esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Study Area  
We selected two forests in Germany where forest inventory data and orthophotos were available for training and validation purposes. Figure 2 shows the project areas, the Ebersberger and the Freisinger Forest. Both regions exhibit almost complete coverage of trees with only few glades, as well as patterns of forest management with rather homogeneous patches of spruce, oak and beech trees as dominant species. The occurrence of similar tree types within patches makes it perfect for the analysis with deca-metric-resolution imagery like Sentinel-2. The characteristics of the test sites are described in detail by [PERSON] et al. (2015). According to the Bavarian State Forest Enterprise (Ebersberger and Forest, 2016), the annual temperature for the Ebersberger Forest is around 7.6\({}^{\circ}\) and the average annual precipitation is 850-950 mm with around 500 mm between April and September. It is one of Germanys largest connected forests with an extent of 76.3 km\({}^{2}\). The Freisinger Forest's size equals to nearly 9 km\({}^{2}\) and was used for a transferability study. The Ebersberger Forest is one out of 10 state forest areas that are controlled by the forest enterprise Wasserburg. In 2015, the tillering ratio consisted of two-third coniferous trees and one-third broad-leaved tree types. Overall, spruce has a dominance of 56%, followed by beech with 11%. Pines reached 9%, whereas fir, larch and Douglas fir form a total of 1-2%. Oak trees have a 4% proportion of all trees species in these forest areas.  
Figure 1: Flowchart showing the methodology of the study.
# Free Vibration Analysis of a Thermally Loaded Porous Functionally Graded Rotor-Bearing System
## 6 Results and Discussions
### The Effect of Porosity on DNFs for Different Power Law Indices (k)  
The dimensionless natural frequencies (\(\overline{\omega}\)) of the FG rotor-bearing system for different volume fractions of porosity are calculated using \(\overline{\omega}^{4}=~{}\rho_{\rm ss}\mathrm{AL}\omega^{2}/\mathrm{E_{ss}I}\), where \(\rho_{\rm ss}\) is the density of stainless steel, A is the area of the cross-section of the shaft, L is the length of the shaft, \(\omega\) is the natural frequency of the shaft in rad/s, \(\mathrm{E_{ss}}\) is Young's modulus of stainless steel at room temperature and I is a diametrical moment of inertia. Dimensionless natural frequencies (DNFs) of FG rotor-bearing systems at different volume fractions of porosity are plotted for different power law indices in Figure 7. It has been observed that the DNFs decrease with an increase in the volume fraction of porosity. The DNFs decrease due to a reduction in the material properties such as Young's modulus and density, while there is an increase in the volume fraction of porosity. Young's modulus and density for various volume fractions of porosity are tabulated in Table 7. However, the DNF curves of different power law indices converge at \(\alpha=0.4\) due to a proportional drop in the stiffness and mass of the porous FG shaft for all power law indices. DNFs also decrease with the increase in k. The reason for this is explained in Section 6.4.  
Figure 6: (**a**) First mode shape of a porous and non-porous FG rotor–bearing system; power law index (k) = 0.5. (**b**) Second mode shape of a porous and non-porous FG rotor–bearing system; power law index (k) = 0.5. (**c**) Third mode shape of a porous and non-porous FG rotor–bearing system; power law index (k) = 0.5.
# Chinese Character Component Deformation Based on AHP
## 4 Control Point Selection and Component Deformation
### Component Deformation  
After hierarchical total ranking, we can find the point set with the highest weight and then find the corresponding points in the reference component for the points in the highest weight point set by using the feature point matching results mentioned before. These points will be used as control points for the calculation of the parameters in Equation (1). Finally, the new component image can be obtained by an affine transformation of the original component image. The overall process is shown in Algorithm 1.  
```
0: Original component image \(T_{1}\), reference component image \(T_{2}\)
0: The component image \(T_{R}\) obtained by deforming \(T_{1}\).
1: Rosenfeld (\(T_{1}\), \(T_{2}\));//obtain the skeleton map of the input image.
2: MatchPairs\(<\)-Feature point detection and matching;
3: A(W,H,N);//Compute the importance matrix A of (W, H, N) according to Equation (2).
4: Weight(W,H,N);//Compute \(\omega_{W}\), \(\omega_{H}\), and \(\omega_{N}\) according to Equation (3).
5: PoSets\(<\)-Group(points);//Grouping the original component feature points in groups of three.
