esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, new RDCA and SCNN methodologies have been proposed with automatic extracted features and classification for both single and combined PQD signals. The automatically extracted features, which are based on the primary novel idea of treating the time-frequency matrix as the whole angle, solve the uncertainty of the issue of manually selected features. For the purpose of POD diagnosis and classification, the parameter sensitivity experiments show that the weight residual structure of the RDCA is helpful to improve the accuracy. Furthermore, the convergence experiment reveals that the BN layer of the SCNN contributes to accelerating convergence and makes the structure of the RDCA more flexible. The visualization results show that the feature maps are able to obtain higher abstractness along with the increment of layers. A variety of comparative experiments manifest that the proposed method has superior precision as well as anti-noise capability than the existing approaches. The designed hardware test platform shows the real-time PQD diagnosis using the proposed method. The implementation performance demonstrates the practicability and robustness of the proposed method. To further improve this methodology, future work will focus on optimization of high-level feature extraction to simplify the model structure.  
Conceptualization, J.L. and W.Q.; methodology, J.L.; software, W.Q.; validation, J.M., Y.Q. and B.S.; formal analysis, J.M.; investigation, J.L.; resources, Q.T.; data curation, Q.T.; writing--original draft preparation, J.L.; writing--review and editing, Q.T.; visualization, W.Q.; supervision, J.L.; project administration, Q.T.; funding acquisition, Q.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China OF FUNDER grant number 5207067.  
The authors declare no conflict of interest.
## 2 Review of Related Literature and Applications
### Review of Approaches for Regulating Smart Device Usage
#### 2.2.2 Co-Use and Guided Mediation  
Co-use refers to shared engagement between parents and children in digital activities, such as watching videos or playing games together [28; 29]. This approach emphasises communication and collaborative meaning-making, which can enhance children's critical thinking and media literacy [28; 29]. When parents actively participate in their children's digital experiences, smart device use can serve as a meaningful opportunity for interaction and learning [30].  
Building on the foundations of co-use, guided mediation introduces direct parental instruction and discussion about content quality, digital safety, and responsible use [31]. These practices have been demonstrated to improve media literacy and inhibit unregulated or risky tendencies among children [31]. However, the success of such strategies is contingent on parental availability and digital competence. Parents with limited technological knowledge may find it difficult to provide effective guidance [32], and sustained co-engagement can be challenging for busy families [33]. Additionally, as children grow older, they may begin to resist parental involvement, which diminishes the long-term effectiveness of these approaches [34].  
These limitations underscore the importance of design solutions that ease the burden on parents while supporting the gradual development of self-regulation among children.
## 4 Numerical Examples
### Analysis and Discussion of Model Performance Comparison Using LSE Estimation Parameter Values
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Parameter Estimation Values** & \(MSE\) & \(R^{2}\) & \(RMSE\) & \(KD\) & \(TS\) & \(Bias\) \\ \hline G-O model & \(a=6735.5\); \(b=0.000023\) & 174.27 & 0.4091 & 13.2 & 0.6948 & 66.13 & 9.88 \\ \hline Delayed S-shaped model & \(a=137\),680; \(b=0.000144\) & 103.14 & 0.6502 & 10.16 & 0.5092 & 50.88 & 7.01 \\ \hline Inflection S-shaped model & \(a=113.1\); \(b=0.027176\); \(\beta=123.06\) & 65.37 & 0.7783 & 8.08 & 0.3873 & 40.5 & 5.54 \\ \hline Yamada Imperfect-2 model & \(a=474.57\); \(b=0.000142\); \(a=0.023482\) & 129.1 & 0.5622 & 11.36 & 0.5723 & 56.92 & 8.1 \\ \hline P-N-Z model & \(a=215.56\); \(b=0.008277\); \(\beta=37.586\); \(a=0.018169\) & 92.29 & 0.687 & 9.61 & 0.4593 & 48.13 & 6.98 \\ \hline Weibull distribution model & \(a=301.19\); \(b=4\times 10^{-6}\); \(c=0.20165\) & 109.29 & 0.6294 & 10.45 & 0.5187 & 52.37 & 6.8 Figure 3: _Cont_. Figure 3: The cumulative number of detected faults over time. (**a**,**c**,**e**) represent the cumulative number of detected faults using 100% of the data for DS3-1, DS3-2, and DS3-3, respectively. (**b**,**d**,**f**) represent the cumulative number of detected faults using 85% of the data for DS3-1, DS3-2, and DS3-3, respectively. From Tables 12-14, we can