esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Definitions  
The Dianzhong Water Diversion Project is the largest water diversion project under construction in China. It is located in the Yunnan province, China, and passes through Lijiang city, the Dali Prefecture, the Chuxiong Prefecture, Kunming city, Yuxi city, and the Honghe Prefecture. The total length of the project is about 664 km, more than 90% of which is tunnel engineering. What is noticeable is that multiple intervals of tunnel engineering in Kunming city are in soft soils, significantly near a rock mountain, as shown in Figure 1, and the burial depth of the tunnel is generally small (tens of meters). In other words, the tunnel might pass through a plain soft-soil site first, then a soft-soil site near a rock mountain, and, subsequently, a plain soft-soil site again, which leads to the fact that the longitudinal non-uniform subsurface geometry along the tunnel, induced by a local rock mountain, may possess a considerable influence on the seismic response of said tunnel. The existence of a local one-sided rock mountain near the tunnel may affect the propagation of incident seismic waves and, thus, brings about differences in the seismic responses between tunnels with and without adjacent rock mountains. The mechanism and regularity of this influence are studied in this paper, where different incidence directions and angles of SV-waves have been considered, as shown in Figure 2.  
Figure 1: The spatial location relationship between the tunnel and the mountain for the Kunming interval of the Dianzhong Water Diversion Project.  
Figure 2: Schematic diagram for the local one-sided mountain–plain site tunnel model: (**a**) left side incidence; and (**b**) right side incidence.  
In summary, the influence of subsurface geometry variations along a tunnel's path on the seismic response and damage characteristics of said tunnel are the main focus of this paper. It is hereby noted that the original and practical subsurface geometry was not completely reproduced in the numerical model in this paper, because the local elevation and site information was classified, and, thus, the authors could not attain or publish the corresponding detailed information at present. Therefore, simplified models were established instead.
# MBSE Testbed for Rapid, Cost-Effective Prototyping and Evaluation of System Modeling Approaches
## 2 Materials and Methods  
The testbed encompasses the following: software programming environment; multiple modeling methods; analysis and optimization algorithms; repositories packages and libraries; simulation environments; and hardware components and connectors. The following list presents the core components of the testbed.  
**Software Programming Environment**  
* Operating system(s): Linux (Ubuntu 16.04) and Windows
* Programming environment: Spyder cross-platform development environment for Python 3.x programming language  
**Systems Modeling Methods** (descriptive, analytic; deterministic, probabilistic)
* Systems Modeling Language (SysML) [6]
* Decision trees
* Hidden Markov Models (HMM) [7]
* Partially Observable Markov Decision Process (POMDP) model [8]  
**Optimization, Control, and Learning Algorithms**  
* Optimization using fitness functions
* N-step Look-ahead decision-processing algorithm
* Traditional deterministic control algorithm (e.g., proportional-integral-derivative controller (PID) algorithm)
* Q-learning algorithm  
**Repositories, Packages, Libraries**  
* NumPy--a Python library for manipulating large, multidimensional arrays and matrices
* Pandas--a Python library for data manipulation and analysis, specifically numerical tables, and time series
* Scikit-learn--a machine-learning library for Python; built on top of NumPy, SciPy and matplotlib
* Python-open CV--a library of Python bindings designed to solve computer vision problems  
**Simulation Platforms**  
* _Hardware-Software Integration Infrastructure:_ Multiple simulation platforms are used for visualization, experimentation, and data collection from scenario simulations for both ground-based and airborne systems; integrate simulations (implemented in Python 3) with hardware platforms, such as Donkey Car [4] and quadcopters.
* _CARLA Simulation Platform:_ CARLA offers an open-source high-fidelity simulator (including code and protocols) for autonomous driving research [5]. CARLA provides open digital assets, such as urban layouts, buildings, city maps, and vehicles. CARLA also supports flexible specification of sensor suites, environmental conditions, and dynamic actors. CARLA provides Python APIs to facilitate integration.
* _DroneKit Platform:_ DroneKit, an open-source platform, is used to create apps, models, and algorithms that run on onboard computers installed on quadcopters. This platform provides various Python APIs that allows for experimenting with simulated quadcopters and drones. The code can be accessed on GitHub [9].  
