Datasets:

Maverick17

/

cira_dataset

like 0

### Instruction: Extract the cause and effect entities from the following sentence: These are additional AOC services not specified in the COCR and therefore not addressed in the ITU Informational Paper ACP– WGF 19/IP01 as all other services ### Assistant: VARIABLE: These CONDITION: addressed in the ITU Informational Paper ACP– WGF 19/IP01 NEGATION: not KEYWORD: and therefore NOT_RELEVANT: as all other services CAUSE_1: These are additional AOC services CAUSE_2: not specified in the COCR EFFECT_1: not addressed in the ITU Informational Paper ACP– WGF 19/IP01 ### END

### Instruction: Extract the cause and effect entities from the following sentence: Conversely, pilot on-the-loop systems often only have single-thread LOS data links and higher latencies because the pilot is not in real-time control of the takeoff or landing maneuvers ### Assistant: NOT_RELEVANT: Conversely VARIABLE: the pilot CONDITION: landing maneuvers AND: and KEYWORD: because NEGATION: is not OR: or CAUSE_1: the pilot is not in real-time control of the takeoff CAUSE_2: landing maneuvers EFFECT_1: pilot on-the-loop systems often only have single-thread LOS data links EFFECT_2: higher latencies ### END

### Instruction: Extract the cause and effect entities from the following sentence: However, the method for monitoring the voice traffic between the pilot and the VHF equipment, when it is carried by the aircraft flying a BLOS mission, is not specifically covered by current regulations ### Assistant: NOT_RELEVANT: However VARIABLE: the voice traffic between the pilot and the VHF equipment KEYWORD: for CONDITION: monitoring NEGATION: is not CAUSE_2: it is carried by the aircraft flying a BLOS mission EFFECT_1: is not specifically covered by current regulations CAUSE_1: monitoring the voice traffic between the pilot and the VHF equipment ### END

### Instruction: Extract the cause and effect entities from the following sentence: As d earlier, voice communication is not currently considered for the manned aircraft applications and therefore was not the focus of prior FCSs ### Assistant: NOT_RELEVANT: As d earlier VARIABLE: voice communication NEGATION: was not CONDITION: the focus of prior FCSs KEYWORD: and therefore EFFECT_1: was not the focus of prior FCSs CAUSE_1: voice communication is not currently considered for the manned aircraft applications ### END

### Instruction: Extract the cause and effect entities from the following sentence: Classes A, B, and, if the UAS is operated in accordance with instrument flight rules (IFR), Class C ### Assistant: NOT_RELEVANT: Classes A, B, and KEYWORD: if VARIABLE: Class C CONDITION: is operated in accordance with instrument flight rules (IFR) EFFECT_1: Class C CAUSE_1: the UAS is operated in accordance with instrument flight rules (IFR) ### END

### Instruction: Extract the cause and effect entities from the following sentence: Latency will be of the utmost importance when establishing a safety case for the operation of UAs, particularly in non-segregated airspace ### Assistant: VARIABLE: a safety case for the operation of UAs CONDITION: establishing KEYWORD: when NOT_RELEVANT: particularly in non-segregated airspace CAUSE_1: establishing a safety case for the operation of UAs EFFECT_1: Latency will be of the utmost importance ### END

### Instruction: Extract the cause and effect entities from the following sentence: UAS complies with ATC procedures and instructions while in ATC ### Assistant: VARIABLE: in ATC CONDITION: instructions AND: and KEYWORD: while EFFECT_1: UAS complies with ATC procedures EFFECT_2: instructions CAUSE_1: in ATC ### END

### Instruction: Extract the cause and effect entities from the following sentence: Specifically, further interference analysis and the development of a joint interference testing program have been postponed until corresponding European L-band activities resume ### Assistant: NOT_RELEVANT: Specifically VARIABLE: corresponding European L-band activities AND: and CONDITION: resume KEYWORD: until CAUSE_1: corresponding European L-band activities resume EFFECT_1: further interference analysis EFFECT_2: the development of a joint interference testing program have been postponed ### END

### Instruction: Extract the cause and effect entities from the following sentence: Because of fewer VHF spectrum depletion concerns in the United States than in Europe for A/G communications, L–DACS development in the United States was determined to be a lower priority compared with the other future communications components, with L-band technology mainly considered to support far-term applications ### Assistant: CONDITION: mainly considered to support far-term applications VARIABLE: VHF spectrum depletion concerns AND: with KEYWORD: Because of CAUSE_1: fewer VHF spectrum depletion concerns in the United States than in Europe for A/G communications EFFECT_1: L–DACS development in the United States was determined to be a lower priority compared with the other future communications components EFFECT_2: L-band technology mainly considered to support far-term applications ### END

### Instruction: Extract the cause and effect entities from the following sentence: Because system capacity and spectrum saturation in the VHF aeronautical communications band appears to be a more pressing issue in Europe, the need for an L-band system is more prevalent in that region ### Assistant: KEYWORD: Because VARIABLE: the need for an L-band system AND: and CONDITION: is more prevalent in that region EFFECT_1: the need for an L-band system is more prevalent in that region CAUSE_1: system capacity CAUSE_2: spectrum saturation in the VHF aeronautical communications band appears to be a more pressing issue in Europe ### END

### Instruction: Extract the cause and effect entities from the following sentence: Several L-band technologies were identified as candidates to support future en route communication due to favorable propagation characteristics and because of spectrum congestion in the VHF band ### Assistant: VARIABLE: spectrum congestion in the VHF band CONDITION: favorable KEYWORD: because of AND: and EFFECT_1: Several L-band technologies were identified as candidates to support future en route communication CAUSE_1: favorable propagation characteristics CAUSE_2: spectrum congestion in the VHF band ### END

