esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 3 GAN-Enhanced CNN for Voiceprint Recognition
### Model Training and Evaluation  
Once the model construction was complete, it was trained to learn patterns from the data. Afterward, its adaptability and accuracy were tested on a separate test dataset. Accuracy, defined as the ratio of correct predictions to total predictions, was evaluated over 10 rounds of training, and the best-performing model was selected for further use. A standard CNN architecture was chosen to ensure the reliability of the classification results. This allowed us to isolate and evaluate the contributions of the iterative reduction technique without introducing confounding variables from complex or proprietary CNN designs.  
The model's performance was optimized using the sparse_categorical_crossentropy loss function, which is particularly suited for multi-class classification tasks with integer labels. This loss function measures the difference between predicted and true labels, with the goal of minimizing this discrepancy to enhance the model's accuracy. To achieve this, the Adam optimizer was employed, which combines the strengths of AdaGrad and RMSProp. By calculating the first and second moment estimates of the gradient, Adam provides greater stability and requires less tuning compared to traditional optimization algorithms like the stochastic gradient descent (SGD). The CNN used in this study is a well-established architecture for classification tasks. It serves as a robust tool for processing the combined dataset of real and GAN-generated samples, supporting the implementation of the iterative reduction technique.  
The process of training the model involves two main phases: forward propagation and backpropagation. In forward propagation, data flow through the network from the input layer to the output layer, with each neuron calculating output values based on the current weights. After this step, backpropagation is used to adjust the weights and biases by computing the gradient of the loss function with respect to each weight. The model then uses this information to update the weights, aiming to minimize the loss by applying the gradient descent algorithm or the Adam optimizer.  
Training is conducted over multiple cycles, called epochs, where the entire dataset passes through the model. Each epoch includes a complete forward and backpropagationcycle. In this study, 30 epochs were performed, meaning the model iterated over the data 30 times, refining its predictions and learning from the patterns in the data.
# Anthropometric Values in Spanish Elite Soccer: Differences between Divisions and Playing Positions
## 3 Results
This was also the case for the second B division, except for stretch stature, where midfielders showed significantly better values than defenders (\(p\) = 0.026) and goalkeepers (\(p\) = 0.017). More differences were found in the third division, with forwards showing higher values than midfielders for body mass (\(p\) = 0.000), stretch stature (\(p\) = 0.011), arm relaxed girth (\(p\) = 0.013), arm flexed and tensed girth (\(p\) = 0.028), thigh middle girth (\(p\) = 0.005), humerus breadth (\(p\) = 0.016), corrected thigh girth (\(p\) = 0.007), sum of corrected girths (\(p\) = 0.004), FM ([PERSON]) (kg) (\(p\) = 0.005), FM ([PERSON]) (kg) (\(p\) = 0.000), FM ([PERSON]) (kg) (\(p\) = 0.018), adipose tissue ([PERSON]) (kg) (\(p\) = 0.023), MM ([PERSON]) (kg) (\(p\) = 0.009), RM (kg) (\(p\) = 0.027), and BMI (\(p\) = 0.007). Also, forwards showed higher values than defenders for body mass (\(p\) = 0.005), stretch stature (\(p\) = 0.047), and FM ([PERSON]) (kg) (\(p\) = 0.025). Midfielders showed lower values compared to goalkeepers for body mass (\(p\) = 0.017), stretch stature (\(p\) = 0.036), and BM (\(p\) = 0.017). \begin{table}
