esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Delay Spread  
The RMS delay spread is a fundamental metric in wireless communications, which allows us to measure the effect of multi-path propagation in this type of system. In our case, it will allow us to evaluate the multi-path effect of the UM-VLC channel components in the UM environment since this model has components that generate temporal dispersion, which degrades the channel bandwidth. The irregular tunnel walls and dust particles in the environment generate multiple random reflections and scattering of the original light signal. These effects cause splitting of the signal that propagates through the channel in different paths and distances, which arrive at the PD in a different period of time and induce a delay. The time difference between the arrival of signals at the PD and the particular delay after the arrival of the first light signal, which is usually the LoS component, is declared as excess delay \(\tau_{p}\).  
The temporal dispersion has a direct relation with the UM-VLC CIR. Therefore, the RMS delay spread of the UM-VLC channel can be expressed as  
\[D_{RMS}=\sqrt{\frac{\sum_{p=0}^{p}(\tau_{p}-\mu_{RMS})^{2}\;h_{mp}^{2}}{\sum _{p=0}^{p}h_{mp}^{2}}}, \tag{43}\]  
Figure 6: Empirical CDF of the SNR obtained for different values of \(N\) in the hemispheric area of the UM-VLC scenario evaluated.  
where \(h_{mp}^{2}\) represents the sum of samples numeric of all the channel components that form it with a maximum number of samples \(P\) and the mean delay spread \(\mu_{RMS}\), which is given by [16]  
\[\mu_{RMS}=\frac{\sum_{p=0}^{P}\tau_{p}\ h_{mp}^{2}}{\sum_{p=0}^{P}h_{mp}^{2}}. \tag{44}\]  
The behavior of the RMS delay spread metric is plotted in terms of empirical CDFs and distributions in the UM-VLC scenario by varying the \(N\) values between 40 and 200 dust particles, as can be seen in Figure 7. If we compare the result of RMS delay spread obtained in work [12] (see Figure 7f), with any of the curves and distributions obtained in Figure 7, we can notice that the numerical values obtained in this manuscript for any value of \(N\) are higher. This finding would also contribute to the deduction that the model of hemispherical dust particles presented in this work is closer to the reality of the UM-VLC scenario because the light signal has a more evident time delay. Although all RMS delay spread distributions for all \(N\) values share the same random trend in the UM scenario, we can observe that the higher the \(N\) values, the RMS delay spread values also tend to grow.  
Figure 7: _Cont._
# Rapid Construction of Aerocapture Attainability Sets Using Sequential Convex Programming
## 5 Numerical Demonstrations  
As Mars is a hot destination for deep space exploration, the Mars aerocapture was taken as an example to verify our proposed algorithm. The radius of Mars is \(R_{M}=3396\) km. The gravitational parameter of Mars is \(\mu_{M}=42\),\(828\) km\({}^{3}\)/s\({}^{2}\). The Orion Multi-Purpose Crew Vehicle (MPCV) [28] was used in our simulations. The mass of the Orion MPCV is 10,387 kg. The spacecraft always flies a trim angle-of-attack profile and the hypersonic lift-drag ratio \(L/D\) is 0.27. The lift and drag coefficients are \(C_{D}=1.37\) and \(C_{L}=0.37\), respectively. The reference area of the vehicle is 19.86 m\({}^{2}\). The initial states entering the atmosphere of Mars are listed in Table 1[3].  
In the construction of the aerocapture attainability set, the lower boundary given in Equation (6) was applied with a minimum altitude of apoapsis \(h_{a,\text{min}}=200\) km after the atmospheric flight. The terminal orbit was estimated with the size of 300 \(\times\) 3000 km to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**States** & \(h_{0}\)**, km** & \(V_{0}\)**, m/s** & \(\gamma_{0}\)**, deg** \\ \hline Values & 128 & 5900 & \(-11\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial conditions of Mars aerocapture.  
