esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
We have proposed a PS-based PP scheme for performance improvement of the DM-CVKQD system by using PS operation on Bob's side. The proposed scheme can tolerate more channel losses compared with the original scheme so that it extends the maximal transmission distances. The secret key rate of the eight-state PP DM-CVQKD system is slightly higher than that of the four-state system for the same parameters in numerical simulation. We demonstrate the relation between secret key rate, channel losses and untrusted source noise (characterized by \(g\)), and find that the PS-based scheme can tolerate more untrusted source noise. In addition, we take the finite-size effect into account and show its influence on the secret key rate with the tunable numbers of subtracted photons and exchanged signals. This research provides a feasible method to improve the performance of PP CVQKD, which has a certain reference value for experiment and application.  
Conceptualization, writing--original draft preparation, C.Y. ; software, Y.M.; validation and formal analysis, Y.G.; supervision, S.Z.; All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (Grant Nos. 61572529, 61871407). The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} QKD & Quantum Key Distribution \\ CVQKD & Continuous-Variable Quantum Key Distribution \\ DVQKD & Discrete-Variable Quantum Key Distribution \\ EPR & Einstein-Podolsky-Rosen \\ PP & Plug-and-Play \\ PS & Photon Subtraction \\ EB & Entanglement-Based \\ PM & Prepare-and-Measurement \\ TMSV & Two-Mode Squeezed Vacuum \\ \end{tabular}  
Figure 6: The secret key rate as a function of channel losses for PP DM-CVQKD protocol in the finite-size scenario. (**a**) PP four-state protocol; (**b**) PP eight-state protocol; (**c**) 1-PS PP four-state protocol; (**d**) 2-PS PP four-state protocol. The parameters are as follows: the modulation variance \(V_{M}=0.5\), the channel excess noise \(\varepsilon=0.01\), the reconciliation efficiency \(\beta=0.95\) and the gain \(g=1.005\).
# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 2 Materials and Methods
### Synthetic Aperture Radar (SAR) Data Mining
#### 2.2.4 Ancillary Data
Alongside terrain slope, the ASTER GDEM has been used to characterize the (geo-)morphology and topography of the areas of interest, and to derive a comprehensive understanding of the physical characteristics that may make those areas prone to flooding, as per the standard practice in flood-risk research (e.g., [83, 84]). This analysis has been embedded in the interpretation process of the observed change patterns (see Section 2.3). * Alluvial deposits: the material deposited or cemented into the lithological unit filling the fluvial valley is the geological factor that testifies a historical predisposition of the territory to alluvial events. The extent of alluvial deposits marks the lowlands of Lima and areas at the highest hazard for inundation. * Land cover: anthropogenic factor that indicates the spatial distribution and extent of the elements (e.g., houses, infrastructure) potentially exposed to risk. For this study, this layer mostly matches with the extent of the built-up areas and it was compiled by combining the information available through the Open Street Map (OSM) accessed from the QGIS service and the Global Urban Footprint (GUF) produced by the German Aerospace Center (DLR) with TerraSAR-X data [74, 75]. Because these two datasets were updated until 2011, the OSM-GUF raster was complemented with new urban areas built between 2011 and 2019 that were digitized from Google Earth imagery. Figure 6: Geographic information system (GIS)-based analysis workflow: datasets showing spatial distribution of hazard factors and elements at risk (i.e., alluvial deposits, slope and land cover) are integrated with the SAR-based red, green and blue (RGB) composites to detect and interpret the observed change patterns of radar backscatter as “Flooding” (F) and “No-Flooding” (NF) changes. Satellite optical imagery from Google Earth is exploited for validation. The same approach applies to the ratio maps. The shapefiles obtained from the aforementioned three datasets (Figure 6) were integrated in QGIS, with the aim of producing basic thematic mapping (BTM) containing all the elements of hazard and risk elements that were used, alongside the SAR change patterns, to generate the final flood risk scenario map.
# Stability Prediction of Soil Slopes Based on Digital Twinning and Deep Learning
As shown in Figure 1, it included: (1) A large number of actual slopes were collected, the DT method was used to establish the digital slope models with similar characteristics to the actual slopes, and the slope sample database was built by fine-tuning the slope profiles; (2) the LEMs were used to calculate the slope safety factor, and then the CNN model was built and trained with the slope samples. The coordinates of the slope profiles and the material parameters of the soil layers were taken as the CNN input, and the corresponding safety factor was taken as the output to train the CNN. To predict the stability of a slope, the inspectors only need to input their field exploration data into the proposed model.
