esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion
### Effect of the Addition of Freeze-Dried GPP on Pasta Gluten Network  
#### 3.2.1 Protein Content of Semolina- and GP-Fresh Pasta  
The protein content of semolina, freeze-dried GPP and raw and cooked fresh pasta samples were shown in Table 2. In total, 100% Raw semolina fresh pasta exhibited roughly the same amount of protein as semolina flour (11.6%), while the cooked one revealed a 48% of protein loss during cooking (5.92%), probably due to a faster starch swelling that slowed down the rate of protein interaction, leading to a weakening of the protein network [30]. It is well established that the drying step appears to be essential to keep the pasta structure [30; 31], especially during overcooking; the protein network of a low temperature (55 \({}^{\circ}\)C) dried pasta is partially disrupted and loses its continuity after the cooking procedures.  
On the contrary, the application of a very high-temperature (100 \({}^{\circ}\)C) process and a low moisture level allows us to preserve the microstructure of pasta, even after a prolonged cooking phase [32]. In our study, the lack, or at least the lower protein interaction, was probably accentuated by the absence of the drying process. Raw GP-fresh pasta sample, both with the addition of 5% and 10% GPP, showed a protein content similar to the 100% semolina fresh pasta sample (11.3% and 10.9%, respectively), being the grape pomace mainly characterized by the presence of grape seed proteins that can range from 6% to 15% [23].  
The major protein component in grape seed protein isolate was found to be a globulin-link protein with subunit molecular weights varying from 25.5 to 40.0 kDa, as determined by SDS-PAGE [33]. Cooked GP-fresh pasta samples showed a significantly (_p_ < 0.05) higher protein content (8.22% and 10.7%, respectively) than the 100% cooked semolina fresh pasta (5.92%). In these samples, the loss of protein during the cooking process was about 27.7% for tagliatelle with 5% of GPP and only of 1.97% for those with 10% of GPP, underlining an improvement of the nutritional characteristics of the GPP-pasta samples after cooking with respect to 100% semolina fresh pasta.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **\%Protein** \\ \hline Semolina flour & 12.0 \(\pm\) 0.01 \({}^{\mathrm{a}}\) \\ \hline Freeze-dried GPP & 10.5 \(\pm\) 0.01 \({}^{\mathrm{f}}\) \\ \hline
100\% Raw Semolina & 11.6 \(\pm\) 0.03 \({}^{\mathrm{b}}\) \\ Fresh Pasta & 5.92 \(\pm\) 0.02 \({}^{\mathrm{h}}\) \\ \hline
5\% Raw & 11.3 \(\pm\) 0.04 \({}^{\mathrm{c}}\) \\ GP-Fresh Pasta & 8.22 \(\pm\) 0.02 \({}^{\mathrm{g}}\) \\ \hline
10\% Raw & 10.9 \(\pm\) 0.04 \({}^{\mathrm{d}}\) \\
10\% Cooked & 10.7 \(\pm\) 0.06 \({}^{\mathrm{e}}\) \\ GP-Fresh Pasta & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Content of total protein expressed as percentage of protein (g/100 g).
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 3 VBDA Architectures and Core Technologies
### VBDA Core Technologies
#### 3.2.5 Visualization and Interpretability
Synthesizing the key findings from Sections 2 and 3, we identify several persistent challenges in the VBDA landscape:  
Figure 2: Key technologies in video big data platforms. * **Latency and Scalability:** Centralized cloud-centric designs often struggle with real-time analytics under high video throughput and geographically dispersed deployments. * **Data Heterogeneity:**ulti-source video data varies in encoding formats, resolutions, frame rates, and modalities (e.g., visual, thermal, audio), posing standardization and fusion challenges. * **Privacy and Federated Learning:** FL adoption in video domains remains limited due to non-IID data distribution, communication overhead, and explainability concerns. * **Semantic Understanding:** While progress has been made in video understanding using AI models, semantic indexing, multimodal fusion, and real-time reasoning remain underdeveloped in practice. * **Deployment and Resource Management:** Lack of modular, open source reference platforms hinders rapid deployment, especially in resource-constrained edge settings. These challenges are synthesized from our survey of both the academic and industry literature between 2018 and 2025. To address them holistically, the following section introduces ViMindXAI, a hybrid, modular VBDA platform designed to operationalize cloud-edge coordination, semantic video analytics, and privacy-preserving learning in real-world scenarios.
