esa-sceva/satcom-chunk-collection huggingface dataset:
# An Analytical Solution for Block Toppling Failure of Rock Slopes during an Earthquake
## 4 Discussion  
A limit equilibrium analytical solution for the block toppling failure is presented and applied to an anti- dip rock slope model under shake table test. The theoretical results can capture the experimental observation well including the block deformation features and overall slope stability condition. Inthe presented analytical analysis, some conditions are simplified, for example, the width of blocks is identical, the strength parameters are also identical for every block, and the failure base is a linear form. It is known that the real slopes in the field are sometimes much more complicated than the idealized slope mode. If so, the engineering geological generalization approach should be adopted to abstract the control factors and reach the simplified but representative engineering geological model. For the very complicated slopes such as those have unequal-sized layers and different variable strength parameters, numerical tools are more suitable [30].  
The cohesion of layers or block base joints is not incorporated in the presented solution, because the cohesions of these discontinuities are effectively lost in most toppling slopes. If not, the presented solution should be modified a little to involve the cohesion of discontinuities, or an equivalent substitution can be used as we did in the case study. Besides, the discontinuity connectivity between adjacent blocks is considered as 1 to represent the bedding plane.  
The acceleration amplification is another problem that should be discussed further. In the case study, the horizontal acceleration amplification coefficient increased with the slope height, over 1.5 near the crest. The vertical acceleration amplification coefficient does not increase as obviously as the horizontal one, i.e., around 1.0 except at the top of the slope where it is 1.35. The coefficient is assigned as 1.5 and 1.0 for the horizontal and vertical amplification respectively in the limit equilibrium analysis for simplicity. In the real slope, the coefficient may be much larger than 1.5 above the crest, so the tensile failure in these positions could always be observed at earliest stage during the failure process [31].  
The presented solution is a form of deterministic analysis. If the inputting variables are not deterministic but probabilistic following certain distribution, the probability of failure can be calculated. This process is beyond the capacity of a sheet program but needs further study.
## 1 Introduction
The stability of steep slopes is a common issue in open-pit mining and large-scale civil engineering projects, particularly in regions with frequent rainfall, where the risk of slope failure is significantly heightened. Compared to FLAC3D or PLAXIS, which require complex three-dimensional modeling and extensive computational time, the two-dimensional modeling approach in GeoStudio ensures computational accuracy while significantly reducing computational costs, making it well-suited for parametric studies involving multiple slope ratios and heights. GeoStudio's SEEP/W module accurately simulates unsaturated seepage and the dynamic variation of pore water pressure, while the SLOPE/W module seamlessly integrates seepage results for limit equilibrium analysis. Furthermore, the reliability of GeoStudio's limit equilibrium method was validated in numerous slope stability studies. In this study, a two-dimensional numerical model was established using GeoStudio, incorporating existing data and regional soil parameters to evaluate the safety factors of single-step slopes under a 10 h short-duration intense rainfall. The study considered different slope ratios (1:1.5, 1:1.75, 1:2.0, and 1:2.25), which are commonly adopted in open-pit dump slope designs. In southern China, where frequent rainfall poses a significant challenge, slope ratios ranging from 1:1.5 to 1:2.25 are widely employed to balance safety and economic feasibility. This study contributes to the existing body of research on slope stability under varying slope ratios, providing scientific guidance for optimizing slope design parameters and laying a foundation for more complex future studies on slope stability.
