esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Description of Data Analysis
#### 2.2.1 Acquisition of Vegetation Index  
The vegetation index can reflect dynamics in canopy structure, pigments, and physiology, and can be calculated using vegetation reflectance. The formulas for the Normalized Difference Vegetation Index (NDVI) and the MERIS Terrestrial Chlorophyll Index (MTCI) are as follows [38; 39]:  
\[\mathrm{NDVI}=\frac{\mathrm{R_{NIR}-R_{red}}}{\mathrm{R_{NIR}+R_{red}}} \tag{2}\]  
\[\mathrm{MTCI}=\frac{\mathrm{R_{NIR}-R_{rededge}}}{\mathrm{R_{rededge}-R_{red}}} \tag{3}\]\(\mathrm{R_{NIR}}\), \(\mathrm{R_{red}}\), and \(\mathrm{R_{rededge}}\) represent the average vegetation reflectance in the spectral bands of 770-780 nm, 650-660 nm, and 700-710 nm, respectively.  
Previous research has found that weather conditions, particularly the state of radiation, can affect leaf photosynthesis and stomatal behavior [40]. We selected clearness index (CI) as the standard for clarify the weather condition, using 0.5 as the threshold to differentiate between sunny days (CI \(\geq\) 0.5) and cloudy days (CI \(<\) 0.5) [41]. The calculation process for CI is as follows [42]:  
\[\mathrm{CI}=\frac{\mathrm{R_{g}}}{\mathrm{R_{0}}} \tag{4}\]  
\[\mathrm{R_{0}}=\mathrm{R_{sc}}\times\left[1+0.033\times\mathrm{cos}\bigg{(} \frac{360\times\mathrm{DOY}}{365}\bigg{)}\right]\times\mathrm{cos}\theta \tag{5}\]  
In the formula, \(\mathrm{R_{g}}\) represents the total solar radiation; \(\mathrm{R_{0}}\) represents extraterrestrial radiation at the top of the atmosphere; \(\mathrm{R_{sc}}\) is the solar constant, noted as 1367 W\(\cdot\)m\({}^{-2}\); \(\mathrm{\theta}\) is the solar zenith angle.
# Low-Light Mine Image Enhancement Algorithm Based on Improved Retinex
## 2 Retinex Theory  
Retinex is a model based on the vision perception and adjustment of the brightness of surrounding objects [PERSON] et al. (2018), and its basic principle is to divide the image \(I(x,y)\) into illumination component \(L(x,y)\) and reflection component \(R(x,y)\)[PERSON] et al. (2018). \((x,y)\) represents the spatial coordinates in the image, that is, the pixel position in the image. \(x\) usually represents the horizontal position, and \(y\) represents the vertical position. The illumination component encompasses the variations in brightness. Object features and color information are presented in the reflection component, and its principle model is presented in Figure 1.  
Based on the principles of Retinex theory, the above process can be formulated as  
\[I(x,y)=L(x,y)\times R(x,y). \tag{1}\]  
Since the logarithmic form is closest to the process attributes of humans experiencing brightness, transferring it to the logarithmic domain yields  
\[\log(I(x,y))=\log(L(x,y)\times R(x,y))=\log(L(x,y))+\log(R(x,y)). \tag{2}\]  
In the Retinex algorithm, the calculation of the illumination component is commonly achieved through the application of a Gaussian filter as the center-surround mechanism, which is estimated as  
\[L(x,y)=I(x,y)*G(x,y), \tag{3}\]  
where \(G(x,y)\) represents the Gaussian filter and \(*\) is the convolution symbol.  
Based on Equation (2), Figure 2 depicts the diagram of the Retinex algorithm.