Figure 6: Illustration of the initial profile of radius distances and flight-path angle.  
initialize the terminal velocity in Equation (8). The initial adaptive step of terminal velocity was \(\Delta_{f}^{1}=100\) m/s. The stopping criterions were \(\Delta\gamma_{f}=\Delta\gamma_{f,\min}=\gamma_{f,\max}^{k}=0.01\) deg.  
In the solving of optimal control problems, the lower and upper bounds of the bank angle in Equation (12) were set to be \(\sigma_{\min}=\)15 deg and \(\sigma_{\max}=165\) deg, respectively. For the case of \(D\leq 0.1\,g_{M}\), the states were integrated directly with zero bank angle because of the weak modulation of the vehicle in the thin atmosphere. The trust-region boundary in Equation (25) was \(\delta=[6000/R_{M},\pi/4]^{T}\). The stopping criterion of successive approximation in Equation (29) was \(\varepsilon=[200/R_{M},0.05\pi/180]^{T}\). The problem was discretized by \(N=300\) and solved by the state-of-the-art primal-dual interior point method (PDIPM), which is efficient and accurate in solving convex programming problems without initial guess [51] using the mature software MOSEK [52]. All computations were performed via MATLAB-R2018a on a desktop with Intel i7-4790K CPU @4.00 GHz.  
In addition, various simulations with different initial states and parameters of the vehicle were also investigated to determine the impact of different parameters on the constructed aero capture attainability set.
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 3 Error Bound Analysis
### Metric of Single Measurement
#### 3.1.2 Metric of TOF  
Here, by selecting the TOF as the measurement to conduct the indoor localization, the distance between the locations \((x_{i},y_{i})\) and \((x_{k}\,y_{k})\) can be calculated by:  
\[\widehat{d_{ik}}=c\cdot t_{ik}+\chi_{2}=\sqrt{\left(x_{i}-x_{k}\right)^{2}+ \left(y_{i}-y_{k}\right)^{2}}+\chi_{2} \tag{14}\]  
where \(t_{ik}\) is the time of flight of the signal from the \(k\)-th AP to the receiver located at \((x_{i},y_{i})\). \(c\) is the flight speed. \(\chi_{2}\) is a random variable that follows the Gaussian distribution with the mean zero and variance \(\sigma_{2}{}^{2}=c^{2}\sigma_{\text{r}}{}^{2}\), \(N\left(0,\sigma_{2}{}^{2}\right)\). \(\sigma_{\text{r}}{}^{2}\) is the variance of time of flight of the signal. The PDF of \(\mathbf{d}_{ik}\) with respect to \(\theta_{i}\) is:  
\[f_{\theta_{i}}\left(\mathbf{d}_{ik}\right)=\prod\limits_{k=1}^{m}\frac{1}{\sqrt{2 \pi}\sigma_{2}}\exp\left(-\frac{\xi_{i2}{}^{2}}{2\sigma_{2}{}^{2}}\right) \tag{15}\]  
where \(\xi_{i2}=\widehat{d_{ik}}-\sqrt{\left(x_{i}-x_{k}\right)^{2}+\left(y_{i}-y_{k }\right)^{2}}\). Based on (5), we can calculate that:  
\[\left\{\begin{array}{c}J\left(\theta_{i}\right)=\left[\begin{array}{cc}J_ {xx}\left(\text{TOF}\right)&J_{xy}\left(\text{TOF}\right)\\ J_{yx}\left(\text{TOF}\right)&J_{yy}\left(\text{TOF}\right)\end{array}\right] \\ J_{xx}(\text{TOF})=\rho_{2}\sum\limits_{k=1}^{m}\left(\cos\alpha_{ik}\right)^{ 2}\\ J_{xy}(\text{TOF})=J_{yx}(\text{TOF})=\rho_{2}\sum\limits_{k=1}^{m}\sin\alpha _{ik}\cdot\cos\alpha_{ik}\\ J_{yy}(\text{TOF})=\rho_{2}\sum\limits_{k=1}^{m}\left(\sin\alpha_{ik}\right)^{ 2}\end{array}\right. \tag{16}\]  
Figure 1: Geometric relations between the \(i\)-th real location and \(k\)-th Access Point (AP).  