see that using 85% of the data (DS1-1, DS1-2, and DS1-3), the \(MSE_{prdict}\), \(RMSE_{prdict}\), \(TS_{prdict}\), and \(Bus_{prdict}\) values of the PM are 45.63, 6.76, 8.02, and 0.84; 211.38, 14.54, 12.52, and 2.05; and 113.91, 10.67, 17.21, and 1.22, respectively. In Table 12, the \(MSE_{prdict}\), \(RMSE_{prdict}\), and \(Bus_{prdict}\) values of the ISS model which ranked second are approximately 1.09, 1.04, and 1.02 times as large as those of the PM, respectively. Table 13 shows that the \(MSE_{prdict}\), \(RMSE_{prdict}\), and \(Bus_{prdict}\) values of the G-O model, which came in second place, are 0.82, 0.35, and 0.4 times higher than those of the PM, respectively. Table 14 reveals that the \(MSE_{prdict}\), \(RMSE_{prdict}\), and \(Bus_{prdict}\) values of the second-placed DSS model are approximately 1.92, 0.71, and 0.91 times greater than those of the PM, respectively. The PM outperforms the G-O model, DSS model, ISS model, Yamada Imperfect-2 model, P-N-Z model, GGO model, Wang model, and Li model in terms of prediction performance. The second best is the ISS model and the worst is the Li model. The PM outperforms the G-O model, DSS model, ISS model, Yamada Imperfect-2 model, P-N-Z model, GGO model, Wang model, and Li model in terms of fitting performance, as shown in Figure 1b. The G-O model comes in second, while the DSS model comes in last, using 85% of the data (DS1-2). Figure 1d shows that the Yamada Imperfect 2 model has superior fitting performance compared with the other models, but the suggested model performs worse than it. However, compared to the Yamada Imperfect 2 model, the PM performs predictions more accurately. The DSS model comes in second with 85% of the data (DS1-3), and the Li
# Wound Dressing Materials: Bridging Material Science and Clinical Practice
## 4 Antimicrobial Dressings
### Antimicrobial-Peptide-Based Dressings  
Antimicrobial peptides (AMPs), also referred to as host defense peptides (HDPs), are small, bioactive molecules that are naturally produced by a wide variety of organisms, including plants, animals, and humans. These peptides are essential components of the innate immune system, acting as the first line of defense against invading pathogens such as bacteria, fungi, and viruses. AMPs are found in many tissues, including the skin, mucous membranes, and blood, where they perform a critical role in immune surveillance and host protection. They provide rapid, broad-spectrum antimicrobial activity, often before the adaptive immune response is activated, making them crucial for immediate defense against infections. AMPs exert their antimicrobial effects through two primary mechanisms: membrane-targeting and non-membrane-targeting. In the membrane-targeting mechanism, AMPs bind to the microbial cell membrane and insert themselves into the lipid bilayer,causing disruption of the membrane's structure. This disruption compromises the integrity of the membrane, leading to leakage of cellular contents and, ultimately, cell death. In the non-membrane-targeting mechanism, AMPs penetrate microbial cells either through passive diffusion or endocytosis. Once inside the cell, AMPs can interfere with essential processes such as protein synthesis, nucleic acid synthesis, and protease activity, inhibiting cell division and overall microbial function. These dual mechanisms of action make AMPs highly effective against a wide range of pathogens and an important area of research for developing new antimicrobial therapies [110]. In addition to their antimicrobial properties, AMPs support wound healing by promoting angiogenesis, cytokine release, dermal cell migration, and cell proliferation [110; 111; 112]. Currently, a growing number of preclinical and clinical trials are investigating AMPs for the treatment of infectious diseases. Several AMP-based or AMP-inspired products have already received FDA approval for managing bacterial skin and wound infections [110; 111; 112].
## 2 Materials and Methods
### Training of Classifiers
#### 2.5.7 Loss Functions for CNN-W and CNN-EW  
Weighted sum of absolute error loss for CNN. The unbalancing between positive and negative number of samples accounts for around 1:19, respectively. This means that loss function has to recover to the high probability to see a negative sample and thus to avoid learning much more about this class. For this reason, the weighting factor \(w_{1}\) has been introduced for positive class and \(w_{0}\) for negative class that balances error on positive class roughly 19 times with respect to the error when true class is negative, multiplied to the absolute error between true and predicted resistance values. It can be argued that this solution does not consider whether the error is a false positive or a false negative.  
