esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results
### Effects of the Anisotropy in Bearings
Figure 8 shows the Campbell diagram of the system with a 20-mm crack, where an isotropic case and an anisotropic case \(\alpha_{k}=0.5\), \(\beta_{k}=0.2\) are included. The natural frequencies are often coming in pairs, which are referred to as the forward and backward frequencies, respectively. As is illustrated in the figure, the anisotropy of bearings mainly affects the eigenvalues of relatively high orders, and the first pair of natural frequencies and the torsional natural frequencies is almost unaffected. With respect to the second and the higher order natural frequencies, the differences between the two cases are relatively obvious. Therefore, the high-order critical speeds will be obviously influenced. These phenomena depend on the characteristics of the mode shapes. The first-order mode shapes of the system tend to be bowed, whereas the high-order ones often involve complex bending deflections. The regions adjacent to the bearing nodes are more likely to participate in the deflection in the high-order modes, and then the high-order natural frequencies are more sensitive to the change of the system's parameters. It can be concluded that the periodically time-variant characteristic caused by the anisotropy of journal bearings makes the dynamic response of the cracked rotor-bearing system more complicated. It should be well dealt with in order to gain accurate insights into the dynamic behaviors of such systems, especially when the responses in the high frequency ranges are of interest.
# Voter Authentication Using Enhanced ResNet50 for Facial Recognition
## 1 Introduction  
The increasing adoption of biometric technologies worldwide has transformed various applications, including voting systems, by enhancing security and efficiency. Biometric modalities such as iris recognition, fingerprint analysis, voice recognition, and facial recognition offer significant advantages for authentication and identification. In many countries, international organizations such as the Friedrich Ebert Stiftung emphasize the crucial role of free and fair electoral participation in the promotion of democratic governance [1]. In response to growing concerns over electoral integrity, several nations have implemented biometric-based voter authentication systems. For example, some electoral authorities have integrated biometric fingerprint systems since the early 2010s to improve the verification process [2]. However, the existing paper-based identification system used in polling stations has faced significant scrutiny. Post-election allegations of electoral fraud, including ballot stuffing, falsification of results, and multiple voting, have become commonplace [3, 4, 5]. The phenomenon of multiple voting is particularly problematic, as it represents a significant loophole in the current system. This issue erodes public trust in the electoral process andposes a threat to national stability, essential for development [3; 4; 5]. To address these challenges, this study proposes a novel voter authentication system based on facial recognition.
## 1 Introduction
A reduction in photochemical yield due to water stress does not necessarily mean that fluorescence yield will increase, since NPQ may reach a high enough level to compensate for the photochemical yield decrease, resulting in a reduction of fluorescence yield and, therefore, SIF. Thus, precipitation and soil moisture can exert significant influence on both SIF and GPP and be significantly correlated with them [37; 38; 39]. It is also noted that plant water need varies with the stage of crop development. According to the Food and Agriculture Organization of the United Nations [40], a typical crop has four development stages: initial stage, crop development, mid-season, and late season. The importance of water availability for a plant is such that if a water stress event occurs in a critical growth development period, the resulting crop production and quality later in the season will be reduced [1]. In this connection, it would be necessary to factor in precipitation and soil moisture information from earlier in the season to be able to produce meaningful estimates of plant production. Therefore, in order to investigate the possibility of using SIF in conjunction with other parameters for GPP quantification, it is imperative that we look into the link between water availability during the critical crop development period (e.g., pod development stage in soybean [41; 42]) and response from the plant machinery, as reflected in photosynthesis and fluorescence levels. In this study, we intended to explore and investigate the influence of soil moisture and precipitation along with SIF on GPP quantification. Accordingly, we needed a statistical tool to establish such relationships between dependent and independent parameters. Thus, this study aims to use multiple linear regression (MLR) analysis in a statistically justified manner to include SIF, precipitation and soil moisture for the purpose of GPP quantification. We proposed to use SIF-based GPP equations constrained by local conditions and formulated such equations to characterize crop and grass plant production over the contiguous US. Note that we did not estimate GPP for vegetation types other than crops and grasses, e.g., mixed forests along the East Coast or shrublands over the Southwestern US and other US regions [43; 44]. We have also tested the relationships between GPP and precipitation, and soil moisture with the inclusion of a lead-lag effect. This effect is likely related to the fact that plant water demand and availability during the development stage influence its production at later stages of the lifecycle.