# Anthropometric Values in Spanish Elite Soccer: Differences between Divisions and Playing Positions
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Position Comparisons** & **Mean Difference \(\pm\) SD** & \(p\) **Value** & **95\% CI** \\ \hline \multicolumn{5}{c}{Second division} \\ \hline Body mass index (kg/m\({}^{2}\)) & Middifielders & Goalkeepers & \(-4.10\pm 1.52\) & 0.047 & \(-8.16\) ; \(-0.04\) \\ \hline \multicolumn{5}{c}{Second B division} \\ \hline Stretch stature (cm) & Middifielders & Defenders & \(-4.53\pm 1.56\) & 0.026 & \(-8.70\) ; \(-0.35\) \\ \cline{2-6}  & Middifielders & Goalkeepers & \(-6.76\pm 2.24\) & 0.017 & \(-12.73\) ; \(-0.79\) \\ \hline \multicolumn{5}{c}{Third division} \\ \hline Body mass (kg) & Forwards & Middifielders & \(10.65\pm 2.31\) & 0.000 & 4.48; \(16.82\) \\ \cline{2-6}  & Forwards & Defenders & \(8.06\pm 2.36\) & 0.005 & 1.75; \(14.36\) \\ \cline{2-6}  & Middifielders & Goalkeepers & \(-8.39\pm 2.78\) & 0.017 & \(-15.80\) ; \(-0.99\) \\ \hline \multicolumn{5}{c}{Forwards} \\ \hline Stretch stature (cm) & Forwards & Middifielders & \(6.37\pm 2.02\) & 0.011 & 0.99; \(11.75\) \\ \cline{2-6}  & Forwards & Defenders & \(5.55\pm 2.06\) & 0.047 & 0.05; \(11.04\) \\ \cline{2-6}  & Middifielders & Goalkeepers & \(-6.73\pm 2.42\) & 0.036 & \(-13.19\) ; \(-0.27\) \\ \hline Arm relaxed girth (cm) & Forwards & Middifielders & \(1.78\pm 0.57\) & 0.013 & 0.26; \(3.29\) \\ \hline Arm flexed and tensed girth (cm) & Forwards & Middifielders & \(1.74\pm 0.61\) & 0.028 & 0.12; \(3.37\) \\ \hline Thigh middle girth (cm) & Forwards & Middifielders & \(3.11\pm 0.91\) & 0.005 & 0.67; \(5.55\) \\ \hline Humerus breadth (cm) & Forwards & Middifielders & \(0.40\pm 0.13\) & 0.016 & 0.05; \(0.75\) \\ \hline Corrected thigh girth (cm) & Forwards & Middifielders & \(2.99\pm 0.90\) & 0.007 & 0.57; \(5.40\) \\ \hline Sum of corrected girths (cm) & Forwards & Middifielders & \(7.08\pm 2.02\) & 0.004 & 1.68; \(12.47\) \\ \hline Fat mass ([PERSON]) (kg) & Forwards & Middifielders & \(1.15\pm 0.34\) & 0.005 & 0.25; \(2.04\) \\ \hline Fat mass ([PERSON]) (kg) & Forwards & Middifielders & \(1.66\pm 0.40\) & 0.000 & 0.58; \(2.73\) \\ \hline Fat mass ([PERSON]) (kg) & Forwards & Defenders & \(1.20\pm 0.41\) & 0.025 & 0.10; \(2.30\) \\ \hline Fat mass ([PERSON]) (kg) & Forwards & Middifielders & \(1.56\pm 0.52\) & 0.018 & 0.18; \(2.93\) \\ \hline Adipose tissue ([PERSON]) (kg) & Forwards & Middifielders & \(2.21\pm 0.76\) & 0.023 & 0.19; \(4.23\) \\ \hline Muscle mass ([PERSON]) (kg) & Forwards & Middifielders & \(3.79\pm 1.17\) & 0.009 & 0.66; \(6.93\) \\ \hline Bone mass ([PERSON]) (kg) & Middifielders & Goalkeepers & \(-1.60\pm 0.53\) & 0.017 & \(-3.02\) ; \(-0.19\) \\ \hline Residual mass (kg) & Forwards & Middifielders & \(3.65\pm 1.26\) & 0.027 & 0.27; \(7.02\) \\ \hline Body mass index (kg/m\({}^{2}\)) & Forwards & Middifielders & \(1.60\pm 0.48\) & 0.007 & 0.31; \(2.88\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Post hoc comparison in anthropometric and derived variables according with division and position.
## 4 Characteristics of Different Time Distribution Systems
### Network Time Protocol
#### 4.1.3 Flexibility  
NTP is a network-based and software-based time synchronization standard, which makes it easy and flexible to access. It allows computers across a network to synchronize their clocks with each other conveniently.  
