esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In [42], the artificial gorilla troops' optimizer (GTO) was presented to solve benchmark functions and engineering problems. The artificial hummingbird algorithm (AHA) [43] was proposed to solve numerical test functions and challenging engineering design cases. In [44], the marine predators algorithm (MPA) was introduced to solve test functions, engineering benchmarks, and real-world engineering design problems. However, most of them have never been investigated in MaOPF problems. MPA is a well-proposed metaheuristic method inspired by predators in the ocean [44]. Many optimization problems in several areas, such as forecasting confirmed cases during the COVID-19 pandemic [45], feature selection problems [46], parameter estimation of photovoltaic models [47], and parameters' identification of triple-diode photovoltaic models [48] have been efficiently solved by MPA. However, MPA has been rarely used to solve OPF problems [49; 50]. Although the multi-objective optimization version of MPA has been proposed [51; 52], MPA has never been used to solve MOOPF and MaOPF problems. So, this work proposes many-objective MPA (MaMPA) and evaluates the effectiveness of the MaMPA in solving SOOPF, MOOPF, and MaOPF problems. The MPA itself cannot solve multi- or many-objective optimization problems, so the non-dominated sorting, crowding mechanism, and leader mechanism are applied to MPA. Two test systems including IEEE 30-, and 118-bus systems are used in the simulation process. Four objectives, which are the following: fuel costs; emissions; transmission losses; and VSI are selected as the objective functions. The results from many other algorithms in the literature are used to compare the simulation results of the MaMPA, and the statistical results of MaMPA are investigated. The main contributions of this work are listed below. 1. The MaMPA is proposed to solve SOOPF, MOOPF, and MaOPF problems;
2. The Pareto dominance concept is used to find Pareto fronts for MOOPF and MaOPF problems, the non-dominated sorting and crowding mechanism are used to organize the full repository, the leader mechanism is used to help calculate the equations of MPA, and the fuzzy decision method is applied to find the best-compromised solution for the MaOPF problems. The Pareto fronts are compared with those of PSO, PESA-II, SPEA2, and SSA based on the hypervolume indicator;
3. The performance comparison of the proposed algorithm with other algorithms introduced in the literature is presented;
4. The statistical results of the MaMPA are investigated. The remainder of the paper is organized as follows. Section 2 introduces the MaOPF problem including objective functions and constraints. Section 3 presents the MPA formulations and implementation of MPA to solve the MaOPF problem. Section 4 provides the simulation results and comprehensive discussions. Finally, Section 5 concludes the work of the paper.
## 2 Applied Signal Processing Techniques
### Intensity of the Digital Images  
Current image sensors acquire digital images, \(I_{m\times n}\), in the form of a matrix of \(m\times n\) pixels. Each pixel is designated as \(I(i,j)\), where \((i,j)\) defines the position in the matrix, with \(1\leq i\leq m\), \(1\leq j\leq n\)[33]. The image \(I_{m\times n}\) has a resolution of \(P=m\times n\) pixels.  
All pixels in RGB images contain a triplet (R,G,B) that defines the color coordinates in the RGB colour space, where \(R\) = red, \(G\) = green, and \(B\) = blue, so each pixel is defined by a (1,3) vector. The image results in  
\[I_{RGB}(i,j,k)=\{\underbrace{I_{RGB}(i,j,R)}_{R(i,j)},\ \underbrace{I_{RGB}(i,j,G)} _{G(i,j)},\ \underbrace{I_{RGB}(i,j,B)}_{B(i,j)}\} \tag{3}\]  
where \(I_{RGB}(i,j,k)\) is represented by 8-bit unsigned integers, and since \(2^{8}=256\), \(0\leq I_{RGB}(i,j,k)\leq 255\).  
To determine the intensity of the images, it is common to transform the original RGB image into a grayscale image using the following transformation [34]:  
\[I_{grayscale}(i,j)=0.299\cdot R(i,j)+0.587\cdot G(i,j)+0.114\cdot B(i,j) \tag{4}\]  
where the weights 0.299, 0.587, and 0.114 of the red, green, and blue channels, respectively, are taken considering the luminous efficacy function of the average human eye, thus approaching the average spectral sensitivity of the human eye.  