\[w_{1}=\frac{Number\ of\ Population\ Samples}{2*Number\ of\ Positives} \tag{3}\]  
\[w_{0}=\frac{Number\ of\ Population\ Samples}{2*Number\ of\ Negatives} \tag{4}\]  
\[e=|Y-\hat{Y}| \tag{5}\]  
\[Error_{i}=\begin{cases}e*w_{1},&\text{if }Y>20\\ e*w_{0},&\text{if }Y\leq 20\end{cases}\]  
\[Weighted\ SAE\ Loss=\sum_{i=1}^{N}Error_{i} \tag{6}\]  
In Equations (3) and (4), the digit 2 represents the number of classes, and in Equation (5), the parameter \(e\) is the absolute difference between true (\(\Upsilon\)) and predicted (\(\hat{\Upsilon}\)) resistance values.  
Extended weighted sum of absolute error loss for CNN. The extended weighted loss modifies the previous, penalizing false positive and false negative twice more. In case of true positive or true negative class recognized (after inverse transforming predictions and ground truth values, since they were normalized as mentioned in Section 2.4), error is considered to guide prediction layer to lower mean squared error (MSE) on resistance prediction. MSE is a metric monitoring overall performance of CNN regression during training. The mathematical explanation is provided in the following piecewise definition, where \(FN\), \(TP\), \(FP\), and \(TN\) symbolize false negative, true positive, false positive, and true negative, respectively.  
\[Error_{i}=\begin{cases}e*w_{1}*2,&\text{if}\quad\quad Y>20\quad\&\&\quad\hat{Y} \leq 20\quad\quad for\quad FN\\ e*w_{1},&\text{if}\quad\quad Y>20\quad\&\quad\hat{Y}>20\quad\quad for\quad TP \\ e*w_{0}*2,&\text{if}\quad\quad Y\leq 20\quad\&\quad\hat{Y}>20\quad\quad for\quad FP \\ e*w_{0},&\text{if}\quad\quad Y\leq 20\quad\&\quad\hat{Y}\leq 20\quad\quad for\quad TN \end{cases}\]  
\[\text{\emph{Extended Weighted SAE Loss}}=\sum_{i=1}^{N}Error_{i} \tag{7}\]  
In both regression implementations, the total loss value accounted for the sum of absolute errors (SAE) reported on the batch data; see Equations (6) and (7).
# N-Trans: Parallel Detection Algorithm for DGA Domain Names
## 4 Experiment and Analysis  
### Dataset and Test Environment  
The dataset used in this paper includes legitimate domain names and malicious domain names. The legitimate domain names are from the collection of the top 1 million domain names collected by Alexa [18]. The malicious domain names are from different DGA families of malicious domain names publicly collected by the 360 Network Security Lab [19] such as _nymaim_, _necurs_, and other well-known malicious domain name families.  
The experiments in this paper are based on the deep learning framework of _keras_, and the back-end uses the _Tensorflow_ module. The specific experimental environment is shown in Table 1.
## 1 Introduction
The results demonstrated that the nanotubes possessed effective adsorptive qualities for BPA, along with commendable recyclability and concentration capabilities. Most studies have focused on MWCNTs, with fewer reports on SWCNTs [30]. Nevertheless, SWCNTs exhibit superior performance; they possess smaller diameters and a larger specific surface area compared to MWCNTs. Therefore, drawing on earlier investigations into phenol elimination from water, this study prepared magnetic SWCNT composites incorporating iron oxide nanoparticles via an electric arc process followed by thermal treatment. The adsorption behavior and underlying mechanisms of the composites towards BPA were investigated. These results offer foundational theoretical and empirical support for the application of magnetic SWCNT nanocomposites in the removal of organic contaminants during water purification processes.
# Variable Scale Pruning for Transformer Model Compression in End-to-End Speech Recognition
## 5 Experiments and Results
### Pruning Evaluation  
The three types of pruning, namely local, global, and variable scale, were applied to the test data without any parameter modifications. The values of \(U_{0}\), \(V_{0}\), \(\alpha\), and \(\beta\) remained the same as those set on the development data.  
Figures 13 and 14 present the error rates for varying Sparsity levels, reaching up to 52.5% for Libri-trans and 62.5% for VoxforgeIT. It is noteworthy that, beyond these thresholds, the application of global pruning results in a significant increase in the error rate. Specifically, the error rate exceeds 24% for Libri-trans and 13.9% for VoxforgeIT.  
In all cases, variable scale pruning demonstrates superior performance compared with global and local pruning techniques.  
