esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 7 Proposed System Architecture
### The SVM Classifier  
Support Vector Machine (SVM) will be our choice to replace the fully connected layers in the proposed model. The ResNet50 model will be connected with the SVM classifier as the feature vector from the CNN model will be the SVM model's input. As we know, SVM offers efficient results in high dimensional spaces and is effective in cases where the total number of dimensions is larger than the total number of samples. In addition, SVM will be intensively employed in image classification scenarios and will achieve high classification accuracy. SVM constructs a hyperplane in multidimensional space in the proposed system to differentiate varying classes.
## 3 Results and Discussion  
### Optimizing Synthesis Conditions  
#### 3.1.1 Effect of Modifier KH570 Concentration  
KH570 played a role in the surface modification of hydroxyl aluminum. The hydroxyl group on the surface of the polymerized hydroxyl aluminum was used to produce a condensation reaction with KH570, so that the inorganic substance and the organic compound were connected by a chemical bond, thus forming polymer products.  
In order to ensure the complete hydrolysis of the silane coupling agent to silanol, KH570 was hydrolyzed under acidic conditions for 12 h. The hydrolyzed solution of KH570 was tested by Fourier-transform infrared spectroscopy. The Fourier-transform infrared spectrum is shown in Figure 2. The wavenumber in the range of 1675-1500 cm\({}^{-1}\) shows two absorption peaks of C=C double bond stretching vibration, while the absorption peak at 1000-650 cm\({}^{-1}\) indicates C-H bending vibration. There is no Si-O-C siloxane stretching vibration located at 850 cm\({}^{-1}\), but instead, a very obvious stretching vibration absorption peak is observed in the range of 3200-3500 cm\({}^{-1}\). This suggests that KH570 has been completely hydrolyzed to silanol, meeting the experimental requirements. Subsequently, different ratios of hydrolyzed KH570 and PAC (such as 3/100, 5/100, 7/100, and 9/100) were mixed, and the solution was alkalized and reacted for 4 h to obtain the optimal modified product. The FTIR technique was used to test these modified products under different KH570 concentrations (Figure 3). According to the modification principle, the success of the modification test was based on the absence of, or the presence of very few, hydroxyl vibration absorption peaks. Upon the observation of Figure 3, it can be seen that, with an increase in KH570 content, the -OH absorption peak at 3200-3500 cm\({}^{-1}\) gradually reduces until it reaches a KH570 and polyhydroxy aluminum quality ratio of 9%, where the alcoholic hydroxyl peak did not appear. Therefore, a quality ratio of KH570 and PAC at 9% was chosen as the best modification ratio based on these results.  
Figure 2: FTIR spectrum of KH570 hydrolysate.
# The Use of Smart Rings in Health Monitoring--A Meta-Analysis
## 3 Results
### Study Selection and Description  
The initial search identified 5786 articles (PubMed n = 276, Scopus n = 351, ProQuest = 5159). A total of 753 studies were excluded after the deduplication process. A total of 5033 articles were screened, with 4977 excluded at the title/abstract screening stage. A total of 56 articles were assessed for eligibility by full-text evaluation. A final number of 19 articles [38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56] met the criteria for final inclusion (Figure 2). In order to synthetize the characteristics and the results of the included studies, all the extracted data are reported in Table 1 (as mentioned above, the information extracted were: first author, year of publication, country, number of participants, mean age, gender distribution, type of SR, control device, total time of recording, type of estimates, specific measured outcome, SR's accuracy results, and conflicts of interest). Ten studies explored sleep quality, one both sleep and cardiovascular parameters, seven only cardiovascular parameters, and one SaO2. All articles included in this systematic review were published between 2017 and 2024, with one published in 2017, four in 2020, four in 2021, four in 2022, two in 2023, and four in 2024. The studies were conducted in various countries, with five studies in the USA, two in Switzerland, three in Singapore, one in Australia, three in South Korea, three in Finland, one in Japan, and one in multiple countries. The populations were comprised of adolescents in one study, adolescents and adults in two, healthy adults in thirteen, adults with persistent atrial fibrillation (AF) in one, adults undergoing invasive blood pressure monitoring in one, and adults with sleep disorders in one. The mean number of participants enrolled was 49, with a range of 5 to 118 patients. Eleven studies analysed the efficacy of SRs in measuring sleep quality parameters, seven assessed their efficacy in measuring cardiovascular parameters, and one analysed the efficacy of SRs on measuring oxygen saturation (Table 1). Moreover, no studies analysing the cost-effectiveness of implementing SRs in sleep, cardiovascular, or respiratory monitoring were found.
