esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Optimal Control of Treadmill Shock Absorption Based on Ground Reaction Force Constraint
## 2 Establishment of the Mathematical Model
### Biomechanical Motion Model
\begin{array}{c}\text{m}_{1}\ddot{\text{x}_{1}}=\text{m}_{1}\text{g}+\text{k} _{1}(\text{x}_{3}-\text{x}_{1})+\text{k}_{2}(\text{x}_{2}-\text{x}_{1})+\text {c}_{1}\big{(}\dot{\text{x}_{3}}-\dot{\text{x}_{1}}\big{)}+\text{c}_{2}\big{(} \dot{\text{x}_{2}}-\dot{\text{x}_{1}}\big{)}-\text{fg}\\ \text{m}_{2}\ddot{\text{x}_{2}}=\text{m}_{2}\text{g}+\text{k}_{2}(\text{x}_{ 2}-\text{x}_{1})+\text{k}_{3}(\text{x}_{3}-\text{x}_{2})+\text{c}_{2}\big{(} \dot{\text{x}_{2}}-\dot{\text{x}_{1}}\big{)}\\ \text{m}_{3}\ddot{\text{x}_{3}}=\text{m}_{3}\text{g}+\text{k}_{1}(\text{x}_{ 3}-\text{x}_{1})+\text{k}_{3}(\text{x}_{3}-\text{x}_{2})+(\text{k}_{4}+\text {k}_{5})(\text{x}_{4}-\text{x}_{3})+\text{c}_{1}\big{(}\dot{\text{x}_{3}}- \dot{\text{x}_{1}}\big{)}+\text{c}_{4}\big{(}\dot{\text{x}_{4}}-\dot{\text{x}_{ 3}}\big{)}\\ \text{m}_{4}\ddot{\text{x}_{4}}=\text{m}_{4}\text{g}+(\text{k}_{4}+\text{k}_{5} )(\text{x}_{4}-\text{x}_{3})+\text{c}_{4}\big{(}\dot{\text{x}_{4}}-\dot{\text{ x}_{3}}\big{)}\\ \text{m}_{5}\ddot{\text{x}_{5}}=\text{m}_{5}\text{g}+\text{k}_{5}(\text{x}_{ 1}-\text{x}_{5})+\text{k}_{6}(\text{x}_{5}-\text{x}_{6})+\text{k}_{7}(\text{ x}_{5}-\text{x}_{7})+\text{c}_{5}\big{(}\dot{\text{x}_{1}}-\dot{\text{x}_{5}} \big{)}+\text{c}_{6}\big{(}\dot{\text{x}_{5}}-\dot{\text{x}_{6}}\big{)}+\text{c }_{7}\big{(}\dot{\text{x}_{5}}-\dot{\text{x}_{7}}\big{)}\\ \text{m}_{7}\ddot{\text{x}_{7}}=\text{m}_{7}\text{g}+\text{f}_{\text{g}2}-\text{ k}_{7}\text{x}_{7}\\ \text{f}_{\text{g}1}+\text{f}_{\text{g}2}=\text{f}_{\text{g}}\end{array}  \tag{1}\]  
In the equations, the coefficients m, c, and k represent mass, damping, and stiffness, respectively, while g denotes gravitational acceleration. In [PERSON] and Zadpoor [13], the magnitude of the ground reaction force (GRF) is represented as Fg, which has been modified. According to the modified model, the vertical contact force Fg is expressed as follows:  
\[\text{Fg}=\left\{ \begin{array}{c}\text{Ac}\big{[}\text{a}\big{(}\text{x}_{ 1}-\text{x}_{5}\big{)}^{\text{b}}+\text{c}\big{(}\text{x}_{1}-\text{x}_{5} \big{)}^{\text{d}}\big{(}\dot{\text{x}_{1}}-\dot{\text{x}_{5}}\big{)}^{\text{e} }\big{]}\text{x}_{1}>0\\ 0\text{x}_{1}\leq 0\end{array} \right. \tag{2}\]  
Ac, a, b, c, d, and e are shoe-specific parameters that correspond to the values for soft and hard shoes. The parameters used in this experiment are consistent with those employed by [PERSON] and [PERSON], as shown in Table 1. In reality, this represents a nonlinear elastic model, where parameters a, b, c, d, and e are unique to the shoes. Figure 1: (a) Human treadmill exercise. (b) Kinematic model of moving surface.
