esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey and Perspectives of Vehicular Wi-Fi versus Sidelink Cellular-V2X in the 5G Era
## 3 Simulation Tool and Settings
### Main Settings  
The main settings are reported in Table 5. The investigated application is the cooperative awareness service, where each vehicle transmits in broadcast periodic messages informing of its status and movements. Such service is at the basis of most other applications, especially those related to safety and coordinated manoeuvres. The messages, which are called cooperative awareness messages (CAMs) by ETSI and are a subclass of the basic safety messages (BSMs) defined by SAE, are sent with a 100 ms period, which is the lowest value considered by ETSI. Messages of 300 bytes are assumed, which is the largest size suggested by 3 GPP in [29].  
The channel bandwidth of IEEE 802.11p, i.e., 10 MHz, is assumed. All nodes transmit with the same transmission power of 23 dBm, adopting antennas with 3 dB gain. The propagation is modelled following the WINNER+ model, scenario B1, with a path loss exponent of 4 (except for small distances) and with correlated shadowing, as suggested by 3 GPP in [29]. The log-normal shadowing has a standard deviation of 3 dB in line-of-sight (LOS) and 4 dB in non-line-of-sight (NLOS), and is correlated with decorrelation distance 10 m in urban and 25 m in highway scenarios. Per each transmission and receiver, the average SINR is calculated, taking into account the interference from all nodes transmitting at the same time. If the bandwidth is not the same, the SINR calculation includes the presence of IBE [30].
## 3 Results and Discussion
### Characterization
After the incorporation of PEI-GA, the peaks appear at 2856 cm\({}^{-1}\) and 2854 cm\({}^{-1}\) for LD/PEI and CC/PEI beads, respectively, and the (CH\({}_{2}\))\({}_{n}\) band around 2947 cm\({}^{-1}\) appears more distinct because of the number of C-H groups present in PEI and GA [39]. The peaks at 1599 cm\({}^{-1}\) for LD/PEI beads  
Figure 2: SEM (Scanning electron microscopy) micrographs of the raw beads and element cartographies of Pd-loaded beads. and 1610 cm\({}^{-1}\) for CC/PEI beads correspond to C=N vibration, which is due to the reaction between primary amine groups of PEI and GA, forming \"[PERSON]'s base\" [40]. The peak at 1605 cm\({}^{-1}\) (COO\(-\) asymmetric stretching) on LD beads disappears, which could be attributed to the overlapping with C=N vibration. Other changes on LD beads include band shift from 1416 cm\({}^{-1}\) to 1397 cm\({}^{-1}\) (COO\(-\) symmetric stretching) and the disappearance of the peak at 1250 cm\({}^{-1}\) (C\(-\)O stretching), confirming the ionic crosslinking reaction between positively charged amino groups of PEI and the negatively charged carboxylate groups on _L. digitata_. At the same time, for CC beads, there are some changes in the wave number for C=C stretching and COO\(-\) symmetric stretching. Moreover, a decrease in the intensity of S\(-\)O stretching mode (1221 cm\({}^{-1}\)) of the sulfate ester group is also observed. This is probably because some of the negatively charged sulfate ester reacted with the positively charged amine groups on PEI and this resulted in physicochemical changes of carrageenanan. Indeed, a previous study [16] showed that protonated amine groups of PEI interact with sulfate groups of \(\kappa\)-carrageenanan (\(\kappa\)C), enabling the formation of the PEI-\(\kappa\)C polyelectrolyte complex. After Pd(II) sorption, the main change is the shift of the C=N vibration peak from 1599 cm\({}^{-1}\) to 1608 cm\({}^{-1}\) for LD/PEI, and from 1610 cm\({}^{-1}\) to 1621 cm\({}^{-1}\) for CC/PEI beads, confirming the contribution to Pd(II) sorption through the incorporation of PEI-GA into the beads. For LD/PEI beads, a new peak began appearing at 1711 cm\({}^{-1}\), assigned to the carboxylic acid C=O stretching, while the COO\(-\) peak at 1397 cm\({}^{-1}\) disappeared; this means that a part of the carboxyl groups are converted to the anhydride form, which could be due to the excess of protons in acid solution (sorption was performed at pH 1) [41].
