esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### The Advantages of Virtual Reality (VR) Teaching for Social Skills Training in Students with Autism  
Virtual reality (VR) training has become an increasingly important tool for promoting social interaction and communication learning in students with autism [12]. Studies highlight its numerous benefits for special needs students, particularly in facilitating skill transfer from virtual environments to real-life interactions [13]. The effectiveness of immersive VR as a training tool stems from its ability to simulate real-world characteristics, incorporating seven key features: controllable input stimuli, modification for generalization, safer learning situations, a primarily visual/auditory world, individualized treatment, preferred computer interactions, and trackers [14]. These attributes make VR an effective method for improving both verbal and non-verbal communication skills while fostering social interaction.  
Research has provided compelling evidence of VR training effectiveness. [PERSON] et al. [15] conducted a study comparing a VR-based program to traditional methodologies such as Discrete Trials Instruction (DTI), Applied Behavior Analysis (ABA), and Naturalistic Developmental Behavioral Interventions (NDBIs). Their findings confirmed that the VR program not only enhanced social interactions, but also improved communication and behavioral outcomes, demonstrating its superiority over conventional approaches.  
Beyond general social interaction, VR-based interventions have also contributed to notable advancements in specific aspects of social behavior. Studies have reported improvements in emotion recognition, collaborative activities, and turn-taking skills among participants [16; 17; 18]. These interventions have proven effective in helping individuals understand and respond to social cues more naturally. Additionally, desktop VR systems have gained popularity due to their user-friendly interfaces and ability to provide accessible social skills training without specialized equipment [19; 20]. These features make desktop VR systems an ideal, scalable, and flexible solution for autism education.  
Overall, the growing body of research underscores the potential of VR in enhancing social skills and emotional intelligence in individuals with autism. By offering diverse tools and techniques, VR-based interventions continue to support social development, providing an immersive and structured learning environment that caters to individual needs.
# Synchronizing TSN Devices via 802.1 AS over 5G Networks
## 3 Related Research  
3 GPP Release 16 and 17 integrate TSN functionality in 5G networks. This is an active and evolving research area, and TSN 5G integration is not feasible until time synchronization between TSN devices over 5G does not fulfill the accuracy requirements of the IEEE 802.1 AS standard.  
In [4], [PERSON] et al. created TSN over Wi-Fi and over 5G mobile networks using common off-the-shelf (COTS) devices. They tested the networks for single-hop and two-hop performance of 802.1 AS over wireless links and found that with COTS devices, successful synchronization, that meets the standards of Industrial TSN (sub-microsecond accuracy), cannot be achieved. Their results show a time synchronization accuracy of up-to 2.5 milliseconds in best-case scenarios for 5G links. They also show that due to the variations in the jitter and the measured propagation delay, accurate synchronization is not achieved.  
[PERSON] et al. in [5] implemented 802.1 AS-based time synchronization over 5 GS in a hardware test setup. Their findings state that <1 millisecond accuracy can be achieved with 802.1 AS time synchronization over 5G.  
[PERSON] et al. [6] published an analytic framework and showed that sub-microsecond accuracy can theoretically be achieved, if different errors, such as error in path delay estimation and error in time of arrival estimation, associated with reference time indication are minimized. They also mention that variations and errors in propagation delay estimation can have significant impact on the synchronization accuracy as we show in our simulations.  
[PERSON] et al. in [7] present a formulation method for error computation in time synchronization mechanisms for TSN and 5G integration. The paper considers a maximum error of 50.5 nanoseconds for propagation delay measurement, and a symmetric 5G link is assumed. Their model calculated synchronization errors of up to 8 ns after successful synchronization. The paper focuses on minimizing the error rate during synchronization while assuming a 50 nanoseconds symmetric link delay in wired and wireless links, which is unrealistic.  
The next section describes the method implemented and used for synchronizing TSN devices over a 5G network and the scenarios simulated to test the synchronization accuracy.
