esa-sceva/satcom-chunk-collection huggingface dataset:
# MCPSHA: A New Tool for Probabilistic Seismic Hazard Analysis Based on Monte Carlo Simulation
###### Abstract  
The utilization of the Monte Carlo method in conjunction with probabilistic seismic hazard analysis (PSHA) constitutes a compelling avenue for exploration. This approach presents itself as an efficient and adaptable alternative to conventional PSHA, particularly when confronted with intricate factors such as parameter uncertainties and diverse earthquake source models. Leveraging the Monte Carlo method and drawing from the widely adopted Cornell-type seismicity model in engineering seismology and disaster mitigation, as well as a seismicity model capturing temporal, spatial, and magnitude inhomogeneity, we have derived a formula for the probability of earthquake intensity occurrence and the mean rate of intensity occurrence over a specified time period. This effort has culminated in the development of a MATLAB-based program named MCPSHA. To assess the model's efficacy, we selected Baoji City, Shaanxi Province, China, as our research site. Our investigation delves into the disparity between occurrence probability and extreme probability (a surrogate commonly employed for occurrence probability) in the Baoji region over the next 50 years. The findings reveal that the Western region of Baoji exhibits a heightened hazard level, as depicted in the maps, which illustrate a 10% probability of exceedance within a 50-year timeframe. The probability of earthquake occurrence under various intensities (VI, VII, and VIII) over 50 years follows a declining trend from west to east. Furthermore, the likelihood of seismic intensity exceeding VI, VII, and VIII indicates the lowest exceeding probability in the northeast and the highest in the northwest. Notably, for intensities VI-VII, the difference between occurrence probability and extreme probability approaches twice, gradually diminishing with increasing intensity. This study underscores the MCPSHA model's efficacy in providing robust technical support for mitigating earthquake risk and enhancing the precision of earthquake insurance premium rate calculations.  
e 2024 14 1079 10.3390/app14031079
# Evaluating the Performance of Lightweight Ciphers in Constrained Environments--The Case of Saturnin
## 2 Background  
### Lightweight Cryptography  
Lightweight cryptography is a subfield of cryptography that aims to provide solutions tailored for resource-constrained devices [9]. Although an obvious approach is to develop efficient implementations of conventional cryptographic standards, the design and analysis of new lightweight primitives seems to be the proper way to address this challenge. To this goal, NIST published in 2018 a document containing the requirements and the evaluation criteria of submissions to the relevant NIST competition for a lightweight cryptography standard [9].  
The NIST competition is ongoing, and it is currently in its third round, in which 10 algorithms (from the initial list of 56 algorithms in the first round) have been selected to continue. The selection of the schemes highly relies on the efforts that the cryptographic community puts in order to explore and evaluate their security arguments. It should be pointed out that, as an explicit requirement that NIST has set, each submission to the NIST competition should contain a family of AEAD (Authenticated Encryption with Associated Data) schemes, so as to ensure that not only confidentiality but also authenticity of the data are being provided.  
It should be stressed that the design of a lightweight cryptographic algorithm achieving all the design goals--i.e., high security, low cost and high performance--is not an easy task. It is generally assumed that, although any two of these three design goals can be (easily) optimized, it is very difficult to simultaneously achieve all these three goals. Therefore, it is of high interest to examine the performance of a lightweight (i.e., of low cost) cipher with high security standards.
