esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The effects of pyrolysis temperature, biochar particle size, and biochar application rate on hydrophysical parameters of sandy soil are presented in Table 3. Differences between control means and soil-BC treatment means were evaluated by \(t\)-test. The three-factor ANOVA testing results of the effects of pyrolysis temperature, biochar particle size, and biochar application rate and their interactions on soil hydrophysical parameters are presented in Table 4. The bold \(p\)-value indicates a statistically significant difference between means on a significance level of \(p\)\(<\) 0.05. Measured values of the hydrophysical parameters for all experiment treatments are presented in Figures 1-4. The box plots denoted with different letters significantly differ (HSD test) on a significance level of \(p\)\(<\) 0.05. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Soil Parameter**} \\ \hline  & & \multicolumn{2}{c}{\(P\) (\%)} & \multicolumn{2}{c}{**AWC (cm\({}^{3}\) cm\({}^{-3}\))**} & \multicolumn{2}{c}{\(K_{S}\) (cm h\({}^{-1}\))} & \multicolumn{2}{c}{**CA (\({}^{\circ}\))**} \\ \hline
**Factor** & **df** & **F** & **\(p\)** & **\(F\)** & **\(p\)** & **\(F\)** & **\(p\)** & **\(F\)** & **\(p\)** \\ \hline Pyrolysis temperature & 1 & 10.37 & **0.003** & 13.95 & **0.001** & 6.84 & **0.01** & 1.78 & 0.191 \\ Particle size & 1 & 217.42 & **<0.001** & 5.84 & **0.021** & 2458.54 & **<0.001** & 9.38 & **0.004** \\ Application rate & 1 & 12.01 & **0.002** & 364.48 & **<0.001** & 218.66 & **<0.001** & 20.96 & **<0.001** \\ Temperature \(\times\) size & 1 & 2.55 & 0.130 & 15.57 & **<0.001** & 0.43 & 0.509 & 0.20 & 0.66 \\ Temperature \(\times\) rate & 1 & 22.06 & **<0.001** & 2.34 & 0.135 & 0.26 & 0.61 & 0.003 & 0.956 \\ Size \(\times\) rate & 1 & 13.60 & **0.001** & 2.26 & 0.142 & 145.84 & **<0.001** & 0.014 & 0.904 \\ Temperature \(\times\) size \(\times\) rate & 1 & 0.09 & 0.765 & 0.009 & 0.927 & 0.002 & 0.963 & 0.072 & 0.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results (\(F\)-statistics and \(p\)-values) from a three-factor ANOVA testing for the effects of pyrolysis temperature, biochar particle size, and biochar application rate and their interactions on hydrophissical parameters (porosity, \(P\); available water content for plants, AWC; saturated hydraulic conductivity, \(K_{S}\); contact angle, CA) of sandy soil. The bold \(p\)-value indicates a statistically significant difference between means on a significance level of \(p\) < 0.05. (df = degree of freedom).
## 2 Experiments
### Experimental Scheme  
Before the main displacement-controlled tests, a group of uniaxial compression tests were first performed to obtain the basic mechanical parameters of rock samples. The average compressive strength of the rock samples was 80 MPa. The loading was imposed under the displacement-controlled mode with three different rates: 0.1, 0.5, and 1 mm/min, respectively. In each group, speckle photography was used for microscopic characterization from the start to the end of the loading process. Later, the collected images were subjected to the digital image correlation (DIC) analysis. The evolution of the displacement vector \(r(x,y)\) field at the marked points on the sample surface was tracked and recorded to provide data for the further analysis.  