## 2 The Proposed Model  
### FLR-FSWGARCH Model  
The proposed model consists of four steps, as shown in Figure 1. The first step is the parameter estimation using fuzzy linear regression, the second step is the return computation, the third step is the fuzzy window variance computation, and the last step is the forecasting. The new combination of the GARCH model with two methods is done in the first and third steps. A fuzzy sliding window algorithm was developed in this model to be used in the first and the third steps.  
The following paragraphs explain the above four steps involved in the proposed model in detail.  
Figure 1: Design algorithm of the fuzzy linear regression sliding window generalized autoregressive conditional heteroskedasticity (FLR-FSWGARCH) model. TFN: triangular fuzzy number.
## 4 Experiments
### Experimental Results and Analysis
#### 4.3.1 Parameter Analysis  
**Analysis of corpus proportion division:** Before conducting an experimental analysis, a reasonable division of the training set, verification set, and test set is crucial in the fields of machine learning and deep learning. When the corpus's sample size is small, it is common to use between 2/3 and 4/5 of the data for training and the remaining data for testing. In order to reduce the impact of data division on the experimental results, we validated the results of three different corpus division ratios, as shown in Figure 11.  
The experimental corpus performs best with a segmentation ratio of 6:2:2, as shown in Figure 11. Thus, the corpus of 2520 chemical accident reports was separated into 1510 training sets, 500 validation sets, and 510 test sets.  
**Analysis of Bert pretraining:** Pre-trained BERT models currently exist in three types: English, Chinese, and multilingual. BERT-Base and BERT-Large models are provided, and the corresponding hyperparameters are shown in Table 2.  
Figure 11: The experimental results of the three division ratios. (**a**) Precision. (**b**) Recall. (**c**) F1.  
BERT-Base model supports Chinese, simplified and traditional, so we adopted BERT-Base. The dimensions of bi-directional long short term memory (Bi-LSTM) were set to 128, and the training and learning rates of Bert were set to \(5\times 10^{-5}\). Due to BERT's strong feature fitting ability, the training learning rate of Bi-LSTM and the conditional random field (CRF) were set to \(5\times 10^{-2}\), and the preheating value of the learning rate was set to 0.1. Dropout technology was adopted to prevent over-fitting in the experiments. The dropout value was set to 0.5.
## 1 Introduction  
Valosin-containing protein (VCP) is encoded by _VCP_ on chromosome 9 and it is implicated in numerous human disease phenotypes with autosomal dominant inheritance. VCP is a highly conserved, ubiquitously and abundantly expressed ATPase. Indeed, VCP may make up as much as 1% of all cytoplasmic protein [1]. VCP mediates ubiquitin-dependent cellular processes through the ubiquitin-proteasome system (UPS) [2], protein quality control [3; 4], transcription factor processing [5], membrane fusion [6], cell cycle control [7], and regulation of autophagy [8; 9].  
Over 50 heterozygous missense mutations in _VCP_ have been identified in patients with multisystem proteinopathy 1 (MSP1), an autosomal dominant, adult-onset progressive disorder, also known as inclusion body myopathy associated with Paget disease of bone and frontotemporal dementia (IBMPFD), or VCP disease (hereafter referred to as VCP-MSP) [10; 11]. The major pathological feature seen in VCP-MSP is the presence of ubiquitin-positive protein aggregates in muscle tissues of patients affected by inclusion body myopathy (IBM). Intranuclear TDP-43+ inclusions are demonstrated in patients with frontotemporal dementia (FTD) and/or amyotrophic lateral sclerosis (ALS) [12]. In Paget disease of bone (PDB), abnormally large osteoclasts containing nuclear inclusions resemble the muscle pathology findings [13]. On the whole, the pathological findings of VCP-MSP suggest that abnormal protein clearance is the major molecular mechanism affected by _VCP_ mutations causing human disease. In other forms of MSP (recently reviewed) [11], it has also been suggested that the common molecular mechanism relates to the disruption of two major protein clearance pathways: the UPS and autophagy [14]. However, future studies may identify others among VCP's diverse molecular mechanisms that are relevant to MSP and other human diseases. The molecular functions of VCP and disease pathogenesis have recently been reviewed [9].  
In this review, we provide an overview of the complex clinical presentations of VCP-MSP, with guidance for genetic diagnosis of this complex condition. A special discussion of the complexities surrounding genetic diagnosis and counselling in VCP-MSP is also included.
