esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Matching Demodulation Transform  
The Hilbert transform cannot provide the orthogonal transform for the signal \(g_{k}(t)\). Thus, we can get the approximate results in Equation (2) under some restrictive assumptions [38]. We can then define two operators, which are called Modulation Operator (MO) and Demodulation Operator (DO), as follows:  
\[\mathrm{M}(t)=\exp\!\left(i2\pi\!\left(\int_{0}^{t}f_{d}(s)ds-f_{c}t\right)\right) \tag{6}\]\[\mathrm{D}(t)=\exp\!\left(-i2\pi\!\left(\!\int_{0}^{t}f_{d}(s)ds-f_{c}t\right)\! \right) \tag{7}\]  
where \(f_{d}(s)\) stands for frequency of the operators, and carrier frequency \(f_{c}\) is a constant value [36]. Demodulation technology can be used to eliminate the FM of component \(g_{k}(t)\), thus generating narrow-band demodulation mode component. The demodulated signal can be expressed as follows:  
\[g_{k}^{d}(t)=g_{k}(t)\mathrm{D}(t) \tag{8}\]  
where the frequency of demodulated signal \(g_{k}^{d}(t)\) is around the carrier frequency. The original signal \(g_{k}(t)\) can be obtained by modulation with the MO, and the equation is as follow:  
\[g_{k}(t)=g_{k}^{d}(t)\mathrm{M}(t) \tag{9}\]  
It should be noted that we get mixed multi-component signals, and each component has its own DO. These DOs demodulate the signal into different forms, and their Time-Frequency (TF) representations are concentrated around the corresponding IF trace. Then, the demodulated signal is represented in the TF plane, i.e., a two-dimensional demodulated signal is converted into another two-dimensional function in TF domain. In this paper, we aim to extract specific mode components, and the extraction strategy is to extract from the corresponding TF plane based on its trajectory. The TF upper boundary and TF lower boundary of the TF subregion of the component \(g_{k}^{d}(t)\) are  
\[\omega_{ub,k}^{d}(t)=\pi f_{i,g_{k}^{d}}^{d}(t)+\pi f_{i,g_{k+1}^{d}}^{d}(t) \tag{10}\]  
\[\omega_{lb,k}^{d}(t)=\pi f_{i,g_{k}^{d}}^{d}(t)+\pi f_{i,g_{k-1}^{d}}^{d}(t) \tag{11}\]  
Then, the TF subregion of \(k-th\) component:  
\[B_{k}=\left\{(t,\omega):\omega_{lb,k}^{d}(t)\leq\omega\leq\omega_{ub,k}^{d}(t)\right\} \tag{12}\]
# Automatic System for the Detection of Defects on Olive Fruits in an Oil Mill
## 2 Materials and Methods
### Image Processing
#### 2.2.2 Detection of Defects  
The detection of defects is based on an iterative process that sequentially applies the segmentation algorithms previously presented (Kapur and Bernsen) to each of the olive contours found in the previous step. Figure 5 shows the detection of defects in the image previously used to illustrate the detection of olives. In order to avoid glows in the olives and enhance potential defects, a preprocessing step, consisting of the inversion of the pixel intensity values with respect to the maximum possible gray level (255), is required. This way, given an image \(I\) defined in the interval \([0,255]\), the inverted image \(I_{INV}\) is obtained with the operation \(I_{INV}=255-I\). The bottom-left area of Figure 5 shows the image after this transformation.  
The extracted features of the regions obtained after applying the segmentation procedures are area, elongation and circularity (_cir_), obtained as:  
\[cir=\frac{4\cdot\pi\cdot area}{perimeter^{2}} \tag{14}\]  
In general, the defects that identify an olive as defective are expected to be small, circular and with a gray level in the inverted image that is larger than the rest of the pixels in the olive surface. With these features, and applying the decision tree of Figure 6, it is possible to discern between acceptable and defective regions. The area, elongation and  
Figure 4: Detection of the contour of the olives. (**a**) Infrared image. (**b**) Global mask (magenta) and adaptive mask (green). (**c**) Active contours obtained with each mask. (**d**) Final active contours.  
