esa-sceva/satcom-chunk-collection huggingface dataset:
# A Thermopile Device with Subwavelength Structure by CMOS-MEMS Technology
## 4 Experimental Results and Discussion
The infrared  
Figure 6: SEM image of the CMOS compatible thermopile with (**a**) rectangular-hole type (RHT) and (**b**) circular-hole type (CHT) subwavelength structures. radiation source is placed 40 mm distance away from the sensor chip. The temperature of infrared radiation source is set to be between 30 \({}^{\circ}\)C and 75 \({}^{\circ}\)C at increments of 15 \({}^{\circ}\)C. In addition, to avoid interference from ambient light or other background signals, we set up a chopper before the sensor chip and a 5-14 \(\upmu\)m infrared filter. To investigate the bandwidth of proposed thermopile, the measurement of frequency response was built and is shown in the Figure 8. The frequency ranged from 1-150 Hz to investigate the bandwidth of the frequency response and the range was much higher than the bandwidth of the proposed thermopile. The results of the infrared modulation measurement for thermal time constants of sensors under various conditions are shown in Table 1. The time constant was about 5 ms and the corresponding bandwidth was 32 Hz, which has proved to be practical for the applications of infrared thermometer and thermal imagery. The prototypes for the thermopile without the SWS, the RHT thermopile with the SWS width of 3 or 3.5 \(\upmu\)m, and the CHT thermopile with the SWS diameter of 3 or 3.5 \(\upmu\)m, were measured and \begin{table}
\begin{tabular}{c c c} \hline
**Target Temperature (\({}^{\circ}\)K)** & **Relative Cut-Off Frequency (Hz)** & **Relative Thermal Time Constant (ms)** \\ \hline
473.15 & 33.1 & 4.81 \\ \hline
573.15 & 32.4 & 4.92 \\ \hline
673.15 & 30.2 & 5.27 \\ \hline \end{tabular}
\begin{tabular}{c} Average cut-off frequency is 31.9 Hz \\ Average of thermal time constant \(\tau\) = 5 ms. \\ \end{tabular}
\end{table} 
Table 1: Frequency response measurement with infrared radiation. Figure 8: Setup of the frequency response measurement for the thermopiles. Figure 7: Setup of the experimental measurement framework for the thermopiles. the results are shown in Figure 9. Here, the output voltage difference is the difference between the thermopile with SWS and the one without SWS. It can be seen that the maximum is the result for the case of the RHT thermopile with an SWS width of 3.5 \(\upmu\)m at a target temperature of 75 \({}^{\circ}\)C. The best case of the measurement is the same as the one of the simulation and is also the RHT case for the SWS width of 3.5 \(\upmu\)m, that is, the maximum is the result of the RHT thermopile with the SWS width 3.5 \(\upmu\)m at a target temperature of 75 \({}^{\circ}\)C. For the CHT thermopiles, the case of the SWS diameter 3 \(\upmu\)m was also higher than the other cases. Comparing Figure 9 with Figure 4, it can be seen that the normalized experiment results are highly consistent with the normalized simulation results.
## 6 Performance Evaluation
### Performance Analysis of CL-APCC
#### 6.1.1 The universal performance of CL-APCC  
Most existing congestion control algorithms only act directly against specific network density and routing protocols. In order to illustrate the universal feature of the proposed scheme, in the first set of simulations, we describe the impact of different network densities and routings for the RPR of network in Figure11.  