## 2 Materials and Methods
### Remote Sensing Data  
#### 2.2.1 Sentinel-2 Data  
The 2020-2021 Sentinel-2A/B Multispectral Instrument (MSI) surface reflectance (SR) data (Level-2A) were utilized to obtain the vegetation indices (VIs) in the Saihanba region. Sentinel-2 offers high-resolution Earth imagery (10-60 m resolution in all bands) and has a revisiting period of 5 days. The sensor on board the Sentinel-2 satellites consists of 13 channels, encompassing the visible spectrum, the near-infrared (NIR), and shortwave infrared (SWIR) bands. The Google Earth Engine (GEE) platform was utilized to process the Sentinel-2 data. Cloud and shadow masks were first applied to the Sentinel-2 dataset to extract cloud-free and shadow-free data. The cloud-masking algorithm devised by the Sentinel Hub [34] was utilized to identify clouds within the Sentinel-2 cloud probability dataset (s2 cloudless), and shadows were determined as the intersections of cloud projections and low-reflection near-infrared (NIR) pixels. However, the algorithm did not completely eliminate clouds and shadows in the Saihanba area, so the QA60 bitmask band provided by the European Space Agency (ESA) in the metadata was additionally used to effectively remove clouds and cirrus. Furthermore, cloud shadows were eliminated using solar geometry and height estimates [35]. During the masking process, the results were visually inspected, and all parameters were adjusted until the best results were obtained. To generate an 8-day composite, the mean synthesis of valid Sentinel-2 VIs was used. Data gaps were then filled using linear interpolation to achieve complete coverage over the entire 8-day period. The resulting time series was smoothed using a Savitzky-Golay (SG) filter with a third-order polynomial and a window size of 56 days (7 observations), as described by [PERSON] et al. [36]. Finally, a consistent 8-day Sentinel-2 time series that was free of clouds and effectively filled in data gaps was acquired (refer to Figure 2).  
This study utilized six spectral VIs obtained from Sentinel-2 SR data, namely the enhanced vegetation index (EVI), near-infrared reflectance of terrestrial vegetation (NIRv), red-edge normalized difference vegetation index (NDVIre), chlorophyll index--red (CIr), chlorophyll sensitive index (CSI), and terrestrial chlorophyll index (MTCI). These indices were computed using bands B3 to B8, with central wavelengths of 560 nm, 665 nm, 705 nm, 740 nm, 783 nm, and 842 nm, respectively. The calculation formulas for each index can be found in Table 1. All VIs resolutions were resampled to 0.0005\({}^{\circ}\) for subsequent SIF downscaling.
# Analysis and Research on Secondary LT Coding Anti-Eavesdropping Scheme Based on LT Code Degree-1
## 3 Program Performance Analysis
### Effect of the Length of the Source Symbol on the Decoding Overhead  
In the decoding process, the ratio of the number of symbols \(m\) required to recover the source information to the original number of symbols \(k\) of the source is known as the decoding overhead \(\varepsilon\), whose mathematical expression is:  
\[\varepsilon=m/k \tag{23}\]The number of overhead symbols needed to decode \(k\) symbols in LT code, according to the literature [15], is:  
\[m_{LT}=k+o\bigg{(}\sqrt{k}\cdot\ln^{2}\bigg{(}\frac{k}{\delta}\bigg{)}\bigg{)} \tag{24}\]  
where \(\delta\) denotes the maximum decoding failure probability and \(\delta\) is fixed.  
