esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Study Selection  
A total of 864 records were retrieved from PubMed/MEDLINE (375 articles), Google Scholar (369 articles), and the COCHRANE library (120 articles). Of these, 502 were excluded because duplicates or full texts were unavailable. Of the remaining 362 records, 280 were not relevant, and 82 full texts were screened; 51 entries were also excluded because the evaluation of the full texts did not reveal study outcomes or intervention methods. Finally, 31 articles were included in the qualitative analysis.  
The flowchart for study selection is shown in Figure 1.  
Figure 1: Study selection flowchart.
# Code Similarity and Location-Awareness Automatic Program Repair
## 7 Conclusions  
To resolve the problem of large search spaces and low search efficiency, we propose an automatic program repair based on code similarity and location awareness. First, in order to improve search efficiency, SLARepair combines code similarity with guided genetic programming. We designed a new fitness function for genetic programming to more efficiently select suitable repair ingredients. In addition, we use a location-awareness strategy to further reduce search space, which significantly improves repair efficiency. Finally, we utilize the test case prioritization approach combined with test case filtering to further reduce patch verification time. Experimental results show that SLARepair has higher repair efficiency and accuracy than existing automatic program repair approaches. SLARepair can repair 43 faults with a success rate of 19.2%, which is 3.6-9.4% higher than other repair tools. Compared with GenProg, SLARepair improves repair accuracy by 72%.  
In the future, we plan to study the following issues: We want to consider the automatic repair of software faults in a multi-fault environment. The automatic program repair works at the sentence level of the abstract syntax tree, but for some faults, the correct repair ingredient may exist at a finer granularity (i.e., variable and expression) than the sentence. We will continue to refine the repair granularity and use fine-grained repair ingredients to generate patches.  
Conceptualization, H.C. and F.L.; methodology, H.C. and D.H.; software, FL.; formal analysis, FL.; investigation, D.H.; resources, TL.; data curation, T.L., C.Z. and J.S.; writing--original draft, H.C.; writing--review and editing, D.H.; visualization, C.Z. and J.S.; supervision, T.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of China (Nos. 62206087, 62276091, and 61602154), the Innovative Funds Plan of Henan University of Technology (No: 2022 ZKCJ14), the Key Laboratory of Grain Information Processing and Control (Henan University of Technology), the Ministry of Education (No. KFJ)2022006), the Cultivation Programme for Young Backbone Teachers in Henan University of Technology, the Key scientific research projects of colleges and universities in Henan Province (No. 22A520024), the Major Public Welfare Project of Henan Province (No. 201300311200), and the Science and Technology Research Project of Henan Province (Nos. 232102210186, 222102210140).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All the data used in this paper can be traced back to the cited references.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction  
Recent research in Collaborative Networks (CN) has focused on the socio-technical aspects of collaboration, aiming to find solutions to prevent collaboration failures and potential conflicts. According to [1], large complex systems, such as CNs, fail because they do not recognize the social and organizational complexity of the environment in which these systems are deployed. Thus, improvements concerning the social interactions among the players, rather than only the technological aspects of such environments, need to be addressed [2; 3; 4].  
In this context, a framework for appraising emotions in a CN environment is proposed--the Collaborative EMOtion (C-EMO) modeling framework--which brings a richer perspective to decision-making processes, management of conflicts and failures of collaborative networks [5; 6]. C-EMO is aimed at improving the performance of existing CNs by adopting some of the models developed in human and computational sciences. As is known, members of a CN are organizations that might be dispersed geographically with different purposes and competences, and not humans, yet they are managed and operated by humans. Emotions are unquestionably related to humans, involving feelings, experience, behaviour, physiology and cognition, and it is evident that organizations cannot feel emotions in the same way humans do. Nevertheless, a kind of emotional state can be appraised when an organization belongs to a virtual environment that presupposes interaction and collaboration among its members. In the same line of thought, the emotional state of each participating organization would contribute to the assessment of the aggregated emotional state of the CN and, in this way, contribute to its success.  