# Learning and Compressing: Low-Rank Matrix Factorization for Deep Neural Network Compression
## 3 Algorithm  
### TSVD Technique  
In a numerical solution, TSVD is reformulated as the optimization problem Equation (1) that minimizes the Frobenius norm between the given matrix \(\mathbf{W}\) and the low-rank approximated matrix \(\mathbf{U}\mathbf{V}\). We assume that there is a given rank \(k\) matrix \(\mathbf{W}\in\mathbb{R}^{m\times n}\) which is decomposed into three matrices: the left singular matrices \(\mathbf{U}\in\mathbb{R}^{m\times m}\), the diagonal singular matrix \(\mathbf{\Sigma}\) and the right singular matrices \(\mathbf{V}\in\mathbb{R}^{n\times n}\), and the closed form is as follows:  
\[\mathbf{W}=\mathbf{U}\mathbf{\Sigma}\mathbf{V}^{\mathbf{T}},\text{s.t.}\mathbf{ U}^{\mathbf{T}}\mathbf{U}=\mathbf{I}_{\mathbf{m}},\mathbf{V}^{\mathbf{T}}\mathbf{V}= \mathbf{I}_{\mathbf{n}}, \tag{2}\]  
where \(\mathbf{U}\) and \(\mathbf{V}\) are orthogonal matrices, \(\mathbf{\Sigma}=\text{diag}(\mathbf{\varpi}_{1},\mathbf{\varpi}_{2},..., \mathbf{\varpi}_{n})\) and the diagonal elements \(\mathbf{\varpi}\) are the singular values ordered as \(\mathbf{\varpi}_{1}>\mathbf{\varpi}_{2}>...>\mathbf{\varpi}_{n}\geq 0\). In many cases, the sum of the top 10% or even 1% of the singular values accounts for more than 99% of the sum of all singular values. In other words, we can approximate the description matrix \(\mathbf{W}\) with the largest \(r\) singular values where \(r\leq k\leq\min(m,n)\). Therefore, the approximated matrix \(\mathbf{\Theta}\) can be reconstructed as follows:  
\[\mathbf{W}\approx\mathbf{\Theta}=\mathbf{U}(:,1:r)\mathbf{\Sigma}(1:r,1:r) \mathbf{V}(:,1,r)^{T} \tag{3}\]  
For the given matrix \(\mathbf{W}\in\mathbb{R}^{m\times n}\), after applying Equation (3), matrices \(\mathbf{U}\), \(\mathbf{\Sigma}\), and \(\mathbf{V}\) are truncated with low-rank \(r\). We assume that \(\mathbf{U}=\mathbf{U}(:,\mathbf{1:r})\) and \(\mathbf{V}=\mathbf{\Sigma}(\mathbf{1:r,1:r})\mathbf{V}(:,\mathbf{1,r})^{ \mathbf{T}}\). Therefore, we can obtain the compression ratio of the TSVD is \(\text{O}\frac{mn}{(m\pi+nr)}\). Obviously, \(mr+nr\) will be smaller than \(mn\) when \(r\) is significantly smaller than \(\min(m,n)\).
## 3 Physics-Informed Neural Networks (PINNs)
### Foundations of PINNs
Assuming that \(\mathcal{L}_{F}\) represents the error of each physical condition, and \(\mathcal{L}_{B}\) represents the error of boundary violations, we can illustrate a general loss function with \(\mathcal{L}_{data}\) as the MSE function, which minimizes the following expression:  
\[\mathbf{\theta}^{*}=\operatorname*{arg\,min}_{\mathbf{\theta}\in\Theta} \Bigg{(}\frac{\lambda_{d}}{N}\sum_{i=1}^{N}(f(x_{i};\mathbf{\theta})-y_{i})^{2}+ \frac{\lambda_{F}}{N}\sum_{i=1}^{N}\mathcal{L}_{F}(f(x_{i};\mathbf{\theta}),\hat{u }_{F}(x_{i}))+ \tag{3}\] \[\frac{\lambda_{B}}{N}\sum_{i=1}^{N}\mathcal{L}_{B}(f(x_{i};\mathbf{ \theta}),\hat{u}_{B}(x_{i}))\Bigg{)},\]  
where \(f(x_{i};\mathbf{\theta})\) is the model with parameters \(\mathbf{\theta}\), \(y_{i}\) are the observed values, \(\mathcal{L}_{F}\) represents the physical loss function, with \(\hat{u}_{F}\) as the conditions imposed on the model, \(\mathcal{L}_{B}\) is another function that represents constraints or boundary conditions imposed trough \(\hat{u}_{B}\), and \(\lambda_{d},\lambda_{F},\lambda_{B}\) are weighting coefficients to the different losses. As shown in Figure 3, the join of the loss functions described above is represented by \(\mathcal{L}_{total}\), which is used to adjust the ith estimated target \(\hat{\mathbf{y}}_{i}\) with respect to the actual value \(\mathbf{y}_{i}\) straitened by the physical rules. Finally, the objective is to minimize deviations from the underlying physics law by monitoring the training process with measured or generated data (i.e., \(\mathcal{L}_{data}\)) and by training to diminish the output from the physics law. To further minimize deviation from the physical law during training, the term \(\mathcal{L}_{F}\) ensures that such a neural network generalizes better given unknown inputs by preventing overfitting. The physical loss enables training the output variables to adhere to rules, typically their first and second-degree derivatives, and a local region around each input value of the given data [19]. Physics-informed neural networks (PINNs) integrate various equations to improve predictive accuracy and adherence to physical laws. These include initial conditions that define the system's state at the start, constraints that impose behavioral limits, governing rules such as differential equations describing system evolution, and boundary conditions that address edge cases or boundary-specific behaviors. By incorporating these elements, PINNs can accurately model complex physical systems and generalize effectively beyond training data, making them highly applicable in scientific and engineering domains.
