esa-sceva/satcom-chunk-collection huggingface dataset:
# Chaos Based Frequency Modulation for Joint Monostatic and Bistatic Radar-Communication Systems
## 6 Monostatic Radar Signal Processing  
### Ambiguity Surface of Monostatic CBFM Radar  
For a monostatic radar configuration, the transmitter and receiver are collocated. Therefore, additional procedures as shown for the bistatic configuration are not necessary. The same waveform that is embedded with digital information \(i(t)\) is used for efficient transmission. The response of the matched filter is \(s(t)\). Hence, the ambiguity function in monostatic configuration is given as  
\[\chi\Big{(}\widehat{\tau}-\tau,\widehat{f}_{D}-d_{D}\Big{)}=\int\limits_{- \infty}^{\infty}s(t-\tau)s^{*}(t-\widehat{\tau})exp^{[-j2\pi\big{(}f_{D}- \widehat{f}_{D}\big{)}t]}dt. \tag{18}\]  
In the above equation,  
\[\bigg{(}\tau=\frac{2R_{R}}{c};f_{D}=\frac{2f_{c}}{c}V\bigg{)} \tag{19}\]  
\[\bigg{(}\widehat{\tau}=\frac{2\widehat{R}_{R}}{c};\widehat{f}_{D}=\frac{2f_{c }}{c}\widehat{V}\bigg{)} \tag{20}\]  
Since the relationship between the range-delay and velocity-Doppler frequency is linear, the ambiguity surface can be plotted either on the delay-Doppler plane, which is traditionally used or on the range-velocity plane. Figure 15 shows the ambiguity surface of the CBFM waveforms on the range-velocity plane. For both the Lorenz CBFM (Figure 15a) and the Burke-Shaw CBFM waveforms (Figure 15b), a near thumbtack ambiguity function is obtained. Similar to the bistatic radar, the sidelobes on the velocity plane die out very quickly. The sidelobes on the range plane for the Lorenz CBFM is at \(0.055\) (\(-25.2\) dB), and for the Burke-Shaw CBFM, it is at \(0.052\) (\(-25.7\) dB). These extremely low sidelobes prove that the CBFM waveforms serve as an excellent candidate for high-resolution radar imaging.  
Figure 14: Sidelobe volume of the bistatic radar configuration for the Lorenz CBFM and Burke-Shaw CBFM waveforms plotted against the signal to noise ratio (\(SNR\)). The squares indicate that for the respective SNR, the sidelobe levels are at the value of \(-13.33\) dB.
## 2 Materials and Methods
### InSAR Data and Processing
Interferograms created during pre-processing were processed using phase unwrapping techniques to resolve ambiguities and extract accurate displacement information. Based on precise satellite ephemeris data, orbital corrections were used to eliminate long-wavelength phase artifacts caused by trajectory errors. Atmospheric noise reduction, incorporating delay models, corrected distortions from water vapor and temperature variability, ensuring clear subsidence signals. The baseline thresholds of 12 days (temporal) and 181 m (spatial), as shown in Figure 6, maintained high coherence across the interferograms while preserving sufficient data density. Figure 5: The PSI network shows coherence connections, where the red lines represent low coherence and the blue lines represent high coherence. Figure 6: The network of interferograms for PSI processing shows connections based on 12-day temporal and 181 m spatial baselines.
# Variable Rate Point Cloud Attribute Compression with Non-Local Attention Optimization
## 4 Experimental Results
### Rate-Distortion Efficiency
#### 4.3.2 Objective Quality Comparison  
We follow the common practice to measure the bit rates (i.e., bits per points, bpp) and distortion in terms of Peak Signal-to-Noise Ratio (PSNR) in dB of the Y channel, which are computed using MPEG PCC pc error tools. As the bit rates generated by various algorithms are different, the BD-Rate (in percentage) is used to measure the overall R-D performance.  
