esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Domain Generalization  
Domain generalization (DG) problems [36] usually occur in deployments of statistical learning techniques due to distinct distributions of offline training data and online data. This phenomenon is very common because collecting a training dataset perfectly representing real-world scenarios tends to be impossible. Although statistical learning methods, especially for DNNs, have recognized generalization capabilities, this is only true for online data with the same distribution as the training data [43].  
There are many research topics addressing the DG problem. Multi-task [44] learning reuses the data representations for training on different related tasks to help the model to perform better for the original task. However, multi-task learning models only work for the domains they have already seen, which hardly fully solves the DG problem. Transfer learning [45; 46] pre-trains the models using data from classes with numerous samples, then fine-tunes the model's parameters with data from other classes. This canonical pipeline can be used for solving the DG problem by fine-tuning the models using data from different domains. However, transfer learning methods require data from other domains for training, which is not always available [47]. Meta-learning techniques [48; 49] recently became popular as promising solutions to DG problems. Meta-learning tries to learn a universal rule from various datasets and various tasks, i.e., learning to learn [50], which is close to human learning habits. Although meta-learning methods are attractive for DG problems, their computational time is always extremely long [51]. Meanwhile, their DG performances are sometimes unsatisfactory due to incidental overfitting on training datasets [51]. Data augmentation [52] aims to generate more samples for training for greater data diversity according to the existing training dataset to fulfill DG. However, in our problem, we cannot create meaningful fingerprints without channel matrices.  
Representation learning [53] aims to unify either the data representations or the inference results, or both training and inference for different domains [36; 54]. When extracted data representations are invariant for different domains and only relevant to the concerned task, domain generalization is achieved. In this context, DANN [37; 55] is a straightforward solution involving adversarial learning on domain labels to generate data representations that are irrelevant to the concerned domain [56; 57]. DANN implements this by reversing the gradient of the domain discriminator during backpropagation [58].
# Thermal Runaway Diagnosis of Lithium-Ion Cells Using Data-Driven Method
## 2 Open Battery Failure Dataset  
The fractional thermal runaway calorimeter (FTRC) aims to quantify the heat and mass released from the battery cell during the thermal runaway [19]. In [18], they conduct extensive experiments and build a large open-source database of battery failure incidents for different commercial cell types and causes. This rich set of experimental data using different cell manufacturers and abuse types provides a valuable benchmark.  
In [18], the thermal runaway of each cell was triggered using three different abuse methods, namely, \"Heat\", \"ISC\", and \"Nail\".  
* Heat: Heat is applied to the battery cell until it reaches thermal runaway.
* ISC: One of the latent defects in cell electrodes is an internal short-circuiting (ISC) device [20]. It can trigger thermal runaway at specific locations within the cell if the temperature reaches about 57 \({}^{\circ}\)C, much lower than that of the Heat abuse method.
* Nail: The pneumatically activated nail of FTRC penetrates 9 mm of the cell body.  
Table 1 summarizes the measured features and the causes of the open-source battery failure databset [18]. The dataset includes the heat and mass ejected data using FTRC from 364 tests on various batteries. Abuse tests use different cylindrical battery models with cell formats such as 18,650 and 21,700 from multiple manufacturers. Each sample of the abuse test consists of 26 measured features and 1 output label of causes. By considering the ejected location within the cell, most heat and mass measurements using FTRC can be categorized into three distinct groups, namely, (1) positive, (2) body, and (3) negative. In addition, the released energy is computed based on the fractional heat output. This dataset offers insights into the heat, mass, and energy changes in each cell under different abuse tests.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Data Type** & **Unit** & **Description** \\ \hline CellCap & Ah & Maximum capacity of the cell. \\ \hline CellEn & Wh & Maximum possible stored energy of the cell. \\ \hline PreCellM & g & Mass of the cell before the experiment. \\ \hline BottomVent & - & Whether the cell includes a bottom vent or not. \\ \hline PrePosM & g & Mass of the copper mesh of the FTRC positive side. \\ \hline PreNegM & g & Mass of the copper mesh of the FTRC negative side. \\ \hline ConfPos & - & Seal material of the FTRC positive side. \\ \hline ConfNeg & - & Seal material of the FTRC negative side. \\ \hline CellFail & - & Mechanism of the cell fails. \\ \hline HeatLossRate & kJs & Heat loss rate \\ \hline DiffM & g & Mass difference between before and after the experiment. \\ \hline PostCellM & g & Mass of the remained cell. \\ \hline PostPosRateM & g & Mass of the positive side ejected mating. \\ \hline PostPosBoreM & g & Mass of the positive side ejected bore. \\ \hline PostPosCuM & g & Mass of the positive side copper mesh. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measured features of open-source battery failure dataset [18].
