esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Feature Selection  
Another major aim of this study was to analyze how different number of features affects physical activity classification and what is the minimal number of features to use without compromising classification performance. For that, two different feature selection schemes were used to optimize the feature set.  
One scheme was based on various methods that were used successively (Figure 2). This scheme used the features extracted with sampling frequency of 50 Hz and window length of 3 s and the achieved optimized feature set was later used with other frequency and window length combinations.  
First, correlating features were removed based on a large correlation matrix that showed each feature's correlation coefficient with other features. From feature pairs or groups with a very high correlation (correlation coefficient larger than 0.9 or lower than \(-0.9\)), only the simpler features in terms of computational power requirements and complexity were kept. By using this method, 67 features were removed from the initial set, and a new subset of 43 features was formed. This method and the results have also been described in the previous study done by the authors [28].  
Further feature optimization was done with one-way analysis of variance (ANOVA). The purpose of one-way ANOVA is to determine whether data from several groups of a factor have a common mean. ANOVA was used in this work to find out which features did not differentiate between any of the activities and thus did not provide any useful information for activity classification. Based on ANOVA results, 15 features were removed that were found not to affect classifier performance, and a new subset of 28 features was formed.  
Finally, a sequential backward selection (SBS) procedure was repeated, where each feature was again removed one-by-one (those calculated similarly over all axes were removed together), and thefeature that decreased the classifier performance the least was removed. After removing features this way, the classifier performance was still persistent with 13 features used. Further removal of features showed a decrease in activity classification sensitivities.  
The second feature selection scheme used in this study was a sequential forward selection (SFS) method similar to the last steps used in the first scheme (Figure 3). In this method, features were added one-by-one by conducting physical activity classification with each feature and, for every iteration, the best feature was kept. Features were added until the overall average classification sensitivity did not improve by more than 0.001. This method was completed for every sampling frequency and window length combination, and was used to compare the results of the first method.  
Figure 3: Forward sequential feature selection (SFS) method used in the second feature selection scheme.  
Figure 2: First feature selection scheme using correlation analysis, ANOVA, and backwards sequential feature selection with the number of features removed in each step.
# On a Certain Research Gap in Big Data Mining for Customer Insights
## 1 Introduction
To drive future decisions, big data analytics leverages various databases from a heterogeneous range of related media and metadata [18]. Organizations, including enterprises, to function properly and in the long term, should advance and implement goals (under constant and dynamic establishment), both short-term and long-term. BDA is seen as the \"enabler of dynamic capabilities\" [19]. Grasping the processes taking place in the organization, of which the evolution of big data analytics is a part, is therefore the foundation for effective counteracting failures in achieving the goals of the organization for its further growth. The organization, being in constant and effective development, can better use BDA and thus more efficiently understand and research the needs of consumers. The main purpose of this article is to provide a theoretically grounded discussion on big data analytics for customer insights and to identify and describe a research gap due to the lack of temporal big data mining methods and tools. The main contributions of this paper are:  
1. To organize knowledge on the role of advanced big data analytics in understanding client behavior;
2. To indicate the importance of the temporal dimension of customer behavior;
3. To identify an interesting research gap: mining of temporal big data for a complete picture of clients. These contributions are due to analyzing the existing research achievements promulgated in the domains of big data analytics and of customer analytics. Based on this analysis, we identify the current state of big data analytics for customer insights, and the challenges to address the time dimension of customer behavior while mining big data. The literature search has employed various bibliographic databases: Scopus, Thomson Reuters Web of Science, ProQuest, and EBSCOhost, as well as open access papers. The research has been conducted in four steps: search, selection, analysis, and synthesis as proposed in [20]. The results presented in the paper have been verified against the Scopus database. The results obtained in this paper may be further utilized by scholars and researchers looking for an engrossing new possible area of investigation and by business practitioners searching for new approaches to customer analytics. The rest of the paper is organized as follows: Section 2 provides a profound theoretical outline for the notions of customer insights and big data. In Section 3 the types of customer insights from big data are discussed in the context of their role in organizations. Section 4 is devoted to the identified challenges in big data mining which leads to the identification of a research gap. In Section 5, the identified research gap is verified against the Scopus database. In Section 6, the discussion of results against existing literature is provided. Section 7 summarizes and concludes the research findings.
