esa-sceva/satcom-chunk-collection huggingface dataset:
# Ground Deformation Associated with Deep Excavations in Beijing, China
## 3 Characteristic Analysis of Deformation  
### Analysis of Final Surface Deformation  
A total of 1354 monitoring points were arranged in 88 cases. Abnormal points and failure points, affected by the leakage of sand and water through the wall, dewatering, and plastic flow during excavation, were eliminated. The valid data of 1231 monitoring points were obtained, which may reflect the cumulative deformation induced by the whole construction process.  
Figure 4 reveals that excavation primarily results in surface settlement, and settlement points comprise 91.9% of the total. Points with settlements between 0 and \(-\)30 mm accounted for 83.9% of the total. Those with settlements exceeding \(-\)30 mm constituted a mere 8% of the total. In addition to causing ground settlement, deep excavation can also lead to ground upheaval. However, few points exhibited upheaval (this was more evident in the deeper excavations in our study), representing just 8.1% of the total points. These upheaval values were typically small, and primarily distributed within the range of 0-5 mm.  
In the early stages of the entire deformation process, when the excavation level was shallow, we noticed the prevalence of upheaval of the points. As the excavation deepened, these upheaval points began to subside, and the settlement value continued to increase along with the excavation level. This could be due to the fact that when the excavation reaches a hard soil layer, the unloading of the soil at the bottom can cause a rise, pushing the surrounding soil upward. Concurrently, the lateral soil starts to move towards the pit and descend, but the presence of the support system weakens this trend. These two trends interact; if the upward force dominates, an uplift of the surface occurs.  
The design and deformation information for 171 foundation pits worldwide were collected, and the data of the final surface settlement for 68 cases have been obtained (Figure 5) [8]. All surface deformations appear to have subsided. The probability of the final surface settlement in \(-\)10\(-\)\(-\)15 mm is the highest, accounting for 23.5% of the total. The proportion of points with settlements larger than \(-\)30 mm was 18.3%.  
Currently, there is extensive research on the relationship between the maximum surface settlement induced by foundation pit excavation and the distance from the excavation face, but studies regarding the magnitude and probability distribution of the final surface deformation are rare. Due to variances in the stratum and support methods, the results presented in this paper slightly differ from those of [PERSON]. [PERSON] [8], but the overall trend is consistent.  
Figure 4: Probability histogram of the final ground surface deformation.
## 3 Methodology
### PS and SBAS-InSAR  
PS-InSAR is one of the promising approaches that improves the precision of conventional InSAR displacement measurements. The PS-InSAR algorithm utilizes a time series of radar images to detect coherent radar signals from PS points to derive information of the terrain motion [4; 5; 40]. Another algorithm called SBAS-InSAR was also employed, which utilizes all available SAR images with small baselines to get high degree of spatial coverage of distributed scatters [6]. Both algorithms can compensate the disadvantages of the conventional D-InSAR, namely, phase errors due to geometrical and temporal decorrelation as well as atmospheric disturbance [31].  
The ENVISAT-ASAR data were processed using both the PS and SBAS-InSAR methods. For PS-InSAR, we selected the slant-range image (28 November 2006) as a master image and generated 20 interferograms. The perpendicular baseline ranged from 43 m to 951 m, the temporal baseline ranged from 68 days to 1277 days, and selected the potential PS candidates with a coherence threshold of 0.67. For SBAS-InSAR, we selected the slant-range image (28 October 2008) as a super master image. To reduce the geometrical and temporal decorrelation, the threshold criteria with a maximum temporal baseline of 735 days and a maximum perpendicular baseline of 483 m were used, and then, 68 interferograms were generated. Figure 2 shows the time-baseline plot for the ENVISAT-ASAR images used for PS and SBAS-InSAR processing. A combination of the minimum cost flow (MCF) network [41] and Delauney 3D method [42] was employed for phase unwrapping with an unwrapping coherence threshold of 0.35. After that, a screening process of flattened, filtered interferograms and unwrapped phases for checking some unwanted behaviors and data problems, which were caused by strong orbit inaccuracy, non-coherent pairs, atmospheric artefacts, residual topography etc., and 12 interferometric pairs were discarded from further processing. For refinement and reflattening, the reference points where the unwrapped phase value close to zero and flat areas identified from a topographic map projected in the line of sight (LOS) direction were chosen [43]. A custom atmospheric filtering was performed with a low-pass spatial filter with a 1.2 km \(\times\) 1.2 km window on each single acquisition, and a high-pass filter at 365 days on these preliminary results to recover the final and cleaned displacement time series. Finally, geocoding was done in the original satellite LOS direction with a pixel resolution of 25 m. The same software package used for D-InSAR was also used in the PS and SBAS-InSAR processing chain.  
