esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Facing the problems in determining the distribution range of karst areas and detecting karst caves under the restrictions of complex building and human exploration environments on the urban surface, taking the karst detection of Tianmeixin village and its southern pond in the north extension section of Guangzhou Intercity Railway Line 18 as the application research object, based on the formation mechanism of karst and the existing geophysical detection methods, the electrical resistivity tomography method with a large detection range and the cross-well seismic computed tomography method with a high detection accuracy are used to carry out application research on concealed karst cave detection, which are two geophysical technical detection methods with strong adaptability and anti-interference ability. The results show that the optimized combination of geophysical exploration techniques can effectively overcome the limitations of the environment, draw the main karst development areas, reveal the interface between rock and soil, and accurately characterize the size and shape of karst caves. The electrical resistivity tomography method was used to find a number of potential water conduction channels in the middle zone between Tianmeixin village and the south river. The overall distribution characteristics of karst in Tianmeixin village were summarized, and the key detection areas were drawn. This conclusion was verified by several sets of cross-well seismic computed tomography profiles, which provided a reference for the layout of the subsequent cross-well seismic computed tomography imaging method and greatly reduced the workload of drilling, shortened the construction period, saved on detection costs, and reduced the impact on the production and life of residents.  
k +
Footnote †: journal: Applied sciences  
Article
# Dual-Indicator Micro-Electro-Mechanical System Monitoring Method for Rock Instability Early Warning
## 3 Experimental Validation and Performance Evaluation
### Experimental Setup and Modeling
#### 3.1.1 Rockfall Model  
In this experiment, the unstable rock mass and bedrock were simulated using proportionally equivalent materials. The material composition consisted of quartz and barite powder as aggregates, gypsum as the cementitious binder, with a retarding agent and glycerol incorporated as admixtures. The optimized mass ratio for the synthetic rock matrix was formulated as: Quartz Sand:Barite Powder:Gypsum:Retarder:Water_Glycerol = 50:30:8:0.02:10:1.5. As detailed in Table 1, the fabricated specimens exhibited comparable physical properties to natural geological formations. Figure 5 illustrates the geometric configuration, where the protrusion (dimensions: 12 cm (L) \(\times\) 10 cm (H) \(\times\) 11.5 cm (W)) represents the unstable rock mass, while the cuboidal bedrock section (30 cm edge length) provides structural constraint. Hydraulic jacks applying 2.4 MPa vertical confinement pressure ensured bedrock stability throughout testing. The failure mechanism was systematically induced through the progressive reduction in the bonded interface area (15% increment steps) via the precision machining of the controlling structural plane.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Density/(kg/m\({}^{3}\))**} & **Modulus of** & **Poisson’s** & **Tensile** & **Cohesion/MPa** \\  & **Elasticity/GPa** & **Ratio** & **Strength/MPa** & **Cohesion/MPa** \\ \hline
2230.00 & 2.40 & 0.28 & 0.098 & 0.926 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical properties of the rock blocks.  
Figure 4: Simulated unstable rock collapse monitoring system.  
Figure 5: Simulation of the collapse process of unstable rock. (**a**) Rock mass formed by pouring. (**b**) Cut of the structural plane.
## 3 First-Passage Reliability Problem
### Parameter Calibration Using Observational Data  
The deformation monitoring data is a set of discrete samples of a random sequence that varies with depth. Note that the relevant parameters (c.f. \(\Omega(x)\) and \(Y(x)\) in Equation (13)) involved in the first-passage reliability problem are continuous along the depth \(x\). This section presents a method for calibrating these quantities by regression analysis of the samples.  
2.1 Fitting \(\Omega\left(x\right)\) and Subsequently Obtaining \(\hat{\Omega}\left(x\right)\)  
The retaining wall deformation caused by foundation pit excavation varies with the construction stage. The damage to adjacent buildings due to pit excavation at the early stages has been rarely reported but occurs mainly at the latter stages [5, 47]. Therefore, the emphasis could be put on the deformation reliability assessment of retaining structures at the later stages of foundation pit excavation. In the presence of a sequence of observed deformation data, taking the depth \(x\) as the argument, the (continuous) deformation with respect to \(x\) can be fitted with an \(m\)-degree polynomial as follows,  
\[\mathbb{E}(S(x))=\sum_{i=0}^{m}\gamma_{i}x^{i}, \tag{14}\]  
where \(\gamma_{i}\) is the coefficient of the polynomial. In order to avoid [PERSON]'s phenomenon in the fitting [48] (the oscillation at the edge of the interval when using a high-order polynomial interpolation), the degree \(m\) should be carefully selected. Furthermore, one can obtain \(\Omega\left(x\right)\) according to Equation (4), and further \(\hat{\Omega}\left(x\right)\) by differentiating \(\Omega\left(x\right)\).