# A Novel Love Wave Mode Sensor Waveguide Layer with Microphononic Crystals
## 2 Materials and Methods
### Transmission Parameters Analysis
In Figure 8, transmitting path 1 indicates that the signal transmits from the emitting IDT to the receiving IDT across the free surface, which can be expressed by the following Equation:  
\[{Y_{21}}^{0}=P_{23}^{IDT}P_{21}^{0}\cdot P_{31}^{IDT} \tag{7}\]  
where \({Y_{21}}^{0}\) is the first signal in the transfer admittance of the Love wave mode delay line with the common waveguide layer transmitting path and \({P_{21}}^{0}\) is the transmission coefficient of the common waveguide layer transmitting path. In transmitting path 2, the signal transmits from the emitting IDT to the receiving IDT across the 2D piezoelectric PnCs, yielding the following equation:  
\[{Y_{21}}^{PnCs}=P_{23}^{IDT}P_{21}^{PnCs}\cdot P_{31}^{IDT} \tag{8}\]  
where \({Y_{21}}^{PnCs}\) is the first signal in the transfer admittance of the Love wave mode delay line with the piezoelectric PnCs, and \(P_{21}^{PnCs}\) is the transmission coefficient of the piezoelectric PnCs. In transmitting path 3, the signal is sent from the emitting IDT, reflected by the piezoelectric PnCs and received by the emitting IDT. Thus, the P matrix provides the relationship shown in the following equation:  
\[{Y_{11}}^{PnCs,r}=P_{23}^{IDT}P_{11}^{PnCs}\cdot P_{32}^{IDT} \tag{9}\]  
where \({Y_{11}}^{PnCs,r}\) is the reflected signal of PnCs in the input admittance of the Love wave mode delay line with the piezoelectric PnCs, and \(P_{11}^{PnCs}\) is the reflection of the piezoelectric PnCs. Figure 8: The P matrix equivalent models of delay line structures with and without micro PnCs. Figure 7: The \(P\) matrix model of an IDT. By comparing transmitting path 1 and transmitting path 2 with Equations (7) and (8), the transmission coefficient of the piezoelectric PrCs \(P_{21}\)\({}^{\textit{P}\textit{P}\textit{C}}\)s can be obtained via Equation (10) when \(Y_{21}\)\({}^{0}\) is not equal to zero:  
\[P_{21}\)\({}^{\textit{P}\textit{P}\textit{C}}=\frac{Y_{21}\)\({}^{\textit{P}\textit{P}\textit{C}\textit{S}}}{Y_{21}\)\({}^{0}\)  
In this way, the transmission of the piezoelectric PrCs was obtained as shown in Figure 9. The results in the valid region [0,1] are valid; beyond this area, the results show errors caused by the numerical calculation for these two models. From the results of Figures 6 and 9, it can be seen that with the specific micro PrCs in the waveguide layer, the propagation attenuation of the Love wave can barely be increased. This shows the potential of combining the Love wave mode sensor with microfluidic devices.
## 3 Results and Discussion
### Batch Adsorption Performance of EGS@APTES-GT  
3.2.1. Influence of Solution pH on Adsorbent Efficiency  
The most influential factors on the adsorption performance are pH-dependent ionic speciation and adsorbent surface ionization. The results of pH\({}_{\text{PZC}}\) determination for EGS@APTES-GT (8.04) suggest that the effective removal of oxyanions could be achieved at pH \(<\) pH\({}_{\text{PZC}}\). Otherwise, the hydrogen bonding/donating capability of Iprodione is not significant and of a low susceptibility to the pH influence. On the contrary, the adsorption effectiveness strongly depends on EGS@APTES-GT surface ionization by creating ionized sites able to be established by either dipolar/polarizable and hydrogen bonding/donating interactions with different moieties present in the Iprodione structure. Due to this, the preliminary was performed with respect to the initial pH of the solution (a pH in the range 5-9). According to the results obtained, an optimal pH of 7 was selected for both As(V) and Iprodione removal. The pH-dependent ions speciation of As(V) was performed using MINTEQ 3.0 software (Figure S6). The selection of an optimal pH provides circumstances for the achievement of a good adsorption capacity, but also for an understanding of the adsorption efficiency under a range of pH values without the prior adjustment of the inlet pH. It could be indicative of the applicability of EGS@APTES-GT for real water applications with a wide range of pHs. It will a subject of further investigation.
