esa-sceva/satcom-chunk-collection huggingface dataset:
# Nature-Inspired Optimization Algorithms for the 3D Reconstruction of Porous Media
## 3 3D Reconstruction as an Optimization Problem
### Data Representation  
In the above-defined optimization problem, the unknowns that need to be found are the phase values of each material image pixel. Therefore, for an image of \(N\times N\times N\) pixels in size, the number of  
Figure 1: Flowchart of the proposed reconstruction algorithm.  
unknowns is \(N^{3}\), and thus the corresponding search space where any nature-inspired algorithm needs to be executed is extremely complex. For example, if a 3D model of \(128\times 128\times 128\) pixels size needs to be derived, a total of 2,097,152 unknowns should be found by the algorithm.  
To overcome the above problem of high dimensional data, an efficient data representation is proposed in this work. For this purpose, the notion of the fundamental element (_fe_), which controls the representation accuracy of the reconstructed 3D material model, was introduced. Considering that the material is composed of homogenous cubic (all pixels of the cube have the same phase) elements of \(N_{fe}\times N_{fe}\times N_{fe}\) pixels in size, the number of unknowns can be reduced to \(\left(\frac{N}{N_{fe}}\right)^{3}\), achieving a significant reduction in the problem's dimension.  
Moreover, considering the case of a two-phase porous media and without loss of generality, each cubic element can be decoded as a single bit. In this way, by using a 64-bit arithmetic representation, one can compactly encode the phase of 64 cubic elements in a single integer value. By applying the proposed data representation, the problem dimension is finally reduced to \(\left(\frac{N}{4N_{fe}}\right)^{3}\).
## 1 Introduction
Thermal stress within the metal-resin composite joint is an additional serious concern because the coefficient of thermal expansion (CTE) of most resins is much higher than that of the metal part. In contrast, the addition of a \"soft\" resin with quite low Young's modulus and yield strength is a promising approach for reducing the joint stiffness to facilitate the utilization of TLPS technique in high-temperature power modules. We have proposed an approach for the stiffness reduction of TLPS joints using a novel Cu-solder-resin composite for die-attach applications. We have already proven the feasibility of this approach [33] and demonstrated excellent thermal stability during thermal storage test at 200 \({}^{\circ}\)C for 1000 h [34]. The TLPS joint was composed of stiff Cu-Sn IMCs, ductile Cu, and soft resin, which were expected to reduce the apparent stiffness of the joint. The added resin plays the role of a \"spacer,\" enabling the formation of an inherent skeleton-shaped microstructure composed of Cu and IMCs. In this study, a unique microstructure of the TLPS joint is proposed and finite element (FE) analysis based on three-dimensional (3D) image reconstruction is used to elucidate the macro- and micro-deformation properties of the microstructure. The macroscopic mechanical properties of the TLPS microstructure are estimated through computational tensile tests, where the constituents are considered as homogenized materials. The mechanical properties of the materials and the ambient temperature are varied to clarify their effect on the stability of the joint. Structural FE simulations mimicking thermal cycling tests of SiC-die-attached specimens are used to predict their reliability by using the calculated homogenized mechanical properties of the microstructure, which are compared with those obtained by experimental thermal cycling results. Finally, the deformation behavior of the microstructure is investigated to identify crack initiation sites microscopically.
# Blocking Cyclic Job-Shop Scheduling Problems
## 3 Mathematical Formulation of a Basic Model  
In the following, we provide details of the mixed-integer linear programming (MILP) model for the basic cyclic job-shop scheduling (CJSS) problem without sequence-dependent setups. Due to the blocking conditions, the scheduling approach we used was based on the departure times of jobs from machines. The decision variables used in the proposed MILP model were:  
\(X_{j,j^{\prime},m}\) 1 if job \(j\) immediately precedes job \(j^{\prime}\) on machine \(m\); 0 otherwise;  
\(R_{j,m}\) 1 if machine \(m\) is occupied by job \(j\) at the beginning of a cycle; 0 otherwise;  
\(B_{j,m}\) 1 if job \(j\) is the first job to depart from machine \(m\) after the beginning of the cycle;  
\(D_{j,i}\)  The departure time of job \(j\) from its \(i^{th}\) operation;  
\(T\)  The cycle time;  
\(U_{j,m}\)  An integer variable used to eliminate jobs' sequence subtours on each machine \(m\);  
\(W\)  A sufficiently large value, typically known as \"Big M\" in integer programming.  
