esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Digitalization of MGs
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Years** & **Milestones** \\ \hline
1970s & The development of supervisory control and data acquisition (SCADA) systems enables remote monitoring and control of power system components. \\ \hline
1980s & The introduction of microprocessor-based digital relays improves the accuracy and reliability of power system measurements and monitoring. The development of phasor measurement units (PMUs) enables real-time monitoring and analysis of power system dynamics. \\ \hline
1990s & The introduction of digital protective relays replaces traditional electromechanical relays, leading to better accuracy, faster response times, and more advanced fault detection and diagnosis. \\ \hline
2000s & The use of digital communication technologies such as fiber-optic cables and wireless networks facilitates the integration of DERs such as solar panels and wind turbines into the power grid. The development of synchro phasors enables the measurement and visualization of power system dynamics in real time, leading to improved situational awareness and enhanced stability. The adoption of smart grid technologies enables the integration of advanced sensors, communication networks, and automation systems, leading to greater efficiency, reliability, and sustainability of the power grid. \\ \hline
2010s & The advent of big data analytics, machine learning, and artificial intelligence (AI) enables advanced data processing and predictive modeling, leading to improved forecasting, fault detection, and outage management. The development of cloud computing platforms enables the processing and storage of large amounts of data generated by the power system, leading to improved data analytics and decision making. The introduction of virtual power plants (VPPs) enables the aggregation and management of DERs, leading to more efficient and flexible energy management. \\ \hline
2020s & The use of digital twins, which are virtual replicas of physical assets, in the power system has the potential to improve asset management, maintenance, and planning, leading to improved reliability and cost-effectiveness. The development of blockchain technology and distributed ledger systems has the potential to revolutionize the way energy transactions are managed, enabling secure and efficient peer-to-peer energy trading and billing. The deployment of 5G wireless networks has the potential to enable the integration of more advanced communication and automation systems in the power grid, leading to greater efficiency, reliability, and sustainability. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Key milestones in the history of digital technologies in electric power systems.
## 2 Datasets
### Landsat-Retrieved Albedo  
The TM onboard the Landsat 4 and 5 satellites with seven spectral bands covered the shortwave range at a resolution of 30 m from 1984 to 2011. It is an optimal data source for regional and global land surface change research because of its long period of operation [48; 49; 50; 51; 52]. The high-spatial-resolution (30 m), long-term albedo product based on Landsat data has been derived [43; 45; 53]. In this study, we adopt the algorithm of [PERSON] et al. [45] to derive the global long-term land surface albedo product from Landsat TM data. LLT data that were temporally consistent with AmeriFlux site observations were downloaded from the U.S. Geological Survey (USGS) website. To minimize the influence of cloud coverage, only the highest data quality (quality flag is 9) and a maximum cloud cover of 10% were used. In this paper, 2034 scenes of TM data were downloaded, covering all land cover types and seasons, where 1820 scenes corresponded to high-quality MODIS data. TM data were first rectified to sinusoidal projections and resampled to 30 m resolution using the nearest neighbor algorithm.  
The accuracy of meso-scale data needs to be verified and calibrated before they are used to bridge field measurements and coarse-resolution products. Landsat TM albedo data was first calibrated with the field data. The Landsat TM overpassed at local time 10:00-11:00 AM. To guarantee the temporal consistency between TM data and field observation, field observations 15 min before and after the TM passing over were averaged. The field observation covers an area larger than 30 m, so we assume that the land surface at 30 m scale is homogeneous and field observation can compare directly with TM data. However, for each AmeriFlux site, the tower-located TM pixel was picked out according to the tower location. Sometimes, the tower was not located at the center of the pixel and possible geometric correction error may also exist, so albedo of 3 \(\times\) 3 TM pixels around the tower were averaged and compared with the field observations.  
Figure 1 shows a scatter plot of the field measurements and the TM albedo. The correction coefficient was derived from least squares regression between TM data and ground measurement. The determination coefficient was 0.877, RMSE and bias were 0.033 and 0.00035, respectively. The calibratedTM data with the determined correlation were then used as a bridge for comparison with the MODIS albedo product as well as for further analysis to determine the spatial representativeness of the later.
