esa-sceva/satcom-chunk-collection huggingface dataset:
# Depth-Extrapolation-Based True-Amplitude Full-Wave-Equation Migration from Topography
## 4 Discussion  
Full-wave-equation depth extrapolation for migration is based on the two-way full-wave equation, therefore, the wave propagation can be described more accurately. However, because the conventional acquisition system cannot provide the vertical partial derivative  
Figure 11: The topographic profile of the Canadian Foothills model.  
Figure 12: The imaging result of our proposed method.  
Figure 13: (**a**) Comparison of the imaging amplitudes of our proposed method (blue line) and the theoretical amplitudes (red line) at log L3; (**b**) Comparison of the imaging amplitudes of our proposed method (blue line) and the theoretical amplitudes (red line) at log L4; (**c**) Comparison of the imaging amplitudes of our proposed method (blue line) and the theoretical amplitudes (red line) at log L5.  
of the surface wavefield, this method is very limited. The traditional method of obtaining the derivative requires the assumption that the surface velocity is constant and it is currently the most common method. This assumption can be satisfied at sea, but it is often unsatisfied on land. When the surface velocity has a lateral variation, there will be a non-negligible error in the calculated derivative. It will affect the amplitude-preserving performance of the migration result. This is proven in our horizontal layer model experiment. The vertical partial derivative calculation method we proposed overcomes the problem in that traditional methods need to assume uniform surface velocity although our method does have a disadvantage.For the reflected wave signal from the shallow part, the vertical partial derivative calculated by our method will have a certain error. From the results of the Marmousi model experiment, we can find that our method is slightly worse than the RTM method in terms of imaging amplitude accuracy in the shallow part (as shown in Figure 8, approximately at a depth of 500 m). However, as a whole, our method has obvious advantages in the description of structural details, the imaging of deep structures, and the ability to maintain amplitude. In actual seismic exploration, especially in petroleum seismic exploration, the depth of the target layer is generally several kilometers deep. Therefore, our method still has great practical application potential.
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 3 Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme
### Multi-Metric Gateway Selection Process  
When the IV wants to communicate with the LTE-eNB and finds that the \(RL_{eNB}\) of the selected eNB is below the threshold and DT application packets needed to transmit to the LTE-eNB, the IV starts the gateway discovery process by sending a GWreq message within its IEEE 802.11p coverage zone. For each NV, it first should check the receive signal strength \(RSS_{\text{NV}}\) from the LTE-eNB before replaying the packets. If the \(RSS_{NV}\) of the NV is greater than the threshold \(RSS_{th}\), the NV will find itself as one of the potential MGW candidates for its neighboring vehicles. The NV replies with a GWrep message, which includes up-to-the-minute information regarding the \(RSS_{NV}\), \(AV_{CNV}\), \(D_{IVN}\)_and \(LLT_{IVN}\)_. However, if the \(RSS_{NV}\) of the NV is less than the threshold \(RSS_{th}\) the NV drops the message. The IV ignores any duplication of the GWreq packet that comes from the same source. The IV will receive several GWrep packets from different NVs which reside inside the IV IEEE 802.11p coverage zone. Upon receiving the GWrep, the IV applies Algorithm 2, which is based on the SAW technique to select the best MGW. The IV computes the scale metric of each received metric. Each metric is assigned the same priority factor (F) (i.e., \(\frac{1}{4}\) priority level for each metric). The IV uses the F value and metric scale value to compute the weight of each NV. The NV with the highest weight is selected as the best MGW. Finally, the IV starts to transmit the packets through the selected MGW.
# A Probabilistic Hyperspectral Imagery Restoration Method
## 5 Experimental Results and Analysis  
### Experimental Setup  
Data sets: In this study, Washington DC Mall dataset, urban image dataset and terrain dataset are utilized to verify the effectiveness of the proposed method. Washington DC Mall dataset has 191 spectral bands and 1208 \(\times\) 307 pixels. In the experiment, we crop a sub-image in size of \(256\times 256\times 191\) for validation, which is same as [11] does. The urban image has \(307\times 307\) pixels and 210 bands. A sub-image of size \(307\times 180\times 210\) is cropped for the experiment. The terrain dataset contains \(307\times 500\) pixels and 210 bands. The entire image of terrain dataset is used for the experiment.  
