esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Machine Learning-Based System Performance Optimization Techniques
## 2 Background
### Machine Learning Based Index Structures
#### 3.1.4 Learned Inverted Index  
An inverted index is a technique that is widely used in a large-scale text search. In contrast to forward index, words become key and documents become value (so, inverted). However, when indexing such a large-scale text, it is generally generated through the same hash function without understanding the distribution of various data. In order to increase the space utilization rate of the inverted list when the large amount of data is given, this paper presents Pavo [8], an RNN-based learned inverted index.  
Pavo conducts four stages as shown in Figure 5. The first stage, the input stage, is a stage for pre-processing input data. The input data string is processed as bigram and used as the input sequence of the RNN. For example, the word \"Documentaries\" can be divided into do, oc, cu,..., es when using bigram. The second stage, the disperse stage, consists of a hierarchical model and plays a role to distribute data evenly among multiple models. By dividing the dataset into several sub-data sets, the low-level model helps to learn the mapping relationship of the dataset. The third stage, the mapping stage, maps each data in the dataset of models in the last layer to a local hash. The last stage, the join stage, is connected by a local hash to create the final hash-table. All local hashes are serially connected to create a global hash table, and as shown in Figure 5, pos1 located in the local hash of RNN 3.2 is mapped to the global hash table to finally find the location of the key. This paper introduces two learning methods in the mapping stage: supervised and unsupervised learning methods. First, supervised learning aligns keys, then sets labels in order, and learns to map keys one-to-one with hash entries. Supervised learning hash-function tends to overfitting the model when the number of labels is small, or the noise is high [17]. Therefore, unsupervised learning is introduced. The second unsupervised learning is a method of self-learning the distribution of data without labels. When each key is input to the model, the model outputs a vector of hash table length. Next, the vector is the input to the Softmax function [18]. It finds the position of the largest value in the output value of Softmax and sets the position's value to 1. At the end of the stage, to obtain a distribution, the value of each category is summed, and the model optimized the computed error through a loss function to distribute the data uniformly. When mapping size is 1000, the average number of look-up of learned hash-function from the entire dataset was approximately 1.0-1.15, which was lower than 1.5 of the existing hash-function.  
Figure 4: Bloom Filter Architecture [7]: The Learned model received a key as an input and predicts a regression value to determine if the key exists in the set or not.
# True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA
## 4 Implementation Results
### NIST Statistical Test Suite  
The quality of the designed entropy source was assessed by the commonly used NIST 800.22 statistical test suite [37]. It consisted of 15 type of tests which were performed to assess the performance of TRNGs. Such tests were carried out for the proposed FiRO-GaRO configurations (i.e., 1 FiRO-1 GaRO, 2 FiRO-2 GaRO and 4 FiRO-4 GaRO) at 50 MHz and 100 MHz. A total of 320 sequences with a length of 1 Gbit were collected and tested for each configuration. The parameters of each test were set following the NIST recommendations [37]. In Table 6 the results obtained with a 50 MHz sampling rate are shown, while in Table 7 we present the results with a 100 MHz sampling rate. Both tests refer to the implementation of the aforementioned Stratix IV FPGA. The statistical _p_-value should be greater than 0.01 if the test item was passed and the proportion of passed sequences to total sequences should be larger than 0.980. All tests were considered passed as they scored above the threshold. The entropy level demonstrated itself to be similar in all the cases, with the 1 FiRO-1 GaRO configuration achieving slightly lower entropy levels. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**1 FiRO-1 GaRO**} & \multicolumn{2}{c}{**2 FiRO-2 GaRO**} & \multicolumn{2}{c}{**4 FiRO-4 GaRO**} \\
**Test Name** & _p_**-Value** & **Proportion** & _p_**-Value** & **Proportion** & _p_**-Value** & **Proportion** \\ \hline Frequency & 0.578763 & 0.987 & 0.959132 & 0.993 & 0.676097 & 0.984 \\ BlockFrequency & 0.701879 & 0.987 & 0.880335 & 0.990 & 0.540457 & 0.987 \\ CumulativeSums * & 0.392456 & 0.990 & 0.637119 & 0.987 & 0.001732 & 0.987 \\ Runs & 0.656634 & 0.996 & 0.141256 & 0.984 & 0.103676 & 0.996 \\ LongestRun & 0.656634 & 0.993 & 0.360699 & 0.990 & 0.005789 & 0.993 \\ Rank & 0.585209 & 0.987 & 0.839722 & 0.996 & 0.202944 & 0.981 \\ FFT & 0.794626 & 0.978 & 0.171276 & 0.990 & 0.930752 & 0.993 \\ NonOverlappingTemplate * & 0.515367 & 0.978 & 0.752361 & 0.978 & 0.019520 & 0.975 \\ OverlappingTemplate & 0.280017 & 0.990 & 0.124566 & 0.990 & 0.727346 & 0.981 \\ Universal & 0.371101 & 0.981 & 0.437274 & 0.984 & 0.875539 & 0.978 \\ ApproximateEntropy & 0.800471 & 0.993 & 0.103676 & 0.996 & 0.817667 & 0.990 \\ RandomExcursions * & 0.590375 & 0.980 & 0.063657 & 0.974 & 0.522989 & 0.984 \\ RandomExcursions Variant * & 0.652733 & 0.976 & 0.153309 & 0.969 & 0.511916 & 0.973 \\ Serial * & 0.817667 & 0.978 & 0.017156 & 0.993 & 0.408942 & 0.984 \\ LinearComplexity & 0.553147 & 0.993 & 0.758528 & 0.993 & 0.656634 & 0.981 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: NIST 800.22 Statistical Test Results with 50 MHz of Sampling Rate. * Worst case reported for tests with multiple outcomes.