# Intelligent Resource Orchestration for 5G Edge Infrastructures
## 7 Results
### Resource Optimization Results
#### 7.3.4 Optimization of Load Balance with Latency Violation and Number of Server Constraints  
In this subsection, we analyze the objective function that optimizes the load balance using both constraints in the number of servers and the number of latency violations. We carried out two sets of experiments, the first with the maximum number of servers (12 servers) and different latency violation constraints (Figure 11), and the second with the minimum number of servers (nine servers) and different latency violation constraints (Figure 12). In this case, we only show the load profile graphs, since the number of servers and number of latency violations values were implicit in each experiment.  
When utilizing 12 servers, we could observe a convergence between the maximum and minimum values of the average daily load as we relaxed the constraint on latency violations. This indicates an improved load distribution among the servers. Furthermore, with a latency violation limit above 40, the maximum peak load remained below 100%, indicating the absence of server overloading in these scenarios. The solver's execution time stayed below two seconds in all experiments.  
On the other hand, if we limit the number of servers to the minimum value of nine, each server has to support a higher load, leaving less room for load balance improvement. However, by establishing a latency violation limit of around 80-90, we can slightly reduce the gap between the maximum and minimum average daily load. This helps to avoid overloading, keeping the maximum peak load at around 100%. In some of these experiments, the solver took longer to converge to an optimal solution, with execution times ranging from 3 to 120 s.  
Figure 11: Actual load per core for the ‘load balancing’ objective function under different latency violation constrains and a server limit of 12.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{71.1 pt} p{71.1 pt} p{71.1 pt}} \hline \hline
**Limit on Number of** **Servers** & **Total Servers Used** &
\begin{tabular}{c} **Total Latency** **Violations** \\ \end{tabular} & **Solver Exec. Time** \\ \hline
9 & 9 & 59 & 8.9 s \\
10 & 10 & 50 & 7.1 s \\
11 & 11 & 40 & 3.0 s \\
12 & 12 & 29 & 1.0 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Allocation results for the ‘latency violations’ objective function under different server constraints.
# Brain Tumor Analysis Using Deep Learning and VGG-16 Ensembling Learning Approaches
## 3 Comparative Analysis
### Existing Methodology  
Current framework systems follow multiple pre-defined procedures to identify brain MRI images [51; 52; 53; 54]. The effective mechanisms involved in recognizing and classifying tumor and non-tumor units in MRI brain imaging is covered in [55; 56]. The following is a brief overview of prospective approaches and strategies. Most images to be entered as input are MRI brain scans [57; 58]. Depending on the architecture and memory limitations, the input might be 2D or 3D. Due to its efficiency in significantly enhancing image data the input regarding images to be entered has proved to be as crucial as any other stage [19; 20; 21; 59]. Segmentation primarily divides the input image into identical sections depending upon specified criteria, allowing only essential data to be extracted and the remainder to be discarded [22; 23]. There are numerous approaches. Some studies segment the actual tumor [48], whereas others segment the image region including the tumor [25]. The goal of the classification stage is to divide the input data into various categories based on comparable behavior patterns inside the group. The third process described in the literature involves directly feeding brain MRI images to a Deep Learning program for categorization with no pre-processing. Statistical techniques or machine learning techniques are used to identify features. The Deep Learning algorithm is then trained using these extracted features. While deep learning methods do not necessitate extraction of features, the study has shown that extracted features using machine learning, or meta-heuristic optimization methods, are still used in different models with reinforcement learning to include efficient and resilient features [27]. Each plan's principal purpose is to change the levels of Supervised Learning based on the experimental criteria and then choose the model with the best performance. Using machine and Deep Learning approaches, researchers employed models to build efficient systems. The dataset is divided into learning, testing, and verification sets before beginning any of the approaches above. Convolutional Neural Network (CNN) has received substantial appreciation and recognition in Deep Learning for its ability to automatically extract and detect deep features by responding to tiny changes in images. A constant comparative table (Table 1) briefly describes all of the essential characteristics of each previously presented study paper in this area. Table 1 essentially provides a concise overview of the tactics employed up to this moment. Some limits and inadequacies, as well as the obtained results, have been highlighted for thorough analysis.
