esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure and Efficient High Throughput Medium Access Control for Vehicular Ad-Hoc Network
## 6 Conclusions  
This work presented a secure MAC design for VANET. This model presented a commutative RSA-based channel allocation scheme on a shared channel network, namely S-PECA. The S-PECA model has overcome the key management and communication overhead issue of exiting third-party server and public-key cryptography schemes. Experiments are conducted to evaluate the overhead incurred in provisioning security to S-PECA and S-ENCCMA. The S-PECA and S-ENCCMA protocols incur an average throughput overhead of 7.2% and 15.91%, average collision overhead of 35.07% and 38.91%, and average success packet transmission overhead of 6.63% and 17.97% when security is provisioned to S-PECA and ENCCMA, respectively, considering the different environmental conditions. The outcome shows that overhead incurred by S-PECA is much lower when compared to S-ENCCMA in terms of throughput, collision, and successful packet transmission considering varied environmental models. The overall outcome shows S-PECA minimizes collision and maximizes system throughput considering different radio propagation environments when compared to state-of-the-art techniques. In future work, we would consider performance evaluation under various modulation schemes and consider designing a new security mechanism for VANET.  
[PERSON] and [PERSON] contributed to the main idea and the methodology of the research. [PERSON] designed the experiment, performed the simulations, and wrote the original manuscript. [PERSON], [PERSON], [PERSON] and [PERSON] contributed significantly to improving the technical and grammatical contents of the manuscript. [PERSON], [PERSON], [PERSON] and [PERSON] reviewed the manuscript and provided valuable suggestions to further refine it. Supervision, [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was a part of the project titled 'Marine digital AtoN' information management and service system development (1/5) (20210650)', funded by the Ministry of Oceans and Fisheries, Korea.  
Not applicable.  
The authors declare no conflict of interest.
# Visible Spectrum and Infra-Red Image Matching: A New Method
## 2 Related Work
Similarly, Sobel filters are used in four different directions to compute Histograms of Directional Maps (HoDM) descriptor [25]. Binary descriptors such as Oriented FAST and Rotated BRIEF (ORB) [26] and Binary Robust Invariant Scalable Keypoints (BRISK) [27] are also used to overcome intensity changes between the images. There are some methods, which employ the theory of physics to understand the phenomena of textural and intensity changes to improve the cross spectral image matching results [28; 29]. In Reference [9], the authors compare different descriptors on multisensor images using the image matching framework of [18]. These descriptors are gradient based descriptors like SIFT, NG-SIFT, LG-SIFT, DE-SIFT, MN-SIFT, and intensity order based descriptors [30], Haar wavelet based descriptors [31] and Local binary pattern based descriptors [13; 14]. It is shown that descriptors computed on normalized image patches extracted through Harris feature points [32] and SIFT feature points demonstrate better performance than descriptors computed on normalized image patches extracted with ORB and Brisk detectors. It is also shown that MN-SIFT compared to others, demonstrates better results on multisensor images. The regression method proposed in this paper is also based on MN-SIFT descriptors. It uses MN-SIFT descriptors to train a regression model to overcome textural and intensity changes between VS-IR images. The regression model processes the MN-SIFT descriptors of test VS images prior to their descriptor matching with the MN-SIFT descriptors of IR images and improves the image matching results.
