esa-sceva/satcom-chunk-collection huggingface dataset:
# All-Optical Non-Inverted Parity Generator and Checker Based on Semiconductor Optical Amplifiers
## 4 Conclusions  
A new kind of 2-bit all-optical non-inverted parity generator and parity checker is proposed with FWM and WGM effect of SOA, where the parity generator and checker are realized by SOAs, which enhances the integratability, configurability, and extensibility of the circuit. The experimental results confirm the feasibility of the scheme and measure the ER and OSNR for every output. The ER and OSNR can show the stability of the system very well in the entire transmission process. The results show that a SA is configured which has a certain improvement effect on ER and OSNR of the parity generator and checker. ER and OSNR of the parity checker are only reduced by 3 dB and 2 dB respectively which are compared with the parity generator. The scheme uses only dual SOAs to implement the parity generator and checker which have the potential to achieve more bits of parity and realize further complex logic functions.  
Conceptualization and methodology, B.H. and J.X.; writing--original draft preparation, P.C.; validation, R.G.; software, Y.G., and Y.N.; writing--review and editing, project administration and funding acquisition, Y.L.; All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Science Fund for Distinguished Young Scholars under grant number 61705157 and 61805167, in part by the National Nature Science Foundation of China grant number 61975142 and 61475112, in part by the Research Project Supported Shanxi Scholarship Council of China grant number 2017-key-2, in part by the Project funded by China Postdoctoral Science Foundation, in part by the Research Project Supported by Shanxi Scholarship Council of China grant number 2020-112, and in part by the Key Research and Development (R&D) Projects of Shanxi Province grant number 201903D121124.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality.  
The authors declare no conflict of interest.
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 3 System Design
### Application Programming Interface  
WISCANet provides a simple, consistent interface to the developer in any of the supported languages and bindings. The first step is to initialize the LocalUSRP object. The baseband software is provided an initial start time as a runtime argument or parameter. Afterward, the baseband software developer can call the tx_usrp and rx_usrp member functions, specifying the start time and number of channels desired. The key function signatures are as follows:  
rx_buffer = rx_usrp(start_time, num_chans); tx_usrp(tx_buffer, start_time, num_chans, reference_power);  
where the buffer's will be complex double matrices of (\(N_{samples}\times M_{channels}\)) containing time-domain I/Q samples. The system expects them to be scaled into \([-1,1]\).
## 1 Introduction
Oil palm land area has increased within 45 years from 54,000 hectares (1960) to 4.05 million hectares (2005) [12]. This shows the successfulness of the oil palm industry in Malaysia as well as the contribution to global food sources. Palm oil mill effluent (POME) is the main pollutant produced in palm oil mills in Malaysia. For one ton of crude palm oil processing, it is estimated that 3.05 m\({}^{3}\) of POME is produced [13]. If there is no proper effluent management, POME will be the main source of air and water pollution in the future. POME contains a high nutrient, organic and carbon contents despite having high biochemical oxygen demand (BOD) and chemical oxygen demand (COD) content (Table 1). It also possesses huge potential for the production of biogas [14]. During POME decomposition of organic matters, there are 60%-70% of methane and 30%-40% of CO\({}_{2}\) produced, with the rest consists of a trace amount of H\({}_{2}\)S [15]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Unit** & **Raw POME** & **Digested POME** & **References** \\ \hline  & & Mean \(\pm\) S.D. * & Mean \(\pm\) S.D. * & \\ \hline pH & – & 4.3 \(\pm\) 0.28 & 7.4 \(\pm\) 0.05 & [15; 16; 17; 18] \\ \hline Volatile fatty acids (VFAs) & mg L\({}^{-1}\) & 470 \(\pm\) 240 & 678.83 \(\pm\) 166.47 & [15; 19; 20] \\ \hline Chemical oxygen demand (COD) & mg L\({}^{-1}\) & 53,450 \(\pm\) 10,350 & 83,800 \(\pm\) 11,000 & [16; 21; 22; 23] \\ \hline Total suspended solids (TSS) & mg L\({}^{-1}\) & 29,000 \(\pm\) 6000 & 10,200 \(\pm\) 2500 & [22; 23; 24] \\ \hline Suspended solids (SS) & mg L\({}^{-1}\) & 23,600 \(\pm\) 4400 & 4126 & [22; 25] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of palm oil mill effluent (POME). In 1978, Environmental Quality Regulations enactment was proposed for POME discharge standards with the focus on BOD. From 25,000 mg L\({}^{-1}\) of untreated POME, the discharge standard limit was reduced to 5000 mg L\({}^{-1}\) in the first generation, down to the current BOD of 100 mg L\({}^{-1}\)[14]. Initiatives are in the progress to decrease the BOD level to 50 mg L\({}^{-1}\), and in places where release into conduits is required. Research and Development (R&D) is effectively sought after to decrease the BOD load to 100 mg L\({}^{-1}\). Table 2 represents POME discharge standards starting from 1978 until 2015 [28]. The palm oil and rubber mills effluent discharge standard were first introduced by Malaysia. In 1977, the Department of Environment (DoE) announced the discharge standard for POME. Before the regulation was implemented by all palm oil mills, crude palm oil seemed to be the worst main source of pollution. The daily discharge was more than 300% increased from 1965 until 1977. Hence, the regulation was made in order to reduce pollution without hindering the growth of oil palm industries.