### Instruction: Extract the cause and effect entities from the following sentence: None of the considered technologies were fully recommended primarily due to concerns about the operational compatibility (spectrum interference) with existing systems in the L-band and/or because of lack of sufficient technical maturity ### Assistant: NEGATION: None of the VARIABLE: sufficient technical maturity CONDITION: lack of KEYWORD: because of AND: and OR: or EFFECT_1: None of the considered technologies were fully recommended CAUSE_1: concerns about the operational compatibility (spectrum interference) with existing systems in the L-band CAUSE_2: lack of sufficient technical maturity ### END

### Instruction: Extract the cause and effect entities from the following sentence: The L–DACS1 option represents the state of the art in the commercial developments employing modern modulation techniques and may lead to utilisation/adaptation of commercial products and standards ### Assistant: VARIABLE: standards NOT_RELEVANT: represents the state of the art in the commercial developments employing modern modulation techniques and KEYWORD: may lead to CONDITION: utilisation/adaptation of AND: and EFFECT_1: utilisation/adaptation of commercial products EFFECT_2: standards CAUSE_1: The L–DACS1 option ### END

### Instruction: Extract the cause and effect entities from the following sentence: The L-band system is proposed to be introduced during the second phase of FRS implementation ### Assistant: EFFECT_1: The L-band system is proposed to be introduced KEYWORD: during CAUSE_1: the second phase of FRS implementation VARIABLE: FRS implementation CONDITION: the second phase of ### END

### Instruction: Extract the cause and effect entities from the following sentence: However, transition issues need to be identified early in the system development process to assure they are properly addressed by the time of system deployment ### Assistant: NOT_RELEVANT: However VARIABLE: they CONDITION: are properly addressed by the time of system deployment CAUSE_1: transition issues need to be identified early in the system development process KEYWORD: to assure EFFECT_1: they are properly addressed by the time of system deployment ### END

### Instruction: Extract the cause and effect entities from the following sentence: We have a plan; now it is time to begin the really difficult work of execution, which is much more difficult than planning because it requires commitment to action ### Assistant: KEYWORD: because CONDITION: requires commitment to action VARIABLE: work of execution CAUSE_1: it requires commitment to action EFFECT_1: work of execution NOT_RELEVANT: We have a plan; now it is time to begin the really difficult ### END

### Instruction: Extract the cause and effect entities from the following sentence: A final selection of services chosen as applications for the new system will greatly influence transition time and process ### Assistant: VARIABLE: A final selection of services AND: and CONDITION: chosen as applications for the new system CAUSE_1: A final selection of services chosen as applications for the new system EFFECT_1: will greatly influence transition time EFFECT_2: process ### END

### Instruction: Extract the cause and effect entities from the following sentence: When implemented, the system will provide a long-term capacity solution and will allow the introduction of new communications services ### Assistant: KEYWORD: When CONDITION: will allow the introduction of new communications services VARIABLE: the system AND: and CAUSE_1: implemented EFFECT_1: the system will provide a long-term capacity solution EFFECT_2: will allow the introduction of new communications services ### END

### Instruction: Extract the cause and effect entities from the following sentence: A gradual transition to the new band and services will lead to mixed equipage aircraft cooperating for extensive periods of time ### Assistant: CAUSE_1: A gradual transition to the new band AND: and CAUSE_2: services KEYWORD: will lead to CONDITION: cooperating for extensive periods of time VARIABLE: services EFFECT_1: mixed equipage aircraft EFFECT_2: cooperating for extensive periods of time ### END

### Instruction: Extract the cause and effect entities from the following sentence: If an evolutionary process is chosen, some communications technologies could be made available for UAS operations quickly while others are being developed ### Assistant: KEYWORD: If VARIABLE: some communications technologies CONDITION: could be made available for UAS operations quickly while others are being developed CAUSE_1: an evolutionary process is chosen EFFECT_1: some communications technologies could be made available for UAS operations quickly while others are being developed ### END

### Instruction: Extract the cause and effect entities from the following sentence: A/A communications is shown as being provided on L-band only ### Assistant: VARIABLE: L-band CONDITION: is shown as being provided KEYWORD: only CAUSE_1: L-band EFFECT_1: A/A communications is shown as being provided ### END

### Instruction: Extract the cause and effect entities from the following sentence: When selecting appropriate centre frequencies for the B–AMC system, all interferers with the power level below –100 dBm can be neglected ### Assistant: KEYWORD: with CONDITION: can be neglected VARIABLE: all interferers CAUSE_1: selecting appropriate centre frequencies for the B–AMC system EFFECT_1: can be neglected CAUSE_2: the power level below –100 dBm ### END

### Instruction: Extract the cause and effect entities from the following sentence: As the En-Route coverage is the most demanding case with respect to the usage of spectral resources, this case has been investigated in detail—TMA and airport planning have been delegated to the future work ### Assistant: KEYWORD: As VARIABLE: this case CONDITION: has been investigated in detail NOT_RELEVANT: TMA and airport planning have been delegated to the future work EFFECT_1: this case has been investigated in detail CAUSE_1: the En-Route coverage is the most demanding case with respect to the usage of spectral resources ### END

### Instruction: Extract the cause and effect entities from the following sentence: Within the initial planning exercise, large 120 nm En-Route B–AMC cells have been considered, with ground B–AMC TX power of +38 dBm ### Assistant: KEYWORD: Within VARIABLE: ground B–AMC TX power CAUSE_1: the initial planning exercise CONDITION: of +38 dBm AND: with EFFECT_1: large 120 nm En-Route B–AMC cells have been considered EFFECT_2: ground B–AMC TX power of +38 dBm ### END