# Many-Objective Optimization for Anomaly Detection on Multi-Layer Complex Interaction Networks
## 4 Results
### Case Study II: Aarhus Real-World Multi-Layer Network  
In our second experiment, we consider the analysis of a first real-world multiplex network. The network application context relates to the scope of social interactions, i.e., interactions in a departmental context between people working at that department [64; 65]. Here, we observed slightly different patterns compared to the synthetic network data, which we discuss in detail below. The applied network data consist of five layers modeling online and offline relationships (Facebook, Leisure, Work, Co-authorship, and Lunch) between the employees of the Computer Science department at Aarhus University. The network includes 61 nodes, representing the respective members at the department. Figure 4 depicts the individual layers of the multiplex network, representing the individual relationships. We observe a rather relative dense structure in layer 5 compared to the other layers, this is also summarized in Table 5 where we present a statistical overview on the network and its layers, respectively. Figure 5 shows a detailed view on layer 5, i.e., the densest layer. Here, we can already observe some interesting findings regarding the connection structure, i.e., regarding relatively loosely connected nodes and one unconnected node. This will be useful when interpreting our results, which we will discuss in the following. As result of estimating the centrality for all nodes in all layers and applying many-objective optimization through minimization, we found a Pareto Front that consists of six nodes. As shown in Figure 6, these are the nodes 1, 8, 12, 14, 37, and 60. For determining the nodes to be categorized as anomalous nodes, we considered the high density \(D\) layers as a basis to find nodes with the highest ACE score. Additionally, we considered the ones closest to the highest ACE score and far from the mean \(\mu\), being contained in the Pareto Front. In Figure 6, we show a visualization of the different layers and the Pareto Front. \begin{table}
\begin{tabular}{c c c c} \hline
**Layer** & \(\mu\) & \(\sigma\) & \(D\) \\ \hline
**L1 (\(p=0.03\))** & 0.194114 & 0.1911789 & 0.028889 \\ \hline
**L2 (\(p=0.05\))** & 0.34386 & 0.1979547 & 0.056566 \\ \hline
**L3 (\(p=0.20\))** & 0.674128 & 0.1472402 & 0.198384 \\ \hline
**L4 (\(p=0.25\))** & 0.651931 & 0.1232115 & 0.235556 \\ \hline
**L5 (\(p=0.30\))** & 0.726498 & 0.1220831 & 0.29899 \\ \hline \end{tabular}
\end{table} 
Table 2: Statistical characterization: synthetic multiplex network generated via an Erdős-Rényi graph generation process. Each layer of the network corresponds to one synthetic network with the link probability \(p\in\{0.03,0.05,0.20,0.25,0.30\}\). Figure 4: Aarhus multiplex network. Here, we show the individual layers of the network, where the different sets of edges of each layer are also illustrated via different colors for the connected edges.
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 5 Visual Simulations and Analysis (Constellation Scenarios)  
Based on the proposed methodology and the autonomous strategies to develop autonomy in the Cislunar space [16], the visual demonstrations are depicted through simulations that propagate the small satellite constellations for a defined period of time. These observations were significant with unique forms of designed constellations, which have different orientations for Earth and Moon as the operating environment that are unique on each side. But the most important aspect is that these fall under a single operating frame of Cislunar space, forming a center stage for deep space exploration. The Simulation configurations are presented in Table 1 for the Earth along with the visual representation in Figure 5.  
The need for these simulations is that the major environmental issues need to be observed in real-time conditions and define the motive of Cislunar space appropriately. There are several concerns in this region that need to be thought of, designed, simulated, and implemented in due course for sustainable missions to be propagated in the near future. These simulations are propagated for the Moon and Earth separately while simultaneously aligning with the proposed methodology. The missions need to use the lower Earth orbits to maintain continuity for making the Moon's orbits operable and viable for movement into deeper space. Hence, these simulations have taken this into due consideration.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value/Comment** \\ \hline Orbits & Hybrid–Flower (5); Mixed walker (4); MEO—Inclined (2); GEO (1) \\ Eccentricity & 0.001 \\ No. of satellites & 27 \\ Sensors (FoV) & 30 \& 40 (Deg.) \\ Altitude & VLEO–GEO (Distributed) \\ SRP & Integrated module \\ Inclination & 0–110 Deg. (varied) \\ Satellite Mass & 85 (Kg) \\ Perturbations & \(j_{2}\) and atm. Drag (included) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Earth-based constellation configuration [16].  
