esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Loss Function  
In the training process, we firstly determine which prior box matches the ground truth, and the bounding box corresponding to the matching prior box is responsible for predicting it. If a prior box does not match any ground truth, then the prior box can only match the background, which is called a negative sample. Intersection over Union (IoU) is used in the matching process. It is the ratio of the area where the two boxes intersect to the combined area. The principle of matching has the following two points: First, we match each ground truth with the prior box with the highest IoU between it to ensure that each ground truth must match a certain prior box; second, for the remaining unmatched prior boxes, the prior box is also matched the ground truth if the IoU is higher than the threshold (0.5 in our experiments). The IoU of ground truth and the prior box can be formulated as follows:  
\[IoU(gt\_box,priorbox)=\frac{area(gt\_box)\cap area(priorbox)}{area(gt\_box )\cup area(priorbox)} \tag{7}\]  
There are a lot more negative samples than positive samples because ground truths are less than prior boxes. SSD7-FFAM uses hard negative mining to sample negative samples and arrange them in descending order of confidence loss and selects top-k with larger loss as negative samples to ensure that the ratio of positive and negative samples is close to 1:3.  
After the training samples are determined, the loss function needs to be calculated. SSD7-FFAM uses the weighted sum of location loss (loc) and confidence loss (conf) as the loss function:  
\[L(x,c,l,g)=\frac{1}{N}\Big{(}L_{conf}(x,c)+\alpha L_{loc}(x,l,g)\Big{)} \tag{8}\]  
where \(N\) is the number of positive samples and the weight parameter \(\alpha\) is set to 1 through cross validation; \(x_{ij}^{p}\in\{1,0\}\) is an indicator parameter. When \(x_{ij}^{p}=1\), it means that the i-th prior box matches the j-th ground truth, the ground truth category is \(p\); \(c\) is the category confidence prediction value, \(l\) is the location prediction value, and \(g\) is the location parameter of the ground truth.  
For the confidence loss, the proposed SSD7-FFAM uses SoftMax loss:  
\[L_{conf}(x,c)=-\sum_{i\in\text{Pos}}^{N}x_{ij}^{p}\log(\hat{c}_{i}^{p})-\sum _{i\in\text{Neg}}\log(\hat{c}_{i}^{0}) \tag{9}\]  
\[\hat{c}_{i}^{p}=\frac{\exp(c_{i}^{p})}{\sum_{p}\exp(c_{i}^{p})} \tag{10}\]  
For the location loss, SSD7-FFAM employs Smooth L1 loss, which is calculated as follows:  
\[L_{loc}(x,l,g)=\sum_{i\in\text{Pos}}^{N}\sum_{m\in\{cx,cy,w,h\}}x_{ij}^{k} smooth_{L1}\Big{(}l_{i}^{m}-\hat{g}_{j}^{m}\Big{)} \tag{11}\]  
\[smooth_{L1}(x)=\left\{\begin{array}{cl}0.5x^{2}&if|x|<1\\ |x|-0.5&\text{otherwise}\end{array}\right. \tag{12}\]
# EE-UWSNs: A Joint Energy-Efficient MAC and Routing Protocol for Underwater Sensor Networks
## 3 The Proposed EE-UWSNs Protocol
### The Mechanism of the EE-UWSNs Protocol
```
Initialize power level;  Set \(INACT_{\mathit{PFC}}\), \(Active_{\mathit{Thr}}\), \(INACT_{\mathit{Period}}\), \(Cone_{\mathit{pos}}\), \(\theta\) ; whileNode has DATA_pktdo  Broadcast \(RTS_{\mathit{pkt}}\);  Waiting time \(=2\times max(D_{\mathit{prop}})\); whileWaiting time!=0 do  Waiting time=!