Figure 2: Perpendicular baseline variations for both PS and SBAS-InSAR interferograms. Green diamonds represent selected interferogram pairs, and the yellow diamonds represent the selected master image of PS (left side: 28 November 2006) and SBAS (right side: 28 October 2008). PS: Persistent Scatterer; SBAS-InSAR: Small Baseline Subset-Interferometric Synthetic Aperture Radar.
## 3 Results
### Accuracy
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Solution** & **GNSS** & **N** & **E** & **U** & **2D** & **3D** \\ \hline \multirow{3}{*}{BRDM} & G & 11.4 & 25.0 & 31.8 & 17.6 & 21.7 \\  & E & 6.8 & 19.6 & 14.9 & 13.1 & 16.8 \\  & GE & 15.7 & 26.6 & 24.1 & 28.5 & 35.8 \\ \hline \multirow{3}{*}{HAS} & G & 21.8 & 41.8 & 47.0 & 38.9 & 47.4 \\  & E & 3.2 & 6.1 & 9.1 & 6.1 & 10.2 \\  & GE & 3.4 & 7.4 & 8.6 & 6.7 & 10.0 \\ \hline \multirow{3}{*}{CNES} & G & 1.7 & 3.1 & 2.2 & 3.0 & 4.2 \\  & E & 1.2 & 2.6 & 3.2 & 2.8 & 4.7 \\ \cline{1-1}  & GE & 1.5 & 2.2 & 2.1 & 2.0 & 2.8 \\ \hline \multirow{3}{*}{CODE} & G & 1.5 & 2.1 & 1.4 & 2.9 & 4.1 \\ \cline{1-1}  & E & 0.6 & 1.1 & 1.3 & 1.9 & 3.0 \\ \cline{1-1}  & GE & 1.2 & 1.1 & 0.8 & 1.8 & 2.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Standard deviation of coordinate differences to reference coordinates of all stations for all analyzed solutions. Values in cm. The CNES solution, based on real-time precise corrections, achieved a markedly better performance. For GPS, the mean errors were \(-0.48\) cm (N), \(-1.25\) cm (E), and \(-0.02\) cm (U), resulting in 2D and 3D accuracies of 5.66 cm and 7.98 cm. For Galileo, there were mean errors of \(-0.95\) cm (N), \(-1.39\) cm (E), and \(-0.17\) cm (U), leading to accuracies of 7.21 cm (2D) and 11.17 cm (3D), which were not as good as for GPS. The GE combination provided the best performance, with mean errors of \(-0.64\) cm (N), \(-1.05\) cm (E), and \(-0.04\) cm (U) and accuracies of 3.95 cm (2D) and 5.73 cm (3D). Standard deviations were exceptionally low (e.g., \(\pm 1.74\) cm in N, \(\pm 3.11\) cm in E, and \(\pm 2.21\) cm in U for GPS), confirming the high precision and stability of the CNES solution. The CODE solution, based on precise products, demonstrated the best performance. For GPS, the mean errors were 0.26 cm (N), \(-0.72\) cm (E), and \(-0.15\) cm (U), corresponding to 2D and 3D accuracies of 3.80 cm and 5.39 cm. For Galileo, the mean errors were minimal (0.06 cm in N, 0.00 cm in E, and \(-0.36\) cm in U), with 2D and 3D accuracies of 3.24 cm and 4.88 cm. The GE combination further improved the results, with mean errors of 0.16 cm (N), \(-0.23\) cm (E), and \(-0.24\) cm (U), and 2D and 3D accuracies of 2.21 cm and 3.26 cm, respectively. Standard deviations remained very low across all components, confirming the superior precision and consistency of the CODE solution. It should be noted that the 2D and 3D values represent the mean absolute differences from the reference coordinates. Therefore, these values may appear large, which is consistent with the large standard deviations. In summary, worse results were obtained for GPS in both BRDM and HAS solutions. The inclusion of Galileo significantly improved the achieved accuracy. The best results for each of the products were obtained by combining GPS and Galileo signals, which provided the best convergence times and the highest measurement accuracy.
