esa-sceva/satcom-chunk-collection huggingface dataset:
# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 4 Spatiotemporal Data Collection and Processing for Bridge Digital Twin
### The 2D Image Data Collection and Processing
#### 4.1.3 The 2D Image Data Processing Using YOLOv8  
After preprocessing, the 2D image data were analyzed using the You Only Look Once, Version 8 (YOLOv8), model to automatically detect cracks [35]. YOLOv8 is a highly efficient and rapid algorithm that performs both object classification and localization in a single step. It facilitates the identification of cracks on the surface of the bridge by predicting their location and size, as shown in Figure 4.  
For each detected crack, YOLOv8 predicts a bounding box, which includes the center coordinates \(\left(b_{x},b_{y}\right)\), the width \(b_{w}\), and the height \(b_{h}\) of the box. These values are calculated based on the model's predictions and the position of the grid cell in the image. The formula for calculating the center of the bounding box is provided in Equation (3):  
\[b_{x}=p_{x}+c_{x}\;,\;b_{y}=p_{y}+c_{y} \tag{3}\]  
where \(p_{x}\) and \(p_{y}\) are the predicted offsets within the grid cell, and \(c_{x}\) and \(c_{y}\) are the coordinates of the top left corner of the grid cell.  
Figure 4: Schematic representation of the YOLOv8 architecture.  
The width and height of the bounding box are also predicted by the model. These values determine the size of the crack and are calculated as follows according to Formula (4):  
\[b_{w}=p_{w}+e^{t_{w}},b_{h}=p_{h}+e^{t_{h}} \tag{4}\]  
where \(p_{w}\) and \(p_{h}\) are the predefined anchor box dimensions (representing average crack sizes), and \(t_{w}\) and \(t_{h}\) are the adjustments predicted by the model.  
In addition to localizing the cracks, YOLOv8 also classifies them. The model assigns a confidence score to each detected crack, indicating the model's certainty that the detected object is indeed a crack. This allows for more accurate and reliable detection results. The YOLOv8 model's architecture ensures that detection is both rapid and precise, making it suitable for real-time monitoring applications in structural health assessments.
# An Efficient QC-LDPC Decoder Architecture for 5G-NR Wireless Communication Standards Targeting FPGA
## 7 Conclusions  
This paper presents an innovative architecture for a layered QC-LDPC decoder that conforms to the 5G-NR standards. Our proposed design enables the processing of the maximum information length in 5G-NR. The decoder features a low latency, flexible model design, and reduced complexity due to the read/write networks, check node units, and shifting block designs. This research has also demonstrated the efficiency of the modified SOLL method for 5G-NR, highlighting its convenience and demonstrating positive outcomes. This shows significant improvements in SNR analysis and the number of iterations when compared to previous works. Moreover, the design supports a wide range of expansion factors and base matrices and can be easily modified to accommodate different code rates. As a next step, we plan to implement the decoder on FPGA; the synthesis process has already commenced. The promising results thus far reinforce our design's potential for practical IMT-2020 applications.  
I.A., methodology, formal analysis, validation, and supervision; M.A.M., visualization, resources, and formal analysis; A.A., methodology, formal analysis, validation, supervision, and data curation; B.M., software, conceptualization, formal analysis, data curation, and writing--original draft preparation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data that support the findings of this study were obtained at the Engineer Sciences Laboratory from the Polydisciplinary Faculty of Taza, University Sidi Mohamed Ben Abdellah (USMBA), Fez. They are available upon reasonable request. Data sharing is subject to ethical and privacy considerations. Do not hesitate to contact [PERSON] at [EMAIL_ADDRESS] for inquiries regarding data access and availability.  
The author would like to express his deepest gratitude to his research supervisors [PERSON] and [PERSON], for their invaluable guidance, insightful critiques, and unwavering support throughout this research. My sincere thanks also go to [PERSON] for her expert advice and assistance in the hardware description language phase of my work. A special appreciation is owed to the Laboratory of Engineer Sciences for providing the necessary facilities and resources.  