where \(\rho_{2}=\left(\frac{1}{c\cdot\sigma_{x}}\right)^{2}\). Based on (8), the localization error bound by using the metric of TOF with respect to \(\theta_{i}\) equals:  
\[\begin{split} V_{m}\left(\text{TOF}\right)&=\frac{{{{ \int_{yy}}\left(\text{TOF}\right)+{{I}_{xx}}\left(\text{TOF}\right)}}}{{{{ \int_{xx}}\left(\text{TOF}\right){{\int_{yy}}\left(\text{TOF}\right)-{{I}_{yy }}^{2}\left(\text{TOF}\right)}}}}\\ &=\frac{1}{\rho_{2}}\cdot\frac{\sum\limits_{k=1}^{m}{{{\frac{ \sin\alpha_{ik}}{{2}}}}^{2}+\sum\limits_{k=1}^{m}{{{\left(\sin\alpha_{ik}\right) ^{2}}}}^{2}}}{\sum\limits_{k=1}^{m}{{{\left(\cos\alpha_{ik}\right)^{2}}}^{2}} \times\sum\limits_{k=1}^{m}{{{\left(\cos\alpha_{ik}\right)^{2}}}^{2}}-\left( \sum\limits_{k=1}^{m}{{{\sin\alpha_{ik}}}\cdot\cos\alpha_{ik}}\right)^{2}} \end{split} \tag{17}\]
## 3 Methodology
### Feature Extraction Methodology
#### 3.1.2 Characteristics of Time-Frequency Domain  
It is not comprehensive to extract the signal features only from the time domain. In order to extract more abundant features, this paper also introduces the empirical mode decomposition (EMD) method to extract the time-frequency domain features of the signal. EMD is a new adaptive signal time-frequency processing method proposed by [PERSON] that is especially suitable for the analysis and processing of nonlinear and nonstationary signals. The essence of the empirical mode decomposition (EMD) method is to identify all vibration modes contained in the signal through the characteristic time scale. In this process, the characteristic time scale and the definition of IMF have certain experiences and approximations. Compared with other signal processing methods, the EMD method is intuitive, indirect, a posteriori, and adaptive. The characteristic time scale used in its decomposition is derived from the original signal.  
The purpose of EMD decomposition is to obtain the eigenmode function. EMD decomposes the input signal into several eigenmode functions and a residual; the process is as follows:  
1. Find all extreme points of signal \(I(n)\).
2. Use a cubic spline curve to fit the envelope \(\text{Emax}(n)\) and \(\text{Emin}(n)\) of the upper and lower extreme points, and find the average value \(m_{1}(n)\) of the upper and lower envelope and subtract it from \(I(n)\): \[h(n)=I(n)-m_{1}(n)\] (4)
3. Judge whether \(h(n)\) is IMF according to preset criteria:In the whole time range of the function, the number of local extreme points and zero crossing points must be equal or be at most one difference; At any time point, the envelope of the local maximum (upper envelope) and the envelope of the local minimum (lower envelope) must be zero on average;
4. If not, replace \(I(n)\) with \(h(n)\) and repeat the above steps until \(h(n)\) meets the criteria; then \(h(n)\) is the \(IMF_{m}(n)\) to be extracted;
5. Each time the \(IMF_{m}(n)\) is obtained, it is deducted from the original signal and the above steps are repeated until the last remaining part \(RES_{M}(n)\) of the signal is only a monotone sequence or a constant value sequence. In this way, the original signal \(I(n)\) is decomposed into the linear superposition of a series of \(IMF_{m}(n)\) and the remaining parts: \[I(n)=\sum_{m=1}^{M}IMF_{m}(n)+RES_{M}(n)\] (5) where \(I(n)\) indicates the input signal, \(IMF_{m}(n)\) represents the eigenmode function of \(m\)th, and \(RES_{M}(n)\) indicates the residual.  
