esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 3 Micro-Supercapacitor Carbon Electrodes
### Carbide-Derived Carbon  
Carbide-derived carbon (CDC) is of interest because of the control of pore size and pore size distribution, achieved by selectively etching metal atoms from carbide powders, such as metal carbides, carbonitrides, or oxycarbides, through a chlorination process at a certain temperature and duration [97; 98; 99; 100]. Alternative ways to prepare CDC include calcium carbide inorganic salt reaction method, hydrothermal leaching, and carbide thermal decomposition [101; 102; 103]. The pore size of these materials can be adapted to match the electrolyte solvated ion size, resulting in higher capacitance [63; 104; 105]. Moreover, CDC has outstanding chemical durability and good electrical conductivity. The high surface area of at least up to 1800 m\({}^{2}\)/g in combination with a narrow pore size distribution makes CDC suitable for EDLC electrodes with excellent capacitance [106]. The energy density can be enhanced by combining CDC with high specific capacitance materials such as metal oxides, noble metals, and conducting polymers [107].  
Polymer-derived ceramic materials, such as spherical and homogeneous polyorganosil-lesquioxane particles, produced by a Microjet reactor technique at large scale, were used as precursor materials for CDC through pyrolysis to silicon oxycarbides [108]. The total pore volume and specific surface area of the produced CDC was 1.3-2.1 cm\({}^{3}\)/g and 2014-2114 m\({}^{2}\)/g, respectively. The specific capacitance of the material was 116 F/g at 5 mA/g and 80 F/g at 100 A/g in 1 M tetraethylammonium tetrafluoroborate (TEA-BF\({}_{4}\)) in acetonitrile [108]. In another study, the electrochemical behaviour and performance of micrometre-thick TIC-CDC in 1-ethyl-3-methylimidazolium tetrafluoroborate, (EMIM-BF\({}_{4}\)) diluted in either acetonitrile or propylene carbonate, was investigated for micro-supercapacitor applications and showed typical capacitive signature with 169 F/cm\({}^{3}\), and energy density of 90 uWh/cm\({}^{2}\) in 2 M EMIBF\({}_{4}\)/acetonitrile [109]. The influence of nitrogen doping on CDC was tested in supercapacitor set-ups with a conventional organic electrolyte, 1 M TEA-BF\({}_{4}\) in acetonitrile, and an ionic liquid, EMIM-BF\({}_{4}\). The results showed that the 1 to 7 mass% nitrogen content did not significantly influence the energy storage capacity, but it had a stronger impact on the rate handling ability [110]. Composites of CDC with titanium oxide were produced with hierarchical structure, using a solvothermal method. The specific capacitance of this composite material for supercapacitors was 173 F/g, and it had an excellent cycle stability [107].
## 3 Performance Analysis  
### Outage Probability  
Outage probability is the probability that the instantaneous SNR, \(\gamma\), is smaller than the threshold value \(\gamma_{th}\), so the mathematical formula of it in the mixed RF-FSO system is defined as \(P_{out}(\gamma_{th})=\Pr(\gamma\leq\gamma_{th})\).  
