esa-sceva/satcom-chunk-collection huggingface dataset:
# Digital Twins in Solar Farms: An Approach through Time Series and Deep Learning
## 2 Background
### Time Series
#### 2.1.1 Time Domain Analysis  
In the time domain, statistical methods are relevant. Characteristics such as the mean, standard deviation and variance provide us with information on the trend, stability and probabilistic distribution of a signal.  
Other more sophisticated techniques, such as kurtosis, skewness, and correlation, are used to understand the normality of a signal, as well as the relationships between this and other signals. We briefly detail these techniques as follows:  
* Kurtosis provides information on the shape of the distribution [3].
* Skewness indicates the asymmetry of the distribution, with negative values indicating a bias to the left and positive values indicating a bias to the right [3].
* Correlation measures the linear relationship between the signals, and it is widely used to detect highly correlated signals to perform dimensionality reduction [4].  
The application of these techniques in combination with smoothing techniques achieves a better representation of the sensor signals, improving the performance of the pattern recognition algorithms. There are different smoothing techniques, including exponential, logarithmic, and exponential decay, among others. The objective of these methods is to clean the signals from noise.  
Other techniques that allow us to carry out an in-depth study of time series in the time domain are the seasonal decomposition, auto-correlation function, partial autocorrelation and the autoregressive models, such as moving average, autoregressive moving average and integrated autoregressive moving average. Each of these techniques provides us information about the predictability of the time series, such as the influence of random processes or the linearity and influence of previous values.
# A UAV-Assisted Stackelberg Game Model for Securing IoMT Healthcare Networks
## 1 Introduction
UAVs are vulnerable to attacks that can compromise user data and disrupt services [19]. Despite existing research on IoMT-UAV integration, little attention has been given to addressing these security concerns. Various types of attacks, such as denial of service attacks, man-in-the-middle attacks, and phishing attacks, can compromise the security and privacy of patient data. Previous research assumes various kinds of attacks are encountered before they harm any data or network resources. However, practically there exist hacker attacks that harm user data. In particular, there is no way to retrieve compromised user or UAVdata in the event of an attack. Therefore, we have introduced a Stackelberg game model based on IoMT networks to tackle this issue to guarantee data safety and recovery success. We have adopted pure and mixed strategies to encounter the black hole attacks. Furthermore, our proposed scheme provokes the SEE technique to recover the resource in case it is hacked. Game theory [20] is a mathematical framework widely used to analyze strategic interactions between multiple decision-makers, where each decision's outcome depends on others' decisions [21; 22]. In the context of UAVs, game theory is an efficient technique for modelling and analyzing the interactions between multiple UAVs, UAVs, and ground-based agents and between numerous ground-based agents coordinating with UAVs [23]. One application of game theory in UAVs is cooperative surveillance, in which multiple UAVs work together to monitor a target area. Using game theory, researchers can model the strategic interactions between UAVs and optimize their surveillance strategies to maximize coverage and minimize resources used. Another application is UAV routing and scheduling, where multiple UAVs need to coordinate their routes and schedules to reduce collision risks and maximize efficiency. Game theory can be used to model strategic interactions between UAVs and optimize their routes and schedules to achieve the best overall outcome [24]. Figure 1 presents a taxonomy that classifies attacks in IoMT UAV networks based on network, device, and physical attacks. The taxonomy is a valuable tool for identifying and mitigating potential security threats in IoMT UAV networks, which are crucial for providing reliable and timely medical services in emergencies. As IoMT and UAVs are increasingly adopted in healthcare systems, the security of communication and data exchange between these devices becomes paramount. Taxonomy in Figure 1 provides a systematic framework for analyzing and categorizing threats to IoMT UAV networks, enabling researchers and practitioners to develop effective security solutions that protect the privacy and integrity of patient data.
## 3 Results and Discussions
### Energy Absorption Capabilities
Nevertheless, the energy absorption values of the HG and VG specimens in the chiral structures were only 7.3% and 14.2% lower, respectively, than the uniform structure. This indicates that the performance of FGLSs with chiral structures can potentially be improved. As a result, when all designs were evaluated, the chiral lattice structure was advantageous compared to other designs in terms of total and specific energy absorption.
