esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Optimization Algorithm--Genetic Algorithm Based on Double-Elite Strategy
### Genetic Algorithm Based on Double-Elite Strategy  
To improve the efficiency of finding the optimal individual, modifications to the traditional genetic algorithm are necessary. One effective method is to implement an elite strategy, including the elite reservation strategy and the elite crossover strategy. An elite individual has the highest fitness during the population evolution and shares partially excellent genes [46].  
The elite reservation strategy involves directly reserving the individual with the best fitness for the next generation, thereby bypassing the genetic operations of the current generation. This approach prevents the excellent genes of the current generation from being damaged during crossover and variation, yet this may cause the result to get stuck in a locally optimal solution. The elite crossover strategy is making the elite cross with every individual in the current population according to a given probability [47]. It allows the excellent genes to be possessed by more offspring individuals, while the elite individual is not fully preserved.  
To improve the performance of the genetic algorithm, this paper adopted a double-elite strategy, as detailed in Figure 7. This approach involves using both elite reservation and elite crossover strategies simultaneously. Specifically, the best individual in the current generation is directly reserved for the next generation while also being allowed to mate with every individual in the population. This approach balances the advantages and disadvantages of the two strategies, resulting in a more effective genetic algorithm.  
Figure 6: The description of assembly sequence in IFC standards and Neo4j. (**a**) The description of assembly sequence in IFC standards; (**b**) The description in Neo4j.
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 5 Conclusions and Future Work  
We suggested a complete process for detecting hate speech in Roman Urdu by making use of classical machine learning along with deep learning models in this study. Data were retrieved from Facebook, sanitized, and enhanced with pre-processing (tokenization, lemmatization, and spelling correction). We tried different embedding methods (TF-IDF, Word2 Vec, FastText) and different ML classifiers (SVM, RF, LR, NB, GBM, KNN) and DL architectures (RNN, GRU, LSTM, CNN). Of the ML models, SVM with TF-IDF had the highest F-score of 94%, while of the DL models, LSTM with FastText achieved 96.2% and CNN reached 95.1%, demonstrating the ability of sequential models to model context-rich code-mixed language. We further verified these results by confusion matrix analysis and explainability methods (LIME and SHAP), which shed light on the influence of tokens in the networks. Although the results are promising, there are challenges, including informal Roman Urdu orthography, code-mixing, unspoken hatred, and fewer resources. However, we believe that our result exhibits that it is possible to maintain an AI-moderated system for Roman Urdu social media content. In the next step, we plan to fine-tune multilingual transformers directly on Roman Urdu, scale up the dataset and annotations to more diverse platforms, and investigate real-time inference with explainable, lightweight models for faster, fair, and accountable moderation systems.  
Conceptualization, A.N., A.Q., N.H., G.S., M.Z. and A.G.; methodology, A.N., A.Q., N.H., G.S. and A.G.; validation, A.N., A.Q., N.H., M.Z. and F.A.; formal analysis, A.N., A.Q., N.H., M.Z. and F.A.; data curation, A.N., A.Q., N.H., M.Z. and F.A.; writing, A.N. and A.Q.; funding acquisition, G.S. and A.G. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Mexican Government through the grant A1-S-47854 of CONACYT, Mexico, and the grants 20254236, 20253468, and 20254341 provided by the Secretaria de Investigacion y Posgrado of the Instituto Politecnico Nacional, Mexico. The authors thank CONACYT for the computing resources brought to them through the Plataforma de Aprendizaje Profundo para Tecnologias del Lenguaje of the Laboratorio de Supercomputo of the INAOE, Mexico, and acknowledge the support of Microsoft through the Microsoft Latin America PhD Award.  
The data is available on request.  
The authors declare no conflicts of interest.