## 2 Materials and Methods
### Methodology  
Figure 4 shows the different phases of this work. A field mission was carried out in the Hassan Addakhil dam on the same day of the satellite visit in order to calibrate the extracted models for the different water quality parameters, for which the image of 14 March 2021 has been used. This mission was done in collaboration with the staff of the Guir-Ziz-Rheris Hydraulic Basin Agency, and the analyses were realized in its laboratory. Later on, a statistical study was carried out to extract the different correlated bands with the different parameters, and a multiple stepwise analysis modeling approach was used in order to set the models. Several models were extracted for the different parameters, and the selection of a suitable one was made on the basis of the compliance index R\({}^{2}\), the root mean square error (RMSE) and p-value. Finally, after the models' validation and the extraction of their equations, spatiotemporal monitoring of the reservoir water quality was performed through multitemporal images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sentinel-2 Bands** & **Wavelength (nm)** & **Spatial Resolution (m)** \\ \hline
**Coastal Aerosol** & 442.7 & 60 \\
**Blue** & 492.4 & 10 \\
**Green** & 559.8 & 10 \\
**Red** & 664.6 & 10 \\
**Vegetation red edge** & 704.1 & 20 \\
**Vegetation red edge** & 740.5 & 20 \\
**Vegetation red edge** & 782.8 & 20 \\
**NIR** & 832.8 & 10 \\
**Narrow NIR** & 864.7 & 20 \\
**Water vapor** & 945.1 & 60 \\
**SWIR-Cirrus** & 1373.5 & 60 \\
**SWIR** & 1613.7 & 20 \\
**SWIR** & 2202.4 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sentinel-2 satellite image characteristics.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Image** & **Acquisition Dates** \\ \hline
1 & 28 April 2020 \\
2 & 3 May 2020 \\
3 & 6 June 2020 \\
4 & 7 July 2020 \\
5 & 6 August 2020 \\
6 & 20 September 2020 \\
7 & 28 October 2020 \\
8 & 19 November 2020 \\
9 & 19 December 2020 \\
10 & 13 January 2021 \\
11 & 17 February 2021 \\
12 & 14 March 2021 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sentinel-2 satellite image acquisition dates.
# Congestion Analysis of Transport Layer in a Multicell 5G DL Communication System
## 1 Introduction
Since UDP cannot control network congestion, this research focuses on TCP, which can run in a variety of different versions, each of which makes use of a congestion control algorithm. TCP is one of the many protocols that are needed to support the varied capabilities of 5G wireless communications [23]. When implementing either TCP or UDP in a mobile network, it is also important to take into account issues such as synchronization and functional slicing. In this work, we studied the transport layer congestion in 5G communication systems by making use of two types of FTP traffic and the impact of the TCP slow-start algorithm. The effectiveness of TCP in a 5G network was evaluated using metrics such as user throughput (TP), cell throughput, cell edge user throughput, delay, and physical resource blocks (PRBs). Two types of FTP traffic were simulated to investigate the effect that the TCP slow-start model has on the levels of network congestion and delay. The goal of the TCP slow-start algorithm is to reach a suitable value for the congestion window as rapidly as possible. Figure 1: Fifth-generation (5G) use cases. Source: [22]. It was observed that adopting the TCP slow-start method in either a single-cell or a multicell scenario can have a significant impact and offer many benefits. The obtained throughput was 1.2 Gbps for the single-cell scenario and 800 Mbps for the multicell scenario, keeping the maximum delay under 50 ms.  
For the scenarios with greater amounts of UE in the cell, the cell edge user throughput decreased when the PRB utilization rate increased. Average throughput of 20 Mbps at the cell edge could be maintained up to around 38% PRB utilization in the cell for the multicell case, which was a similar value as obtained for the single-cell scenario. Up to about 40% of the cell's PRBs could be used without negatively impacting the average throughput of 20 Mbps per second at the cell edge for the single-cell scenario.
## 4 Conclusions  
This paper reports a first characterization of the novel SiC detectors developed by the STLab company with UHDR 9 MeV electron beams produced by the SIT Sordina ElectronFLASH accelerator.  
The response in the charge of a 10 \(\upmu\)m thick SiC detector was investigated as a function of the dose delivered per pulse up to 5 Gy/pulse. SiC response was found to be perfectly linear with the dose per pulse up to 2 Gy/pulse, while a slight saturation was found for the dose per pulse higher than 2 Gy/p. This is fully ascribable to the saturation of the used electrometer, while no degradation of the sensor's response was observed at up to 90 kGy. The results demonstrated, for the first time, the potentialities of Silicon Carbide detectors as possible new dosimeters to employ in FLASH-RT. Nevertheless, the issue related to the saturation of the instrument that has clearly affected the linearity response at higher dose per pulse needs to be addressed in the future employment, for instance, by using an additional electronic circuit to avoid the saturation or by using different instrumentations. New measurements with UHDR electron and proton beams are already planned in the future and will focus on extending the linearity curve up to a higher dose per pulse. Detectors with different active areas and thicknesses will also be tested under the same conditions to investigate their response.  
Moreover, the newly developed ultrathin, free-standing membranes will also be experimentally characterized with UHDR beams to confirm the results obtained with the Monte Carlo Geant4 simulation shown in this paper. Thanks to the simulations, we expect that in the case of ultrathin, free-standing sensors without the bulk substrate, a significative reduction on the beam angular scattering and energy degradation will be observed, which will definitively demonstrate their suitability for the UHDR beam monitoring in FLASH-RT.  