# Detection of Safety Signs Using Computer Vision Based on Deep Learning
## 3 Deep Neural Network
### Improved NMS Algorithm  
In the object detection task, the original output results have a large number of duplicate detection prediction boxes. As a method of model post-processing, a Non-Maximum Suppression (NMS) algorithm can effectively suppress the duplicate prediction boxes. The traditional NMS first retains one prediction box with the highest confidence score, moves it into the result set in each loop, and then iterates through the other prediction boxes. If the overlap degree between prediction boxes with the highest confidence score exceeds a threshold, the prediction box is considered to be detected repeatedly. In this case, the candidate box should be deleted to retain the prediction box with an overlap degree less than the threshold and the prediction box without overlap.  
The NMS algorithm has obvious disadvantages. Firstly, a threshold, which is determined by subjective experience, needs to be set manually. Secondly, when similar targets are dense, and the detected objects are highly overlapped, the NMS algorithm is easy to delete the prediction box belonging to another target, resulting in missed detection.  
By using a smoother suppression method, the Soft-NMS algorithm solves the problem of traditional NMS. When the overlap between the current prediction box and the prediction box with the highest confidence level exceeds a threshold, the prediction box is not immediately removed from the result set, but the confidence score of the current prediction box is reduced using a Gaussian weighted function (see Equation (4)).  
\[s_{i}=s_{i}e^{\frac{\text{{i}u(MN)}^{2}}{\sigma}},\forall bi\
ot\in D \tag{4}\]  
where, \(s_{i}\) is the classification confidence, \(M\) is the current highest scoring detection box, \(bi\) is the box to be processed, \(iou\) represents the ratio of the intersection area of the two candidate boxes to the union area, its fomula is shown in Equation (5).  
\[IOU=\frac{area(B_{gt}\cap B_{p})}{area(B_{gt}\cup B_{p})} \tag{5}\]  
The Gaussian weighting method avoids the issue that when there is no overlap, the penalty function has no penalty for prediction boxes, while when the prediction boxes are highly overlapped, a higher penalty function will be generated. Subsequently, bounding boxes with scores below a certain threshold are removed, and the process is repeated by selecting the bounding box with the highest score and adding it to the final list of detection results. The scores of the remaining bounding boxes are updated based on the IOU with the highest-scoring bounding box. Repeat these steps until all bounding boxes are processed, then return the final detection results list.  
Through this method, Soft NMS can more delicately handle situations with overlapping bounding boxes, reducing the incorrect elimination of correct detection results, especially in scenes with dense targets, effectively improving the accuracy and recall rate of detection.
###### Abstract  
In this paper, a compact microstrip rat-race coupler at a 950 MHz operating frequency is designed, simulated, and fabricated. New branches are proposed in this design using high-/low-impedance open-ended resonators. In the conventional rat-race coupler, there are three long \(\lambda/4\) branches and a 3\(\lambda/4\) branch, and they occupy a very large area. In the presented designed, three compact branches are proposed for use instead of three \(\lambda/4\) branches and an ultra-compact branch is suggested for use instead of the 3\(\lambda/4\) branch. Additionally, an artificial neural network (ANN) approach is incorporated to improve the performance of the resonators using a radial basis function (RBF) network. The proposed compact structure has achieved a reduction of more than 82% compared with the size of the conventional coupler structures. Additionally, the proposed coupler can suppress the 2 nd up to the 5 th harmonic to improve the performance of the device.  
rate race coupler; resonator; neural network; filter; GSM +
Footnote †: journal:  
1  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] A Compact Rat-Race Coupler with Harmonic Suppression for GSM Applications: Design and Implementation Using Artificial Neural Network.
