esa-sceva/satcom-chunk-collection huggingface dataset:
# Routing Schemes in FANETs: A Survey
## 5 Routing Schemes in FANETs
### Hybrid
#### 5.4.1 Enhancing Route Stability Using Energy-Efficient Technique  
[PERSON] et al. [102] propose a hybrid routing scheme that enables nodes to form clusters and routing by adjusting the transmission range of nodes in order to reduce energy consumption and computational complexity. The main objectives are to enhance route stability (O.1) and network coverage (O.2) in FANETs. The routing scheme addresses two challenges, namely high dynamicity (X.1) of network topology and low residual energy (X.3) as a result of long transmission range and packet retransmission. This routing scheme is designed for source routing (C.1.2).  
During the neighbor and route discoveries stage, each node periodically broadcast messages (e.g., hello messages) to form a neighbor set comprised of two-hop neighboring nodes--the hello message contains its geographical location (M.3) and node identity (M.5)--as well as to predict the geographical locations of two-hop neighboring nodes based on coordinates, which indicates the distance between nodes. During the route selection stage, in order to increase network stability and hence the route lifetime, the underlying network is clustered using the K-mean algorithm based ona) residual energy (M.4) and mobility metrics (M.1), whereby nodes with higher residual energy and moving close to the average speed of the average speed are selected as CHs, and b) on geographical location (M.3) and node identity (M.5), whereby nodes moving close to the average speed of a CH joins its cluster. The CH collects data from CMs and then aggregates and forwards the data. The transmission power of CHs and CMs are adjusted to further reduce energy consumption. CHs are responsible for sending the data between the clusters. The route between CHs are established by considering the residual energy (M.4) of the CH. Hence, a CH with higher residual energy is selected as intermediate node. During the route maintenance stage, link disconnection occurs due to the dynamicity of network topology, and so, the route discovery mechanism is initiated to select a new CH or to join another CH as well as to reestablish routes.  
The routing scheme is compared with a state-of-the-art scheme that uses the swarm-based algorithm to provide an optimized resource utilization. Random walk mobility model (A.1) is used in simulation. The routing scheme has been shown to reduce energy consumption (P.2) and the number of clusters in the network (P.3).
# A Comparison between Two Reduction Strategies for Shrouded Bladed Disks
## 4 Numerical Results
### First Test Cases for Accuracy and Efficiency Analysis
In order to check the numerical efficiency of the two reduction approaches described in this paper versus the size of the full model, they are applied to Test Cases A and B in both their configurations (linear and quadratic elements). In particular, the direct method is applied by retaining 60 fixed-interface modes, while the two-step method is applied by retaining \(Z_{1}=180\) modes at the 1 st step and \(Z_{1}=60\) modes in the 2 nd step. In this way, the ROMs will have the same final size and, according to the previous analysis, comparable accuracy. The total computation times, corresponding to the time necessary to generate ROMs for all the harmonic indices needed to build the full wheel ROM by means of Equation (4), are shown in Table 4, whose last column represents the ratio between the two-step and the direct method's calculation time. A ratio higher than one means that the direct method is more efficient and vice versa. The four test cases are ordered according to the number of nodes in the fundamental sector. The results in Table 4 suggest that the two-step method becomes more and more convenient from the computational point of view as the number of nodes (and of dofs) of the fundamental sector becomes higher. The obtained results give a preliminary indication, but they are not conclusive and, therefore, a third test case is analyzed in the next section for the sake of completeness. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Case** & **Node \#** & **Direct (s)** & **Two-Step (s)** & **Two-Step/Direct** \\ \hline B-Lin & 2454 & **328** & 641 & 1.95 \\ B-Quad & 6052 & **779** & 1169 & 1.50 \\ A-Lin & 11,672 & **322** & 336 & 1.04 \\ A-Quad & 30,920 & 1120 & **620** & 0.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Total calculation times for the proposed reduction techniques (Test Cases A and B). Figure 7: Test Case A (linear elements, \(Z_{2}=60\), \(h=1\))—accuracy of the two-step method.
