esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Pneumatic down-the-hole (DTH) hammer drilling technology has been used extensively in the fields of heat reservoir exploitation and geological exploration owing to its advantages of high efficiency and low pollution. However, the vibration near the bit is up to 40 g while DTH hammer drilling, which significantly affects the performance and longevity of the near-bit measurement while drilling (MWD). To enhance the environmental adaptability of the near-bit MWD in pneumatic DTH operations, a design method for a vibration-damping system based on the parameter optimization of a non-dominated sorting genetic algorithm II (NSGA-II) is proposed in this study. First, the whole structure of the near-bit MWD is designed, including the MWD sub-shell, sensors, measurement circuits, batteries, and connecting structures (the circuit unit). Secondly, this study analyzes the vibration characteristics of the pneumatic DTH hammer near the bit. According to the damping structure, the vibration response model for the circuit unit and the damping model are established. Thirdly, NSGA-II is employed to optimize the parameters of the damping model in terms of the low-frequency, high-intensity vibration characteristics near the bit in pneumatic DTH operations, thereby devising a damping scheme tailored to the unique conditions of DTH hammer drilling. Finally, vibration experiments were conducted to verify the effectiveness of the vibration-damping device. The experimental results indicate that within the vibration frequency range of 5-20 Hz and vibration level of 10-40 g, the peak attenuation rate of the circuit unit is more than 86.446%, and the improvement rate of the vibration stability of the system is more than 75.214%; the anti-vibration performance of the near-bit MWD system in DTH hammer drilling is improved remarkably. This study provides strong technical support for the stability of MWD equipment under such special working conditions. It has broad engineering application prospects.  
pneumatic down-the-hole hammer drilling; anti-vibration; MWD; optimization design Article
# Image-to-Image Translation Using a Cross-Domain Auto-Encoder and Decoder
## 1 Introduction  
A variety of network models have been introduced to address the issue of image-to-image translation, which is a hot topic in the field of computer vision and graphics. Image-to-image translation involves learning the mapping from a source domain image to a target domain image. Early image-to-image translation methods used convolutional neural networks (CNN), which learn to minimize the loss of a pixel value between the source domain image and the target domain image but had the limitation of failing to produce more photorealistic images [1; 2].  
To solve this problem, a generative adversarial network (GAN) approach was recently suggested [3]. This approach used the information necessary to enhance the quality of the target translation image, rather than just using pixel-to-pixel dissimilarity, by additionally utilizing adversarial feedback to improve image translation quality [4; 5; 6; 7].  
Pix2 pix [3] tried to solve the blurring problem, which is one of the problems faced by many CNN-based approaches, and had, thus, made various image translation tasks possible, such as changing a label (segmentation) image to an actual image, a daytime image to a night image, and a satellite map to a graphic map. Despite their promising results, there were still various problems, as depicted in Figure 0(a). Furthermore, obtaining enough paired data to learn image translation was another difficult problem. For example, if we wanted to transform landscape photographs into paintings in the style of [PERSON], we might have to prepare real paintings by [PERSON] for all the landscape pictures, which would be almost impossible in practice.  
CycleGAN [8] enabled image-to-image translation even for unpaired image pairs and produced quite successful results. However, its unsupervised learning with unpaired data often causes instability of the images generated. When comparing the results of CycleGAN with the ground truth images of the cityscapes data, two different segments (building and tree) seemed to be confused with each other, as shown in Figure 0(b). Such problems, as explained already, may raise the necessity for a more effectivetranslation method. In this paper, we propose a new translation method where the mapping process from source domain to target domain can work better, and thus suppress mistranslation by transferring the learning of domain and target feature spaces using both an auto-encoder and decoder. In contrast to commonly-used feature space learning methods [9; 10; 11; 12; 13], our approach utilizes an auto-encoder and an auto-decoder--which are pretrained for the source domain and target domain, respectively--and cross-connects them by mapping their feature spaces across the two domains.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 5 Potential Research for ISAC towards 6G ISTNs
### Reconfigurable Intelligent Surface  
Reconfigurable intelligent surfaces (RISs), consisting of a series of reflective components, have become a focus of 6G wireless communication research for reconstructing incident signals and mitigating various challenges in wireless networks [136]. In [137], the authors provided a comprehensive survey on the recent development and advances of intelligent surfaces. In recent years, several contributions have extensively studied RIS-aided ISAC systems [138; 139; 140; 141; 142]. Through RIS-aided ISAC, the directionality and energy control of ISAC beams can be further achieved, especially in environments with high shadowing. When ISAC signals experience fading, RIS can effectively adopt beamforming technology during transmission [143], achieve rational energy allocation, and save energy. In the scenario where the target is blocked by surrounding obstructions, the authors utilize intelligent reflecting surfaces to assist with localization [144]. Several studies have also focused on RIS in relation to satellites [145; 146]. It was shown that placing RIS units on satellite reflector arrays can provide significant gains in signal transmission for broadcasting and beamforming [147]. In [148], the authors investigated RIS-assisted satellite-to-ground covert communication in a satellite communication environment and proposed an iterative algorithm for optimization.  