The authors declare no conflicts of interest.
# A Case Study on User Evaluation of Scientific Publication Summarization by Japanese Students
## 6 Discussion
### Characteristics of Manual and Automatic Summarization  
We evaluated five types of summaries generated by manual or automatic summarization methods (in addition to a machine translation system). We found the following characteristics of each type summary from the experimental results:  
* Manual Japanese was easy to read compared with automatic summarization because of the terminology explanation as well as fluency. However, there were some negative opinions as well, such as \"it is too simple\" and \"there is insufficient information on summaries.\"
* Automatic summarization methods obtained negative feedback, for example, \"there is too much unnecessary information.\" On the other hand, subjects also answered \"there is important information (in automatically generated summary) that is not included in another one (manual summary)\" when they compared automatically and manually generated summaries. There is the possibility of reducing or removing the perceptual biases of the authors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **ROUGE-1** & **ROUGE-2** & **ROUGE-L** & **Reference** \\ \hline AuE & 20.70 & 5.71 & 10.81 & AbE \\ AbJ & 36.09 & 7.54 & 19.40 & MJ \\ AuJ & 30.62 & 6.27 & 16.13 & MJ \\ AuJ & 49.30 & 16.18 & 22.56 & AbJ \\ \hline \hline \end{tabular}
\end{table}
Table 12: ROUGE scores of summaries used in experiment 1.  
* Machine translation systems may make grammatical errors and word errors due to the existence of specialized terminology that is not used in other domains (e.g., news articles), but some subjects preferred summaries written in Japanese, even if grammatically incorrect, over English ones.
## 5 Model Training and Result Discussion
### Performance Evaluation Indicators  
To better evaluate the detection capability of the developed model, this study used multiple indicators for evaluation. Among them, accuracy was the most used indicator in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Leak Rate** & \multicolumn{4}{c}{**Sliding Window Sizes**} \\ \cline{2-5}
**Level** & **5** & **10** & **25** & **50** \\ \hline
1 & 2247 & 2244 & 2235 & 2220 \\
2 & 2247 & 2244 & 2235 & 2220 \\
3 & 2247 & 2244 & 2235 & 2220 \\
4 & 2247 & 2244 & 2235 & 2220 \\
5 & 2247 & 2244 & 2235 & 2220 \\
6 & 2247 & 2244 & 2235 & 2220 \\ Total & 13,482 & 13,464 & 13,410 & 13,320 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of videos with different leak rate levels (vertical axis) and sliding window sizes (horizontal axis).  
Figure 10: Schematic diagram of the image data enhancement.  
the classification problems, which was usually used to evaluate the overall performance of the model. Its calculation equation is shown as follows:  
\[\text{Accuracy}=\frac{\text{Number of Correct Predictions}}{\text{Total Number of Predictions}} \tag{14}\]  
However, to better evaluate the advantages of the model, precision (\(P\)) and recall (\(R\)) were used, which are also common typical evaluation indicators for neural network models. The calculation equations are shown in Equations (15) and (16), where \(P\) reflects the detection performance of the model, expressed as the ratio of the number of correctly detected objects to the total number of detected objects, and \(R\) reflects the recall performance of the model, expressed as the ratio of the number of correctly detected objects to the total number of objects detected by the model [34]. However, there is a certain complementarity between precision and recall, that is, the recall rate of a model with high precision will decrease to a certain extent and vice versa. For this reason, researchers often consider the F1 score to comprehensively weigh \(P\) and \(R\), which can effectively avoid the short board effect and is mathematically called the harmonic mean. This study uses the F1 score as an evaluation indicator to evaluate the training effect of the model, as shown in Equation (17).  
