esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Assessment
### Measurements Acquisition and Characterization  
In order to carry out a study that was as detailed as possible on the impact of public attendance on the service performance, measurements at different layers were gathered. As described in Section 3.1.2, in the scenario, two probes were deployed at the same site within the data-center of the stadium where LTE coverage exists. The probes were node 371, equipped with two radio interfaces connected to both operator A and operator B, and node 361 with one interface connected to operator B.  
This deployment was carried out in such a way that, despite being physically in the same place, two different average values of the signal received from operator B could be used for comparison. Furthermore, the existing hardware resources in both nodes were identical in terms of RAM (4 GB), Ethernet connection for management (1000 MBps) and available hard disk (16 GB), terebys emulating the same configuration (instantiation in the case of vUE probes). These settings emulate two possibilities of use under the same resources.  
In addition to the MODEM parameters described in the previous section, an open-source traffic sniffer Mobile Insight [22] was used to incorporate the signalization packets from all LTE radio layers: Packet Data Convergence Protocol (PDCP), Network Access Stratum (NAS), Radio Resource Management (RRM), Radio Resource Control (RRC), Media Access Control (MAC) and physical (PHY). Based on these traces, radio layer indicators were extracted to help deepen the understanding of the influences on service performance and cross-layer effects.  
Two measurement campaigns have been conducted over 16 months between August 2018 and December 2019, capturing the event days. operator 1 recorded the impact of the Web service in 12 soccer matches every hour from 10:00 to 00:00 jointly with full-day MODEM Logs. In campaign 2, the impacts on FTP, NetTest and ping services (jointly with MODEM and Mobile Insight Logs) of 6 matches were captured with a finer granularity (15 min) from 2 h before and until 2 h after. The Table 3 shows the average number of samples captured per node.  
Figure 2: Approximate locations [41] of network elements in the stadium scenario.
## 2 Configurations and Design Principles
### S-/C-Band Elements and Generation of CP  
It is well known that increasing the dielectric constant of the substrate (while reducing the dimensions of the antenna) results in a narrower bandwidth [17]. At the same time, increasing the thickness of the substrate is helpful to increase the operating bandwidth  
Figure 1: Shared-aperture phased array lattice (**a**) array lattice; (**b**) array lattice with traditional PCB substrate. (The yellow ones are C band elements and the red ones are S band elements).  
(by reducing the quality factor). To ensure the spacing between the two nearest elements is as large as possible, we truncated the ceramic substrate to a size that is slightly larger than the size of the printed patch, which shows an extra contribution to reduce the surface wave loss.  
Figure 2 shows the configuration of the CP element for the C-band. The antenna is composed of a top circular metallic patch (radius R\({}_{\rm p}\)), a ceramic substrate (with a dielectric constant of 15, tan\(\delta\) = 2.2 \(\times\) 10\({}^{-4}\), and thickness h of 6 mm), and a metal ground plane. Notably, a slot is cut on the top metallic patch, which produces the CP radiation characteristics and increases the bandwidth by changing the current flow path on the surface of the patch antenna (the slot is deviated from the \(y\)-axis by 45\({}^{\circ}\)). Furthermore, the slot also contributes to a further diminution of the antenna's overall dimensions, thereby enhancing its suitability for the compact layout of the array where space is also a limiting factor. The feed probe is offset from the \(Y\)-axis to ensure a good impedance match (the proposed antennas are all fed by coaxial cables). In order to ensure the flexibility of the sequential rotating installation of the above structure, the corners are cut on both sides of the patch and the ceramic substrate. Other detailed structural parameters of the element antenna are listed in the caption of Figure 2.  
The configuration of the S-band element is shown in Figure 3. The square substrate with the same dielectric constant as that of the C-band antenna element is adopted. The element incorporates four truncated corners, a design feature that helps in optimizing the radiation characteristics and impedance matching. A slot in the center of the metallic patch is used to increase impedance bandwidth and decrease the antenna size further (the slot is deviated from the \(y\)-axis by 45\({}^{\circ}\)).  
Figure 3: Geometry of the S-band antenna. Dimensions are Ws = 32, Wp = 28, h = 6, S1 = 1.7, S2 = 13.5, L1 = 3.8, L2 = 3.7. (unit: millimeter).  
