esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Power Consumption Models
### Energy Harvesting Techniques  
Energy scavenging or harvesting involves a massive amount of energy from different environments, such as thermal, solar, vibration, and wind. Energy harvesting approaches are effective in improving network life span [52]. Among various forms of environmental energy, solar cell energy is selected in the current work to supply power to the sensor, router, and main router nodes. In energy harvesting, a battery is cyclically recharged to preserve the life span of nodes that continuously operate in the network rather than focusing on reducing energy depletion. However, an energy-harvesting platform must be incorporated into the network to efficiently use the harvested energy.  
The current consumption of the sensor nodes from the battery depends on the type of application [53]. In our work, the solar panel is positioned in front of the sensor node box. The solar cell angle is oriented toward the sun with an incident angle of 20\({}^{\circ}\)-30\({}^{\circ}\) relative to the ground [8]. KINGRO-004V solar cell (KINGRO, Shaoxing, China), which is a first-generation polycrystalline solar cell (12 V/5 W) that delivers a maximum current of 416 mA, is selected (Figure 3). Table 3 provides the characteristics of the adopted solar cell.  
Mathematical models should be created for the battery and solar cells of the sensor node to investigate the harvested energy from the solar cell and battery consumption. The power consumption model of the sensor, router, and main router nodes based on the battery is presented in Equations (2)-(15), as shown in Section 7.1 and 7.2. The solar cell is formulated in the current section. Solar cell efficiency (\(\eta\)) can be expressed as [25]:  
\[\eta_{solar}=(P_{max}/S\;\times\;R), \tag{16}\]  
where \(P_{max}\) is the output power of the solar cell (measured in W); \(S\) is the solar cell surface area (measured in m\({}^{2}\)); and \(R\) is the radiation, which can be defined as the intensity of the incident light power on the surface of a solar cell (measured in W/m\({}^{2}\)).
## 2 Materials and Methods
### Feature Extraction Methods
The third category of methods investigated was in regard to deep learning methods and more specifically in regard to CNN, which are known to be well-suited methods for image classification, thanks to robust feature patterns [5]. Many architectures were used to address ImageNet challenges, and their associated performances were analyzed [31]. Instead of training this network from scratch, as we have data constraints and also computational constraints, we choose a Domain Adaptation approach for these models. Most of the papers to date have dealt with CNN trained on ImageNet [7], as this database contains thousands of classes and more than 14 million images, meaning that the extracted features from these networks can be used in various fields. As discussed in a previous work [32], Inception-V3 architecture pre-trained on ImageNet is thought to be the most relevant for medical applications. As RCM images can have various forms and can contain specifics details compared to other image modalities, this research compares the most well known CNN architectures: VGG-16 [33], Inception-V3 [34], ResNet [35] and Inception-ResNet [36], with accuracies of 0.71, 0.76, 0.78, and 0.80, respectively, on the ImageNet database [31]. This method involves the use of Transfer Learning, by removing the last layers devoted to the classification task, in order to obtain \begin{table}
\begin{tabular}{l l} \hline \hline
**GLCM Features** & **GLH Features** \\ \hline Angular Second Moment & Mean value \\ Difference Moment & Mean square deviation \\ Correlation & Skewness \\ Sum of Squares & Kurtosis \\ Inverse Difference Moment & Entropy \\ Summed Average & \\ Sum Variance & \\ Entropy & \\ Sum Entropy & \\ Difference Entropy & \\ Measure of Correlation 1 & \\ Measure of Correlation 2 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The statistical measures derived from GLCM and GLH, respectively, and extracted in order to perform “Spatial” extraction methods. a new representation of the image data as features. Furthermore, in order to reduce the number of features provided by the previous step, a global pooling layer on each activation layer was performed. For convenience, this whole method is called \"Transfer Learning\" in the next paragraphs and the names of the respective networks are used. The CNN computation was implemented using the \"Keras\" library [37].
## 5 Simulation Results and Analysis
### Performance of Osnr  
In Section 5.2, we evaluate the performance on the FISRSA-S algorithm through the OSNR. Specifically, the calculation of the \(OSNR\) is conducted in accordance with the mathematical formula provided in Equation (21):  
\[OSNR_{\text{average}}=\frac{\sum OSNR_{r,\text{success}}}{N_{r,\text{total}}}, \tag{21}\]  
where \(OSNR_{r,\text{success}}\) denotes the \(OSNR\) on a successfully allocated request, whereas \(N_{r,\text{total}}\) signifies the number of arriving requests.  
