esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
The load balancing algorithm, designed to dynamically transfer functionality between cloud and fog servers based on their capacity, has proven essential for maintaining a balanced distribution of load, even under challenging game conditions. This dynamic approach has not only improved user coverage but also effectively addressed server overload, ensuring a satisfactory gaming experience for most players across all clusters. Simulation results have highlighted the scalability and flexibility of the architecture, especially when employing multiple fog servers. As the number of available servers increases, the average load on each fog server decreases, indicating potential for further optimization and improvement in load balancing within the cluster. This suggests that incorporating more fog resources can lead to an even more efficient load distribution, thereby continuously improving the gaming experience for users. The paper in [27] presents an economic approach to balance load in MMOGs using cloud resources. They introduce an event-based algorithm that dynamically manages game session loads, allowing real-time adjustments to meet QoS requirements. Experimental results validate the effectiveness of the algorithm and show a potential reduction in cloud hosting costs. The research in [28] develops dJay, a cloud game server that dynamically adjusts rendering configurations to optimize visual quality and frame rate. The system evaluation in commercial games demonstrates improvements in multitenancy and visual quality. Another study, shown in [29], implements a server cluster for MMOGs, introducing mechanisms for load balancing and dynamic distribution of maps and players. The results show significant improvements in system performance and service capacity. Finally, the article in [30] proposes a load balancing system for MMMORPs that focuses on user preferences and interactions. Results demonstrate a 40% efficiency in server relocation, improving interactivity and reducing frequent client migrations. Some of the articles that motivated the development of the main paper are analyzed in more detail in Table S1, which can be found in the supplementary material for reference. Additionally, the supplementary table provides an in-depth analysis of articles [31; 32; 33; 34; 35], which have played a crucial role in the development of the proposed model. This body of research provides a solid foundation for developing a dynamic load balancing model and low-latency architecture in hybrid fog and edge environments, essential for enhancing QoE in MMO games. While the proposed model primarily addresses latency, other critical factors influencing QoE--such as user feedback, visual quality, and perceived responsiveness--also warrant attention.
## 3 Methods  
For a given matrix, which can be factorized into two matrices, \(\mathbf{P}\in\mathbb{C}^{m\times r}\) and \(\mathbf{Q}\in\mathbb{C}^{m\times r}\) (size \(\mathbf{r}\) is unlimited), its nuclear norm can be computed as the following [33; 34]:  
\[\|\mathbf{X}\|_{*}=\underset{\mathbf{P},\mathbf{Q}}{min}\;\frac{1}{2}\Big{(}\| \mathbf{P}\|_{F}^{2}+\|\mathbf{Q}\|_{F}^{2}\Big{)}\,s.t.\mathbf{X}=\mathbf{P} \mathbf{Q}^{H}, \tag{5}\]  
where \(\|\cdot\|_{F}\) denotes the Frobenius norm of a matrix and \(\cdot^{H}\) means conjugate transpose. In this work, we propose a self-learning subspace matrix factorization (SLSMF) method to accelerate the state-of-the-art SLS method:  
\[\underset{\mathbf{x},\mathbf{P},\mathbf{Q}}{min}\;\frac{1}{2}\Big{(}\| \mathbf{P}\|_{F}^{2}+\|\mathbf{Q}\|_{F}^{2}\Big{)}-Tr\Big{(}\mathbf{A}_{ \Omega,l}^{H}\mathcal{R}_{\mathbf{x}}\mathbf{B}_{\Omega,l}\Big{)}+\frac{ \lambda}{2}\|\mathbf{y}-\mathcal{U}_{\mathbf{x}}\|_{F}^{2}s.t.\mathcal{R}_{ \mathbf{x}}=\mathbf{P}\mathbf{Q}^{H}, \tag{6}\]  
It requires no SVD computations in the inner loop, which greatly speeds up the reconstruction. Next, we will solve the optimization problem of Equation (6). The augmented Lagrange form of Equation (6) is:  