## 4 Test and Result
### Ambiguity Resolution Analysis
It can be inferred the proposed filter strategy suppresses the noise in DD measurements on the whole, as the correntropy can measure the similarity between the random variables through PDF. For AMC-KF, the RMS is improved by (+78.60%, +88.85%, +77.74%) at BONE-WEEE and (+57.49%, +69.52%, +42.31%) at BONE-GSBN than DD-KF. The STD is improved by (+64.97%, +66.26%, +60.81%) at BONE-WEEE and (+51.10%, +46.89%, +40.34%) at BONE-GSBN, respectively. The proposed filter strategy can reduce the positioning error significantly for the long baseline.
## 8 Future Directions
### AI in 6G Networks
These developments highlight the importance of AI-driven algorithms capable of handling real-time, high-throughput data streams, while simultaneously ensuring energy efficiency and security compliance. The integration of AI in 6G networks will not only enhance performance but will also facilitate the next wave of technological innovations. Recent studies have shown that AI can significantly improve network management and user experience in 6G, as demonstrated in works like [148], where AI was applied for massive MIMO optimization, and [149], which explores AI for secure data transmission in satellite-terrestrial integrated networks. The continuous evolution of AI technology is essential for addressing the complexities of 6G networks, and ongoing research will further enhance its role in enabling more efficient, secure, and scalable communication systems.
## 3 Proposed Ground Subsidence Prediction Method
### Selection of Extraction Elements within the Calculation Area
\right.\quad\text{(Case B)} \tag{8}\]  
where \(P_{i}\) and \(P_{i+1}\) are two endpoints of a mining boundary line. Once a BP is determined, the mining depth on the BP can be easily obtained by making use of the points for which the mining depth is known in advance and determined via the interpolation method (e.g., Kriging interpolation method). The deviation of the inflection point corresponding to the BP can be calculated by substituting the mining depth on the BP into Equation (1). If Equation (9) is true for each BP of an extraction element, the extraction element could be treated as an element within the calculation area:  
\[S^{\prime}_{i}<d_{i} \tag{9}\]  
where \(d_{i}\) is the BD of \(i\)-th mining boundary line, and \(S_{i}\)' is the deviation of the inflection point corresponding to the BP of \(i\)-th mining boundary line. Figure 4: Geometry of mining area boundary lines and the incenter of an extraction element. Figure 5: Relative position of the incenter of the extraction element and mining boundary lines: (**a**) the incenter of the extraction element located between two endpoints of a mining boundary line; (**b**) the incenter of the extraction element located out of two endpoints of a mining boundary line. A flowchart of the determination of an extraction element within the calculation area is shown in Figure 6. Note that \(n\) is the amount of mining boundary lines. The corresponding algorithm can be described as follows:  
1. Input vertex coordinates of a triangular extraction element and the vertex coordinates of the mining area;
2. Calculate the incenter of the extraction element by Equation (6);
3. Determine the relative position of the incenter and the \(i\)-th mining boundary line by Equation (7) and Equation (8); then calculate the BD (\(d_{i}\)) and BP for the \(i\)-th mining boundary line;
4. Calculate the deviation of the inflection point on the BP, that is, \(S_{i}\)';
5. Compare \(d_{i}\) and \(S_{i}\)'. If the former is smaller than the latter, the extraction element is not in the calculation area. Otherwise, make \(i\) equal to \(i+1\), and repeat (c)-(e) for the \((i+1)\)-th mining boundary line;
6. If \(d_{i}\) is greater than or equal to \(S_{i}\)' for each mining boundary line, the extraction area is in the calculation area. Repeating the above process for each extraction element, all the extraction elements within the calculation can be obtained. Note that, in order to guarantee the sufficient approximation to the in-situ calculation area, the area of the extraction element should be small enough. Assuming the minimum mining depth of the mining area is \(H_{min}\), the data processing results shows that there is a good agreement between the proposed method-based calculation area and the in-situ one if the mesh size is smaller than 0.5\(\cdot k\cdot H_{min}\). Note that \(k\) is the inflection point offset coefficient.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 3 Morphological Characteristics of the Vascular Network
### Functional Characteristics of the Tumor Vascular System
#### 3.4.2 Extensive Vascular Permeability  
Vascular permeability refers to the passage of substances through the endothelial barrier. Small molecules (less than 40 kDa) are extruded spontaneously, whereas larger molecules require the disruption of the vascular barrier [75]. There are two pathways that control the passage of solutes across the endothelial barrier: the intercellular (ortranscellular) pathway, i.e., the vesicle-mediated transport of small molecules (less than 3 nm), and the paracellular pathway, which limits the diffusion of macromolecules and is controlled by endothelial cell-cell junctions [99; 100]. The permeability of the paracellular pathway is influenced by the opening and closing of endothelial junctions, which are the final barrier to the passage of solutes [85; 99; 100].  