## 3 Test Results
### Development of Secondary Cracks on the Tunnel-Surrounding Rock  
By separating the internal video into individual frames, the deformation and failure characteristics of the tunnel-surrounding rock are captured. The most important results are as follows (shown in Figure 5):  
(1) Cavern-free sample: The internal surrounding rock keeps a stable status and no visible secondary failure appears in the majority of tests. As the loading reaches 447 s, the typical local failure initiates at the bottom of sidewalls, and some thin rock debris is ejected. Cracks that develop along the strike direction of the tunnel start at 499 s, and a large range of failures appear in both sidewalls. Splitting failures quickly develop when the z-axis loading reaches the maximum value (507 s), and cracks on both sidewalls present a mirror-symmetric distribution.  
(2) Cavern-existing sample: The occurrence of visible secondary failure (about 262 s) is earlier than that in the cavern-free sample; the local failure in the left sidewall only distributes at the bottom position, while the secondary failures occur in the right sidewall bottom and right arc waist (neighboring the prefabricated cavern). The rock block ejected from the right sidewall moves about 2 mm at 300 s, and the secondary failure at the left sidewall remains stable. When the z-axis loading reaches the peak value at 402 s, the destruction degree of the left and right sidewalls is obviously different, and the volume of the rock block at the right sidewall (near the invisible cavern) is significantly larger than the other locations, which means that the cavern has an obvious influence on the distribution of local secondary failure.  
(3) Comparing the above results with the mechanical-acoustical data in Figure 4, the initiation and extension periods of the visible secondary cracks in the cavern-free sample are highly consistent with the AE bursts (from 447 s). In addition, the first appearance of visible cracks (262 s) and rock block movement (300 s) in the cavern-existing sample activate typical AE bursts; the AE data reflect that the destruction intensity of the first local failure is more intensive (high AE events and energy) than the following rock block movement. Accordingly, the AE data are important evidence for identifying the development of secondary failures.  
Figure 4: Time–stress–AE relationships among two groups of specimens: (**a**) mechanical and acoustic data obtained in the cavern-free sample; (**b**) mechanical and acoustic data obtained in the cavern-existing sample.
# A Practical Multiclass Classification Network for the Diagnosis of Alzheimer's Disease
## 4 Dataset, Experiments and Discussions  
### Dataset  
In this work, the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset [44] is utilized. ADNI brings together experts and study data to help characterize Alzheimer's disease progression (ADP). MRI and PET imaging, genetics, cognitive tests, CSF, and blood bio-markers are all used by ADNI researchers to gather, validate, and use data as disease predictors. ADNI includes Alzheimer's disease patients, mild cognitive impairment individuals, and older controls, as well as research tools and data from the North American ADNI project. The information of the datasets are present in Table 1. Subjects in this dataset are scanned with respect to different durations of time and visits. Each of the scans in this study is considered a different subject. The overall dataset is divided into 70% as the training dataset, 20% as testing data, and 10% as the validation dataset. In this work, we are handling the complex challenges of multiclass classification for a limited amount of available data. An imbalanced dataset balanced with the augmentation technique is made suitable for the optimal performance of the PMCAD-Net. Multiple categories are available in the present case, ranging from normal controls (NC) to mild cognitive impairment (MCI) and AD. In comparison, MCI can be classified as early mild cognitive impairment (EMCI) and late mild cognitive impairment (LMCI). It is important to note that the collection of large-scale datasets for MRI imaging is one of the major challenges. A dataset of various patients, including 75 AD, 75 EMCI, and 70 LMCI patients, and 80 NC patients, was obtained from the ADNI database. These images were pre-processed, and augmentation was applied to obtain a uniform and equivalent number of images for multiclass classification. It facilitates handling the major challenge of the early detection of AD.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 3 Methodology
### Field Measurement Techniques
#### 3.1.1 Ground Resistance Testing Using the Wenner and Schlumberger Methods  
To accurately measure the ground resistance, two primary methods are used: the Wenner method and the Schlumberger method. Both methods involve injecting a current into the ground and measuring the resulting potential difference.  
Wenner Method: This method uses four equally spaced electrodes placed in a straight line. A current is injected into the outer electrodes, and the voltage is measured between the inner two electrodes. The ground resistance \(R_{ground}\) is calculated using  
\[R_{ground}=\frac{2\pi\alpha V}{I} \tag{7}\]  
where \(\alpha\) is the spacing between the electrodes, \(V\) is the voltage between the inner electrodes, and \(V\) is the injected current.  
