esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
With the development of vehicular communication technology, the electromagnetic compatibility requirements of vehicular communication systems are becoming more demanding. The traditional four-level electromagnetic compatibility evaluation model is widely applied in many scenarios. However, this model neglects the mutual interference of electronic devices inside a vehicle, and it cannot evaluate whether reduced radio receiver sensitivity, antenna isolation, and communication distance satisfy the system requirements for vehicular communication, thus making it unsuitable for digital communication systems. With the development of remote sensing technology, high-precision digital maps are easy to acquire and thus widely used. In this work, a modified five-level evaluation model based on digital maps is proposed, where digital maps are employed to support receiver sensitivity, antenna isolation, and communication performance evaluation. Through remote sensing technology and digital maps, a terrain profile is obtained, and a more accurate vehicle communication propagation model is established. In the experiment, an actual armored vehicular communication system example is applied to verify the performance of the proposed five-level evaluation model. Compared with the free-space propagation model, the error of the actual power received by the receiver is reduced by 0.97%, and the error of the communication distance where the sensitivity of the receiver is reduced by more than the system EMC threshold is reduced by 16.78%. The calculated antenna isolation degree is basically consistent with the actual measurement data. The model is able to evaluate the electromagnetic compatibility of an armored vehicular communication system more quickly, accurately, and comprehensively compared to previous evaluation models.  
electromagnetic compatibility (EMC); digital maps; armored vehicle; wireless communication system; remote sensing; evaluation model Article Construction and Analysis of the EMC Evaluation Model for Vehicular Communication Systems Based on Digital Maps [PERSON]  
1 School of Electronic Engineering, Xidian University, Xi'an 710071, China; [EMAIL_ADDRESS] (G.Z.); [EMAIL_ADDRESS] (F.W.); [EMAIL_ADDRESS] (Y.Q.); [EMAIL_ADDRESS] (B.J.)
## 2 Materials and Methods  
### Study Design  
In accordance with the Declaration of Helsinki, the research protocol, designed as a cross-sectional study, was approved on 9 June 2021 by the Institutional Review Board of the Department of Human Sciences, Society, and Health of the University of Cassino and Lazio Meridionale (approval number 11748) to examine the effects of wobble-board hand exercises on the inter-limb asymmetry of tremor. This observational cohort study was conducted in the Sport and Exercise Physiology Laboratory of the University of Cassino and Lazio Meridionale. All participants gave their written consent to participate after receiving both written and oral information regarding the procedures. Participants were informed that they could withdraw from the study at any time without incurring any adverse consequences. Consent for the publication of figures was also obtained for the participant's photographs.  
The inter-limb asymmetry of tremors is an important parameter often evaluated in clinical populations, such as those affected by essential tremors or Parkinson's disease. The assessment of the reduction in the inter-limb asymmetry of tremor is commonly carried out after 6-week generalized strength, resistance, and yoga interventions [22; 25]. However, to the best of our knowledge, no studies have investigated the acute effect of a single session specifically focused on hand exercises. To address this gap and provide scientific evidence regarding the influence of hand exercise on the asymmetry of tremors, our study took a new methodological approach introducing wobble-board hand exercise as an effective methodology involving movement in all the anatomical body planes and axes, usually involved in all daily tasks and not considered in generalized training programs. The new methodology was ideated using dynamic tasks with real-time performance displayed to simulate the innovative hand training method, such as gaming and e-sports, which could attract several populations from athletes to sedentary participants and from children to older adults [29; 30]. Considering that wobble-board hand exercise training includes hand movement in different motion patterns to promote the contraction of all the small muscles of the hand and fingers, it could provide more information on fine motor skills, in comparison with generalized strength and resistance training. Thus, to establish whether the inter-limb asymmetry of tremor could be acutely modified by hand exercise training, the assessment of hand tremor was performed before (PRE) and after (POST) hand exercises on a wobble board.
## 5 Evaluation
### Overhead Evaluation
#### 5.5.1 Upstream Overhead  
In the case of packet loss, the packets sent upstream by the MTNs and the receivers are NACK packets. As shown in Figure 7, all the upstream overhead curves show a downward trend with increasing cache size, because more and more recovery packets are retransmitted by MTNs. It is worth noting that when the cache size is larger than 600, the upstream overhead curve in Figure 6(c) starts to drop significantly. When the cache size is less than 500, the upstream overhead curve in Figure 6(d) decreases significantly, while when the cache size is greater than 500, the curve is in a stable state. The upstream overhead curve in Figure 6(b) has the largest drop, which shows that caching chunks of each multicast group based on the DCAM allocation result can achieve the lower upstream overhead in the process of reliable multicast loss recovery.