# Frequency-Based Adaptive Interpolation Filter in Intra Prediction
## 1 Introduction
Since the Cross Component Linear Model (CCLM) uses the relationship between luma samples and chroma samples, the linear model parameters for chroma samples are obtained using the down-sampled luma sample values and the coding efficiency is improved due to the accurate prediction values of the chroma samples. The Most Probable Mode (MPM) list increased from the three (3) intra prediction modes in HEVC to the six (6) intra prediction modes in VVC. In this paper, first, we design and propose two 8-tap interpolation filters with better high-pass filter (HPF) characteristics and low-pass filter (LPF) characteristics which are designed to generate the fractional prediction samples considering the frequency characteristic of the integer reference samples. One 8-tap DCT-IF with HPF characteristics is designed with DCT and the other 8-tap SIF with LPF characteristics is designed with the convolution of a linear filter and [1; 6; 15; 20; 15; 6; 1] low-pass filter which comes from a three times convolution of [1; 2; 1]. Second, a filter selection method is proposed, that uses transformed reference samples to investigate energy in the frequency domain. Finally, the proposed frequency-based Adaptive Filter, which uses the 8-tap DCT-IF and SIF and frequency domain filter selection, is combined with a conventional VVC filter. The proposed method brings better coding efficiency than VVC intra coding. This paper is structured as follows: Section 2 provides an overview of reference sample filtering for video coding standards and related studies are described. The proposed method based on both the two 8-tap interpolation filters and the filter selection in the frequency domain is explained in Section 3. Then, the experimental results of the proposed method are reported in Section 4. Finally, Section 5 concludes the paper.
# Elastic Hop Count Trickle Timer Algorithm in Internet of Things
## 7 Results of Grid Topology
### Convergence Time
#### 7.1.6 Power Consumption of 40 Nodes  
As illustrated in Figure 17, the elastic algorithm consumed less power than dynamic, e-trickle, and original algorithms. The power consumed in a scenario of 40 nodes is greater than the power consumed in a scenario of 20 nodes because more nodes mean more communication that consumes power. In addition, the energy consumed using grid topology is almost the same as the power consumed using random topology on 40 nodes.  
Figure 16: Power consumption for 20 nodes with different RX ratios.  
Figure 17: Power consumption for 40 nodes with different RX ratios.
# Robust Static Structural System Identification Using Rotations
## 3 Theoretical Motivation for Measuring Rotations
### Statistical Analysis of a Two-Story One-Bay Frame  
To further verify the proposed method, statistical analysis was carried out for a two-story one-bay frame. The elastic modulus was 3.5 \(\times\) 10\({}^{7}\) kN/m\({}^{2}\). The area and inertia of the columns were 0.24 m\({}^{2}\) and 7.2 \(\times\) 10\({}^{-3}\) m\({}^{4}\), while the area and inertias of the beams were 0.15 m\({}^{2}\) and 3.125 \(\times\) 10\({}^{-3}\) m\({}^{4}\). The parameterization of the FEM for this structure is shown in Figure 7a. A uniform load of 45 kN/m was applied to the second story. The bending stiffness \(EI_{9}\) in the middle part of this story was the target parameter. To identify \(EI_{9}\), four rotations or five deflections were required. Without loss of generality, two sets were used for structural system identification. Set 1 comprised four rotations (green) and set 2 comprised five deflections (red) (see Figure 7a). As the analytical expression for \(EI_{9}\) is not concise, it is not provided here. The probability density curves for the estimations of \(EI_{9}\) for these sets under different error levels (indicated in the brackets) are presented in Figure 7b. For set 1 (dashed lines), the positive modes always center around 1 and the negative ones are negligible. As the errors increase, the probability density curve for the estimation of \(EI_{9}\) using rotations becomes wider and flatter. At the error level of 5%, the mean and the positive mode are 1.025 and 0.957, respectively. However, the estimations using deflections (solid lines) are very sensitive to error levels. The bimodality of the distribution for \(EI_{9}\) is observed with a minor error of 0.2%. In addition, the positive modes are greatly deviated from 1. The positive modes related to 0.2% error and 1% error are 0.695 and 0.222, respectively.  
This analysis shows that: (1) the estimations using deflections are extremely sensitive to measurement errors in frame structures. (2) Measuring rotations outperforms measuring deflections regarding both the unbiasedness and the extent of variation in the estimations of the target parameter. It should also be pointed out that in frames, measuring rotations might be more practical since finding a reference point to measure deflections is non-trivial.  
Figure 7: (**a**) FEM for a two-story one-bay frame (arrows denote deflections, arcs denote rotations). (**b**) The distributions of the estimations using rotations (\(w\)) or deflections (\(v\)) under different error levels (indicated in the brackets).
