esa-sceva/satcom-chunk-collection huggingface dataset:
# Fragile Watermarking of 3D Models in a Transformed Domain
## 6 Experimental Results
During preprocessing, for every vertex, a signature of the polygons to which it belongs is computed. In the worst case scenario, a vertex is connected to all other vertices, so this computation would take \(O(n^{2})\) time, where \(n\) is the number of vertices. The embedding phase, instead, is linear in the number of vertices of the model, and it is upper bounded because the GA is run for a given predefined number of generations. Finally, the verification phase is very fast, and again, it is linear in the number of vertices (it usually takes a few hundreds of milliseconds on a standard laptop). One of the original models (lion) is shown in Figure 4a. To present how the algorithm operates on the model from a subjective point of view, in Figure 4b, the same model after the fragile watermark embedding is reported. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**3D Model** & **Sensitivity \(\pm\)1 (\%)** & **Sensitivity \(\pm\)2 (\%)** & **MAE \(\times\)10\({}^{-6}\)** & **RMSE \(\times\)10\({}^{-6}\)** & **PSNR (dB)** \\ \hline lion & 93.85 & 99.82 & 0.65 & 2.76 & 312.85 \\ cat & 93.68 & 99.84 & 0.63 & 2.74 & 310.07 \\ dog & 93.83 & 99.81 & 0.75 & 3.08 & 310.85 \\ fish & 93.71 & 99.85 & 0.50 & 2.30 & 311.03 \\ horse & 93.79 & 99.84 & 2.30 & 2.96 & 306.21 \\ raptor & 93.82 & 99.83 & 0.69 & 2.95 & 314.29 \\ suit & 93.71 & 99.84 & 0.071 & 0.36 & 299.61 \\ wolf & 94.01 & 99.88 & 0.63 & 2.80 & 309.04 \\ AK & 93.94 & 99.83 & 1.52 & 8.85 & 317.46 \\ snake & 93.81 & 99.85 & 0.73 & 3.10 & 302.39 \\ \hline Average & 93.82 \(\pm\) 0.20 & 99.84 \(\pm\) 0.02 & 0.43 \(\pm\) 0.35 & 3.19 \(\pm\) 2.04 & 309.36 \(\pm\) 5.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of the proposed algorithm on a set of ten publicly available 3D models. Figure 3: Original models used in the experiments along with [number of vertices]/[number of polygons]composing them: (**a**) lion, (**b**) cat, (**c**) dog, (**d**) fish, (**e**) horse, (**f**) raptor, (**g**) suit, (**h**) wolf, (**i**) AK, and (**j**) snake. An example of tampering is shown in Figure 4c, and the resulting tamper detection highlights the modified vertices in red color, as can be seen in Figure 4d. To give more insights into how the algorithm detects tampering, we report a simple model in Figure 5a and show how the removal of a single vertex (Figure 5c) from the watermarked model (Figure 5b) results in a detection (Figure 5d) that highlights all the vertices contiguous to the removed one; these vertices are marked as forged because, as said in Item 2. of Section 5.3, the polygons containing them were altered: indeed, they were removed, deleting the vertex in the cone apex. Figure 4: Application of the proposed algorithm to a model (lion). Figure 5: Application of the proposed algorithm to a simplified model.
# Fuzzy-Based Dynamic Time Slot Allocation for Wireless Body Area Networks
## 6 Performance Analysis and Comparison  
### Simulation Setup  
The performance was evaluated using the network simulator version 2 (NS-2) simulating tool. NS-2 is an object-oriented discrete event simulator for research in wired and wireless networks that can simulate newly designed network protocols. It has a number of wireless networking supported platforms and protocols for detailed study of simulated results. A random WBAN network was considered with 15 sensor nodes. The network used IEEE 802.15.4 as the MAC protocol. The simulation  
Figure 4: Example of a tree network.  
Figure 5: Slot allocation and parent node active state duration in conventional method.  
Figure 6: Slot allocation and parent node active state duration in DTS method.  
time was selected as 200 s, and the packet interval was varied from 0.1 s to 3 s in steps of 0.5. Table 2 summarizes the simulation parameters used.