### Instruction: Extract the cause and effect entities from the following sentence: It is recommended that further evaluation on the impact of L–DACS transmissions on the incumbent L-band system receiving systems be performed when the L–DACS design has sufficiently matured ### Assistant: NOT_RELEVANT: It is recommended that VARIABLE: the L–DACS design CONDITION: has sufficiently matured KEYWORD: when CAUSE_1: the L–DACS design has sufficiently matured EFFECT_1: further evaluation on the impact of L–DACS transmissions on the incumbent L-band system receiving systems be performed ### END

### Instruction: Extract the cause and effect entities from the following sentence: Safety is enhanced by situation displays that depict airborne and surface traffic as well as aerodrome information ### Assistant: VARIABLE: situation displays CONDITION: aerodrome information KEYWORD: that AND: as well as CAUSE_1: depict airborne CAUSE_2: surface traffic CAUSE_3: aerodrome information EFFECT_1: Safety is enhanced ### END

### Instruction: Extract the cause and effect entities from the following sentence: As more sources of position data become available, more traffic is under some form of improved surveillance ### Assistant: KEYWORD: As CAUSE_1: more sources of position data become available CONDITION: is under some form of improved surveillance VARIABLE: more traffic EFFECT_1: more traffic is under some form of improved surveillance ### END

### Instruction: Extract the cause and effect entities from the following sentence: Improved weather information integrated into DSSs and disseminated via data link reduces encounters with hazardous weather ### Assistant: CAUSE_1: Improved weather information integrated into DSSs CONDITION: reduces CAUSE_2: disseminated via data link VARIABLE: encounters with hazardous weather AND: and EFFECT_1: reduces encounters with hazardous weather ### END

### Instruction: Extract the cause and effect entities from the following sentence: The introduction of data-linked meteorological information improves overall situational awareness ### Assistant: CAUSE_1: The introduction of data-linked meteorological information EFFECT_1: improves overall situational awareness CONDITION: The introduction of VARIABLE: data-linked meteorological information ### END

### Instruction: Extract the cause and effect entities from the following sentence: SWIM provides access to weather and information via data link to flight crews, allowing them to develop near-real-time picture of the surrounding environment ### Assistant: VARIABLE: them CONDITION: to develop near-real-time picture of the surrounding environment AND: and KEYWORD: allowing EFFECT_1: them to develop near-real-time picture of the surrounding environment CAUSE_1: SWIM provides access to weather CAUSE_2: information via data link to flight crews ### END

### Instruction: Extract the cause and effect entities from the following sentence: Automatic exchange of information between aircraft and ground-based DSSs improves the accuracy and coordination of arrival profiles ### Assistant: CAUSE_1: Automatic exchange of information between aircraft and ground-based DSSs VARIABLE: coordination of arrival profiles CONDITION: improves AND: and EFFECT_1: improves the accuracy EFFECT_2: coordination of arrival profiles ### END

### Instruction: Extract the cause and effect entities from the following sentence: When operationally advantageous and mutually agreed upon, flight deck separation is authorized by ATC ### Assistant: KEYWORD: When CAUSE_1: operationally advantageous CAUSE_2: mutually agreed upon AND: and VARIABLE: flight deck separation CONDITION: mutually agreed upon EFFECT_1: flight deck separation is authorized by ATC ### END

### Instruction: Extract the cause and effect entities from the following sentence: The flight planning system accommodates all uses of the airspace as the flight profile evolves to include real time SUA operations scheduling information ### Assistant: VARIABLE: the flight profile CONDITION: evolves to include real time SUA operations scheduling information KEYWORD: as CAUSE_1: the flight profile evolves to include real time SUA operations scheduling information EFFECT_1: The flight planning system accommodates all uses of the airspace ### END

### Instruction: Extract the cause and effect entities from the following sentence: When the profile is filed, it is automatically checked against various conditions and constraints ### Assistant: KEYWORD: When VARIABLE: it CONDITION: constraints CAUSE_1: the profile is filed AND: and EFFECT_1: it is automatically checked against various conditions EFFECT_2: constraints ### END

### Instruction: Extract the cause and effect entities from the following sentence: At the completion of the planning process, the user supplies the service provider with both the flight profile that best balances the NAS constraints and the user’s preferred flight profile ### Assistant: KEYWORD: that CONDITION: the user’s preferred flight profile VARIABLE: the user AND: and CAUSE_1: the completion of the planning process EFFECT_1: the flight profile CAUSE_2: best balances the NAS constraints EFFECT_2: the user’s preferred flight profile ### END

### Instruction: Extract the cause and effect entities from the following sentence: This information, including any subsequent changes, is available electronically to all service providers until the termination of the flight ### Assistant: VARIABLE: the flight AND: including CONDITION: the termination of NEGATION: until EFFECT_1: This information EFFECT_2: any subsequent changes, is available electronically to all service providers CAUSE_1: the termination of the flight ### END

### Instruction: Extract the cause and effect entities from the following sentence: As conditions change during the planning phase or during the flight, the user is notified, and he/she is able to interactively determine the impact of the changes on the flight and modify the flight profile as desired ### Assistant: KEYWORD: during VARIABLE: she CONDITION: modify the flight profile as desired CAUSE_1: conditions change OR: / CAUSE_2: the planning phase CAUSE_3: the flight AND: and EFFECT_1: the user is notified EFFECT_2: he/she is able to interactively determine the impact of the changes on the flight EFFECT_3: modify the flight profile as desired ### END

