esa-sceva/satcom-chunk-collection huggingface dataset:
# No-Reference Blurred Image Quality Assessment by Structural Similarity Index
## 2 Related Works
### Distortion-Specific NR IQA Algorithms  
Distortion-specific NR IQA algorithms assume that the distortion medium is known. Popular blur IQA algorithms model edge spreads and relate these spreads to perceived quality. [PERSON] [5] proposes a blur IQA model by using singular value decomposition (SVD) to evaluate image structural similarity. [PERSON] [6] computes sharpness using the average 2D kurtosis of the 8 \(\times\) 8 DCT blocks and spatial edge extent information. [PERSON] [2] evaluates image quality by Just Notice Blur (JNB). [PERSON] [7] presents an NR IQA method based on continuous wavelet transform. Similarly, the general approach to NR JPEG IQA is to measure edge strength at block boundaries and relates this strength as well as possibly some measure of image activity to perceived quality. [PERSON] [8] measures the visual impact of ringing artifacts for JPEG images. [PERSON] [9] detects the low-amplitude edges that result from blocking and estimating the edge amplitudes. [PERSON] [10] evaluates image quality by designing a computationally inexpensive and memory-efficient feature extraction method and estimating the activity of the image signal. JPEG2000 ringing artifacts in an image are normally modeled by measuring edge-spread using an edge-detection-based approach. For example, [PERSON] [11] computes simple features in the spatial domain, [PERSON] [12] assesses image quality by natural scene statistics (NSS) models, and [PERSON] [13] calculates edge width by finding the start and end positions of the edge of each corresponding edge in the processed image.
## 2 Materials and Methods
### Sample Collection and Laboratory Analysis  
A 30-cm long sediment core was collected by a Uwitec gravity sampler (Uwitec, Austria) in the North Aral Sea in June 2018, and the sampling location is shown in Figure 1 (NAS-01; 60\({}^{\circ}\)57\({}^{\prime}\)37.87\({}^{\prime\prime}\) E and 46\({}^{\circ}\)19\({}^{\prime}\)31.12\({}^{\prime\prime}\) N). Sub-samples were taken at 1-cm intervals on site, sealed, and stored in plastic bags for dating and analysis of the environmental proxy indicators. The specific activity of 137 Cs was used to determine the age of the sedimentary strata, the activity was measured, and the depth-age model was based on the former published literature [16].  
The total organic carbon (TOC) and total nitrogen (TN) were determined using a CE-440 element analyzer (Exerter Analytical Inc., North Chelmsford, MA, USA) with a relative error (RE) of \(<\)2% [39]. The determination and analysis of organic carbon isotopes (8\({}^{13}\)C\({}_{\rm{org}}\)) was achieved using a MAT-251 isotope mass spectrometer (Thermo Finnigan, Bremen, Germany) (relative error \(<\) 0.05%, expressed in per mil (%) relative to Vienna Peedee belemnite), and the experiments were performed at the State Key Laboratory of Lakes and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences.  
Figure 2: (**a**) Temperature change of the Aral Sea Basin from 1950 to 2018; (**b**) the annual average precipitation variations of the Aral Sea Basin. The blue lines demonstrate the five-year moving average (5a MA) and the dashed lines for the linear fitting. Note: This figure is original and the data used for Figure 2 were extracted from the CRU datasets [38].  
The organic carbon burial rate (OCBR; g\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)) of the Araal Sea was calculated from the TOC content (%) in the sediments, the deposition rate SR (m\(\cdot\)a\({}^{-1}\)), and the dry matter bulk density (DBD) (g\(\cdot\)m\({}^{-3}\)). The calculation formula is as follows:  
\[\text{OCBR}=\text{TOC}\times\text{SR}\times\text{DBD} \tag{1}\]  
\[\text{DBD}=\text{M}_{\text{d}}/\text{V} \tag{2}\]  
where the sedimentation rate (SR) was calculated from the sediment depth-age model, and the dry matter bulk density (DBD) was derived from the ratio of the dry weight (M\({}_{\text{d}}\)) of the sediment sample to the dry weight volume (V).