Once the RGB images have been transformed into grayscale images, their intensity [35; 36] can be calculated as:  
\[INT_{image}(I_{grayscale})=\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}I_{grayscale }(i,j) \tag{5}\]  
The digital image contains sensor and background noise. To reduce the noise effect, a window around the discharge is selected and the effect of the background noise is removed (see Figure 3):  
\[INT_{image-background}=\sum\limits_{i=1}^{m*}\sum\limits_{j=1}^{n*}[I_{grayscale }(i,j)-B_{grayscale}(i,j)] \tag{6}\]  
where \(m^{*}<m\) and \(n^{*}<n\) are the vertical and horizontal dimensions of the selected window and \(B_{grayscale}(i,j)\) is the 8-bit unsigned value of the \(i\)-th \(j\)-th position of the background. Equation (6) assumes that in the grayscale image, the value of each pixel (in the 0-255 range) is proportional to the amplitude of the incident light integrated over the exposure time and the pixel area. Since the energy of a wave is proportional to the square of its amplitude, in this paper, it is assumed that the square root of the energy dissipated by the discharges calculated from (2) is proportional to the intensity of the images calculated from (6). Section 5 assesses the accuracy of this approach by means of experimental data.  
Figure 3 summarizes the procedure applied to determine the intensity of the discharges calculated from the digital images.  
Finally, (6) is normalized to return values within the range
# A Brief Review of Mueller Matrix Calculations Associated with Oceanic Particles
## 3 General Scattering Method for Suspended Particles
### Physical-Geometric Optics Method  
When the particle size is much larger than the incident wavelength, [PERSON]'s equations can be approximated by the eikonal equation [55]. The eikonal equation is the theoretical foundation of the geometric optics method. The key process for the geometric optics method is ray-tracing. The ray-tracing process of the geometric optics method consists of two parts: one is the diffracted rays and another is the transmitted rays, including external reflection, refraction without internal reflection, refraction with one internal reflection, and so on. The conventional geometric optics method (CGOM)considers the diffracted and the transmitted rays separately and it assumes equal contributions from the diffracted and transmitted rays under the assumption that the extinction efficiency is 2. Moreover, the CGOM does not consider the ray spreading effect from the near field to the far field, that is, there is no mapping process for the CGOM. The CGOM is applied to compute the light scattering of large particles with a large refractive index, such as ice particles (e.g., [56; 57]). The CGOM can be improved by considering the ray spreading effect for a particle in random orientation. The improved geometric optics method (IGOM) can be used to compute light scattering of an intermediate particle or even a small particle [58; 59].  
For oceanic particles, the diffracted and transmitted rays have strong destructive interference so they cannot be separately handled. The physical-geometric optics method (PGOM) considers not only the interference between the diffracted and transmitted rays, but also the ray spreading effect in the far field [60]. Equations (7) and (8) are fundamental to the PGOM, which substantially extend the applicability of the principles of geometric optics in conjunction with physical optics to from large to moderate particles. For faceted particles, the ray-tracing process can be analytically accomplished since the phase change on a facet is linear [61; 62; 63]. The PGOM can be effectively used to compute the light scattering properties of oceanic particles.
# DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Detection
## 3 Detection Schemes for GOMIMO Systems
### Joint ML Detection  
Assuming perfect CSI, joint ML detection is the optimal detection scheme for GOMIMO systems with \(M\)-PAM modulation. More specifically, the joint ML detector estimates the transmitted constellation and spatial information simultaneously in a joint manner. By applying the joint ML detector, the transmitted signal vector \(\mathbf{x}\) can be estimated by  
\[\mathbf{\hat{x}}_{\text{JML}}=\arg\min_{\mathbf{x}\in\mathbb{X}}\|\mathbf{y}- \mathbf{H}\mathbf{x}\|^{2}, \tag{7}\]  
where \(\|\cdot\|_{2}\) denotes the modulus operator and \(\mathbb{X}\) represents the set of all the considered transmitted signal vectors.  
Although the joint ML detection can achieve optimal performance, it suffers from high computational complexity. Therefore, it is usually not feasible to apply the joint ML detector in practical GOMIMO systems.