Hence, the expression of outage probability can be obtained by \(P_{out}(\gamma_{th})=F_{\gamma}(\gamma_{th})\),  
\[P_{out}(\gamma_{th})=1-\frac{2^{(\alpha+\beta)}A\sqrt{C}}{16\pi\sqrt{\overline {\gamma_{1}}}}\sqrt{\gamma_{th}}\exp(-\frac{\gamma_{th}}{\overline{\gamma_{1} }})\times G\begin{array}{c}6,0\\ 1,6\end{array}\left(\frac{(\alpha\beta)^{2}C\gamma_{th}}{16\overline{\gamma_{1 }}\overline{\gamma_{2}}}\right]\begin{array}{c}\frac{\xi^{2}+1}{2}\\ \kappa_{1}\end{array}\right). \tag{5}\]
# A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition
###### Abstract  
Birdsong is a valuable indicator of rich biodiversity and ecological significance. Although feature extraction has demonstrated satisfactory performance in classification, single-scale feature extraction methods may not fully capture the complexity of birdsong, potentially leading to suboptimal classification outcomes. The integration of multi-scale feature extraction and fusion enables the model to better handle scale variations, thereby enhancing its adaptability across different scales. To address this issue, we propose a multi-scale hybrid convolutional attention mechanism model (MUSCA). This method combines depthwise separable convolution and traditional convolution for feature extraction and incorporates self-attention and spatial attention mechanisms to refine spatial and channel features, thereby improving the effectiveness of multi-scale feature extraction. To further enhance multi-scale feature fusion, a layer-by-layer alignment feature fusion method is developed to establish a deeper correlation, thereby improving classification accuracy and robustness. Using the above method, we identified 20 bird species on three spectrograms, wavelet spectrogram, log-Mel spectrogram and log-spectrogram, with recognition rates of 93.79%, 96.97% and 95.44%, respectively. Compared with the resnet18 model, it increased by 3.26%, 1.88% and 3.09%, respectively. The results indicate that the MUSCA method proposed in this paper is competitive compared to recent and state-of-the-art methods.  
birdsong recognition; hybrid convolution attention mechanism; multi-scale feature extraction; deep learning Article
## 5 Discussion
### Particularities of Task Implementation  
When discussing task influences on human-CNN similarity, we need to consider a number of problems with our tasks. A first problem is that they were quite arbitrary in some implementation details that are likely to affect the results. For instance, if we had used fewer or more categories, or categories that were more or less similar, this might have changed which areas participants attended to. If participants had needed to choose between only two categories, they would probably have made very few fixations because response selection would have been much easier. Conversely, if they had needed to choose between 20 or 200 categories, our procedure would not have been feasible at all because participants would have been unable to memorize the key mapping. Thus, our tasks do not easily scale to more complex categorization demands. Some of these implementation issues could be solved by minor changes to the procedure, such as performing verbal instead of manual labelling (e.g., [56]) or presenting a category label beforehand and then having participants indicate whether it matches the image or not (e.g., [67; 68]).  
The available category alternatives can also affect eye movements in other ways. Given the obvious differences between our image types, it presumably was sufficient to scan the image superficially: even just looking at a person in an office will tell you that this is not a desert or lighthouse and probably also not a dining room. Conversely, if there had been several similar categories (e.g., office, home office, reception desk, computer lab, computer store), participants would have been forced to fixate the most informative, discriminative areas. A similar point has been made for human-CNN comparisons that relied on fine-grained classification [8] but, in these images, only a singular feature differentiated between the categories. Thus, an interesting question for future research is how humans and CNN differ in discriminating complex scenes that consist of similar objects. It is questionable, however, whether this can be tested with spatial representations like attention maps because the differences between categories may depend on the relations between objects rather than their mere presence.  
Another important task factor is the time available for inspecting the images. We imposed no time constraints, just like most previous studies on human-CNN similarity [48; 50; 51; 53; 54; 57]. However, this created a large variance in fixation counts as viewing times ranged from less than 1000 ms up to our data inclusion threshold of around 7000 ms. To test how time constraints affected our results, we repeated our analyses with attention maps that only included the first fixation. The impacts on the results were negligible. Overall, overlaps with Grad-CAM were almost identical between maps derived from all fixations versus only the first fixation (spontaneous gaze: 0.24 vs. 0.26, gaze-pointing: 0.26 vs. 0.25). Also, in the interaction with image type, we only observed minor changes. Thus, future studies could constrain viewing time for efficiency purposes.