## 3 Proposed Solution
### Chromosome Decoding
#### 3.2.1 Permutation Decoding  
* In permutation decoding, each gene is a number indicating an index in a complete feature set, which is expressed by numbers along with expressions with permutations.
* Each chromosome represents a selected feature subset.
* The order of genes in a chromosome is not considered, and duplicate values are not permitted.
* The length of the chromosome is set to the same length as that of the partial feature set. Chromosomes are expressed as \(c=(f_{1}\), \(f_{2}\), \(f_{3}\),..., \(f_{n})\), where \(f_{i}\) expresses the \(i\)th feature, \(i=(1,2,3,\ldots n)\), \(n\) is equal to the number of total features, and \(f_{i}\in F\). The value of each gene of a chromosome is chosen by an initialization method, which is random for the initial population step and located in the problem space. The length of the chromosome is the number of partial features to be selected. For example, the chromosome initialized as \(c=(1,6,25,3,9)\) in Figure 3 indicates that five features (the 1 st, 6 th, 25 th, 3 rd, and 9 th) are to be selected, and detection models will be generated by building training models with only these features.  
The advantage of permutation decoding is that users can obtain as many feature sets as they want and operation time can be reduced, depending on the length of the chromosome, compared to binary decoding. The disadvantages are that experiments must be performed  
Figure 3: An example of permutation decoding.  
to determine the optimal size of the feature subset and that local optima can be encountered during crossover owing to the order-independent selection.
# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 3 Methodology
### Feature Functions  
Features are calculated over epochs composed of \(N\) samples. They are then stored as vectors in a feature matrix, which serves as the training database for an ML model. The features such as mean, variance, skewness, kurtosis, entropy, and energy are calculated for labeled segments as either seizure or non-seizure. By incorporating these features, EEG signal analysis becomes more nuanced and informative, allowing for the detection of subtle patterns and abnormalities associated with epileptic seizures. Furthermore, the utilization of half-epoch overlap ensures that interactions between successive epochs are adequately captured, enhancing the robustness of the feature extraction process.
## Methods
### Experimental Design  
In order to investigate the effects of root dynamics on global climate, two sets of numerical experiments were conducted using the original CESM1.2.0 (referred as CTL) and CESM1.2.0_DR coupled with the dynamic rooting scheme (referred as NEW). In these experiments, only the atmospheric (CAM4) and land (CLM4.5) components were integrated, whereas ocean surface conditions were prescribed using monthly observations of sea-surface temperatures (SSTs) and sea-ice concentration from the HadISST dataset [36]. To establish the C and N pools and fluxes, the 1000-year initialization results were used as initial conditions. The greenhouse gas forcing data are from the 2000 observations, and the land-use data are derived from the Moderate Resolution Imaging Spectroradiometer satellite data. The monthly results at 0.9\({}^{\circ}\)\(\times\) 1.25\({}^{\circ}\) resolution, from 1982 to 2005, were analyzed, focusing on gross primary production (GPP), evapotranspiration (ET), 2-m air temperature (TA), and land precipitation (PR). Here, the Model Tree Ensembles (MTE) datasets were used as a reference for benchmarking these comparisons: The global GPP and ET (monthly, 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\)) were up-scaled from FLUXNET observations using a machine-learning technique for 1982-2010 [37; 38].  
Figure 1: Schematic diagram of the impacts of the dynamic rooting scheme on land–atmosphere interactions.  
The precipitation data (from 1901 to 2014, monthly, \(1^{\circ}\times 1^{\circ}\)) from the Global Precipitation Climatology Centre (GPCC) ([[https://www.esrl.noaa.gov/psd/data/gridded/data.gproc.html#detail](https://www.esrl.noaa.gov/psd/data/gridded/data.gproc.html#detail)]([https://www.esrl.noaa.gov/psd/data/gridded/data.gproc.html#detail](https://www.esrl.noaa.gov/psd/data/gridded/data.gproc.html#detail))), and the air temperature reanalysis data (from 1948 to 2014, monthly, \(0.5^{\circ}\times 0.5^{\circ}\)) from the National Centers for Environmental Prediction (NCEP), were also used as benchmarks.
## 3 Experiment and Results  
### Visual Alignment  
For the measurement method, both probes must move along the maximal cross-sectional profile. Otherwise, an alignment error occurs in the direction of measurement as shown in Figure 9.  