## 5 Conclusions  
In this study, we evaluated the characteristics of a single-crystal CZT detector to validate its applicability in SRS and SBRT. The fabricated CZT detector demonstrated excellent performance in terms of dose linearity, dose rate dependence, energy dependence, field size dependence, reproducibility, and depth dependence. Notably, the stable linearity observed in both the high- and low-dose regions is conducive for both SRS and SBRT, in which abrupt dose variations are common. However, further investigations are required with the established techniques for developing submicron pixel structures and to assess the potential for performance enhancement. Detectors with a pixelated CZT structure are expected to have significantly higher performance and an optimized design for SRS and SBRT compared to the single-pixel detector studied in this research. The CZT detector is a highly promising device for application in SRS and SBRT and can be used in various radiation therapy environments.  
Conceptualization, S.C. and S.K.; methodology, J.-Y.S. and J.-U.J.; software, M.S.Y.; validation, S.C., S.K. and J.-U.J.; formal analysis, J.-Y.S. and Y.-H.K.; investigation, T.-K.N. and Y.-H.K.; resources, J.-U.J.; data curation, T.-K.N.; writing--original draft preparation, S.K.; writing--review and editing, M.S.Y. and S.-J.-A.; visualization, M.S.Y.; supervision, S.C.; project administration, S.C.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a grant (BCRI24053) of Chonnam National University Hospital Biomedical Research Institute.  
Not applicable.  
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
This study was supported by a grant (BCRI24053) from the Chonnam National University Hospital Biomedical Research Institute.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Experiment and Analysis
### Experiment Result
#### 4.3.1 The Result of the First Experiment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Motion** & **Algorithm** & **AE** & **RMSE** & **ME** & **CEP of 75\%** & **CEP of 95\%** \\ \hline \multirow{3}{*}{Calling mode} & PDR & 3.64 & 4.06 & 6.64 & 4.97 & 6.62 \\  & WiFi & 1.06 & 1.26 & 2.64 & 1.37 & 2.62 \\  & FFF & 1.07 & 1.26 & 2.27 & 1.28 & 2.22 \\  & AO-PSO-PF & 0.82 & 1.03 & 2.29 & 1.08 & 2.21 \\ \hline \multirow{3}{*}{Dangling mode} & PDR & 6.24 & 7.54 & 15.37 & 9.38 & 15.32 \\  & WiFi & 1.04 & 1.17 & 2.25 & 1.42 & 2.22 \\  & FFF & 0.98 & 1.14 & 2.34 & 1.28 & 2.32 \\  & AO-PSO-PF & 0.85 & 0.99 & 1.77 & 1.23 & 1.72 \\ \hline \multirow{3}{*}{Handheld mode} & PDR & 14.78 & 16.81 & 26.19 & 20.44 & 26.12 \\  & WiFi & 1.17 & 1.26 & 1.89 & 1.48 & 1.87 \\  & FPF & 1.36 & 1.52 & 2.45 & 1.68 & 2.42 \\  & AO-PSO-PF & 1.11 & 1.22 & 1.87 & 1.48 & 1.82 \\ \hline \multirow{3}{*}{Pocketed mode} & PDR & 4.26 & 4.61 & 7.42 & 5.18 & 7.42 \\  & WiFi & 1.19 & 1.37 & 2.31 & 1.76 & 2.36 \\  & FPF & 1.36 & 1.58 & 2.97 & 1.98 & 2.92 \\  & AO-PSO-PF & 1.02 & 1.25 & 2.16 & 1.68 & 2.12 \\ \hline \multirow{3}{*}{General} & PDR & 7.23 & 8.26 & 13.91 & 9.99 & 13.37 \\  & WiFi & 1.12 & 1.27 & 2.27 & 1.51 & 2.27 \\ \cline{1-1}  & FPF & 1.19 & 1.32 & 2.51 & 1.48 & 2.47 \\ \cline{1-1}  & AO-PSO-PF & 0.95 & 1.12 & 2.02 & 1.37 & 1.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The detailed data for the PDR, WiFi, FPF, and AO-PSO-PF with four postures.