6: PoSets\(<\)-Filter(PoSets);//Filter the sets of feature points obtained in step 5.
7:forC in [W,H,N]do
8: B(PoSets);//Compute the importance matrix B of the feature point sets.
9:forpset in PoSetsdo
10: Weight(pset);
11:endfor
12: Consistency test(B);//Consistency test according to Equation (10).
13:endfor
14:forpset in PoSetsdo
15: Total-Weight(pset);//Compute the total weight of each point set according to Equation (11).
16:endfor
17: CPoints\(<\)-Control-Points(PoSets, MatchPairs);//Determine control points.
18: Parameters\(<\)-Calculation(CPoints);//Calculating affine transformation parameters.
19:\(T_{R}\)\(<\)-Affine-transformation(\(T_{1}\), Parameters);
```  
**Algorithm 1** AHP-based Chinese character component deformation.
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 4 Design of Control Strategies
### Control Scheme  
The control scheme for a nonlinear system, in this case for an application using a robot, is presented below and shown in Figure 5[3; 14; 30].  
Similarly to other feedback control systems, the following terms can be distinguished:  
* \(q_{d}\): reference signal called the desired trajectory.
* \(q_{d}\), \(q^{\prime}\), \(y\), \(q^{\prime\prime}\): signals to be controlled called the actual trajectories associated with motion, velocity, and acceleration.
* \(e\): error produced by the difference between \(q_{d}\) and \(q\).
* Controller: generally, includes parts of the robot's dynamic equation.
* System model: corresponds to the mathematical model of the robot, represented as a matrix and vectors.
* Disturbance: external variables that cause changes in the robot's motion.
* Sensors: elements in the feedback loop that allow the identification of the variables to be controlled.
## 1 Introduction
Our research focuses on the deployment of an efficient multipath routing protocol to manage video-warning messages in MANETs. In this work, we aimed to design a dynamic selection of the data forwarding paths using a game-theoretical approach plus a multipath multimedia routing protocol that we previously introduced in [3]. This contribution seeks to further enhance the overall performance of the service. The rest of the paper is structured as follows. Section 2 includes some relevant related work. In Section 3 we summarize the features of our multipath routing protocol. Section 4 gives a brief explanation of the game-theoretical proposal. Section 5 describes analytically our novel game-theoretical model to send video-warning messages over MANETs. Section 6 describes a method to calculate a specific parameter of the model. Simulation results are shown and analyzed in Section 7. Finally, conclusion and future work are given in Section 8.
## 3 Semantic Service Description and Context-Based Orchestration
### Construction of Ontologies
#### 3.2.8 Device Ontologies  
While all other ontologies are unique in our ontological structure, there is a device ontology for each field device or component. The device ontologies contain information about the device category, the operations provided by the device as well as its inherent characteristics. To model this information, each device ontology instantiates the concepts supplied by the ontologies belonging to the domain level and assigns concrete instances or values to the ObjectProperties or DatatypeProperties respectively. To uniquely identify a device, we use a combination of manufacturer, device type and serial number. This information is important for the later automatic registration and discovery of devices and their provided services.
###### Abstract  
The mevalonate pathway is an attractive target for many areas of research, such as autoimmune disorders, atherosclerosis, Alzheimer's disease and cancer. Indeed, manipulating this pathway results in the alteration of malignant cell growth with promising therapeutic potential. There are several pharmacological options to block the mevalonate pathway in cancer cells, one of which is zoledronic acid (ZA) (an N-bisphosphonate (N-BP)), which inhibits the farnesyl pyrophosphate (FPP) synthase enzyme, inducing cell cycle arrest, apoptosis, inhibition of protein prenylation, and cholesterol reduction, as well as leading to the accumulation of isopentenyl pyrophosphate (IPP). We extrapolated the data based on two independently published papers that provide numerical data on the uptake of zoledronic acid (ZA) and the accumulation of IPP (Ag) and its isomer over time by using in vitro human cell line models. Two different mathematical models for IPP kinetics are proposed. The first model (Model 1) is a simpler ordinary differential equation (ODE) compartmental system composed of 3 equations with 10 parameters; the second model (Model 2) is a differential algebraic equation (DAE) system with 4 differential equations, 1 algebraic equation and 13 parameters incorporating the formation of the ZA+enzyme+Ag complex. Each of the two models aims to describe two different experimental situations (continuous and pulse experiments) with the same ZA kinetics. Both models fit the collected data very well. With Model 1, we obtained a prevision accumulation of IPP after 24 h of 169.6 pmol/mgprot/h with an IPP decreasing rate per (pmol/mgprot) of ZA (\(k_{XGZ}\)) equal to 13.24/h. With Model 2, we have comprehensive kinetics of IPP upon ZA treatment. We calculate that the IPP concentration was equal to 141.6 pmol/mgprot/h with a decreasing rate/percentage of 0.051 (\(k_{XCIU}\)). The present study is the first to quantify the influence of ZA on the pharmacodynamics of IPP. While still incorporating a small number of parameters, Model 2 better represents the complexity of the biological behaviour for calculating the IPP produced in different situations, such as studies on \(\gamma\delta\) T cell-based immunotherapy. In the future, additional clinical studies are warranted to further evaluate and fine-tune dosing approaches.  