## 3 Verification of the HTP-MC Criterion
Their expressions are as follows:  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{N}{(\sigma_{1,i}^{T}-\sigma_{1,i}^{p})}^{2}} {\sum\limits_{i=1}^{N}{(\sigma_{1,i}^{T}-\sigma_{1,i}^{avg})}^{2}} \tag{12}\]  
\[MRE=\frac{\sum\limits_{i=1}^{N}{\left|\frac{\sigma_{1,i}^{T}-\sigma_{1,i}^{p} }{\sigma_{1,i}^{T}}\right|}}{N}\times 100\% \tag{13}\]  
where \(N\) is the number of data points in the rock triaxial compression dataset, dimensionless; \(\sigma_{1,i}^{p}\) and \(\sigma_{1,i}^{T}\) represent the predicted strength and experimental strength of the \(i\)-th rock sample under confining pressure \(\sigma_{3}\), respectively. High temperatures cause the thermal expansion of mineral grains within the rock. Due to differences in the thermal expansion coefficients of different minerals, uneven deformation and constrained stress develop between grains, resulting in thermal stress. When the thermal stress exceeds the bonding strength between mineral grains, microcrack propagation occurs, reducing the overall strength of the rock. As shown in Figure 2, under high-temperature conditions, the failure strength of granite, mudstone, and carbonate rock decreases significantly. Since the HB and Bieniawski criteria cannot account for the influence of temperature on the rock strength, this study analyzes only the predictions from the HTP-MC criterion. As shown in Figure 3c, the \(R^{2}\) values of the HTP-MC criterion for predicting the strength of granite, mudstone, and carbonate rock range from 94.1% to 99.8%, with an average of 97.1%. The _MREs_ range from 2.54% to 8.55%, with an average of 5.25%. These results indicate that the HTP-MC criterion predicts the strength of granite and mudstone more accurately than that of carbonate rock. This discrepancy may be attributed to the heterogeneous nature of carbonate rocks, which increases the prediction error. Overall, the HTP-MC criterion shows low _MRE_ values and high accuracy in predicting rock strength under high-temperature and high-pressure conditions. It is also worth noting that, in this study, some of the high-temperature and high-pressure triaxial compression data were obtained from rocks that had been subjected to high-temperature treatment prior to testing. This may affect the accuracy of parameter fitting and strength prediction of the proposed criterion. Therefore, in future work, our research will focus on investigating the mechanical behavior of rocks under real-time high-temperature and high-pressure conditions. Figure 3: Comparison of the _MRE_ and \(R^{2}\) of prediction strength of three rock types: (**a**) The comparison of the \(R^{2}\) of three criteria. (**b**) The comparison of the _MRE_ of three criteria. (**c**) Comparison of the _MRE_ and \(R^{2}\) of the HTP-MC criterion.
# An Evaluation of Directive-Based Parallelization on the GPU Using a Parboil Benchmark
## 5 Implementation Details
### MRI Non-Cartesian Q Matrix Calculation (MRI-Q)  
This application has two OpenACC implementations. The main goal of both is to avoid the usage of atomic operations. In the first implementation (labeled as OPENNACC_INTERCHANGE), this is achieved by interchanging the two main loops. However, only the outer loop can be parallelized because parallelizing both would require the use of atomic directives. The second implementation (labeled as OPENACC_REDUCTION) goes a step further by allocating additional memory. This enables the usage of the collapse directive on the two main loops, which results in a higher level of parallelism because the result of each iteration is stored in a different memory location. However, this approach requires an additional step. The result has to be calculated with the OpenACC reduction directive. The code for the OPENACC_INTERCHANGE implementation is given in Listing 3.  
```
1#pragmaaccparallellooppresent(x[0:mmX],y[0:mmX],z[0:mmX],Qr[0:mmX],
2 Qi[0:mmX],kVals[0:mmK])
3//interchangedloops
4 for(intindexX=0;indexX<mmX;+indexX){
5 for(intindexK=0;indexK<mmK;++indexK){
6 floatexpArg=PIx2*(
7 kVals[indexK].K*x[indexX]+
8 kVals[indexK].K*y[indexX]+
9 kVals[indexK].Kz*z[index]
10);
11 floatcosArg=cosf(expArg);
12 floatsinArg=sinf(expArg);
13 floatphi=kVals[indexK].PhiMag;
14
15//#pragmaaccatomicupdate
16 Qr[indexX]+=phi*cosArg;
17
18//#pragmaaccatomicupdate
19 Qi[indexX]+=phi*sinArg;
20}
21}
```  
Listing 3: OPENACC_INTERCHANGE implementation of the MRI-Q application.