The EMD energy of \(m\)th is determined by the amplitude of \(IMF_{m}(n)\), which is defined as follows:  
\[E_{m}=\sum{(IMF_{m}(n))^{2}} \tag{6}\]  
The EMD energy spectrum is defined as follows:  
\[E=\sum_{m=1}^{n}E_{m} \tag{7}\]  
\[Level\ m=E_{m}/E \tag{8}\]
## 3 Data and Model
### A Lagged Dependent Variable and Control Variables  
In addition to the dependent and independent variables, we included a lagged dependent variable or the dependent variable of one previous period (\(\text{WSV}_{\text{t}-1}\)) as an independent variable. This variable captured the effect of the volume of the previous day's web searches on the volume of the present-day web searches without considering marketing activities. We did this to test whether a dynamic effect existed in the web search volume.  
We also included two sets of control variables in our model. First, we identified the characteristics of the movies. We included the genre (eight different genres), format (whether they were regular movies or three-dimensional stereoscopic (3D) ones), and IP (whether they were adapted from intellectual properties (IPs) which had been popular in recent years). Specifically, IP movies can be described in the following way: \"IP is a pre-existing property that sees a film studio hire screenwriters, actors, and producers to adapt into a film. This means huge franchises, comic book characters such as SpiderMan, and all remakes of other movies and shows are IP\" (source: [[https://www.thefocus.news/culture/ip-movies-meaning/](https://www.thefocus.news/culture/ip-movies-meaning/)]([https://www.thefocus.news/culture/ip-movies-meaning/](https://www.thefocus.news/culture/ip-movies-meaning/))) (accessed on 30 December 2021). We considered these variables because public reactions toward movies are known to be influenced by such movie characteristics [42]. We also included a monthly dummy to control the seasonality effect.
## 5 Conclusions  
This study presents the development and validation of the MANTiS-32 wireless sensing system, a low-cost, high-precision monitoring solution tailored for performance-based maintenance of small- to medium-span bridges. Built upon an open-source platform, the system integrates ESP32 microcontrollers with MPU-6050 inertial sensors and supports  
Figure 23: Real-time mode shape identification using MANTiS-32.  
Figure 24: MAC analysis for validation of MANTiS-32 modal sensing performance.  
real-time data transmission via Wi-Fi communication. Each MANTiS-32 hub is capable of interfacing with up to eight MPU-6050 units, enabling scalable deployment across bridge structures.  
Initial validation confirmed the system's fundamental sensing capabilities, including accurate acceleration measurements under both impulse loads and ambient excitations, such as human footsteps. The MANTiS-6050 sensors demonstrated sufficient sensitivity for dynamic response capture, and the MANTiS-32 hub processed and transmitted the data with no observable latency or signal bottlenecks.  
Wireless communication between MANTiS-32 hubs and the access point was executed using predefined packet blocks, effectively mitigating synchronization issues. Reliable data acquisition was achieved in real time, even when concurrently streaming from 26 distributed sensors across four hubs. The system maintained stable performance at up to an 80 m communication range, and the potential for further extension was verified by the scalable use of additional APs.  
To assess the system's capability in real-world applications, a 1:30-scale model of a cable-stayed bridge was instrumented with 26 sensors for real-time ambient modal analysis. The modal parameters--natural frequencies and mode shapes--were successfully extracted. Comparison with finite element analysis revealed frequency discrepancies within 1.6%, and mode shape correlation using the Modal Assurance Criterion (MAC) showed a high level of agreement (90-98%).  
In conclusion, the MANTiS-32 system demonstrates strong potential as a robust, energy-efficient wireless SHM solution for small- to medium-scale bridges. Its ability to acquire synchronized, high-fidelity data and support real-time modal analysis validates its effectiveness for deployment in structural performance monitoring and condition-based maintenance strategies. However, the MANTiS-32 system may be limited in its ability to accurately capture the dynamic responses of long-span bridges, particularly those characterized by low-frequency (0.1-1 Hz) and low-amplitude vibrations.