## 4 Conclusions  
RT PPP can provide centimeter accuracy-level solutions based on real-time precise products, for orbits, clocks, and code/phase biases, provided by CNES. Nevertheless, a significant challenge for the RT PPP to achieve high-accuracy position solutions is its long convergence time, i.e., up to a few hours. Thanks to the standardization of RT message of VTEC models in CLK92/CLK93 stream from CNES, the convergence time of RT PPP can be reduced by using an uncombined functional model with RT ionospheric correction products. In order to significantly reduce the convergence time and achieve the high-accuracy positioning solutions after filter convergence, the proper weight of ionospheric constraints are important.  
To solve this issue, a weight factors searching algorithm with a moving-window average filter is proposed. This approach is similar to the method of Helmert variance component estimation; it searches for the optimal weight of ionospheric constraints according to the principle that the sum of the quadratic forms of weighted residuals is the minimum, and makes good use of the weight information at previous epochs. Datasets collected at 31 MGEX stations on 30 consecutive days are exploited to evaluate the proposed approach. Both static and kinematic experiments have been carried out in dual- and single-frequency, and the statistical results indicate that the new method significantly improves the performance of RT PPP convergence. The maximum improvement reaches 35.9% in comparison to the method of priori variance. By using the new method, the final positioning accuracy is not affected by the accuracy of RT ionosphere products, and the same accuracy as that of without constraints can be achieved. Overall, our proposed weighting approach can not only accelerate the convergence at the beginning of filter processing, but can also achieve high-accuracy position solutions after filter convergence. Future work will include the application of the proposed weighting approach to multi-GNSS combinations with tropospheric constraints.  
[PERSON], [PERSON] and [PERSON] came up with the idea of the constrain method, [PERSON] and [PERSON] analyzed the data, [PERSON] and [PERSON] conceived and designed the experiments. [PERSON] wrote the paper.  
The Fundamental Research Funds for the Central Universities with a grant number as 2015 XKMS051.  
The authors gratefully acknowledge Center National d'Etudes Spatiales (CNES) and IGS Multi-GNSS Experiment (MGEX) for providing GNSS data and precise products.  
The authors declare no conflict of interest.  
Figure 8: RMS statistics of positioning errors for different RT PPP processing methods at the beginning of 30 min (**left**) and all day (**right**).
## 5 Conclusions  
Based on the obtained results, it can be summarized that:  
* A shooter could appear GSR-negative by SEM/EDS seven hours after firearm discharge;
* Depending on the number of shots and the ammunition in high-caliber and low-caliber weapons, the duration of GSR persistence on a shooter's hands is extended but does not exceed 7 h;
* The results of this work are to be considered valid only for mobile people who follow normal office activities but not for victims (immovable). Our previous investigation showed that GSR particles could be detected in samples taken from people who had committed suicide, even two days after the incident. This long residual time highlights the limited possibilities of transferring the GSR from one surface to another;
* The results could be influenced by the postdischarge activity of the person involved in the tests, and interference or contamination by secondary transfer during the working day should also be considered.  
The obtained results are in good agreement with the SWGGSR recommendations to collect GSR within 4-5 h after a shooting incident [27]. In each of the tests, GSR particles were detected on the shooter's hands 4 h after firearm discharge. However, later GSR sampling should not be excluded, as on average, from six series of single-shot experiments, GSR could be detected on the shooter's hand 6 h after outdoor firearm discharge. If the number of shots increased, GSR particles could be detected on the shooter's hands even later, 7 h after firing a high-caliber firearm. This evidence contrasts some forensic protocols that required the sampling to be carried out no later than 6 h after the shooting event. Hence, the forensic protocol for GSR sampling should take into account, from one side, the wide range of reported time intervals for GSR persistence on the shooter's hands and, from another side, the fact that a shooter may appear GSR-negative if sampling was carried out at longer postdischarge intervals.  