Figure 2: Geometry of the C-band antenna. Dimensions are R\({}_{\rm s}\) = 9.5, R\({}_{\rm p}\) = 8.2, h = 6, a = 11.2, b = 7.5, u = 1.3. (unit: millimeter).
# Acoustic Emission Analysis of the Cracking Behavior in ECC-LWSCC Composites
## 7 Results and Discussion
### Crack Progression Investigation Using b-Value and Ib-Value Analyses
#### 7.2.2 Evaluation of Crack Development by \(b\)-Value and \(lb\)-Value Analyses  
The ECCP-T (sensor 1) beam was chosen as a typical example for all beams and sensors. Figure 3 shows the trends resulting from both the \(b\)-value and \(lb\)-value analyses for the ECCP-T beam (sensor 1). Referring to Figure 3a, the \(b\)-value followed a generally decreasing trend. The decrease in \(b\)-value magnitudes indicates crack propagation leading to failure throughout the test duration. Generally, lower \(b\)-values correspond to higher cracking activity. Furthermore, the first noticeable decrease, around 120 s, represents the occurrence of the first crack. The sudden drops in \(b\)-value were also correlated with changes in the AE parameters' slopes.  
Figure 3: \(b\)-value and \(lb\)-value variations for ECCP-T sensor 1: (**a**) \(b\)-value; (**b**) \(lb\)-value.  
In Figure 3b, the _lb_-value does not follow a clear trend, unlike the _b_-value. However, the sudden fluctuations (vertical drops) in Figure 3b indicate high cracking activity [50]. To pinpoint the periods of highest cracking activity, the difference between the maximum and minimum points of fluctuation is calculated. For example, in Figure 3b, the highest cracking activity (ultimate load) is observed around 3450 s (_lb_-value of 2.052 \(-\) 0.069 = 1.983). The first significant vertical drop (first crack) in Figure 3b is also around 120 s, similar to the _b_-value analysis for the first crack. Moreover, the fluctuation trends in Figure 3b resemble those in Figure 3a, showing similar cracking activities at corresponding time intervals.  
Table 5 presents the _b_-values and _lb_-values for all the beams. The ECCP and ECCS beams showed average _b_-values and _lb_-values of 2.19, 1.39, 1.516, and 1.62 at the first crack, respectively. The ECCP-C and ECCS-C beams exhibited average _b_-values and _lb_-values of 2.44, 1.39, 1.74, and 1.85 at the first crack, respectively. ECCP-T and ECCS-T resulted in average _b_-values and _lb_-values of 0.84, 0.95, 1.84, and 1.74 at the first crack, respectively. Both b-values and _lb_-values yield promising results for understanding and analyzing crack onset and development throughout the testing process.
# A Two-Dimensional mKdV Linear Map and Its Application in Digital Image Cryptography
## 1 Introduction  
The mathematical principles used in operating systems, database systems, and computer networks play an important role in cryptography. There are many algorithms for securing data that involve mathematics, two of which can be found in [1]. In addition to mathematical principles, mapping concepts can be used in encryption-decryption algorithms. A cryptographic encoding algorithm for digital images, namely, Arnold's Cat Map (ACM), was studied in [2], whereas the logistic map was outlined in [3], and the Nonlinear Chaotic Algorithm (NCA) was investigated in [4]. Additional cryptographic encoding algorithms can be found in [5], which outlines a chaotic cryptosystem based on a Henon map [6] and investigates an independent component analysis, and [7; 8; 9], which provide algorithms for all data file types. Furthermore, in [10], the authors suggest a fast color image encryption scheme containing both permutation and substitution key streams that have been quantified from a sequence extracted from the orbit of [PERSON]'s chaotic system.  
Recently, several approaches used in various encryption algorithms have concerned digital images. Two of the techniques were proposed in 2017 in [10; 11]. In [11], the authors proposed an algorithm that involves scrambling pixel positions and changing the intensities of the pixels (see [12] for a different method in hiding an image using reference point coding to embed data in the spatial domain). In this article, a two-dimensional linear map derived from a partial discrete equation (partial discrete mKdV equation) with Consistency Around the Cube (CAC) properties will be discussed (see [13; 14] for the construction of a generalized mKdV map and the investigation of its properties) and applied in cryptographic encoding algorithms for digital image data (see [15] for digital text data). This article consists of four Sections. In Section 1, previous research results (related to the use of mathematical concepts and algorithms in cryptography) are provided. In Section 2, the CAC properties of the partial discrete mKdV equation will be shown. In addition, a two-dimensional mKdV map derived from a partial discrete mKdV equationis described. Section 3 details the construction of a two-dimensional linear mKdV map and the application of the map in an encryption-decryption algorithm for image data. The Mathematica programming language was used to implement the algorithm. In the last section, the concluding remarks are provided.