Figure 7 shows the R-D curves, and Table 1 shows BD-rate gains. The proposed method outperforms TMC13v6 by a large margin, having 31.5% BD-rate reduction. However, the proposed method still has performance losses compared with the state-of-the-art  
Figure 5: Examples from ScanNet. (**a**) scene-0233 (**b**) scene-0262 (**c**) scene-0483 (**d**) scene-0699.  
Figure 6: Test dataset. (**a**) longdress. (**b**) readblack. (**c**) soldier. (**d**) loot.  
algorithms TMC13v14. The results show that our scheme has 29.3% performance gap. For learning-based method SparsePCAC, the proposed method achieves 6.3% BD-rate reduction. For Deep-PCAC, the test sample \"soldier\" is used for comparison since the other three point clouds are used for training by Deep-PCAC. The proposed method achieves 50.7% BD-rate reduction.
## 3 Low Power Cusped Field Thruster Development at HIT  
In recent years, research on the cusped field thruster have been majorly focused on the low power types since the demand on low power electric thrusters increased with the development of small satellites. The major research interests include configuration and physical mechanism studies, cusped field thruster downscaling and the recent microwave discharge cusped field thruster concept.
# Landslide Susceptibility Assessment by Using Convolutional Neural Network
###### Abstract
Using machine learning to residual uncertainty within landslide susceptibility assessments has led to the development of more complex approaches to attain acceptable levels of accuracy. The largest sources of uncertainty in susceptibility modeling are related to the inventory database. Geological complexity, geomorphological deformations, and land-use and land-scape changes are the main causes of the uncertainties. In this regard, the development of more accurate models is important. Recently, machine-learning techniques have been applied to landslide susceptibility mapping to increase mapping accuracy, which provides the learning rate from the inventory database and reduces errors. These errors were estimated based on loss functions and error tables (e.g., MSE, MAPE, and RMSE) [29; 30; 31]. This study has attempted to use CNN for susceptibility zonation of the Gorzineh-khil region. The innovation point in this research is the application of a deep learning-based method to CNN models in order to provide a landslide susceptibility assessment for the study region. The application of CNN, which has received significant success in landslide susceptibility, is one of the most recent geo-hazard evolution methods in deep learning. CNN is capable to apply on satellite images to extract features, classify the prone area for sliding and predict the hazard risk potential. In this regard, a 15-layered CNN model was performed to detect and classify the sliding features based on the triggering factors. As a justification, the proposed model was justified through a comparison of the common machine-learning classifiers such as SVM, k-NN, and DT algorithms based on performance indices.
# A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method
## 5 Proposed Fault Diagnosis and Severity Analysis Method  
### Fault Detection Process  
For rolling bearings, when bearing failure occurs, the fault defect triggers an instantaneous impact with non-stationarity. When a rolling bearing undergoes local failure, every contact in the process of operation triggers an instantaneous impact, resulting in a large number of impact waveforms in the measured vibration signal of the rolling bearing. Therefore, the vibration signal of the rolling bearing shows impact characteristics and the impact waveform changes with the severity of the fault. This impact waveform causes the complexity of the vibration signal to increase, leading to abrupt changes in the SampEn value. The fault detection of the rolling bearing vibration signals can be realized by using the SampEn to measure the complexity of the time series. The more serious the fault is, the more complex the vibration signal is. The threshold of fault detection is determined by the SampEn of the vibration signal under normal conditions and different fault conditions of the rolling bearing. If the SampEn value of the vibration signal of the rolling bearing is greater than the set threshold value, it indicates that the rolling bearing has failed. To improve the real-time performance of the fault detection process, the calculation method based on the kd tree is applied to the calculation of the SampEn. Although rolling bearing faults can be detected through SampEn, the identification of the fault types needs to be realized through the fault diagnosis process.