### Instruction: Extract the cause and effect entities from the following sentence: For users equipped with data link, the capability exists to load a flight profile directly into the aircraft FMS ### Assistant: KEYWORD: For VARIABLE: the capability CONDITION: exists to load a flight profile directly into the aircraft FMS CAUSE_1: users equipped with data link EFFECT_1: the capability exists to load a flight profile directly into the aircraft FMS ### END

### Instruction: Extract the cause and effect entities from the following sentence: SWIM information is available to all users and service providers until the termination of the flight ### Assistant: VARIABLE: the flight CONDITION: the termination of AND: and NEGATION: until EFFECT_1: SWIM information is available to all users EFFECT_2: service providers CAUSE_1: the termination of the flight ### END

### Instruction: Extract the cause and effect entities from the following sentence: On taxi out, the flight’s time-based trajectory is updated in SWIM, and projections are made based on prevailing traffic conditions ### Assistant: KEYWORD: On VARIABLE: projections CONDITION: are made based on prevailing traffic conditions AND: and CAUSE_1: taxi out EFFECT_1: the flight’s time-based trajectory is updated in SWIM EFFECT_2: projections are made based on prevailing traffic conditions ### END

### Instruction: Extract the cause and effect entities from the following sentence: Both the initial values and subsequent adjustments are incorporated into the surface management information system to ensure consistency and an integrated approach across systems ### Assistant: AND: and VARIABLE: consistency CONDITION: an integrated approach across systems CAUSE_1: the initial values CAUSE_2: subsequent adjustments are incorporated into the surface management information system EFFECT_1: to ensure consistency EFFECT_2: an integrated approach across systems ### END

### Instruction: Extract the cause and effect entities from the following sentence: As required navigation performance (RNP)/RNAV capabilities increase, the low-altitude airspace structure remains largely unchanged ### Assistant: KEYWORD: As VARIABLE: the low-altitude airspace structure CONDITION: remains largely unchanged EFFECT_1: the low-altitude airspace structure remains largely unchanged CAUSE_1: required navigation performance (RNP)/RNAV capabilities increase ### END

### Instruction: Extract the cause and effect entities from the following sentence: Widespread area navigation equipage and expanded surveillance coverage with new technology provide increased access to airports and airspace in all weather conditions ### Assistant: KEYWORD: provide CONDITION: expanded VARIABLE: surveillance coverage with new technology AND: and EFFECT_1: increased access to airports EFFECT_2: airspace in all weather conditions CAUSE_1: Widespread area navigation equipage CAUSE_2: expanded surveillance coverage with new technology ### END

### Instruction: Extract the cause and effect entities from the following sentence: Seamless communications and coordination, coupled with information accessible through SWIM, allow real-time reassignment of airspace between facilities to meet contingencies such as equipment outages ### Assistant: CAUSE_1: Seamless communications AND: coupled with CAUSE_2: coordination CAUSE_3: information accessible through SWIM KEYWORD: allow VARIABLE: information CONDITION: accessible through SWIM EFFECT_1: real-time reassignment of airspace between facilities to meet contingencies such as equipment outages ### END

### Instruction: Extract the cause and effect entities from the following sentence: Moving map displays enhance pilot familiarity with the airport, leading to better planning and increased safety ### Assistant: CONDITION: increased VARIABLE: safety KEYWORD: leading to AND: and CAUSE_1: Moving map displays EFFECT_1: enhance pilot familiarity with the airport EFFECT_2: better planning EFFECT_3: increased safety ### END

### Instruction: Extract the cause and effect entities from the following sentence: This data sharing allows service providers to coordinate local operations with airline ramp and airport operators, thus improving overall airport operations ### Assistant: VARIABLE: overall airport operations CONDITION: improving AND: and KEYWORD: thus EFFECT_1: improving overall airport operations CAUSE_1: This data sharing allows service providers to coordinate local operations with airline ramp CAUSE_2: airport operators ### END

### Instruction: Extract the cause and effect entities from the following sentence: The redundancy in the NAS is applied expeditiously to maintain flow and reduce operational impact ### Assistant: VARIABLE: operational impact CONDITION: reduce AND: and CAUSE_1: The redundancy in the NAS is applied expeditiously EFFECT_1: to maintain flow EFFECT_2: reduce operational impact ### END

### Instruction: Extract the cause and effect entities from the following sentence: With the completion of the National Airspace Redesign, airspace is restructured to meet future traffic requirements ### Assistant: KEYWORD: With CONDITION: to meet VARIABLE: future traffic requirements EFFECT_1: airspace is restructured EFFECT_2: to meet future traffic requirements CAUSE_1: the completion of the National Airspace Redesign ### END

### Instruction: Extract the cause and effect entities from the following sentence: Static restrictions due to fixed sector boundaries are reduced or eliminated ### Assistant: VARIABLE: sector boundaries KEYWORD: due to CONDITION: eliminated OR: or CAUSE_1: fixed sector boundaries EFFECT_1: are reduced EFFECT_2: eliminated ### END

### Instruction: Extract the cause and effect entities from the following sentence: If the transmitter has too high a duty cycle, this pulse-blanking method could unacceptably degrade receiver performance ### Assistant: KEYWORD: If VARIABLE: this pulse-blanking method CONDITION: could unacceptably degrade receiver performance CAUSE_1: the transmitter has too high a duty cycle EFFECT_1: this pulse-blanking method could unacceptably degrade receiver performance ### END

