esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Super Oxide Dismutase Level on Control and Treated Groups (SOD)  
The values from the results presented in Table 5 can be seen. The SOD level was \(4.763+0.480\) units/mg in the control group; this value decreased significantly \((p\leq 0.05)\) in the Streptozotocin group \(1.673+0.205\) units/mg, while in both groups that were treated with metformin and selenium extract, it rose at the same level, which was \(2.321+0.399\) units/mg and \(2.128+0.166\) units/mg, respectively.  
The green SeNPs group showed the best result, which was \(2.917+0.234\), and these results showed a significant increase \((p\leq 0.05)\) in the level of SOD compared to DM. Additionally, as shown in Figure 11, the group indicates its excessive use in mitigating free radicals I born during DM. This note has already been reported in animals with diabetes [41].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**Comet Score Program (Mean \(\pm\) SD)**} \\ \cline{2-4}  & **Tail Length (px)** & **Tail DNA (\%)** & **Tail Mean Moment** \\ \hline Neg. control & D65.044 + 8.035 & D0.0246 + 0.00089 & E0.000231 + 0.0000521 \\ \hline STZ & A303.775 + 24.336 & A12.114 + 2.007 & A7.3351 + 2.155 \\ \hline STZ + Met & C90.261 + 11.074 & B4.125 + 1.008 & B4.224 + 1.007 \\ \hline STZ + Se & B105.752 + 13.276 & B4.026 + 0.986 & C2.965 + 0.8112 \\ \hline STZ + Se NPs & D75.842 + 8.117 & C2.243 + 0.411 & D1.7775 + 0.477 \\ \hline \(p\)-Value & 0.000431 & 0.000512 & 0.000116 \\ \hline \(\bullet\) & STZ = streptozotocin diabetic mice & & \\ \(\bullet\) & Met = metformin & & \\ \(\bullet\) & Different capital and small latter represent significant \(p\leq 0.05\). & & \\ \(\bullet\) & LSD: least significant difference. & & \\ \(\bullet\) & \(p\) value: probability value. & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measurement of the tail length, tail DNA percentage and tail moment by using comet score program.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Parameters**} & **SOD U/mg (Mean \(\pm\) SD)** \\ \hline Neg. control & A4.763 + 0.480 \\ \hline STZ & D1.673 + 0.205 \\ \hline STZ + Met & C2.321 + 0.399 \\ \hline STZ + Se & CD2.128 + 0.166 \\ \hline STZ + Se NPs & B2.917 + 0.234 \\ \hline LSD & 0.482896 \\ \hline \(p\)-value & 0.00040 \\ \hline \(\bullet\) & STZ = streptozotocin diabetic mice \\ \(\bullet\) & Met = metformin \\ \(\bullet\) & Different capital and small latter represent significant \(p\leq 0.05\). \\ \(\bullet\) & LSD: least significant difference. \\ \(\bullet\) & LSD: least significant difference.  
The effect of SeNPs on plasma oxidative enzymes and their increased activity is an indication of their presence and the ability to search for reactive oxygen species, thus contributing to the protective effect against oxidative stress and preventing further damage to membrane lipids [42].
## 3 Control Architecture  
A general schematic of the control architecture is provided in Figure 2 and is composed by three layers: the Planning Layer, the Control Layer, and the Simulated System. The force that each thruster must exert, which is an input to the Simulated System, is found using the thruster allocation mechanism that maps the desired wrench on the body to the forces that need to be generated by each of the thrusters. Such a desired wrench is the output of the Dynamic Control Layer (DCL), which tracks the desired system velocities. The Kinematic Control Layer (KCL) encompasses the definition of the control tasks, which are the basic building block of the architecture. Within the KCL, basic tasks are arranged into task hierarchies with priorities that constitute the control actions. The KCL receives the action to be executed from the Planning Layer, and it exploits the proposed task priority framework to find out the desired system velocity to accomplish it. The Planning Layer contains the mission manager that supervises and manages the execution of the current mission. It also addresses conflicting tasks by sequencing different actions or by providing a new set of parameters, thus providing an alternate solution. We recall that in the scope of this paper, we will focus on the properties of the KCL without going into details of DCL and Planning Layer.
# Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks
## 3 System Model and Problem Formulation
### Problem Formulation and Mathematical Modeling
#### 3.2.2 Cell Coverage  
Achieving a balance between coverage and energy consumption in the BS switching-off/on approach for cellular green networks is important. The closed form for the cell coverage can be expressed as follows [39]:  
\[\begin{array}{c}C=Q(a)+\exp\left(\frac{2-2 ab}{b^{2}}\right)Q\left(\frac{2-ab}{ b}\right)\\ a=\left(\frac{P_{\min}-P_{xx}(r)}{\sigma_{\varphi}}\right),\ b=\left(\frac{10a \log_{\min}(\exp)}{\sigma_{\varphi}}\right),\end{array} \tag{10}\]  
where \(\sigma_{\varphi}\) is the standard deviation of the shadow fading component; and \(\alpha\) is a path loss exponent component. In Equation (10), the functional representation of the cell coverage area is denoted by \(C=f(a,b)=f\big{(}P_{\min},P_{rx},\alpha,\sigma_{\varphi}\big{)}\), where the \(P_{\min}=f\Big{(}N_{o},BW,N_{f},SINR,IM\Big{)}\) and the received power is \(P_{rx}=f(P_{tx},G,L,\sigma)\). The problem formulation is described as follows:  
\[(p:)\underset{P_{tx},G,BW,SINR,\sigma}{\text{maximize}}\bigg{[}Q(a)+\exp\left( \frac{2-2 ab}{b^{2}}\right)Q\left(\frac{2-ab}{b}\right)\bigg{]}, \tag{11}\]  
which is subject to the following constraints:  
\[P_{tx}^{\min}<P_{tx}\leq P_{tx}^{\max} \tag{12}\]  
\[G_{\min}\leq G\leq G_{\max} \tag{13}\]  
\[BW_{\min}\leq BW\leq BW_{\max} \tag{14}\]  
\[SINR_{\min}\leq SINR\leq SINR_{\max} \tag{15}\]  
\[\sigma_{\min}\leq\sigma\leq\sigma_{\max}. \tag{16}\]  
Equation (11) is a nonlinear problem including a \(q\)_-function_ and five input constraint parameters, wherein differential mathematics is used as a complex approach to find a solution. Hence, an optimization technique has been chosen that requires differentiation of an objective function. Furthermore, the technique generates multiple solutions during each iteration, unlike a numerical method that generates a single point (solution) at each iteration. The sequence of these points approaches the optimal solution.  
PSO is preferred because of its capability to maximize the cell coverage area when studied under the \(P_{tx}\), \(BW\), \(G\), \(SINR\), and \(\sigma\) constraints. Moreover, the MCS, data rate, and EE were taken into consideration. The PSO approach are equipped with the following attributes: flexibility, adaptability to the problems, strong global search ability, and robust performance. PSO performance is comparable to that of genetic algorithms or the ant colony algorithm because it is faster and less complicated. PSO has also been successfully applied to a wide variety of problems. Moreover, PSO is simple to implement and is a very efficient global optimizer for continuous variable problems. See [40; 41; 42; 43] for applications in which PSO has been successfully applied to the field of wireless communication networks.
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 7 Current and Future Research Directions
### Current Work in the Field
#### 7.2.1 Data Sources and Benchmarking  
Some words such as \"real-world\", \"dataset\", and \"synthetic\", indicate that research is conducted in the field of benchmarking data sets. At the moment, especially the IoT sector and activity recognition problems are prevalent in the literature [9; 13; 15]. Recent works related to the COVID-19 pandemic have been published, in which stream classification methods are used, e.g., for facial mask detection or the processing of ultrasound images [29; 30]. Considering the mentioned examples, it becomes clear that a special interest is observed regarding the issue of privacy protection. Since data sources, such as IoT devices or recordings of individuals, are often based on sensitive user data, this is an additional challenge for stream classification algorithms [182]. In the future, researchers must test modern methods of preserving data privacy, such as decentralized data storage,  
Figure 10: Wordcloud of titles and abstracts of papers published in 2020 and 2021.  
for their suitability for stream classification. In decentralized storage, data are not stored in a central location such as a cloud but in individually encrypted blocks [183; 184].  
A similar problem exists in the social media domain, since social networks restrict data sharing among researchers for the same reasons. Thus, finding benchmark solutions for social media stream data--an image and textual-based--remains a challenge for future works [46].  
As shown in Figure 3 as well as Table 3, more numerical data sources are available than image and textual data sources. Generally, the generation and sharing of more real-world benchmark datasets remain an open task for the future. Existing datasets are often too small or have other flaws such as dependent labels [34; 86]. Recently, published works incorporate multi-object detection and tracking, as well as multi-stream input [28; 185; 186]. Thus, more challenging datasets are also required, which reflect the current state of technology, e.g., for processing images of highly sensitive cameras that produce numerous images per second [187].  