NTP requires only an Internet connection for time synchronization. Since the Internet is widely available today, the implementation cost is very low.
# LungVision: X-ray Imagery Classification for On-Edge Diagnosis Applications
## 4 Experimental Results and Discussion  
In this section, we examine the effect of different quantization techniques on the models at hand in terms of the generated model size, model performance and inference time. Following the work reported in the literature, the dataset was split 80-20 for training and testing. All models used were pre-trained on ImageNet and were followed by a simple classification head that consists of a dense layer of 512 nodes, followed by another dense layer for classification utilizing a softmax activation function. The hyperparameters tuned for this experiment include the number of neurons in the final dense layer between [64; 128; 256], the dropout rate between [0.3, 0.5, 0.7], and the learning rate used [0.1, 0.01, 0.001]. The optimal parameter choice was 256, 0.5, and 0.001, respectively, based on hyperparameter tuning. The models were later trained using a categorical cross entropy loss for 15 epochs. An early stopping criterion was used to avoid overfitting with a patience of 3 monitoring accuracy.  
Having generated the classification models using the 6 CNN architectures and converting them to TensorFlow Lite using the 3 quantization techniques, it was noticed that the all the quantization techniques of interest reduce the model's size by varying ratios. Additionally, the influence of quantization on the model's performance degradation is based on the combination of CNN architecture and quantization technique used. The impact of quantization on the user's experience, in terms of app inference time, seems to be faster across all models, with some exceptions.
# Influence of Synthesis Parameters in Obtaining KIT-6 Mesoporous Material
## 4 Conclusions  
The KIT-6 mesoporous material have been efficiently synthesized through a typical procedure, reported in the literature. Based on this procedure, modifications to the synthesis parameters have been presented and show a significant influence on the structure and textural properties in the ordered mesoporous materials obtained. For the formation of _Ia3d_ cubic structures, characteristic of the KIT-6 material, concentrations of P123 below 0.017 M are required. The alcohol probably acts on the generation of the micelle, and the size of its carbonic chain possibly interferes in the formation of the porous system and ordered structure of the synthesized mesoporous materials. In order to obtain the 3D cubic structure, a longer synthesis time is required, otherwise SBA-15 or any ordered structures are formed. In other words, below 4 h and 18 h for P123 dissolution and aging gel, respectively, the SBA-15 ordered structure is generated. The heat treatment time has a significant influence on the textural properties of the synthesized materials. Thus, this parameter revealed that the longer the time employed, the better the materials that are produced, by demonstrating elevated specific area, high volume and pore diameter. The data obtained have confirmed the influence of the synthesis parameters in the formation, organization and textural properties of mesoporous material like KIT-6. However, it is important to emphasize that the cubic structure of KIT-6 can be obtained by reducing the concentration and dissolution time of P123, and times of heat treatment and aging, when compared to the synthesis reported in the literature. The modifications carried out in the synthesis procedure have resulted in materials with different characteristics, which makes it possible to employ them in applications in the areas of catalysis and adsorption.  
The following are available online at [[http://www.mdpi.com/2076-3417/8/5/725/s1](http://www.mdpi.com/2076-3417/8/5/725/s1)]([http://www.mdpi.com/2076-3417/8/5/725/s1](http://www.mdpi.com/2076-3417/8/5/725/s1)), Figure S1: Curves of (A) TG and (B) DTG of samples with cubic structures, Figure S2: Curves of (A) TG and (B) DTG of samples with hexagonal structures, Figure S3: FTIR spectra of all samples with ordered structures.  
**Author Contributions:** [PERSON] and [PERSON] performed the experiments. [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper and analyzed the data. [PERSON], [PERSON] and [PERSON] supervised the whole work.  
**Acknowledgments:** The authors thanks to the CAPES/Brazil for the financial support, and the Magnetic and Optical Analysis Laboratory (LAMOp/State University of Rio Grande do Norte), and GERATEC-PPGQ-CCN/State University of Piaui, for the analysis performed.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 3 Methodology
### Verification Experiment Using the Flow Visualization Method
#### 3.3.3 Flow Field Extraction Method  
The method for extracting flow information using laser flow visualization is explained. First, the video captured is stored as an image for each frame. Red-green-blue (RGB) values were extracted from the flow information image and stored with RGB representing the three primary colors of light in computer graphics. The RGB color coordinate system is attached in Figure 5. It represents a cube's R, G, and B values with each cube axis as a color scale expressed in RGB space [22; 23]. Each light source has a maximum value of 255. The coordinates of red are defined as (255, 0, 0), green as (0, 255, 0), blue as (0, 0, 255), Magenta as (255, 0, 255), Cyan as (0, 255, 255) and Yellow as (255, 255, 0). The origin of the coordinates (0,0,0) is represented in black, and the coordinates (255,255,255) are represented in white. The line connecting the origin and the white point is grayscale. In this study, flow information was extracted using  
Figure 4: Flow visualization experimental device using the laser tomography method: (**a**) shaded CSS-scale model and fluid equalizer; (**b**) wind generator and air tunnel.  
