esa-sceva/satcom-chunk-collection huggingface dataset:
# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
## 2 Signal Collection and Preprocessing  
### Signal Sources  
The PhysioNet/CinC dataset, widely utilized due to its substantial volume of data, limited number of channels, and available reference annotations, was employed in this paper's experiments. Specifically, the PhysioNet/CinC Challenge 2013 dataset, Set A, was used. This subset offers 75 multi-channel non-invasive fetal ECG signals along with reference annotations marking the locations of each fetal QRS complex. Each record includes four channels and lasts 1 min, with a sampling frequency 1000 Hz and a 16-bit resolution. Following the challenge organizers' advice, due to inaccurate annotations, seven recordings (a33, a38, a47, a52, a54, a71, and a74) were discarded [26; 27; 28]. The abdominal ECG signals of pregnant women primarily consist of maternal ECG signals, fetal ECG signals, and various noise components. As illustrated in Figure 2, the signal source represents the four abdominal ECG signals from different electrode placements on a pregnant woman.  
The Abdominal and Direct Fetal ECG Database (ADFECGDB) from PhysioNet comprises ECG recordings from five pregnant women [26; 29]. Each record includes four abdominal channels and one direct fetal channel, with each segment lasting 5 min. The signals were sampled at 1 kHz with a 16-bit resolution. We utilize the four abdominal  
Figure 2: Four-channel signal sources.  
channels of this database as our test set to validate the reliability and accuracy of the proposed algorithm in this paper.
# Feature-Level Camera Style Transfer for Person Re-Identification
## 3 Feature-Level Camera Style Transfer
### Network Architecture  
The main architectures of generator \(G\) and discriminator \(D\) are illustrated in Figure 1. Since the whole camera style transfer process took place at the feature level, a convolutional layer is not necessary, and only fully connected (FC) layers are used in the network. The dimensions of input identity feature and camera-style feature are 2048 and 512, respectively. For generator \(G\), a U-Net based architecture is adopted as the backbone. The identity branch consists of three parts: the downsampling part, the transition part and the upsampling part. The downsampling part acts as the role of an encoder, reducing the dimension of features, while the upsampling part acts as a decoder and has the opposite effect. Batch normalization layers are applied after each FC layer in these two parts. The transition part, namely a serial of ABN blocks, plays the key role of fusing two branches of features and makes a style transfer, where the proposed ABN layers are applied after each FC layer in this part. All normalization layers in \(G\) are followed with a Leaky ReLU layer (the negative slope is set to be 0.2, which is the same as below). The camera style branch is a Multi-Layer Perception (MLP) for mapping camera style information into ABN layers. For \(D\), a series of FC layers reduce the dimension of input features from 2048 to 1 with a reduction rate of 0.5 per layer. An additional classifier is connected to the 512-dimensional layer for camera classification loss. All FC layers in \(D\) are followed with a Leaky ReLU layer except the last one, while normalization layers are not used in \(D\), since they would do harm to Conditional GAN training. The image in the red dotted box in Figure 1 is not generated; it is placed here only to demonstrate that the generated feature \(f^{\prime}\) maintains identity information while transferring camera style.  
Through the proposed CST approach, given an input feature, multiple features that maintain the same identity but vary in camera style can be generated. These augmented features can be used to alleviate camera style variance during the training process of the re-ID model.  
Figure 1: The structure of generator \(G\) and discriminator \(D\) of the proposed CST approach. \(E_{I}\) and \(E_{C}\) are the identity feature extractor and camera style feature extractor, respectively.
# Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
## 3 Results and Discussion in Axle Recognition
### Test Set Results

Table 2: Performance of the Transformer-GRU model on the test set. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Transformer-RNN** &
\begin{tabular}{c} **Transformer-LSTM** \\ \end{tabular} & **Transformer-GRU** \\ \hline Accuracy & 0.7507 & 0.9491 & 0.9951 \\ Training time (h) & 8.3967 & 10.4222 & 9.8864 \\ Inference time (s) & 0.1734 & 0.1883 & 0.1802 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of accuracy of different models. the issues of vanishing gradients and exploding gradients of traditional RNN, although the training and testing time is increased, there were no special requirements on the hardware. This enhancement significantly boosted the accuracy of sequence-to-sequence models utilizing Transformer-LSTM and Transformer-GRU. The LSTM model has a more complex network structure than the GRU models; therefore, it can capture contextual dependencies in sequences more effectively. However, the Transformer encoder already captured critical information of the weighing signal in long sequences, whereas the decoder needed to pay more attention to capturing the short and significant signal changes caused by instantaneous axle load fluctuations. The GRU controls the hidden state by integrating the forget and input gates into the update gates, and the structure was relatively simple with fewer parameters, faster training speed, and easier convergence, particularly when working with smaller datasets. Additionally, due to the varying lengths of weighing signals from different vehicles, the simple gating structure of the GRU models allowed them to adapt more flexibly to different timescales. These factors contributed to the Transformer-GRU model performing better on the test set compared with the Transformer-LSTM model.
# Unlocking the Potential of RFA and Stabilizers in High Moisture Geotechnical Applications
###### Abstract  
In recent decades, rapid urbanization has generated a large amount of waste soft soil and construction debris, resulting in severe environmental pollution and posing significant challenges to engineering construction. To address this issue, this study explores an innovative approach that synergistically applies recycled fine aggregate (RFA) and soil stabilizers to improve the mechanical properties of soft soil. Through laboratory experiments, the study systematically examines the effects of different mixing ratios of RFA (20%, 40%, 60%) and soil stabilizers (10%, 15%, 20%) with red clay. After standard curing, the samples underwent water immersion maintenance for varying durations (1, 5, 20, and 40 days). Unconfined compressive strength (UCS) tests were conducted to evaluate the mechanical performance of the samples, and the mechanisms were further analyzed using scanning electron microscopy (SEM) and particle size distribution (PSD) analysis. The results indicate that the optimal performance is achieved with 20% RFA and 20% stabilizer, reaching the highest UCS value after 40 days of water immersion. This improvement is primarily attributed to the formation of a dense reticulated structure, where RFA particles are effectively encapsulated by clay particles and stabilized by hydration products from the stabilizer, forming a robust structural system. Unconsolidated undrained (UU) tests reveal that peak deviatoric stress increases with confining pressure and stabilizer content but decreases when excessive RFA is added. Shear strength parameter analysis demonstrates that both the internal friction angle (\(\varphi\)) and cohesion (c) are closely related to the content ratios, with the best performance observed at 20% stabilizer and 20% RFA. PSD analysis further confirms that increasing stabilizer content enhances particle aggregation, while SEM observations visually illustrate a denser microstructure. These findings provide a feasible solution for waste soft soil treatment and resource utilization of construction debris, as well as critical technical support and theoretical guidance for geotechnical engineering practices in high-moisture environments.  
Keywords: recycled aggregate; soil stabilizer; UCS; UU; FESEM; PSD; water stability +
Footnote †: journal: Applied Sciences
# Dependable and Secure Voting Mechanism in Edge Computing
## 3 System
### System Model
#### 3.2.1 Algorithm  
The execution is replicated in \(2f+1\) cloud instances for ensuring the existence of \(f+1\) identical outputs even in the presence of arbitrary or malicious faults. Each cloud instance proposes a value \(v\) to the particular edge node. Each edge node begins the voting mechanism. The algorithm must enforce a set of properties to ensure correct operation:  
1. **Voting:** All correct processes must terminate with single value \(v\).
2. **Agreement:** All correct processes must choose the same value \(v\).
3. **Termination:** Processing or communication delays are bounded to a value \(\Delta\).  
The first two properties are concerning safety. The algorithm must guarantee that no correct process should ever make a decision that is contrary to what the remaining correct processes have chosen. The third property is concerning liveness.  
