esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Intelligent Fuzzy Controller for a Two-Axis Solar Tracking System
## 4 Sun Track System Software  
### System Programming  
This paper proposes the design of system software using LabVIEW (2012, National Instruments, Austin, TX, USA, 2012) and MATLAB (R2012a, MathWorks, Natick, MA, USA, 2012). Real-time detection software is written in LabVIEW. The intensity of the sunlight and the solar panel output current are passed through an A/D converter and fed to the computer's serial port to enter the PC. These two variable values, through the FUZZY program of MATLAB, determine whether the solar panels need to be rotated or not. If they do not need to be rotated, we return to LabVIEW for detection. If they need to be rotated, we start the search of the database. The current position of the sun and its horizontal angle is then found, and the angle values of \(X\) and \(Y\) are passed to the main LabVIEW program to calculate the required pulse number to drive the stepper motor. Through the data line of the Print Port, the Darlington circuit of the stepper motors is driven to rotate the stepper motor. The worm gear reducer enables the rotating solar panel to follow the sun's path. Therefore, the solar cells receive the sunlight directly, resulting in an increased generating capacity.  
Figure 9: Flowchart of solar tracking system.
# Analysis of Shielding Effectiveness of a Cavity with Internal Dielectric Plate and Apertures
## 2 Theoretical Analysis of Cavity Shielding Effectiveness
### Model of a Perforated Single Cavity with Built-In Dielectric Plate
Using [PERSON]'s theory, the size of the circular opening is expressed as \(l=w=\sqrt{\pi}\cdot r\), where \(Z_{g0}\), \(Z_{PCB}\), and \(Z_{ap}\) are represented as  
\[\Gamma_{1}= \begin{bmatrix}0&e^{ik_{g0}d_{3}}\\ e^{ik_{g0}d_{3}}&0\end{bmatrix} \] \[\Gamma_{2}= \begin{bmatrix}0&e^{ik_{g0}d_{2}}\\ e^{ik_{g0}d_{2}}&0\end{bmatrix} \] \[\Gamma_{3}= \begin{bmatrix}0&e^{ik_{g0}(d-d_{1}-d_{2}-d_{X})}\\ e^{ik_{g0}(d-d_{1}-d_{2}-d_{X})}&0\end{bmatrix}  \tag{19}\] \[\Gamma_{4}= \begin{bmatrix}0&e^{ik_{PCB}d_{X}}\\ e^{ik_{PCB}d_{X}}&0\end{bmatrix} \] \[\Gamma_{5}= \begin{bmatrix}0&e^{ik_{g0}d_{1}}\\ e^{ik_{g0}d_{1}}&0\end{bmatrix} \] \[k_{0}=2\pi/\lambda\] (20) \[k_{g0}=k_{0}\sqrt{1-\left(\frac{m\lambda}{2a}\right)^{2}-\left( \frac{n\lambda}{2b}\right)^{2}}\] (21) \[k_{PCB}=k_{1}\sqrt{1-\left(\frac{m\lambda_{1}}{2a}\right)^{2}- \left(\frac{n\lambda_{1}}{2b}\right)^{2}} \tag{22}\]  
Figure 8: Signal flow diagram of single-hole cavity with built-in dielectric plate. According to the BLT equation, the voltage \(V_{p}=V_{22}+V_{31}\) at the corresponding point P can be obtained. The shielding effectiveness of point P can be calculated as  
\[SE_{E}=-20\log_{10}\left|\frac{2V_{p}}{V_{0}}\right| \tag{23}\]
## 2 Mmwave 8 \(\times\) 8 AiP Array Platform
### Aip Array Platform Experimental Validation  
#### 2.2.1 Stability Validation  
The measurements are performed in the compact antenna test range (CATR) chamber to ensure the AiP is measured in a clean plane wave condition, as shown in Figure 3. Additionally, the measurement system consists of:  
1. A VNA operating from 10 MHz to 43.5 GHz.
2. A laptop as the control computer that controls the AiP and VNA.
3. An adjustable DC Power Supply working on 12 V for the AiP module.  
Figure 3: Photograph of the AiP calibration measurement setup.  
Figure 2: Block diagram of the 8 \(\times\) 8 AiP.  
