esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Datasets and Methods
### Methods
#### 2.3.4 Accuracy Assessment  
As the most standard method for remote sensing image classification accuracy, the confusion matrix (also called error matrix) was employed to quantify misclassification results. The accuracy metrics derived from the confusion matrix include overall accuracy (OA), Kappa coefficient, user's accuracy (UA), producer's accuracy (PA), and F1-score [64]. The number of validation samples per class used to evaluate classification accuracy is shown in Table 3. A total of 98,009 samples were applied to assess the classification accuracies.  
The OA describes the proportion of correctly classified pixels, with 85% being the threshold for good classification results. The UA is the accuracy from a map user's view, which is equal to the percentage of all classification results that are correct. The PA is the probability that the classifier has labeled a pixel as class B given that the actual (reference data) class is B and is an indication of classifier performance. The F1-score is the harmonic mean of the UA and PA and gives a better measure of the incorrectly classified cases than the UA and PA. The Kappa coefficient is the ratio of agreement between the classification results and the validation samples, and the formula is shown as follows [22].  
\[Kappa\ coefficient=\frac{N\sum\limits_{i=1}^{r}X_{ii}-\sum\limits_{i=1}^{r}X_{i+}X _{+i}}{N^{2}-\sum\limits_{i=1}^{r}X_{i+}X_{+i}} \tag{18}\]  
where \(r\) represents the total number of the rows in the confusion matrix, \(N\) is the total number of samples, \(X_{ii}\) is on the \(i\) diagonal of the confusion matrix, \(X_{i+}\) is the total number of observations in the \(i\) row, and \(X_{+i}\) is the total number of observations in the \(i\) column.
# Enhancing Kazakh Sign Language Recognition with BiLSTM Using YOLO Keypoints and Optical Flow
## 1 Introduction  
Effective communication is essential to bridging social divides, yet for many in the deaf and hard-of-hearing community, sign language is the primary medium for expression. Unlike spoken languages that rely on auditory signals, sign language presents unique challenges to automated recognition due to its reliance on rapid hand movements, subtle gestures, and intricate facial expressions. These visual cues require systems that not only detect static features but also understand complex motion dynamics over time.  
Early efforts in sign language recognition depended on handcrafted features and conventional machine learning techniques, which often struggled to account for the variability inherent in natural signing [1]. The emergence of deep learning, however, has significantly shifted this paradigm by enabling models to learn rich representations directly from raw data [2]. Techniques leveraging convolutional neural networks (CNNs) [3] and recurrentneural networks (RNNs) [4] have shown promise, yet many such methods tend to overlook the detailed dynamics necessary for capturing fast, fluid gestures.  
In addressing these challenges, our work proposes an integrated deep learning framework specifically tailored for Kazakh Sign Language--a language that remains underrepresented in current research, unlike the commonly studied American Sign Language and, recently, Arabic Sign Language [5,6].
## 5 Conclusions and Future Work  
In this paper, we study the power allocation problems of SCO\(-\)AN under perfect and imperfect CSI. First, the power allocation model of SCO\(-\)AN with perfect channel estimation is constructed. Then, the effect of the imperfect channel estimation error is examined. The power allocation model of SCO\(-\)AN is constructed for the first time in this paper, along with the expression of the imperfect channel estimation's effect on power allocation. The power allocation optimization problem is a crucial contribution to optimizing secrecy capacity under imperfect channel estimation. The power allocation problem's objective function is non-convex, which poses challenges to the solving process. Therefore, we solve this problem by adopting the ISQP algorithm. We compare ISQP with the other three algorithms-SQP, BPA, and COCOA. Although ISQP is slightly worse than SQP in terms of the optimization effect, the ISQP algorithm far exceeds other algorithms. Moreover, ISQP requires the least complex computation. Therefore, we decide to choose the ISQP algorithm. Our simulation results show that the secrecy capacity of SCO\(-\)AN wireless communication system increases the most under ISQP algorithm. We then conclude that the ISQP algorithm is the most effective for this purpose.  
There is much room for future research. For any optimization problem, there is an upper bound to be reached. What is the upper bound on secrecy capacity for SCO\(-\)AN under a specific power? This question lays an exciting background for future research directions. Since 2019, the research on the physical layer security of reflective intelligence surfaces has become a research hotspot. The application of SCO\(-\)AN in intelligent reflector technology is one of our future research contents as well. As inspired by many papers, the features of mixing other SCO\(-\)AN signals also pose a meaningful research question, such as SCO\(-\)AN with interference alignment characteristics and SCO\(-\)AN with channel coding characteristics. Among these proposed topics for future studies, we will first study the secrecy capacity's upper bound of SCO\(-\)AN.  