## 1 Introduction  
Ge incorporation into the kesterite (Cu\({}_{2}\)ZnSnSe\({}_{4}\)) system in order to form Cu\({}_{2}\)Zn(Sn,Ge)Se\({}_{4}\) (CZTGSe) material is considered to be a promising approach for obtaining photovoltaic absorbers with a higher and tunable band gap in the range of 1.0 eV to 1.5 eV [1, 2]. In addition, Ge is known to occupy the Sn sites, possibly reducing or passivating Sn-related deep defects, i.e., \(V_{\text{Sn}}\), Sn\({}_{2}\)n and Cu\({}_{\text{Sn}}\), which are assumed to be a major cause of high \(V_{\text{OC}}\) deficits in kesterite [3, 4, 5, 6, 7]. Commonly reported approaches for Ge incorporation into kesterites are based on the inclusion of Ge in the precursor layer, which can be grown by spray coating, electrodeposition and sputtering [8, 9, 2, 10]. It has been reported that such approaches can lead to the localized incorporation of Ge [6], significant variation in grain size [8] and inferior device performance, which can be improved by ambient storage [2]. However, most of the reported processes have in common that they are based on processes that were previously optimized for a Ge-free-material system. As a consequence of adding Ge to the precursor, the entire alloy configuration, distribution of elements, and overall composition ratios in the precursor are changed, altering the reaction pathway for the kesterite-phase formation. In such a case, a well-optimized process for Ge-free-kesterite growth may need further optimization steps for Ge incorporation. The precursor structure and further process parameters may need a full re-optimization or even significant modifications to avoid the formation of other secondary phases and further undesirable defects.  
Here we report an approach in which we avoid the aforementioned problems by using an alternative method to incorporate Ge into the layer after the high-temperature phase of the selenization process, thereby maintaining the previously optimized precursor structure and the main formation pathway identical to the Ge-free growth process. In brief, the Ge is incorporated into the already-formed kesterite-absorber layer via the vapor phase during the high-temperature phase of the selenization process. During this step the composition of the layer is usually shifted from Cu-rich to Cu-poor [11; 12]. Since the main kesterite phase formation route is unaltered, the process offers high reproducibility in terms of layer morphology and targeted final-layer composition. The Ge-based devices show slightly lower efficiencies and could reach the range of 10%, which is slightly below the as-grown efficiencies of Ge-free devices. However, investigations into long-term device stability indicate an improvement of the long-term performance by adding Ge to kesterite solar cells.
## 2 Mathematical Modeling and Analysis of the Proposed Detector Algorithm  
### Clutter Map CFAR Detector  
#### 2.1.1 Description of the CMAP-CFAR Process  
The CMAP-CFAR detector is a system using a first-order recursive filter to update the clutter power estimate. This detector uses a sliding window estimator that takes the average over m scans. Each resolution cell's exponential smoothing updates the clutter power estimate at each scan of the radar space (see Figure 1).  
In this technique, we use only the latest estimate instead of data from m previous scans. At each output of the kth cell of the Nth scan, the factor \(\alpha\) is equal to q(k), where \(\alpha\) is the filter's coefficient gain used to ensure system stability (\(0<\alpha<1\)) and is added to the previous estimate of clutter power y(n) weighted by the factor (\(1-\alpha\)) to obtain the new estimate.  
Figure 2 represents the block diagram of our proposed detector where the threshold. Th is calculated in a way to ensure a constant false alarm probability. By comparing the test cell q(k) with the weighted threshold, it is ultimately determined whether the target is present [16]. The background estimate is formed from the previous scans and for mth scan; the output of the recursive filter is given by the following equation:  
\[\mathrm{y_{m}(k)}=(1-\alpha)\times\mathrm{y_{m-1}(k)}+\alpha\times\mathrm{q_{m }(k)} \tag{1}\]  
where \(\mathrm{q_{t}}\) is the output of the tth resolution cell and \(\alpha\) is the filter's coefficient gain. We can generalize Equation (1) recursively and obtain a more compact form, as shown below:  
\[\mathrm{y_{m}(k)}=\sum_{i=0}^{m}\alpha(1-\alpha)^{i}\mathrm{q_{m-i}(k)} \tag{2}\]
# Autonomous Prostate Segmentation in 2D B-Mode Ultrasound Images
###### Abstract  
Prostate brachytherapy is a treatment for prostate cancer; during the planning of the procedure, ultrasound images of the prostate are taken. The prostate must be segmented out in each of the ultrasound images, and to assist with the procedure, an autonomous prostate segmentation algorithm is proposed. The prostate contouring system presented here is based on a novel superpixel algorithm, whereby pixels in the ultrasound image are grouped into superpixel regions that are optimized based on statistical similarity measures, so that the various structures within the ultrasound image can be differentiated. An active shape prostate contour model is developed and then used to delineate the prostate within the image based on the superpixel regions. Before segmentation, this contour model was fit to a series of point-based clinician-segmented prostate contours exported from conventional prostate brachytherapy planning software to develop a statistical model of the shape of the prostate. The algorithm was evaluated on nine sets of in vivo prostate ultrasound images and compared with manually segmented contours from a clinician, where the algorithm had an average volume difference of 4.49 mL or 10.89%.  