# Side-Channel Vulnerabilities of Unified Point Addition on Binary Huff Curve and Its Countermeasure
## Appendix A Weierstrass Elliptic Curve
### Edwards Elliptic Curve  
An Edwards elliptic curve has the parameters \(c\) and \(d\) that satisfy the following equation:  
\[x^{2}+y^{2}=c^{2}(1+dx^{2}y^{2}) \tag{11}\]  
The inverted projective coordinates represent \(x,y\) as \(X,Y,Z\) to satisfy the following equation:  
\[x=Z/X\quad\text{and}\quad y=Z/Y\]  
The equivalence class containing \((X,Y,Z)\) is  
\[(X:Y:Z)=(\lambda X,\lambda Y,\lambda Z):\lambda\in\mathbb{F}. \tag{12}\]  
We describe a inverted projective form of the unified point addition formula (add-2007-bl) given in [21]. Let \(P_{1}=(X_{1}:Y_{1}:Z_{1})\) and \(P_{2}=(X_{2}:Y_{2}:Z_{2})\). Then, we get \(P_{1}+P_{2}=(X_{3}:Y_{3}:Z_{3})\) by the unified point addition formula for the Edwards elliptic curve:  
\[\begin{cases}X_{3}=c(E+B)H\\ Y_{3}=c(E-B)I\\ Z_{3}=AHI,\end{cases} \tag{13}\]  
where  
\[\begin{array}{llll}A=Z_{1}Z_{2},\quad B=dA^{2},\quad C=X_{1}X_{2},\quad D=Y _{1}Y_{2},\\ E=CD,\quad H=C-D,\quad I=(X_{1}+Y_{1})(X_{2}+Y_{2})-C-D.\end{array}\]  
This formula requires 9 field multiplications and 1 field squaring. We can identify vulnerabilities of Type 1 and Type 2 during the computations of \(P_{1}+P_{2}\) for \(P_{1}\
eq P_{2}\) and \(P_{1}=P_{2}\).  
**Type 1 vulnerability**: Let us consider the computation \(A=[Z_{1}]\cdot[Z_{2}]\). In this formula, it is computed as \([Z_{1}]\cdot[Z_{1}]\) for \(P_{1}=P_{2}\), whereas it is computed as \([Z_{1}]\cdot[Z_{2}]\) for \(P_{1}\
eq P_{2}\). Similarly, in \(C=[X_{1}]\cdot[X_{2}]\), \(D=[Y_{1}]\cdot[Y_{2}]\) and \(I=[(X_{1}+Y_{1})]\cdot[(X_{2}+Y_{2})]-C-D\), and these are computed as \([X_{1}]\cdot[X_{1}]\), \([Y_{1}]\cdot[Y_{1}]\), and \([(X_{1}+Y_{1})]\cdot[(X_{1}+Y_{1})]-C-D\) for \(P_{1}=P_{2}\), respectively. Thus, we can distinguish between \(P_{1}=P_{2}\) and \(P_{1}\
eq P_{2}\) using ROSETTA.  
