esa-sceva/satcom-chunk-collection huggingface dataset:
# A Research on Manipulator-Path Tracking Based on Deep Reinforcement Learning
## 2 Method  
### Deep \(Q\) Network  
The value-function approach in deep reinforcement learning, which originally evolved from the Q-Learning algorithm in classical reinforcement learning, is the basis of the Deep Q-Network (DQN) algorithm [23], a classic algorithm. The algorithm known as Q-Learning [24] is based on the \(Q\) value, which is the projected future cumulative reward value of action \(a\) (activity a must be finite and discrete), taken in accordance with policy \(\pi\) in state \(S\).  
\[Q_{\pi}(s,a)=E_{\pi}\Bigg{[}\sum_{t=0}\gamma^{t}r(s_{t},a_{t})|s_{0}=s,a_{0}=a \Bigg{]} \tag{1}\]  
In Equation (1), \(\gamma\) is the discount factor determining the agent's horizon. The optimal value \(Q^{*}\) is defined as the maximized \(Q\) value, and the strategy that can obtain the optimal value is defined as the optimal strategy \(\pi^{*}\). The DQN uses a deep neural network \(Q_{\pi}(s,a;\theta)\) with parameters \(\theta\) to replace the \(Q\) value \(Q_{\pi}(s,a)\), which allows the algorithm to remain valid in the case of high-dimensional and continuous input states \(S\). In addition, the experience-playback pool Replay buffer and a target \(Q\) network with parameters \(\theta^{\prime}\) are also added to DQN. The Replay buffer improves the utilization efficiency of samples, and the use of the target \(Q\) network solves the loss in the neural-network-function problem. The target \(Q\) network is defined as Equation (2).  
\[y=r+\gamma\underset{a^{\prime}}{\max}Q\big{(}s^{\prime},a^{\prime};\theta^{ \prime}\big{)} \tag{2}\]  
Therefore, this problem can be transformed into a supervised learning problem to solve, that is, \(\underset{\theta}{min}\Sigma(y-Q(s,a;\theta))\), where \(\theta\) in ever \(\tau\) step copy their own parameters to \(\theta^{\prime}\).
## 3 Results
### Characterization of the Materials
#### 3.1.6 UV-Vis Diffuse Reflectance Spectroscopy  
The photoresponse of the samples was evaluated by UV-vis diffuse reflectance spectroscopy. The DRUV-Vis spectra and Kubelka-Munk function plots of samples Ti, TiLAC-7/3, and TiLAC-9/1 are displayed in Figure 6. Sample Ti had the lowest absorption in the UV region (\(<\)400 nm), while the absorption curves of TiLAC samples presented higher absorption in this region, associated to the intrinsic absorption of TiO\({}_{2}\). The presence of activated carbon in the composites strongly affects light absorption, explaining the high intensity of the absorption behavior of these samples in the same region. It is worth mentioning that the DRUV-Vis spectra of the activated carbon is constant and shows a strong absorption in the whole studied wavelength range. This is attributed to the effect of its black color, as reported in many previous studies [49; 50]. The band gap energy, \(E_{g}\), was determined through the \(\alpha\) value (\(m^{-1}\)) from a plot of (\(\alpha\)hv)\({}^{1/n}\) versus photon energy (\(h\)v), where h is [PERSON]'s constant, \(v\) is the frequency (\(s^{-1}\)) and the exponent \(n\) is the power factor of the optical transition mode. Note that n depends on the nature of the electronic transitions responsible for the absorption and is equal to two for allowed indirect transitions. The intercept of the tangent to the absorption curves was used to estimate the band gap (\(E_{g}\)) values of the samples, that were found to decrease according to the increasing amount of activated carbon content: 3.10, 2.85, and 2.66 eV for samples Ti, TiLAC-9/1 and TiLAC-7/3 respectively, indicating an enhancement of TiO\({}_{2}\) response in visible light region [51].  
Figure 5: FTIR spectra for Ti, TiLAC-7/3, and TiLAC-9/1 samples.