The percentile analysis of the measurements included in the study depending on the division can be observed in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Percentile** & **10** & **20** & **30** & **40** & **50** & **60** & **70** & **80** & **90** & **100** \\ \hline \multicolumn{10}{c}{Second division} \\ \hline Body mass (kg) & 66.57 & 69.00 & 72.45 & 74.88 & 76.00 & 77.12 & 80.17 & 82.50 & 84.13 & 90.90 \\ \hline Stretch stature (cm) & 172.00 & 175.76 & 178.00 & 180.00 & 180.90 & 182.10 & 185.49 & 186.06 & 190.00 & 193.00 \\ \hline Triceps skinfold (mm) & 3.20 & 3.80 & 4.21 & 4.96 & 5.40 & 6.00 & 6.40 & 7.32 & 8.06 & 9.40 \\ \hline Subscapular skinfold (mm) & 5.80 & 6.28 & 7.00 & 7.08 & 7.45 & 7.82 & 8.40 & 8.86 & 9.52 & 13.30 \\ \hline Biceps skinfold (mm) & 2.60 & 2.80 & 3.01 & 3.20 & 3.70 & 4.12 & 6.07 & 7.00 & 7.75 & 10.50 \\ \hline Iliac crest skinfold (mm) & 4.97 & 5.70 & 6.20 & 7.00 & 7.10 & 7.82 & 8.79 & 9.52 & 10.60 & 13.40 \\ \hline Supraspanile skinfold (mm) & 4.27 & 4.60 & 5.00 & 5.40 & 5.80 & 6.00 & 6.59 & 7.52 & 8.86 & 14.90 \\ \hline Abdominal skinfold (mm) & 5.80 & 6.10 & 6.60 & 6.88 & 7.45 & 8.10 & 8.98 & 10.10 & 11.53 & 14.80 \\ \hline Thigh skinfold (mm) & 5.20 & 6.00 & 6.22 & 7.00 & 7.60 & 8.24 & 9.19 & 10.10 & 11.29 & 14.40 \\ Calf skinfold (mm) & 3.20 & 3.60 & 3.80 & 4.00 & 4.20 & 4.52 & 4.80 & 5.20 & 6.00 & 10.10 \\ Arm relaxed girth (cm) & 28.24 & 29.16 & 29.92 & 30.16 & 30.60 & 31.20 & 31.50 & 32.20 & 33.12 & 35.70 \\ \hline Arm flexed and tensed girth (cm) & 31.43 & 32.50 & 33.09 & 33.50 & 34.00 & 34.40 & 34.80 & 35.38 & 36.27 & 40.00 \\ \hline Waist girth (cm) & 74.09 & 75.86 & 77.49 & 78.62 & 79.30 & 80.00 & 80.55 & 81.38 & 82.41 & 87.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Percentiles of anthropometric and derived variables of soccer players according to the division.
## 5 Active Coating and its Control Algorithm
### The Cross-Section Structure of the Protected Body  
Each Huygens' piston (or active piston) \(\overline{S}_{k}\) represents two layers (two piezoelectric layers, which were polarized identically along the axis \"z\", Figure 6c) in cross-section. In top view Huygens' piston presents a planar convex polygon. In other words the active coating is divided into a lot (\(N_{i}\)\(>\)\(>\)1) of Huygens' pistons (Figure 6b) analogous to the pistons \(S_{k}\), called above radiating pistons. Equivalent pistons \(S_{k}\) and active pistons \(\overline{S}_{k}\) (Huygens's) have the same \"mass's center\" \(\rho_{k}\), the same square \(\sigma_{k}\) and the same characteristic linear spatial scale \(\sim\)\(L_{V}\). Surfaces of each piezoelectric layer are covered by thin metal weightless films and equipped by electric contacts (Figure 6c). Between contacts of each \(k\)-th Huygens' piston the differences \(\overline{\varphi}_{k}\)(t) and \(\overline{\overline{\varphi}}_{k}\)(t) of the electric potentials are supported by active control system.
## 3 Experimental Results and Analysis
### Analysis of Improved Haar-Like Feature  
Figure 9 shows the detection results of the circles with different illuminations, using ORB, KAZE, traditional Haar feature, and improved Haar feature, respectively. Model_1, Model_2, and Model_3 on the left side of the dashed line are original circular models for different regions. On the right of the dashed line are the images of three models under two different illumination conditions. The pink line is the connecting line between the original feature point and the center, while the red line is the connecting line between the matching feature point and the center. It is obvious that under different illumination conditions the matching effect of the improved Haar-like feature is better than that of ORB, KAZE, and the traditional Haar feature.  
Table 1 compares the average pixel errors of the matching results of three circular model feature points under two illumination conditions, using KAZE, the traditional Haar feature, and the improved Haar-like feature, respectively. It can be found from the table that the average pixel error of traditional Haar feature algorithm is smaller than that of the KAZE algorithm, and the average pixel error of improved Haar-like feature algorithm is much smaller than that of the traditional Haar feature.  
Figure 9: Matching results of different illumination images by four matching algorithms.  
Table 2 compares the average pixel errors of circle radii obtained by the matching results of three circular model feature points under two illumination conditions, using KAZE, the traditional Haar feature, and the improved Haar feature, respectively. It can be found from the table that the average pixel error of the improved Haar feature algorithm is much smaller than that of the KAZE algorithm and the traditional Haar feature algorithm, which is approximately 1-2 pixels.