The transmission S-parameters S21 between the AiP and the CATR probe antenna is measured by the VNA at 28 GHz, with the broadside direction of the AiP aligned with the center of the CATR reflector. For this measurement campaign, the AiP is set as the Rx, and the CATR probe antenna is the Tx. In this setup, the probe antenna and the reflector generates plane waves impinging at the AiP phased array. In addition, the Tx power is set to 10 dBm and the intermediate frequency (IF) is set to 10 Hz in the VNA.  
The complex least squares method in [23; 36] is used here to estimate the calibration coefficients of the AiP elements for 10 repetitions. To investigate the effect of temperature on the stability of the AiP, the AiP is turned on for a period of time to warm up before the calibration measurements, and the first to sixth calibration measurements are performed after the temperature has stabilized. After the sixth calibration measurement, the AiP is turned off to allow it to cool down, and then the AiP is turned on again to perform the seventh through tenth calibration measurements immediately. It is worth mentioning that during each calibration measurement, the temperature of the 16 element chips of the AiP is recorded before each elements phases are reset.  
If the tenth calibration measurement is chosen as the reference, the difference between the first nine measurements and the tenth one is shown in Figure 4. From the measurement result, it can be analyzed that, except for the seventh measurement, the rest of the measurements have a good agreement with the tenth one, both in terms of the amplitude and phase excitation of array elements. In addition, the chips temperature records from the measurements show that the average AiP chips temperature is consistent throughout the measurements, except for the seventh measurement. For the 6 th, 7 th and 10 th AiP calibration measurements, the average chips temperature trends are shown in Figure 5. In the 7 th measurement, since the AiP is turned off for cooling and then turned on to perform calibration measurements immediately, the average temperature of the AiP chips rises during the early part of the measurement process until it stabilizes. In summary, the operating temperature of the AiP has a significant effect on elements' stability. Therefore, the AiP should be used in its stable state after the temperature reaching a stable level.  
Figure 4: AiP elements stability measurement results.
## 3 Results
### Petroleum Potential of the Albert Rift
#### 3.2.2 Source-Rock Characteristics  
Analysis of the Rock-Eval pyrolysis data revealed crucial parameters indicative of organic richness, thermal maturity, and depositional environment. The parameters include the total organic carbon (TOC) content; S1 and S2 peak areas, representing free and kerogen-bound hydrocarbons, respectively; and T\({}_{\rm max}\) values reflecting thermal maturity. Additionally, the hydrogen index (HI) offers insights into the type and quality of organic  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & \
\begin{tabular}{c} **Thickness** \\ **Top (m)** \\ \end{tabular} & **Bottom (m)** &
\begin{tabular}{c} **Age** \\ **Top (Ma)** \\ \end{tabular} & **Bottom (Ma)** \\ \hline Nyabusozi & 0 & 1056 & 0 & 2.5 \\ Nyakabingo & 1056 & 1392 & 2.5 & 3.8 \\ Nyaburogo & 1392 & 1632 & 3.8 & 4.8 \\ \hline Oluka & 1632 & 2208 & 4.8 & 7 \\ Kakara & 2208 & 3456 & 7 & 11.9 \\ Kasande & 3456 & 4008 & 11.9 & 14 \\ Kisegi & 4008 & 4800 & 14 & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Stratigraphic dataset derived from the integration of TRC well data with previous seismic interpretations used to model the burial–thermal and hydrocarbon-generation history of the rift at pseudo-well X.  
Figure 8: Simplified stratigraphic cross-section along the NW–SE direction showing different formations penetrated by wells.  
matter and the presence of mineral matter. These parameters collectively provide a comprehensive understanding of the organic composition and thermal history of the analyzed samples. These parameters are provided in the respective tables and plots for each well.  
In the NGJ well, the total organic carbon (TOC) contents range from 0.11% to 0.48%. Hydrogen index (HI) values vary between 9 mgHC/g TOC and 123 mgHC/g TOC. T\({}_{\rm max}\) values range from 345 \({}^{\circ}\)C to 424 \({}^{\circ}\)C. Additionally, the S1 and S2 values are in the ranges of 0.00-0.03 mg/g and 0.02-0.6 mg/g, respectively. The samples also exhibit production index (PI) values ranging from 0.01 to 0.32 (Table 7).  