This work is guided by the following research question: \"What could be an adequate modeling methodology approach to instantiate the C-EMO modeling framework and which methodologies would be suitable for the estimation/appraisal of collaborative network emotions?\". Regarding this, the work presented in this article proposes two simulation approaches for materializing the C-EMO modeling framework: the system dynamics and agent-based techniques.  
The remainder of this article is organized as follows: Section 2 presents an overview of the C-EMO framework; Section 3 briefly introduces the system dynamics and agent-based simulation modeling methodologies; Section 4 describes the proposed system dynamics simulation models to appraise the individual member and aggregated network emotions, and the agent-based simulation model to represent the CN environment; Section 5 presents the implementation aspects; Section 6 is dedicated to the validation aspects of the proposed models and, finally, Section 7 concludes and identifies future work directions.
# Generating Low-Earth Orbit Satellite Attitude Maneuver Profiles Using Deep Neural Networks
## 6 Conclusions  
For LEO satellites to perform observation missions and ground contact, it is essential to generate attitude profiles. Reference profiles for maneuvers between target pointings are directly related to the mission performance and accuracy of the antenna position to the ground contact. However, considerations, such as the initial and final boundary conditions (angles and angular velocities), constraint conditions (maximum jerk, angular acceleration, and angular rate), and throughput and upload capacity to be applied to the satellite must be taken into account. Therefore, this study used a data-based learning model and an optimization algorithm to develop a technique for generating reference profiles for the maneuvering phase of LEO satellites. The proposed technique demonstrated effective performance under various initial and final maneuvering conditions. In conclusion, it is expected that the proposed technique will optimize the LEO mission design performance and increase the satellite antenna pointing accuracy to the ground station.  
This research received no external funding.
# Coupling and Optical Analysis of a Round-Cornered Square-Shaped Microresonator
## 4 Results and Discussion
### Effect of Rotation in RCSS
By comparing the results of Figures 8 and 10, it can be seen that the same microresonators exhibit different optical characteristics when rotated, i.e., the values of the quality  
Figure 10: Optical parameters for the rotated RCSS microresonators of the varying radius of curvature \(R_{c}\). The rotation angle is 45\({}^{\circ}\) with respect to the feed waveguide around the coupling point. The coupling region is in a central corner of the RCSS. factor and the extinction ratio vary significantly. Rotating changes the coupling characteristics, which demonstrates the importance of coupling to the optical performance. This phenomenon is further investigated for the RCSS microresonator with a radius of curvature of \(R_{c}=50\)\(\upmu\)m with FEA analysis (see Figure 12), whose results have been validated with the data from the optical experiments. The results of both the FEA and the optical experiments demonstrate a significant improvement in the quality factor (from \(\sim\)10\({}^{3}\) to \(\sim\)10\({}^{5}\)) and transmission dip (\(r_{e}\) increases from 6 dB to 8.2 dB) at resonance, with the rotation of the RCSS resonator (\(R_{c}=50\)\(\upmu\)m) around the coupling point. This finding can be envisioned in Figure 13, noting that the corresponding values of the quality factor for the experimental results are lower than those for the FEA analysis due to several experimental and fabrication limitations. For instance, the coupling gap and width of the waveguide and microresonator inputs have experimental uncertainties (a sigma variation of 3.84 nm in width and 1.38 nm in thickness), and may not be the same as the dimensions used for the FEA and theoretical analyses. Additionally, optical microresonators are extremely sensitive to the thermal environment during the optical experiment, both due to the temperature-dependent refractive index of the microresonator and the bulk refractive index of the surrounding medium [7,8,16]. Other experimental uncertainties are the roughness of microresonator surfaces and attenuation/noise at input and output connections. However, the trend of the experimental results on the effect of rotation around the coupling point in RCSS microresonators is in a good agreement with the FEA analysis.
