esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Review of Mask-Face Detection Methodologies
### Two-Class Mask-Face Detection Methodologies
#### 4.1.1 Traditional Machine Learning Based Approaches  
Before deep learning methods became popular, researchers tended to use traditional machine learning for feature extraction. The location and range of the region of interest are determined by letting candidate frames of different sizes traverse the entire image, and then feature extraction algorithms are used such as Haar and Histogram of Oriented Gradient (HOG) to extract features from the candidate region, so as to obtain the required features and classify them.  
In the study of [PERSON] et al. [25], their feature-based adaptive boosting and cascade classifiers trained Haar-like features, Local Binary Pattern (LBP), and HOG, respectively, to obtain the required features and perform sorting. Then, since Haar-like features focused more on facial contours (nose and mouth), its accuracy reached 86.9%, surpassing the 81.7% accuracy of LBP and 74.5% of HOG.
# Polar Codes and a M-ary Modulation-Based OFDM-PLC System
## 5 Performance Simulation and Results Analysis
### BER for Different Modulation Methods  
Modulation schemes of the same order are compared under an OFDM-PLC system using the same polar coding method and setting the improved impulse noise parameter to \(m=10\times 5^{-3}\). Three types of modulation, 16 QAM, 16 APSK, and 16 PSK, were evaluated, and the BER performance is shown in Figure 8. It can be seen that, as the SNR increases, 16 QAM has the fastest decrease in BER, followed by 16 APSK, and 16 PSK has the slowest decrease. 16 QAM has slightly better noise resistance than 16 APSK and 16 PSK in the same order. The performance of 16 QAM is superior to 16 APSK by about 1 dB at BER of \(10^{-2}\) and by about 3 dB at BER of \(10^{-3}\). As the SNR increases, the performance advantage of 16 QAM is more obvious than that of 16 APSK under the same polar code coding conditions. Because of the different constellation architectures, the minimum spacing of all adjacent constellation points in the QAM modulated constellation is the same, while the minimum spacing of adjacent constellation points in APSK modulated constellation is only equally spaced on the same circumference, so the distribution of QAM modulated symbols of the same order is more uniform than that of APSK. At the same time, as the modulation order increases, the number of APSK modulation radii of the same order increases, and the phase modulation level difference becomes larger, resulting in more difficult modulation and demodulation and lower modulation performance. However, when the load on the power line changes, channel distortion may occur. The 16 QAM is the most sensitive to amplitude distortion because it has the most amplitude values, while 16 PSK is the most sensitive to phase distortion because all its signal points are on the same circle, and the phase spacing between adjacent points is minimal. The sensitivity of 16 APSK to both types of distortion is between 16 QAM and 16 PSK, and it is more balanced in terms of noise resistance and distortion resistance, making it more suitable for PLC communication systems.
Sender nodes wait for an ID packet of a receiver node and when it acquires such an ID packet from an appropriate receiver, it transmits a send request (SREQ) packet to establish a link. Note that we have developed IRDT as a protocol which has actually been implemented  
Figure 1: Asynchronous intermittent transmission methods. in meter products [12]. Furthermore, we are currently proposing this technique to IEEE 802.15 Task Group 4 as part of a standard protocol for smart meter systems [11]. In this paper, we clarify the performance characteristics of IRDT by comparing them with those of X-MAC protocols and another receiver-driven MAC protocol (RI-MAC) through computer simulations. The long-term operation of IRDT is examined by comparing the energy consumption under conditions of low data incidence. Additionally, we implement improvements in IRDT by decreasing the incidence of control packet collisions. Control packet collisions are classified into two types, namely ID collisions, occurring between ID packets and other packets, and SREQ collisions, occurring between multiple SREQ packets. Such collisions drastically reduce the performance of IRDT, and we discuss them in detail in Section 4. Furthermore, we propose a simple and effective routing algorithm for mesh networks with IRDT, as well as novel improvement mechanisms for IRDT, and evaluate the impact of these improvements. The rest of this paper is organized as follows. In the following section, we present some related work, and in Section 3, we describe the details of IRDT. In Section 4, we discuss control packet collisions in IRDT and some approaches to avoiding it. Finally, we present the simulation results in Section 5 and our conclusions in Section 6.