The endothelial cells lining the inner lumen of blood vessels are connected in three ways: through tight junctions (TJs), adhesion junctions (AJs), and gap junctions (GJs). TJs and AJs regulate the permeability of the vascular barrier, whereas GJs connect adjacent endothelial cells, transporting water and ions [99; 100]. The former are composed of transmembrane proteins such as claudins and nectins, which bind to peripheral proteins such as ZO family kinases and catenins, regulating vascular permeability and cell adhesion [99; 100]. Vascular endothelial cadherin (VE-cadherin) is expressed in blood vessels and its dysfunction leads to a hyperpermeability phenotype. Other adhesion proteins are N-cadherin and platelet-endothelial cell adhesion molecules (PECAM-1) and junctional adhesion molecules (JAMs) [100].  
Vascular permeability is regulated by several physiological signals, such as VEGF [99]. In cancer, tumor size, high protein content, increased interstitial fluid pressure, large vessel diameter, and reduced blood flow slow down the extrusion of molecules from blood vessels. However, changes in endothelial cell properties lead to increased plasma and plasma protein extraction compared to normal vessels [85]. The tumor vasculature is characterized by extensive angiogenesis and the overexpression of angiogenic factors that increase endothelial cell proliferation and permeability. This results in the disruption of the endothelial barrier, creating pores (0.1-3 um), and allows the diffusion and extrusion of membrane proteins and nutrients, especially macromolecules, into the interstitial space [75; 100; 101]. Vascular permeability varies depending on the type of tumor and during tumor growth, regression, and recurrence [46].
# Structuring Reference Architectures for the Industrial Internet of Things
## 6 Limitations  
The modeling of relations between reference frameworks, concerns, technologies and standards in the knowledge graph provides a flexible format for the modeling of the various relevant dimensions and dependencies. Nevertheless, the selected model has several disadvantages. First of all, the presented graph is by its nature incomplete. The amount of publications in the IIoT domain, considering its velocity and variety, do not allow a complete coverage. Therefore, the non-existence of an entity or a link between two entities must not be interpreted as though the two nodes are unrelated. For instance, not having an 'alignedWith' relation between classification A and B does not imply that A and B are not aligned. Nevertheless, the application of the so-called Open World Assumption inherent to ontology-based reasoning allows the application of machine supported knowledge graph completion. The number of explicitly stated facts can thereby automatically increased from more than 15,000 to up to 24,000.  
Second, the detailed characteristics of a single relation cannot be presented, that is, there is either a relation or not instead of having, for instance strong and weak relations. For example, a reference architecture such as the IDS views usage control as at its main scope whereas the IIRA merely mentions it. As a matter of readability and simplicity and restricted by the expressiveness of the RDF model, the gradations of this relation and other potentially relevant features are not part of the graph. The knowledge graph model solely allows qualitative statements of relations (relation exists or not). Consequently, a link between two entities states that a relation between these entities exists but says nothing about the nature or degree of this relation. Applying automated logical reasoning, this restriction makes it hard to depict whether one statement 'is stronger' or 'has more support' than another or which degree of uncertainty is included. Other approaches, in particular property graphs, have aligning capabilities but, in general, lacks the web-based nature of the RDF graph.  