In Figure 9, \(d\) is the diameter of the sphere, \(h\) is the offset distance between the ideal maximum cross section and the actual cross section in the experiment, and \(s\) is the alignment error caused by \(h\). The formula for \(h\) is:  
\[h^{2}=s(d-s) \tag{6}\]  
As \(s\ll d\), the formula above can be simplified as:  
\[h=\sqrt{s\cdot d} \tag{7}\]  
If \(d\) and \(h\) are 300 and 1.7 \(\upmu\)m, respectively, then \(s\) is 10 nm. The probes and the maximum cross section should be aligned very well to reduce the alignment error. Herein, the alignment is adjusted by the 3D macro platform and the two CCD microscopes. The pixel numbers of the camera is 1920 \(\times\) 1080, the pixel size is 2.75 \(\times\) 2.75 \(\upmu\)m, the objective lens magnification is 4.5, and the field of the view is 1.2 \(\times\) 0.7 mm. The alignment result is calculated by edge detection of the images based on the wavelet analysis, and \(h\) can be controlled at about 1 \(\upmu\)m.
# Research Challenges for the Internet of Things: What Role Can OR Play?
## 3 How can OR Support the Future IoT?
### The Mathematical Tools and Techniques of OR Which May Support the IoT
#### 3.1.4 Simulation  
Discrete-event simulation simulates the operation of a system as a discrete sequence of events with each event marking a change of state of the system. Between events, no change in the system state is assumed. Discrete-event simulation can be applied to the study of issues in IoT networks. [PERSON] et al. [51] applied discrete-event simulation to model a heterogeneous sensor network with smart devices connected. The model can incorporate the effects of phenomena such as weather and crisis situations on the network state. This could be readily extended to examining similar effects for heterogenous IoT networks.  
There are many simulation models available for networks that can be applied to IoT. [PERSON] and [PERSON] [52] identified 36 such systems for WSNs ranging from low level emulators to simulators for topology and environment (see Section 4.1). Atarraya, for example, is a discrete-event simulation that simulates topologies for networks enabling testing of algorithms and protocols [53]. An earlier survey of WSN simulation tools was done by [PERSON] et al. in 2009 [54].  
Discrete-event simulation would appear to be an ideal approach to studying many of the design and engineering challenges for IoT such as scalability and energy efficiency by constructing a synthetic environment where new concepts can be safely tested. We discuss in more detail how simulation can support the IoT research challenge of Scalability in Section 4.1.
# Haptic Texture Rendering of 2D Image Based on Adaptive Fractional Differential Method
## 3 Differential Order for Adaptive Selection Algorithm
We use it to compare the differences between the extraction results after using the adaptive method and the specified order differential method, thereby further verifying that the texture extraction effect of the adaptive method has obvious advantages. A comparison method for structure, brightness, and contrast is given in Ref. [39]:  
\[ \begin{cases}l(\mathbf{x},\mathbf{y})=\frac{2\mu_{x}\mu_{y}+C_{1}}{\mu_{x}^{2} +\mu_{y}^{2}+C_{1}}\\ c(\mathbf{x},\mathbf{y})=\frac{2\sigma_{x}\sigma_{y}+C_{2}}{\sigma_{x}^{2}+ \sigma_{y}+C_{2}}\\ s(\mathbf{x},\mathbf{y})=\frac{2\sigma_{x}\sigma_{y}+C_{3}}{\sigma_{x}\sigma_{y} +C_{3}}\end{cases}  \tag{6}\]  
where \(\mathbf{x}=\{x_{i}|i=1,2,\cdots,N\}\), \(\mathbf{y}=\{y_{i}|i=1,2,\cdots,N\}\) are two image patches extracted from the same spatial location from two images, respectively. And \(\mu_{x}\), \(\sigma_{x}^{2}\) and \(\sigma_{xy}\) are the mean of \(\mathbf{x}\), the variance of \(\mathbf{x}\), and the covariance of \(\mathbf{x}\) and \(\mathbf{y}\), respectively. \(C_{1}\), \(C_{2}\) and \(C_{3}\) are small constants given by  
\[C_{1}=(K_{1}L)^{2},\ \ \ C_{2}=(K_{2}L)^{2},\ \ \ C_{3}=C_{2}/2 \tag{7}\]  
where \(L\) is the dynamic range of the pixel values (\(L=255\) for 8 bits/pixel gray scale images), and \(K_{1}<<1\) and \(K_{2}<<1\) are the two scalar constants. The procedure of the MS-SSIM method for image structural similarity assessment is illustrated in Figure 2. The two images to be compared are used as input signals, and then the low-pass filter is applied for iteration, and the filtered image is down sampled by factor 2. The original image index is scale 1, and the highest index is scale \(M\). At the \(j\)-th scale, the contrast comparison \(c_{j}(\mathbf{x},\mathbf{y})\) and the structure comparison \(s_{j}(\mathbf{x},\mathbf{y})\) are calculated, respectively. The luminance comparison is computed only at scale \(M\) as \(l_{m}(\mathbf{x},\mathbf{y})\). Thus, the MS-SSIM evaluation is obtained by combining the measurements at different scales using:  
\[SSIM\left(\mathbf{x},\mathbf{y}\right)=[l_{M}(\mathbf{x},\mathbf{y})]^{\alpha }\cdot\prod_{j=1}^{M}[c_{j}(\mathbf{x},\mathbf{y})]^{\beta}[s_{j}(\mathbf{x},\mathbf{y})]^{\gamma} \tag{8}\]  
where \(\alpha\), \(\beta\) and \(\gamma\) are parameters to define the relative importance of the three components. To simplify parameter selection, we set \(\alpha=\beta=\gamma=1\).
