esa-sceva/satcom-chunk-collection huggingface dataset:
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 5 BackCom: Wireless Communications
### Multiple Access Techniques for BackCom
#### 5.3.4 Non-Orthogonal Multiple Access (NOMA)
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**References** & **Key points** & **Objective(s)** \\ \hline \multicolumn{3}{c}{**Time Division Multiple Access**} \\ \hline
[68] & \(\bullet\) Backscatter Spike Train (BST) proposed. & \(\bullet\) Enable concurrent transmissions from many devices. \\
[99] & \(\bullet\) Hybrid Access Point (H-AP) used. & \(\bullet\) Allow two users to harvest energy simultaneously. \\
[100] & \(\bullet\) Wireless AP transmitting energy to many users. & \(\bullet\) Broadcast energy to multiple users. \\
[101] & \(\bullet\) TDMA employed in the data transmission segment. & \(\bullet\) Treat multiple users at the same time. \\ \hline \hline \multicolumn{3}{c}{**Code Division Multiple Access**} \\ \hline
[102] & \(\bullet\) Code shift modulation proposed. & \(\bullet\) Separate overlap signals from multiple tags using CDMA. \\
[103] & \(\bullet\) CDMA principles discussed. & \(\bullet\) Predict the number of tags. \\ \hline \hline \multicolumn{3}{c}{**Orthogonal Frequency Division Multiple Access**} \\ \hline
[104] & \(\bullet\) Based on the division of received power. & \(\bullet\) Allow synchronous power and information transfer simultaneously. \\ \hline \hline \multicolumn{3}{c}{**Non-Orthogonal Frequency Division Multiple Access**} \\ \hline
[101] & \(\bullet\) Data transfer. & \(\bullet\) Allow for spectral efficiency and user equity. \\
[105] & \(\bullet\) NOMA with successive interference canceller (SIC). & \(\bullet\) Improve NOMA performance, such that OMA is outperformed. \\
[106] & \(\bullet\) Working principles for NOMA. & \(\bullet\) Create differences in the uplink and downlink. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Multiple access. Figure 28: NOMA in BackCom.
# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## 4 Results and Discussions
### Reliable Aerial Cellular Coverage
Figure 7 illustrates a two-dimensional representation of RSRP and RSRQ around a 4G BS, based on the RSRP and RSRQ prediction model in [51], Equations (14) and (15). Note that the utilized equations were developed based on the same dataset used in this study. The results show how RSRP and RSRQ degrade by increasing distance and flight height. In addition, based on the results and radiation pattern of directional antennas, in a radius of roughly less than 50 m, the main lobe is not able to provide sufficient coverage, especially at altitudes above the antenna height. In conclusion, the results show that for a flight height up to 85 m above ground level (AGL) and a 2D distance up to 500 m, the considered parameters, RSRP, RSRQ, and handover, are primarily in a reliable range, where a BS can provide RSRP and RSRQ better \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Height (m)** & **Rout I** & **Rout II** & **Rout III** \\ \hline
65 & 7 & 7 & - \\
85 & 8 & 8 & - \\
105 & 13 & 10 & 9 \\
125 & 6 & 10 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The number of handovers in different routes and flight heights. Figure 7: 2D representation of (**a**) RSRP and (**b**) RSRQ around a 4G BS. than \(-80\) dBm and \(-14\) dB, respectively. Therefore, it is expected that the existing 4G network can provide a good link for telemetry communications for drones within a 2D distance between 50 to 500 m and heights up to 85 m AGL in suburban areas, where the heights of buildings, trees, and other obstacles are almost lower than the heights of BSs.
## 2 ML Methodologies
### Supervised Learning
#### 2.1.1 Classification
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \multicolumn{1}{c}{**Algorithm**} & \multicolumn{1}{c}{**Ref.**} & \multicolumn{1}{c}{**Description**} \\ \hline Elastic net classifier & [38] & The elastic net classifier combines both L1 (lasso) and L2 (ridge) regularization terms to overcome the limitations of each. It strikes a balance between feature selection and feature grouping, making it effective in scenarios with correlated features and when there are more predictors than observations. & \\ \hline Least squares classifier & [39] & The least squares classifier, also known as linear regression for classification, fits a linear function to the input features using the least squares method. It assigns class labels based on the threshold of the predicted continuous values. It can be used for both binary and multiclass classification. & \\ \hline Stochastic gradient descent (SGD) classifier & [40] & The SGD classifier optimizes the model parameters using stochastic gradient descent. It updates the weights with a small subset of training samples (minibatches) at each iteration, making it efficient for large-scale datasets. It is widely used for linear classification problems and can be extended to handle nonlinear data using kernel tricks. & Naive Bayes is a probabilistic linear classification algorithm based on Bayes’ theorem. It assumes that the features are conditionally independent given the class label. Naive Bayes calculates the probability of each class and predicts the class with the highest probability. When using linear kernels, naive Bayes can be considered a linear classifier. & \\ \hline Linear discriminant analysis (LDA) & [42] & LDA is a linear classification algorithm that models the distribution of each class by assuming a Gaussian distribution. It projects the input data onto a lower-dimensional space while maximizing the class separability. The algorithm then assigns the class based on the projected values. & \\ \hline Passive aggressive classifier & [43] & The passive aggressive algorithm is a linear classification algorithm that is especially useful for online learning scenarios. It updates the weights based on misclassification errors, but in a more “passive” or “aggressive” manner depending on the confidence of the prediction. This algorithm is suitable for situations where the data distribution might change over time. & \\ \hline Quadratic discriminant analysis (QDA) & [44] & QDA is a variant of LDA that allows for quadratic decision boundaries. While it involves quadratic terms, it can be considered a linear classifier if the feature space is transformed to include those quadratic terms. QDA models the distribution of each class using quadratic terms and assigns class labels based on the calculated probabilities. & \\ \hline \end{tabular}
\end{table} 
Table 2: Linear classification algorithms for water resources management.