# Design of Shadow Filter Using Low-Voltage Multiple-Input Operational Transconductance Amplifiers
## 1 Introduction
Those in [18; 19; 20; 21; 22; 23; 24] provide low-pass filters (LPFs) and band-pass filters (BPFs) based on the foundational principles outlined in [6], while the designs in [25; 26] extend the concept to include LPFs, BPFs, and high-pass filters (HPFs) using the approach described in [7]. Additionally, the current-mode shadow filters in [27; 28; 29; 30; 31; 32] support LPFs, BPFs, HPFs, band-stop filters (BSFs), and all-pass filters (APFs) within the same topology. Shadow filters can be categorized into three main types. The first category consists of filters that adjust the natural frequency using an amplifier [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. However, altering the natural frequency in this approach also impacts the quality factor. The second category includes filters that modify the quality factor via an amplifier [25; 26]. In this case, adjusting the quality factor results in an increase or decrease in the passband gains of the LPF and HPF, depending on whether positive or negative feedback is applied. Consequently, only the BPF maintains a standard form. The third category comprises filters capable of varying both the natural frequency and the quality factor through amplifiers [25; 26; 27; 28; 29; 30; 31; 32]. However, changing the natural frequency in these filters affects both the quality factor and the passband gain. Fortunately, the quality factor can be adjusted to remain constant while varying the natural frequency. However, the passband gain cannot be maintained at unity; it will either increase or decrease depending on the feedback connections. To address these limitations, shadow filters with a constant passband gain are proposed in this work. Two types of shadow filters are introduced. The first shadow filter allows the quality factor to be adjusted without affecting the passband gains of the BPF, LPF, and HPF when the input signals are appropriately applied. Consequently, the frequency responses of this shadow filter maintain the standard forms of BPF, LPF, and HPF. The second shadow filter enables the natural frequency to be adjusted without influencing the passband gains of the HPF, BPF, and LPF under proper input signal conditions. Both the natural frequency and the quality factor of the second-order shadow filter can be independently and electronically controlled. These proposed shadow filters are proposed using multiple-input operational transconductance amplifiers (MI-OTAs). The use of MI-OTAs allows for the realization of multiple filtering responses and the incorporation of external amplifiers, as demonstrated in this study.
# Robust Adaptive Control for an Aircraft Landing
## 4 Algorithm Control Designation  
### Performance Measure of Impact Energy Absorption  
After a landing gear touches the ground, the initial kinetic and potential energy of the system is absorbed and dissipated with the back-and-forth motion of the damper in the landing gear. The work done by the damper can be calculated as the integral of the damper force \(F_{d}\) over the stroke. The total work done by the damper until the landing gear system is stabilized and is then equal to the initial kinetic and potential energy, and thus it is constant in a given landing condition. The total energy absorber efficiency is defined as the ratio between the total work done by the damper during landing and the product of the maximum force \(F_{\text{max}}\) and stroke \(s_{\text{max}}\) during the same time period. The latter means the maximum possible work by the force \(F_{\text{max}}\) traversing \(s_{\text{max}}\). Therefore, the total energy absorber efficiency \(\eta\) is the measure of damper performance of absorbing the dynamic energy through the whole period of impact absorption:  
\[\eta=\frac{\int\limits_{0}^{s_{final}}F_{d}ds}{5_{\text{max}}F_{\text{max}}} =\frac{\int\limits_{0}^{s_{\text{max}}}F_{d}ds}{5_{\text{max}}F_{\text{max}}} +\frac{s_{\text{max}}}{5_{\text{max}}F_{\text{max}}}=\eta_{1}+\eta_{2} \tag{19}\]  
In [4, 22, 23, 24], the shock strut efficiency is used to assess the damper performance, which is defined as the ratio \(\eta_{1}\) between the damper work from 0 to \(s_{\text{max}}\) and the product of the maximum force and stroke. \(\eta_{1}\) approximates \(\eta\) because the most impact energy is absorbed and dissipated in the first contraction phase of the damper moving from 0 to \(s_{\text{max}}\). However, there is still more than 10% of the total energy stored in the air spring at \(s_{\text{max}}\), and thus, the damper will still actively oscillate until it arrives at the equilibrium state. In this paper, the total energy absorber efficiency \(\eta\) is used instead, in order to evaluate the damper efficiency of the landing impact absorption in the overall process.
## 2 Materials and Methodology
### Method of Measurement  
The tensile properties of thin-walled Al-Mg-Si alloy profiles were tested using the WDW-300S universal testing machine (Virtual Expo, Marseille, France), in which the tensile specimens were cut out along the extrusion direction with a thickness of 2.8 mm. The dimensions of the tensile specimen are shown in Figure 2. Subsequently, the fracture surface of the tensile specimen was manually sawed off, and the tensile fracture micro-morphology was observed using a Prisma E scanning electron microscope (Tescan Brno, Brno-Kohoutovice, Czech Republic).  
The bending properties of thin-walled Al-Mg-Si alloy profiles were tested using the WD-20B universal testing machine (Instron Limited, High Wycombe, UK). The bending specimens were 60 mm in length, 30 mm in width, and 2.8 mm in thickness.  