## 2 Monostatic-Bistatic Geometry for Stereo-Radargrammetric Reconstruction  
Relief reconstruction by spaceborne Stereo-Radargrammetry is based on the detection of target relative displacements between two SAR images and on the equations relating the positions and heights of the viewed targets [9]. Different acquisition geometries are exploitable if two monostatic images are used [13]: same and opposite sides, steep and shallow look angles. From a geometric point of view larger stereo-intersection angles (i.e. very large baselines and opposite-side stereo pairs) provide the best results [9]. Actually, when dealing with real-world data, large radiometric discrepancies are generated in opposite-side geometry thus limiting previous advantages [21], especially as terrain slope [14] and time decorrelation effects due to non-simultaneity between the images [11] increase. Therefore, provided that a minimum intersection angle is guaranteed in repeat-pass same-side geometry (8\({}^{\circ}\) has been suggested in [22]), it is possible to state that there is not a significant correlation between DEM accuracy and acquisition geometry.  
In a spaceborne monostatic-bistatic scenario further considerations are needed:  
* the bistatic image quality in terms of resolutions, ambiguities, signal-to-noise-ratio (SNR) is greatly affected by the monostatic-bistatic configurations, even before stereo reconstruction, therefore the effect of the acquisition geometry on bistatic image parameters must be accounted for when selecting the optimum configuration for radargrammetric DEM generation;
* when a monostatic image and a bistatic one are adopted to form a stereoscopic pair, new relations are needed to define target height as a function of the peculiar parameters of monostatic-bistatic surveying geometry [23]. Namely, the models of classical stereo-radargrammetry can be specialized to the monostatic/bistatic configuration, but also new models can be developed [17; 24].
# Depth Estimation of a Deformable Object via a Monocular Camera
## 3 Skeleton-Based Topological Segmentation  
This section introduces how to provide the reconstructed point cloud with a robust topological segmentation, so as to deal with the case where the target object is not rigid. In this way, each sub-point-cloud derived from the topological segmentation is expected to be rigid. Topological segmentation based on the surface information of the target object is inclined to getting influenced by the surface noise, causing weak robustness. Therefore, the proposed topological segmentation is executed through two steps. First, we extract the skeleton of the reconstructed point cloud [21] and segment the skeleton based on its curvature. Second, we dilate the sub-skeletons [22] to yield the sub-point-clouds, which are the results of the topological segmentation.  
**Skeleton extraction and segmentation**. Given an object denoted as \(\Omega=\left\{P_{j}^{*}\right\}_{j=1...M}\), we denote \(CORE\left(\Omega\right)\) as the set of all the maximally inscribed spheres in \(\Omega\), none of which has common points of tangency with noisy surface. Then, the skeleton of \(\Omega\) is denoted as \(S\left(\Omega\right)\).  
After extracting the skeleton of the reconstructed point cloud, we segment the skeleton according to its curvature. Supposing \(C\left(\Omega\right)\subset S\left(\Omega\right)\), an equivalence relation \(\sim_{C}\) implicated by \(C\left(\Omega\right)\) is defined as \(\forall p_{1},p_{2}\in S\left(\Omega\right)\), \(p_{1}\sim_{C_{1}}p_{2}\) if and only if \(p_{1}\), \(p_{2}\) are on a curve segment whose ends are two points in \(C\left(\Omega\right)\) and no other points in \(C\left(\Omega\right)\) are on the same curve segment.  
Thus, the curve segments determined by \(C\left(\Omega\right)\) are the equivalence classes [23]\(S_{C}\left(\Omega\right)\) defined as  
\[S_{C}\left(\Omega\right)=S\left(\Omega\right)/\sim_{C}=\left\{\left\{p\in S \left(\Omega\right):p\sim_{C}q\right\}:q\in S\left(\Omega\right)\right\}. \tag{7}\]  
It is clear that the curve segments in \(S_{C}\left(\Omega\right)\) are separated from each other. In this paper, we propose two categories of points, respectively, denoted as \(C_{1}\left(\Omega\right)\) and \(C_{2}\left(\Omega\right)\) in order that \(C\left(\Omega\right)=C_{1}\left(\Omega\right)\cup C_{2}\left(\Omega\right)\) determines \(\sim_{C}\) in Equation (7).