In Figure 11(a), when the node number is 50, the average RPR of BS is 97.5%. In the same way, when the node number is 75, the average RPR of BS is 97.1%, and when the node number is 100, the average RPR of BS is 95.1%. It can be seen that admission ratio of BS has a small extent of decline with the number of nodes increased. That is because data packets generated by the network correspondingly increase with the number of node increased. Consequently, there is a high probability of data sending collisions between nodes, and all the factors mentioned above result in the increase of lost packets ratio. However, the network has a stable trend, and after 40 seconds the PRP of the network is stabilized from 95% to 97%. The average RPR of network stays above 95%, which can better meet the goal of network monitoring. Now, we analyze the reason that the RPR of the network is lower in the previous 40 seconds. When the node number is 100, the average admission ratio in the pre-40s of the  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Values** \\ \hline NODE-PLACEMENT & Random \\ PROPAGATION-PATHLOSS & Two-Ray \\ PACKET-SIZE & Packet Head Size 12 byte \\  & Packet Data Size 500 byte \\ ROUTER-PROTOCOL & ADOV \\ MAC-PROTOCOL & The IEEE 802.11 Protocol \\ INITIAL-ENERGY & 2 J \\ Eelec & 50 nJ/bit \\ \(\xi\)amp & 100 pJ/bit/m\({}^{2}\) \\ TANSMISSION-RANGE (\(r\)) & 40 m \\ GRIP-NUMBER (\(K\)) & 4 \(\times\) 4 \\ BS-POSITION & Left-Upper of Monitor Region \\ NODE-NUMBER (\(N\)) & 50, 75, 100 \\ NETWORK-SIZE & 100 m \(\times\)100 m \\ MEMORY-SIZE (\(m\)) & 3 M \\ ORIGINAL-PRIORITY OF PACKET & 1, 2, 3 \\ THRESHOLD (\(L_{\max}\)) & 0.75 \(\times\) 3 M \\ INITIAL \(\rho\) & 0.6 \\ SIMULATION-TIME & 200 S \\ SAMPLING-INTERVAL & 5 S, 40 S \\ \hline \end{tabular}
\end{table}
Table 1: The setting of parameters in the simulation.  
experiment is only 80.6%. This is because at the initial time, data packets are temporarily cached in some nodes, which results in data packets which are generated by data sources not arriving at BS in time. As time goes by, CL-APCC adjusts the data sending rate of the node based on the memory conditions of the node which makes the RPR of the network achieve balance.  
In Figure 11(b), the node number is 100. The network trends to stable after 40 seconds. It is less than 1% the impact to CL-APCC, which is caused by the different routing protocols. The RPR of the network is kept balanced at 97%. Everything mentioned above illuminate that CL-APCC is irrelevant to the specific routing protocol. It has the higher universal feature. Furthermore, the reason that the RPR is smaller in the pre-40s of the network is the same as Figure11 (a).
## 4 Conclusions  
Taking into consideration the results of the research conducted, the following conclusions can be drawn:  
1. With accumulation time of 10 s, the most notable differences of the signal at the output of program module that implements AGC from the Gaussian distribution are observed in both areas of high  
Figure 8: White noise after normalization by the level. Lines of equal probability for consecutive PDF for T = 1 s. This is lines of equal probability of the values of w(y): 0.000134 (red), 0.004432 (green), 0.053991 (blue), 0.1295 (cyan), 0.241971 (magenta), 0.3521 (yellow), 0.3867 (black). For the Gaussian distribution with the mean equal to 0 and variance equal to 1, this would be corresponding to the straight lines—also in accordance with values of y: \(\pm\)4, \(\pm\)3, \(\pm\)2, \(\pm\)1.5, \(\pm\)1, \(\pm\)0.5, \(\pm\)0.25.  
Figure 7: White noise after normalization by the level. Lines of equal probability for consecutive PDF for T = 10 s. This is lines of equal probability of the values of w(y): 0.000134 (red), 0.004432 (green), 0.053991 (blue), 0.1295 (cyan), 0.241971 (magenta), 0.3521 (yellow), 0.3867 (black). For the Gaussian distribution with the mean equal to 0 and variance equal to 1, this would be corresponding to the straight lines—also in accordance with values of y: \(\pm\)4, \(\pm\)3, \(\pm\)2, \(\pm\)1.5, \(\pm\)1, \(\pm\)0.5, \(\pm\)0.25.  
and low probable sample values; differences from the Gaussianity in the intermediate area of sample values are less notable.
2. With the decrease of accumulation time, the differences from the Gaussian distribution increase most notably in the areas that correspond to the most probable sample values and the least probable sample values; changes in the intermediate area of probable sample values are less notable.
3. The research confirms the possibility of using the stationary noise model at the output of the described AGC system for the studied fragment of hydroacoustic noise, including fragments with propeller cavitations as an initial approach.  
All sources of funding of the study should be disclosed. Please clearly indicate grants that you have received in support of your research work. Clearly state if you received funds for covering the costs to publish in open access.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments, analyzed the data and wrote the paper.  
The authors declare no conflict of interest.
# Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform
## 3 Materials and Methods  
In this study, we adhered to the following workflow: first, time-domain samples containing six-channel sensor readings were preprocessed using the CWT. Transform configurations with different mother wavelets and scale values were considered, resulting in the generation of eight scalogram sets. This operation was performed on both the source KU-HAR dataset and the target UCI-HAPT dataset. In addition, the UCI-HAPT dataset was preliminary preprocessed so that the sample size was identical to that of the KU-HAR dataset.  