## 4 Results from the Literature Review
### The Use of Blockchain
#### 4.2.5 Development of Transport Infrastructure  
Infrastructure plays a foundational role in any transport system, supporting both existing and future solutions. A review of the literature identified three primary research areas in transport infrastructure, with a focus on sustainable development and adaptability during global crises (see Table 12). The electrification of transport is a prominent focus in recent studies. Electric vehicles (EVs) are increasingly accepted by the public, steadily growing their market share. Key factors influencing EV popularity include driving range, as well as the convenience and cost of energy supply--both of which largely depend on the availability and strategic placement of charging infrastructure [212]. Research underscores the importance of government financial support for initiatives such as fast-charging stations, which are essential to the wider adoption of EVs, fostering economic growth and promoting sustainability [213]. Expanding EV infrastructure, particularly through an accessible charging network, has a direct impact on consumer purchasing decisions and supports EV adoption [214]. The resulting rise in EVs on the roads, facilitated by adequate charging access, can significantly reduce exhaust emissions, contributing to improved air quality [215], which is especially critical in urban centers with severe pollution issues. As shown in [216], the electrification of transport can reduce harmful airborne pollutants, a vital factor for public health. Other studies suggest that the environmental and health impacts of transport could be further minimized through the integration of renewable energy sources, V2G technology, and smart charging systems [217]. These approaches not only reduce greenhouse gas emissions but also lessen reliance on fossil fuels [218], a key consideration in the face of potential supply disruptions. The literature also highlights the need for equitable development of EV infrastructure, extending coverage to less affluent areas [219], where electrification can improve quality of life and reduce social inequalities. Smart roads employ IoT technology, combining physical infrastructure, such as sensors, cameras, LED lighting, and solar panels, with advanced software solutions, such as AI or Big Data [220]. The concept of smart roads enhances existing road systems to meet
# Real-Time Psychological Stress Detection According to ECG Using Deep Learning
###### Abstract  
Today, excessive psychological stress has become a universal threat to humans. That stress can heavily affect work and study when a person repeatedly is exposed to high stress. If that exposure is long enough, it can even cause cardiovascular disease and cancer. Therefore, both monitoring and managing of stress is imperative to reduce the bad outcomes from excessive psychological stress. Conventional monitoring methods firstly extract the characteristics of the RR interval of an electrocardiogram (ECG) from a time domain and a frequency domain, then use machine learning models, like SVM, random forest, and decision tree, to distinguish the level of that stress. The biggest limitation of using these methods is that at least one minute of ECG data and other signals are indispensable to ensure the high accuracy of the results. This will greatly affect the real-time application of the models. To satisfy real-time detection of stress with high accuracy, we proposed a framework based on deep learning technology. The proposed monitoring framework is based on convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM). To evaluate the performance of this network, we conducted the experiments applying conventional methods. The data for the 34 subjects were collected on the server platform created by the group at the Institute of Psychology of the Chinese Academy of Sciences and our group. The accuracy of the proposed framework was up to 0.865 on three levels of stress using a 10 s ECG signal, a 0.228 improvement compared with conventional methods. Therefore, our proposed framework is more suitable for real-time applications  
psychological stress; deep learning; CNN; BiLSTM; real-time
# Bindings and RESTlets: A Novel Set of CoAP-Based Application Enablers to Build IoT Applications
## 6 Implementation and Evaluation  
### Implementation  
The selection of a good implementation platform is crucial to demonstrate the feasibility of new concepts and to show performance gains obtained through the proposed solutions. We used  
Figure 10: CoAP Messages used to create the required Binding Relationship.  
Figure 9: RESTlet block diagram for the smart home scenario.  
