esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Smart Water Methods (SWMs)
The iWIDGET project is another notable smart water project, providing an online platform for efficient urban water management through information and communication technology (ICT), rendering near-real-time water consumption data and decision-support tools to households and utilities [123]. The project primarily focuses on the processing and analysis of water use data and providing feedback. Operating across Greece, Portugal, and the United Kingdom, this project empowers water utilities to devise water pricing models and forecast demand, offering a pivotal utility in the efficient management of water resources [124]. Within the Scientific City in northern France lies the SunRise Smart Water project, a demonstration of smart water systems within the \"SmartWater4 Europe\" European initiative. Encompassing a wide range of urban networks, including water distribution systems, the Scientific City faces water loss challenges due to various factors, including aging infrastructure, soil movement, and unaccounted water volumes for specific purposes, such as network cleaning or construction works. To overcome these challenges, hydraulic sensors, automatic metered readers (AMRs), and other techniques are employed to measure flow, pressure, and noise levels in pipelines and inside the buildings. The combination of various methods, including water balance, minimum night flow, leak noise loggers, hydraulic modeling, and artificial neural network approaches, has helped to detect multiple leaks quickly and to reduce the non-revenue water by 36% [125]. Smart water management systems play a crucial role in improving water leak detection. However, they face challenges, including high implementation costs, cybersecurity concerns, and data volume management. The heterogeneity of existing water infrastructure, often aged and incompatible with the sophisticated sensor and communication technologies necessary for smart monitoring, is also another challenge. Moreover, the lack of standardized protocols and interoperability intensifies the complexity of integrating smart systems. These challenges must be addressed, and future research should focus on several areas that require improvement. First, enhancing the integration of advanced data analytics, machine learning, and artificial intelligence can refine the leak detection accuracy and facilitate automated decision-making processes. Research into more cost-effective and energy-efficient sensors and communication technologies is essential to lower barriers to adopting smart water management solutions. Furthermore, developing standardized cybersecurity protocols and data encryption measures is crucial for safeguarding these systems from cyber threats. Initiatives promoting universal communication protocols may enhance interoperability, facilitating seamless integration of diverse technologies across water networks.
# Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture
## 3 Numerical Tests and Results
### Feature Datasets
#### 3.2.2 Multi-Event Microseismic Records  
In our simulation of multi-event microseismic records, we used the same configuration for the observational system as previously described. Regarding the source parameters, we placed an unspecified number of underground sources, ranging from 10 to 15, within a depth range below 500 m. For excitation, we maintained the use of a Ricker wavelet with a dominant frequency of 30 Hz. To enhance the realism of the simulated microseismic signals, this paper incorporated random time delays within the range of (0, 100 ms) for  
Figure 4: The Marmousi velocity model used for generating the labeled dataset.  
Figure 5: The extracted local Marmousi models obtained from the data subset.  
Figure 6: (**a**) The original velocity model. (**b**) The vertically flipped velocity model derived from (**a**). (**c**) The horizontally flipped velocity model derived from (**a**).  
each sub-wavelet during numerical simulations. By using this methodology, we were able to replicate phase variations and interference phenomena that occur during sub-wavelet propagation, considering factors such as reflection and refraction. This approach facilitated a more accurate reconstruction of vibration signals, thereby improving the representational capabilities of the results. Figure 7 shows an example of a single multi-event microseismic record obtained through this process.
# A Physicist's View on Partial 3D Shape Matching
## 3 Implementation
### Computing the LD-SIFT Signature  
Let \(\mathcal{M}\) be a triangular mesh, with \(\mathcal{M}=(V,E,T)\), and \(V=\{v\}\), \(E=\{e\}\), and \(T=\{t\}\) denoting its vertices, edges, and triangles, respectively. Our implementation directly follows the method described in [46], with the following practical details. The scale \(S(v)\) of a vertex \(v\) in \(\mathcal{M}\) is given by Equation (1), i.e., it is the mean edge length over all edges attached to \(v\), multiplied by a constant \(K\) which we set to 3. \(N(v)\) is the set of all vertices of \(V\) that are within the distance \(S(v)\) of \(v\). Let \(N=|N(v)|\) and let \(w\) be one of those vertices,and let \(r_{w}\) be its position in the ambient space \(\mathbb{R}^{3}\). To compute a normal of the surface at \(v\), we first compute a covariance matrix over \(N(v)\):  
\[\mathcal{C}=\frac{1}{N}\sum_{w\in N(v)}r_{w}r_{w}^{T}-\frac{1}{N^{2}}\left(\sum _{w\in N(v)}r_{w}\right)\left(\sum_{w\in N(v)}r_{w}\right)^{T}. \tag{28}\]  
The normal \(n\) at \(v\) is then computed as the cross product of the two dominant eigenvectors of \(\mathcal{C}\). The whole mesh \(\mathcal{M}\) is then rendered onto the plane \(P(v)\) that is normal to \(n\) and passing through \(v\), using a method adapted from Patch Software Render by [PERSON] (2016). The corresponding image size is set to \(21\times 21\) pixels, with each of its dimensions covering \(2\times S(v)\) on \(P(v)\), centered at \(v\). SIFT descriptors are then computed for the point at the center of the image (which corresponds to \(v\)) using the method implemented in VLFeat (2016). Two sets of descriptors are computed, for the image and for image rotated by \(180^{\circ}\), and averaged out. This leads to a vector of 128 descriptors that forms the LD-descriptor of \(v\).  