# A Multi-Agent Deep Reinforcement Learning System for Government Interoperability
## 6 Discussion
### Benchmarking Against Traditional Systems  
To evaluate the effectiveness of the proposed framework, a comparative analysis against traditional governmental systems is essential. Traditional systems often rely on rule-based decision-making, manual coordination, and rigid workflows, limiting adaptability and efficiency. In contrast, the JADE-based multi-agent system (MAS) with deep reinforcement learning (DRL) optimizes decision-making through real-time learning and adaptive automation. Quantitatively, the proposed system achieves a 95% task completion rate, a 96% decision accuracy, and an average communication latency of 120 ms, significantly outperforming traditional approaches that suffer from slower processing, high error rates, and a lack of interoperability. Additionally, bureaucratic workload reduced by 40%, enhancing administrative efficiency. While traditional systems struggle with cross-departmental communication, policy adaptation, and scalability, the MAS-DRL integration enables seamless interoperability, automated compliance updates, and dynamic scalability across governmental domains.
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 4 Results  
During the first test, the effect of the Kriging range and nugget value on the frequency response was evaluated according to the method described in Section 3.1. Even a small nugget of \(n_{g}=10^{-6}\) has a significant effect on the frequency response (Figure 4). The corner frequency drops from 0.5 Hz to 0.35 Hz. Kriging with \(r=5\cdot T_{S}\) or \(r=6\cdot T_{S}\) has almost the same frequency response as windowed WSI. Higher range values create ripples and slightly decrease the corner frequency. The values \(r=6\cdot T_{S}\) and \(n_{g}=0\) were used as standard setting until further testing.  
Figure 5 shows an example test signal with alternating jitter, generated by bandlimited noise with \(f_{\mathrm{L}}=0.3\) Hz and reconstruction by Lagrange interpolation and by Kriging. The Kriging reconstruction had an RMSE of 0.0014 at the margins of the sampled time window and was nearly indistinguishable from the reference signal. On the other hand, Lagrange exhibited a significant deviation at the end of the time window, with an RMSE of 0.158.  
The process of frequency swapping to estimate \(f_{40}\) with an RMSE below \(E_{Max}=-40\) dB is illustrated in Figure 6 for the group-delay scenario with a uniform sine wave test signal. Intersections of the frequency diagram with the line for \(E_{Max}=0.01\) or \(-40\) dB are marked by diamonds. The resulting OSR values for the centre of the available time window were  
Figure 4: Spectrum of the impulse response for modified Kriging parameters.  
Figure 5: Reference, measured, and reconstructed signals in the time domain. Bandlimited noise with \(f_{\mathrm{L}}=0.3\) Hz as test signal.  
calculated using (13) and listed in the legend. Kriging enables lower OSR compared to Margolis, Lagrange, and Akima. Akima has the advantage that it only requires a window of \(\pm 3\) samples and thus provides the same stability at the margins. However, the RMSE was so high that it can only be applied to signals with a band limit below 0.1 Hz, which is equivalent to an OSR of 5. The other methods besides Akima were all highly sensitive to the insufficient number of input samples at the margins, leading to an increase of RMSE by a factor of more than 100, which must be compensated by higher OSR.  
Sine wave input signals always produced a higher RMSE than bandlimited noise, resulting in lower \(f_{40}\) values. Depending on the interpolation method, the required OSR was between 5% and 8% higher for sine waves, which were therefore considered as the worst-case scenario and used as standard input signals in the following tests.  
The three different jitter modes produced almost the same \(f_{40}\) values. The OSR varied by 2%. Random jitter was taken as the default in the subsequent tests.
## 2 Materials and Methods
### Test Materials
#### 2.1.3 Testing Instruments  
In the test, HOBO temperature and humidity recorder UX100-011 (accuracy: temperature \(\pm\)0.20 \({}^{\circ}\)C, relative humidity \(\pm\)2.50%) produced by Onset, USA was used to measure the environmental temperature and humidity of the greenhouse. The light intensity was measured by HOBO light intensity recorder UA002-64 (accuracy: \(\pm\)10 lx). The wind speed of indoor import and export was detected by Testo 425 (accuracy: wind speed \(\pm\)0.03 m/s + 5% of the measured value) produced by Testo, Germany. Heat flow density was measured by the portable heat flow meter JTDL-4 type (accuracy: heat flow \(\pm\)4%, resolution 0.1 W/m\({}^{2}\)) produced by Beijing Century Jintong company.  