# Optical Convolutional Neural Networks: Methodology and Advances (Invited)
## 2 Development of Optical Convolution Neural Network  
Generally, a CNN is composed of convolutional layers, pooling layers, fully connected layers, and nonlinear activations. With the convolutional layer, convolution operations are conducted to extract the features of input images. The typical CNN architecture of LeNet-5 is shown in Figure 1a. The convolutional layer with multiple kernels concurrently performs the convolutional operations to extract various feature images. The pooling layer following the convolutional layer is used to subsample and compress features, reduce the amount of calculation, and alleviate overfitting. Immediately afterwards, the fully connected layers are able to realize the full connection of parameters and generate the final classification results. Additionally, various nonlinear activation functions are employed following the convolutional layers and the fully connected layers, aiming to lead the nonlinear properties to the network.  
Figure 1b shows the principle of one convolution operation with an \(m\times n\) kernel. One convolution operation can be divided into two processes: (1) multiplication between elements in the kernel matrix and data matrix and (2) addition of all multiplication results, which can be expressed as follows:  
\[y=\sum_{i=1}^{m}\sum_{j=1}^{n}w_{ij}x_{ij}, \tag{1}\]  
Figure 1: Basics of convolutional neural networks. (**a**) Scheme of typical LeNet-5 [3]. (**b**) Principle of one convolution operation. (**a**) Reprinted with permission from Ref. [3]. 1998, IEEE.  
where \(w_{ij}\) is the element of the convolution kernel, \(x_{ij}\) is the element of the input data, \(m\) is the row count, and \(n\) is the column count in the convolutional kernel. From Equation (1), one convolution operation is converted into vector-vector multiplication (VVM), and parallel convolution of multiple kernels is represented as a VMM [58]. At the same time, the optical matrix operation has been widely investigated [59], which makes it convenient to accelerate the convolution operation process with optical methods.  
The optical architecture enabling the convolution operation has been blooming. For most of the reported optical CNNs, the convolution operations of CNNs are accelerated in the optical domain, and the rest remain in the electrical domain, which absorbs both the respective advantages for the ultra-bandwidth and low loss of light and the high precision recognition of electricity. Optical CNNs based on the implementation principle are generally divided into four categories: diffraction-based optical CNNs, interference-based optical CNNs, wavelength division multiplexing-based (WDM-based) optical CNNs, and tunable optical attenuation-based optical CNNs.
## 5 Experimentation and Performance Evaluation of Obfuscation Methods
### Evaluation of Obfuscation Features
#### 5.3.1 High Obfuscation Coverage  
In practice, when simulating RTL code, different macro definition parameters can affect the actual operation and results of the project. Certain obfuscation tools might face interference from these macro definitions, potentially leading to incomplete code obfuscating. This paper addresses this scenario by specifically analyzing XML files generated under different macro definition parameters. The goal is to identify discrepancies and obfuscate the code comprehensively, ensuring that no portion of the code is overlooked. The resulting obfuscated code is likely to have a coverage rate of 98% or more, and the coverage rate of some project code can reach 100%.
# InSAR-CTPIM-Based 3D Deformation Prediction in Coal Mining Areas of the Baisha Reservoir, China
###### Abstract
However, the prediction of vertical displacement uses a static PIM model, which is used to multiply the Knothe time function to obtain a dynamic prediction along the vertical direction. Vertical displacement is the main indicator of 3D displacement in a mining area, and the Knothe time function is an empirical function fitted along the time series; the physical meaning is not clear enough, the model effect is not ideal, and the accuracy of predicting the vertical displacement is still debatable. Based on this, a 3D deformation dynamic prediction model (InSAR-3D-CTPIM) between InSAR deformation observations and coordinate-time PIM (CT-PIM) parameters has been proposed in this paper. Based on the literature [19], this model replaces the union of the static PIM and the Knothe function with a more theoretical coordinate-time function that can directly solve the PIM parameters by using the InSAR phase observations and fully reflect the mining geological conditions in the prediction of vertically oriented deformation, with higher modeling accuracy. Based on the literature [16], the one-dimensional vertical deformation prediction was extended to 3D deformation prediction, which can realize the deformation prediction in the three directions of vertical, east-west and north-south. Then, the historical deformation of the Jiansheng Coal Mining Area, which may have collapsed southwest of the Baisha Reservoir of Henan Province during the period of 2015-2017, was reconstructed. The research results can more comprehensively decipher the developmental history of basin-wide deformation dynamics of the surface in this mining area, providing a reference for the subsequent early warning and prediction of geological hazards in this area.