# Modeling and Multi-Objective Optimization Design of High-Speed on/off Valve System
## 4 Utilizing AFSA for Multi-Objective Optimization Design Based on HSV
### Objective Functions for Multi-Objective Optimization Design  
Six main parameters influence the performance of the HSV. When optimizing these parameters, they are mutually constrained. Increasing the plunger diameter can enhance the operating force of the electromagnet and reduce the valve opening time. However, increasing the volume of the plunger leads to an increase in the inductance of the solenoid coil and the mass of the armature, resulting in a prolonged valve closing time. Similarly, increasing the thickness of the permanent magnet can decrease eddy current losses but may also weaken the output force of the actuator, potentially leading to a valve malfunction. The fluctuation of each parameter presents challenges in achieving an optimal solution through individual parameter optimization. The optimization design problem for a new HSV is a multi-objective optimization problem. In such problems, the sub-objectives often conflict with each other, and improving one sub-objective may compromise the performance of others. Thus, it is necessary to coordinate and compromise among sub-objectives to optimize each to the fullest extent [31]. Unlike single-objective optimization problems, multi-objective optimization problems do not have a unique solution but rather a set of Pareto optimal solutions consisting of numerous non-dominated solutions. These non-dominant solutions constitute the set of decision vectors, while the corresponding objective functions are graphically represented as the Pareto front.  
Prior to performing target optimization for the ball valve, a preliminary design is conducted, where the following parameters are manually defined: ball diameter, valve seat diameter, ball valve opening, number of coil turns, and coil resistance. The parameters to be optimized in the design are \(R_{\text{P}}\), \(R_{\text{t}}\), \(R_{\text{c}}\), \(H_{\text{b}}\), \(H_{\text{m}}\), and \(H_{\text{c}}\). Based on the system analysis in Section 4.1, the optimization design problem for the HSV is described as follows: under given constraints, select appropriate design variables \(x\) to achieve the optimal value of the function \(f(x)\). The mathematical model is represented as follows:  
\[\left\{\begin{array}{l}\min f(x)=\left\{f_{ton}(x),f_{off}(x)\right\}\\ X_{\min}<x<X_{\max}\end{array}\right. \tag{14}\]  
Figure 12: Flowchart of the HSV-optimized AFSA algorithm based on BPNN.  
In the equation, \(x=(x_{1},\,x_{2},\,\ldots,\,x_{6})\) represents all optimization variables, which respectively represent \(R_{\text{p}}\), \(R_{\text{t}}\), \(R_{\text{c}}\), \(H_{\text{b}}\), \(H_{\text{m}}\), and \(H_{\text{c}}\). \(f(x)\) represents the objective function variable. \(x_{\text{min}}\) and \(x_{\text{max}}\) represent the upper bound and lower bound of the optimization variables. There are two optimization objective functions, representing the valve opening and closing times.
## 4 Model to Minimize Cost Dynamically
### Network Model  
The network is composed of mobile IoT devices, MEC servers, and a cloud provider. The wireless links determine the communication channels between IoT devices and MEC servers, as in Figure 1. The system network has a finite set \(D=\{1,2,3,\ldots,d\}\) of mobile IoT devices, \(S=\{1,2,3,\ldots,s\}\) of local MEC servers, and of wireless communication channels \(W_{\rm C}=\{1,2,3,\ldots,w\}\). Each scheduled task has a set of the available wireless channels (\(H=\{h_{1},h_{2},\ldots,h_{a}\}\)) from the IoT device to MEC and from the MEC to CC with its correspondent bandwidth. The TEMS algorithm will choose a channel with a lower energy consumption cost. If the task runs in the local device, it does not have an associated channel. Coding and decoding costs are built into the channel like in the approach [32].