**Type 2 vulnerability**: The vulnerability of Type 2 does not exist.  
When applying \(w\)RPC to unified point addition for the Edwards elliptic curve, the two inputs are expressed as follows:  
\[\begin{cases}P_{1}=(X_{1}:Y_{1}:Z_{1}),P_{2}=(r_{1}X_{1}:r_{1}Y_{1}:r_{1}Z_{1} )&\text{If }k^{\prime}=0,\\ P_{1}=(X_{1}:Y_{1}:Z_{1}),P_{2}=(r_{2}X_{2}:r_{2}Y_{2}:r_{2}Z_{2})&\text{If }k^{ \prime}=1.\end{cases} \tag{14}\]where \(r\
eq 1\). It is sufficient to secure against a Type 1 vulnerability by applying \(w\)RPC to unified point addition. The application of \(w\)RPC to unified point addition is evaluated in Table A4. Table A4 shows that vulnerability of Type 1 no longer exists.
## 3 Entropy Feature-Based LNMS LDPC Decoding Optimization  
In this section, we first introduce the basic framework of the LNMS decoding algorithm for LDPC codes, and then we explain the working principle and key technologies of the algorithm. Then, we propose improved algorithms for serial and parallel architectures and explain in detail how these algorithms adjust their internal operations according to the reliability evaluation results of the LPU to improve decoding efficiency.
# Balanced Loss Function for Accurate Surface Defect Segmentation
## 3 Experiments
### Experiment Settings
The models are trained with Adam optimizer [29] (learning rate \(10^{-4}\), weight decay \(10^{-6}\)) and an exponential learning rate regularizer (\(\gamma=0.995\)). The training epoch number for KolektorSDD2, Severstral and the aluminum alloy dataset are 100, 50 and 100, respectively. Data augmentation techniques are used for all training, including random flipping, random rotation and random color jittering. We do not blur or sharpen the images because the defect can be very tiny and sensitive to such transformations. The balanced loss is compared with other loss functions as counterparts, including cross-entropy loss (CE), weighted cross-entropy loss (WCE), focal loss (FL), Dice loss (Dice), inverse weighting loss (iw) [13] and Lovasz loss [30]. For WCE, the weight of positive/negative examples is set according to image-level label imbalance. For example, there are about nine times as many negative images as positive images in KolektorSDD2 (see Table 2), so the positive class loss weight is 9 in WCE and 1 for negative class loss. For focal loss, we use the same rule to set a. \(\beta\) is set to 2 or 4 (both cases are evaluated). In comparative experiments, the hyper-parameters of the balanced loss are chosen empirically. For dynamical class weighting, the scaling factor \(\beta\) is 0.99; for truncated cross-entropy loss, the two thresholds are \(\epsilon_{inf}=0.3\) and \(\epsilon_{sub}=0.7\). For label confusion suppression, the size of the neighboring region is \(r=7\). The sensitivity to the hyper-parameters is tested in the experiments. Figure 6: Images from aluminum alloy surface dataset, containing defect type (**left** to **right**): corrosion spot, corrosion product, corrosion pit and chemical denudation.
# Target Image Mask Correction Based on Skeleton Divergence
## 3 Results and Discussion
### Effect of the Algorithm on Different Sequences  
As shown in Table 3, the dynamics of the scenes in sequences f3/w_h and f3/w_r are slightly higher than those of sequences f2/d_p and f3/s_hz. These four sequences are all video sequences in the TUM RGBD data set. The scenes are all office scenes, in which there are two people in the f3/w_h and f3/w_r sequences; and one person in the f2/d_p and f3/s_h sequences. The complexity of the motion of dynamic participants in a scene affects motion compensation, which renders mask correction more difficult. However, the results provided in Table 3, which represent a variety of scene complexities, indicate that the proposed algorithm is robust to dynamic scenes. The information fusion decision method significantly reduces the influence of uncertain factors on pixel-attribute judgment decisions; thus, the robustness of mask correction is improved. The results for f2/d_p are slightly better than those of f3/s_h mainly because the people in the f2/d_p scene wore clothes that are easier to distinguish from the rest of the scene.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mask Correction Algorithms** & **Computing Time Cost (s)** \\ \hline GOP & 0.532 \\ CUR & 0.618 \\ IFMCaS & 0.471 \\ AIF & 0.672 \\ Prop & 0.301 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time cost in different mask correction algorithms.  
