esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Influence Map
### Spread and Attenuation Modes
#### 3.2.2 Attenuation Modes  
Attenuation modes are generally divided into exponential and linear attenuation modes. As the distance between the starting point and the target point increases, the influence gradually decreases. In addition, when the influence is attenuated to a certain degree, it is usually set to 0 to reduce the amount of calculation.  
Consider point \(a\) as the starting point of spreading and point \(b\) as a target point on the map. \(I_{a}\) represents the initial influence of point \(a\), and \(I_{a,b}\) represents the exerted influence from point \(a\) to point \(b\).  
**Exponential attenuation:** The relationship between the influence and distance is given by Equation (4).  
\[I_{a,b}=\left\{\begin{array}{cc}I_{a}\cdot\gamma^{d_{a,b}},&d_{a,b}<D_{max},\\ 0,&d_{a,b}\geq D_{max},\end{array}\right. \tag{4}\]  
where \(\gamma\) is the exponential attenuation parameter, \(d_{a,b}\) represents the distance from point \(a\) to point \(b\), and \(D_{max}\) represents the maximum spread distance. After exceeding this distance, the influence value drops to 0. The value of the exponential attenuation parameter (\(\gamma\)) is usually between 0 and 1. The lower the value, the faster the attenuation speed of the starting influence.  
**Linear attenuation:** The relationship between influence and distance is given by Equation (5).  
\[I_{a,b}=\left\{\begin{array}{cc}I_{a}-\beta\cdot d_{a,b},&d_{a,b}<D_{max},\\ 0,&d_{a,b}\geq D_{max},\end{array}\right. \tag{5}\]  
where \(\beta\) is the linear attenuation parameter.  
When these two attenuation modes use adjacent spread, the adjacent spread no longer spreads after the influence value of a certain point is less than a preset value instead of stopping spreading after reaching the maximum spread distance.
# Experimental and Numerical Evaluation of Calcium-Silicate-Based Mineral Foam for Blast Mitigation
## 4 Results from Simulations
### Comparison Between Experimental and Numerical Results  
Figure 18 compares deflections at the center of the backplate between the experimental and numerical results for configuration A. The SPH method is a robust approach for predicting the deflection at the backplate's center. The Fu Chang Foam and Crushable Foam material models predict maximum deflections of 11.9 and 11.3 mm, respectively, with a relative error of 3% compared to the mean experimental data of 11.6 mm. Furthermore, the Crushable Foam model considers a mean strain rate, yet it performs commendably. However, for practical design applications, the Fu Chang Foam model offers excellent suitability due to its consideration of a range of strain rates rather than a single value. Therefore, the Fu Chang Foam material model is adopted for further discussion in subsequent sections.  
Figure 19 compares the deflection profiles from the numerical simulations to the experimental curves for configurations A to C. The simulations can predict the experimental results regarding deflection profiles with a relative error of about 5%.  
Figure 17: Comparison of numerical and experimental reflected over-pressure measured by Gauge 1. (**a**) Results without the protective foam and (**b**) results with 60 mm thick foam cladding.  
Figure 18: Comparison between the experimental and numerical deflection at the backplate’s center with the protective foam for configuration A.
# Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks
## 4 Experimental Results and Analysis
### Comparative Analysis of Alternating Training and Joint Training  
In order to prove the superiority of using alternating training in this paper, this paper uses the learning curves of the two training strategies to compare the applicability of the MTL-DGCNR model. In addition, this paper also includes pretrained variants for comparison. The item embedding is first pretrained by TransH, then they are input into the MTL-DGCNR model and are done parameter tuning only by the loss \(L_{\text{S}}\) of the equation. This paper measures the pros and cons of the three training methods through the changes of the _MRR@_20 curve on the Retailrocket and JData datasets, because _MRR@_20 can reflect a better ranking than _Recall@_20. The learning curves of the three specific training strategies are shown in Figures 8 and 9.  
The curves in Figures 9 and 10 show that although the pretraining model achieves better results at the beginning of training, it is significantly inferior to alternating and joint training at the convergence stage. This also proves that multi-task learning is significantly superior to single-task knowledge graph pre-training. In addition, according to Formula (20), the commodities that appear multiple times in the training set session will adjust the loss \(L_{K}\) multiple times in each epoch of alternate training so that the knowledge learning embedding is gradually biased towards the auxiliary task \(L_{K}\). This method can reduce the loss (\(L_{S}\)) of the main task DGCNR and improve the learning performance of the overall model.  