# Design and Optimization of Lattice Structures: A Review
## 5 Summary and Overview
Because additive manufacturing technology adopts the manufacturing method of material layer printing, different materials can be used to manufacture lattice structure in order to meet the demand of using performance. For example, with the wide application of composite materials, a variety of composite materials can be mixed for designing lattice structures. 2. Multi-structure lattice structures. At present, the research of Poisson's ratio structure has changed from uniform Poisson's ratio to non-uniform Poisson's ratio. From the former positive Poisson's ratio structure to the present negative Poisson's ratio structure, the design and optimization of multiple structures in lattice structures are bound to be the future research trend. 3. Multi-method to design the lattice structures. With the development of CAD technology and programming language, the design of lattice structures in the future will rely heavily on computer technology to develop lattice structure databases with different topology of unit cells. At the same time, equipped with detailed performance parameters using standards for various industries. 4. Micro-scale lattice structures. Because of different design methods and fabricating methods (such as additive manufacturing technology) of lattice structures, the scales of lattice structures are divided into macro size (\(>\)0.5 mm), micro size (0.1 mm-5 mm), micro size (micron level: 100 nm-100 um; nano level: \(<\)100 nm) [74; 207]. Micro-nano scale lattice structures can be realized in the future, and will be a hot spot in research field. In brief, lattice structures will become a kind of material with special properties, which combines multi-material, multi-structure, multi-method, and micro-scale. In the future work, scientists should establish a huge lattice structure database, which contains various forms of unit cell topology, and has performance standards for researchers to choose. [PERSON] searched and classified the literature, wrote the original draft preparation, and wrote the manuscript; [PERSON] edited and polished the manuscript, made the project administration and funding acquisition; [PERSON] made the project administration. All authors have read and agreed to the published version of the manuscript. This work was supported by a grant from the \"General program of Natural Science Foundation of Guangdong Province\" (NO. 2019A1515011964). The authors would like to thank the School of Mechanical Engineering of Beijing Institute of Technology (BIT), and Institute of Engineering Medicine of Beijing Institute of Technology (BIT). The authors declare that they have no competing/financial conflict of interests in this paper. Appendix AFigure A5: (a) Body centered cubic (BCC); (b) reinforced body centered cubic (RBCC) [147]. Figure A7. (a) Cubic. ; (b) G7; (c) Rhombic dodecahedron [150].
## 3 Results and Analysis
### Finite-Element Dynamic Characteristics Analysis  
According to the drawings of the structure, SAP2000 was used to analyze the dynamic characteristics of the structural floor before and after vibration reduction. Calculation results were analyzed according to the three-dimensional spatial structure, and the structure was modeled using SAP2000 as shown in Figure 6. The used material properties were according to the specification [24], and the damping ratio of the structure was taken to be 1.22% (see in Table 2). According to Technical Standard for Vibration Comfort of Building Floor Structures JGJ/T441-2019 [13], the elastic modulus of reinforced concrete floor slabs was enlarged by 1.2 times. The equivalent uniform live load of people in the stands was 1.5 kN/m\({}^{2}\). When performing structural modal analysis, the quality source was selected: 1 time (constant load) + 1 time (live load at the stand) + 0.5 times (live load in other areas). Since the grandstand structure was not fully completed during the test, there was no decoration surface, no seats, walkways, and low live load. Therefore, the load conditions of the original calculation model were modified according to actual conditions.  
Due to the complex structure, the natural vibration period of the structure is very dense. The 100-order mode shape was selected to meet the requirements of the specification for vertical mass parameter coefficients. Table 3 shows the period of the first 30 modes and their participating masses. Because the problem that affected the comfort of the structure was the cantilever part of the stand, by observing the shape of each order, the vibration mode with the larger vertical vibration of the 4 th order cantilever part is selected. The natural frequencies were 3.056, 3.470, 3.747, and 4.215 Hz, and the local mode diagrams are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measuring** & **Damping** & **Measuring** & **Damping** & **Measuring** & **Damping** \\
**Point** & **Ratio (\%)** & **Point** & **Ratio (\%)** & **Point** & **Ratio (\%)** \\ \hline
1 & 0.73 & 9 & 1.69 & 17 & 0.98 \\
2 & 1.25 & 10 & 2.92 & 18 & 1.23 \\
3 & 0.83 & 11 & 1.42 & 19 & 0.75 \\
4 & 1.59 & 12 & 1.63 & 20 & 1.40 \\
5 & 0.79 & 13 & 0.96 & 21 & 1.41 \\
6 & 1.94 & 14 & 1.40 & 22 & 0.98 \\
7 & 0.29 & 15 & 1.74 & 23 & 0.83 \\
8 & 1.31 & 16 & 1.17 & - & - \\ average & 1.22 & & & & \\ value & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Damping test results.  
Figure 6: Structure model diagram.  
edge of measurement area 1; the 20 th order was the wave-shaped local vibration at the edge of measurement area 2; in the 28 th order, wave-shaped vibration occurred in both measurement areas.  
We analyzed the modal assurance criteria (MAC) of the finite-element model-calculated mode shape to check the similarity of each order mode shape. Table 4 and Figure 8, show that, in the estimation of the mode vector of different modes, the MAC is close to 0, indicating that the two are mutually independent and orthogonal, and the modal identification results of each order were clear and reliable.
# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 2 Dynamic Modeling  
Dynamic modeling, known as _simulation modeling_, is described mainly using mathematical models. [PERSON] et al. [9] assume that dynamic models (DM) are defined using a set of rules. These rules take the current states as inputs and study how the modeled systems change over time. In this subsection, a taxonomy of the main dynamic approaches applied to complex systems will be shown. [PERSON] et al. [10] describe DM as a relationship providing the next state of the studied system based on its present state. [PERSON] and [PERSON] [11] categorized the model variables as follows:  
* Non-probabilistic/Deterministic models that use static crisp parameters. They are decomposed into two categories: (1) Single objective models and (2) multi-objective models;
* Probabilistic/Stochastic models that include unknown or random parameters [12; 13] where Markov models can be used to model such stochastic events. Those approaches can be further classified as (1) the optimal control theory and (2) the dynamic programming;
* Hybrid models have mixed elements from both the deterministic and the stochastic models. Hybrid models include both the simulation aspects and the inventories theory to cover crisp and uncertain parameters;[PERSON] [14] and [PERSON] [15] have classified the models based on the used tools (such as economic, analytical, simulation, and organizational approaches). One of the important strengths of dynamical modeling is the ability to illustrate the temporal aspect throughout the simulation. Three modeling approaches of the temporal aspect could be observed in Figure 1: Random Number Models, Continuous Time Models, and Discrete Event Models. Hybrid modeling illustrates the combination of those approaches.
# Cryogenic System for FTIR Analysis of Hydrocarbon Fuels at Low Temperature and Atmospheric Pressure
## 5 Conclusions  
The idea for this work sprang from the desire to obtain new fundamental data about the low-temperature properties and behavior of hydrocarbons under non-vacuum conditions. For this purpose, we used modified cryogenic DRIFT spectroscopy based on a standard diffuse reflection attachment of a Fourier spectrometer, as a result of which we acquired a cryogenic setup, connected to the spectrometer. This cryogenic setup is a device capable of cooling hydrocarbon substances at atmospheric pressure and in an IR-inactive environment, subsequently allowing us to obtain a mid FTIR spectra of the samples. This study shows the behavior and properties of hydrocarbons, or more precisely--methanol, kerosene and ethanol--by band strengths of their characteristic vibrations' specific wavenumbers. All spectral data are confirmed by comparison with the results of other authors studying hydrocarbon substances in this field and showed good correlation. The cryogenic setup allowing precise temperature control and a thorough measurement technique involving FTIR spectroscopy demonstrates effective operability in obtaining hydrocarbon spectral data.  
Thus, the conducted study lays the foundation for subsequent fundamental and applied work on cryo-spectroscopy of hydrocarbon materials, offering both confirmation of theoretical ideas about the vibrational properties of cooled fuels and a tool for solving practical problems of the fuel and energy complex.
# An Agent-Based Simulation of How Promotion Biases Impact Corporate Gender Diversity
## 3 The Simulation
### Core Elements of Our Simulation  
The simulation we developed for the present study is based on simple assumptions about the operation of a typical company. Figure 1 is a screenshot of the simulation, which was developed with the NetLogo simulation platform [47]. Employees fall into one of four increasing ranks: entry-level employees, managers, VPs and executives. Because we are exploring gender biases, in our simulated company there are two types of employees: men (in blue--or the darker shade if viewed in grayscale) and women (in yellow--lighter shade). Our simulation assumes that men and women have identical abilities and that their performance is also identical (Note that although many other personal traits could easily be added, for the present study we wanted to focus exclusively on how gender biases in promotions and hiring impact the overall behavior of a company. However, this is not meant to suggest that gender should be seen as binary, nor that we suggest that the experiences of all those who do not identify as men are identical).  
At the start of each simulation \"run,\" employees are distributed across ranks in a way that mimics a typical company, with smaller numbers of employees at higher ranks. Unless otherwise stated, the simulation starts with a 50-50 gender split at each level.  
The company is assumed to grow over time, creating vacancies within each rank. In addition, employees may be promoted or may leave the company, creating additional  
Figure 1: Screenshot of the simulation, see text.  
vacancies. Vacancies at the entry level are filled through hiring from a hypothetical external pool of candidates, while vacancies at all other ranks are filled by promoting individuals from the rank immediately below.  
For this version of the simulation, we do not simulate direct hiring into higher ranks, nor do we allow promotions to skip levels. However, these and other details could be added if we were interested in exploring the effects of such modifications.  
While the simulation runs, the employees move back-and-forth across the floor of their rank as time elapses (this movement is not relevant to the results, but it helps to visualize the activities unfolding over time). Employees who separate from the company rise just above the other employees, turn gray and gradually fade. Employees who are promoted are seen floating up to the next level. These animations help to visualize the main events that occur to the simulated employees during each run.  
In addition, to help visualize the gender balance for each employee rank, the floor of each level of the simulation acts as a simple histogram, showing the percentage of men and women at that rank. The two vertical bars seen in Figure 1 on the left side of the screen show the overall gender balance across the entire organization.  
