esa-sceva/satcom-chunk-collection huggingface dataset:
# Salt and Pepper Noise Removal with Multi-Class Dictionary Learning and L0 Norm Regularizations
## 4 Discussions  
To further confirm the advantage of using the L\({}_{0}\) norm over the L\({}_{1}\) norm, FDLCP-L\({}_{1}\) is compared with the proposed method FDLCP-L\({}_{0}\). Typical denoised images are shown in Figure 7 and quantitative measures are shown Figure 8, Tables 2 and 3. Image features are preserved much better using FDLCP-L\({}_{0}\) than that using FDLCP-L\({}_{1}\). Consistent improvements at all noise levels are observed for FDLCP-L\({}_{0}\). Therefore, replacing the L\({}_{1}\) norm with the L\({}_{0}\) norm is valuable to improve the quality of the images that are contaminated by the salt and pepper noise.  
Figure 6: Quantitative measure at different noise levels. (**a**)–(**c**) are PSNRs of _Boat_, _Lena_, and _Barbara_ images, respectively; (**d**)–(**f**) are MSSIMs of _Boat_, _Lena_, and _Barbara_ images, respectively.  
Figure 7: Comparisons on denoised images using FDLCP-L\({}_{1}\) and FDLCP-L\({}_{0}\). (**a**) and (**b**) are noise-free and noisy images, (**c**) and (**d**) are denoised images using FDLCP-L\({}_{1}\) and FDLCP-L\({}_{0}\), respectively. The first row and the second row are _Boat_ and _Lena_ images. Note: The salt and pepper noise level is 0.5 and the denoised _Barbara_ images is shown in Figure 3.
# Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques
## 2 Coordination Protection Problem Formulation  
### Objective Function  
Equation (1) represents the objective function (OF) of the protection coordination problem that aims at minimizing the total operation time of the DOCRs. In this case, \(t_{if}\) corresponds to the operation time of relay \(i\) when fault \(f\) occurs, while \(m\) and \(n\) are the number of relays and faults in the system, respectively. It is worth to mention that, according to the specialized literature, the most common objective function of the protection coordination problem is the minimization of the total operation time. Regarding reliability, this one is taken into account within the constraints of the optimization problem. as described in Section 2.2.  
\[Min\sum_{i=1}^{m}\sum_{f=1}^{n}t_{if} \tag{1}\]
# Using Long-Range Wireless Sensor Network to Track the Illegal Cutting Log
###### Abstract  
Nowadays, the need for wireless sensing applications is increasing. Along with the increased illegal cutting of logs in the forest, however, it requires the integration application to tackle the illegal logging and forest preservation. The wireless sensor network is a suitable network architecture for remotely monitoring or tracking applications in the environment. This paper proposed an integrated system that can identify and track the position of a moving cutting log. An Arduino Uno, Raspberry Pi 3 B+, sound sensor, accelerometer sensor, LoRa GPS HAT Shield, and Outdoor LoRa Gateway OLG01 performed the hardware monitoring and tracking of the proposed system. The network of STAR topology configuration between master and slaves is represented by the LoRa Network embedded with the sensors, as an architecture of the wireless sensor network. The system was examined the performance of the network and the tracking process. The result determined that the LoRa can detect and identify the occurrence of the illegal cutting of logs in real-time. Meanwhile, in terms of the tracking performance, a duration of 5-46 s was required to track the new position of the moving cutting log.  
long range communication; illegal cutting log; wireless sensor network; tracking and monitoring; sound sensor; accelerometer sensor +
Footnote †: journal: Applied Science and Technology  
1
## 3 Methodology
### Proposed Framework
#### 3.1.2 Edge Layer  
Sensors, referred to as nodes or edges, were put in the field at various locations and connected to a low-power microcontroller designed for the IoT. We used Node MCU ESP 32 in our experiment, which could gather and analyse data from sensors before transferring them to the edge layer's base station. Sensors had to be calibrated and checked against an expected value to collect data in analogue or digital form according to the requirements. Data were collected for various climate variables, both healthy and unhealthy, to better comprehend all possible environmental situations and to assure crop survival through accurate crop management.
