esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 2 Image Encryption Algorithms
### Chaotic Systems
#### 2.2.5 Henon Maps  
The Henon map is a two-dimensional map that maps a point \((x_{n},y_{n})\) to a new point \((x_{n+1},y_{n+1})\). The Henon map can be defined as follows:  
\[\begin{split} x_{n+1}&=1-ax_{n}^{2}\\ y_{n+1}&=bx_{n},\end{split} \tag{5}\]  
where \((x_{n},y_{n})\) and \((x_{n+1},y_{n+1})\) are the coordinates of a point before and after the mapping, respectively; \(a\) and \(b\) are the control parameters for the Henon map; however, the map starts to behave chaotically when \(a=1.4\) and \(b=0.3\)[22]. The bifurcation diagram for the Henon map is depicted in Figure 4.  
[PERSON] et al. [23] proposed an image encryption algorithm using the Henon map. The proposed algorithm consists of three stages. In the first stage, the Henon map generates two sequences of random values. In the second stage, the first randomly generated sequence is XORed with the plaintext image to diffuse the pixel values. Finally, the diffused image is permuted using the second sequence of values generated by the Henon map to produce the cipher image.  
Figure 3: Tent map bifurcation diagram.  
In [24], [PERSON] et al. introduced a new permutation-substitution algorithm based on the Henon map. The algorithm utilizes the Henon map to generate two sets of keystreams, namely X and Y, each having a length of (height \(\times\) width \(+\) 100), where the first 100 generated values are removed from the keystream. The plaintext image is then reshaped into a one-dimensional array, and the elements are permuted using the random numbers from keystream X for two rounds. The Y keystream is converted into a three-dimensional array to make it compatible with the three color channels (RGB) of the permuted array. Then, each color channel of the permuted array elements is diffused using an XOR operation with the corresponding three-dimensional Y keystream.
## 3 Proposed Methodology
### Proposed Multi-Fractal Dimension
#### 3.5.2 Testing Stage  
Figure 5 illustrates the steps required in this stage. The segmentation step is the same method presented in the training stage to derive the ROI. Five different blocks are generated from the ROI before noise removal. A model based on the blocking technique is built to (1) obtain specific and local information from the image; (2) acquire a binary image for FD using the threshold technique, and thus use blocks to determine the suitable threshold for extracting effective texture structures; and (3) build classifiers by using information from different regions of the image. The final decision depends on all trained classifiers.  
The proposed EWBFEM is applied to produce blocks in the enhancement step. This step, which plays a crucial role in extracting powerful features, is performed separately on each block to remove noise prior to feature extraction. Texture features are extracted from each block based on FD after grouping and enhancing each ROI block in the feature extraction step. This step extracts 25 texture features from each block (total features = 125) according to threshold value generation, binary image generation, and FD calculation for  
Figure 5: Testing stage of the proposed models.  
each binary image step in the training stage. The genetic algorithm is used as the feature selection method in the next step to reduce the number of features and achieve the two objectives of accelerating the process and reducing the overlapping between classes.  
The model created in the training stage can classify breast cancer into benign or malignant cases in the classification step. The ANN classifier is trained into five classifiers; each block contains its own classifier. Classifiers 1 to 5 are trained for blocks 1 to 5 and are denoted C1 to C5, respectively. Each block is tested based on its classifier, and each classifier produces a binary decision of 0 or 1 in the testing stage. D1 to D5 represent the first to fifth binary decisions, respectively.  
The fusion process is conducted based on produced binary decisions to decide whether the selected ROI is malignant or benign, as shown in Figure 5. The fusion stage can determine the breast cancer class of the ROI based on the results of five blocks as the final decision after testing all blocks. The confidence level that links diagnosis decisions is reflected in the evaluation of the risk of malignancy. Improvement in the confidence level of diagnosis through the fusion process leads to increasingly accurate diagnosis decisions. Occasional conflicts that occur between classifiers can lead to uncertain cases based on the fusion process; hence, such cases require further investigation. The majority vote fusion technique is used to obtain the final decision considering the five decisions of each ROI. The final decision for each ROI is subsequently determined using majority voting. Five different decision-based trained classifiers are fused to obtain a single decision at the final decision level.