## 2 Construction of the Defocus and Tilt Compensation Theoretical Model
### Dual-Prism Compensation Theoretical Model
In the diagram, the gray image plane represents the changes caused by the rotation angle \(\omega\) and the black line indicates the position changes of the image plane after one rotation of the wedge-shaped prism. Four feature positions are selected and displayed in the figure. Similarly, the blue image plane represents positional changes due to variations in parameter \(D\), which corresponds to the positional change of the image plane caused by altering the spacing between two wedge prisms; the yellow image plane corresponds to changes from parameter \(\Delta T\), which is the change in image plane position caused by the oblique motion of the second prism. This is abstracted as a mathematical model, specifically the change in image plane position resulting from the change in the center thickness of the second prism.
## 4 Innovative Approach to Building a Metaverse Classroom
### Unique Technological Integration  
#### 4.1.1 Cross-Platform Accessibility  
Cross-platform accessibility in a Metaverse classroom ensures equal participation for all students, regardless of the devices they use [137]. By making the classroom compatible with VR headsets, smartphones, tablets, and traditional computers, students from diverse backgrounds can access immersive learning experiences without requiring specialized  
Figure 2: Innovative approach to building a Metaverse classroom.  
hardware [223]. This device flexibility promotes inclusivity and flexibility by accommodating students with varying technological resources [97]. A key feature of cross-platform accessibility is seamless transitions between devices, allowing students to switch from one device to another without losing progress [56]. For example, a student could start a lesson on a desktop computer at home and continue on a tablet while commuting, with progress synced in real time [53]. Cloud-based data storage and session synchronization ensure a smooth learning experience, allowing students to participate in lessons from various locations and on different schedules [224]. Optimized performance across devices is essential to ensure smooth functionality on various platforms [225]. VR headsets require high frame rates and detailed 3D rendering, while smartphones need efficient resource management to handle processing and battery limitations [54]. To meet these varied needs, adaptive rendering technologies adjust graphics and interactive features based on the capabilities of the device [226].  
Additionally, low-latency connections and data compression techniques help devices with limited bandwidth maintain access to core Metaverse features without significant lag or quality loss [58]. Bandwidth adaptability is crucial for students with unstable or low-speed internet, especially in remote areas [54]. The Metaverse classroom integrates features such as data compression, offline access to resources, and progressive content loading to reduce dependence on continuous high-speed connections [227]. For example, students can download essential materials, such as lesson videos or 3D models, ahead of time and complete activities offline [15]. This design ensures that students in areas with limited internet can still participate effectively in virtual learning [228]. A unified user interface (UI) across all platforms provides students with a consistent and intuitive experience, regardless of their device [1]. The interface adapts to different screen sizes and input methods, ensuring smooth navigation with a VR headset or a mobile phone [71]. Customizable controls and personalized onboarding help students quickly familiarize themselves with the virtual environment, reducing the cognitive load and making the learning process more intuitive [132]. By prioritizing accessibility and adaptability across platforms, the Metaverse classroom creates a flexible, inclusive, and equitable learning environment that meets the needs of all students, regardless of their technological or connectivity limitations [18].
## 4 Simulation Results and Discussion
### Presentation and Discussion of Results  
We consider the following three cases for testing the effect of scheduling of DR programs and EVs on operation costs of the MG, load profile curve characteristics and profit of customers during the scheduling period. * Case 1: without demand side participation and EVs commitment,
* Case 2: with demand side participation and without EVs commitment,
* Case 3: with demand side participation and EVs commitment. It should be noted that Case 1 is considered to be a base case, so operating costs, load profiles and reserve scheduling in other cases are evaluated compared to the base case. Case 1: In this case, DR programs are not considered and there is no contribution from EVs side. The scheduled energy and reserve capacity in this case is illustrated in Figure 5a,b, respectively. As shown in Figure 5a, based on the economic dispatch results, low-cost MT1 and GE are used as base units to provide the energy. These generators are dispatched during the entire scheduling horizon to reduce the overall operating cost, while the other units (especially FC1 and FC2 due to their higher operating cost) are only dispatched at peak hour periods. As shown in Figure 5b, all the scheduled reserve capacity is provided by dispatchable DG units, including up-spinning reserve (Up/DGs), down-spinning reserve (Down/DGs), and non-spinning reserve (Non/DGs) in this case. Since the output power of WT and PV are intermittent, the required reserve power is provided by MTs, FCs and GE. It can be observed that when RESs power productions in scenarios are relatively different from the forecasted values, (i.e., in 10:00-14:00 and 19:00-22:00), more reserve capacity is scheduled accordingly.