Contiki 2.7 [18] as a base system for all experiments, which was the latest stable version available at the time of starting our experiments. Contiki is an open-source embedded operating system suitable for constrained systems. Its innovative IP implementation, uIP, makes it a good solution for experiments that involve IP-based communications in the constrained world. In addition to this, all required features for our tests such as 6 LoWPAN, RPL, and CoAP have all been implemented. The CoAP implementation of Contiki is known as Erbium [19].  
Next to this, we also need to run some tests on non-constrained devices for comparison purposes. This requires a CoAP implementation that runs on non-constrained devices (e.g., gateways, Cloud servers, etc.). For this, we have used our own C++ based implementation of CoAP and its extensions, named CoAP++. Since both Erbium and CoAP++ do not support the proposed binding and RESTlet concepts, some modifications have been made, including the addition of new CoAP options and the introduction of a mechanism to define and instantiate RESTlets.
## 4 Open-Source Libraries
### Feast  
* Summary: FEAST [43] is the eigensolver that implements the FEAST algorithm, which was first proposed in [35]. Among other characteristics, the package includes the option to switch to IFEAST, which uses an inexact iterative solver to avoid direct matrix factorizations. This feature is particularly useful if the sparse matrices are very large and carrying out direct factorization is very expensive.
* Eigenvalue Methods: FEAST.
* Arithmetic: both real and complex types.
* Matrix Formats: FEAST depends on LAPACK as an interface for dense matrix, on SPIKE as an interface for banded matrix and on MKL-PARDISO [65] for sparse CSR matrix formats. In addition, FEAST includes RCI and, thus, data formats can be customized by the user. Using the sparse interface requires linking FEAST with Intel MKL. Linking the library with BLAS/LAPACK and not with MKL is possible, but seriously impacts the performance and the results of the library.
* Returned Eigenvectors: the FEAST algorithm implementation is two-sided.  
* Dependencies: FEAST requires LAPACK/BLAS.
* GPU-based version: currently not supported.
* Parallel version: PFEAST and PIFEAST are the parallel implementations of FEAST and IFEAST, respectively. Both support three-Level MPI message parsing layer, with available options for the MPI library being Intel MPI, OpenMPI, and MPICH. PFEAST and PIFEAST employ MKL-Cluster-PARDISO and PBiCGStab, respectively, as their parallel linear sparse solvers.
* Development: the first version of FEAST (v1.0) was released in 2009 and supported only symmetric matrices. Through the years FEAST added many functionalities, such as support for non-symmetric matrices, parallel computing, and support for polynomial eigenvalue problems. Since 2013, Intel MKL has adopted FEAST v2.1 as its extended eigensolver. The latest version is FEAST v4.0, which was released in 2020.
## 2 Materials and Methods
### Answers Evaluation
The resulting kappa values were 0.79 for accuracy and 0.73 for completeness, indicating substantial inter-rater agreement. These results support the robustness of the subjective evaluation process used in this study. In parallel with expert evaluation, the readability of each chatbot's response was assessed using the online tool [[https://readable.com](https://readable.com)]([https://readable.com](https://readable.com)). Readability scores were determined based on five established metrics:  
* **Flesch-Kincaid Reading Ease Score (FRES)**: Calculated using the average number of syllables per word and words per sentence. The FRES produces a score ranging from 0 to 100, with higher values indicating easier-to-read text [24; 25]. * **Flesch-Kincaid Grade Level (FKGL)**: Estimates the United States school grade level required to comprehend the text, based on syllables per word and sentence length [26]. * **Gunning Fog Index (GFI)**: Provides a score typically between 6 and 17, where 6 corresponds to the reading level of an 11-12-year-old, 12 to a high school graduate, and 17 to a college graduate [24]. * **Simple Measure of Gobbledygook (SMOG) Index**: Considered the gold standard for evaluating healthcare materials, as it estimates the reading level required for 100% comprehension [25]. * **Coleman-Liau Index (CLI)**: Calculates readability based on the average number of characters per 100 words and the average sentence length, producing a United States grade-level score; lower values denote easier readability [26]. These readability metrics were used to assess the accessibility of AI-generated content for general audiences, with a particular focus on healthcare-related communication. Clinical readability guidelines, including those from the National Institutes of Health and the American Medical Association, recommend that patient-facing materials be written at a sixth- to eighth-grade reading level. Accordingly, we used the following thresholds: FKGL \(\leq\) 8, SMOG < 9, GFI \(\leq\) 12, CLI \(\leq\) 8, and FRES \(\geq\) 60 to determine the proportion of chatbot responses that met these standards [24; 25; 26].