## 1 Introduction
For these reasons, in the literature [18], three norm-based commutation methods are compared and one of them--the clipped l\({}^{2}\)-norm based commutation (CL2C) method--is proposed to commutate the ironless, over-actuated electromagnetic actuators with moving magnets. The realized algorithm of CL2C method, which is based on iteration, offers the advantage of fast calculation time over quadratic programming. However, this heuristic algorithm is not inevitable to obtain the optimal solution of commutated currents. In this paper, the distribution of currents and power loss of coil arrays take into account coils commutation, and a convex quadratic optimization commutation method is proposed to improve it. The global power loss, local power loss, and power loss equalizing degree (PLED) of coil array are defined to evaluate the power loss distribution. The approach to raising PLED is validated by giving a lower boundary constraint in the convex quadratic optimization commutation, which is implemented by using the convex optimization routine with an active-set algorithm, quadprog  
Figure 1: Basic control structure including the commutation (algorithm) of coil array and the electromagnetic forces/torques model. in Matlab Optimization Toolbox. It is also verified by measuring real electromagnetic forces and torques on the constructed force/torque measurement platform. The convex quadratic optimization commutation can obtain the optimal currents solution in the sense that power loss distribution in all the coils is balanced. In the meantime, the lower boundary constraint in the convex quadratic optimization commutation can be used to select DC (direct current) power amplifiers with lower capacities and costs.
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 6 Experimental Results
### Pose Estimation on Antennas  
#### 6.3.1 Estimation with Camera-IMU Calibration  
In this subsection, we evaluated the effects of the various camera-IMU calibration methods on the antenna downtilt and azimuth estimation accuracies using AntennaL. We performed the estimations using the pipeline proposed in this paper, in which we configured the camera-IMU calibration parameters offline using the results of the various calibration methods. For [37; 50; 51], we used the calibration results obtained from BoardF; for our method, we used the calibration results obtained from BoardL with \(w\) set to 0.1.  
Table 4 compares the performances of all calibration methods in terms of the mean, standard and maximum errors for both downtilt and azimuth estimation. All four methods show improvements in downtilt estimation accuracy compared with an uncalibrated phone in terms of both the average and standard errors. Among them, the method proposed in [37] and our method yield results that are very close to the best result achieved using the method of [51], with mean/standard errors lower than 0.5/0.3. However, in terms of the azimuth accuracy, our method not only yields a reduction in the mean/standard error of up to 1.5, but also shows good control over the maximum error, making it the only method to achieve a lower azimuth error than the conventional maximum tolerance of 15 degrees, whereas the other methods do not show obvious improvements (compared with the default camera-IMU relation). These findings confirm the superior performance of our method in simultaneously improving both the downtilt and azimuth estimation accuracy.
# Stability Analysis of Jinchuan Hydropower Station Hydraulic Tunnels during Excavation and Unloading
## 4 Numerical Modeling of the Excavation of Adjacent Tunnels
### Mechanical Response of the Surrounding Rock during Step-by-Step Exavation  
Monitoring the mechanical response of the surrounding rock is essential to the success of the entire tunnel excavation progress and even the entire hydropower project because the excavation of the first layer of the spillway tunnel and the diversion tunnel in the Jinchuan Hydropower Station destroyed the stress state of the primary surrounding rock and directly exposed faults and other weak structural planes. Therefore, this section focuses on the analysis of the excavation and penetration of the first layer of the spillway tunnel and the diversion tunnel, and the excavation process was modeled in strict accordance with the construction process and construction logs.  