## 4 Results and Discussion
### Comparison of Natural Gas and Hydrogen
#### 4.2.4 Temperature Variations in Surrounding Rock at Different Burial Depth  
As illustrated in Figure 8(a), three monitoring points are established at varying distances horizontally from the cavity, designated as A, B, and C, progressing from the closest to farthest. The temperature variations at these horizontal monitoring points during the injection and extraction process are depicted in Figure 8(b). The temperature of the surrounding rock fluctuates in response to changes in the gas temperature within the cavity during the injection and extraction cycle; as the distance from the cavity increases, the amplitude of temperature fluctuations in the surrounding rock diminishes. For the same monitoring point, the temperature of the surrounding rock storing hydrogen is consistently higher than that storing natural gas. When storing hydrogen, the rate of increase or decrease in the surrounding rock temperature is greater compared to natural gas, indicating a more pronounced thermal exchange effect between hydrogen and the surrounding rock.
# Secure Information Transmission with Self Jamming SWIPT
## 2 System Model
### Secrecy Rate Analysis  
The secrecy rate analysis of this system model is based on the Gaussian wiretap channel secrecy capacity, which deduces the difference between [PERSON]'s capacity of \(R\) and \(\xi\). The maximum achievable secrecy rate is the secrecy capacity of \(R\)[8; 9]. The secrecy capacity of \(R(a)\) with respect to \(\xi(a)\) and the secrecy capacity of \(R(a)\) with respect to \(\xi(c)\), respectively, are given (The notation for the secrecy rate of \(R(a)\) with respect to \(\xi(c)\) is given as \(\mathbb{C}_{(c),A}\), where \((c)\) denotes \(\xi(c)\) and \(A\) denotes \(R(a)\). A similar rule is followed for the other three secrecy rate notions.) as:  
\[\begin{split}\mathbb{C}_{(a),A}&=\frac{\Delta f}{ k}\sum_{k=0}^{K-1}\bigg{(}\log\bigg{(}\frac{1+|H_{\mathcal{T}R}^{(k)}|^{2} \mathbb{T}_{R(a)}}{1+|H_{\mathcal{T}\xi}^{(k)}|^{2}\mathbb{T}_{\xi(a)}}\bigg{)} \bigg{)},\\ \mathbb{C}_{(c),A}&=\frac{\Delta f}{k}\sum_{k=0}^{K- 1}\bigg{(}\log\bigg{(}\frac{1+|H_{\mathcal{T}R}^{(k)}|^{2}\mathbb{T}_{\xi(a)} }{1+|H_{\mathcal{T}\xi}^{(k)}|^{2}\mathbb{T}_{\xi(c)}}\bigg{)}\bigg{)},\end{split} \tag{17}\]  
where \(2\Delta f\) is the bandwidth and \(\Delta f\) is the peak frequency deviation. The secrecy capacity of \(R(b)\) with respect to \(\xi(a)\) and the secrecy capacity of \(R(b)\) with respect to \(\xi(b)\), respectively, are given as:  
\[\begin{split}\mathbb{C}_{(a),B}&=\frac{\Delta f}{ k}\sum_{k=0}^{K-1}\bigg{(}\log\bigg{(}\frac{1+|H_{\mathcal{T}R}^{(k)}|^{2} \mathbb{T}_{R(b)}}{1+|H_{\mathcal{T}\xi}^{(k)}|^{2}\mathbb{T}_{\xi(a)}} \bigg{)}\bigg{)},\\ \mathbb{C}_{(b),B}&=\frac{\Delta f}{k}\sum_{k=0}^{K- 1}\bigg{(}\log\bigg{(}\frac{1+|H_{\mathcal{T}R}^{(k)}|^{2}\mathbb{T}_{R(b)}}{ 1+|H_{\mathcal{T}\xi}^{(k)}|^{2}\mathbb{T}_{\xi(b)}}\bigg{)}\bigg{)}.\end{split} \tag{18}\]  
Here, the interference of the EH signals is used as the external noise to improve the maximum achievable secrecy rate of \(R\), and by increasing \(\beta\), the secrecy capacity can be improved in all scenarios. Even though \(\beta\) can reduce the capacity of \(R(b)\) due to the amplification of channel estimation errors, since \(\xi(b)\) also has channel correlation error in addition to the channel estimate errors, \(\beta\) will improve \(R\)'s secrecy capacity.