## 3 System Model and Problem Statement
### Problem Statement  
In this paper, we aim to design a transmission scheduling algorithm that can improve the network performance. Message conflicts not only severely reduce network throughput, but also cause energy waste. DRLSA-IA is based on the TDMA, so the main task is to reasonably schedule the transmission time slots to eliminate message collisions and increase network throughput.  
Due to large propagation delays of UWANs, simultaneous transmissions may be collision-free, while nonsimultaneous transmissions may still collide at the receivers. For example, as shown in Figure 3, both node \(A\) and node \(B\) transmit messages in time slot 2, but the two messages reach node \(C\) in different time slots. The reason is that the propagation delays are different, i.e., \(d_{AC}\
eq d_{BC}\). The two messages can both be received successfully. However, node \(A\) and node \(D\) transmit messages in different time slots, and the two messages conflict in time slot 4. Therefore, large propagation delays need to be considered when designing the transmission scheduling algorithm.  
In addition, it is worth noting that the underwater environment is ever-changing and the propagation delays between nodes may be different at each time. For example, as shown in Figure 4, the propagation delays of node \(A\), node \(B\), node \(D\), and node \(C\) at time \(t1\) are \(d_{AC}=2,d_{BC}=1,d_{CD}=1\), respectively. However, due to environmental factors, nodes move, and the propagation delays of node \(A\), node \(B\), node \(D\), and node \(C\) at time \(t2\) are \(d_{AC}=2,d_{BC}=3,d_{CD}=2\) respecti  
Figure 3: Complex collision situations caused by large propagation delay.  
In summary, we need to take into account the above characteristics of UWANs when designing a transmission scheduling algorithm. Additionally, the algorithm produces a schedule that maximizes the network throughput. A schedule \(\mathbf{S}\) determines when each node transmits and receives messages, where \(S_{it}\) is an element of \(\mathbf{S}\). If \(S_{it}=j>0\), then node \(i\) transmits a message to node \(j\) in time slot \(t\). If \(S_{it}=0\), then node \(i\) is idle in time slot \(t\). If \(S_{it}=-j<0\), then node \(i\) receives a message from node \(j\) in time slot \(t\). The average throughput \(thr\) (unit: packets/slots) with period \(T\) can be computed from the number of successful transmissions in schedule \(\mathbf{S}\), as shown in Equation (3),  
\[thr=\frac{1}{T}\sum_{t=1}^{T}\sum_{i=1}^{N}\mathbb{I}(S_{it}>0), \tag{3}\]  
where \(\mathbb{I}(X)\) is the indicator function. The function value is \(1\) if the event occurred, \(0\) otherwise.  
Therefore, we formulate the throughput maximization problem as follows:  
\[\max_{\mathbf{S}}\mathbb{E}\left[\sum_{t=1}^{T}\sum_{i=1}^{N}\mathbb{I}(S_{it} >0)\right]. \tag{4}\]
## 4 Experimental Setup
### Attack Scenarios  
Both black hole and gray hole attacks were simulated in the network. In the black hole scenario, malicious nodes dropped all the packets they received, whereas, in the gray hole scenario, malicious nodes selectively dropped packets based on their content. Varying numbers of malicious nodes (5%, 10%, and 20% of the total nodes) were introduced to evaluate the robustness of the proposed detection method.
