esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Seismic isolation and damping technologies, though extensively used in buildings, are less common in large hydraulic structures, underscoring the importance of researching seismic mitigation methods for these constructions. This research first establishes that saturated sandy soil can act as a damping material through experimental and theoretical analysis. Subsequently, a novel hollow concrete gravity dam containing saturated sandy soil is proposed, utilizing the EOS (equation of state) subroutine for viscous fluids to model the liquefied sand. The findings indicate that the new-type dam exhibits a reduction in displacement of approximately 20% along the flow direction under an 8-degree seismic event compared to conventional gravity dams. This decrease correlates inversely with the characteristic wave speed of the saturated sandy soil, while the energy dissipation capacity of the saturated sandy soil is directly proportional to the soil layer's thickness. Finally, a small-scale shaking table test revealed that saturated sandy soil effectively reduces displacement and acceleration at the dam crest. These findings were corroborated by numerical simulations, which further substantiated the reliability of both the experimental and simulated data. Utilizing saturated sandy soil for energy dissipation and seismic damping in dams offers cost benefits, high durability, and significant effectiveness, representing a promising direction for the advancement of seismic mitigation in concrete gravity dams.  
Keywords:hollow concrete gravity dam; saturated sandy soil; damping material; numerical simulation; shaking table test; displacement response Article
## 3 Experimental Setup: Methods and Procedures
### Using Neural Networks for Hardness Determination
#### 3.2.3 CNN Training  
The stochastic gradient descent with momentum (SGDM) method was employed to train the model. The conventional gradient descent (GD) algorithm updates the network parameters (weights and biases) by iteratively minimizing the loss function. In GD, at each iteration \(l\), the parameters are adjusted toward the negative gradient of the loss function \(E(\theta)\), with the step size determined by the learning rate \(\alpha>0\). Specifically, the update rule for the gradient descent method can be expressed as follows:  
\[\theta_{l+1}=\theta_{l}-\alpha\
abla E(\theta_{l}) \tag{4}\]  
where \(\theta\) represents the parameter vector, and \(\
abla E(\theta)\) denotes the gradient of the loss function concerning the parameters. In traditional gradient descent, the gradient is computed over the entire training dataset, meaning the algorithm processes the full dataset in each iteration. In contrast, SGD evaluates the gradient and updates the parameters using a mini-batch of the training data at each iteration, rather than the entire dataset. A mini-batch is a randomly selected subset of the training set, and each algorithm iteration involves a different mini-batch. A full pass over the entire training dataset, using different mini-batches, constitutes one epoch. The stochastic nature of this method arises because the parameter updates computed from a mini-batch are noisy estimates of the true parameter update that would be derived from the entire dataset. Consequently, the stochastic gradient descent algorithm exhibits oscillatory behaviour along the steepest descent path, which can slow down convergence. The SGDM introduces a momentum term to address this issue that helps reduce oscillations and accelerates convergence. The SGDM update rule is given by the following:  
\[v_{l+1}=\gamma v_{l}+\alpha\
abla E(\theta_{l})(\theta_{l}) \tag{5}\]  
\[\theta_{l+1}=\theta_{l}-v_{l+1}(\theta_{l}) \tag{6}\]  
where \(v\) is the velocity term (momentum), \(\gamma\) is the momentum coefficient (typically between 0 and 1), and \(\alpha\) is the learning rate. The parameter \(\gamma\) determines how much of the previous gradient's influence persists in the current iteration. This allows the algorithm to overcome  
Figure 6: Preparation of input data for CNN.  
local minima and make smoother progress toward the global minimum. This approach mitigates the oscillations inherent in SGD and facilitates faster convergence by building up velocity in the gradient direction [27,28]. The progression of the training process using SGDM is visualized in Figure 7, which illustrates the improvement in loss function minimization over time.