## 2 Results and Discussion
### Stability in Human Plasma  
Sialorphin, spinorphin and their heterodimer (peptide **X**) were incubated in the human plasma to determine their stability. In stability assays in human plasma, peptide **X** was included because this compound was selected for in vivo assays of the anti-inflammatory activity. The combination of sialorphin and spinorphin analogs in a heterodimer resulted in peptide **X**, which exhibited increased stability in human plasma. As shown in Figure 1, peptide **X** displayed greater stability in plasma than did sialorphin and spinorphin. After 2 h of incubation, the levels of peptide **X** were, respectively, 10.3 and 4 times higher in relation to those of sialorphin and spinorphin. Sialorphin and spinorphin after 4 h of incubation in human plasma were completely decomposed, while as much as 42% of peptide **X** was detected. After 24 h, the level of peptide **X** dropped to 8%. This research showed that the combination of the sialorphin analog (peptide **IV**) and the spinorphin analog (peptide **VI**) in the heterodimer (peptide **X**) would be effective in the protection of the peptide against enzymatic degradation in plasma.
# Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review
## 3 Interpretation of Mining-Induced Microseismicity
### Spectral Analysis  
Microseismic waveforms are complicated and unstable time series, and these signals contain abundant information related to the coal/rock rupture process [87]. The frequency-spectrum characteristics of microseismic waveforms are robust due to their consistencies with physics and mathematics [88]. The commonly used waveform analysis methods for microseismicities consist of frequency spectrum analysis and time-frequency analysismethods based on the Fourier transform. The Fourier transform can transfer the seismic waveform into the signal series in the frequency domain conveniently, as follows:  
\[F(f)=\int\limits_{-\infty}^{+\infty}x(t)e^{-j2\pi ft}dt \tag{5}\]  
where \(x(t)\) is the signal series of the seismic waveform, \(f\) is the frequency, \(t\) is the time, and \(j\) denotes the imaginary unit. The Fast Fourier transform is the most famous method for frequency-spectrum analysis. However, the Fast Fourier transform cannot realize frequency localization and is not able to provide information in the time domain. The time-frequency analysis methods, e.g., the Wavelet transform, the S transform, and the Hilbert-Huang transform, are utilized to study the time-frequency characteristics of mining-induced microseismicities. [PERSON] et al. [88] studied the frequency-spectrum characteristics of microseismic signals recorded in the laboratory coal/rock compression test and coal mine, and they concluded that the increase in the vibration velocity of the lower-dominant-frequency microseismic signal is an important precursory indicator for rockbursts. [PERSON] et al. [89] studied the frequency and time-frequency characteristics of microseismic signals generated in different construction procedures in the heading face, and some frequency parameters were employed in the long short-term memory network to warn of the outburst disaster. [PERSON] et al. [90] proposed a dual-channel convolution neural network to classify different seismic signals in coal mines, and the new network improved the classification performance by taking advantage of wavelet packet decomposition coefficients in terms of signal enhancement and noise cancellation. [PERSON] et al. [55] used the frequency parameters extracted by the S transform and Manifold Learning methods to identify the microseismic signals in the Xiashijie coal mine with a high accuracy of 94 percent. [PERSON] et al. [87] extracted microseismic features such as the waveform energy (Figure 7) using the Hilbert-Huang transform to study the time-frequency and energy characteristics of microseismic waveforms prior to the rockburst in the Qianqiu coal mine and drew some valuable conclusions for the improvement of rockburst prediction accuracy concerning the dominant frequency variation and energy accumulation.  
Figure 6: Seismic velocity tomography around longwall panels [86].