Figure 5: _Cont._
###### Abstract  
Particulate volume scattering function (VSF), especially at angles larger than 170\({}^{\circ}\), is of particular importance for interpreting ocean optical remote sensing signals and underwater imagery. In this study, a laboratory-based VSF instrument (VSFlab) adopting the periscopic optical system was developed to obtain VSF measurements from 1\({}^{\circ}\)-178.5\({}^{\circ}\). In the VSFlab, a new prism design that simply combines a single prism and a neutral density filter was proposed to more efficiently reduce the stray light in the backward direction, while a detailed calibration procedure was given. A full validation based on standard beads of various sizes and a comparison with the results from LISST-VSF and POLVSM indicated that the VSFlab can provide reliable results from 1\({}^{\circ}\) to 178.5\({}^{\circ}\). VSFlab measurements in the East China Sea (ECS) exhibited a moderate increase (not more than 5 times) in VSF from 170\({}^{\circ}\) to 178.5\({}^{\circ}\) rather than a sharp increase of more than one order of magnitude presented in other instrument results measured in other coastal regions. The estimates of the particulate backscattering coefficient using single angle scattering measurements near 120\({}^{\circ}\) or 140\({}^{\circ}\) and suitable \(\chi_{p}\) were justified. Two types of the VSFs with different size distribution and shape parameters in the ECS can be distinguished based on the variability of \(\chi_{p}\) after 155\({}^{\circ}\). The measured VSF could provide a basis for the parameterization of VSF in the radiative transfer model and the variability of \(\chi_{p}\) in the backward direction had the potential to be used to characterize the particles in the coastal region of the ECS.  
ocean optics; volume scattering function measurements; new prism design; calibration and validation; field observation in the East China Sea
# Dynamic Scene Segmentation and Sentiment Analysis for Danmaku
## 2 Related Works
### Association Rules Based Emotion Analysis  
Mining rules of association, a common technique in data mining, works to find meaningful and often happening patterns in sets of items. This way has been used efficiently in studying emotion and sentiment to find hidden patterns and trends within text data. This gives useful information about how users behave, their emotional tendencies, and feedback mechanisms.  
[PERSON] et al. [16] studied the application of machine learning and association rule mining in tweet analysis. Airlines pay much attention to improving customer experience, and Twitter is an important platform for passengers to share their opinions. In their research, they used the Glove dictionary and n-gram methods to extract features from tweets for word embedding. They explored different artificial neural network (ANN) architectures and support vector machines (SVM) to create a classification model that can classify tweets into positive and negative sentiment categories. They also developed a convolutional neural network (CNN) for tweet classification and compared its performance with the most accurate model among SVM and multiple ANN architectures. The results showed that the CNN model performed better. To gain more insights, they applied association rule mining to different tweet categories. For example, they found that certain word combinations, like the simultaneous appearance of \"flight on time\" and \"good service\", are strongly associated with the positive sentiment category, while \"flight delay\" and \"poor attitude\" are related to the negative sentiment category. These findings are valuable for airlines to optimize their customer experience strategies.  
[PERSON] et al. [21] proposed two methods, quantitative and fuzzy, to extract meaningful rules to reveal the associations between different emotion categories in users' posts on social media platforms. They used a Twitter user dataset and classified emotions according to [PERSON]'s six emotion categories. They conducted several experiments with different minimum support and minimum confidence thresholds. In the quantitative method, they counted the frequencies of different emotion-related words in users' posts and their co-occurrence situations to mine the association rules between emotion categories. For example, they found that when words expressing \"joy\" frequently appear in a user's post, the probability of words expressing \"anticipation\" also increases. In the fuzzy method, they used fuzzy logic to deal with the uncertainty of emotion expressions and capture the fuzzy associations between emotion categories more flexibly. For example, for some ambiguous words, they determined their associations with different emotion categories through fuzzy membership functions. The experiments achieved promising results. Both methods can effectively identify the associations between different emotion categories in users' tweets, which is of great significance for psychologists in studying people's emotional psychology and personality.
