esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analysis
### Analysis of Experimental Results  
A matching experiment was carried out on 21 SAR images covering the southeast coastal area of China. The matching resolution was set to a fixed ground sampling interval of 1.8 m, and the initial absolute positioning deviation of the image was about 30 m. Therefore, the matching window was set to 300 pixels, which could ensure that enough similar ground object information was found in the window area of the two images, so as to ensure the effectiveness of the matching results.  
For 21 scene images, the feature points of overlapped images are extracted and matched. After resampling, the maximum overlap area of the two scene images is  
By comparing the experimental results in Table 3, it can be seen that:  
1. When matching SAR images for a single node, the SAR-SIFT algorithm after GPU processing is nearly 96% higher than that of the single CPU node SAR-SIFT algorithm. After the improvement of the GPU algorithm, the performance of single-node GPU optimization algorithm is improved by 97%, which shows the effectiveness of the optimization strategy of the SAR-SIFT algorithm based on GPU mapping;
2. After multi-node processing, SAR image matching performance has been significantly improved. After 16-node parallelism, the GPU optimization algorithm based on multi-node parallelism has improved by 99.75% compared with the traditional single-node CPU, which shows the effectiveness of the multi-node parallelism strategy.  
To sum up, by optimizing the multi-node parallel strategy for computing time-consuming content in SAR image feature point extraction and matching, the resource utilization of computing nodes can be effectively improved, thus improving the efficiency of SAR image feature point extraction and matching.
## 12 Evolving Fuzzy Techniques for EEG Artifact Removal
### Takagi-Sugeno (TS) Fuzzy Techniques for Removing Artifacts in EEG Signals  
The use of fuzzy TS systems in removing artifacts from EEG signals greatly improves the analysis, effectively addressing the complexity and ambiguity of the signals. These systems simultaneously evaluate multiple signal characteristics for more complete and precise artifact removal.  
In fuzzy TS systems, the output is a function of multiple inputs. Consider a system with \(n\) inputs (extracted features), \(c_{1}\), \(c_{2}\),...,\(c_{n}\), and one output, \(y\). Each rule can be expressed as:  
\[\text{If }c_{1}\text{ is }A_{1}\text{ and }c_{2}\text{ is }A_{2}\text{ }\dots\text{ and }c_{n}\text{ is }A_{n}\text{ Then }y=f(c_{1},c_{2},\dots,c_{n}), \tag{37}\]  
where \(A_{1}\), \(A_{2}\),...,\(A_{n}\) are fuzzy sets associated with the various inputs, and \(f(c_{1},c_{2},\dots,c_{n})\) is a function describing the linear or non-linear relationship between inputs and output. As in [144], each \(c_{i}\) is evaluated through fuzzy membership functions, \(\mu_{A_{i}}(c_{i})\) to determine its degree of membership in the relevant fuzzy sets.  
With fuzzy evaluations of each feature, inference rules are implemented to establish how different degrees of membership influence artifact removal. For each rule \(R_{i}\), the fuzzy output \(y_{i}\) corresponds to the filtering or correction decision based on the combination of the input characteristics.  
