esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsharp Mask Guided Filtering for Acoustic Point Cloud of Water-Conveyance Tunnel
## 2 Background and Related Work
### Classical Guided Filtering  
Guided image filtering is a well-known time-efficient feature-preserving smoothing operator [9]. The filtered results are obtained by considering the content of the guidance image, and the guidance image can be the input image itself or a different image. This assumes that the filtered output image is a linear transformation of the guidance image in a window. It can transfer the structure of the guidance image to the filtered output, enabling new filtering applications, such as dehazing and feathering. Therefore, guided filtering is essential to the rapid production of edge-feature-preserving denoising applications.  
The guided image filtering is given by  
\[\hat{I}_{i}=a_{k}G_{i}+b_{k},\ \forall i\in\omega_{k}. \tag{1}\]  
where \(\hat{I}\) and \(G\) are the filtered output image and the guidance image, respectively. \(i\) is the index number of the pixel within window \(\omega_{k}\) centered at pixel \(k\). \(a_{k}\) and \(b_{k}\) are two constants confined to the window \(\omega_{k}\). Their values can be calculated by solving  
\[E(a_{k},b_{k})=\sum\limits_{i\in\omega_{k}}{((a_{k}G_{i}+b_{k}-I_{i})^{2}+ \varepsilon a_{k}^{2})}. \tag{2}\]  
where \(\varepsilon\) is a regularization parameter that penalizes large values of \(a_{k}\) and \(I\) is the original input image. The optimal values of \(a_{k}\) and \(b_{k}\) are calculated by  
\[a_{k}=\frac{\frac{1}{|\omega|}\sum\limits_{i\in\omega_{k}}{I_{i}G_{i}-\overline {I}_{k}\overline{G}_{k}}}{\sigma_{k}^{2}+\varepsilon},\ b_{k}=\overline{I}_{k} -a_{k}\overline{G}_{k}. \tag{3}\]  
Here, \(\overline{G}_{k}\) and \(\sigma_{k}^{2}\) are the mean and variance of \(G\) in \(\omega_{k}\), respectively. \(|\omega|\) is the number of pixels in \(\omega_{k}\) and \(\overline{I}_{k}\) is the mean of \(I\) in \(\omega_{k}\). To simplify the computation, we assume the guidance image to be the same as the input image [9]. Therefore, we can obtain  
\[a_{k}=\sigma_{k}^{2}/(\sigma_{k}^{2}+\varepsilon),\ b_{k}=(1-a_{k})\overline{ G}_{k}. \tag{4}\]  
According to Equations (1) and (4), the regions with variance (\(\sigma_{k}^{2}\)) much larger than \(\varepsilon\) are preserved, and the regions with variance (\(\sigma_{k}^{2}\)) much smaller than \(\varepsilon\) are smoothed. However, because \(\varepsilon\) is a constant in all the neighborhood windows, halos are unavoidable when the filter is forced to smooth some edge features.
## 2 Materials and Experimental Procedures
### Doehlert Design for Optimization of Parameters  
The experimental approach using Doehlert design [27; 28] was applied to develop a series of experiments to investigate the influence of the three processing parameters (laser power, scan speed, and hatching distance) and arrive at a set of optimum parameters to meet the component integrity objectives pertaining to porosity and cracking.  
If this is the case, the result of Doehlert design with three parameters (laser power, scanning speed, and hatching distance) will be a second order equation with one constant term, three linear terms, three interaction terms, and three square terms, as follows:  
\[y=a_{0}+a_{1}x_{1}+a_{2}x_{2}+a_{3}x_{3}+a_{11}{x_{1}}^{2}+a_{22}{x_{2}}^{2}+a _{33}{x_{3}}^{2}+a_{12}x_{1}x_{2}+a_{13}x_{1}x_{3}+a_{23}x_{2}x_{3}, \tag{6}\]  
where \(x_{1}\), \(x_{2}\), and \(x_{3}\) are the experimental factors and \(y\) is the estimated response. Coefficients \(a_{0}\), \(a_{1}\)... \(a_{23}\) in Equation (6) were calculated from the vector of experimental response (\(\gamma\)) and the matrix of factors (\(X\)):  
\[a=\left({}^{t}X\cdot X\right)^{-1}\cdot X\cdot Y, \tag{7}\]  
where \({}^{t}X\) is transpose \(X\) matrix, \(X\) is the matrix of experimental factors as follows:  
\[X=\left[\begin{array}{cccc}1&x_{11}&\cdots&x_{12}x_{13}\\ 1&x_{21}&\cdots&x_{22}x_{23}\\ \vdots&\vdots&\cdots&\vdots\\ 1&x_{n1}&\cdots&x_{n2}x_{n3}\end{array}\right], \tag{8}\]  
where \(n\) is the total observations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Si** & **Mg** & **Cu** & **Fe** & **Zn** & **Al** \\
**wt.\%** & **wt.\%** & **wt.\%** & **wt.\%** & **wt.\%** & **wt.\%** \\ \hline
10.6 & 0.45 & 0.02 & 0.19 & \textless{}0.01 & Bal. \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of AlSi10 Mg alloy by inductively coupled plasma atomic emission spectrometry (ICP-AES) [10].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample** & **\(P\) (W)** & **\(v\) (mm/s)** & **\(h\) (mm)** & **Energy Density (J/mm\({}^{2}\))** \\ \hline
1 & 625 & 1400 & 0.35 & 1.28 \\
2 & 950 & 1400 & 0.35 & 1.94 \\
3 & 788 & 2300 & 0.35 & 0.98 \\
4 & 463 & 2300 & 0.35 & 0.58 \\
5 & 300 & 1400 & 0.35 & 0.61 \\
6 & 463 & 50
## 7 Evaluation  
We implemented a prototype of CanaryExp based on the binary analysis platform angr [28], well-known fuzzer AFL++ [29], whole-system symbolic executor S2E [30], and the exploit development library pwnotools [36].  
