dataesr/scientific-paragraphs-categorization huggingface dataset:
Technology The technological dimension looks at the intrinsic elements that cause a chatbot system to succeed or fail. We focus here on algorithm requirements and human resources that underlie the chatbot's AI. AI can be developed at various levels of autonomy with respect to the interaction between programmers and the machine. The first level is called supervised learning, which implies a high degree of human control and intervention over the curation of content that will compose the database for the chatbot  (Tegmark, 2018). From our interviews, we note that sensitive issues are likely to arise in relation to the Brazilian Congress's communication with society, notably regarding biases, prejudices, and other distortions that could negatively affect certain interest groups and/or protected minorities.
Acknowledgments xxx ACKNOWLEDGMENTS Acknowledgements We thank  Zih-Yang Zhou  and  Ping-Hsuan Liu  for their contributions as research assistants to this project. Funding -This research is supported by the  YuShan Scholar Program ,  Ministry of Education , R. O. C. Taiwan [No. 110V0301 ] and the  Ministry of Science and Technology , R. O. C [No. 110CD804-1 ]. Declaration of Conflicting Interests -There is no conflicting interest.
Acknowledgments The authors would like to thank  Isa Liang ,  Sravya Yandamuri ,  Shouzhuo Sun , and  Nikhil Garg  for their help in building the platform. They thank the  Asian Energy Studies Centre at Hong Kong Baptist University  led by  Professor Daphne Mah  for their work on the discussed deliberations. They also thank anonymous reviewers as well as colleagues for their feedback on earlier drafts of this manuscript. This work is supported by the  Stanford Institute for Human-Centered Artificial Intelligence , the  David and Helen Gurley Brown Institute for Media Innovation  through a  Magic Grant , and the  Office of Naval Research.
Acknowledgments The authors thank  Rick Bonney ,  Børge Damsgaard ,  Ian Davies ,  Ilja Lang ,  Verena Meraldi ,  Naja E. Mikkelsen ,  Stein Sandven ,  Kai Sørensen ,  Agnieszka Tatarek , and  Penelope Wagner  for their advice. Thanks are also due to the participants in the CS programs, the CS project organizers, the expedition cruise operators, and the staff of AECO. The authors are grateful for the support from the  EC H2020 projects INTAROS and CAPARDUS  (grants  727890  and  869673 ).
We present an important milestone for the CMS High Granularity Calorimeter (HGCAL) event reconstruction: the deployment of the GPU clustering algorithm (CLUE) to the CMS software. The connection between GPU CLUE and the preceding GPU calibration step is thus made possible, further extending the heterogeneous chain of HGCAL's reconstruction framework. In addition to improvements brought by CLUE's deployment, new recursive device kernels are added to efficiently calculate the position and energy of CLUE clusters. Data conversions between GPU and CPU are included to facilitate the validation of the algorithms and increase the flexibility of the reconstruction. For the first time in HGCAL, conditions data are deployed to the GPU and made available on demand at any stage of the heterogeneous reconstruction.
Introduction The High Luminosity LHC (HL-LHC) will start taking data in 2029, achieving unprecedented instantaneous luminosities of ∼5 × 10 34 cm 2 s -1 (more than twice LHC's current value) and a pileup of up to 200. An integrated luminosity of ∼3 ab -1 will be reached over 10 years  [1, 2]. In order to cope with the above, a major upgrade of the CMS endcap calorimeters  [3, 4 ] is being prepared. The novel High Granularity Calorimeter (HGCAL)  [2]  is an extremely challenging project, requiring the development of reconstruction code capable of fully exploiting the increased granularity under the expected extreme conditions. The biggest contributor to CPU usage is event reconstruction, of which currently ∼5% is used by HGCAL  [5]. CMS plans to port part of its reconstruction to Graphics Processing Units (GPUs), which represent one of the most promising hardware accelerator technologies on the market.