## 4 Results and Analysis of Three-Dimensional Forward Modeling
### Discussion  
The case studies presented in this paper, based on 1D and 2D measurements, demonstrate that the proposed configuration effectively identified the characteristics of anomalous bodies. However, for more complex 3D observations, additional boreholes oriented in various directions were required to provide adequate support, which is a crucial consideration for future practical applications. With sufficient exploration data, more accurate 3D inversion becomes feasible. It is noteworthy that boreholes in roadways are often distributed in different directions and at varying depths, which can complicate the processing and interpretation of inversion. Nevertheless, since this exploration method improves detection accuracy, the ability to achieve 3D detection and inversion is expected to offer new opportunities for the precise exploration of underground spaces.
# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
## 3 Experiments and Results  
### Data Collection  
To construct the IPSO-SVM model for fault prediction in high-impact recorders, high-impact experiments were conducted. The specific steps for experimental design and data collection are as follows:
## 2 Basic Theory
### Sparse Dictionary Learning  
The principle of Sparse Dictionary Learning rests on the assumption that signals can be compactly represented by a sparse linear combination of basis functions, known as atoms, which are collected in a dictionary. Sparse Dictionary Learning involves two main components: the construction of the dictionary and the sparse decomposition of the signal over this dictionary.  
Figure 1: Flowchart of the AO-VMD.
# A Security and Privacy Scoring System for Contact Tracing Apps
## 3 Results
### Analysis of the Vulnerability of Contact Tracing Applications
#### 3.3.1 Vulnerability Review  
The ranking corresponds to how serious a vulnerability in the system is. High indicates that there is nothing of serious concern. Medium means that there are some areas of concern in the security of the system. Low indicates that critical security flaws are present in the system. The apps were given the following initial rankings.  
* Canada Covid Alert--Medium.
* France TousAntiCovid--Medium.
* Iceland Rakning C-19--High.
* India Aarogya Setu--Low.
* Singapore TraceTogether--Medium.  
**Canada's Covid Alert** application is ranked as medium because while many of the proposed vulnerabilities have the potential to be exploited within the system, the highest has a totaled average of 7.3. This vulnerability is concerning but not a critical issue with the system.  
**France's TousAntiCovid** application is ranked as medium because while many of the proposed vulnerabilities have the potential to be exploited within the system, the highest has a totaled average of 7.06. This vulnerability is concerning but not necessarily a critical issue with the system.  
**Iceland's Rakning C-19** application is ranked as high because of the lack of vulnerability. When the attack tree was held against the system of the Rakning C-19 application, none of the proposed vulnerabilities were a viable option against the system, due to the nature of GPS logging. The system is secure against these attacks.  
**India's Aarogya Setu** application is ranked as low because of the nature of the vulnerabilities found. When the attack tree was held against the system of the Aarogya Setu application two of the vulnerabilities received a totaled average of higher than 8. The functionalities within the system leave opportunities for attackers to take advantage and maliciously target users. Allowing the users to see how many individuals are near them currently, or that have been near them, have symptoms or have tested positive for the virus is a dangerous amount of information to be publishing.  
**Singapore's TraceTogether** application is ranked as medium because, similar to the Covid Alert app, while there are vulnerabilities that have the potential to be exploited and two with average totals of 7, these are only concerning. The application has some areas of concern, though there is no critical vulnerability in the system.
# Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems
## 3 System Model and Performance Metrics
### Performance Metrics
#### 3.2.3 Fairness  
The fairness evaluation of the proposed power allocation and user pairing methods, their gain index (or simply fairness index), is calculated and compared with the OFDMA scheme. In this aspect, the mathematical formulation of the fairness index is as follows [31]:  
\[F=\frac{\left(\sum\limits_{w=1}^{M}R_{w,s}\right)^{2}}{M\sum\limits_{w=1}^{M} (R_{w,s})^{2}} \tag{12}\]  
where \(0\leq F\leq 1\). The fairness performance of a resource allocation method is higher when its \(F\) value is closer to one.