\[ \begin{array}{c}L(\mathbf{x},\mathbf{P},\mathbf{Q},\mathbf{D})=\underset{ \mathbf{x},\mathbf{P},\mathbf{Q}}{min}\;\underset{\mathbf{D}}{max}\;\frac{1}{2} \|\mathbf{P}\|_{F}^{2}+\frac{1}{2}\|\mathbf{Q}\|_{F}^{2}-Tr\Big{(}\mathbf{A}_{ \Omega,l}^{H}\mathcal{R}_{\mathbf{x}}\mathbf{B}_{\Omega,l}\Big{)}+\\ \frac{\lambda}{2}\|\mathbf{y}-\mathcal{U}_{\mathbf{x}}\|_{2}^{2}+\Big{(} \mathbf{D},\mathcal{R}_{\mathbf{x}}-\mathbf{P}\mathbf{Q}^{H}\Big{)}+\frac{ \beta}{2}\|\mathcal{R}_{\mathbf{x}}-\mathbf{P}\mathbf{Q}^{H}\|_{F}^{2},\end{array}  \tag{7}\]  
where \(\mathbf{D}\) is a dual variable used to improve the convergence speed of the algorithm, \(\langle\cdot,\cdot\rangle\) is the inner product in the Hilbert space of matrices, and \(\beta>0\) is the regularization parameter. The proposed algorithm consists of two main loops. The outer one iteratively updates the signal subspace \(\mathbf{A}_{\Omega,l},\mathbf{B}_{\Omega,l}\) to determine the best subspace under the prior information. Once the subspace is obtained, in the inner one, in order to solve Equation (7), it is converted into four sub-problems by adopting alternating direction methods of multipliers (ADMM) [35]:\[\left\{ \begin{array}{l}\mathbf{x}_{k+1}=\underset{\mathbf{x}}{arg\underset{ \mathbf{x}}{min}L(\mathbf{x},\mathbf{P}_{k},\mathbf{Q}_{k},\mathbf{D}_{k})}\\ \mathbf{P}_{k+1}=\underset{\mathbf{P}}{arg\underset{\mathbf{P}}{min}L(\mathbf{ x}_{k+1},\mathbf{P},\mathbf{Q}_{k},\mathbf{D}_{k})}\\ \mathbf{Q}_{k+1}=\underset{\mathbf{Q}}{arg\underset{\mathbf{Q}}{min}L(\mathbf{ x}_{k+1},\mathbf{P}_{k+1},\mathbf{Q},\mathbf{D}_{k})}\\ \mathbf{D}_{k+1}=\mathbf{D}_{k}+\left(\mathbf{R}\mathbf{x}_{k+1}-\mathbf{P}_{k +1}\mathbf{Q}_{k+1}^{H}\right)\end{array}
## 3 State-of-the-Art Results on the Detection of Identity Deception
### Criteria for Evaluation Used by Models  
Studies [17; 26; 31] use several metrics (e.g., precision and recall) to evaluate the performance of an identity deception detection model. These metrics attempt to represent how the model would perform in a real-world OSN. To evaluate the effectiveness of a detection approach, there are two essential components that are considered: the statistical measure (e.g., 80% recall), which reflects how well the model detected malicious accounts in some dataset of OSN traffic, and the dataset that is representative of real-world traffic, which affects the external validity of a model.  
The predominant metrics that are used in the literature for identity deception detection relate to binary classification and are precision, recall, and \(F_{1}\) score [32]. Precision is the ratio of true positives to the sum of true and false positives. It effectively measures the true positive rate. Recall, on the other hand, is the ratio of true positives to the sum of true positives and false negatives. It is thought to be an indicator of the degree of malicious accounts that are identified out of the complete sample. In practical terms, a high recall would mean that a model detects most malicious accounts, whereas a high precision would mean that moderators can trust the model to correctly identify accounts as malicious. The \(F_{1}\) score is a weighted harmonic average of precision and recall. ROC curves are also frequently used to measure performance [33].  
The second criterion for the evaluation of a model's performance is the dataset that the model uses and on which it is evaluated. This will influence the ability of the model to generalize well in other contexts [19]. Typically, researchers develop and subsequently test their model on numerous datasets of OSN traffic. These datasets usually contain data extracted from an OSN and contain primarily benign users alongside a small portion of malicious users (i.e., the low prevalence of malicious users will result in a low count of malicious account cases in a dataset). These datasets typically contain labels of what users or actions are considered \"normal\" or \"not normal.\" Since these datasets are needed to confirm a model's performance, their quality and integrity are essential for the external validity of a model.