The WAW-100 FC (Shenzhen Wance Testing Machine Co., Shenzhen, China) single-space hydraulic universal testing machine was utilized to test the crush performance of the thin-walled Al-Mg-Si alloy profiles, with a downward speed of the press of 100 mm/min and a termination displacement of 200 mm. Figure 3 shows the schematic of the crush-performance test.  
Thereafter, metallographic tests were performed on the thin-walled Al-Mg-Si alloy profiles. Figure 4 shows the location of metallographic sampling. First, the metallographic samples were sanded and polished in order. Second, a reagent with a volume ratio of HF:HCL:HNO3:H2O = 1:1.5:2.5:95 was chosen to corrode the sample, with a corrosion time of 30 S. Third, after the corrosion process, the sample was rinsed with water. Finally, the rinsed samples were placed under the VHX-7000 metallographic microscope (Japan Keyence Co., Akishima-shi, Japan) for observation.  
Figure 1: Dimensional drawings of the double-cell thin-walled tube interface: (**a**) Cross-sectional dimensions (mm); (**b**) Schematic of the extruded workpiece profile (from Ref. [28]).  
Figure 2: Schematic diagram of the dimensions of tensile specimens (mm).  
The number and size of particles in the second phase of the Al-Mg-Si alloy thin-walled profiles were observed using a Prisma E scanning electron microscope, and the second-phase particles were analyzed by energy spectroscopy using the Oxford EDS-type energy spectrometer, which is a component of the scanning electron microscope.  
Transmission samples with a length of \(\sim\)5 \(\upmu\)m and thickness of \(\sim\)45 nm were prepared using the focused ion beam technique, and the microstructures of the Al-Mg-Si alloy thin-walled profiles were observed using a Tecnai G2F20 transmission electron microscope (Thermo Fisher Scientific, Middlesex County, MA, USA) at an accelerating voltage of 200 KV.
### Conventional Communication Channels
#### 2.1.2 Through-Earth (TE) and Through-Air (TA)  
The through-earth (TE) scheme is data transmission using the propagation of EM signals through the surface of the earth. These signals penetrate in depth through the multiple layers of underground mines; however, such propagation weakens the power of EM signals [10]. Even so, the range of signal transmission can be improved using particular frequencies of less than 10 kHz [11]. The attenuation of EM signals in the TE scheme depends on multiple factors such as antenna size, transmit power, bandwidth, and noise levels, etc. TE operates normally between 90 Hz to 9 kHz, and it is necessary to launch EM signals using large loop antennas [12]. On the other hand, through-the-air (TA) represents the communication framework that uses wireless links through free space inside mines for data transmission. The major problem in this approach is its adaptability in a mine's critical environment. Physical characteristics of a mine, inefficient channel modeling, and mining material create a high-level scattering for propagated signals, which reduces the efficiency of this approach [13].
## 2 Failure Probability Analysis  
### Method  
Fragility analysis can be used to calculate the failure probability of structures in the case of single disasters or double disasters. However, when calculating the failure probability of the structure under double disasters, the fragility analysis ignores the correlation between the two disasters. Therefore, based on the joint probability distribution of WS-WD and IT-WS, this paper calculates the failure probability of transmission lines under combined ice and wind loads. Equations (1) and (2), respectively, give the calculation formulas of the failure probability density and the cumulative failure probability of the transmission line under combined ice and wind loads [29]:  
\[P_{\varphi}=F_{\varphi}(im_{1},im_{2})\cdot f_{IM_{1},IM_{2}}(im_{1},im_{2}) \cdot P(\varphi|im_{21},im_{22}) \tag{1}\]  
\[T_{\varphi}=\int_{im_{21}}^{im_{22}}\int_{im_{11}}^{im_{12}}F_{\varphi}(im_{1}, im_{2})\cdot f_{IM_{1},IM_{2}}(im_{1},im_{2})\cdot P(\varphi|im_{21},im_{22}) dim_{1}dim_{2} \tag{2}\]  
where \(P_{\varphi}\) and \(T_{\varphi}\) are the failure probability density and the cumulative failure probability, respectively; \(im_{1}\) and \(im_{2}\) are the ice thickness and wind speed; \(F_{\varphi}(im_{1},im_{2})\) is the fragility function of tower-line system under combined ice and wind loads at wind direction \(\varphi\); \(f_{IM_{1},IM_{2}}(im_{1},im_{2})\) is the joint probability distribution of IT-WS; \(P(\varphi|im_{21},im_{22})\) is the probability of wind direction \(\varphi\) in the \(im_{21}-im_{22}\) wind speed range.