# Imperfect Wheat Grain Recognition Combined with an Attention Mechanism and Residual Network
###### Abstract
[22] used four CNNs (LeNet-5, AlexNet, VGG-16, and ResNet-34) to identify perfect and broken wheat grains, and compared them with a traditional support vector machine (SVM) and BP neural network, and the results showed that the identification accuracy was greatly improved by using CNN. In 2021, [PERSON] et al. [23] used the LeNet-5, ResNet-34 and VGG-16 model combined with an image enhancement method to highlight the characteristics of imperfect particles, and the test accuracy was improved by 1% compared with the model without image enhancement. The image features of imperfect grains of wheat are not clearly distinguished and the overall similarity is high. Therefore, its classification and recognition can be classified as a fine-grained image classification problem. In 2017, [PERSON] et al. [24] pointed out that, different from ordinary image classification tasks, the signal to noise ratio (SNR) of fine-grained images is very small, and the information containing a sufficient degree of discrimination usually only exists in a very small and local area. How to effectively extract and utilize useful information from these local regions is the key to the success of a fine-grained image classification algorithm. In 2017, the Google team [25] proposed a simple network structure based on an attention mechanism and applied it to machine translation. Later, the attention mechanism was widely applied in the deep learning field. In 2018, [PERSON] [26] proposed an attention module, the convolutional block attention module (CBAM), and added it to the CNN model. After testing, it was found that the network model with the attention module was also better than the network model without it in terms of image recognition. In 2019, [PERSON] et al. [27] added a channel attention mechanism to VGG-16 and compared three types of fine-grained image datasets. The results showed that the network with the attention mechanism, not only improved classification accuracy, but also had a good generalization ability. In 2020, [PERSON] et al. [28] applied CNN with an attention mechanism to soybean aphid identification which resulted in higher accuracy. Based on the above, this study will attempt to combine the attention mechanism and residual network to classify and recognize six kinds of wheat grains: scab wheat grains, insect-damaged wheat grains, sprouted wheat grains, mildew wheat grains, broken wheat grains, and perfect grains. The aim is to explore a more accurate deep learning model that is suitable for imperfect wheat grain recognition, and to provide guidance for intelligent detection and recognition methods of wheat. The structure of this paper is as follows: the second section introduces the methods used in this paper, the third section shows the experimental results, tables, and discussions, and the conclusions are presented in the fourth section.
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 4 Simulation Results
### Impact of Noise Power  
Based on the previous discussion in Section 3, the deviation of the metrics of RSS, AOA and TOF (or called noise power) determines the values of \(\rho_{1}\), \(\rho_{2}\) and \(\rho_{3}\) and thereby affects the error bound. Then, in this section, we will focus on the impact of the deviation of the metrics of RSS, AOA and TOF on error bounds.
Although CAs in other geometrical shapes have been used in multiple biomedical applications such as drug delivery [32; 33], tissue engineering scaffolds [34; 35], and antibacterial and wound dressing [36; 37], the potential of CA microfibers for biomedical application has not been determined. Therefore, the processing, cytotoxicity, and biological assessment of these nanobiomaterials produced by assisting inorganic salts remain essential. This study aims to understand the correlations between initial polysaccharide characteristics, dissolution, and processing of the cellulose using Ca(SCN)\({}_{2}\cdot\)6H\({}_{2}\)O on the final aerogel's internal structure and biological properties. In particular, this research inspects the effect of two different molecular weights of microcrystalline cellulose on the physicochemical and cytotoxicity of the wet spun microfiber CAs that originated from them. The microcrystalline cellulose and CA fibers are characterized in terms of crystallinity, morphology, physical and textural properties, thermal stability, and cytotoxicity. The CA microfibers produced from high molecular weight powder proved to have a higher surface area and lower crystallinity with a smaller average fibrillar diameter compared to the fibers with lower molecular weight. In comparison to powders, both CA fibers went through cellulose I to cellulose II transformation and were mainly in an amorphous form with lower thermal stability. Fibers were nontoxic and showed cell viability and proliferation as promising indications for using these fibers in biomedical applications where porous fibers are required.
# Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing
## 3 The MOTSO Model in SC
### Multi-Objective Particle Swarm Optimization  
MOPSO consists of several significant steps included in each iteration, as shown in Figure 4. The MOPSO algorithm starts with initializing the swarm, velocity, position, and PBestz values. The velocity of all particles is initialized randomly. Due to the huge search space (i.e., the thousands of available workers and spatial tasks), we propose utilizing the output solutions of the pre-computation phase to initialize the position of the swarm directly. We used the ranked table values as the position of the particles at the first iteration to enhance the MOPSO's capability for achieving satisfying solutions, through accelerating the convergence effectively and to be close to the global target instead of the local target. The steps of our MOPSO and the fitness function were explained in more detail in our previous study [19].  
Figure 4: Flowchart of the multi-objective particle swarm optimization (MOPSO) [19].