## 1 Introduction  
During the last decade, UAV-imaging has developed rapidly, enabling advancements in three-dimensional (3D) modeling for preservation, documentation, and management of cultural heritage sites [1; 2]. Terrestrial surveying using terrestrial laser scanners is accepted for producing high-quality cultural heritage images, while the advent of robotic total stations has enabled researchers to gather large quantities of data more easily than ever before [3; 4; 5; 6]. Unfortunately, the use of such geomatics-based approaches requires considerable expertise and a large surveying budget [2]. Alternatively, unmanned aerial vehicles (UAVs) can be used to easily and feasibly acquire 3D models. Furthermore, imaging using UAVs can be conducted economically and offers an ideal means for surveying complex archaeological sites [7; 8; 9]. There are different forms of UAVs, including fixed-wing [10], multi-rotor [11], and gyro. Furthermore, current platforms such as quadrotor [12] are more robust than their previous forms, due to their vertical take-off and landing capability, insensitivity to varying environments, high mobility and stability, and ease of operation [13; 14]. The combination of computer vision and photogrammetry present in [15] has the following advantages: (1) Remote control systems, permitting the UAV to be perfectly positioned to collect images at varying heights and angles; (2) different sensors, ensuring that different image types can be used, including infrared,visible-spectrum, and thermal images captured from both calibrated and non-calibrated cameras, and (3) high-quality outcomes, enabling a researcher to control the reliability and accuracy of the results.  
This study focuses on the low cost, portability, and completeness provided by the unmanned aerial vehicle structure-from-motion (UAV-SfM) method for cultural heritage site management. Two pagodas in Wat Maha That, Thailand (Chedi and Prang) are considered as a case study. The results demonstrate the accuracy of 3D models obtained using ground control points (GCPs), checkpoints (CPs), and terrestrial laser scanning (TLS). The study aims to apply the UAV-SfM method, which is cost-effective, convenient, and provides acceptable accuracy for building the 3D models compared with traditional techniques, such as TLS. The constructed 3D model could be employed in different applications in the future, such as tourism and generating the rapid mapping of cultural heritage in a short period, while ensuring acceptable quality.
## 3 Results and Discussion
### Mathematical Model
#### 3.2.3 The Effect of Temperature (T)  
Since the water temperature of aquifers is mainly in the range of 9-20 \({}^{\circ}\)C, in order to simulate the relationship between CaCO\({}_{3}\) and toluene at different temperatures, mathematical model diagrams of 10 \({}^{\circ}\)C, 15 \({}^{\circ}\)C, and 20 \({}^{\circ}\)C are presented in Figure 4. This paper assumes that the nitrate is entirely transformed into nitrogen and that the consumption of microbial growth is 6.5%.  
As the temperature increases, the activity of ions increases, and the equilibrium constant changes. In addition to directly affecting the temperature assignment in the model, temperature also affects the equilibrium constant of the reaction. As shown in Figure 4, the higher the temperature is, the higher the concentration of H\({}^{+}\). The trend of Ca\({}^{2+}\) and H\({}^{+}\) changes could be more consistent, possibly due to the combination of temperature and the equilibrium constant. Therefore, the influence of temperature on toluene mineralization is complicated. The Ca\({}^{2+}\) concentration in the system is not a monotonic function that varies with temperature.
# A Study of Correction to the Point Cloud Distortion Based on MEMS LiDAR System
###### Abstract  
Active imaging technology can perceive the surrounding environment and obtain three-dimensional information of the target. Among them, light detection and ranging (LiDAR) imaging systems are one of the hottest topics in the field of photoelectric active imaging. Due to the small size, fast scanning speed, low power consumption, low price and strong anti-interference, a micro-electro-mechanical system (MEMS) based micro-scanning LiDAR is widely used in LiDAR imaging systems. However, the imaging point cloud will be distorted, which affects the accurate acquisition of target information. Therefore, in this article, we analyzed the causes of distortion initially, and then introduced a novel coordinate correction method, which can correct the point cloud distortion of the micro-scanning LiDAR system based on MEMS. We implemented our coordinate correction method in a two-dimensional MEMS LiDAR system to verify the feasibility. Experiments show that the point cloud distortion is basically corrected and the distortion is reduced by almost 72.5%. This method can provide an effective reference for the correction of point cloud distortion.  