Figure 5: Example of the detection of defects in olives.  
circularity thresholds have been set heuristically after the analysis of the 50% of the images acquired the first day. The elongation and circularity, since they are rations, are quite insensitive to small variations in the distance between the camera and the conveyor, which might affect the spatial resolution of the system. The area, on the other hand, is directly impacted by these variations. However, the design of the chamber includes a holder for the camera that prevents its movement. The installation of the system at a different distance from the conveyor would require a recalibration of the system and the definition of new thresholds. An olive is considered acceptable if there is no defective area in its contour. Figure 5 shows the result of the classification where only one defective olive is found.  
In addition, the percentage of defective olives is also given and it is obtained from:  
\[KO\ \ area=\frac{defective\ \ area}{total\ \ area}\cdot 100. \tag{15}\]
###### Abstract  
In order to meet the reliability requirements of communication for underwater resource exploration, this study develops an underwater wireless optical communication (UWOC) system utilizing a blue semiconductor laser as the light source. At the receiver, a fully digital automatic gain control (AGC) module, implemented on a field-programmable gate array (FPGA), is designed to mitigate signal fluctuations induced by underwater turbulence. Digital filtering techniques, including median filtering (MF) and bilateral edge detection filtering (BEDF), are also employed to improve signal demodulation reliability. An improved Reed-Solomon (RS) coding scheme is further adopted to significantly reduce the bit error rate (BER). The design of a highly reliable UWOC system was realized based on the above techniques. The system's performance was evaluated across a range of signal-to-noise ratios (SNRs) and bubble intensities. The results show that the digital AGC module can provide a gain range from \(-3.2\) dB to \(16\) dB, adapting to varying signal strengths, which greatly bolsters the system's resilience against underwater turbulence. Filtering techniques and RS coding further enhance the system's immunity to interference and reduce the system BER. Communication experiments were conducted over various distances under three distinct water quality conditions. The results demonstrate that, within the detection range of the avalanche photodiode (APD), the system consistently maintained a BER below \(3.8\times 10^{-3}\) across all water types, thereby confirming its high reliability. In clear seawater, the system demonstrated reliable information transmission over a \(10\) m distance at a data rate of \(10\) Mbps, achieving a BER of \(2\times 10^{-8}\). Theoretical calculations indicate that the maximum transmission distance in clear seawater can reach \(111.35\) m.  
UWOC; underwater turbulence; RS coding; digital filtering; BER; reliability
###### Abstract  
Global tourism development can be seen as a tourism network evolution; however, how the network structure influences the tourism industrial performance has not been clearly outlined. This paper utilizes complex network theory to understand the global tourism network changes and detect the global network structure effects on international tourism industrial performance, aiming to explain the tourism development from a network perspective and help to organize international tourism effectively. Using the data of 222 regions' statistics from 1995 to 2019, this paper explores the influence of the global-level network structure on the tourism industry through Pearson's correlations test and the individual-level effects through a combination of the gravity model with the mixed-effect model. At the global level, results indicate that a network structure with a higher density or clustering coefficient can improve the global tourism arrivals, but the high value of the network average path length and small-worldness characteristic have negative effects. At the individual level, the node's characteristics including the high degree, closeness, and betweenness centrality of a region in the network positively improve its international tourism arrivals, while the eigenvector centrality and local clustering coefficient generate negative effects. Additionally, most network structure measurements of a region show stronger effects on its own tourism performance than the regions with which it connects. This paper verifies that the network structure has significant impacts on tourism performance and development, which can aid international tourism development both globally and individually.  
global tourism network; network structure; tourism performance; global level; individual level; effect +
Footnote †: journal: Applied Sciences
## 3 HMC-MAC Protocol  
In this section, we explain how the network is created and neighbor discovery is done according to HMC-MAC. A time segmentation approach is used by HMC-MAC and is decided by a special multi-interface node, the Network Coordinator (\(NC\)). We explain how TDMA is used to ensure a multi-hop beacon propagation for synchronization and neighbor discovery. Then, we explain how the neighborhood information is used to allocate channels.
# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 3 Methodologies
### Dspo  
#### 3.4.1 Particle Motion Direction and Target Definition  
The PSO is usually used to solve the optimization problem of continuous space. The problem of the optimal allocation of transmission tasks mainly involves solving the mapping problem between the tasks and equipment, which is obviously a discrete allocation problem. The solution space of this problem is equivalent to the total arrangement COP of allocating \(n\) tasks to \(n\) equipment items, and the number of all solutions in the space is \(P_{n}^{n}\).  