The results presented in Figure 5 demonstrate that the proposed algorithm surpasses benchmark algorithms in terms of OSNR. Specifically, in the NSFNET network topology, at a traffic load of 400 Erlang, the proposed algorithm exhibits an improvement in OSNR by approximately 4.17 dB. Similarly, in the COST239 network, under a traffic load of 3800 Erlang, the OSNR is enhanced by approximately 4.71 dB. The primary path of the KSP(L)-MSP(C)-EFM algorithm employs the KSP routing method, and compared to the minimum congestion routing method, the algorithm based on KSP routing is more prone to traffic aggregation, which significantly aggravates the physical layer impairment among services. Therefore, the OSNR of the KSP(L)-MSP(C)-EFM algorithm is poor. The backup path of the LC(L)-DP(C)-EFM algorithm utilizes the DPP routing approach. Compared to algorithms  
Figure 4: The resource utilization in (**a**) the NSFNET network and (**b**) the COST239 network.  
that employ SBPP routing, the backup path resources of the LC(L)-DP(C)-EFM algorithm cannot be shared, resulting in increased resource consumption as well as an augmentation of physical layer impairments among wavelength bands. Consequently, the OSNR performance of the LC(L)-DP(C)-EFM algorithm is inferior to algorithms that utilize SBPP routing. The proposed algorithm's significant enhancement can be attributed to the implementation of a least congestion routing algorithm, which effectively distributes primary path requests across diverse paths. Additionally, the utilization of a maximum shared routing algorithm for discovering backup paths serves to minimize the occupation of backup resources. Therefore, it reduces the ISRS between requests significantly, leading to improved network performance.
# I/Q Imbalance and Imperfect SIC on Two-Way Relay NOMA Systems
## 2 System Model
### I/Q Imbalance Signal Model  
Considering IQI at the transmitter (TX) [37], the time domain baseband signal can be obtained as:  
\[\hat{s}=\mu_{t}s+v_{t}^{*}s^{*}, \tag{1}\]  
where \(s\) and \(s^{*}\) denote the baseband IQI-free signal and the IQI impaired signal, respectively; \(\mu_{t}\) and \(v_{t}\) are the IQI coefficients; and \(v_{t}^{*}\) is the conjugation of \(v_{t}\). As in [37], \(\mu_{t}\) and \(v_{t}\) are given as follows:  
\[\mu_{t}\ \overset{\Delta}{=}\ \frac{\left(1+g_{t}e^{i\varphi_{t}}\right)}{2}, \tag{2}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Acronyms** & **Definition** \\ \hline NOMA & Non-orthogonal multiple access \\ UE & User devices \\ OMA & Orthogonal multiple access \\ ipSIC & Imperfect successive interference cancellation \\ pSIC & Perfect successive interference cancellation \\ PDF & Probability density function \\ CDF & Cumulative density function \\
5G & 5 th Generation \\ TWR C-NOMA & Two-way relay cooperative NOMA \\ DF & Decode-and-forward \\ BS & Base station \\ COMP & Coordinated multi-point transmission \\ MRC & Maximal ratio combining \\ IGS & Improper Gaussian signalling \\ TX & Transmitter \\ RX & Receiver \\ IQI & In-phase and quadrature imbalance \\ OFDM-IM & Orthogonal frequency division multiplexing with index modulation \\ LoS & Line-of-sight \\ AWGN & Additive white Gaussian noise \\ SNR & Signal-to-noise ratio \\ SINR & Signal-to-interference plus noise ratio \\ IS & Interference signals \\ MIMO & Multiple-input multiple-output \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of acronyms.  