## 4 Evaluation Results
### Impact of Error Probability  
As can be seen from the SNR thresholds listed in Table 4, stronger reliability will require slightly higher SNR. This can be explained by observing the transmission rate given in Equation (7), which is re-written here as follows:  
\[R_{2}(n,P,\epsilon,M)=I^{\prime}(P,M)-Loss(P,\epsilon,n), \tag{25}\]  
where  
\[Loss(P,\epsilon,n)=\sqrt{\frac{V(P)}{n}}Q^{-1}(\epsilon)-\frac{\log_{2}(n)}{n}. \tag{26}\]  
The second item \(Loss(P,\epsilon,n)\) is composed of two parts and is introduced because of reliability requirement (denoted by \(\epsilon\)) and finite coding length (denoted by \(n\)). Here, the capacity loss \(\eta(n,P,\epsilon,M)\) is defined and discussed  
\[\eta(n,P,\epsilon,M)=\frac{Loss(P,\epsilon,n)}{I^{\prime}(P,M)}\times 100\%. \tag{27}\]  
Figure 5 plots the capacity loss under different configurations, where the modulation order is set to 4. It can be seen that the loss increases when reliability requirement becomes stronger. However, the incremental rate of \(\eta\) is quite slow compared with the decreasing rate of \(\epsilon\).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**MCS Index** & **1** & **5** & **11** & **20** & **27** \\ \hline One time & transmission & & & & \\ \((\epsilon=10^{-5})\) & & & & & \\ One time & transmission & & & & \\ \((\epsilon=10^{-3})\) & & & & & \\ \hline Dual duplication transmission & & & & & \\ \((\epsilon_{t}=\sqrt{10^{-5}})\) & & & & & \\ Dual duplication transmission & & & & & \\ \((\epsilon_{t}=\sqrt{10^{-3}})\) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: SNR thresholds for MCS selection (when \(k=256\) bits).  
Figure 5: Capacity loss under different reliability requirements.
# An Algorithm for Rescheduling of Trains under Planned Track Closures
## 6 Simulation Experiments
### Network of Tracks Case
A similar situation occurs when the number of train runs \(TR\) grows for the fixed number of groups \(\eta\) (Figure 14 and Table 13). Nevertheless, the worsening is less significant in the latter case. The performance indexes \(\hat{Q}\) values are jointly compared with the computational time \(\widetilde{T}\) in Figure 15 and Table 14, while the performance index \(\widetilde{Q}\) are jointly compared with the computational time \(\widetilde{T}\) in Figure 16 and Table 15. The values of \(\hat{Q}\) are marked on the abscissa axis, and \(\widetilde{Q}\) values are represented by dots, in Figure 15. The opposite presentation is used in Figure 16, where values of \(\widetilde{Q}\) are represented on the logarithmic abscissa axis, while \(\hat{Q}\) values are represented on the logarithmic abscissa axis. Figure 14: Dependence of \(\widetilde{Q}\) on the number of train runs for 4 groups. Figure 13: Dependence of \(\widetilde{Q}\) on the number of groups for 10 train runs. However, it is worth noting that the main challenge consists of answering the question if there is any feasible solution, and it is necessary to obtain the answer in the limited time. So, the most important issue is to obtain any solution in such time. On the other hand, the decision-maker has to be aware that the decreasing of computational time by applying bigger values of \(\eta\) can result in the lack of any solution. Even though the feasible solution of the considered problem may exist, the algorithm can fail to find it. Such a situation can be observed in Table 16, where the algorithm for \(\eta\geq 20\) did not find any solution in 20% of all examined cases.
# Future Renewable Energy Communities Based Flexible Power Systems
## 6 Flexible Energy Resources  
Flexible energy resources aim to increase the flexibility of the power system. A flexible power system is able to host a large share of intermittent renewable resources because it has the ability to continuously adjust its operating point through the use of its flexible energy resources [18]. In general, the community's flexible energy resources are categorized as follows [34]:  
Figure 10: Energy community management.  
* Atomic loads (shiftable loads) include those loads with a flexible start time. Although atomic load's start times can be shifted according to grid's flexibility needs, they cannot be interrupted once they start operating. Wet appliances (such as household washing machines) and some industrial processes fall within \"shiftable loads\" category.