Schlumberger Method: In this method, the current electrodes are placed farther apart than the voltage electrodes. The resistance is calculated similarly, but the setup is more flexible for varying electrode spacings. The equation is as follows:  
\[R_{ground}=\frac{\pi(M-N)}{I}V \tag{8}\]where \(M\) and \(N\) represent the positions of the current and voltage electrodes, respectively.
## 4 Coordination of PSS and BESS Controllers to Enhance Oscillatory Stability Using Proposed Method
### Power-System-Controller Modeling
#### 4.1.2.3 PQ Controller  
The PQ controller has two main loops, the active-power loop and reactive-power loop. The modeling of these loops is based on decoupled control theory, where d-axis current \(i_{d}\) controls real power, and the q-axis current \(i_{q}\) controls reactive power [8], as shown in Figure 9. The active-power  
Figure 8: \(i^{th}\) POD structure.  
control loop has four main input signals: set point active power \(P_{ref}\), actual BESS output power \(P\), modulating signal from POD \(P_{mod}\), and signal from charge controller \(P_{c}\). The difference between the sums of \(P_{ref}\), \(P_{mod}\) and \(P_{c}\) with \(P\) is passed through a simple PI controller to synthesize \(i_{d}\). Similarly, \(i_{q}\) is synthesized by measuring the difference between setpoint reactive-power \(Q_{ref}\) with actual reactive power \(Q\) and passing it to a PI controller. \(Q_{ref}\) commonly depends on the requirement for power-factor regulation, but is assumed to be zero for this paper (unity power factor).  
The current reference signals from the PQ controller are then fed to the charge controller, which controls battery charging and discharging, and provides reference currents \(i_{dref}\) and \(i_{qref}\), which are used to generate pulse-width modulating signals to control VSC switching.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 7 Software and Wireless Modules  
### Radio Mobile  
Radio Mobile software version 11.6.7 is one of the most well-known tools in designing wireless networks. This tool is particularly noted for its precision in calculating link budgets for long-distance wireless networks, demonstrating its widespread adoption and effectiveness in determining link feasibility, as demonstrated by [32; 33; 34].  
The software calculates path loss based on the terrain between the sender and receiver. Using the longitude and latitude of the location where the network is intended to be set, the software draws a digital image with natural obstacles, such as trees displaying the condition of the LoS and Fresnel zone, accompanied by the values of the link budget calculations. The tool's ability to produce the image shown in Figure 8 hinges on the parameters of the wireless module at each location, such as TX power, antenna gain, and receiver sensitivity.
## 2 Grid Forming and Small-Signal Modeling
### Base Case and Theoretical NFP Results  
In order to verify the theoretical background presented in previous subsections, a base case using the system presented in Figure 1 with the parameters listed in Table 1 is considered. For this base case, the NFP plot for different sets of parameters are obtained using the general Equation (5) particularized to Equation (6).  
\[\frac{\Delta P}{\Delta\omega_{g}}=-\frac{(\tau_{p}s+1)\omega_{b}K_{t}}{\tau_{p }s^{2}+s+m_{p}\omega_{b}K_{t}} \tag{6}\]  
In looking into more detail in Figure 2, there is more than one set of parameters to synthesize the same inertia. Thus, to analyze the inherent GFM contribution, various values of \(m_{p}\) for synthesizing the same inertia considering two different cases (H = 2.5 s and H = 5 s) are presented in Figure 5.  
As expected, at low frequencies, the droop region is characterized by a response dominated by the droop term \(m_{p}\) with a phase shift around 180\({}^{\circ}\). In the present base case, the droop region goes from 0 to 0.5 Hz approximately. The amount of exact damping provided by each control setting can be seen in this region. The larger the dB-value, the more damping it provides to the grid. As the phase shift remains almost constant. It is also possible to see that the increase in the damping makes it harder to see the inertia resonance. But the value of the gain around the inertia asymptote is similar. In summary, the static gain represented by the active power droop (\(m_{p}\)) provides damping at control frequencies, while inertia supplies active power in response to frequency variations within the range of the inertia constant. Thus, depending on the inertia constant and how the  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Var Name** & **Value** \\ \hline Inertia 2.5 s & \(m_{p}\) & 0.005/0.01/0.015 pu \\  & \(t_{f}\) & 0.025/0.05/0.075 s \\ Inertia 5 s & \(m_{p}\) & 0.0025/0.005/0.0075 pu \\  & \(t_{f}\) & 0.025/0.05 /0.075 s \\ \hline  & \(L_{c}\) & 0.1 pu \\ LCL filter & C\({}_{f}\) & 0.05 pu \\  & \(L_{t}\) & 0.05 pu \\ \hline Grid & \(L_{g}\) & 0.1 pu \\  & \(R_{g}\) & 0 pu \\ \hline \hline \end{tabular}
\end{table}
Table 1: Base case parameters.  
inertia is implemented in the GFM control, the same IBR with the same inertia will provide a different damping at low frequencies.  
