esa-sceva/satcom-chunk-collection huggingface dataset:
# Q-Learning Approach Applied to Network Security
## 4 Results
### Hyperparameters
```
Input: A exploration rate \(0<\epsilon<1\); integer number of iterations \(n\geq 1\); learning rate \(0<\alpha<1\); discount factor \(0<\gamma<1\); exploration rate rate decay \(0<decay<1\); initial state \(s_{0}\); Output: A state-action matrix \(Q(s,a)\) Initialize Q-table \(Q(s,a)\) with zeros; Set learning rate \(\alpha\), discount factor \(\gamma\), exploration rate \(\epsilon\), decay rate, and minimum exploration rate \(\epsilon_{\text{final}}\); for each episode in num_episodes do \(keep\_going\leftarrow\text{TRUE}\);  Initialize state \(s\gets 0\) (Normal);  total_reward \(R\gets 0\); while\(keep_{g}oing\)do for each step in episodedo ifrandom number \(<\epsilon\)then  Select random action \(a\) Exploration else  Select action \(a\leftarrow\arg\max_{a}Q(s,a)\)Exploitation  end if  Take action \(a\), observe reward \(r\) and next state \(s^{\prime}\);  Update Q-value: \(Q(s,a)\gets Q(s,a)+\alpha\bigg{[}r+\gamma\max_{a^{\prime}}Q(s^{\prime},a^{ \prime})-Q(s,a)\bigg{]}\) \(s\gets s^{\prime}\)  end if  total_reward \(\leftarrow\) total_reward \(+r\)  end for  Decay exploration rate: \(\epsilon\leftarrow\max(\epsilon\cdot(1-\text{decay}),\epsilon_{\text{final}})\) Store total_reward for this episode  end for
```  
**Algorithm 1**The Q-learning training algorithm \begin{table}
\begin{tabular}{l l c} \hline \hline
**Hyperparameter** & **Description** & **Value** \\ \hline Learning rate \(\alpha\) & Controls how much the weights are updated during training. & 0.2 \\ \hline Discount factor \(\gamma\) & Determines the importance given to future rewards in the Q-learning equation. & 0.1 \\ \hline Exploration rate \(\epsilon\) & The probability of selecting a random action instead of following the learned policy. & 0.9 \\ \hline Epsilon decay & Reduces exploration over time in favor exploitation. & 0.05 \\ \hline Epsilon minimum & Ensures that the Q-learning agent retains a minimum level of exploration & 0.01 \\  & throughout training. & \\ \hline Number of episodes & The total number of iterations when training the agent. & 200 \\ \hline Steps per episode & The maximum number of steps before ending an episode. & 50 \\ \hline Moving average window & This is used to smoothen the cumulative reward over time. & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameters used in Q-learning.
## 2 Background
### Driving Technologies of Industry 4.0
#### 2.2.4 Cyber-Physical Systems (CPS)  
Cyber-physical systems are considered one of the key technologies of Industry 4.0. They consist of intelligent machines and physical systems that can be monitored and integrated through communication and computation, including sensors and actuators, connecting humans, machines, and products [62; 85; 87; 82].  
This integration between the real and virtual worlds allows for real-time data transfer, facilitating the monitoring and diagnosis of production systems [77]. CPS have four main elements: computers and software, computing networks, sensors and actuators, and data capture and transmission [83; 96].