Figure 5: Mask correction of the 82 nd frame in f3/w_h. (**a**) GOP, (**b**) CUR, (**c**) IFMCaS, and (**d**) AIF.
## 4 Comparison and Discussion
### Ablation Study  
#### 4.1.1 Differential Prediction Method  
The ablation experiments are conducted for the differential prediction method. The differential prediction method was used for the control group. The experimental group used the direct prediction method. Because of the change in the prediction method, the final bed pressure prediction results are used as the target value. The results are shown in Table 6 and Figure 11.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Control Group**} & \multicolumn{2}{c}{**Experimental Group**} \\ \cline{2-5}  & **MAE/kPa** & **MAPE/\%** & **MAE/kPa** & **MAPE/\%** \\ \hline Dataset 1 & 0.0443 \(\pm\) 0.0004 & 0.40 \(\pm\) 0.00 & 0.3779 \(\pm\) 0.0399 & 3.39 \(\pm\) 0.36 \\ Dataset 2 & 0.0931 \(\pm\) 0.0030 & 0.79 \(\pm\) 0.03 & 0.4985 \(\pm\) 0.0404 & 4.22 \(\pm\) 0.32 \\ Dataset 3 & 0.0345 \(\pm\) 0.0022 & 0.29 \(\pm\) 0.02 & 0.5816 \(\pm\) 0.0771 & 4.91 \(\pm\) 0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The prediction comparison table of differential prediction method.  
Figure 10: The prediction results diagram of bed pressure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Bed Pressure Value**} & \multicolumn{2}{c}{**Differential Value**} \\ \cline{2-5}  & **MAE/kPa** & **MAPE/\%** & **MAE** & **MAPE/\%** \\ \hline Dataset 1 & 0.0443 \(\pm\) 0.0004 & 0.40 \(\pm\) 0.00 & 0.0379 \(\pm\) 0.0003 & 6.81 \(\pm\) 0.07 \\ Dataset 2 & 0.0931 \(\pm\) 0.0030 & 0.79 \(\pm\) 0.03 & 0.0165 \(\pm\) 0.0005 & 3.33 \(\pm\) 0.12 \\ Dataset 3 & 0.0345 \(\pm\) 0.0022 & 0.29 \(\pm\) 0.02 & 0.0327 \(\pm\) 0.0021 & 6.68 \(\pm\) 0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Prediction results table.  
It can be seen from the results that the use of the differential prediction method greatly improves the prediction performance of the model. The direct prediction approach makes it difficult for the model to learn the mapping relationship between the input and the bed pressure. The main reason is that the model has a limited input length and cannot fully characterize the initial values of bed pressure resulting from the entire past operation. After adopting the differential prediction approach, the model predicts by learning the bed pressure variation, thus achieving better prediction performance. Moreover, the neural network model is more likely to learn low-frequency information and ignore high-frequency information during the training process [38]. The differential processing of bed pressure similarly extracts the high-frequency information of the data, allowing the model to learn the high-frequency information of the variables directly. Therefore, the predictive performance of the model is improved.
