esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In turn, not only the data supporting decision-making affects the efficiency of ML algorithms, but also the data on which they are trained [29]. For various tasks in V2X, there are several approaches to obtaining a dataset for training: using data from field experiments, including readings from actual sensors [30; 31; 32; 33], using synthetic data obtained by simulating the V2X system [34; 35; 36; 37], and a combination of the first two options to evaluate the performance of the algorithm consistently [22; 38; 39; 40; 41; 42]. The data from actual sensors is an essential element of testing algorithms since it directly reflects the processes occurring in V2X networks, while their simulation will inevitably diverge from natural conditions. However, it is essential to remember that the frequency of emergencies is significantly lower than their absence [43]. Therefore, datasets of sensor data recorded in a specific period will contain only several representative examples, affecting the ML algorithm's generalizing ability. This paper presents an approach for learning an intelligent beamsteering algorithm based on synthetic vehicle-positioning data without relying on a GNSS system. The approach is universal, simulating various situations within the V2I network, including rarer events, increasing the ML algorithm's generalizing ability, and considering a large number of factors to adequately match the natural conditions. Following [44], we used the obtained synthetic data to train the XGBoost algorithm to control the beamforming circuit of the PAA, aiming at channeling the power into the intended positioning area of the vehicle. The system operation results were demonstrated on a linear PAA simulation model with predetermined parameters. The ML algorithm was tested using the same environment, simulating traffic in a specific existing road section and wireless signal propagation. We estimated the algorithm performance as the probability of an incorrect choice of the vehicle's location sector (that is, the probability of power loss at the receiver of more than 3 dB)  
The paper is organized as follows: Section 2 introduces the beamsteering system concept and the simulation model of the PAA. Section 3 presents an approach for learning and testing an intelligent algorithm, including the dataset structure, the simulation model of the propagation environment, the traffic model, and the applied ML algorithm. Section 4 presents the results of training and testing the intelligent algorithm, while the conclusions are provided in Section 5.
# Problems and Design of a Powered Mechanism-Based Car Child Restraint System
## 2 Materials and Methods
### Dimensions of the Back Seat of Vehicles  
There are essential factors to consider when inserting the car seat mechanism for a child restraint system to adapt it to the back seat of the vehicle, such as the height of the seat, the seat back, and the length of the seat. It directly influences the safety and ergonomics  
Figure 4: Children’s sizes by section [7]. (A, B and C are reference sizes as in Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Orientation** & **Position** & **A (cm)** & **B (cm)** & **C (cm)** & **Weight (kg)** & **Age** \\ \hline \multirow{2}{*}{Rearward facing} & Supine & 40–65 & 25–45 & 10–20 & 3.5–8 & 0–3 mph \\  & Reclining 15\({}^{\circ}\) & 60–105 & 43.5–61.5 & 18–30 & 7–18.5 & 3 mph-4 yrs \\ \hline \multirow{2}{*}{Forward facing} & Seated & 75–135 & 50–74.5 & 24–37 & 9.6–36 & 1–10 yrs \\  & Seated & 125–150 & 70–81 & 30–41.5 & 25–43 & 8–13 yrs \\ \hline \hline \end{tabular}
\end{table}
Table 3: Children’s sizes are measured by section and growth according to height, weight, and age [14].  
of the infant. The dimensions of a rear seat are considered, as shown in Figure 5, with the dimensions where the mechanism is incorporated within the dimensions of the rear seat.  
Proper ergonomic posture ensures that the design provides a correct ergonomic posture at each stage of growth, adapting to the head, neck, and back support needs of a toddler and pre-teen. Four requirements are identified for the seat to be considered ergonomic: safe, adaptable, comfortable, and robust.
