esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Micro-Cracking Behaviors of Shale under Different Bedding Parameters
### Effects of Bedding Strength
#### 4.1.2 Change of Crack Types  
Four crack types are identified in numerical simulations: tensile crack in matrix (Pbt), shear crack in matrix (Pbs), tensile crack in beddings (Sjt), shear crack in beddings (Sjs). This classification is based on the failure mode and location of micro-cracks. For convenient comparison, the percentage of each type of crack is the ratio of the number of those in the same crack type to the total number of micro-cracks at the end of loading process.  
The influence of dip angle on the crack type is investigated. The percentage of each type is presented in Figure 9. Figure 9a shows that the percentage of shear cracks in the matrix and beddings (Pbs+Sjs) increases first and then decreases with the dip angle. As the dip angle increases from 0\({}^{\circ}\) to 90\({}^{\circ}\), the percentage of shear cracks is 50.6%, 72.2%, 92.2% and 48.3%, respectively. This indicates that the percentage of each crack type varies with not only the dip angle but also the bedding strength. The first three pictures in Figure 9 show that the average ratio of the number of tensile cracks to shear cracks in the shale model is 0.628, 0.758 and 0.776 as the JSR increases from 0.8 to 1.2. This implies that the percentage of tensile cracks in the shale specimen increases with the increase of bedding strength. The impact of the JSR on the percentage of matrix cracks is presented in Figure 9d. The percentage of matrix cracks increases with the increase of bedding strength. A larger bedding strength results in a higher growth rate of the percentage of matrix cracks. For instance, when the dip angle is 60\({}^{\circ}\), the percentage of matrix cracks (Pbt+Pbs) increases from 2.5% to 6.2% and subsequently to 14.9% as the JSR increases from 0.8 to 1.0 and then to 1.2, respectively. The growth rate of matrix cracks becomes larger when the bedding strength increases in uniaxial compression loading. These simulation results indicate that the number of tensile cracks and matrix cracks increases with the increase of joint strength. During the exploration of shale gas, the increasing number of tensile cracks in the shale matrix will facilitate the shale gas transportation during hydraulic fracturing. The increase of tensile cracks (Pbt+Sjt) in fracturing behaviors could also contribute greatly to shale gas production.
## 2 Islanding Detection Methods
### Remote Methods
#### 2.4.2 Signal Produced by Disconnect Method (SPD)  
In this method, islanding can be detected based on the signal transmission between DGs inverters and the main grid which is exactly the same as the PLCC method but the signal transmission is based on a different form than the PLCC form, such as microwave ortelephone line. This method has no NDZ, and it has full control of both the main grid and DG. However, it is expensive, and the design is complicated [32].
## 3 Results
### Comparison between Time-Series Phenological Estimation Portfolios for Crop Growth Stages
#### 3.2.1 SIF vs. EVI
EVI data had limited utility in estimating winter wheat growth stages, while SIF data proved inadequate for T&S (in maize) and heading (in winter wheat) estimation. Additionally, TROPOMI SIF was found to offer accuracy with \(R^{2}\) at approximately or over 0.6 for estimating most crop growth stages of maize and winter wheat.
# Attention-Based Active Learning Framework for Segmentation of Breast Cancer in Mammograms
## 1 Introduction
Afterwards, a novel attention mechanism is used to evaluate the similarity between the unlabeled and segmented samples, thus offering weights as criteria to measure the uncertainty or informativeness of unlabeled samples with respect to the trained and basic segmentation model. Finally, an active learning strategy is used to sort unlabeled samples with weights, thus determining selections that need to be further manually labelled. We observed that these selected samples often contained appearance or shape features which were unacknowledged by the basic segmentation model subjected to the current training dataset. With iterations of retraining with the most informative unlabeled samples, the proposed model stably approached the upper bound of segmentation performance for high accuracy. The contributions of this paper are summarized as follows:  
* We propose an attention-based active breast cancer segmentation framework which effectively improved the accuracy of segmentation with few training samples, thus alleviating the high cost of labeling breast cancer images. * A novel attention-based sampling scheme is proposed which measures the most informative unlabeled samples via calculating similarity weights. * We adopt an active learning strategy for the global optimization of accuracy performance, which iteratively selects appropriate unlabeled samples to first manually label and then retrain the model to boost performance. The rest of this paper is organized as follows. Section 2 reviews related work on the segmentation of breast cancer images. Section 3 presents an overview of the proposed method. Details of the basic segmentation model, attention-based sampling scheme and active-learning-based labeling strategy are also discussed in Section 3. Section 4 presents and discusses the experimental results. Finally, Section 5 concludes the paper.