# Light-YOLOv5: A Lightweight Algorithm for Improved YOLOv5 in Complex Fire Scenarios
## 5 Discussion  
The performance of Light-YOLOv5 when applied to complex scenarios was discussed in Section 4.4. As illustrated in Table 5, by comparing the more popular lightweight algorithms at this stage, it can be observed that the mAP of Light-YOLOv5 was 6.1% higher than that of YOLOv3-tiny, 5.5% higher than that of YOLOv-s, and 6.8% higher than that of YOLOv7-tiny; in terms of the F1 scores, Light-YOLOv5 was 5% higher than YOLOv3-tiny and 4% higher than YOLOv7-tiny. Light-YOLOv5's parameters were only 1.29M, and FLOPs were only 3.4G, which means that the algorithm is easier to deploy in low-cost devices. As shown in Figure 7a,b, Light-YOLOv5 had a higher confidence level compared to the original algorithm in all cases, and it was better at detecting small targets, as presented in Figure 7a, where some of the undetected small-target flames were detected in Figure 7b.  
However, we observed that the algorithm still presented limitations during the tests, such as a low accuracy in detecting small targets and semi-obscured flames or smoke, which can lead to the failure to detect the device in the first instance when it is more than a certain distance away from the target. These issues may be caused by the fire dataset's age and the lack of clarity of some images; on the other hand, it may be that the model loses too much information in the downsampling process. However, these problems are not insurmountable. For the semi-obscuration problem, we can add more smoke and fire-specific data enhancement during the data pre-processing; for the detection of small targets, we can add a detection head for small targets at the network input, or redesign an anchor for small targets. For example, in Figure 7b, it can be observed that even Light-YOLOv5 had some small targets that were not detected.
## 3 Theoretical Background
### Proposed DCGOSM-Based Speech Enhancement Technique
#### 3.9.1 Process of Obtaining the Required Sensing Matrix
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Techniques**} \\ \hline  & \multicolumn{2}{c}{**OMP**} & \multicolumn{2}{c}{**CoSaMP**} & \multicolumn{2}{c}{**StOMP**} & \multicolumn{2}{c}{**Proposed**} \\ \hline \multirow{2}{*}{**SNR (dB)**} & **SNR** & **SSNR** & **SNR** & **SSNR** & **SNR** & **SNR** & **SNR** & **SSNR** \\  & **(dB)** & **(dB)** & **(dB)** & **(dB)** & **(dB)** & **(dB)** & **(dB)** & **(dB)** & **(dB)** & **(dB)** \\ \hline
0 & 5.74 & 4.631 & 3.113 & 2.814 & 4.417 & 3.581 & **6.380** & **5.874** \\
5 & 9.124 & 8.049 & 7.481 & 6.328 & 8.392 & 7.164 & **9.627** & **9.157** \\
10 & 12.339 & 11.385 & 11.542 & 10.221 & **11.916** & 10.773 & **12.639** & **12.298** \\
15 & 14.862 & 14.101 & 14.658 & 13.527 & 14.696 & 13.807 & **14.993** & **14.745** \\
20 & 16.266 & 15.941 & 16.267 & 15.765 & 16.299 & 15.944 & **16.390** & **16.386** \\ \hline SNR (dB) & PESQ & STOI & PESQ & STOI & PESQ & STOI & PESQ & STOI \\ \hline
0 & 1.801 & 0.688 & 1.759 & 0.634 & 1.796 & 0.608 & **2.296** & **0.748** \\
5 & 2.319 & **0.764** & 2.069 & 0.689 & 2.173 & 0.699 & **2.795** & 0.731 \\
10 & 2.570 & 0.793 & 2.403 & 0.734 & 2.509 & 0.768 & **3.092** & **0.809** \\
15 & 2.759 & **0.837** & 2.611 & 0.776 & 2.708 & 0.817 & **3.201** & 0.825 \\
20 & 2.957 & **0.877** & 2.760 & 0.824 & 2.874 & 0.869 & **3.220** & 0.862 \\ \hline \hline \end{tabular} The bold font indicates the best obtained result for the selected measure in that row.  