=1;  end while ifCTS_pkt receivedthen  Calculate Score values according to Equation 1;  Arrange Responded_Node in descending order based on Score values;  Set a node with min(Score) as a relay; ifNum(Responded_Node>2)then \(List_{\mathit{INACT}}\)=\(\mathit{Pect}\)(Responded_Node) based on \(INACT_{\mathit{PFC}}\) ; if((\(N-N_{\mathit{INACT}}\))> \(Active_{\mathit{Thr}}\))then  Add nodes to \(List_{\mathit{INACT}}\) based on Score values;  end if  Broadcast \(List_{\mathit{INACT}}\);  end if  Send DATA_pkt  end if else ifpower level == \(PN\)then  ifcone does not return to the original positionthen  Shift the cone;  power level = \(P1\);  end if  end if else  power level \(+=1\);  end if  end if ifNode receives \(RTS_{\mathit{pkt}}\)then ifNode exists within the conethen ifNode engages in another communicationthen  Send \(S1L_{\mathit{pkt}}\);  end if  else  Send \(CTS_{\mathit{pkt}}\)  end if  end if else  Node will not respond  end if else ifNode receives \(INACT_{\mathit{pkt}}\)then ifNode \(\in List_{\mathit{INACT}}\) & \(\theta\) has no DATA_pktthen  Node enters \(INACT\) mode; if\(INACT_{\mathit{Period}}\) finishesthen  Node exits from \(INACT\) mode  end if  end if  end if else ifNode receives \(DATA_{\mathit{pkt}}\)then  Send \(ACK_{\mathit{pkt}}\);  end if
```  
**Algorithm 1**Pseudocode for the proposed EE-UWSNs protocol.
### High-Resolution Imagery and the Benefits of Urban Forests  
Remote sensing is a well-established field and tool used in managing natural resources and in analyzing land cover change [11]. Since the launch of NASA's integral Landsat program in 1972, Landsat and other sensors have collected millions of Earth images, providing valuable insights into a changing planet [12]. Most of these sensors collect images at a 10 to 30 m resolution, which allow for large-scale analysis but lack granularity when analyzing city neighborhood levels or finer resolutions. In recent years, widespread advances, predominantly led by commercial imagery vendors providing high-resolution imagery (1 m or finer) at higher temporal frequencies, have revolutionized landscape monitoring. For example, the Earth observation company Planet provides customers with analysis-ready multispectral imagery on demand. Their \"PlanetScope\" system maps the entire Earth daily, providing customers with 3-m-resolution multispectral imagery. Similarly, their \"SkySat\" sensors offer customers 50-cm imagery for any location on Earth at any time through custom tasking. Some of their notable customers include NASA and the Jane Goodall Institute.  
The GIS software and data provider Esri recently announced an offering of 60-cm-resolution imagery to its customers at no additional cost, in partnership with the imagery vendor Maxar. This high-resolution imagery will be incorporated into their base maps as multispectral imagery that can be pulled directly into GIS software from the cloud [13]. Organizations and individuals can collect their own high-resolution imagery with inexpensive unmanned aerial vehicles (UAVs), and LiDAR data are also readily available.  
Because of this wealth of affordable high-resolution imagery, GIS and remote sensing analysis techniques are adapting and evolving rapidly to unlock the vast geographic information that can be derived from these resources, often in near-real time, creating new opportunities and tools for researchers, natural resource managers, conservationists, and other decision-makers [14].
## 3 Scheme Design
### Iterative Algorithm Based on FPP-SCA  
Since the problem in Equation (11) is non-convex and basically intractable, in this section, an iterative algorithm based on FPP-SCA [41] is proposed to decouple the primal problem into four subproblems corresponding to four variables: \(\mathbf{W}_{i}\), \(\mathbf{F}_{r}\), \(\mathbf{F}_{1}\), and \(\mathbf{F}_{2}\), and to solve them alternately. At each iteration, one variable is optimized while keeping the other fixed. Starting from Equation (12), the \(\mathbf{W}_{i}\) is optimized, and then, the \(\mathbf{F}_{r}\) is optimized by using Equation (14), following this, Equation (16) (actually two subproblems) is formulated to optimize \(\mathbf{F}_{1}\) and \(\mathbf{F}_{2}\) separately. Finally, the four subproblems are solved, and the four variables are optimized. Details are given below.  