The system output, which determines the artifact removal action, is obtained by aggregating the outputs of all the rules. The aggregation can be and via the centroid method, where the overall output \(y\) is given by the weighted average of the outputs of each rule, weighted by their degrees of truth, \(w_{i}\):  
\[y=\frac{\sum_{i=1}^{m}w_{i}\cdot y_{i}}{\sum_{i=1}^{m}w_{i}} \tag{38}\]  
in which:  
\[w_{i}=\prod_{j=1}^{n}\mu_{A_{j}}(c_{j}). \tag{39}\]
## 3 Results
### Spatial Comparison of Bottom-Mounted and Towed ADCPs  
This study aimed to correct the radar data effect on the towed ADCP by identifying the best route based on a spatial and temporal scale. The towed ADCP was used as the target value of the LSTM output for the corrected u-component and v-component radar data. The corrected radar oceanic current u-component and v-component were used as the target value for the LSTM output. The radar provided the original flow rate of the u-component and v-component, while the original bottom-mounted ADCP flow rate yielded the EOF ellipse, as shown in Figure 8. At the coordinate points of our experiments, it was evident that the ellipse using bottom-mounted ADCP as the target value was similar to the original ellipse of the bottom-mounted ADCP, while the ellipse using the towed ADCP as the target value was almost identical to the ellipse of the original bottom-mounted ADCP currents. The reason for the difference in the LSTM output when using the bottom-mounted ADCP as the target value was likely due to the limited amount of data used, which was only 3 days. This highlights the superiority of radar data, which, when corrected by using towed ADCP data, can capture the magnitude and direction of ocean currents more accurately. In our previous study, we obtained ADCP-corrected radar data within a specific spatial range. However, we were unable to obtain additional ADCP data for validation during the same time period. Therefore, we conducted an experiment to randomly compare the ellipses of six other points in the existing radar data after correcting them using the previous training parameters of the LSTM. It is evident that both training methods produce errors larger than the experimental points, and these errors tend to increase as the distance from the experimental points increases. Therefore, the closer the corrected radar data align with the towed ADCP trajectory, the better the result is.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Target Value Time Length** & **Correlation Coefficient** & **RMSE(m/s)** \\ \hline Exp 1 & Towed ADCP (1.5 d) & 0.49 & 0.21 \\ Exp 2 & Towed ADCP (2 d) & 0.57 & 0.18 \\ Exp 3 & Towed ADCP (3 d) & 0.64 & 0.17 \\ Exp 4 & Bottom-mounted ADCP (3 d) & 0.59 & 0.15 \\ Exp 5 & Bottom-mounted ADCP (5 d) & 0.65 & 0.14 \\ Exp 6 & Bottom-mounted ADCP (10 d) & 0.75 & 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensitivity experiment of training data quantity for towed and bottom-mounted ADCPs.
# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 3 Proposed DOA Estimation Method  
### Preprocessing and IQ Extraction  
Unlike traditional DOA estimation methods based on covariance matrices, this paper adopts a strategy of directly using the \(IQ\) components of the received signals as the input. For the original signal \(x_{m}(n)\), which is a discrete signal received by each array element, its \(Q\) signal \(I_{m}(n)\) can be obtained using the discrete Hilbert transform, defined as  
\[I_{m}(n)=\frac{2}{\pi}\sum_{k=-\infty,k\
eq 0}^{\infty}x_{m}(n-k)\cdot\frac{ \sin^{2}(\pi k/2)}{k} \tag{5}\]  
Furthermore, the analytic signal \(x_{a}\) can be derived from the formula as follows:  
\[x_{a}(n)=R_{m}(n)+jl_{m}(n) \tag{6}\]  
where \(R_{m}(n)\) represents \(I\) signal, which is numerically equal to the original signal. After that, the \(I\) and \(Q\) components are obtained by  
\[R_{m}(n)=\Re(x_{a}(n)),I_{m}(n)=\wp(x_{a}(n)) \tag{7}\]Then, the \(IQ\) components of all array elements are sequentially concatenated to form the input matrix, which is explained as  
\[M_{IQ}=\left[\begin{array}{cccc}R_{1}(0)\ R_{1}(1)&\cdots&R_{1}(T-1)\\ R_{2}(0)\ R_{2}(1)&\cdots&R_{2}(T-1)\\ \vdots&\vdots&\vdots&\vdots\\ R_{M}(0)\ R_{M}(1)&\ldots&R_{M}(T-1)\\ I_{1}(0)&\mathrm{I}_{1}(1)&\ldots&\mathrm{I}_{1}(T-1)\\ I_{2}(0)&\mathrm{I}_{2}(1)&\ldots&\mathrm{I}_{2}(T-1)\\ \vdots&\vdots&\vdots&\vdots\\ I_{M}(0)&\mathrm{I}_{M}(1)&\ldots&\mathrm{I}_{M}(T-1)\\ \end{array}\right]\rightarrow\theta\in[0,360^{\circ}) \tag{8}\]  
This matrix \(M_{IQ}\) has a dimension of \(2M\times(f_{s}\times T)\). Using this approach, directional information is directly extracted from the raw signals.  