# Fast Reroute Mechanism for Satellite Networks Based on Segment Routing and Dual Timers Switching
## 2 Related Work
This approach reduces the resources needed for path recalculations during failures and shortens repair times. Design and Implementation of Fast Rerouting Scheme Based on Segment Routing and Dual Timers Switching  
This section presents the design and implementation of the SRDT-FRR scheme. First, it outlines the conceptual framework of the proposed approach, with a focus on the classification of link failures and the joint sensing mechanism for transient and stable failures. Next, the design of each functional module is detailed, including the onboard control plane, the link failure detection module, the backup path activation timer module, and the backup path deactivation timer module, highlighting their respective roles in failure detection and traffic recovery. Furthermore, the section discusses the construction and utilization of SR backup paths, with particular emphasis on the label stack-based path encoding strategy and its application logic during failure switching. Finally, a comprehensive analysis of the router behaviors is provided, illustrating the key operations performed by different types of nodes (PLR nodes, intermediate nodes, and egress nodes) in backup path activation, label processing, and underlay routing restoration. This analysis offers thorough theoretical and technical support for the effectiveness and feasibility of the fast rerouting scheme.
# A Study of Friction Nonlinearity and Compensation for Turntable Servo Systems
###### Abstract  
In view of the worse dynamic performance and steady-state accuracy caused by nonlinear friction in turntable servo systems, challenges are posed in precise positioning tasks. However, most of the existing research ignores the effect of friction on system performance. Therefore, it is of great significance to analyze the nonlinear characteristics of the transmission mechanism and study compensation strategies for improving the control quality of non-direct drive turntable servo systems. Therefore, an improved active disturbance rejection control (ADRC) based on state feedback compensation is proposed in this paper to optimize the accuracy of the turntable servo system and improve the robustness of the system under nonlinear friction conditions. Firstly, friction is modeled and analyzed through offline identification, which is the basis for nonlinear friction compensation. Subsequently, the two methods of friction compensation are compared. Since feedforward compensation is prone to under-compensation and over-compensation, it is highly dependent on the parameters, while the traditional ADRC compensation method has poor dynamic performance under gap conditions. Therefore, the advantages of ADRC and state feedback are combined together to reduce the steady-state error and optimize the control performance of the system. Lastly, the effectiveness of the proposed compensation method is verified and compared through simulations and experiments. The method is able to comprehensively compensate the gap and friction nonlinearities, and the experimental steady-state error is reduced from 0.55\({}^{\circ}\) to 1/3 (0.19\({}^{\circ}\)), which improves the load-side positioning accuracy. Finally, a conclusion can be drawn that the new compensation method can improve the parameter adjustability, speed estimation precision, and system robustness.  
friction nonlinearity; servo drive systems; closed-loop control; extended state observer; positioning accuracy
# Integrated Robotic and Network Simulation Method
## 4 A Case-Study Illustrating the Use of the IRoNS Method
### Choosing Solutions
#### 4.2.3 MPC Rendezvous  
The MPC (model prediction control) rendezvous [34] is an algorithm that uses a consensus formulation with receding horizon stated as a quadratic optimization problem. The optimization problem in robot \(i\) can be stated as finding the minimum of an objective function \(J_{i}\) constrained by saturation velocities (6) [34].  
The objective function is composed of a consensus function \(f_{i}\) for robot \(i\) and its neighbors, a 2D velocity vector \(V_{i}\) and auxiliary matrix \(H_{i}\). This auxiliary matrix includes the adjacent matrix elements and a ponderation factor that penalizes changes in control.  
\[\begin{array}{c}\text{minimize}\left(J_{i}=\frac{1}{2}\left[\begin{array}{ cc}V_{i}^{x}&V_{i}^{y}\end{array}\right]\left[\begin{array}{cc}H_{i}&0\\ 0&H_{i}\end{array}\right]\left[\begin{array}{cc}V_{i}^{x}\\ V_{i}^{y}\end{array}\right]+\left[\begin{array}{cc}f_{i}^{x}&f_{i}^{y}\end{array} \right]\left[\begin{array}{c}V_{i}^{x}\\ V_{i}^{y}\end{array}\right]\right)\\ s.t.\begin{array}{c}v_{\text{max}}^{x}\leq v_{i}^{x}\leq v_{\text{max}/f}^{x} \\ v_{\text{max}}^{y}\leq v_{i}^{y}\leq v_{\text{max}/f}^{y}\end{array}\\ \end{array} \tag{6}\]  
This technique is considerably more complex than the rest and it was selected to show that even demanding algorithms that rely on optimization could be utilized in this type of complex simulation.