## 5 Case Study: CV Joint Replacement in Korean Automotive Market
### Environmental Impact of CV Joint Replacements  
A CV joint mainly consists of three parts: the inboard joint, outboard joint, and shaft [31]. The bill of materials for each part is described in Table 3. The shaft is typically reused unless cracks or severe damage are observed. Most components of the inboard and outboard joints are also reused, but certain components, such as the boot, clamp, retaining ring, retainer, and grease, must be replaced with new ones during the remanufacturing process. The environmental impact of remanufactured parts can be evaluated by considering these components throughout their life cycle.  
The ReCiPe method has been used to assess the environmental impact of both new and remanufactured CV joints, with the results shown in Figure 4. Three impact categories such as ecosystem quality, human health, and resource were calculated. The calculated points for ecosystem quality, human health, and resources for a new CV joint are 2.63, 1.35, and 8.22, respectively, whereas the points for remanufactured CV joints are 1.67, 0.92, and 5.00, respectively.  
Figure 3: The demand for CV joint replacements with respect to vehicle sales.  
The \"human health\" impact includes factors such as the number and duration of diseases and life years lost due to premature death caused by environmental factors. \"Ecosystem quality\" measures the effects on species diversity, particularly in vascular plants and lower organisms. Additionally, the \"resources\" category indicates the surplus energy required in the future to extract lower-quality mineral and fossil resources. The total environmental impact points for each CV joint are 12.20 for a new CV joint and 7.59 for a remanufactured CV joint. The results show that remanufactured CV joints have a lower environmental impact, approximately 62% of that of new ones.
# Screen-Based Sports Simulation Using Acoustic Source Localization +
## 1 Introduction
proposed a 3D position estimation technique by applying a dual position-based beamforming technique without using the candidate 2D planes [17]. However, their approach demanded a large number of microphones, making the system complicated for use in sports simulation. [PERSON] et al. proposed a system based on a direction-based scheme [18]. In their approach, various factors such as sound pressure, calibration, sensitivity, and signal-to-noise ratio (SNR) of the microphone array were used for the position estimation process. In this paper, we introduce a novel acoustic source localization in a 3D space, based on a direction estimation technique. Assuming the distance an acoustic source is from adjacent microphones, its waves spread in a planar form called planar wavefront. In our system, the directions and steering angles between the acoustic source and the microphone array are estimated based on the planar wavefront model using a delay and sum beamforming (DSBF) system and an array of 2D microelectromechanical system (MEMS) microphones. The proposed system is designed with a small number of input parameters such as time delays for candidate degrees between the acoustic source and the microphones, and it is implemented using a cost-effective field programmable gate array (FPGA) and a micro control unit (MCU). The experimental results show that the localization errors of the proposed system are less than 3 cm when an impulsive acoustic source is generated over 1 m away from the microphone array, which is comparable to a position-based system with reduced computational complexity. One of the main contributions of this paper is to analyze and compare two different types of DSBF algorithms in terms of computation complexity, processing performance, and accuracy to locate the accurate 3D source location of impulsive sounds between the position-based and direction-based methods. Furthermore, we introduce a low-cost high-speed DSBF hardware engine that processes the DSBF algorithm in real time, making our system suitable for use in time critical systems such as sports simulation. On the basis of the high accuracy and real-time performance of localizing an impulsive acoustic source, such as striking a ball, the proposed system is applied to improve the user's sports skills. As shown in the experimental results, users trained with the simulation program demonstrated noticeable improvements in their soccer kicks and baseball hits as compared with outdoor users. The remainder of this paper is organized as follows: The direction and position-based estimation methods of an impulsive acoustic source are analyzed in Section 2. The proposed system to locate the 3D position of an impulsive acoustic source based on a geometric model is described in Section 3. An overview of proposed system and the detailed architecture of our hardware design is described in Section 4. The experimental results are presented in Section 5. We conclude this paper with ongoing improvements in Section 6.