# Algebraic Point Projection for Immersed Boundary Analysis on Low Degree NURBS Curves and Surfaces
## 3 Background on Algebraic Level Sets
### Time Complexity of the Algebraic Level Sets Algorithm  
The algorithmic complexity of Algorithm 1 is given in [23] and reproduced here. Consider a NURBS curve of degree \(p\). Further, assume that the NURBS curve decomposes into \(n\) Bezier curves. This decomposition process can be carried out in \(O(n)\) time. For each step in constructing the algebraic level sets of the Bezier curve, the time complexity is a function of its degree \(p\) and is given in Table 2.  
Figure 7: Algebraic level sets from a symmetric quadratic NURBS surface. (**a**) The valley of the surface contains only a \(\mathcal{G}^{0}\) continuity across the plane of symmetry. The level sets are plotted over three principal planes slicing the surface: (**b**) x-y plane; (**c**) y-z plane; and (**d**) x-z plane.  
Since each Bezier curve comprises of \(p+1\) control points, the construction of its convex hull requires \(O(p\log p)\) time. Since the convex hull contains at most \(p+1\) edges, computing the normalized distance field for the hull requires \(O(p)\) time. Finding the Bezout/Dixon resultant involves evaluating the determinant of the Bezout matrix of size \(p\times p\) and costs \(O(p^{3})\) time. Normalization of this resultant requires the gradient of the resultant, which in turn requires solving a linear system (see Equation (21)) and costs \(O(p^{3})\) time. The trimming operation only evaluates Equation 9 and can be carried out in \(O(1)\) time. Thus, constructing algebraic level sets for a Bezier curve segment requires \(O(p^{3})\) time. These level sets constructed on Bezier curve segments can then be combined to construct level sets for the NURBS curve in \(O(np^{3})\) time. The computational time complexity for a NURBS surface of degree \(p\times q\) may be obtained analogously. For a Bezier segment of such a NURBS surface, the convex hull contains at most \((p+1)(q+1)\) points and edges, and the Dixon matrix used for the resultant is of size \(2 pq\times 2 pq\). From the step-by-step time complexities listed in Table 2, construction of algebraic level sets for a Bezier surface segment requires \(O(p^{3}q^{3})\) time, while that of the NURBS surface requires \(O(np^{3}q^{3})\) time.
# A Comprehensive Experimental Emulation For OTFS Waveform RF-Impairments
## 2 Waveform Modulation and Demodulation
### OTFS Waveform  
OTFS modulation is composed of cascaded two-dimensional (2D) transforms at both transmitter and receiver, as shown in Figure 1. At the transmitter side, the information symbols \(X[l,k],k=0,\ldots,N-1,l=0,\ldots,M-1\) are mapped in the two-dimensional delay-Doppler domain from the modulation alphabet \(\mathbb{A}\) to be transmit over \(NT\) time duration and using bandwidth \(B=M\Delta f\) where \(\Delta f=1/T\), and \(N\) and \(M\) are the delay and Doppler bins, respectively. Then, inverse symplectic finite Fourier transform (ISFFT) using to map the \(N\times M\) delay-Doppler grid points into the time-frequency plane as follows  
\[x[n,m]=\frac{1}{\sqrt{NM}}\sum_{k=0}^{N-1}\sum_{l=0}^{M-1}X[k,l]e^{j2\pi( \frac{nk}{N}-\frac{ml}{M})}. \tag{5}\]As illustrated by the dashed box in Figure 1, the time-frequency plane signal is transformed into a time domain signal for transmission using the Heisenberg transform, which is given by [6; 7]  
\[x(t)=\sum_{n=0}^{N-1}\sum_{m=0}^{M-1}X[n,m]g_{tx}(t-nT)e^{j2\pi m\Delta f(t-nT)}, \tag{6}\]  
where \(g_{tx}\) denotes transmit pulse shaping. Unlike OFDM where CP is added for each of \(N\) symbols in the frame, the CP is added for each frame in the time domain in OTFS. This considerably reduces the CP overhead. Then the signal will be transmitted through the time-varying wireless channel. The time domain received signal is expressed as  
\[y(t)=\int_{\
u}\int_{\tau}h(\tau,\
u)x(t-\tau)e^{j2\pi\
u(t-\tau)}\mathrm{d} \tau\mathrm{d}\
u, \tag{7}\]  