## Discussion  
### Convex Quadratic Versus Hybrid Piecewise Logistic Modeling of LSP for Land Cover Classification  
The hybrid piecewise logistic model (HPLM) was first designed to detect vegetation phenology from MODIS time series [34]. The HPLM has a well-refined fitting algorithm, but strict requirements regarding numerical and temporal distributions of observations [22; 34]. On the other hand, the convex quadratic model (CxQ) has recently characterized seasonal patterns of vegetated surfaces by incorporating the use of MODIS LST 8-d composites at 1 km spatial resolution [11; 46; 47]. The fitting algorithm and data requirements for the CxQ are more flexible than for the HPLM, due to fewer parameter coefficients to estimate [18]. When data requirements were satisfied, the HPLM could fit  
Figure 3: 2016 ARD-based land cover maps of area around Sisseton, the county seat of Roberts County, South Dakota. Note the barren/developed class in the phenometrically-based RFC maps (CxQ and HPLM) is not as accurate as in the SPL or CMB RFC maps.  
the observed EVI2 pattern more precisely than the CxQ, leading to a higher classification accuracy. However, when fewer observations were available, e.g., outside the Landsat sidelap zones, the CxQ model could serve as a back-up algorithm in this temperate climate where temperatures constrain the initiation and tempo of spring growth. The fundamental challenge for both CxQ and HPLM is dealing with gaps in observations during the growing season, arising from few good observations available in some years and/or over some areas. Although many observations were available for the study area in both years (Figure S4), a lower minimum number of observations (at least ten) was required to fit the HPLM in this study (compared to the fitting for MODIS data in [34] and for AVHRR (Advanced Very High Resolution Radiometer) data in [35]) to generate a map without gaps. Even within the Landsat sidelap zones, we were not always able to retrieve sufficient observations to fit the LSP model [18]. However, the temporal density of observations could be increased by bringing together complementary sensors. For example, our results show that Sentinel-2 data can be used with Landsat ARD in a phenometrically-based classification. An alternative feasible solution may be to leverage very high spatiotemporal but low spectral resolution data from a small satellite constellation to fill any gaps [48].
## Materials and Methods
### Characterization of the Electrospun Fibers and Films
#### 2.7.5 Color Measurements  
The color of the pectin-based interlayers and the resultant multilayers in PHBV was carried out in a benchtop spectrophotometer Konica Minolta CM-5, from Hunter Associates Laboratory, Inc. (Reston, VA, USA). The Commission Internationale de l'Eclairage (CIE) standard illuminant D65 was used to assess the CIE Lab color space coordinates L*a*b* using an observer angle of 10\({}^{\circ}\). L* represents the luminance (black to white), a* indicates the change between green and red, and b* represents the change from blue to yellow. The colorimeter was calibrated with a white standard tile and a mirror device for the black (no light reflection). The L*a*b* coordinate values were obtained on five different samples and the color difference (\(\Delta\)E*) was calculated following Equation (1).  
\[\Delta\mathrm{E}^{*}=\left[(\Delta\mathrm{L}^{*})^{2}+(\Delta\mathrm{a}^{*}) ^{2}+(\Delta\mathrm{b}^{*})^{2}\right]^{0.5} \tag{1}\]  
where \(\Delta\mathrm{E}^{*}\), \(\Delta\mathrm{a}^{*}\), and \(\Delta\mathrm{b}^{*}\) corresponded to the differences between the color parameters of the multilayer samples and the values of the PHBV/PHBV multilayer. Color change was evaluated as follows: Unnoticeable (\(\Delta\mathrm{E}^{*}{}_{\mathrm{ab}}<1\)), only an experienced observer can notice the difference (\(\Delta\mathrm{E}^{*}{}_{\mathrm{ab}}\geq 1\) and \(<\)2), an unexperienced observer notices the difference (\(\Delta\mathrm{E}^{*}{}_{\mathrm{ab}}\geq 2\) and \(<\)3.5), clear noticeable difference (\(\Delta\mathrm{E}^{*}{}_{\mathrm{ab}}\geq 3.5\) and \(<\)5), and the observer notices different colors (\(\Delta\mathrm{E}^{*}{}_{\mathrm{ab}}\geq 5\)) [59].
