esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Public Safety Network
### Drones for Public Safety
#### 2.1.1 Coverage Area  
The optimal placement of drones for public safety communications to enhance the coverage performance is discussed in [60]. The coverage can be extended with a number of drones, as shown in Figure 4. These drones can connect with each other and with first responders over an emergency or disaster area for delivering services accordingly. A drone lifts a flying platform by incorporating communication technologies such as ad hoc, WiFi, WiMAX, or LTE equipment to provide efficient communication services. A disaster generally destroys all of the communication and electric supply infrastructure. For some people, the availability of electricity means the difference between life and death. Electric power is required for lighting medical aid stations, ventilation, and delivering water to people in shelters, as shown in Figure 5. The figure illustrates the architecture power beaming transport from a drone. Therefore, power is beamed from a drone to the rescue and relief team receiver devices over the disaster area.  
During an earthquake, power may go out immediately. During a tsunami, the water level remains high for several hours, and many people are swept away several kilometers out to sea, creating an urgent need for rescue and relief over the sea. Therefore, the emergency communication system must take into consideration that no support can come via ground transportation. Hence, power transmission technologies must have a time-varying electromagnetic field [61]. Wireless transmission power is electric power that transmits from the power source to destination machines without discrete human-made conductors [62]. Wireless power transmission is useful in case of hazards. A transmitter machine connected to the source of power, then using an electromagnetic field for power transmission  
Figure 3: Public safety DJI drone [23].  
across an intervening space to one or more receivers, will finally be converted back to electric power for utilization [61].
## 3 Results  
### Correlations of Running Kinematics Variables with Maximal Velocity  
Table 1 shows the results of the variables selected from RusScribe due to their significant correlation with maximum running speed.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Kinematic Variables**} & \multirow{2}{*}{**Correlation with V max.**} & \multicolumn{2}{c}{**95\% C.I**} \\ \cline{3-4}  & & **Lower C.I.** & **Upper C.I.** \\ \hline
**Distance and angles** & & & \\ Stride Length (m) & 0.648 *** & 0.510 & 0.754 \\ Stride Angle (\({}^{\circ}\)) & 0.302 ** & 0.102 & 0.479 \\ Swing Excursion (\({}^{\circ}\)) & 0.599 ** & 0.453 & 0.714 \\ Step Length (m) & 0.668 *** & 0.536 & 0.768 \\ \hline
**Velocity and frequency** & & & \\ Maximal Speed (km/h) & 1 *** & 1 & 1 \\ Max Pronation Velocity (\({}^{\circ}\)/s) & 0.448 ** & 0.271 & 0.595 \\ Max Stance Velocity (FS-MP) (\({}^{\circ}\)/s) & 0.423 ** & 0.238 & 0.579 \\ Max Stance Velocity (MP-TO) (\({}^{\circ}\)/s) & 0.686 ** & 0.559 & 0.782 \\ Step rate & 0.556 ** & 0.395 & 0.684 \\ Vertical Speed (m/s) & \(-\)0.491 *** & \(-\)0.633 & \(-\)0.317 \\ \hline
**Times** & & & \\ Contact Time (ms) & 0.649 ** & \(-\)0.755 & \(-\)0.512 \\ Contact Ratio (\%) & \(-\)0.536 ** & \(-\)0.668 & \(-\)0.370 \\ Flight Time (ms) & 0.490 ** & 0.335 & 0.645 \\ Flight Ratio (\%) & 0.532 *** & 0.364 & 0.667 \\ Time (MP-TO) (ms) & \(-\)0.677 *** & \(-\)0.775 & \(-\)0.547 \\ Time (TO-Min Swing) (ms) & 0.442 *** & 0.265 & 0.590 \\ Time (Max Swing-FS) (ms) & 0.538 ** & 0.373 & 0.670 \\ \hline \hline \end{tabular} Max_stance_vel_MP—Maximum stance velocity from the point of maximum pronation to toe-off (\({}^{\circ}\)/s); Max_stance_vel_FS—Maximum stance velocity from the foot strike to the point of maximum pronation to toe-off (ms); Time_TO_min_swing—Time from the top of minimum portion to the toe-off (ms); Time_TO_min_swing—Time from the top-off to minimum swing point (ms); Time_TO_max_swing—Time from the maximum swing point to the foot strike (ms). *** = \(p<0.001\); ** = \(p<0.01\).  