## 3 CENTERA: Our Centralized Trust-Based Efficient Routing Protocol with Authentication
### Initialization Block  
Initially and prior to network launching, the WSN administrator must study the network specifications and needs, including the expected size of the network, the nature of the application, the sensitivity of the data being sensed, and the hostility of the environment where the network will be deployed, among others. Based on the results, the administrator decides on the different parameters for the function of the system.  
Among the decisions is the message size that depends on the number of nodes in the network; _i.e._, any network having less than 256 nodes requires an ID size of 8 bits, whereas the ID size must be greater than that for a network with more than 256 nodes.  
Another very important decision is the authentication technique, if any, to be used in the network. So, depending on the hostility of the network and its unattended environment, the administrator chooses to use a strong asymmetric authentication system, like IBE-ECC or PBC, a lighter symmetric system such as the RC5, or not to use any authentication. The administrator decides on the sizes of the used keys and appended MAC, considered x bytes in size. Following this decision, the administrator creates and installs the network master key in all the nodes, in case of the RC5 symmetric cipher choice, or each node's unique private key, in case of the PBC choice.  
Other parameters include the hop costs used in [PERSON]'s algorithm, the activity period to send an activity/neighbor report, the keep-alive period, the number of allowed problems before a node is considered bad, and the number of periods to decrement the number of problems or level of bad behavior. These parameters will be explained in more details in subsequent subsections. Of course, in this block the administrator installs all the identities, the chosen authentication algorithm, and all the required algorithms and parameters for the proper functioning of CENTERA.
## 3 Results
### Classification Results after Wavelength Reduction Using LDA  
#### 3.4.1 Tissue Slices  
Table 4 shows the classification results using LDA and the tissue slices dataset before and after wavelength reduction. In a similar approach described in [15], the splitting of the whole dataset into a training set (70% of the images) and a test set (30% of the images) was performed randomly while keeping spectra from one patient together. The recall was the percentage of pixels that were correctly classified as tumor or healthy tissue, using histopathologic assessment as ground truth. The classification results decreased after wavelength reduction: The performance of both tumor and connective tissue detection decreased from values of 99% to 79% and 94% to 68%, respectively. Only the detection of adipose tissue, with values higher than 94%, remained high after wavelength reduction.
# Reliable Semantic Communication System Enabled by Knowledge Graph
## 2 Related Work
### Performance Metrics
#### 2.2.2 METEOR Score  
METEOR extends the synonym set by introducing external knowledge sources, such as WordNet [38]. Furthermore, it uses precision \(P_{m}\) and recall \(R_{m}\) to evaluate the similarity between transmitted and received texts. The formula is given as follows  
\[F_{\text{mean}}\ =\ \frac{P_{m}R_{m}}{\alpha P_{m}+(1-\alpha)R_{m}} \tag{2}\]  
\[\text{METEOR}=(1-\text{Pen})F_{\text{mean}} \tag{3}\]  
where \(\alpha\) is the hyperparameter according to WordNet, \(F_{\text{mean}}\) represents the harmonic mean combining \(P_{m}\) and \(R_{m}\), and Pen is the penalty coefficient.
###### Abstract  
We aimed to examine the associations between ankle instability, identified by mechanical and functional assessments, and an individual's ability to cope unexpected perturbations. Sixty students were assessed for _five_ different mechanical/functional ankle instability assessments: the Cumberland Ankle Instability Tool (CAIT), history of previous ankle strains, the Ankle Instability Instrument (AII) questionnaires, proprioception ability, and mechanical instability. The point where participants lost postural balance due to an unexpected perturbation was recorded when participants were standing on BalanceTutor-Treadmill(r) with eyes open-SO, eyes closed-SC, tandem-dominant-leg forward-TD, tandem non-dominant-leg forward-TND, single-leg same side-SLSS, single-leg opposite side-SLOS and walking. Significant correlations were found between: CAIT and perturbation in the TND-position; AI scores and perturbations in TD and TND positions; ankle strains and perturbations in SC, ND, and NTD positions; and proprioception ability and perturbations in SO, SC, TD, TND, and walking (_p_ < 0.05). No correlations were found between mechanical assessments and perturbations. Survival-analyses showed significant differences in coping with perturbations between individuals identified with CAI in 4/5 mechanical/functional assessments compared to those with no-CAI in 4/5 assessments (_p_ < 0.05). Functional ankle instability and proprioception ability were associated with the ability to cope with unexpected perturbations when starting from different standing/walking positions. Individuals with'stable' ankle in most mechanical/functional assessments had better ability to cope perturbations than those with 'unstable' ankle.  