## 4 Discussion
### Effects of Distance Between Structures
Therefore, it is reasonable to expect that \(\Delta\)PSD of structure will decrease from the highest value when the surrounded structure is located at a distance smaller than \(\lambda/4\), or it will increase from the lowest value when neighboring building moves out of the range of \(\lambda/2\) (arrow lines shown in Figure 9). Figure 9: Cont. Figure 9: Variation of \(\Delta\)PSD with distance between foundations (**a**) for S1 structure in adjacent to S2 structure in S1-S2 case; (**b**) for S1 structure adjacent to S3 structure in S1-S3 case; (**c**) for S2 structure in S1-S2 case; (**d**) for S3 structure in S1-S3 case. \(\lambda\) is wavelength emitted from adjacent structure.
## 3 Methodology
### Particle Swarm Optimization  
In 1995, [PERSON] and [PERSON] proposed a parallel algorithm called particle swarm optimization (PSO), which is a stochastic optimization technique that was inspired by the social behavior of fish schooling patterns [34]. The basic concept of PSO starts by initializing a group of random particles (solutions) and then searches for optimal parameter values by updating the generations in a \(D\)-dimensional solution search space. For each iteration, the particle updates itself based on tracking two parameters which consist of the personal best value \((P_{p\_best})\) and the group best value \((P_{g\_best})\). The target value is set as the best value of the fitness function. This new position is simply calculated as the sum of the previous position and the new velocity as follows:  
\[X_{i,t+1}=X_{i,t}+V_{i,t+1}, \tag{4}\]  
\[V_{i,t+1}=\omega_{i}V_{i,t}+c_{1}r_{1}(P_{p\_best_{i,t}}-X_{i,t})+c_{2}r_{2},(P _{g\_best_{i,t}}-X_{i,t}), \tag{5}\]  
where \(X_{i,t}\) and \(V_{i,t}\) are the position and velocity of the particle for iteration \(t\), respectively. \(\omega_{i}\) is the weight of the inertia factor, \(c_{1}\) and \(c_{2}\) are the acceleration coefficients, and \(r_{1}\) and \(r_{2}\) are random numbers in the range [0,1]. The weight inertia is designed to reduced linearly and can be expressed by:  
\[\omega_{i}=\omega_{max}-\frac{\omega_{max}-\omega_{min}}{iter_{max}}\times t, \tag{6}\]  
Figure 3: The architecture of a feed-forward ANN for wind speed estimation using the reflected signal from the BeiDou satellite.  
where \(\omega_{min}\) and \(\omega_{max}\) are the minimum and maximum inertia weights. \(iter_{max}\) is the maximum number of iterations. A summary of the PSO approach is shown in Algorithm 1.  
```
Input: Problem size, Population size Output: Optimal point \((P_{g\_best})\)
1 Population \(\leftarrow\) []
2\(P_{g\_best}\leftarrow\) []
3 fori = 1:Population sizedo
4\(P_{velocity}\leftarrow\) Random Velocity() // Initialize the position and
5\(P_{position}\leftarrow\) Random Position() // velocity of particles
6\(P_{p\_best}\gets P_{position}\)
7 if\(Cost[P_{p\_best}]\leq Cost[P_{g\_best}]\)then
8\(P_{g\_best}\gets P_{p\_best}\) // Update the global best position
9 whileStop condition()do
10 forP \(\in\) Populationdo
11\(P_{velocity}\leftarrow\) Update Velocity(\(P_{velocity},P_{g\_best},P_{p\_best}\)) // Update the position and
12\(P_{position}\leftarrow\) Update Position(\(P_{position},P_{velocity}\)) // velocity of particles
13 Evaluate the fitness value of the particle \(f(P_{position})\)
14 if\(Cost[P_{position}]\leq Cost[P_{p\_best}]\)then
15\(P_{p\_best}\gets P_{position}\)
16 if\(Cost[P_{p\_best}]\leq Cost[P_{g\_best}]\)then
17\(P_{g\_best}\gets P_{p\_best}\) // Update the global best position
18 return\(P_{g\_best}\)
```  
**Algorithm 1**Pseudocode for particle swarm optimization
## 4 Measurement and Comparison of Related Antenna
Table 3 shows that at 0.5 GHz, practically all models still have S21 \(>-20\) dB because the distance between components is less than 0.5, implying that another strategy for mutual coupling reduction is required. Table 3 further shows that the maximum directivity attained by MLPT is 16.56 dBi, whereas ELPT produced the lowest SL, best beam squint, and lowest beam width.