\end{table}
Table 1: Correlations of running kinematics variables with maximal velocity.
## 4 Results and Discussion
### Effect of Roof PV Forms on Natural Ventilation
In comparison to the condition without PV panels, the ventilation speed losses caused by the PV systems in these cases are 2.3%, 3.4%, 2.9%, 1.7%, 5.7%, and 0.6%, respectively. Figure 7: Schematic diagram of the designated ventilation areas. Among these, the unilateral pitched roof configurations demonstrate consistent improvement, while the symmetrical pitched roof cases exhibit greater variation in performance. Under the leeward condition of the main transformer room, the proposed layout configurations in this study (Case 7, Case 8, Case 11, Case 12, Case 14, and Case 16) exhibit relatively small fluctuations. Among them, Case 8 performs less favorably than Case 4, while the other configurations show improvements. In the unilateral pitched structure, smaller angles are more advantageous. The symmetrical pitched structures consistently demonstrate improved performance, nearly equivalent to that of Case 2, which represents the condition without the PV system. Finally, taking into account ventilation performance (wind velocity contours, vector diagrams, and velocity), PV back surface cooling, and structural risks due to wind loads, the most balanced configuration is the 10\({}^{\circ}\) symmetrical PV system (Case 15/16). This configuration achieves the best ventilation performance among all PV-installed cases, with only a 0.6% reduction compared to the no-PV baseline and a 14.6% improvement over the horizontal PV setup. It also features minimal indoor stagnant zones and smooth airflow beneath the PV panels for effective cooling. Additionally, it avoids high wind pressure loads, offering greater structural safety. Horizontal configurations (Case 3/4) are not recommended due to their significant reduction in ventilation performance. The use of single-pitched structures should be approached with caution, especially at larger slopes, as they may introduce increased wind loads.
# An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs
## 3 Techniques and Methods
### The QoS Parameters
#### 3.2.3 Residual Energy  
Energy is consumed when a packet is received or sent or when a node is idle overhearing traffic from neighboring nodes. Energy consumption of a node can be determined using the following formula:  
\[E_{\mathrm{c}}(n)=\left[P_{\mathrm{t}}\times\frac{D_{\mathrm{s}}}{D_{\mathrm{ r}}}-P_{\mathrm{r}}\times\frac{D_{\mathrm{s}}}{D_{\mathrm{r}}}\right]+nP_{ \mathrm{o}} \tag{3}\]  
where \(E_{\mathrm{c}}(n)\) is the energy consumed by the node, \(P_{\mathrm{t}}\) is the power consumption for signal transmission, \(D_{\mathrm{s}}\) is the size of the data packet, \(D_{\mathrm{r}}\) is the transmission rate, \(P_{\mathrm{r}}\) is the power consumption for the reception, and \(P_{\mathrm{o}}\) is the power consumption during the period of overhearing the neighbor nodes.  
Residual energy is available or remaining energy in a node which can be calculated as follows:  
\[Er(n)=E_{\mathrm{i}}(n)-E_{\mathrm{c}}(n) \tag{4}\]  
where \(E_{\mathrm{i}}(n)\) is the amount of initial energy of a node and \(E_{\mathrm{c}}(n)\) is the consumed energy of the node.