ankle stability; functional assessment; mechanical assessment; proprioception +
Footnote †: journal: _Applied Sciences_
# A Review of MEMS Scale Piezoelectric Energy Harvester
## 2 Research Progress of PEH
### Optimization of Energy Harvesters' Structure
#### 2.4.4 Impedance Matching for the PEH  
Once the PEH is designed and placed in the circuit, assuming that the output is simply terminated with a resistive load, the equivalent circuit can be constructed as shown in Figure 12. The PEH is regarded as an AC voltage in series with a capacitor [77]. Based on Equations (5) and (6), the system model is given in Equations (10) and (11).  
\[\ddot{\delta}=\frac{-k_{sp}}{mk}\delta-\frac{b_{m}}{m}\dot{\delta}+\frac{k_{ sp}d}{mt}V_{R}+\frac{3b}{2l2}\ddot{y} \tag{10}\]  
\[\dot{V}_{R}=\frac{-Yd\dot{g}}{k\epsilon}\dot{\delta}-\frac{1}{RC}V_{R} \tag{11}\]  
where  
\[k_{sp}=\frac{3 YI}{l^{3}} \tag{12}\]  
\[k=1-\frac{Yd^{2}}{\epsilon}=1-k_{ij}^{2} \tag{13}\]  
where \(k_{ij}\) represents the piezoelectric coupling coefficient, \(V_{R}\) the voltage across load resistance, \(C\) the capacitance of PEH, \(R\) the load resistance, \(I\) the moment of inertia of beam, \(t\) the thickness of piezoelectric material and \(y\) the displacement of input vibrations.  
Figure 11: Nonlinear PEH combining bi-stability and internal resonance. Reprinted by permission from [PERSON] et al. Mech. Syst. Signal Pr. 90, 317 (2017). Copyright 2017 Elsevier.  
When the external excitation frequency is equal to the resonant frequency of PEH, the output power can be analyzed from Equations (10) and (11) as given in Equation (14).  
\[|P|=\frac{RC^{2}{(\frac{\gamma dg}{\mathcal{K}})}^{2}}{\left(2\zeta\omega^{2}RC \right)^{2}+\left(\omega^{2}RC(1-k)+2\zeta\omega\right)^{2}}\frac{3b}{2l^{2}} |A|^{2} \tag{14}\]  
where \(A\) is the acceleration magnitude of the external excitation.  
According to Equation (14), the output power of the PEH is relevant to the load resistance. It can be maximized by match the internal impedance and the load resistance.  
In general, more efforts are made to design proper circuits to match the impedance for the PEH. The interface circuit includes three components: an AC-DC rectifier, a voltage regulator and an energy storing device. The most commonly used rectifiers are full-wave and half-wave bridge rectifiers [78]. When the load impedance is the complex conjugate of the source impedance, the maximum power transfer is achieved.  
In addition, the internal impedance of the PEH can be reduced. [PERSON] et al. [79] fabricated multilayer piezoelectric thick films by using the tape-casting process. By using an impedance analyzer, the capacitance of the five-layer device was measured to be 241.04 nF, which was 32.88 times higher than that of the single-layer device. It resulted that the impedance matching load of the five-layer device for maximum power transfer was on average 34.05 times smaller than that of single-layer. As capacitance increased, the energy stored in the 220 \(\mu\)F capacitor for 10 s using the five-layer device was 920% larger compared to the energy stored using a single-layer.  
The improved PEHs mentioned in this section are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
Figure 12: Equivalent circuit for a PEH.