To incorporate data sources and other parts of the stream classification pipeline, _frameworks_ can be used. The literature review demonstrated that few frameworks exist for stream classification purposes, while many more exist for handling stream data but cannot be used for classification. River and MOA are the most comprehensive frameworks under active development. Until now, MOA is the most cited framework in recent literature [13; 124; 188]. However, River also provides a broad range of stream classification data sets, algorithms, drift detection, and evaluation methods for smaller tasks or situations where researchers want to execute the stream classification pipeline in Python.
## 3 Structural Parameter Optimization of the MRE Isolator
### Optimal Results  
The process of optimizing the size parameters of the MRE isolator is shown in Figure 8. A two-dimensional axisymmetric model of the MRE isolator is established after the global definition is carried out in COMSOL and the geometric parameters and interpolation functions are input. In the optimization module, the optimization solver, objective function, control variables, and parameters are set up, including optimization method selection, optimization tolerance and model maximum calculation number input, variable range setting, etc. Then, the material definition, meshing, model calculation, and the average magnetic induction intensity at the MRE are solved by the AC/DC electromagnetic field module and optimization module. The B-H curve is used to define the permeability of the magnetic steel material and the MRE material. The condition for the end of parameter optimization is that the magnetic induction intensity at the optimization target MRE reaches the maximum value or reaches the maximum number of calculations of the model; otherwise, the variable value is updated to repeat the model calculation and other operations. The final parameter optimization results are represented by the magnetic induction intensity distribution cloud map in Figure 9.  
According to the optimization results, when \(r_{1}\) is 15.0 mm, \(r_{4}\) is 36.0 mm, and \(h_{3}\) is 53.5 mm, the average magnetic induction intensity at the MRE reaches the maximum at 3 A current, which is about 417.46 mT. The optimization results obtained more accurate geometric parameters and mechanical structure of the MRE isolator. The main geometric parameters of the MRE isolator after parameter optimization are shown in Table 2, and the three-dimensional structure profile is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Unit** & **Dimension (mm)** & **Unit** & **Dimension (mm)** \\ \hline MRE width & 5.0 & Iron core diameter & 30.0 \\ MRE height & 6.0 & Outer diameter of & 54.0 \\ Coil outer diameter & 55.0 & Outer sleeve thickness & 6.0 \\ Coil inner diameter & 45.0 & Outer sleeve height & 67.0 \\ \hline \end{tabular}
\end{table}
Table 2: Main geometric dimensions of MRE isolator.  
Figure 8: Optimization process of the MRE isolator size parameters.  
Figure 9: Cloud map of magnetic induction intensity distribution after parameter optimization.
# EconLedger: A Proof-of-ENF Consensus Based Lightweight Distributed Ledger for IoVT Networks
## 3 EconLedger: Rationale and Architecture
### Network Model  
EconLedger relies on a permissioned network, and we assume that the system administrator is a trust oracle for maintaining global identity profiles for all valid nodes. We adopt a standard asymmetrical algorithm such as Rivest-Shamir-Adleman (RSA) for key generation (\(RSA.gen\)) and digital signature scheme (\(RSA.sign\), \(RSA.verify\)). During the registration process, signing-verification key pair \((sk_{i},pk_{i})\gets RSA.gen(i)\) is generated by PKI and assigned to the authorized node \(u_{i}\). Additionally, a node's public key \(pk_{i}\) is associated with its credit stake \(c_{i}\leq C_{max}\), where \(C_{max}\) is the maximum value of credit stake defined by the system. Therefore, all registered nodes can be represented as \(U=\{(pk_{1},c_{1}),(pk_{2},c_{2}),...,(pk_{n},c_{n})\}\), where \(n\) is the total number. As the above security assumptions depend on the system administrator's behavior, our EconLedger is a partially decentralized blockchain model.  
EconLedger assumes a synchronous network environment. Operations in consensus protocol are coordinated in rounds with upper bounded delay \(\mathcal{T}_{\Delta}\). Thus, the time is divided into discrete \(slots\), which can be indexed by logical clocks _ticks_ to synchronize the events in a distributed system [34]. Given a certain tick \(t\in\{1,2,3,...\}\), slot \(sl_{t}\) represents the length of time window to measure \(\mathcal{T}_{\Delta}\). The time window of \(sl_{t}\) should be sufficient to guarantee that the message transmitted by a sender is received by its intended recipients (accounting for local time discrepancies and network delays). Thus, we require \(sl_{t}\geq\mathcal{T}_{\Delta}\) in order to ensure the liveness of consensus protocol.