A typical stress-strain curve of an intact rock sample can be roughly subdivided into the following four stages. The first stage is the compaction stage, where the curve bends slightly upwards, and the initial microcracks are closed due to the compression. The curve is a straight line at the second stage, so this is the linear elastic stage. At the third stage, the curve bends slightly downwards, and unstable microcracks are generated in the direction parallel to the loading direction. The third stage is called a non-elastic (plastic) stage, while the fourth one is referred to as the failure stage. At the plastic and failure stages, cracks appeared on the sample surface, so the DIC technology would fail to achieve the desired effect. Therefore, DIC is only applicable to the data at the first two (compaction and elastic) stages. In metals and alloys, features of strain localization that resemble those of the deformation waves have been observed at the linear elastic stage in the samples under compression or tension [31; 32]. In the present study, the deformation waves' propagation velocities at the linear elastic stage were of primary concern during the  
Figure 1: Red sandstone samples and relevant experimental equipment. (**a**) The rock samples, (**b**) the loading and speckle photography system, (**c**) the local image of loading.  
loading process. At this stage, the wave propagation velocity was closer to the slow waves' velocities observed in Earth's crust and exhibited similar evolution patterns.
# Test Method for Single Satellite's Inter-Satellite Link Pointing and Tracking via Ground Station
## 4 Numerical Simulation  
### Validation of Virtual Satellite with Respect to the Ground Stations  
In this section, the proposed ground station-based test method for inter-satellite link pointing and tracking is validated and evaluated.  
The behavior of the virtual satellite with respect to the ground stations is first validated. A single satellite test scenario for inter-satellite link pointing and tracking is established. The longitudes, latitudes, and altitudes of the ground stations are listed in Table 1. The mission epoch is set as 5:00 a.m. (UTC) on 1 September 2023. The local satellite is located in a geostationary orbit with a longitude of \(100^{\circ}\) E. The orbital altitude of the virtual target satellite is set as 600 km. The pointing vectors and time difference are calculated accordingly.  
Following the simulation conditions and the Newton-Raphson method, the orbital parameters of the virtual satellite are obtained as follows:  
\[a=6978\,\mathrm{km},\ e=0,\ i=30.9103^{\circ},\ \Omega=50.6987^{\circ},\ \omega=79.2623^{\circ},\ f=15.4974^{\circ} \tag{23}\]  
The iterative convergence curve is shown in Figure 5. The time difference \(\Delta t\) converges to 400.1 s after five iterations. The final error of the time difference is 5 ms. The results demonstrate that the proposed method can simulate the position of the virtual satellite using two ground stations.
## 2 Materials and Methods
### LSSC  
This study mainly focuses on LSSC, and its detailed description is presented in this section. The explanation on the other selection criteria can be found in [26; 27]. The conventional selection criteria for the MS of SVM assume that all the data in the training set have similar characteristics. However, there are certain degrees of differences in the characteristics among data from different subjects because of ISV. This study utilizes the LSSC to consider the ISVs of the physiological data and to measure the performance of the SVM model during MS. Algorithm 1 shows the detailed procedure used to calculate the LSSC, which is obtained for each tested SVM model, that is, the pair of \(C\) and \(\gamma\) values designated by the MS method. In the procedure, available data for MS are normalizedand separated into datasets for each of the subjects. If the data were measured from \(N\) subjects, then a total of \(N\) datasets are considered. Subsequently, the divided datasets are cross validated using the LOSOCV approach; this means that the dataset of a single subject is selected as the test data (\(TE\)), and the datasets of the remaining subjects are used as the training data (\(TR\)). The SVM is trained using the \(TR\) with the designated \(C\) and \(\gamma\) values, and the classification accuracy of the SVM is evaluated with the \(TE\). This evaluation process is repeated \(N\) times, and the LSSC is obtained as the averaged result from the \(N\) evaluation processes. This selection criterion is mainly used to represent the difference in data characteristics among the data of each subject to MS. Therefore, each SVM model is evaluated for the data of an unseen subject, and the results are averaged to derive the performance of the tested SVM model for data that had specific characteristic differences with the training data. A sample MATLAB code (version R2017b, MathWorks, Natick, MA, USA) that obtains the LSSC can be observed online ([[https://github.com/minho17/paper-Comparison-of-performance-measures](https://github.com/minho17/paper-Comparison-of-performance-measures)]([https://github.com/minho17/paper-Comparison-of-performance-measures](https://github.com/minho17/paper-Comparison-of-performance-measures)) (accessed on 3 December 2021)).  