At high frequencies, a gain of \(-20\) dB/decade and a phase shift of \(90^{\circ}\) can be seen, which ensures proper synchronization power.
## 5 Materials and Methods: Modeling Verification  
The values calculated using the integrated model employing ammonia as fuel, which was proposed in this study, and the corresponding values listed in the literature are presented in Table 6. The estimated values are consistent with the data in the literature, and the discrepancy between the current data and the literature data is maintained within a tolerable range.  
The proposed system has the potential to simultaneously supply power for the propulsion plant and other electric equipment, and provide hot water for the seafarers on board ships.  
Since the subsystem is responsible for 26.59% of the total power production of the integrated system, its verification is critical. Unfortunately, there are no experimental installations of the SOFC-GT subsystem with direct ammonia injection accessible [9];  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Modelling** & **Reported [31]** & **Different (\%)** \\ \hline SOFC temperature (\({}^{\circ}\)C) & 857.8 & 870 & 1.06 \\ \hline Gas turbine inlet temperature (\({}^{\circ}\)C) & 1192 & 1201 & 0.61 \\ \hline Cell voltage (V) & 0.71 & 0.747 & 4.9 \\ \hline Current density (A/m\({}^{2}\)) & 1430 & 1429 & 0.06 \\ \hline SOFC efficiency & 56.8 & 50.96 & 5.84 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the simulation results of the proposed integrating model with the corresponding values obtained from the literature.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Parameter** & **Value** \\ \hline SOFC & Ambient temperature (\({}^{\circ}\)C) & 29.85 \\  & Ambient pressure (bar) & 1.013 bar \\  & Operating pressure (bar) & 4 \\  & Operating temperature (\({}^{\circ}\)C) & 878.1 \\  & Number of single cells & 17,360 \\  & Fuel cell current density (A/m\({}^{2}\)) & 1430 \\  & Active surface area (m\({}^{2}\)) & 0.22 \\  & Anode thickness (cm) & 0.0020 \\  & Cathode thickness (cm) & 0.0020 \\  & Electrottive thickness (cm) & 0.0040 \\  & Stoichiometric rate of hydrogen & 1.2 \\  & Stoichiometric rate of oxygen & 2 \\  & Fuel utilization factor in SOFCs & 85\% \\  & Gas turbine cycle compression ratio & 13 \\ Compressor & Isentropic efficiency (\%) & 82 \\ Pumps & Isentropic efficiency (\%) & 82 \\ Expanders & Isentropic efficiency (\%) & 80 \\ Heat exchangers & Minimum temperature approach (\({}^{\circ}\)C) & 7 \\ Converter & DC-AC converter efficiency (\%) & 98 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The design and operating parameters of system.  
however, there are experimentally validated models in the literature. [PERSON] et al. [32] reported a multigeneration system, SOFC-GT, that generated a total power of 4910.4 kW (0.07% difference) with energy and exergy efficiencies of 56.9% and 54.7%, respectively. The present model has relative errors of 3.5% and 2.6% for the SOFC-GT energy and exergy efficiencies, respectively. These error values demonstrate that the proposed model yields reasonable results.
# Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector
## Materials and Methods  
### Study Areas  
Hurricane Sandy is the second most costly hurricane in US history and resulted in nearly $70 billion in losses [28]. A total of 24 states were affected by this disaster. At least 650,000 houses were damaged or destroyed, with a total damage of approximately $50 billion [29].  
In this study, a team from Drexel University used an aerial RGB camera to investigate the images of the New Jersey coastline. The resolution of the image is 1920 \(\times\) 1080. The survey area was approximately 7.8 square kilometers. The survey area coverage was from the Seaside Height on the New Jersey coastline to the borough of Mantoloking. Figure 1 presents an aerial RGB image showing some debris (red circles) and partially damaged (yellow circle) buildings.  