where \(\tau\) and \(\
u\) denote the delay and Doppler variables, respectively. Furthermore, \(h(\tau,\
u)\) represent the complex channel response in delay Doppler domain. The Wigner transform is used at the receiver side to transform the time domain received signal \(y(t)\) to time-frequency domain, by matching it with the receiver pulse shaping \(g_{rx}\). Assuming that the transmit pulse shaping \(g_{tx}\) and the receiver pulse shaping \(g_{rx}\) satisfy the bi-orthogonality conditions then the time-frequency signal is given by [6; 7; 35]  
\[Y[n,m]=H[n,m]X[n,m]+W[n,m], \tag{8}\]  
where \(W[n,m]\) is the noise matrix and \(H[n,m]\) is given by  
\[H[n,m]=\int_{\tau}\int_{\
u}h(\tau,\
u)e^{j2\pi\
u nT}e^{-j2\pi(\
u+m\Delta f )\tau}\mathrm{d}\
u\mathrm{d}\tau. \tag{9}\]  
Then, the symplectic finite Fourier transform (SFFT) is used to map the time-frequency signal in delay-Doppler domain, which is defined as follows  
\[\begin{split}\hat{y}[k,l]=\frac{1}{\sqrt{NM}}\sum_{n=0}^{N-1} \sum_{m=0}^{N}Y[n,m]e^{-j2\pi\left(\frac{N}{M}-\frac{m}{M}\right)}\\ =\frac{1}{\sqrt{NM}}\sum_{n=0}^{N-1}\sum_{m=0}^{M-1}x[n,m]h(\tau^ {{}^{\prime}},\
u^{{}^{\prime}})+w[k,l],\end{split} \tag{10}\]  
Message passing (MP) detection will be used after OTFS demodulation to detect the received symbols \(\hat{x}[k,l]\) from the delay Doppler received signal \(\hat{y}[k,l]\), as will be illustrated in the following subsection. This will be performed so that the symbols can be extracted from the received signal.
## 4 Simulation and Hardware-in-the-Loop Results
### Simulation Studies  
For simulation study, different scenarios were implemented to achieve de-loading. The first scenario was based on varying the rotor speed only, while keeping pitch angle constant. The second scenario was performed using the proposed method, but allowing pitch angle variation without wind speed forecasting. In the last scenario, the proposed de-loading method was implemented to obtain an optimal pitch angle that is valid for a longer time frame, and updating the rotor speed instantaneously.  
The simulation experiment was repeated again to perform de-loading for PFC purposes. In this scenario, the pitch angle was kept constant (i.e., 0 degrees), while the de-loading was performed using a rotor speed controller. The maximum power point tracking (MPPT) was modified to track the output power by subtracting the maximum power by the required power to be reserved. Then, the reference rotor speed was calculated to provide the required reserve power.  
The proposed algorithm was used to de-load the WT, in order to maintain an adequate amount of power for PFC use. In this scenario, the wind profile was assumed to be predicted by wind forecast. The averaged estimated wind speed was used to find the optimum pitch angle for a longer period. Then, the current and future wind speed is used to estimate the optimal possible pitch angle for that time frame. The rotor speed was extracted using the nonlinear relation, using Equation (10).
# Calculation of 1/f Fluctuation from Sound Signal and Comfort Evaluation
## 2 Calculation Method for Fluctuation Coefficient of the Sound Signal
### Verification of Fluctuation Calculation Method  
In the previous section, we demonstrated that the fluctuation coefficient of the whole sound signal, the fluctuation coefficient of the frequency, and the fluctuation coefficient of the amplitude were calculated from a wav data by the proposed calculation method. However, the accuracy of each calculated fluctuation coefficient was not proven. Therefore, we calculated each fluctuation coefficient using pink noise to prove the calculation method's accuracy. Pink noise is the noise at which sound pressure is inversely proportional to a frequency, and the strength of every octave is a constant noise. Therefore, the fluctuation coefficient of the whole pink noise will be close to \(-1\) when calculated by the proposed calculation method in the previous section.  