Hardware and Connectors
* Donkey Car platform: an open-source platform for conducting autonomous vehicles research
* Raspberry Pi (onboard computer): on the Donkey Car
* Quadcopters (very small UAVs used in surveillance missions), 1/16 scale robot cars
* Video Cameras: mounted on Donkey Car and quadcopters
* Socket communication: used to send commands from a computer to a Donkey Car or a quadcopter
## 1 Introduction
For a large and complex CPS, it is of great significance to clarify the fault identification and the correlation between the fault factors to master the influence degree of the CPS fault and create a maintenance plan for different parts. At present, the classifications of CPS faults are not comprehensive, the fault statistical sample data are sparse, and there are no research results on the influencing factors of a production system fault. Therefore, determining how to identify the key influencing factors of a CPS fault, take targeted protection or preventive measures, prevent and reduce the occurrence of faults effectively, improve the utilization efficiency of production systems, and ensure the construction progress and construction quality of concrete dams is important. Based on the literature reviews and actual engineering CPS fault statistics of Shatuo hydropower station, and considering mechanical structure characteristics and operation characteristics, this study proposes a fault classification system for a concrete dam production system and summarizes the root causes of faults in CPSs by taking a concrete dam production system as the research object. In addition, a fault identification model of the CPS is established, a correlation analysis of the fault statistical sample data is carried out, and the key influencing factors of the CPS fault are determined. The research results of the case study show that the service life factors of mechanical components have the greatest impact on batching metering system faults and mixer faults. Environmental factors have the greatest impact on material delivery system faults and pneumatic system faults. The main contribution of the study is the realization of fault pattern recognition of CPSs in dam engineering, and it provides a guiding reference for targeted fault handling. The remainder of the paper is organized as follows. Section 2 introduces the component and process flow of a CPS in dam engineering. Section 3 presents the fault classification results for the CPS. Section 4 presents a fault identification model of the CPS based on the grey relational analysis method. Section 5 introduces the application results of the proposed model to a concrete dam project.
# Optimized Score Level Fusion for Multi-Instance Finger Vein Recognition
###### Abstract  
The finger vein recognition system uses blood vessels inside the finger of an individual for identity verification. The public is in favor of a finger vein recognition system over conventional passwords or ID cards as the biometric technology is harder to forge, misplace, and share. In this study, the histogram of oriented gradients (HOG) features, which are robust against changes in illumination and position, are extracted from the finger vein for personal recognition. To further increase the amount of information that can be used for recognition, different instances of the finger vein, ranging from the index, middle, and ring finger are combined to form a multi-instance finger vein representation. This fusion approach is preferred since it can be performed without requiring additional sensors or feature extractors. To combine different instances of finger vein effectively, score level fusion is adopted to allow greater compatibility among the wide range of matches. Towards this end, two methods are proposed: Bayesian optimized support vector machine (SVM) score fusion (BSSF) and Bayesian optimized SVM based fusion (BSBF). The fusion results are incrementally improved by optimizing the hyperparameters of the HOG feature, SVM matcher, and the weighted sum of score level fusion using the Bayesian optimization approach. This is considered a kind of knowledge-based approach that takes into account the previous optimization attempts or trials to determine the next optimization trial, making it an efficient optimizer. By using stratified cross-validation in the training process, the proposed method is able to achieve the lowest EER of 0.48% and 0.22% for the SDUMLA-HMT dataset and UTFVP dataset, respectively.  