# Gearbox Fault Diagnosis Based on Multi-Sensor and Multi-Channel Decision-Level Fusion Based on SDP
## 3 Fitting Fault Identification Based on SDP Technology
### Analysis of Single-Channel Experimental Results  
In this experiment, 1000 sets of experimental data were selected, and each set of data consisted of 4096 points. It can be obtained from the experimental results that the accuracy of the single-channel gear teeth fault diagnosis was not high, even when the accuracy of each sensor was improved after the DS fusion theory. The accuracy was not as high as the accuracy when the three sensors were fused simultaneously when the two sensors were fused with each other. The simultaneous fusion of three sensors improved the accuracy by 11.67%, 17.60%, and 11.63% over sensor one and two fusion, sensor one and three fusion, respectively. The experiments showed that the single-channel diagnosis method does not collect comprehensive data of the gearbox gear teeth, which leads to low diagnosis results, however, using the DS fusion method can make up for the disadvantage of incomplete data collection by a single sensor and can improve the gear teeth fault diagnosis results. Therefore, the fusion algorithm is of great significance for the fault diagnosis field.
# Horizontal Polarized DC Grounded Omnidirectional Antenna for UAV Ground Control Station
## 4 Simulation Verification
### Determination of Pin Diameter and Slot Size Effect  
Figure 4 helps us to see the impact of the slot-feed pin diameter on the antenna input reflection. The change of diameter changed the antenna's matching, as shown in Figure 4. Figure 4 depicts how the slot-length variations shifted the antenna resonance region.
# Evaluation of Algal Control Measures in Eutrophic Reservoir Based on Aquatic Ecosystem Models
## 2 Materials and Methods
### Aquatic Ecological Model Framework  
The Shanzai eutrophication model reflects a redeveloped and enhanced version of the eutrophication and sediment system template found in MIKE 21 (DHI) in the ECO Lab [43]. It simulates and adjusts the biomass of blue-green algae, green algae, diatoms, and fish (using ABM simulation) while tailoring the aquatic ecological cycling mechanism to accurately replicate the eutrophication process in the Shanzai reservoir.  
This model is derived from the established eutrophication and sediment system template in the ECO Lab [44]. It simulates and adjusts the biomass of blue-green algae, green algae, diatoms, and fish (employing ABM simulation). Furthermore, it customizes the aquatic ecological cycling mechanism to represent the eutrophication process in the Shanzai reservoir.  
The Shanzai eutrophication model articulates the nutrient cycling process in the reservoir and the growth dynamics of phytoplankton and zooplankton. It also encompasses the cycling processes of various forms of nitrogen and phosphorus nutrients in the sediment,capturing their interactions with the overlying water. Utilizing a suite of 129 differential equations, the Shanzai eutrophication model describes the variations in 29 distinct state variables, encompassing phytoplankton (blue-green algae, green algae, diatoms, and dinoflagellates), chlorophyll-a, zooplankton, humic substances, dissolved oxygen, and inorganic nutrients.  
Additionally, the model calculates various derived variables, such as primary productivity, total nitrogen and phosphorus concentrations, sediment oxygen demand, and water clarity. Figure 2 illustrates the carbon, nitrogen, phosphorus, and oxygen cycling and transformation processes described in the eutrophication and sediment system template. The entire model is constructed around the carbon cycling and transformation processes of different nutrient pools, including phytoplankton, zooplankton, humic substances, and sediment.  
\[\frac{\partial c}{\partial t}+u\frac{\partial c}{\partial x}+v\frac{\partial c }{\partial y}+w\frac{\partial c}{\partial z}=D_{x}\frac{\partial^{2}c}{ \partial z^{2}}+D_{y}\frac{\partial^{2}c}{\partial z^{2}}+D_{z}\frac{\partial ^{2}c}{\partial z^{2}}+S_{c}+P_{c} \tag{4}\]  
\[P_{c}=\frac{dc}{dt}=\sum_{i=1}^{n}\text{process}_{i} \tag{5}\]  
where \(c\) is the concentration of the ECO Lab state variable; \(u,v,w\) are the flow velocity components; \(p,q(x,y,t)\) are the single-width flow in the \(x\) and \(y\) directions; \(D_{x},~{}D_{y},~{}D_{z}\) are the dispersion coefficients; \(S_{c}\) is the sources and sinks; \(P_{c}\) is the ECO Lab processes, process\({}_{i}\) stands for various biochemical processes.
# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
###### Abstract  
The path planning of a ship requires much information, and one of the essential factors is predicting the ocean environment. Ocean weather can generally be gathered from forecasting information provided by weather centers. However, these data are difficult to obtain when satellite communication is unstable during voyages, or there are cases where forecast data for a more extended period of time are needed for the operation of the fleet. Therefore, shipping companies and classification societies have attempted to establish a model for predicting the ocean weather on its own. Historically, ocean weather has been primarily predicted using empirical and numerical methods. Recently, a method for predicting ocean weather using deep learning has emerged. In this study, a deep learning model combining a denoising AutoEncoder and convolutional long short-term memory (LSTM) was proposed to predict the ocean weather worldwide. The denoising AutoEncoder is effective for removing noise that hinders the training of deep learning models. While the LSTM could be used as time-series inputs at specific points, the convolutional LSTM can use time-series images as inputs, making them suitable for predicting a wide range of ocean weather. Herein, using the proposed model, eight parameters of ocean weather were predicted. The proposed learning model predicted ocean weather after one week, showing an average error of 6.7%. The results show the applicability of the proposed learning model for predicting ocean weather.  
ocean weather; deep learning; denoising AutoEncoder; convolutional LSTM +
Footnote †: journal: Marine Science and Engineering
# An Adaptive Process Monitoring Method Based on an Improved ADALINE Neural Network
## 3 Adaptive Process Monitoring Approach
### Data Preprocessing Method  
The directly collected original signal data of a machining process often contain noise; moreover, the original data for analysis will affect the experimental results. Therefore, preprocessing is extremely necessary. Preprocessing includes screening, cleaning, and normalizing the original data. Noise reduction is an important part of preprocessing. Nonlinear wavelet transform threshold denoising is one of the most effective algorithms for signal noise reduction and was proposed by [PERSON] and [PERSON] [42; 43]. For the nonlinear wavelet threshold, the specific steps of denoising are as follows.  
Step 1: Select the appropriate wavelet bases and set the decomposition layer number as \(J\);  
Step 2: Perform discrete wavelet transform on the noisy signal to obtain wavelet decomposition coefficients \(d_{j,k}\);  
Step 3: Select the appropriate threshold and threshold function to obtain the estimated wavelet coefficient \(\overset{\wedge}{d_{j,k}}\);  
Step 4: Finally, use \(\overset{\wedge}{d_{j,k}}\) for reconstruction to obtain the denoised signal.  
From these steps, we can see that the selection of a suitable number of decomposition layers has a great influence on the denoising effect. In the traditional wavelet threshold noise reduction method, the number of decomposition layers is usually set based on experience. However, the experience-oriented method is subjective and restricts the denoising effect. Thus, here, we present a method for testing the wavelet transform maximum modulus of the signal to implement the adaptive selection of wavelet decomposition layers. The flow chart of this method is shown in Figure 3.  
First, the noisy data are decomposed into \(J\) layers to obtain and retain the maximum modulus of each layer, recorded as \(w_{peak}(j)(j\in 1,2,3,...,J)\). Then, the decomposition layer number is set to \(j=0\). By considering the other decomposition layers, a determination of whether \(w_{peak}(j)\) satisfies \(\left|w_{peak}(j)\right|\geq MAX\left|w_{peak}(J)\right|\) is made. Then, the number of decomposition layers is output. Otherwise, \(j=j+1\) is set, and the process is repeated until \(w_{peak}(j)\) satisfies \(\left|w_{peak}(j)\right|\geq MAX\left|w_{peak}(J)\right|\). Then, a discrete wavelet transform is performed on the noisy signal to obtain a series of wavelet decomposition coefficients \(d_{j,k}\), and the appropriate threshold and threshold function are selected to obtain the estimated wavelet coefficients \(\overset{\wedge}{d_{j,k}}\). Finally, \(\overset{\wedge}{d_{j,k}}\) is used for reconstruction to obtain the denoised signal.
## 5 Improved DSR Using Phonological Features in Vocabulary-  
### Mismatched Setting  
#### Motivation  
Previous research on dysarthric speech recognition explored the significant impact of training vocabulary on enhancing the accuracy of recognizing target dysarthric speech in both speaker-independent and vocabulary-mismatched contexts. When using very-low-resource data for model training or working in a cross-lingual setting, models frequently encounter speech with previously unseen words and even phones, leading to poor recognition accuracy. While using a vocabulary-informed base model with extensive vocabulary  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**EXP** & **Training Strategy** & **PER** \\ \hline
**E18** & **C\_rich\_extgt** & **0.694** \\ \hline
**E19** & **D\_extgt** & 0.840 \\
**E20** & **E18**\(\rightarrow\)**D\_extgt** & 0.818 \\ \hline \hline \end{tabular}
**Bold numbers**: Best PER(s) within the statistical significance level in each case.  
\end{table}
Table 6: Averaged PER results across all dysarthric datasets using vocabulary-informed base model and using models trained with **D_extgt** with different model initializations. (A \(\rightarrow\) B: train model A with data B.)knowledge in model initialization can enhance performance, such base models are not always available or suitable in practical scenarios.  