Figure 6 shows the calculated pink noise result of the spectral slope obtained by the above equations. The figure shows that (a) is the fluctuation coefficient of the whole sound  
Figure 5: The \(1/f\) fluctuation coefficient \(\lambda\) of each fluctuation for each piece of music.  
signal, (b) is the fluctuation coefficient of the frequency, and (c) is the fluctuation coefficient of the amplitude. For result (a), the fluctuation coefficient of the whole sound signal of pink noise was limitlessly close to \(-1\), as we expected before the calculation. On the other hand, for results (b) and (c), each fluctuation coefficient was far from \(-1\). Pink noise does not change frequency and amplitude even after the play time of the sound signal. Therefore, the frequency and amplitude fluctuation coefficient were not near \(-1\).  
The fluctuation coefficient of the whole pink noise was close to \(-1\) according to the proposed calculation method in this paper. Furthermore, using this algorithm to  
Figure 6: The calculation results of each fluctuation coefficient of pink noise. (a) Spectrum of the whole sound signal. (b) Spectrum of frequency. (c) Spectrum of amplitude.  
calculate the fluctuation coefficient of the frequency and amplitude yielded the same result as calculating the spectrum slope of the whole sound signal. We considered that the calculation accuracy of the fluctuation coefficient of the frequency and amplitude was secured. Therefore, the proposed method can calculate the fluctuation coefficient accurately.  
Psychological Condition Estimation and Comfort Evaluation by the Difference in Fluctuation Coefficients for Humans  
The previous section clarified that the fluctuation coefficient of the whole sound signal, frequency, and amplitude can be derived using the proposed calculation method. Therefore, we studied the relationship between the calculated fluctuation coefficient of music and human comfort. This study focused on frequency fluctuation and comfort as fundamental considerations. In this section, we investigated the comfort of music with different fluctuation coefficients and its influence on a human using the sound signal of the same melody.
# A New Method for 2D-Adapted Wavelet Construction:
## 3 Results
### Detection of 2D Mass-like Patterns in Digital Mammography Images  
Breast cancer remains one of the most commonly detected cancers in the world population. In Cuba, it ranks second in incidence and mortality [42]. X-ray imaging of the breast,  
Figure 3: Locating the pattern of the letter \(a\) in an image. From left to right: the original image, the pattern to be detected, the region given by the algorithm based on _shapelets_, and the measure \(\mathbb{S}\) of the diagonal coefficients of detail. Maxima of the measure \(\mathbb{S}\) of the diagonal coefficients \(=\{143,\ 375,\ 606\}\), Rect(xy = (26, 3), width = 8, height = 9), Rect(xy = (28, 25), width = 8, height = 9), Rect(xy = (28, 47), width = 8, height = 9).  
Figure 2: Locating the pattern of the letter \(c\) in an image. From left to right: the original image, the pattern to be detected, the region given by the algorithm based on _shapelets_, and the measure \(\mathbb{S}\) of the diagonal coefficients of detail. Position of the maximum value for the measure \(\mathbb{S}\) of the diagonal coefficients \(=371\), Rect(xy = (20, 25), width = 8, height = 9, angle = 0).  
known as mammography, is the most popular and widely used breast cancer screening modality because the tumor can be detected before it can be physically felt [1]. Mammograms are inherently noisy, have poor contrast, and are less sensitive to dense breast tissues [2] due to the low radiation doses used and shorter exposure times necessary for the safety of human health. Analysis and interpretation of mammograms require practice, experience, and training. Radiologists may miss certain abnormalities on mammograms due to the subtlety of the suspicious lesions, or because these are masked in the breast parenchyma [43]. That is why radiologists have used computer-aided analysis (CAD) to improve the detection of subtle signs and provide a consistent and accurate diagnosis from mammograms. There are various mammographic lesions; in particular, a mass is a voluminous lesion that occupies a space with different shapes and has a denser appearance in the center while it is relatively thin towards the edges [44]. A mass can be classified according to its shape, its density (number of fat cells present along with the density of suspicious cells), and its margin (characteristics of its border) [44]. In this section, the 2D strategy proposed to detect mammography masses using adapted wavelets will be applied.