According to the previous description of the PSO, the PSO needs to be improved to adapt to the characteristics of the problem. The particle motion direction and target are redefined.  
1. Definition of particle motion direction: According to the description of the PSO, the particle motion speed is calculated by the position vector, which contains the direction and displacement information. In TTSA, due to the discrete characteristics, the decomposition speed is divided into two items: direction and displacement. Each node of the task sequence is defined as the particle motion direction. The particle motion direction suitable for the problem in this paper is \(VD_{i}\), defined as: \[VD_{i}=\left(vd_{i1},vd_{i2},\ldots,vd_{ij},\ldots,vd_{in}\right),\ i=1,2, \ldots,K,\ vd_{ij}=0,1\] (20) Only one \(vd_{ij}\) of the \(VD_{i}\) sequence values represented by each is 1, and the other is 0. The position of 1 represents the task of replacing the node when the particle updates its position, that is, the particle's motion direction.
2. Definition of particle motion target: According to the discrete characteristics of TTSA, the velocity displacement is defined as the number of transmission tasks to be replaced by the node representing the particle motion direction. Referring to the PSO Formula (12), the particle motion target suitable for the problem in this paper is defined as: \[VT_{i}=\left(vt_{i1},vt_{i2},\ldots,vt_{ij},\ldots,vt_{in}\right),\ \ i=1,2,\ldots,K,\ \ vt_{ij}=0,\textit{TaskNo}\] (21) Only one \(vt_{ij}\) value of each represented \(VT_{i}\) sequence is taken as _TaskNo_, and the others are taken as 0. The value taken as _TaskNo_ indicates that the particle replaces the node with the representative task when updating the position, that is, the replacement target of the particle position code in the particle motion direction, which is defined as the particle motion target.
# Efficient Key Exchange Using Identity-Based Encryption in Multipath TCP Environment
## 3 Related Work: Identity-Based Encryption (IBE) and Elliptic Curve Cryptosystem (ECC)
This algorithm computes the Cipher Text \(C\) using \(\left\langle rP,\ \sigma\ \oplus H2\ (g_{ID}{}^{r})\right\rangle\), and \(g_{ID}=\ell\Big{(}Q_{ID},\ P_{pub}\Big{)}\in G_{2}\), \(\sigma\) is a random number and \(r=H3(\sigma,\ M)\). * Decryption: The private key generated by the KeyGeneration algorithm, system parameters generated by the SetUP algorithm, and the \(ID\) of the receiver will be used to decrypt the ciphertext. The Plaintext message \(M\) can be computed as below: if \(C=\left\langle U,\ V,\ W\right\rangle\) is a cipher text encrypted using public key \(ID\), compute \(V\ \oplus\ H2\ (\ell\left(d_{ID}\,\ U\right))=\sigma\) and \(W\oplus H4\ (\sigma)=M\) and \(r=H3(\sigma,\ M)\). Test that \(U=rP\). If not, reject the cipher text. Here, \(M\) is the decryption of \(C\).
# Combined Mie Resonance Metasurface for Wideband Terahertz Absorber
###### Abstract  
In this paper, we propose a combined metasurface consisting of an aluminum substrate and an array of TiO\({}_{2}\) blocks to achieve a wideband terahertz absorber. We incorporated several similar dielectric blocks with different side length into each unit cell. Each dielectric block could cause magnetic-resonance-inducing absorption effect with different peak wavelengths. Thus, our combined metasurface could achieve wider absorption frequency band than the traditional design when these dielectric blocks were properly designed. The absorption bandwidth could be widened nearly 2.5 times and 5 times compared to a single block case when there were four and nine blocks, respectively, andcouldbe further improved by increasing the number of combinations in structures (variable parameters included number, spacing, dimensions etc.). For both TE\({}_{00}\) (the electric fields of the light polarized along the \(y\)-axis) and TM\({}_{00}\) (the electric fields of the light polarized along the \(x\)-axis) polarization states, the absorption bandwidth could be widened effectively; even when the incident angle was 45\({}^{\circ}\), the absorption rate could still reach about 75%. This structure is simple and easy to fabricate, and this design concept can also be used in various other application fields.  