## 3 Analysis and Results of CGAN-IFCM
### Comparison Between CGAN, SMOTE, and CSL  
To study the effectiveness of CGAN in addressing the issue of an imbalanced dataset, a comparison is made with two typical approaches: the synthetic minority oversampling technique (SMOTE) [42; 43] and cost-sensitive learning (CSL) [44; 45]. Table 8 presents the performance of CGAN-IFCM, SMOTE-IFCM, and CSL-IFCM in binary and multi-class voice disorder detection. The results show that the proposed CGAN-IFCM model outperformed SMOTE-IFCM and CSL-IFCM by 4-6% in terms of the TNR and TPR. This result can be explained by the following factors: (i) the datasets SVD and VOICED contain conditional information that fits well with the theory of CGAN, and (ii) SMOTE does not consider the possibility of neighboring points that belong to other classes. This could potentially increase the overlapping of classes and introduce additional noise; (iii) CSL is, moreover, prone to overfitting.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cases** & **IFCM TNR** & **Proposed CGAN-IFCM** & **Percentage Improvement by** \\  & **(\%)/ TPR (\%)** & **TNR (\%)/TPR (\%)** & **Proposed Work** \\ \hline Binary detection model & & & \\ with SVD & 85.9/89.8 & 94.7/95.6 & 10.2/6.5 \\ Binary detection model & & & \\ with VOICED & 83.3/89.2 & 91.6/94.4 & 10.0/5.8 \\ Multi-class detection model with SVD & 81.9/76.5 & 92.2/88.9 & 12.6/16.2 \\ Multi-class detection model with VOICED & 80.7/79.2 & 89.4/90.1 & 10.8/13.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison between CGAN-IFCM and IFCM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cases** & **CGAN-FCM TNR** & **Proposed CGAN-IFCM** & **Percentage Improvement** \\  & **(\%)/TPR (\%)** & **TNR (\%)/TPR (\%)** & **by Proposed Work** \\ \hline Binary detection model & & & \\ with SVD & 87.8/91.3 & 94.7/95.6 & 7.9/4.7 \\ Binary detection model & & & \\ with VOICED & 85.3/91.6 & 91.6/94.4 & 7.4/3.1 \\ Multi-class detection model with SVD & 84.6/79.4 & 92.2/88.9 & 9.0/12.0 \\ Multi-class detection model with VOICED & 83.3/81.6 & 89.4/90.1 & 7.3/10.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison between CGAN-IFCM and CGAN-FCM.
# A Turbo Q-Learning (TQL) for Energy Efficiency Optimization in Heterogeneous Networks
## 7 Conclusions  
In order to jointly optimize resources to maximize the energy efficiency of HetNets by RL, there is a problem of too large action space and state space. We propose a novel QL algorithm (TQL) based on the multi-stage decision process to improve the QL algorithm to effectively solve the problem of excessive action space. Through the analysis of Algorithm 5, the advantage is greater in the case of large action space. For the dimension problem of state space, DNN is designed to classify states where the optimization policy of novel Q-Learning is set to label samples. At the same time, compared with the general RL algorithm, there is only one set of adjustable parameters, and the TQL learning proposed in this paper can further adjust the parameters according to the system requirements to further optimize the system. Thus far, the dimensions of action and state have been solved. The algorithm proposed in this paper is more flexible. Finally, the simulation result proves that the algorithm proposed in this paper is effective and feasible, and improves the convergence speed by 60% compared with the tabular QL.  