a green laser to track the R-value. Big and small eddies exist in the visualized flow field, including various turbulence components. The turbulent flow component with volatility is identified as noise and eliminated. Therefore, it is converted into a 9 \(\times\) 16 main matrix to normalize the visualization result. The area of each matrix is used for visualization analysis. There are flow objects within the segmented area of the flow field. The top 0.5% of the RGB value is defined as a flow object, and the segmented area tracks the flow object's progress.
# GNSS/INS Integration Based on Machine Learning LightGBM Model for Vehicle Navigation
## 2 Introduction to LightGBM Machine Learning Model Based on PUA
### Position Update Architecture and PLA-LightGBM Model  
The position update architecture is a data fusion architecture for GNSS/INS integrated navigation that uses the AI method, which defines the AI-based integrated navigation model's inputs and outputs (Figure 1A). The architecture trains the AI model based on INS data and GNSS data, with the inputs to the model being the velocity (\(V_{\text{INS}}\)) and azimuth (\(\theta_{\text{INS}}\)) given by the INS and the outputs being the differences in longitude (\(\delta E\)) and latitude (\(\delta N\)) between the two epochs. Based on the linear and non-linear relationships between the motion data and vehicle position coordinates, PUA provides a training model for predicting vehicle position changes. Hence, it is reasonable to use PUA to determine the inputs and outputs of AI. PUA makes it possible to apply AI methods to find relationships between vehicle dynamics data and position changes [18]. Initially, [PERSON] et al. implemented an AI-based integrated navigation algorithm using ANN and PUA; in addition, AI-based integrated navigation algorithms using PUA have been proposed successively: DSNN [11], RFR [19], DS-SVM [20], etc.  
This study proposes to use the PUA-based LightGBM machine learning model to predict changes in vehicle position during GNSS outage conditions. During vehicle operation, the data from the INS are combined with data from the GNSS to construct a training dataset and train a position change prediction model. When there is a GNSS outage, the INS data are used in the model to predict the change in vehicle position in real-time, as shown in Figure 1B. It is important to note that this paper uses vehicle three-axis acceleration as a feature input based on PUA. One of the main advantages of LightGBM is its high training efficiency [17]. From a practical deployment point of view, LightGBM models for vehicle position prediction in the event of a GNSS outage could collect more data. In this way, the model can update or expand the dataset while the vehicle moves, allowing it to be retrained at a lower cost and with greater adaptability.
## 3 Results
### Spatiotemporal Changes in Cropland CWUE, GPP, NPP, and ET
#### 3.1.2 Spatiotemporal Changes in Cropland GPP, NPP, and ET  
Figure 4 presents the spatial distribution and spatiotemporal dynamics of the GPP, NPP, and ET for cropland in Henan Province from 2001 to 2019. The spatial patterns of the GPP and ET are similar, displaying east-west and south-north gradients, while the NPP shows a meridional decline from south to north. The GPP, NPP and ET mainly exhibited increasing trends, covering approximately 95.70%, 84.11%, and 92.77% of the cropland area, respectively. Notably, areas with significant increases accounted for about 73.14%, 18.88%, and 53.04% of cropland for the GPP, NPP, and ET, respectively. The spatial distributions of the increasing GPP and ET largely overlapped, whereas increases in the NPP were primarily concentrated in western Henan. Areas where the GPP, NPP, and ET showed significant decreases were minimal, representing only about 0.64%, 0.18%, and 0.96% of the cropland area, respectively.  
Figure 4: Spatial distribution and spatiotemporal trends of GPP, NPP, and ET in cropland in Henan Province from 2001 to 2019. GPP mean (**a**), GPP annual mean change trend (**d**), NPP mean (**b**), NPP annual mean change trend (**e**), ET mean (**c**), and ET annual mean change trend (**f**).