## 3 Results and Discussion  
### Model Solution Experiments  
As(III/V) Stability in Preserved and Unpreserved Model Solutions  
Differential pulse anodic stripping voltammetry (DPASV) was used to evaluate the sensitivity for arsenic analysis and speciation of groundwater. The DPASV voltammograms of As(III) for a 10 \(\upmu\)L\({}^{-1}\) standard solution with the addition of NaC with scRNA Gold electrode are shown in Figure 1.  
Figure 1: Resulting voltammogram obtained for the determination of As(III) in As(III)/As(V) 10 \(\upmu\)L\({}^{-1}\) model sample with the addition of NaC solution in a final concentration c = 2 \(\times\) 10\({}^{-3}\) mol L\({}^{-1}\) (1); (2) after the first addition 30 \(\upmu\)L of 1 \(\times\) 10\({}^{-3}\) mol L\({}^{-1}\) As(III) standard solution; (3) after the second addition of the same standard solution and corresponding analytical curve. Voltammograms obtained for As(tot) determination in As(III)/As(V) 10 \(\upmu\)L\({}^{-1}\) model sample with OX solution as a preservative (c(OX) = 2 \(\times\) 10\({}^{-3}\) mol L\({}^{-1}\)) and corresponding analytical curve are shown in Figure 2. Measurements were performed one hour after the preparation of the samples.  
The voltammograms were recorded for all model samples at 10 days and results are presented in Table 1.  
The redox species distribution remained unchanged for 3 days in double-distilled water without preservatives (Figure 3a). However, with the addition of preservatives, the stability time was increased to 5 days and longer. For example, the addition of CA/HAc mixture (Figure 3b) prolonged the solution's stability up to 6 days. After that period, the reduction of As(V) occurs and the increase of As(III) concentration was the same as in unpreserved sample.  
The same trend was observed for samples with higher arsenic concentrations (\(\gamma\) (As(III)) = \(\gamma\) (As(V)) = 50 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbol
# Underwater Communications for Video Surveillance Systems at 2.4 GHz
## 4 Results of Maximum Distances
### Measurements for OFDM at Different Temperatures
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Origin of** & **Sum of** & **Degree of** & **Average** & **F** & **Probability** & \multicolumn{2}{c}{**Critical Value for F**} \\
**variations** & **squares** & **Freedom** & **Squares** & **F** & **Probability** & \multicolumn{2}{c}{**Critical Value for F**} \\ \hline Temperatures & 1270.41604 & 35 & 36.297601 & 3.24754764 & 1.3182 \(\times\) 10\({}^{-8}\) & 1.45688671 \\ Frequency & 17105.803 & 10 & 1710.5803 & 153.04568 & 2.998 \(\times\) 10\({}^{-121}\) & 1.85779125 \\ Error & 3911.92424 & 350 & 11.1769264 & & & & \\ Total & 22288.1433 & 395 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 20: ANOVA for OFDM. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Data Transfer** & **2412** & **2417** & **2422** & **2427** & **2432** & **2437** & **2442** & **2447** & **2452** & **2457** & **2462** \\
**Rates** & **MHz** & **MHz** & **MHz** & **MHz** & **MHz** & **MHz** & **MHz** & **MHz** & **MHz** & **MHz** & **MHz** \\ \hline
48 Mbps at 16 \({}^{\circ}\)C & 3 & 2 & 0.125 & 0.01 & 0.25 & 1.875 & 1.625 & 1 & 1.25 & - & - \\
48 Mbps at 18 \({}^{\circ}\)C & 2 & 0.5 & 1.625 & 2 & 1.25 & 0.125 & 0.03 & 2.25 & - & - \\
48 Mbps at 20 \({}^{\circ}\)C & 2 & 1 & 1.375 & 2 & 1.75 & 1.875 & 1.125 & 2 & 1.25 & 0.01 & 0.02 \\
48 Mbps at 22 \({}^{\circ}\)C & 3 & 1.5 & 0.375 & 0.02 & 0.25 & 3.625 & 2.875 & 3 & 4.75 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Calculation of residuals for OFDM at 48 Mbps. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Data Transfer** & **2412** & **2417** & **2422** & **2427** & **2432** & **2437** & **2442** & **2447** & **2452** & **2457** & **2462** \\
**Rates** & **MHz** & **MHz** & **MHz** & **MHz** & **MHz** & **MHz** & **MHz** & **MHz** & **MHz** & **MHz** \\ \hline
48 Mbps at 16 \({}^{\circ}\)C & 3 & 2 & 0.125 & 0.01 & 0.25 & 1.875 & 1.625 & 1 & 1.25 & - & - \\
48 Mbps at 18 \({}^{\circ}\)C & 2 & 0.5 & 1.625 & 2 & 1.25 & 0.125 & 0.03 & 2.25 & - & - \\
48 Mbps at 20 \({}^{\circ}\)C & 2 & 1 & 1.375 & 2 & 1.75 & 1.875 & 1.125 & 2 & 1.25 & 0.01 & 0.02 \\
48 Mbps at 22 \({}^{\circ}\)C & 3 & 1.5 & 0.375 & 0.02 & 0.25 & 3.625 & 2.875 & 3 & 4.75 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Calculation of residuals for OFDM at 54 Mbps.