Figure 3: Active heat storage cycle system composition diagram.  
Figure 2: Solar greenhouse structure site map. (**a**) Soil module solar greenhouse (SG). (**b**) Pebble module solar greenhouse (PG).
# Bulk Solids Stacking Strategy of a Rectangular Ship Cabin
## 2 Strategy Design of Bulk Solids Stacking  
### Stacking Route Planning  
In order to reduce the equipment loss, energy consumption, and the labor intensity during operation, it is necessary to reduce the movement of the stacker in the stacking process. The loading process is summarized as follows:  
* According to the stacking site size and the total loading capacity, the stacking locations and the corresponding stacking volume at each stacking location are planned.
* A stacker stops at a certain stacking point for continuous loading bulk solids to form a certain size of a stockpile, and then the stacker moves to the next stacking point to continue loading bulk solids.  
A bulk carrier with three cabins is considered as an example. The moving route of a ship loader is shown in Figure 1. During loading, the frequent start, stop, and movement of the ship loader will affect the service life of the equipment. Therefore, the moving route of the ship loader should be as short and continuous as possible. After loading at one stacking location, the ship loader shall continue loading at its adjacent location as far as possible, so as to ensure the continuity and high efficiency of loading. In addition, the moving route of the loader must also consider the stability and strength of the ship during loading. If the unilateral load is too large, the roll angle and trim angle of the hull will be greater than the stable value. On the basis of the above considerations, the moving route of the ship loader was designed as shown in the figure. First, 15-35% of the bulk solids is loaded from the bow to the stern along the center line of the cabin to stabilize the hull. Then, the remaining bulk solids is loaded alternately on the left or right side of the three cabins according to the double zigzag route shown in Figure 1.
# A Multi-Scale Deep Back-Projection Backbone for Face Super-Resolution with Diffusion Models
###### Abstract
[25], we propose BPSR3, a conditional generation diffusion model that replaces the U-Net with a multi-scale DBPN, and apply it to the SISR task. BPSR3 follows the idea of SR3 [23] using LR directly to guide the reconstruction of images and keeping the same parameter continuity settings as SR3. Meanwhile, we note that Simple Diffusion [25] demonstrated the usability of replacing the underlying network, ResDiff [24] found the effectiveness of using a convolutional structure to guide the reconstruction, and DBPN [7] achieved some success in SISR. Therefore, we tried to replace the U-Net with a structure from the DBPN family, through which the scaling is conducted in the convolutional structure. Unlike in Simple Diffusion, we completely discarded the U-shaped structure in our proposed model and instead completed the network replacement by splicing parallel sequences formed by DBPNs with different scales. The different scales allow the DBPN to capture information at multiple image scales like U-Net. The skip connections in the DBPN help the diffusion model enhancement to preserve the Markov condition, making the results at a certain time step depend on previous or subsequent effects. Such a parallel structure approach boasts a reduced parameter count compared to the traditional U-Net, thereby simplifying the model and aiding in the reduction in training costs while accelerating convergence. Furthermore, at the zoom level of the image, leveraging convolution operations enables a more precise extraction of pixel proximity compared to interpolation methods. This enhancement leads to improved reproduction of the reconstruction. Our experiments on facial datasets show that (1) BPSR3 has only \(1/4\) of the parameters of SR3 and converges faster and more stably; (2) BPSR3's multi-scale design is more effective than single-scale; and (3) BPSR3 can reconstruct HR images without affecting the potential for accelerated sampling. Our contribution to face SR can be summarized as follows:  
**Less time and space consumption:** we propose a multi-scale BP-based diffusion model BPSR3 for the facial SISR task, which improves the reconstruction convergence speed significantly compared to SR3 using U-Net. Moreover, BPSR3 uses fewer parameters and computes fewer feature maps for the same magnification task. It achieves successful optimization in both time and space. **More restored reconstruction results:** using learnable convolutional structures to scale images can capture the image domain relationships better than U-Net with linear interpolation methods. This can enhance the visual similarity between their constructed image and the reference image.