# Buckling Analysis of Piles in Multi-Layered Soils
## 2 Methodology  
Buckling analysis is often neglected in the design of foundation piles, but in the case of highly slender piles in soft soils, the pile load-bearing capacity can be reduced by buckling. Therefore, in this case, an elastic buckling analysis is equipped to calculate the critical buckling load of such slender compression members. Typically, buckling analysis involves solving an eigenvalue-eigenvector problem; that process was used in this article to perform buckling analysis of piles in multi-layered soils. In general, such a problem is faced using finite differences or finite elements, where one could even consider different soil stiffness values for each pile element. In contrast, by using a Fourier development of soil stiffness along the pile, this method considers continuous functions only, notwithstanding the stiffness discontinuity between different soil layers. Moreover, the variational approach was pursued using a direct method (the Rayleigh-Ritz method) to implement the analytical model. In the following, considering the applied load P as a function of the coefficients ak (k = 1, 2,..., m) of another Fourier series describing the pile modal shapes, the critical buckling load can be identified through minimising function P = P(a1, a2,..., ak,..., am) of m unknowns ak. Figure 1 shows the flowchart of the methodology adopted for buckling analysis of piles in multi-layered soils.
# Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
## 6 Conclusions  
In this paper, a new spatial modulation technique named SS-FGSM is proposed for the underwater acoustic channel. The proposed SS-FGSM scheme uses two axes (antenna index and spreading codes index) for the spatial domain to convey additional information bits and increase the achievable data rate. Unlike the traditional SMTs schemes, the proposed scheme uses three dimensions for data transmission, the habitual M-ary signal constellation, antenna index, and spreading code index. Two axes of the spatial domain have been used for conveying data transmission leading to reduce the physical bits transmitted over the channel and increase the SE and EE of the communication system. Additionally, the power consumption has been saved by transmitting only a few bits physically over the channel and most bits are transmitted based on indices of either the transmit antennas or the spreading codes. For the receiver's complexity point of view, the proposed scheme is evaluated based on two types of the detector; ML-based detector and DL-based detector. Due to the high computational complexity of the ML decoder, this paper provides a deep learning-based detector to be used in underwater communication to avoid wasting additional computational power. The general ABER formula for MLSS-FGSM wasderived to evaluate the proposed scheme performance, and the simulation analysis confirmed the corresponding mathematical expressions used in the ABER analysis. Furthermore, the SE, EE, and receiver's complexity of the proposed scheme is analyzed and compared with different SMTs. The proposed scheme has been evaluated by using measured underwater acoustic channels conducted from one sea-going experiment. In particular, the proposed scheme outperforms the conventional schemes in terms of energy and spectrum efficiency, as well as in terms of ABER performance. In our future work, real experimental tests for the proposed scheme is going to be conducted.  
**Author Contributions:** Formal analysis, Z.A.H.Q. and J.Q.; Funding acquisition, H.S.; Investigation, J.W.; Methodology, Z.A.H.Q. and H.E.; Supervision, H.S. and J.Q.; Writing--original draft, Z.A.H.Q.; Writing--review & editing, H.E., J.Q. and J.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by The National Natural Science Foundation of China (61671394), the Fundamental Research Funds for the Central Universities (20720170044), Science and Technology Program of Shenzhen, China (JSGG20170414090428464) and Natural Science Foundation of Tianjin (16)CQNJC01100).  
