esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Limitations of the Study
### Expanding Validation to Strengthen Conclusions  
The framework's validation is currently focused on a single use case, which restricts the results' applicability to different kinds of merchants or geographic contexts. Future research should involve larger-scale studies that span many use cases to validate the framework's adaptability to varied retail settings, product kinds, and data from other areas, even if this case study with Carrefour and Monoprix has shown encouraging findings. To assess the system's adaptability in many market segments, future research should test it with merchants of varying sizes, such as independent shops, regional chains, and e-commerce platforms. To evaluate the framework's effectiveness in various cultural and demographic situations, it is also essential to validate it in a variety of geographic locations. In order to assess the system's long-term influence on consumer behavior and the suggestions' ongoing applicability, longitudinal studies will also be required to examine its performance over lengthy time periods.
# Kalman-Filter-Based Tension Control Design for Industrial Roll-to-Roll System
## 3 Design of a Signal Filter
### Kalman Filter Algorithm  
The KF algorithm is divided into two stages: prediction process and correction process. Based on the system model variables A and Q, the prediction process guesses how the estimated value \(\hat{x}_{k}\) changes when the time changes from \(t_{k}\) to \(t_{k+1}\). A is the system matrix and Q is the covariance matrix of W. Depending on how the system model variables, such as A, Q, R, and H, are selected, the performance of KF varies. Equations (12)-(17) refer to the KF algorithm mentioned in Reference [15]. The following equation is the state variable and error covariance, which are the predicted variables as follows:  
\[\hat{x}_{k}^{-}=A\hat{x}_{k-1}+Bu_{k} \tag{12}\]  
\[{P_{k}}^{-}=AP_{k-1}A^{T}+Q \tag{13}\]  
In the correction process, the KF's final estimated value \(\hat{x}_{k}\) is calculated using the system model variables \(H\) and \(R\). \(H\) is the measurement matrix and \(R\) is the covariance matrix of \(V\). The KF computes  
Figure 3: Kalman filter (KF) structure.  
the final estimated value \(\hat{x}_{k}\) by adding the predicted value \(\hat{x}_{k}{}^{-}\) and the current measurement \(Z_{k}\) multiplied by the appropriate weight value as follows:  
\[\hat{x}_{k}=\hat{x}_{k}{}^{-}+K_{k}(Z_{k}-H\hat{x}_{k}{}^{-}) \tag{14}\]  
Here, the weight value \(K_{k}\) is known as a Kalman gain, and is newly calculated by repeating the algorithm differently from the LPF and high pass filter (HPF) as follows:  
\[K_{k}=P_{k}{}^{-}H^{T}\Big{(}HP_{k}{}^{-}H^{T}+R\Big{)}{}^{-1} \tag{15}\]  
\(P_{k}\) is the error covariance. The error covariance reflects how far the estimated value is from the true value as follows:  
\[P_{k}=(I-K_{k}H)P_{k}{}^{-} \tag{16}\]  
According to the definition of error covariance, if \(P_{k}\) is large, the estimation error is large, and if \(P_{k}\) is small, the estimation error is then small. Error covariance is defined as follows:  
\[P_{k}=E[(x_{k}-\hat{x}_{k})(x_{k}-\hat{x}_{k})^{T}] \tag{17}\]  
where \(E(\cdot)\) refers to the expected value.  
Noise covariance matrices Q and R have limitations to be determined analytically because it is difficult to grasp noise characteristics with multiple errors. In this paper, the Q and R values were determined experimentally through trial and error process. The larger the Q, the more influenced by the measured value. Since R has the opposite tendency to Q, the larger the R, the less affected the measured value.
## 4 Analytical Study
### Verification of the Analytical Models  
This section provides a comparative examination of the findings derived from the analytical model in relation to the outcomes observed in the shake table experiments. Initially, the eigenvalue results obtained from the specimens undergoing testing are scrutinized. This is then succeeded by an evaluation of the LVDT outcomes in connection with the nonlinear time history analysis extrapolated from the analytical models. Following the calibration of the analytical models with the empirical data collected from the shake table tests, the values of damped hysteretic energy for these models are calculated and discussed in the context of the established code threshold limits.  
