esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Model
Hence, we choose a mother polar code with block length \(N_{M}=32\), and the number of information bits in the 3 rd transmission can be calculated as \(N_{3}R_{3}=6\); then, the code-rate of the mother code is \(R_{M}=3/16\). The 3 rd polar code \(C(N_{3},R_{3},\mathcal{A}_{3}^{(3)})\) has been punctured eight bits from the mother code, and the information bits in \(\mathcal{A}_{3}^{(3)}\) are \(\{u_{1},u_{2},u_{5},u_{6},u_{7},u_{8}\}\). Then, the sender finishes all three transmissions of the 3-level RC PCPP coding blocks. As shown in Figure 2b, the backward decoding process begins when the 3 rd PCPP coding block can be decoded successfully. Figure 2: The encoder and decoder structure of a 3-level PCPP code, where \(N_{1}=16,N_{2}=8\), and \(N_{3}=24\): (**a**) encoder structure; (**b**) decoder structure. **Step 1**: the receiver starts by decoding the 3 rd PCPP coding block \(C(N_{3},R_{3},\mathcal{A}_{3}^{(3)})\) and recovers the information bits \(\{u_{1},u_{2},u_{5},u_{6},u_{7},u_{8}\}\), which can be used to decode the other two previously received PCPP coding blocks that were stored in the buffer. **Step 2**: according to the nested property, the decoded bits \(\{u_{1},u_{2}\}\) are regarded as frozen bits in \(C(N_{2},R_{2},\mathcal{A}_{2}^{(2)})\), and the 2 nd PCPP coding block turn to a \(C(N_{2},R_{3},\mathcal{A}_{3}^{(2)})\) coding block, where the rate 2 nd PCPP coding block is reduced to \(R_{3}\). Then, the remaining information bits \(\{u_{3},u_{4}\}\) can be decoded successfully. **Step 3**: the information bits \(\{u_{1},u_{2},u_{3},u_{4},u_{5},u_{6},u_{7},u_{8}\}\) in the two previously decoded PCPP coding block are regarded as frozen bits in \(C(N_{1},R_{1},\mathcal{A}_{1}^{(1)})\), to decode the remaining information bits \(\{u_{9},u_{10},u_{11},u_{12}\}\) with the code-rate also being reduced to \(R_{3}\). Therefore, all of the 12 information bits have been decoded through the 2 times backward decoding. According to the above analysis, it is worth noting that, although the RC PCPP coding scheme can achieve arbitrary rates and block lengths, the coding complexity needs to be optimized. First, assume that \(K\) information bits are transmitted successfully after \(k\) time transmissions and the sender has been transmitting \(k\)-level PCPP coding blocks; then, there are \(k\) pairs of encoders and decoders that are needed to construct the \(k\)-level PCPP codes, which lead to a complex system structure. The key to addressing this problem is by considering the continuous block transmission over time-varying channels, where the last successive transmission of the PCPP coding blocks can help the sender to determine the optimization coding parameters for the next data block encoding in the first transmission. We will discuss this optimization RC IR-HARQ transmission scheme in detail in the following.
## 5 Applications and Services
### UAV Design  
Depending on the applications, communication technologies, available resources, and UAV navigation objectives, the aerial UAV package can be designed as an aerial base station (UAV-BS) or equipped with an RIS to form a RISeUAV.
# Generation of Data-Driven Expected Energy Models for Photovoltaic Systems
## 2 Methodology
### Variable Standardization  
The specific inputs used for model training mimic commonly available parameters used in current expected energy models (e.g., [15]), such as irradiance and site capacity. However, with covariates at different scales (e.g., \(\{0\frac{W}{m^{2}}<I<1.2\times 10^{3}\frac{W}{m^{2}}\}\) while \(\{1\times 10^{2}\ kW<C_{DC}<1.3\times 10^{5}\ kW\}\)), variable standardization is required to reduce model sensitivity to parameter scales. In particular, without standardization, weights generated  
Figure 3: An example of anomaly-based filter (energy production vs. irradiance) for a particular site. Anomalous data points (visualized as Xs) are often lower than non-anomalous values within the distribution-derived bands (red lines).  
for each parameter are more likely to reflect scalar nuances rather than the relative importance of the parameter to the outcome of interest. Variable standardization centers the data by subtracting data points in a feature from its associated mean value (\(\mu\)) and then scales the data by dividing by the associated standard deviation (\(\sigma\))--i.e., \(Z=\frac{\mu-\hat{\mu}}{\sigma_{X}}\). The resulting standardized variables have a mean of zero and a standard deviation of one. This process makes parameters easier to rank in terms of influence; the variable with the larger parameter holds a more important effect on the output response. Thus, variable standardization also aids in the interpretability of the derived parameters, especially when variable interactions (e.g., \(I\times C_{DC}\)) are introduced. The mean and standard deviation parameters used to standardize irradiance, capacity, and energy values are captured in Table 1.