First, with \(\mathbf{F}_{i}\) and \(\mathbf{F}_{r}\) fixed, the receiver \(\mathbf{W}_{i}\) is first optimized. As \(\mathbf{W}_{i}\) is only involved in \(\mathcal{J}_{i}\), the optimal \(\mathbf{W}_{i}^{opt}\) can be derived using \(\partial\mathcal{J}_{i}/\partial\mathbf{W}_{i}^{*}=0\), which yields  
\[\mathbf{W}_{i}^{opt}=\mathbf{J}_{i_{B}}^{H}\mathbf{R}_{wi}^{-1}, \tag{12}\]  
where \(\mathbf{R}_{wi}=\left(\mathbf{J}_{i_{B}}\mathbf{J}_{i_{B}}^{H}+\sigma_{r}^{2 }\mathbf{J}_{i_{A}}\mathbf{J}_{i_{A}}^{H}+\beta_{i}\sigma_{S_{i}}^{2}\mathbf{ J}_{M_{R}}\right)\).
## 2 Numerical Method and Validation
### Validation
#### 2.2.1 LSSDW  
The LSSDW model provided by NLR, as shown in Figure 1, was used to verify the low-speed method. Its basic configuration is a trapezoidal main wing with a simple stake. The computing states from reference [24] are listed in Table 1, where \(\alpha\) represents the angle of attack.  
A multi-block structured H-H type grid consisting of 181 \(\times\) 125 \(\times\) 81 grid nodes is generated, with an O-type mesh around the model, as shown in Figure 2. The grid grows by 1.2 with a y+ of 1. The far field is 20 times the length of the mean aerodynamic chord.  
The CFD calculation results (Calculation) are obtained utilizing the above grid, while the experimental (EXP) results are gained from the reference [24]. The axial force (C\({}_{\rm N}\)), normal force (C\({}_{\rm T}\)), and pitching moment (C\({}_{\rm m}\)) of the experimental [24] and CFD calculations, are shown in Table 2. A comparison of the pressure coefficients (C\({}_{\rm p}\)) between the experimental and CFD calculation, at typical sections on the upper surface, are shown in Figure 3. At the angle of attack \(\alpha=10.033^{\circ}\), the calculated aerodynamic forces, moment, and pressure distribution are in good agreement with the experimental values. In contrast, at \(\alpha=22.495^{\circ}\), there are differences between the calculated and experimental. Considering the C\({}_{\rm p}\) distributions at Sections 3 and 4, it can be inferred that the stake vortices breakdown at the trailing edge of the main wing, resulting in an extremely unsteady phenomenon that is difficult to simulate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ma** & **Re** & \(\alpha\) & **Description** \\ \hline
0.225 & \(3.7\times 10^{6}\) & \(10.033^{\circ}\) & stake/wing vortexes develop independently \\
0.225 & \(3.7\times 10^{6}\) & \(22.495^{\circ}\) & vortexes interact; trailing edge vortex breakdown \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computing states of LSSDW.  
Figure 1: LSSDW model.  