## 4 Temperature and Humidity Fitting Model for Stone Burial Relics
### CFD Airflow Modeling
#### 4.3.1 Fluid Flow Control Equations  
1. Continuity equation  
The mass conservation equation is often referred to as the continuity equation, and the introduction of vector notation \(div(a)=\partial a_{x}/\partial x+\partial a_{y}/\partial y+\partial a_{z}/ \partial z\), combined with the fact that the density of an incompressible fluid is a constant, yields the following fluid flow continuity equation:  
\[div(u)=0 \tag{5}\]where \(u\) is the velocity vector.  
2. Momentum conservation equation  
For any fluid flow system, the law of momentum conservation must be obeyed. Within the same time interval, the rate of change of the total momentum of each microelement of the flow system is equal to the sum of the various forces acting on this microelement. The equation of momentum conservation is also known as the Navier-Stokes equation (the N-S equation), and combined with the nature of fluid, the following momentum conservation equation for fluid flow can be obtained:  
\[\frac{\partial(\rho u)}{\partial t}+div(puu)=div(\mu\ grad\ u)-\frac{\partial p }{\partial x}+F_{x} \tag{6}\]  
\[\frac{\partial(\rho v)}{\partial t}+div(pvu)=div(\mu\ grad\ v)-\frac{\partial p }{\partial y}+F_{y} \tag{7}\]  
\[\frac{\partial(\rho w)}{\partial t}+div(pvu)=div(\mu\ grad\ w)-\frac{\partial p }{\partial z}+F_{z} \tag{8}\]  
where \(\rho\) is the fluid density; \(u,v,\) and \(w\) are the velocity vectors \(u\) along the x, y and z directions, respectively; \(grad(\ )=\partial(\ )/\partial x+\partial(\ )/\partial y+\partial(\ )/ \partial z\); \(F_{x},F_{y},\) and \(F_{z}\) are the volumetric forces acting on the microelement body along the x, y, and z directions, respectively; \(p\) is the pressure acting on the microelement body; and \(\mu\) is the dynamic viscosity.  
3. Energy conservation equation  
For any fluid flow system, the law of energy conservation must be obeyed. Within the same time interval, the rate of change of the total energy of each microelement of the flow system is equal to the sum of the heat flux given to the microelement from the outside and the work done by the external force on the microelement, and combined with the nature of fluid, the energy conservation equation can be expressed as follows:  
\[\frac{\partial(\rho T)}{\partial t}+div(\rho uT)=div\bigg{(}\frac{k}{C_{p}} gradT\bigg{)}+S_{T} \tag{9}\]  
where \(C_{p}\) is the specific heat capacity; \(k\) is the heat transfer coefficient of the fluid; and \(S_{T}\) is the viscous dissipation term of the fluid.  
In summary, the governing equations for fluid flow can be expressed in the following common form:  
\[\frac{\partial(\rho\phi)}{\partial t}+div(\rho u\phi)=div(\Gamma grad\phi)+S \tag{10}\]  
where \(\phi\) is the common variable; \(\Gamma\) is the broad diffusion term (generalized diffusion tensor (GDT)); \(S\) is the broad source term; and the remaining physical quantities are the same as above. The terms of Equation (10) comprise transient, convective, diffusive, and source terms.
# Secure and Efficient Federated Gradient Boosting Decision Trees
## 3 Preliminaries
### Federated Learning for GBDTs  
#### 3.2.1 GBDTs  
Gradient Boosting Decision trees (GBDTs) is a machine learning algorithm to improve decision trees. After multiple weak learners train regression trees through local data sets, they are aggregated into a group of trees in a specific order, thus forming strong learners. As shown in Figure 1, in each tree, whenever the weak learner inputs instance data, its prediction results are divided into a certain leaf node. In this way, the leaf weights of the same prediction results are accumulated to obtain the prediction results of the strong classifier.  
\[\hat{y}=\sum_{t=1}^{T}f(x) \tag{2}\]  
where \(T\) represents the number of decision trees and \(f_{t}(x)\) represents the prediction result of the \(T\) decision tree.  