Based on the above foundation, it is expected that in the ISTNs, RIS-aided ISAC will optimize beam intensity and power allocation, improve power efficiency, and ultimately enhance system performance.
# Spatial Sound Rendering Using Intensity Impulse Response and Cardioid Masking Function
## 1 Introduction
At the same time, the convolution methods require a set of impulse responses dedicated to appropriate channels and source positions to render individual signals efficiently. To date, multi-microphone techniques have been used to record impulse responses, either directly corresponding to the main microphone systems used during recordings [20; 21] under actual reverberant conditions or using universal systems (e.g., Soundfield ambisonic microphones or high-order ambisonic (HOA) designs). In this case, the corresponding impulse responses were generated by processing the recorded signals using appropriate mathematical models. A typical property of these methods is their multi-channel nature and, thus, the need to use multiple acoustoelectric transducers (microphones) with often varying directivity characteristics and multiple recording paths. The microphones themselves are usually pressure-gradient transducers. Such design forces compromises related to an ideally flat frequency response over the entire audible frequency range. Despite technological advances, the same is true for the ideal repeatability of signal transmission parameters in individual audio paths. It must be noted that the final spatial effect is best if all microphone channels (microphone plus amplifier and recorder paths) are identical. Increasing the number of channels makes it increasingly difficult and expensive to achieve high compatibility. Even the use of carefully selected microphones will only ensure a partial identity. In this paper, the authors propose and describe the testing of a method for obtaining impulse response sets intended for a convolution processor for the case of 5.1 surround sound. The authors also propose a general method for obtaining impulse response sets intended for a convolution processor for multichannel sound; it was tested for the case of 5.1 surround sound. Among the significant advantages of the proposed method are the following:  
* Elimination of microphone directional characteristic mismatch,
* Use of omnidirectional microphones with the best possible sound timbre preservation,
* The possibility of obtaining any set of required impulse responses intended for the convolution processor. At the same time, these responses are determined according to the requirements of the target sound system at the post-production stage. This involves taking measurements in a reference room according to the fixed procedure.
## 2 Proposed Method
### The Fuzzy Learning Vector Quantization (FLVQ)  
FLVQ is a fuzzy variant of LVQ3 for which all neurons are winners but to different degrees. It is an optimization procedure that tries to better exploit the structural information carried by each training example [20] by minimizing the following objective function  
\[J_{m}(U,V,X)=\sum_{k=1}^{n}\sum_{i=1}^{c}(u_{ik})^{m}\|x_{k}-v_{i}\|^{2} \tag{4}\]  
where \(U\) is a matrix of membership degrees, with \(u_{ik}\) denoting the membership degree of the \(k\)th object to the \(i\)th class; \(V\) is the matrix of prototypes, with \(v_{i}\) representing the \(i\)th class prototype; \(m>1\) is a weighting exponent that controls the fuzziness degree of candidate partitions during the learning process; and \(n\) is the total number of training examples.  
Figure 1: Structure of a competitive neural network. \(\{x_{1}\), \(x_{2}\),..., \(x_{n}\}\) and \(\{y_{1}\), \(y_{2}\),..., \(y_{n}\}\) are the components of input and output vectors of the network, respectively, and \(\{u_{11},w_{12},\ldots,w_{cn}\}\) are the synaptic weights of the \(c\) output neurons. This structure is the basis of the multi-competitive-layer architecture we propose in this work.  