# A Method of Dual-AGV-Ganged Path Planning Based on the Genetic Algorithm
## 2 Establishment of Environmental Model  
This study employs a two-dimensional grid map, one of the most commonly used map types in path planning [28]. As shown in Figure 1, each grid cell was quantified by an occupancy probability value, where a probability of 1 indicates occupancy, 0 indicates unoccupied, and values between 0 and 1 indicate unknown states. The AGV can pass through a grid when it is unoccupied. Initial information is obtained through topic communication in the Robot Operating System (ROS). ROS, a middleware framework for robot software development, was created by Willow Garage in 2007 [29]. The coordinates retrieved from the subscribed topic information were then converted into decimal form using real-number encoding. In AGV navigation, an inflation radius was typically set in the cost map to account for the dimensions of the AGV, thereby reducing the risk of collisions [30]. However, this method has limitations when dealing with AGVs that have significantly different length and width dimensions as it requires a trade-off between safety and efficient map utilization [31].  
To address potential collision issues during straight-line movement in a dual-AGV-ganged system, this study adjusted the inflation radius in the cost map. Additionally, a dual-AGV-ganged safety steering radius was introduced in the global path-planning algorithm to ensure collision-free turning, significantly enhancing the safety of the dual-AGV-ganged system during navigation.  
As shown in Figure 2, the black dashed box outlines the overall profile of the dual-AGV-ganged system, while the blue rectangles represent the contours of the leader AGV and the follower AGV, maintaining a fixed safety distance between them. The green arrows on the leader AGV's contours indicate the shared orientation of the two vehicles. The parameters for the dual-AGV-ganged system are based on the center-to-center distance c between the two AGVs, as well as the horizontal distance a and vertical distance b from the center of a single AGV to its contour. The specific settings are detailed below.  
The inflation radius of the cost map was set based on the horizontal and vertical distances from the center of gravity to the outer contour of a single AGV, along with a buffer distance, as described by Equation (1):  
\[R_{\text{Map}}=\sqrt{a^{2}+b^{2}}+d_{s}, \tag{1}\]  
where \(R_{\text{Map}}\) is the global cost map inflation radius; \(a\) and \(b\) are the horizontal and vertical distances from the center of gravity of a single AGV to the profile, respectively; and \(d_{s}\) is the buffer distance.  
This paper comprehensively considers factors such as the center of gravity distance of the leader-follower AGV and the resolution of the cost map to establish a safety steering radius for dual AGVs, as specified in Equation (2):  
\[R_{\text{Turn}}=\frac{\sqrt{a^{2}+c^{2}}+d_{s}}{r}, \tag{2}\]  
where \(R_{\text{Turn}}\) is the safety steering radius for dual AGVs, \(c\) is the distance between the centers of gravity of the leader and follower AGVs, and \(r\) is the resolution of the cost map.
## 3 Anomaly Detection and Quantification of Seismicity Parameters
### SGS Seismographic Network (SNSN)  
In 2005, the Saudi Geological Survey (SGS) commenced the operation of the Saudi National Seismographic Network (SNSN). The network comprises 27 broadband stations across the Arabian Shield and Platform, equipped with state-of-the-art seismic monitoring tools and satellite-based data transmission and remote control systems. Centralized in Jeddah, it efficiently manages seismic data collection, processing, and analysis. The network operates on specialized software, designed to withstand harsh environments, and maintains rigorous station performance monitoring, ensuring high reliability and efficiency in seismic activity monitoring [6,40]. Figure 2d illustrates the spatial distribution of earthquake activity reported by SNSN in the Red Sea region, covering the period from 2005 to 2020.  