In the KWB well, the total organic carbon (TOC) contents range from 0.17% to 0.68%. Hydrogen index (HI) values vary within the range of 28-259 mgHC/g TOC. T\({}_{\rm max}\) values range from 403 \({}^{\circ}\)C to 433 \({}^{\circ}\)C. Additionally, the S1 and S2 values are in the ranges of 0.00-0.21 mg/g and 0.05-1.75 mg/g, respectively. The samples also exhibit production index (PI) values ranging from 0.02 to 0.13 (Table 8).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Depth (m)** & **Quantity (mg)** & **TOC (\%)** & **S1 (mg/g)** & **S2 (mg/g)** & **T\({}_{\rm max}\) (\({}^{\circ}\)C)** & **TpkS2 (\({}^{\circ}\)C)** & **PI** & **HI** \\ \hline
1467.00 & 50.8 & 0.24 & 0.02 & 0.23 & 407 & 444 & 0.07 & 98 \\
1476.00 & 44.9 & 0.22 & 0.01 & 0.21 & 414 & 451 & 0.0
# A Simple and Efficient Local Search Algorithm for the Machine Reassignment Problem
## 1 Introduction  
The emerging development of technologies that provide cloud services and data storage is generating great interest in improving service quality and minimizing the usage of resources. This paper addresses the Machine Reassignment Problem (MRP) proposed by Google in 2012 in the context of the ROADEF/EURO challenge [1]. This problem consists of a set of machines with available resources such as CPU, memory, and Hard Disk and processes requiring significant computational resources. Initially, each process is assigned to a machine. The MRP consists of optimizing the process reassignments for the best available resource usage. This is measured by objective functions, satisfying several constraints such as machines' capacities, dependencies, and process distribution. Many approaches have been proposed to solve MRP from the ROADEF competition, which we briefly review in Section 2. A brief introduction to the MRP is presented in Section 3. The problem is a NP-hard problem [2] that involves several types of constraints to satisfy and also considers five objectives functions that, properly weighted, should be able to determine optimal solutions. In this work, we propose a simple local search algorithm based on Hill Climbing (HC) and Simulated Annealing (SA). Our goal is to propose a simple and adaptable approach that can compete with sophisticated algorithms that require much computational resources. Our approach has been specially designed to manage all constraints and all objectives of the problem. This is an important contribution in solving this problem compared to othermethods proposed in the literature [2]. Moreover, the structure of the proposed approach could be extended to some other problems that present similar characteristics.  
In Section 4, we present the details of our approach. We evaluate it using 30 benchmark instances from the ROADEF competition. Section 5 presents the results and statistical comparison with the best well-known results from the literature. It is important to remark that our approach can reach high-quality solutions in most problem instances using a simple but specialized local search approach. Finally, we present some conclusions and future work in Section 6.
## 4 Experimental Results
### Evaluation Metrics  
The accuracy [27], false alarm rate, and underreporting rate are usually used to evaluate the model in an UGS system. Accuracy is the proportion of correctly predicted samples,  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN}, \tag{11}\]  
where _TP_ (true positive) is the number of correctly classified intrusion events, _TN_ (true negative) is the number of correctly classified noise samples, _FN_ (false negative) is the number of missed intrusion events, and _FP_ (false positive) is the number of intrusion events misjudged as noise. The false alarm rate is the proportion of noise samples misjudged as intrusion events,  
\[FAR=\frac{FP}{TP+TN}, \tag{12}\]  
where _TP_, _TN_, and _FP_ are defined by Equation (11). The underreporting rate is the proportion of samples that misjudge an intrusion event as noise,  
\[UR=\frac{FN}{TP+TN}, \tag{13}\]  
where _TP_, _TN_, and _FN_ are defined by Equation (11).
# Reversible Data Hiding in Encrypted Images Based on the Mixed Multi-Bit Layer Embedding Strategy
## 2 Related Works  
This section mainly reviews the relevant RDH-EI algorithms from recent years on the study of available hidden spaces.  
In [19; 20], block encryption was adopted. Then, the encrypted image was divided into 2 \(\times\) 2 blocks, and lossless compression techniques such as Huffman coding were used to compress the bit plane to vacate space. Finally, the cover image was recovered by the shared MSBs. However, the block was too small, which affected the embedding capacity.  