## 3 Results
### The Lateral Gradient Analysis  
The ESV gradient was calculated across 10 zones of distance from the river, as shown in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Land Use** & **Area/km2** & **The Proportion of the Area/\%** & **Ecological Services** & **The Proportion of** \\  & & **Area/\%** & **Value/CNY 104** & **ESV/\%** \\ \hline Grassland & 0.24 & 0.00 & 61.68 & 0.00 \\ Scrub & 1056.47 & 15.60 & 360,747.12 & 13.72 \\ Built-up land & 1121.44 & 16.56 & 0.00 & 0.00 \\ Broad-leaved forest & 1342.16 & 19.82 & 691,059.56 & 26.28 \\ Artificial wetlands & 500.71 & 7.39 & 381,829.72 & 14.52 \\ Natural wetlands & 5.75 & 0.08 & 6705.38 & 0.25 \\ Paddy fields & 2156.18 & 31.84 & 188,176.53 & 7.16 \\ Water bodies & 317.66 & 4.69 & 894,546.93 & 34.02 \\ Coniferous forests & 271.22 & 4.01 & 106,669.80 & 4.06 \\ Total & 6771.83 & 100 & 2,629,796.72 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the land use area and corresponding ecosystem service value in the Guangdong section of the Xijiang River. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Previousing Services**} & \multicolumn{3}{c}{**Regulating Services**} & \multicolumn{3}{c}{**Support Services**} & \multicolumn{3}{c}{**Cultural Services**} \\ \cline{2-9}
**Type of Ecosystem** & \multicolumn{1}{c}{**Value/CNY 10\({}^{4}\)**} & \multicolumn{1}{c}{**Proportion/\%**} & \multicolumn{1}{c}{**Value/CNY 10\({}^{4}\)**} & \multicolumn{1}{c}{**Proportion/\%**} & \multicolumn{1}{c}{**Value/CNY 10\({}^{4}\)**} & \multicolumn{1}{c}{**Proportion/\%**} & \multicolumn{1}{c}{**Value/CNY 10\({}^{4}\)**} & \multicolumn{1}{c}{**Proportion/\%**} \\ \cline{2-9}
**Grassland ecosystems** & 3.9 & 0.0039 & 39.8 & 0.002 & 14.9 & 0.0039 & 3 & 0.0028 \\
**Scrub ecosystems** & 19,909.8 & 19,9293 & 243,421.3 & 11,9564 & 81,061.4 & 20,9925 & 16,354.5 & 15,3531 \\
**Broad-leaved forest ecosystems** & 38,843.9 & 38,8818 & 461,919.8 & 22,6648 & 158,386.6 & 41,0174 & 31,918.2 & 29,9639 \\
**Artificial wetland ecosystems** & 25,837.1 & 25,8623 & 244,890.9 & 12,0268 & 75,264.6 & 19,4913 & 34,823.9 & 23,6917 \\
**Natural wetland ecosystems** & 464.0 & 0.4644 & 429e-2 & 0.211 & 1335.4 & 0,3458 & 609.7 & 0.5724 \\
**Paddy field ecosystems** & \(-\)57,081.8 & \(-\)57,1376 & 221,071.1 & 10,857 & 19,833.5 & 5,1363 & 4353.7 & 4,0871 \\
**Water bodies ecosystems** & 65,779.7 & 65,844 & 789,997.8 & 38,7973 & 25,299.9 & 6,5519 & 13,469.5 & 12,6448 \\
**Conforvers forest ecosystems** & 6145.8 & 6,1519 & 70,585.8 & 3,4665 & 24,948.4 & 6,4609 & 4,899.7 & 4,6842 \\
**Total** & 99,902.4 & 100 & 2,036,213.7 & 100 & 386,144.7 & 100 & 106,522.2 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Total table of four service values of the water-land ecotone in the Guangdong section of Xijiang River.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
### Motivation and Contributions  
Considering the crucial role smart grid systems play in controlling essential infrastructure, any compromise in their security or system failure can have severe consequences for public safety and security. Besides causing physical damage, such incidents can also lead to significant harm and loss of life for individuals. Therefore, it is crucial to address these risks by identifying and understanding various threats, vulnerabilities, and attacks that would jeopardize the smart grid's stability and security. Recognizing the need for enhanced security measures, this work focuses on developing a comprehensive list detailing these threats, aiming to safeguard data integrity and privacy within the smart grid domain. This requires protecting sensitive information from unauthorized access or exploitation, which is crucial in preventing identity theft and data leaks. In particular, this work contributes to the security and safety of smart grids by achieving the following security objectives, which must be integrated into smart grid systems and devices to accomplish the required protection and prevention against cyber-physical risks and attacks:  
* **Maintaining Security Goals:** Essential to maintain availability by not interrupting power supplies and disrupting the supply chain integrity. This can be achieved by maintaining secure communication networks and channels, as well as ensuring confidentiality by protecting users' data and implementing privacy-preserving measures.