zoledronic acid; gamma-delta T cells; immunotherapy; isopentenyl pyrophosphate; ATRAID; pharmacodynamics; mathematical modeling; parameters estimation 2022
## 3 Results
The tracks are colored differently to indicate the corresponding date. Similar to Figure 7, the red dots represent the geographic locations of the four ionosondes used in this study. Due to the high inclination of CHAMP, the local time of descending tracks has little change during the three days [55], which is approximately at a fixed local time near 2230 LT. We plotted the measured electron density in Figure 10b-g, respectively, following the orbital arrangement from west to east in Figure 10a. To be convenient for comparison, we adjusted the position of the corresponding tracks in Figure 10d,e. The date marked with the corresponding color is drawn in the bottom right corner of each subfigure. The most prominent feature is the wide and deep plasma depletion that was clearly seen symmetrically at the magnetic equator in Figure 10c,f on 16 July. As shown in Figure 10f, the latitudinal coverage even reached \(\sim\)30\({}^{\circ}\)N over the two lower latitude ionosondes in Japan. The relatively small latitudinal extension of plasma depletion in Figure 10c reveals the possible reason why plasma depletion was not observed at the crossed longitude on track b7 of ROCAST-1 in Figure 8b at the same time. At 14 UT (23 JST) on 16 July, the only observation of spread F at OK426 of the four stations in Figure 6 might be associated with the northern edge of plasma depletion on the track of CHAMP in Figure 10c. On 15 and 17 July, we note that no signatures of plasma depletion were detected by CHAMP, which may explain why no enhancement of ROTI was observed later hours in Figure 1a,c. Due to the gradual decay of the ionosphere after sunset [53], a single equatorial anomaly crest appeared over the magnetic equator on 15 and 17 July. Notably, the overall ambient electron density detected by CHAMP was indeed enhanced as expected on 16 July. The observations of CHAMP further suggested that the magnetic storm had an important effect on the variations in the ionosphere and the generation of plasma depletion related to EPBs.
# Noncoherent Channel Combining for GNSS Signal Tracking with an Adaptive Antenna Array
## 2 Mathematical Model  
This section introduces the signal model for antenna arrays and anti-jamming methods using array weighting.