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 2 Related Work
### FPGA Acceleration of CNN
Key components of Vitis AI include its compilers and runtime libraries. They facilitate the transformation of models trained using popular deep learning frameworks like TensorFlow, PyTorch, and Caffe into highly efficient hardware accelerators through quantization and compilation techniques. By harnessing the parallel computing capabilities of FPGAs and SoCs, these accelerators enable real-time applications and edge devices to benefit from low-latency, high-throughput inference acceleration. By utilizing Vitis AI, users can perform pruning, quantization, and compilation operations on network models, and deploy them onto the FPGA of the Zynq-ultraScale series, including KV260, ZCU102, ZCU104, and ZCU106. [PERSON] et al. [44] proposed a YOLOv3 convolutional neural network accelerator based on the FPGA+ARM architecture of the AXI bus. The YOLOv3 network model was quantified and pruned through the Vitis AI tool chain, and finally deployed on Xilinx Zynq ZCU104 accelerating convolutional neural networks. The power consumption of this solution is 25 W, and the FPS is 84.5518. Compared with GPU GeForce GTX1080, it has lower power consumption and higher detection real-time performance. [PERSON] [45] and others used the Vitis AI 1.4 framework to complete the deployment of the neural network model on the Xilinx ZCU 104 FPGA, and finally achieved 88.8% mAP and 28 FPS, realizing the real-time detection of the road unevenness detection system.
## 5 Experimental Results
### Design of the TIBuck DC/DC Converter  
The TIBuck DC/DC converter must be able to generate a 1 MHz communication signal with low distortion and high efficiency. Its switching frequency, \(\rm f_{sw}\), the order and the cutoff frequency of the filter, \(\rm f_{c}\), were designed according to the modulation scheme chosen previously. There is also a trade-off between efficiency, resolution achieved for tracking the output voltage and filter design. The lower the switching frequency, the higher the efficiency and the resolution. Moreover, there is a limit to how much the switching frequency can be reduced. As shown in Equation (7), a condition must be fulfilled regarding \(\rm f_{fit}\), \(\rm f_{c-ii}\) and \(\rm f_{sig-max}\) (which is \(\rm f_{ws}=f_{ti}\) and \(\rm f_{c}=f_{c}-t_{i}\)), allowing the filter to separate the signal spectrum from the switching harmonic components of the output voltage. Studies and guidelines about how close these frequencies can be placed were performed and reported in [18; 21]. The switching frequency, \(\rm f_{ti}\), was selected at 10 MHz, a decade above the carrier frequency. Following the aforementioned guidelines, the filter used was a 6 th Butterworth  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\rm Q_{bu-1}\) & \(\rm Q_{bu-2}\) & **Gate Driver** & \(\rm L_{bu}\) & \(\rm C_{bu}\) \\ \hline CSD88539 & \(\rm ISL6700\) & 49 \(\rm\mu H\) & 9 \(\rm\mu F\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of components of the auxiliary Buck DC/DC converter.  
filter with a cut-off frequency, f\({}_{\text{c-ti}}\), of 2.5 MHz. The reactive elements for the output filter are shown in Table 2. The converter was implemented in an asynchronous configuration using the high frequency RF MOSFETs PD84010S-E for Q\({}_{\text{ti}}\) and the fast diode UPS115 UE3 for S\({}_{\text{ti}}\). Due to its high switching frequency, a high-speed gate driver, EL7155 CSZ, was used. Since Q\({}_{\text{ti}}\) is not referred to the circuit ground, a fast signal isolator, ISO721, is needed between the FPGA and the gate driver.
# Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems
## 3 Materials and Methods
### High-Order Tensor Model for ETC Gantry Data
#### 3.1.3 Extension of the Subtensor Block Models to the Tensor Block Model  
The subtensor block models are integrated into a higher-order tensor space; components within the same dimension and of identical order are merged, preserving the unique orders. The amalgamation of subtensor blocks is executed as follows:  
\[\begin{array}{c}A\in R^{I\times I\times K}\\ I=\left[i_{1},i_{2},i_{3}\right],\ J=\left[j_{1},j_{2},j_{3}\right],K=\left[k_ {1},k_{2}\right]\\ B\in R^{I\times I},I=\left[i_{1},i_{2},i_{4}\right],J=\left[j_{1},j_{2}\right] \\ f:A\overset{\sim}{\times}B\to C,\ C\in R^{I\times I\times K}\\ I=\left[i_{1},i_{2},i_{3},i_{4}\right],J=\left[j_{1},j_{2},j_{3}\right],K= \left[k_{1},k_{2}\right]\end{array} \tag{3}\]Equation (3) delineates the method for merging subtensor blocks. In this context, \(A\in R^{I\times J\times K}\) and \(B\in R^{I\times J}\) represent tensor blocks with different dimensions, where \(A\in R^{I\times J\times K}\) has tensor dimensions \(I=[i_{1},i_{2},i_{3}],I=[j_{1},j_{2},j_{3}]\), and \(K=[k_{1},k_{2}]\), and \(B\in R^{I\times J}\) has tensor dimensions \(I=[i_{1},i_{2},i_{4}]\) and \(J=[j_{1},j_{2}]\). \(A\in R^{I\times J\times K}\) and \(B\in R^{I\times J}\) are superimposed to form tensor \(C\in R^{I\times J\times K}\) with dimensions \(I=[i_{1},i_{2},i_{3},i_{4}],I=[j_{1},j_{2},j_{3}]\), and \(K=[k_{1},k_{2}]\).  
By expanding each subtensor block model, we construct a tensor block model, which is then sequentially organized along the timeline to form a composite, higher-order tensor model. Parameters of identical order within each dimension constitute the foundational attribute parts, while those of varying orders are categorized as extensible attribute parts, facilitating a modular approach to model construction.
# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 1 Introduction
On the other hand, according to the construction of different channels, the transmit power of each buoy can be optimized according to the dynamic environment, which ensures the localization accuracy requirement for radar sensing.
## 2 Fault Signal Characteristics of Test Rig
### Test Rig and Test Conditions
#### Loading System  
The loading system adopts screw manual loading, and the pressure sensor with a digital display installed on its head is used to monitor the load in real-time. Meanwhile, data are transmitted software through the acquisition card, and the load value in the experiment process is effectively stored.
# Prediction Equation for Horizontal Ground Motion Peak Values in the Loess Plateau Region
## 3 Attenuation Models and Regression Algorithms
### Attenuation Models Used in This Study  
This study develops two types of ground motion parameter attenuation models. Model I is the attenuation model used in the \"Seismic Ground Motion Parameters Zonation Map of China\" (GB18306-2015) [17], expressed as follows:  
\[\text{lg}y=\text{A}+\text{B}M+\text{Clg}\left(R+\text{De}^{\text{EM}}\right)+\xi \tag{5}\]  
where E\(M\) represents the products of E and \(M\). This notation is introduced for concise representation.  
Preliminary analysis indicates a significant correlation between the attenuation rate of ground motion parameters and magnitude. To more accurately describe this relationship, this study references the related research findings [18,19] and improves the attenuation model. In the improved model, the attenuation coefficient term is a linear function of magnitude, forming Model II, proposed in this article, expressed as follows:  
\[\text{lg}y=\text{A}+\text{B}M+(\text{F}+\text{G}M)\text{lg}\left(R+\text{De} ^{\text{EM}}\right)+\xi \tag{6}\]  
\[\text{lg}y=\sum_{i=1}^{m}H_{i}E_{i}+\text{Clg}\left(R+\sum_{i=1}^{m}R_{0i}E_ {i}\right)+\varepsilon \tag{7}\]  
\[R_{0}=\text{De}^{\text{EM}} \tag{8}\]  
\[H=\text{A}+\text{B}M \tag{9}\]  
\[C=\text{F}+\text{G}M \tag{10}\]  
Model II is introduced as part of the improved model and is described by Equation (6). Where y represents the ground motion parameter, such as PGA or PGV; M denotes the surface wave magnitude; \(R\) is the epicentral distance, measured in kilometers (km); A, B, C, D, E, F, and G are regression coefficients; and \(\xi\) is a random variable with a mean of 0 and a standard deviation of \(\sigma\).