Note that the variable \(X\) determines the cyclic sequence of jobs on each machine. However, to do the scheduling of departure times, we need variables \(B\) to determine the first job on each machine. Similarly, \(R\) is used to capture the possibility that this first job has started processing before the start of the cycle so that the interval from the arrival of the part on the machine to the departure overlaps the cycle start time.
# Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G
## 1 Introduction
### Paper Structure  
The organization of the paper is given here. The description of the adaptive modulation is given in Section 2, and the system model and associated channel modeling are given in Section 3. In Section 4, a detailed description of the mathematical modeling of atmospheric imperfection and the ML-assisted adaptive modulation algorithm is given. Section 5 describes numerical results, and the conclusions are drawn in Section 6.
###### Abstract
Adaptive filtering mechanisms classify and prioritize notifications based on sensitivity and context, as researched in [30]. Lastly, privacy-preserving frameworks integrate these technologies into cohesive systems for robust protection, as detailed in [31]. These approaches collectively enhance the security and privacy of IMA notifications against various physical attack vectors. Therefore, in this paper, we introduced an innovative key management system that improves the security of off-site key storage against various external threats. Furthermore, we present a notification display on locked devices for Signal messenger, which is a secure messaging application. The proposed system is crafted to securely exhibit notifications to the user on a phone that is locked, and it exhibits a great way for key management that enhances off-site security. The paper is organized as follows: Section 2 covers the basic definitions and notations used in the paper. Section 3 is devoted to the methodology. A detailed explanation of the proposed key management system is presented in Section 4. A performance analysis of the proposed system is discussed in Section 5. Section 6 concludes the discussion.
## Appendix A Appraisal of Significance of Acoustic Parameters in Contribution to Human Annoyance  
**[PERSON] *, [PERSON](r), [PERSON] and [PERSON]**  
Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia;  
[EMAIL_ADDRESS] (S.G.); [EMAIL_ADDRESS] (I.D.); [EMAIL_ADDRESS] (A.P.)  
* Correspondence: [EMAIL_ADDRESS]  
Received: 8 May 2020; Accepted: 28 May 2020; Published: 29 May 2020  
**Featured Application: The results of this research can serve as a basis for the further improvement of acoustic environments and making new architectural and urban planning solutions that give rise to overall acoustic comfort.**  
**Abstract:** The goal of this paper was to describe a study which aimed to determine the significance of acoustic parameters in terms of some typical audio signals which occur in common urban environments. The focus when establishing the significance was set on their relevance with respect to the annoyance of the study participants. In order to carry out this experiment, an acoustic environment recording was made in which short distracting signals were inserted into the acoustic environment background sound. The recording obtained in the described way was then reproduced to listeners in laboratory conditions. Furthermore, the experiment was envisaged in a way that the participants had the option to adjust the following acoustic parameters: the amplitude, duration and the amplitude growth rates of the short signals. In order to prove the statistical significance of the obtained results from the experiment and the study premise, the chi-square test was applied. Thus, according to the results of the study, the ranking of the objective acoustic parameters was achieved with respect to the human annoyance i.e., it can be concluded that the most common reason for the human annoyance in different acoustic environments is the sound amplitude, then its duration and finally its amplitude growth rate.  
**Keywords:** noise pollution; human annoyance; acoustic environment; amplitude; duration; amplitude growth rate
## 5 Simulation Results  
To validate the feasibility of the serial submodule reinjection circuit and assess the effectiveness of the modulation and control strategies, a 7-level RMVSC-STATCOM system model was developed using PSCAD/EMTDC. The reactive power capacity of the system is \(\pm\)10 Mvar, with a rated voltage of 10 kV. It has transformer turn ratios of 1:1 (Y/Y) and 1:\(\sqrt{3}\) (Y/D), with a leakage reactance of 10%.
## 4 Results  
At the operational level, the system architecture, as freely illustrated in the image below (Figure 3), includes the distinct users/actors and the data, services and equipment technologies involved:  
1. The repository of virtual geospatial world models, together with data and metadata that document the cultural-tourism resources represented through these models;
2. Web services and servers that make cultural content available;
3. Global positioning, video capture and internet connection technologies.  
Two distinct software components are identified: (a) the \"Mergin' Mode\" manager component concerning the authority responsible for the preparation of the cultural content which will be being served or deposited in the open repositories and (b) the \"Mergin' Mode\" end-user component (app) concerning the visitor of the site which will be perceiving the digital content (virtual world) merged with the real world in a MR.