**Author Contributions:** Formal analysis, Z.L.; Methodology, X.W. and J.L.; Project administration, J.L.; Software, L.L. and J.L.; Writing--original draft, L.L.; Writing--review & editing, X.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Zhejiang Provincial Natural Science Foundation of China under Grant No. Y20F010069, as well as supported in part by the National Natural Science Foundation of China (NSFC) under Grant No. 51874264.  
**Acknowledgments:** This work was supported by the Zhejiang Provincial Natural Science Foundation of China under Grant No. Y20F010069, as well as supported in part by the National Natural Science Foundation of China (NSFC) under Grant No. 51874264 and Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou 310018, China. (Corresponding author: [PERSON].)  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Sporiasis Pathogenesis and Treatment
## 2 Pathogenesis
### Genetics
#### Antigen Presentation  
Genome-wide linkage studies of psoriasis-affected families have so far detected at least 60 chromosomal loci linked to psoriatic susceptibility [86; 87; 88]; the most prominent locus is PSORS1, which has been attributed up to 50% of the heritability of the disease [89]. PSORS1 is located on chromosome 6p21 within the major histocompatibility complex (MHC), which is specifically in the class I telomeric region of HLA-B, and spans an approximately 220 kb-long segment and corresponds to HLA-Cw6 (C*06:02). HLA-Cw6 is strongly linked to early and acute onset psoriasis [90; 91]. The HLA-C*06:02 allele is present in more than 60% of patients, and increases the risk for psoriasis nine to 23-fold [92]. Nevertheless, no link between late-onset psoriasis or pustular psoriasis and PSORS1 could be established, possibly reflecting a genetically heterogenic background associated with different clinical phenotypes [93]. PSORS2 spans the CARD14 gene, while PSORS4 is located in the epidermal differentiation complex [94; 95; 96; 97; 98; 99; 100; 101].  
The results of numerous genome-wide association studies (GWAS) in psoriasis are consistent with the prominent role of PSORS1 as a risk factor, but have also revealed over 50 single-nucleotide polymorphisms (SNPs) to be associated to psoriasis [102; 103; 104]. Variants involving the adaptive and immune system are a constant result in these studies [53; 105; 103].
After the disastrous earthquake of 26 November 2019, investigating the underlying geologic formation's role in seismic response was deemed necessary. Basic soil dynamic properties of surficial geologic strata (e.g., fundamental frequency, amplification, shear wave velocity) must be estimated in order to be implemented in respective seismic response analyses. For this purpose, in the present work, ambient noise measurements at 80 sites were performed in the urban area of Durres, and the HVSR method was applied to estimate their fundamental and dominant frequencies, as well as their corresponding amplitudes. In addition, spatial distribution of these parameters in the city is presented and compared alongside other available geological or/and geophysical characteristics of Durres complex 3D basin. The experimental HVSRs for selected sites are compared with HVSRs from synthetic ambient noise data based on available 1D profiles, thus assessing their reliability. In addition, classification of all experimental HVSRs using their entire curve is attempted, and four discrete classes are proposed for the city of Durres. The correlation of the proposed four zones/classes with the observed damage is discussed and these zones are proposed as a satisfactory basis for any future microzonation and/or seismic risk analyses of the Durres built environment.