## 3 Mesh and Validation  
First, let us introduce, for a fixed geometry configuration (Figure 1), a sensitivity analysis of the model described through Equations (14)-(23). The constants range values used for investigating the thermophoresis and Brownian diffusion are: \(\text{Pr}=6.2,50\leq\text{Re}\leq 500,0.1\leq\text{Le}\leq 5,0.1\leq N_{B}\leq 1\), and \(0.1\leq N_{T}\leq 1\).  
The mesh used for the domain discretization is made up of quadrilateral elements. In Figure 2 some of the considered meshes are shown. In particular, in order to identify the optimal number of computational elements, the following quantities are evaluated as average in the domain of interest: velocity, temperature, particle concentration, and Nusselt number on the hot wall.  
For the sake of clarity, the mean volume--related to a unit depth--quantities were evaluated according to the expression  
\[\overline{\xi}=\frac{1}{\Omega}\;\iint\xi\;d\Omega \tag{29}\]  
where \(\xi\) is the general non-dimensional quantities and \(\Omega\) is the non-dimensional volume under consideration. Otherwise, the mean Nusselt number is evaluated along the hot wall by the expression \[\overline{\text{Nu}}=\frac{1}{l}\ \int_{0}^{l}\frac{d\Theta}{dx}\ dx \tag{30}\]  
where \(l=l_{1}+l_{2}+l_{3}\) is the total length of the hot wall.  
In Table 1 the grid independence tests are reported. In the following, grid #3 will be employed.
# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 5 System Model
### Vehicle to Road Side Unit (V2R) Communication Model  
Vehicle to RSU communication; coverage area is divided into zones \(k=7\), range of each zone is \(d_{1}\), \(d_{2}\), \(d_{3}\)... \(d_{k}\), transmission rate at each zone is denoted as \(R_{k}\) and equally allocated to each vehicle. The bit rate of node at time \(t\) from RSU to \(V\) is expressed in Equation (2).  
\[R_{R,i}(t)=R_{k}/N_{k}\;(t) \tag{2}\]  
where \(N_{k}\;(t)\) shows the number of vehicles in \(k\)th zone at time \(t\). The amount of data provided by RSU as a download service to all corresponding nodes is expressed in Equation (3).  
\[T_{R}=\sum_{k=1}^{7}\;N_{k}\cdot\frac{dk}{E[v]}\cdot\frac{Rk}{Nk} \tag{3}\]  
There is a control buffer in RSU with the size of \(T_{R}\) that stores all the data requested by the node and deletes the data when the buffer overflows.
# A Multi-Mode Recognition Method for Broadband Oscillation Based on Compressed Sensing and EEMD
## 4 Analysis and Comparison of the Results
### Analysis and Comparison Based on Measurement Signals  
A wind farm model consisting of six wind turbines was established. Each wind turbine was connected to a 25 kV distribution system through a corresponding transformer and output power to a 120 kV grid through a 30 km 25 kV feeder. The simplified model is shown in Figure 5. It was run in discrete mode with a sampling time of 1 \(\times\) 10\({}^{-5}\) s and a running time of 0.2 s.  