## 3 Plasma Application to Eliminate Organic Pollutants in Water
### Common Industrial Pollutants
The outcomes have shown that considerable reductions in lead, chromium, and nickel were accomplished utilizing high-voltage corona discharges, while virtually little decrease was obtained in the case of cadmium (Cd). It is evident that plasma discharge was a successful method for removing heavy metals from wastewater. According to another study [140], it is possible to simultaneously decrease Cr(VI) to Cr(III) and oxidize As(III) to As(V) using glow discharge plasma. By increasing the voltage input from 530 V to 600 V, the conversion of As(III) and Cr(VI) can also be effectively increased from 96% to 100% and 53% to 77%, respectively. Additionally, the authors pointed out that H\({}_{2}\)O\({}_{2}\) produced in the glow discharge plasma can reduce Cr(VI) and produce highly oxidizing -OH, which is accountable for the oxidation of As(III). In turn, the reaction between As(III) and -OH prevented Cr(III) from being re-oxidized to Cr(VI) by -OH, favoring the net conversion of Cr(VI). Recently, [PERSON] et al. [141] pointed out that plasma-generated reactive species, which are O, -OH, H\({}_{2}\)O\({}_{2}\), and NOx, were promoted from As(III) to As(V) and Fe\({}_{3}\)O\({}_{4}\) to Fe\({}_{2}\)O\({}_{3}\). The plasma catalytic removal of As(III) was confirmed by the high eradication %, when compared without plasma. These investigations show that plasma may co-convert Cr(VI) and As(III) in aqueous solutions, which has great promise for global wastewater remediation.
## 4 FLAC-Based 3D Numerical Modeling
### Calculation Results  
The powerhouse is the most important structure among the underground caverns of the diversion and power generation systems, and the deformation characteristics of the rock mass greatly affect the construction processes. Figure 12 shows the cross-sectional displacement of each turbine after the excavation of the powerhouse for bench I. The rock mass primarily deformed near the vault or spandrel, and the bottom also experienced a large displacement. The maximum cross-sectional displacement for turbines #1 and #2 was approximately 3.5 mm, and that of turbines #3 and #4 was approximately 5.1 mm and 5.6 mm, respectively. The SPD9-F1 fault appeared near turbines #3 and #4, causing significant deformation in the area. In addition, under the unfavorable influence of the steeply dipping lamprophyre dyke, the calculated maximum deformation of 7 mm in the powerhouse area was located at the downstream spandrel at stake 0 + 116.80 m, as shown in Figure 13. This indicates that the risk is the greatest in this area, and microseismic  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Structural Plane** & \begin{tabular}{c} **Normal** \\ **Stiffness** \\ **(GPa/m)** \\ \end{tabular} & \begin{tabular}{c} **Shear** \\ **Stiffness** \\ **(GPa/m)** \\ \end{tabular} & \begin{tabular}{c} **Cohesion** \\ **(MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Internal** \\ **Friction Angle** \\ **(\({}^{\circ}\))** \\ \end{tabular} &
\begin{tabular}{c} **Tensile** \\ **Strength** \\ **(MPa)** \\ \end{tabular} \\ \hline SPD9-F1 & 6 & 3 & 0.05 & 20 & 0.05 \\ SPD9-F2 & 6 & 3 & 0.05 & 20 & 0.05 \\ Lamprophyre dyke & 10 & 5 & 0.1 & 25 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mechanical parameters of the structural planes.  
Figure 11: Three-dimensional numerical model: (**a**) 3D numerical model; (**b**) numerical model of three major caverns; (**c**) completion of bench I; (**d**) schematic diagram of the support.  
events are also concentrated near the steeply dipping lamprophyre dyke, indicating that the numerical simulation results are in good agreement with the microseismic monitoring results. Microseismic events accumulated in large numbers near the steeply dipping lamprophyre dyke, causing the rock mass near the steeply dipping lamprophyre dyke to be further fragmented and reduced in strength, which finally led to the collapse of the downstream spandrel on 17 May 2019. Fortunately, due to the early microseismic warning, there were no casualties, which means that microseismic monitoring can provide technical support for on-site construction, and the evolution characteristics of microseismic excavation of underground caverns with high geostress can provide a reference for on-site construction.  
Figure 12: Cross-sectional displacement of each turbine after excavation and support of bench I: (a) turbine #1; (b) turbine #2; (c) turbine #3; (d) turbine #4.
# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
## 3 Experiment and Results
### Experimental Setup
#### 3.1.2 Metrics  
The segmentation-extraction results of different transformer models can be evaluated on the basis of both subjective and objective aspects [38]. The subjective aspects include whether the segmentation-extraction results of the remote-sensing image features are complete and whether the segmentation edges of the features are clear and consistent. The objective aspect can be quantitatively calculated on the basis of the evaluation criteria, and afterward, the model classification accuracy can be assessed. The following criteria were used in this study mainly to evaluate the classification performance of the three transformer models for the data in the training data set.  
\(F1\) score:the F1 score is a metric to evaluate the model proposed on the basis of precision and recall, which explains the extent to which the true value overlaps with the predicted outcome pixels. The F1 score also serves as a summed average of precision and recall as a whole, as defined below:  
\[F1 score\ =2\cdot\frac{Recall\ \cdot\ Precision}{Recall\ +\ Precision} \tag{14}\]  
\[Recall\ =\frac{TP}{TP+FN} \tag{15}\]  
\[Precision\ =\frac{TP}{TP+FP} \tag{16}\]  
where _TP_ is a positive sample predicted by the model as a positive class, _FP_ is the negative sample predicted by the model as a positive class, and FP is the negative sample predicted by the model as a positive class.  
Overall classification accuracy (_OA_): the ratio of the number of correctly classified samples to the number of all samples can be defined as follows.  
\[OA=\frac{TP+TN}{TP+FN+FP+TN} \tag{17}\]  
_TN_ is the negative sample predicted by the model as the negative class.  
Figure 10: Proportion of each feature type in the Potsdam data set.  
Mean intersection and merge ratio (_MloU_): the MIoU is the calculation of the ratio of the intersection of two sets of true values to the merged set of predicted values, and it is a global evaluation of the image classification results, as defined below.  
\[MloU=\frac{1}{k+1}\sum_{i=0}^{k}\frac{TP}{FN+FP+TP} \tag{18}\]  
Kappa coefficient: the Kappa coefficient can be used to measure classification accuracy and also to test consistency. In practical classification problems, the Kappa coefficient is often used as an indicator to evaluate the \"bias\" of the model if the consistency between samples is poor. If \(P_{o}\) is the overall classification accuracy and if the number of real samples in each category is \(a_{1}\), \(a_{2}\),..., \(a_{c}\), the predicted number of samples in each category is \(b_{1}\), \(b_{2}\),..., \(b_{c}\), and the total number of samples is \(n\), then \(P_{e}\) is the consistency error.  
\[p_{e}=\frac{a_{1}\times b_{1}+a_{2}\times b_{2}\ldots a_{c}+b_{c}}{n\times n} \tag{19}\]  
\[kappa=\frac{p_{o}-p_{e}}{1-p_{e}} \tag{20}\]
## 4 Discussion and Conclusions  
In the off-axis PMD, the sampling points are no longer equally spaced on a regular rectangular grid due to the tilted camera, and a distorted quadrilateral geometry is generated, making it difficult to reconstruct a wavefront with higher accuracy. In this paper, the quadrilateral geometry under this perspective effect is simulated at different angles by performing ray tracing for different surfaces to acquire the reflection point coordinates. Numerical simulation demonstrates that for the reconstruction of all three different surfaces, the RBF interpolation algorithm works better than the improved Southwell algorithm, which can reach a higher accuracy than the traditional Southwell. To further verify the reconstruction capability of the RBF interpolation algorithm for this quadrilateral geometry, experimentally, the camera is tilted at different angles, and the repeatability experiments also are implemented three times at five different angles. All the experimental results show that RBF is able to reconstruct the surface shape even the camera is tilted at a large angle and with about tens of nm RMS higher reconstruction accuracy than the Southwell algorithm. Finally, the front surface shape of the KDP crystal is reconstructed using the RBF interpolation algorithm, and the reconstruction accuracy is higher compared with the reconstruction result of the improved Southwell algorithm. However, some issues, such as lens distortion, system configuration and alignments, etc., need to be considered if a significant improvement in accuracy for the RBF is experimentally pursued.  
Conceptualization, H.W. and D.L.; methodology, H.W. and D.L.; software, H.W., D.L., X.Z., R.G. and W.Z.; data curation, H.W., D.L. and X.Z.; writing--original draft preparation, H.W.; writing--review and editing, D.L., X.Z., R.G. and W.Z. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the National Natural Science Foundation of China (grant numbers: U20A20215 and 61875142) and Sichuan University (grant number: 2020 SCUNG205).  
Not applicable.  
We would like to thank [PERSON], who kindly helped us with the RBF algorithm.  