Comparison methods and parameter setup: We term the proposed method as 'low-rank' in this study. We compare it with several spectral-spatial methods including video block matching 3-D filtering (VBM3D) based method (a representative local patch-based method), and two low-rank based methods. One low-rank based method is Godec the (low-rank based method proposed in [11]). The other low-rank based method is structured low-rank based method (termed as SLrank for simplification [35]), which is recently proposed and obtains the state-of-the-art performance compared with other competing low-rank based method for HSI denoising. In the experiments, the noise variation is set as 15 for VBM3D method. The parameter is optimally assigned according to [11; 41] for Godec method. For the proposed low-rank method, we set \(\mu_{0}\) as 0. Other parameters including \(a_{0}\), \(b_{0}\), \(c_{0}\), \(\mathbf{d}_{0}\) in addition with the component of \(\alpha_{0}\) are set as \(10^{-5}\).  
Evaluation measures: We adopt two commonly used measures including peak signal-to-noise ratio (PSNR) and structure similarity (SSIM) to measure the quality of the denoising effect.  
Given two images \(\mathbf{A}\) and \(\mathbf{B}\) whose sizes are \(\mathrm{M}\times\mathrm{N}\), PSNR can be calculated by  
\[\mathrm{PSNR}=10\times\log_{10}\frac{M\times N}{\sum_{\mathrm{x}=1}^{M}\sum_{ \mathrm{y}=1}^{N}[\mathbf{A}(\mathrm{x},\mathrm{y})-\mathbf{B}(\mathrm{x}, \mathrm{y})]^{2}}. \tag{18}\]  
\(\mathbf{A}(\mathrm{x},\mathrm{y})\) represents the elements in image \(\mathbf{A}\). Denote the mean value and variance of \(\mathbf{A}\) as \(\mu_{\mathrm{A}}\) and \(\sigma_{\mathrm{A}}\), respectively. Similar, \(\mu_{\mathrm{B}}\) and \(\sigma_{\mathrm{B}}\) is the mean value and variance of \(\mathbf{B}\), respectively. \(C_{1}
# Geographical Information System Enhancement Using Active Learning-Enhanced Semantic Segmentation
## 2 Materials and Methods
### Semantic Segmentation Algorithms for Roof  
In this study, UNet, SegNet, DeepLab18, and DeepLab50 were evaluated as building detection methods for urban areas. Figures 2 and 3 show the network architecture used in this study. Various networks were employed to identify the one with the best performance, taking into account both accuracy and learning time. The input represents images captured by drones which have been resized to a format of 256 \(\times\) 256 \(\times\) 3, indicating RGB channels. The output, on the other hand, consists of values in a 256 \(\times\) 256 format, which determine whether a given pixel corresponds to a roof or not.  
After capturing drone images with a resolution of 2560 \(\times\) 2560 \(\times\) 3, they were georeferenced using RTK-based GPS systems to achieve centimeter-level accuracy in latitude and longitude information. These images were then labeled and divided into 10 \(\times\) 10 segments corresponding to the labeled image. Four different networks, UNet, SegNet, DeepLab18, and DeepLab50, were applied to image learning in both urban and rural areas. The input network size was 256 \(\times\) 256 \(\times\) 3, and the output network size was 256 \(\times\) 256 \(\times\) 1. The input comprised a color image, and the output was divided into roof and non-roof areas using two classes.  
Figure 1: Proposed procedure for building registration ledger inspection using semantic segmentation.  
and learning rate to 1/1000. The performance of each network was evaluated to determine its suitability for utilizing existing networks.  