Figure 3: Pressure coefficients comparisons of the experiment ([PERSON] et al., 2018) and numerical calculation on the upper surface of LSSDW at typical sections for Ma = 0.225, Re = 3.7 \(\times\) 10\({}^{6}\): (**a**) Section 1; (**b**) Section 2; (**c**) Section 3; (**d**) Section 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**States** & **Method** & \(\mathbf{C_{N}}\) & \(\Delta\mathbf{C_{N}}\)\% & \(\mathbf{C_{T}}\) & \(\Delta\mathbf{C_{T}}\)\% & \(\mathbf{C_{m}}\) & \(\Delta\mathbf{C_{m}}\)\% \\ \hline \multirow{2}{*}{10.033\({}^{\circ}\)} & Calculation & 0.5027 & \(-\)0.06\% & \(-\)0.0045 & \(-\)12.50\% & 0.0315 & \(-\)4.26\% \\  & EXP & 0.5030 & 0 & \(-\)0.0040 & 0 & 0.0329 & 0 \\ \hline \multirow{2}{*}{22.495\({}^{\circ}\)} & Calculation & 1.1604 & 0.70\% & \(-\)0.0189 & 5.50\% & 0.0880 & \(-\)5.22\% \\  & EXP & 1.1523 & 0 & \(-\)0.0200 & 0 & 0.0923 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Forces results of Exp and CFD.  
Figure 2: The grid topology of LSSDW: (**a**) Volume grid; (**b**) Surface grid of LSSDW.
# Comparison of Different Algorithms to Orthorectify WorldView-2 Satellite Imagery
## Materials and Methods
### Algorithms for Orthorectification
Clearly, the source, accuracy, distribution and number of GCPs and CPs are very important for a correct orthorectification [2]. To obtain a reliable measurement of GCPs and CPs with adequate planimetric accuracy, their (x, y) coordinates in UTM-WGS84 were derived from orthophotos of Campania with 0.20 m resolution (nominal scale: 1:5000). It should be noted that errors may occur when manually selecting corresponding points. Thus, specific GCPs and CPs were detected in well-defined locations such as road junctions and corners of buildings or swimming pools. For any ambiguous points, Canny edge detection algorithm [56] was used to support the identification of the corresponding points. In accordance with the literature [25], this helps in ensuring that the accuracy of the GCP and CP identification on the imagery was definitely below one pixel. Figure 2 reports an example of CP identification. GCP and CP elevations were obtained by DEMs of the study area with a different horizontal resolution (cell size: 1 m, 20 m, 75 m). The DEM with a cell size of 1 m derived from laser-scanning  
Figure 2: Check point (CP) identification: (**a**) CP in orthophoto; (**b**) corresponding CP in the WV-2 primary panchromatic image. The coordinates reported are referred to as UTM/WGS84 (33 N—zone T) and are expressed in meters. data was supplied by the local administration (Provincia di Napoli) which indicates residuals of 0.16 m between the heights from the grid and those of the 3D points acquired with the RTK (real-time kinematic) survey. The DEM with a cell size of 20 m was provided by IGM (Istituto Geografico Militare), which indicates residuals of 7-10 m between the heights from the grid and those of the 3D points in plane and hilly zones. The DEM with a cell size of 75 m was provided by ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale) and presents a vertical accuracy of 16-22 m in the considered area. The flowchart in Figure 3 shows the inputs (orthophotos, WV-2 panchromatic image, DEMs), the algorithms used (2D PFs--fourth- and fifth-order; 3D RPFs with and without RPCs; [PERSON]'s rigorous model; and a combination of 3D RPFs and 2D PFs), and the most important steps (GCP and CP residuals calculation, positional accuracy evaluation based on minimum, maximum, mean, standard deviation, RMS calculation, orthorectification process, and output). Figure 3: Flowchart of the orthorectification process. The orthorectification in two steps was introduced to increase the geopositional accuracy of the final WV-2 panchromatic image. In the first step, the best resulting 3D RPFs model (75 GCPs-15 CPs, DEM = 1 m) was applied. For the second step, first-order PFs using 15 GCPs was performed on the orthoimage resulting from the first step.
## 2 RDFTA (Repairable Dynamic Fault Tree Analysis)
### RDFTA Priority AND Logic Gate  
The priority AND gate logically conforms to the AND gate, but the events occur in a certain order. As can be seen from Figure 5, state 4 represents the output event fault and only the success probability of state 4. The output event \(y(t)\) only occurs when the basic events \(x_{1}(t)\) and \(x_{2}(t)\) occur and when event \(x_{1}(t)\) occurs before event \(x_{2}(t)\).  