Following the extraction of the \(IQ\) components, a dedicated preprocessing pipeline is applied to prepare the data for neural network input. First, a fourth-order Butterworth bandpass filter is applied to preserve frequency components near 2000 Hz while removing out-of-band frequencies, thereby eliminating external noise and interference as the acoustic source emits a pure 2000 Hz sinusoidal tone. Subsequently, the denoised signal was transformed into its analytic representation, using the Hilbert transform to extract the \(IQ\) components. The real and imaginary parts are concatenated to form a \(2M\times(f_{s}\times T)\) matrix. Prior to network input, all angle data are normalized to a range from 0 to 1. This normalization uniformly scales the input features, mitigating the influence of potential variations in signal amplitude across different recordings and ensuring stable gradient behavior during neural network optimization. Finally, the \(IQ\) components and the normalized angles are concatenated to form an input feature matrix for the model.
## 2 Literature Review
### Volume Estimation Methods  
Volume estimation has significant applications in geoscience and construction. Traditional methods, including manual surveying [12] and photogrammetry [13], provided reliable but labor-intensive results. The advent of LiDAR [13] offered high accuracy but incurred high costs.  
Algorithmic approaches like the marching cubes algorithm [14] transformed scalar fields into volumetric models. The marching cubes algorithm is mainly used in the health industry to convert the layered images obtained during the computed tomography (CT) and magnetic resonance (MR) scans into a 3D volumetric representation. Enhancements by [PERSON] et al. [8] improved the handling of ambiguous cases. Adaptive marching cubes [14] introduced further computational optimizations but required preprocessing. Dual contouring methods [15] provided higher accuracy in regions with sharp features.
## 5 Discussion
Our study, unlike the one of [PERSON] et al. [13], showed a decrease in performance enhancement in the applied PAPE protocol. [PERSON] et al. [13] found a slight increase in squat jump performance after an upper-body CA (bench press, 4 repetitions at 90%1 RM). Of course, this result may also be related to a short intra-complex rest interval selected by the participants of our study. However, an alternative interpretation of the data could be provided. The reasons for these discrepancies can be found in the differences between the protocols. In our study, a CA involving the lower body was used, which resulted in a significant decrease in the performance of the upper body; the opposite was applied in the case of the [PERSON] et al. [13] study. It seems possible that the intense activation of nonspecific muscle groups may impair performance, particularly when the CA engages such a large muscle area as the lower limbs before an upper-body explosive activity, as in our exercise protocol. Since the CA used in the present study decreased the capacity of the upper body, possibly by inducing fatigue, it cannot exclude that it could also cause potentiation. Nevertheless, due to the insufficient intra-complex rest intervals, augmentation of performance did not occur; therefore, this remains a problem to be resolved for further PAPE studies. Even though pylometric CA with self-selected intra-complex rest interval might be an attractive approach to elicit PAPE due to easy practical application, it was ineffective on the participants of this study and in such a configuration. However, in light of the limitations of this study, appropriate caution is needed in interpreting the obtained results. First, there is no certainty that the applied CA would effectively induce the PAPE effect because there was no control group with fixed intra-complex rest intervals. Second, the sample size of this study was small; therefore, similar studies on large sample sizes are needed. Third, only a single PAPE protocol was investigated, so it cannot be ruled out that other CA might be effective. Fourth, the impact of the menstrual cycle on obtained results cannot be ruled out, since not all participants were in the same phase. As a final point, this finding has implications for the testing of female athletes and highly sport-specific tasks, thus the results of this investigation may not translate to alternative samples.
## 2 Materials and Methods
### Qualitative Content Analysis  
For the analysis of the transcripts, the qualitative data analysis software ATLAS.ti was applied. We used the structuring qualitative content analysis as there is consensus in social research that this is the core method of qualitative content analysis procedures [34; 35; 36]. It allows us to answer explorative research questions [37] and thus was considered adequate with respect to the presented research aims. For the qualitative content analysis, we used [PERSON]'s approach [34; 38], which builds upon the concept of \"the psychology of text processing\" introduced in the early 1980s by [PERSON] [39]. [PERSON]'s approach is recognized and widely used in German-speaking countries [40]. His work provides a clearly defined and structured analysis process. It is suitable for systematically processing and examining large quantities of text and was thus considered appropriate for the presented research. The procedure aims at bundling and summarizing the content of texts. [PERSON] developed this approach further by suggesting working through transcripts line per line and documenting each work step in a table with the following columns: (1) quotation, (2) paraphrase, (3) generalization, and (4) reduction. These four columns represent the process of [PERSON]'s approach, starting with the raw material, shortening the quotations by extracting the core statement, leaving any additional but irrelevant information aside, generalizing the message for its usage in the text, and reducing redundant text passages. The output of this analysis essentially allows us to make general statements about the problem context, yet it also provides important details and thus leads to relevant conclusions about interrelations between two or more codes.  