\(J_{m}\) can be interpreted as a fuzzy measure of the global error incurred in representing the \(n\) training vectors by the \(c\) prototypes. It can be minimized using an alternating optimization procedure that consists in repeatedly recalculating \(U\) and \(V\) matrices using the expressions:  
\[u_{ik,t}=\left[\sum_{l=1}^{c}\left(\frac{\|x_{k}-v_{i,t}\|^{2}}{\|x_{k}-v_{l,t}\| ^{2}}\right)^{\frac{1}{m-1}}\right]^{-1} \tag{5}\]  
and  
\[v_{i,t}=\frac{\sum_{k=1}^{n}(u_{ik,t})^{m}x_{k}}{\sum_{k=1}^{n}(u_{ik,t})^{m}} \tag{6}\]  
Hence, starting from a randomly chosen matrix of initial prototypes \(V_{0}=\{v_{1,0},v_{2,0},\ldots,v_{c,0}\}\), if we iteratively recalculate the components of \(U\) and \(V\) according to (5) and (6), the process will converge to a point \((U^{*},V^{*})\) where the \(c\) prototypes are stabilized, which means a point from which the difference \(\|V_{t}-V_{t-1}\|\) becomes insignificant. To evaluate this difference, we use the expression:  
\[\|V_{t}-V_{t-1}\|=\max_{1\leq i\leq c}\left(\max_{1\leq j\leq p}\left(|v_{ij,t }-v_{ij,t-1}|\right)\right) \tag{7}\]  
Depending on the way \(m\) varies throughout iterations, two versions of FLVQ have been developed: FLVQ\(\downarrow\) and FLVQ\(\uparrow\). In FLVQ\(\downarrow\), \(m\) decreases according to the relation  
\[m=m_{t}=m_{max}-\frac{t}{t_{max}}(m_{max}-m_{min}) \tag{8}\]  
and in FLVQ\(\uparrow\), it increases according to  
\[m=m_{t}=m_{min}+\frac{t}{t_{max}}(m_{max}-m_{min}) \tag{9}\]  
where \(t\) is the index of iterations and \(t_{max}\) the maximal number of iterations.
# Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation
## 3 Evaluation Model of Foundation Cushion Filling Effect Based on Elastic Wave Monitoring
### Elastic Wave Monitoring: BP Neural Network  
#### 3.2.1 BP Neural Network Method  
A BP neural network is a multilayer feedforward network trained by an error BP algorithm. To establish the relationship between the elastic wave data and the state of the sand deposit, a neural network evaluation model was constructed. The input layer contained the characteristic parameters of the collected elastic wave: \(PP_{1}\), \(PP_{2}\), \(PP_{3}\), \(F_{1}\), \(F_{2}\), \(A_{1}\), \(A_{2}\) and \(T\). The output layer is the two states corresponding to the monitoring points. The hidden layer is the \"hub\" connecting the input and output of the neural network. At present, there is no fixed theoretical basis for the choice of the hidden layer. Through repeated practice, the hidden layer is selected as three layers, and the numbers of nodes are 15, 20, and 10.  
Based on the above analysis, the BP neural network structure of the sand-filling state of the immersed tube tunnel was established. A graphical representation of the architecture of the proposed system is shown in Figure 9. The output of each processing node is connected to each processing unit in the next layer, but the processing units in the same layer do not form a connection.
## 5 Thermal Analysis and Results Discussion
### Radiator Performance Evaluation Studies  
As illustrated in Figures 22-25, the temperature of the body-mount and deployable radiators remains almost constant for different inclinations varying from 0 to 90 degrees for both worst orbital conditions. However, the temperature increases significantly with a reduction in radiator surface area. As described using Equation (23), \(\beta\) varies with varying orbital inclination (i). For every \(i\), \(\beta\) changes due to Earth's seasonal variation. In this study, it is considered that the satellite is orbiting clockwise when observed from the sun or the sun is at north of the orbit plane and hence \(\beta\) assumed to vary from 0 to 90\({}^{\circ}\)[42]. As seen from Figure 13, there is a maximum \(\beta\) for every i and this maximum \(\beta\) us considered and plotted against varying inclination angles as shown in figures As illustrated in Figures 22-25. These figures illustrate the relationship between the temperature of radiator panels, \(i\), \(\beta\), and radiator surface area. For a constant radiator area, the temperature of the radiator panel will fluctuate while varying \(i\) and \(\beta\). However, due to radiator's position in the satellite (placed just under the solar panels in this case), the temperature remains almost constant for all inclinations.  