Given LD-SIFT signatures for all vertices in a mesh, the distance between two such vertices is computed as the L2-norm of the differences between their signatures. Note that under this framework, vertices are represented in the same feature spaces; therefore, the same procedure applies for computing the distance between two vertices of two different meshes.
# Mechanism to Estimate Effective Spectrum Availability Inside Smart Buildings
## 3 System Model
### Effective Spectrum Availability Model  
The ESA uses a metric that represents the percentage of the internal area of the building in which the primary spectrum can be reused. It is obtained from the ratio between the number of apartments within an SB from where an SR-CRWS can operate as an SS and the quantity of building apartments.  
The ESA estimation mechanism proposed is based on the simultaneous fulfillment of the two interference conditions in each apartment of the SB; to estimate the ESA on each SB floor (\(ESAF\)), both C1 and C2 must determine the apartments that meet both conditions at the same time. To identify these apartments on each floor (\(IDAF\)), they are assigned binary values as follows:  
\[\left(IDAF\right)_{i}=\begin{cases}1,&if\ C1\ and\ C2\ are\ true\\ 0,&if\ any\ is\ false\end{cases}\qquad\forall i=1,\ldots,N \tag{13}\]  
Once the apartments have been correctly identified, they are grouped by floor to determine the \(ESAF\), as shown below:  
\[\left(ESAF\right)_{j}=\frac{p}{N}\sum_{i=1}^{N/p}\left(IDAF\right)_{i,j};\ \ j=1,\ldots,\ p. \tag{14}\]  
Finally, the ESA is determined using  
\[ESA=\sum_{j=1}^{p}\left(ESAF\right)_{j} \tag{15}\]  
\[ESA=\frac{p}{N}\sum_{j=1}^{p}\sum_{i=1}^{N/p}\left(IDAF\right)_{i,j} \tag{16}\]  
In other words,  
\[ESA=\frac{p}{N}\sum_{j=1}^{p}\left(\sum_{i=1}^{N/p}\left(\begin{array}{c}1, \ if\ \ I_{BS-SS}<P_{TH}\ \wedge\ I_{SS-PS}<P_{TH}\\ 0,\ if\ \ I_{BS-SS}>P_{TH}\ \vee\ I_{SS-PS}>P_{TH}\end{array}\right)_{i} \right)_{j} \tag{17}\]  
and finally,  
\[ESA=\frac{p}{N}\sum_{j=1}^{p}\left(\begin{array}{c}\left(\begin{array}{c}1, \ if\ \ \left(\begin{array}{c}\frac{5}{N}\\ 10\log cos\theta_{k}-\left(W_{e}+W_{ge}+\max(\Gamma_{1},\Gamma_{2  
where the LOS component \(L_{outside}\) is evaluated and validated in the next section.
# A High-Resolution Spectral Analysis Method Based on Fast Iterative Least Squares Constraints
## 2 Theory
### Fast Iterative Shrinkage-Thresholding Algorithm
To address the oscillation issue caused by \(a_{k}\to 1\) in the FISTA, the Greedy FISTA introduces a restart mechanism where \(a_{k}\to 1\) periodically. This forces \(a_{k}\) to start increasing from 0 again, effectively preventing oscillations that occur when \(a_{k}\to 1\) and restarting the process in a cycle. To minimize the interval between restarts and reduce the oscillation period, thereby improving the convergence rate, \(a_{k}\) is set to a constant value of 1. This restart framework ensures convergence while shortening the restart interval and oscillation period. Compared to the ISTA and FISTA, this approach enhances convergence speed.
