esa-sceva/satcom-chunk-collection huggingface dataset:
# Research and Application of Slag-Nanosilica Stabilizer for Silt Subgrade
## 4 Results and Discussion
### Micromechanism of Stabilization
#### 4.3.2 XRD  
Figure 6 gives a comparison of the diffraction peaks of plain soil and specimens under different conditions. As can be seen, there were many diffraction peaks of nonsilt minerals, such as quartz and K-feldspar, and silt minerals, such as muscovite, chlorite, and kaolinite, can be seen. Comparing the XRD spectrum lines of silt before and after the addition of stabilizer, it can be seen that the spectrum lines of plain soil were extremely similar to that of stabilized soil at each content and curing age, which indicates that no new diffraction peak was generated after the addition of SNS and that the addition of stabilizer did not result in new minerals. With the increase in SNS content and curing age, it can be observed that the diffraction peak value of quartz (about 26.6\({}^{\circ}\)) decreased significantly, which indicates that the quartz (SiO\({}_{2}\)) in soil was involved in the stabilizing reaction and that the reaction of the stabilizer was better with the increase in SNS content and curing age. Because the C-S-H gel and C-A-S-H gel that were generated were amorphous products, there was no obvious diffraction peak in the spectrum lines. However, there was a corresponding diffuse diffraction peak (about 29\({}^{\circ}\)) on the XRD spectrum lines of SNS-S, which indicates that the addition of stabilizer generated hydration gel products. The diffraction peak (about 29.4\({}^{\circ}\)) of calcite (CaCO\({}_{3}\)) can be found by observing the XRD spectrum lines of SNS-S at 6%/28 d, which indicates that the stabilizer with low content had hydration reaction in the early period. Due to the low content of Ca\({}^{2+}\), the Ca(OH)\({}_{2}\) generated reacted with CO\({}_{2}\) to generate more calcite.  
Figure 6: XRD results.  
The diffraction peaks (about 27.5\({}^{\circ}\) and 23.1\({}^{\circ}\)) of albite (Na[AlSi\({}_{3}\)O\({}_{8}\)]) and K-feldspar (K[AlSi\({}_{3}\)O\({}_{8}\)]) in the XRD spectrum lines of plain soil and SNS-S cured for 7 days were observed, and it was found that the corresponding diffraction peak values decreased with the increase in curing age. The ions of Na\({}^{+}\) and K\({}^{+}\) in the soil get exchanged with Ca\({}^{2+}\) in SNS, which reduces the thickness of the double electric layer and increases the molecular attraction between soil particles [6], thus leading to the aggregation and condensation of soil particles, a denser soil structure, and increased strength [41].
## 4 Comparison with Experimental Data  
Despite the difference of geometry between our urban array and the one used by [PERSON] et al. [9] for their experiments, as well as the differences in inflow conditions, it is possible perform some comparisons between the well-resolved LES and the stereoscopic PIV measurements. The aspect ratio of the urban array used by [PERSON] et al. [9] is over 0.67, which mimics an urban environment with relatively high buildings compared with the street width. Therefore, contrary to what we expected from the numerical data, [PERSON] et al. [9] expected a skimming-flow regime to be reached. In Figure 6, we show mean streamwise velocity \(U/U_{H}\) and the mean-wall normal velocity \(W/U_{H}\) at \(y/W_{b}=0\), where the data is extracted from the experimental campaign by [PERSON] et al. [9].  
It is possible to observe very similar flow patterns in the experimental and the numerical databases. However, in the experimental data, the bulk of the flow from above the urban canopy does not enter the streets as is shown by the very low magnitude of the reversed flow observed in the streets, compared with the well-resolved LES. In particular, the region of negative wall-normal velocity on the windward side of the downstream building is of higher intensity in the LES. Therefore, a higher flow rate from above the urban canopy enters the streets when the streets are wider. The streamwise and spanwise velocity fluctuations, shown in Figure 7, confirm that a skimming flow is reached almost immediately, with high level of turbulence at the roof level but not in the streets. The main difference between the two databases can be observed in the first street: In the well-resolved LES, Figure 5 exhibits moderate levels of turbulence in the street, which is not the case in the experiments shown in Figure 7 (experiments). This synergistic use of simulations and experiments can help to address the limitations from both research,eventually resulting in more accurate flow data, for instance, through the development of pertinent corrections [51; 52].