## 4 c-di-AMP Binding Molecules
### TetR-Family Transcription Factor, DarR  
DarR from _Mycobacterium smgematis_, a member of the TetR-family regulator proteins, was identified through a c-di-AMP transcription factor binding screen [38; 55]. TetR regulators are DNA binding proteins whose affinity towards DNA is altered upon interaction with their cognate small molecule. Following small molecule interaction, these regulators undergo a structural change, thereby altering their affinity towards specific DNA binding sites and giving rise to the altered expression of target genes. Like the other members of the TetR family, DarR represses its own promoter as well as the downstream genes. DarR homologs are found primarily in Gram-positive bacteria such as _Bacillus_ spp., _Staphylococcus_ spp., _Listeria_ spp., _Lactobacillus_ spp., and _Clostridium_ spp. [38; 55].  
DarR contains two domains: a C-terminal QacR-like domain, and an N-terminal TetR-like helix-turn-helix DNA binding domain [38; 55]. Interestingly, not all c-di-AMP-producing bacteria possess QacR-like domains in their DarR proteins. The domain is found in _Staphylococcus_, _B. subtilis_, and _Pelotomaculum thermopropionicum_[55]. The detailed mechanism of c-di-AMP allosteric regulation of DarR is not well understood, nor has the binding site of c-di-AMP been identified, but c-di-AMP has been shown to stimulate the binding of DarR to its target DNA [55]. DarR binds to the promoter regions of genes that are involved in fatty acid metabolism and the expression of the cold shock protein in _M. smegmatis_ as _darR_ deleted _M. smegmatis_ exhibits larger cell size and reduced fatty acid metabolism [38; 55].
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 3 Derivation of the BECPB for Rayleigh Channel
### BECPB for Battery with MDC-Based BUF  
Substituting Equation (16) into Equation (6), the power consumption for the battery with MDC-based BUF is given by  
\[P_{\text{bat}}(t)=\left\{\begin{array}{ll}\frac{f_{\text{ray}}(B,d)+P_{\text{ a}}+P_{\text{d}}}{\eta(1-\gamma P_{\text{mean}})}&\text{when emitting symbol `1'}\\ \frac{P_{\text{d}}}{\eta(1-\gamma P_{\text{mean}})}&\text{when emitting symbol `0'}\end{array}\right. \tag{20}\]  
where \(P_{\text{mean}}\) is the average of \(P_{\text{out}}(t)\) and can be expressed as  
\[P_{\text{mean}}=\frac{f_{\text{ray}}(B,d)+P_{\text{a}}+2P_{\text{d}}}{2\eta} \tag{21}\]  
In the same way used in the former section, the BECPB for the battery with MDC-based BUF in Rayleigh channel is  
\[E_{\text{bat,M}}=\frac{1}{2B}\frac{f_{\text{ray}}(B,d)+P_{\text{a}}+2P_{\text{ d}}}{\eta-\gamma\frac{f_{\text{ray}}(B,d)+P_{\text{a}}+2P_{\text{d}}}{2}} \tag{22}\]  
Note that an ideal battery has a BUF always equal to 1. Moreover, it can be easily seen that the BECPB for the ideal battery can be evaluated by either Equation (19) or Equation (22) by setting \(\gamma\) as 0. As a result, it can be readily shown that the BECPB for the ideal battery is  
\[E_{\text{bat,ideal}}=\frac{1}{2B}\frac{\xi Bd^{k}+P_{\text{a}}+2P_{\text{d}}} {\eta}. \tag{23}\]  
Since both Equations (19) and (22) are increasing with \(\gamma\), \(\gamma=0\) corresponds to the smallest BECPB, meaning that the battery with either IDC or MDC base BUF corresponds to a larger BECPB than the ideal battery, and the gap increases as \(\gamma\) increases.
# Docker Performance Evaluation across Operating Systems
## 3 Methodology
### Iperf3 Tool  
Iperf3 is a network performance measurement tool that creates controlled data flow between two instances in a network. The primary purpose of this program is to gather information on network connection performance such as throughput, jitter, latency, and efficiency. This benchmark utility is designed to operate in a client-server architecture. This benchmark's primary goal is to determine Docker network performance between containers and a host. The iperf3 scenario will cover three cases:  
1. inter-container TCP,
2. inter-container UDP,
3. client on host--server on container TCP.  
To provide accurate and replicable test results, the time of program measurements has been extended to 60 s. The metrics that will be measured are throughput and packet loss. The bridge network driver was used in all three test cases. To provide connectivity between the host and the container, port 5201 has to be exposed by a server in the container.