* **Advanced Security Analysis:** By presenting the most prominent security threats, vulnerabilities, attacks, and challenges, as well as the impact and risk of the occurrence of these attacks, especially against the smart grid's critical infrastructure.
* **Automated Security Measures:** By presenting several types of reactive and preventive security measures and countermeasures and analyzing them to show how large and hybrid smart grid systems and devices are protected.
* **Dynamic Study:** By studying and analyzing both cryptography and non-cryptography solutions that maintain the security and resiliency properties of smart grid systems.  
This work selectively emphasizes the most impactful and under-addressed threats in the literature, thereby prioritizing actionable insights for researchers and system designers.
## Underlying Theories
### 5G Spectrum Usage Fe  
Previous studies of spectrum usage fees include [4; 5; 6], which examine the formulation of spectrum usage fees for service operators (2G/3G) in Taiwan and Indonesia. The formulas used by these nations vary depending on various factors, including competition among service operators, geographical condition, population, bandwidth, and expected income from spectrum usage fees for the government. The goals of each nation are the same--namely to formulate a fair and technology-neutral fee model to enhance the efficiency of spectrum utilization. The framework was developed based on frequency bands below 3 GHz, operated on narrow bandwidths of up to 20 MHz for the 4G network.  
5G provides three different usage scenarios that require simultaneous access to low, mid, and high bands to meet 5G capacity, latency, coverage, and quality requirements to comply with advanced spectrum range use cases [20; 21]. Therefore, the formulation of the spectrum usage fee may have a different focus for different bands. Recent research has begun to conduct spectrum valuation and pricing of private LTE and 5G networks at the 3.5 GHz frequency band [7; 8; 9; 10].
# Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines
## 2 Individual Methods
### Machine Learning Algorithms
#### 2.1.7 Hyper-Parameter Optimization of the Model  
The XGBoost model has many hyper-parameters, and different combinations can significantly affect the model's computational efficiency and generalization performance. Therefore, optimizing the hyper-parameters to obtain a model with good computational efficiency and generalization performance is essential. Hyper-parameter optimization is a black-box problem, meaning that the functional expression is unknown during the optimization process, and only the corresponding objective function values can be obtained based on discrete independent variables. This characteristic poses numerous difficulties for hyperparameter optimization. Currently, an increasing number of hyper-parameter optimizations are accomplished through automated methods. These methods aim to find the optimal parameter combination with less resource consumption following specific strategies. Commonly used hyper-parameter optimization methods include grid search, random search, and Bayesian optimization algorithm et al. [38]. Among them, grid search determines a set of optimal values as the final hyper-parameter combination of the model by traversing and looping through all the points on the grid in the hyper-parameter space within the given hyper-parameter range. However, this method is highly time-consuming and computationally resource-intensive in the case of multiple parameters and large datasets. The idea of random search is quite similar to that of grid search. It searches for the global optimal point by randomly sampling points within the search range, saving time for hyper-parameter optimization, but cannot guarantee to obtain the best combination. Bayesian optimization (BO) is a global optimization algorithm based on probability distribution. Unlike grid search and random search in constructing the objective function, BO constructs a surrogate function using a limited number of sample points to approximately substitute the aim of the black-box function. This reduces the  
Figure 7: Schematic diagram of the XGBoost algorithm.  
time cost of calculating the objective function and can achieve the optimal solution of complex objective functions with fewer evaluation times, thus significantly improving the optimization efficiency and convergence speed of hyper-parameters. Therefore, in this paper, BO is proposed to optimize the hyper-parameters of the established model. Substituting the optimized hyper-parameters into the model can yield the trained remaining strength prediction model.