[PERSON] proposed the framework of the whole algorithm. [PERSON] handled all the simulations and made all figures and tables in the manuscript. [PERSON] was a major contributor in writing the manuscript. All authors have read and agreed to the published version of the manuscript.  
The research in this article is supported by \"the National Natural Science Foundation of China\" Grant nos. 61571167, 61471142, 61102084 and 61601145).  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of NAME OF INSTITUTE (protocol code XXX and date of approval).  
Informed Consent Statement:Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement:Data sharing not applicable to this article as no datasets were generated or analysed during the current study.  
The authors declare that they have no competing interests.
# Application of a New Enhanced Deconvolution Method in Gearbox Fault Diagnosis
## 3 Limitations and Improvements of MOMEDA  
The MOMEDA algorithm has a good effect in the extraction of periodic fault features. The algorithm can obtain the optimal filter without iteration and can extract continuous pulses, but it also has certain limitations. For example, noise has a great influence on it, and it is not ideal to extract weak shock signal under normal working conditions. Aiming at this limitation of the MOMEDA fault extraction method, this paper proposes a fault diagnosis method based on ARMA-MOMEDA. The parametric ARMA model can accurately describe the objective law of the dynamic system; the autoregressive parameters are most sensitive to the change of working conditions, and have obvious advantages in small damage identification, noise reduction, and operability. By building an autoregressive moving average model for fault signals, a set of optimal models is obtained. The model is used to filter the fault signal and separate the uncorrelated impact components. Therefore, ARMA is used as a pre-filter of MOMEDA to denoise the signal to improve the limitation of MOMEDA in extracting fault features in a strong noise environment.
# Cross-Regional Pavement Temperature Prediction Using Transfer Learning and Random Forest
## 3 Analysis of Meteorological Characteristics
### Pavement Temperature Characteristics
In summary, the internal temperature of the pavement structure is affected by both depth and regional conditions. With increasing depth, the temperature response becomes progressively attenuated, shifting from strong fluctuations at the surface to relative thermal stability in deeper layers. Regional influences are reflected in the amplitude of temperature variation, the occurrence of extreme values, and the characteristics of statistical distributions. These differences highlight the fundamental variations in pavements' thermal behavior under distinct climatic environments.
## 6 Robustness with Respect to Uncertainty in the Time Delay  
In practice, a control strategy that provides stability with respect to the uncertainties of the model is desired. In this section, uncertainty with respect to the time delay is considered for the proposed control strategy.  
Following [30], the robustness properties of the control strategy with respect to the time delay are analyzed. For this purpose, let us consider a characteristic quasi-polynomial of the form  
\[p(s)=p_{0}(s)+p_{1}(s)\mathrm{e}^{-\tau s}+p_{2}(s)\mathrm{e}^{- \tau_{0}s}, \tag{27}\]  
where its stability properties are established as a function of the delay times \(\tau\) and \(\tau_{0}\). Using the results presented in [30], it is possible to give a general framework for our particular case. Let \(T\) be the set of all points \((\tau,\tau_{0})\in\mathbb{R}_{+}^{2}\) such that \(p(s)\) (Equation (27)) has at least one zero on the imaginary axis. Any \((\tau,\tau_{0})\in T\) is known as a crossing point, and \(T\) is the collection of all stability crossing curves. Consider now system (1) and the proposed scheme shown in Figure 2 with \(G_{S}(s)=1\) and \(C(s)\) given by (22), \(\tau\) being the delay time at the observer, and \(\tau_{0}\) being the delay time of the process. The closed-loop characteristic equation is given by  
\[p_{A}(s)=p_{a}(s)+p_{b}(s)\mathrm{e}^{-\tau s}+p_{c}(s)\mathrm{e}^{-\tau_{0}s}, \tag{28}\]  
with  
\[p_{a}(s) =(s-a)(s+b_{1})(s^{3}+(bk_{\mathrm{d}}+b_{1}-a)s^{2}+(bk_{\mathrm{ p}}-ab_{1})s+bk_{\mathrm{i}}),\] \[p_{b}(s) =b(s(k_{\mathrm{d}}s+k_{\mathrm{p}})+k_{\mathrm{i}})(s^{2}+(bg_{ 1}+b_{1}-a)s+bg_{2}+bb_{1}g_{1}-ab_{1}),\] \[p_{c}(s) =-b(s-a)(s+b_{1})(s(k_{\mathrm{d}}s-g_{1}s+k_{\mathrm{p}}-g_{2}- b_{1}g_{1})+k_{\mathrm{i}}).\]  
Note that the characteristic Equation (28) has form (27), so it is possible to identify the regions of \((\tau,\tau_{0})\in\mathbb{R}_{+}^{2}\) such that \(p_{A}(s)\) is stable. Figure 4 shows a numerical example of the stable region \((\tau,\tau_{0})\) for the characteristic Equation (28). This figure illustrates the interval of values \(\tau_{0}\) such that the proposed observer-based controller remains stable for a nominal delay \(\tau\) at the observer stage.