Table 3 presents a statistical overview of seismic activity in the Red Sea Area from 2005 to 2020, as recorded by SNSN, offering essential insights into the seismicity of the region. The data set encompasses 23,731 seismic events. The temporal analysis shows a mean year of seismic activity centered around 2010, with a standard deviation indicating a spread of events over the 15-year period. The skewness value suggests a slight concentration of events in the latter part of the period, which could indicate either an increase in seismic activity or advancements in seismic detection capabilities. The distribution of events across months and days shows a fairly uniform spread, with minimal skewness and kurtosis, indicating a non-seasonal pattern of seismic occurrences. From a spatial perspective (Figure 2d), the data suggest a broader range of seismic activity across the region, as indicated by the higher standard deviations compared to earlier periods. This might suggest improvements in the network's spatial coverage. The depth data reveals a wide range of seismic event depths, with a significant standard deviation and extremely high kurtosis. In terms of magnitude, the majority of seismic events were of low to moderate intensity, with a mean magnitude of 1.586. The presence of events with magnitudes up to 5.8, however, highlights the potential for significant seismic occurrences within the region.
## 2 Near-Fault and Far-Fault Ground Motions
### Ground Motions with Soil Conditions  
The selected ground motions were revised to match the design response spectrum in the entire period. In this study, spectrum matching was performed for a period of 0.02 to 10 s using the algorithm of [PERSON] and [PERSON] (2018). Ground response analysis was then performed according to the site condition of a target bridge to revise the ground motions. The local site condition is essential in the seismic response of a structure. Table 3 lists the site classifications specified in several main design standards: EC 8 (Eurocode 8) ([PERSON] and [PERSON], 2018), UBC 97 (Uniform Building Code) (Kosel, 2018), KDS (Korean Design Standard) (Kosel, 2018), and RPA 99/2003 (Regles Parasismiques Algeriennes) (Kosel, 2018). Eurocode 8 and UBC 97 define the site classifications based on the shear wave velocity averaged at a bedrock depth of 30 m (V\({}_{\text{s}30}\)). KDS and RPA 99/2003 use the shear wave velocity (V\({}_{\text{s}}\)) in the range of bedrock depths (H) from 10 to 20 m to define the site classifications.  
The target site of this study is a shallow and hard ground area with a bedrock depth of approximately 17 m and a shear wave velocity higher than 260 m/s. The Eurocode 8 and UBC 97 standards classify the target site as soil type C and RPA 99/2003 as S3. According to KDS, which accounts for both the bedrock depth and shear wave velocity, the target site was included in the S2 type. Based on the site conditions defined from the shear wave velocity of the actual ground, nonlinear ground response analysis was performed to generate near-fault and far-fault artificial ground motions ([PERSON], 2018; [PERSON], 2018; [PERSON], 2018). The dynamic properties of the soil used in the ground response analysis involved the unit weight, plasticity index, and shear wave velocity. The property values were determined by the shear wave velocity corresponding to the site classification. Figure 2 shows the response spectrum obtained from the nonlinear ground response analysis according to the amplification of ground motions. The near-fault ground motions included a relatively low-frequency range compared with the far-fault ground motions, but no significant differences were found. The peak values of the response spectrum were approximately 5 to 15 Hz in the near-fault ground motions and 10 to 20 Hz in the far-fault ground motions. The acceleration of the ground motions was rapidly reduced to maintain an almost constant value with increasing frequency after the peak value.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Eurocode 8** & **UBC 97** & **KDS** & **RPA 99/2003** \\ \hline Soil type A: & Soil type A: & S1: & S1: \\ V\({}_{\text{s}30}\geq\) 800 m/s & V\({}_{\text{s}30}\geq\) 1500 m/s & H \textless{} 1 m & V\({}_{\text{s}}\) \textgreater{} 800 m/s \\ \hline Soil type B: & Soil type B: & S2: & S2: \\