HGCAL and its Reconstruction Chain HGCAL will be a sampling calorimeter. The proposed design includes an electromagnetic section of silicon sensors as active material in the first 28 layers. A hadronic section comprises 8 silicononly layers followed by 14 silicon-scintillator hybrid layers, where the scintillation light is read out by silicon photo-multipliers. Both sections are interleaved with absorber layers. HGCAL will comprise ∼620 m 2 of silicon and ∼400 m 2 of plastic scintillators for a total of, respectively, ∼6 million and ∼240 thousand channels. Three subdetectors form HGCAL's hybrid detection technology: the first 28 layers made exclusively of silicon (CE-E) and the silicon and scintillator parts of the hadronic section (CE-H Si and CE-H Sci ). The reconstruction model envisioned for HGCAL is intended to be fast and flexible, comprising a sequence of modules/stages which transform raw data into physics objects.
Methodology The CPU and GPU workflows available in CMSSW follow the same structure (fig. 1 ). For the GPU case, the "Device to Host", "D→H" and "SoA to Legacy" modules guarantee a robust validation and increase the framework's flexibility. The implementation of all GPU code relies on Nvidia's CUDA  [9]. Conditions data are deployed from the CPU to the GPU and remain available in the GPU during an "interval of validity". Algorithms might run in parallel for the CE-E, CE-H Si and CE-H Sci subdetectors, using different CUDA streams, as is the case for the hit calibration and clustering stages. A GPU standalone version of CLUE  [11]  is currently available and being actively used for testing and optimization purposes. In the following we list the improvements and the features brought by deploying CLUE to CMSSW: • Data Formats: The standalone version is currently using Structure of Arrays (SoAs) where each array performs its own independent memory management.
Deployment of conditions data to the GPU HGCAL will have approximately 6 million detector elements; a fast and straightforward mechanism to transfer conditions data to the GPU is needed. An achievement of this work is to completely remove host-device transfers of conditions data between modules, making them available to all modules of the reconstruction chain via a single transfer per interval of validity, per CPU job and per available device. This approach offers several advantages: • Alleviates module (including CLUE) memory consumption, by avoiding the storage of conditions data; • Avoids slow and recurrent CPU→GPU data transfers; • Makes data transfers faster, as they are now done in parallel. Indeed, conditions data are mapped via a CMSSW indexing class which allows to retrieve information for each particular detector element.
Once all indexes have been transferred (serially), conditions (sensor positions, for instance) can be filled in the GPU in parallel, as they are independent from one another. • Simplifies the implementation of future GPU reconstruction modules, given that all geometry information is already accessible on the device. Position information is stored according to HGCAL's geometry: two endcaps, subdetectors, layers, and silicon cells (or tiles for the hadronic section). A single SoA-like memory block is allocated. The same specific order is later exploited on the GPU side to retrieve the correct information, mapping detector elements to their memory locations (O(1) lookup complexity). The procedure keeps data transfer to a minimum while ensuring spatial locality: conditions of adjacent detector elements will often be located very close in memory and be requested at close moments in time.
Validation and Performance Both validation and performance measurements are executed starting from three uncalibrated CPU collections of hits (one per subdetector). The hits are transferred to the GPU, are calibrated using techniques described in  [7] , and are directly forwarded to CLUE. Finally, GPU data in SoA format is converted back to the Legacy CPU format (as in fig. 1 ). For the validation, the CPU-only and the GPU steps just described are run over the same input dataset. The clusters thus obtained are compared in terms of their underlying quantities: no differences are found for hit energy densities and distances while minor discrepancies, currently under investigation, are observed for cluster energies and positions. Performance is assessed using a T4 Nvidia and V100 GPU, and an Intel(R) Xeon(R) Silver 4114 CPU (40 logical cores), using a custom benchmarking tool  [14].
Several pileup scenarios are assessed: 0, 50, 100, 140 and 200. We measure the throughput, defined as the number of events processed per second. Throughput measurements are performed using 4 and 8 jobs for the CPU and 1 job for GPU, with 10 CPU threads each and 400 events, skipping the first 100 to mitigate GPU initialization costs. 512 threads per block are tested in the GPU. Larger values were tested with no performance benefit. The measurements include initial uncalibrated hits data transfer from the CPU to the GPU. A single interval of validity was considered  1. The measurements in fig. 3  show the throughput for multiple CPU and GPU scenarios (left) and the obtained speedups (right), defined as the throughput of a GPU scenario divided by the most performant CPU throughput. We stress that the comparison is being made between a full CMS node and a single GPU.