The natural vibration periods were determined by considering the stiffness of the fissured sections in both longitudinal and transverse orientations, and these computations for the two specimens are compared with the empirical data as illustrated in Table 2. The derived eigenperiods exhibit alignment with the findings from the shake table experiments for each specimen. An assessment of the LVDT data acquired from the shake table tests, compared with the calculated displacement trajectory generated from the analytical model, is presented in Table 3. The information displayed in the tables signifies a robust correlation between the results of the analytical model and the shake table experiments. This finding implies that the analytical model has been successfully calibrated.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Specimen No** & **Shake Table Test (s)** & **Analytic Model (s)** & **Error (\%)** \\ \hline
1 & 0.495 & 0.463 & 6.45 \\
2 & 0.151 & 0.147 & 2.65 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of the natural vibration periods.  
Figure 10: Cyclic stress–strain response of the Stl_BRB material model implemented in the software for the conventional BRB benchmark.
## 2 Materials and Methods
### SD Approach Using XGB and k-Means  
This section estimates the weights of the features, i.e., vehicle groups, charging levels, charging locations, temperature, electricity rates, seasonal category, weather events, day type, etc. [25], using the XGB algorithm. Then, the weighted features are integrated using the k-means clustering.  
XGB is a decision tree-based ensemble ML algorithm that applies a gradient boosting framework. XGB is a supervised algorithm that assembles all base learners into strong learners. The prediction result by XGB is equal to the total of all base learners.  
The XGB process is as follows:  
\[\hat{y}_{i}=\sum_{n=1}^{N}f_{n}(x_{i}),\ f_{n}\in F\, \tag{1}\]  
where \(f_{n}\) represents the nth decision tree, \(N\) is the number of decision trees, \(F\) is the space that covers a set of decision trees. The objective function of XGB includes the loss function and the regularization term, which are defined as:  
\[obj=\sum_{i=1}^{n}l(y_{i},\ \hat{y}_{i})+\sum_{m=1}^{n}\Omega\ (f_{n}), \tag{2}\]  
\[\Omega\ (f_{n})=\gamma T+1/2\ \beta\ \sum_{j=1}^{T}w_{j}{}^{2}\, \tag{3}\]  
where \(l\ (y_{i},\ \hat{y}_{i})\) denotes the loss function, \(y_{i}\) and \(\hat{y}_{i}\) are the prediction and the target values, \(\Omega\) states the regularization term, \(n\) is the number of targets \(y_{i}\), \(T\) specifies the leaf node number in a decision tree, \(w_{j}\) is the score of leaf nodes, \(\gamma\) and \(\beta\) act as the penalty factors. Then, \(k\)-means clustering, which is an unsupervised learning algorithm, is used to partition \(n\) observations into different clusters, so that similar data points are grouped together and underlying patterns can be discovered.
# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
###### Abstract  
The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), a crucial preprocessing step in machine learning that seeks out the ideal set of characteristics with the maximum accuracy possible. Due to their dominance over traditional optimization techniques, researchers are concentrating on a variety of metaheuristic (or evolutionary) algorithms and trying to suggest cutting-edge hybrid techniques to handle FS issues. The use of hybrid metaheuristic approaches for FS has thus been the subject of numerous research works. The purpose of this paper is to critically assess the existing hybrid FS approaches and to give a thorough literature review on the hybridization of different metaheuristic/evolutionary strategies that have been employed for supporting FS. This article reviews pertinent documents on hybrid frameworks that were published in the period from 2009 to 2022 and offers a thorough analysis of the used techniques, classifiers, datasets, applications, assessment metrics, and schemes of hybridization. Additionally, new open research issues and challenges are identified to pinpoint the areas that have to be further explored for additional study.  
metaheuristics; feature selection; hybridization; evolutionary methods; classification 2023
# Landslide Prediction with Model Switching +
## 1 Introduction  
Landslides are natural disasters which can cause huge damage to properties and severe loss of lives. Many studies have focused on how to detect and monitor landslides. Though landslide detections could be performed in real-time, there might not be enough time to react, so as to save human lives and properties. In order to minimize the losses caused by landslides, an early prediction mechanism, with pre-warnings, is necessary. Once the system can give an alarm in advance, people would have more response time to evacuate before the landslide occurs.  