The algorithm that is depicted in the Figure 3 is composed of three phases--PROPOSE, prepare, commit. In this example, we are considering \(f=1\), which means that three edge nodes and three cloud instances are necessary. In the next paragraph, we detail the execution of the system from the client and server perspectives.
## 3 Experimental Program  
### Mix Proportion and Steel Fiber  
Table 1 summarizes the mixture proportions of high-strength concrete and SFRC used in this study. The Type I Portland cement, ground granulated blast-furnace slag, and fly ash were used as cementitious materials, and a water-to-binder ratio of 0.35 was adopted. River sand was used as fine aggregate, whereas gravel with a maximum size of 25 mm was used as coarse aggregate. A sand-to-totalaggregate ratio of 0.48 was applied. To achieve a self-consolidating property, a water-reducing admixture was additionally included into the mixture as 1.0% of total binder contents. The slump flow values, which were measured according to ASTM C1611 [17], were obtained in a range of 490-630 mm, and the air contents measured were approximately 1.7%-1.8% for all tested mixtures. Typical slump flow test results for the 3D hooked-end fiber specimens are shown in Figure 2.  
In order to evaluate the effect of end-hook geometry of steel fibers on the flexural behavior of concrete slabs, three types of hooked-end steel fibers with different end geometries, i.e., 3D, 4D, and 5D, were adopted. A picture of the hooked-end steel fibers used is shown in Figure 1. Their density was 7.9 g/cm\({}^{3}\) and the tensile strength was approximately 1100 MPa. Because the post-cracking tensile strength of fiber-reinforced concrete is affected by the fiber reinforcing index, \(v_{f}\times l_{f}/d_{f}\) identical volume fractions and aspect ratios were applied for all hooked-end steel fibers with different end deformations [18]. Here, \(v_{f}\) is the fiber volume fraction, \(l_{f}\) is the fiber length, and \(d_{f}\) is the fiber diameter. The length and diameter of all 3D, 4D, and 5D hooked-end steel fibers were 60 mm and 0.9 mm, respectively, resulting in an aspect ratio (\(l_{f}/d_{f}\)) of approximately 65. Owing to a better mechanical anchorage effect of the highly deformed hooked-end fibers, the order of bond performance was reported as follows: 5D > 4D > 3D [10]. [PERSON] and [PERSON] [10] noted that the maximum pullout load and energy absorption capacity of 5D hooked-end steel fibers embedded in normal-strength concrete are obviously higher than those of 3D and 4D fibers, respectively. In addition, three different volume fractions, i.e., 0.37%, 0.60%, and 1.00%, of hooked-end steel fibers were adopted to produce deflection-softening and deflection-hardening SFRCs, based on three-point bending test results [19].
###### Abstract  
Segment measurement data are essential for sediment transport analysis and therefore highly important in overall river planning. Extant sediment measurement methods consume considerable manpower and time and are limited by factors including economic reasons and worker risks. This study primarily aimed to predict the changes in SSC (Suspended Sediment Concentration) and turbidity by examining the change in color in underwater images. While maintaining a constant flow in a channel, the turbidity and concentration were measured under different SSC. Multiple regression models were developed using turbidity measurement results, and they exhibited high explanatory powers (adjusted R\({}^{2}\) > 0.91). Furthermore, upon verification using the verification dataset of the experimental results, an excellent predictive power (RMSE \(\approx\) 0.4 NTU) was demonstrated. The model with the highest predictive power, which was inclusive of red and green bands and showed no underlying multicollinearity was used to predict turbidity. Finally, the turbidity and suspended sediment concentration relationship determined from the experimental results was used to estimate the sediment concentration from the color changes in the underwater images. The concentrations that were predicted by the model showed satisfactory results, compared to the measurements (RMSE \(\approx\) 21 ppm). This study indicated the feasibility of continuous SSC monitoring using underwater images as a new measurement method.  