## 2 Materials and Methods
### Sample Preparation  
The coal materials used in this study were collected in Pucheng City, Shanxi Province, China. A total of 61 coal samples were analyzed, comprising two types: anthracite and bituminous coal. The primary difference between these two types lies in their volatile content, with bituminous coal containing the majority. It has been shown that the particle size of coal can affect the spectra reflectance; within a certain range, the coal particle size decreases, the spectra reflectance increases [31]. However, the spatial information of coal with a smaller particle size (e.g., 0.2 mm) is relatively poor. Therefore, to enhance the spectral reflectance and utilize the spatial information of coal, a toothed-roll crusher (Haoxin DG200*150, Ganzhou, China) was employed to reduce the coal size from 50 mm to approximately 3 mm. The crushed coal was dried and sieved using a 3 mm diameter mesh screen and divided into two parts, one for testing and the other for acquiring hyperspectral image data.  
The proximate analysis of the 61 coal samples included moisture content (MC, %), ash content (Ash, %), volatile matter (VM, %), fixed carbon (FC, %), and coal calorific value (CV, MJ/kg). The first four indices of coal were determined according to the Chinese standard GB/T 212-2008 [32], which aligns with ISO 11722 (MC), ISO 1171 (Ash), and ISO 562 (VM and FC). The fixed carbon was determined as Equation (1). The calorific value (CV) of coal was determined in the laboratory using an oxygen bomb calorimeter according to GB/T 213-2008 [33].  
\[FC=100\%-Ash-MC-VM \tag{1}\]
# Emerging Decision-Making for Transportation Safety: Collaborative Agent Performance Analysis
## 3 System Architecture and Design
### Agent Vehicle Sensor Configuration  
For the detection of objects in the environment, a multi-sensor approach that provides data fusion of camera, radar, and Lidar information enhances the capabilities of AVs to perceive their environment, compared to using data from these sensors in isolation, improving accuracy and resilience to adverse weather conditions. A simplified example of the hierarchy of different sensors, actuators, and communication and control elements is shown in Figure 5.  
The AV must be equipped with an appropriate combination of sensors correctly positioned for optimal performance, as observed in Figures 6 and 7.  
Figure 4: Comparison of simple reflex agent vs. model-based utility agent.  
Figure 5: Simplified example hierarchy of elements used for sensing, actuation, control, and communications.  
This also provides a backup system with sensor redundancy and helps overcome specific machine vision issues on AVs such as vibrations, motion blur, and poor lighting, increasing resilience to sensor failures and increasing robustness. Improving ADAS safety is critical to improving public confidence in AVs, especially with media coverage of high-profile incidents such as a fatality caused by Tesla autopilot failing to detect a truck crossing its path. Ref. [44] recommends these sensors are oriented in the direction of travel to accurately detect road users, hazards, road signs, and lane markings. Data from wheel encoders and an Inertial Measurement Unit (IMU) can be combined with road maps to supplement Global Positioning System (GPS) data (visual odometry) and improve the reliability of calculations that determine the position of the vehicle, which is vital in ensuring the vehicle can accurately and reliably navigate through its environment. Odometric navigation based on inertial sensors alone is unreliable because of measurement bias and drift. This is studied in further detail with respect to the Localisation/Navigation using Lidar data case study in Section 4.2.  
Figure 6: Arrangement of sensors on an AV.  
Figure 7: Placement of AV sensors and their roles in perceiving the environment.  
The AV sensors and actuators are designed to work in combination to perceive the environment as shown in Figure 7 and Table 5. Actuators have been broken down from the vehicle system they belong to (e.g., braking) to provide more detail about how ADAS functions are implemented. This analysis highlights how an AV can have considerable ADAS functionality if combinations of even a small number of sensors and actuators, many of which are already implemented on vehicles, are used. For intelligent AVs, this sensing and actuation capability can be enhanced through the application of artificial intelligence algorithms to enhance road safety as the human driver is gradually phased out.