# Security Enhancement for Data Migration in the Cloud
## 1 Introduction
### Cloud Data Storage versus Traditional Data Storage
#### 1.1.1 Lower Complexity and Costs  
Almost six out of 10 providers say that cost reduction is the main goal for customers to use cloud services [2]. Consumers do not need to buy and configure new equipment. Cloud storage service allows them to get their application started immediately, and the providers charge the users based on the usage of resources (Pay As You Go). The service usually costs a fraction of what it would cost to implement an on-site solution.
# Automatic Fabric Defect Detection Method Using PRAN-Net
## 2 PRAN-Net Fabric Defect Detection Method Based on Faster R-CNN
### Priori Anchor Generation  
Region Proposal Network (RPN) [13] is used to predict defect proposals in feature maps by anchors. Generally, a series of fixed-size anchors set for each pixel of the feature map in RPN work well to detect the moderate aspect ratio defects. Due to the extreme aspect ratios of most fabric defects, fixed-size anchors cannot match defective regions exactly, which lead to defect location deviation. Furthermore, a large number of fixed anchors are set in each feature map by sliding windows, most of which have no contribution to detect defects and lead to extra calculation time. PRAN-Net generates sparse priori anchors in images to locate diverse fabric defects accurately and efficiently.  
The fabric defect in images determined by the location and the shape is represented by a quaternary tuple \((x,y,w,h)\), where \((x,y)\) is the center coordinate of the defect, \(w\) and \(h\) are the width and height of the defect, respectively. Assuming that the probability of a defect \((x,y,w,h)\) locating in the image \(I\) satisfies the following condition:  
\[p(x,y,w,h|I)=p(x,y|I)p(w,h|x,y,I) \tag{1}\]  
where \(p(x,y|I)\) is the probability of the existing defect in the image \(I\), and \(p(w,h|x,y,I)\) is the probability of the specific shape \((w,h)\) defects. Based on formula (1), the priori anchors are generated adaptively in different scale feature maps in two steps: predicting coordinates of the defect center and predicting the shape of the defect guided by the ground truth (GT) box of defect as shown in Figure 3.  
Figure 3: The schematic of the priori anchor generation in the Priori Anchor Convolutional Neural Network (PRAN-Net).  
Figure 2: The schematic of features extraction in the PRAN-Net.
## 3 Conceptual Framework and System Overview
### Theoretical and Developmental Basis of the User Experience Design
#### 3.1.3 Psychological Frameworks Applied to User Experience Design  
The UX strategy of the system draws on multiple psychological frameworks that guide its structure, tone, and interaction logic developmentally appropriately. One such framework is [PERSON]'s concept of animistic thinking, describing the tendency of children to attribute lifelike qualities to inanimate objects. The system employs emotionally expressive characters to frame screen-time disengagement as a socially contextualised interaction, supported by facial expressions, animation, and tone [5].  
The design also draws on key principles from Acceptance and Commitment Therapy (ACT), which encourages individuals to acknowledge difficult emotions while committing to value-driven actions [66]. Adapting ACT for early childhood, the system employs simplified language and metaphorical prompts reflecting the perspective of the child. For example, instead of directive commands, the interface offers emotionally attuned phrases such as 'It's hard to stop, I understand' to acknowledge resistance without judgement. These verbal cues are intended to support emotional processing and guide the child toward established, developmentally appropriate goals.  
This approach aims to foster psychological flexibility by helping children acknowledge and manage difficult emotions without becoming overwhelmed. It also reduces the risk of oppositional behaviour by avoiding rigid, externally imposed control, instead framing disengagement as a collaborative, meaningful choice.  
Additionally, the design draws on executive function (EF) theory, particularly the distinction between hot and cool EF domains [67].  
As screen-time transitions are often emotionally charged and impulsive, the UX incorporates paced cues, rituals, and reflective dialogue to support cool EF processes such as planning and voluntary control. These elements act as external scaffolds that gradually support the internalisation of self-regulatory strategies.  
Finally, Self-Determination Theory (SDT) guides the UX approach to intrinsic motivation by addressing three basic psychological needs: autonomy, competence, and relatedness [68]. The system supports autonomy by offering end-of-session choices (e.g., through gestures or voice input), reinforces competence through positive feedback, and fosters relatedness via emotionally attuned character interaction.  
Together, these psychological frameworks provide a cohesive foundation for the UX design of the system--shaping its affective structure, motivational logic, and interaction model. Rather than enforcing compliance, the system supports developmentally appropriate self-regulation through socially grounded cues and structured interaction tailored to the developmental capacities of children, as illustrated in Figure 1.