# Design of an Open Remote Electrocardiogram (ECG) Service
## 3 An Open Service for Remote ECG
### Patient's Side Sensor Device
However, we come to the challenge of implementing a WebSocket-capable client on a device with constrained capabilities like the ECG sensor. An appealing alternative to HTTP is the Constrained Application Protocol (CoAP) [22], which follows HTTP guidelines and is specially adapted to operate on constrained devices. In essence, CoAP is based on UDP (instead of TCP) and does not enforce a request/response protocol. It neatly fits the requirements of our design with a marked advantage on bandwidth: comparing the headers involved in HTTP/WebSocket over TCP versus CoAP over UDP, we have 16 bytes for CoAP against 30 bytes for WebSockets. However, the relevance of such an advantage depends on the payload size. The advantage of HTTP over CoAP is that it is an established and widely supported technology. Such features reduce coding effort and allow using widely diffused hardware devices and services, thus reducing development and deployment costs. Again, we are facing a critical decision: a more efficient solution, though less supported by experience (CoAP standard is dated 2014), versus a less efficient one, but for which we find well-supported tools. We finally consider the _hardware design_ of the sensor. We address MCUs for cost-related reasons, but we must take into account that both the sampling process and data delivery are demanding tasks. One option is to have two of them running in parallel using one for ECG acquisition, another for network operation. The communication bitrate between the two depends on the sampling frequency: taking 250 Hz as a reference, and assuming that a 3-leads ECG transmits a 16-bit integer for each sample, the net baud-rate is of 4000 baud, which should be supplemented by timing and protocol overhead. Such a data rate is compatible with protocols that are commonly used in embedded systems so that the option of a design based on two specialized MCU is acceptable.
# A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps
## 1 Introduction  
In recent decades, a continuous growth in the number of digital images has been observed, due to the spread of smart phones and various social media. As a result of the huge number of imaging sensors, there is a massive amount of visual data being produced each day. However, digital images may suffer different distortions during the procedure of acquisition, transmission, or compression. As a result, unsatisfactory perceived visual quality or a certain level of annoyance may occur. Consequently, it is essential to predict the perceptual quality of images in many applications, such as display technology, communication, image compression, image restoration, image retrieval, object detection, or image registration. Broadly speaking, image quality assessment (IQA) algorithms can be classified into three different classes based on the availability of the reference, undistorted image. Full-reference (FR) and reduced-reference (RR) IQA algorithms have full and partial information about the reference image, respectively. In contrast, no-reference (NR) IQA methods do not posses any information about the reference image.  
Convolutional neural networks (CNN), introduced by [PERSON] et al. [1] in 1988, are used in many applications, from image classification [2] to audio synthesis [3]. In 2012, [PERSON] et al. [4] won the ImageNet [5] challenge by training a deep CNN relying on graphical processing units. Due to the huge number of parameters in a CNN, the training set has to contain sufficient data to avoid over-fitting. However, the number of human annotated images in many databases is rather limitedto training a CNN from scratch. On the other hand, a CNN trained on ImageNet database [5] is able to provide powerful features for a wide range of image processing tasks [6; 7; 8], due to the learned comprehensive set of features. In this paper, we propose a combined FR-IQA metric based on the comparison of feature maps extracted from pretrained CNNs. The rest of this section is organized as follows. In Section 1.1, previous and related work are summarized and reviewed. Next, Section 1.2 outlines the main contributions of this study.