The canary leakage POC generation is implemented on S2E with 2342 lines of C++ code. As for canary leaking analysis time exploit generation, there are 154 lines of Python code on the angr side implementing the canary check patcher, 4814 lines of C code on AFL++ implementing canary-leakage-guided fuzzing, 3445 lines of C++ code implementing the exploitation technique scheduler on S2E. For dynamic canary-relocation-based exploitability evaluation, the IO sequence analysis and canary metadata collection are implemented on S2E, consisting of 1546 lines of C++ code. The exploitability session construction process is implemented on pwnotools, consisting of 564 lines of Python code.  
In this section, we present the evaluation results of this system. These experiments are all carried out in a 64-bit Ubuntu 22.04.3 LTS system on a workstation with 128 G RAM and Intel(R) Core(TM) i9-12950 HX CPU @ 2.5 GHz. These experiments are conducted to answer the following questions:  
* RQ1: Can CanaryExp generate canary leaking input and incremental POC input samples more efficiently than current test case generation techniques?RQ2: Can Can CanaryExp generate an exploitation session that can automatically evaluate the exploitability of the vulnerable programs with the StackGuard protection mechanism effectively bypassed?
# A Survey of of Side-Channel Attacks and Mitigation for Processor Interconnects
###### Abstract
As of now, one of the latest multi-core processor architectures, Intel's Sierra Forest (Intel, Santa Clara, CA, USA), features a product with 144 cores: the Intel Xeon 6700 series, aimed at providing enhanced AI performance and handling compute-intensive workloads for high-throughput cloud servers [25]. According to Intel's official reports, the Xeon 6900 series, featuring 288 cores, is expected to be released in the first quarter of 2025 [26]. Additionally, Kalray's 256-core CPU (Kalray, Montbonnot-Saint-Martin, France), the MTPA2, is widely used in data centers to accelerate data processing. In recent years, with the increasing complexity of on-chip systems and the rapid expansion of applications, security issues related to on-chip interconnects and Network-on-Chip have gradually received attention. Although some relatively well-known attacks have emerged, discussions on side-channel and covert-channel attacks on on-chip interconnects and in the SoC security field remain scarce and outdated, lacking a comprehensive investigation of state-of-the-art attacks and defenses. In this survey, we focus on some of the most exploitable characteristics of processor-on-chip interconnect architectures, including contention, distance, and planar layout. Specifically, we analyze three types of side-channel attacks and corresponding countermeasures found in the existing literature:  
1. **Contention**: Traffic generated when cores communicate with LLC or IMC located on other tiles causes interconnect congestion. 2. **Distance**: Signal propagation delay created by the distance between cores constructs a channel. 3. **Planar Layout**: Thermal radiation from heavily loaded cores to neighboring cores constructs a channel. The subsequent sections of this paper will provide a detailed discussion of each of these exploitable characteristics. Each section will introduce the attacks within the context of on-chip interconnects, provide an overview of threat models, and discuss mitigation strategies. We will compare and contrast various defense mechanisms, considering both their advantages and disadvantages. This survey primarily includes papers published in the past decade. Papers related to processor-on-chip interconnect or NoC security that do not focus on side-channel attacks, such as NoC denial-of-service attacks and information leakage, are not included in this survey. The structure of this paper is as follows: Sections 2 and 3 provide an overview of on-chip interconnects and processor microarchitectural side channels, respectively. Sections 4-6 describe these attacks and their corresponding countermeasures in the context of processor-on-chip interconnects. Section 7 discusses the future development trends of interconnect side channels. Section 8 concludes this survey.