multi-instance finger vein biometrics; histogram of oriented gradients; score level fusion; Bayesian hyperparameter optimization
# Compression and Fungal Heat Production in Maize Bulk Considering Kernel Breakage
###### Abstract  
Breakage in maize kernels and vertical pressure in grains lead to the uneven distribution of grain bulk density, which easily causes undesired problems in terms of grain storage. The objective of this study was, therefore, to determine the compression and heat production of the whole kernel (WK) and half kernel (HK) under two different loadings, i.e., 50 and 150 kPa, in maize bulk. An easy-to-use element testing system was developed by modification of an oedometer, and an empirical-analytical-numerical method was established to evaluate fungal heat production, considering kernel breakage and vertical pressure. Based on the experimental results, it was found that breakage induced larger compression, the compression of HK was 62% and 58% higher than that of WK at 50 kPa and 150 kPa, respectively. The creep model of the Hooke spring-Kelvin model in series can be used to accurately describe the creep behavior of maize bulk. Fungi and aerobic plate counting (APC) were affected significantly by the breakage and vertical pressure. APC in HK was 19 and 15 times that of WK under 150 and 50 kPa, respectively. The heat released by the development of fungi was found to be directly related to the APC results. The average temperatures of WK and HK under 150 and 50 kPa were 11.1%, 9.7%, 7.9%, and 7.6% higher than the room temperature, respectively. A numerical method was established to simulate the temperature increase due to fungi development. Based on the numerical results, heat production (Q) by fungi was estimated, and the results showed that the Q in HK was 1.29 and 1.32 times that of WK on average under 150 and 50 kPa. Additionally, the heat production results agreed very well with the APC results.  
maize bulk; kernel breakage; vertical pressure; deformation; heat production +
Footnote †: journal: Applied Sciences
# A Technical Survey on Delay Defects in Nanoscale Digital VLSI Circuits
## 11 Commercially Available EDA Tools
### Synopsys  
Synopsys offered a test solution through the TestMAX product [88]. It supports delay fault testing through TDF ATPG, PDF ATPG, slack-based transition fault ATPG, N-detect ATPG, and CA ATPG. The IC Compiler II [89] is a place and route tool through which we can extract the timing information in the SDF format. The StarRC [90] extracts the parasitic information after the placement and routing. The PrimeTime [91] is an STA tool that uses the timing and parasitics information to generate the pin slack and critical paths. The slack-based TDF ATPG [88] uses pin slack information to propagate the effect of the fault through the longest possible path. But the disadvantage here is that the tool will first try to propagate the fault through the longest path possible. If the longest path is untestable, it does not guarantee to propagate the fault through the second-longest path. It will propagate through any available testable path. Hence, this does not guarantee the detection of all the SDDs through TA mode.  
The TestMAX CTMGen [88] utility creates the cell test model (CTM) for CA testing by using the simulation results of fault-free and faulty transistor netlist obtained from HSPICE [92]. The CA ATPG uses the CTM to generate the test patterns. Moreover, the Design for Testability (DFT) compiler supports the insertion of OCC circuitry. The OCC is compatible with the TestMAX and can be used for selecting appropriate capture and launch clocks for at-speed testing or FAST.
## 1 Introduction
### Objective  
The objective of this study was the development of a dual robust stochastic fuzzy optimization--energy and environmental systems (DRSFO-EES) model for planning EES while considering the traffic sector. This study is the first attempt at planning an EES while considering the traffic sector by integrating TSP, FPP, RTSO, and RFPP into a single framework. The proposed model can effectively tackle stochastic and fuzzy uncertainties as well as their combinations, capture associated risks from fuzzy and stochastic uncertainties, and thoroughly analyze trade-offs between system costs and reliability. The proposed DRSFO-EES model was applied to the Beijing-Tianjin-Hebei (BTH) region in China, which experiences severe smog and haze associated with high concentrations of air pollutants. The following is a detailed enumeration of the capabilities of the proposed model: (i) exploration of the impacts of different vehicle policies (such as regarding EVs usage, EV's power source, and vehicular emission standards) on vehicular emission mitigation via scenario analysis; (ii) generation of robust optimized solutions for energy allocation, coking processing, oil refining, heat processing, electricity generation, electricity power expansion, electricity importation, energy production, as well as emission mitigation under multiple uncertainties; (iii) identification of the study regional atmospheric pollution contributions of different energy activities such as coke processing, heat processing, oil refining, electricity generation, and motor vehicle operation.