# MCST Scheme for UAV Systems over LoRa Networks
## 4 MCST Scheme  
The mixture of concurrent and sequential transmission (MCST) scheme is a cooperative transmission scheme operated over the BRF transmission with the relaying node concept in multihop wireless networks. With the idea of TDMA in wireless networks, the transmissions are sequentially executed through the sequential transmission (ST) scheme in the half-duplex (HD) communication system. The ST scheme can give lower interference with limited network capacity. On the other hand, through the concurrent transmission (CT) scheme, several transmissions are executed concurrently in the same transmission medium via the help of potential techniques such as the full-duplex (FD) system. The CT scheme can achieve a higher network capacity. However, the interference issue becomes a considerable factor because of the concurrent transmissions. By applying the concept of the hybrid HD/FD system method, the MCST scheme combines both sequential transmission and concurrent transmission in a shared timeslot to obtain the highest transmission capacity with the least interference power level. For the BRF transmission, the transmission rates of \(SR\) and \(RD\) transmissions are different if the transmission scheme is operating as ST or CT. However, as the principle feature, MCST controls the time fraction to equal the transmission rates of \(SR\) and \(RD\) transmissions. Figure 4 illustrates an example of MCST operated over a BRF transmission, in which the \(SR\) transmission and \(RD\) transmission perform the sequential transmission in the time fraction of \((1-\tau)\) as case 1 and case 2, respectively. Furthermore, for both cases, the time fractions for concurrent transmissions, i.e., \(\tau_{SR}\) and \(\tau_{RD}\), are computed. Then, the transmission capacity for both cases is computed and MCST optimally selects the one that can give the higher transmission capacity. In this way, the MCST scheme optimizes the transmission capacity of the BRF transmission by optimally selecting the sequential transmission and time fraction of the concurrent transmissions. The MAC protocol to assist the MCST scheme was proposed in WLAN based on the basic access mechanism of distributed coordination function (DCF) in WLAN [26]. By keeping the originality of the MAC protocol with the MCST scheme, the paper designs the MCST scheme to operate over the LoRa network.
# Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine
## 4 Discussion
Unlike prior studies that primarily compare different classifiers or broad preprocessing categories, this work isolates the direct influence of parameterized signal preprocessing on an SVM-based classifier, providing insights into how signal preprocessing choices affect feature distribution and classification performance. This result highlights the importance of selecting an appropriate signal preprocessing strategy to enhance the robustness of RF fingerprinting systems. Furthermore, while we focused on SVM to maintain a controlled evaluation, future studies could extend our approach to other classifiers to assess whether similar trends hold across different classification models. The results of this study underscore the potential of MNRS signal preprocessing but indicate the need for further refinement, such as exploring hybrid methods or using deep learning-based classifiers to improve accuracy and reduce computational time. These findings highlight a trade-off between simplicity and performance, suggesting that while MNRS used in an SVM-based classifier provides a baseline, the integration of more sophisticated signal preprocessing and classification techniques could lead to competitive results aligned with the best-performing studies. It should be noted that the studies shown in Table 9 use devices similar to or even older than those used in this study, and although newer Bluetooth chipsets may have variations in their radio signal characteristics, the basic principles of classification based on RF fingerprinting remain the same for devices of any generation or operating in different frequency bands. However, it should also be noted that all of the works mentioned in Table 9 make significant contributions to the field of device identification based on RF fingerprints. In this regard, the creation of a large-scale Bluetooth signal database that includes modern devices is extremely valuable, but this remains beyond the scope of this study. It should be noted that collecting a sufficiently diverse dataset requires extensive infrastructure and a significant amount of resources to capture signals from a wide variety of devices under controlled conditions. Finally, it should be emphasized that although a signal preprocessing technique adds computational overhead, there is a significant improvement in classifier algorithm accuracy at a minimal computational cost. For instance, the classifier's accuracy increased from 30.59% for raw signals to 86.21% for normalized signals. The additional computational cost was estimated at 73 ms per signal during the classification process for device authentication. Note that this cost was estimated when transforming a dataset of raw signals to a dataset of normalized signals using the algorithms developed with Matlab R2023b on a Ryzen 5 computer. However, it is expected that the cost of an actual implementation will be less than 73 ms.