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 7 Performance Evaluation
### Evaluation in Real Systems
#### 7.2.2 Link Quality Reconstruction  
We first evaluate LESS with three proposed scheduling algorithms by examining the link quality reconstruction accuracy in Figure 11(a) with sampling rates from 0.2 to 0.9 in Zhoushan system. For each type of scheduling algorithm, 10 rounds are performed. Generally speaking, the reconstruction accuracy of all scheduling algorithms increases with the growth of sampling rates, especially for sampling rates from 0.2 to 0.5. In the following, we reveal several important findings from the comparison of the three algorithms in terms of link quality reconstruction accuracy.  
**Random Sampling vs. K-Spatial Sampling:** RS and K-S demonstrate similar performance when the sampling rate is above 0.7. However, when the sampling rate is less than 0.6, K-S outperforms RS. This is because nodes in Zhoushan system (Figure 11) are densely deployed, which brings spatial characteristics to the link quality matrix to some extent. In this case, K-S performs better than RS, especially when the sampling rate is small.  
**K-Spatial Sampling vs. K-Temporal Sampling:** Comparing the reconstruction accuracy between K-S and K-T in Figure 11(a), we observe that K-T behaves better than K-S for all sampling rates. The reason is that K-T benefits from system temporal characteristics, i.e., tide cycle, which is helpful information the intertidal WSNs share.
## 2 Materials and Methods
### Data Processing  
Eddy Pro software (Version 6.0, LI-COR Bioscience, Inc., Lincoln, NE 68504, USA) was adopted to compute, perform quality control on and correct the half-hourly CO\({}_{2}\) and CH\({}_{4}\) fluxes. The correction practices included coordinate rotation via double rotation [55], correction for density fluctuations (WPL correction) [56], spectral correction [57], and spike detection. The following types of CH\({}_{4}\) data were excluded from subsequent analyses: (1) those flagged as low quality by EddyPro (0-the best quality, 1-medium quality suitable for general analysis, 2-poor quality that should be discarded); (2) those obtained during periods of rainfall, instrument malfunction or human disturbance; (3) those obtained when the U* was lower than 0.15 m s\({}^{-1}\); and (4) those with relative signal strength indicator (RSSI) values below 10% [34; 49]. The filtering of CO\({}_{2}\) data used criteria (1)-(3) above. After the data cleaning procedure, the 63.8%, 69.2%, 59.8% and 58.8% data were retained for 2018-2020 late rice and 2018 early rice respectively.  
The mean diurnal variation (MDV) [58] method was adopted to fill the gaps in the micrometeorological and CH\({}_{4}\) flux data. The daytime and nighttime net ecosystem exchange (NEE) were filled in separately on the basis of the assumption that at night, NEE equals the total ecosystem respiration (R\({}_{\text{eco}}\)) due to the absence of photosynthesis, while during the day, NEE is the difference between R\({}_{\text{eco}}\) and gross primary production (GPP) (Equation (1)):  
\[\text{NEE}=\left\{\begin{array}{c}\text{R}_{\text{eco}}(\text{nighttime})\\ \text{R}_{\text{eco}}-\text{GPP}(\text{daytime})\end{array}\right. \tag{1}\]  
where GPP is the sum of available daytime NEE and R\({}_{\text{eco}}\) (\(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) and R\({}_{\text{eco}}\) is the respiration of the ecosystem (\(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)).  
An empirical function estimating R\({}_{\text{eco}}\) was fitted according to [PERSON] [59] based on the assumption that respiration is controlled mainly by temperature.  
\[\text{R}_{\text{e}}=\text{a}\bullet\text{e}^{\text{b}\bullet\text{T}_{\text {s}}-\text{T}_{\text{ref}}} \tag{2}\]  
where a and b are empirical constants, T\({}_{\text{s}}\) is the soil temperature at 5 cm depth, and T\({}_{\text{ref}}\) is a reference temperature (25 \({}^{\circ}\)C). It was assumed that Equation (2) is applicable for the daytime and that GPP can be estimated by Equation (3) [60] based on the assumption that photosynthesis is controlled mainly by PAR in a short period of time.  
\[\text{GPP}=\frac{\text{P}_{\text{max}}\alpha\text{PAR}}{\text{P}_{\text{max} }+\alpha\text{PAR}} \tag{3}\]  
where P\({}_{\text{max}}\) is the hypothetical maximum GPP, \(\alpha\) is the initial slope of the function of ecosystem quantum yield, and PAR is the photosynthetically active radiation flux measured above the canopy.