ultrasound image processing; active shape model; superpixels; prostate brachytherapy
## Nomenclature  
* _AS_ Acquired signal
* _RS_ Reconstructed signal
* _FT_ Fourier transform
* _EA_ Equivalent amplitude
* _PC_ Plunging component
* _RC_ Rotating component
* _D\({}_{t}\)_ [m] Reference diameter of the throat_ \(D_{t}=0.1\) _[m]_ \(f\) _[Hz] Dominant frequency_ \(Q\) _[_m\({}^{3}\)/s_] Main discharge at the inlet of the test section_ \(c_{p}\) _[_\(-\)_] Dimensionless pressure coefficient_ \(N\) _Number of measured samples_ \(\overline{p}\) _[Pa] Mean pressure_ \(p_{RMS}\) _[Pa] Root mean square of the pressure signal_ \(Sh\) _[_\(-\)_] Strouhal number_ \(V_{t}\) _[m/s] Bulk velocity in the throat of convergent- divergent test section_ \(\sigma\) _Standard deviation_ \(\rho\) _[_kg/ m\({}^{3}\)_] Density_
## 2 Data and Methods
### Methods
#### 2.3.3 Phenological Metrics Extraction  
The phenological metrics were extracted from MOD13A2 NDVI data. [PERSON] et al. reported that there was little difference among Gaussian, logistic, and Savitzky-Golay fitting methods in the TIMESAT tool [23]. We used TIMESAT software in MATLAB R2015b (The Mathworks, Inc., Natick, MA, USA) with a seasonal parameter of 0.5, an adaptation strength of 2.0, a Savitzky-Golay window size of 2, and an amplitude of 20% to calculate the start of season (SOS), the end of season (EOS), and the length of season (LOS) in northeast China from 2007 to 2015.
## 2 Proposal of Vehicle-Mounted Transient Electromagnetic Method
### Principle and Process of Vehicle-Mounted Transient Electromagnetic Method  
TEM technology can effectively distinguish the surrounding rock media based on the different electrical resistivity between the rock and water, and the development scale of the hidden and undiscovered water can also be assessed to some extent [3; 9; 17; 18]. The principle and process of vehicle-mounted TEM can be shown in Figure 6. The vehicle-mounted TEM measurement system includes the transmitter-receiver coil system and the movable platform, as shown in Figure 6a.  
In operational tunnels, the detailed detection process of the vehicle-mounted TEM measurement system can be shown in Figure 6b, and the vehicle-mounted TEM measurement system can realize the simultaneous operation of detection and movement. The detection process of the vehicle-mounted TEM measurement system mainly consists of two steps, and the two steps are interlaced with each other [3]. For the first step, the transient pulse signal in the transmitter coil is instantly sent to the whole space of surrounding rock, and the receiver coil captures the second signal caused by the surrounding rock. For the second step, the movable platform moves quickly through the tunnel, and its position condition is recorded. It is important to note that the movable platform is not recommended to be made of metal, to avoid ineffective electromagnetic signals. Based on these two steps, the induction signal and position information of the entire tunnel can be acquired, and so the spatial information map of the tunnel defects in the entire tunnel can be identified and obtained through the fast inversion method.