In this section, we explore methods to improve model performance in the vocabulary-mismatched case without relying on vocabulary-informed base models. We propose using phonological features (PFs) as the intermediate representations in the phone recognition task.
# All-Optical Tuning of Fano Resonance for Quasi-BIC and Terahertz Sensing Applications
###### Abstract  
The bound states in the continuum (BIC) support anomalous resonances in the optical or terahertz band with a theoretically infinite quality factor. Therefore, it has great application prospects in the field of sensors. However, the current regulation of BIC mainly relies on the asymmetry of the material structure, which requires high processing technology. The structure can hardly be effectively adjusted once it is formed. In this work, we propose a new metasurface consisting of an array rectangular hole structure combined with aluminum and photosensitive silicon, which supports quasi-BIC to achieve ultrasensitive sensing in the terahertz range. By introducing photosensitive silicon, the asymmetry of the structure is efficiently controlled by the light field, thus realizing the bidirectional continuous control from quasi-BIC to BIC-like states. Through the optimization of the structure, a class of highly sensitive terahertz sensing based on optical tuning is finally proposed. The narrow-band quasi-BIC resonance is sensitive to medium thickness and refractive index, and compared with pure metal structure, the sensitivity and dynamic range can be increased by 2.60 times and 2.63 times, respectively. Due to the high slope of the Fano lineshape, sensitivity can reach 9.41 GHz/RIU and 0.65 GHz/\(\upmu\)m, respectively. Furthermore, this feasible and practical structure provides an ideal platform for highly sensitive sensing.  
quasi-BIC; terahertz sensing; Fano resonance
## 4 Mathematical Modeling and Analysis
### Optimal ABS Ratio  
In order to maximize the throughput of CUEs and DUEs, the ABS ratio should be optimally determined. Based on mathematical analysis of Shannon formula, we define data rate \(R\) as follows :  
\[R_{mode}^{link}=log_{2}(1+SINR_{mode}^{link}), \tag{7}\]  
where a link can be a D2D or cellular link. To be short, a cellular link can be denoted as CUE and be applied to FFR or FFR-ABS. FFR-ABS is shortened as ABS. From Equation (7), we define the totalnetwork throughput to maximize the data rate of CUE and DUE. Firstly, the total network throughput of conventional FFR can be expressed as the following:  
\[R_{total}^{FFR} = \sum R_{FFR}^{D2D}+\sum R_{FFR}^{CUE}.\]  
In FFR-ABS, a part of CUEs have ABS applied and others operate by the FFR method. The nodes that are on the ABS scheme have to transmit data on their subframes only. Thus, the total data rate of FFR-ABS is as follows:  
\[R_{total}^{ABS} = \beta(Pr_{ABS}^{D2D}\sum R_{ABS}^{D2D}+(1-Pr_{ABS}^{D2D})\sum R _{FFR}^{D2D})\] \[+(1-\beta)(Pr_{ABS}^{CUE}\sum R_{ABS}^{CUE}+(1-Pr_{ABS}^{CUE}) \sum R_{FFR}^{CUE}),\]  
where \(Pr_{ABS}^{D2D}=\frac{n(\bigcup_{\text{$\text{$\text{$\text{$\text{$\text{ $\text{$\text{$\text{$\text{$\text{$\text{$\text{$\text{$\text{$}}$}$}$}$}}}$}}}} \mathcal{ARS}_{\text{$\text{$\text{$
## 7 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Radar Type** & **Center Freq/GHz** & **Features** & **Dataset, Samples** & **Activities** & **Class. Model** & **Process.** & **Max. Accuracy/\%** \\ \hline
[40] & 2010 & FMCW & 4.3 & Time-based RF signatures & Owre: 40 per class (5 \&
\begin{tabular}{c} Walk, run, rotate, \\ punch, crawl, \\ standing still, \\ transition / sitting) \\ \end{tabular} & SVM & PCA & 89.99 \\ \hline
[53] & 2018 & CW & 4.0 & DT, CV, etc. & Owre: 50-149 &
\begin{tabular}{c} Walk, jog, limp, walk \\ + cone, walk + walk, \\ crawl, energy, \\ \end{tabular} & CAE & - & 94.2 \\ \hline
[47] & 2018 & CW & 24.0 & DT & Owre: 50-149 &