Second, the generated scalograms were used to evaluate nine popular CNN architectures, yielding 72 possible combinations. In this study, the following network architectures were considered: DenseNet121, DenseNet169, DenseNet201, ResNet50, ResNet101, ResNet152, Xception, InceptionV3, and InceptionResNetV2. The mentioned architectures were selected because they are among the most popular [53], with proven performance and ready-to-use implementations in most ML and AI software packages. It is important to note that the Xception, InceptionV3, and InceptionResNetV2 architectures have constraints on the input sample shape; therefore, scalograms with scale values less than 128 could not be utilized, resulting in a total of 60 feasible combinations tested. Each combination was tried five times to avoid the problem of suboptimal local minima, resulting in the training and evaluation of over 300 models. From the evaluated models, the best-performing combination of the network architecture and CWT parameters was then chosen. Classification accuracy was chosen as the criterion for model selection, as we consider all classes equally important for classification and it is the most intuitive metric for selecting a model from a group of comparable models. For the selected model, we provide other commonly used metrics, such as precision, recall, AUC, and F1-score.  
Third, the selected pre-trained model was evaluated with different numbers of frozen layers on the scalograms generated from the target UCI-HAPT dataset. The whole UCI-HAPT dataset and its subset were used to determine how the selected model performs on the target datasets of various sizes. The obtained results were then compared to the non-pre-trained control model with the same network architecture and input shape.  
During the model selection and model testing, 70% of the respective datasets were randomly selected for training and 30% for testing. For validation, 10% of the training subsets were used. We used the Adam optimizer with the initial learning rate set to \(1*10^{-3}\) and the categorical cross-entropy loss function. Additionally, the callback that decreases the learning rate when the loss function stops improving (i.e., ReduceLROnPlateau) was employed. During model pre-training, the number of epochs was set to 50. During fine-tuning and testing, this number was set to 100 for most models; however, it was increased to 120 for some models because of observed underfitting.
# An Improved Empirical Wavelet Transform and Its Applications in Rolling Bearing Fault Diagnosis
## 4 Simulated Signal Verification  
EWT needs to set many parameters when dividing boundaries, and different parameters will get different results. This affects its adaptability to some extent. The time required to repeatedly modify a large number of parameters exceeds the time saved by its self-adaptation. Sometimes it is difficult to distinguish two components whose frequencies are close. In a low signal-to-noise ratio signal, EWT divides more invalid components to bring unnecessary trouble to the analysis. In view of the above problems, three sets of simulation signals are given in this section to prove that FEWT boundary demarcation is more reasonable, faster, and more efficient than EWT.
## 4 Proposed Methodology
### CatBoost Classifier (Cb-C) for WSN Attack Detection  
The CatBoost Classifier (Cb-C) is a gradient boosting library-based, open-source, non-linear, tree-based ML approach. It outperforms more sophisticated boosting techniques,including Light GBM, XG Boost, and others, and delivers optimal results even during initial runs. The Cb-C exhibits equivalent performance across a wider variety of data types. The Cb-C was implemented in this pipeline to classify the pre-processed data. By implementing this classifier, the data were categorized with enhanced precision and increased speed without requiring additional time for processing. The Cb-C classifier was supplied with the data for the purpose of classifying the data during the training and testing phases. Each weight was initially established as identical. Figure 3 depicts the flow diagram Cb-C classifier.  
1. [label=()]
2. **Objective function (loss function):** The objective function is a classification loss function, and the base equation for distinguishing between the normal traffic and attack traffic in WSN is given by \[L(y,\hat{y})=\sum\limits_{i=1}^{n}y_{i}\log(\hat{y_{i}})+(1-y_{i})\log(1-\hat{ y_{i}})\] (4) where \(y_{i}=1\) for attack traffic (DoS) and \(y_{i}=0\) for normal traffic. \(\hat{y_{i}}\) is the predicted probability that sample 'i' is an attack.
3. **Handling categorical data in WSNs:** CatBoost's ordered target statistics help in managing the categorical data present in the WSN-DS dataset, and this prevents leakage and overfitting. The equation for the ordered target statistics is given by \[TS(c)=\frac{\sum\limits_{i=1}^{n-1}y_{i}}{n-1}\] (5) where c is the categorical feature (attack type) and \(y_{i}\) represents the attack labels.  
Figure 3: Flowchart of the CatBoost classifier.  