In [23], selecting available blocks is in the pre-processing step. A function is defined to measure the first-order smoothness of blocks, and the blocks are classified into smooth blocks and complex blocks by the function value. The LSB bits of the pixels in the smooth blocks are then reserved for hiding. This scheme provides multi-layer embedding, but with the increase in the used bit planes, the image recovery performance decreases significantly. Therefore, this scheme studies three LSB planes at most.  
In [24], the threshold \(T\) is designed in advance, ranging from 5-100. The smaller \(T\) is, the fewer image blocks are available. The space can be reserved by compressing the LSB layer in the block set of the smooth region. Because only part of the image block is modified during data embedding, the quality of the directly decrypted image is satisfactory, but the embedding capacity is small.  
In [25], the cover image was processed into blocks with different scales after three rounds of segmentation and marked. The lowest two LSBs of each pixel in the available block are reserved spaces. Then, the average and reference values of pixels in the block are embedded as auxiliary data. The algorithm is affected by the image block size and fixed threshold, and the space needs further improvement.  
In the above mentioned works, lossless compression technology, the block size, the fixed threshold, single-layer embedding, and other factors affect the algorithms' performance. Although these algorithms have made some accomplishments, they still need to be improved in terms of embedding capacity and decryption quality.
## 1 Introduction
However, traditional markets are not able to absorb crude glycerol from biodiesel plants, both because of its low purity and the amount generated [16]. One of the most promising chemicals derived from glycerol is solketal (2,2-Dimethyl-4-hydroxymethyl-1,3-dioxplane), an oxygenated cyclic ketal with two methyl groups. It is produced by the ketalization of glycerol with acetone in the presence of an acidic catalyst, with water as the by-product (Figure 2). It is an emerging fuel additive by reason of improving the fuel octane number, enhancing liquid properties at low temperatures, reducing gum formation, thus aiding maintenance and cleanliness of engines, and reducing particle emission, which diminishes atmospheric pollution [10, 19, 20]. Its potential as a fuel additive for the aviation industry, responsible for 2% of the global CO2 emissions, is noteworthy because of its icing inhibition and de-icing properties, improving the storage stability of aviation fuels [21, 22, 23]. Figure 1: Biodiesel production with glycerol as by-product. Glycerol valorization into solketal is attractive from the environmental and economical perspectives [24]. The reaction conforms to several of the Green Chemistry principles; as all the reactants are from renewable sources, the reaction can be catalyzed by a heterogeneous catalyst and performed under mild conditions, the chemicals involved present relatively low toxicity and the by-product of the reaction is water. Furthermore, by using it as fuel additive, this chemical is reinserted in the biofuel production chain, constituting a mass and energy closed flow that complies with the Circular Economy Model and improves the carbon footprint and life cycle parameters. Economically, the by-product is valorized and capitalized on, once the selling price of crude glycerol is US$210-390/ton (pre-pandemic scenario) and of solketal is around US$3000/ton, clearly enhancing the industry profitability [14, 24, 25]. Over the years, especially from 2015 on, numerous technologies have been proposed for the synthesis of solketal, notably concerning the use of heterogeneous catalysts and continuous processes. This review aims to summarize these new catalysts and the Process Intensification strategies that have been investigated for solketal production. Moreover, it is aimed to deeply explore the kinetic models usually proposed and explain the reasons why different models better describe the reaction behavior for each type of catalyst. Furthermore, the works and the patents with proposed industrial plants for the production of this chemical are exposed, discussed, and compared to appoint solutions for the implementation of large-scale processes, a literature examination never reported before, to the authors' knowledge. This work also briefly discusses the biofuel and glycerol markets and the influence that the COVID-19 pandemic had and will have over it.
## 2 System Model and Problem Formulation  
### System Model  
In order to facilitate the research, the following hypotheses are made in this paper:  
* In a wireless communication network, there are \(N\) communication nodes. The wireless communication links are non-interfered links. The communication node set is defined as \(\{C=\{C_{1},C_{2},\ldots,C_{N}\}\}\), among which \(C_{i}(i\in\{1,2,\ldots,N\})\) is the \(i\)th node. In particular, \(C_{1}\) is the source node, and \(C_{N}\) is the destination node, where \"node \(i\)\" and \"node \(C_{i}\)\" refer to the \(i\)th node. If the energy of all nodes is large enough, there are \(N-1\) next hop nodes for the current node to select. The location set of all communication nodes is denoted as \(\Lambda=\{L_{1},L_{2},\ldots,L_{N}\}\). The adjacent node set of the current node can be judged by the location set of all communication nodes [24]. In practice, all nodes have limited energy. The current node can only select neighboring nodes. Within an area, there is a jammer \(J\) that continuously interferes with the surrounding area at constant power \(P_{|0}\), as shown in Figure 1.