```
Input - C and \(\gamma\) values to be tested - Data of \(N\) subjects that can be used for MS Output: evaluated performance (\(P\)) for the tested model \(P=0\)  Normalization for the data of \(N\) subjects For\(i=[1,N]\) \(TE\) = Dataset of subject \(i\) \(TR\) = {Dataset of subject \(j\) \(|\)\(j=1,...,N,j\
eq i\) }  Train SVM using \(C\), \(\gamma\), and \(TR\) \(ACC\) = classification accuracy of the SVM for \(TE\) \(P=P+ACC\) End For \(P=\frac{P}{N}\) Return \(P\)
```  
**Algorithm 1**Pseudocode used for LSSC in MS
## 1 Introduction
### Related Works
The first ensemble model used a linear meta-learner in form of logistic regression and the second ensemble model used a neural net as the meta-learner for classification. The second ensemble model achieved 98.1% accuracy. There is significant published work on the segmentation and classification of mammographic images. The fuzzy sets are notably useful and often used to address uncertainty issues and quantify uncertainty. [PERSON] et al. [26] proposed a fuzzy-based novel quality control algorithm for the segmentation of medical images. [PERSON] et al. [27] presented an algorithm based on fuzzy set methods for segmenting the boundaries of breast masses. [PERSON]. [28] refers to thresholding techniques in general and proposes a method where intuitionistic fuzzy sets can be used as a segmentation method for images that contain blood vessels and blood cells. [PERSON] et al. [29] used the BGWO feature selection technique and analyzed the percentage of features that the algorithm could reduce in order to achieve a high ROC area. The researchers achieved an accuracy of 78% and AUC 0.871 for the classification as benign or malignant. [PERSON] et al. [30] analyzed 14 classifiers of deep learning methods that were categorized into five classes. Their study showed that the CNN GoogLeNet classifier achieved the best results with an accuracy of 91.92% and AUC of 99.29%. [PERSON] et al. [31] proposed a CNN model, namely CoroNet, to perform automated breast cancer detection. It attained an accuracy of 94.92% on a four-class classification (benign/malignant mass and calcification). [PERSON] et al. [32] studied CNN with and without attention mechanisms (AMs). The implementation of a deep learning algorithm on infrared thermal breast images showed an accuracy of over 99% with AMs and 92.32% without. [PERSON] et al. [33] presented a procedure that classifies architectural distortion, a feature of breast cancer. Enhancing the images with top-bottom hat and exponential transformation, reducing the noise with NSCT and finding a threshold for the segmentation of the image using the improved PCNN achieved an accuracy of 93.16% and an AUC of 0.93.
# End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks
## 8 The Energy Consumption Model  
In this section we provide a model to evaluate the energy consumption of the nodes of the WSN. Quite often the following energy model has been adopted in the literature The amount of energy expended to transmit, or receive, a message of unit length at a distance \(d\) between sender and receiver, is proportional to, respectively,  
\[E_{t}(d)=\alpha_{11}+\alpha_{2}d^{\eta};\quad E_{r}(d)=\alpha_{12}; \tag{33}\]  
We could also consider the energy of sensing a message, \(E_{s}=\alpha_{3}\), but typically it is much smaller compared to \(E_{t}\) and \(E_{r}\), so we ignore \(E_{s}\) in this paper. Following [37], typical values are \(\alpha_{1}=\alpha_{11}+\alpha_{12}=180\) nJ/bit and \(\alpha_{2}=10\) pJ/bit/m\({}^{2}\) (if \(\eta=2\)) or \(\alpha_{2}=0.001\) pJ/bit/m\({}^{4}\) (if \(\eta=4\)).  