# Approximation of Non-Linear Stress-Strain Curve for GFRP Tensile Specimens by Inverse Method
## 1 Introduction  
Many studies have been conducted to reduce the weights of automotive [1; 2; 3; 4] and aircraft parts [5; 6]. A few of these parts are made of metals; replacing them with polymer plastics satisfies the demand for weight reduction [7; 8]. However, polymer plastics exhibit lower strength compared with metal materials [9; 10]. To this end, many studies have been conducted to improve the strength of plastics by adding glass or carbon fibers [11; 12]. The plastics with such fibers vary the pattern of the stress-strain (S-S) curve, a macroscopic property, and also make it difficult to predict microscopic mechanical properties [13; 14]. [PERSON] [15] proposed a method for estimating the S-S curve of a specific or composite material with a large fluctuation in material properties during processing, through fuzzy theory. As there is a considerable difference between the S-S curve derived through fuzzy theory and the behavior of an actual specimen, it is difficult to use the derived S-S curve as the material property for a finite element analysis (FEA).  
Recently, studies on models for estimating the S-S curve using the response surface method of design of experiment (DOE), which are referred to as inverse methods, have been conducted [16; 17; 18]. For inverse methods, basic theories are provided by the American Society for Testing and Materials (ASTM) standards; further, studies to improve the accuracy of property data through mathematical modeling that uses power and exponential functions have been proposed [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22].  
However, as mathematical models may vary depending on the materials, inverse methods have been mostly applied to metals and alloy models. Therefore, it is necessary to select and optimize inverse methods to secure the macroscopic properties of composite materials.  
In this study, a method for estimating the macroscopic properties of glass fiber-reinforced polymer (GFRP) tensile specimens, which are commonly used as the lightweight materials of automotive parts, using an inverse method through the finite element method (FEM) was proposed. GFRP specimens were fabricated, and their mechanical properties for tensile deformation were analyzed through tests. Based on the results, various inverse methods were applied for modeling, and the analysis and experiment results were compared. Moreover, parameter optimization was conducted using DOE to select an inverse method most suitable for GFRP among various models. In this process, the maximum number of iterations for optimization was limited to the same value. Moreover, whether the actual properties of GFRP could be estimated was verified through advanced inverse methods--by combining the function with the lowest error.
# Computer-Aided Detection of Hypertensive Retinopathy Using Depth-Wise Separable CNN
###### Abstract
A normal retinal fundus image and images of HR affected fundi with different symptoms are illustrated in Figure 1. Eye specialists can use CAD systems to diagnose certain retinal diseases, such as HR-related eye illness. These technologies help academics and the health care industry by self-diagnosing. Ophthalmologists use such systems to diagnose and treat eye-related diseases, particularly HR-related diseases. The authors provided a recent survey for automated HR identification techniques, as illustrated, and compared in Section 2. A few studies suggested that hypertensive retinopathy (HR) might be automatically identified by segmenting the retina's structural features, such as the macula, optic disc (OD), blood vessels (BV), and microaneurysms (MA), as illustrated in Figure 1. Deep-learning techniques also automatically extract certain structures. These characteristics were then quantitatively assessed to determine irregularities and ultimately identify HR or non-HR cases. Extracting clinical features for HR disease diagnosis is a time-consuming and challenging operation for computerized algorithms. Instead of focusing on more serious  
Figure 1: An instance of main HR symptoms are: (**a**) optic disk, cotton wool patches, hemorrhages and microaneurysms, (**b**) A/V ratio and (**c**) tortuosity. symptoms like cotton-wool patches or hemorrhages, researchers spent a lot of time only extracting the features. Microaneurysms were characteristic of early HR, but other lesions were critical in diagnosing malignant HR-related illnesses. In contrast, recently, deep learning methods have been deployed extensively for various computer vision applications and biological imaging analysis tasks.