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
###### Abstract  
In recent decades, enhancing underwater images has become a crucial challenge when obtaining high-quality visual information in underwater environment detection, attracting increasing attention. Original underwater images are affected by a variety of underwater environmental factors and exhibit complex degradation phenomena such as low contrast, blurred details, and color distortion. However, most encoder-decoder-based methods fail to restore the details of underwater images due to information loss during downsampling. The noise in images also influences the recovery of underwater images with complex degradation. In order to address these challenges, this paper introduces a simple but effective denoising multiscale back-projection feature fusion network, which represents a novel approach to restoring underwater images with complex degradation. The proposed method incorporates a multiscale back-projection feature fusion mechanism and a denoising block to restore underwater images. Furthermore, we designed a multiple degradation knowledge distillation strategy to extend our method to enhance various types of degraded images, such as snowy images and hazy images. Extensive experiments on the standard datasets demonstrate the superior performance of the proposed method. Qualitative and quantitative analyses validate the effectiveness of the model compared to several state-of-the-art models. The proposed method outperforms previous deep learning models in recovering both the blur and color bias of underwater images.  
underwater image enhancement; image restoration; back-projection algorithm; multiscale feature fusion; knowledge distill; multi-type degradation image enhancement +
Footnote †: journal: Applied  
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Footnote †: journal: Applied  
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Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## Materials and Methods
### SPME Procedure Optimization  
The SPME-GC-MS method was optimized using 500 mg of fresh leaves. The leaves were ground under liquid nitrogen in a mortar and pestle. The ground leaves were placed in 10 mL glass vials (Supelco, Bellefonte, PA, USA) with 1.0 mL of 100 mM EDTA (pH 7.5 with NaOH) containing 5 M CaCl\({}_{2}\) to inhibit enzyme activity [42] and 10 \(\upmu\)L of 3-pentanol (0.82 mg/mL in 1% MeOH in H\({}_{2}\)O) as an internal standard. Nitrogen gas was bubbled through the EDTA and CaCl\({}_{2}\) solution for 90 min prior to addition to leaf samples, and this process dramatically reduced contaminants in the solution. The vial was tightly sealed with a screw cap containing a PTFE/silicone septum (Supelco, Bellefonte, PA, USA) and then sonicated for 5 min. The vial was placed in a Multipurpose autosampler MPS2 (Gerstel, Munich, Germany) and kept at the same temperature as the extraction during the 20 min equilibration between the solution and the headspace. After the equilibration, the selected SPME fiber was inserted into the headspace of a sample vial for extraction of volatiles.  
SPME extraction parameters including fiber composition, extraction temperature and extraction time were optimized to detect the widest quantitative and qualitative array of volatile metabolites in alfalfa plants. All analyses for SPME extraction optimization were performed in triplicate. Three fibers with different polarity were tested: non-polar polydimethylsiloxane (PDMS, 100 \(\upmu\)m film thickness, 1 cm fiber length) for extraction of non-polar volatiles; carboxen/polydimethylsiloxane (CAR/PDMS, 75 \(\upmu\)m, 1 cm) for extraction of gases and volatiles; and divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS, 50/30 \(\upmu\)m, 2 cm) for extraction of volatiles and semi-volatiles with short chains. The extractions were carried out for 20 min at 50 \({}^{\circ}\)C. Prior to extraction, each SPME fiber was preconditioned according to the manufacturer's instructions. Extractions were carried out for 20 min at 40, 50, 60, and 70 \({}^{\circ}\)C using DVB/CAR/PDMS fiber to select an optimized extraction temperature, and three time periods (10, 20, and 30 min) were tested at 60 \({}^{\circ}\)C using DVB/CAR/PDMS fiber to establish an appropriate extraction time.
## 3 Results  
The track of nadir points and altitude are often utilized to present the features of Earth satellites of different kinds of Earth orbits and play an influential role in the further performance of observation characteristics. The angular and spatiotemporal coverage characteristics are the two commonly concerned features in Earth observation, which have been studied carefully in many previous studies on Moon-based Earth observation [34; 35]. Thus, the first step of this work is to compare the different performances of halo orbits in these aspects.
## 4 Visual Interfaces for Ontology Alignment
### Interfaces Based on Graph Visualization  
The structure of an ontology is basically a graph, which makes graph visualization probably the most natural, and definitely the most commonly used, visual representation of an ontology. There is a plethora of different graph visualization approaches, with comprehensive surveys available in [36] and [37]. Interactive alignment systems employ graph visualization for the navigation and exploration of ontologies, to provide insight into the ontology structure, and to show detailed information of every ontological element. Matching information is usually encoded through color or through additional links.  