# Speckle Noise Removal in OCT Images via Wavelet Transform and DnCNN
## 5 Conclusions  
In the domain of OCT image processing, the presence of speckle noise significantly compromises the diagnostic image quality, necessitating advanced denoising strategies to enhance the clinical interpretation accuracy. This study introduces an enhanced wavelet-based framework integrating the DWT and SWT for multiscale decomposition, coupled with a DnCNN architecture for adaptive noise suppression. The proposed methodology achieves a structure-preserving reconstruction through hierarchical feature extraction, demonstrating a superior capability in mitigating speckle artifacts while retaining critical anatomical details in OCT imagery.  
Quantitative evaluations utilizing the MSE, PSNR, and SSIM confirm that the algorithm achieves an optimal trade-off between noise reduction and detail preservation, outperforming conventional approaches. Notably, the edge detection analysis corroborates the framework's efficacy in maintaining subtle structural boundaries. These results collectively validate the clinical applicability of the proposed method for improving the OCT diagnostic precision in ophthalmic and cardiovascular imaging contexts.  
However, the computational complexity remains a challenge due to the iterative multiscale wavelet transformations required for model training. Future work should focus on optimizing the implementation efficiency through parallel computing architectures and exploring hybrid deep learning paradigms that balance performance with computational feasibility. This study not only underscores the potential of wavelet-CNN synergies in biomedical imaging but also advocates for an adaptive algorithm development tailored to complex noise environments, providing both theoretical foundations and the empirical validation for advancing OCT image quality in clinical practice.  
Writing original draft preparation, F.L.; Software, Z.Y.; formal analysis, B.J.; data curation, B.J.; supervision, Q.W. All authors have read and agreed to the published version of the manuscript.  
Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province, grant number 20210038.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Thanks to the Shanxi Eye Hospital for providing data support for this work.  
The authors declare no conflicts of interest.
## 8 DECODE Service  
DECODE is a symptom checker tool used to assist patients and family doctors in the preliminary screening and early detection of COVID-19.  
In the DECODE service, a patient has the opportunity to fill out a questionnaire regarding his/her health condition and obtain a preliminary assessment of their possibility carrying COVID-19 and, consequently, the need to be tested for SARS-CoV-2.  
The questions (Figure 7) cover many problem areas related to possible COVID-19 disease. In addition to the year of birth, sex, the occurrence of signs that are typical for coronavirus, such as fever over 38 degrees of Celsius, cough, dyspnoea, loss of smell or taste, etc., the patient may add other disturbing symptoms and state how long they last. Moreover, the person completing the questionnaire has the option of providing the names of any chronic diseases--if any--and the names of the medications that are constantly taken. Blood type is also taken into account. To avoid possible inconsistencies, the questionnaire fields are validated. For example, if a patient checks any symptoms, the length of their presence must be greater than 0.  
After completing and sending the questionnaire, the patient receives suggestions in the form of a Negative/Positive statement, a quantitative score, and an illustration of how the obtained score compares with the distribution of other results stored in the system. An example of such an illustration is presented in Figure 8. It contains two distribution charts presenting the frequency of scores for patients who are positive or negative for SARS-CoV-2 infection. The system informs users that the obtained results are only a prediction and only a medical test can fully confirm the presence of SARS-CoV-2. The link that allows the patient to return to the survey is active for 14 days. Using this link, the patient can, for example, report new symptoms, if they occur. In particular, when the patient knows their PCR test results, they can share this information and help to improve the service.  
DECODE is available in two language versions (Polish and English) and is integrated with the CIRCA diagnostic service, which was also developed at the Silesian University of Technology to support and accelerate COVID-19 imaging diagnostics.  
Figure 7: Questionnaire collecting data on patient’s condition, implemented in the DECODE online tool to perform patient preliminary screening for COVID-19.