The voltage waveform of 0.02-0.08 s (12,000 points in total) when the broadband oscillation occurred was collected. After compressed sampling by a Gaussian random matrix, it was transmitted to the main station, then the compressed sampled data were reconstructed according to the SP algorithm. Figure 6 shows a comparison between the reconstructed and original signals. The RE was 0.1263, and the basic shape of the original signal was restored.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Oscillation Mode** & **Oscillation Information** & **Theoretical Value** & \begin{tabular}{c} **Identification Result** \\ **(15 dB/20 dB/25 dB/30 dB)** \\ \end{tabular} \\ \hline Mode 1 & \begin{tabular}{c} Frequency/Hz \\ Amplitude/p.u. \\ \end{tabular} & 50 & \begin{tabular}{c} 50/50/50/50 \\ 0.8291/0.8958/0.8573/0.8547 \\ \end{tabular} \\ \hline Mode 2 & \begin{tabular}{c} Frequency/Hz \\ Amplitude/p.u. \\ \end{tabular} & 75 & \begin{tabular}{c} 75/75/75/75 \\ 0.1775/0.1860/0.1814/0.1743 \\ \end{tabular} \\ \hline Mode 3 & \begin{tabular}{c} Frequency/Hz \\ Amplitude/p.u. \\ \end{tabular} & 200 & \begin{tabular}{c} 200/200/200/200 \\ 0.45 \\ \end{tabular} \\ \hline Mode 4 & \
\begin{tabular}{c} Frequency/Hz \\ Amplitude/p.u. \\ \end{tabular} & 400 &
\begin{tabular}{c} 400/400/400/400 \\ 0.6 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recognition results of broadband oscillation signals with different SNR signals.  
Figure 5: Simplified model for a power generation system composed of six wind turbines.  
Figure 6: Comparison of the broadband oscillation signal with a reconstructed signal.  
EEMD was used to decompose the reconstructed signals, and a set of IMFs was obtained. Any irrelevant IMF components were eliminated by the energy coefficient, and IMF components containing broadband oscillation information were retained. Finally, FFT was used to calculate each IMF component. In addition to a 50 Hz fundamental frequency, there was also 150 Hz and 200 Hz medium and high-frequency oscillation information in the measured signal, as shown in Figure 7.
# Inversion of Chlorophyll-a Concentration in Wuliangsu Lake Based on OGolden-DBO-XGBoost
## 4 Results and Discussion
### Comparison and Analysis of Improvement Strategies  
In order to verify the performance of OGolden-DBO-XGBoost in the inversion of Chl-a concentration with the introduction of the month feature, this paper compares the performance of DBO-XGBoost, Obl-DBO-XGBoost, Golden-DBO-XGBoost, and OGolden-DBO-XGBoost.  
As can be seen in Table 9, the XGBoost model, improved by different strategies, performs well on both the training and test sets, with the Obl-DBO-XGBoost model achieving an R\({}^{2}\) of 0.8618 on the test set, the RMSE decreasing to 3.2515 \(\mathrm{\SIUnitSymbolMicro\text{  
In summary, the OGolden-DBO-XGBoost model improved by these two strategies showed good results in the inversion of Chl-a concentration in Wuliangsu Lake. It also outperformed the other models in terms of R\({}^{2}\), RMSE, and MAE in both the training and test sets, with smaller errors between the projected and measured values. This demonstrated its superior capacity to invert the Chl-a concentration in Wuliangsu Lake. The enhanced DBO's efficacy and excellent performance were proven.
# A Scalable and Consistent Method for Multi-Component Gravity-Gradient Data Processing
###### Abstract  
We demonstrate the potential of using the convolutional equivalent layer to jointly process large gravity-gradient datasets. Based on the equivalent-layer principle, we assume a single fictitious physical property distribution on a planar layer can approximate all components of the gravity-gradient tensor. Estimating this distribution using the classical technique ensures physical consistency among components. However, the classical approach becomes computationally prohibitive for large datasets due to the need to solve a large-scale inversion with a massive sensitivity matrix. To overcome this limitation, we exploit the block-Toeplitz Toeplitz-block structure of the sensitivity matrix for data on a regular horizontal grid. This structure significantly reduces computational cost--by orders of magnitude--compared to the classical method. Applications to synthetic and real datasets show that our method offers a computationally efficient alternative for processing large gravity-gradient data from various acquisition systems (AGG and FTG), even when data are irregularly spaced or flight lines are misaligned. On a standard laptop configuration, our method processed over 290,000 AGG data points in a few tens of seconds. It also handled between 726,000 FTG and 1,250,000 AGG data points within seconds to a couple of minutes, demonstrating practical computational efficiency for large-scale datasets.  
large-scale gravity-gradient data; jointly processing; FTG andFalcon AGG acquisition systems; equivalent-layer technique; reduced floating-point operations; Kauring gravity gradiometry test site; Vinton salt dome; Canobie airborne gravity survey +
Footnote †: journal: Applied Sciences  
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