* Thermostatically controllable loads include loads that are related to the consumers' thermal needs. Electric heaters, air conditioning systems, water heaters, and heat pumps are some examples. The operation of these devices can change based on the grid's flexibility needs. However, they should still fulfil the thermal comfort of the customers.
* Curtailable loads consist of those loads which can be curtailed based on the grid's flexibility needs. However, their curtailment should not have any serious rebound effect. Lightings are an example of curtailable loads.
* Curtailable generation includes those generation units whose production can be reduced or curtailed based on the grid's flexibility needs. Run-of-river hydropower, wind power, and PV power are some examples falling into this category.
* Storage systems are an important flexible energy resource that is able to both consume and inject power whenever the system needs flexibility. Batteries and pumped hydro are two examples of stationary storage whereas electric vehicles are another example of storage that can be charged at different sites and charging stations.
* Frequency response, frequency control, and voltage regulation capability of distributed power generation systems can enhance the flexibility of power systems [35]. For example, the distributed power generation system in Denmark was able to decrease its generation by 2-12% of the nominal power using some active power control functions [35].  
Flexible energy resources are mainly scheduled to fulfil a part of the grid's flexibility needs. To provide flexibility, they need to participate in different demand-response programs [36] or different flexibility (ancillary) services markets [37].
## 3 Results
### Classification Tasks
#### 3.2.2 Deep Learning
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**First Author (Year)**} & \multicolumn{2}{c}{**Database**} & \multirow{2}{*}{**Description**} & \multirow{2}{*}{**Results**} \\  & & & **2 DVD** & & & \\  & & & **Field of View (FOV)** & & & \\ \hline \multirow{9}{*}{**Machine**} & \multirow{9}{*}{**Machine**} & \multirow{9}{*}{**Machine**} & 82 DR, 23 healthy & Features: blood vessel density, blood vessel & \multirow{9}{*}{AUC = 95.22\%} \\  & & & 2D & califer, distance map of FAZ area. & & \\  & & & \(6\times 6\) mm\({}^{2}\) & Classifier & **SVM classifier with RBF** & \\ \cline{2-5}  & \multirow{9}{*}{**Aharony 2019**[21]} & 20 DR, 6 AMD, 4 RVO, 26 healthy & Features: mean, standard deviation, skewness, & \multirow{9}{*}{Accuracy = 83.9\%} \\  & & 2D & and kurtosis of gray level histogram. & & \\  & & \(3\times 3\) mm\({}^{2}\) & No formal classifier. & & \\ \cline{2-5}  & \multirow{9}{*}{**Abdealsalam 2020**[32]} & 30 DR, 30 NFDR, 40 healthy & Features: mean of the intercapillary areas, FAZ & Total Accuracy = 97\% \\  & & 2D & perimeter, circularity index, and vascular & Precision \\  & & \(3\times 3\) mm\({}^{2}\) & density. & 95.2\% (healthy vs. diabetic) \\  & & & **Classifier neural network** & & \\ \cline{2-5}  & \multirow{9}{*}{**Cano 2020**[65]} & 33 no DR, 26 mild NPDR, 22 healthy & Features: vessel transotiotiy, fractal dimension ratio (FDR), & \multirow{9}{*}{AUC = 95.22\%} \\  & & 2D & Classifier **Ordinary least squares modeling** & Mild NPDR vs. healthy \\  & & 6 \(\times\) 6 mm\({}^{2}\) & method. & Accuracy = 91\% \\ \cline{2-5}  & \multirow{9}{*}{**Abdealsalam 2021**[33]} & 80 DR, 90 healthy & Features: multifractal parameter computation (maximum, shift, width, lucantarity, box & \\  & & 2D & counting dimension, information