Figure 9: \(MRR\)@20 curve change chart—JData.
## 3 Data Preprocessing
### Vectorization of Sample Data  
Before model training, the sample data should be vectorized, and the corresponding label value should be set for each group of feature vectors.  
The mathematical explanation of the forecasting problem of daily power generation of a group of small hydropower stations is to obtain a mapping function \(f\) that satisfies \(f(\mathbf{X})=Y\). Suppose the power generation in HC to be forecasted on day \(t\) is \(E_{t}\); then, \(Y=E_{t}\). According to the analysis in Sections 3.3 and 3.4, precipitation and power generation in the 6 days before the forecasted date \(t\) should be selected as predictors, which are denoted as:  
\[\mathbf{X}_{1}=[\mathbf{PS}_{t-1},\mathbf{PS}_{t-2},\mathbf{PS}_{t-3},\mathbf{PS}_{t-4},\mathbf{PS}_{t- 5},\mathbf{PS}_{t-6}] \tag{9}\]  
\[\mathbf{X}_{2}=[E_{t-1},E_{t-2},E_{t-3},E_{t-4},E_{t-5},E_{t-6}], \tag{10}\]  
where \(t-m\) (\(m=1\), \(2\), \(3\), \(4\), \(5\), \(6\)) represents the \(m\)-th day before the forecasted date \(t\). \(\mathbf{PS}_{t-m}\) represents the spatial distribution of precipitation on the previous \(m\)-th day (its structure corresponds to the precipitation grid on the right side of Figure 3):  
\[\mathbf{PS}_{t-m}=\left[\begin{array}{cccccc}0&P_{m,1}&P_{m,2}&P_{m,3}&P_{m,4}&P _{m,5}&P_{m,6}&0\\ P_{m,7}&P_{m,8}&P_{m,9}&P_{m,10}&P_{m,11}&P_{m,12}&P_{m,13}&P_{m,14}\\ 0&P_{m,15}&P_{m,16}&P_{m,17}&P_{m,18}&P_{m,19}&P_{m,20}&P_{m,21}\\ 0&P_{m,22}&P_{m,23}&P_{m,24}&P_{m,25}&P_{m,26}&P_{m,27}&P_{m,28}\\ 0&P_{m,29}&P_{m,30}&P_{m,31}&P_{m,32}&P_{m,33}&0&0\\ 0&0&0&0&P_{m,34}&0&0&0\end{array}\right] \tag{11}\]  
In Equation (11), \(P_{m,j}\) (\(j=1\), \(2\), \(3...\), \(34\)) represents the precipitation value of the \(j\)-th precipitation node (corresponding to the precipitation grid point on the right side of Figure 3). In addition, \(0\) means that the point is not in the HC region, so the influence of its precipitation value is not considered. Finally, the input feature vector and label are:  
\[\left\{\begin{array}{l}\mathbf{X}=\{\mathbf{X}_{1},\mathbf{X}_{2}\}\\ Y=E_{t}\end{array}\right. \tag{12}\]  
Similarly, we can also obtain the vectorized sample data of GL city.
## 4 Results
### Dynamic Responses of the One-Layered Soil
#### 4.1.2 Effect of Train Speed  
The vibration responses of the saturated and dry soils underneath the slab track are shown in Figure 9 when the train speed increased from 100 km/h to 400 km/h.  
The pore water pressure of the saturated soil decreased rapidly at the relatively lower soil depth (<4 m) and kept to a constant value at deeper depths, as shown in Figure 9a. The maximum pore water pressure occurred at the soil surface as the water at the surface was not easy to dissipate immediately. In addition, the train speed had an insignificant influence on the pore water pressure.  
Unlike the pore water pressure, the effective stress of the saturated soil exhibited a different tendency with soil depth, as shown in Figure 9b. The maximum effective stress occurred at a depth of around 2 m and then decreased to a relatively small value with depth. Furthermore, the train speed had a small influence on the effective stress. At the soil surface, the effective stress was much lower than the pore water pressure, indicating that the pore water undertook the majority dynamic loads instead of the soil particles. When the depth was lower than 2 m, the soil particles undertook more and more dynamic loads and the effective stress thus increased. When the soil depth was higher than 2 m, the values of pore water pressure was quite close to the values of effective stress, indicating the water and soil particles carried the dynamic loads together at the deeper soil.  