## 3 Proposed Architecture
### Justification for Using Class Anchors  
When utilizing the SNN for disease evolution assessment, the conventional representation of similarity between two images, as depicted in Figure 2, does not suffice for our purpose. To illustrate this, we present a hypothetical scenario in Figures 7-9, where we compare two images, Image A and Image B, belonging to the same patient at different time points. In this context, direct image comparison using a similarity score alone does not necessarily provide insights into whether the situation is improving or deteriorating. Hence, we demonstrate the necessity of comparison with an anchor image to discern the progress or regression of the disease.  
The DFU-Helper framework is a novel system that utilizes a Siamese Neural Network for implementing similarity learning. The SNN in DFU-Helper consists of subnetworks, which are ensembles of CNN and Vision transformers. The training of the SNN is performed using the LMCoT loss function, specifically designed for similarity learning. This  
Figure 5: Block diagram summarizing how the similarity is calculated and plotted once the anchors of the classes are known and a test image is received.  
Figure 6: Visual illustration of cosine similarity and Euclidean distance [46].  
framework represents a pioneering effort in the field of the application of deep learning for the longitudinal evaluation of DFU diseases over time.  
Figure 8: Example 2: Image A from Figure 7 is fed in the SNN together with an Anchor representing Healthy image. We obtain a similarity score of 0.02, which means very low similarity. This means we have a situation that is clearly far from being healthy, and the information immediately becomes more mean.  
Figure 7: Example 1: When we input Image A and Image B into the SNN, the calculated similarity score is 0.02, indicating a low level of similarity between the two images. However, this score alone does not provide any indication regarding whether the condition is improving or worsening. This highlights the necessity of refining the model to gain better insights into the disease’s progression.
# Study on the Population Balance Dynamics Simulation of Grinding under Impact Crushing
## 3 Results and Discussion
#### 3.1.2 Calculation of Breakage Distribution Function  
In Table 5, the \(E_{\text{\tiny{CS}}}\) and corresponding \(t_{10}\) values of the quartz with different particle sizes in the mill are given. Combining the \(t_{10}\)-\(t_{xx}\) function relationship, a series of \(t_{xx}\) values for different particle sizes can be obtained, that is, the element \(B_{\bar{y}}\) of the cumulative breakage distribution matrix \(\mathbf{B}_{\bar{y}}\) of the quartz. According to Equation (5), the breakage  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{t_{xx}}\) & **Fitting Equation** & \(\mathbf{R^{2}}\) \\ \hline \(t_{1.4}\) & \(t_{1.4}=50.3655+3.9373t_{10}-0.0976t_{10}{}^{2}+7.6231\times 10^{-4}t_{10}{}^{3}\) & 0.9582 \\ \(t_{2.0}\) & \(t_{2.0}=11.5492+5.0587t_{10}-0.0988t_{10}{}^{2}+6.4790\times 10^{-4}t_{10}{}^{3}\) & 0.9584 \\ \(t_{2.8}\) & \(t_{2.8}=6.7042+3.3822t_{10}-0.0311t_{10}{}^{2}\) & 0.9953 \\ \(t_{3.9}\) & \(t_{3.9}=-0.0723+2.8244t_{10}-0.0213t_{10}{}^{2}\) & 0.9991 \\ \(t_{5.6}\) & \(t_{5.6}=-1.7103+1.971t_{10}-0.0097t_{10}{}^{2}\) & 0.9958 \\ \(t_{7.9}\) & \(t_{7.9}=-0.53+1.33t_{10}-0.0028t_{10}{}^{2}\) & 0.9999 \\ \(t_{11.1}\) & \(t_{11.1}=0.1882+0.8837t_{10}+8.0415\times 10^{-4}t_{10}{}^{2}\) & 1 \\ \(t_{15.8}\) & \(t_{15.8}=0.9599+0.5256t_{10}+0.0033t_{10}{}^{2}\) & 0.9969 \\ \(t_{22.3}\) & \(t_{22.3}=0.2401+0.514t_{10}-6.9004\times 10^{-4}t_{10}{}^{2}+3.1204\times 10^{-5}t_{10}{}^{3}\) & 0.9991 \\ \(t_{31.5}\) & \(t_{31.5}=0.8033+0.3217t_{10}+0.0023t_{10}{}^{2}\) & 0.9980 \\ \(t_{44.6}\) & \(t_{44.6}=1.0755+0.2106t_{10}+0.0027t_{10}{}^{2}\) & 0.9952 \\ \(t_{62.6}\) & \(t_{62.6}=0.7647+0.2124t_{10}+0.0018t_{10}{}^{2}\) & 0.9945 \\ \(t_{144.2}\) & \(t_{144.2}=0.7007+0.1579t_{10}+0.0013t_{10}{}^{2}\) & 0.9877 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Functional relation of \(t_{10}\)-\(t_{xx}\) for quartz.  