\end{table} 
Table 1: Performance comparison for white noise using the NOIZEUS dataset sample sp05.wav. Figure 7: Block diagram of the proposed algorithm. The process of obtaining the required sensing matrix involves the following steps:  
1. In the first step, the clean speech sample is mixed with the noise using the mixer block, which adjusts the amplitude of the noise signal according to the given SNR and then adds it to the clean speech to generate the noisy speech sample. 2. The noisy speech sample is passed through the preprocessing block, which normalizes the signal. Normalization helps to reduce the impact of variations in recording conditions, microphone characteristics, and speaker differences. By normalizing the speech signals, the relative differences in amplitude caused by these factors are minimized, making the subsequent processing algorithms more robust and less sensitive to such variations. 3. Subsequently, the signal is divided into frames using overlapping Hamming windows. Considering the block-based processing nature of the CS, these windows are required to divide the speech signal into frames. This process also reduces the computational complexity of subsequent processing steps, and the analysis can be performed on smaller segments, reducing the amount of data to be processed and
# Fault Reconstruction for a Giant Satellite Swarm Based on Hybrid Multi-Objective Optimization
## 3 Fault Reconstruction Based on a Hybrid Multi-Objective Genetic Algorithm  
After the reconstruction indicator system is established, the reconstruction strategy must be determined. Since the satellite numbers are discontinuous, the process requires optimizing a discrete system. Commonly used multi-objective optimization algorithms obtain a set of optimal solutions at the Pareto front for continuous variables, so they are unsuitable for this research scenario. Therefore, consistent with the optimization indicators established in the previous section, the linear weighted sum method is adopted, and a utility function is formed to represent the overall optimization goal after normalization; then, the reconstruction strategy is determined by a genetic algorithm. In summary, the decision tree of multi-objective fault reconstruction is shown in Figure 4.
# Photovoltaics on Landmark Buildings with Distinctive Geometries
## 3 Presentation of the Samples
### Paris Courthouse  
Signed by Renzo Piano Building Workshop (Pritzker awarded architect in 1998), Paris Courthouse is a 160 m tall building, consisting of four different cuboids. It is built beside the Port de Clichy, and adjacent to the Martin Luther King Park on the east side (Figure 9) of Paris, France (FR). The idea of the architect was to reunite all the courthouse functions into one building. The project started in 2010 and the building was completed in 2017. Through its size and status, the building is regarded as a starting point for the rehabilitation of its neighbourhood [42]. Apart from its size, robustness and orthogonal geometry, the building is distinguished by its glazed, dematerializing facades and the horizontal and vertical arrays of photovoltaic sunshades. According to ISSOL, the company in charge for the photovoltaic aspect of the building, the installation consists of more than 1590 sun shades (1930 m\({}^{2}\)) on facade and 152 PV modules (360 m\({}^{2}\)) on the roof, with an installed capacity of 325 kW. The expected annual power production is 312 MWh [43]. By exposing the PV modules on the east and west facades, the architect wanted to underline the environmental responsibility of the public building. Furthermore, apart from their main roles of producing energy and protecting from the sun, the PV modules are expressive architectural element, emphasizing the horizontal lines of the cuboids, and the vertical spines connecting the floating building parts.