dimension, & \\  & & 3 \(\times\) 3 mm\({}^{2}\) & correlation dimension). & \multirow{9}{*}{Accuracy = 98.5\%} \\  & & \multirow{9}{*}{**Lau 2021**[84]} & 114 DR, 132 healthy & Features: wavelet transform on SVD, PVP, RVN. & Sensitivity = 84\% \\  & & \(3\times 3\) mm\({}^{2}\) & Classifiers: LR, LR-EN, SVM, XGBost. & Specificity = 80\% \\ \hline \multirow{9}{*}{**Deep**} & \multirow{9}{*}{**Le 2020**[89]} & 463 volumes & **VCG19, ResNet50**, and **DenseNet** with & Ensemble network \\  & & 2D & superficial and deep plexus images, majority & accuracy = 92 \(\pm\) 1.92\% \\ \cline{1-1}  & & 3 \(\times\) 3 mm\({}^{2}\) & soft voting. & Accuracy = 87.27\% \\ \cline{1-1} \cline{2-5}  & \multirow{9}{*}{**Le 2020**[99]} & 75 DR, 24 diabetes, 32 healthy & \multirow{9}{*}{**VCG16.**} & AUC = 9.7\% (healthy) \\ \cline{1-1}  & & 2D & 0.98 (ne DR) \\ \cline{1-1}  & & 6 \(\times\) 6 mm\({}^{2}\) & 0.97 (DR) \\ \cline{1-1} \cline{2-5}  & \multirow{9}{*}{**Zang 2021**[90]} & 303 images & A densely and continuously connected neural network with adaptive rate dropout & 96.5\% (two class) \\ \cline{1-1}  & & \(3\times 3\) mm\({}^{2}\) & (DcardNet). & 67.5\% (four classes) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification tasks summary.
## 2 Materials and Methods  
### Applied Titanium Arrays and Their Properties  
Planar silicon arrays sputtered with 100 nm titanium (Ti) were obtained from the Center for Microtechnologies (ZFM, [PERSON], University of Technology Chemnitz, Germany) at 1 \(\times\) 1 \(\times\) 0.075 cm in size (length \(\times\) width \(\times\) depth). Ti is covered by a natural oxide layer. The Ti specimens exhibited a negative \(\zeta\)-potential of \(-\)87.5 mV at pH 7.4, a water contact angle (WCA) of 87.4\({}^{\circ}\), and a surface free energy (SFE) of 37.6 mN/m [34]. On the one hand, these Ti substrates were coated with the plasma polymerized alkylamine (PPAAm) nanolayer by a low-pressure plasma reactor [34; 42] and were acquired from the Leibniz Institute for Plasma Science and Technology e.V. (INP). They displayed a moderately positive \(\zeta\)-potential in the physiological range (+7.1 mV), a WCA of 66.9\({}^{\circ}\), an SFE of 46.2 mN/m, and a layer thickness of 24.5 nm [34]. On the other hand, the substrates were modified by a polyelectrolyte multilayer (consisting of 10.5 polycation/polyanion pairs) terminated with polydiallydimethylammonium chloride (PDADMA) by dip coating oppositely charged polyelectrolytes with a deposition robot (Riegler & Kirstein, Potsdam, Germany) [34; 43]. They featured the following characteristics: +51.4 mV \(\zeta\)-potential at pH 7.4 (0.15 bar), 38.7\({}^{\circ}\) WCA, 62.0 mN/m SFE, and 38.0 nm layer thickness [34]. The positive charge originates from weakly bound polycation chains, which can protrude into the solution under suitable conditions [43]. For a detailed description of surface preparations and characteristics, please refer to [PERSON] et al. [34]. Briefly, concerning characteristics, the \(\zeta\)-potentials of the Ti samples were determined with the SurPASS\({}^{\text{TM}}\) system ([PERSON], Ostfildern, Germany) by analyzing the streaming potentials at pH 6.5-8.0 in a 1 mM KCl solution. The exact values at pH 7.4 (physiological conditions for the cells in vitro) were calculated via a linear regression (GraphPad Prism version 6.05) [34]. The WCA and the SFE of the substrate/air interface were determined by the sessile drop method (distilled water and diiodomethane) using the Drop Shape Analyzer-DSA25 ([PERSON], Hamburg, Germany) [34].