There are several problems in landslide prediction. First, just as in most safety-critical applications, landslide prediction also exhibits the same data imbalance problem, where the class of stable data has much more data than the class of unstable data. Stable data, here, refers to the normal conditions (where there is no landslide), while unstable data represents landslide-related information. Second, a low true-positive rate (TPR) problem is often found in safety-critical applications, because of the interference in learning between two or more classes in the datasets. For example, learning from the normal stable conditions often affects the learning from the unstable (landslide) conditions, thus resulting in a low TPR. Third, predictive applications are often faced with the problem of determining an appropriate prediction horizon; that is, the size of the time window of past history to be used for predictions. Finally, real-time applications face the problem of determining when to re-train the models.  
To address the above-mentioned four problems existing in landslide prediction, this work provides a total solution in the form of an early warning system, called the Model Switch-based Landslide Prediction System (MoSLaPS). To address the data imbalance problem, we adapt the popular Adaptive Synthetic Sampling (ADASYN) method [1] to landslide prediction. To address the low TPR problem, we propose a novel event-class model switch predictor design that significantly improves TPR. To address the problem of customizing the prediction horizon, we also propose a novel dynamic tuning method for the prediction horizon, in order to achieve the goal of early warning. To address the problem of determining model re-training time, we propose a novel learning-based re-training method, based on an error model which considers both the long-term and short-term accumulated errors. Errors are also predicted, so re-training can be done earlier in preparation for future data changes; as a result of which, our proposed system can achieve the goal of early warning.  
Section 2 introduces some related work. Section 3 presents the proposed model switching method. Section 5 presents and analyzes the experimental results. In Section 6, the conclusions and future work are described.
## 4 Reliable and Secure Probability Analyses
### Outage Probability Analysis for Untrusted Relays  
Utilizing the description from (8), we can deduce the outage probability \(P_{\text{out}}^{\text{UR}}\) as follows:  
\[P_{\text{out}}^{\text{UR}}= \text{Pr}\Big{\{}R_{\text{S}_{1}}^{\text{D}}(\epsilon_{1})\hat{R}_ {\text{S}_{1}}\leq C(\gamma_{\text{S-UR}_{1}})\Big{\}}\times\text{Pr}\Big{\{}R _{\text{S}_{2}}^{\text{D}}(\epsilon_{2})\hat{R}_{\text{S}_{2}}\leq C(\gamma_{ \text{S-UR}_{1}})\Big{\}}\] (20) \[= \text{Pr}\bigg{\{}0\leq\frac{\gamma_{\text{S-UR}_{1}}}{1+\gamma_ {\text{D-UR}_{1}}}\leq 2^{R_{\text{S}_{1}}^{\text{D}}(\epsilon_{1})\hat{R}_{\text{S}_{1}}}-1 \bigg{\}}\text{Pr}\bigg{\{}0\leq\frac{\gamma_{\text{S-UR}_{2}}}{1+\gamma_{ \text{D-UR}_{2}}}\leq 2^{R_{\text{S}_{2}}^{\text{D}}(\epsilon_{2})\hat{R}_{\text{S}_{2}}}-1 \bigg{\}}\] (21) \[= \int_{0}^{\infty}f(\gamma_{\text{D-UR}_{1}})d\gamma_{\text{D-UR} _{1}}\int_{0}^{\left(2^{R_{\text{S}_{1}}^{\text{D}}(\epsilon_{1})\hat{R}_{ \text{S}_{1}}}-1\right)(\gamma_{\text{D-UR}_{1}}+1)}f(\gamma_{\text{S-UR}_{1} })d\gamma_{\text{S-UR}_{1}}d\gamma_{\text{D-UR}_{1}}\] \[\times \int_{0}^{\infty}f(\gamma_{\text{D-UR}_  
For the sake of clarity, symbol indexes are disregarded. The justification for (20) comes from the [PERSON] separation theorem combined with the assumption of independent fading; (21) is derived from the presumption of [PERSON]'s Gaussian codebook, while the rationale for (22) and (23) is due to the nonergodic Nakagami-m fading channels under consideration. Given the block fading assumption, quasi-static fading channels are perceived as AWGN channels with consistent channel gains as per the fading distribution in every fading realization. As a result, outage probability \(P_{\text{out}}^{\text{UR}}\) can be interpreted as a series of dual integrations spanning the rate region regarding the PDF of the current SNRs for the involved channels.