Figure 2a,b show the architectures of UNet and SegNet, respectively, with \(\times\) 256 \(\times\) 3 color image input and a 2-class output. Figure 3a,b illustrate DeepLap18 and DeepLap50, which are similar, but with DeepLap50 having 49 block connections. The learning network used in this study was the same as that used in a previous study. I/O changed in the network model.
## 4 Simulation, Results, and Discussion
### Enhancing PSO Accuracy via Particle Initialization  
A critical aspect of the performance of the PSO algorithm within the proposed VLC system for localization is the strategy employed for particle initialization. Traditionally, particles are distributed randomly within the search space. However, this approach can lead to premature convergence to local optima because of the potential clustering of particles in suboptimal regions. To mitigate this problem, the proposed system incorporates a space partitioning method for particle initialization, which systematically segments the search space and ensures a more evenly distributed initialization of particles.  
This novel strategy was compared to the conventional random initialization technique. As depicted in Figure 6, the partition initialization method consistently resulted in lower localization errors across a spectrum of receiver heights. This suggests a more thorough exploration of the search space and indicates a higher probability of escaping local optima, thereby enhancing the likelihood of converging to the global optimum.  
Figure 5: Real and estimated positions with the receiver at 1.5 m.  
The efficacy of the partition initialization method is further underscored by the Cumulative Distribution Function (CDF) depicted in Figure 7. This graph is based on the results obtained for Figure 6, where the localization of a target is performed at various heights. The steeper ascent of the CDF curve for the partition method indicates a higher concentration of localization estimates with smaller errors. Remarkably, this method achieved a faster convergence rate, since a significant proportion of the localization tasks were completed with minimal errors, demonstrating its robustness to achieve precise localizations.  
These findings underscore the importance of the initialization phase in the performance of the PSO algorithm for the intended VLC-based localization system. The systematic distribution of particles across the search space not only provided better accuracy but also demonstrated a consistent performance advantage over the random initialization method, as evidenced by the empirical data.  
The implications of these results are profound, suggesting that the initialization process plays a crucial role in the algorithm's ability to navigate complex search spaces effectively.  
Figure 6: Localization error [cm] vs. the height of the receiver [m].  
Figure 7: CDF vs. error [cm].  
This advancement in particle swarm initialization presents a promising avenue for future research and application, particularly in the critical context of hospital environments where precise localization is paramount.
# Study on the Seismic Effect of the Pebble Soil Site in the Zhongwei Basin
## 4 Results  
### The Result of Variation in the Pebble Thickness  
According to the geophysical exploration and drilling work conducted for this study, there are pebble beds with different thicknesses in the Zhongwei Basin. The overlying silty clay beds are approximately 5 m in most areas and are overlaid with sticky sand of varying thicknesses. To study the influence of the pebble layer thicknesses on the ground motion parameters, ten one-dimensional calculation models with pebble layers of different thicknesses were established in this paper, as shown in Table 1. Among them, the soil shear wave velocity was obtained by in situ testing. Figure 4 shows the wave velocity test results of typical drilling holes, and it can be seen from the shear waveform that the test results are true and reliable. The dynamic nonlinear parameters of each type of soil were given by a third-party laboratory, and the test results of similar soils in other local engineering projects were referred to (Table 2). According to the soil layer models in Table 3, the Zhongwei Basin is a class II site. This paper used SEC computing software