## 7 Discussion
### Economic Impact of 5G-mmWave Private Network in Industrial Areas
Thus, Table 7, row (19) shows that the 5G-mmWave private network in the Jakarta industrial area will produce an output of 308,384 million IDR for the Pulogadung industrial area and 424,820 million for the KBN industrial area. These values are the increase in the contribution to the output of the Indonesian economy as an effect of the multiplier. Table 6, column (3), row (13), shows that implementing the new formula to calculate the spectrum usage fee in the Pulogadung area would result in a loss of IDR 347 million in state income. However, it will be compensated for by contributing IDR 308,384 million to the Indonesian national economic output, accounting for a total economic output increase of 0.244% over a 10-year period. By detailing the 17 sectors, we see that the 5G-mmWave private network deployment cost would benefit each sector. For instance, the information and communication section (row (11), column (2)) has a multiplier of 1.59. The value indicates that if there is a final demand of IDR 1 million, the output of this sector will increase by IDR 1.59 million. Translated into this case, this means that the 5G-mmWave private network deployment in the Pulogadung area will create a national economic impact of IDR 224,419 million in the information and communications sector. As another example, the health services and social activities sector (row (17), column (2-4)) has a multiplier of 1.73, and the national economic value of the output is approximately IDR 129 million and IDR 178 million due to 5G-mmWave private network deployment in the Pulogadung and KBN areas, respectively. Finally, it should be noted that countries with advanced telecommunications are not solely concerned with an annual increase in state income. Optimizing the mmWave frequency for the 5G-mmWave private network with a broader bandwidth will provide the country with even better value for the multiplier coefficient supporting Industry 4.0 and promoting digital economic growth and national digital transformation.
# Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication
## 3 OCC System Overview
### Proposed SVM Classifier Assisted Intelligent Receiver
#### 3.3.3 Data Decoding Method from the Accurate LED Region  
After recognizing accurate data transmitting LED regions, the data bits have been decoded from the accurate LED regions by analyzing the produced bright stripes and dark stripes on the image frame. The overall demodulation scheme of our proposed intelligent receiver is shown in Algorithm 1. In Algorithm 1, the detection of LED regions using CNN to the separation of accurate data transmitting LED region have been pointed out from step 1 to step 9. From each separated region, normalized intensity value \(Z_{i}\) is measured where \(i\) specifies the \(i\) th row of total \(p\) no. of rows in the separated accurate LED region. Now, the median value of the normalized intensities is calculated and set as threshold \(Z_{th}\). Now, we have assigned \(Z_{i}\) = '1' if \(Z_{i}>Z_{th}\) and vice versa to represent the 'ON' state, i.e., bright stripe and the 'OFF' state, i.e., dark stripe, respectively. In our experiment, the frequency to encode bit '1' is chosen as double that of the frequency to encode bit '0'. Therefore, the time period for the encoding bit '1' is half that of encoding bit '0' as well. This leads to the variation in the no. of pixel rows in the image frame, where bright stripes and dark stripes are generated, concerning bit '0' and bit '1'. Thus, we have calculated the no. of rows, which is represented as width, of successive 'OFF' and 'ON' states sequentially using 'OpenCV' module in Python, and they are denoted as \(W_{0}\) and \(W_{1}\). Finally, if both the width is greater than the ratio (\(\delta\)) of each row read out time and half of the time period to encode bit '1', the output is derived as bit '0'; otherwise, it is derived as bit '1'.  
```
0: Captured image of all LEDs state.
0:\(x_{o}\) = \"11101011...\" from accurate data transmitting LED region.