## 6 Conclusions  
Source node location privacy protection is an important issue in widely-used WSNs. In this paper, we propose two new grid-based source location privacy protection schemes in WSNs to ensure the location privacy of source nodes. The sink node with high power resource is used to determine the PNCS, which reduce the total communication overhead. The phantom nodes selected in our proposed schemes can be distributed anywhere in the network and thus have stronger positional randomness. As our proposed schemes ensure the diversification of the routing path by increasing the number of phantom nodes used, the security performance of the source location privacy protection is further improved. The simulation results show that compared with other schemes, our proposed schemes have higher safety period and less communication overhead, thus their application prospect can be expected. Our proposed schemes are especially suitable for resource-constrained scenarios. However, considering the balance of performance of the schemes, it is necessary to select the most suitable solution according to different requirements of different scenarios. For example, when the security requirement for the network is very high and the communication overhead is not a constraint, RDPS can be used. Instead, if the network's communication conditions are limited and the security requirement is not so high, NRSPS and NRDPS can be adopted.  
In the future, we will continue to improve the performance of our schemes, and aim to adapt them to their practical environments. Further research should consider the following aspects:  
(1) In this paper, in order to simplify the problem, referring to other relevant pieces in the literature, we evaluate our schemes in the deterministic network model. However, in some practical applications as described in Reference [28], the stochastic deployments should be paid attention too. Therefore, we will study the source node location privacy protection in the stochastic deployments in our subsequent research;  
(2) In the network model of this paper, only one source node and one sink node are considered, but in practical applications, there may be multiple source nodes and multiple sink nodes. Hence, in future research, we will investigate the multiple objects tracking scenarios.  
The idea and primary algorithm were proposed by [PERSON]; [PERSON] carried out the simulations and wrote the draft of the manuscript, [PERSON] and [PERSON] critically reviewed the algorithms, results and manuscript for important intellectual content, and gave constructive discussions; [PERSON] revised the manuscript and [PERSON] polished the paper. All authors read and approved the final manuscript.  
This work was partially supported by Zhejiang Province Natural Science Foundation (No. LY19F020039, No. LY18F020017), National Natural Science Foundation of China (No. 61401128, No. 61872120) and Key technologies, system and application of Cyberspace Big Search, Major project of Zhejiang Lab (No.2019 DH0 ZX01).  
The authors declare no conflict of interest.
# Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications
## 1 Introduction
### Organization  
The rest of the paper is organized as follows. Section 2 presents the RIS-aided UAV communication system and formulates the problem. In Section 3, the problem is reformulated as a path-finding problem by analyzing the inter-cluster interference. The LIM and RIM schemes are then proposed. Section 4 shows the simulation results, and the final section concludes the paper.
# Photo-Stimulated Luminescence Approach for Effective Identification of Irradiated Fruit
## 2 Materials and Methods  
### Samples and Participants  
Preliminary tests were conducted by Istituto Superiore di Sanita and Istituto Zooprofilattico Sperimentale del Lazio e della Toscana on 10 types of fresh and dried fruit selected on the basis of the results of previous studies and purchased from local markets: fresh kiwis, lemons, mangoes, oranges, papayas, pineapples, tangerines, dried dates, dried prunes and raisins. For each matrix, 3 samples were prepared: one non-irradiated, one irradiated at 0.5 kGy and one irradiated at 1 kGy. In the inter-laboratory test, 8 types of fruit were proposed, namely, 2 of fresh fruit, 2 of dried fruit and 4 of nuts: kiwis, oranges, hazelnuts, walnuts, peanuts, pistachios, dried figs and raisins. For each matrix, 3 samples were prepared: one non-irradiated, one irradiated at 0.5 kGy and one irradiated at 1 kGy. In total, 24 samples, i.e., 8 non-irradiated and 16 irradiated, were coded in order not to disclose the treatment carried out. Four official control laboratories, which met the requirements in terms of equipment and skills, participated in the test: ATS-Milano; Istituto Superiore di Sanita; Istituto Zooprofilattico Sperimentale del Lazio e della Toscana; Istituto Zooprofilattico Sperimentale della Puglia e della Basilicata. All the samples of the preliminary tests as well those of the inter-laboratory test were purchased and irradiated by the Istituto Zooprofilattico Sperimentale della Puglia e della Basilicata, and they were then sent to the participants. The treatment with ionizing radiation was carried out at room temperature by means of a biological X-ray radiator (model RS 2400, RAD SOURCE Inc., North Las Vegas, NV, USA), at a peak voltage of 150 kV and anode current of 45 mA. The doses were selected in the range used for fruit, including a very low value (0.5 kGy) to check the detection limit of the method. Before measurement, the samples were stored inside their packaging in the dark at room conditions.