## 5 Verification of the Prediction Model for Height of WFFZ  
### Engineering Background  
Coal Mine A is in the Hujirt mining area, Jurassic coalfield, Ordos Basin (Figure 1). The main coal seam is the Jurassic Yan'an Formation 3\({}^{-1}\) coal seam. It is 610.62-626.17 m deep, with an average depth of 620 m, and it has inclination angles of 1-4\({}^{\circ}\) and an average thickness of 5.72 m. The long-wall fully mechanized mining scheme is adopted for panel 31110X, with a mining height of 5.3 m, a panel width of 260 m, and a mining length of 2589.3 m. The full caving method is used for roof management.  
The positions of the roof aquifers relative to the 3\({}^{-1}\) coal seam are shown in Figure 8. The immediate roof of the 3\({}^{-1}\) coal seam is mainly sandy mudstone, and its basic roof is  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **MAE** & **RMSE** \\ \hline SSA-ELMAN & 3.93 & 4.93 \\ ELMAN & 55.75 & 64.54 \\ BP & 43.29 & 53.92 \\ Empirical Equation (1) * & \(H=4.82M+60.13\ln\frac{\epsilon}{100}\) & 61.70 & 64.72 \\ Empirical Equation (2) & \(H=\frac{1000}{1.6M+3.6}\pm 5.6\) & 67.86 & 92.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Contrast of prediction performances of various models in the evaluation stage.  
Figure 7: Comparison of prediction performances of different models in the evaluation stage.  
mainly sandstone. There are multiple aquifers above the 3\({}^{-1}\) coal seam, and the bedrock aquifers from the top to the bottom are as follows: the Lower Cretaceous Zhidan Formation (K\({}_{1}\)zh) pore confined groundwater aquifer, the Middle Jurassic (J\({}_{2}\)) clastic rock confined groundwater aquifer, and the Middle-Lower Jurassic Yan'an Formation (J\({}_{1}\)-zy) clastic rock confined water aquifer. The direct filling groundwater source of the 3\({}^{-1}\) coal seam mining is the top aquifer of the Yan'an Formation and the middle aquifer of the Zhiluo Formation. According to the hydrogeological survey, this aquifer group has a strong water abundance, and it is prone to causing abnormal roof water busting during roadway excavation and face mining, which threatens the safety of the mining operations.  
Input the relevant mining conditions of panel 31110X in the Coal Mine A into the trained prediction model, and the predicted height of the water-flowing fractured zone at the mine resulted in 124.9 m, with mining fissures lead to the aquifer of Zhiluo Fm. In order to verify the performance of the model, we measured the actual height of the water-flowing fractured zone of panel 31110X.
## 2 Background
### Target Area
Due to this difficulty, the application to SHARAD data over the Phoenix landing site should be tested to prove the robustness of developed algorithms. Figure 1: **(a)** The map of Martian surface and location of target areas using Mars Orbiter Laser Altimeter (MOLA) digital elevation model (DEM), **(b)** Southern polar region of Mars (between latitude 55\({}^{\circ}\) S and 90\({}^{\circ}\) S), (c) Phoenix landing site, **(d)** Anonymous LDA and **(e)** Euripus Mons LDA. The same colormap has been used in all maps throughout the paper.
# Structural Information Reconstruction of Distorted Underwater Images Using Image Registration
## 2 Methods
### Guided Filter  
The edges of the fused image, which plays a critical role in the reconstruction of the text, are closer to their true positions. In order to guide the registration to focus on the more important region, a guided filter proposed in [27] is employed to enhance the text boundary. In general, a general linear translation-variant filtering process involves a guidance image \(G\), an input image \(p\), and an output image \(q\). Both \(G\) and \(p\) are given beforehand according to the application, and they can be identical. The filtering output \(q\) at the position \(i\)  
\[a_{k}=\frac{{\sigma_{k}}^{2}}{{\sigma_{k}}^{2}+\varepsilon} \tag{5}\]  
\[b_{k}=(1-a_{k})\overline{p_{k}} \tag{6}\]  
where \({\sigma_{k}}^{2}\) is the variance of \(G\) in \(\omega_{k},\overline{p_{k}}\) is the mean of \(p\) in \(\omega_{k}\). Based on the Equation (5) and Equation (6), the windows can be divided into \"flat area\" and \"high variance area\". The former means that the guidance \(G\) is approximately constant in \(\omega_{k}\), and the solution is \(a_{k}=0,b_{k}=\overline{p_{k}}\), whereas the latter implies that the intensity of guidance \(G\) changes a lot within \(\omega_{k}\), then the solution becomes \(a_{k}\approx 1,b_{k}\approx 0\). More specifically, the \(\varepsilon\) is selected as the discrimination threshold of \"flat area\" and \"high variance area\". Therefore, the edge patches with \(\sigma^{2}\) much larger than \(\varepsilon\) are preserved, while non-edge patches with smaller variance are smoothed.  