The decoding overhead \(\varepsilon_{LT}\) is obtained from Equations (23) and (24).  
\[\varepsilon_{LT}=1+o\bigg{(}\sqrt{k}\cdot\ln^{2}\bigg{(}\frac{k}{\delta} \bigg{)}\bigg{)}/k \tag{25}\]  
Since LT-a in double LT is the LT code, the decoding overhead \(\varepsilon_{LT-a}=\varepsilon_{LT}\).  
LT-b decoding is LT encoding and decoding of \(k\mu(1)\) degree-1 symbols, and the decoding overhead \(\varepsilon_{LT-b}\) is obtained from Equation (25):  
\[\varepsilon_{LT-b}=\mu(1)+o\bigg{(}\sqrt{k\mu(1)}\cdot\ln^{2}\bigg{(}\frac{k \mu(1)}{\delta}\bigg{)}\bigg{)}/k \tag{26}\]  
The total of LT-a code and LT-b code constitutes the double LT decoding overhead. The decoding overhead \(\varepsilon_{double\_LT}\) of the double LT code method is obtained by adding (25) and (26):  
\[\varepsilon_{double\_LT}=1+u(1)+\frac{o\Big{(}\sqrt{k}\cdot\ln^{2}\Big{(} \frac{k}{\delta}\Big{)}\Big{)}+o\Big{(}\sqrt{k\cdot u(1)}\cdot\ln^{2}\Big{(} \frac{k\cdot u(1)}{\delta}\Big{)}\Big{)}}{k} \tag{27}\]  
The larger \(k\) is, the smaller \(u(1)\) is from Equation (3), and the smaller \(\Big{(}o\Big{(}\sqrt{k}\cdot\ln^{2}\Big{(}\frac{k}{\delta}\Big{)}\Big{)}+o \Big{(}\sqrt{k\cdot u(1)}\cdot\ln^{2}\Big{(}\frac{k\cdot u(1)}{\delta}\Big{)} \Big{)}\Big{)}/k\) is in Equation (27). Comparing Equations (25) and (27), \(u(1)>0\), obviously, \(\varepsilon_{double\_LT}>\varepsilon_{LT}\).  
It is clear from the aforementioned derivation that \(\varepsilon_{double\_LT}\) is greater to \(\varepsilon_{LT}\). However, as \(k\) rises, \(\varepsilon_{double\_LT}\) moves closer to \(\varepsilon_{LT}\).
# An Overview of Fog Data Analytics for IoT Applications
## 4 Current State-of-the-Art
### Home Automation  
IoT devices have a huge role in home automation, making it a smart home with connected devices and data synchronization over the network of IoT devices. These devices are connected with wireless access technologies and can make intelligent decisions according to situations and conditions. Devices such as intelligent lights that adjust the lights according to the available sunlight at a given time, smart thermostats that dynamically adjust temperatures in combination with devices such as air conditioners or heaters, and security cameras that intelligently recognize people and track their movements are driving home automation and the quality of living to new heights. Data from all of these devices need to be processed in real-time for the best experience and to receive immediate support in emergencies. In addition, with adaptive learning mechanisms to constantly shape the services of all of these smart devices to serve the best experience to the user, fog nodes embedded with machine learning and deep learning hardware provide fully customized and user-centric automation without any direct human interaction. In modern scenarios, all smart IoT devices are connected to the central network and can be accessed and controlled from mobile and wearable devices. These smart devices, in coordination with each other, through sharing real-time data, have also resulted in an overall reduction in energy consumption.