## 4 w-SMNO: Performance Analysis
### Simulation Outcomes
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Factors**} & \multicolumn{5}{c}{**E2E Delay (msec)**} & \multicolumn{5}{c}{**Network Coverage (\%)**} \\ \hline \multicolumn{1}{c}{**State-of-the-Art**} & \multicolumn{1}{c}{**Proposed**} & **SES** & **AISM** & **AJ-MOFA** & **Proposed** & **SES** & **AISM** & **AJ-MOFA** \\ \hline \multirow{10}{*}{**Time (s)**} & 30 \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Factors**} & \multicolumn{5}{c}{**E2E Delay (msec)**} & \multicolumn{5}{c}{**Network Coverage (\%)**} \\ \hline
**State-of-the-Art** & **Proposed** & **SES** & **AISM** & **AJ-MOFA** & **Proposed** & **SES** & **AISM** & **AJ-MOFA** \\ \hline \multirow{5}{*}{**Local Leader Density**} & 50 & 127.85 & 173.46 & 159.79 & 150.99 & 87.66 & 76.107 & 80.251 & 83.127 \\ \cline{2-9}  & 60 & 142.6 & 202.15 & 180.97 & 169.41 & 87.49 & 78.348 & 85.813 & 82.195 \\ \cline{2-9}  & 70 & 160.43 & 243.82 & 207.85 & 193.15 & 92.319 & 80.443 & 83.477 & 84.264 \\ \cline{2-9}  & 80 & 182.49 & 286.78 & 244.02 & 224.37 & 97.861 & 84.837 & 84.698 & 86.236 \\ \cline{2-9}  & 90 & 210.38 & 364.02 & 286.83 & 262.8 & 98.332 & 86.304 & 87.435 & 85.117 \\ \cline{2-9}  & 100 & 244.93 & 435.66 & 342.5 & 312.08 & 99.157 & 87.774 & 91.738 & 88.421 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Factors**} & \multicolumn{5}{c}{**Message Delivery Rate (\%)**} & \multicolumn{5}{c}{**Collision Rate (\%)**} \\ \hline
**State-of-the-Art** & **Proposed** & **SES** & **AISM** & **AJ-MOFA** & **Proposed** & **SES** & **AISM** & **AJ-MOFA** \\ \hline \multirow{5}{*}{**Time (s)**} & 30 & 98.56 & 85.49 & 77.21 & 68.43 & 2.35 & 5.73 & 3.76 & 6.11 \\ \cline{2-9}  & 60 & 97.24 & 82.76 & 78.66 & 72.59 & 3.61 & 10.26 & 8.83 & 8.95 \\ \cline{2-9}  & 90 & 89.37 & 84.64 & 82.42 & 77.91 & 5.42 & 18.46 & 9.48 & 10.37 \\ \cline{2-9}  & 120 & 78.19 & 74.28 & 86.57 & 77.02 & 8.68 & 25.54 & 12.2 & 16.77 \\ \cline{2-9}  & 150 & 72.43 & 65.36 & 80.62 & 73.25 & 12.7 & 35.76 & 20.27 & 24.62 \\ \cline{2-9}  & 180 & 75.41 & 52.81 & 74.18 & 71.84 & 20.35 & 44.68 & 31.18 & 29.43 \\ \cline{2-9}  & 210 & _77.52_ & 53.16 & 67 & 64.73 & 12.8 & 30.57 & 24.84 & 36.82 \\ \cline{2-9}  & 240 & 83.77 & 57.33 & 55.07 & 63.34 & 13.21 & 31.22 & 20.26 & 40.41 \\ \cline{2-9}  & 270 & 85 & 65.74 & 62.46 & 60.08 & 14.55 & 34.37 & 26.34 & 33.08 \\ \cline{2-9}  & 300 & 92.6 & 69.35 & 73.58 & 65.16 & 15.16 & 39.63 & 32.08 & 37.51 \\ \hline \multirow{5}{*}{**Population (Count)**} & 100 & 99.48 & 87.44 & 85.2 & 88.76 & 3.36 & 4.35 & 4.08 & 5.45 \\ \cline{2-9}  & 200 & 98.75 & 81.43 & 84.68 & 81.28 & 3.92 & 5.73 & 5.46 & 6.75 \\ \cline{1-1} \cline{2-9}  & 300 & 96.07 & 77.6 & 81.7 & 75.19 & 4.71 & 11.91 & 7.52 & 8.39 \\ \cline{1-1} \cline{2-9}  & 400 & 85.4 & 70.09 & 72.16 & 70.32 & 6.84 & 15.43 & 11.61 & 12.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Simulations result of proposed for message delivery rate and collision rate.