Figure 1: The relation curves of \(t_{10}\)-\(t_{xx}\) for quartz.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Particle Size/mm**} & **Nominal Particle Size/mm** & **M/kg** & \(E_{\text{\tiny{CS}}}\)/kWh/h** & \(t_{10}\)**\%** \\ \hline \(-3.35\pm 2.36\) & 2.8118 & \(3.0830\times 10^{-5}\) & 1.3994 & 46.3585 \\ \(-2.36\pm 1.7\) & 2.003 & \(1.1145\times 10^{-5}\) & 3.8713 & 64.6617 \\ \(-1.7\pm 1.18\) & 1.1463  
distribution function \(b_{ij}\) of the quartz can be obtained, which is the complete particle size distribution of the quartz under the impact of the steel ball media, as shown in Table 6.  
Table 6 lists the breakage distribution function \(b_{ij}\) of the quartz only under impact, and each element \(b_{ij}\) in the matrix represents the yield of particle size \(i\) generated by particle size \(j\) fragmentation. In a mill, the \(i\) particle size originates from the crushing of any particle size coarser than it, while the \(j\) particle size will be crushed to produce a finer particle size.
## 5 Performance Evaluation
### Comparison with Other Metrics  
We compare the throughput and packet loss rate of RECN with other metrics. We adopt RTS retransmission times [9], FSD [10], \(R_{b}\)[13] and \(R_{FTE}\) as the metrics of the indicator signal respectively, and also compare their performances with the case of no ECN.  
In this section of the experiment, each sending node sent a TCP flow using UDP flows as the background flows. With the experiment, the number of sending nodes changed from one to 10. The results of throughput and packet loss rate are shown in Figure 4(a),p, respectively.  
Figure 4(a) shows that with the increasing number of sending nodes, the throughput decreases as a whole. However, compared with the no ECN situation, the decline of throughput is not so obvious as using ECN with the MAC layer's metric. Specifically, the more sending nodes, the more obvious the improvement of the throughput is. This is because with more sending nodes, the possibility of congestion also increases. The sender using the ECN mechanism will reduce its sending rate when the congestion occurs.  
In addition, Figure 4(b) tells us that the packet loss rate of no ECN is far greater than the others. For example, when number of flows is nine, the packet loss rate with no ECN is 0.35, with RTS, FSD and \(R_{b}\) is 0.15 and with the \(R_{FTE}\) we propose in this paper is only about 0.014.  
To sum up, in the multi-hop ad hoc networks, the four metrics mentioned above can all improve network performance. However, the RECN metric we proposed can achieve the best improvement of both throughput and packet loss rate.  
Thus, in the following sections, we focused on experiments to compare the performance of TCP with or without RECN.
# Evaluating the High Frequency Behavior of the Modified Grounding Scheme in Wind Farms
## 2 Wind Turbine Model Description
### Vertical Rod Model  
A vertical rod [37; 38] is an electrical device designed to dissipate a static discharge voltage, such as lightning, to the ground. For the frequency-dependent distributed parameters grounding model, the rod is divided into N segments, where each segment is a part of the R-L-Cbranch with similar parameters.This study assumes that each segment is divided into three parts with a length of 3 m.  
Unlike other research discussed in this article, this study addresses the nonlinear resistance behavior of the grounding rod to accurately obtain the performance of the rod in less conductive soil. During a lightning strike, it would be possible for the electric field around the grounding rod to be larger than the rod's electrical strength, resulting in a breakdown and electrical discharge. For a nonlinear resistor, this can be represented as a function of the current through the rod [30]:  
\[R_{t}(t)=\frac{R_{0}}{\sqrt{1+\frac{i(t)}{I_{g}}}}, \tag{1}\]  
where \(R_{t}(t)\) is the nonlinear resistance of the ground rod, \(R_{0}\) is the tower footing resistance at low current and low frequency and:  
\[I_{g}=\frac{E_{0}\rho}{2\pi R_{0}^{2}}, \tag{2}\]  
where \(E_{0}\) is the critical electric field intensity (about 300 kV/m) and \(\rho\) is the soil resistivity (in \(\Omega\)m).