## 3 Results and Discussion
At high temperatures, the lasing wavelength of the DFB laser will move to a long wavelength. This means that the mode competition will also move to a long wavelength. However, as the performance of the device will decrease at high temperatures, non-radiative recombination will consume more carriers, and the carriers are mainly provided to the lasing mode. This means that the side modes will not receive many carriers, and so the SMSR will increase with the increase in I\({}_{\text{DFB}}\). The 3 dB linewidth test results are shown in Figure 7. The measured spectral range was 160 MHz with a resolution of 1 MHz. Figure 7a shows the 3 dB linewidth measured at 20 \({}^{\circ}\)C using a delay self-heterodyne linewidth test system. In the spectrogram, the 3 dB linewidth represents the spectral width corresponding to the 3 dB drop from the maximum value of the spectral line amplitude. The measured spectral lines are of the Lorentz line type, so more accurate results can be obtained by performing a Lorentz fit on the measured spectral lines. The measured results have been fitted by the Lorentz method, and the fitted 3 dB linewidth was 18.86 MHz. As the temperature increased, the linewidth generally displayed a clearer trend. In theory, when the output power is not saturated, the linewidth of the semiconductor laser is proportional to the derivative of the output power. However, in practice, the linewidth expands twice due to the linewidth enhancement factor and the spatial hole burning effect. Therefore, the actual measured line width is affected by multiple factors. Increasing the differential gain and optimizing the coupling coefficient had a beneficial impact on reducing the linewidth of the semiconductor laser. The device maintained a stable single longitudinal mode output throughout the test period. Figure 6: **(a)** SMSR of the device with I\({}_{\text{DFB}}\) = 700 mA at 20 \({}^{\circ}\)C; **(b)** SMSR at different temperatures and I\({}_{\text{DFB}}\). The performance characteristics of the DFB laser fabricated in this study were compared with those of other reported DFB lasers. The results are listed in Table 1. As can be seen from Table 1, all devices do not perform very well on all characteristic parameters. The device fabricated in this paper is much higher than that reported in other literature in terms of output power and can have a high SMSR while maintaining high output power. In terms of tuning range and line width, it is not as excellent as the output power and SMSR performance. In the following research, we will focus on how to improve the tunable range and reduce the linewidth and then make our device more perfect.
# A Low-Cost Printed Log-Periodic Dipole Array for DVB-T2 Digital TV Applications
## 3 Results
The far-field pattern shows a satisfying end-fire behavior in the whole DVB-T2 UHF band, confirming that the proposed LPDA can be successfully used as a compact low-cost broadband antenna for Digital TV applications in the frequency range 470-860 MHz, with a fractional bandwidth greater than 70%.
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 5 Covert Channel Attacks in 2.5D/3D ICs  
Even though hardware security in 3D ICs has received some attention [67; 68; 69; 70], covert channel attacks in 2.5D-/3D-based integration are largely unexplored. The most related existing works are on thermal side-channels [71; 51; 72]. Other hardware security studies related to 2.5D/3D integration primarily focus on supply chain vulnerabilities, such as the  
Figure 6: Power spectral density (PSD) of instructions per cycle (IPC) simulated during the execution of (**a**) random applications (sequentially) from SPLASH-2 and PARSEC benchmark suites on an Intel Haswell processor core (no covert channel) (**b**) a low power benchmark, raytrace, encoded with the secret data (i.e., with TCC).  
presence of malicious chiplets (in the form of both software and hardware Trojans) and IP piracy [73; 74; 75; 76]. For example, an active interposer in a 2.5D system can be leveraged as a root-of-trust to host a hardware security module and various security features, assuming that the interposer is designed and fabricated by trusted entities [77]. This approach, however, is highly vulnerable to semi-invasive and invasive physical attacks, since the interposer is relatively accessible to a malicious user who can, potentially, bypass these security features while maintaining the functionality of the chiplets. Furthermore, these hardware security features do not protect the 2.5D/3D IC against malicious end users who can exploit tightly coupled chiplets to establish efficient covert channel communication. Since individual compute units (i.e., chiplets) are expected to be much smaller in 2.5D/3D integration, the impact of thermal noise from other chiplets is be weaker, exacerbating the security threat caused by such covert channel attacks. A concise introduction to 2.5D/3D integration is provided in Section 5.1. Existing covert channel attacks in 2.5D/3D ICs are discussed in Section 5.2. A power covert channel attack model is proposed in Section 5.3. Finally, design-time techniques, as potential countermeasures, are discussed in Section 5.4.