# Secure Cloud Infrastructure: A Survey on Issues, Current Solutions, and Open Challenges
## 4 Security Issues in Cloud Computing Infrastructure
### Network Level Issues
#### 4.3.1 Integrity Issues  
Integrity refers to the confidence in the protection against changes by unauthorized persons. The issues of integrity came in many contexts in cloud infrastructure [36]. In the context of the network, IP addresses are seized by corrupting the Internet routing tables by BGP hijacking or prefix hijacking. Normally, the border gateway protocol (BGP) is utilized to stabilize the network and transfer the packets from one path route to another route if the original path is down. However, attackers could hijack the BGP and redirect the data to other destinations. BGP hijacking leads to data leakages, and compromises the integrity and sometimes availability [43]. In addition, in the cloud environment, IP addresses are usually reassigned and reused. This occurs when customers change their location. The old \"aged\" IP can be assigned to other customers. This might lead to increased risk when reusing an existing IP address for a new device/customer happens faster than removing its old assignment from DNS caches [44].
## 3 Bridge Type, SNR and Multipath in Geodetic Deflection Measurements
### Long, Flexible Bridges  
The accuracy and versatility of GPS encouraged its use in bridge dynamics, and the first efforts to measure dynamic deflections were made for long-span bridges; this is because of their importance and because they were expected to be characterized by long-period, large-amplitude deflections (see above). Measured dynamic deflections of long bridges were found of the order of 10-100 cm (Figure 1), i.e., of a level much higher than the corresponding measurement noise. In such cases the SNR (signal-to-noise ratio) of dynamic displacements remains typically high for Geodesy, SNR \(>\) 1, and permits safe results. The excellent results of geodetic monitoring permitted to establish GPS as a valuable tool for monitoring of long, flexible bridges [4; 10; 11].  
It must be noticed that very large-amplitude deflections as those described in Figure 1 do not cause any damage to modern healthy, flexible bridges, but they may cause some discomfort to users and perhaps a risk of loss of control of vehicles. For this reason, in the case of real-time monitoring, the amplitude of deflections can be used as criterion for interruption of circulation.
## 4 Experiments and Results
### Optimal Network Topology  
Next, we study the optimal network topology. We consider five different network topologies that maintain the total number of neurons (1800). The first topology, denoted as FIRST, consists of a neural network with six hidden layers and 300 neurons evenly distributed in each layer. The second topology (SECOND) considers fewer hidden layers, with more neurons per layer than the FIRST case. Specifically, the neural network consists of three hidden layers, with 600 neurons per layer. The third topology (THIRD) considers more hidden layers, with fewer neurons per layer than the FIRST case. It consists of 12 layers, with 150 neurons per layer. The fourth topology (FOURTH) assumes there is an increase in the number of neurons for deeper hidden layers. The number of neurons is increased for each hidden layer as follows: 200 (for the first hidden layer), 200, 300, 300, 400, and 400 (for the last hidden layer). The last topology (FIFTH) assumes there is a decrease in the number of neurons for deeper hidden layers (the reverse order of the proposed FOURTH topology). The modelling performance using the five topologies is shown in Figure 14. The fifth topology (FIFTH) achieves the best overall performance, with a median MAE of 0.02735, which is 9.59, 12.34, 23.82 and 1.26% more accurate than the models FIRST, SECOND, THIRD and FIFTH, respectively. The FOURTH and FIFTH topologies achieve a better modeling performance. Therefore, we conclude that a fatter and especially a thinner topology improves the modeling performance.