\begin{tabular}{c} Walk, run, rotate, \\ punch, crawl, \\ standing still, \\ transition / sitting) \\ \end{tabular} & DCNN & - & 94.2 \\ \hline
[48] & 2019 & FMCW & 76.0-81.0 & \begin{tabular}{c} Range, velocity, \\ power, angle, time \\ \end{tabular} & \
\begin{tabular}{c} Owre (\#MAtivity): \\ Training 12,097; Test: \\ 3538; Validation: 2419; \\ \end{tabular} & Box, jump (acks),
\begin{tabular}{c} SVM (with RBF), \\ MLP, LSTM, CNN + \\ LSTM \\ \end{tabular} & PCA (for SVM) & 80.34 Bi-LSTM:88.42 \\ \hline
[41] & 2020 & FMCW & 5.8 & \
\begin{tabular}{c} RT, RD, \\ amplitude/phase, \\ CV \\ \end{tabular} & Owre: 249 per class &
\begin{tabular}{c} Walk, sit down, stand \\ up, pick up object, \\ drift, fall \\ \end{tabular} & SVM, SAE, CNN & SBS & SVM: 95.24; SAE, 91.23; CNN: 96.65 \\ \hline
[46] & 2020 & FMCW & 77.0 & RDT & \
\begin{tabular}{c} Owre, Events: 1505; \\ Gestures: 2347 \\ \end{tabular} & CNN, CNN + LSTM & n.a. &
\begin{tabular}{c} Event-related: 97.03; \\ Gesture-related: \\ 87.78 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Radar classes and echo signals used for coarse-grained HAR.
# A Novel Generative Model for Face Privacy Protection in Video Surveillance with Utility Maintenance
###### Abstract  
In recent years, the security and privacy issues of face data in video surveillance have become one of the hotspots. How to protect privacy while maintaining the utility of monitored faces is a challenging problem. At present, most of the mainstream methods are suitable for maintaining data utility with respect to pre-defined criteria such as the structure similarity or shape of the face, which bears the criticism of poor versatility and adaptability. This paper proposes a novel generative framework called Quality Maintenance-Variational AutoEncoder (QM-VAE), which takes full advantage of existing privacy protection technologies. We innovatively add the loss of service quality to the loss function to ensure the generation of de-identified face images with guided quality preservation. The proposed model automatically adjusts the generated image according to the different service quality evaluators, so it is generic and efficient in different service scenarios, even some that have nothing to do with simple visual effects. We take facial expression recognition as an example to present experiments on the dataset CelebA to demonstrate the utility-preservation capabilities of QM-VAE. The experimental data show that QM-VAE has the highest quality retention rate of 86%. Compared with the existing method, QM-VAE generates de-identified face images with significantly improved utility and increases the effect by 6.7%.  
face de-identification; autoencoders; privacy protection; vector quantization; deep learning; differential privacy +
Footnote †: journal: Medical Informatics
# Edge-Aided Reliable Data Transmission for Heterogeneous Edge-IoT Sensor Networks
## 3 System Design
### Edge-Aided Retransmissions  
When the edge server overhears one packet has been repeatedly retransmitted to the same destination node, it estimates the expected link quality of the target link and then decide whether to retransmit the packet for that link.  
We identify the poor links as links with a quality smaller than \(t_{p}\). If the edge server overhears the same packet transmission \(n_{r}\) times, then it can estimate the link quality of the target link \(k\) as:  
\[q_{k}=\frac{1}{n_{r}+1}. \tag{6}\]  
When a poor link is identified, the edge node keeps this link in a set \(N_{p}\). When the edge node overhears transmissions to certain destinations, it first estimates the probability that the packet is transmitted on a poor link as follows:  
\[p_{k}=\frac{n_{k}^{on}}{n_{k}^{all}} \tag{7}\]  
where \(n_{k}^{on}\) denotes the number of packet transmissions on the link \(k\), and \(n_{k}^{all}\) denotes the number of packet transmissions of which the destination node is the same with \(k\)'s destination node.