In this work, our objective is to build a DT of a standalone PV system to deal with real-time energy management challenges. Figure  1   Sensors 2023,  23,  x FOR PEER REVIEW 2 of 23 consumption. It also enables predictive maintenance  [7]. Therefore, it is crucial to implement techniques that provide a wide range of operational data about the actual system  [8]. IoT technology can be associated with DT thanks to their ability of actuation and sensing. In this work, our objective is to build a DT of a standalone PV system to deal with real-time energy management challenges. Figure  1    To experimentally validate the proposed framework, we have developed a lab demonstrator according to IoT-based architecture for embedded and distributed instrumentation. Furthermore, to cope with the devices' heterogeneity, we use MATLAB Simulink.
Literature Review Several studies have considered photovoltaic systems monitoring. In  [9] , the authors used the IoT and MQTT (MQTT: Message Queuing Telemetry Transport) in web-based monitoring. They implemented this approach to monitor the performance of the solar panels and the battery system, as well as the energy consumption of a living laboratory. Similar work is reported in  [10]. When compared to other protocols, MQTT has a small footprint, making it much more suitable for resource-constrained environments. Despite To experimentally validate the proposed framework, we have developed a lab demonstrator according to IoT-based architecture for embedded and distributed instrumentation. Furthermore, to cope with the devices' heterogeneity, we use MATLAB Simulink. Indeed, this is a comprehensive software environment that can communicate with sensors and Programmable Logic Controllers through client/server applications.
We find the same confusion in the articles  [29] [30] [31] [32] [33]. Relatively to our contribution, Table  1  summarizes the state of art of the current literature dealing with digital twin applications. In this work, we propose a Digital Twin of a complete photovoltaic system using MATLAB software as a unified environment. This framework is suitable to address the following topics: -Real-time access to multi-protocol data for monitoring purposes. -Modelling, simulation, and real-time control of PV systems -Implementing of innovative energy management systems -Reporting In other words, this experimental platform can be used to compare simulation results and monitored data in real time. Indeed, it could be used to develop new approaches for fault detection and prediction issues. This integrated environment allows on the one hand to have a large panel of toolboxes (modelling, code generation, machine learning, advanced control, cloud computing.
PV System Description The stand-alone system is composed of the following elements (Figure  2 ): ✓ - ✓ ✓ - - ✗ Proposed ✓ Unified environment ✓ ✓ ✓ ✓ ✓ In this work, we propose a Digital Twin of a complete photovoltaic system using MATLAB software as a unified environment. This framework is suitable to address the following topics: -Real-time access to multi-protocol data for monitoring purposes. -Modelling, simulation, and real-time control of PV systems -Implementing of innovative energy management systems -Reporting In other words, this experimental platform can be used to compare simulation results and monitored data in real time. Indeed, it could be used to develop new approaches for fault detection and prediction issues. This integrated environment allows on the one hand to have a large panel of toolboxes (modelling, code generation, machine learning, advanced control, cloud computing…).
Sensors and Data Aquisition Developing a digital twin for renewable energy systems requires constant data collection and monitoring  [40]. Therefore, it is necessary to implement techniques that provide a wide range of data: -Weather data (temperature, irradiance, wind speed. ); -Real time electrical data (energy, currents, voltages, batteries' state of charge. ). The experimental platform is equipped with sensors of various technologies that do not use the same communication protocol. To transmit measurement data to a unified software environment, we have developed a hardware and software architecture based on the following components: -An IoT architecture combining smart sensors for electrical data  [41]  and a weather station. As IoT, these devices use heterogeneous communication protocols  [42] , including Modbus TCP/IP, HTTP, and PROFINET; -MATLAB Simulink, as an integrated environment concentrating all the operational data of the system, constitutes the core of the digital twin.