## 3 Methodology  
### Design of Device  
The working principle of the proposed sensing device can be seen in Figure 1a. A PTC heater is used to generate heat at a constant temperature. Since PTC heating devices are self-regulating [59; 60] and can maintain a constant temperature without a dedicated close-loop control. An aluminum heat sink is mounted on the PTC heater to act as a heat exchanger. The maximum temperature of the PTC heater was set at 60 \({}^{\circ}\)C using a 5-V direct current supply. In this study, a thermopile array [61] with an 8 \(\times\) 8 grid arrangement was used to collect the temperature data. The thermopile array was mounted 45 mm above the heat sink, so that the field of view could completely cover the heat sink area. The thermopile array had 64 thermopile sensors and a symmetric arrangement of the thermopile array allowed the temperature data to be collected from the heat sink surface as uniformly distributed grids (8 \(\times\) 8). An ESP32 module [62] was used to collect the temperature data from thermopile array through an I\({}^{2}\)C interface. The collected data were then wirelessly transferred to a notebook PC via Wi-Fi. In Figure 1b, an assembled version of the proposed sensing device is shown. The PTC heater and heat sink are mounted at the bottom of the device frame, while the thermopile array and ESP32 are mounted at the top part of the frame. The device frame is supported by three cylindrical structures to keep the interference of the wind interacting with the heat sink to a minimum. In Figure 1c, a picture of the 3D printed version of the proposed device is shown, which is used for the measurements and experiments in this study.  
Figure 1: _Cont._The experimental measurement data on temperature change on the surface of the heat sink are due to wind flow. In Figure 2, some measurements collected by the thermopile array are shown in form of a heat map. Each square block on the heat map represents temperature measurements by one thermopile in the thermopile array. The color bar to the right of each heat map shows the color that corresponds to the measured temperature. In total, 64 blocks of temperature data were measured by the 8 \(\times\) 8 thermopile array. Visually, at low wind speeds, the center area of the heat sink is hotter than the exterior. However, as wind speed increases, the temperature at the center starts decreasing. In addition, at higher wind speeds, the temperature is more dispersed in the opposite direction to the wind. Wind direction has a significant effect on the temperature distribution on the surface of the heat sink. The direction of higher temperature zones follows the direction of the wind. However, at lower wind speeds, the temperature at the center of the heat increases up to 60 \({}^{\circ}\)C, making the wind direction effect negligible.  
Figure 1: Diagram of proposed sensing device. (**a**) Diagram showing the working principle, (**b**) completely assembled system, (**c**) picture of proposed system assembled on a 3D printed fixture.  
Figure 2: _Cont._
## 4 Experiments
### Ablation Study
#### 4.5.1 Contribution of Self-Training with Noise  
To evaluate noise-enhanced self-training, three configurations were analyzed:  
1. Baseline: standard NLSPN model without self-training or noise augmentation.
2. Self-training only: teacher-student iterative training framework implemented, excluding noise perturbations.
3. Full framework: the integration of self-training with dual noise strategies--RGB Gaussian blur and stochastic perturbation are applied to sparse depth data.  
The experimental outcomes are summarized in Table 4. Implementing the self-training framework (Configuration 2) notably elevated model performance over the baseline (Configuration 1), reducing the RMSE from 0.240 to 0.184. Incorporating noise augmentation (Configuration 3) further decreased the RMSE to 0.090, lowered the REL from 0.048 to 0.011, and achieved a \(\delta_{1.25}\) of 99.7%. These improvements suggest that simulating real-world input variations through noise strategies strengthens the model robustness and generalizability.
# Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks +
###### Abstract  
Community Networks have been around us for decades being initially deployed in the USA and Europe. They were designed by individuals to provide open and free \"do it yourself\" Internet access to other individuals in the same community and geographic area. In recent years, they have evolved as a viable solution to provide Internet access in developing countries and rural areas. Their social impact is measurable, as the community is provided with the right and opportunity of communication. Community networks combine wired and wireless links, and the nature of the wireless medium is unreliable. This poses several challenges to the routing protocol. For instance, Link-State routing protocols deal with End-to-End Quality tracking to select paths that maximize the delivery rate and minimize traffic congestion. In this work, we focused on End-to-End Quality prediction by means of time-series analysis to foresee which paths are more likely to change their quality. We show that it is possible to accurately predict End-to-End Quality with a small Mean Absolute Error in the routing layer of large-scale, distributed, and decentralized networks. In particular, we analyzed the path ETX behavior and properties to better identify the best prediction algorithm. We also analyzed the End-to-End Quality prediction accuracy some steps ahead in the future, as well as its dependency on the hour of the day. Besides, we quantified the computational cost of the prediction. Finally, we evaluated the impact of the usage for routing of our approach versus a simplified OLSR (ETX + Dijkstra) on an overloaded network.  
Community Networks; End-to-End Quality Prediction; time-series analysis Article