# Efficacy of Maximally Expansion with Clear Aligner in the Mixed Dentition: A Systematic Review
## 3 Results
### Study Characteristics  
Type of Study and Location  
Tables 2 and 3 presented the characteristics of the eighteen included studies. Three of these [27; 28; 29] were prospective studies, seven were retrospective studies [11; 15; 18; 19; 25; 26; 30], one was a case series [9], one [7] was a preliminary study, and two [1; 2] were randomized controlled trials. The studies were conducted at multiple institutions, including the Oral Pathology and Rehabilitation Research Unit (UNIPRO) at the University Institute of Health Sciences (IUCS), Cooperativa de Ensino Superior Politecnico e Universitario (CESPU) [9; 30]; the Department of Systems Medicine at the University of Rome 'Tor Vergata', Italy [11; 15; 26]; the Department of Surgical Sciences, CIR Dental School, University of Turin [1; 2]; the Department of Orthodontics at Xiangya Stomatological Hospital, Central South University, China [28; 29]; the Department of Dentistry at UNSBC, Tirana, Albania [27]; the Department of Human Sciences, Innovation and Territory, School of Dentistry, Postgraduate Orthodontics Program at the University of Insubria, Italy [7]; the Department of Orthodontics, University of the Pacific, Arthur A. Dugoni School of Dentistry, USA [19]; the Department of Oral Health Science, Faculty of Dentistry, University of British Columbia, Canada [19]; the Surgical, Medical and Dental Department, University of Modena and Reggio Emilia, Italy [1]; the Dentistry Unit, Department of Health Sciences, University of Catanzaro \"Magna Graecia\", Italy [1]; the Department of Orthodontics, School of Dentistry, Baskent University, Turkiye [25]; the Department of Orthodontics, School of Dentistry, Izmir Tinaztepe University, Turkiye [25]; and the Department of Health Science, Unicamillus-Saint Camillus International Medical University, Italy [18]. These investigations took place in Portugal [9; 30], Italy [1; 2; 7; 11; 15; 26], Albania [27], China [28; 29], the USA [19], and Turkiye [25]. The studies were published between 2021 [7; 27], 2022 [2; 9; 11; 15], 2023 [26; 28; 29; 30], and 2024 [1; 18; 19; 25]. All of them involved young patients in the mixed dentition stage presenting with maxillary transverse deficiency,  
Figure 1: Information flow of search and selection of studies. who were treated with Invisalign(r) First clear aligners aimed at achieving dentoalveolar maxillary expansion. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**References (Authors, Year of Publication)** & **Study Design** & **Sample Size and (Number of Female)** & **Mean Age \(\pm\) DS and/or Interval (Years)** & **Type of Variance (Ved)** & **Measurement Technique** & **Average Duration of Treatment (Months)** \\ \hline
**[PERSON] et al., 2023**[29] & Prospective cohort study & 51 (N.R.)
## 4 Experimental Results and Analysis  
The ice-covered transmission line dataset used in this experiment consists of two parts. One part comes from the ice-covered transmission line images collected by ourselves in winter, and the other part is a dataset collected on the Internet. Among these, the self-collected dataset uses the transmission lines and cables built outdoors when snow falls in  
Figure 5: Model deployment flow chart.  
winter to restore the actual ice-covered transmission lines. When the cable is ice-covered, a camera is used to take pictures and collect ice-covered images. The other part is to search for ice-covered transmission line images on the Internet, download them, and manually screen them to remove poor quality images. Finally, after sorting, 939 images of ice on transmission lines were obtained and labeled using the Labelme annotation tool to obtain authentic label images. During training, the dataset is expanded through online data augmentation methods and divided into training and testing sets in an 8:2 ratio.  
The experiment uses the PyTorch deep learning framework for model training, the GPU used for training is an RTX4090, and the model inference device is an OrangePi5 Plus. During training, the BatchSize is set to 24, the number of iterations is 1600, and the Stochastic Gradient Descent (SGD) optimizer is selected for gradient optimization. The evaluation indicators of the final segmentation effect are mean intersection over union (mIoU) and balanced F-Score (F1-Score). IoU represents the intersection-over-union ratio of the real mask to the predicted mask. mIoU averages the IoUs of different categories to get the average intersection-over-union ratio. The calculation formula is  
\[mIoU=\frac{1}{K}\Sigma\frac{TP}{TP+FP+FN} \tag{12}\]  
where K represents the classification category. TP means that the predicted and true labels are consistent and both are true. FN means that the predicted result is wrong; that is, the predicted value is false and the true label is true. FP means that the true label is false and the predicted value is true. F1-Score is the harmonic mean of precision and recall, which is used to comprehensively evaluate the performance of the model. The calculation formula is  
\[F1=\frac{Precision\times Recall\times 2}{Precision+Recall} \tag{13}\]