## Materials and Methods
### DART Simulation
#### 2.2.3 Simulation of fAPAR  
fAPAR\({}_{canopy}\) was defined as the fraction of absorbed photosynthetically active radiation (APAR) by the entire canopy (i.e., leaves and wood elements without ground) over the PAR region from 400 nm to 700 nm [30]. Accordingly, fAPAR\({}_{leaf}\) was defined as the fraction of absorbed photosynthetically active radiation (APAR) only by leaves. To evaluate the impact of woody elements on the simulation and estimation of SIF escape probabilities, two types of fAPAR (i.e., fAPAR\({}_{canopy}\) = fAPAR\({}_{leaf+novody}\) and fAPAR\({}_{leaf}\)) were used to estimate SIF escape probabilities (Section 3.4).  
In DART, fAPAR\({}_{canopy}\) and fAPAR\({}_{leaf}\) were derived from the radiative budget of facets per type of scene element (i.e., leaf, wood element, ground).  
\[\text{fAPAR }=\frac{\int_{400\ nm}^{700\ nm}RB^{absorbed}(\lambda)d \lambda}{\int_{400\ nm}^{700\ nm}E(\lambda)d\lambda}, \tag{4}\]  
where \(E(\lambda)\) is the incident irradiance, and \(\lambda\) is wavelength over the spectral region from 400 to 700 nm, \(RB^{absorbed}(\lambda)\) is the irradiance absorbed by leaf only (i.e., \(RB_{leaf}^{absorbed}(\lambda)\)) or entire canopy (i.e., \(RB_{canopy}^{absorbed}(\lambda)\) = \(RB_{leaf}^{absorbed}(\lambda)+RB_{novody}^{absorbed}(\lambda)\)). In addition, \(RB^{absorbed}(\lambda)\) was calculated by area-weighted average as below:  
\[RB^{absorbed}(\lambda)=\sum_{i=1}^{n}\left(RB_{i}^{absorbed}\cdot S_{i}^{ element}\right)/S_{canopy}^{element}, \tag{5}\]where \(n\) is the number of leaves in the canopy, \(RB_{i}^{absorbed}\) and \(S_{i}^{element}\) are the absorbed irradiance and area of scene elements of type \(i\) (e.g., leaf, woody element), respectively, and \(S_{canopy}^{element}\) is the sum of areas of all elements within the canopy.
## 4 Case Study
### Prediction Results  
The rockburst hazards in the Zhongnanshan Tunnel, the Daxiangling Tunnel, and the Cangling Tunnel were predicted by the PSO-BPNN-AdaBoost and PSO-BPNN-XGBoost models. The results are shown in Table 1.  
Figure 16: Geographical locations of the engineering cases.  
The results obtained by the PSO-BPNN-AdaBoost matched well with actual results, while the PSO-BPNN-XGBoost model produced three prediction deviations. However, as shown in Table 1, the PSO-BPNN-AdaBoost model also produced one prediction deviation. This deviation was observed in prediction instance 10, where the actual rockburst hazard level was III, but the model incorrectly classified it as level II. Several factors may account for this deviation, including the presence of noise or outliers in the dataset, as well as the complexity of feature interactions that the model may not have fully captured. Specifically, the oversampling technique used during data preprocessing might have introduced synthetic samples that do not entirely represent real-world conditions, thus affecting the model's ability to generalize to specific cases. Based on the above analysis, the constructed PSO-BPNN-AdaBoost and PSO-BPNN-XGBoost algorithms can be effectively applied to rockburst prediction in practice.
## 4 Simulation Results
### Transitions in Islanded Operation  
Figure 12 shows the steady-state simulation of interval B, and Figure 13 shows the results of the dynamic simulation corresponding to the transition from the charging mode to the power injection mode in the presence of the SST and then the passage to the voltage regulation mode after a shutoff of the grid.  
When initially only ChP1 is occupied, the SST supplies all the required current, and the energy surplus allows the battery to charge. The arrival of a second electric vehicle at ChP2 requires more power than that available in the SST, which provokes the transition to the power injection mode.  
Figure 11: Operating modes of the HESS in interval A: (**a**) operation modes of the HESS; (**b**) current supply of ChPs.  
Figure 12: Details of interval A in Figure 7: **(a)** ChPs occupancy, **(b)** power flow, **(c)** sequence of UFCS operating modes.  