Hurricane Irma hit the US East Coast in September 2017. The NOAA remote sensing department collected images of the area [30]. The size of the Irma image is 18681 \(\times\) 18681. The disaster scene caused by Hurricane Irma is shown in the Figure 2.  
Figure 1: Damage examples. An example aerial image of an aerial image of the impacted area. The red circles highlight the ruins of destroyed houses, and the yellow circles highlight the houses that were displaced or slightly damaged by the hurricane.  
Figure 2: Post-disaster scene map of Hurricane Irma. Show the details in the satellite image because the large size of original remote sensing satellite image.  
The Hurricane Sandy dataset was used for the next major work. The Hurricane Irma dataset was used to validate the proposed method at the end of the experiment.
## 2 Materials and Methods
### Main Outcome Measures  
#### 2.3.1 Bruce Protocol  
The Bruce Protocol (BP) was used in order to evaluate maximal oxygen uptake (VO2 max) as well as the accumulation of lactate in the peripheral blood samples of professional football players. The evaluation of physical capacity using progressive maximum exercises on a treadmill and with the simultaneous use of ergospirometry can further determine aerobic capacity through the athlete's maximal oxygen uptake (VO2 max), which is directly related to strength, endurance, and above all to the indicators of cardiovascular adaptation and sports performance [32; 33]. Furthermore, the evaluation of lactate at the anaerobic threshold is another indicator of aerobic capacity for a professional football player that is directly linked to their athletic performance [5]. Therefore, the improvement of aerobic fitness indices in professional football players is directly linked to their sports performance and their return to play after a Grade II LAS.  
A treadmill (Excite Run 1000, Technogym, London, UK), a heart rate monitor (Polar Electro Oy, Helsinki, Finland) and an ergospirometry recording system (Quark CPET, COSMED Inc., Rome, Italy) were used to evaluate the maximum oxygen uptake (VO2 max), in addition to a lactate analyzer (Lactate Scout 4, EKF Diagnostics, Penarth, UK) for the evaluation of the accumulation in a peripheral blood sample. Prior to initiating the procedure, each athlete was informed about the BP stages and was asked to perform a 5 min warm-up (speed 5 km/h, incline 0%) on the floor ergometer for familiarization with the equipment and preparation to perform the evaluation. In addition, each professional football player was informed about all safety and communication parameters during BP execution with the researcher.  
BP consists of eight stages with a gradual increase in the speed and incline of the treadmill. Specifically, the BP stages change every 3 min and the speed and inclination parameters are as follows: (a) 2.7 km/h, 10%, (b) 4.0 km/h, 12%, (c) 5.5 km/h, 15%, (d) 6.8 km/h, 16%, (e) 8.0 km/h, 18%, (f) 8.9 km/h, 20%, (g) 9.7 km/h, 22% and (h) 10.5 km/h, 25% [32; 35]. The test was completed as soon as the athlete requested to stop the procedure due to fatigue, when the maximal oxygen uptake (VO2 max) and lactate values were recorded by taking capillary peripheral blood from the index finger. After recording the maximum oxygen uptake (VO2 max) and lactate levels, the recovery period started as soon as the heart rate decreased \(<\)80% (based on the maximum heart rate and the age of the athlete). Subsequently, for the next 10 min, the athlete had to do no physical activity in order to restore his cardiorespiratory function, with a decrease in heart rates of approximately \(>\)20% compared to the maximum rate [5; 35].
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 1 Introduction
* We conduct a performance evaluation of the considered topologies in terms of practical parameters (like reliability, power density, size, efficiency, redundancy, modularity, and performance in harsh environmental conditions) and identify the best topology for a specific satellite application. * We compare the topologies based on various applications and types of satellites. Figure 4: Graphical abstract of this paper. Figure 3: Applications of DC-DC converters for satellites. Given a satellite application, we expect that this paper will guide researchers and the space industry in selecting the best topology. The remainder of this paper is arranged as follows. Section 2 discusses the various electrical subsystems in satellites and the design requirements of DC-DC converters for satellites. Section 3 reviews the various isolated and non-isolated topologies of DC-DC converters, and Section 4 compares them based on practical performance indicators. Section 5 discusses future directions in the area and provides insights into current and future deep space missions. Section 6 concludes this paper.