# Application of Multi-System Combination Precise Point Positioning in Landslide Monitoring
## 1 Introduction
[27] used a special instrument to carry out a landslide simulation experiment and used PPP technology to solve the problem. The study showed that the average deviation of real-time PPP in the north, east and elevation directions was 2 cm. [PERSON] et al. [28] calculated 6-12 hours of monitoring data of a landslide area in southern Gansu through GAMIT and PANDA PPP modules. The experimental results showed that using the GAMIT PPP module in calculating the coordinates of the accuracy and precision of relative positioning algorithms to obtain the coordinates of the amount of change can be reflected to five decimal places, and GAMIT and PANDA PPP modules of coordinate difference variation can be reflected to more than six decimal places, which satisfies the precision requirement of rapid landslide monitoring. [PERSON] et al. [29] discussed the calculation accuracy of GNSS PPP and RTK in a large-scale loess landslide, and the study showed that the BDS/GPS combination significantly improved the accuracy compared with the single system. The positioning accuracy of RTK was better than 3 mm and could be used for real-time monitoring of the landslide, while the positioning accuracy of PPP was better than 6 cm and could be used for the maintenance of deformation data. [PERSON] et al. [30] took the data of eight monitoring stations in the Xishan Village landslide area of Sichuan Province for 16 months as the data source and discussed the positioning accuracy and reliability of GPS PPP, BDS PPP and GPS/BDS combined PPP. The results showed that the mean square error of GPS PPP, GPS/BDS PPP, GPS/BDS combined PPP was about 1 cm, 2 cm, and 5 cm, respectively, which meets the accuracy requirements of landslide monitoring in mountainous areas. At present, the application of PPP in landslide monitoring is mostly single-system PPP or dual-system combined PPP [31; 32], and scholars have barely discussed the accuracy, reliability and calculation rate of multi-system combined PPP in landslide monitoring in a continuous period. Their conclusions show that the combined system has greater improvement in positioning stability, continuity and positioning accuracy than the single positioning system, but the specific values are not shown. Therefore, based on the static experiment and dynamic simulation experiment research, this paper experimented with monitoring data of one single landslide disaster area in Fujian province. The coordinates of the monitoring points obtained by static relative positioning between the continuously running reference station and the monitoring station are used as the reference value, and the GPS system is used as the reference system, showing the multi-system combination of G/R/C, G/R/E and G/R/E/C to have better positioning performance, which verifies the positioning performance and reliability of multi-system combination PPP in landslide monitoring.
# Invariant Feature Encoding for Contact Handprints Using Delaunay Triangulated Graph
## 3 Proposed Method
### Delaunay-Triangle-Graph (DTG) Algorithm
#### 3.1.1 Base-Triangle Generation  
The algorithm initially searches a set of a possible number of matching triangles (\(N\)) from Delaunay triangulation. A collection of reference locations is initially identified from a set of matched minutia-based triangles, namely, _base-triangles_, as shown in Figure 3. These triplets are a set of possible matched or similar triangles which are then identified as reference locations for further extension and used for image alignment as well. The feature vector for the triangle match is stated in Equation (1).  
\[fv=\{\theta_{p},t_{p},o_{p},od_{p},\theta_{q},t_{q},o_{q},od_{q},\theta_{r},t_{ r},o_{r},od_{r}\} \tag{1}\]  
where  
\(p\), \(q\) and \(r\)--minutiae right to left order;  
\(\theta\)--triangle angle;  
\(t\)--minutiae type;  
\(o\)--orientation of a minutiae from right to left;  
\(od\)--orientation difference between adjacent minutiae.
## 3 Receiver Design for NOMA  
An advantage of power-ordered NOMA lies on the ability to allow sharing of the spectrum by different users [3; 4; 5; 6; 7]. This characteristic represents a great advantage in scenarios where the spectrum is scarce, which is the situation of the extremely high number of IoT devices (e.g., Smart Cities or Autonomous Vehicles) supported by 5G. The signals of different users are separated in the power domain. Users closer to the base station use lower transmit powers, while users further from the base station require higher transmit powers levels. Consequently, since the signals of different users that share the spectrum present different received power levels, NOMA uses such property to differentiate signals. Naturally, besides the near-far problem, fading and power control are also factors that make received power levels of users suffer variations. Detecting the signals by their descending power levels, the NOMA receiver also takes these factors into account.  
As can be seen in Figure 3, two different scenarios exist in conventional NOMA: (1) signals with power levels higher than the reference user are estimated and cancelled using a Successive Interference Cancellation (SIC), before the reference user's signal is detected; (2) signals with power levels lower than the reference user are not cancelled, as they represent low level of interference. Instead of conventional NOMA, cooperative NOMA can be employed. In this latter case, all interfering signals can be cancelled, including those with lower powers than the reference one.
# Improved Convergence Speed of a DCD-Based Algorithm for Sparse Solutions
## 1 Introduction
In this paper, we considered a combination of leading and cyclic DCD iterations and proposed a leading-cyclic DCD algorithm for obtaining a sparse solution in systems requiring a large number of updates. The idea is that the leading DCD algorithm uses a small number of iterations to obtain the initial input for the cyclic DCD algorithm. The number of iterations used in the leading DCD algorithm has been thoroughly investigated under different system matrix conditions and solution sparsities. In the proposed algorithm, a range of the number of iterations is determined that will produce an optimal number of updates for the leading DCD algorithm. The results show that the proposed leading-cyclic DCD algorithm improves the convergence speed of the cyclic DCD algorithm and lowers the steady-state level. Notation: Boldface uppercase and boldface lowercase letters represent matrices and vectors (e.g., \(\mathbf{R}\) and \(\mathbf{\Gamma}\)). Elements of matrix and vector are denoted as \(R_{p,n}\) and \(\Gamma_{n}\), respectively. The \(n\)-th column of \(\mathbf{R}\) is denoted as \(\mathbf{R}_{:,n}\). \((\cdot)^{T}\) denotes a matrix transpose, and \((\cdot)^{H}\) denotes the Hermitian transpose.