### Instruction: Extract the cause and effect entities from the following sentence: The architecture will not be usable if excessive burden (such as Size, Weight, and Power (SWaP) and complexity) is needed for a single mission to realize the multi-mission benefits ### Assistant: VARIABLE: excessive burden NEGATION: will not CONDITION: is needed for a single mission to realize the multi-mission benefits EFFECT_1: The architecture will not be usable KEYWORD: if NOT_RELEVANT: (such as Size, Weight, and Power (SWaP) and complexity) CAUSE_1: is needed for a single mission to realize the multi-mission benefits ### END

### Instruction: Extract the cause and effect entities from the following sentence: Rationale: Managing the complexity of the design through a common architecture and reducing the amount of interdependence on other systems will enhance reliability ### Assistant: NOT_RELEVANT: Rationale CONDITION: will enhance VARIABLE: reliability AND: and EFFECT_1: will enhance reliability CAUSE_1: Managing the complexity of the design through a common architecture CAUSE_2: reducing the amount of interdependence on other systems ### END

### Instruction: Extract the cause and effect entities from the following sentence: Leveraging their software and hardware development and products to the extent reasonable may reduce NASA’s costs and risks ### Assistant: CONDITION: may reduce VARIABLE: risks AND: and EFFECT_1: may reduce NASA’s costs EFFECT_2: risks CAUSE_1: Leveraging their software CAUSE_2: hardware development CAUSE_3: products to the extent reasonable ### END

### Instruction: Extract the cause and effect entities from the following sentence: Deviating from current practices would increase design cost and schedule and decrease reliability ### Assistant: CAUSE_1: Deviating from current practices VARIABLE: reliability CONDITION: Deviating from current practices AND: and EFFECT_1: would increase design cost EFFECT_2: schedule EFFECT_3: decrease reliability ### END

### Instruction: Extract the cause and effect entities from the following sentence: An implementation may not require all aspects of the architecture; therefore, all requirements may not apply to each STRS-compliant software defined radio ### Assistant: VARIABLE: all requirements NEGATION: may not CONDITION: apply to each STRS-compliant software defined radio KEYWORD: therefore EFFECT_1: all requirements may not apply to each STRS-compliant software defined radio CAUSE_1: An implementation may not require all aspects of the architecture ### END

### Instruction: Extract the cause and effect entities from the following sentence: By having a single radio be capable of running more than one waveform, this can reduce the number of radios for a mission ### Assistant: KEYWORD: this can VARIABLE: the number of radios for a mission CONDITION: reduce EFFECT_1: reduce the number of radios for a mission CAUSE_1: a single radio be capable of running more than one waveform ### END

### Instruction: Extract the cause and effect entities from the following sentence: The radio could also be used for simultaneous GPS reception, thus eliminating the need for a separate receiver ### Assistant: VARIABLE: the need for a separate receiver CONDITION: eliminating KEYWORD: thus EFFECT_1: eliminating the need for a separate receiver CAUSE_1: The radio could also be used for simultaneous GPS reception ### END

### Instruction: Extract the cause and effect entities from the following sentence: Future NASA missions will probably need significantly larger science data transfers from space, requiring even higher frequency bands to be implemented ### Assistant: VARIABLE: even higher frequency bands CONDITION: to be implemented KEYWORD: requiring EFFECT_1: even higher frequency bands to be implemented CAUSE_1: Future NASA missions will probably need significantly larger science data transfers from space ### END

### Instruction: Extract the cause and effect entities from the following sentence: Multiple simultaneous channels can also allow for increased throughput or redundancy ### Assistant: CONDITION: increased VARIABLE: redundancy KEYWORD: can also allow for OR: or CAUSE_1: Multiple simultaneous channels EFFECT_1: increased throughput EFFECT_2: redundancy ### END

### Instruction: Extract the cause and effect entities from the following sentence: The pilot may deviate only in response required for collision avoidance ### Assistant: VARIABLE: response CONDITION: required for collision avoidance KEYWORD: only in EFFECT_1: The pilot may deviate CAUSE_1: response required for collision avoidance ### END

### Instruction: Extract the cause and effect entities from the following sentence: Norden #2 hosts the guidance, navigation, and control software; hence it is also referred to as the Flight Managemenmight Control host computer ### Assistant: VARIABLE: it CONDITION: is also referred to as the Flight Managemenmight Control host computer AND: and KEYWORD: hence CAUSE_1: Norden #2 hosts the guidance CAUSE_2: navigation CAUSE_3: control software EFFECT_1: it is also referred to as the Flight Managemenmight Control host computer ### END

### Instruction: Extract the cause and effect entities from the following sentence: Neither the original (i.e., essential) TSRV FCS requirements nor the evolution of the baseline software are well documented, thus the “essence” of the system was backed out of the existing implementation ### Assistant: NEGATION: nor VARIABLE: the “essence” of the system NOT_RELEVANT: (i.e., essential) CONDITION: was backed out of the existing implementation KEYWORD: thus CAUSE_1: TSRV FCS requirements CAUSE_2: nor the evolution of the baseline software are well documented EFFECT_1: the “essence” of the system was backed out of the existing implementation ### END

### Instruction: Extract the cause and effect entities from the following sentence: In the analysis stage, the specification of the system should not be influenced by past design solutions -- “We’ve always done it that way” -- or by current technology constraints such as processor speeds or memory limitations ### Assistant: KEYWORD: In VARIABLE: the specification of the system NEGATION: should not CONDITION: by current technology constraints NOT_RELEVANT: such as processor speeds or memory limitations OR: or CAUSE_1: the analysis stage EFFECT_1: the specification of the system should not be influenced by past design solutions EFFECT_2: by current technology constraints ### END