## 4 Numerical Optimization Experiments
### Simulation Results Analysis
& \(4.46\times 10^{3}\) & \(1.58\times 10^{3}\) & \(4.79\times 10^{3}\) & \(1.56\times 10^{3}\) & \(1.25\times 10^{3}\) & \(\mathbf{2.74\times 10^{3}}\) & \(1.60\times 10^{3}\) \\  & & Rank & 2 & 4 & 5 & 6 & 7 & **1** & 3 \\ \hline \multirow{3}{*}{F9} & \multirow{3}{*}{30} & Mean & \(\mathbf{0}\) & \(1.67\times 10^{2}\) & \(7.47\times 10^{1}\) & 3.00 & **0** & \(1.67\times 10^{1}\) & **0** \\  & & S.D. & \(0\) & \(1.25\times 10^{2}\) & \(1.21\times 10^{2}\) & \(1.23\times 10^{1}\) & **0** & \(9.15\times 10^{1}\) & **0** \\  & & Rank & **1** & 7 & 6 & 4 & **1** & **5** & **1** \\ \hline \multirow{3}{*}{F10} & \multirow{3}{*}{30} & Mean & \(5.15\times 10^{-15}\) & \(1.01\times 10^{1}\) & 2.76 & \(5.84\times 10^{-14}\) & \(5.86\times 10^{-15}\) & \(5.42\times 10^{-8}\) & \(\mathbf{3.25\times 10^{-15}}\) \\  & & S.D. & \(8.40\times 10^{-15}\) & \(2.76\times 10^{1}\) & 4.21 & \(2.87\times 10^{-14}\) & \(5.50\times 10^{-15}\) & \Figure 4: Optimization results for F1 (Dim = 30). Figure 5: Optimization results for F3 (Dim = 30). Figure 6: Optimization results for F5 (Dim = 30). Figure 7: Optimization results for F7 (Dim = 30). Figure 11: Optimization results for F15 (Dim = 4). Figure 8: Optimization results for F10 (Dim = 30). Figure 10: Optimization results for F13 (Dim = 30). Figure 9: Optimization results for F11 (Dim = 30). Figures 4-7 illustrate the convergence curves of the average values of 30 runs for part of the unimodal benchmark functions (F1, F3, F5, and F7). Figure 4 shows that the MSWOA algorithm had the best search accuracy and the fastest convergence speed. Figure 5 indicates that MSWOA overtook the other six algorithms in terms of search results and convergence rate, while PSO-GWO ranked second. Figure 6 demonstrates that WOA, GWO, PSO-GWO, LWOA, and MSWOA had very close search accuracy, but the convergence speed of the proposed MSWOA was super to that of the other four algorithms. From Figure 7, it can be seen that MSWOA converged faster in the initial stage of the searching process, yet PSO-GWO obtained slightly higher search precision in the latter period. algorithms, except MFO, obtained very close performances in the early stage; however, LWOA showed a promising final search accuracy.
# Multi-Label Classification Based on Associations
## 2 Literature Review
### CBA and msCBA Algorithms  
CBA is one of the earliest algorithms that merge the ARM and classification tasks. CBA was introduced in [48]. Since then, several more techniques based on the combination of ARM and classification have been presented. The MMAC algorithm [43] and the multi-class associative classification (MAC) algorithm [49] are examples of algorithms that adhere to the AC methodology. CBA employs the a priori method in a classification dataset by the use of three key phases. At first, all continuous attributes are discretized.discretization is the step of converting any continuous variable or attribute into a discrete one. This step is compulsory for any AC-based classifier. Then, CARs are generated. CARs consider rules with arbitrary combinations of elements on antecedent (the left-hand side) and a single class on the consequent (the right-hand side). CARs are chosen using two metrics (support and confidence). The objective of the final phase is to construct a classifier using the best CARs [50]. CBA was subsequently enhanced in [18] by removing two flaws in the original CBA algorithm. The first problem is the use of a single minsup (minimum support) threshold value, which may result in an unbalanced class distribution. Using several minsup criteria, the modified version has addressed this problem. The exponential increase in the number of rules issued by CBA is the second flaw of the original CBA. This problem was fixed by combining CBA to a decision tree, as in C4.5, resulting in more precise rules. The modified version of CBA is referred to as CBA2 or msCBA, which is short for multiple support classification based on associations. Algorithm 1 illustrates the first CBA algorithm.  