# Special Issue on \"Advances in Organic Corrosion Inhibitors and Protective Coatings\"
## 1 Introduction  
Metallic components consisting of magnesium, aluminum, steel, and their alloys are widely used in a plethora of applications because of their exceptional mechanical properties and their overall durability. However, they are often damaged by mechanical or chemical processes. Mechanical damage that is caused by the impact of solid particles is called erosion, while electrochemical damage that results from chemical degradation is termed corrosion. The corrosion phenomena, to which every metallic substrate is subjected, eventually result in the degradation of the metal and the deterioration of its properties.  
The interaction of a metal with its environment that results in its chemical alteration is called metallic corrosion. According to the literature, corrosion is classified into two types; uniform and localized corrosion. The intervention of either in the alloy environment or in the alloy structure can influence the corrosion protection of metallic materials. Furthermore, the interference in the metal alloy environment can be conducted with the utilization of cathodic or anodic protection via the corresponding inhibitors. Therefore, the most common categorization is among cathodic, anodic and mixed-type inhibitors, taking into account which half-reaction they suppress during corrosion phenomena. The majority of the organic inhibitors are of mixed-type and perform through chemisorption.  
Corrosion control of metallic structures is an important task in technical, economic, environmental, and safety terms. Several types of corrosion inhibitors are being employed to prevent metallic dissolution in corrosive media, for which the use of organic inhibitors is one of the most frequent and economic methods. Heteroatoms (O, S, N, and P) and \(\pi\)-electrons in the conjugated form act as excellent corrosion inhibitors for metals and alloys in aggressive solutions [1]. These inhibitors can be incorporated into corrosion-protective coatings. Coatings considered for corrosion inhibition must offer an effective physical barrier, impeding the access of violent materials to the metal surfaces.  
In order to update the field of corrosion protection of metal and metal alloys with the use of organic inhibitors, a Special Issue entitled \"Advances in Organic Corrosion Inhibitors and Protective Coatings\" has been introduced. This editorial manuscript gathers and reviews the collection of ten contributions (nine articles and one review), with authors from Europe, Asia and Africa accepted for publication in the aforementioned Special Issue of Applied Sciences.
## 3 Results and Discussion  
### Effect of the Inlet Gas Flow Rate on Outlet CO\({}_{2}\) Volume Fraction  
The absorber test rig is run for 10 min under specified conditions for every measurement, resulting in time-dependent values for each measured parameter (i.e., pressure, temperature, and gas concentrations). The standard deviation, which indicates the range of variation of each measured parameter, is then calculated to estimate the random error. The systematic error of the measuring instruments is constant for all tests and is therefore not presented additionally in this chapter. In general, the measurement uncertainty of directly measured values (e.g., temperature, pressure, and flue gas concentrations) depends only on the relative uncertainty of the measuring instruments and is given by the relative error. For indirectly measured parameters or calculated values (e.g., volumetric flow rate, where the pressure difference and temperature are used in the calculation), the Gaussian error propagation method is applied, assuming normally distributed uncertainties. In this study, the volumetric concentrations are determined with the gas analysis unit, and the maximum relative error for CO\({}_{2}\) in the different process streams is about 3%.  
Figure 4 demonstrates the effect of inlet gas flow rate on the outlet volume fraction of CO\({}_{2}\) at pressures of 0.22, 0.24, and 0.26 MPa. From Figure 4, it can be shown that increasing the inlet gas flow rate has a significant effect on the volume fraction of CO\({}_{2}\). The volume fraction of CO\({}_{2}\) goes up with the increase of the inlet gas flow rate from 12 to 16 Nm\({}^{3}\)/h and from 20 to 24 Nm\({}^{3}\)/h, while an increase of the inlet gas flow rate between 16 and 20 Nm\({}^{3}\)/h has a slight effect on the volume fraction of CO\({}_{2}\). The trend of this effect is similar for all pressure values investigated.  