## 5 Characterization Using the Time Domain Measurement Data
### Measurement Results
#### 5.1.2 NAD Results  
NAD results of the measured noise are shown in Figure 7. The NAD is defined as the number of pulses whose value is higher than the threshold divided by the total measurement time. It measures the intensity of electromagnetic noise from another perspective. Figure 7 shows that the NAD values measured by the horizontal and vertical polarization modes of the antenna were approximate. The head (at the level of \(10^{-2}\)) of the NAD curve indicates that the power of electromagnetic noise measured in the office area was smaller than the power level measured in the manual spot welder and welder robot, which was due to the office area being less affected by electromagnetic noise. The NAD values  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Frequency (MHz)** & **315** & **433** & **779** & **868** & **916** \\ \hline Gain (dB) & 0 & \(-0.3\) & \(-0.6\) & \(-0.5\) & \(3.8\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Gain at different frequencies.  
Figure 6: Amplitude probability distribution (APD) results. (**a**) APD of vertical polarization at 315 MHz; (**b**) APD of horizontal polarization at 315 MHz; (**c**) APD of vertical polarization at 779 MHz; (**d**) APD of horizontal polarization at 779 MHz; (**e**) APD of vertical polarization at 916 MHz; (**f**) APD of horizontal polarization at 916 MHz.  
of electromagnetic noise measured near the manual spot welder and the welder robot were similar, indicating that the intensity of electromagnetic noise near them was similar.
## 3 Results  
During the experiment, the average methane production rate (MPR) in the digester was the highest at 200 kPa overpressure in the range of the applied hydraulic retention times (HRT). At the 5-day HRT, the MPR at an overpressure of 200 kPa was higher than the MPR at an overpressure of 150 kPa by 3.5% (Figure 5b). During the stepwise increase in OLR in control, there was observed a deterioration in the process stability. During the stepwise increase in OLR in a digester with an overpressure of 100 kPa, an increase in the MPR was observed at the 7-day HRT, and a deterioration in the process stability at 5-day HRT (Figure 5a). A sharp increase in the MPR was observed with a decrease in the HRT to 7 days (64% in ratio to 10-day HRT) and 5 days (79% in ratio to 10-day HRT) in a digester with an overpressure of 150 kPa.  
At the same time, the average methane content (MC) in biogas remained at the level of 65% \(\pm\) 3% throughout the experiment in all digesters.  
Figure 4: Change in organic loading rate (OLR) throughout the experiment.  
Figure 5: Cont.  
Methane yield (MY) in all digesters at 10 days HRT was practically the same (\(\pm\)3%), except for digester without overpressure (Figure 6a). However, a stepwise decrease in the HRT to 5 days led to a decrease in MY by 60%, 7% and 6% for digesters with an overpressure of 100, 150, and 200 kPa, respectively (Figure 6b). At the same time, with an HRT of 7 days, an increase in MY was observed at an overpressure of 150 and 200 kPa by 16 and 7%, respectively, compared with HRT of 10 days.  
Figure 5: Dynamics of methane production rate (MPR) (**a**) and its average value (**b**) depending on the overpressure and HRT in digesters.  
Figure 6: _Cont._  
Throughout the experiment, the pH in the digesesters was in the range of 6.4-7.3 (Figure 7a), except for control and digester having an overpressure of 100 kPa (Figure 7a). At an HRT of 7 days for digester without overpressure there was a constant decrease in pH from 6.3 to 4.2. At an HRT of 5 days for digester with an overpressure of 100 kPa there was a constant decrease in pH from 6.9 to 5.2. The highest degree of vs. removal (63.21%) was observed at an overpressure of 150 kPa and HRT of 7 days (Figure 7b).  
Figure 6: Dynamics of methane yield (MY) (**a**) and its average value (**b**) depending on the overpressure and HRT in the digesters.  
Figure 7: Cont.  
The highest energy production rate (EPR) (94.269 kJ/(L-day)) and the highest energy yield (EY) (8.069 kJ/g VS) were observed at an overpressure in the digester of 150 and 200 kPa and the HRT of 5 and 7 days (Figure 8a,b).  
Figure 8: _Cont._  
Figure 7: Dynamics of pH values (**a**) and average vs. removal (**b**) depending on the overpressure and HRT in the digesters.