The total stress of saturated soil was composed of both pore water pressure and effective stress, while the total stress of dry soil was normal stress. As shown in Figure 9c, the total stresses of saturated soil were a little bit higher than those of dry soil for most cases especially when the depth was lower than 2 m, but the differences were insignificant.  
The displacement of saturated soil was lower than that of dry soil, as shown in Figure 9d. The train speed had an evident influence on the displacement. The displacement increased with train speed for both saturated and dry soils.  
Figure 8: Time history curves of saturated ground: (**a**) Pore water pressure and (**b**) effective stress.
# Assessment of Hongtu-1 Multi-Static X-Band SAR Constellation Interferometry
## 3 Results
### Height Update
This is also confirmed by the shaded reliefs of the 1\" Copernicus DEM and the height updated with the HT1 data shown in Figure 7. Consequently, in addition to updating a DEM temporally, improving the spatial resolution of a DEM with a spatial resolution larger than 10 m may be an attractive application for HT1 data.
# Noise-Assessment-Based Screening Method for Remote Photoplethysmography Estimation
## 3 Method  
In this study, we propose a method for extracting robust pulse signals in NIR environments through noise-assessment-based rPPG screening. Our approach leverages motion, illumination, and pulse signal quality as indicators to evaluate the presence of noise, thereby enhancing the performance of pulse signal extraction compared to existing methods. Notably, our method offers the advantage of minimal computational requirements, making it suitable for real-time applications. Moreover, the rPPG screening technique can be seamlessly integrated with various existing methods, enabling performance improvements and adaptability to diverse scenarios. Overall, this research contributes to the advancement of efficient and effective heart rate extraction methods in infrared environments, with potential applications in healthcare and other relevant fields.
## 4 Conclusions
These were nitrogen oxides (NO\({}_{\text{x}}\)), carbon oxides (CO) and carbon dioxide (CO\({}_{2}\)). The decrease in NO\({}_{\text{x}}\) when the engine is operated under load was shown to be effectively influenced by the silver solution, which at a concentration of 2% resulted in a reduction in NO\({}_{\text{x}}\), by 3.2% at 20 kW load and by 4% at 30 kW load. It is probably due to the drop in temperature in the cylinder, as the intensive synthesis of oxygen and nitrogen occurs at very high temperatures. However, the mechanism of the effect of the additive used on heat generation in the cylinder and temperature is not known to the authors. The presence of a 2% silver solution in the fuel also resulted in a significant reduction in carbon monoxide in the flue gas: by 28.7% at 20 kW and 25.3% at 30 kW. Increasing the concentration to 5% proved ineffective. This also applies to the other additives. Because carbon monoxide is produced by incomplete combustion of the hydrocarbons contained in the fuel. The likely reason for the decrease in CO in the flue gas is the improved combustion process, which is indirectly evidenced by the demonstrated decrease in fuel consumption. For carbon dioxide, the best results were also obtained with a silver solution of 2%. At idle, the reduction was up to 10% compared to pure oil. The higher the engine load, the lower the reduction, e.g., 4.6% for 30 kW. Carbon dioxide is produced during the combustion of hydrocarbon fuels and its content in the exhaust gas depends on the chemical composition of the fuel and the amount burned. Studies with the additive have shown that there is a noticeable decrease in fuel consumption, which is likely to result in a decrease in CO\({}_{2}\) emissions. In summary, it can be concluded that in order to reduce the NO\({}_{\text{x}}\), CO and CO\({}_{2}\) content in the exhaust gas and achieve a noticeable decrease in hourly fuel consumption, a silver solution with a 2% concentration in MDO is the best additive. The above would also need to be confirmed in further tests on an engine operating under real operating conditions. It is also important that the fuels used with individual additives, regardless of their percentage of MDO, do not require structural changes to the fuel infrastructure. In further studies, it will be necessary to investigate the effect of this additive on the combustion process in the engine and the wear and tear of its components, and to confirm the results obtained under real operating conditions. Conceptualization, R.K.; methodology, R.K.; software, R.K.; validation, K.W. ; formal analysis, K.W. ; investigation, R.K.; resources, R.K.; data curation, K.W. ; writing--original draft preparation, R.K.; writing--review and editing, K.W. ; visualization, R.K.; supervision, R.K. and K.W. ; project administration, K.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this study are available in the article. The authors declare no conflicts of interest.