In Figure 5, \(S_{x(i)}(t)\) represents the normal working probability of the PAND gate input event \(x_{i}(t)\) at time \(t\), \(F_{x(i)}(t)\) is the failure state probability of the PAND gate input event \(x_{i}(t)\) at time \(t\), \(\mu_{x(i)}\) is the maintenance rate of the input event \(x_{i}(t)\), and \(\lambda_{x(i)}\) is the failure rate of the input event \(x_{i}(t)\). \(S_{y}(t)\) represents the normal working probability of the PAND gate output event \(y(t)\) at time \(t\), \(F_{y}(t)\) is the failure state probability of the PAND gate output event \(y(t)\) at time \(t\), \(\mu_{y}(t)\) is the maintenance rate of the output event \(y(t)\), and \(\lambda_{y}(t)\) is the failure rate of the output event \(y(t)\).  
A conversion rate matrix can be derived from Figure 5 as follows:  
\[\mathbf{A}=\begin{bmatrix}-\lambda_{1}-\lambda_{2}&\lambda_{1}&\lambda_{2}& 0&0\\ \mu_{1}&-\lambda_{2}-\mu_{1}&0&\lambda_{2}&0\\ \mu_{2}&0&-\mu_{2}-\lambda_{1}&0&\lambda_{1}\\ 0&\mu_{2}&0&-\mu_{2}&0\\ 0&0&\mu_{1}&0&-\mu_{1}\end{bmatrix}. \tag{11}\]  
According to the Formula State Matrix (11), the differential Equation (12) can be solved:  
\[\left\{\begin{array}{c}\left(P_{0}^{\prime}(t),P_{1}^{\prime}(t),P_{2}^{ \prime}(t),P_{3}^{\prime}(t),P_{4}^{\prime}(t)\right)=\left(P_{0}(t),P_{1}(t), P_{2}(t),P_{3}(t),P_{4}(t)\right)A\\ \left(P_{0}(t),P_{1}(t),P_{2}(t),P_{3}(t),P_{4}(t)\right)=\left(1,0,0,0,0\right) \end{array}\right..\]Quantitative operation rule  
In the priority AND logic gate, the input event \(x_{i}(t)(i=2)\) and output event \(y(t)\) have two states: state 0 is the normal working state, while state 1 is the fault state. The state in which \(x_{1}(t)\) fails before \(x_{2}(t)\) is recorded as 2. According to the relationship between the input and output events \(x_{i}(t)\) and \(y(t)\), a list of the quantitative operation rules of the priority AND can be obtained, as shown in Table 6.  
2. The quantitative calculation formula of the priority AND gate is as follows: \[\left\{\begin{aligned} S_{y}(t)=\sum\limits_{i=0}^{n}P_{i}(t),(n<4) \\ F_{y}(t)=P_{4}(t)\\ \lambda_{y}=\sum\limits_{i=1}^{n}\lambda_{i}\\ \mu_{y}(t)=\frac{\lambda_{y}\sum\limits_{i=0}^{n}P_{i}(t)}{S_{y}(t)} \end{aligned}\right.\] (13)
# A Convolutional Neural Network SAR Image Denoising Algorithm Based on Self-Learning Strategies
## 4 Conclusions and Future Work  
At present, traditional denoising methods based on manual feature extraction have certain drawbacks, such as the assumption that encoding relies on the original image, low  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Noise Category** & **Noise** & **Denoise** & **BSF** \\ \hline Stripe noise & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Denoising Method** & **SNR** & **BSF** & **SSIM** \\ \hline Mean filtering & 16.52 & 2.58 & 0.39 \\ Median filtering & 16.25 & 2.54 & 0.53 \\ Gaussian filtering & 26.45 & 4.58 & 0.45 \\ Bilateral filtering & 9.56 & 1.73 & 0.36 \\ Non-local average denoising & 29.47 & 5.45 & 0.41 \\ Directly load pre training parameters (gray blind) & 18.25 & 2.86 & 0.65 \\ SAR image denoising based on self-learning & 35.27 & 7.61 & 0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation of denoising effect of classic image denoising methods and the proposed DnCNN model on SSDD-noise dataset.  