The energy model is applied to the CONT sub-frame and to the TDMA sub-frame. In the CONT sub-frame the energy consumption is due to the sensors when transmitting a sensed message to its CH and to this CH when receiving this message. Since all sensors within the CH area are uniformly distributed, for simplicity, the distance \(d\) in Equation (33) is assumed equal to the distance average value between sensors and their CH, that is, \(d_{\text{CONT}}(r_{\text{max}})=(2/3)R_{c}(r_{\text{max}})\), see Section 4.3. Observe that when \(N_{c}=1\) the sink is the only CHs in the WSN, and only the CONT sub-frame without spatial reuse is implemented. Due to the randomness of the protocol in the CONT sub-frame, the number of attempts to successfully transmit a sensed message is a random variable. We define a contention factor, \(c_{f}(N_{c}(r_{\text{max}}))\) as the mean number of attempts to transmit a message. \(c_{f}(N_{c}(r_{\text{max}}))\) will be greater than one and depend basically on the type of RAP implemented, in our case FSA, and the number of sensors, \(N_{c}(r_{\text{max}})\), that belongs to cluster.  
In the TDMA sub-frame the communication is between CHs. Assuming that the CHs are located in the center of their hexagons, the shortest distance between two CHs belonging to two neighboring rings is \(\sqrt{3}R_{h}(r_{\text{max}})\), see Section 4.3. In practice, due to the irregular location of the CHs, when applying Equation (33) we have identified \(d_{TDMA}(r_{\text{max}})=2\sqrt{3}R_{h}(r_{\text{max}})\).
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 5 Security Analysis  
In terms of security, the system must be analysed in the following aspects: exhaustive key search, ciphertext only attack and statistical attack. We assume that the attacker knows the details of the scheme and intercepts some stego signals, but not the value of the key.
## 2 Time-Frequency Image Feature-Based HI
### Continuous Wavelet Transform (CWT)  
The CWT decomposes the signal into a time-scale plane by scaling and shifting the basis wavelet. A mother wavelet, \(\psi\in L^{2}(R)\), is a function of zero average and finite length, where \(L^{2}(R)\) is the space of square-integrable complex functions [24; 26; 28]. The family of time-scale waveforms is obtained by shifting and scaling the mother wavelet:  
\[\psi_{a,\ b}(t)=|a|^{-\frac{1}{2}}\psi\bigg{(}\frac{t-b}{a}\bigg{)}, \tag{1}\]  
where \(a\) is the scale parameter for dilating or contracting the wavelet, and \(b\) is the shifting parameter for transitioning the wavelet along the time axis. Thus, we obtain the components of the lower frequency band using a long wavelet. Alternatively, we obtain a component of the high-frequency band using a short wavelet. For the given signal, \(x(t)\), wavelet coefficient \(wt(a,b)\) can be represented as  
\[wt(a,\ b)=|a|^{-\frac{1}{2}}\int_{-\infty}^{\infty}x(t)\psi^{*}\bigg{(}\frac{t -b}{a}\bigg{)}dt, \tag{2}\]  
Figure 1: Framework of the Continuous Wavelet Transform and Convolutional Neural Network-based Health Indicator (CWTCNN-HI)-based prognostics method.  
where \(\psi^{*}\) denotes the complex conjugate of \(\psi\). The CWT is useful for obtaining both frequency and spatial information. It is also better for visualizing the frequency components at different time scales and resolutions. The details of the CWT can be found in the work of [PERSON] [32].