Although msCBA demonstrated higher performance in single-label classification compared to other classifiers from different learning strategies [16], it is incapable of handling multi-label datasets. The msCBA method assumes that each instance input has a single class label associated with it. Hence, it generates single-label rules with a single class label as the rule's consequence. When extending the msCBA method to accommodate multi-label datasets, this assumption should thus be discarded. In addition, the msCBA method captures the global relationships between features (attributes) and class labels, despite the fact that local dependencies and associations outperform global dependencies and associations [51; 52].  
```
1:\(F_{1}=\{large1-ruleitems\}\);
2:\(CAR_{k}=genRules(F_{1})\);
3:\(prCAR_{1}=pruneRules(CAR_{1})\);
4:for(\(k=2\); \(F_{k-1}\
eq\phi;k++\)) do
5:\(C_{k}=candidateGent(F_{k-1})\);
6:for each data case \(d\in\mathcal{D}\)do
7:\(\mathcal{C}\)=ruleSubset(\(C_{k}\),d);
8:for each candidate \(c\in\mathcal{C}\)do
9: c.condsupCont++;
10:if\(d.class=c.class\)then
11: c.rulesupCount++;
12:endif
13:end
14:end
15:\(F_{k}=\{\mathcal{C}\in C_{k}|c.rulesupCount\geq minsup\}\);
16:\(CAR_{k}=genRules(F_{k})\);
17:\(prCAR_{k}=pruneRules(CAR_{k})\);
18:endfor
19:\(CAR_{s}=U_{k}CAR_{k}\);
20:\(prCAR_{s}=U_{k}prCAR_{k}\);
```  
**Algorithm 1** CBA algorithm.
# Characterisation of Dust Particles Deposited on Photovoltaic Panels in the United Arab Emirates
## 3 Results and Discussion
### Dust Particle Morphology (SEM Characterisation)
#### 3.3.1 Dust T-10  
Figure 4 depicts SEM micrographs representing dust particles identified as T-10. The micrographs illustrate varying sizes and morphologies of the dust particles, characterised by irregular shape distribution and rough surfaces. Some particles exhibit an oval shape while others present a square or rectangular shape. The particle size ranged from 0.01 to 30 \(\upmu\)m, with a Gaussian distribution centred around 15 \(\upmu\)m, and a higher concentration of medium-sized particles. The SEM micrographs reveal the presence of small cavities, with other areas mainly composed of closely packed dust particles. Observations of the magnification at 10 \(\upmu\)m suggest that larger particles are predominantly covered and surrounded by smaller particles, measuring less than 5 \(\upmu\)m.  
The presence of small cavities and closely packed dust particles revealed by SEM micrographs suggests that these particles may be cohesive, potentially due to electrostatic forces, and could possibly form aggregates. Furthermore, the observation that larger particles are mostly surrounded by smaller particles (<5 \(\upmu\)m) suggests that there may be a size-based sorting mechanism in place, whereby smaller particles tend to accumulate around larger particles.