for calculation analysis, which is a mature and advanced computing software in China [23]. Through calculation, the PGA amplification ratio of the different pebble layer thicknesses under different input intensities was obtained. As shown in Figure 5, under the same input, the variation in the pebble layer thickness had no obvious influence on the amplification ratio of site PGA. Under the same pebble layer thickness, the amplification ratio of PGA decreased with the increase in input. Moreover, the site PGA under different inputs was significantly higher than the corresponding zoning results of the Zhongzhongwei Basin in the \"China Seismic Parameter Zoning Map (GB 18306-2015)\", and the amplification ratio was also significantly higher than the correspond  
Figure 4: The wave velocity test results of typical drilling holes. ing site PGA adjustment coefficient. The analysis showed that the main reason for the large PGA value of the site was the large input ground motion obtained by the probability hazard calculation, and the high amplification factor was caused by the overburden of the shallow silty clay. As shown in Figure 6, the influence of the pebble layer thickness on the response spectrum of ground acceleration was primarily concentrated in the middle/high-frequency bands of 0.2-0.6 s, and with the increase in the input ground motion, the differences in the response spectrum curves in this range also increased. The acceleration response spectrum curves of sites with a pebble layer thickness greater than 65 m (Models 1-9, 1-10) were significantly larger than those with a pebble layer thickness less than 65 m. In the period
# Residual-Attention UNet++: A Nested Residual-Attention U-Net for Medical Image Segmentation
## 4 Experiments and Results
### Dataset  
#### 4.1.1 Skin Cancer Segmentation  
The dataset used in this work was from ISIC 2017 Challenge [34], which includes 2000 lesion images in JPEG format and 2000 corresponding binary mask images in PNG format. Among these 2000 samples, 1280 samples are for training, 320 samples for validation, and the rest for testing and we resized the images to 256 \(\times\) 256 pixels in the experiment. For the target pixels, 0 and 255 represent the area outside the lesion and the area inside the lesion, respectively.
## 3 Concepts and Problems
### General Compressors  
A comprehensive study about the general compressors can be found in [7]. They can be categorized into three different groups based on the idea of how they work.  
The first one consists of Sorting-based compressors, that use different approaches to move similar data together in order to obtain better compression ratios. A conventional method is the Burrows-Wheeler transformation (BWT) [29]. It rearranges characters based on context, thus creating runs of similar characters. This is useful, since techniques such as run-length encoding tend to more easily compress strings that have runs of the same character. It is also important to point out that this transformation can be reversed. To achieve this, only the position of the first original character has to be stored. The BWT is used by Bzip2.  
There are Dictionary-based compressors that maintain a dictionary based on the already processed data, which is used to replace duplicate instances of data. One such algorithm is the Lempel-Ziv-Markov-chain algorithm (LZMA). It is similar to LZ77 [30], except it supports dictionary sizes of up to 4 GB and has a special scheme that chooses phrases (not greedily as in LZSS or LZ77) and a particular scheme of encoding for phrases. The algorithm produces phrases and a stream of literals which are then encoded bit by bit with the use of a range encoder.  
Prediction-based compressors apply statistical models in order to predict upcoming symbols based on context. This can be used to lower the number of bits that are needed to encode the next character. For example, prediction by partial matching (PPMd) [31; 32] predicts the upcoming symbol in an uncompressed stream of characters with the use of a set of previously known symbols. A probability is allocated to each previously seen symbol, and these probabilities are then used to compress the sequence. The PPMd uses the previous 16 tokens while assigning probability, and its memory limit is set to 256 MB.