## 3 Results and Discussions
### Wind Retrieval and Validation
The incorporation of the Doppler centroid anomaly significantly enhances the accuracy of both wind speed and direction, although the standard deviation remains unchanged (see Table 1). This might be attributable to the fact that the CDOP was developed using global sea surfaces, primarily encompassing open oceans. In these open oceanic regions, the influence of ocean currents on Doppler centroid anomalies is notably smaller compared to that of winds. However, in coastal areas, where currents and waves play a more substantial role in Doppler centroid anomalies relative to winds, the improvement in wind field inversion results is not as pronounced. A comparison with in situ data underscores the Bayesian method's consistency and practical efficacy in wind field inversion. The retrieved wind speed gives an overall good performance with a correlation coefficient of 0.931, a mean bias of 0.33 m/s, and a root-mean-square error (RMSE) of 1.45 m/s. For the wind direction, the overall correlation coefficient of wind direction reaches 0.661 with a bias of 6.94\({}^{\circ}\) and a RMSE of 35.74\({}^{\circ}\). The empty circles correspond to the cases where the wind speed is lower than 4m/s. It can be clearly seen that when the wind speed is lower than 4 m/s, the error of the wind direction increases significantly, which might be due to the weaker sensitivity of GMF to low wind speed. The scatter index (SI) also greatly decreases at higher wind speeds. These metrics improve greatly when calculating the deviation and standard deviation of wind direction excluding the cases of wind speeds of less than 4 m/s as annotated. By comparison, the performance metrics of wind retrieval based on NRCS plus model winds without input of Doppler information is listed in Table 1. The bias is 0.34 m/s and 7.25\({}^{\circ}\) for wind speed and direction, respectively, with the RMSE values being 1.45 m/s and 35.84\({}^{\circ}\). While the improvement may not be substantial, the slightly improved bias and RMSE still suggest a degree of accuracy in our wind retrieval. Figure 7: Illustration of contribution of each term in the cost function. (**a**) NRCS term; (**b**) a priori (model) term; (**c**) Doppler term; (**d**) NRCS and model; (**e**) NRCS and Doppler; (**f**) NRCS, Doppler, and model. ‘ indicates the true wind and ‘ indicates the local minima for cost function.
# Matched Filtering for MIMO Coherent Optical Communications with Mode-Dependent Loss Channels
## 4 SDM Communication System Model
### Matched Filter-Based Receiver for SDM  
In this subsection we will explore the optical channel requirements to reduce the linear MIMO receiver \(\underline{Q}(\omega)\) in Figure 1 to a simple matched filter-based receiver. Furthermore, we will show that the resulting receiver is optimal in the sense that the discrete-time system response of the SDM communication system is the identity matrix, followed by the addition of the AWGN noise. Let us first write out:  
\[\underline{\underline{Q}}(\omega)=P(\omega)\cdot\underline{\underline{H}}_{tot} (\omega), \tag{21}\]  
where \(\underline{\underline{H}}_{tot}(\omega)\) and \(P(\omega)\) are the Fourier transforms of \(\underline{\underline{H}}_{tot}(t)\) and \(p(t)\), respectively and according to what is plotted in Figure 2. It follows that:  
\[\underline{\underline{Q}}(\omega)^{H}=P^{*}(\omega)\cdot\underline{\underline{ H}}_{tot}(\omega)^{H}. \tag{22}\]  
The signal at each of the \(D\) branches \(y_{i}(t)\), defined in (15), is processed before sampling by the continuous-time filter \(\underline{\underline{Q}}^{H}(\omega)\). The equivalent scheme for this matched filter-based MIMO receiver is shown in Figure 3a. By using the linearity of the system we can rearrange Figure 3a to obtain Figure 3b. Then, elaborating the expression \(\underline{\underline{Q}}(\omega)\underline{\underline{Q}}^{H}(\omega)\) we obtain that:  
\[\underline{\underline{Q}}(\omega)\underline{\underline{Q}}^{H}(\omega)=P( \omega)\cdot\underline{\underline{H}}_{tot}(\omega)\cdot\underline{\underline {H}}_{tot}^{H}(\omega)  
And the diagonal matrix:  
\[ \begin{split}|\underline{\Delta}^{(K_{Amp})}(\omega)|^{2}& =\\ &\text{diag}\bigg{(}\left[e^{\frac{1}{2}\underline{s}_{1}^{(K_{Amp} )}-i\omega\tau_{1}^{(K_{Amp})}},\ldots,e^{\frac{1}{2}\underline{s}_{D}^{(K_{Amp })}-i\omega\tau_{D}^{(K_{Amp})}}\right]\bigg{)}.\\ &\text{diag}\bigg{(}\left[e^{\frac{1}{2}\underline{s}_{1}^{(K_{Amp })}+i\omega\tau_{1}^{(K_{Amp})}},\ldots,e^{\frac{1}{2}\underline{s}_{D}^{(K_{Amp })}+i\omega\tau_{D}^{(K_{Amp})}}\right]\bigg{)}\\ &=\bigg{|}\text{diag}\bigg{(}\left[e^{\frac{1}{2}\underline{s}_{1} ^{(K_{Amp})}},\ldots,e^{\frac{1}{2}\underline{s}_{D}^{(K_{Amp})}}\right] \bigg{)}\bigg{|}^{2}\end{split}  \tag{26}\]  
that does not allow simplifying (25) unless the following holds:  
\[|\underline{\Delta}^{(K_{Amp})}(\omega)|^{2}=e^{\big{(}\bar{s}^{(K_{Amp})} \big{)}}\cdot\underline{I}. \tag{27}\]  
This latter condition is equivalent to assuming that:  
\[e^{\big{(}\frac{1}{2}g^{(K_{Amp})}\big{)}}=e^{\big{(}\frac{1}{2}\underline{s} _{1}^{(K_{Amp})}\big{)}}=e^{\big{(}\frac{1}{2}\underline{s}_{2}^{(K_{Amp})} \big{)}}=\cdots=e^{\big{(}\frac{1}{2}\underline{s}_{D}^{(K_{Amp})}\big{)}}, \tag{28}\]  
or, in other words, that the MDL is negligible for the \(K_{amp}\)-th span.