Sensors and Data Aquisition Developing a digital twin for renewable energy systems requires constant data collection and monitoring  [40]. Therefore, it is necessary to implement techniques that provide a wide range of data: -Weather data (temperature, irradiance, wind speed…); -Real time electrical data (energy, currents, voltages, batteries' state of charge…). The experimental platform is equipped with sensors of various technologies that do not use the same communication protocol. To transmit measurement data to a unified software environment, we have developed a hardware and software architecture based on the following components: -An IoT architecture combining smart sensors for electrical data  [41]  and a weather station. As IoT, these devices use heterogeneous communication protocols  [42]  , including Modbus TCP/IP, HTTP, and PROFINET ; -MATLAB Simulink, as an integrated environment concentrating all the operational data of the system, constitutes the core of the digital twin.
Control System At first level, the embedded control system is based on a S7-1200 PLC which controls solar emulator and relays. This allows experimental tests to be carried out according to real-time weather data or with historical data. At the second level, the overall control of the PV system is performed within MATLAB Simulink programs using the embedded PLC as a slave. Data communication is carried out using OPC-UA and Industrial Communication Toolbox™. Figure  4  describes the unified architecture proposed in this work.
6 10 C),  is the temperature of the PV cell,  is Boltzmann's constant (1. 38 10 /), and A is the diode ideality factor. A PV panel is made up of numerous identical PV cells connected in series to provide a higher voltage. A PV module composed of  identical cells in series can also be represented by the equivalent circuit shown Figure  4a  but the circuit needs to be modified  [43]  as follows (Figure  5b ): 𝑅 _ = 𝑛 𝑅 , 𝑅 _ = 𝑛 𝑅 , 𝐴 = 𝑛 𝑛, 𝐼 _ = 𝐼 , 𝐼 _ = 𝐼 (3) The solar panel datasheet does not include some important parameters, such as  and. To obtain them, we used the PV array tool of Matlab by setting the PV parameters of the studied solar panel. Table  3  shows the extracted parameters. The mathematical model of the studied solar panel is then implemented under MATLAB Simulink and the results are compared to datasheet data as shown in Figure  6.
A PV panel is made up of numerous identical PV cells connected in series to provide a higher voltage. A PV module composed of n s identical cells in series can also be represented by the equivalent circuit shown Figure  4a  but the circuit needs to be modified  [43]  as follows (Figure  5b ): R s_ns = n s R s , R sh_ns = n s R sh , A ns = n s n, I 0_ns = I 0 , I ph_ns = I ph (3) The solar panel datasheet does not include some important parameters, such as R s and R sh. To obtain them, we used the PV array tool of Matlab by setting the PV parameters of the studied solar panel. Table  3  shows the extracted parameters. The mathematical model of the studied solar panel is then implemented under MAT-LAB Simulink and the results are compared to datasheet data as shown in Figure  6. The dotted curves in Figure  5  represent data from the datasheet and the cont ones come from the mathematical model.
These results show a good correlation be the model and the solar panel manufacturer's data. To assess the model's effectiveness, we conducted indoor tests of the PV pan PV panel was illuminated using an artificial light source, while its output was con to a rheostat. To obtain the I-V curves, measuring instruments were integrated, as de in Figure  7a. The dotted curves in Figure  5  represent data from the datasheet and the continuous ones come from the mathematical model. These results show a good correlation between the model and the solar panel manufacturer's data. To assess the model's effectiveness, we conducted indoor tests of the PV panel. The PV panel was illuminated using an artificial light source, while its output was connected to a rheostat. To obtain the I-V curves, measuring instruments were integrated, as depicted in Figure  7a.
The dotted curves in Figure  5  represent data from the datasheet and the continuous ones come from the mathematical model. These results show a good correlation between the model and the solar panel manufacturer's data. To assess the model's effectiveness, we conducted indoor tests of the PV panel. The PV panel was illuminated using an artificial light source, while its output was connected to a rheostat. To obtain the I-V curves, measuring instruments were integrated, as depicted in Figure  7a. Figure  7b  shows the simulation and experimental results, confirming the accuracy of the implemented mathematical model.