Owing to these properties, studies have utilized CaOH\({}_{2}\) NPs alone or combined with various materials to achieve enhanced efficacy against pathogens, especially the resistant strain _E. faecalis_[26; 27; 29]. Most recently, [PERSON] et al. reported high antibacterial effectiveness of CaOH\({}_{2}\) NPs against Gram-positive and Gram-negative bacteria [28]. Carob-mediated CaOH\({}_{2}\) NPs (C-CaOH\({}_{2}\) NPs) are a new type of NP developed by mediating carob aqueous extract in the green synthesis of CaOH\({}_{2}\) NPs [30]. We reported their characterization and biocompatibility in a recent study [30]. They exhibited a hexagonal shape with a size ranging from 31.56 to 81.22 nm. In addition, X-ray diffraction analysis (XRD) showed high crystalline peaks at (100) 30.90, (111) 32.06, and (202) 60.52 of 2-theta degrees that were characteristic of CaOH\({}_{2}\); and an amorphous peak at (220) 70.08 that may be linked to carob-CaOH\({}_{2}\) interaction. Moreover, C-CaOH\({}_{2}\) NPs demonstrated the presence of carbon (28.58%), oxygen (22.10%), magnesium (21.22%), zinc (19.95%), and calcium (8.15%) elemental contents using energy dispersive X-ray analysis (EDS) and displayed hydroxyl (OH), carboxyl (C-O-OH), and carbon (C-C) functional groups on the attenuatedtotal reflection spectrum (ATR). The presence of OH groups is related to both CaOH\({}_{2}\) dissolution by linking to H\({}_{2}\)O and carob structural phenolic OH groups [31; 32]. Carboxyl and carbon groups were linked to carob polyphenol content (C-O-OH in phenolic acid and C-C in flavonoids) [17; 33; 31]. Finally, C-CaOH\({}_{2}\) NPs exhibited good in vitro biocompatibility; however, their antibacterial potential has yet to be investigated. Hence, the aim of this study is to investigate the antibacterial activity of the green-synthesized C-CaOH\({}_{2}\) NPs against different aerobic and anaerobic pathogens. This study is designed to provide preliminary information on the inhibitory and bactericidal activity of C-CaOH\({}_{2}\) NPs. The hypothesis proposed is that the combination of carob and CaOH\({}_{2}\) may yield NPs with efficient antibacterial properties. Therefore, C-CaOH\({}_{2}\) NPs may provide a new eco-friendly antibacterial agent that can be used in various biomedical applications as, for example, coatings for metal implants to prevent site infections.
## 2 Security and Privacy Challenges in IoE
### Next-Generation Attack Vectors in IoE
#### 2.2.3 Risks in Edge and Cloud Security for IoE  
In the IoE ecosystem, edge computing and cloud systems are commonly used for data processing and data sharing between the edge and the cloud. However, security vulnerabilities can arise between these two layers, making these architectures susceptible to cyber-attacks [70]. In particular, physical attacks, hardware manipulations, and security flaws are frequently observed in edge devices. Since IoE systems generally consist of small and low-power devices, their security measures are often limited. This situation can lead to unauthorized access and the takeover of devices by attackers [70].  
On the cloud side, data leaks and unauthorized access issues are more prominent. When large volumes of data generated by IoE devices are uploaded to cloud platforms, ensuring the security of this data becomes critically important. However, in centralized cloud systems, the lack of encryption or the use of weak authentication mechanisms can lead to large-scale data breaches [10].  
As a result, it is essential to understand next-generation attack vectors in the IoE ecosystem and develop effective solutions against these threats. AI-driven attacks, quantum threats, and edge-cloud security vulnerabilities are among the most significant risks to the sustainability of IoE. In this context, it is crucial to work on new approaches such as PQC, trusted AI models, and advanced edge-cloud security protocols [68, 69, 70].