## 2 Related Work
The approach to modeling multi-service overflow systems used in the above studies includes three stages: Stage 1, which involves the development of a model of the primary resources; Stage 2, which includes the determination of the parameters of overflow traffic; and Stage 3, which involves the determination of the traffic characteristics of alternative resources. Although there is extensive literature on several solutions to the problems of the first and third stages, the main problem--concerning a determination of the parameters of traffic that overflows from primary resources that service multi-service mixtures of Erlang-Engset-Pascal traffic--still has only rough solutions. This article aims to analyze the accuracy of determining the parameters of overflow traffic, in particular the accuracy of the calculations of traffic streams of multi-service Pascal traffic, and to develop a new and significantly more accurate method for determining the parameters of overflow traffic. Further on in the article, a scheme of traffic overflow and a model of primary and secondary resources will be briefly described, and a new method for determining the traffic parameters of overflow traffic for multi-service Pascal streams will be proposed. This method will allow the accuracy of modeling traffic overflow systems to be significantly increased.
# Performance Analysis of IoT-Based Health and Environment WSN Deployment
## 3 Health and Environment Sensor Network Deployment
### GIS-Based WSN Deployment
#### 3.1.1 Coverage  
The coverage which is the fundamental issue in WSNs addresses how to deploy sensor nodes in the geographic space so that every point of the space is sensed at least by one sensor node [16; 73; 77]. This is an optimization problem to find optimal locations of sensor nodes in the network so that the coverage of the space is maximized [17; 73]. This issue is addressed as the network coverage in the WSN deployment which is calculated using the sensor coverage. The sensor coverage is defined as the area of the \(R_{s}\)-meter circle, where \(R_{s}\) is the sensing range of the sensor [78]. The total coverage of WSN, which is shown in grey colour in Figure 2, is calculated as follows:  
\[\text{coverage}=\sum_{i=1}^{n}(C_{i}-C_{i}^{o})/C_{R} \tag{3}\]  
where \(C_{i}\) is the coverage of the ith sensor, \(C_{i}^{o}\) is the overlapped coverage between the ith sensor and other sensors, \(C_{R}\) is the total area of the region of interest (ROI). In this formula, \(C_{R}\) is used to normalize the total coverage. So, the total coverage is a normalized value between 0 and 1. The total coverage of the sensor network should be maximized in the design process. By assuming that the existing sensors can rotate 0-360\({}^{\circ}\) horizontally [73], each sensor coverage and total network coverage are calculated by spatial analyses. To this end, first, a point geometry layer is created using the locations of the sensors in which a point represents a sensor node. Then, the buffer polygon around the points are created to display the coverage area of the sensor in the sensing distance using buffer analysis. After that, the overlapped area between two polygons are removed from using Intersection and Union analysis. Buffer, Intersection, and Union are the spatial analyses that are performed on different types of the geometry in GIS [79].
# Development of a High-Precision Touch-Trigger Probe Using a Single Sensor +
## 4 Experimental Results and Discussion
### Probe Repeatability  
The touch-trigger repeatability of the probe along \(X\)+, \(X\)\(-\), \(Y\)+, \(Y\)\(-\), and \(Z\) directions was also tested. The probe was touched seven times in each direction with a displacement of 1 \(\upmu\)m of the Physik Instrumente (PI) stage after the temperature in the chamber had stabilized. The residual errors of the test results in each direction are shown in Table 2. The maximum single directional repeatability was 4 nm (\(K=2\)).
# Cooperative Caching and Resource Allocation in Integrated Satellite-Terrestrial Networks
## 2 Related Work
### Cooperative Caching in Terrestrial Networks  
For terrestrial networks which are content-centric, cooperative edge caching can effectively alleviate the heavy burden in bandwidth consumption and service latency, where popular contents are cached and forwarded at the edge of networks [25; 26; 27]. Specifically, a heterogeneous information network-based content caching is discussed to reduce the network load while improving the quality of service in [28], and a content caching placement approach is also proposed to maximize the cache hit-rate in [29]. Furthermore, a cooperative edge caching approach is investigated to optimize the content placement and cluster size in large-scale user-centric mobile networks in [30]. In addition, a mobility-aware edge caching strategy is also implemented to improve the content hit-rate in the internet of vehicles [31].  
However, different from terrestrial networks, there are time-varying topologies and restricted onboard resources in satellite networks, which can bring a difficulty for cooperative caching.