## 3 Results and Discussion
### Optimization Results Using PSO without and with DG integration  
In considering the integration of DGs into the system, the problem was formulated as a multi-objective function such that the voltage stability was maximized and the power losses were minimized, considering sets of constrained elements using a PSO algorithm. In this paper, six different scenarios were considered based on the network bus CF priority results presented in Table 1 to investigate the influence of DG integration on a radial power system. These scenarios were classified as either an optimal case or an arbitrary case depending on the identified location as specified by the CF results. The first scenario represents the base case where the system topology still remained intact. In this scenario, no DG was installed and the power-flow results represent the steady-state simulation of the system. The results  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bus No.** & **CF** & **CF Ranking** & **Bus No.** & **CF** & **CF Ranking** \\ \hline  
& \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bus coupling factor (CF) for identifying weak buses in the IEEE 33-bus distribution system.  
obtained for this scenario are presented in Table 2. In the second scenario, only one DG, whose size was determined using the PSO algorithm, was integrated into the system at bus 18, which was the optimal location as specified by the lowest CF value. The third scenario involved the installation of only one DG, whose location was arbitrarily chosen. In this arbitrary case, bus 16 was used. For the fourth scenario, two DGs were integrated into the system at buses 18 and 33. These were the optimal locations for the integration of two DGs since the CFs associated with buses 18 and 33 were ranked 1 and 2, respectively. The fifth scenario was the arbitrary case where two DGs were installed at buses 16 and 32, which were arbitrarily chosen. The last scenario involved the installation of three DGs at the locations identified by the CF ranking method. These locations were buses 18, 33 and 32 with CF ranking values of 1, 2 and 3, respectively.
# From Climate Conditions to the Numerical Slope Stability Analysis of Surface Coal Mines
## 6 Discussion  
In this work, a practical framework is proposed (see Figure 1) to assess the effect of rainfall on coal and lignite mines, including details on how to quantify rainfall based on measurements and climate projections. For the case example of Greek lignite mines, it is concluded that slope stability is not jeopardized due to rainfall for the range of the parameters involved. Nevertheless, the changes of the SF due to the four parameters examined above are of general interest. All four parameters are essential for stability and they affect the way rainfall infiltrates the soil. The soil-water characteristic curve affects the unsaturated zone and was investigated in this study through \(\mathrm{g_{a}}\). The considered range of \(\mathrm{g_{a}}\) was rather broad, including the whole range of fine-grained materials, and thus its effect, although important, is not critical. Notice, however, that, in practice, having an accurate estimation of the SWCC is rare, and a broad range should generally be examined. The soil saturation complements the SWCC for the infiltration analysis, which has a similar effect, important but not critical.  
Furthermore, the groundwater table's height in the soil slope is critical for stability. That is related to the deviating operations during a mining excavation and the groundwater conditions before the mine creation. The higher groundwater table suggests greater caution with respect to stability and a more significant impact of rainfall on stability. Finally, the intensity of rainfall is crucial. Of particular interest is medium-to-high rainfall intensity, as this range most affects stability. Very low intensity is inconsequential, while heavy rain practically has the same effect as a medium-to-high intensity. Overall, the results are in accordance with those reported in the previous literature, offering some additional insights into the particular case of slopes of coal and lignite mines.  