## 3 Materials and Methods
### Graph-Based Differential Privacy for Frequent Sequential Patterns  
Previous methods start from the sequences and provide differential privacy when extracting the frequent patterns, while the proposed method can be applied to protect frequent patterns. Hence, it does not require having access to users' sequences, only to their patterns. It consists of the following steps (as depicted in Figure 1):  
1. Frequent sequential pattern mining (pre-processing);
2. Graph representation of frequent patterns;
3. Adding noise to the client-pattern graph;
4. Publishing the frequent sequential patterns with noise.  
For graph representation of frequent patterns, we mine the sequential patterns from the sequences dataset to obtain the frequent **sequential patterns**\(pDB\) and associate each user \(i\) with its patterns through a dictionary \(\mathcal{D}=\{client:pattern\}\). This can be considered as a pre-processing step. After extracting the frequent patterns, we generate a **bipartite graph**\(G\) from the dictionary \(\mathcal{D}\), as follows: We define \(N\) as the set of clients \(i\) in the sequence database \(sDB\) and \(M\) as the set of frequent sequential patterns \(j\) in \(pDB\) and \(E\) as the pairs \(ij\), for each client \(i\) and each pattern \(j\in\mathcal{D}[i]\). Thus, \(G=G(N,M,E)\) is the bipartite graph with nodes \(N,M\) and edges \(E\). We assume that \(|N|=n\) and \(|M|=m\).  
To add noise to the client-pattern graph, we apply the bipartite noise graph mechanism, after obtaining the graph \(G=G(N,M,E)\), which provides differential privacy from Theorem 1. We obtain a **protected bipartite graph**\(\hat{G}=\mathcal{A}_{n,m,p}(G)\) as a result.  
Finally, to publish the frequent sequential patterns with noise from the protected graph \(\hat{G}\), we obtain a protected client-pattern dictionary \(\hat{\mathcal{D}}=\{client:pattern\}\). From the client-pattern dictionary \(\hat{\mathcal{D}}\), we re-calculate the supports of the frequent patterns and publish the frequent sequential patterns with differential privacy guarantees; see Figure 1.
# Highly Sensitive SPAD-Based Receiver for Dimming Control in LiFi Networks +
## 1 Introduction
### Related Works  
Dimming is an essential feature in VLC systems, allowing for dynamic and energy-efficient illumination control. Over the years, several dimming techniques have been proposed to achieve different illumination levels in VLC systems.  
One of the most common techniques is dimming VLC with single-carrier modulation, which involves using pulse-amplitude modulation (PAM) or pulse-width modulation (PWM) to vary the light intensity. Analogue dinming is a continuous dimming method that relies on varying the amplitude of the carrier signal as the case in on-off keying (OOK) transmission, whereas digital dimming is a discrete method that uses PWM to turn the light on and off at a specific duty cycle such as variable OOK (VOOK) transmission. These techniques have been shown to work well in indoor VLC systems [21; 22], but they suffer from poor spectral efficiency (SE) when extreme light requirements are targeted.  
To overcome these limitations, researchers have explored dimming VLC with multi-carrier modulation, such as optical orthogonal frequency division multiplexing (O-OFDM) with quadrature amplitude modulation (QAM). These techniques use multiple subcarriers to modulate the light intensity, enabling more precise and flexible dimming control. In particular, OFDM-based dimming techniques have been shown to provide higher SE and robustness to inter-symbol interference (ISI) and multipath fading. OFDM and its variations, such as (DC)-biased optical OFDM (DCO-OFDM) [23], asymmetrically clipped optical OFDM (ACO-OFDM) [24], and PAM-DMT [25], have received the most research attention. However, their energy efficiency and SE vary. Hybrid and layered schemes have been proposed to improve SE, including dimming control strategies such as adjusting the DC bias in DCO-OFDM and using reverse-polarity optical OFDM with pulse-width modulation (PWM) during \"On\" and \"Off\" stages [26; 27; 28].  
However, most of the previous works on dimming VLC have considered using linear photodetectors (PDs) for signal detection, which have limited sensitivity and dynamic range, strongly limiting the communication performance when the dimming level is low. To address this issue, SPAD receivers can be employed to improve receiver sensitivity and detection accuracy in low-light environments. The application of SPADs in OWC has been widely investigated in the literature, with previous studies demonstrating their ability to achieve high sensitivity, low noise, and excellent timing resolution. For instance, SPADs have been used in OWC systems to improve the performance of underwater communication [29], long-distance free-space communication [30], and high-speed indoor communication [31]. However, the investigation of SPADs' application in dimming VLC remains scarce, and there is a need for further exploration of their potential in this context to the best of the authors' knowledge. This work aims to address this research gap by investigating the performance of a novel adaptive SPAD-based receiver in indoor dimmable VLC systems.