# Bioactive Compounds and Nanodelivery Perspectives for Treatment of Cardiovascular Diseases
## 5 Nano-Delivery Systems of Bioactive Compounds
### Nanoparticles for Theranostic  
The concept of a theranostic is derived from combined therapy and diagnostic tools assembled into a single platform [174], while the nano-theranostic embodies the most advanced technological approach with multifunctional attributes such as multimodal imaging, controlled, and localized drug targeting, allowing the development of personalized medicine. In this respect, engineered nanomaterials consisting of magnetic nanoparticles, liposomes, carbon-based nanomaterials, metal and/or polymeric based nanoparticles are good candidates for dual applications in terms of both diagnostics and therapeutic approaches [175; 176; 177]. For example, magnetic nanoparticles coated with natural compounds have proved their efficiency in the imaging of CVDs. [PERSON] et al. [178] reported ultra-small superparamagnetic iron oxide nanoparticles coated with a specific polysaccharide (fucoidan) which have been successfully employed in MRI as contrast agents for arterial thrombus and elastase-induced vascular injury in a rat model. Another example is the case of gold nanoparticles used as contrasting agents, for the efficient detection and diagnosis of myocardial infarction. The method is based on engineered gold NPs conjugated with collagen, which demonstrated high-resolution detection of myocardial and ischemic injuries, along with adequate therapeutic tools [179]. Lipsomal platforms have also proved to be a successful tool for diagnostic and therapeutic methods for platelet targeting in CVDs. The surface of liposomes with natural peptides was demonstrated to facilitate the drug delivery of active compounds and at the same time to target the cardiovascular tissues or damaged areas [180]. Many evidences for nano-based theranostics in terms of prognosis of atherosclerosis, myocardial infarction, aneurysms, angiogenesis, and other CVDs [175] have been reported, highlighting the benefit of sensitive detection of pathophysiological conditions combined with concomitant therapeutic measures. Although there has been reported success of preclinical studies related to theranostic nanoplatforms, the translational approach to clinical sectors remains unexplored, and moreover, the innate toxicity and stability of the nanoformulations are less studied. Most of the in vivo modelswere considered with small animals, and hence, huge differences might be detected by comparison with human anatomical features. On the other hand, the costs/benefits ratio must be considered, as there is a lack of studies reporting these aspects.  
Developing innovative multifunctional nanomaterials with qualities that allow them to deliver specialized therapies through various physiological obstacles and to target specific cell types, tissues, and organs in the body is a major challenge for this project. Effective nano-delivery systems have ideal characteristics for controlled release of medicament, long storage life, and enhanced therapeutic efficacy with no or minimal side effects [181; 182].
# A Graduated Non-Convexity Technique for Dealing Large Point Spread Functions
## 1 Introduction
Experimental evidence suggests that the most computationally expensive minimization is the first energy function approximation since subsequent minimizations start from a good solution approximation. In this paper, we propose a method to minimize the first convex approximation by approximating each block of the blur operator using matrices that can be efficiently treated using a fast discrete transform. Since each block of the blur operator is a symmetric Toeplitz matrix, we here focus on finding a class of matrices that is easy to handle computationally while providing a good approximation of the Toeplitz matrices. Specifically, we approximate each Toeplitz matrix as the sum of a symmetric circulant and a reverse circulant matrix (cf. [19]). Symmetric circulant matrices have several applications in ordinary and partial differential equations (cf. [20; 21; 22; 23; 24]), images and signal restoration (cf. [25; 26]), and graph theory (cf. [27; 28; 29; 30; 31; 32]). Reverse circulant matrices have different applications, such as exponential data fitting and signal processing (cf. [33; 34; 35; 36; 37]). By theoretical and experimental results, we choose a suitable subclass of matrices to use for our approximations. This subclass of matrices is the set of the \(\gamma\)-matrices presented in [38]. We tested the proposed algorithm in reconstructing artificially blurred images and those affected by natural blurring. These experiments show how using such approximations reduces about a fifth of the CATILED algorithm's computational costs without affecting the result's quality. We refer to the technique here proposed as E-CATILED (_Extended Convex Approximation Technique for Interacting Line Elements Deblurring_). The paper is structured as follows: in Section 2, we present the problem of image deblurring and the related regularization technique; in Section 3, we recall the CATILED algorithm for the minimization of the energy function; in Section 4, we present the proposed E-CATILED technique; in Section 5, we report our experimental results.
# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 3 Fall Detection Based on the Improved YOLOv5s  
### Introduction of the YOLOv5s Model  
YOLOv5 is a widely used algorithm with good accuracy and speed, becoming a popular algorithm in the field of object detection. It is an object detection algorithm developed by [PERSON], [PERSON], and other authors [28]. It adopts  
Figure 1: A dual model of human posture detection in this work.  
an anchor-based detection method and uses a series of optimization strategies, such as feature pyramid network and adaptive incremental training, to improve detection accuracy and speed. YOLOv5s is the smallest version in the YOLOv5 series, with a fast detection speed and low model size, making it suitable for object detection in resource-constrained environments such as mobile devices. The YOLOv5s network's structure is introduced in detail below.  
As shown in Figure 2, the YOLOv5s network structure can be divided into three parts: Backbone, Neck, and Head. Among them, the Backbone is used to extract features from the image, the Neck is used to fuse features of different resolutions, and the Head is used to predict the category and position of objects. In detail, each part functions as follows.  
Backbone: The Backbone network mainly extracts image feature points. It converts original input images into multi-layer feature maps for subsequent target detection tasks. The Backbone mainly comprises Conv modules, C3 modules, and Spatial Pyramid Pooling--Fast (SPPF) modules.  
Neck: In the target detection algorithm, the Neck usually combines feature maps of different levels and generates feature maps with multi-scale information to improve the target detection accuracy.  
Head: The object detection Head detects the object of the feature pyramid, which includes some convolutional layers, pooling layers, and fully connected layers.
# Data Exfiltration through Electromagnetic Covert Channel of Wired Industrial Control Systems
## 4 Detecting Radiation Frequency
### Hardware and Software Setup for Experiments  
The experimental hardware setup uses a computer connected to a router through a Cat5 UTP Ethernet cable, representing the target ICS's wired network. The resultant radiation signals are observed through a _HackRF One_ SDR device [22], along with a magnetic H-loop antenna, connected to the attacker's computer. The observed EM radiation data are stored on the attacker's laptop for postmortem analysis. Figure 1 illustrates the arrangement of the target network and the attacker's equipment in a realistic scenario. For the experimental evaluation of this phase, i.e., detecting the radiation frequency, the computer to which the Cat5 cable is connected and the attacker's computer to which the SDR hardware are connected are the same machine.  
The software running on the attacker's laptop consists of an EM trace collector with three parallel threads collecting data. The first thread sends a set of predetermined network packet patterns through the Ethernet cable to the router, causing the Ethernet cable to produce intentional EM radiation patterns. The second thread reads the EM radiation data captured through the SDR hardware and saves them into EM trace files. The third thread sniffs the Ethernet network interface of the attacker's computer (to which the Ethernet cable is connected for experimental purposes), and saves network traffic packets as PCAP files. These three threads operate in a synchronized fashion so that each captured EM radiation data file corresponds to a specific transmitted network packet pattern through the wired network. The objective is to use these synchronized data to identify two network traffic patterns that cause two uniquely distinguishable EMR patterns.
###### Abstract  
In recent years, the need for computation-intensive applications in mobile networks requiring more storage, powerful processors, and real-time responses has risen substantially. Vehicular networks play an important role in this ecosystem, as they must support multiple services, such as traffic monitoring or sharing of data involving different aspects of the vehicular traffic. Moreover, new resource-hungry applications have been envisaged, such as autonomous driving or in-cruise entertainment, hence making the demand for computation and storage resources one of the most important challenges in vehicular networks. In this context, Mobile Edge Computing (MEC) has become the key technology to handle these problems by providing cloud-like capabilities at the edge of mobile networks to support delay-sensitive and computation-intensive tasks. In the meantime, researchers have envisaged use of onboard vehicle resources to extend the computing capabilities of MEC systems. This paper presents a comprehensive review of the most recent works related to MEC-assisted vehicular networks, as well as vehicle-assisted MEC systems. We illustrate the MEC system architecture and discuss its deployment in vehicular environments, as well as the key technologies to realize this integration. After that, we review the recent literature by identifying three different areas, i.e.: (i) MEC providing additional resources to vehicles (e.g., for task offloading); (ii) MEC enabling innovative vehicular applications (e.g., platooning), and (iii) vehicular networks providing additional resources to MEC systems. Finally, we discuss open challenges and future research directions, addressing the possible interplays between MEC systems and vehicular networks.  