After computing coefficients for all windows \(\omega_{k}\) in image, the entire output image can be given by:  
\[q_{i}=\overline{a_{i}}G_{i}+\overline{b_{i}} \tag{7}\]
# A Reflection-Type Dual-Band Phase Shifter with an Independently Tunable Phase
## 3 Simulation and Measurement Results  
The proposed dual-band tunable PS was designed and fabricated at \(f_{\rm L}\) = 1.88 GHz and \(f_{\rm H}\) = 2.44 GHz with an operating bandwidth of 100 MHz, using a substrate RT/Duroid 5880 with a dielectric constant (\(\epsilon_{r}\)) of 2.2 and a thickness (\(h\)) of 0.787 mm. The simulation was performed by using advanced design system (ADS) simulator. The variable capacitance \(C_{\rm v}\) was implemented using a varactor diode SMV-1231 from SKYWORKS. A 3-dB hybrid coupler S03A2500N1 from ANAREN was also used. Figure 8 shows a layout and photograph of the designed dual-band tunable PS.  
Figure 8a defines the transmission line width, length, and components in the entire circuit, and their values are listed in Table 3. The part number and measured value of DC block capacitor, RF choke inductor, bypass capacitor, \(L_{\rm C}\), and \(C_{\rm C}\) at\(f_{\rm L}\) and \(f_{\rm H}\) are also listed. The overall size of the fabricated circuit was 85 mm \(\times\) 52 mm.  
Figure 8: (**a**) Layout and (**b**) photograph of fabricated dual-band tunable reflection-type PS.  
Figure 7: PSR with and without compensating for the effects of a \(\lambda/4\) shunt open stub at the high band.
# Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
## 4 Experimental Results
### Crossband Filtering--Length Extension
#### 4.4.1 Optimal FWSegSNR (\(L_{\text{bb}}=15\))  
The observations depicted in Figure 2a,c provide valuable insights into the impact of incorporating the first crossband component on the dereverberation performance, specifically in terms of FWSegSNR and PESQ. Surprisingly, it is evident that introducing the first sample of the crossband component leads to a decrease in performance compared to the conventional WPE method. Conversely, when considering the CD measure (as illustrated in Figure 2b), adding the first sample of the crossband component improves the dereverberation performance. Further analysis reveals that the optimal performance, in terms of FWSegSNR and CD, is achieved when \(L_{\text{cb}}\) is set to 2, as shown in Table 1. On the other hand, the optimal performance in terms of PESQ is attained when \(L_{\text{cb}}\) is set to 3. It is worth noting that for \(L_{\text{cb}}>6\), the performance starts to decline, surpassing the level achieved by the conventional WPE method. These intriguing findings shed light on the intricate relationship between different choices of \(L_{\text{cb}}\) and their impact on the overall dereverberation performance.
## 3 Graph Theory and Sensor Networks
### Hamiltonian Paths  
Computing Hamiltonian paths allows us to visit all sensors once, collecting all the data saved by them. Analyzing the existence of this type of trail can be applied to analyze network connectivity, optimize data collection, enhance routes efficiently, and improve network resilience.  
In the field of sensor networks, the study of Hamiltonian paths within the Delaunay graph associated with such a network allows us to analyze the interconnectivity among sensors. These paths, covering every sensor node precisely once, provide optimal routes for traversing the sensor network.  
Moreover, the analysis of this type of path contributes to the enhancement of routing efficiency; identifying paths that cover all sensors aids in designing communication routes that minimize delays and reduce the risk of congestion.  
Figure 3: Example of a group of sensors and its associated Delaunay graph.  