* **Prediction of attack likelihood:** After building the trees iteratively, the final prediction for a given instance is \[\hat{y}_{i}=\sum_{m=1}^{M}\eta\cdot h_{m}(X_{i})\] (6) where \(\hat{y}_{i}\) is the predicted likelihood of an attack on the i-th instance of sensor data and \(X_{i}\) represents features like the packet size, transmission rate, and node energy level.
## 5 Datasets and Experimentation Setup
### Pruning
#### 5.3.1 Experimentation Setting 1  
The first experiment consists of performing the pruning on each layer of the model in order to obtain a completely sparse model. Each layer has the same sparsity level with \(L_{2}\) regularization. For the same model we observed the accuracies in four cases. We removed 80%, 85%, 90%, and 95% of parameters.
## 6 Discussion  
The price of SLM equipment is higher than traditional equipment and forming powder has a higher price than traditional raw materials. Therefore, SLM is not suitable for processing general parts, the size range for forming parts is limited, and it cannot process large-sized parts. Spheroidizing and warpage are major defects in the SLM forming process. The SLM method can be used to obtain arbitrarily-sized shapes and fully-metallurgical functional parts. The density can reach 100%, a size precision of 20-50 \(\upmu\)m, and a surface roughness of 20-30 \(\upmu\)m. It can greatly shorten the manufacturing cycle, and its application scope has been extended to the fields of aerospace, medicine, automotion, molds, etc.
# Outage Performance of SWIPT-D2D-Based Hybrid Satellite-Terrestrial Networks
## 3 Performance Analysis
### Ts-D2d Scheme
#### 3.2.1 Outage Probability of \(\mathrm{U}_{1}\)
\begin{split}\varphi_{2}&=\text{Pr}\left[\Lambda_{ \text{su1,ts}}<\gamma_{p,ts}-\Lambda_{\text{su1,ts}},\gamma^{\prime}_{p}\geq \gamma_{p,ts}-\Lambda_{\text{su1,ts}}\right]\\ &=\text{Pr}\left[\Lambda_{\text{su1,ts}}<\gamma_{p,ts}-\Lambda_{ \text{su1,ts}},\Lambda_{\text{su1,ts}}\geq\gamma_{p,ts}-\gamma^{\prime}_{p} \right]\\ &=\int_{0}^{\gamma_{p,ts}}\int_{\gamma_{p,ts}-\gamma^{\prime}_{p}} ^{\gamma_{p,ts}-y}f_{\Lambda_{\text{su1,ts}}}(x)f_{\Lambda_{\text{su1,ts}}}(y) \text{d}x\text{d}y\\ &=\int_{0}^{\gamma_{p,ts}}F_{\Lambda_{\text{su1,ts}}}(\gamma_{p,ts} -y)f_{\Lambda_{\text{su1,ts}}}(y)\text{d}y-\int_{0}^{\gamma_{p,ts}}F_{\Lambda_ {\text{su1,ts}}}(\gamma_{p,ts}-\gamma^{\prime}_{p})f_{\Lambda_{\text{su1,ts}}}( y)\text{d}y.\end{split}  \tag{47}\]  
Adopting the \(L\)-step staircase approximation approach, we can rewrite the probability of case 2 as  
\[ \begin{split}\varphi_{2}&\approx\sum\limits_{i=0}^{L-1 }\left\{F_{\Lambda_{\text{su1,ts}}}\left(\frac{i+1}{L}\gamma_{p,ts}\right)-F_{ \Lambda_{\text{su1,ts}}}\left(\frac{i}{L}\gamma_{p,ts}\right)\right\}\\ &\quad\times\text{F}_{\Lambda_{\text{su1,ts}}}\left(\frac{L-i}{L} \gamma_{p,ts}\right)-F_{\Lambda_{\text{su1,ts}}}(\gamma_{p,ts}-\gamma^{ \prime}_{p})F_{\Lambda_{\text{su1,ts}}}(\gamma_{p,ts}).\end{split}  \tag{48}\]  
Substituting Equations (41) and (42) into Equation (48), we can obtain \(\varphi_{2}\). Then, substituting Equations (46) and (48) into Equation (45), we can obtain the OP of \(U_{1}\) for the TS-D2D scheme.