# Joint Radar and Communications Waveform Design Based on Complementary Sequence Sets
## 6 Conclusions  
In this paper, we designed a JRC waveform group based on CSSs called the JRC-CWG. In order to achieve better radar performance, the JRC-CWG was optimized to obtain the SACF with a low sidelobe level. However, it has poor Doppler resilience. To improve the Doppler resilience of the JRC-CWG, the DR-JRC-CWG design method was proposed. To devise the JRC-CWG and DR-JRC-CWG, the algorithm based on the framework of the FR-CG algorithm and the FFT algorithm was employed, which can be implemented using FFT to decrease the calculation complexity. Compared with the OPP waveform and LFM-BPSK waveform, the designed JRC-CWG and DR-JRC-CWG have favorable radar and communications performances.  
Conceptualization, H.L., Y.L. and G.L.; writing--original draft preparation, H.L.; writing--review and editing, Y.L. and Y.C.; supervision, G.L.; funding acquisition, Y.L. and G.L.; All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (NSFC) under Grants 62001352 and 61931016, by the Fundamental Research Funds for the Central Universities under grant no. JS220207, and by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under grant no. 61621005.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# NOMA-Based VLC Systems: A Comprehensive Review
###### Abstract  
The enhanced proliferation of connected entities needs a deployment of innovative technologies for the next generation wireless networks. One of the critical concerns, however, is the spectrum scarcity, due to the unprecedented broadcast penetration rate nowadays. Based on this, visible light communication (VLC) has recently emerged as a viable solution to secure high-speed communications. VLC, a high data rate communication technology, has proven its stature as a promising complementary to its radio frequency (RF) counterpart. VLC is a cost-effective, energy-efficient, and secure technology that exploits the current infrastructure, specifically within indoor and underwater environments. Yet, despite their appealing capabilities, VLC systems face several limitations which constraint their potentials such as LED's limited bandwidth, dimming, flickering, line-of-sight (LOS) requirement, impact of harsh weather conditions, noise, interference, shadowing, transceiver alignment, signal decoding complexity, and mobility issue. Consequently, non-orthogonal multiple access (NOMA) has been considered an effective technique to circumvent these shortcomings. The NOMA scheme has emerged as a revolutionary paradigm to address the shortcomings of VLC systems. The potentials of NOMA are to increase the number of users, system's capacity, massive connectivity, and enhance the spectrum and energy efficiency in future communication scenarios. Motivated by this, the presented study offers an overview of NOMA-based VLC systems. This article provides a broad scope of existing research activities of NOMA-based VLC systems. This article aims to provide firsthand knowledge of the prominence of NOMA and VLC and surveys several NOMA-enabled VLC systems. We briefly highlight the potential and capabilities of NOMA-based VLC systems. In addition, we outline the integration of such systems with several emerging technologies such as intelligent reflecting surfaces (IRS), orthogonal frequency division multiplexing (OFDM), multiple-input and multiple-output (MIMO) and unmanned aerial vehicles (UAVs). Furthermore, we focus on NOMA-based hybrid RF/VLC networks and discuss the role of machine learning (ML) tools and physical layer security (PLS) in this domain. In addition, this study also highlights diverse and significant technical hindrances prevailing in NOMA-based VLC systems. We highlight future research directions, along with provided insights that are envisioned to be helpful towards the effective practical deployment of such systems. In a nutshell, this review highlights the existing and ongoing research activities for NOMA-based VLC systems, which will provide sufficient guidelines for research communities working in this domain and it will pave the way for successful deployment of these systems.  