Furthermore, the chosen graphical representations are limited to two dimensional relations. While the knowledge graph itself is a highly interconnected and non-planar structure, the visualizations in the form of Venn diagrams and co-occurrence matrices limit the comparisons to a maximum of two dimensions. For instance, frameworks can be plotted in relation to concerns, their classifications or their alignments to other frameworks but not in the same view. Extending the existing plotting options for more-dimensional presentations including more kinds of attributes will increase the amount of information presented to the user.
## 6 Synthetic Scaffolds
### Polymers  
The most commonly used synthetic polymers are polyurethane (PUR) and saturated aliphatic polyesters such as poly(lactic acid) (PLA), poly(glycolic acid) (PGA), poly(lactic-co-glycolic acid) (PLGA) and poly(carprolactones) (PCL) [17; 26; 59]. Like natural scaffolds, these products are generally biocompatible and biodegradable [59]. However, synthetic polymers have longer release rates and can be produced under more controlled manufacturing methods [35]. This means that they have predictable physicochemical properties and can be engineered, for example, by adjusting the molecular weight, to yield specific desired characteristics [35]. The versality of these scaffolds is their largest advantage [26]. One disadvantage though, is that compared to native bone, synthetic polymers have relatively poor mechanical properties [59]. This makes them unsuitable for use in high load-bearing areas [26]. Moreover, they degrade by hydrolysis which produces carbon dioxide. This has been reported to lower the surrounding pH, resulting in tissue necrosis [60; 61]. In scaffolds that rapidly dissolve, this change in pH may even be large enough to reduce the functional efficacy of the local antibiotic [17]. For these reasons, antibiotic-eluting synthetic polymers remain the focus of _in vitro_ and _in vivo_ experiments [45; 61; 62].  
An example of the utility of these materials is provided by [PERSON] et al. (2010) [62]. This group used biodegradable PUR scaffolds impregnated with free-base vancomycin, to treat 40 rats withpost-traumatic osteoarthritis [62]. Using this method, the authors found the bacterial load at four weeks (measured in colony forming units) was significantly lower than that of negative controls [62]. The effectiveness of this resorbable scaffold was comparable to PMMA which is the current standard of care [62]. In another study by [PERSON] et al. (2014), a biodegradable PLGA scaffold was used to deliver gentamicin and clindamycin to 30 sheep with osteomyelitisitis [45]. These animals were sacrificed at two- and six-weeks post-implantation to look for evidence of infection. No bacteria were isolated from animals treated with antibiotic-eluting scaffolds, but _Staphylococcus aureus_ was successfully isolated from control groups [45]. The authors also showed that at 13 weeks the scaffold material had fully degraded [45]. Any area of the defect that was not filled with new bone contained cartilaginous tissue that would be expected to eventually turn into mineralised bone [45]. Synthetic polymers are therefore promising biomaterials. They are contained in many recent composites that have been used in clinical trials as discussed below.