Figure 11: The development error rate is plotted as a function of Sparsity for the Libri-trans system after applying local, global, and variable scale pruning techniques.  
Figure 12: The development error rate is plotted as a function of Sparsity for the VoxforgeIT system, after applying local, global, and variable scale pruning techniques.  
The Word Error Rate (WER) of the non-pruned Libri-trans system is reported to be 6.6% in Section 3.2. According to Figure 13, when the WER is 7.3%, variable scale pruning achieves a Sparsity gain of 7%. This allows the model to be compressed to 52.5% while maintaining a WER of 8.9%. This corresponds to a relative increase in the error rate of approximately 13.5%.  
In Section 3.2, it is stated that the non-pruned VoxforgeIT system achieved a Character Error Rate (CER) of 9.3%. The information presented in Figure 14 clearly shows that, for a CER of 9.5%, the variable scale pruning models exhibit 6% higher Sparsity values compared with the locally or globally pruned models. Furthermore, when the pruned system achieves a Sparsity level of 57%, the CER remains at approximately 10%. As a result, we can deduce an absolute increase in the error rate of 0.7% when the model is compressed to 57%.  
It is worth noting that VoxforgeIT's transformer model demonstrates a higher level of over-parameterization compared with that of Libri-trans, as evidenced by its larger Sparsity at the same increase in error rate.  
Overall, these results highlight the effectiveness of variable scale pruning in achieving a favorable trade-off between Sparsity and error rate for both datasets. Setting a maximum allowable relative increase of 10% in the error rate (i.e., WER = 7.3% for Libri-trans and  
Figure 14: The test error rate is plotted as a function of Sparsity for the VoxforgeIT system after applying local, global, and variable scale pruning techniques.  
Figure 13: The test error rate is plotted as a function of Sparsity for the Libri-trans system after applying local, global, and variable scale pruning techniques.  
CER = 10.3% for VoxforgeIT), the most optimal pairs of (error rate, pruning rate) were found to be (7.6%, 43%) for the Libri-trans system and (10.3%, 59.5%) for the VoxforgeIT system.
# Photon Enhanced Interaction and Entanglement in Semiconductor Position-Based Qubits
## 3 Position-Based Semiconductor Qubits in the Frame of Semiconductor Photon Communication
### Maximally Entangled States and Entanglement Entropy
**(b)** Evolution of the probability of the localized state \(\left|0^{A}\right\rangle\) of system-A, \(P_{\left|0^{A}\right\rangle}(t)=P_{\left|0^{A}_{S}\right\rangle}(t)+P_{\left|0 ^{B}_{S}\right\rangle}(t)\), together with the evolution of probability of the localized state \(\left|0^{B}\right\rangle\) of system-B, \(P_{\left|0^{B}\right\rangle}(t)=P_{\left|0^{B}_{S}\right\rangle}(t)+P_{\left|0 ^{B}_{S}\right\rangle}(t)\) as a function of time. In this case, the anti-correlation is not trivial, and the result will depend on initial conditions. This is expected since we assumed that there is no electrostatic interaction between the two particles, i.e., the Coulomb force is negligible. Considering this, the position of each particle is not expected to be \"strongly\" affected by the position of the other, only their energy states. Figure 7: **(a)** Quantum paths of system-A and system-B. The methodology to obtain these graphs is similar to the one discussed for the system of a single qubit. It can be seen that the quantum trajectories of the two systems are anti-correlated. In other words, when system-A is in the ground state \(\left|n+1,g\right\rangle\), system-B is in the excited state \(\left|n,e\right\rangle\); **(b)** The emitted and absorbed photons between the two systems are anti-correlated. When system-A emits a photon, system-B absorbs a photon, and vice versa. this, the position of each particle is not expected to be \"strongly\" affected by the position of the other, only its energy states. Here we should note that if one considers a non-negligible Coulomb force, the positional anti-correlation will be also observed. This was also demonstrated in [26] where the quantum states entanglement was due to a Coulomb interaction. To conclude this section, we investigate the entanglement in the system under study. Figure 8 plots the Von Neumann entanglement entropy \(S_{\mathrm{N}}\) defined between system-A and system-B. As shown in Section 4.4, the maximum value of \(S_{\mathrm{N}}\) for this system is \(2\ln 2\). At such an instance of time, when \(S_{\mathrm{N}}=\mathrm{max}\), the quantum states are maximally entangled (inseparable). Thus, the wave-function cannot be represented as a product of the wave-functions of each particle. From the plot, one can observe that \(S_{\mathrm{N}}\) is a time-dependent quantity and for this system, it reaches the maximum value of \(2\ln 2\). We conclude that the states of the two coupled qubits via the waveguide are entangled. Therefore, when a photon communication is allowed between two qubits, as constructed via the methodology discussed in this study, one is expected to achieve quantum operations between the qubits over longer distances.