## 5 Conclusions  
To address the issues in parameter estimation for the generalized Pareto distribution, this study successfully solved the problem of non-closed-form expression using the PSO algorithm and obtained accurate parameter estimates. By expanding the search space of the likelihood function to two dimensions, the stagnation issue in the one-dimensional search was resolved. Fit analysis experiments showed that the positive 0.5 th/1 st-order fractional moment and positive 0.25 th-order logarithmic moment estimation methods performed well in fitting heavy-tailed sea clutter data. To improve the parameter estimation of the PSO algorithm, this research proposed an HPSO algorithm for parameter estimation of the generalized Pareto distribution. Through simulation verification and analysis, the following results were obtained:  
* The HPSO algorithm overcame the premature convergence problem of the PSO algorithm and demonstrated better parameter estimation performance.
* The parameters of the HPSO algorithm were optimized, resulting in good performance.
* Through the analysis of parameter estimation variations, it was found that the parameter estimation results of the PSO algorithm were unstable.
* Compared to other methods, the generalized Pareto distribution estimated using the HPSO algorithm exhibited the most stable and optimal fitting results for real data, and it was not influenced by the range of shape parameter values.
* The HPSO algorithm achieved high-precision parameter estimation results while maintaining fast computational speed.  
These research findings provide new insights and practical value for parameter estimation of the generalized Pareto distribution. For future research, it is suggested that the influence of different parameter settings in the HPSO algorithm on the optimization process is further investigated and detailed performance analyses using real-world data are conducted.  
Conceptualization, B.Y. and Q.L.; data collection, B.Y.; data analysis, B.Y. and Q.L.; data interpretation B.Y.; methodology, B.Y.; software, B.Y.; writing--original draft, B.Y.; writing--review and editing, B.Y. and Q.L.; final approval, B.Y. and Q.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Director's Foundation of Institute of Microelectronics, Chinese Academy of Sciences, under grant no. E0518101.  
Not applicable.  
The data that support the findings of this study are available within the article.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter Estimation Methods** & **PSO** & **HPSO** & **2 nd/4 th-MoM** & **1 st-Order ZlogZ** & **MLE** \\ \hline Running time (s) & 3.42 \(\times\) 10\({}^{-1}\) & 3.22 \(\times\) 10\({}^{-1}\) & 5.81 \(\times\) 10\({}^{-2}\) & 2.53 \(\times\) 10\({}^{-2}\) & 5.56 \(\times\) 10\({}^{-1}\) \\ \hline \end{tabular}
\end{table}
Table 6: Estimate time of different parameter estimation methods.
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 5 Open Issues and Challenges
### Analysis of Big Data  
In a smart health-care network, big data analysis is a dominant research direction. The future smart health-care network will consist of millions of devices that will generate an enormous volume of information and data for analysis [80]. These data contain private user information (i.e., Data of Patient)  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Features** & **Advantages** & **Research Challenges** & **Key Requirements** \\ \hline Achieving Interoperability & A significant platform for communication between different IoT devices by using various protocols. & \(\bullet\) & Incorporating devices for retailer secured administrations. & \(\bullet\) & Adaptable, universal and integrated models are needed for incorporation and communication (i.e., CoAP, IP) for IoT devices. \\ \hline Analysis of Big data & Enhance the network performance by processing data received from valid sources (i.e., analysis of patient data with an intelligent method can minimise congestion of network). & \(\bullet\) & Limitation of useful tools to process heavy amount of information generated by devices in the network. & \(\bullet\) & Need for a centralized big data centre for processing. \\ \hline Performing Connectivity & IoT devices from various domain. & \(\bullet\) & How to assure the connectivity of various devices from a different domain in high mobility? & \(\bullet\) & Usage of the spectrum with an efficient technique for IoT devices communication. Intelligent algorithms that guarantee the connectivity of different devices with various domains in the network. \\ \hline Achieving Security & Provides a secure platform (free of attacks) to deploy services. & \(\bullet\) & Secure deployment and integration of cloud-based services at levels. & \(\bullet\) & Recognizable proof of vulnerabilities at a different level in the system. which function as entry points for different attacks. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Future research challenges.  
and surrounding environment information of the patient (i.e., Heartbeat rate, ECG, etc.). Therefore, intelligent algorithms and approaches are needed for data analysis. For instance, the information that is generated by local devices in the network must be efficiently analysed with the help of machine learning algorithms. The main concerns that must be addressed are:  
* For data analysis privacy must be provided to the user data.
* For sensitive data secrecy must be provided.
* For data collection and analysis, a well-defined infrastructure must be provided.
* For information extraction, computation power must be provided.