Although GBDTs can obtain good prediction results, it cannot make full use of the local hardware resources of weak learners, and it also brings challenges to the data security of weak learners. Therefore, the literature [28] combines GBDTs with federated learning to achieve better results.
## 3 Problem Statement  
### Grasp Pose Definition  
During the process of grasp pose generation, precise grasping can be achieved by establishing a mathematical model that encompasses the robot arm, object, and camera. Typically, the grasp pose in the camera coordinate system is represented as a five-dimensional grasp representation [6], which can be visualized as depicted in Figure 1b.  
The grasp pose of a pixel point in the camera coordinate system is initially defined as follows:  
\[g=(p,\theta,w,q) \tag{1}\]  
Figure 1: Definition of grasp pose: (**a**) the network-derived grasp pose, encompassing the quality score, angle, and width of each pixel point. (**b**) A visual representation depicting a point grasp pose.  
The grasping point is denoted by coordinate \(p=(u,v)\) in the plane, while angle \(\theta\) represents the orientation of the width edge of the grasping box with respect to the horizontal \(X\)-axis. The range of angle \(\theta\) is defined as \(\left[-\frac{1}{2}\pi,\frac{1}{2}\pi\right]\). \(w\) denotes the gripper's opening and closing width; \(q\) quantifies the grasping quality of a point on a scale ranging from 0 to 1.  
The grasping pose set of each pixel in the image is defined as follows:  
\[\hat{G_{i}}=(\hat{P_{i}},\hat{\theta_{i}},\hat{W_{i}},\hat{Q_{i}}) \tag{2}\]  
The grasping posture is defined within the world coordinate system as follows:  
\[G_{i}=(P_{i},\theta_{i},W_{i},Q_{i}) \tag{3}\]  
\(P_{i}=(X_{i},Y_{i},Z_{i})\) represents the point in relation to the world coordinate system, where its elevation remains constant relative to the spatial relationship between the camera and the object, thereby ensuring a fixed value for \(Z_{i}\) within the global coordinate system.  
Finally, the transformation relationship \(T_{camera}^{base}\) between the robotic arm base and the camera is determined by employing DH modeling [21] for coordinate system conversion.  
The grasping pose in the camera coordinate system is transformed into the grasping pose in the world coordinate system, which can be achieved through Equation (4):  
\[G_{i}=T_{camera}^{base}*(\hat{G_{i}}) \tag{4}\]  
The grasp detection network utilizes camera image \(I\in\mathbb{R}^{n\times h\times w}\) as an input to compute the set of grasp poses \(\hat{G_{i}}\) for each pixel point in the camera coordinate system, followed by determining the optimal grasp pose using formula \(\hat{G}^{*}=\max_{Q}\hat{G}\) based on the highest grasp quality. Consequently, this paper focuses on investigating the set of grasp poses \(\hat{G_{i}}\) for every pixel point in the camera coordinate system.
## 2 Theory Background
### Floating-Point Multiplier  
Designers can implement multiplication using a fixed- or floating-point multiplier using the previously described number formats. In general, floating-point arithmetic implementation involves processing the \(S\), \(E\), and \(M\) parts separately and then combining them after rounding and normalization. Figure 2 shows the multiplication process for 32-bit floating-point numbers. It involves a fixed-point adder for the exponents, a fixed-point  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Precision** & \(S\) **(bits)** & \(E\) **(bits)** & \(M\) **(bits)** & **Bias** & **E-Bias Range** \\ \hline Half & 1 & 5 & 10 & \(2^{5-1}-1=15\) & \(-14\)+\(15\) \\ Single & 1 & 8 & 23 & \(2^{8-1}-1=127\) & \(-126\)+\(127\) \\ Double & 1 & 11 & 52 & \(2^{11-1}-1=1023\) & \(-1022\)+\(1023\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Floating-point formats.  