Conceptualization, F.R., M.C. and G.M.; methodology, F.R., G.M. ([PERSON]), G.F., F.G., G.M. ([PERSON]) and M.P.; software, G.M. ([PERSON]); data analysis, G.M. ([PERSON]), F.R., M.C.D., A.G., M.M., V.V., D.V., E.M., E.S. and A.V.; writing--original draft preparation, F.R. and G.M. ([PERSON]); writing--review and editing F.R., G.M. ([PERSON]), M.C., F.D.M., A.V., A.G.; supervision, F.R., M.C.; All authors have read and agreed to the published version of the manuscript.  
Partially supported by INFN CSN5 funded project \"FRIDA\" and by the \"SAMOTHRACE\" project (ECS00000022) Missione 4 Istruzione e Ricerca-Componente 2-Investimento 1.5 (\"PNRR\"), NGEU.  
Not applicable.  
Figure 6: Charge per pulse measured with the SiC detector after each irradiation as a function of the cumulative dose. The dotted lines delimit the region within the \(\pm\) 0.75% variation in charge.  
Data Availability Statement: Data will be provided on request. Conflicts of Interest: The authors declare no conflict of interest.
# Early Neighbor Rejection-Aided Tabu Search Detection for Large MIMO Systems
###### Abstract  
In this study, a low complexity tabu search (TS) algorithm for multiple-input multiple-output (MIMO) systems is proposed. To reduce the computational complexity of the TS algorithm, early neighbor rejection (ENR) and layer ordering schemes are employed. In the proposed ENR-aided TS (ENR-TS) algorithm, the least promising \(k\) neighbors are excluded from the neighbor set in each layer, which reduces the computational complexity of neighbor examination in each TS iteration. For efficient computation of the neighbors' metrics, the ENR scheme can be incorporated into QR decomposition-aided TS (ENR-QR-TS). To further reduce the complexity and improve the performance of the ENR-QR-TS scheme, a layer ordering scheme is employed. The layer ordering scheme determines the order in which layers are detected based on their expected metrics, which reduces the risk of excluding likely neighbors in early layers. The simulation results show that the ENR-TS achieves nearly the same performance as the conventional TS while providing up to 82% complexity reduction.  
multiple-input multiple-output (MIMO); tabu search (TS); neighbor examination
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 6 The 6G Network Infrastructure to Manage V2X Application Lifecycles
### Implementation Solutions:  
Technical Framework: Establish a system where vehicles utilize 6G to communicate data regarding speed, distance, and road conditions to each other, employing AI algorithms to make cooperative decisions regarding speed adjustments and lane changes.  
Addressing Challenges: Ensure that the AI algorithms are robust enough to manage varied traffic scenarios and implement redundant communication channels to enhance reliability.  
Practical Considerations: Address issues related to user adoption, update current vehicular communication systems to be 6G-compatible, and navigate regulatory compliance related to autonomous driving features.
## 2 Materials and Methods
### Algorithm Workflow
#### 2.3.1 Reading Inputs  
Inputs are requested in a.txt file named criteria; here, the user can control the scale factor from pixels to millimetres, give an indication on whether the deposited material is darker than the substrate's material, the fraction of the height considered when computing the wettability angles, whether or not visual outputs are to be saved, if a.csv file with the outputs is to be saved, the path to save data and lastly, the input image formats. The algorithm opens.png,.jpeg and.tiff files by default, and additional formats must be inserted in the file.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{5}{c}{**DOEs**} \\ \cline{2-6}  & **1 st** & **2 nd** & **3 rd** & **4 th** & **5 th** \\ \hline Laser Power (W) & 1500 & 1400 & 1400 & 1400 & 1550 \\ \(\pm\) & 250 & 150 & 150 & 150 & - \\ Scanning Speed (mm s\({}^{-1}\)) & 6 & 6 & 9 & 3 & 12 \\ \(\pm\) & 3 & 3 & 3 & 1 & - \\ Feeding rate (g min\({}^{-1}\)) & 10 & 10 & 7.5 & 7.5 & 12 \\ \(\pm\) & 5 & 5 & 5 & 5 & - \\ Shielding gas (L min\({}^{-1}\)) & 25 & 25 & 25 & 25 & 25 \\ \(\pm\) & - & - & - & - & 5 \\ Carrier gas (L min\({}^{-1}\)) & 4 & 4 & 4 & 4 & 4 \\ \(\pm\) & - & - & - & - & 1 \\ Nozzle distance (mm) & 11.5 & 11.5 & 11.5 & 11.5 & 12 \\ \(\pm\) & - & - & - & - & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Five DOEs conducted during the 18 Ni300 process parameter optimisation. The \(\pm\) sign indicates the variation of a parameter in one DOE: for example, the first DOE consisted of line beads with laser powers of 1250, 1500 and 1750 W, scanning speeds of 3, 6 and 9 mm s\({}^{-1}\) and feeding rates of 5, 10 and 15 g/ min.