* When each node transmits information, the channel bandwidth is \(B\). Each packet transmitted from the source node \(C_{1}\) to the target node \(C_{N}\) has a length of \(D\) bits. Each communication node receives all bits of the whole packet before forwarding it to the subsequent nodes. Assuming that all nodes have limited energy, the current node can only select neighboring nodes.
* Location information and link status between nodes are shared through a common control link. However, the specific position and jamming range of jammers are unknown to communication nodes.
* The jammer executes precise jamming aimed at tracking frequency and communication signal time alignment, which will lead to effective jamming in both the time domain and frequency domain. This jamming effect is equivalent to full-frequency blocking jamming. Additive White Gaussian noise exists in all channels, and its power spectral density is \(n_{0}\).
* It is assumed that when a communication node that is not jammed is selected, namely, \(SJNR_{tth}\leq SINR\), the modulation mode of the current communication node is \(QPSK\), and the transmission is carried out at the full rate \(v\). If a communication node that is partially disturbed is selected, namely, if \(SJNR_{tth2}<SJNR<SJNR_{tth}\), the modulation mode of the current communication node is \(BPSK\), and the transmission is carried out at the half-rate \(v/2\). If a communication node that is seriously disturbed is selected, namely, if \(SJNR\leq SJNR_{th}\), the transmission rate is 0, and the nodes cannot communicate with each other. Here, \(SJNR_{tth1}\), \(SJNR_{tth2}\), and \(SJNR_{th}\) are the threshold values of \(SJNR\), which gradually decrease.  
Figure 1: System model.
# Enhanced Curvature-Based Fabric Defect Detection:
###### Abstract  
Quality control at every stage of production in the textile industry is essential for maintaining competitiveness in the global market. Manual fabric defect inspections are often characterized by low precision and high time costs, in contrast to intelligent anomaly detection systems implemented in the early stages of fabric production. To achieve successful automated fabric defect identification, significant challenges must be addressed, including accurate detection, classification, and decision-making processes. Traditionally, fabric defect classification has relied on inefficient and labor-intensive human visual inspection, particularly as the variety of fabric defects continues to increase. Despite the global chip crisis and its adverse effects on supply chains, electronic hardware costs for quality control systems have become more affordable. This presents a notable advantage, as vision systems can now be easily developed with the use of high-resolution, advanced cameras. In this study, we propose a discrete curvature algorithm, integrated with the Gabor transform, which demonstrates significant success in near real-time defect classification. The primary contribution of this work is the development of a modified curvature algorithm that achieves high classification performance without the need for training. This method is particularly efficient due to its low data storage requirements and minimal processing time, making it ideal for real-time applications. Furthermore, we implemented and evaluated several other methods from the literature, including Gabor and Convolutional Neural Networks (CNNs), within a unified coding framework. Each defect type was analyzed individually, with results indicating that the proposed algorithm exhibits comparable success and robust performance relative to deep learning-based approaches.  
machine vision; quality control; gabor transform; curvature; intelligent methods Article  
1  
[PERSON], [PERSON], [PERSON], M.  
The edges of faulty fabric images are extracted using edge detection methods. By applying contour algorithms to the edge-detected images, contours are stored as an array to be used in the curvature algorithm [11]. The developed curvature algorithm then determines the presence of any deviations by calculating edge coordinates, such as radius and angle. Based on these calculations, the discrete curvature algorithm is modified and adapted to solve the problem at both small-scale and neighboring pixel levels [12; 13].  
The curvature algorithm is compared with widely used detection algorithms, such as the Gabor transform, as well as deep learning-based algorithms, including CNN and YOLO. In this study, the modified curvature algorithm and other methods are compared using the same dataset, and the results are presented with a focus on their performance, accuracy, and practical applicability.