## 2 Materials and Methods
### Kinanthropometric Assessment  
Four basic measurements--eight skinfolds; six girths (arm relaxed, flexed and tensed arm, waist, hips, middle thigh and calf); five breadths (biacromial, biliocristal, humerus, femur and bi-styloid); three lengths (acromiale-radiale, radiale-stylion and stylion-medio dactylion); and a height (ilioespinale)--were measured following the guidelines of the International Society for the Advancement in Kinanthropometry (ISAK) [23]. All measurements were performed by level 2 and 3 kinanthropometrists accredited by the ISAK. The intra- and inter-evaluator technical error of measurement (TEM) were calculated in a sub-sample of 30 participants (age = 14.47 \(\pm\) 1.10) in the pilot study. For this purpose, a sample with similar characteristics to the study sample was chosen, and the number of participants included was based on previous research [24], being the minimum number of participants indicated to perform the inter- and intra-evaluator error calculations [25]. The intra-evaluator TEM was 0.03% in basic measurements, lengths, heights and girths and 1.07% in skinfolds; the inter-evaluator TEM was 0.05% in basic measurements of lengths, heights and girths and 2.83% in skinfolds.  
The following material was used for kinanthropometric assessment: a SECA 862 scale (SECA, Hambourgh, Germany) with an accuracy of 100 g; a SECA stadiometer (SECA, Hambourgh, Germany) and an arm span meter (Smartmet, Jalisco, Mexico) with an accuracy of 0.1 cm; a skinfold caliper (Harpenden, Burgess Hill, UK) with an accuracy of 0.2 mm; and an inextensible tape (Lufkin, Missouri, TX, USA), a segmenter (CESCORF, Porto Alegre, Brazil), an anthropometer (Realmet, Barcelona, Spain) and a small girth sliding caliper (Holtain, Crymych, UK) with 0.01 cm accuracy. All measurements were taken twice. When the difference between the first and second measurements was greater than 5% for the folds or 1% for the other measurements, a third measurement was taken. The final value used for the analysis was the mean between measurements in the case of two attempts and the median in the case of three measurements.  
The final values of the kinanthropometric measurements were used to calculate the variables of the body mass index (BMI), fat mass [26], muscle mass [27], bone mass [28], somatotype [29], \(\leq\)6 skinkolds [30], \(\leq\)8 skinkolds [30; 31], Cormic index [(sitting height/height) \(\times\) 100], relative arm span [(arm span/height) \(\times\) 100], upper limb length [acromiale \(-\) radiale \(+\) radiale \(-\) stylion \(+\) stylion \(-\) medo dactylion], corrected girths of the arm [arm relaxed girth \(-\) (\(\pi\)\(\times\) triceps skinkold)], thigh [middle thigh girth \(-\) (\(\pi\)\(\times\) thigh skinfold)] and calf [ calf girth \(-\) (\(\pi\)\(\times\) calf skinfold)], the muscle-bone index [muscle mass/bone mass] and waist-to-hip ratio (waist/hip girth).