absorption; metamaterials; resonance +
Footnote †: journal: Applied Sciences
## 3 Related Works
### Modeling of Video as Curve on Riemannian Manifold  
The key point here is to account for the temporal features and spatial features of video simultaneously. Profiting from explicit landmark configurations, several recent works modeled each sequence of facial expressions in video as a curve or a trajectory on Riemannian manifolds. [PERSON] et al. [25] represented a sequence of faces as a sequence of facial landmarks. Since landmark configuration on each face is a full rank \(D\times d\) matrix encoding \(d\) dimensional coordinates of \(D\) landmarks, a sequence of facial expression can be considered as a curve on the Grassmannian manifold \(Gr(d,D)\) with the neutral face as the starting point. To classify the curves modeled, the linear discriminant analysis (LDA) and multi-class SVM are applied. Taking texture information into consideration, [PERSON] et al. [16] encoded deep convolutional neural network features extracted from human faces by covariance descriptors so as to model the temporal revolution of facial expression as trajectory on SPD manifolds.  
Besides its role in the field of facial expression recognition, temporal modeling based on landmarks on Riemannian manifolds can extend to action recognition [17; 26; 27; 28; 29]. [PERSON] et al. [17] represents each \(D\times d\) landmark matrix of skeleton by a \(D\times D\) positive semidefinite Gram matrix. By doing so, the temporal evolution of skeletons is represented by a time series of their corresponding Gram matrices, which can be considered as a trajectory connected by pseudo-geodesics [23] on positive semidefinite cone \(Sym_{+}^{D}\). [PERSON] et al. [26] used square-root velocity function to construct trajectories in a n-dimensional space representing skeleton sequences. [PERSON] et al. [27] represented the landmark configuration sequence as the trajectory in Kendall shape space and encoded the trajectory through the dictionary learned in the sample set to generate Euclidean sparse time series.  
However, all these works rely on geometric information from landmarks. These methods not only consume too much to detect landmarks in each frame, but also are not friendly to video without clear landmarks. Moreover, features extracted from pixel-level landmarks are limited. Distinct from works introduced above, in this paper, we focused on modeling curves of ROIs. By determining the pattern of ROIs, different applications of video recognition can be realized, but not only facial expression recognition and action recognition.
## 2 Materials and Methods
### Data Analysis  
The force signals were sampled at 1000 Hz and were further automatically processed in the manufacturer's software (Analysis and Reporting Software, S2P, Ljubljana, Slovenia). A moving average filter (window: 5 ms) was applied, after which the RFD was calculated as \(\Delta\) force/\(\Delta\) time for 0-50, 0-100, 0-150 and 0-200, as well as for 50-100, 100-150 and 150-200 ms time intervals, after the onset of the contraction. To ensure an accurate determination of force-rise onset, manual marker positioning was done within the software, as recommended in the literature [1]. Peak torque was determined from the sustained contraction as the mean value of the 1 s interval. For all of the outcome variables, the best repetition (i.e., highest value) was considered for the main statistical analyses (differences and correlations). All values for the respective task (3 for sustained and 6 for ballistic contractions) were considered for the reliability analysis. The outcomes were not normalized to peak force or body mass, as the main purpose was to investigate the effect of contraction type (within-subject factor).
###### Abstract  
The microwave planar cutoff probe, recently proposed by [PERSON] et al. is designed to measure the cutoff frequency in a transmission (S\({}_{21}\)) spectrum. For real-time electron density measurement in plasma processing, three different types have been demonstrated: point-type, ring-type (RCP), and bar-type (BCP) planar cutoff probes. While [PERSON] et al. has shown that the RCP and BCP are more suitable than the point-type probe for process monitoring, the basic characteristics of the ring- and bar-type probes have yet to be investigated. The current work includes a computational characterization of a RCP and BCP with various geometrical parameters, as well as a plasma parameter, through a commercial three-dimensional electromagnetic simulation. The parameters of interest include antenna size, antenna distance, dielectric thickness of the transmission line, and input electron density. Simulation results showed that the RCP has several resonance frequencies originating from standing-wave resonance in the S\({}_{21}\) spectrum that the BCP does not. Moreover, the S\({}_{21}\) signal level increased with antenna size and dielectric thickness but decreased with antenna distance. Among the investigated parameters, antenna distance was found to be the most important parameter to improve the accuracy of both RCP and BCP.  