Optima [38] displays both ontologies separately in graph visualization components. Various graph layouts, such as a tree, a circle, or random, are available. Because large ontologies would appear cluttered, node clustering and node filtering are supported. Matched nodes are highlighted and displayed in blue and the user can select a node to identify the matching node in the other ontology.  
The work in [39] describes extensions for the PROMPT [18] alignment system. Figure 5 shows a variant of the interface shown in Figure 4, but with tree representations of class hierarchies replaced by graph visualizations showing fragments of the ontologies. The list in the center (3) shows matching pairs, while the graph visualization (1 and 2) display neighborhoods of the currently selected mapping suggestion.  
Figure 5: Graph-based user interface supporting interactive alignment in the PROMPT [18] system.  
AlViz [39] combines four different views: two tree representation for the class hierarchies and two graph visualizations for representation of the ontologies (see Figure 6). The clustering of nodes is performed in the graph visualization according to the selected level of detail using similarities provided by the matching algorithm. Nodes are positioned using a spring-embedded algorithm so that tightly coupled groups of nodes appear positioned close together, while loosely coupled ones are placed further apart. Node size corresponds to the number of clustered concepts, while color is used to indicate similarity between concepts from the two ontologies.
## 5 Feasibility and Measurement
The measurement uncertainty was estimated by combining contributions from calibration accuracy, cable repeatability, and connector variability. Based on this assessment, the overall uncertainty in the measured S-parameter magnitude is approximately \(\pm\)0.5 dB across the frequency range of interest. Table 3 compares the performance of the new antenna to those of published sub-6 GHz MIMO designs in terms of bandwidth, radiation efficiency, the ECC, and physical size. Notably, the proposed design achieves superior isolation (>12 dB) and a lower ECC (<0.12), while maintaining a competitive size and good wideband matching. Figure 11: Measured far-field radiation patterns of the antenna ports at 3.3, 3.5, and 3.8 GHz: (**a**) the E-plane of ANT#1, (**b**) the H-plane of ANT#1, (**c**) the E-plane of ANT#2, (**d**) the H-plane of ANT#2.
## 4 Security Attacks
### Application Layer
#### 4.5.3 Attacks on Hot Wallets  
Hot wallets [27; 31] are internet-connected applications that store private cryptographic keys. Although cryptocurrency exchanges often claim to keep user data in wallets isolated from the internet, history has shown otherwise. For instance, a $500 million attack on Coincheck in 2018 exposed significant vulnerabilities in hot wallet security. In June 2019, an attack on GateHub resulted in unauthorized access to dozens of native XRP wallets and the theft of various crypto assets. Similarly, the Singapore-based crypto exchange Bitrue experienced a hot wallet breach around the same period, with attackers stealing funds worth over $4.5 million in XRP and $237,500 in ADA. These incidents underscore the importance of robust security measures and constant monitoring for online wallets.
## 4 IoT Deployment Opportunities in Developing Countries
### Leveraging Cellular Networks for IoT Deployments in Nigeria
#### 4.1.1 Policy and Regulatory IoT Issues  
To accelerate IoT development and investment in developing countries, a good policy and regulatory framework are needed in areas of security and privacy, standards, and government regulation [1]. These can be seen, for example, in the code of practice for consumer IoT security in the UK [36], the Unmanned Aircraft Systems (UAS) regulations and guidelines of the United States Federal Aviation Administration [37], and the security requirements for consumer IoT devices by ETSI [38]. Other policy and regulatory themes to be addressed include fair access to a sufficient spectrum range, managing communication taxes, ensuring end-to-end security, numbering, and addressing [39]. Radio spectrum is critical to IoT deployment and the regulation of radio frequency (RF) which, according to the International Telecommunication Union (ITU), can be performed independently at the national, regional, and global levels. In Africa, the African Telecommunication Union (ATU) comprises mobile and fixed telecom companies that formulate policies and strategies that promote technology integration across the continent. The regional approach sees regulatory bodies such as the West Africa Telecommunications Regulatory Assembly (WATRA), the East Africa Communication Organization, and the Communication Regulators' Association of Southern African (CRASA) managing regulations. Nigeria is a member of ITU Region 1 (ITU-R1) with the Spectrum Administration Department of National Communications Commission (NCC) responsible for managing, planning, and licensing the RF spectrum, as specified in the NCC Spectrum Administration Charter (SAC) [40]. The Federal Ministry of Communications (FMC) is the regulatory body governing the communication policy. Poor management of the RF spectrum is the result of a lack of adequate regulatory frameworks for an expensive and scarce natural resource described as a technological service of economic interest to all [33]. RF harmonisation facilitates use of the same spectrum for different services without issues in different countries in the same ITU region, since RF crosses national boundaries. However, different countries usually have different operators with varying individual allocations of spectrum based on their auction's strategy.