Figure 21: 6U Satellite deployable radiator with Thermal Straps—Radiator Temperature.  
Figure 20: 6U Satellite deployable radiator with Thermal Straps—CC and HX Temperature.  
Figure 23: DR temperature variation with inclination and beta angle—Worst hot condition.  
Figure 24: BMR temperature variation with inclination and beta angle—Worst cold condition.  
As with the body-mount radiator, the temperature of the deployable radiator for the orientation shown in Figure 6 also remains approximately constant for different inclination angles. Hence, the radiator designed for one orbit can also be used for other orbit conditions without altering too much and this is applicable only for the radiators that are not pointed to the sun at any instant in the orbit. If the mission requires specific pointing, additional environmental heat loads should be considered for the radiator design. It is observed from the plots that the effect of beta angle variation is not affecting the radiator temperature as the radiator is kept below the solar panels and the solar panels are assumed to have pointed to the sun.
## 3 Results
### Proposed Solutions for Fault Management  
A total of 128 articles (69.2%) proposed a concrete solution beyond fault detection. These included predictive maintenance frameworks, robust control models, and simulation-based validations. Figure 4 presents the solution distribution.  
Beyond fault detection, the provision of actionable solutions--either through control adjustments, system reconfiguration, or maintenance recommendations--is a critical step in transforming a diagnostic framework into a practically deployable system. In this review, each article was examined to determine whether it proposed a concrete solution or intervention following the detection or diagnosis of a fault.  
The results indicate that 128 out of 185 articles (69.2%) went beyond simple detection to propose specific measures for managing or resolving the identified fault conditions. These ranged from predictive maintenance protocols and early warning mechanisms to robust control strategies and real-time optimization schemes.  
The proposed solutions can be broadly categorized into the following types:  
* Predictive Maintenance and Early Warning Models: These rely heavily on supervised and unsupervised learning to forecast equipment degradation and issue alerts before critical thresholds are exceeded. Such models often incorporate time-series analysis and recurrent neural networks (e.g., LSTM, GRU).
* Control-Oriented Interventions: Many studies proposed enhancements to existing PID controllers through adaptive techniques, such as Model Predictive Control (MPC), Robust MPC (RMPC), or fuzzy-PID hybrids. These strategies were particularly common in thermal systems where superheated steam regulation or turbine control is involved.
* Simulation-Based Evaluation and Validation: Several articles implemented simulation environments (e.g., MATLAB/Simulink, ANSYS, or custom-built platforms) to test the effectiveness of proposed solutions under faulted conditions. These environments allow researchers to tune parameters and assess system performance under realistic, yet safe conditions.
* Intelligent Recovery and Reconfiguration Logic: A smaller group of papers dealt with self-healing systems capable of rerouting functions or reconfiguring subsystems dynamically in response to detected faults. These were most often found in publications related to smart grids or distributed generation environments.  
The inclusion of a proposed solution was strongly correlated with the level of diagnostic pipeline (as seen in Section 3.10). Articles that addressed the full diagnostic pipeline were significantly more likely to include system-level recommendations or control logic, whereas those limited to detection often remained theoretical.  
This observation underlines the increasing emphasis placed on integrated diagnostic frameworks, where detection and decision-making are part of the same operational architecture. As power plants modernize and integrate with AI-driven infrastructure, the ability to transition from diagnosis to intervention will become an essential feature of next-generation monitoring systems.
# Formation Control Technology of Fixed-Wing UAV Swarm Based on Distributed Ad Hoc Network
## 1 Introduction
[PERSON] and [PERSON] [24] proposed a decentralized design method based on a UAV distributed-formation maintenance controller, decomposing the UAV formation model into decoupled parts and associated parts using robust control methods, and improved the distributed control method of the associated system designs of the controller to control the UAV formation flying. However, most of the research content was based on the formation controller design coupled with the UAV's underlying control system. It was assumed that the UAV had a three-channel autopilot and had the ability of instantaneous response [25]. These assumptions made it difficult to apply the formation algorithm to an actual UAV swarm control. Furthermore, the research content was insufficient in the control algorithm of real flight environments such as network topology jump, communication delay, and even weak communication. Based on the consistency theory, this paper proposes a method for formation switching and obstacle avoidance based on waypoint planning for the problem of formation control of a fixed-wing UAV swarm in a distributed ad hoc networks. The organizational structure of the paper is as follows. The first part gives a general description of the problem of a fixed-wing UAV swarm formation in a distributed ad hoc network; the second part proposes a method for switching the formation of the UAV swarm based on the consistency theory; the third part designs a UAV swarm formation obstacle avoidance algorithm; the fourth part deals with the problems of flight abnormality and communication abnormality of the UAV swarm during the flight; the fifth part simulates and verifies the formation switching of the UAV swarm, the formation obstacle avoidance, and handling of anomalies during the flight; the sixth part analyzes and discusses the results; and finally, the seventh part summarizes the article.