# Business Impact Analysis of AMM Data: A Case Study
## 4 Business Impact Analysis of AMM Datasets  
Business Impact Analysis (BIA) is widely used to assess the impact on an organization when there is an interruption in the supply of products or services that are critical to its operations. It is primarily used as an input to business continuity management (ECM) and risk analysis. BIA primarily involves expert determination of the minimum levels of resources required to restore critical activities within specified times and levels.  
The methodological evaluation of BIA for AMM purposes involves, in the first phase, the assessment of the value/quality of information assets using the Confidentiality, Integrity, Availability (CIA) triad. The task is to determine the significance of homogeneous data assets, which then influences the choice of protection level.  
This is followed by the implementation of standard procedures for BIA, which starts by defining and qualifying the basic risks for BIA related to the area of reregulated electricity distribution service to end customers.  
This is followed by the establishment of service availability parameters in the form of Maximum Tolerable Period of Disruption (MTPD), Medium Impact Period of Disruption (MIPD), and Maximum Tolerable Data Loss (MTDL) In parallel with this, recovery parameters in the form of Recovery Time Objective (RTO) and Recovery Point Objective (RPO) must be established. For uniform parameters, threshold values are established that reflect the legislative and regulatory requirements for this type of regulated business.  
BIA focuses primarily on metering data as important datasets. Metering data are used by different distribution companies for settlement purposes with different market participants, as well as to ensure grid stability, especially given the growth of small-scale renewables. Without these data, the energy market could not function, and the risk of grid instability would increase. Therefore, the analysis needs to assess the risk of their unavailability to the distribution company. As an example of a distribution company, CEZ Distribuce, a.s., one of the three licensed distribution system operators, will be used in the currently deployed variant of meter communication with a point-to-point data center.
# PNER: Applying the Pipeline Method to Resolve Nested Issues in Named Entity Recognition
## 1 Introduction
For the sequence labeling task, we employed the BERT-Bi-LSTM-CRF model, a state-of-the-art approach. Leveraging BERT [13] for pre-training, this model adeptly constructs word embeddings. It integrates bi-directional long short-term memory (Bi-LSTM) for comprehensive feature extraction, accommodating both long- and short-term word embeddings. Finally, conditional random field (CRF) was utilized for NER labeling, a conventional method for NER tasks. For the classification task, we utilized transfer learning, specifically fine-tuning a pre-trained BERT model, to address this challenge. Our main contributions are listed as follows:  
Figure 1: An example of nested entities from GENIA dataset; protein and DNA are categories of entities. * We refined the sequence labeling to effectively tackle nested entity challenges. Our proposed approach involves initially utilizing the merged label layer method for identifying entity boundaries, followed by the classification of entity categories. This strategy preserves the benefits of the merged label layer method while mitigating the issue of exponentially increasing label counts. * Introducing an entity hierarchical system allowed us to discern between different entities nested within each other. This hierarchical grading of entities, from outermost to innermost, ensures the precise identification of entity boundaries in instances with nesting complexities. * In our approach to entity classification, we were inspired by the pipeline method employed in entity relationship extraction. Our innovation involved introducing entity hierarchies as text identifiers. This decision was rooted in our belief that modifications in entity hierarchies would significantly influence the identification of entity classes. * Following experimentation across three diverse datasets, our findings underscore that our methodology not only refines the merged label layer sequence labeling method but also maintains robust competitiveness when compared to existing models. The structure of this paper, following the already completed Introduction section (Section 1), is organized as follows: Section 2 provides a review of related work. Section 3 describes the three datasets used in our experiments, along with a statistical analysis of these datasets. Section 4 is dedicated to the proposed method, with Section 4.1 detailing the labeling method for the fusion labeling layer, Section 4.2 discussing the labeling method with a focus on the uniformity of entity categories, and Section 4.3 explaining the process of inserting identifiers for entity category recognition. Subsequently, Section 5 presents the results of our experiments, including comparisons and analyses relative to both the model before improvement and other benchmark models. We conclude our findings in Section 6.