## 3 Results  
### Imaging Acquisition and Mesh Processing  
The first case study involved a CT of a femur comprising 986 images and we implemented a semi-automatic approach. Due to the number of images, a predefined density value for the 3D reconstruction was applied. Since the femur geometry was quite simple, holes and segmentation errors were easily repaired with the mesh repairing software. For more complex geometries, for example foot CT, we adopted a semi-automatic approach: firstly, we applied a predefined density value for the overall 3D reconstruction. Then, we changed the density value to edit the complex geometries of: cuneiform bones, metatarsal bones, and phalanges (Figure 5).  
This semi-automatic approach was necessary in order to simplify the subsequent use of mesh repairing software. In fact, in these areas, holes and segmentation errors were significant and automatic mesh repairing algorithms could not repair the mesh in a consistent way (Figure 6).  
Depending on the complexity of the anatomical district, the meshing process took about 1 to 4 h for the whole model. The femur of the case study was 463 mm long. Since the building volume of the 3D printer is 320 mm height, we divided the femur into three sections. With the insertion of cubes and the application of Boolean operation (Figure 7), dedicated pins were created to allow the mechanical connection of the three portions printed separately (Figure 8).  
Figure 5: Segmentation errors in the cuneiform bones of a child foot.
## 4 Results and Performance Analysis
### Performance Analysis  
Both codes have been shown to be robust, efficient and reliable in simulating the analysed phenomena. However, in terms of computing cost, the Eulerian method was faster than the Lagrangian method. In fact, tracking sufficient number of particles and analyzing them in the Lagrangian approach required more time than the Eulerian iterations.  
With the large numbers of neighboring particle interactions and the time step restricted by the Courant condition for weakly compressible flow, SPH is computationally demanding.  
However, in this work the power computing of GPUs was used to accelerate DualSPHysics when compared with the performance achieved using a classic version of SPHysics code [25].  
This important acceleration of the code using the new GPU technology can be observed in Figure 9 where the runtime of the hardware accelerated DualSPHysics code is shown by comparing its performance against the classic version of SPHysics code using a single core. In particular, in [PERSON] et al. [25], the open-source code SPHysics [23], was executed up to t = 25 s from the beginning for test T1 and T2 and it cost about 40 h of CPU time using a personal computer (CPU 2.66 GHz and RAM 4.0 GMB PC). An initial particle distance of \(\Delta\)x = \(\Delta\)z 0.022 m was chosen for these two tests, resulting in a total number of 30,000 particles.  
In this study, with an initial particle distance of \(\Delta\)x = \(\Delta\)z = 0.01 m and a total number of 62,892 particles, the hardware accelerated smoothed particle hydrodynamics code DualSPHysics was executed up to t = 100 s from the beginning and cost about 12 h of CPU time using a personal computer (CPU 2.70 GHz and RAM 384 GB PC, equipped with a NVIDIA Quadro K2000 GPU). Furthermore, with an initial particle distance of \(\Delta\)x = \(\Delta\)z 0.003 m and a total number of 389,000 particles, the hardware accelerated smoothed particle hydrodynamics code DualSPHysics was executed up to t = 100 s from the beginning and cost about 225 h of CPU time using a personal computer (CPU 2.70 GHz and RAM 384 GB PC, equipped with a NVIDIA Quadro K2000 GPU).  
Instead, the discontinuous Galerkin FEM was executed up to t = 100 s from the beginning and cost about 9.75 h of CPU time using a personal computer (CPU 2.66 GHz  
Figure 9: Comparison of runtime in T1 and T2.  
and RAM 4.0 GMB PC). Square elements with sizes \(\Delta\)x = \(\Delta\)y = 0.04 m were chosen for these two tests, resulting in a total number of 5607 nodes.
# Performance Analysis of Troposphere Scattering Communication Channel with Chirp-BOK Modulation
## 2 System Model with Chirp-BOK Modulation
### Chirp Signal Model  
Within a symbolic period, a chirp signal can be expressed as  
\[s(t)=a(t)e^{j\left(2\pi f_{0}t+\pi\mu^{2}\right)},0\leq t\leq T_{b}, \tag{1}\]  
where \(a(t)\) represents the chirp signal envelope, which is usually a rectangular pulse; \(f_{0}\) is the initial frequency; \(\mu\) is the chirp rate, which denotes the instantaneous frequency change rate of the chirp signal; \(T_{b}\) denotes the duration of the chirp signal; and \(B=|\mu|T_{\text{b}}\) is used to represent the chirp signal bandwidth. When \(\mu>0\) or \(\mu<0\), the frequency of the chirp signal increases or decreases linearly during the sweep time, which is called the up-chirp signal or down-chirp signal.  