## 4 Results and Analysis
### Test Environment and Experimental Design
#### 4.1.2 Experimental Design  
The experiments were carried out in the following two parts to evaluate and verify the performance and characteristics of the status-byte-assisted RDMA transmission mechanism:  
1. Comparison of transmission mechanisms: This part compares the performance of the status-byte-assisted RDMA transmission mechanism and the sliding window transmission mechanism in terms of multiple metrics, including synchronization control latency, transmission latency, throughput, and CPU utilization.
2. Validation of transmission mechanism specificity: This part aims to verify the advantages of the status-byte-assisted RDMA transmission mechanism in terms of flexible memory block control and its application capabilities in multi-task data transmission scenarios.  
The goal of the above experiments is to comprehensively evaluate the effectiveness of the status-byte-assisted RDMA transmission mechanism in an edge computing environment and provide a detailed performance comparison with the existing methods.
## 2 Materials and Methods
### Data Collection
#### 2.1.1 Search Methodology  
The articles were retrieved from multiple databases, including Scopus, Google Scholar, and Web of Science. To ensure consistency and relevance in the search results, a set of base keywords was used: (\"Hyperspectral\" OR \"Spectroscopy\") AND (\"Lettuce\" OR \"Spinach\" OR \"Basil\") AND PUBYEAR > 2019 AND PUBYEAR < 2025. Additional keywords related to specific chemical compounds and elements of interest, such as chlorophyll, carotenoids, anthocyanin, nitrogen, nitrate, and nitrite, were sequentially incorporated into the search. For Scopus and Web of Science, the database results were exported and subsequently uploaded to the Rayyan [7] software for systematic screening and selection. In the case of Google Scholar, where search queries frequently yielded tens of thousands of results, a tailored screening methodology was implemented. The search process involved reviewing results page by page, identifying relevant articles by reading titles and abstracts, and determining a stopping criterion. Specifically, the search continued until n pages, where n represents the last page containing at least one relevant article, with an additional page (+1) screened to ensure no relevant studies were overlooked. This structured approach allowed for efficient handling of large volumes of search results while maintaining the integrity and relevance of the selected studies. Studies were deemed eligible for synthesis if they matched predefined search and inclusion criteria explicitly.
## 3 Materials and Methods
### Methodology
#### 3.5.3 Recognition  
The ship bBox with various sizes was obtained as the output of the localization stage. However, the input for the classification nets should have a fixed size. The general approach to overcome this challenge was to resize input images according to the network requirements. The VHRShips dataset includes ship bBox ranging between 42 and 283,336 square pixels in size (Table 2). If the standard image resizing is applied, some ships will be exposed to extreme up-scaling, while others will be exposed to down-scaling. As previously mentioned, this research aims to perform DLRI stages in the optical satellite images, which are scaled. Thus, to avoid losing the scale information via down-scaling or up-scaling, it was decided to keep the extracted ships at their original sizes except for the patches that include ships bigger than the input sizes of the recognition and the identification stages. Firstly, we implemented a zero-padding process around the ships to fit all the extracted images into the input sizes of the classification stages. Afterwards, we determined that the zero-padded pixels, especially in very small objects, should avoid the weights of networks when adequately trained. Finally, the Pattern Forming via Mirror Overlaying (PFMO) approach was proposed to overcome this problem. The PFMO approach follows the steps indicated below.  
* Analyzing the length-width ratios of the bBox and rotating it to ensure that the long side is horizontally aligned.
* Putting the rotated bBox in the center and overlaying the rotated bBox around the center bBox until the input size of the stage is filled.
* While overlaying the bBox, the reflection in the X and Y directions is applied to prevent the unintended gradient forming.  
The recognition stage uses the Xception network, the same as the detection and identification stages. Even though the same network was used in the detection and the recognition stages, few of the tuning parameters were optimized for the recognition stage, which is briefly explained as:  
* The input is gathered through PFMO with 416 \(\times\) 416 \(\times\) 3 patch size.
* The output is the probability of 16 parent classes.
* No threshold is applied at the classification level output, the class with the highest score is selected.  