The authors declare no conflict of interest.  
This research is funded by the National Natural Science Foundation of China (grant numbers: U20A20215 and 61875142) and Sichuan University (grant number: 2020 SCUNG205).  
Figure 17: The experimental results at the angle of 13.1\({}^{\circ}\). (**a**) The result of interferometer without the Chebyshev's first three terms; (**b**) reconstruct wavefront without the Chebyshev's first three terms using Southwell; (**c**) reconstruct wavefront without the Chebyshev’s first three terms using RBF; (**d**) is the difference between (**a,b**); (**e**) is the difference between (**a,c**).
## Appendix B Features Description  
This section provides definitions for the features used in Table 3.  
_LST/SWIR2 \(\cdot\) Mean_Solar_1 week_--LST divided by SWIR2 and multiplied by 1 week mean solar radiation  
_LST/SWIR2 \(\cdot\) Solar_--LST divided by SWIR2 and multiplied by current daily solar radiation _Cum_Solar_2 week_--2 week cumulative solar radiation  
_Cum_Solar_4 days_--4 days cumulative solar radiation  
_Cum_Temperature_1 week_--1 week cumulative temperature  
_Cum_Temperature_4 days_--4 days cumulative temperature  
_Delta_T_--difference between current daily temperature and daily dew-point temperature  
_EVI \(\cdot\) DewPoint_--EVI multiplied by dew-point temperature  
_Mean_Solar_2 week_--2 week averaged solar radiation  
_Mean_Solar_4 days_--4 days averaged solar radiation  
_Mean_Solar_4 week_--4 week averaged solar radiation  
_Mean_Temperature_1 week_--1 week averaged temperature  
_NDVI \(\cdot\) Cum_Solar_3 week_--NDVI multiplied by 3 week cumulative solar radiation NDVI - Mean_Solar_3 week_--NDVI multiplied by 3 week averaged solar radiation  
NDVI - Mean_Solar_4 week_--NDVI multiplied by 4 week averaged solar radiation  
NDVI - Solar--NDVI multiplied by current daily solar radiation  
TCG - Cum_Solar_3 week_--TCG multiplied by 3 week cumulative solar radiation  
TCG - Cum_Solar_4 days_--TCG multiplied by 4 days cumulative solar radiation  
TCG - DewPoint--TCG multiplied by dew-point temperature  
TCG - Mean_Temperature_2 week_--TCG multiplied by 2 week averaged temperature  
TCG - Solar--TCG multiplied by current daily solar radiation  
_blue - SWIR--blue multiplied by SWIR_  
_blue/green_--blue divided by green  
_blue/red_--blue divided by red  
_blue/NIR--blue divided by NIR_  
_blue/SWIR2--blue divided by SWIR2_  
_green red--green multiplied by red_  
_green/red_--green divided by red  
_green/NIR--green divided by NIR_  
_red/blue_--red divided by blue  
_red/green_--red divided by green  
_red/NIR--red divided by NIR_  
_red/SWIR2_--_Cum_Solar_2 week_--red divided by SWIR2 and multiplied by 2 week cumulative solar radiation  
_NIR - SWIR--NIR multiplied by SWIRIR_  
_NIR/LST_--NIR divided by LST  
_NIR/LST_--_Solar_--NIR divided by LST and multiplied by current daily solar radiation  
_NIR/green_--NIR divided by green  
_NIR/SWIR--NIR divided by SWIR  
_NIR/SWIR2_--NIR divided by SWIR2_  
_NIR/SWIR2_--_Mean_Solar_2 week_--NIR divided by SWIR2 and multiplied by 2 week averaged solar radiation  
_NIR/SWIR2_--_Solar_--NIR divided by SWIR2 and multiplied by current daily solar radiation  
_NIR - Cum_Solar_3 week_--NIR multiplied by 3 week cumulative solar radiation  
_NIR - Solar--NIR multiplied by current daily solar radiation  
_SWIR/green_--SWIR divided by green  
_SWIR/red_--SWIR divided by red  
_SWIR/NIR_--SWIR divided by NIR  
_SWIR/NIR_--_Cum_Temperature_over_5_4 week_--SWIR divided by NIR and multiplied by 4 week cumulative temperature over 5 Celsius degree  
_SWIR2/blue_--SWIR2 divided by blue  
_SWIR2/green_--SWIR2 divided by green  
_SWIR2/NIR_--SWIR2 divided by NIR  
_SWIR2/SWIR_--SWIR2 divided by SWIR