###### Abstract  
The operation mode of a single strip provides incomplete side-scan sonar image in a specific environment and range, resulting in the overlapping area between adjacent strips often with imperfect detection information or inaccurate target contour. In this paper, a sum-modified Laplacian energy filtering (SMLF) and improved dual-channel pulse coupled neural network (IDPCNN) are proposed for image fusion of side-scan sonar in the domain of nonsubsampled contourlet transform (NST). Among them, SMLF energy is applied to extract the fusion coefficients of the low frequency sub-band, which combines the characteristics of energy information, human visual contrast, and guided filtering to eliminate the pseudo contour effect of block flow. In addition, the IDPCNN model, which utilizes the average gradient, soft limit function, and novel sum-modified Laplacian (NSML) to adaptively represent the corresponding excitation parameters, is applied to improve the depth and activity of pulse ignition, so as to quickly and accurately select the image coefficients of the high frequency sub-band. The experimental results show that the proposed method displays fine geomorphic information and clear target contour in the overlapping area of adjacent strips. The objective index values are generally optimal, which reflect the information of image edge, clarity, and overall similarity.  
sonar image fusion; overlapping area between adjacent strips; nonsubsampled contourlet transform; sum-modified Laplacian energy filtering; dual-channel pulse coupled neural network +
Footnote †: journal: Applied Sciences
# Fusion Classification Method Based on Audiovisual Information Processing
## 3 Proposed Method
### Feature Extraction  
#### 3.3.1 Sound Feature Extraction  
In Section 3.2, preprocessing operations were applied to the audio data. This paper employs Mel-Frequency Cepstral Coefficients (MFCC) to extract features of sound targets in complex environments. The principle behind MFCC extraction is based on the spectrogram of the sound. However, when fusing these features with image features, MFCC is unable to capture higher-level phonetic information from the sound. Therefore, this paper further  
Figure 2: Spectrum diagram.  
extracts the semantic information of the sound using a one-dimensional convolutional neural network (1D CNN). During the extraction process, the sound features are used as the input, denoted as \(Y_{audio}=(y_{1},y_{2},\dots,y_{n})\). As shown in Equation (6),  
\[\widetilde{Y}_{audio}=conv1D(Y_{audio},k_{audio}). \tag{6}\]  
Among them, \(\widetilde{Y}_{audio}\) represents the semantic features of the sound, \(conv\) denotes the convolution operation, and \(k_{audio}\) represents the size of the convolution kernel.
# LPWAN-Based Vehicular Monitoring Platform with a Generic IP Network Interface
## 2 Related Work
As a difference, with the work presented in this paper, none of the studies mentioned above deployed an end-to-end monitoring platform, neither including real data extracted from the vehicle nor a visualization system nor, as a prime advance, integrating a generic UNIX-based network interface for LPWAN. In fact, we explore the entire process as follows: (i) Vehicle data collection by means of an OBD-II interface, (ii) data compression by using the novel IETF Static Context Header Compression (SCHC) scheme, (iii) data transmission via the LoRaWAN link, through the developed LPWAN UNIX network interface, and (iv) data visualization from a data server hosted in the cloud, by means of a web-based dashboard.
## 3 Methodology
### The Proposed ConvResBiGRU-SE Model
#### 3.3.2 Structure of GRU  
The GRU was created as a novel architecture derived from RNNs to mitigate the challenges of expanding or vanishing gradients. Nonetheless, the memory cells within its architecture lead to increased memory consumption [28]. The GRU is a streamlined variant of the LSTM model, lacking a distinct memory cell in its architecture [29,30].  