Figure 4: Effect of the inlet gas flow rate on the volume fraction of CO\({}_{2}\) at the outlet of the absorber.  
The principal explanation for this effect may be that a change in the gas flow rate will influence the hydrodynamic characteristics of the tray--such as clear liquid height, the liquid holdup of the tray, and the froth height on the tray--which will be studied later.  
The standard errors of the measurements of CO\({}_{2}\) volume fraction are shown in Table 1.
## 6 Conclusions  
In this study, we addressed the challenge of removing chaos in long-haul WDM optical communication systems utilizing the DP-16 QAM modulation scheme. DeepChaos+ introduced two key components to enhance the performance of chaos removal. Through extensive experiments, we demonstrated the effectiveness of DeepChaos+ in accurately reconstructing the original signal with a significantly reduced bit error rate. The achieved bit error rate of approximately \(10^{-5}\) highlighted the superiority of DeepChaos+ compared to traditional methods. Additionally, DeepChaos+ exhibited high efficiency in terms of processing time, enabling fast signal reconstruction. By enhancing the reliability and efficiency of chaotic secure channels in optical fiber communication systems, DeepChaos+ has the potential to significantly improve data transmission in high-speed networks.  
Conceptualization, D.A.V., Q.X.N. and D.C.T.; methodology, D.A.V., D.C.T. and N.K.H.D.; data collection, H.N.T.N., T.T.T.T. and D.A.V.; software, T.T.T.T., H.N.T.N., H.M.D. and N.K.H.D.; validation, D.A.V., T.T.T.T., N.K.H.D., Q.X.N. and D.C.T.; formal analysis, H.N.T.N., H.M.D. and N.K.H.D.; investigation, D.A.V., T.T.T.T., H.N.T.N. and H.M.D.; resources, D.C.T. and Q.X.N.; data curation, T.T.T.T., N.K.H.D., H.N.T.N. and H.M.D.; writing--original draft preparation, D.A.V., T.T.T.T., H.M.D. and H.N.T.N.; writing--review and editing, H.M.D., H.N.T.N. and D.C.T.; visualization, T.T.T. and H.N.T.N.; supervision, D.C.T. and Q.X.N.; project administration, D.C.T.; funding acquisition, D.A.V. and D.C.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The simulation dataset used in our experiments is publicly available at: [[https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) PwM4Z79 csBFTIqdQcsq_gdbEwMzloxd]([https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) PwM4Z79 csBFTIqdQcsq_gdbEwMzloxd).  
The authors declare no conflicts of interest.
# On the Rate-Distortion Theory for Task-Specific Semantic Communication
## 5 Experimental Results
### Results for Classification Tasks  
To further evaluate the effectiveness of our proposed semantic communication framework across different tasks, we conduct classification experiments using the MNIST dataset under different divergence metrics.  
In Figure 8a, we present the classification accuracy of the recovered images under KL divergence and reverse KL divergence. Here, \(R(D)\) represents the simulation curves under only MSE distortion, which is obtained by setting \(\lambda_{\text{cls}}=0\) in (25). The results demonstrate that, at any given transmission rate, our proposed models achieve significantly higher classification accuracy compared to conventional bit-wise approaches, highlighting the  
Figure 7: Image quality evaluation of generated samples on MNIST under different divergence metrics. (**a**) Results on MNIST; (**b**) Results on CIFAR10.  
effectiveness of our task-specific semantic communication architecture. Moreover, the results exhibit the following tradeoff relationship: (1) higher transmission rates consistently lead to improved classification accuracy across all models; (2) a more stringent constraint on semantic distance, i.e., higher \(P_{\text{gen}}\), results in higher classification accuracy. This also aligns with the theoretical and empirical findings of the semantic rate-distortion tradeoff, confirming that increased transmission rates enhance the preservation of semantic information. A 3D surf plot of the semantic rate-distortion function for the classification task is shown in Figure 8b.  