Figure 1: Comparison between fixed-point notation and floating-point notation.  
multiplier for the mantissas, and normalization logic for the result. This can be computed using the following steps. First, the sign bits are XORed to obtain the sign bit of the product. Subsequently, the exponents of both operands are added. The _bias_ (127, as shown in Table 1) is subtracted from the sum to allow for both the negative and positive exponent values. Finally, the two mantissas are multiplied and shifted to the range of 1 and 2 to produce a normalized representation. As shown in Figure 2, the exponent is adjusted if a shift happens [22]. The multiplication of mantissas of floating-point numbers can always be viewed as a special case of fixed-point multiplication. Thus, the most critical operations in fixed- and floating-point multiplications can be considered the same.
# Intelligent Transportation System Technologies, Challenges and Security
## 4 Intelligent Transportation System Security Challenges and Measures
### Basic Security Requirements for ITS  
ITS has become an important issue to consider in all communication systems regarding security. It is possible to implement security requirements that are common to all systems. The main goal is the complete security of interoperable systems and services in a sustainable working environment. All standards have been examined, and studies on this subject have been discussed. According to ETSI, one of the international security standards on which ITS systems are based, the minimum security requirements are below [39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50]. These requirements aim to protect systems against cyber attacks. The most important point is to protect the systems in the best way possible in case of an attack and to ensure that the necessary precautions are taken. The minimum precautions do not mean that the entire system will be protected one hundred percent. For this reason, experts and institutions should take necessary precautions by considering the requirements below.  
In ITS applications, futuristic technologies such as AI, quantum calculation, blockchain, data science, deep learning, and generation security algorithms should be integrated, and security measures should be taken. They should also use the entire infrastructure to protect hybrid models safely [41].  
Basic security requirements for ITS [39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50]:  
* No universal default passwords,
* Implement manage reports vulnerabilities,
* Keep software updated,
* Securely store sensitive security parameters,
* Communicate securely,
* Ensure software integrity,
* Ensure that personnel data are secure,
* Make it easy for users to delete user data,
* Validate input data,
* Maintenance of devices easy,
* Minimize exposed attack surfaces,
* Examine system telemetry data,
* Make systems resilient to outages,
* Using quantum security method, and
* Using a new generation of security algorithms.
## 5 Discussion  
This work proposes a backbone network termed WDARFNet, which fuses frequency domain analysis with adaptive receptive-field modeling to enhance oriented object detection in remote-sensing imagery. Extensive experiments on three widely used benchmarks were conducted to substantiate the effectiveness of the proposed model. For instance, when embedded into Oriented R-CNN under a single-scale training protocol, WDARFNet achieves an mAP of 81.06 % on DOTA-v1.0. On DIOR-R, WDARFNet elevated the baseline mAP by 7.16%, achieving 67.48 %. Collectively, the findings indicate that synergistically coupling the discrete wavelet transform with Selective Kernel module delivers markedly superior performance relative to either component in isolation.  
The ablation study further quantifies the individual contribution of each module. Introducing WDCSM or the WDSSM in isolation increased mAP by 1.47 and 2.06 percentage points, respectively. When combined, the two modules yielded a 3.24-point overall gain over the ResNet-50 baseline, confirming their complementary effects across the channel and spatial domains. WDARFNet also exhibits outstanding robustness to noise. When Gaussian noise with a standard deviation of 25 is injected during the inference stage, the mAP of ResNet-50 decreases by 29.14 percentage points, whereas that of WDARFNet decreases by only 17.14 percentage points. Heat-map visualizations corroborate these  
Figure 11: Visualization analysis of heatmaps illustrating feature activation for ResNet50 and WDARFNet.  
quantitative results, revealing that, even under severe noise perturbations, the network activations remain concentrated on the target rather than on the noisy background, thereby highlighting the superior noise robustness of WDARFNet.  
Despite the above advances, several limitations remain. First, the multi-branch large-kernel design introduces additional parameters relative to standard ResNet, which may hinder deployment on edge devices. Second, the current framework targets single-image tasks; incorporating temporal cues into video or sequential remote-sensing scenarios could further exploit the adaptive receptive-field mechanism.  