# Shared Tuned Mass Dampers for Mitigation of Seismic Pounding
## 3 Numerical Study
### An Optimization Example
This, however, comes at a cost of increased stroke of the device, which is due to low damping values of the locally optimal STMD, as is evident from the bottom left panel of Figure 3. The stroke of the locally optimal STMD is much larger than the stroke of the TMD placed on building A. As the frequency ratio is increased, the displacement response of both the buildings show a decreasing trend followed by an increasing trend. This trend is also visible in the cost function shown in Figure 2. If we consider the minima (based on the cost function) at a higher frequency ratio as the global minima, the optimal frequency ratio decreases with increasing damping, and the optimal solutions are more effective than TMD placed on building A alone. However, these optimal solutions correspond to very stiff springs in the TMD. For a damping ratio as large as 40%, the optimal frequency ratio is about 10. With a mass ratio of 3%, the stiffness of the TMD is about 3 times the stiffness of building A. From a practical point of view, it might not feasible to construct a TMD which is so much stiffer than the building it rests on. Such a solution, although not realistic, will be hereafter referred to as a globally optimal solution referring to the cost function. At the locally optimal solution, the shared TMD reduces the minimum separation distance to some extent. As the frequency ratio and damping ratio are increased, the bounding distance decreases further. With a very stiff TMD and sufficiently high damping ratio, it appears the bounding distance can be reduced to a large extent. While this seems like a plausible mitigation strategy against bounding, it is deficient in both conceptual and practical terms. In practical terms, having a TMD 3 times stiffer than the building itself is not attractive. The conceptual deficiency is that such a stiff device cannot be considered as a TMD. This can be better understood by inspecting the results shown in the bottom left panel of Figure 3. Irrespective of the damping ratio, as the frequency ratio increases above 1, the  
Figure 3: Peak displacement of building A, displacement of building B, stroke of the TMD, and minimum separation. stroke of the TMD reduces rapidly, and for a frequency ratio of 10, the device does not move relative to building A. Therefore, the device is not acting like a TMD and any reduction in response is due to the coupling of the two buildings with a viscous damping device. It is also interesting to note that unlike the cost function which increases after the global minima, the bounding distance decreases with increasing frequency ratio.
## 1 Introduction
By combining traditional FCS-MPC with an enhanced version that incorporates a term into the cost function (\(g\)), this study examines the effects on a three-phase, two-level voltage source inverter (VSI). The main goal is to control the AC voltage across the capacitor in the \(LC\) filter and assess how switching frequency minimization affects load current and capacitor voltage control. This analysis provides essential insights into the trade-offs and synergies associated with improved control in power converters. This study highlights the transformative power of real-time tools on the OPAL-RT platform to enhance predictive control strategies. The objective of this work is to redefine FCS-MPC applications, providing practical solutions for high-performance power conversion in modern energy systems. Thus, this paper bridges the gap between theoretical advancements in finite-control-set model predictive control (FCS-MPC) and practical implementation by minimizing switching frequency in the cost function. The approach is validated through real-time experiments on the OPAL-RT platform, effectively reducing switching losses and providing insights into FCS-MPC behavior under non-linear loads. This strategy is robust and practical for high-performance power converters in industrial applications. The remainder of this paper is organized as follows. Section 2 presents the mathematical model of the plant, including the two-level, three-phase voltage source inverter with an _LC_ filter; the load; and the essential equations for the prediction controller of the finite-control-set model predictive control. Section 3 discusses the developed FCS-MPC techniques and their real-time results. Section 5 concludes with the main findings and key points of the study.
# An Efficient and Secure Certificateless Aggregate Signature Scheme for Vehicular Ad hoc Networks
## 6 Performance Comparison
### Computational Cost  
The computational cost reveals how much processing is required on both the sender and receiver ends of the communication. Major procedures like modular exponential, bilinear pairing operations, elliptic curve point multiplication, and hyperelliptic curve divisor scalar multiplication are used to calculate the computational cost. In this section, we have compared our proposed CLAS scheme in terms of computational cost with the relevant schemes of [PERSON] et al. [30], [PERSON] and [PERSON] [19], [PERSON] and [PERSON] [28], and [PERSON] et al. [29], as depicted in Table 3. For this purpose, we then show that \(\mathcal{B}\mathcal{P}\mathcal{M}\), \(\mathcal{B}\mathcal{P}\), \(\boldsymbol{\delta}\mathcal{C}\), \(\boldsymbol{\mathcal{H}\delta}\mathcal{C}\) denote bilinear point multiplication, bilinear pairing, elliptic curve multiplication, and hyperelliptic curve multiplication, respectively.  