###### Abstract
However, the image co-registration of lung perfusion information with CT scan images may be useful during the RT treatment planning. A prospective study showed that the functional dose-volume-normalized SPECT/CT-weighted parameters (functional MLD, V20 and V30) are more reliable to evaluate the risk of RIP than the commonly used standard dose-volume parameters based on simulation CT scan [9]. Lung perfusion images, using SPECT/CT, provide information about the different lung perfusions showing a concrete gradient. Different functional lung regions could be integrated into the global information available in the treatment planning process. Furthermore, this information may be used to optimize radiation treatment plans in terms of dose painting in order to preserve the best perfused lung regions without compromising target coverage and respecting dose constraints for organs at risk. Several studies in the literature showed that 8-10% reduction in V20/V30 significantly reduces the risk of RIP using SPECT/CT during the planning workflow [10; 11]. The exploratory nature of this descriptive analysis investigated the potential impact of the SPECT-CT scan on the RT treatment plan workflow for LA-NSCLC. The aim of this dosimetric study is to set the basis for a pilot and prospective clinical trial to evaluate the integration of SPECT data into treatment planning plans to more accurately estimate the risk of RIP.
## 8 Toward a Theoretical Framework
### Trust in the IoHE  
Researchers use trust in the IoHE as a conventional trust framework to discuss security in cell networks as the important anchor of monitoring device behaviors, identifying devices, connection protocols, and the related procedures to devices. Security measures at the device level could be adopted to enhance security. At the network level, security could be improved by using point-to-point encryption techniques based on cryptography algorithms, message integrity verification strategies, and trusted routing mechanisms. Security measures to prevent data security and privacy are required to be adopted at the cloud level, and suitable training concerning awareness is needed at a human level.  
Researchers proffered the following to attain trust in the IoHE: (i) design of a trust framework to be included within the development of any IoHE entity; (ii) advanced dynamic protocol for trust management which enables IoHE systems to deal with misbehaving nodes whose status or conduct might change dynamically; (iii) offered an extensible trust model that was seamlessly integrated into the IoHE ontology and focused on the IoHE-trust modeling, an ontology for fuzzy semantics reusing existing trust models; (iv) designed meta-models for contractors by defining privacy and quality of context conventions independently of those of the users and the creators for the independent management of privacy in the IoHE; and (v) established a proper trust-management control mechanism primarily based on the architecture modeling of the IoHE.  
However, the dearth of consideration of trust in relation to the quality of experience (QoE) is also seen as a shortcoming and is one of the significant challenges concerning trust in the IoT establishment of remote IoHE devices.  
Devices on the Internet must be trusted to ensure privacy and security. Trust management is important for IoHE devices and applications to provide security and privacy of data. Hackers could connect devices to manipulate data in the IoHE applications. Data collection trust is a serious issue because a massive amount of data is collected from devices and are utilized by IoHE applications, which need make the right decisions about patients to improve the quality of healthcare.
# High-Fold 3D Gaussian Splitting Model Pruning Method Assisted by Opacity
###### Abstract  
Recent advancements in 3D scene representation have underscored the potential of Neural Radiance Fields (NeRFs) for producing high-fidelity renderings of complex scenes. However, NeRFs are hindered by the significant computational burden of volumetric rendering. To address this, 3D Gaussian Splatting (3 DGS) has emerged as an efficient alternative, utilizing Gaussian-based representations and rasterization techniques to achieve faster rendering speeds without sacrificing image quality. Despite these advantages, the large number of Gaussian points and associated internal parameters result in high storage demands. To address this challenge, we propose a pruning strategy applied during the Gaussian densification and pruning phases. Our approach integrates learnable Gaussian masks with a contribution-based pruning mechanism, further enhanced by an opacity update strategy to facilitate the pruning process. This method effectively eliminates redundant Gaussian points and those with minimal contributions to scene construction. Additionally, during the Gaussian parameter compression phase, we employ a combination of teacher-student models and vector quantization to compress the spherical harmonic coefficients. Extensive experimental results demonstrate that our approach reduces the storage requirements of original 3D Gaussian models by over 30 times, with only a minor degradation in rendering quality.  