In future research work, we will focus on reducing model complexity while maintaining accuracy. Specifically, we plan to explore quantization and pruning strategies and to devise lighter-weight architectures through channel trimming and knowledge distillation. In addition, we will broaden the data spectrum by validating the model on larger-scale datasets--for example, by systematically assessing its performance in SAR-image object detection--to obtain a more comprehensive evaluation.
## 5 \"Migrate-Transfer-Control\" Technology of Roadway Surrounding Rock and Its Application Effect
### Roadway Support Scheme Optimization Design and Simulation Analysis
#### 5.2.2 Simulation Analysis of Supporting Effect  
Figure 22 shows the vertical stress cloud diagram of different support schemes. When the scheme is the original support, the maximum vertical stress of the roadway reaches 26.23 MPa. When the roof is strengthened, the maximum vertical stress is 24.67 MPa, which is 5.94% lower than the original support scheme. When the sidewall is strengthened, the maximum vertical stress of the roadway is 25.19 MPa, which is lower by 3.96%. When both the roof and sidewall are strengthened, the maximum vertical stress of the roadway is 22.77 MPa, which is lower by 13.19%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Supporting Parts** & **ID** & **Diameter/mm** & **Length/mm** & **Preload/kN** & **Elastic Modulus/GPa** \\ \hline Roof bolt & 1, 2, 3, 4, 5, 6 & 20 & 2000 & 60 & 200 \\ Roof anchor cable & 7, 8, 9 & 21.6 & 6700 & 50 & 200 \\ Wall side bolt & 10, 11, 12, 13, 14, 15 & 20 & 2000 & 60 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Supporting parameters.  
Figure 21: Schematic diagram of different support schemes. (**a**) The original support scheme. (**b**) Roof strengthening support scheme. (**c**) Strengthening support scheme of sidewall. (**d**) Strengthening support scheme of roof and sidewall.  
Figure 23 shows the vertical displacement cloud diagram of different support schemes. When the scheme is the original support, the maximum subsidence can reach 94 mm. When the roof is strengthened, the maximum subsidence is 63 mm, which is 33.0% lower than the original support scheme. When the sidewall is strengthened, the maximum roof subsidence of the roadway is 66 mm, which is less by 29.8%. After strengthening both the roof and sidewall, the maximum roof subsidence of the roadway is 50 mm, which is less by 46.8%.  
Based on the above, after strengthening the roof, the effect of deformation control is slightly greater than that of strengthening the support of the sidewall. The effect of strengthening the support of both the roof and sidewall is undoubtedly the best, and the effect of deformation control is much greater than that of a single factor. Therefore, for deformation control, the roof support should be strengthened first, and then the support of the sidewall. Improving the overall strength is the most effective way to control the deformation of the roadway.
# Pixel Circuit Designs for Active Matrix Displays
## 1 Introduction
### TFT Devices
#### 1.3.4 Ltpo Tft  
LTPO TFT consists of LTPS TFT and MO TFT, first proposed by Apple Inc. [44]. In the pixel circuit, MO TFIs with low leakage current are connected to the signal lines, and LTPS TFIs with high driving capability are used for power supply. This enables low frame rate (1 Hz) operation with low power consumption [45; 46]. LTPO TFTs are usually available in two combinations. The first is a combination of top-gate LTPS TFIs and bottom-gate MO TFTs (Figure 2), while the second combination includes top-gate LTPS TFIs and top-gate oxide TFTs [47].  
Hydrogen is often used to enhance the characteristics of LTPS TFTs. However, MO TFTs are easily affected by hydrogen, resulting in reduced reliability [45]. Therefore, balancing the total amount and distribution of hydrogen atoms in LTPO stacks during manufacturing is crucial. Current solutions include low hydrogen insulating silica layer techniques. [46], metal barrier processes [48] that prevent hydrogen diffusion from the bottom layer, combined BG LTPS TFT and TG MO TFT configurations [47], and others.