LiDAR; MEMS mirror; 3D imaging; point cloud distortion; coordinate correction method +
Footnote †: journal: Applied Sciences
# Application and Performance Evaluation of Resource Pool Architecture in Satellite Edge Computing
## 3 Proposed Architecture
### Satellite Node Selection  
Assume that there are \(N\) satellites in the space-based system, and \(M\) resources in each satellite. The dynamic task set of a satellite \(sat_{i}\) is defined as \(T_{sat_{i}}=\{task_{k}|1\leq k\leq K\}\), and expression of the task is \(task_{k}=\left\{T_{i}^{k},R_{i}^{k}\right\}\). \(T_{i}^{k}\) is the execution time window of the task \(k\), which corresponds to the task target area and \(T_{i}^{k}=\langle t_{start},t_{end}\rangle\). \(R_{i}^{k}\) refers to the resource requirements of the task. The tasks generated by satellite \(i\) can be expressed by the required resources and the data size of the task itself, i.e., \(R_{i}^{k}=\left\{rc_{i}^{k},rs_{i}^{k},d_{i}^{send},d_{i}^{receive}\right\}\), where \(rc_{i}^{k}\) indicates the number of computing resources required to perform the task, which can be quantified by the number of CPU cycles \(C_{i}^{(s)}\). \(rs_{i}^{k}\) is the storage requirement. \(d_{i}^{send},d_{i}^{receive}\) represent the input and output file sizes, respectively, describing task information such as program code and response data.  
An advantage of using the pooled mode over the cooperative mode is that it can pool more resources for tasks, especially for tasks with large resource requirements [22]. It can dynamically split the tasks and distribute them to the satellites with idle resources in the resource pool for execution, realizing satellite resource sharing [23]. The problem of satellite selection can be described as selecting \(k\) satellites from the whole space-based system to share resources. In Figure 3, satellites one to five are used to build the resource pool.
# Parallel Computation of CRC-Code on an FPGA Platform for High Data Throughput
###### Abstract  
With the rapid advancement of radiation hard imaging technology, space-based remote sensing instruments are becoming not only more sophisticated but are also generating substantially more amounts of data for rapid processing. For applications that rely on data transmitted from a planetary probe to a relay spacecraft to Earth, alteration or discontinuity in data over a long transmission distance is likely to happen. Cyclic Redundancy Check (CRC) is one of the most well-known package error check techniques in sensor networks for critical applications. However, serial CRC computation could be a bottleneck of the throughput in such systems. In this work, we design, implement, and validate an efficient hybrid look-up-table and matrix transformation algorithm for high throughput parallel computational unit to speed-up the process of CRC computation using both CPU and Field Programmable Gate Array (FPGA) with comparison of both methods.  
FPGA; CRC; parallel computing; error check +
Footnote †: journal: Medical Computing  
0
Footnote 0: Copyright**: © 2021 by the authors. Licenses 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/))).
## 2 Related Works
Conversely, fully decentralized approaches are more scalable but often lead to suboptimal traffic management due to lack of coordination. Additionally, reward function [25] design plays a critical role in RL-based traffic signal control. Many existing methods use reward functions based on average waiting time or queue length but designing an effective reward function that balances multiple objectives--such as throughput [26], fairness [27], and robustness to traffic fluctuations [28]--remains an open research problem. To address these limitations, this study proposes a multi-agent deep reinforcement learning (MADRL) framework that integrates spatio-temporal attention networks for enhanced traffic signal coordination. By leveraging Graph Attention Networks (GATs) to model spatial dependencies and recurrent neural networks (RNNs) to capture temporal variations, the proposed approach enables more efficient traffic control in large-scale networks. The hierarchical reinforcement learning structure ensures that local and global objectives are balanced, improving both scalability and coordination. Extensive experiments using Simulation of Urban Mobility(SUMO) demonstrate that the proposed framework outperforms baseline methods in terms of reducing vehicle waiting times and improving traffic throughput. These findings contribute to the growing body of research on intelligent traffic management and highlight the potential of deep reinforcement learning for large-scale urban mobility optimization. To distinguish our work from existing literature, we highlight three core innovations. First, unlike STMARL or MARL-DSTAN that primarily focus on spatial neighborhood interactions, our approach incorporates both graph-based spatial attention and LSTM-based temporal encoding to better capture cross-time dependencies. Second, our framework introduces a hybrid hierarchical structure where global agents guide sub-region controllers, achieving improved coordination across large networks without incurring full centralization costs. Third, we propose a customized reward function that jointly balances throughput, waiting time, and fairness--an aspect often treated separately or implicitly in prior work. These contributions collectively enable our model to scale effectively while maintaining robust performance under dynamic traffic loads.
# Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images
###### Abstract  
Addressing the common issues of low brightness, poor contrast, and blurred details in images captured under conditions such as night, backlight, and adverse weather, we propose a zero-reference dual-path network based on multi-scale depth curve estimation for low-light image enhancement. Utilizing a no-reference loss function, the enhancement of low-light images is converted into depth curve estimation, with three curves fitted to enhance the dark details of the image: a brightness adjustment curve (LE-curve), a contrast enhancement curve (CE-curve), and a multi-scale feature fusion curve (MF-curve). Initially, we introduce the TCE-L and TCE-C modules to improve image brightness and enhance image contrast, respectively. Subsequently, we design a multi-scale feature fusion (MFF) module that integrates the original and enhanced images at multiple scales in the HSV color space based on the brightness distribution characteristics of low-light images, yielding an optimally enhanced image that avoids overexposure and color distortion. We compare our proposed method against ten other advanced algorithms based on multiple datasets, including LOL, DICM, MEF, NPE, and ExDark, that encompass complex illumination variations. Experimental results demonstrate that the proposed algorithm adapts better to the characteristics of images captured in low-light environments, producing enhanced images with sharp contrast, rich details, and preserved color authenticity, while effectively mitigating the issue of overexposure.  
low-light image enhancement; zero-reference; curve tuning; deep learning Article
## 2 Materials and Methods
### RNA-Seq and Transcriptome Analyses  
Transcriptomics analyses were performed using grapes sampled at E-L 35, E-L 36, and E-L 38. Total RNA was extracted from grapes using the TRIzol reagent (Life Technologies, Carlsbad, CA, USA), and the concentration and integrity were evaluated using an Agilent Bioanalyzer 2100 system (Agilent Technologies). cDNA library construction and RNA-Seq were performed by the Biomarker Technology Company (Beijing, China) on an Illumina HiSeq(tm) system [22]. The clean data were mapped to the grape reference genome ([[http://plants.ensembl.org/Vitis_vinifera/Info/Index](http://plants.ensembl.org/Vitis_vinifera/Info/Index)]([http://plants.ensembl.org/Vitis_vinifera/Info/Index](http://plants.ensembl.org/Vitis_vinifera/Info/Index)), 26 August 2007) using the program TopHat v2.4.0 [28].  
The expression levels of transcripts were measured using fragments per kilobase of transcript sequence per million base pairs sequenced (FPKM) values using Cufflinks software [28]. The differentially expressed genes (DEGs), defined using the criteria of an FDR significance score <0.01 and an absolute value of fold change \(\geq\)1.5, were then subjected to KEGG pathway analyses and Gene Ontology (GO) enrichment [28].
## 4 Matrix Converters in the Connection of Renewable Energies to the Grid  
Increasing environmental concerns have led to growing efforts in the pursue of a 100% renewable carbon-free electrical grid [39]. In order to achieve such a goal, the integration of renewable energy sources in the grid will have to continue increasing. PV and wind energy are two of the most relevant renewable energy resources. The connection of these systems to the grid demands high efficiency and reduced costs to ensure economic viability as well as a high degree of controllability that meets the demanding criteria for a resilient grid composed mostly of distributed energy resources.  
With the increasing use of non-linear loads and the growing penetration of solar and wind renewable energy in smart grids, new PQ issues are arising that create challenges for distribution system operators (DSOs). These include issues such as low-order current harmonics, non-unity displacement power factor (DPF) or voltage-related disturbances at the point of common coupling (PCC). These issues can be mitigated by adding enhanced PQ compensation functionalities to the PV array converter, with shunt and/or series connection to the SG.  
Although matrix converters are primary AC-AC converters, best suited for wind energy conversion systems, they can be advantageously used as DC-AC converters in Photovoltaic (PV) arrays. Figure 10 shows a potential application of a DMC in the injection of P- generated energy into the grid.  
Figure 9: Representation of a matrix converter-based unified power flow controller (UPFC) using a topology as suggested in [37].