## 3 DQN-Based Offloading Design
### System State and Action Spaces  
**System State Space**: In the considered DQN paradigm, the state space observed by the agent includes the CSI of the overall network and the received task size \(C_{k}(t)\) at time \(t\). As the agent needs to consume extra communication overhead to connect the CSI from all MEC servers, the MEC server at time \(t\) observes a delayed version of CSI at time \(t-1\), i.e., \(\left\{h_{m,k}(t-1)\right\}\). Denote  
\[\begin{split}\mathbf{h}(t)&=\big{\{}h_{1,1}(t),h_{1,2 }(t),\cdots,h_{M,K}(t)\big{\}},\\ \mathbf{C}(t)&=\big{\{}C_{1}(t),C_{2}(t),\cdots,C_{K }(t)\big{\}}.\end{split} \tag{6}\]  
The state space observed at time \(t\) can be represented as  
\[S(t)=\big{\{}\mathbf{h}(t-1),\mathbf{C}(t)\big{\}}. \tag{7}\]  
**System Action Space**: The agent will take certain actions to interact with the environment with the observed state space \(S(t)\). As DQN can only take care of the discrete actions, the actions defined in the proposed DQN paradigm constitute only the MEC server selection. The MEC server selection action is denoted as \(a(t)\), which can be represented as  
\[a(t)=\big{\{}x_{m,k}(t)|x_{m,k}(t)\in\{0,1\}\big{\}}, \tag{8}\]  
where \(x_{m,k}(t)=0\) means that the intelligent terminal \(k\) does not select the MEC server \(m\) at \(t\)-th time slot, while \(x_{m,k}(t)=1\) indicates that the intelligent terminal \(k\) selects the MEC server \(m\) at \(t\)-th time slot.
## 1 Introduction
Effects of Chl\(a\)(z) vertical distribution on \(R_{rs}(\lambda)\) and Chl\(a\) estimation have been studied based on the relationship between \(R_{rs}(\lambda)\) and structure parameters of Chl\(a\)(z) profiles in Lake Chaohu, China [21]. With the knowledge of Chl\(a\)(z) profile parameters, \(R_{rs}(\lambda)\) of nonuniform waters can be corrected to the \(R_{rs}(\lambda)\) of uniform waters with same average Chl\(a\) across the water column [21]. However, structure parameters of Chl\(a\)(z) profiles were difficult to obtain from \(R_{rs}(\lambda)\). In addition, how the Chl\(a\)(z) vertical inhomogeneous affect the underwater light field of eutrophic lakes was not known. In this study, on the basis of radiative transfer simulation, effects of Chl\(a\)(z) vertical profiles on underwater light field in eutrophic lakes were first analyzed. The objectives are to (1) analyze the influence of Chl\(a\)(z) vertical distribution on \(K_{x}(\lambda,\) z) and \(r_{rs}(\lambda,\) z); (2) calculate the contribution of IOPs(\(\lambda,\) z) between depths \(z_{1}\) and z\({}_{2}\) to \(r_{rs}(\lambda,0^{-})\) by evaluating different weighting average functions; and (3) establish a simple remote sensing reflectance model considering the vertical distribution of phytoplankton in eutrophic lake. Contribution of IOPs(\(\lambda,\) z) to \(r_{rs}(\lambda,0^{-})\) of a certain water depth could provide useful guidance to field measurement. Considering the vertical distribution of phytoplankton on underwater light fields can help to understand the light fluctuations and improve the inversion of optically active constituents (OACs) in eutrophic lakes in the future.
# Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost
## 5 Results and Discussion
### Prediction Model for Maximum Longitudinal Tensile Stress on the Pavement Surface  
In the training set, a combination of grid search and five-fold cross-validation was used to optimize the parameters of the machine learning model, and R\({}^{2}\) was chosen as the main evaluation index to select the optimal combination of parameters, and the final evaluation result was the mean value of the five-fold cross-validation results. The optimal combination of parameters is learning_rate = 0.09; max_depth = 2; min_child_weight = 4; and n_estimators = 276. The prediction performance of the model after parameter optimization was verified on the test set. The relationship between the predicted and true values of the XGBoost model on the test set is shown in Figure 14, from which it can be seen that the predicted values of most samples in the model are very close to the true values.  