360 \(\leq\) V\({}_{\text{s}30}\) \textless{} 800 m/s & 760 \(\leq\) V\({}_{\text{s}30}\) \textless{} 1500 m/s & 1 \(\leq\) H
# Enhanced Ant Colony Algorithm Based on Islands for Mobile Robot Path Planning
## 3 Brief Introduction of ACO
### Modified Heuristic Function  
In the basic ACO, the heuristic value only considers the distance between each free grid and the target grid, neglecting the impact of the turning angle in the optimal path calculation. However, a larger turning angle results in higher energy consumption. To address this issue, this study modifies the heuristic function by incorporating the turning angle variable. The specific calculation is shown in Equation (16):  
\[\eta_{ij}=\frac{1}{d_{ig}+\sigma_{ij}}, \tag{16}\]  
where \(d_{ig}\) signifies the distance between the next position \(j\) and the ending position, and \(\sigma_{ij}\) indicates the angle coefficient. The distribution of angle coefficients from node \(i\) to node \(j\) is illustrated in Figure 7 and can be calculated using Equation (17):  
\[\sigma_{ij}=\left\{\begin{array}{ll}0\times\varepsilon,&if\ <\overrightarrow{p_{i-1}P_{i}}, \overrightarrow{p_{i}P_{j}}>=0,\\ 1\times\varepsilon,&otherwise\ if\ <\overrightarrow{p_{i-1}P_{i}}, \overrightarrow{p_{i}P_{j}}>=\frac{\pi}{4},\\ 2\times\varepsilon,&otherwise\ if\ <\overrightarrow{p_{i-1}P_{i}}, \overrightarrow{p_{i}P_{j}}>=\frac{\pi}{2},\\ 3\times\varepsilon,&otherwise\ if\ <\overrightarrow{p_{i-1}P_{i}}, \overrightarrow{p_{i}P_{j}}>=\frac{3\pi}{4},\end{array}\right. \tag{17}\]  
where \(\varepsilon\) signifies the angle coefficient; \(\overrightarrow{p_{i-1}P_{i}}\) refers to the vector that starts with node \(i-1\) and ends with node \(i\); node \(i-1\) denotes the previous node of node \(i\) in the path; node \(i\) implies the current position of the robot; \(\overrightarrow{p_{i}P_{j}}\) refers to the vector that starts with node \(i\) and  
Figure 6: Island search maps.  
ends with node \(j\); node \(j\) indicates the next node of node \(i\) in the path; node \(j\) represents the node to be selected by the robot in the path planning; and \(<\overline{P_{i-1}P_{i}},\overline{P_{i}P_{j}}>\) refers to the angle between vector \(\overline{P_{i-1}P_{i}}\) and vector \(\overline{P_{i}P_{j}}\).
# Defect Detection on Rolling Element Surface Scans Using Neural Image Segmentation
###### Abstract  
The surface inspection of steel parts like rolling elements for roller bearings is an essential component of the quality assurance process in their production. Existing inspection systems require high maintenance cost and allow little flexibility. In this paper, we propose the use of a rapidly retrainable convolutional neural network. Our approach reduces the development and maintenance cost compared to a manually programmed classification system for steel surface defect detection. One of the main disadvantages of neural network approaches is their high demand for labeled training data. To bypass this, we propose the use of simulated defects. In the production of rolling elements, real defects are a rarity. Collecting a balanced dataset thus costs a lot of time and resources. Simulating defects reduces the time required for data collection. It also allows us to automatically label the dataset. This further eases the data collection process compared to existing approaches. Combined, this allows us to train our system faster and cheaper than existing systems. We will show that our system can be retrained in a matter of minutes, minimizing production downtime, while still allowing high accuracy in defect detection.  
automated optical inspection; image segmentation; convolutional neural networks; surface defects; quality control Article
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 2 Datasets
### Handwritten Text
[37] presents some datasets of handwritten Urdu text recognition, which give outstanding results; the dataset descriptions and their availability are also shown in Table 3.  