## 3 Proposed Solution
### Flow-Centric Visibility to Assist Networking Control
Similarly, we identify flows by comparing application id in collected flows from TCP-sync collection with a list of known applications from legitimate users, which is pre-defined as an input part of tagging. To facilitate networking control, our flow-centric visibility solution includes a mapping function that recommends control actions for each flow type. Clustered and identified flows are typically allowed through the network, while attacked flows are recommended for dropping. A small number of unknown flows are redirected to temporary storage for inspection in near real time by a network intrusion detection system (IDS) or other similar tools. Following the processing of each stage of flow-centric visibility, produced data and recommended actions are stored in the respective DataStores of the DataLake, ensuring traceability and compliance. The overall design of flow-centric visibility to assist networking is illustrated in Figure 4.
# Experimental Study on Mechanical Properties of Graded EPS-Steel Fiber-Reinforced Concrete
## 3 Analysis of Experimental Phenomena and Results  
### Failure Mode of Compression Test Specimens  
Due to the similar phenomena observed in the compressive strength tests of both gravel and fine aggregate concrete specimens at different concrete strength grades, the failure mode of the concrete containing a 1.75% volume fraction of steel fibers and graded EPS (expanded polystyrene) was selected for analysis, as shown in Figure 3. Overall, the specimens showed no large-scale fragment detachment after compression, due to the buffering effect of the EPS particles on energy absorption. The failure mode of the specimens shifted from the brittle failure typical of conventional concrete to a more ductile  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Type**} & \(\mathbf{\alpha_{a}}\) & \(\mathbf{\alpha_{b}}\) & \(\sigma\) & \(\beta_{s}\)(\%) & \(\mathbf{m_{co}}\) & \(\mathbf{m_{wo}}\) & \(\mathbf{m_{so}}\) & \(\mathbf{m_{go}}\) & \multicolumn{2}{c}{**Superplasticizer**} & \multirow{2}{*}{\(\delta\)**(\%)**} & \multirow{2}{*}{\(\mathbf{W/C}\)} & **Experimental** \\ \cline{5-10}  & & & & & & & & \multicolumn{2}{c}{**kg/m\({}^{3}\)**} & & & \\ \hline C30 gravel & 0.49 & 0.13 & 5.0 & 28 & 318 & 147 & 542 & 1388 & 5 & 0.8 & 0.46 & \\ C30 fine aggregate & 0.53 & 0.20 & 5.0 & 33 & 367 & 176 & 604 & 1228 & 5 & 0.5 & 0.48 & \\ C40 gravel & 0.49 & 0.13 & 5.0 & 29 & 349 & 150 & 561 & 1369 & 5 & 0.7 & 0.43 & \\ C40 fine aggregate & 0.53 & 0.20 & 5.0 & 35 & 368 & 169 & 643 & 1192 & 5 & 0.9 & 0.46 & \\ C50 gravel & 0.49 & 0.13 & 6.0 & 27 & 345 & 145 & 521 & 1408 & 5 & 0.7 & 0.42 & \\ C50 fine aggregate & 0.53 & 0.20 & 6.0 & 34 & 365 & 164 & 640 & 1230 & 5 & 0.6 & 0.45 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculation parameters of concrete mix proportions for each grade and optimal mix proportions in the laboratory.  
failure. However, the failure pattern remained similar to that of ordinary concrete, with vertical, penetrating cracks propagating from top to bottom.  
In Figure 2(a), when the volume fraction of graded EPS was 15%, the specimens still tended towards brittle failure, exhibiting numerous wide penetrating cracks and larger volumes of unbroken fragments, with only slight ductility. As the volume fraction of graded EPS increased to 25%, the number and width of penetrating cracks significantly decreased, with most of the cracks being fine vertical, non-penetrating cracks, indicating improved ductility, as shown in Figure 2(b). When the volume fraction of graded EPS increased to 35%, the replacement rate of aggregates with EPS particles was higher. At this stage, the EPS particles, with their strong hydrophobicity, failed to retain free water inside them, preventing adequate bonding with the cementitious materials. Additionally, the strength of the EPS particles was much lower than that of conventional aggregates, leading to the formation of wider penetrating cracks, though the number of cracks was fewer compared with the specimens with 15% graded EPS, as shown in Figure 2(c).