A GRU contains update and reset gates that regulate the revised magnitude of each hidden state. These gates dictate the data that should be transmitted to the subsequent state and withheld, as depicted in Figure 4(a). The concealed state \(h_{t}\) at time \(t\) is determined by the output of the update gate \(z_{t}\), the reset gate \(r_{t}\), the current input \(x_{t}\), and the preceding hidden state \(h_{t-1}\). The mathmatical formulas of the gates in GRU are shown as follows:  
\[z_{t}\ =\ \sigma(W_{z}x_{t}\oplus U_{z}h_{t-1}\ +\ b_{z}), \tag{5}\]  
\[r_{t}\ =\ \sigma(W_{r}x_{t}\oplus U_{t}h_{t-1}\ +\ b_{r}), \tag{6}\]  
\[g_{t}\ =\ \tanh(W_{g}x_{t}\oplus U_{g}(r_{t}\otimes h_{t-1})\ +\ b_{g}), \tag{7}\]  
\[h_{t}\ =\ ((1-z_{t})\otimes h_{t-1})\oplus(z_{t}\otimes g_{t}) \tag{8}\]  
where \(W\) represents the weight matrices for the current input, \(U\) represents the weight matrices for the previous hidden state, and \(b\) are bias vectors. The function \(\sigma\) represents a sigmoid function. The symbol \(\oplus\) denotes a basic addition operation, while \(\otimes\) signifies a fundamental multiplication operation.  
Figure 5: Structure of BiGRU: (**a**) GRU cell and (**b**) unroll BiGRU.  
In 1997, [PERSON] and [PERSON] designed the bidirectional RNN (BiRNN) to overcome the constraints of the traditional unidirectional RNN [31]. In contrast to standard RNNs, the output at any specific time step in a BiRNN incorporates information from preceding and subsequent inputs. This is achieved by simultaneously training two separate RNN layers: one processes the sequence in the forward direction, while the other processes it in reverse.  
In a BiRNN, the neurons of a conventional RNN are partitioned into two segments: one for interpreting data in the forward direction and the other for the backward direction. Conversely, the outputs of positive neurons are not linked to negative neurons. This yields the overall structure illustrated in Figure 5b. The subsequent equations delineate the mathematical operations involved:  
\[\overrightarrow{h_{t}} = GRU(x_{t},\overrightarrow{h_{t-1}}) \tag{9}\] \[\overleftarrow{h_{t}} = GRU(x_{t},\overleftarrow{h_{t+1}})\] (10) \[h_{t} = [\overrightarrow{h_{t}},\overleftarrow{h_{t}}] \tag{11}\]  
where \(GRU()\) represents the GRU function that processes the current input \(x_{t}\) along with the previous hidden state in either the forward (\(\overrightarrow{h_{t-1}}\)) or backward (\(\overleftarrow{h_{t+1}}\)) direction.
# Low Discrepancy Sparse Phased Array Antennas
## 2 Sampling Points on a Planar Aperture
### Random Sampling Approaches
#### 2.1.2 Random Sampling with Jitter  
Jitter adds a small random variation to the element location given by  
\[(x_{n},y_{n})=\big{(}x^{\prime}_{n}+\alpha_{n}r_{\text{max}}\cos(2\pi\beta_{n }),y^{\prime}_{n}+\alpha_{n}r_{\text{max}}\sin(2\pi\beta_{n})\big{)} \tag{4}\]where \((x^{\prime}{}_{n},y^{\prime}{}_{n})\) are the rectangular lattice coordinates and \(r_{\max}\) is the maximum distance that the new sample point moves from the regular lattice.