# Surface Electromyography-Based Action Recognition and Manipulator Control
## 4 Results
### Performance of Real-Time Control  
The overlapping sliding window and the synchronous command transmission were conducive to shorten the running time. We set the baud rate of the serial port to 115,200. The specific correspondence between the labels and the control commands ensured consistency. When the classifier outputs the label of resting state, a corresponding resting-state command was also sent to the manipulator to keep it still. After network training, the action of the manipulator could be controlled synchronously. For instance, when the subject did not make any movement and remained at a resting state, the manipulator also kept still. When the subject did fist clenching, so did the manipulator. When the subject's movement was hand opening, the manipulator also opened its fingers to follow the action. The synchronization speed was fast, without excessive delay. After calculation, it was noticeable that the entire process took  
Figure 4: The positions of the electrodes. We adopted 4-channel differential input in signal acquisition and took the elbow as the reference. The acquisition positions located on extensor pollicis longus, finger extensor muscle, and the palm long extensor muscle.  
Figure 5: Five actions to be recognized. (**a**) Fist clenching. (**b**) Hand opening. (**c**) Wrist flexion. (**d**) Wrist extension. (**e**) Calling me. These actions are more common in daily lives and relatively easier to be recognized. Patients usually start with these basic movements in restoring hand function.  
less than 200 ms from the sEMG signal acquisition to the manipulator's response. The manipulator was able to track the actions of the subject consistently.  
Figure 6: Spatial distribution of each feature. (**a**) MAV, (**b**) ZC, (**c**) WL, (**d**) RMS, (**e**) MF, and (**f**) MPF. The x, y, and z labels are channel 1, 2, and 3. The actions represented by different colors are marked—the blue points represent fist clenching, the red points represent hand opening, the yellow points represent wrist flexion, the purple points represents wrist extension, and the green points represents calling me. The classification effect is better if the difference can be seen with the naked eye. Or if it is difficult to distinguish the scattered points, the classification effect is poor.
# Spatiotemporal Evolution and Impact Mechanisms of Gross Primary Productivity in Tropics
## Discussion
### Analysis of the Transmission Situation of GPP  
Over the past two decades, transformations in land cover types have significantly influenced GPP in two primary ways. Firstly, the conversion of various land uses into forests, shrublands, and other green spaces has led to an increase in GPP. Secondly, transformations into bare areas and cropland have resulted in a decrease in GPP. These changes underscore the critical role that land degradation and the conversion of forested areas into agricultural land play in diminishing GPP. Consequently, it is crucial to focus on mitigating the decline in productivity that results from the conversion of broadleaf forests into cropland. Such efforts are aimed at enhancing forest cover, thereby promoting increased vegetation productivity [49]. Additionally, specific measures to implement the Returning Farmland to Forest Program (RFFP) should be considered. To ensure the effectiveness of future forest policies, they must be crafted with a comprehensive understanding of the specific regional context and the prevailing natural and socioeconomic conditions [50].
## 2 Materials and Methods
### Statistical Indicators for Performance Analysis  
The performance of the bias-corrected reanalysis data and of the spatially interpolated data, computed from ground sensors by cross-validation, were evaluated at each AWSs, by assuming the weather observations retrieved from the AWS sensors as benchmarks.  
Similarly, for what concerns the reference evapotranspiration, the benchmark was assumed to be the reference evapotranspiration computed with Equation (1) by using the weather data retrieved from ground sensors as input variables. This benchmark ET\({}_{0}\) was, respectively compared with ET\({}_{0}\) computed by using the bias-corrected reanalysis weather data and the spatially interpolated weather data from ground sensors as input.  