# Towards Automatic Detection of Social Anxiety Disorder via Gaze Interaction
## 3 Materials and Methods
### Criteria for the Selection of Participants  
As explained in the related work section, subjective assessment scales have various disadvantages. However, to predict SAD automatically, there is a requirement to have subjective assessment data of the participants to overcome some of the disadvantages like difficulty in filling out scale by children and non-literate. The following four conditions were applied to make the results correct:  
1. Participants above the age of 18 were considered.
2. The educational qualification of all participants was taken above 10 th grade.
3. For each class of SAD, the number of male and female participants was taken equally.
4. The number of participants selected was greater than the existing literature review on SAD prediction.  
The experimenter informed participants about the experiment verbally and in the form of written information on the provided consent form. It was necessary to fill out the form to show the participants' volunteer participation agreement for the experiment. One of the participants supplied informed consent to publish the information/image(s) in an online open-access publication. All the methods were conducted following World Health Organization (WHO) relevant guidelines and regulations. The process of data collection, taking consent, and the ethical procedure followed was approved by the COMSATS University Islamabad, Research, and Evaluation Committee (CUI-REC) co-located in COMSATS University Islamabad, Abbottabad Campus, Pakistan. The committee also looks after the ethical issues in research.  
After the consent, the participants were provided with the printed LSAS scale. The experimenter also supplied instructions on filling it out accurately. After filling out the scale, the participants returned it to the experimenter. The filled scales by 180 participants were manually analyzed one by one, and the final score of each participant was computed. Each of the participants was grouped based on the final score. The grouping was based on the LSAS threshold values to declare someone with \"No SAD\" or to any of the four classes: Marked, Moderate, Severe, and Very Severe. Most of the participants i.e., 66 had a \"No SAD\" group, 43 had Moderate SAD, 39 were with Marked SAD, 10 were identified as having Severe SAD, and 22 had \"Very Severe\" SAD as per an analysis of LSAS scores. In the phase, we investigated 180 participants, among which 77.2% were male and 22.8% were female. As there were only 10 participants in the severe class, therefore, it was decided to collect data from only 50 participants (10 from each class) in the second phase.  
The percentage distribution of the data collection at various locations is illustrated in Table 2 below.
# Spatial Monitoring of Wafer Map Defect Data Based on 2D Wavelet Spectrum Analysis
## 1 Introduction
[10] extracted wavelet basis functions from textile image data to detect out-of-control conditions using a generalized likelihood ratio chart, which was proposed by [PERSON] and [PERSON] [11]. Existing approaches employ signal processing methods in SPC to improve the detection accuracy of abnormal status from (spatial) image data. The majority of studies assume normality or a pre-specified distribution a priori to calculate the statistics designating the control limits of a control chart. In reality, the process variables from (spatial) image data hardly follow a normal distribution. Moreover, almost entirely abnormal (or defective) regions in an image present relatively irregular patterns for each faulty area. In this research, we propose a wavelet spectrum based 2D SPC (WS-2 DSPC) to provide a robust output by considering the energy of wavelets, mainly under a nonparametric framework. A hierarchical structure decomposed by 2D discrete wavelet transform (DWT), which was proposed by [PERSON] et al. [12], is introduced to obtain the directional and spectral information of the faulty area. Then, the Hurst index as the representative statistic is estimated to reflect the nonstationary property of wavelet coefficients in fractional Gaussian fields. Finally, WS-2 DSPC is constructed by setting the control limits based on the density of Hurst indices from image windows. The proposed method is applied to real WBM image data to prove its potential in fault detection or process monitoring. Figure 1: Fabrication procedure of wafers in the manufacturing process [5]. The rest of this study is organized as follows. In Section 2, we briefly review 2D DWT and wavelet spectrum analysis. The conceptual procedure of the proposed idea is presented in Section 3. Section 4 provides an application of the proposed idea to WBM image data for SPC purposes. This study is concluded in Section 5, along with possible future research.