A first draft of the code system assisted in formulating adequate interview questions with respect to the research aims. The final code categories were derived inductively based on the topics and transcript material and in accordance with the process model provided by [PERSON] [38].  
We developed a code scheme consisting of 5 code groups and 115 codes in total. The code groups with respect to sleeping assets are as follows: (1) advantages, (2) barriers, (3) requirements, (4) sleeping asset types, and (5) types of usage concepts. The code groundedness, defined as the \"number of quotations coded by it\" [41], gives an indication about the relative importance of a certain code compared to the other codes of the code system and with respect to all conducted interviews. An overview of the complete code system and the codes' respective code groundedness are provided in Appendix B (Table A1).  
Since the content of the interviews was confidential, the results of the analysis according to [PERSON] cannot be included in Appendix A. The analysis served to derive the following results and to explain them in the context of the findings from literature.
## 3 Results and Discussion
### Method Comparison  
Ultimately, a comparative analysis was conducted between the efficacy of the established method and several prior techniques for measuring the selected steroids in both the water and urine samples, and the results of this evaluation are presented in Table 6. It is worth noting that the proposed method involved the direct immersion of HDE-SBME devices into urine and water samples without any pre-treatment. On the other hand, most of the other methods listed in the literature necessitate multiple procedures before the extraction process. The proposed method offers a broader linear range (0.57 to 104 \(\mu\)g L\({}^{-1}\)), satisfactory LOD (\(\leq\)0.407 \(\mu\)g L\({}^{-1}\)) and LOQ (\(\leq\)1.357 \(\mu\)g L\({}^{-1}\)) values, and comparable extraction recovery (\(\geq\)91.70%) for all chosen analytes, except for those obtained from the expensive LC-MS/MS methods [9; 14; 40], when compared to the referenced techniques. In general, most of the methods in the literature use volatile toxic organic solvents for extracting and eluting selected steroids from the urine and water samples. The developed method, however, uses HDE in the extraction process and limits the use of organic solvents during elution, making it more straightforward, cost-effective, and environmentally friendly. Furthermore, only 29 extraction tests were necessary to determine the optimal extraction conditions for the optimized HDE-SBME-HPLC-DAD method using CCD. Moreover, due to the similarity of most steroid hormones in their physical properties and the utilization of highly flexible HPLC-DAD for analysis, the proposed method can efficiently extract a range of steroid hormones, including the targeted ones.  
Figure 7: The chromatograms of the HDE-SBME method under optimized conditions for **(A)** pregnant women’s urine, **(B)** boy’s urine, **(C)** steroid-free urine, **(D)** groundwater samples, and **(E)** tap water samples.
## 5 Experiments and Results
### Benchmark Model Comparison and Estimation Metrics
#### 5.2.2 Estimation Metrics  
To quantitatively measure the prediction capability of the proposed scheme and benchmark models, we adopt the root mean square error (RMSE) to weight the differences between ground truth and predicted results, which is calculated as follows,  
\[RMSE_{t}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(Y_{t}[i]-\tilde{Y}_{t}[i]\right) ^{2}} \tag{11}\]  
where \(Y_{t}\) denotes the real-world position of vehicle \(i\) at moment \(t\), and \(\tilde{Y}_{t}[i]\) is the vehicle \(i\) prediction coordinates in time \(t\). The RMSE is reported in every second of the prediction time window, not as mean values over the entire horizons.