# A Comparative Study of Local Descriptors and Classifiers for Facial Expression Recognition
## 3 An Approach for Optimized Facial Expression Recognition  
A simple and effective method for facial expression recognition based on local descriptors and conventional classifiers was defined by [PERSON] et al. [14]. The method involves dividing the face region into several non-overlapping regions and extracting local descriptors from each sub-region. The histogram of each sub-region is calculated separately and concatenated to form a single feature vector for classification. The current work extends [PERSON] et al.'s [14] approach by optimizing the feature extraction process. Figure 2 summarizes the proposed approach for optimized facial expression recognition, consisting of two phases: a training phase and a testing phase. In the training phase, images are pre-processed using face detection and registration. Next, a local descriptor is applied to the registered faces to extract facial features. Subsequently, feature vectors are generated by dividing a face region into several sub-regions and calculating the histograms of each sub-region. The feature vectors are normalized based on the type of local descriptor used (see Section 3.2). To optimize the process of feature vector calculation, the extraction parameters (i.e., the sub-region size and the number of histogram bins) are varied, producing several feature sets. Then, the best extraction parameter settings are found by comparing the classification performances produced by each feature set. Finally, the best feature set and corresponding expressions are used to train a machine-learning model.  
In the testing phase, images are processed, and the feature vectors are generated based on the best extraction parameter values obtained from the training phase. After that, the feature vectors are classified as facial expressions using the trained classifier. Additionally, the classifier's performance is evaluated by 10-fold cross-validation. The novelty of this approach is in using a variable sub-region size and a variable number of histogram bins to optimize the performance of each local descriptor.
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 2 Research Status of Tunnel Lighting System
### Influences of Color Temperature on Driver Behavior
#### 2.5.2 Measurement of Outside Color Temperature  
Weather always changes, and so does the color temperature of natural light sources, therefore, dynamic determination of the color temperature outside the tunnel is essential to execute accurate lighting adjustment, so as to achieve an intelligent lighting strategy. If there is no requirement to create close-looped lighting control, acquisition of the color temperature inside the tunnel is optional.  
Color temperature can be measured in many ways; in this project, one color luminance meter (Konica Minolta CL-500A) is used, located ahead of the tunnel entrance and close to the luminance detector. This meter acquires color measurements frequently and these can be fetched by user as requested.  
In this way, research groups have collected much data on natural light conditions. Here, we present one example of color temperature records for the same geographic location (30\({}^{\circ}\)15\({}^{\prime}\)\(\sim\)31\({}^{\circ}\) north latitude and 120\({}^{\circ}\)20\({}^{\prime}\)\(\sim\)121\({}^{\circ}\) east longitude) that were measured every 2 h (8:00 a.m.-17:00 p.m.) on sunny and cloudy days. When sunny (cloud coverage below 0.3), the color temperature is lower than 6000 K. When cloudy (cloud coverage above 0.8), the color temperature is about 6000\(\sim\)8000 K and it changes relatively little. Compared to cloudy days, on a sunny day the scenery is illuminated by direct sunlight that is yellow in color. At this location, the color temperature of sunny days is lower than that of cloudy days. Figure 7 shows the process of color temperature change under two weather conditions.  
Figure 6: Recognition time with CRI and color temperature (luminance: 4.5 cd/m\({}^{2}\)).