## 3 Suggested Techniques
### Suggested Routing Protocol
#### 3.3.1 CH Selection  
The proposed protocol works for each round like LEACH. CH selection for each cluster is selected in consideration of the residual energy of each sensor node and the position of the sensor node derived from the WSN performance improvement considerations of Table 1. Each cluster selects a CH that transmits data to BS every round, and a score function is introduced for CH selection. The conditions of the node to be selected as CH are as follows:  
1. Nodes with high residual energy.
2. Nodes close to the center of the cluster. It is possible to calculate the center of the cluster and the distance of the sensor nodes by using the Global Positioning System (GPS) installed by each sensor node.  
The reason for introducing the weight is to determine which of the above conditions should be weighted. The following score function is introduced for this purpose:  
\[\text{Score}(CH_{i})=\alpha E_{i}+(1-\alpha)\frac{1}{D_{i}^{2}} \tag{1}\]  
In Equation (1), \(E_{i}\) is the amount of remaining energy of sensor node \(i\) in a cluster, \(D_{i}\) is the distance between sensor node \(i\) and the center of the cluster, and \(\alpha(0\leq\alpha\leq 1)\) is the weight. By applying Equation (1), the node with the highest score in each cluster is selected as CH. When selecting the CH node, the selection of the weight (\(\alpha\)) can affect the performance of the proposed routing protocol, so the optimal weight was selected by performing the following simulation.  
Figure 6 shows that the results in a round with 100 nodes are down when weight (\(\alpha\)) is 0.8. If 100 nodes go down in the 522 nd round, it provides optimal results. Based on this result, this study applied 0.8 to weight (\(\alpha\)).
## 2 Materials and Methods
### Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR)  
Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was utilized to analyze differential gene expression between untreated and treated samples. Cells for total RNA extraction were cultured overnight in 6-well plates at a seeding density of 200,000 cells per well. Following overnight culture, the cells were incubated with respective treatments for 48 h. Total RNA was then obtained using Invitrogen(tm) TRIzol(tm) Reagent, according to the manufacture's recommendations. After the cells were lysed and homogenized in TRIzol(tm) Reagent, the homogenate was shaken with chloroform to facilitate phase separation. Total RNA was precipitated from the aqueous layer using isopropanol, and purified by washing in 75% ethanol. Finally the purified RNA resuspended in nuclease free water. Reverse transcription was performed with 0.5 \(\upmu\)g of total RNA using RevertAidFirst Strand cDNA Synthesis kit (Thermo Scientific, USA) according to the manufacturer's instructions. 1 \(\upmu\)L of cDNA (5 \(\mathrm{ng/\upmu L}\)) was used per reaction. qPCR was performed using PowerUpTM SYBRTM green master mix (ThermoFisher Scientific, Waltham, MA, USA) according to the manufacture's recommendation. GAPDH was used as an endogenous control for all reactions; a list of all genes tested and primers used is summarized in Table 4. The final primer concentration per reaction was 500 nM while the thermo-cycling program used for all genes was 50 \({}^{\circ}\)C for 2 min, 95 \({}^{\circ}\)C for 2 min, 95 \({}^{\circ}\)C for 15 s, and 60 \({}^{\circ}\)C for 1 min, for a total of 40 cycles. Annealing temperature for all primers was set to 60 \({}^{\circ}\)C. A dissociation step was additionally performed using the following program: 95 \({}^{\circ}\)C for 15 s, 60 \({}^{\circ}\)C for 1 min, and 95 \({}^{\circ}\)C for 15 s. All reactions were performed using the 7500 Real-Time PCR system from Applied Bio-systems, USA, and expression fold changes determined using the 2\(-\Delta\)ACt method [43]. Statistical significance was measured using GraphPad Prism 6.01 software's multiple T-tests [33].
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 2 Literature Review
### UAV Image Segmentation with Deep Learning Enhancements  
In 2020, ([PERSON] et al., 2020) introduced a novel approach for multifeature semantic segmentation derived from UAV photogrammetry images, leveraging deep learning techniques. The incorporation of the SegNet model notably enhanced the accuracy of building extraction in this context. Additionally, in the same year, [PERSON] and colleagues conducted a study in a related field [20].