A grid shutoff can be observed in second 130 of the simulation in Figure 13a, which implies that current \(i_{SST}\) is zero. The voltage regulation mode activates the SESS module, causing an initial power peak. Subsequently, the battery injects the required current, nullifying the contribution of the supercapacitor, as shown in Figure 13b. In order to maintain the nominal voltage of the supercapacitor, the battery injects an extra current after the transition to compensate for the capacitance loss of the former. In addition, Figure 14 illustrates the bus voltage, which is regulated to 1500 V with the contribution of both the battery and supercapacitor. Finally, the corresponding supply of the charging points and the operating modes of the HESS are depicted in Figure 15.  
Figure 14: Bus voltage and ESD voltages during interval B.  
Figure 13: PSIM simulation during interval B showing the current flow: (**a**) in the main bus; (**b**) in the HESS.
# Causality in Control Systems Based on Data-Driven Oscillation Identification
## 4 Results
### Results for Simulation Data
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{\(T_{row\to column}\)} & \(e_{1}\) & \(e_{2}\) & \(e_{3}\) & \(e_{4}\) & \(e_{5}\) \\ \hline \multirow{3}{*}{\(e_{1}\)} & \(d_{4}\) & NA & **0.1146** & 0.0344 & 0.0242 & 0.0297 \\  & \(d_{5}\) & NA & **0.1438** & 0.0496 & 0.0495 & 0.0468 \\  & \(d_{6}\) & NA & **0.1515** & 0.0750 & 0.0643 & 0.0908 \\  & \(d_{7}\) & NA & **0.1299** & 0.0826 & 0.0730 & 0.1071 \\ \hline \multirow{3}{*}{\(e_{2}\)} & \(d_{4}\) & **0.0834** & NA & 0.0300 & 0.0227 & 0.0251 \\  & \(d_{5}\) & **0.1504** & NA & 0.0587 & 0.0429 & 0.0536 \\  & \(d_{6}\) & **0.0753** & NA & 0.0560 & 0.0549 & 0.0646 \\  & \(d_{7}\) & **0.0953** & NA & 0.0612 & 0.0755 & 0.0940 \\ \hline \multirow{3}{*}{\(e_{3}\)} & \(d_{4}\) & 0 & 0 & NA & 0.0358 & 0.0244 \\  & \(d_{5}\) & 0.0467 & **0.0520** & NA & 0.0358 & 0.0411 \\  & \(d_{6}\) & 0.0734 & **0.0914** & NA & 0.0508 & 0.0698 \\  & \(d_{7}\) & 0.0788 & **0.1058** & NA & 0.0665 & 0.0767 \\ \hline \multirow{3}{*}{\(e_{4}\)} & \(d_{4}\) & 0 & 0 & 0.0233 & NA & 0.0219 \\  & \(d_{5}\) & **0.0429** & 0.0362 & 0.0350 & NA & 0.0402 \\  & \(d_{6}\) & **0.0701** & 0.0600 & 0.0637 & NA & 0.0625 \\  & \(d_{7}\) & **0.0958** & 0.0947 & 0.0641 & NA & 0.0752 \\ \hline \multirow{3}{*}{\(e_{5}\)} & \(d_{4}\) & 0 & 0 & 0.0223 & 0.0275 & NA \\  & \(d_{5}\) & 0.0432 & **0.0518** & 0.0410 & 0.0447 & NA \\ \cline{1-1}  & \(d_{6}\) & 0.0611 & **0.0709** & 0.0573 & 0.0656 & NA \\ \cline{1-1}  & \(d_{7}\) & 0.1442 & **0.1657** & 0.0633 & 0.0612 & NA \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Transfer Entropy coefficients for simulated data (Gaussian noise and Cauchy disturbance). pairs of variables. The expected causality diagram for the simulation model (Figure 7) assumes direct and indirect relations, but focusing on causality for the highest value of TE in a row, such a complicated graph cannot be determined. However, when comparing the two graphs, there are some subtle differences. There is the lack of dependence between the variables \(e_{3}\) and \(e_{1}\), and the direction of causality of the variables \(e_{5}\) and \(e_{2}\). Considering the second largest value of TE coefficient for the pair \(e_{3}\) with the other variables, the causality graph would show the relationship between \(e_{3}\) and \(e_{1}\), as originally assumed. The inverse, presumed causality between \(e_{5}\) and \(e_{2}\) should be considered in further research. Correctly obtained results are the effect of stimulating the simulation system not only with additional Cauchy noise but also with a sinusoidal signal with a known frequency. The causality graph obtained as a result of calculations should be considered correct and sufficient.