**Author Contributions:** Conceptualization, writing--original draft preparation, and investigation, M.T.; methodology and supervision, A.S. and B.O.; validation, resources, and writing--review and editing, P.V. and A.V.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research publication was supported by TUIASI from the University Scientific Research Fund (FCSU).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** Special thanks to the Department of Ballistics, Kosovo Forensic Agency, for participating in the shooting experiments. The support of the Kosovo Police and the Kosovo Forensic Agency by providing forensic materials (sampling stubs, ammunitions, etc.) for this study is acknowledged. [PERSON] acknowledges the support by the Erasmus+ program for training mobility in the Technical University \"[PERSON]\" in Iasi, Romania.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Evaluation Results and Discussion
### Objective Evaluation
#### 5.1.3 Measuring Speech Quality after Analysis and Re-Synthesis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**GPE \%**} & \multicolumn{3}{c}{**MFPE**} & \multicolumn{3}{c}{**STD**} \\  & **BDL** & **JMK** & **SLT** & **BDL** & **JMK** & **SLT** & **BDL** & **JMK** & **SLT** \\ \hline baseline & 25.041 & 26.870 & 33.124 & 2.919 & 2.799 & 2.845 & 3.061 & 2.936 & 3.180 \\ contF0\_AKF & 24.548 & 28.034 & 31.103 & 2.285 & 2.293 & 2.284 & 2.338 & 2.327 & 2.468 \\ contF0\_TWRP & 21.512 & 22.329 & 29.893 & 2.256 & 2.482 & 2.472 & 2.253 & 2.702 & 2.787 \\ contF0\_STMSK & 24.371 & 26.131 & 32.775 & 1.429 & 1.179 & 1.387 & 1.686 & 1.981 & 1.140 \\ \hline YANGaf & 15.401 & 12.509 & 22.186 & 1.419 & 1.307 & 1.393 & 2.282 & 2.732 & 2.022 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average performance score per each speaker in the presence of pink noise (SNR = 0 dB).
# Response Statistics of a Shape Memory Alloy Oscillator with Random Excitation
## 3 Theoretical Analysis of the SMA Oscillator Model  
In this section, the nonlinear dynamical behaviors are examined for the proposed stochastic SMA oscillator model (11). From Equation (11), we find that the nonlinearity of the system is caused by the constitutive relationship of the SMA material. The dynamic characteristics of the system are also affected by the temperature and the random external excitation, and therefore the nonlinear dynamic behaviors of the system may be complicated. The multiple-scale method is implemented only for the primary resonance, i.e., the excitation frequency is close to the natural frequency of the SMA oscillator system. Thus, a detuning frequency \(\sigma\) is introduced, such that  
\[\Omega^{2}=\alpha+\varepsilon\sigma. \tag{12}\]  
Then, Equation (11) can be rewritten in the following form:  
\[\ddot{x}+\varepsilon\zeta\dot{x}+\Omega^{2}x-\varepsilon\sigma x+\varepsilon \beta x^{3}+\varepsilon\gamma x^{5}=\varepsilon f\cos[\Omega\tau+hW(\tau)]. \tag{13}\]  
Introducing fast and slow time scales as follows:  
\[T_{0}=\tau,\ T_{1}=\varepsilon\tau,\ T_{2}=\varepsilon^{2}\tau,\cdots \tag{14}\]  
Using the differential operators  
\[D_{0}=\frac{\partial}{\partial T_{0}},\ D_{1}=\frac{\partial}{\partial T_{1}},\ D_{2}=\frac{\partial}{\partial T_{2}},\cdots \tag{15}\]Then, applying the chain rule, we can transform the ordinary derivatives into partial derivatives [26] as  
\[ \begin{array}{l}\frac{d}{d\tau}=D_{0}+\varepsilon D_{1}+\varepsilon^{2}D_{2}+ \cdot\cdot\cdot,\\ \frac{d^{2}}{d\tau^{2}}=D_{0}^{2}+2\varepsilon D_{0}D_{1}+\varepsilon^{2}(D_{1 }^{2}+2D_{0}D_{2})+\cdot\cdot\cdot.\end{array}  \tag{16}\]  
According to the standard procedure of the multiple-scale method, the approximate analytic solutions of the SMA oscillator systems (13) can be expressed in the following form:  
\[x=x_{0}(T_{0},T_{1})+\varepsilon x_{1}(T_{0},T_{1})+\cdot\cdot\cdot \tag{17}\]  
Substituting Equations (16) and (17) into Equation (13) and separating the term of the yielded equation by the power of \(\varepsilon\), we derive the following equations  