# Underground Garage Patrol Based on Road Marking Recognition by Keras and Tensorflow
## 4 Software Design and Neural Network Training
### Edge Detection  
Edge detection is also a means of detecting lanes. Taking the grayscale image as an example, the grayscale value of each pixel is in the interval [0, 255], and the color of the lane is usually quite different from that of the road surface. We used the color change of the road surface to detect the lane [30].  
Figure 5: Processed image.  
The Canny edge detector is one of the edge detection methods. The general process of the Canny edge detector is as follows:  
Filter out any noise. The Gaussian filter is used for this purpose. An example of a Gaussian kernel of size = 5 that might be used is shown below:  
\[\mathrm{K}=\frac{1}{159}\begin{bmatrix}2&4&5&4&2\\ 4&9&12&9&4\\ 5&12&15&12&5\\ 4&9&12&9&4\\ 2&4&5&4&2\end{bmatrix} \tag{5}\]  
Find the intensity gradient of the image. For this, we followed a procedure analogous to Sobel:  
Apply a pair of convolution masks (in X and Y) directions:  
\[\mathrm{G_{x}}=\left[\begin{array}{ccc}-1&0&+1\\ -2&0&+2\\ -1&0&+1\end{array}\right]\mathrm{G_{y}}=\begin{bmatrix}-1&-2&-1\\ 0&0&0\\ +1&+2&+1\end{bmatrix} \tag{6}\]  
Find the gradient strength and direction with:  
\[\mathrm{G}=\sqrt{\mathrm{G_{x}^{2}+G_{y}^{2}}} \tag{7}\]  
\[\theta=\arctan\left(\frac{\mathrm{G_{y}}}{G_{x}}\right) \tag{8}\]  
The direction is rounded to one of four possible angles (namely 0, 45, 90, or 135). Non-maximum suppression is applied. This removes pixels that are not considered to be part of an edge. Hence, only thin lines (candidate edges) will remain.  
Hysteresis: The final step. [PERSON] uses two thresholds (upper and lower):  
If a pixel gradient is higher than the upper threshold, the pixel is accepted as an edge.  
If a pixel gradient value is below the lower threshold, then it is rejected.  
If the pixel gradient is between the two thresholds, then it will be accepted only if it is connected to a pixel that is above the upper threshold.  
[PERSON] recommended an upper: lower ratio between 2:1 and 3:1.
## 2 Modeling
### Path-Tracking Model  
The path tracking model is shown in Figure 5, illustrating the relationship between the lateral deviation \(e\), the heading deviation \(\theta_{\mathrm{e}}\), and the distance \(s\) along the path. In most of the existing path-tracking controllers, the lateral deviation \(e\), the heading deviation \(\theta_{\mathrm{e}}\) are chosen as the reference states [28; 29; 32; 33], solving the optimization problem by minimizing \(e\) and \(\theta_{\mathrm{e}}\). However, path tracking lateral deviation is minimized when vehicle sideslip is held tangent to the desired path at all times [19; 20; 37]. Therefore, the course deviation \(\Delta\varphi\) is chosen as the reference state in this paper, and the path tracking model can be expressed as:  
\[\left\{\begin{array}{l}\dot{e}=v_{\mathrm{y}}+v_{\mathrm{x}}\theta_{\mathrm{ e}}\\ \dot{\theta}_{\mathrm{e}}=r-v_{\mathrm{x}}k(s)\\ \dot{s}=v_{\mathrm{x}}\cos(\theta_{\mathrm{e}})-v_{\mathrm{y}}\sin(\theta_{ \mathrm{e}})\\ \Delta\varphi=\theta_{\mathrm{e}}+\beta=\theta-\theta_{\mathrm{d}}+\beta\end{array}\right. \tag{6}\]  
where \(k(s)\) is the curvature of the reference path at the position of \(s\).