3D gaussian splatting; novel view synthesis; 3D compression Article
## 2 System Identification Technique by Parameter Estimator
### System Identification Improvement Using Null-Space Solution  
In the least-squares method-based system identification technique, the estimation of terms of the \(X\) depends on the relative size of each term of the multiplied \(N\). To adjust each term of the \(N\), weights are added to the dynamic equation of the loading device. Equations (9) and (10) are the dynamic equation added by the weights.  
\[\tilde{\varnothing}=X_{1}\left\{w_{1}+\sin(\varnothing)\ \right\}+X_{2}\left\{\ w_{2}+\cos( \varnothing)\ \right\}+X_{3}\{\ w_{3}+D_{m}\left(\dot{\theta}-\tilde{\varnothing}\right)+ \mathrm{T}_{m}\}-X_{1}\ w_{1}-X_{2}\ w_{2}-X_{3}\ w_{3} \tag{9}\]  
\[\tilde{\varnothing}=\left[\ w_{1}+\sin(\varnothing)\quad w_{2}+\cos(\varnothing )\quad w_{3}+D_{m}\left(\dot{\theta}-\dot{\varnothing}\right)+\mathrm{T}_{m} \quad-w_{1}\quad-w_{2}\quad-w_{3}\right]\begin{bmatrix}X_{1}\\ X_{2}\\ X_{3}\\ X_{1}\\ X_{2}\\ X_{3}\end{bmatrix}={N^{*}}^{T}\,X^{*} \tag{10}\]  
The \(w_{1},\ w_{2}\) and \(w_{3}\) are the weights, the \(N^{*}\) is the known parameter vector, and the \(X^{*}\) is the unknown parameter vector. Equation (11) is the desired solution of the \(\Delta X^{*}(k)\) by adding a null-space solution to Equation (8). Equation (8) is the final equation of estimated \(X\) in system identification process and it is used to optimize the controller in gain scheduling. Equations (12) and (13) is derived from Equation (8).  
\[\Delta X^{*}(k)=F^{+}\ Z+\left(\mathrm{I}-F^{+}F\right)\,V=Q+R\times\mathrm{V} \tag{11}\]  
\[F=\left[N^{*}(k)\ N^{*}(k)^{T}\right] \tag{12}\]  
\[Z=[\sum_{i=1}^{k}\{N^{*}(i)\ \tilde{\varnothing}_{\mathrm{real}}(i)-N^{*}(i)N^{*} (i)^{T}\ X^{*}_{\mathrm{empty}}\}-\sum_{i=1}^{k-1}\left\{N^{*}(i)N^{*}(i)^{T} \,\Delta X^{*}(i)\right\}\ ] \tag{13}\]  
\((\mathrm{I}-F^{+}F)\ V\) in Equation (11) is a null-space solution. Since the weights of Equation (10) should not affect the system of the loading device, they should be offset. The offsetting of the weights is set as a constraint to specify the null-space solution. The constraint is Equation (14). The \(V\) of Equation (11) is derived as Equation (15) using this constraint. Then, the null-space solution is calculated by multiplying Equation (15) and the three rows of the \(R\) in Equation (11).  
\[X^{*}(1)=X^{*}(4)\,X^{*}(2)=X^{*}(5)\,\ X^{*}(3)=Y^{*}(6) \tag{14}\]  
\[[p]=Q_{p},\ \ \ R[p,q]=R_{pq}(\,p,\ q=1\ \sim 6)\]  
\[V=\begin{bmatrix}R_{11}-R_{41}&R_{12}-R_{42}&R_{13}-R_{43}\\ R_{21}-R_{51}&R_{22}-R_{52}&R_{23}-R_{53}\\ R_{31}-R_{61}&R_{32}-R_{62}&R_{33}-R_{63}\end{bmatrix}^{+}\begin{bmatrix}Q_{ 4}-Q_{1}\\ Q_{5}-Q_{2}\\ Q_{6}-Q_{3}\end{bmatrix} \tag{15}\]  
The system identification technique using a null-space solution can control the \(X^{*}\) by adjusting the relative size of each term of the \(N\) with the \(w_{1},\ w_{2}\), and \(w_{3}\). And the accuracy of the \(X_{\mathrm{estimated}}\) can be improved.