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
###### Abstract
This paper aimed at describing the salient developments in mobile radiation detection systems coupled to ground-based (handheld equipment included) and air-based platforms from the era after the Fukushima Daiichi nuclear power plant accident (FDNPP), considering four reference scenarios. The advantages and limitations of each detection system are also analyzed, highlighting the challenges and future research needed in these fields. The literature review used three different bibliographic databases, Scopus, Web of Science, and Google Scholar, and it was limited to articles published between the era post-Fukushima accident and the end of July 2020. Different keywords were used, like mobile, radiation, gamma imaging, and nuclear, and the subject area was limited to engineering, physics, computer science, and environmental science. For the search, original peer-reviewed research articles and literature reviews were included. In total, more than 200 bibliographic references were identified and analyzed. Data were compiled from books, published journal articles, conference proceedings, and grey literature, in particular: technical reports from international agencies, like the IAEA, or national agencies, such as the United States Department of Homeland Security, and information obtained from manufacturer websites (e.g., Saint-Gobain) that are not normally subjected to peer review and, therefore, may contain biased data. However, all the data were cross-checked to guarantee validity of the conclusions. The remainder of this paper is divided into six sections. The \"Scenarios\" section briefly describes the four scenarios considered in this article. The \"Mobile platforms\" section gives an insight of the different platforms that can be used for radiation detection, as well as challenges and ongoing research. The \"Mobile Radiation Detection Systems\" section covers the existing gamma and neutron detectors described in the literature. In the \"Results and Discussion\" section, the benefits and limitations of the different combinations of mobile platform, detection sensors, and contextual sensors, considering the four scenarios, are analyzed. In the last section, conclusions are presented, and prospective views are provided.
# A Feasibility Study for a Hand-Held Acoustic Imaging Camera
## 2 Acoustic Imaging Concepts
### Array Geometry  
Microphone array geometry plays a critical role in acoustic imaging performance. Key factors are:  
* Aperture --The overall physical size determines angular resolution. Larger apertures improve discrimination.
* Number of microphones--More microphones provide enhanced spatial sampling at the cost of complexity.
* Layout--Positions within the aperture area. Uniform grids simplify analysis but suffer aliasing. Randomized arrangements help reduce lobes.
* Symmetry--Circular/spherical arrays enable uniform coverage but planar designs are easier to manufacture.  
The greater the physical dimensions that an array of sensors has compared to what a single transducer allows, with the same wavelength \(\lambda\) considered, the greater the capacity for spatial discrimination of the directions of origin of the signals, and therefore the greater the resolution, referred to in this context as angular resolution. Usually, the dimensions of an array are quantified by evaluating its spatial opening \(D\) defined as the maximum distance that separates two elements belonging to it. Therefore, the spatial discrimination capacity of an array coincides with a value proportional to \(D/\lambda\). An array has properties of flexibility unattainable by the single sensor with the same implementation simplicity. In fact, in many applications, it may be necessary to modify the spatial filtering function in real time to maintain an effective attenuation of the interfering signals to the advantage of the desired ones. This becomes essential in imaging applications in which the pointing  
Figure 4: Two\(-\)dimensional beam pattern using a periodic positioning of 32\(-\)microphones on a planar array 25 \(\times\) 25 cm\({}^{2}\) (Figure 3) at different frequencies: **(a)** beam pattern at 2 kHz, **(b)** beam pattern at \(\approx\)6 kHz, both functions of \(\theta\) and \(\phi\) (\(u\) and \(v\); see later on). Increasing the frequency, the grating lobes equal the main central lobe. The colorbar maps from 0 dB (yellow) to -30 dB (blue).  