### Instruction: Extract the cause and effect entities from the following sentence: In the case of the TSRV FCS, the systems analysis process started from a specification of the system “as built” ### Assistant: KEYWORD: In the case of VARIABLE: the systems analysis process CONDITION: started from a specification of the system “as built” EFFECT_1: the systems analysis process started from a specification of the system “as built” CAUSE_1: the TSRV FCS ### END

### Instruction: Extract the cause and effect entities from the following sentence: When several attributes form the primary id, each attribute is separated by a plus sign ("+"), and should appear on a line of its own; the last attribute may be followed by a period (".") ### Assistant: KEYWORD: When VARIABLE: the last attribute CONDITION: may be followed by a period CAUSE_1: several attributes form the primary id NOT_RELEVANT: (".") AND: and EFFECT_1: each attribute is separated by a plus sign EFFECT_2: should appear on a line of its own EFFECT_3: the last attribute may be followed by a period ### END

### Instruction: Extract the cause and effect entities from the following sentence: Common domains are created whenever two or more attributes share a common domain of values, due to an essential similarity (not a coincidence) ### Assistant: VARIABLE: similarity CONDITION: essential KEYWORD: due to OR: or CAUSE_3: an essential similarity NOT_RELEVANT: (not a coincidence) EFFECT_1: Common domains are created CAUSE_1: two CAUSE_2: more attributes share a common domain of values ### END

### Instruction: Extract the cause and effect entities from the following sentence: For example, the reader immediately knows that the attribute “wfp-l-Flight-- Plan-Name-RO1 A” is a referential attribute, because it has the ‘R’ tag “ROl A” ### Assistant: NOT_RELEVANT: For example VARIABLE: it CONDITION: has the ‘R’ tag “ROl A” KEYWORD: because CAUSE_1: it has the ‘R’ tag “ROl A” EFFECT_1: the reader immediately knows that the attribute “wfp-l-Flight-- Plan-Name-RO1 A” is a referential attribute ### END

### Instruction: Extract the cause and effect entities from the following sentence: The description for an ADCEP may be omitted where obvious from the name ### Assistant: VARIABLE: The description for an ADCEP CONDITION: obvious from the name KEYWORD: where EFFECT_1: The description for an ADCEP may be omitted CAUSE_1: obvious from the name ### END

### Instruction: Extract the cause and effect entities from the following sentence: Teamwork checks referenced ADCEPs when validating a nonprimitive definition ### Assistant: VARIABLE: a nonprimitive definition CONDITION: validating KEYWORD: when CAUSE_1: validating a nonprimitive definition EFFECT_1: Teamwork checks referenced ADCEPs ### END

### Instruction: Extract the cause and effect entities from the following sentence: When two or more ADCEPs are referenced, this indicates that the ADCEP is actually composed of two or more meaningful parts, each with its own domain ### Assistant: KEYWORD: When VARIABLE: the ADCEP OR: or CONDITION: each with its own domain NOT_RELEVANT: this indicates that CAUSE_1: two CAUSE_2: more ADCEPs are referenced EFFECT_1: the ADCEP is actually composed of two EFFECT_2: more meaningful parts EFFECT_3: each with its own domain ### END

### Instruction: Extract the cause and effect entities from the following sentence: An attribute in a supertype which names the subtype object class is called a subtype indication, and its domain of valid values is, by definition, the object ids of the subtype object classes ### Assistant: VARIABLE: its domain of valid values KEYWORD: which CONDITION: the object ids of the subtype object classes AND: and NOT_RELEVANT: by definition CAUSE_1: names the subtype object class EFFECT_1: is called a subtype indication EFFECT_2: the object ids of the subtype object classes ### END

### Instruction: Extract the cause and effect entities from the following sentence: Transition labels have been augmented in several ways in order to simplify otherwise busy STDs ### Assistant: VARIABLE: otherwise busy STDs CONDITION: to simplify KEYWORD: in order EFFECT_1: to simplify otherwise busy STDs CAUSE_1: Transition labels have been augmented in several ways ### END

### Instruction: Extract the cause and effect entities from the following sentence: The event name carries the usual meaning: when an object of the class is in the source state, and the named event occurs, then the transition will be taken (unless the transition’s guard is false -- see below) ### Assistant: NOT_RELEVANT: (unless the transition’s guard is false -- see below) KEYWORD: then VARIABLE: the transition CONDITION: will be taken CAUSE_1: an object of the class is in the source state AND: and CAUSE_2: the named event occurs EFFECT_1: the transition will be taken ### END

### Instruction: Extract the cause and effect entities from the following sentence: The boolean expression becoming true while the object is in the source state is treated as an event in the usual sense ### Assistant: VARIABLE: the object CONDITION: is treated as an event in the usual sense KEYWORD: while CAUSE_1: The boolean expression becoming true CAUSE_2: the object is in the source state EFFECT_1: is treated as an event in the usual sense ### END

### Instruction: Extract the cause and effect entities from the following sentence: When one of the events occurs, or when one of the boolean expressions evaluates to true, it is said that an event in the event block has occurred ### Assistant: KEYWORD: when VARIABLE: an event in the event block CONDITION: has occurred CAUSE_1: one of the events occurs OR: or NOT_RELEVANT: it is said that EFFECT_1: an event in the event block has occurred CAUSE_2: one of the boolean expressions evaluates to true ### END

### Instruction: Extract the cause and effect entities from the following sentence: If the guard evaluates to false, then the transition is not taken: the event in the event block is ignored ### Assistant: KEYWORD: in VARIABLE: the event block CONDITION: is ignored NEGATION: is not CAUSE_1: the guard evaluates to false EFFECT_1: the transition is not taken EFFECT_2: is ignored CAUSE_2: the event block ### END