## Materials and Methods
### ANN Structure, Training Algorithm, and Feature Input Selection
The training goal is naturally to reduce modified error function expressed as [32]:  
Figure 2.3  
where  
Figure 2.4  
Figure 2.5  
the \"black box\" regularization parameters \(\alpha\) and \(\beta\) are responsible for penalizing the cost function (F) which affects the generalization of the trained model. In general, the higher the regularization constant (\(\beta\)), the more network weight connections will be dropped to prevent overfitting. Table 2 summarizes the ANN parameters and algorithm setup that was investigated in this study. Similarly, in order to have consistent results and fair comparison for each different ANN setup, the initial random seeds for weights and biases were set to the same state of random number generator in the software. All activation functions in each neuron are sigmoid for the hidden layer and linear function of the output layer as expressed in Equations (3) and (4), respectively. Moreover, the regularization constant (\(\beta\)) was set to 0.2, and the maximum number of iterations was set to 1000. There is no cross-validation procedure performed during the training. Once the ANN training was performed using the training dataset, the trained ANN model was then validated using the testing dataset. The obtained model accuracy was evaluated by calculating the regression coefficient (\(R^{2}\)), \(RMSE\) (root mean squared error) value and mean of percentage error (\(MPE\)) which are defined as:  
Figure 2.6 \begin{table}
\begin{tabular}{c c} \hline \hline
**ANN Model Properties** & **Experimentation** \\ \hline Feature Input Selection & To use all features or reduced features using correlation score \\ \hline ANN structure/architecture & To use feed-forward and cascade-forward structure \\ \hline \multirow{3}{*}{Number of hidden layer and its neurons} & To use 1 or 2 hidden layers with 5, 10, or more neurons in each \\  & layer e.g., \([n_{1}+n_{2}]\) means \(n_{1}\) neurons in hidden layer 1 and \\ \cline{1-1}  & \(n_{2}\) neurons in hidden layer 2 \\ \hline \multirow{2}{*}{Training algorithms} & To use Levenberg–Marquardt (LM), Bayesian Regularization \\  & (BR) and Particle Swarm Optimization (PSO) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Artificial neural network (ANN)-based model setup. \[MPE\ =\ \sum_{i\ =\ 1}^{n}\frac{(y_{i}-\hat{y}_{i})}{y_{i}}\times\frac{100\%}{n} \tag{14}\]  
The overall process of the river flow modelling using ANN is illustrated in Figure 3. It begins with raw data collection, as explained in Section 2.1, followed by data analysis and pre-processing, as explained in Section 2.2. The ANN training and some related experimentations are explained in Section 2.3. This procedure can be considered a general procedure for ANN-based predictive model building.
## 2 Conventional Current Detection Scheme with \(N_{\text{P}}\) Hall Effect Sensors  
For an \(N_{\text{P}}\)-phase SRM, \(N_{\text{P}}\) hall effect sensors are usually used. Each phase conductor vertically passes through the aperture of a sensor, and each phase current is detected individually by a sensor. The AHBC with four hall effect current sensors for a four-phase 8/6 SRM is shown in Figure 1, where \(i_{\text{A}}\), \(i_{\text{B}}\), \(i_{\text{C}}\), and \(i_{\text{D}}\) are the currents of phases A, B, C, and D, respectively; \(L_{\text{A}}\), \(L_{\text{B}}\), \(L_{\text{C}}\), and \(L_{\text{D}}\) represent the phase inductances; \(U_{\text{dc}}\) is the dc-link voltage; \(\text{HS}_{1}\), \(\text{HS}_{2}\), \(\text{HS}_{3}\), and \(\text{HS}_{4}\) denote the current sensors.  
There are four operation modes for each phase in an AHBC. Take phase A for example, with the power switches \(S_{1}\) and \(S_{2}\) both on, positive dc-link voltage is applied to the phase winding, and phase A is in the magnetization mode as shown in Figure 2a; with \(S_{1}\) and \(S_{2}\) both off, negative dc-link voltage is applied and demagnetization mode is formed as shown in Figure 2b; with only one switch on, zero voltage is applied; with only the lower switch on, freewheeling mode I is established as shown in Figure 2c; with only the upper switch on, the phase is in freewheeling mode II as shown in Figure 2d.  
\[i_{\text{A}}=i_{\text{HS1}};\ i_{\text{B}}=i_{\text{HS2}};\ i_{\text{C}}=i_{ \text{HS3}};\ i_{\text{D}}=i_{\text{HS4}} \tag{1}\]  
Figure 1: Asymmetric half-bridge converter (AHBC) with four hall effect current sensors for a four-phase 8/6 switched reluctance motor (SRM).  
Figure 2: Operation modes of AHBC. (**a**) Excitation mode. (**b**) Demagnetization mode. (**c**) Freewheeling mode I. (**d**) Freewheeling mode II.