\[P=\frac{TP}{TP+FP} \tag{15}\]  
\[R=\frac{TP}{TP+FN}\times 100\% \tag{16}\]  
\[F1=\frac{2\times P\times R}{P+R} \tag{17}\]  
However, in order to better evaluate the advantages of the model, the evaluation indicators are often not limited to the traditional accuracy and F1 indicators. In the field of object detection, average precision (AP) and mean average accuracy (mAP) are also the evaluation indicators of different model training effects. Equations (18) and (19) are mathematical expressions of AP and mAP, respectively. The AP represents the area under the precision-recall curve for each category, while the mAP is the average AP for all classes.  
\[\text{AP}=\int_{0}^{1}P(R)dr \tag{18}\]  
\[\text{mAP}=\frac{1}{N}\sum_{i=1}^{N}AP \tag{19}\]  
where \(P\) and \(R\) represent the precision and recall, and \(N\) is the number of all of the categories in this study.
# A High-Resolution SAR Focusing Experiment Based on GF-3 Staring Data
## 2 Focusing Experiment Based on GF-3 Staring Data
### Data Acquisition and Preprocessing  
GF-3 uses an active phased array antenna to achieve azimuth beam steering, which can steer faster compared to traditional mechanical antenna. In [21], [PERSON] et al. introduced GF-3's active phased array antenna, which contains 12 columns (azimuth) and 64 rows (elevation) that have transmit-receive channels measuring 7.5 m (azimuth) and 1.232 m (elevation) that are distributed in two panels in SL/ST mode. In consideration of its grating lobes (in Section 3.4), this antenna can achieve a steering angle within \(\pm\)1.9\({}^{\circ}\). At the same time, the steering angle varies discretely but continuously, and the step steering angle is 0.01\({}^{\circ}\). In other words, the steering angle varies 0.01\({}^{\circ}\) after many pulses, which we call the stationary number of pulses (SNP). In effect, the SNP controls the VRP's position when the PRF is constant; the larger the SNP, the farther the VRP is from the satellite. In this focusing experiment, the data was acquired on 11 March 2017 in Nanjing, China. The SNP is 90 and the SNP of a SL acquisition at the same scene is 120.  
After receiving echoes reflected from ground, the processor on GF-3 compresses the data with a block adaptive quantization (BAQ) (8:4) algorithm. Every frame of the compressed data contains not only an echo, but also some essential auxiliary parameters, such as the radar system parameters, the satellite attitude and position, the antenna steering angle, and the beam position.  
Because the VRP is fickle due to the SNP, some tricks may be needed to determine the VRP's position. We combine two frames of raw data into one equation and calculate the VPR. Assuming N frames of raw data are acquired during the integration time, there will be N/2 equations (i.e., the number of the VRP is N/2). Then, the final VRP is estimated by the least squares (LS) means. Moreover, some essential parameters like scene size, Doppler bandwidth (the whole scene and single point), and nominal resolution can be calculated.  
At this point, both echo data and some essential auxiliary parameters have been acquired.
# Reliable Fault Tolerant-Based Multipath Routing Model for Industrial Wireless Control Systems
## 3 Methodology and System Model
### Network Deployment Structures  
The primary method for meeting the routing reliability requirements is to find appropriate WirelessHART mesh network deployment structures. We use the developed structure in Figure 2 as an example. The figure indicates the possibility of obtaining a reliable network layout to guarantee transmission reliability by overlapping three separate routing graphs and ignoring the connection directions, as shown in Figure 5. One can see that there are more than three paths, while nodes 5 and 4 are only used for backup. The gateway is represented by node 9, and the access points AP1 and AP2 are represented by nodes 7 and 6, respectively. The multipath structure conveys the data through three different paths. Thus, between the gateway and the sink node, at least three different routes exist. For instance, when considering the paths from the gateway to Node 1, there are three redundant routes, (9\(\rightarrow\)7\(\rightarrow\)3\(\rightarrow\)1), (9\(\rightarrow\)8\(\rightarrow\)1), and (9\(\rightarrow\)6\(\rightarrow\)2\(\rightarrow\)1). Nodes 5 and 4 are backup nodes that hold the same set of data being forwarded by the access points to the successive nodes. The data is being momentary stored in the case of any data interruption in the main route. As seen in Figure 5, there are two types of routes, represented by solid and dashed lines. The solid line represents a default path (DP) from a specific node, while the dashed line represents an alternative path (AP) from the previous node. Usually, the main node has the tendency to receive the data forwarded through the default path.  