## 3 System Model
### Computation Model  
This subsection will introduce the offloading model where the simulation has a number \(\mathcal{M}\) of small UAVs that are connected to the centralized cloud computing via a backbone router. In addition, each UAV is connected to a number \(\mathcal{N}\) of mobile devices that have highly intensive computation tasks that should be finished. For each mobile device \(j\), the tuple \(\{b_{i,j},c_{i,j},\tau_{i,j}\}\) represents the task requirement, where \(b_{i,j}\) indicates the size of the dataset that needs to be transmitted from mobile device \(j\) to UAV \(i\), whereas \(c_{i,j}\) and \(\tau_{i,j}\) denote the CPU cycles' number and the required deadline for the task of mobile device \(j\). The values of \(b_{i,j}\) and \(c_{i,j}\) can be acquired via task execution profiling [40; 43].  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(\omega_{i}\) & Density of noise power of channel which is connected with UAV \(i\) \\ \(\xi_{i,j}\) & Energy consumed per CPU cycle of mobile device \(j\) which is connected with UAV \(i\). \\ \(\delta\) & Propagation delay between UAV and cloud \\ \(f_{i,j}^{l}\) & Computational capability of mobile device \(j\) which is connected with UAV \(i\). \\ \(f_{i,j}^{c}\) & CPU resource of UAV \(i\) assigned to mobile device \(j\) \\ \(f_{i,j}^{c}\) & CPU resource of cloud assigned to mobile device \(j\) which is connected with UAV \(i\). \\ \(T_{i,j}^{l}\) & Time for local execution for the task of mobile device \(j\) which is connected with UAV \(i\). \\ \(f_{i,j}^{l}\) & Energy for local executing task of mobile device \(j\) which is connected with UAV \(i\) \\ \(T_{i,j}^{off}\) & Time for transmitting task of mobile device \(j\) to UAV \(i\). \\ \(T_{i,j,exec}^{c}\) & Time for executing task of mobile device \(j\) at UAV \(i\). \\ \(T_{i,j,exec}^{c}\) & Time for executing task of mobile device \(j\) at Cloud server. \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._
# Improved FraSegNet-Based Rock Nodule Identification Method and Application
## 5 Experimental Results and Analysis  
### Data Analysis  
The accuracy of the training and validation sets is shown using Python 3.10.12, and the accuracy curves for the nodal fissure features are shown in Figure 9. The loss rates of the training set and the validation set are shown in Figure 10.  
The two charts illustrate the model's accuracy and loss curves during both the training and validation phases. As the number of training epochs progressed, both metrics demonstrated consistent improvement, indicating effective learning and good generalization capability. Specifically, both training accuracy (reaching 0.9711 by the 150 th epoch) and validation accuracy (reaching 0.9348 at the same point) exhibited steady upward trends, reflecting the model's effective adaptation to the data. Although minor fluctuations were observed in validation accuracy, likely attributed to data complexity or overfitting concerns,  
Figure 8: Generalization ability test.  
Figure 9: Training and validation set accuracy.  
the overall upward trajectory suggested robust generalization performance. The relatively small gap between the training and validation accuracies further indicated that significant overfitting was effectively mitigated.  
For the loss rate, both the training and validation losses showed a consistent decline as training advanced, reflecting continuous improvement in the model's predictive capabilities. By the final stages of training, the model achieved a training loss of 0.0717 and a validation loss of 0.1642, with these relatively low values suggesting a strong fit to the data. While minor fluctuations were present in the validation loss during intermediate and later training stages--typical behavior for deep learning models--the training loss curve maintained its smooth progression, indicating stable performance on the training set without significant oscillations.  
Since feature extraction in deep learning models is often hard to interpret, this study employed the Grad-CAM algorithm [32] to generate activation heatmaps for the FraSegNet model. These visualizations provided insights into how different regions of the rock images influenced classification outcomes and verified whether the FraSegNet architecture successfully captured key features from the input data. The corresponding results are presented in Figure 11.  
The first column shows the original image and the second column shows the heat map. The heat map highlights how different regions affect the predicted output, with darker areas indicating higher responses and greater contributions to the network. The results show that the optimized FraSegNet network accurately identifies the locations of dense nodal fissures.
# Exploration of Applying Pose Estimation Techniques in Table Tennis
## 2 Literature Review  
### Computer Vision Technique for Gesture Recognition Applications  
Due to the development of technology, there is a long history of development of algorithmic techniques for tracking human movement using images [4; 5]. However, in the past, studies often relied on a single image or sensor to provide visual data, which also led to inconvenience in the operation of the subject and limitations in the recording of the object [6; 7]. Recent advances in image recognition technology have produced a variety of visual recognition systems that make it possible to more easily perform pose estimation on test subjects; thus, these systems are gradually being applied to motion pose estimation [8; 9].  