plasma diagnostics; electron density measurement; planar microwave cutoff probe; bar-type cutoff probe; ring-type cutoff probe; computational characterization +
Footnote †: journal: Applied Physics Lab for PLasma Engineering (APPLE), Department of Physics, Chungnam National University, Daejeon 34134, Korea; [EMAIL_ADDRESS]
## 6 Proposed Methods
### Model Evaluation  
With its purpose of evaluating the robustness of an algorithm applied in a system with numerous variables that can affect or generate perturbations in the input parameters, the total sensitivity model evaluation method was used. [PERSON] et al. evaluated different stochastic approaches to evaluate the sensitivity indexes, allowing a comparison to be made between the input parameters with respect to their influence on the points of interest [48]. To evaluate the results of the time series analysis we used the root mean square error (\(RMSE\)) [49] and the correlation coefficient (\(CC\)) [50]. The variable \"\(M\)\" represents the values of the records modeled by the recurrent networks (Elman, LSTM, and GRU), the variable \"\(R\)\" is the actual records, and \"\(n\)\" the number of total data.  
\(RMSE\) represents the difference between the data generated by the network and the actual data [49].  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(M_{i}-R_{i})^{2}} \tag{13}\]  
\(CC\) generates values between \(-1\) and \(1\), being the closest to one in a positive way means that the predicted values (\(M\)) are closer to the real values (\(R\)). In other words, if the \(CC\) is equal to \(1\), there is no difference between the modeled data and the real data, while if a negative value is obtained it means that a mirror behavior to the real data was obtained [50]. Equation (14) shows the sections corresponding to the calculation.  
\[CC=\frac{S_{MR}}{\sqrt{S_{M}S_{R}}} \tag{14}\]  
where:  
\[S_{MR}=\frac{\sum_{i=1}^{n}\bigl{(}M_{i}-\overline{M}\bigr{)}\bigl{(}R_{i}- \overline{R}\bigr{)}}{n-1} \tag{15}\]  
\[S_{M}=\frac{\sum_{i=1}^{n}\bigl{(}M_{i}-\overline{M}\bigr{)}}{n-1} \tag{16}\]  
\[S_{R}=\frac{\sum_{i=1}^{n}\bigl{(}R_{i}-\overline{R}\bigr{)}}{n-1} \tag{17}\]
## 4 Verification and Discussion of the Proposed Method
### The Fault Analysis of the SNR Effect  
The detection of the side-band frequency was applied to the simulated signal for 2/3 tooth breakage with different levels of noise added. Because there is no external noise, the simulated signal for the faulty gear is very a clear impulse signal, as shown in Figure 9a. However, the vibration signal is weak for an early fault diagnosis of a gearbox and often buried under background noise. Next, the simulated signal with different white noise (10 dB, 5 dB, 0 dB, \(-\)5 dB, and \(-\)10 dB) was processed by the proposed method. The waveforms are shown in Figure 9b-f in the time domain. The impulse signals become increasingly misty as the SNR decreases, especially with noise under 0 dB.  
The corresponding waveforms and envelope spectra of the faulty component information are extracted by using EWT and a square envelope, respectively. The results are shown in Figures 10 and 11. The fault feature information is basically reserved above \(-\)5 dB of noise, and the side-band components are obtained by envelope analysis. This indicates that the frequency feature of the faulty component of a gear is effectively extracted by the proposed method. The fundamental frequency equals the result of the theoretical calculation, and the amplitude value of the order frequency remains stable.  
Figure 10: The impulse component with EWT method, (**a**) no noise, (**b**) 10 dB, (**c**) 5 dB, (**d**) 0 dB, (**e**) \(-\)5 dB, and (**f**) \(-\)10 dB of noise.  
Figure 9: The simulated signal with added noise, (**a**) no noise, (**b**) 10 dB, (**c**) 5 dB, (**d**) 0 dB, (**e**) \(-\)5 dB, and (**f**) \(-\)10 dB of noise.  
In Figure 12, the proposed signal processing can effectively extract the side-band frequency component, and stably obtain the fixed amplitude value where the SNR level is low. As a result, the proposed method indicates that it can largely enhance the representation of the faulty gear component and make the amplitude of the side-band frequency more stable under the strong noise.