Figure 1: System model.  
\[\
u_{t}\overset{\Delta}{=}\frac{\left(1-g_{t}e^{-j\varphi_{t}}\right)}{2}, \tag{3}\]  
where \(g_{t}\sim N\left(0,\sigma_{g_{t}}^{2}\right)\) and \(\varphi_{t}\sim N\left(0,\sigma_{\varphi_{t}}^{2}\right)\) are the TX amplitude and phase mismatch, respectively, which follow the Gaussian distribution. Similarly, the time domain baseband representation of the RX IQI impaired signal can be obtained as:  
\[\hat{y}=\mu_{r}y+\
u_{t}y^{*}, \tag{4}\]  
where \(y^{*}\) is referred to as the mirror signal introduced by the IQI. \(\mu_{t}\) and \(\
u_{t}\) are the IQI coefficients caused by RX, which can be expressed as:  
\[\mu_{r}\overset{\Delta}{=}\frac{\left(1+g_{r}e^{-j\varphi_{r}}\right)}{2}, \tag{5}\]  
\[\
u_{r}\overset{\Delta}{=}\frac{\left(1-g_{r}e^{j\varphi_{r}}\right)}{2}, \tag{6}\]  
where \(g_{r}\sim N\left(0,\sigma_{g_{r}}^{2}\right)\) and \(\varphi_{r}\sim N\left(0,\sigma_{\varphi_{r}}^{2}\right)\) denote the RX amplitude and phase mismatch, respectively, which follow the Gaussian distribution. Therefore, the severity of TX IQI and RX IQI can be expressed as an image rejection ratio, which are defined as follows [38]:  
\[IRR_{T}\overset{\Delta}{=}\frac{\left|v_{t}\right|^{2}}{\left|\mu_{t}\right|^ {2}}, \tag{7}\]  
\[IRR_{R}\overset{\Delta}{=}\frac{\left|v_{r}\right|^{2}}{\left|\mu_{r}\right|^ {2}}, \tag{8}\]
# A New DGNSS Positioning Infrastructure for Android Smartphones
## 4 Experimental Studies
### Walking in Urban Areas  
Another test was also conducted to evaluate the positioning performance of smartphones in the urban area. In the test, the pedestrian holding the equipment walked around a mountain. The distance between the reference station and the mountain is approximately 2.5 km. Figure 8 shows the trajectory of the pedestrian. Along the sidewalks shown in the green oval, the GNSS signals from satellites were affected by the trees, and the multipath error was not large. The accuracy improvement by using the infrastructure can still be seen from the figure. When the pedestrian was in the section denoted by the black oval, however, the multipath error was very large, and the Trimble receiver could not fix the ambiguities. In this section, the positioning accuracy of smartphones was not improved  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test** & **Mode** & **Average** & **RMS** \\ \hline \multirow{2}{*}{Morning} & Standalone & 0.9 & 1.7 \\  & DGNSS & 0.4 & 1.5 \\ \hline \multirow{2}{*}{Afternoon} & Standalone & 4.0 & 4.4 \\  & DGNSS & 0.4 & 1.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Smartphone performance in the morning and in the afternoon.  
Figure 7: Smartphone performance comparison between the morning and the afternoon.  
significantly. This result agrees with the fact that the differencing process could not remove multipath errors. The mitigation of the multipath errors on the GNSS positioning will be conducted in our future study.
# Integrated Photodetectors Based on Group IV and Colloidal Semiconductors: Current State of Affairs
## 1 Introduction
Such quantum detectors exhibit high performance with high efficiency and high speed. However, the production of these detectors continues to be very expensive. In addition, considering their low operating temperature, they require a cooling system, so their use is limited to defense and astronomical research. There is a plenty of materials studied in different configurations to realize an efficient photodetector, however most of them show complexity, high cost and difficulty to integrate with the standard CMOS process technologies. The research of alternative solutions that will address these problems requires a new active materials development or, in alternative, exploits the already known materials generally used for opto-electronic integration. Significant progress has been made in the past decade, overall using nanocrystal formations and colloidal quantum dot for IR sensing developments [23; 24]. For these reasons, we focus our review paper on the current state of photodetectors based on group IV and colloidal semiconductors, which allow high compatibility with CMOS process and good performance.