The performances were evaluated by computing the BIAS, which was used as an indicator of accuracy of data. and the root mean square error, RMSE, which gives insight about both accuracy and precision of data. These two statistical indicators were calculated as follows:  
\[\text{BIAS}=\frac{\sum_{\text{i=1}}^{\text{n}}\left(\text{X}_{\text{p}}^{ \text{i}}-\text{X}_{\text{b}}^{\text{i}}\right)}{n} \tag{2}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{\text{i=1}}^{\text{n}}\left(\text{X}_{\text{p}} ^{\text{i}}-\text{X}_{\text{b}}^{\text{i}}\right)^{2}}{n}} \tag{3}\]  
where \(\text{X}_{\text{p}}^{\text{i}}\) and \(\text{X}_{\text{b}}^{\text{i}}\) are, respectively, the variable for which the performance evaluation is desired and the corresponding variables assumed as benchmark, at the i-th day; \(n\) denotes the number of examined days (183 days of the annual irrigation season multiplied by the 11 years analyzed, i.e., 2013 days in total). The more the BIAS differs from zero the less accurate is the estimate. The greater is the RMSE, the less are accuracy and precision of the estimates.  
In the following, differences between the performance indicators were computed for comparing the performance of different methods each other. For comparing BIAS, the differences between the corresponding absolute values resulting from Equation (2) were computed.  
When monthly analyses for the reference evapotranspiration were performed, the BIAS and RMSE were divided by the mean daily ET\({}_{0}\) on the examined j-th month computed over the analyzed years (values specified in Table 3), in order to consistently compare monthly performances.  
These normalized indicators are hereinafter referred to as nBIAS and nRMSE, computed as follows for the j-month:  
\[\text{nBIAS}=\frac{1}{\text{X}_{o}^{j}}\frac{\sum_{\text{i=1}}^{k}\left(\text {X}_{\text{p}}^{j,}-\text{X}_{\text{b}}^{j,i}\right)}{k} \tag{4}\]  
\[\text{nRMSE}=\frac{1}{\text{X}_{o}^{j}}\sqrt{\frac{\sum_{\text{i=1}}^{k} \left(\text{X}_{\text{p}}^{j,i}-\text{X}_{\text{b}}^{j,i}\right)^{2}}{k}} \tag{5}\]  
where \(k\) refers to the number of days in the j-month of interest and \(\overline{\text{X}_{o}^{j}}\) is the mean observed value of the generic variable X in the examined j-month over the analyzed years at a specific site of interest. In the following, we express nBIAS and nRMSE in percent.
# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 2 Materials and Methods
### Data Acquisition  
The first step was to implement a GNSS measurement using a Leica GNSS antenna in order to obtain data correction information. Three points were placed near the tunnel outdoors. After measuring them, a prism was placed at the center of each control point to measure the geo-referenced coordinates. The control points were only located on the ground (due to the urban restriction on mounting control points on the underpass wall).  
After measuring the control points, the main step of data acquisition began with capturing images using both spherical cameras. The technical settings of the spherical  
Figure 4: Two network design strategies were employed to achieve the highest possible accuracy of the 3D point clouds. In the graffiti underpass (top view: **(a)**), data capturing was repeated two times for each spherical camera with two different orientations: stereo mode (**b**) and sideways mode (**c**). Accordingly, the raw file images (**d**) and the panorama images (**e**) were exported for each implemented strategy.  