Figure 14: Relationship between predicted and true values of XGBoost model on the test set. Line: predicted values, points: true values.  
Figure 13: Importance of input feature variables.  
The XGBoost model was compared with other traditional machine learning models. The models were evaluated using three metrics, MAE, RMSE, and R\({}^{2}\), and a grid search combined with cross-validation was used for each model to optimize its parameters. Table 6 shows the prediction results of each traditional machine learning model on the test set, and it can be seen that the XGBoost model exhibits good performance. The XGBoost model was compared with the KNN model, and the RMSE was reduced by 43%, the MAE was reduced by 50%, and the R\({}^{2}\) was improved by 9%. The XGBoost model was compared with the SVM model, and the RMSE was reduced by 71%, the MAE was reduced by 74%, and the R\({}^{2}\) was improved by 61%.  
The importance of the input feature variables for the interpretability analysis of the developed prediction model using the SHAP architecture is shown in Figure 15. The most important characteristic variable is the modulus of elasticity of the upper layer of pavement \(E1\), and the relatively more important characteristic variables are the thickness of the steel bridge panel \(T\) and the thickness of the upper layer of pavement \(H1\).
## 5 Case Studies
### Real-Time Scheduling Results  
The real-time scheduling results shown in Figure 10 indicate that GT and power purchases provide a stable foundation for electricity supply, while PV reaches its peak during the midday period. ESS are charged during periods of low electricity prices at night and high PV output at midday. During peak load periods, ESS actively discharge, alleviating the pressure on power supply and reducing the amount of load shedding.
# Readiness of Enterprises in Czech Republic to Implement Industry 4.0: Index of Industry 4.0
## 2 Theoretical Background
### Data Collection and Analysis  
The process of data collection process includes retrieving, searching, selecting, and generating. As more and more business activities are digitized, new data sources arise, and the equipment to process these data becomes increasingly cheaper; we are entering a new era [14]. The volume of stored data is growing four times faster than the world economy, and computing power is increasing nine times faster. Interestingly, in 2000, only one-quarter of the world's information volume was stored in digital form; today, the figure is close to 95% [15]. The creation of large volumes of data is supported by digitization, aiming to convert all possible information and media--text, sounds, photos, videos, and instrument and sensor data--to the natural language of computers. Big data is a versatile term for any collection of datasets that are very large and complex. Big data are quantified in petabytes (\(10^{15}\)), as it is not possible to receive, store, secure, process, and visualize them with common hardware and software in a reasonable time [15].  
Big data are defined as a cultural, technological, and scientific phenomenon that rests on interplay [16] of technology and analysis. [PERSON] [17; 18] defined big data through three characteristics: volume, variety, and velocity. The Gartner company introduced the term big data. [PERSON] and [PERSON] [19] complemented the big data characteristics with a fourth Vs value. [PERSON] [20] classified big data through a data life cycle that includes data, process, and management activities. [PERSON], [PERSON], and [PERSON] [21] classified big data in eight areas: healthcare, automation, transport, energy, smart cities, agriculture, industry, and military.  
Data analysis can be expressed through different terms such as data mining, clustering, classification, analytics, aggregation, annotation, combining, extraction, evaluation, and filtering. Data analysis is performed either directly through a variety of cloud computing services (PaaS -- platform as a Service; SaaS -- Software as a Service), or in a conventional way on the user's end computers [19]. The main advantage of cloud services is effective integration with other applications, scalability, performance, multitasking, and configurability [22]. According to [PERSON] et al. [23], data analysis methods have the following limitations for big data usage: centrality and unscalability, dynamics (inability to analyze data on the fly), and data structure format (data inconsistency).
# A Mobility-Assisted Localization Algorithm for Three-Dimensional Large-Scale UWSNs
## 3 System Model  
In the section, we mainly introduce the network structure of the proposed localization scheme and related distance measurement methods.