Pose estimation, a computer vision technology in AI, has good potential for development and application in sports and fitness activities, motion analysis, and 3D clothing fitting. Currently, various techniques have been developed for body pose estimation, for instance, the Mask R-CNN technique [10] or AlphaPose [11; 12; 13], which is now available in v0.3.0. A multipose estimation system can simultaneously achieve a mean average precision (mAP) of over 60 and multiple object tracker accuracy (MOTA) of over 50 on the PoseTrack Challenge dataset [14]. OpenPose can simultaneously integrate body posture estimation, face estimation, and hand and leg posture estimation [15]. The OpenPose tech nology provides a real-time multiperson pose recognition system. It can instantly analyze the body, hand, face, and foot posture key point estimation. It provides up to 135 posture key points in total. The related programs and descriptions are available on Carnegie Mellon University GitHub ([[https://github.com/CMU-Perceptual-Computing-Lab/openpose](https://github.com/CMU-Perceptual-Computing-Lab/openpose)]([https://github.com/CMU-Perceptual-Computing-Lab/openpose](https://github.com/CMU-Perceptual-Computing-Lab/openpose)), access date: 1 October 2022), and its technical development is based on published research and extensions [15; 16]. Figure 1 shows the execution results of two studies developed for face and hand gesture recognition [15].
## 4 Results
### Implementation Details
#### 4.1.3 Loss Function  
During computer-aided diagnosis or clinical processes, achieving high recall is a critical performance indicator for models. The presence of unbalanced data in medical datasets makes a network easily fall into the local optimum. This, in turn, adversely impacts segmentation performance, often leading to a high precision but low recall. In order to balance the differences between categories among the training samples, Tversky loss was experimentally selected as the loss function to calculate the similarity between the predicted labels and the ground truth, with the following equation:  
\[T(\alpha,\beta)=\frac{\sum_{i=1}^{n}p_{0i}g_{0i}}{\sum_{i=1}^{n}p_{0i}g_{0i}+ \alpha\sum_{i=1}^{n}p_{0i}g_{1i}+\beta\sum_{i=1}^{n}p_{1i}g_{0i}} \tag{9}\]  
where \(p_{0i}\) denotes the probability that the \(i\)th voxel is a tumor; \(p_{1i}\) denotes the probability that the \(i\)th voxel is not a tumor; \(g_{0i}=1\) denotes a lesion voxel; \(g_{0i}=0\) denotes a normal voxel; and \(g_{1i}\) the opposite. \(\alpha\) and \(\beta\) are two hyperparameters, set to \(\alpha+\beta=1\), reducing the effect of positive and negative sample imbalance on model performance by adjusting the values of \(\alpha\) and \(\beta\). When \(\alpha=\beta=0.5\), Tversky loss [24] simplifies to the Dice coefficient while equating to the balanced F score (F1 score).
# Secure Transmission in mmWave Wiretap Channels: On Sector Guard Zone and Blockages
## 6 Conclusions  
In this paper, we investigated secrecy performance under the Nakagami fading channel in an mmWave wiretap network. Then, an adaptive transmission scheme according to the locations of exavesdroppers is adopted for secrecy transmission, and we derived the SOP, COP and secrecy throughput under stochastic geometry. Specifically, there exists an optimal transmission power for the direct transmission and an optimal power allocation factor for the AN-assisted transmission by maximizing secrecy throughput. When the system parameters are set properly, AN can improve the secrecy throughput of the system. We got a meaningful conclusion that choosing sector secrecy guard zone with a larger radius according to the density of physical barriers and the distance of receiver can improve the secrecy throughput. In addition, it provides an important perception into the interaction among the transmitting power, main-lobe gain and the mmWave frequency. In future works, complex scenarios such as imperfect CSI, base-station (BS) cooperation and nonorthogonal multiple access (NOMA) will be considered. Furthermore, the results presented here can be combined with unmanned aerial vehicle (UAV) to analyze secrecy transmission capability.  
[PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] conceived of the main proposal of the secure transmission schemes, conducted system modeling, and derived analysis and numerical simulation of the proposed schemes. [PERSON] and [PERSON] wrote the manuscript. [PERSON] and [PERSON] provided considerable comments and technique review of the proposed scheme and contributed to the revision of the paper. [PERSON] read and approved the final manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61471393 and 61771487.  
The authors declare no conflict of interest.