Finally, understanding Hamiltonian paths also plays a very important role in improving the resilience of a sensor network. By identifying paths that visit all the nodes, the network can adapt to changes, such as sensor failures, ensuring continuous data flow.  
An example of a Hamiltonian cycle is given by the trail \(\{v_{1},v_{2},v_{3},v_{4},v_{5},v_{7},v_{6},v_{1}\}\) in the Delaunay graph from Figure 3 since it is a closed trail on the network visiting every node (vertex) once.
## 3 Data and Methodology
### Data Pre-Processing
#### 3.1.1 Identification of Flaws in the Data  
The task of identifying flaws in the provided dataset arose from the need to create time series, which cannot have interruptions. This analysis was conducted in two stages: an initial one, in which flaws in all grids lasting over 24 h were identified; and a more in-depth one, in which all 5-min periods with flaws were identified for each grid individually.  
The records were registered in intervals of 5 min, and all the intervals that contained flaws were identified. Total and partial flaws were distinguished, with total flaws corresponding to failures in all grids, and partial flaws corresponding to failures in only some grids.
# Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices
## 3 NAND Flash Memory Reliability
### Random Telegraph Noise (RTN)
#### 3.2.4 Effect on Programmed \(V_{t}\) Distribution  
Figure 14 shows pictorially what the effect of RTN on the \(V_{T}\) levels is and how it affects the array reliability, for the case of an MLC array. Exponential tails due to RTN extend from the ideal Gaussian distribution, their magnitude increasing with \(N_{C}\) and time. This results in a reduced separation between adjacent \(V_{T}\) levels (the so-called _window_), compromising the accuracy of the read operation and leading eventually to a read failure: the dashed lines in Figure 14 indicate the reference \(V_{T}\) levels used during read, to which the cell \(V_{T}\) is compared. When a distribution crosses a reference level, a read error is bound to occur.  
Figure 13: (**Left**) Comparison between experimental data and model for the RTN distribution in \(1\times\) and \(2\times\) NAND arrays and different \(N_{C}\); (**Right**) same comparison for the tail probability at a fixed \(|\Delta V_{T}|\) as a function of the number of reads for a 65 nm NOR Flash array.  
To investigate this issue in more detail, Figure 15 shows the effects of RTN on the programmed \(V_{T}\) distribution [157] following ISPP and successive read. The left-hand side shows the \(V_{T}\) pdf right at the end of the ISPP algorithm, for two values of the ISPP step: \(V_{s1}\) and \(V_{s2}>V_{s1}\). In the ideal case, the algorithm is expected to result in a \(V_{T}\) distribution exceeding the program-verify level \(PV\) by an amount at most equal to the ISPP step (dashed lines in Figure 15). In reality, the distributions are widened by the program noise (not discussed here, see [158; 159]) and by RTN and display the characteristic exponential tails. Note that no cell can have \(V_{T}\) values below the program-verify level, as the algorithm would apply an additional step to them. However, a negative RTN tail also arises as soon as the cells are read (right-hand side of Figure 15). This figure also shows that the RTN tail height is smaller for larger values of the ISPP step; however, increasing its value will lead to an overall larger \(V_{T}\) distribution. On the other hand, reducing the ISPP step is effective--apart from the increase in program time--only until its value becomes comparable with the RTN distribution width.
## 3 Compressive Sensing Framework
### Reconstruction Methods  
There are some convex optimization algorithms that can be used to solve (3). Following, three distinct recovery methods considered in this work are briefly mentioned: The Newton algorithm with a log-barrier method, used in the L1-magic MatLab toolbox [30]; the greedy-based algorithm called A*OMP [31]; and the shrinkage-based algorithm called Least Absolute Shrinkage and Selection Operator (LASSO) [32].
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 3 Proposed Scheme
### Room Reserving
#### 3.2.3 Vacating Room  
After self-reversible embedding, all bits of the target pixels \(P_{t}\) and \(n\) LSBs of the reference pixels \(p_{r}\) are saved in C. The image owner sets \(n\) LSBs of the reference pixels \(P_{r}\) to zero, and then the additional data are embedded into \(n\) LSBs of the target pixels \(P_{t}\) that have been set to zero by homomorphic addition in ciphertexts. This operation can also avoid overflow when embedding data into the encrypted domain. Figure 5 describes the proposed scheme more intuitively.