# A Survey on Emerging Blockchain Technology Platforms for Securing the Internet of Things
## 5 Effect of Blockchain
### Security Effect  
Table 4 presents a few of the most significant security issues that blockchain technology has helped to tackle. It emphasises the technology of blockchain that was utilised to solve the security challenges as well as the research that led to the development of the solution. Considering the security concerns of weak authentication and unrestricted access to systems, decentralised authentication and access control on a permissioned blockchain like Ethereum is an intriguing solution that was proposed in [40]. This method utilises an \"authorisation sensitivity factor\" stored on the blockchain. This factor determines a client device's access level, eliminating the need for a central authority to manage permissions. This system was tested against distributed denial of service attacks (DDOS) and was found to be secure against them. Another good example is delivering secured firmware update to devices. [PERSON] et al. [53] adopted blockchain as the secure network to deliver the  
Figure 8: Cryptography in Blockchain platform for IoT.  
updates to various devices on the chain whose firmware versions are verified for already validated firmware files. Similar to the above, [PERSON] et al. [47] enhanced IoT network communication by adopting blockchain as the medium of exchange of information between devices. Data and privacy protection are critical challenges in IoT systems. To address this, researchers in [49] proposed a three-phase approach. This involves establishing secure authentication through decentralised blockchain-based authentication (DBA), safeguarding data with blockchain encryption, and ensuring data integrity through secure decryption. Insufficient privacy protection, which is common in IoT systems, was also addressed in [42] by encrypting the identity of devices on the block and managing access to information using data access control and auditable data provenance of ethereum blockchain. Blockchain offers a secure architecture for IoT applications, but that does not mean it is immune to security challenges. Resource constraints in some IoT devices might limit the feasibility of certain attacks, but others remain a significant concern. These include Sybil attacks, 51% attacks, replay attacks, wallet poisoning, and smart contract vulnerabilities. Carefully evaluating these risks is crucial when choosing a blockchain platform for IoT security.
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Effective Date** & **Global Area Fuel Sulfur Limits** & **Emission Control Areas (ECAs) Fuel Sulfur Limit** \\ \hline
19 May 2005 & \textless{4.5\% m/m} & \textless{1.5\% m/m, Baltic Sea region and North Sea Region} \\
1 January 2010 & & \textless{1.0\% m/m, Baltic Sea region and North Sea Region} \\
1 January 2012 & \textless{3.5\% m/m} & 1 August 2012, With North America included \\
1 January 2014 & & With US Caribbean included \\
1 January 2015 & & \textless{0.1\% m/m, including the Baltic Sea, the North Sea, North America,} \\
1 January 2020 & \textless{0.5\% m/m} & \\
1 March 2020 & \textless{3.5\% m/m + desulfurization} & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Chronological evolution of IMO fuel sulfur regulations in maritime industry. To address the above-mentioned CO2 emission concerns, this paper compares two candidate fuel oils to evaluate a ship's practical fuel consumption and CO2 emission during oceanographic navigation; these fuels oils are HFOWD and VLSFO. The measurement on the board is under the sulfur restriction policy during a voyage of 18 months in the Pacific Ocean. Consequently, the analysis is significant to the marine industry in mitigating carbon dioxide emissions and global warming effects.
# GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube
## 3 Experiment and Results
### Experimental Results  
#### 3.3.1 Comparative Analysis of Time Efficiency for the Two Strategies  
To evaluate the computational time efficiency of the two data organization strategies based on ODC grid and scene, we recorded the time consumption and time efficiency improvement of the two strategies for calculating NDVI and NDWI, as shown in Figure 5. The red bars and green bars in Figure 5a indicate the time consumption of the ODC grid- and scene-based approaches for calculating NDVI. The red bars and green bars in Figure 5b indicate the time consumption of the ODC grid- and scene-based approaches for calculating NDWI. The curves in Figure 5c,d show the time efficiency improvement of the ODC grid-based approach over the scene-based approach for calculating NDVI and NDWI, respectively. Taking the year 2015 as an example, it took 41.2 s to calculate NDVI with the ODC grid-based approach and 65.18 s with the scene-based approach, i.e., the proposed framework improved the time efficiency by 58.2%. According to the trends of the bars in Figure 5a,b and the time efficiency improvement curves in Figure 5c,d, the time complexity of calculating NDVI and NDWI was consistent. Therefore, the ODC grid-based framework was more efficient than the scene-based approach in terms of NDVI and NDWI calculation in the study area.  
Figure 5: ODC grid-based and scene-based approaches processing time consumption and time efficiency improvement. (**a**) NDVI calculating time consumption (s) based on ODC grid-based approaches (red bars) and scene-based approaches (green bars). (**b**) NDWI calculating time consumption (s) based on ODC grid-based approaches (red bars) and scene-based approaches (green bars). (**c**,**d**) Time efficiency improvement percentage (%) of the ODC grid-based approach over the scene-based approach for calculating NDVI and NDWI, respectively.