### Instruction: Extract the cause and effect entities from the following sentence: Other than this, no special conventions were adopted for Process Model diagrams ### Assistant: NEGATION: no VARIABLE: Process Model diagrams CONDITION: were adopted EFFECT_1: no special conventions were adopted KEYWORD: for CAUSE_1: Process Model diagrams NOT_RELEVANT: Other than this ### END

### Instruction: Extract the cause and effect entities from the following sentence: If the object class has a Behavior or Process Model, su zh is indicated ### Assistant: KEYWORD: If VARIABLE: su zh CONDITION: is indicated OR: or EFFECT_1: su zh is indicated CAUSE_1: the object class has a Behavior CAUSE_2: Process Model ### END

### Instruction: Extract the cause and effect entities from the following sentence: The Altitude-Hold-Mode-Controller, when engaged, generates steering commands so as to capam and hold a pilot-selected altitude ### Assistant: VARIABLE: a pilot-selected altitude KEYWORD: when CONDITION: hold CAUSE_1: The Altitude-Hold-Mode-Controller AND: and EFFECT_1: generates steering commands EFFECT_2: to capam EFFECT_3: hold a pilot-selected altitude ### END

### Instruction: Extract the cause and effect entities from the following sentence: This is the pilot-indicated altitude which shall be captured and held when the Altitude-Hold-Mode-ConController is engaged ### Assistant: NOT_RELEVANT: This is VARIABLE: the Altitude-Hold-Mode-ConController KEYWORD: when CONDITION: is engaged AND: and EFFECT_1: shall be captured EFFECT_2: held CAUSE_1: the Altitude-Hold-Mode-ConController is engaged ### END

### Instruction: Extract the cause and effect entities from the following sentence: When fewer than four dimensions are specified, the remaining dimensions are unconstrained and are determined by the pilot or system “on the fly” during flight ### Assistant: NOT_RELEVANT: When CONDITION: system “on the fly” VARIABLE: flight AND: and OR: or KEYWORD: during CAUSE_1: fewer than four dimensions are specified CAUSE_2: flight EFFECT_1: the remaining dimensions are unconstrained EFFECT_2: are determined by the pilot EFFECT_3: system “on the fly” ### END

### Instruction: Extract the cause and effect entities from the following sentence: The Manual-Electric-Mode_Controller, while engaged, provides mdimentaxy transformation of the AFD control inputs; the aimaft is essentially being flown manually ### Assistant: VARIABLE: The Manual-Electric-Mode_Controller KEYWORD: while CONDITION: provides mdimentaxy transformation of the AFD control inputs NOT_RELEVANT: the aimaft is essentially being flown manually CAUSE_1: The Manual-Electric-Mode_Controller EFFECT_1: provides mdimentaxy transformation of the AFD control inputs ### END

### Instruction: Extract the cause and effect entities from the following sentence: This is the instantaneous longitudinal acceleration of the aircraft, where forward acceleration is positive ### Assistant: KEYWORD: where VARIABLE: acceleration of the aircraft CONDITION: instantaneous longitudinal CAUSE_1: forward acceleration is positive NOT_RELEVANT: This is EFFECT_1: the instantaneous longitudinal acceleration of the aircraft ### END

### Instruction: Extract the cause and effect entities from the following sentence: This is the instantaneous lateral acceleration of the aircraft, where acceleration towards the right wing is positive ### Assistant: NOT_RELEVANT: This is VARIABLE: acceleration towards the right wing CONDITION: is positive KEYWORD: where CAUSE_1: acceleration towards the right wing is positive EFFECT_1: the instantaneous lateral acceleration of the aircraft ### END

### Instruction: Extract the cause and effect entities from the following sentence: This is the instantaneous "vertical" acceleration of the aircraft, where the vertical axis completes the right-hand coordinate system of the aircraft ### Assistant: NOT_RELEVANT: This is VARIABLE: the vertical axis CONDITION: completes the right-hand coordinate system of the aircraft EFFECT_1: the instantaneous "vertical" acceleration of the aircraft KEYWORD: where CAUSE_1: the vertical axis completes the right-hand coordinate system of the aircraft ### END

### Instruction: Extract the cause and effect entities from the following sentence: If the Waypoint is &fined by the pilot via reference to existing Waypoint(s) (e+, SPO/30/30 or SPO/180/EAT/270), then the pilot-entered reference shall be used as the name ### Assistant: KEYWORD: then VARIABLE: the pilot-entered reference CONDITION: shall be used as the name NOT_RELEVANT: (e+, SPO/30/30 or SPO/180/EAT/270) EFFECT_1: the pilot-entered reference shall be used as the name CAUSE_1: the Waypoint is &fined by the pilot via reference to existing Waypoint(s) ### END

### Instruction: Extract the cause and effect entities from the following sentence: Likewise, if the Waypoint is defined by the pilot via latitude and longitude coordinates, then these coordinates shall be used as the name ### Assistant: NOT_RELEVANT: Likewise KEYWORD: then VARIABLE: these coordinates CONDITION: shall be used as the name AND: and EFFECT_1: these coordinates shall be used as the name CAUSE_1: the Waypoint is defined by the pilot via latitude CAUSE_2: longitude coordinates ### END

### Instruction: Extract the cause and effect entities from the following sentence: In order to maximize the scope of analysis on this project, the development of OCCDs was given a low priority, and only an example OCCD was generated ### Assistant: KEYWORD: only CONDITION: was generated VARIABLE: an example OCCD EFFECT_1: to maximize the scope of analysis on this project CAUSE_1: the development of OCCDs was given a low priority AND: and CAUSE_2: an example OCCD was generated ### END