The execution time for several time-consuming cryptographic operations is summarized in Table 4[33]. The experiment is conducted through Intel Core i5-6300 CPU, 2.40 GHz processor,8 GB of RAM, Windows 10 Ultimate edition, and Multiprecision Integer and Rational Arithmetic C Library (MIRACL). Then, the following formulas are used to compute the computational cost of the proposed CLAS scheme in milliseconds (ms) with the relevant schemes of [PERSON] et al. [30], [PERSON] and [PERSON] [19], [PERSON] and [PERSON] [28], and [PERSON] et al. [29], as presented in Table 5:  
* For bilinear pairing-based scheme, we used the following formulas for computational cost: _Signing Cost_ = Number of Bilinear Pairing Operations
* Time required for Single Bilinear Paring Operation
* _Verification Cost_ = Number of Bilinear Pairing Operations
* Time required for Single Bilinear Paring Operation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Operation**} & **Bilinear Point** & \multirow{2}{*}{**Bilinear Pair**} & \multirow{2}{*}{**Elliptic Curve**} & **Hyper Elliptic** \\  & **Multiplication** & & & **Curve** \\ \hline
**Time in ms** & 4.31 ms & 14.90 ms & 0.97 ms & 0.48 ms \\ \hline \hline \end{tabular}
\end{table}
Table 4: Execution time of different cryptographic operations.
* For elliptic curve-based scheme, we used the following formulas for computational cost: _Signing Cost_ = Number of Elliptic Curve Operations
* Time required for Single Elliptic Curve Operation
* For hyperelliptic curve-based scheme, we used the following formulas for computational cost: _Signing Cost_ = Number of Hyper Elliptic Curve Operations
* Time required for Single Hyper Elliptic Curve Operation  
Therefore, Table 5 shows that the new method uses less computing power by using hyperelliptic curve cryptography with a key size of only 80 bits and provides the same level of security as RSA and elliptic curve cryptography. Figure 4 depicts that our scheme outperforms [19; 28; 29; 30].
# Correlation between Temporomandibularibular Disorders and
## 4 Discussion
### Factors Influencing TMD and Timitus Association  
Some authors identified specific factors influencing the TMD and tinnitus association. [PERSON] et al. (2016) suggested that a certain type of TMD (PTF type 1) might be associated with an increased incidence of tinnitus. The authors employed a tac cone beam (CBCT) to assess the relationship between PTF subtype and tinnitus in TMD patients [54]. The study of [PERSON] et al. aimed to investigate the relationship between the displacement of the condyle in the temporomandibular joint (TMJ), the structure and position of the petrotympanic fissure (PTF), and comorbid tinnitus in patients with temporomandibular joint and muscle disorder (TMD) [65]. The researchers enrolled 331 TMD patients (268 women and 63 men), with an average age of approximately 40 years for women and 38 years for men. They used imaging studies of the facial part of the skull to analyze the TMJ and PTF configurations; around 10% of the TMD patients reported having tinnitus [65].  
[PERSON] et al. (2020), using magnetic resonance imaging, found that patients with TMJ dysfunction and tinnitus presented a displacement of the joint disc with greater reduction than those with TMD but without tinnitus [53]. Furthermore, in the study of [PERSON] et al. in 2020, the authors described a rare condition of chewing-induced tinnitus that occurs because of a defect in the normal ossification of the tympan portion of the temporal bone that creates a communication pathway between the external auditory canal and the infratemporal fossa [64]. Instead, [PERSON] et al. aimed to use computed tomography (CT) to evaluate glenoid fossa depth and the horizontal angle of the ramus mandible in patients with TMD with tinnitus and without tinnitus to determine the association between TMD and tinnitus [55]. It was found that the glenoid fossa depth was decreased in patients with TMD and tinnitus compared to those with TMD only. This suggests a possible link between TMD and tinnitus due to the close anatomical relationship between the temporomandibular joint and the ear. Further research is needed to explore this association in more detail [55]. Another factor considered is the tympan foramen or foramen of Huschke (FH), which is closed during the first five years of life [66]. The persistence of this defect can cause spontaneous herniation of the retrodiscal tissue of the TMJ in the middle ear with consequent mandibular clicking and tinnitus enlargement of the tympan foramen, which can increase with age, resulting in tinnitus and hearing loss, as assessed by [PERSON] et al. [63]. Assessment by high-resolution TBCT of the external auditory canal may be useful for diagnosis. Treatment in these cases requires surgery to correct the defect, which can be obliterated using tragus cartilage [67].
# Fast Gaussian Filter Approximations Comparison on SIMD Computing Platforms
## 8 Conclusions  
Approximations of convolution with Gaussian filters offer a practical alternative to the computationally expensive approach of exact Gaussian smoothing. In this paper, we compared several of these methods in terms of computational complexity and accuracy. All the considered approximations demonstrated acceptable results and 10-20\(\times\) times speedup compared to the naive approach. The choice between these approximations depends on the specific requirements of the application and hardware, striking a balance between precision and efficiency.  