# Stabilization of Argan Oil Nanoemulsions Using Chitosan Extracted from Pink Shrimp Shells
## 2 Materials and Methods
### Chitosan Characterization  
#### 2.2.1 Deacetylation Degree (DDA) and Molecular Weight  
The degree of deacetylation (DDA) of CS was determined via potentiometric titration [11]. A 0.2 g sample of dried chitosan was dissolved in 20 mL of 0.1 M HCl, followed by the addition of 25 mL of distilled water, with 30 min of agitation after each addition. The solution was then titrated with 0.1 M NaOH while monitoring pH changes. The resulting titration curve displayed two inflection points, and the DDA was calculated using the following equation:  
\[DDA(\%)=\frac{2.03\times(V_{2}-V_{1})}{m+0.0042\times(V_{2}-V_{1})} \tag{1}\]  
where \(m\) is the sample weight; \(V_{1}\) and \(V_{2}\) represent the NaOH volumes at the two deflection points; 2.03 is a coefficient derived from the molecular weight of the acetylated monomer unit; and 0.0042 accounts for the molecular weight difference between the acetylated and deacetylated monomer units.  
The molecular weight of chitosan was calculated using the viscosity-average molecular weight derived from viscosity measurements. Viscosity was determined using an Ubbelohde viscometer (CANNON) at 25 \({}^{\circ}\)C in a thermostatic water bath, with an aqueous acetic acid/sodium acetate buffer (0.3 M/0.2 M, pH 4.6) as a solvent. CS was dissolved at 1.0 wt% and filtered, followed by five serial 1:1 (\(v/v\)) dilutions. Flow times for each solution and the solvent were recorded, and the specific viscosity (\(\eta_{sp}\)) and reduced viscosity (\(\eta_{red}\)) were calculated as follows:  
\[\eta_{sp}=\frac{t-t_{0}}{t_{0}} \tag{2}\]  
\[\eta_{red}=\frac{\eta_{sp}}{C} \tag{3}\]  
where \(t\) is the solution's flow time; \(t_{0}\) is the solvent's flow time; and \(C\) is the CS concentration in g/mL.  
The intrinsic viscosity ([\(\eta\)]) was determined by plotting the reduced viscosity (\(\eta_{red}\)) versus concentration (\(C\)) and extrapolating to zero concentration. The molecular weight was calculated using the Mark-Houwink Equation (4),  
\[[\eta]=K\times M_{w}^{\alpha} \tag{4}\]  
where [\(\eta\)] is in mL/g; \(M_{w}\) is the average molecular weight in Da; K (74.0 \(\times\) 10\({}^{-5}\) mL/g) is the solvent constant; and a (0.76) is the Mark-Houwink constant. The parameters \(K\) and a are specific to the solvent system and depend on the temperature and chitosan DDA [12].
# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
## 4 Experiments
### Datasets
#### 4.1.1 Utdac2020  
UTDAC2020, originating from the 2020 Underwater Object Detection Algorithm Competition, serves as an underwater dataset with four classes: echinus, starfish, holothurian, and scallop. The dataset included 5168 training images and 1293 validation images, with four resolutions: 3840 \(\times\) 2160, 1920 \(\times\) 1080, 720 \(\times\) 405, and 586 \(\times\) 480. Notably, it presented  
Figure 4: Illustration of Deformable ConvNets v2 on the underwater image.  
with significant imbalances in resolution and category samples, posing challenges for model training.