s +
Footnote †: journal: Medical Imaging  
supended sediment concentration; turbidity; underwater image; RGB; multiple regression analysis
## 3 Materials and Methods
### Parameters Extraction and Singograms Obtention  
As described in Section 2.1, the sinogram is a two-dimensional representation in which the projections are placed according to the different directions through which the object is illuminated. As shown in Figure 14, for each obtained signal, a parameter estimation was applied [33]. In this work, the attenuation that the signal experienced with respect to the transmitted signal was obtained [34;35] (Equations (4)-(6)).  
\[\alpha_{ij}\left[\frac{\mathrm{dB}}{\mathrm{cm}}\right]=\frac{P_{tx,i}[ \mathrm{dB}]-P_{rx,j}[\mathrm{dB}]+G[\mathrm{dB}]}{d_{ij}[\mathrm{cm}]} \tag{4}\]  
\[P_{tx,i}[\mathrm{dB}]=10\cdot log_{10}\left(\frac{1}{N_{tx}}\sum_{n=0}^{N_{tx} }\left|x_{rx,i}[n]\right|^{2}\right) \tag{5}\]  
\[P_{rx,j}[\mathrm{dB}]=10\cdot log_{10}\left(\frac{1}{N_{rx}}\sum_{n=0}^{N_{rx} }\left|x_{rx,j}[n]\right|^{2}\right) \tag{6}\]  
where  
* \(i\) represents the position of the transmitter (\(i=[1,\ldots,180]\));
* \(j\) represents the position of the receiver with respect to the position of the transmitter (\(j=[1,\ldots,25]\));
* \(\mathrm{P_{tx,i}[\mathrm{dB}]}\) represents the power of the transmitted signal for position i. It is a constant value and it does not depend on i. \(\mathrm{x_{tx,i}[n]}\) is the discrete transmitted signal;
* \(\mathrm{P_{rx,j}[\mathrm{dB}]}\) represents the power of the received signal for sensor \(j\). It depends on the transmitter position, receiver position, and the properties and morphology of the sample. \(\mathrm{x_{rx,j}[n]}\) is the discrete received signal;
* \(\mathrm{N_{tx}}\) and \(\mathrm{N_{rx}}\) are the number of transmitted and received signals, respectively. \(N_{rx}\) was set to 7000 samples, corresponding to 70 \(\mathrm{\SIUnitSymbolMicro s}\), to prevent unwanted signals such as those from surface propagation and/or reflections from the container walls;
* \(\mathrm{d_{ij}[cm]}\) is the distance between the transmitter and the receiver;
* \(G\) [dB] represents the gain of the amplifier used. In this work and for the real measurements, its value was fixed at 40 dB;  
In this work, the columns of the sinogram correspond to the rotation angle of the transmitter, while the rows correspond to the position of the receivers. Each position of this matrix contains the value of the attenuation parameter extracted for that specific measurement. The sinogram is then converted into a column vector that will feed the neural network, either for training or for testing. It is important to notice that, although the reconstruction of the sinogram from the parameters of each signal is not necessary because the neural network does not require it, it is important to readjust/synchronize the size of the sinograms between the real measurements and simulated ones (see Figure 14). The sinogram interpolation module readjusts the size of the sinogram to have the same dimensions as the simulated sinograms and the real signals. The interpolation process is a cubic interpolation between adjacent samples (Figure 14).
## 4 Proposed Algorithm
### Proposed Machine Learning-Based Opportunistic Routing (OR) Protocol
#### 4.2.3 Energy Model  
The lifespan of a WSN depends on the endurance of each node, while performing network operations. The sensor nodes rely on the battery life to perform network operations. The energy cost model considered here is the first-order energy model for WSN [25]. Various terms used in Equations (1)-(3) are defined in Table 2.  