**Conflicts of Interest:** The authors confirm there is not any conflict of interests.
# Preparation and Properties of Epoxy Composites with Multi-Scale BN Sheets
###### Abstract  
Epoxy resin is one of the most widely used thermosetting polymers and commonly applied in power electronics field. The intrinsic properties of epoxy can be improved by the introduction of inorganic filler, thus fabricating a composite material. In this paper, different scales of modified boron nitride (BN, 1 \(\upmu\)m, 10 \(\upmu\)m) were used to improve the thermal conductivity of epoxy resin. The surfaces BN were modification by a silane coupling agent to improve the compatibility between BN and epoxy resin. The effects of micro-and nano-BN sheets on the microstructure, breakdown strength, thermal and mechanical properties of epoxy resin composite were studied. The characterization of its morphology by scanning electron microscopy shows that nano-BN distribution is in the middle of micro-BN, forming a better bridging effect. The data of the breakdown strength and thermal conductivity indicated that when the content of micro-BN is 30 wt% and nano-BN is 20 wt%, the thermal conductivity of BN/epoxy composite was 1.52 W/m\(\cdot\)K. In addition, the breakdown strength is 77.1 kV/mm. Thus, this type of BN-filled BN/EP composites with remarkable insulation and thermal conductivity properties would have potential for power engineering materials.  
boron nitride; epoxy resin composites; thermal conductivity; insulation properties +
Footnote †: journal: Applied sciences
## 4 Results and Discussion
### Feature Extraction  
The GGA+ELM algorithm performs feature extraction optimizing the accuracy classification of samples according to their fructose concentration. The genetic algorithm is fed with spectral information as shown in Figure 4. Each feature is a spectral line. The optimal solution, if it exists, is unknown. The algorithm delivers two of the best solutions found in the execution. Each solution is composed of a set of frequencies (feature extraction) that classify with high accuracy. Not all solutions have the same number of frequencies.  
A 2.7 GHz Intel Core i7 processor was used. The specific parameter values of the GGA and ELM are summarized below. Five independent simulations were performed for each of the three sets of samples. The time for each simulation was approximately 1 h, so the total computation time was about 15 h.  
Figure 4: Spectral information of the vibrational absorption bands of ten different concentrations of fructose in distilled water (from 0 g/L to 9 g/L). The curves represent average and normalized values of sound pressure level (dB/Hz) over the audible frequencies range (20 Hz–22.05 kHz) for the samples from Table 1.  
* Maximum number of generations = 50 generations;
* Training data size = 80%;
* Testing data size = 20%;
* Population size = 50 individuals;
* Mutation probability = 0.1;
* Number of neurons of ELM = 10 in Table 1; ELM = 11 in Tables 2 and 3.  
No information on which frequencies to be tested first was given to the algorithm. Table 4 lists the frequencies (kHz) of the independent classifiers. Classifier 1 consists of 4 frequencies in the range (8-15) kHz, and Classifier 2 selects five frequencies in the range (3-15) kHz. The two classifiers are combined into a single classification system. It is noteworthy that with only nine features can characterize the 28 concentrations in Tables 1-3.
# Cloud Storage Data Verification Using Signcryption Scheme
## 2 Preliminaries  
### Bilinear Pairings  
Let \(G_{1}\) be a cyclic additive group and \(G_{2}\) be multiplicative cyclic groups with prime order \(p\), \(P\) is the generator of group \(G_{1}\). The mapping \(e:G_{1}\times G_{2}\to G_{2}\) is a bilinear map with the following properties:  
1. **Bilinearity:**\(e(aP,bQ)=e(P,Q)^{ab}\), and \(e(P+R,Q)=e(P,Q)\cdot e(R,Q)\ \forall P,Q\), \(R\in G_{1}\), \(a,b\in Z_{p}\).
2. **Computability:** There is an efficient algorithm to compute \(e(P,Q)\ \forall P,Q\in G_{1}\).
3. **Non-degeneracy:** There exists \(P\in G_{1}\) such that \(e(P,P)\
eq 1\). We consider the following problems in the additive group \(G_{1}\).
4. **Discrete Logarithm Problem (DLP):** Given two group elements P and Q, find an integer \(n\in Z_{p}^{*}\), such that \(Q=nP\) whenever such an integer exists.
5. **Computational Diffie-Hellman Problem (CDHP):** For \(a,b\in Z_{p}^{*}\), given \(P,aP,bP\), compute \(abP\). There are two variants of CDHP:
6. **Inverse Computational Diffie-Hellman Problem (Inv-CDHP):** For \(a\in Z_{p}^{*}\), given \(P,aP\), compute \(a^{-1}P\).
7. **Square Computational Diffie-Hellman Problem (Squ-CDHP):** For \(a\in Z_{p}^{*}\), given \(P,aP\), compute \(a^{2}P\).