Figure 3: Simulation of a periodic 32\(-\)microphone positioning on a planar array 25 \(\times\) 25 cm\({}^{2}\).  
direction changes constantly in order to scan all possible directions of arrival of the signal. This change, in a system that adopts an array of transducers, is achieved simply by varying the way in which the beamforming combines the data coming from each sensor in a linear fashion; in the case of a single transducer, the change is impractical as it would be necessary to act directly on the physical characteristics of the sensor.
## 2 The LVC Converter in Their Traditional Operation
### Voltage Ripple at the Output Port in the Traditional Operation  
The voltage ripple at the output port is the main variable of the present work since the objective of the proposed operation is to maintain the converter operating as in the traditional mode with the advantage of reducing the output voltage ripple. The ideal output voltage is a constant (pure DC) signal, but in duty cycles different from 0.5, a small ripple is present due to the switching action. The ripple may be reduced by increasing the capacitance of capacitors or the switching frequency, but the aim of this work is to maintain the capacitance of capacitors as equal to the traditional operation (and also with the switching frequency of transistors) and reduce the output voltage ripple with capacitors of the same size.  
The selection of capacitors (15) and (16) involves the capacitors' voltage ripples \(\Delta v_{\alpha}\) and \(\Delta v_{cb}\). From Equations (15) and (16), the voltage ripple in the capacitors can be expressed using Equations (17) and (18).  
\[\Delta v_{Ca}=\frac{I_{out}}{2C_{a}}(1-D)T_{S} \tag{17}\]  
\[\Delta v_{Cb}=\frac{I_{out}}{2C_{b}}(1-D)T_{S} \tag{18}\]  
The voltage ripple at the output port results in the simultaneous charging and discharging of the capacitors. If the capacitors are assumed to have equal capacitance, \(C_{a}=C\), and \(C_{b}=C\), then the voltage ripple at the output port can be calculated by adding (17) and (18), leading to Equation (19). Let us recall that this expression is valid for the former operation and considering capacitors of equal capacitance.  
\[\Delta v_{out}=\frac{I_{out}}{C}(1-D)T_{S} \tag{19}\]  
We will compare this expression against their value in the proposed operation.
## 4 Conclusions  
In this paper, we proposed an LEA detection and tracking method for a color-independent visual-MIMO system utilizing image processing technique that is applicable to various applications. To increase the reliability of LEA detection, we selected the ROI using color-space-based analysis of the received image with a complex background. Next, the LEA with a square shape was detected using the Harris corner method. Furthermore, we used a Kalman filter to better track the moving LEA, which can be disturbed by obstacles. Finally, to facilitate the color (or symbol) decision for each LED, the perspective projection process was performed on the distorted image. Experimental results show that our proposed method provides reliable LEA detection and tracking. Furthermore, SER performance is improved when using perspective projection with a Kalman filter.  
The following are available online at [[http://www.mdpi.com/1424-8220/16/7/1027/s1](http://www.mdpi.com/1424-8220/16/7/1027/s1)]([http://www.mdpi.com/1424-8220/16/7/1027/s1](http://www.mdpi.com/1424-8220/16/7/1027/s1)). Figure S1: The illustration of LEA Detection and Tracking Method for Color-independent Visual-MIMO.  
This research was supported by Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (2015 RID1A1A01061396) and was also supported by the National Research Foundation of Korea Grant funded by the Ministry of Science, ICT, Future Planning (2015R1A5A7037615).  
[PERSON] designed and performed the main experiments and wrote the manuscript. [PERSON] performed the experiments and analyzed the experimental results. [PERSON] as corresponding author initiated the idea and supervised the whole process of this research and wrote the manuscript.  
The authors declare no conflict of interest.  
Figure 12: SER performance comparison with and without Kalman filtering.