Our experiments also reveal a significant divergence in task performance between the KL-optimized and reverse KL-optimized models. As shown in Figure 8a, the standard KL divergence approach demonstrates superior classification accuracy compared to its reverse KL counterpart, particularly in low-rate regimes (\(R\leq 5\) bits) where the performance gap exceeds 11% when \(R=\text{bits}\) and \(P=0.01\). This empirical observation corroborates fundamental information-theoretic principles: the forward KL divergence directly minimizes the expected negative log-likelihood of the true distribution under the approximated distribution [33], making it theoretically optimal for discriminative tasks like classification.  
The performance advantage diminishes at higher transmission rates (\(R>6\) bits), where both divergence metrics achieve comparable accuracy within a 2% margin. This convergence suggests that sufficient channel capacity can compensate for the inherent limitations of reverse KL divergence in classification scenarios. The model minimizing KL divergence to produce distributions with \"mode-seeking\" behavior [34], while beneficial for perceptual quality, as shown in Figures 6 and 7a, appears less suitable for preserving the complete feature distributions needed for robust classification at low bitrates.  
In summary, the classification results validate the practical utility of our semantic communication framework to classification tasks, demonstrating the flexibility of the proposed architecture to different tasks.
# OmniSR-M: A Rock Sheet with a Multi-Branch Structure Image Super-Resolution Lightweight Method
## 3 Experimental Results and Their Analysis
### Pore Space Segmentation Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Sample No. 0003**} & \multicolumn{2}{c}{**Sample No. 0311**} & \multicolumn{2}{c}{**Sample No. 0054**} & \multicolumn{2}{c}{**Sample No. 0617**} \\  & **Surface** & **Relative** & **Surface** & **Relative** & **Surface** & **Relative** & **Surface** & **Relative** \\  & **Porosity (\%)** & **Error (\%)** & **Porosity (\%)** & **Error (\%)** & **Porosity (\%)** & **Error (\%)** & **Porosity (\%)** & **Error (\%)** \\ \hline HR & 40.74 & — & 24.68 & — & 36.23 & — & 32.72 & — \\ LR & 41.43 & 0.69 & 24.31 & –0.37 & 35.84 & –0.39 & 33.89 & 1.17 \\ ESPCN & 42.25 & 1.51 & 24.77 & 0.09 & 36.07 & –0.16 & 33.76 & 1.04 \\ IDN & 42.17 & –0.08 & 24.82 & 0.14 & 36.28 & 0.05 & 33.7 & 0.98 \\ RCAN & 40.82 & 0.08 & 24.82 & 0.14 & 36.33 & 0.1 & 33.74 & 1.02 \\ RDAN & 40.92 & 0.18 & 24.93 & 0.25 & 36.4 & 0.17 & 33.75 & 8.97 \\ OmniSR & 40.81 & 0.07 & 24.78 & 0.1 & 36.23 & 0 & 33.25 & 0.53 \\ OmniSR-M & 40.71 & –0.03 & 24.72 & 0.04 & 36.18 & –0.05 & 33.17 & 0.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of relative errors of different methods of determining the facies porosity rate.
###### Abstract
The EMA method uses a hammer to excite the structure, which cannot replicate the real excitation of agricultural machinery in the field, and the excitation of OMA method is unknown; EFDD and SSI algorithms are used to calculate the modal frequency, white noise signal is used to simulate environmental excitation, decreasing the accuracy of result. The PolyMax method estimates the number of poles and participation factors to obtain modal frequency, and deviations generated from the above step will cause the modal missing phenomenon. Using a time-frequency transform to estimate modal frequency also has problems. For example, the window function of short-time Fourier transform needs to be selected in advance. Frequency accuracy decreases as the window function narrows, while time accuracy decreases as the window function widens. Wavelet analysis requires a large number of basis functions, causing leakage of the signal's energy. Due to the limitation of the wavelet basis functions, when the frequency increases, the resolution ratio of frequency decreases. The above methods have certain limitations in specific environments. To solve these problems, this paper used EMD and EEMD analysis methods to decompose the vibration signal, and obtained its main frequency. Comparing the results of EMD, EEMD and SSI, we propose a practical method for estimating modal frequency. Firstly, based on OMA, we collected the vibration time domain signals of the harvester under operation conditions and analyzed their stationarity. Then, the main frequencies of 16 test points were extracted from the EMD and EEMD results. Finally, we calculated the fitting degrees of EMD, EEMD and SSI to estimate the modal frequency of the harvester. The results of the research can provide a new idea for modal frequency identification and structural optimization of non-stationary random vibration under mass time-varying conditions.