# Algorithm for Extraction of Reflection Waves in Single-Well Imaging Based on MC-ConvTasNet
## 2 Theory and Method
### Dataset
An inherent flaw of single-well imaging is that reflectors vertical to the borehole cannot be imaged. Hence, the dip angles in Table 1 do not include vertical or near-vertical situations. The azimuth angles of the dataset vary from 0\({}^{\circ}\) to 90\({}^{\circ}\), which covers most types of reflected waves. Introducing more azimuthal angle variations does not significantly enhance the diversity of the data. The detection distance of single-well imaging is several meters to tens of meters [2]. While our analytical algorithms can theoretically analyze longer ranges, we set 13 m as the maximum distance in our dataset since reflected waves become more readily extractable as the reflector's distance from the well increases. Of the dataset, 90% is selected as the training set, while the remaining 10% is used as the validation set. The test set consists of 34,944 waveforms (=1092 spatial locations \(\times\) 4 formation combinations \(\times\) 8 receivers). The four formation combinations contain soft and hard formations and do not appear in the training and validation sets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Geometry** & **Lower Limit** & **Upper Limit** & **Step Size** \\ \hline Distance (m) & 3 & 13 & 1 \\ Dip angle (\({}^{\circ}\)) & \(-\)70 & 70 & 10 \\ Azimuth angle (\({}^{\circ}\)) & 0 & 90 & 45 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Variation range of the geometric characteristics of reflectors in the dataset. Figure 5: Illustration of geometric characteristic parameters for a single reflector. A dipole source is placed along the x-axis of the Cartesian coordinate \((x,y,z)\). The cylindrical coordinate \((r,z,\varphi)\) is also shown in the figure. \(\psi\) is the dip angle of the reflector relative to the wellbore axis. \(H\) is the distance from the source to the reflector. \(\varphi\) is the azimuth of the reflector. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Formation Parameters** & **Lower Limit** & **Upper Limit** \\ \hline P-velocity (m/s) & 2595 & 5080 \\ S-velocity (m/s) & 1170 & 3200 \\ Density (kg/m\({}^{3}\)) & 1743 & 2702 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Variation range of parameters of formations on both sides of the reflector in the dataset.
# Cooperative Downlink Listening for Low-Power Long-Range Wide-Area Network
## 5 Performance Evaluations  
In this section, we test each function of CDL, and conduct performance comparison with LoRa standard. While using basic functions of standard as it is, CDL is implemented by adding additional functionalities at the high level, so that data success ratio and delay analysis are skipped in this performance evaluation. As a result of our experiments, it is shown that the two metrics have almost similar performance to those of the standard, so that they are not arguable in this paper. Instead, we evaluate entire network traffic compared with LoRa standard, and energy consumption, which is the most important feature of LoRa, is analyzed.
# Quantum Image Steganography Schemes for Data Hiding: A Survey
## 3 Quantum Image Steganography Schemes
### Pixel Value Differencing  
A good steganographic algorithm, pixel value differencing (PVD), is known for its superior spatial visual recognition as well as its high payload [31]. The PVD technique determines how many hidden bits can be inserted by finding the subtraction of two successive pixels.  
In 2019, [PERSON] et al. [11] proposed a scheme for implementing quantum steganography by adopting the PVD approach to enhance the robustness and performance of his quantum steganography algorithm. The concept of PVD was initially proposed by [PERSON] et al. [32] in 2003; it was discovered that each pixel in a digital image may survive a different degree of alteration because the human eyes exhibit more sensitivity to any changes to pixels applied in the smooth section of an image rather than its edges. That is, every pixel in an image can contain a varied quantity of covert bits without creating detectable sensory distortion. However, with the LSB steganography technique, the amount of alteration in each pixel is uniform. As the LSB method alone does not provide an examination of each pixel and ignores the effect of using an image's edges, its performance is generic.  
In [PERSON]'s approach [11], the PVD method first partitions a cover picture into blocks with each containing two successive pixels. Then, by subtracting the two pixels, the algorithm will extract the difference between them, signified by d. The scheme then takes the absolute value for every d, dividing them into several neighboring ranges known as Ri, where i represents a sequence of numbers from 1 to n. The total bits to be embedded in each pair of pixels are then determined by the corresponding range's extracted difference value. After that, the difference will be subsequently substituted with the secret information bits. This approach is a straightforward way to generate a more unnoticeable outcome than the simple LSB procedures. Furthermore, the covert data contained in the stego image may be recovered without referencing the original image. To achieve this method, a sequence of reversible logic circuits is designed.