## 6 Experimental Setup and Results
### Benchmarking with TE-CNN-RNN
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **TSMixer** & **SAMformer** & **TE-CNN-RNN** \\ \hline \multirow{2}{*}{Max Task} & MA (\%) & 73.31 (\(\pm\)2.26) & 73.53 (\(\pm\)1.71) & **74.04 (\(\pm\)2.74)** \\ \cline{2-5}  & RMSE & 130.67 (\(\pm\)15.96) & 134.61 (\(\pm\)18.39) & **124.23 (\(\pm\)14.75)** \\ \hline \multirow{2}{*}{Avg Task} & MA (\%) & 80.50 (\(\pm\)0.88) & 80.87 (\(\pm\)0.95) & **81.90 (\(\pm\)0.97)** \\ \cline{2-5}  & RMSE & 71.29 (\(\pm\)10.96) & 71.11 (\(\pm\)11.30) & **66.42 (\(\pm\)10.50)** \\ \hline \multirow{2}{*}{Min Task} & MA (\%) & 79.50 (\(\pm\)2.26) & 78.27 (\(\pm\)2.67) & **81.07 (\(\pm\)2.60)** \\ \cline{2-5}  & RMSE & 64.11 (\(\pm\)7.56) & 64.78 (\(\pm\)7.64) & **59.56 (\(\pm\)5.53)** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model performance comparison across different tasks multi-run with 95% confidence intervals. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **CNN-RNN (INT)** & **TSMixer** & **SAMformer** & **TE-CNN-RNN** \\ \hline \multirow{2}{*}{Task Max} & MA (\%) & 73.6 & 74.60 & 74.69 & **75.57** \\ \cline{2-5}  & RMSE & 206.28 & 126.51 & 126.05 & **120.90** \\ \hline \multirow{2}{*}{Task Avg} & MA (\%) & 78.9 & 81.53 & 82.64 & **82.70** \\ \cline{2-5}  & RMSE & 116.45 & 70.18 & 68.14 & **65.35** \\ \hline \multirow{2}{*}{Task Min} & MA (\%) & 72.8 & 79.97 & 79.05 & **81.55** \\ \cline{2-5}  & RMSE & 133.56 & 63.75 & 61.32 & **58.94** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative metrics for CNN-RNN (INT), TSMixer [17], SAMformer [18], and CNN-RNN transformer models. efficacy of the carefully engineered architecture that leverages data fusion. The results also propel the development of our MSCR model, designed to harness multi-faceted data sources' potential.
## 3 Results and Discussion
### Impact of Acoustic Transmission Characteristics on Acoustic Signal Propagation
#### 3.4.2 Multipath and Doppler Effects  
Multipath signals, such as reflected signals from the seabed, the sea surface, the equipment, etc., affect acoustic signal propagation [19], and misrecognizing reflected signals as direct signals also leads to acoustic time delay errors [41]. The time deviation of the reflected signal from the direct signal was simulated by BELLHOP for base station depths of 500 m and 2998 m, as shown in Figure 8a,b. The results show that the multipath signals caused time deviations of milliseconds to seconds. In general, methods such as the signal cross-correlation function (CCF) accompanied by the Energy Ratio (ER) algorithm and Phase-Correlation-Only (PCO) method can be utilized to identify the direct acoustic signal from the multipath signal [19; 20; 21; 41; 42].  
Figure 8a,b also show that the propagation time deviation decreased with increasing distance between the base station and the user. For a fixed user, the propagation time deviations of the shallow base station are significantly less than those of the deep base station. Therefore, the subsurface buoy helps to reduce the propagation time deviation. However, for the impact of decreasing the time deviation, on the one hand, the time devia  
Figure 7: Simulation of acoustic ray bending. (**a**) Acoustic propagation path of the base station at different depths, with the rays grazing the sea surface or the seafloor. Blue and orange colors denote the rays grazing the sea surface and the seafloor, respectively; (**b**) base station maximum service range on the sea surface and the seafloor. Blue and orange colors denote the rays grazing the sea surface and the seafloor, respectively.  
tion can be neglected when its value is close to a microsecond; on the other hand, it's hard to distinguish the multipath signal when a sub-millisecond time deviation exists [19; 21; 43]. Nevertheless, these cases were not present in the simulation.  
In addition, the acoustic signal cannot reach the user directly due to ray bending; e.g., with the seafloor base station at 2998 m depth and the user at 2900 m depth, there was no direct acoustic signal when the horizontal distance was more than 5 km. In contrast, the acoustic signal from the subsurface buoy of 500 m depth could reach the user directly under the same conditions, which also shows that the deployment of a subsurface buoy could increase the service range.  
Due to the relative motion between the target and the base station, the acoustic signal generated a Doppler shift. During acoustic signal detection, the optimal frequency shift rate of the signal was generally estimated by searching the maximum cross-correlation coefficient to correct for the effect of the Doppler shift [20].  
In general, both multipath and Doppler effects can be weakened or corrected during acoustic signal detection, which is beyond the scope of this paper and will be considered in further studies.