## 8 Future Developments
Emerging technologies, such as continuouslearning, virtual reality, biometric sensors, and telemedicine, will be able to transform this prototype into an advanced and scalable solution for assisted rehabilitation. The tests carried out in the current version have already highlighted the solidity of the adopted approach, providing a promising basis for the future use of the system in real clinical contexts. Future developments could offer an even greater level of adaptability, contributing to the innovation of rehabilitation therapies based on this technology.
# Comparing EEG-Based Epilepsy Diagnosis Using Neural Networks and Wavelet Transform
## 3 Methods and Materials
### Classification
#### 3.7.2 Support Vector Machine  
Support vector machine (SVM) is a popular supervised learning method that maps data from an N-dimensional input space to an M-dimensional feature space (M > N) in order to linearly separate classes. Based on statistical learning theory, SVM is considered an extension of the generalized portrait algorithm, which analyzes data for classification and regression. In this article, the SVM classifier was employed due to its widespread usage in data classification, pattern recognition, and regression analysis. SVM is a supervised classifier known for its effectiveness in these domains. The purpose of regression is to determine the best version out of a set of models, known as estimating functions, to accurately approximate target values. The standard support vector regression estimating function is as follows:  
\[\mathrm{f}(x)=(\mathrm{w}\cdot\Phi(x))+\mathrm{b} \tag{2}\]  
where \(\mathrm{w}\subset\mathrm{R}_{\mathrm{n}}\), \(\mathrm{b}\subset\mathrm{R}\) and \(\Phi\) are a nonlinear function that maps \(x\) into a higher dimensional space. W and b are the weight vector and bias, respectively. The weight vector (w) can be expressed as follows:  
\[w=\sum_{i=1}^{L}(\alpha_{i}-\alpha_{i}^{*}) \tag{3}\]By substituting Equation (2) into Equation (3), the generic equation can be rewritten as follows:  
\[f(x)=\sum_{i=1}^{L}\left(\alpha_{i}-\alpha_{i}^{*}\right)\left(\Phi(\text{xi}). \Phi(\text{x})\right)+\text{b} \tag{4}\]  
\[f(x)=\sum_{i=1}^{l}\left(\alpha_{i}-\alpha_{i}^{*}\right)k\left(xi.i.\right)+b \tag{5}\]  
In Equation (5), the function k (\(x_{i}\). \(x\)) = (\(\Phi(x_{i})\). \(\Phi(x)\)) is replaced with the dot product and is referred to as the kernel function and \(\propto\) = (\(\propto\)1, \(\propto\)2, \(\ldots\), \(\propto\)4) consists of nonnegative Lagrange multipliers. The selection of kernel functions and kernel parameters depends largely on the specific application. Some commonly used kernel functions include radial basis functions (RBFs) and polynomial kernel functions. The formulas for these kernel functions are shown below, respectively:  
\[\frac{-\left|x-xi\right|^{2}}{2\sigma^{2}} \tag{6}\]  
\[\left[(x*x_{i})+1\right] \tag{7}\]  
where, \"\(\sigma\)\" represents the kernel width and \"d\" represents the order, which were experimentally determined to produce the best classification results. In this work, RBFs and polynomial kernel functions were used with various values of sigma (\(\sigma\) = 0.8, 1, 1.2) and orders (d = 1, 2, 3), respectively.