If the value of the difference is not equal to zero and if an increase in PV voltage generates an increase in power, this means that there is a convergence to MPP (Maximum Power Point). However, if the power decreases, the PV voltage reference must be reduced to converge to the MPP. The developed algorithm is implemented by the MPPT strategy block (dark blue block) as shown in Figure  15a. The following figure (Figure  16 ) presents the implemented digital model under MATLAB Simulink thanks to a Simulink library containing the EMR basic pictograms. To evaluate the effectiveness of the developed digital model, simulations have been carried out with real-time solar irradiance and temperature data (Figure  17a ) for a typical day (30 June 2022). The digital model can be run and connected to real time weather conditions thanks to Simulink Desktop Real Time.
This latter provides a real-time kernel for executing Simulink models on a laptop or desktop running Windows or Mac OS X. The following figure (Figure  16 ) presents the implemented digital model under MAT-LAB Simulink thanks to a Simulink library containing the EMR basic pictograms. The following figure (Figure  16 ) presents the implemented digital model under MATLAB Simulink thanks to a Simulink library containing the EMR basic pictograms. To evaluate the effectiveness of the developed digital model, simulations have been carried out with real-time solar irradiance and temperature data (Figure  17a ) for a typical day (30 June 2022). The digital model can be run and connected to real time weather conditions thanks to Simulink Desktop Real Time. This latter provides a real-time kernel for executing Simulink models on a laptop or desktop running Windows or Mac OS X.
Indeed, the maximum relative deviations between the real and digital model data are 8% for PV power, 12% for battery power, and 2% for load power. To establish a feedback loop between the virtual and real word of the digital twin, we implement an energy management algorithm in MATLAB Simulink. The rule-based energy management algorithm sends control signals to the PLC (s7 1200). The advantage of using a rule-based control approach is its ease of implementation in real-time  [52]. The flowchart of the energy management algorithm is illustrated in Figure  24. It receives measurement data as inputs and generates relay control signals as outputs, which are subsequently transmitted to the PLC. To establish a feedback loop between the virtual and real word of the digital twin, we implement an energy management algorithm in MATLAB Simulink.
The rule-based energy management algorithm sends control signals to the PLC (s7 1200). The advantage of using a rule-based control approach is its ease of implementation in real-time  [52]. The flowchart of the energy management algorithm is illustrated in Figure  24. It receives measurement data as inputs and generates relay control signals as outputs, which are subsequently transmitted to the PLC. To establish a feedback loop between the virtual and real word of the digital twin we implement an energy management algorithm in MATLAB Simulink. The rule-based energy management algorithm sends control signals to the PLC (s7 1200). The advantage of using a rule-based control approach is its ease of implementation in real-time  [52]. The flowchart of the energy management algorithm is illustrated in Figure  24.
The objective of the research is to analyze whether the elaboration and implementation of biodiversity offsetting policies depend on the quality of spatialized ecological data. We compared the availability and use of such data in Colombia and France, at various stages of the process: (i) anticipation (policy elaboration), (ii) planning, and (iii) monitoring and control. We show that spatialized ecological data are used to implement offsetting policies (France), but may also be a tool to develop such policies, in particular through the elaboration of predetermined ratios based on available scientific data (Colombia). We also show, based on geographical, ecological and legal aspects of the data, that the geographical characteristics of a country and its legal framework are an important determinant of the efficiency of offsets.
, 2014). In Latin America, some countries, such as Colombia and Peru, have formalized their offsetting policies in the last decade  (Alonso et al. , 2020). The majority (about 77%) of countries with offsetting policies do not, however, strictly require that the offsetting takes place in relation to the mitigation hierarchy  (GIBOP, 2019). A prerequisite for BO is to accurately quantify the biodiversity losses caused by development projects  (Bull and Strange, 2018). The application of the mitigation hierarchy thus requires the ecological characterization of the territory under consideration. Although interoperability of databases can still be improved  (Edwards et al. , 2000) , the geospatial revolution  (Dangermond, 2011)  renders data on biodiversity increasingly available. Remote sensing and geographic information system -GIS-technologies help to survey and monitor biodiversity  (Geller et al.