NOMA; successive interference cancellation (SIC); spectral efficiency; massive connectivity; multiple access; visible light communication; resource allocation +
Footnote †: journal: Sensors
## 3 Materials and Methods
### Traffic Scenario Map Construction
#### 3.1.3 Spatial Scene Maps  
At each frame \(t\) of the video, spatial information is captured based on the relative positions of different objects in the bird's-eye view and represented as a scene graph \(S_{t}^{i,j}\). At time \(t\), the spatial relationship between subject \(i\) and object \(j\)\(S_{t}^{ij}\) is defined by the quadrant where the object is located relative to the subject, i.e., \(S_{t}^{i,j}\in R_{s}\), where \(R_{s}=\{\text{top left, top right, bottom left, bottom right}\}\). An example of a spatial scene graph is illustrated in Figure 2e. These spatial relationships are determined by the 3D position information of the objects in the bird's-eye view. At each time step \(t\), the spatial relationship \(S_{t}^{i,j}\) between subject \(i\) and object \(j\) is represented as one of the four quadrants: upper left, upper right, lower left, and lower right. An example of a spatial scene graph is shown in Figure 2e.
## 3 Results
### Wireless Sensor Network Results  
In Section 2.1.1, an overview of the main components of the WSN is provided. The WSN was successfully deployed in the field for a duration of approximately 36 days, during which a substantial amount of data was collected. A total of 5432 measurements were made by the sensor nodes, and these measurements were securely transmitted and stored in the web platform for further analysis.  
To illustrate the captured data, Figure 12 shows a plot representing one day of monitoring. The plot reveals interesting insights regarding the dynamics of the monitored variables. Notably, at around 9:00 a.m., there was an irrigation event, which is evident from the increase in the humidity levels in the crop. Throughout the day, the humidity gradually decreased, indicating the gradual water loss due to environmental factors. On the other hand, variables such as macronutrients remained relatively stable, showing minimal fluctuations during the observed day. According to public weather data, for that day, the temperature was 22-32 \({}^{\circ}\)C and the sky was sunny; therefore, there was no precipitation, and the temperature matches our measurements.  
This snapshot of data provides a glimpse into the temporal dynamics and patterns of the monitored variables within the stevia crop. It demonstrates the effectiveness of the WSN in capturing and recording these measurements, enabling a comprehensive understanding of the crop's environmental conditions and their potential impact on its growth and development.  
Figure 11: (**a**) Export data section of the web platform, showing the query menu that allows for date range selection and node selection. (**b**) CSV file downloaded using the data export section of the web platform.
## 4 Experimental Results
### Comparison of Visualization Results  
In order to better compare the recognition performance of the prebuilt neural networks, the T-SNE algorithm was used to visualize the recognition features of four classification models: backpropagation neural network, support vector machine (SVM) classification model, convolutional neural network (CNN), and deep residual network (ResNet18), as shown in Figure 15. The depicted figure distinctly showcases that the residual network, following meticulous layer-by-layer processing, facilitates precise discrimination among 18 distinct types of damage features, outperforming the classification efficacy of the remaining three models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation** & **BP Neural** & & & \\
**Indicator** & **Network** & & & \\ \hline OA coefficient & 36.43\% & 64.70\% & 88.44\% & 94.29\% \\ Kappa coefficient & 32.61\% & 62.62\% & 87.76\% & 93.95\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical table of classification results.  
Figure 14: Comparison chart of classification results.
## 5 Experimental Results
### Throughput Analysis
#### 5.3.4 Comparison  
The structure of the AC algorithm, WM algorithm, and the algorithm proposed by this study can cause misaligned and uncoalesced memory access operations. Also, numerous threads are inactive in the parallel region due to warp divergence. These situations decrease the number of active threads per warp and negatively affect the pattern-matching performance. In this direction, the kernel performance can be increased by using shared memory. Figures 15 and 16, respectively, compare the packet-processing times and throughputs of these pattern-matching versions applied to datasets with different numbers of patterns. Shared memory located on the SM has a much lower memory processing latency than global memory. Therefore, processing packets in shared memory instead of global memory will reduce the packet-processing time (Figure 15). As a result, the highest throughput is obtained from the versions using shared memory in the pattern-matching process for all three algorithms (Figure 16).  
As seen in Figure 16, the PA achieved the highest throughput among these three algorithms. Similar to the AC and WM algorithms, the pattern-matching performance of the PA is also related to the memory space required by the algorithm and access overheads. However, the memory space required is much less than that of the other two algorithms. This allows efficient use of the cache space and shared memory.  
Figure 14: Comparison of the throughputs obtained from the pattern-matching tests performed using the PA.