There are other methods to build the WirelessHART's mesh network besides the above structure. The authors in [52] proposed triangle and square structures that can also produce reliable transmission. Each node in the triangle-based network structure gets two degrees of incoming and outgoing communication. As a result, a minimum of three separate paths that exist between the gateway and each node are formed. On the other hand, every node in the square structure has three outgoing and incoming communication degrees. As a result, there are four possible routes between the gateway and each node. In this article, we constructed the network with more focus on the triangle-based network structure.  
\begin{table}
\begin{tabular}{l l} \hline \(G\) & Graph routing \\ \hline \(V\) & Set of Nodes \\ \hline \(E\) & Set of Edges \\ \hline GW & Gateway \\ \hline \(A_{1},A_{2}\) & Access Points \\ \hline AP & \((A_{1},A_{2})\) \\ \hline \(T\) & Edge length of each triangular cell \\ \hline \(\mathbb{N}_{R}\) & Routers subset \\ \hline \(\mathbb{N}_{D}\) & Field Devices \\ \hline \(\mathbb{N}_{V}\) & Number of Vertices \\ \hline \((X_{i},Y_{i})\) & Position of field device \\ \hline \(H(a,b)\) & Shortest number of hops between nodes \(a\) and \(b\) \\ \hline \end{tabular}
\end{table}
Table 2: Variables and symbols notations
## 2 Methodology
### Prediction of Building Typologies from Geospatial Attributes  
Once all the attributes have been obtained using GTs, the next step is to infer those that cannot be directly determined by these methods [69]. Building Typologies (BTs) can be defined by combining the material of the lateral load resisting system (MLLRS), the building height, and ductility. Vulnerability assessment involves the assignment of damage probability curves to each BT to proceed with the generation of seismic risk scenarios. Previous studies [44] classified the BT in San Jose into 20 categories, which were later integrated into eight classes by expert criteria based on the similarity of their seismic damage probabilities [70] (see Table 3).  
Predictive models were trained and developed using Random Forest (RF) algorithms to classify buildings into the defined BTs based on geospatial variables. In the case of San Jose, the models were trained with the validation database of 7296 buildings using all geospatial variables to predict the eight BT classes in Table 3.  
In the case of height, as the LiDAR-derived height did not cover the entire area, the visually surveyed height was included as a proxy of the geospatially obtained height. To check the relevance of height, the first model was run without height (RF1) and the second one was run including the attribute (RF2). The models were evaluated in terms of their mean estimated error (%) and the confusion matrices between classes. A feature importance ranking of the attributes was conducted with the Mean Decrease Gini index calculated by the Random Forest (RF) algorithm [71].
## 3 Feasibility of Construction Plan  
### Establishment of Numerical Model  
#### 3.1.1 Model Establishment  
A three-dimensional numerical model was constructed using the 3D finite element software Midas GTS NX (2020). The nonlinearity of materials and the initial stress state of soils could be considered in the software, which could further reflect the actual site condition. Under the different loads and boundary conditions, static, dynamic, consolidation and construction phase analysis can be carried out. Midas GTS NX works using a multi-frontal solver. The solver has the advantage of reducing the number of calculations and memory requirements. In addition, computational efficiency may be improved. It can provide more dependable results for complex engineering analysis and design. The model's dimensions are as follows: along the \(x\)-axis, it spans 79 m; along the \(y\)-axis, it covers 101 m; and along the \(z\)-axis, it extends 30 m. The spatial layout of the tunneling section, which includes the shield tunnel and the underground pedestrian passages (comprising 4 pedestrian passages and 4 interchange halls), faithfully replicates the actual project layout,  
Figure 4: Steel support layout (unit: mm). (**a**) Longitudinal section; (**b**) cross-section.  
as illustrated in Figure 5. The model consists of 199,429 mesh elements and 106,404 nodes. In the simulation, the outer diameter of the shield tunnel lining is 6.2 m, with a width of 1.5 m and a thickness of 0.45 m. The thickness of the underground pedestrian passages is also 0.45 m.