## 1 Introduction
### Real-Time Malicious Node Detection  
VANET is always vulnerable to attacks due to its open wireless access. The wireless network is susceptible to attacks [23]. VANET is also vulnerable due to wireless technology. In VANET, there is real-time communication among the vehicles, so the detection of the malicious nodes may also come in real time; otherwise, the network is always targeted with high vulnerabilities. The security requirements must be best fitted to communicate and provide real-time malicious node detection using the scenario [24]. Malicious node detection comes with confidentiality, non-repudiation, integrity, authentication, and privacy to protect against attackers and intruders. In our case, we encounter real-time malicious node detection using the machine learning approach. This happened due to the protection and detection of different schemes that can demonstrate and provide adequate knowledge and discovery. The main idea in this context is real-time malicious node detection. Real-time means whenever a node starts behaving differently, we encounter the machine learning algorithm that starts detection of the malicious nodes [25].
## 3 Dynamic Link Establishment of Mega-Constellations
### Optimization Matching Algorithm for Dynamic Links
This improvement offers a more reliable solution for optimizing satellite communication systems, enabling them to better cope with the challenges brought about by time-varying conditions, thereby enhancing the overall performance of the systems.
# PECSA: Practical Edge Computing Service Architecture Applicable to Adaptive IoT-Based Applications
## 4 Materials and Methods
### Trust Evaluation Mechanism
#### 4.1.3 Comprehensive Trust Computation  
The trust values established in the IoT and edge networks are utilized to support service operations. Generally, the service provider selects an edge node with a high trust value for data processing, while the nodes with low trust values are rejected. Thus, a comprehensive trust of each node should be established for the classification of honest nodes and malicious nodes. Let us suppose that there is a \(q\) direct trust value about node \(j\) submitted to the server; the mean value of all the direct trust values is as follows:  
\[mt(j)=\frac{\sum_{i=1}^{q}Tu(i,j)}{q}, \tag{3}\]  
while the comprehensive trust \(T_{C}(j)\) of a node \(j\) is given as  
\[T_{C}(j)=\sum_{i=1}^{q}\frac{e^{-(Tu(i,j)-mt(j))^{2}}\cdot Tu(i,j)}{q\sum_{i=1} ^{d}e^{-(Tu(i,j)-mt(j))^{2}}}, \tag{4}\]  
The comprehensive trust \(T_{C}(j)\) changes with the real-time performance of the edge nodes. Furthermore, the changes lead to offloading workload from the nodes with lower trust values to the nodes with higher trust values. When the comprehensive trust \(T_{C}(j)\) of an edge node is less than a threshold \(Tu_{e}\), its workload is reallocated to other edge nodes for security and efficiency. The value of \(Tu_{e}\) is set according to the whole edge network's state. If the trust values of most edge nodes are increasing, the value of \(Tu_{e}\) should be increased; otherwise, it should be decreased. When the \(T_{C}(j)\) of a node in the IoT is less than a threshold \(Tu_{i}\), the node is blacklisted and its service requirements are rejected. The trust estimation process is presented in Algorithm 1.  
```
Input: \(NT\):node type// node in IoT or node in the edge network; \(Td_{it}\),\(Tu_{e}\), \(Tu_{i}\):thresholds used in the algorithm; \(N\):number of currently available nodes in the edge network; Output: \(Tu_{i}(i,j)\),\(T_{C}(j)\),\(N\) Begin
01: each node calculates \(Tu(i,j)\) using (2) and sends it to the associated server;
02:if NT=1 // the node is in IoT
03: {if\(Tu(i,j)\leq Td_{it}\)
04: moves node \(j\) out of the route list of node \(i\);
05:else
06: moves node \(j\) into the route list of node \(i\);
07: the server calculates \(T_{C}(j)\) using (4);
08:if\(T_{C}(j)\leq Tu_{i}\)
09: blacklists node \(j\); }
101:else if NT=2/ // the node is in the edge network
11: {the server calculates \(T_{C}(j)\) using (4);
12:if\(T_{C}(j)\leq Tu_{e}\)
13:if node \(j\) is on the available list
14: {\(N=N-1\);
15: moves node \(j\) out of the available list;}
16:else
17: if node \(j\) is on the available list
18: \(N=N\);
19:else
20: moves node \(j\) into the available list;