\[O\left(\varepsilon^{0}\right):D_{0}^{2}x_{0}+\Omega^{2}x_{0}=0, \tag{18}\]  
\[ \begin{array}{l}O\left(\varepsilon^{1}\right):D_{0}^{2}x_{1}+\Omega^{2}x_{ 1}=-2D_{0}D_{1}x_{0}-\zeta D_{0}x_{0}+\sigma x_{0}-\beta x_{0}^{3}-\gamma x_{0 }^{5}\\ +f\cos[\Omega\tau+hW(\tau)],\end{array}  \tag{19}\]  
The general solutions of Equation (18) can be expressed as  
\[x_{0}(T_{0},T_{1})=A(T_{1})e^{i\Omega T_{0}}+c.c.\, \tag{20}\]  
where \(c.c.\) denotes the complex conjugate of the preceding terms. Substituting Equation (20) into Equation (19), we arrive at  
\[ \begin{array}{l}D_{0}^{2}x_{1}+\Omega^{2}x_{1}=-\left(2i\Omega D_{1}A+i \zeta\Omega A-\sigma A+3\beta A^{2}\overline{A}+10\gamma A^{3}\overline{A}^{ 2}\right)e^{i\Omega T_{0}}\\ -\left(\beta A^{3}+5\gamma A^{4}\overline{A}\right)e^{i\Omega T_{0}}-\gamma A ^{5}e^{i\Omega T_{0}}\\ +f\cos\left[\Omega T_{0}+\overline{h}W(T_{1})\right]+c.c.\,\end{array}
## 3 Construction of the Risk Assessment Model
### Expert Questionnaire Survey  
#### 3.1.1 Traditional Expert Questionnaire Survey  
Traditional expert survey questionnaires utilize pairwise comparisons among evaluation criteria within a certain criterion to calculate the relative importance weights of the indicators at that level concerning the higher-level membership criterion. The specific form of this calculation is illustrated in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Risk Level** & **Low** & \begin{tabular}{c} **Relatively** \\ **Low** \\ \end{tabular} & **Medium** & \begin{tabular}{c} **Relatively** \\ **High** \\ \end{tabular} & **High** \\ \hline \multirow{7}{*}{Visibility} & \begin{tabular}{c} Attenuation \\ coefficient \\ K (10\({}^{-3}\) m\({}^{-1}\)) \\ \end{tabular} & \(\leq\)70 & (70,100) & (100,150) & (150,200) & \(>\)200 \\ \cline{2-7}  & \multirow{4}{*}{Visibility} & \begin{tabular}{c} Attenuation \\ coefficient \\ K (10\({}^{-3}\) m\({}^{-1}\)) \\ \end{tabular} & \(\leq\)5.0 & (5.0,7.0] & (7.0,9.0] & (9.0,12.0] & \(>\)12.0 \\ \cline{2-7}  & \multirow{4}{*}{Luminance} & \begin{tabular}{c} Meteorological \\ visibility \\ (km) \\ \end{tabular} & \(\geq\)0.5 & [0.2,0.5] & [0.1,0.2) & [0.05,0.1) & \(<\)0.05 \\ \cline{2-7}  & \multirow{4}{*}{Luminance} & \begin{tabular}{c} Enhanced \\ lighting in \\ transition \\ sections \\ \end{tabular} & \(\geq\)13.0 & [11.0,13.0) & [9.0,11.0) & [7.0,9.0) & \(<\)7.0 \\ \cline{2-7}  & \multirow{4}{*}{Luminance} & \
\begin{tabular}{c} General \\ illumination \\ \end{tabular} & \(\geq\)8.0 & [6.0,8.0) & [4.0,6.0) & [2.0,4.0) & \(<\)2.0 \\ \cline{2-7}  & \multirow{2}{*}{Noise decibel value (dB)} & \(\leq\)75 & (75,85) & (85,90) & (90,95) & \(>\)95 \\ \cline{2-7}  & \multirow{2}{*}{Proportion of adverse weather (\%)} & \(\leq\)10 & (10,30) & (30,50) & (50,70) & \(>\)70 \\ \hline \multirow{7}{*}{Operations} &
\begin{tabular}{c} Organizational structure (score) \\ \end{tabular} & [80,100] & [60,80) & [40,60) & [20,40) & [0,20) \\ \cline{2-7}  & \multirow{2}{*}{Education and training (score)} & [80,100] & [60,80) & [40,60) & [20,40) & [0,20) \\ \cline{2-7}  & \multirow{2}{*}{Emergency management (score)} & [80,100] & [60,80) & [40,60) & [20,40) & [0,20) \\ \cline{2-7}  & \multirow{2}{*}{Traffic management (score)} & [80,100] & [60,80) & [40,60) & [20,40) & [0,20) \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._When evaluating extensive indicator systems, the traditional expert questionnaire surveys require a significant number of pairwise comparisons between factors, resulting in a laborious and time-consuming process. Moreover, the constructed comparison judgment matrix may not guarantee consistency. Based on the current application status of this method, this paper introduces a new questionnaire survey method and combines it with Triangular Fuzzy Analytic Hierarchy Process (FAHP) to enhance the manipulability of the assessment method and improve the accuracy of evaluation results.