[PERSON] et al. [38] summarized the state-of-the-art in OCR research for Urdu-like cursive scripts, concentrating on Nastaliq and Naskh scripts in the Urdu, Pushto, and Sindhi languages. The study discusses the quirks of these scripts as well as the text-picture databases that are readily accessible. Three categories have been established: printed, handwritten, and internet character recognition. The database is discussed, which includes 60,329 isolated digits, 12,914 strings, 1705 symbols, 14,890 isolated characters, and 318 different patterns of dates. [PERSON] and [PERSON] [39] discussed the most commonly used datasets for training and evaluation of OCR systems for printed Arabic script, including the IFN/ENIT Arabic handwritten dataset, the \"Handwriting Arabic Corpus\" (HAC) dataset, and the RIMES dataset containing a large collection of printed and handwritten documents. The authors provide an overview of the available datasets and emphasize the importance of high-quality datasets for improving the accuracy of OCR systems. Publicly available scanned image datasets are tested by [40], e.g., the WATAN and APTI datasets with extensive vocabularies. The datasets are split into a training set and a testing set, where training data contain 282,000 word images and 1,200,000 characters images while testing 5500 words, and 100,500 characters are used. The trained model \begin{table}
\begin{tabular}{l l} \hline \hline
**Description** & **Stats** \\ \hline Total text lines of dataset & 4,000,000 \\ Total words & 15,000,000 \\ Unique words & 200,000 \\ Text lines per image & 70 \\ Total used fonts (with sizes) & 11 fonts (sizes:12, 14, and 18) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Example Persian dataset collected from different news websites. \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **UPTI** & **CALAM** & **UNHD** \\ \hline Total writers & 250 & 725 & 500 \\ Text lines & 60,000 & 3043 & 10,000 \\ Words & 240,000 & 46,664 & 187,200 \\ Characters & 970,650 & 101,181 & 312,000 \\ Availability & Private & Private & Public \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sample handwritten Urdu datasets. achieves an overall accuracy of 97.94%. As there are many challenges in Arabic optical character recognition (AOCR), [41] surveys various approaches and methods to detect and reduce errors. In [42], the authors proposed a system synthesizing Arabic handwritten words and text pages to generate comprehensive databases for training and validating OCR systems. In the database, vocabulary of the 50,000 most-common Arabic words are used for error correction.
## 5 Engineering Applications
### Equivalent Conversion Results  
The results of the analytic conversion \(\mathbf{Q}\)-\(\mathbf{s}\) curve and comparison with the simplified conversion method are shown in Figure 17. It is clear that the \(\mathbf{Q}\)-\(\mathbf{s}\) curve belongs to the category of slowly changing types. In the case of smaller loads, the equivalent conversion displacement also increases taught slowly. As the load increases, the displacement growth amplitude gradually increases until the final phase of the ultimate load displacement changes suddenly and the load is terminated.  
In this study, the single pile's maximum bearing capacity count by the conversion method is 14,510.05 kN, and the single pile's maximum bearing capacity count by the conversion factor of 0.85 is 15,686 kN. Due to the large differences in the properties and shear strength of various soil layers in actual projects, when using the simplified conversion method for super-long and large-diameter piles, the conversion coefficient method is adopted for the soil layer at the pile side according to the homogeneous soil, and the  
Figure 16: Distribution map of pile side friction under different loads.  
Figure 17: Comparison of \(\mathbf{Q}\)-\(\mathbf{s}\) curves of anchor pile method and different conversion methods.  
difference in the soil layer's bearing properties is ignored. The bearing capacity of the pile can be guaranteed in the pile foundation design, but the analysis of the working performance and the test pile's maximum bearing capacity is not accurate enough. The calculation accuracy of the analytical conversion method can be improved by about 12%. In addition, the difference in the nature of the soil layers on the side of the pile and the interaction between the upper and lower piles are considered. It has systematic theoretical analysis and can better reflect the deformation characteristics and internal force distribution of the self-balanced test pile. Therefore, the conversion method in this study has certain engineering guidance practices for self-balanced test piles.
## 3 Results and Discussion
### Evaluation of Soil Heavy Metal Pollution and Ecological Risk
In contrast, the ecological risk levels in the eastern and western regions are characterized as low, while the central and northwestern regions exhibit moderate ecological risk levels. Interestingly, the _RI_ value in the northeast, where there is a dense distribution of key enterprises, is also at a low level, possibly due to its proximity to the Yangtze River. However, the two very strong ecological risk zones are located along critical waterways, with numerous key enterprises situated in the upstream areas of these watercourses. Figure 6 reveals significant changes in the types of land use within the study area from 1990 to 2020. Notably, the northeastern region has experienced rapid urban land expansion, leading to a marked reduction in the area of Poyang Lake, with the lost area converted into arable land. The increase in urban land in the central-western region corresponds to the zones identified in Figure 5 as having very strong ecological risk, suggesting a direct relationship between high ecological risk in these areas and human activities. When analyzing the spatial distribution of the _RI_ in conjunction with land use types, it appears that the low ecological risk areas in the eastern and western regions correspond to agricultural land, while the moderate ecological risk areas in the central and northwestern regions are primarily characterized by forest, shrub, and grassland uses. This indicates that the cultivation of crops has a minimal impact on soil heavy metal pollution in the study area, allowing for the conclusion that agricultural sources of pollution can be largely excluded. Figure 4: Boxplot of the evaluation results for the \(E_{r}^{i}\).