# Time-Dependent Probabilistic Approach of Failure Mode and Effect Analysis
## 2 Time-Dependent FMEA Model (TD-FMEA Model)
### Risk Evaluation Modeling
#### 2.2.1 Occurrence Time of a Failure and Its Failure-Cause  
Under the assumption of a homogeneous Poisson process (HPP), the occurrence time \(T\) becomes a function of the failure occurrence rate \(\lambda\). \(T\) can be considered exponentially distributed with mean \(1/\lambda\), and its probability density function is expressed as  
\[h_{T}(t)=\lambda e^{-\lambda t},\ t>0. \tag{1}\]  
Additionally, \(T^{\prime}\) is defined by the occurrence time of the failure-cause from its operation. As in Equation (1), \(T^{\prime}\) can be considered as a function of the failure-cause occurrence rate \(\varepsilon\) with its average value \(1/\varepsilon\). The corresponding probability density and cumulative distribution functions are  
\[h^{\prime}{}_{T^{\prime}}(t)=\varepsilon e^{-t}\text{and} \tag{2}\]  
\[H^{\prime}{}_{T^{\prime}}(t)=1-e^{-\varepsilon t}\text{with }t>0. \tag{3}\]
## 2 Modeling of UAV Observations
### Crop Canopy Reflectance  
The classic PROSAIL model is adopted to describe the contribution of crop canopy \(R_{\rm crop}\). The PROSAIL model, a fusion of PROSPECT (leaf reflectance and transmittance) and SAIL (plant canopy reflectance), is widely used for quantitative inversion of vegetation parameters [40; 41]. PROSAIL provides the calculation of the spectral and directional reflectance of the plant canopy [42]. However, the application of PROSAIL for crop parameter inversion requires reflectance after atmospheric correction. Traditionally, atmospheric correction is a separate process before inversion, executed by radiative transfer models, such as 6S and RT3. The inputs of the PROSAIL model and the range of parameters are given in Table 2.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter Types** & **Parameter Symbols** & **Parameter Description** & **Unit** \\ \hline \multirow{3}{*}{Geometry} & SZA & Solar zenith angle & Degrees (\({}^{\circ}\)) \\  & VZA & Viewing zenith angle & Degrees (\({}^{\circ}\)) \\  & SAA & Solar azimuthal angle & Degrees (\({}^{\circ}\)) \\  & VAA & Viewing azimuthal angle & Degrees (\({}^{\circ}\)) \\ \hline \multirow{3}{*}{Atmosphere} & Atmospheric type & The meteorological and air density profile & – \\  & Pressure & Surface pressure & hPa \\  & Altitude & Surface altitude & m \\ \hline \multirow{3}{*}{Aerosol} & AOD & Aerosol optical depth & – \\  & Ri & Complex refractive index of aerosol & – \\  & Profile & The vertical profile of aerosol & – \\  & PSD & Aerosol particle size distribution & – \\ \hline \multirow{2}{*}{Surface} & Lambertian & Lambertian surface reflectance & – \\  & BRDF & Surface bidirectional reflectance & – \\ \hline \multirow{2}{*}{Spectra} & Wavelength & Central wavelength of spectral channel & nm \\  & FWHM & Full width at half maximum of spectral channel & nm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main input parameters of the UNL-VRTM model.
## 4 Multi-Objective Optimization and Experimental Verification
### Verification of Experimental Results  
In this study, the design values of key parameters after optimization were verified through full-load condition optimization results, using existing test benches. The results before and after optimization are illustrated using the examples of 80%, 60%, and 40% loads. As shown in Figure 14, the vibration shock loads were 80%, 60%, and 40%, respectively, from top to bottom, the peak value of the ejection vibration shock decreased by 30-40%, and the peak value of the withdrawal shock decreased by 30-50%. Moreover, the impact phase of withdrawal did not deviate significantly, and the end-time of return basically remained unchanged; therefore, the regulation performance of the flow regulating system did not change significantly.  
To better verify the experimental results, we performed an RMS analysis of the jacking and withdrawing vibration impacts under different loads over a set period. As shown in Figure 15, the effective value of the optimized impact decreased significantly, indicating that the energy generated by the impact decreased significantly, which was conducive to reducing the impact wear of the unloader on the valve plate and of the valve plate on the valve seat.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Load**} & \multirow{2}{*}{**Evaluation Index**} & \multicolumn{6}{c}{**Test Project**} \\ \cline{3-8}  & & **Opt\_90** & **Opt\_80** & **Opt\_70** & **Opt\_60** & **Opt\_50** & **Opt\_40** & **Opt\_30** \\ \hline \multirow{4}{*}{90} & \(\Delta\
u^{re}_{uu}
## 2 Prognostic Approach
### Bayesian Posterior Estimation of Stochastic Parameters  
It is noticed that the key parameter for determining the degradation state is \(\theta\) in Equation (1). \(\theta\) is a random parameter and will be updated with the data obtained at the current moment \(t_{k}\). \(\theta\) is distributed as \(\theta\sim N\big{(}\mu_{\theta,k},\sigma_{\theta,k}^{2}\big{)}\), such parameter distributions are consistent with existing methods [17; 23].  