During the site construction process, the excavation of the spillway tunnel and the diversion tunnel was divided into 71 sections, as shown in Figure 7b. The penetration of the first layer was the research focus, and the excavation steps prior to it are listed in Tables 3 and 4. The two adjacent tunnels were excavated in 10 steps before the first layer was penetrated, as seen in the tables, for a total of 28 sections excavated. The spillway tunnel and the diversion tunnel were excavated in opposite directions at the same time. Excavation from the inlet to the outlet is referred to as \"leftward excavation,\" and excavation from the outlet to the inlet is referred to as \"rightward excavation.\"  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Rock**} & **Deformation** & **Elastic** & **Poisson’s** & \multicolumn{3}{c}{**Rock/Rock**} & \multicolumn{1}{c}{**Allowed**} \\ \cline{3-8}  & **Modulus** & **Modulus** & **Ratio** & \multicolumn{1}{c}{**Shearing Strength**} & \multicolumn{1}{c}{**Shear Strength**} & \multicolumn{1}{c}{**Shear Strength**} & \multicolumn{1}{c}{**Bearing Capacity**} \\ \cline{2-8}  & **E0** & **ES** & \multirow{2}{*}{\(\mu\)} & **f** & **c** & **f** & **c** & **[R]** \\  & **(GPa)** & **(GPa)** & & & **(MPa)** & & **(MPa)** & **(MPa)** \\ \hline
4–5 & 4.5–6 & 0.30–0.35 & 0.65–0.70 & 0.30–0.40 & 0.55–0.60 & 0 & 2.0–3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of the surrounding rock.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Structural Plane Type**} & \(\mathbf{\ell^{\prime}}\) & \(\mathbf{c^{\prime}}\) (MPa) \\ \hline f31 & Debris with mud & 0.35–0.40 & 0.05–0.07 \\ f27 & Debris with mud & 0.35–0.40 & 0.05–0.07 \\ f54 & Debris with mud & 0.35–0.40 & 0.05–0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recommended values for the mechanical parameters of the structural planes.
## 3 Dual Sparsity Pruning Federated Learning Framework
### Overview of DualPFL
2:for global round \(t=1\)do
3:for participating client \(i\) in parallel do
4: Quantize the KL divergence \(k_{i}\) and transform;
5: Generalize the binary mask \(\mathbf{M}_{i}\);
6:for each step \(l=1,2,\cdots,L\)do
7: Sparse Pre-training: \(g_{i,l}\odot\mathbf{M}_{i}\);
8: Local Update \(\theta_{i,t}^{l}\);
9:endfor
10: Upload model updates to server;
11: Aggregate updates and initialize \(p_{i}\)
12:endfor
13:for global round \(t=2,\cdots,T\)do
14:for participating client \(i\) in parallel do
15: Local Update \(\theta_{i,t}^{l}\);
16: Sparsity pruning: \(\Delta\theta_{i,l}\odot\mathbf{M}_{i,t}\);
17: Upload model updates to server;
18:endfor
19: Aggregate updates;
20: Dynamically adjust sparsity ratio \(p_{i}\);
21: Broadcast the global model and \(p_{i}\). 22:endfor
23:Output: global model \(\theta_{t}\). ```  
**Algorithm 1** DualPFL Algorithm  
Next, we propose a dynamic sparsity pruning mechanism for the second phase. After completing local updates, the update of the model is \(\Delta\theta_{i,t}=\theta_{i,t}-\theta_{t-1}\). The server then updates the sparsity ratio \(p_{i,t}\) for the current round \(t\). A renewed mask matrix \(\mathbf{M}_{i,t}\) is generated according to the \(\textit{Bernoulli}(p_{i,t})\) distribution. By performing element-wise multiplication between the updates \(\Delta\theta_{i,t}\) and the binary mask \(\mathbf{M}_{i,t}\), we obtain the channel-pruned sparse updates: \(\Delta\widetilde{\theta}_{i,t}=\Delta\theta_{i,t}\odot\mathbf{M}_{i,t}\). Finally, the server employs the scale-based model aggregation (SMA) algorithm to compute the new global model \(\theta_{t+1}\), which can be formulated as follows:  
\[\theta_{i+1}=\theta_{t}+\sum_{i=1}^{N}\frac{p_{i}}{\sum_{i}p_{i}}\Delta \widetilde{\theta}_{i,t}. \tag{3}\]
## 1 Introduction
Even though all the equipment works, the process does not work; it leads to waste, and it makes the job harder instead of helping. The authors, recognizing the above-mentioned shortcomings, present a proposal for a method for determining process reliability, taking into account the reliability of the information. In this case, the method is based largely on the analysis of fault trees, while the article itself is a summary of a larger work, and the whole can be read in publication [13]. The source publication discusses the topic in more detail, taking into account the issue of data quality, information, etc. The definitions [14] describe logistics as the flow of materials and the accompanying information flow. The first element being distinguished in logistics is the customer's purchase order, and the last is the execution of the task specified by the customer in the said purchase order. There are several processes related to the relocation of goods: storage, warehousing, distribution and transportation. All these processes are accompanied by the flow of information within the system: the transfer of information between the customer and the company, within the company and between the supplier and the company. The essence of logistics is to deliver the right goods at the right time to the right consignee. In their papers conducted on logistic flows, the authors very often ignore the issues related to information unreliability, focusing mainly on solutions in the field of technology or economics. The purpose of the article is to present the method of assessing the logistics process in terms of the unreliability of information flow. However, to evaluate logistic processes concerning the reliability of the information, its components should be taken into account, which is regarded as the quality of information. The review of the literature was carried out in three areas:  
* Information flow;
* Assessment of information quality;
* Assessment of reliability. The summary allowed for drawing conclusions, which then resulted in the solution described in Section 3. Section 4 presents an example of the reliability assessment of a logistics system based on the sample container terminal. This is a curious example because, in the system of handling container units at the contact of rail and road transportation, there are very frequent changes in the information, its redundancy, lack of topicality, or simply scarcity, which translates into the overall functioning of the system. The analysis presents two approaches to calculating reliability--with the omission and consideration of the information flow assessment.