## 6 Discussion and Conclusions
### Future Research Directions
#### 6.2.2 Security  
In contrast to the current Internet, which secures the \"path\" through which traffic flows, ICN secures each \"content chunk\". This is equivalent to securing a road vs. the vehicles. Common attacks in ICN include content poisoning, interest flooding, forwarder watchlist, timing analysis, cache flooding, cache replay, and cache pollution [126; 127]. To secure data, ICN uses per-packet encryption, and this additional feature may potentially result in larger packets/more overload. For example, encryption can introduce latency and affect overall user satisfaction [128]. Therefore, security mechanisms designed for ICN must be lightweight and efficient to avoid degrading QoS and, consequently, the QoE.  
A critical issue in existing literature is the predominant focus on evaluating network performance (QoS or QoE) based solely on the intrinsic security features of ICN, which is insufficient for real-world network environments and the growing landscape of cyber threats [127]. For instance, implementing encryption--including at the ICN packet header level--makes caching non-trivial [129; 130]. Therefore, further research is essential to examine how additional security measures may influence QoS and QoE within ICN frameworks.
# Advanced Optimization Techniques for Federated Learning on Non-IID Data
## 4 Proposed Approach  
### Federated Learning with Fixed Learning Rate  
This section focuses on implementing and evaluating the Federated Averaging (FedAvg) algorithm across different data partitioning scenarios, specifically examining its performance under both IID (Independent and Identically Distributed) and non-IID data distributions among clients. For this purpose, three datasets, MNIST, Fashion MNIST, and CIFAR-10, were used, and different Convolutional Neural Networks (CNNs) were implemented for each dataset, as described in Section 3.2. The goal is to observe the behavior of FedAvg when training CNN models with Stochastic Gradient Descent (SGD) using a fixed learning rate in environments with strongly non-IID data compared with more balanced (IID) distributions.  
To simulate the federated learning environment, the datasets were partitioned into clients using Apache Spark, as discussed in Section 3.3, and all federated learning algorithm implementations were carried out using TensorFlow and TensorFlow Federated.  
The implementation of the FedAvg algorithm with a fixed learning rate followed these steps:  
1. **Client Selection:** At the beginning of each training round, the server randomly selects a subset of the available clients based on specific criteria such as availability and Wi-Fi connection stability. These selected clients participate in the current training round.
2. **Model Broadcast:** The selected clients receive the current global model weights from the server, distributed via the tff.federated_broadcast function.
3. **Local Training:** Each selected client trains the model locally using its own data. Clients use the tff.learning.Model and tf.GradientTape to compute gradients on their batches of data and update the model weights through the client optimizer, which, in this case, is SGD with a fixed learning rate.
4. **Aggregation of Updates:** After local training, the clients send their model updates, which include the changes in weights calculated during local training, back to the server. This aggregation is handled by the tff.federated_aggregate function, which combines the client updates on the server.
5. **Global Model Update:** Once the server receives all the client updates, it computes a federated average by weighing each client's contribution based on the number of images used in the training. The server then updates the global model by averaging the client weights, ensuring that each client's contribution is proportional to the size of their dataset.
6. **Repeat:** This process is repeated for a predetermined number of training rounds.  
After each training round, the global model is evaluated using the test dataset of each corresponding dataset used. This evaluation process is repeated after every round, allowing us to monitor the accuracy of the model throughout the training process in both IID and non-IID scenarios.