## 3 Novel Human Machine Team Design
### Human-in-the-Loop (HIL) Simulation Method
#### 3.3.1 Experiment Case 1: Emergency Care Provider Training  
Once the VBSA is developed, it has been tested on a MARCHE protocol scenario, \"massive hemorrhage,\" in which a patient who is badly wounded with heavy bleeding and needs immediate medical attention by an ECP is treated using a VBSA to stop bleeding. Though VBSA cannot replace a trainer, it enhances the training by providing a sequence of steps to be followed during treatment. A leg manikin model is considered as the patient is treated using an Amazon Echo device that acts as the VBSA interface, as shown in Figure 6. Here, the trainer follows the series of actions step-wise, assisted by the Amazon Echo, and helps the patient stop bleeding. This experiment has been conducted using multiple categories of participants, and the data is collected and analyzed based on the survey that is conducted on the participants. Ref. [8] provides a detailed explanation of this experiment including data analysis and results.  
Figure 6: Experimental setup: Participants treating model patient leg with massive hemorrhage (reproduced from [8]).
## 3 ME-SSA Transmitter Design and Waveform Definition
### QPSK Modulation  
As described in [9; 12], the E-SSA waveform design was originally based on the 3 GPP Wideband CDMA (W-CDMA) up-link air interface [12; 13]. Common features include the FEC coding scheme (turbo code with rate 1/3), symbol spreading, as well as a physical layer control channel (PCCH), carrying known symbols for the channel estimation at the receiver.  
One of the concepts investigated in the MASSIVE project was the use of a linear MMSE detector at the receiver to increase the spectral efficiency. For a conventional receiver with a single user matched filter (SUMF), the use of BPSK modulation is known to reach the optimal capacity (constrained by the receiver SUMF structure) [7; 8]; however, for a linear MMSE detector structure the BPSK modulation no longer provides the highest aggregate throughput. In the MASSIVE project, a QPSK modulation was considered for the transmit signal and a linear MMSE detector (one of its approximations) and successive decoding at the receiver to improve the aggregate throughput, as also indicated in [7; 8; 9].  
The throughput of the linear MMSE detector decreases for a higher system loading, i.e., the ratio between the number of interfering bursts and the spreading factor [7]. For QPSK modulation, the system loading is almost halved with respect to that of BPSK modulation (assuming the same FEC code rate for both options and a low number of pilot and preamble symbols compared to the data symbols). This is due to higher signal space dimensions (almost double, ignoring the impact of the preamble and pilot symbols) for QPSK compared to that of BPSK modulated waveforms. The adoption of QPSK modulation requires a modification of the E-SSA waveform design. In particular, the quadrature multiplexing between the data and control channel was no longer supported. Instead, known pilot symbols were inserted regularly among the QPSK data symbols, as shown in Figure 2.
## 2 Method  
### Complete Bouguer Gravity Correction  
A gravity anomaly is the difference between the measured gravity at a particular location and the theoretical gravity given by a reference Earth model for the same location. It is widely used in the study of density inhomogeneities inside the Earth.  
To obtain accurate Bouguer gravity anomalies in the study area, we have used the complete Bouguer correction program package FA2 BOUG ([[http://www.iamg.org/CGEditor/index.htm](http://www.iamg.org/CGEditor/index.htm)]([http://www.iamg.org/CGEditor/index.htm](http://www.iamg.org/CGEditor/index.htm)), 7 December 2021) [34] for continental areas with large topographic relief to perform the correction calculation. The initialized free-air gravity anomaly was provided by [PERSON] et al. (2014) [35]. The topography or complete Bouguer correction has been historically performed in three steps: the Bouguer slab correction (Bullard A), which approximates the local topography (or bathymetry) by a slab of infinite lateral extent, with constant density and thickness equal to the elevation of the point with respect to the mean sea level; the curvature correction (Bullard B), which replaces the Bouguer slab by a spherical cap of the same thickness to a distance of 166.735 km; and the terrain correction (Bullard C), which consists of the effect of the surrounding topography above and below the elevation of the calculation point. The program performs Bullard A, B and C corrections, defining several zones depending on the horizontal distance (\(R\)) to the point where the Bouguer anomaly is to be calculated. FA2 BOUG defines three zones: an inner zone (\(R<\Delta x_{i}/2\)), an intermediate zone (\(\Delta x_{i}/2<R<R_{i}\)) and a distant zone (\(R_{i}<R<R_{d}\)). The basic grid cell for the proximity correction calculation in the inner zone is a square area with the calculation point as the center and \(\Delta x_{i}\) as the side length. \(R_{i}\) and \(R_{d}\) are the restricted distance for the range of the intermediate zone and distant zone, respectively.