# Sparsity-Robust Feature Fusion for Vulnerable Road-User Detection with 4D Radar
###### Abstract  
Detecting vulnerable road users is a major challenge for autonomous vehicles due to their small size. Various sensor modalities have been investigated, including mono or stereo cameras and 3D LiDAR sensors, which are limited by environmental conditions and hardware costs. Radar sensors are a low-cost and robust option, with high-resolution 4D radar sensors being suitable for advanced detection tasks. However, they involve challenges such as few and irregularly distributed measurement points and disturbing artifacts. Learning-based approaches utilizing pillar-based networks show potential in overcoming these challenges. However, the severe sparsity of radar data makes detecting small objects with only a few points difficult. We extend a pillar network with our novel Sparsity-Robust Feature Fusion (SRFF) neck, which combines high- and low-level multi-resolution features through a lightweight attention mechanism. While low-level features aid in better localization, high-level features allow for better classification. As sparse input data are propagated through a network, the increasing effective receptive field leads to feature maps of different sparsities. The combination of features with different sparsities improves the robustness of the network for classes with few points.  
4D radar; 3D object detection; attention +
Footnote †: journal: Applied Sciences
## 3 Results
### Performance of the Different Extraction Methods under the Double-Exposure Algorithm
This advantage stems from the fundamental difference in approach between the two algorithms. The SHAR-LABFVC algorithm directly employs statistical methods to optimize the intensity value of all pixels; therefore, its enhancement approach is not as refined. The double-exposure algorithm specifically divides the image into shadow and non-shadow regions based on the intensity value, and image enhancement is completed for shadow areas by combining overexposed images on a pixel-by-pixel basis. This targeted approach enables more accurate reconstruction of shadowed image information and, consequently, yields more precise vegetation extraction results.
# Construction of Robotics and Application of the Optical-Flow Algorithm in Determining Robot Motions
###### Abstract  
This article presents the research results in the application of image processing in determining the position, direction, and moving speed of different objects when they move in the free space in the field of view of measuring cameras. The research includes developing an algorithm to detect, identify, and locate objects and an algorithm to calculate the movement direction and instantaneous velocity of the object. Two robots with measurement systems were designed for testing, one carries the investigating object, and the other the camera. These robots can communicate with a computer system using a tele-wireless system. A program was also built for capturing images and analyzing the state of the model automatically. Experimental results show that the position, angle, and velocity of different objects can be captured well. The average error in determining the direction of movement is an average of 1.25\({}^{\circ}\), and the error of the moving speed is less than 0.5 m/s. The research results provide a potential tool for designing robots for highly effective detection. The algorithm and the measurement system are simple and inexpensive but highly effective, and they can be used in the initial process of determining an object. A detailed description of the algorithm, robot system, and testing will be presented in this study.  
optical flow; robot; computer vision; controlling robot; image processing Article