## 5 Key Enabling Technologies of Healthcare 4.0
### Blockchain
medical data IoMT devices generate, reducing the risk of tampering or unauthorized access. This strengthens data security and privacy, which is critical in healthcare. * Interoperability and standardization: IoMT devices often come from different manufacturers and use various data formats and protocols. A decentralized and standardized framework can be established with blockchain, allowing seamless data exchange and interoperability between different IoMT devices. This promotes collaboration, efficiency, and better patient care. * Decentralization: By leveraging distributed ledger technology, blockchain eliminates the need for a central authority to govern data transactions. This can enhance trust among stakeholders within the IoMT ecosystem, such as patients, healthcare providers, insurers, and regulators. The decentralized nature of blockchain ensures that no single entity controls the stored data, reducing the potential for abuse or manipulation. * Data control and consent management: Patients can have greater control over their health data through blockchain. They can grant or revoke access permissions to their medical records, ensuring data privacy and enabling consent-based data sharing. This empowers patients to have a say in how their data is used, fostering trust and transparency. * Data sharing: Smart contracts are self-executing agreements embedded in the blockchain that can be used to define access rights and data-sharing permissions. These contracts can automate consent management, ensuring that data is only shared with authorized entities or for specific purposes. It eliminates the need for intermediaries and strengthens data privacy. * Patient-centricity: Patients can have greater control over their healthcare information through blockchain. Patients can grant specific healthcare providers or researchers permission to access their data, enhancing patient privacy and consent management. * Supply chain integrity: In the healthcare industry, blockchain can improve the traceability and transparency of medical devices, pharmaceuticals, and supplies. It enables the verification of the entire supply chain journey, ensuring authenticity, reducing counterfeit products, and improving patient safety. However, deploying a blockchain in Healthcare 4.0 and IoMT faces many challenges. Some of these include the scalability of the blockchain network to handle a massive amount of medical device data, ensuring data privacy compliance, addressing regulatory concerns, and establishing standardized protocols for data exchange. Considering these challenges and potential benefits, designing, and implementing blockchain solutions in the healthcare sector is essential.
## 3 Results and Discussion
### Analytical and Deep Learning Results
Figure 5: Required system QoS versus required interference power (\(P_{I}\)) (dBm). Figure 8: The transmission distance between V2X (m) versus required interference power (\(P_{l}\)) (dBm). Figure 6: The transmission distance between V2X (m) versus required interference power (\(P_{l}\)) (dBm). Figure 7: The transmission distance between V2X (m) versus overall achievable data rate (bit/s). Figure 9 demonstrates the performance of the previous scenario assumed in Figure 8 in terms of achievable data rate. It can be noted that for numerical and deep learning methods, the proposed approach can attain the same achievable data rate for different values of \(P_{V}\). The obtained results show the ability of the proposed model to control the interference power to satisfy the required system performance. Additionally, it can be deduced that increasing the distance between V2X decreases the overall achievable data rate, which can be mainly affected by the path loss that increases with the increment of the transmission distance. Figure 10 shows the required interference power (\(P_{I}\)) versus vehicle transmission power (\(P_{V}\)). Three transmission distances between V2X have considered to be 50 m, 100 m, and 130 m; it has also been assumed that \(SINR_{th}\) is 10 dB and QoS is equal to 0.99. It can be observed that increasing \(P_{V}\) increases \(P_{I}\) as when \(P_{V}\) increases the system will be able to overcome the interference such that any interfering device can increase its transmission power when the main transmitting device has its maximum transmission power. Additionally, as discussed previously, increasing the transmission distance between V2X will not affect the required interference power but the main parameters that will affect the required interference power are the vehicle transmission and the required QoS. Figure 11 depicts the effect of changing vehicle transmission power on the overall achievable data rate for three different transmission distances between V2X and with system parameters equal to 10 dB and 0.99 for \(SINR_{th}\) and QoS, respectively. It can be noticed from Figure 11 that, to achieve the maximum achievable data rate, each transmission distance (\(d_{V2X}\)) will need an optimum vehicle transmission power (\(P_{V}\)). For example, when \(d_{V2X}\) is 50 m the maximum achievable data rate can be obtained when \(P_{V}\) is 2 dBm. Alternatively, when \(d_{V2X}\) is 100 m, the required \(P_{V}\) is between 9 and 11 dBm to attain the maximum, while when increasing \(d_{V2X}\) to 130 m the required \(P_{V}\) is 23 dBm, which is the maximum vehicle transmission power. It can be concluded from this figure that the maximum achievable data rate cannot always be achieved by the maximum vehicle transmission power but it can be achieved based on system parameters and QoS requirements, which by default affect the required value of vehicle transmission power (\(P_{V}\)) and the required interference power (\(P_{I}\)). Figure 9: The transmission distance between V2X (m) versus overall achievable data rate (bit/s).