In order to estimate hyper-parameters \(\mu_{\theta,k},\sigma_{\theta,k}^{2}\) in the random parameter \(\theta\), the Bayesian posterior estimation is used in this paper.  
Firstly, it is assumed that the prior distribution of \(\theta\) is \(N\big{(}\mu_{0},\sigma_{0}^{2}\big{)}\). Then the chain recursion was incorporated into the calculation. The posterior distribution of \(P(\theta|X_{0:k})\) can be expressed as [17]:  
\[p(\theta|X_{0:k})=\frac{1}{\sigma_{\theta,k}\sqrt{2\pi}}\exp\Bigg{\{}-\frac{( \theta-\mu_{\theta,k})^{2}}{2\sigma_{\theta,k}^{2}}\Bigg{\}} \tag{3}\]  
Where  
\[\mu_{\theta,k}=\frac{\big{(}\mu_{0}\sigma^{2}+x_{k}\sigma_{0}^{2}\big{)}}{ \big{(}t_{k}\sigma_{0}^{2}+\sigma^{2}\big{)}},\sigma_{\theta,k}^{2}=\frac{ \sigma^{2}\sigma_{0}^{2}}{\big{(}t_{k}\sigma_{0}^{2}+\sigma^{2}\big{)}} \tag{4}\]  
It can be found in Equation (4) that the posterior estimate of the random parameter \(\theta\) can be updated after the new monitoring data.
# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 5 Discussion
### Limitations and Future Enhancement  
The precision of GPP estimation with SA-LGBM could be impacted by the composition of training data and the design of model variables. While we have screened flux sites for model training, data from regions with comparatively modest vegetation productivity are scarce. This limitation may result in reduced applicability of SA-LGBM in such areas, as observed in the less stable OSH results shown in Figure 10. Enhanced generalizations of SA-LGBM could be achieved by incorporating more flux tower observations encompassing a wider rang of vegetation types. This study considers the impact of spatial autocorrelation on GPP estimation models. Furthermore, it is essential to investigate the influence of spatial heterogeneity on modeling. Recent research has indicated the presence of additional information related to GPP in SIF data [69; 70; 71]. Whether the use of SIF can lead to improved GPP estimation beyond reflectance-based approaches needs to be confirmed.
# Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT
## 6 Results and Discussion
### Performance Based on End-to-End Delay  
The performance analysis of end-to-end delay with CAR-MOSOA and existing methods (FDEAM [30], EOMR [31], TSGWO [32], and CoCoA [33]) is illustrated in Figure 5. From Figure 5, it can be seen that CAR-MOSOA achieves less delay and continually attempts to transfer the data packet to the finest intermediary node, which gratifies the optimality feature. Quite the opposite, the conventional FDEAM [30], EOMR [31], TSGWO [32], and CoCoA [33] take a considerable period to determine the endpoint node. Moreover, the reply message of the existing methods returns to the source node, only after determining the destination node. Therefore, the typical end-to-end delay in the associated system is longer than that in the CAR-MOSOA method. Table 3 shows the comparative analysis of end-to-end delay performance. From the table, it can be seen that the proposed CAR-MOSOA achieves less time delay when compared with existing techniques.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of** **Nodes**} & \multicolumn{5}{c}{**Energy Consumption (J)**} & \multirow{2}{*}{**Proposed**} \\  & & & & **TSGWO** [32] & & **CoCoA** [33] \\ \hline
100 & 0.8 & 1 & 8 & 11 & 0.8 \\
150 & 7 & 9 & 15 & 15 & 5 \\
200 & 10 & 14 & 19 & 24 & 6 \\
250 & 14 & 19 & 26 & 29 & 9 \\
300 & 26 & 30 & 31 & 32 & 24 \\
350 & 29 & 36 & 37 & 41 & 24 \\
400 & 41 & 47 & 44 & 49 & 33 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative analysis of energy consumption.  
Figure 4: Performance based on energy consumption.