Energy Consumed in the transmission of \(n\)-bit packet up to \(l\) distance:  
\[E_{trs}(n,l)=n*E_{radio}+n*E_{amplification}*l^{2} \tag{1}\]  
Energy Consumed in the transmission of n-bit packet:  
\[E_{rsx}(n)=n*E_{radio} \tag{2}\]  
\[E_{ACK}(n,l)=E_{T_{delic}}(n,l)+E_{R_{delic}}(n) \tag{3}\] \[=2 nE_{elect}+n*E_{amplification}*l^{2},\ if\ l<l_{0}\] \[=2 nE_{elect}+n*E_{amplification}*l^{4},\ if\ l>l_{0}\]  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Parameter** & **Description** \\ \hline \(E_{trsNi\to fud}\) & Transmission vitality ingestion for node N\({}_{i}\) \\ \(E_{rsxNi}\) & Receiving vitality ingestion for node N\({}_{i}\) \\ \(E_{retNi\to fud}\) & Retransmission vitality ingestion for node N\({}_{i}\) \\ \(E_{ACKNi\to source}\) & Vitality spent by node N\({}_{i}\) in transmitting and receiving acknowledgments \\ \(RE_{Ni}\) & The residual vitality of node N\({}_{i}\) \\ \(E_{Truns}\) & Transmission vitality cost of the radio board of a sensor \\ \(E_{Rective}\) & Reception vitality cost of the radio board of a sensor \\ \(E_{Forward}\) & Combined vitality cost of radio board of a sensor for communication of a data packets \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of various terms used in the equations.
## 2 Materials and Methods
### Feature Selection Methods  
To eliminate redundancy and improve processing efficiency, SPA and CARS algorithms were compared for extracting feature bands from the processed kiwifruit spectra. Both algorithms were implemented under MATLAB software. The SPA is a forward variable selection technique aimed at minimizing collinearity within a vector space [36]. By employing vector projection analysis, it identifies a set of variables from the spectral data containing the least redundant information, minimizing collinearity among variables within the dataset. This, in turn, reduces the number of variables used for detection and enhances detection efficiency [37]. The CARS algorithm involves the selection of variables with large absolute regression coefficients using an adaptive weighted algorithm. It selects a subset of variables through cross-validation, aiming to minimize the cross-validation RMSE [29]. In this study, the CARS method was implemented utilizing ten-fold cross-validation and 100 Monte Carlo sampling iterations to determine the features with the lowest cross-validation RMSE.
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 9 BC and Cyberattack Prevention
### BC Features  
The main keywords or features that describe BC are illustrated below.  
* Data immutability: data are protected using cryptographic hashes unique to each block, disallowing manipulation or alteration after registration in the BC network.
* Decentralization: the absence of a trusted supervised centralized authority; decentralization ensures a lower failure rate, makes the network less prone to malicious attacks, and reduces reliance on a third party.
* Transparency: every involved node in the network is aware of the updated stored data.
* Security and Resilience: any data manipulation requires the approval of more than half of the miner nodes, which is extremely difficult to obtain practically.
* Data Encryption: the provision of public and private keys for data encryption and decryption, respectively, via the use of an asymmetric encryption algorithm for every two communicating nodes; the public key is shared between all nodes in the network to encrypt the data, and the targeted receiver can decrypt the data using its own private key.
* Digital Signatures: digital signing of transactions using a digital signature algorithm, such as the elliptic-curve digital signature algorithm (ECDSA), to approve transaction content and originate node identities.
* Consensus: every node in the network should agree on the current state of the distributed ledger, which is made possible using one of several popular consensus mechanisms, such as Proof-of-Work (PoW), Proof of Authority (PoA), Proof of Capacity (PoC),Proof of Share Stake (PoS), Delegated Proof of Stake (DPoS), Raft, Proof of Elapsed Time (PoET), and practical Byzantine fault tolerance (PBFT) [111].
* Smart contract: a piece of code that adds customizability to a BC. It represents an arrangement and executes itself automatically under a predetermined set of rules and conditions without a third party. Smart contracts can be used for node verification and authentication. The input of the smart contract is the transaction, which is executed with a corresponding code that consists of the value, address, functions, and state to generate the output events (see Figure 9) [112].