21: \(N=N+1\); }
```  
**Algorithm 1**Trust estimation algorithm.
## 3 Results  
### FTIR-ATR Result  
Figure 2a,b shows the FTIR-ATR results for the silicone elastomer before and after 500 h of artificial aging without AgNPs and with 0.2% AgNPs, respectively. Prior to artificial aging, as can be seen from Figure 2a, all of the characteristic peaks of silicone were exhibited, including the Si-O-Si (1018.14 cm\({}^{-1}\)), Si-(CH3)\({}_{2}\) (800.46 cm\({}^{-1}\)), C-H (3000 cm\({}^{-1}\)), and Si-CH\({}_{3}\) (1255 cm\({}^{-1}\)) bonds. Nonetheless, after 500 h of artificial aging, the peaks had deteriorated.  
Figure 2b shows the FTIR-ATR results of silicone elastomer with 0.1, 0.15, and 0.2% AgNPs after 500 h of artificial aging. When 0.2% AgNPs were added to the silicone  
Figure 2: (**a**) FTIR-ATR results of silicone elastomer without AgNPs before and after 500 h of artificial aging (black line = silicone before aging, red line = silicone after aging). (**b**) FTIR-ATR results of silicone elastomer with 0.1%, 0.15%, and 0.2% AgNPs after 500 h of artificial aging.  
elastomer, the silicone exhibited all of its characteristic peaks, indicating that the AgNPs did not affect the silicone's matrix and bonds. Even after 500 h of artificial aging, it was also found that all of the silicone peaks remained intact and undeterred. This demonstrates the protective role of AgNPs within the silicone, protecting the silicone elastomer matrix and silicone peaks.
## 5 Experimental Results
### SVM  
Initially, SVM were mainly applied to pattern classification problems such as character recognition, face identification, text classification, etc. However, soon, researchers found wide applications in other domains as well, such as function approximation, regression and time series forecasting. SVM techniques are based on the structural risk minimization rule. The objective of SVM is to find a decision rule with good generalization capability through selecting some particular subset of training data called support vectors. In this method, a best possible separating hyperplane is constructed, upon nonlinearly mapping of the input space into a higher dimensional feature space. Thus, the quality and complexity of SVM solution is not directly dependent on the input space. Another important characteristic of SVM is that the training process is equivalent to solving a linearly inhibited quadratic programming problem.
## 7 Discussion and Future Perspectives
Due to the advancements of the solar PV system worldwide, a validation of the data acquired from the sensor nodes is required. Thus, a common simulation platform is essential which could be interfaced with data transmission modules for the evaluation of the data received from the simulation results. * For the development of a reliable, robust, and efficient wireless solar PV monitoring system, the validation of the data under different environmental conditions should be observed. Therefore, the monitoring system should be tested in changing environmental settings to evaluate the robustness and overall efficiency of the system. * One of the critical issues related to the energy efficiency of the sensor nodes is the transmission of the data. Any failure of the node battery results in the low life of the network, thus disturbing real-time communication. Therefore, further research works are required to design modules for a long-duration operation without interruptions in sending the data. * The data acquired from the solar panel can be affected by the degradation of the solar panel as well as dust, humidity, irradiance, and temperature. Therefore, an in-depth study is required to develop a low-cost intelligent real-time PV monitoring system to identify the degradation. * The development of open-source platforms and software with regard to data processing modules such as Arduino, Raspberry Pi, etc. affects the availability of information in the internet as well as in the cost of acquisition, programming, and modification of devices. Further, the application of open-source platforms will accelerate the development of low-cost programmable devices for innumerable tasks in various applications such as Science, Technology, Engineering and Mathematics (STEM) in the coming years. Additionally, the development of open-source modules would lead to a reduction in the gap between the prototyping and the product development of PV panels due to fault conditions. * The implementation of IoT based wireless solar PV monitoring systems consisting of sophisticated sensors, data processing boards, and communication protocols could be developed to achieve an efficient, accurate, and robust monitoring system for the solar PV environment.
### Design of the HF-Band RFID System
#### 1.2.1 Controller  
The most important element of the HF-band RFID system is the controller (Figure 2). It is responsible for controlling the actuators of the system, such as barriers (bars), gates, and electromagnetic locks, based on the data on accepted identifiers programmed and stored in it. In addition, it controls the badge readers by supplying power to them and activating or blocking the badge reading function. In complex systems (e.g., time and attendance control), the controller cooperates with the computer, sending to it information about the read identifier data, along with information about the reading time and the access zone. The controllers can operate one door zone (entrance and exit) as well as several door zones (also entry and exit).