## 4 Practical Experiments
### Comparison with Agisoft Metashape  
To further evaluate the precision of DEMs processed using Planet4 Stereo, we compared it with Agisoft Metashape (version 2.0.2), a leading Structure-from-Motion tool supporting RPC processing since version 1.5.0. Using Metashape's standard pipeline, we processed the 2019 Shisper glacier dataset with default settings, performing image alignment at \"high\" accuracy and generating the 3D terrain model from depth maps. The final model was exported in ellipsoidal coordinates (WGS84, EPSG:4326). This study also references [16], where Metashape was employed for DEM reconstruction with pre-optimized RPCs. We focus on comparing our pipeline, which incorporates RPC optimization via ASP, to Metashape as a closed solution. Figure 17 illustrates a DoD calculation against the GLO-30 DEM, following methods outlined in Section 4.1.  
The qualitative analysis reveals that the DEM generated using Metashape appears more complete, likely due to fewer restrictions in image pair selection and potentially superior matching algorithms. Additionally, the Metashape DEM exhibits finer detail despite comparable resolution, possibly attributed to differences in the parameters, such as window size, for dense image matching in DEM reconstruction.  
While the DoD comparison shows similar results between the Metashape and Planet4 Stereo DEMs, notable discrepancies arise in specific regions. For instance, in steep terrain near the terminus of the Muchowar glacier, the Metashape DEM performs slightly worse. In the stable area of Ali Abad, Metashape reconstructs a region in the southwest exhibiting negative deviations. This anomaly could stem from an image pair with poor stereo  
Figure 17: (**A**) DEM of the Shisper glacier (2019) processed using Agisoft Metashape v. 2.0.2. (**B**,**C**) DoD analysis against the GLO-30 DEM, serving as reference data, for the areas of Ali Abad/Karimabad and near the terminus of Muchowar glacier, similar to the analyses conducted with the DEMs from Planet4 Stereo. (**D**) DoD analysis against GLO-30 DEM for the stable area of Ali Abad village, with the corresponding histogram shown in (**E**).  
capability, which was pre-filtered in Planet4 Stereo's pipeline but included by Metashape due to its lack of preselection.  
In conclusion, while the Metashape DEM qualitatively appears more complete, its accuracy is not necessarily superior, with completeness influenced by differences in image pair filtering.
# Dynamic Characteristics of Sandstone under Coupled Static-Dynamic Loads after Freeze-Thaw Cycles
## 1 Introduction
In previous analyses of the results of research on the mechanical properties of rock after freeze-thaw cycles, scholars mainly focused on the static mechanic properties, physical properties, and X-ray, CT (Computed Tomography), SEM (Scanning Electron Microscope), NMR (Nuclear Magnetic Resonance) technique results, while few were found to focus on the dynamic mechanical behavior of rocks after freeze-thaw weathering. The microscopic damage characteristics and dynamic mechanical parameters of sandstone subjected to freeze-thaw cycles were investigated from NMR tests and impact loading tests [2,12]. Considering the strain-rate effect, some empirical expressions for the dynamic mechanical degradation of sandstone were proposed after long-term freeze-thaw weathering [20,21]. However, these studies only explain the effect of freeze-thaw processes without considering the combined effect of the freeze-thaw process coupled with static (pre-compression) and dynamic loads. The natural rock mass is under three dimensional stresses, however, the stress state of a rock unit in a pillar. coupled with static-dynamic compression is often encountered, such as in underground mines and deep tunnels. The static pre-stress is geo-stress. The sources of dynamic loads may come from impact, explosion, blasting, drilling, earthquake, etc. Therefore, in this paper, the sandstone specimens were tested under different numbers of freeze-thaw cycles and different axial pre-compression stresses using the split Hopkinson Pressure Bar testing system. Stress-strain curves were obtained, and the peak stress, peak strain, failure characteristics and energy absorption rate of the specimens were studied in detail. The results indicate that the pre-compression stress and the number of freeze-thaw cycles together have a very significant effect on the rock mechanical properties and failure characteristics.