For floating-point input data and implementations, we recommend using the second-order Vliet-Young-Verbeek filter since the other methods superior to it in time or accuracy are significantly inferior in other aspects. However, in cases where accuracy is unimportant, one can use Stack blur as the fastest method.  
For integer inputs, we recommend quantized methods. Bell blur or running sums are the most effective variants depending on the platform and additional memory use restrictions. These methods provide a combination of reasonably high accuracy and speed.  
Figure 10: Results of the considered approximations of Gaussian filter (\(\sigma=10\)): (**a**) original image; (**b**) Gaussian filter; (**c**) Gaussian filter (quantized); (**d**) Stack blur (\(r=24\)); (**e**) Stack blur (quantized) (\(r=24\)); (**f**) Bell blur (\(r=12\)); (**g**) Bell blur (quantized) (\(r=12\)); (**h**) DFT-based convolution; (**i**) running sums (\(k=3\)); (**j**) running sums (quantized) (\(k=3\)); (**k**) running sums (\(k=4\)); (**l**) running sums (quantized) (\(k=4\)); (**m**) running sums (\(k=5\)); (**n**) running sums (quantized) (\(k=5\)); (**o**) PcParab; (**p**) PcParab (quantized); (**q**) Deriche filter (the 1 st order); (**r**) Deriche filter (the 2 nd order); (**s**) VYV filter (the 2 nd order); (**l**) VYV filter (the 3 rd order).  
Quantized Stack blur also works faster but is inferior in accuracy and, therefore, has limited use.  
Conceptualization, D.P.N.; methodology, E.E.L.; software, E.O.R.; validation, E.E.L. and D.P.N.; formal analysis, D.P.N.; writing--original draft preparation, E.O.R.; writing--review and editing, E.E.L.; visualization, E.O.R.; supervision, D.P.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the grant from the Ministry of Science and Higher Education of the Russian Federation, internal number 00600/2020/51896, Agreement dated 21 April 2022 No. 075-15-2022-319.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
[PERSON], [PERSON] and [PERSON] were employed by the company Smart Engines Service LLC. The company was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
## 2 Materials and Methods
### Assessment of Health Benefits Related to Air Quality  
The attributable fraction (AF) method was adopted to evaluate avoided deaths due to PM\({}_{2.5}\), PM\({}_{10}\), and NO\({}_{2}\) during the COVID-19 pandemic. Concentration-response (C-R) functions are widely used to evaluate the relationship between air pollution and health outcomes [42; 43; 44]. In this study, log-linear C-R functions were used as follows:  
\[\textit{Avoidable}\ (\textit{preventable})\ \textit{deaths}=\left(1-exp^{-\beta \times\Delta x}\right)\times\textit{number}\ \textit{of}\ \textit{deaths} \tag{2}\]  
where \(\beta\) represents C-R coefficients, \(\Delta x\) is the decrease in the level of air pollutants, and the number of deaths was based on mortality over the same period in past years (2016-2018).  
In this study, the C-R coefficients of each air pollutant adopted values derived from well-designed previous studies (Table 1). In addition, we used AirQ+ software tool that was developed by WHO to quantify the health impacts of air pollution.  
Finally, we evaluated health outcomes to investigate the cost of mortality at the society level. The value of statistical life (VSL) represents how much individuals are willing to pay (WTP) to reduce the risk of death [48], and has been widely used in studies dealing with air quality and health [49; 50; 51; 52; 53]. The economic cost of a mortality impact was calculated through VSL and multiplied by premature deaths (COVID-19) or avoided deaths. In our study, we evaluated economic outcomes related to health by adopting USD 3.53 million proposed by the OECD as a representative VSL in Korea [54].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Air Pollutants** & **Estimated C-R Regression Coefficients** & **Epidemiological References** \\ \hline PM\({}_{2.5}\) & 0.00405 & [11; 45] \\ PM\({}_{10}\) & 0.0008 & [46] \\ NO\({}_{2}\) & 0.00135 & [47] \\ \hline \hline \end{tabular}
\end{table}
Table 1: The concentration-response (C-R) coefficients for PM\({}_{2.5}\)-, PM\({}_{10}\)-, and NO\({}_{2}\)-induced mortality.