# Sensitive Information Detection Based on Deep Learning Models
## 3 Improved YOLOv8 Model
### Optimizing Feature Extraction Capability  
In complex backgrounds or when parts of the target are occluded, the YOLOv8 model struggles to accurately extract target features, resulting in a decrease in detection performance. To improve the model's adaptability to images of different scales and complexities and enhance the model's receptive field, we introduce the InceptionNeXt structure as part of the YOLOv8 backbone network. InceptionNeXt is a deep learning module that effectively captures multi-scale information in images through parallel branches and deep convolution optimized feature extraction [17]. One of the key innovations of InceptionNeXt is the Inception Depthwise Separable Convolution (Inception DWConv), which decomposes traditional large kernel convolutions into three branches of small kernel convolutions and one identity mapping branch. Specifically, for the input feature map X, we divide it into four groups along the channel dimension:  
\[X_{lmw},X_{w},X_{h},X_{id}=\text{Split}(X) \tag{1}\]  
The segmented input is fed into four different parallel branches:  
1. Small square kernels (3 \(\times\) 3): \[X^{\prime}_{lmw}=DWConv_{C\to g_{c}}(X_{lmw})\] (2) where \(C\) represents the number of channels, \(g_{c}\) is the processed number of channels, and \(DWConv_{C\to g_{c}}\) denotes depthwise separable convolution.  
Figure 2: Improved YOLOv8 network structure.  
2. Horizontal stripe-shaped kernel (1 \(\times\) 11): \[X^{\prime}_{w}=DWConv_{g_{c}\to g_{c}}(X_{w})\] (3) 3. Vertical stripe-shaped kernel (11 \(\times\) 1): \[X^{\prime}_{h}=DWConv_{g_{c}\to g_{c}}(X_{h})\] (4) 4. The identity mapping branch remains unchanged: \[X^{\prime}_{id}=X_{id}\] (5) Finally, the outputs of these four branches are merged: \[X^{\prime}=Concat(X^{\prime}_{hw},X^{\prime}_{w},X^{\prime}_{h},X^{\prime}_{ id})\] (6) The structure of InceptionNeXt is shown in Figure 3.
## 4 Numerical Results  
In this section, we evaluate the performance of the GWO, the SSA, and the WOA algorithms by utilizing several benchmark functions. We extend the algorithms' assessment by optimizing various antenna design problems. A comparative study among these three algorithms is carried out in conjunction with the well-known Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Firefly Algorithm (FA). For the performance evaluation, we utilize custom tools developed in MATLAB (@ 1994-2020, The MathWorks, Inc., Natick, MA, USA). Finally, the commercial high-frequency electromagnetic solver (HFSS, @ 2020 ANSYS, Inc., Canonsburg, PA 15317, USA) is combined with the implementation of the WOA in MATLAB environment.
# Analysis of Scintillation Effects on Free Space Optical Communication Links in South Africa
## 2 Wind Speed Distribution
The climate of South Africa is considered to be highly variable, both spatially and temporally. Spatial variations in elevation across the country contribute significantly to this variability. According to the Council for Scientific and Industrial Research's (CSIR) Koppen-Geiger climate classification for South Africa, the country is predominantly semi-arid, with influences from temperate and tropical zones [40; 41; 42]. A large part of the geographical space of South Africa and Namibia is characterized by arid and hot climates, with clear skies and low annual rainfall [28; 29]. Due to the similarities in the two countries' climatic patterns, the \(C_{n}^{2}\) based on the HAP model are expected to adequately estimate the atmospheric turbulence losses encountered by FSOC links deployed in various cities of South Africa considered in this work. Other values where \(M=1\), \(b_{s}=0.1\) mrad/s, \(a_{o}=10\) m, and \(a=15\) m were used in computing the \(C_{n}^{2}\) throughout this work. The altitude measurements above sea level are given in Table 1, and the ground wind speed data from SAWS when inserted into Equations (1) and (2) are used for determining the \(C_{n}^{2}\) of the various locations shown in Figure 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**City** & \(a_{s}\) **(m)** \\ \hline Cape Town & 42 \\ Port Elizabeth & 69 \\ Durban & 106 \\ Mbombela & 865 \\ Polokwane & 1226 \\ Kimberley & 1196 \\ Mafikeng & 1281 \\ Bloemfontein & 1354 \\ Johannesburg & 1695 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reference altitude of the ground above sea level for various cities in South Africa.
# High-Reliability Underwater Acoustic Communication Using an M-ary Cyclic Spread Spectrum
## 4 Experimental Results  
In March 2022, an experiment was conducted off the coast to evaluate the proposed method's efficiency in an actual underwater acoustic channel. By comparing the BER of the UWA communication, the performance of the communication system was determined.