Visualization of the PFMO process is provided in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value/Type** \\ \hline Network & YOLO v4 pre-trained with Coco dataset \\ Backbone & Cross Stage Partial Network Darknet-53 \\ Network input & 608 \(\times\) 608 \(\times\) 3 (original images are resized) \\ Network output & [x, y, width, height] bBox with the score \\ Loss function & Complete Intersection Over Union (CIoU) \\ Data augmentation & Cutmix \\ Initial learning rate (LR) & 0.001 \\ LR decay factor & 0.0005 \\ Number of epochs & 160 \\ Batch size & 64 \\ Mini batch size & 2 \\ bBox score threshold & 0.4 \\ Ground truth overlap threshold & 0.05 \\ Non-maximum suppression threshold & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The parameters used in the localization stage.
## 1 Introduction
proposed a big data-based algorithm to build a battery pack dynamic model and a probabilistic model for energy consumption prediction [11]. [PERSON], et al. revealed that to estimate the SOC of Li-ion batteries, we derive a normalized state-space model based on Li-ion electrochemistry and apply a Bayesian algorithm. Simulation results reveal that the PST can estimate SOC with accuracy higher than 95% without experiencing divergence. Bayesian algorithm is obtained by modifying [PERSON]'s square root filter and naming the Potter SOC tracker (PST) in this paper [12]. As a research on energy storage device for electric vehicles, [PERSON] et al. expressed that super capacitor (SC) is suffering plenty of limitation factors: high leakage current, thermal aging, high equivalent series resistance (ESR), low voltage window, and so forth. To overcome such drawbacks, one should consider the variety of materials in a smart way. There are materials providing higher capacity values such as graphite. However, their performance in SC is limited and saturated. We believe new intelligent materials with alternative sources may have high impact in engineering to produce next-generation SC while there is plenty of space on the materials selection compared to Li-ion batteries. So, such hybrid SC can be used as a standard high-power source in electrical vehicles in the near future [13]. [PERSON] et al. studied comparison of energy consumption of electric vehicles in three different energy storage systems consisting of lead-acid battery, lithium-ion battery, and hybrid energy storage system (HESS) incorporating lithium-ion battery and ultra-capacitor. As a result, lowest specific energy and power cause battery oversizing of the lead-acid type to use with an intermittent load. For city traffic under frequent stop-and-drive conditions, HESS is an appropriate solution. However,lithium-ion battery and HESS do not have a significant difference on the extra urban driving cycle route [14]. In this study, the research target is to evaluate charging characteristics and propose a new algorithm for improving determination accuracy of SOC during the regenerative braking period of TMED type parallel hybrid electric vehicles.
## 2 Background
### Relationship between Wavelength and Refractive Index  
As mentioned in Section 2.1, light is divided into visible and invisible spectra. Table 1 presents the refractive indices of light at different wavelengths at a water temperature of 20 \({}^{\circ}\)C. A shorter wavelength results in a higher refractive index; conversely, the longer the wavelength, the lower the refractive index.
## 3 Modeling
### Modeling Parameters  
The first quantitative results obtained by the 3D model for one layer of Ti-6 Al-V powder were presented in the previous paper [39] but were unrealistic due to the arbitrary shape of the particles. The previous paper also details the properties of Ti-6 Al-V, bioactive glass, and air (nitrogen) needed for adequate modeling and describes the methodology for the calculation of the simulation parameters. Some parameters of the materials are collected in Table 1, which is based on [39] with corrections and extension.  
Four stages of the simulation of the SLM process are presented here. The first three layers are filled with Ti-6 Al-V alloy powder with atomized particles with diameters of 12.5-25 \(\upmu\)m, and the last layer is filled with bioactive glass powder with particles of arbitrary shape and sizes of about 20-40 \(\upmu\)m. The laser power is 300 W. The diameter of the laser spot is 80 \(\upmu\)m. The velocity of the laser movement is 1 m/s. The initial temperature of the powder and atmosphere is \(T_{i}\) = 373 K (100 \({}^{\circ}\)C). The representative volume element (RVE) for the LBM simulation is represented by the space with 256 \(\times\) 256 \(\times\) 80 (= 5,24,880) cells with cell size \(\Delta x_{r}\) = 1.25 \(\upmu\)m; that is, the RVE sizes are 320 \(\times\) 320 \(\times\) 100 \(\upmu\)m\({}^{3}\). The time step is about tr = 26 ns.  
According to the assumptions, all the variables in the LBM calculations are dimensionless. The cell size and time step are equal to unity: \(\Delta x=\Delta t=1\). The temperature scale is chosen based on the initial \(T_{i}=0\) and the melting temperature of the titanium alloy (\(T_{m\text{T}1}=1\)). The density of the titanium alloy is \(\rho_{\text{T}1}=1\).  