# Development of a Path Tracker Based on a 4 WS Vehicle for Low-Speed Automated Driving Systems
## 4 Evaluation
### Real-Car Experiment  
#### 4.2.1 Real-Car Experiment Environment  
The reference path for the real-car experiment was designed with both straight and curved sections, as shown in Figure 9, to verify the tracking performance in each section. The driving experiment architecture is shown in Figure 10. The driving experiment en  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**Type of Path Tracker**} \\ \cline{3-10}
**Simulation Results** & **Unit** & \multicolumn{3}{c}{**Conventional 2 WS**} & \multicolumn{3}{c}{**Conventional 4 WS**} & \multicolumn{3}{c}{**Proposed 4 WS**} \\ \cline{3-10}  & & **RMS** & **MAX** & **SD** & **RMS** & **MAX** & **SD** & **RMS** & **MAX** & **SD** \\ \hline Lateral distance error & [m] & 0.00158 & 0.00660 & 0.00002 & 0.00132 & 0.00619 & 0.00001 & 0.00090 & 0.00510 & 0.00001 \\ Heading angle error & [deg] & 2.33378 & 4.57429 & 0.02335 & 1.26000 & 2.65473 & 0.01261 & 0.30013 & 0.64969 & 0.00300 \\ Side-slip angle & [deg] & 2.33282 & 0.07629 & 0.00041 & 1.25862 & 0.04232 & 0.00022 & 0.29665 & 0.01115 & 0.00005 \\ Yaw rate & [deg/s] & 13.9915 & 26.2022 & 0.13999 & 14.0075 & 26.9349 & 0.14015 & 13.9944 & 25.6563 & 0.14002 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation results.  
Figure 8: Simulation results: (**a**) lateral distance error; (**b**) heading angle error; (**c**) side-slip angle; (**d**) yaw rate.  
vironment was built on the Ubuntu 18.04 operating system, and the path tracker was designed using the NDT (Normal Distributions Transform)-based localization algorithm of Autoware, which is an open-source autonomous driving software package, along with ROS (Robot Operating System) in C++ [31; 32]. The specifications of the 4 WS-based autonomous vehicle platform used for the driving experiment are listed in Table 3, which supports front- and rear-wheel steering, as shown in Figure 11. Lateral control was verified by transmitting the control commands calculated by the proposed path tracker to the vehicle via the CAN (controller area network) communication interface.  
The driving experiment was conducted at two speeds, 5 KPH and 10 KPH, on the Kongju National University campus road in Korea, which has a speed limit of 10 KPH and is primarily paved with asphalt, as shown in Figure 12. This figure presents a PCD (point cloud data) map image and satellite photo of the campus road, depicting the experimental environment. These speeds were chosen to comply with the low-speed autonomous driving criteria defined in the ISO 22737 standard and to satisfy the assumptions of the kinematic model within the permissible speed range of the campus road. Additionally, conducting experiments at 5 KPH and 10 KPH is appropriate for verifying the performance of the proposed controller, as these speeds fall within the target range of low-speed autonomous driving, which is the primary focus of this study.
# Exploring Multi-Stage GAN with Self-Attention for Speech Enhancement
## 4 Experiments  
### Baseline and Objective Evaluation  
To have a comparative baseline reference, the ISEGAN and the DSEGAN networks [22] are used as the main baseline to compare the performance of the proposed ISEGAN-Self-Attention and DSEGAN-Self-Attention networks. Additionally, the traditional speech enhancement network, SEGAN [16], and the state-of-the-art method, the SASEGAN [23], are selected as baseline networks as well. With the implementations of the self-attention blocks in the proposed ISEGAN-Self-Attention and DSEGAN-Self-Attention networks, we consider the SASEGAN as one of the best gauges to observe how well the self-attention performs in multi-generator settings as compared to a single generator. Also, as adopted in SEGAN, we compare the performance to the noisy signals and to signals filtered via the Wiener method based on a priori signal-to-noise estimation [48]. The objective evaluation metrics used in the selected baselines are adopted to evaluate the quality of the generated enhanced speech signals. These metrics include the five objective signal-quality metrics (PESQ, CSIG, CBAK, COVL, and SSNR) suggested by [PERSON] [4], as well as the speech intelligibility metric (STOI) introduced by [PERSON] et al. [49]. The five objective signal-quality metrics are computed using the implementation in SEGAN [16]. This set of metrics focuses on assessing various aspects of speech signal quality. Specifically, PESQ and STOI measure perceived speech quality and speech intelligibility, respectively. SSNR and CBAK evaluate the distortion caused by background noise, while COVL provides an overall quality assessment of the speech signal. Additionally, SSNR compares the enhanced speech signal with the original clean speech on a segment-by-segment basis, offering insights into the preservation of speech in various segments [4; 49].