# C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
## 2 Related Works
Clustering techniques, such as K-means, have been extensively used in feature selection and optimization to improve the efficiency and accuracy of machine learning models. K-means helps group data points based on feature similarity, which not only reduces redundancy but also enhances the computational efficiency of feature attribution methods. For example, [37] demonstrated the use of kernel SHAP with the clustering of K-means to improve the performance of network anomaly detection models by selecting the most important features for anomaly detection. Similarly, [31] explored clustering to enhance SHAP's capability in the financial domain by clustering feature importance scores, improving both interpretability and execution speed. Hybrid approaches that combine clustering with interpretability methods, like SHAP, have demonstrated significant potential in reducing computational overhead while maintaining interpretability [47; 48]. For instance, ClusteredSHAP [30] used K-means clustering to reduce the number of gradient evaluations in deep learning models. ClusteredSHAP's focus is primarily on minimizing gradient evaluations for deep learning model explanations, with a particular emphasis on enhancing the efficiency of explanations for complex models, such as a 12-lead ECG classification model. Building on SHAP's computational limitations and inspired by the clustering approach, we were motivated to introduce a novel method called C-SHAP. Unlike [30], which targets gradient evaluations in deep learning, C-SHAP broadens the clustering concept to enhance the efficiency of SHAP computations in more general models and datasets, and this is basically a paper that introduces a method concept and method that can be applied to any dataset and domain. C-SHAP clusters similar data points using K-means and computes feature attributions for representative clusters. This approach minimizes redundant calculations while ensuring interpretability and accuracy in feature attributions. By focusing on optimizing SHAP computations more generally, C-SHAP aims to provide a comprehensive solution applicable to various domains, including software engineering, and its performance can be compared with alternative interpretable methods like LIME. In Table 1, a summary of the interpretability techniques, their associated models, and their execution times across various datasets is provided to complement the insights discussed. \begin{table}
\begin{tabular}{l|l|l|l|l|l} \hline
**Ref. ** & **Dataset** & **Machine Learning Model** & **Interpretability Techniques** & **Execution Times of Interpretability Techniques** \\ \hline \multirow{2}{*}{[4]} & Wisconsin Branch Center (ROC), KDC (Lab
# Intrusion Detection Model Based on Improved Transformer
## 5 Discussion  
To address the three issues of slow model training, imbalanced datasets, and poor multi-class detection performance in intrusion detection models, this paper proposes an improved Transformer model. Through multiple experiments, we have demonstrated the effectiveness of the proposed model in addressing these issues, not only improving the model's training speed but also enhancing its classification detection ability. Despite the notable improvements in detection accuracy attained by the proposed model, there remain certain limitations that warrant attention. Specifically, the multi-class detection capability of the model, although enhanced, still requires further development. Future research endeavors may include the exploration of alternative strategies to enhance the binary classification performance of the proposed model. This may involve investigating diverse model architectures, such as incorporating attention mechanisms or exploring novel loss functions that are better equipped to capture the unique characteristics of the dataset. Additionally, efforts could be made to improve the interpretability of the model by analyzing the attention weights of the Transformer model and identifying significant features for intrusion detection. These approaches may culminate in further improvements in the overall multi-classification detection performance and can have far-reaching implications beyond the scope of intrusion detection. Overall, this study contributes to the knowledge system by proposing a new approach and demonstrating its effectiveness in  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Module**} \\ \hline \multirow{3}{*}{**Transformer**} & **Improved Positional Encoding** & **Hybrid Sampling** & **Two-Stage Learning Strategy** & **Accuracy** & **F1-Score** \\ \hline \(\surd\) & & & & 0.787 & 0.782 \\ \(\surd\) & \(\surd\) & & & 0.801 & 0.794 \\ \(\surd\) & \(\surd\) & \(\surd\) & & 0.830 & 0.825 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 0.841 & 0.838 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Ablation experiment of the multi-classification model.  
addressing the three issues in intrusion detection models, while also emphasizing the need for further research and improvement in this area.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 4 Proposed SLAM Classification Based on UWSNs
### Traditional SLAM Classifications
#### 4.1.3 Environmental Adaptability  
Environmental adaptability is another critical aspect of classifying SLAM techniques. Systems designed for structured environments, such as pipelines or seabed infrastructure, perform well in settings with geometric regularities that can be exploited for localization and mapping [37]. In contrast, techniques suitable for unstructured environments, such as open water or coral reefs, handle complex terrains that may lack distinctive features, requiring more sophisticated perception algorithms [36]. SLAM systems operating in dynamic environments, where conditions such as water currents and marine life introduce additional noise, must be capable of handling moving objects and changing conditions to ensure reliable performance [29].