Then, the laser spot diameter is 64. The simulation laser velocity is 0.0208 or about 47 iterations (time steps) per cell for laser movement. The area of the laser spot is 3215.36. The laser heat per cell and iteration is about 2.43 \(\times\) 10\({}^{-9}\) J, and is used to calculate the temperature decrement \(\Delta T\). The calculated gravity acceleration is about 5.3 \(\times\) 10\({}^{-9}\) but due to very low Rayleigh and Bond numbers, the gravity acceleration is increased to a value of 8 \(\times\) 10\({}^{-4}\). The simulated buoyancy coefficient is 0.01. Other simulation parameters dependent on the materials are collected in Table 2.  
The calculations are mainly performed on the CUDA (Compute Unified Device Architecture) developed by NVIDIA with the use of the GeForce RTX-3800 graphic card.
## 4 Fire detection algorithms  
Taking into account that HJ-1B was used to detect fire in various atmospheric conditions, the HJ-1B fire detection algorithm was designed to be an adaptive, contextual algorithm rather than a fixed threshold method.  
Brightness temperatures derived from channel 3 (MIR channel) and channel 4 (TIR channel) of the thermal sensor onboard HJ-1B, which are denoted as \(T_{3}\) and \(T_{4}\), are used in the algorithm. Channel 4 is also used for cloud screening as the temperature of cloud appears rather lower in this channel than others. The data of red and near-infrared channels are aggregated to 300 m for false alarm rejection and cloud masking. The reflectance of these two channels is denoted as \(\rho_{{}_{1}}\) and \(\rho_{{}_{2}}\), respectively.
# Multi-Topology Routing Algorithms in SDN-Based Space Information Networks
## 5 Design and Experiment Results  
### Optimal Routing Strategy  
After obtaining the comprehensive weights of each link in the SIN, the optimal routing strategy of the SIN is analyzed. The classical shortest path algorithms mainly include Warshall-Floyd algorithm and Dijkstra algorithm. Dijkstra algorithm mainly calculates the shortest path from a single source to multiple target nodes. It is mainly used when the nodes and links are not fully understood. Warshall-Floyd algorithm can be used to calculate the shortest path of multiple sources, and is more suitable for the situation that the whole network has been mastered. Therefore, this paper intends to use Warshall-Floyd algorithm. The Warshall-Floyd algorithm calculates the optimal path between two nodes as follows:The graph is \(G=(V,E)\), and the vertex set is \((v_{1},v_{2},\cdots v_{n})\), calculate the integrated link weight _fee_ of each side of \(G\), where \(fee_{ij}\) represents the weight of edge \(v_{ij}\), and if \(v_{i}\) and \(v_{j}\) are not adjacent, then \(fee_{ij}=+\infty\).
2. The basic idea of dynamic programming algorithm is used. For any vertex \(v_{k}\in V\), the shortest path from vertex \(v_{i}\) to vertex \(v_{j}\) passes through vertex \(v_{k}\) or without vertex \(v_{k}\). Let \(d_{ik}\) be the shortest distance from vertex \(v_{i}\) to \(v_{k}\), \(d_{kj}\) be the shortest distance from vertex \(v_{k}\) to \(v_{j}\),\(d_{ij}\) is the shortest distance from vertex \(v_{i}\) to vertex \(v_{j}\) without vertex \(v_{k}\).
3. If \(d_{ij}>d_{ik}+d_{kj}\), then \(d_{ij}=d_{ik}+d_{kj}\).
4. Repeat (3) to search all vertices \(v_{k}\), \(d_{ij}\) is the shortest distance from vertex \(v_{i}\) to vertex \(v_{j}\)[28].  
Suppose there are eight nodes in the SIN. For Task1, after the parameters are set, using the algorithm proposed in Section 4, the weight of the integrated link between any two nodes can be obtained as shown in Figure 5.  
Taking Node 3 to Node 7 as an example, for Task1, the Warshall-Floyd algorithm is used to obtain the optimal path between two nodes as shown in Figure 6.  
Figure 5: Schematic diagram of integrated link weight of Task1.  
For Task2, after the parameters are reset, using the algorithm proposed in Section 4, the weight of the integrated link between any two nodes can be obtained as shown in Figure 7.  
For Task2, the best path from Node 3 to Node 7 can be obtained by Warshall-Floyd algorithm as shown in Figure 8.  
Figure 6: Task1 best route diagram.  
Figure 7: Schematic diagram of integrated link weight of Task2.  
According to the simulation, the integrated link weights of the same link are different for different spatial tasks, and different tasks choose the path with the smallest integrated link weights to transmit, forming a multi-topology routing transmission, which can effectively improve the resource utilization efficiency of the SIN and meet the multi-task requirements of the SIN.