## 2 Materials and Methods
### Microstructural Characterization  
The morphology of samples was investigated by means of scanning electron microscopy (SEM) analysis (FE-SEM-EDS (Auriga Base, Zeiss, Jena, Germany)). Sputter Coater (Q150T, Quorum Technologies, Darmstadt, Germany) was used to sputter with gold the samples before SEM analysis. ImageJ (NIH, Bethesda, MD, USA) was used to calculate fibers average diameters of 50 fibers from each sample [54]. SEM analysis was performed before and after immersion in simulated body fluid (SBF) solution (see Section 2.6); the eventual formation of hydroxy-carbonate apatite (HCA) layer on samples' surface was investigated by means of SEM. Furthermore, FTIR spectra before and after immersion in SBF were obtained using the FTIR spectrometer (Shimadzu, IRAffinity-1S, Fourier Transform infrared spectrophotometer, Duisburg, Germany). For the analysis, 40 spectral scans at resolution of 4 cm\({}^{-1}\) were repeated over the wavenumber range 4000-400 cm\({}^{-1}\).
# Device-Free Indoor Location Estimation System Using Commodity Wireless LANs
## 3 DF Indoor Location Estimation
### Experiment Scenario
#### 3.1.1 Small Office  
The room model of the small office and the position of the antenna are as shown in Figure 9a,b. The receiving and transmitting antennas were placed at a height of 0.9 m and 1.9 m, respectively. For this environment, thirty CSIs were captured while a person moved randomly in a sub-area; thus, one-hundred eighty samples were captured in total.
# A Hybrid CNN-GRU-LSTM Algorithm with SHAP-Based Interpretability for EEG-Based ADHD Diagnosis
## 5 Discussion
### Limitations and Challenges  
In spite of the heartening performance of the suggested models, a number of limitations must be noted that can affect their generalizability and everyday deployment. In the first place, the dataset employed for this research was task-evoked and well-balanced but also small in terms of size and demographic diversity. This drawback may impair the generalizability of the model to a wider group of clinical populations with diverse ages, the presence of more than a single comorbidity, and various EEG acquisition techniques. Collaboration across multiple sites and cultures should be pursued for enhancing external validity. Second, while transformer-based models achieved remarkable results during cross-validation, they suffered from performance inconsistency on separate test partitions. This asymmetry indicates over-sensitivity to distribution shifts and potential overfitting to validation-marked folds. Sophisticated regularization techniques, domain adaptation, or ensemble techniques might be required for obtaining strong generalization in practical situations. Third, the SHAP-based interpretability framework, though helpful for post-analysis, is unable to offer a full explanation for the dynamics at play due to the fact that it fails to record how EEG signals progress temporally with regard to the classification tasks. Future research could pursue the use of intrinsically interpretable models or temporal attention models that explain in real-time the reasons for model predictions at every step in a sequence.  
Last but not least, the computational requirements of the suggested hybrid model--specifically arising from the use of convolutional, recurrent, and attention-based layers--continue to be non-negligible. Excessive training time and resource usage could restrict the model's viability for low-resource setups or real-time usage. Model pruning, quantization, and knowledge distillation are optimizations that need to be investigated to facilitate implementation on handheld EEG recorders or clinical equipment.
## 3 Results
### Cross-Comparison with In-Situ Measurements  
With the purpose of validating satellite-based observation with field measurement, Figure 9 presents the relationship between tower-based GPP and satellite-based SIF, EVI in 2018-2019 across five selected sites at different spatial and temporal resolutions. Despite diverse spatiotemporal resolution, both satellite-observed SIF, EVI were overall significantly (\(p\)-value \(<\) 0.001) associated with in-situ measurement GPP (\(r^{2}\) ranging from 0.65 to 0.94) in the extremely dry year of 2018-2019. There was an enhanced correspondence between GPP and SIF with increasing spatial resolution (from 0.5\({}^{\circ}\) to 0.05\({}^{\circ}\) grid) for both monthly and 16-day temporal scales (Figure 9a,b,e,f). Besides, we found a stronger relationship between field measurement and satellite-based observation at monthly series (\(r^{2}\) = 0.77, 0.94) relative to 16-day series (\(r^{2}\) = 0.65, 0.88). The close correlation between GPP and EVI remained across different spatial and temporal resolution (\(r^{2}\) ranging from 0.86 to 0.91, Figure 9c,d,g,h). For three southern sites (AU-Stp, AU-ASM, AU-TTE) that suffered from extreme dryness in 2018-2019 there was large reduction, and seasonal amplitude was barely detected by either in-situ measurement or satellite observation (EVI).