The company's overall gender balance is an emergent behavior that results from three main HR activities which take place as the company grows over time: hiring, promotion and separation. We now describe each of these processes.
# Adaptive Leakage Protection for Low-Voltage Distribution Systems Based on SSA-BP Neural Network
## 1 Introduction
To solve those problems, this paper proposes an adaptive leakage protection method that uses the SSA-BP neural network to predict the normal leakage current of the line and adjust the protection action threshold in advance. By setting multi-level action gears, the method selects the action threshold based on the predicted leakage after a specific period of time. The switching of gears effectively solves the problem when the normal leakage current changes greatly, and meets the dynamic demand of leakage protection. This research is not only applied to household leakage protection, but can also be used for microgrid leakage prediction [22; 23; 24]. The method's concepts can also be used in short-term prediction fields such as short-term wind power prediction and advance perception of energy storage charge and discharge [25]. The rest of this article is organized as follows. Section 2 analyzes the generation mechanism of normal leakage current. The framework of adaptive leakage protection, construction and training of the prediction model are introduced in Section 3. In Section 4, adaptive leakage protection strategies are introduced in detail. The experimental results and the comparative study are presented in Section 5. This article concludes with a summary in Section 6.
## 6 Utilizing MRSO for Solving Engineering Design Issues
### Performance Comparison of MRSO with SCA, LCA, TSA, and DOA on Seven Engineering Design Problems
In the Welded Beam and Tire Design, the MRSO also achieved the best ranking, further highlighting its robustness. However, in Pressure Vessel Design and Gear Train Design, the MRSO ranked second, being narrowly outperformed by the DOA in both cases. In String Design, the MRSO came in second again, slightly behind the SCA. Overall, the MRSO demonstrated strong competitive performance, securing first or second place in six of the seven problems, as shown in Table 10.  
The convergence rates, shown in Figure 10, indicate that the MRSO converges more quickly than the other algorithms in most problems, particularly in Pressure Vessel Design, Three Bar Truss, and Cantilever Beam. This rapid convergence showcases the MRSO's efficiency in finding optimal solutions early in the iteration process. However, the graph also reveals that for certain problems, such as the String Design and Gear Train Design, the MRSO struggles to outperform other algorithms like the DOA and the SCA, where the DOA's performance fluctuates but often outpaces the MRSO in terms of final optimal values. These findings suggest that while the MRSO is highly effective in most cases, there are specific areas, such as high-dimensional or more complex designs, where further tuning may be required to enhance its optimization capability. Figure 10: Convergence rate comparison of MRSO and metaheuristic algorithms across engineering design problems. A detailed analysis of the failures reveals that the MRSO and some of the competing algorithm's struggle with high-dimensional problems like String Design and Gear Train Design. In the case of String Design, both the MRSO and the DOA produce suboptimal solutions compared to the SCA, showing that the MRSO's exploration capabilities might need improvement for problems with numerous variables. Similarly, in Gear Train Design, while the MRSO performs better than most algorithms, it fails to achieve the best performance, falling behind the DOA. These failures provide valuable insights into the MRSO's limitations, particularly in handling more complex optimization landscapes. These problems could be fixed by making the algorithm better and mixing it with other metaheuristic methods. This would make it better at high-dimensional or non-convex optimization tasks.
## 2 Materials and Methods  
### Setup and Data Acquisition  
The setup used for the system for experiment one is as shown in Figure 1a. The target neck muscles were the left and right sternocleidomastoid (SCM), recorded with gel-based adhesive electrodes, Biorode SDC-H\({}^{\otimes}\) from Sekisui Plastics Co. Ltd., Tokyo Japan. The recorded EMG was amplified using polyam4B and converted from A/D using National Instrument USB 6211 with a 16 bit 250 kS/s multifunction IO device at a sampling rate of 2000 Hz. Data were recorded using the Data Acquisition Toolbox 4.0 from MATLAB. The setup for experiment two is as shown in Figure 1b. In this case, the target muscles were the left and right SCM and Masseter EMG. The same data acquisition protocol was observed, albeit with three channels as opposed to the previous case. The setup was performed on a laptop with Windows 10, 16 GB RAM and an NVIDIA graphics card GTX 1060, MATLAB 2019a, and Unity 3D version 2019.4.12.  
Figure 1: Data acquisition and game concept: (**a**) experiment one; (**b**) experiment two.  
The raw EMG obtained is integrated and the FIR is filtered using moving averages to generate the associated processed signal. In the case of an ML model, the trained model is used for inference to classify which direction is selected. The user sits in front of the PC monitor at a comfortable distance (approximately 0.5 m). The game setup requires the user to maintain visual feedback with the game scene. Thus, the rotation is within a normal head rotation range with little or no strain.