1: read each RGB image \(I_{u\times v\times 3}\);
2: detection of each LED region by CNN and separate regions \(I_{u1\times v1\times 3}^{1},I_{u2\times v2\times 3}^{2},I_{u3\times v3\times 3}^{3}, \ldots,I_{u\times m\times 3}^{n}\);
3: convert to grayscale image \(I_{u\times v\times 3}^{n}\to f_{p\times q}(gray)\);
4: draw contour on each of the \(f_{p\times q}^{n}\);
5:for\(i=0:\) no. of separated LED region do
6: extract features \([M_{00},S_{T},P_{R},N_{1}]\);
7: pass features to trained SVM classifier;
8: recognize accurate data transmitting LED region;
9:endfor
10: normalize each row intensities' values \(\sum_{i=1}^{p}Z_{i}\);
11: set median of normalized values as threshold \(Z_{th}\);
12:for\(i=0:\) pdo
13:if\(Z_{i}>Z_{th}\)then
14: assign \(Z_{i}\) = \"1\";
15:else
16: assign \(Z_{i}\) = \"0\";
17:endif
18: calculate width \(W_{0}\) of successive \(Z_{i}\) = \"0\" and width \(W_{1}\) of successive \(Z_{i}\) = \"0\";
19:endfor
20:if\(W_{0}>\delta\) and \(W_{1}>\delta\)then
21: set \(x_{o}\) = \"0\";
22:else
23: set \(x_{o}\) = \"1\";
24:endif
```  
**Algorithm 1** Demodulation Scheme at the Receiver.
## 4 Artificial Neural Networks
### Simulations
#### 6.2.3 End-to-End Delay  
The end-to-end packet delay is calculated as the time interval between the time instant when the packet is generated and is ready for the transmission, and the moment when it reached the destination node.  
Simulation results, depicted in Figure 19, show that the OSPF is better than the proposed NN method (for about 20%) while AODV is significantly worse, but note that we have the full benefits of neural networks can be achieved only through the hardware implementation, when the parallel work of all neurons is possible. Instead of that, we are using simulation, meaning our model was working in sequential mode which is unnatural for neural networks.
# Exploring Convective Drying Behavior of Hydroxide Sludges Through Micro-Drying Systems
## 3 Results and Discussion
### Volume and Shrinkage  
The average height and surface area of the various cross-sections are used to calculate the volume of the sample at various time intervals, enabling a shrinkage curve to be determined. Figure 8 shows an example of a shrinkage curve, showing the normalized  
Figure 7: Drying flux versus moisture content.  
volume (V/V\({}_{o}\)) as a function of the water content of the sample, expressed on a dry basis (W/W\({}_{o}\)) (kg water/kg solid). (V/V\({}_{o}\)) represents the ratio of sample volume at a given point in the drying process to its initial volume.  
Figure 8 shows that shrinkage is almost linear with residual water content (W) up to a critical threshold (between 0 and 0.1 kgwater/kgDS), for all three air velocities (1, 2, and 3 m/s). Volumetric shrinkage is considerable, with a maximum reduction in volume of around 80%. This volume reduction shows that shrinkage plays a crucial role in controlling the drying process.  
In the previous part of this article, X-ray tomographic images shown in Table 2 were used to determine the external perimeter of the sampled cross-sections, from which the external surface area of the hydroxide sludge samples was calculated at a fixed temperature of 110 \({}^{\circ}\)C for three air-drying velocities. These cross-section images highlighted in Table 2 can also be used to analyze and monitor the evolution of internal texture during drying. As can be observed, the internal texture of the sludge remains unchanged for the air velocity of 1 m/s, that is, homogeneous (see the images corresponding to W equal to 5.3 and 3.7 kgwater/kgDS). In contrast, more changes in texture are noticeable for the two air-drying velocities of 2 m/s and 3 m/s. The only visible change is the reduction in the size of the cross-sections. Some cracks appear and their significance increases as drying progresses (see the images corresponding to W equal to 1.5, 1.2, and 0.9 kgwater/kgDS). It is therefore clear that crack formation is directly associated with the development of internal transfer limitations.  
Figure 8: Normalized volume versus water content.  
However, a key distinction in our findings is the characterization of crack formation at the final stages of drying, which has been briefly mentioned in the prior research [14; 18] but not systematically analyzed. Our results indicate that the onset of cracks is intrinsically linked to internal moisture transfer limitations, a phenomenon that has critical implications for optimizing sludge drying processes and minimizing structural degradation.  
If there are no significant internal transfer limitations, the moisture and temperature profiles inside the sample remain flat. But when internal transfer resistances develop, the mechanical stress induced by temperature and moisture distributions exceeds the maximum fracture strength of the material, and cracks progressively develop.
# Fault Reconstruction for a Giant Satellite Swarm Based on Hybrid Multi-Objective Optimization
## 4 Simulation and Analysis  
According to the model in Section 2.1, fault reconstruction simulation analysis is carried out first to verify the effectiveness of the reconstruction algorithm; then, Monte Carlo simulation is used to verify the correctness of the reconstruction algorithm.