### Instruction: Extract the cause and effect entities from the following sentence: Individuals who require a precise mathematical definition of thermal remote sensing properties, such as directional and hemispherical radiometric temperatures, or directional and hemispherical emissivities, are referred to [RD-2] ### Assistant: VARIABLE: Individuals KEYWORD: who CONDITION: are referred to [RD-2] NOT_RELEVANT: such as directional and hemispherical radiometric temperatures OR: or AND: and EFFECT_1: are referred to [RD-2] CAUSE_1: require a precise mathematical definition of thermal remote sensing properties CAUSE_2: directional CAUSE_3: hemispherical emissivities ### END

### Instruction: Extract the cause and effect entities from the following sentence: To address these themes a number of task-orientated groups are being considered ### Assistant: CONDITION: are being considered VARIABLE: a number of task-orientated groups CAUSE_1: a number of task-orientated groups are being considered EFFECT_1: To address these themes ### END

### Instruction: Extract the cause and effect entities from the following sentence: However, it must be stated that since the ILSTE-WG operates on a “best efforts” basis for most members the review of deliverables is entirely on the goodwill of international colleagues, and the maintenance and growth of the community takes priority ### Assistant: NOT_RELEVANT: it must be stated that KEYWORD: since VARIABLE: growth of the community CONDITION: takes priority CAUSE_1: the ILSTE-WG operates on a “best efforts” basis for most members EFFECT_1: the review of deliverables is entirely on the goodwill of international colleagues AND: and EFFECT_2: the maintenance EFFECT_3: growth of the community takes priority ### END

### Instruction: Extract the cause and effect entities from the following sentence: Where questions did not permit different levels to be specified then requirements were considered to be at the threshold level ### Assistant: KEYWORD: then VARIABLE: requirements NEGATION: did not CONDITION: were considered to be at the threshold level CAUSE_1: questions did not permit different levels to be specified EFFECT_1: requirements were considered to be at the threshold level ### END

### Instruction: Extract the cause and effect entities from the following sentence: Finally, if this type of requirement could not be issued, a decision could be made to construct a ‘soft’ requirement ### Assistant: NOT_RELEVANT: Finally KEYWORD: if VARIABLE: a decision NEGATION: could not CONDITION: could be made to construct a ‘soft’ requirement CAUSE_1: this type of requirement could not be issued EFFECT_1: a decision could be made to construct a ‘soft’ requirement ### END

### Instruction: Extract the cause and effect entities from the following sentence: Once a requirement was identified then it was first checked against the outcomes of the first user consultation meeting (UCM#1) for agreement, differences or contradictions ### Assistant: KEYWORD: then VARIABLE: it CONDITION: contradictions CAUSE_1: a requirement was identified OR: or EFFECT_1: it was first checked against the outcomes of the first user consultation meeting (UCM#1) for agreement EFFECT_2: differences EFFECT_3: contradictions ### END

### Instruction: Extract the cause and effect entities from the following sentence: Where a contradiction occurred, the outcomes from both UCM#1 and the current survey were weighed to construct the final requirement, which was justified in the comments section of the requirements table along with a description of the discrepancy ### Assistant: KEYWORD: which VARIABLE: the current survey CONDITION: with a description of the discrepancy CAUSE_1: a contradiction occurred AND: along EFFECT_1: the outcomes from both UCM#1 EFFECT_2: the current survey were weighed to construct the final requirement CAUSE_2: was justified in the comments section of the requirements table CAUSE_3: with a description of the discrepancy ### END

### Instruction: Extract the cause and effect entities from the following sentence: Where the information was provided, the same process was reiterated with breakthrough level specifications ### Assistant: KEYWORD: Where VARIABLE: the same process CONDITION: was reiterated with breakthrough level specifications CAUSE_1: the information was provided EFFECT_1: the same process was reiterated with breakthrough level specifications ### END

### Instruction: Extract the cause and effect entities from the following sentence: In each sub-section where the implementation of a requirement is described then this is clearly indicated to provide additional traceability ### Assistant: VARIABLE: additional traceability KEYWORD: then CONDITION: to provide EFFECT_1: this is clearly indicated EFFECT_2: to provide additional traceability CAUSE_1: In each sub-section CAUSE_2: the implementation of a requirement is described ### END

### Instruction: Extract the cause and effect entities from the following sentence: For single-sensor datasets which explicitly exploit surface emissivity in their retrieval, such as MODIS, this will be provided as mandatory information ### Assistant: KEYWORD: which VARIABLE: this CONDITION: will be provided as mandatory information NOT_RELEVANT: such as MODIS EFFECT_1: this will be provided as mandatory information CAUSE_1: explicitly exploit surface emissivity in their retrieval ### END

### Instruction: Extract the cause and effect entities from the following sentence: These temporary user workspaces will be created when users place bulk orders for data via the Data Portal, with files deleted after a period of two weeks ### Assistant: VARIABLE: files CONDITION: deleted after a period of two weeks EFFECT_1: These temporary user workspaces will be created KEYWORD: when CAUSE_1: users place bulk orders for data via the Data Portal AND: with CAUSE_2: files deleted after a period of two weeks ### END

### Instruction: Extract the cause and effect entities from the following sentence: The GlobTemperature User List is being maintained as new users are both identified externally and through registration via the GlobTemperature Website ### Assistant: VARIABLE: new users CONDITION: through registration via the GlobTemperature Website KEYWORD: as AND: and CAUSE_1: identified externally EFFECT_1: The GlobTemperature User List is being maintained CAUSE_2: through registration via the GlobTemperature Website ### END