# Location Optimization of Emergency Bell Based on Coverage Analysis for Crime Prevention
## 3 Materials and Methods  
In this section, emergency bell location data and crime data were collected from sources such as the Gwangju Metropolitan City Hall. Exploratory data analysis (EDA) was conducted through correlation analysis and PCA for dimension reduction. The EBM-CLP/EBLSCP methods were then employed to determine the optimal locations for emergency bells.  
We initially conducted a correlation analysis on the original 27 variables. Ten significant variables were extracted after excluding variables with significance levels exceeding 0.05. Subsequently, we performed PCA to reduce dimensionality, resulting in three principal components. Using these three principal components, we conducted K-means clustering to classify administrative districts into three groups: low-level, intermediate-level, and high-level. This classification was based on the number of crimes, which exhibited the highest correlation with the number of emergency bells. Therefore, to derive the weights(\(w_{i}\)) for EBMCLP, we utilized the variable number of crimes. Moreover, we preassigned (F) as the number of new emergency bells with values of 250, 500, 800, 1000, and 1500.  
The optimal location for installing a new emergency bell in Gwangju has been determined to be outside the current range of the emergency bell monitoring system. To determine the optimal location, we collected population data for a 100 \(\times\) 100 (m\({}^{2}\)) grid of each administrative dong and performed data cleaning procedures. An administrative dong is a primary division of districts, which refers to the smallest administrative unit in South Korea, typically located in districts (gu). It plays a crucial role in various administrative functions, including population registration, address management, and electoral processes [28]. Specifically, EDA was conducted to select important variables. Meanwhile, PCA and correlation analysis were conducted to extract features relevant to the installation of emergency bells. Consequently, these variables were used to determine the optimal location through spatial optimization models EBMCLP and EBLSCP. The results were displayed on a map.
## 1 Introduction  
AZ series magnesium alloys, with aluminum and zinc as main alloying elements [1], are widely used as materials for structural elements due, mainly, to their low weight and high strength-to weight-ratio [2]. However, reduced wear resistance [3] and high corrosion rates [4] limit their use in top applications for the automotive, aero-spatial and medical industries [5]. Thus, further applications of magnesium alloys require an improvement of those properties [6]. The mechanical and anti-corrosion properties' enhancement is usually done by adding new alloying elements or by surface modification (coating or surface treatments) [7]. The surface modification approach can be divided into three major techniques classes [8; 9; 10]: chemical conversion coating (including PEO), physical or chemical deposition methods and modification of the surface microstructure.  
One of the most promising, inexpensive and environmentally friendly surface modification techniques is plasma electrolytic oxidation (PEO) [7; 11; 12; 13]. By using high voltages and, in general, an aqueous alkaline electrolyte, the ceramic PEO-obtained coatings exhibit well improved mechanical and corrosion protection properties compared to the base alloy. Silicates, phosphates and aluminates with a series of additives are often used as base electrolytes for plasma electrolytic oxidation of magnesium and its alloys [14]. According to the reviewed scientific literature [4; 15; 16], PEO surface modification of AZ magnesium alloy series has focused on AZ91D and AZ31 alloys. Several research groups showed that the nature and composition of the substrate affects the composition and the properties of the PEO coatings [17; 18].  
A widely used AZ series magnesium alloy is AZ63 [19]. Different approaches for mechanical and corrosion properties' improvement were studied: heat treatment [20; 21], thermomechanical treatment [22], reinforcement by alloying with gallium and adding ZrO\({}_{2}\) particles [23], calcium doping [24] and tailoring the microstructure of the AZ63 Mg alloy [25].  
Very few studies [26] reported the surface modification of AZ63 by PEO coating. We chose to use the AZ63 magnesium alloy because of its relative high content of aluminum (approx. 6%) and zinc (approx. 3%), which could provide better corrosion and mechanical protection properties for PEO coatings obtained on this kind of substrate, as reported by the above-mentioned authors.  
Therefore, this study aims to investigate the influence of processing time on the morpho-structural characteristics and mechanical and anticorrosion properties of the PEO coatings on the AZ63 magnesium alloy in an aluminate-based electrolyte.