# A Single-Rate Multicast Congestion Control (SRMCC) Mechanism in Information-Centric Networking
## 4 Rate Adaptation
### Congestion State Value Definition  
In wired links, the queue overflow of routers is the main cause of congestion packet loss. Therefore, we take queue occupancy ratio as the main indicator of current congestion condition. First, based on the queue occupancy condition, we set two queue occupancy ratio thresholds, \(O_{th1}\) and \(O_{th2}\). Then, according to the relationship between the current queue occupancy ratio \(O(t)\) and the two thresholds, we define four congestion state values (0, 1, 2, 3) corresponding to the stages of no occupancy, light occupancy, moderate occupancy, and heavy occupancy.  
\[\text{congestion state value}=\left\{\begin{array}{ll}0,&O(t)=0\\ 1,&0<O(t)\leq O_{th1}\\ 2,&O_{th1}<O(t)\leq O_{th2}\\ 3,&O(t)>O_{th2}\end{array}\right. \tag{5}\]
# Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering
## 2 Soil Layer Representation and Sparse Borehole Modeling
### Sparse Borehole Modeling  
Borehole drilling refers to the method of drilling into the ground to investigate the distribution of subsurface soil layers. In engineering projects, borehole data are typically obtained through engineering drilling. When a borehole penetrates the ground, it passes through different soil layers, thereby revealing the thickness of each encountered layer. Borehole data include key parameters such as the elevation and coordinates of the borehole, the types of soil penetrated and their respective thicknesses, and other details like borehole radius, name, drilling time, color codes, etc. Borehole data are represented as a series of discrete points, with variations primarily observed in the vertical dimension. While vertical boreholes are the most common, inclined boreholes are also used in some cases. Utilizing these data allows for the direct acquisition of detailed soil distribution information and supports the creation of three-dimensional digital stratigraphic models. To accurately reconstruct soil layers, we designed a specific file format to systematically store and manage borehole data.  
Figure 3 shows the structure of the borehole data. Each borehole file contains several boreholes, and the set of boreholes is represented by \(B\), \(B=\{b_{1},b_{2},\ldots,b_{n}\}\); there are \(n\) boreholes in \(B\); for all \(b_{i}\in B\), there exists a tuple, \(b_{i}=\{u,e,r,c,l\}\), where \(n\) represents the name of the borehole, \(e\) represents the elevation value of the borehole, \(r\) represents the radius of the borehole, and \(c\) represents the coordinates of the borehole. Here, \(c\) is a tuple, \(c=(x,y)\), where \(x\) and \(y\) are the coordinates of the \(X\)-axis and \(Y\)-axis directions of the borehole. \(l\) represents the thickness of the soil layer and the soil layer through which the borehole passes, and \(l\) is a nest tuple, \(l=((1,t_{1}),\ldots(a,t_{a}))\), where \((a,t_{a})\) represents the label of the soil layer and the thickness of the soil layer through which the borehole passes.  
Further, the borehole file also contains some other auxiliary information, such as file name, data type, file creation time, etc.  
Figure 3: Schematic diagram of sparse borehole mode.  
Figure 2: Data structure diagram of soil layer meta-information.
# Fault Detection Method for Wind Turbine Generators Based on Attention-Based Modeling
## 3 Experimental Analysis  
### Description of Gearbox Data  
The gearbox is the core component in improving the speed of fan blades: its failure can easily to lead to wind turbine shutdown and other serious conditions. The gearbox is mainly composed of four parts: bearing, gear, transmission, and drive shaft. The mechanical energy generated by blade rotation is passed through the spindle to the gearbox, to increase the speed of the gears, which accelerate in order to provide more mechanical energy to drive the wind turbine. After the transmission is put into use, its load is unable to maintain a stable state, coupled with its long-term maintenance in a harsh environment, so that the gear in the transmission can easily break. In the process of manufacturing, gear tooth shape and tooth gap and error can easily occur, coupled with wear and tear in the process of transportation and installation. These are important reasons for the failure of gearbox. Combined with the theory of vibration dynamics, a simple vibration model of gearbox vibration can be established, to analyze the phenomenon of gear modulation, the time-frequency characteristics of the vibration signal when the gear fails, and the common vibration signal analysis methods of the gearbox.  
For this paper, two types of wind turbine gearbox datasets were used for experimentation: the SCADA (supervisory control and data acquisition) dataset and the OWT (offshore wind turbine) dataset. The wind turbine experimental platform mainly included five components: (1) magnetic particle loader, (2) gear accelerator, (3) torque sensor and speed detection, (4) three-axis accelerometer, and (5) three-phase asynchronous AC motor. The wind turbine gearbox speed was 1500 rpm, and the sampling frequency was 10 KHz. The vibration signal was collected through two accelerometers. The two bearing models were NJ210 and NJ405.