## 3 Methods  
In this section, we introduce our proposed incremental SAR target recognition method based on a class-specific dictionary. The schematic diagram of our method is shown in Figure 2. First, for each class of targets, we learn the dictionaries individually using the improved dictionary learning method. These dictionaries are also called class-specific dictionaries. Then, these dictionaries are collected into a dictionary set \(D(D=\{D_{1},D_{2},\ldots,D_{C}\})\), where \(D_{i}\) represents the sub-dictionary corresponding to the \(i-th\) class, and \(C\) denotes the number of classes. Finally, the target is classified by comprehensive utilization of the sparse representation coefficient and reconstruction error. When encountering the targets from the new category, the class-specific dictionary \(D_{new}\) is learned in the same way. At the same time, the dictionary set \(D\) is updated with \(D=\{D,\ D_{new}\}\). The following parts introduces the class-specific dictionary learning, target recognition strategy, and incremental learning.
# Flaw Size Quantification for Cable Flaw Inspection System with Inductive Search Coil Sensor
## 4 Experimental Result Analysis  
In this section, the experiment was conducted under the condition depicted in Section 3. The variation of the coil's self-inductance response was measured, and the processed experimental results were employed to explore the corresponding relations between the coil's self-inductance and the flaw axial length as well as the flaw cross-sectional area.  
Figure 8 presents a preliminary comparison of the effects of the PE sheath on detected signals at two representative frequencies under \(D_{2-6}\). The detection results without a 1 mm thick PE sheath are shown in Figure 8a,b for excitation frequencies of 1000 Hz and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Flaw axial length & 10 mm, 20 mm, 30 mm, 40 mm \\ Excitation frequency & 1000 \& 2500 \& 4000 \& 6000 \& 10,000 Hz \\ Layer NO. & \(P_{d}=1\), \(P_{d}=2\) \\ Flaw cross-sectional area & \{1/19, 2/19,..., j/19\} PLA \\ \hline \hline \end{tabular}
\end{table}
Table 1: The whole experiment parameter.  
Figure 6: Flaw position diagram (**a**) Sectional diagram (**b**) Side diagram (**c**) Diagram of the flaw.  
Figure 7: Entire experiment system flowchart.  
10,000 Hz, respectively. Figure 8c,d depict the detection results with the sheath for the same excitation frequencies. The data was smoothed and presented on a uniform scale. The distance between one side of the flaw and the sensor was positioned at 150 mm, while the other side was incrementally adjusted. Notably, the \"0 mm\" condition denotes that the two sides of the flaw were in contact. The presented graphs demonstrate that the signal response exhibited a decrease prior to the flaw front edge and an increase following the flaw rear edge. Moreover, the coil achieved its minimum inductance response during scanning when passing through the center of the flaw. It is noteworthy that when excited with higher frequencies, the coil demonstrated a higher inductance response baseline with reduced noise but lower response amplitude. The variation in the slope and shape of the response curve revealed a correlation between the coil self-inductance and flaw size, where longer axial lengths produced broader and more prominent response curves. The comparison of these results demonstrated that the presence of the PE sheath had a little impact on the amplitude of the inductance response, meanwhile, it exhibited a denoising effect to some extent. These findings provide the groundwork for the further experiments. Subsequent experiments will be conducted using the same cable with the PE layer.
## 4 Reproducibility and Scalability
### Challenges and Limitations  
While our proposed implementation demonstrates significant potential, it is important to acknowledge and address several challenges and limitations. One of the foremost concerns is ensuring the privacy and security of sensitive government data. The deployment of LLMs in e-government applications requires robust encryption methods, stringent access controls, and strict adherence to relevant legal frameworks to mitigate risks. Moreover, the proposed system does not incorporate users' feedback considerations in the retrieval (whether the retrieved documents are relevant to the query or not) and generator (whether the generated answer is correct and sufficient or not) components. This will be considered in future research.  
Another critical challenge is efficiently allocating computational resources, particularly in cloud environments, where costs can rapidly escalate with increased usage. The performance and efficiency of GAI models are closely tied to the size and complexity of the models used. Larger models often deliver higher performance but have significantly higher computational and financial costs. Additionally, using LLMs provided by corporate services may further increase expenses, depending on the specific models and services employed.  
To address these challenges, continuous monitoring and fine-tuning of system performance are essential. This approach ensures that deployment remains cost-effective while maintaining optimal performance across various implementations. Moreover, the proposed framework can be implemented with relatively lower costs by leveraging open-source LLMs and Haystack's LLM orchestration framework, based on each use case's specific data sources and requirements. This flexibility allows for a scalable and economically viable solution, making advanced AI technologies more accessible for e-government applications.