A matched filter is a linear filter in which the ratio of signal power to noise power at the output is maximized, and receivers use matched filters to detect received signals. The optimal matched filter for an up-chirp signal is a down-chirp filter, and vice versa. Taking the up-chirp signal as an example, the output waveform of the signal after the matched filter is  
\[y_{1}(t)=\sqrt{BT_{b}}\cdot\frac{\sin\left[\pi BT_{b}\left(1-\frac{|t|}{T_{b} }\right)\right]}{\pi BT_{b}}\cdot\cos(2\pi f_{0}t), \tag{2}\]  
and the output waveform of the signal after the unmatched filter is  
\[y_{2}(t)=\frac{1}{\sqrt{BT_{b}}}\exp\left(\frac{j\pi\mu^{2}}{2}\right)[C(X)+ jS(X)], \tag{3}\]  
where \(X=\frac{\pi}{2}\left(\sqrt{BT_{b}}-\sqrt{\mu}|t|^{2}\right)\), \(C(\cdot)\), and \(S(\cdot)\) are the standard Fresnel cosine and sine integrals, respectively, and are given by  
\[C(x)=\int_{0}^{x}cos(\frac{\pi}{2}t^{2})dt,\;S(x)=\int_{0}^{x}sin(\frac{\pi}{ 2}t^{2})dt. \tag{4}\]  
As shown in Equation (2), the output of the matched filter has similar characteristics to the sinc function, which can convert the input chirp signal with low peak power into an output signal with high peak power and concentrated energy in a very short period of time. The feature is beneficial for detecting chirp signals. Finally, the information bit can be restored by sampling and judging the output of the matched filter.  
Figure 1: Toposcatter propagation path.
# Implementation of Climate Change Effects on Slope Stability Analysis
## 4 Study of the Effect of Climate Change on Slope Stability
### Sensitivity Analysis  
Because there is some uncertainty in determining soil properties and water net infiltration, the effects of key input parameters on slope stability were examined by means of sensitivity analyses using 27 combinations as follows:  
1. Three different values of rainfall intensity: current extreme (P = 139 mm/day) and future extreme obtained by increasing the current one by 5% and 15%. Consequently, there are three different values of water net infiltration 1.61\(\cdot\)10\({}^{-7}\) m\({}^{3}\)/m\({}^{2}\)/s, 4.02\(\cdot\)10\({}^{-7}\) m\({}^{3}\)/m\({}^{2}\)/s and 7.24\(\cdot\)10\({}^{-7}\) m\({}^{3}\)/m\({}^{2}\)/s;
2. Three different values of soil cohesion, c : 1 kPa, 3 kPa and 5 kPa;
3. Three different values of soil permeability, k: 1\(\cdot\)10\({}^{-7}\) m/s, 5\(\cdot\)10\({}^{-7}\) m/s and 1\(\cdot\)10\({}^{-6}\) m/s.  
The water table and the change in porewater pressure (climate change effect A5) were calculated as a function of the input parameters. The time spans from zero to 3 days of rainfall and then another 4 days after rainfall.  
The change in the safety factor by days for the current extreme net infiltration is shown in Table 4. The results show that the slope is unstable for the actual extreme rain-fall at given water net infiltration, soil strength and physical properties. However, with climate change, extreme rainfall is likely to be higher in the future.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rainfall Time** & **Precipitation Level Today** & **Precipitation Level 2050 (RCP4.5) (Min-Max)** \\ \hline
**(min)** & **(mm)** & **(mm)** & **(mm)** \\ \hline
5 & 19 & 20 & 24 \\ \hline
10 & 33 & 35 & 41 \\ \hline
15 & 41 & 43 & 51 \\ \hline
20 & 46 & 48 & 57 \\ \hline
30 & 54 & 56 & 67 \\ \hline
120 & 77 & 81 & 95 \\ \hline
180 & 84 & 88 & 104 \\ \hline
1440 & 139 & 138 & 149 \\ \hline \end{tabular}
\end{table}
Table 3: Estimate of current and in 2050 precipitation level.