Figure 5: Total station and GNSS receiver used for defining the network of control points (**a**). The control point plates with 40 \(\times\) 40 cm dimensions were big enough to be visible in the fisheye images of the spherical and DSLR cameras (**b**).  
cameras, specifically those pertaining to ISO and shutter speed (ISO value range: 100 to 250, shutter speed value range: 1/25 to 1/350), were automatically adjusted based on the lighting sensitivity. This was due to the lack of sufficient lighting conditions in the middle of the underpass. Each spherical camera, with an f/2 aperture and a focal length of 1.32 mm, captured 40 images (20 images in stereo mode and 20 images in sideways mode). Additionally, 54 photos were captured with the DSLR camera. In our DSLR data collection, only a specific section of the underpass was considered to account for quality comparison and not as a reference dataset. Therefore, the consistent value of ISO allowed for the capture of clear and detailed images with minimal graininess and noise (shutter speed value range: 1/13 to 1/160). Other settings included an ISO of 500, an aperture of F/4, and a focal length of 10.5 mm. In the final step, a reference measurement was taken using the Leica RTC 360 laser scanner from two equally distant viewpoints located at the entrance and exit of the underpass. The first laser scanner station was near the beginning of the underpass and the second one was almost at the end. The settings such as grid configuration, the quality of each scan, and double scan mode were checked. The scan time on both viewpoints was about 3 min.
# A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
## 4 Experimental Results and Analysis
### Experimental Results
The model preserves clear boundaries in challenging regions like the myocardium, which often suffers from segmentation ambiguity due to its thin and non-uniform geometry. Table 3 presents a detailed comparison of segmentation performance across models, where HCMUNet attains the highest overall Dice score of 92.11%, in particular, it outperforms the standard U-Net by 4.27% and exhibits a 1.39% improvement over a Mamba-only variant, highlighting the synergy of convolutional and sequence modeling components in our design. In addition, HCMUNet consistently achieves high performance across all cardiac substructures, including a Dice score of 91.50% for the RV, which often presents irregular boundaries, and 90.20% for the MYO, which is particularly challenging to segment due to its thin and variable shape. Although marginally lower scores are observed for the left ventricle when compared to EMCAD and TransCASCADE, HCMUNet demonstrates superior overall accuracy, stability, and generalization, further validating the robustness of the proposed hybrid framework under diverse anatomical and imaging conditions. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Model** & **DSC** & **HD95** & **Aorta** & **Gallbladder** & **Kidney (L)** & **Kidney (R)** & **Liver** & **Pancreas** & **Spleen** & **Stomach** \\ \hline U-Net [2] & 75.92 \(\pm\) 2.71 & 37.55 \(\pm\) 4.80 & 87.39 \(\pm\) 2.87 & 67.52 \(\pm\) 3.11 & 78.72 \(\pm\) 2.04 & 68.87 \(\pm\) 3.19 &
# The Challenges of Advanced CMOS Process from 2D to 3D
## 5 The Etching Challenges and Solution of 3D
### STI Process for the Gate  
The STI process of Si fins is described in Figure 17[129]. One of the critical processes is to control the fin height (\(H_{fin}\)) since it has influence on the electrical properties of transistors, e.g., the threshold voltage (\(V_{th}\)) decreases as \(\mathrm{H_{fin}}\) decreases. In order to etch silicon dioxide, hydrogen fluoride acid (HF) based reagent is used. However, wet etching normally causes footing due to its isotropic etching nature as shown Figure 18a,b[130]. In any case, high temperature annealing has to be avoided to prevent the material inter-diffusion but low temperature processing leaves low quality of STI oxide [4; 130].  
Figure 16: Cross-section SEM images of self-aligned contact (SAC) structure after the conventional etch and optimized Quasi-ALE. Reproduced with permission from [128]. Copyright IEEE, 2016.  
Figure 15: The concept of Quasi-ALE with (**a**) process started; (**b**) reactant feed deposited; (**c**) adsorb saturated; (**d**) active by Ion bombardment and (**e**) film removed.  
In general, the lower quality of oxide, the higher etch rate (ER) is anticipated. The method to moderate the elevated ER for better process control is to either reduce the concentration of the main species or to decrease the etching time. Normally, the former has better process window compared to the latter.
## 2 Analysis of the State of the Art
### Related Standardization Work  
Regarding the standardization, there are several initiatives (dealing with HCI) that, considering users and devices, are now presented.