# Advanced Applications of Industrial Robotics: New Trends and Possibilities
## 2 Main Robotiisation Strategies
### Modern Robotiisation Strategy
A new assembly strategy was described in a previous study [20], in which a cobot learnt skills from manual teaching to perform peg-in-hole automatic assembly when the geometric profile and material elastic parameters of parts were inaccurate. The results showed that the manual assembly process could be analysed mathematically, splitting it into a few stages and implementing it as a model in robot control. Using an Elite EC75 manipulator (Elite Robot, Suzhou, CN, an assembly time of less than 20 s was achieved, ensuring a 100% success rate from 30 attempts when the relative error between the peg and hole was \(\pm\)4.5 mm, and the clearance between the peg and the hole was 0.18 mm. As a result of the development of sensor and imaging technologies, new applications in robotics are emerging, especially in human-robot collaboration. In [21], detailed research focused on identifying the main strengths and weaknesses of augmented reality (AR) in industrial robots applications. The analysis shows that AR is mainly used to control and program robotic arms, visualise general tasks or robot information, and visualise the industrial robot workspace. Results of the analysis indicate that AR systems are faster than traditional approaches; users have greater appreciation for AR systems in terms of likeability and usability; and AR seems to reduce physical workload, whereas the impact on mental workload depends on the interaction interface [16]. Nevertheless, industrial implementation of AR is still limited by insufficient accuracy, occlusion problems, and the limited field of view of wearable AR devices. A summary of the analysed robotisation strategies indicates that they both have their specific implementation fields. The classical strategy is well suited to strictly controlled environments. The modern strategy ensures more flexible operation and is suitable for non-predictable environments. Nevertheless, it is necessary to note that the strict line between these strategies has gradually disappeared due to advances in sensing technologies, artificial intelligence, and computer vision. A typical industrial robot equipped with modern sensing and control systems can operate similarly to a cobot. According to [22], collaborative regimes can be realised using industrial robots, laser sensors, and vision systems, or controller alteration if compliance with the ISO/TS 15066 standard--which specifies parameters and materials adapted to safe activities with and near humans--is ensured [23]. This standard defines four main classes of safety requirements for collaborative robots: safety-rated monitored stop; hand-guiding; speed and separation monitoring; and power and force limiting. In addition, it is essential to mention that all improvements and advances in robotics can be classified into two main types: universal and application dependent. The remaining part of this article reviews and classifies the latest advances in robotics according to the areas of their implementation.
## 3 Data and Methodology
### Estimation of the \(V_{\text{s30}}\)  
3.5.1. V\({}_{\text{s30}}\) from NSPT Measurements  
There are a considerable number of studies that propose equations relating shear-wave velocity to the Normalized Standard Penetration Test (NSPT). However, the equations are specific to the region under study. [PERSON] et al. [53] compiled data from different continents and proposed empirical equations correlating NSPT values with shear-wave velocity for sands (Equation (4)), clays (Equation (5)), and for all soil types (Equation (6)):  
\[\text{V}_{S} =79.217\times N^{0.3699} \tag{4}\] \[\text{V}_{S} =99.708\times N^{0.3358}\] (5) \[\text{V}_{S} =75.478\times N^{0.3799} \tag{6}\]  
where V\({}_{S}\) is the shear-wave velocity and N-value is the number of blows in the SPT measurements. However, in the case the SPT borehole does not reach 30 m, which is the case in this study, V\({}_{\text{s30}}\) can be correlated from the average velocity at depth \(z\) using the following equation from [54]:  
\[Log~{}V_{\text{s30}}=a+(b\times Log~{}V_{\text{s}}z) \tag{7}\]  
Figure 4: Rayleigh wave dispersion curves obtained from the F-K, SPAC, and ESAC methods.  
\(V_{s}z\) is the velocity at depth \(z\), and \(a\) and \(b\) are coefficients that vary with depth (see Table 2 in [54]).
# Improving the Performance of Turbo-Coded Systems under Suzuki Fading Channels
## 4 Numerical Results
### Complexity Calculations  
In this section, the number of operations required to calculate the derived noise distribution and the corresponding LLRs are calculated utilizing the \(gaxpy\) algorithm [22]. The complexity of calculating Equations (13)-(16), and the Gaussian PDF and its LLRs is illustrated in Table 1.  
Figure 4: Performance of the turbo-coded system with 4-quadrature amplitude modulation (QAM) and 16-QAM schemes, with the proposed log-likelihood ratios (LLRs) and Gaussian LLR, and for rate 1/3.  
Figure 5: Performance of the turbo-coded system with 4-QAM and 16-QAM schemes, with the proposed LLRs and Gaussian LLR, and for rate 1/2.  
It is observed from Table 1 that the number of operations required to calculate the PDFs and the LLRs for the proposed detector compared to Gaussian PDF are relatively low and cost-effective for implementation with regards to the improvement in BER performance.  
Practically, we selected four SNR values to calculate the running time of the simulations, such that SNR = 0, 1, 1.5, and 2 dB, while data size was \(2\times 10^{6}\) bits. As a result, the simulation time required for utilizing Gaussian LLRs was 198.9063 s, while it was 217.2656 s for the proposed LLRS considering the 4-QAM scheme. As a result, the cost for improvement in the BER of 2 dB increases running time by factor  
\[\frac{217.2656-198.9063}{198.9063}=9.23\%.\]