mobile edge computing: MEC; vehicular networks; intelligent transportation systems; vehicle-to-everything +
Footnote †: journal: _Future Internet_  
energy consumption, has driven the advancement of new technologies to improve data gathering, processing, storage, and communication. Technologies such as _cloud_ and _fog computing_ have been introduced to enhance computation capabilities [5]. However, these have limitations when it comes to handling an enormous amount of latency-sensitive data and computing millions of tasks correctly in a time-sensitive way. Moreover, sending such amount of data to cloud or fog layers generates high traffic load on the network, which may become congested.
## 4 Results
### Landslide Risk  
The landslide risk index (LRI) can be calculated by multiplying the LHI and vulnerability value [6; 14]. We selected the result of the DFR-RF model, which performed the best in the model evaluation, as the input variable for the calculation of the landslide risk in this study. According to Equation (12), the landslide risk map was generated by the GIS software. Finally, the normalized landslide risk indices were divided into five categories using the natural breakpoint method: very low (0-0.04), low (0.04-0.12), moderate (0.12-0.29), high (0.29-0.54), and very high (0.54-1).  
\[LRI=LHI\times V \tag{12}\]  
where \(LRI\) is the value of the landslide risk index of each slope unit, \(LHI\) is the value of the landslide hazard index of each slope unit, and \(V\) is the vulnerability value.  
Figure 10 shows that the zones with very high and high landslide risks in the study area are mainly located on the southern side of Aoyitake Town, accounting for 0.18% and 1.16% of the whole area, respectively. These zones have very small coverage and are characterized by wide bands along the KKH. Moreover, the zones with a moderate landslide risk are mainly distributed in the Gaia River valley between Aoyitake Town and Bulunkou and are also found around Dabudaer and the county seat of Taskorgan. These zones account for 7.93% of the study area, and 65.88% of the landslides are distributed in them. They are strip-like, and most of them are close to the KKH. Additionally, the zones with very low and low landslide risks have a wide distribution range, accounting for 64.68% and 26.05%, respectively. Furthermore, due to the fact that most of the study area is uninhabited, many zones with high landslide hazards have also been classified as zones with a low landslide risk based on Equation (12).  
Figure 9: Vulnerability map of the study area.
Sensors_**2011**, _11_, 8430-8455; doi:10.3390/s110908430  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Novel Addressing Scheme for PMIPv6 Based Global IP-WSNs**  
**[PERSON] and [PERSON] ***  
Department of Computer Engineering, College of Electronics and Information, Kyung Hee University, Yongin-si 446-701, Korea; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-31-201-2454; Fax: +82-31-204-3778.  
_Received: 1 August 2011; in revised form: 24 August 2011 / Accepted: 25 August 2011 / Published: 29 August 2011_  
**Abstract:** IP based Wireless Sensor Networks (IP-WSNs) are being used in healthcare, home automation, industrial control and agricultural monitoring. In most of these applications global addressing of individual IP-WSN nodes and layer-three routing for mobility enabled IP-WSN with special attention to reliability, energy efficiency and end to end delay minimization are a few of the major issues to be addressed. Most of the routing protocols in WSN are based on layer-two approaches. For reliability and end to end communication enhancement the necessity of layer-three routing for IP-WSNs is generating significant attention among the research community, but due to the hurdle of maintaining routing state and other communication overhead, it was not possible to introduce a layer-three routing protocol for IP-WSNs. To address this issue we propose in this paper a global addressing scheme and layer-three based hierarchical routing protocol. The proposed addressing and routing approach focuses on all the above mentioned issues. Simulation results show that the proposed addressing and routing approach significantly enhances the reliability, energy efficiency and end to end delay minimization. We also present architecture, message formats and different routing scenarios in this paper.  
**Keywords:** PMIPv6; IP-WSN; IETF; 6 LoWPAN; IEEE 802.15.4; IP-sensor node