## 1 Introduction
The use of single MICP reinforcement is generally considered to be more cost-effective than the use of traditional reinforcement materials such as cement, but the cost of reinforcement is influenced by various factors, including the use of additives, solution concentration, bacterial strain, and the type of soil being reinforced. Due to the particles of clay being very fine, MICP treatment may necessitate greater quantities of bacterial strains and solutions, along with the associated costs of additives such as activated carbon. Additionally, the penetration and uniform distribution of MICP in clay can be more challenging, potentially resulting in higher construction costs compared to sand. Previous studies in the literature have found that methods such as the fiber-based reinforcement of soft clay and sand-clay mixtures are effective for MICP reinforcement. Incorporating fibers not only enhances the shear strength of clay but also increases the colonization space for microorganisms, thereby improving the deposition efficiency and yield of calcium carbonate. The cementing effect of calcium carbonate further promotes fiber reinforcement [22]. Using controlled amounts of clay in clean sands can enhance the efficiency of MICP treatment. Pore clogging, caused by low percentages of clay, may not be a significant issue, and clay minerals can provide additional surface area for bacteria to attach to, leading to the precipitation of larger quantities of bio-cement [23]. The above methods can optimize the migration and distribution of microorganisms within the soft clay. Future research will consider incorporating these methods into further experimental studies on MICP reinforcement in soft clay to enhance the reinforcement performance. The research described in this paper employed a method consisting of mixing bacterial fluid, cementation liquid, and soft clay to simulate the process of soft clay ground reinforcement via MICP. The mineral composition of the soil following MICP reinforcement was analyzed by measuring the physical and mechanical indices and conducting microanalyses of soil cultivated over a specific period. This study investigated this treatment's effects on soft clay and examined the reinforcement mechanisms of MICP technology.
## 3 Finite-Element Modelling of Ventilated Brake Discs under the Action of Flow Fields
### Finite-Element Modeling  
The geometric model meshes with a free hexahedral network for the brake pad and the internal clearance structure of the ventilated brake disc and a free tetrahedral mesh for the ventilated brake disc surface. There are 59,744 cells in the model, consisting of 2520 hexahedral cells and 51,926 tetrahedral cells. The ventilated brake disc material is made of low-alloy steel. The brake pad material is made of copper-based powder metallurgy; the relevant parameters of the materials are shown in Table 1.  
The initial speed of the high-speed train before the emergency braking is set to \(V_{1}\) = 250 km/h, the radius of the train wheels is \(r_{\text{b}}\) = 0.43 m, the initial temperature of the brake and environment is set to \(T\) = 26.85 \({}^{\circ}\)C, the total mass of a single carriage is m = \(6.88\times 10^{4}\) kg under full load conditions, and the friction coefficient is set to \(\mu_{0}\) = 0.25 when the brake pad is in contact with the ventilated brake disc. The rotational motion of the ventilated brake disc is converted into a translational movement in coordinate form and loaded onto the disc, thus realizing its braking process.  
The heat generated by the brake disc and brake pad is loaded on the contact surface of the brake disc and brake pad in the form of a frictional heat source and is evenly distributed on the frictional contact surface. The brake disc friction contact surface and gap structure are set up as a longitudinal swept plate convection heat transfer boundary, a transverse swept tube boundary, and radiant heat exchange at all edges of the brake disc in contact with the air. The finite-element model of the high-speed train ventilated brake disc and brake pad was finally obtained by constraining the three degrees of freedom of the disc in the \(x\), \(y\), and \(z\) directions, as shown in Figure 4.  
Before the start of numerical simulation, it is necessary to check the grid independence [35]. Three coordinate points are selected on the ventilated brake disc friction surface, as shown in Figure 4. The coordinate points are, respectively, (\(-\)0.26, 0, 0.054), (0, \(-\)0.26, 0.054), and (0.26, 0, 0.054). It can be seen from Table 2 that the number of grids in Grid 3 and Grid 4 increases, but the change of the number of grids has little impact on the temperature nodes. Therefore, the third grid number is adopted for calculation in this paper.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material Properties** & **Brake Disc** & **Brake Pads** \\ \hline Density (kg/m\({}^{3}\)) & 7850 & 5250 \\ Elastic modulus (GPa) & 208 & 180 \\ Poisson’s ratio & 0.31 & 0.3 \\ Conductivity (W/m\(\cdot\)K) & 48 & 30 \\ Specific heat (J/kg\(\cdot\)K) & 462 & 550 \\ Linear expansion coefficient (/K) & \(1.28\times 10^{-6}\) & \(1.5\times 10^{-6}\) \\ Surface emissivity & 0.28 & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of the ventilated brake disc and brake pads.  
Figure 4: Finite-element model of ventilated brake disc and brake pad.