The current framework could be further advanced in future works incorporating more details in each of its components (climate measurements, climate projections, numerical models) according to the demands of different projects. Climate measurements have been evaluated herein based on average values and trends, and generalizations based on a few registered extreme events. A more advanced statistical analysis could increase accuracy but would require more data and experience. Furthermore, climate models for projections are constantly updated, and thus any advances could be incorporated directly. Finally, spatial and temporal variability in soil properties could be implemented in the seepage and stability analysis. This direction increases accuracy if relevant data are available but complicates the numerical modeling and requires extra caution in the interpretation of results.  
Figure 11: Slope safety factor variation with rainfall time duration, for various values of (**a**) \(\mathrm{k_{s}}\) and (**b**) \(\mathrm{q}\).
## 3 Results
### Multi-Task Classification of APS Types (NS, EAS, MS) and PA Types (SS, SB, TR)  
#### 3.2.1 Multi-Task Classification of APS Types and PA Types with Weighted Training  
Figure 6a shows the confusion matrix of PA types classification (SS, SB, TR) using multi-task classification of APS types (NS, EAS, MS) and PA types (SS, SB, TR) obtained from multi-task RNN model tuned with weighted training, Figure 6b also shows the confusion matrix APS types classification (NS, EAS, MS) using multi-task classification of APS types (NS, EAS, MS) and PA types (SS, SB, TR) with weighted training. PLS-DA is used for feature selection for the classification tasks. A total of 296 features were selected by combining features from APS and PA. Weighted training is used to handle the issue of imbalanced classes. The architecture of the multi-task classification of APS types and PA types is shown in Figure 4b.  
Figure 5: (**a**) Confusion matrix of PA classification using the multi-task LSTM RNN model designed to simultaneously perform classification of APS types, classification of PA types and estimation of EE; (**b**) Confusion matrix of APS types using the multi-task LSTM RNN model designed to simultaneously perform classification of APS types, classification of PA types, and estimation of EE which excludes the results for MS because not enough EE data were collected during MS sessions to do the multitask classification for this case.  
Figure 6: (**a**) Confusion matrix of PA types classification using the multi-task LSTM RNN classification of APS types and PA types (class imbalance mitigated by weighted training); (**b**) Confusion matrix of APS types classification using the multi-task LSTM RNN classification of APS types and PA types (weighted training).
## 2 Materials and Methods
### DTF and STF Differences in Energy and Angular Distributions
#### 2.2.2 Angular Distributions
In Figure 10 the \({}^{48}\)Ca angular and energy distributions, under the condition of angular symmetric emission of the medium and heavy fragment, i.e., \(\theta_{1}=-\theta_{2}=-70^{\circ}\), are presented. The \({}^{48}\)Ca fragments are emitted at \(60^{\circ}\pm 10^{\circ}\) and \(90^{\circ}\pm 15^{\circ}\) for DTF and STF, respectively. Furthermore, these events correspond also to well separated kinetic energy ranges 40-190 MeV and 240-310 MeV for STF and DTF, respectively, as shown in Figure 10b. Therefore, by including two detectors covering these angles a solid event-by-event identification of DTF and SFT products is possible. Figure 10: Energies and angles of the light fragments from the \({}^{132}\)Sn + \({}^{68}\)Zn + \({}^{48}\)Ca tripartition. Under the condition of angle symmetric emission of the medium and heavy fragment at \(\theta_{1}=-\theta_{2}=-70^{\circ}\), the angular and energy distributions of the \({}^{48}\)Ca fragments are shown in panels **(a,b)**, while the angle vs. energy correlations are shown in panel **(c)**, assuming both DTF (black) and STF (pink) processes. For details on energy, angular intervals, and steps considered in calculations, see the text. Figure 9: Energy correlations of the \({}^{132}\)Sn + \({}^{68}\)Zn + \({}^{48}\)Ca tripartition. The DTF (blue) and STF (pink) energy correlations \(E_{2}\) vs. \(E_{3}\) are presented for the fixed heavy fragment angle \(\theta_{1}\) described at the top of each panel.
# Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
## 2 Related Work
### Prior Regularized Reconstruction Method  
Compressive snapshot reconstruction is an ill-posed inverse problem. The early work formulates it as a prior regularized optimization problem consisting of a prior regularization term and a data fidelity term. GAP-TV [8] uses total variation as a prior regularization constraint, and DeSCI [14] uses sparse representation and non-local self-similarity as a priori representation of hyperspectral images. However, these prior regularizations cannot effectively represent the complex spatial-spectral structure in hyperspectral images. At the same time, these reconstruction models require iterative optimization. Using the alternating  
Figure 1: The architecture of the proposed reconstruction network DualCT-Net. DualCT block acts as the basic building block of DualCT-Net.  
direction multiplier method (ADMM) as an example, each iteration needs to solve the proximal operators of the prior regularization, and the computational complexity is high.
# Selection Criterion of Reanalysis Methods for Plane Truss Optimization
## 3 Quantitative Analysis of Reanalysis Methods
### Computational Accuracy Requirements  
The accuracy of the structural reanalysis method is typically measured by the error between the computed response \(\widetilde{r}\) and the actual response \(r\), as shown in Equation (14).  
\[\varepsilon=\frac{\|\widetilde{r}-r\|}{\|r\|}. \tag{14}\]  
However, the actual response is often unknown in practical computations. Therefore, the following equation is generally used to estimate the computational accuracy in place of the actual response. The calculation formula for the error in this study is also based on Equation (15).  
\[\varepsilon=\frac{\|K\widetilde{r}-f\|}{\|f\|}. \tag{15}\]  
Both excessively large and excessively small values of error will impact the performance of the algorithm. An error that is too small can compromise the effectiveness of reanalysis methods by sacrificing computational efficiency to ensure accuracy, sometimes even resulting in lower efficiency than complete analysis. On the other hand, an excessively large error will affect the optimization process and results of the algorithm. In order to determine a reasonable value of error, a series of numerical tests was conducted. As a result, a value of 0.01 was chosen, indicating that the overall error \(\varepsilon\) of this study should not exceed 0.01. For structural calculations during the optimization process, once the error exceeds 0.01, the reanalysis method should be discontinued, and the accurate solution method should be applied to eliminate the error in the structural response calculation.
## 3 Methodology
### Balanced-Relative Density Peaks Clustering (BRDPC)  
When large density differences among different clusters exist, DPC cannot eliminate the density difference among different clusters and will easily ignore the cluster centers with relatively low local density in datasets. Although NADPC [36] considers the issue of cluster centers with low local density, it cannot get desirable results for the density difference among different clusters. The mutual nearest-neighbor relationship depends on the distribution of data points and is insensitive to the change in distance. It can eliminate the influence of density differences among different clusters [44]. Therefore, a novel balanced-relative density peaks clustering (BRDPC) algorithm is proposed. The schematic diagram of the BRDPC algorithm is shown in Figure 3.  
As shown in Figure 3, \(\rho\) is the local density of any point \(x_{i}\), its calculation formula is shown in Equation (13). \(MH_{i}\) is the set of mutual nearest neighbors of any point \(x_{i}\), its calculation formula is shown in Equation (12). The balanced-relative density of \(x_{i}\) is shown in Equation (18):  
\[brd(x_{i})=\left\{\begin{aligned} 0,&|\mu_{1}(i)|=0\\ \frac{\sum_{\forall x_{j}\in\mu_{1}(i)}\frac{\rho(x_{i})}{\rho(x_ {j})}}{|\mu_{1}(i)|}+|MH_{i}|,&|\mu_{1}(i)|>0\end{aligned} \right., \tag{18}\]  
where \(|\bullet|\) is the cardinality of set.  
The balanced-relative density can improve the possibility of selecting the right cluster center. Furthermore, it can eliminate the influence of density differences among different clusters by considering the local density and the number of the mutual nearest neighbor of point \(x_{i}\).  
Steps of the schematic diagram of BRDPC algorithm are shown as follows:  
Step 1: For any point \(x_{i}\) in \(X\), \(MH_{i}\) of \(x_{i}\) is calculated by Equation (12).  
Step 2: The local density of any point \(x_{i}\) is calculated by Equation (13).  
Step 3: The balanced-relative density of \(x_{i}\) is calculated by Equation (18).  
Figure 3: The schematic diagram of the BRDPC algorithm.