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 5 Results
### Model Predictive Control  
Figure 25 illustrates the trajectory profiles of \(q_{d}\) and \(q\) with sequence A.  
Figure 23: Sequence of trajectory A, as a function of time, using PD-PI fuzzy control.  
Figure 24: 3D graph of trajectory A.  
Figure 25: Sequence of trajectory A, as a function of time, using MPC.  
Figure 26 shows the graph of the actual trajectory in 3D.  
Figures 25 and 26 show that the controller successfully followed the desired trajectory, with minimal displacement in the XY coordinate axes.  
Table 12 presents the performance indices achieved by the three controllers that were analyzed, designed, and simulated.  
Next, a quantitative comparison and interpretation of the dynamic performance of the designed controllers is presented. It is important to note that the RMS values range from 0.0720 to 0.1447 for the Computed Torque PD controller, indicating precision in tracking the planned trajectory. For the Residual Standard Deviation index, the values range from 0.0161 to 0.0572 for the Model Predictive Control controller, indicating good trajectory tracking, although with vibration in some segments. For the Index of Agreement, the values range from 0.9976 to 0.9991 for the PD-PI fuzzy controller, indicating smoothness and stability of the robot.  
When comparing the three control methodologies, the Computed Torque Controllers were the most efficient, under conditions influenced by fixed loads applied to the robot joints. The Computed Torque PD controller stands out for its advantages in terms of accuracy, robustness, simplicity, and applicability in real-world scenarios, such as medicine and industrial agriculture; in the latter case, to perform tasks such as painting, welding, assembly, and manipulation of parts and components, among others.
# Sensitivity Analysis of an Implanted Antenna within Surrounding Biological Environment
## 3 Polynomial Chaos Expansion (PCE)  
Polynomial chaos expansion methods are non-intrusive methods and use 3D solvers as black boxes. Let \(M\) be a mathematical model that from \(N\) input values (parameters) \(x=(x_{1},\ x_{2},\ldots,x_{N})\) generates the output (observable) y given by:  
\[y=M(x), \tag{2}\]  
The input values maybe affected by some random variations or uncertainties. Assuming that the components of the input random vector \(X\) are independent, it can be shown that if the random response of the physical phenomena \(Y\) has a finite variance, then it can be expressed as an infinite modal expansion, denoted polynomial chaos:  
Figure 3: Reflection coefficient of the antenna embedded in the arm model.  
\[Y=\sum_{\alpha}y_{\alpha}\Psi_{\alpha}(X) \tag{3}\]  
where \(\alpha\) is a multi-index, \(y_{\alpha}\) are the coefficients, \(\Psi_{\alpha}\) are basis functions, multivariate orthonormal polynomials. These polynomials are built using tensor products:  
\[\Psi_{\alpha}(x)=\prod_{i=1}^{N}\Psi_{\alpha_{i}}^{(i)}(x_{i}) \tag{4}\]  
where \(\alpha\) denotes the \(N\)-uplet \((\alpha_{1},\ \alpha_{2},\ldots,\alpha_{N})\) divided by subheadings.  
These univariate polynomials are a family of orthonormal polynomials with respect to the margin probability density functions (pdf) given by:  
\[E\Big{(}\Psi_{k}^{(i)}(X_{i}),\Psi_{j}^{(i)}(X_{i})\Big{)}=\delta_{jk} \tag{5}\]  
where \(\delta_{jk}\) is the Kronecker symbol.  
If \(f_{X_{i}}(x_{i})\) is the marginal pdf of the random input variable \(X_{i}\), then from the independence of the input variables then the pdf of \(X\) is given by:  
\[f_{X}(x)=\prod_{i=1}^{N}f_{X_{i}}(x_{i}) \tag{6}\]  
In case of uniform or Gaussian input distributions, the corresponding polynomial basis are the Legendre and Hermite polynomials families, respectively. The PCE coefficients can be estimated by using spectral projections or via the use of least-square regressions. The \"projection\" approach takes advantage of the orthogonality of the chaos polynomials [23].