# Radiometric Method for Determining Canopy Stomatal Conductance in Controlled Environments
## 2 Materials and Methods
### Canopy Decoupling Coefficient  
At the canopy level, relative magnitudes of \(\mathrm{G_{S}}\) and \(\mathrm{g_{A}}\) determine the effect of changes in stomatal conductance on the transport of heat and water vapor from an average leaf surface, through leaf and canopy boundary layers to an effective sink for heat and water vapor above the canopy [3]. The boundary layer surrounding vegetation allows transpired water vapor to humidity air near the leaf surface (e.g., it lowers \(\mathrm{D_{S}}\) compared to \(\mathrm{D_{Bulk}}\)), altering the driving force for transpiration; thus, \(\mathrm{E_{can}}\) becomes less sensitive to changes in stomatal conductance. This feedback between \(\mathrm{E_{can}}\) and \(\mathrm{D_{S}}\) is important for diminishing the sensitivity of \(\mathrm{E_{can}}\) to proportional changes in \(\mathrm{G_{S}}\)[1,3,4].  
The dimensionless decoupling coefficient, \(\Omega\), quantifies the sensitivity of \(\mathrm{E_{can}}\) to changes in stomatal aperture and depends on the influence that \(\mathrm{G_{S}}\) and \(\mathrm{g_{A}}\) exert on how closely conditions at the leaf surface (e.g., \(\mathrm{D_{S}}\)) are linked to \(\mathrm{D_{Bulk}}\) of the free air stream. Equation (10) calculates \(\Omega\) from \(\mathrm{g_{A}}\), \(\mathrm{G_{S}}\), and \(\mathrm{\varepsilon}=\mathrm{s}/\gamma\), where \(\mathrm{s}=\mathrm{the}\) slope of the saturation vapor pressure versus temperature, and \(\gamma=\mathrm{the}\) psychrometric constant [10].  
\[\Omega=(\mathrm{\varepsilon}+1)/[\mathrm{\varepsilon}+1+(\mathrm{g_{A}}/ \mathrm{G_{S}})]. \tag{10}\]  
Equation (10) assumes that the available energy is independent of surface temperature and neglects changes in leaf temperature due to changes in stomatal conductance [4]. In spite of this simplification, \(\Omega\) is useful for (1) exploring how differences in canopy architecture (e.g., wheat and soybean) affect canopy transpiration and (2) quantifying the sensitivity of \(\mathrm{E_{can}}\) to changes in stomatal conductance. Typical values for \(\mathrm{g_{A}}\), \(\mathrm{G_{S}}\), and \(\Omega\) for crops and forests are depicted in Table 1. The magnitude of \(\Omega\) effectively determines whether Ecan is primarily controlled by stomata or by the supply of energy. Generally, forests are well coupled, and their transpiration rate is accurately predicted by the Priestley-Taylor equation [3,4]. The sensitivity of transpiration to stomatal control, \(\mathrm{dE_{can}}\), is determined by the degree of coupling \((1-\Omega)\) between \(\mathrm{D_{S}}\) and \(\mathrm{D_{Bulk}}\) (Equation (11); [3,6,7]).  
\[\mathrm{dE_{can}}=(1-\Omega)\times(\mathrm{E_{can}}/\mathrm{G_{S}})\times \mathrm{dG_{S}}. \tag{11}\]