matching degree of encoded features in real images, reduced performance and flexibility of the method in practical applications, and the feature extraction process of the method is cumbersome, time-consuming, and computationally intensive, which is not suitable for processing real noise with complex distributions. At the same time, the denoising effect on SAR images is poor, and so on. In this paper, we first proposed a self-learning based DnCNN denoising model to effectively solve these problems. By using noisy image datasets and corresponding noise free image datasets to train and optimize the model, it can automatically learn the features and noise distribution in the image, greatly improving the denoising effect of the model on SAR images. Compared with traditional denoising algorithms based on manual feature extraction, the self-learning based SAR image denoising algorithm can provide better denoising effect and stronger generalization ability. In future work, we plan to conduct more extensive experiments to quantify performance variations across different noise models and refine the training process to further reduce any sensitivity to the specific noise type used during training, as well as the trade-offs between denoising effectiveness and processing speed for potential optimization strategies. Alternative loss functions, such as perceptual loss and total variation loss, can better capture perceptual and structural aspects of the images. A more comprehensive evaluation of alternative loss functions will be left for our future work.  
Conceptualization, [PERSON]; Methodology, J.W. and K.X.; Software, J.W.; Writing--original draft, J.W.; Writing--review & editing, J.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# A Cache Policy Based on Request Association Analysis for Reliable NAND-Based Storage Systems
## 4 A Cache Policy Based on Request Association Analysis for Reliable NAND-Based Storage Systems  
### Overview  
In the paper, we will propose a cache policy based on request association analysis for reliable NAND-based storage systems. The proposed method contains two components: request association analyzer and request association cache, as shown in Figure 5. The request association analyzer is to find out strong and weak requests after performing request association analysis. With the request association analyzer, the request association cache can dynamically separate strong and weak request data and then keep the strong request data in the cache as much as possible to improve the cache hit ratio.
###### Abstract  
Virtual Private LAN Services (VPLS) is an ethernet-based Virtual Private Network (VPN) service that provides multipoint-to-multipoint Layer 2 VPN service, where each site is geographically dispersed across a Wide Area Network (WAN). The adaptability and scalability of VPLS are limited despite the fact that they provide a flexible solution for connecting geographically dispersed sites. Furthermore, the construction of tunnels connecting customer locations that are separated by great distances adds a substantial amount of latency to the user traffic transportation. To address these issues, a novel Hierarchical VPLS (H-VPLS) architecture has been developed using 802.1Q tunneling (also known as Q-in-Q) on high-speed and commodity routers to satisfy the additional requirements of new VPLS applications. The Vector Packet Processing (VPP) performs as the router's data plane, and FRRouting (FRR), an open-source network routing software suite, acts as the router's control plane. The router is designed to seamlessly forward VPLS packets using the Request For Comments (RFCs) 4762, 4446, 4447, 4448, and 4385 from The Internet Engineering Task Force (IETF) integrated with VPP. In addition, the Label Distribution Protocol (LDP) is used for Multi-Protocol Label Switching (MPLS) Pseudo-Wire (PW) signaling in FRR. The proposed mechanism has been implemented on a software-based router in the Linux environment and tested for its functionality, signaling, and control plane processes. The router is also implemented on commodity hardware for testing the functionality of VPLS in the real world. Finally, the analysis of the results verifies the efficiency of the proposed mechanism in terms of throughput, latency, and packet loss ratio.  
Virtual Private LAN Service (VPLS); Vector Packet Processing (VPP); FRRouting (FRR); Q-in-Q tunneling; MPLS; LDP +
Footnote †: journal: Computer Physics Communications  
0
Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).