# Changing the Formations of Unmanned Aerial Vehicles
## 2 Organization of Flights in Shared Airspace
### Flights in UAV Formations  
Enhancing the system's efficiency can be achieved by organizing UAVs into formations. Increased striking power or expanded search areas can be obtained through systematic coordination within a formation. Maintaining a formation requires orchestrating collaboration scenarios among UAVs and imposing a cohesive technical specification for each aircraft.  
The basis for creating formations is a hierarchical structure that encompasses various levels related to leading/commanding each formation. The formation commander issues orders to subordinate members of the formation, and depending on the levels of autonomy of the UAVs, this information undergoes analysis by the commander. Commanders at different levels and formation members create an arrangement that should be tailored to the specifics of the assigned task. Such an arrangement should be characterized by the following:  
* Flexibility during maneuver execution;
* Enabling reconfiguration into various constellations;
* Facilitating the introduction of individual UAVs into operation;
* Preventing collisions between UAVs operating in formation.  
It is assumed that the UAV members of the formation are identical while commanders may differ structurally from the formation members. The ground station (GS) communicates with and issues commands only to the commanders. Due to the hierarchical structure, this communication occurs only with the highest-level commander. There is no need to communicate with individual UAVs. Such a solution significantly simplifies and limits radio emissions, which can ensure the covert operation of the system. The downside of this approach is the complex structure of data exchange and the system's dependence on a built chain of actions. Establishing a rigid chain of command without mechanisms for competence transfer can lead to a loss of task execution capabilities.
## 3 Geophysical Data and Methodology  
The geophysical data used in this study consisted of two datasets, namely electrical resistivity data and seismic data. Resistivity data were obtained from the Electrical Resistivity Tomography (ERT) method, while seismic data (Vp and Vs) were obtained from the multi-channel analysis of surface waves (MASW) and P-wave travel time tomography. The resistivity and seismic data used in this study were acquired within the INTER GEO research project, which was funded by the Spanish national research program.
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
## 4 Hamming Error-Correcting Code, IIR and FIR Filters  
In this section, we will describe the Hamming Error-correcting code based on the work done in [41], as well as the IIR and FIR filters.
# Reducing Video Coding Complexity Based on CNN-CBAM in HEVC
## 2 The Proposed Methods
### Proposed Algorithm
#### 2.2.2 Structure of CBAM
The results by [33] showed that the filter size of \(7\times 7\) had good performance, so this paper selected this filter without loss of generality. In summary, the CBAM introduces the spatial and channel attention from the spatial and channel dimensions to notice important information about the object's content and thus better describe the key features of the image. Secondly, adding CBAM to the network induces the original network to spotlight the target objects, thus improving the generalization and characterization ability of the network. More importantly, CBAM is a lightweight network that improves network effectiveness without adding additional computational complexity. Based on the above advantages, we introduced the CBAM into the proposed CNN and then achieved a better texture representation performance. Figure 4: Structure of the convolution block attention module.
## 3 Results and Analysis
### Physical Properties of AgW Materials  
The electrical conductivity, relative density, and Brinell hardness of the AgW (70 wt%), AgW (75 wt%), AgW (80 wt%), and AgW (85 wt%) materials are presented in Figure 15.  
Figure 15 indicates that as the W content increased, the Ag content decreased. Meanwhile, the relative density and electrical conductivity of the AgW materials decreased, whereas the hardness increased. Among the samples, AgW (70 wt%) exhibited the highest Ag content and relative density. This is because the Ag fully infiltrated the pores of the W skeleton, minimising the porosity of the skeleton. As the Ag content decreased, the amount of Ag filling the W skeleton decreased. Thus, the residual porosity increased from AgW (70 wt%) to AgW (85 wt%), corresponding to a reduction in the relative density. Ag has a higher electrical conductivity than W; therefore, AgW materials with higher Ag contents exhibited higher electrical conductivity. Conversely, W has a higher hardness than Ag; thus, AgW materials with higher W contents exhibited higher hardness. Compared with the AgW (70 wt%) without Ag and Ni in the reference [10], the AgW (70 wt%) with Ag and Ni prepared in this paper had a higher density, lower resistivity, higher hardness. Compared with the AgW (70 wt%) without Ag and Ni in reference [11], the AgW (70 wt%) with Ag and Ni prepared in this paper had a higher density, lower resistivity, higher hardness.