# Effect of Cu, Cr, S Doped TiO\({}_{2}\) for Transparent Plastic Bar Reinforced Concrete
## 3 Experimental Results
### Doping TiO\({}_{2}\) Photocatalyst Results  
Calcination changed to TiO\({}_{2}\) with mass, as shown in Figure 9a. As demonstrated in Figure 9b,c, the specimens were ground to 75 micrometers and 30 nm particles with an attrition mill. The microstructural characterization of doped TiO\({}_{2}\) specimens was carried out via SEM. Figures 10 and 11 show the SEM images and EDS (Energy dispersive spectroscopy) spectrum of the doped TiO\({}_{2}\) specimens. As shown in Figure 10, the Cu-doped TiO\({}_{2}\) sample appears to be the most homogeneous, followed by Cr-doped TiO\({}_{2}\), then the S-doped TiO\({}_{2}\) sample. It is confirmed that the Cu-doped TiO\({}_{2}\) sample has a relatively good dispersibility of nanoparticles in Figure 10a. According to the EDS spectrum in Figure 11, it was found that the S-doped TiO\({}_{2}\) sample had little sulfur content. As a result of the X-ray diffraction, TiO\({}_{2}\) has not gone through any process and has been identified to represent the anatase peak as shown in Figure 12. An anatase peak and Cu peak were found in Cu-doped TiO\({}_{2}\) specimens. On the other hand, the Cr-doped TiO\({}_{2}\) and S-doped TiO\({}_{2}\) specimens were composed of two types, an anatase peak and a rutile peak, and a Cr peak and S peak appeared. It was confirmed that TiO\({}_{2}\) can be doped by a simple process for calcination at 1000 \({}^{\circ}\)C for 24 h. Figure 13 shows the UV-visible absorption spectrum and Figure 14 shows the energy band gap of TiO\({}_{2}\) specimens by Tauc plot. Since Figure 13d is a non-doped anatase type TiO\({}_{2}\), it was confirmed that the absorption rapidly decreased from 350 to 380 nm. Figure 13a-c all show an increase in absorbance compared to Figure 13d. Figure 13a has confirmed that Cu-doped TiO\({}_{2}\) has strong absorption bands up to 700 nm.  
Figure 8: Compressive and flexural strength results.  
Figure 7: J-Ring and L-Box test results.  
It was also confirmed that in Figure 14a, inherent band gaps of TiO\({}_{2}\) were reduced from 3.3 to 2.8 eV. Cr-doped TiO\({}_{2}\) confirmed a rise in absorbance at Figure 14b and a decrease in band gaps from 3.3 to 3.0 eV at Figure 14b. From 400 nm, the absorbance tended to gradually decrease, indicating a somewhat lower absorbance of 0.2. S-doped TiO\({}_{2}\) showed somewhat minor visible light reactivity compared to other specimens in Figure 13c. As shown in Figure 14c, the bandgap was not much improved as 3.2 eV. Accordingly, it is expected that utilizing Cu in transparent bar reinforced concrete production in the future will improve indoor air quality due to its excellent visible light absorption performance.  
Figure 11: EDS (Energy dispersive spectroscopy) spectrum of doped TiO\({}_{2}\) specimens.  
Figure 10: SEM (Scanning electron microscope) microstructure images of doped TiO\({}_{2}\) specimens.  
Figure 9: Results of calcination and milling of TiO\({}_{2}\).
# Development and Characterization of Tailored Polyurethane Foams for Shock Absorption
## 2 Materials and Methods
### Physical-Chemical Characterization
#### 2.3.2. X-ray Scattering (WAXS)  
Porous phase dimensional characterization was mainly achieved by X-ray diffraction (XRD). This device has a GD 2000 goniometer and is managed by WinDust32. The diffractogram was deconvolved with the Gaussian function to compute the amorphous and crystalline phases. The peak areas were calculated using WinAcq32. The radiation used was of CuK\(\alpha\) type with a voltage of 40 kV and 30 mA and an angle speed of 0.01\({}^{\circ}\)/s. The fillers bentonite and alumina were crystalline. The software WinDust 32 and WinSearch indicated a tetragonal crystalline system with mesh a = b = 3.78 and c = 9.51 for bentonite. In addition, the hexagonal system for alumina with parameters of mesh a = b = 4.98 A and c = 17.02 A.
###### Abstract  
The reconstruction of bridge responses has been a significant area of focus within the field of structural health monitoring. This process entails the cross-reconstruction of responses from various cross-sections to identify any anomalies at specific locations, which may indicate the presence of structural defects. Traditional research has concentrated on simulating the relationships between different cross-sections for both high- and low-frequency components in isolation. However, this study introduces an innovative approach using a residual network (ResNet) to reconstruct high-frequency bridge responses under vehicular loading and demonstrates its applicability to low-frequency response reconstruction as well. The theoretical basis of this method is established through an analysis of the dynamics within a simplified vehicle-bridge-interaction (VBI) system. This analysis reveals that the transfer matrices for both high- and low-frequency components remain consistent across various loading conditions. Then, a data interception technique is introduced to separate high-frequency, low-frequency, and temperature-related components based on their spectral characteristics. The ResNet modeled the inter-sectional relationships of the high-frequency components and was then used to reconstruct the low-frequency responses under vehicular loading. The methodology was validated using a series of finite element models, confirming the uniformity of the transfer matrix between high- and low-frequency vibration components of the bridge. Field testing was also conducted to evaluate the practical effectiveness of the method. The proposed transfer-reconstruction method is expected to significantly reduce training dataset requirements compared with existing methods, thereby enhancing the efficiency of structural health monitoring systems.  
Keywords:response reconstruction; structural health monitoring; vehicle-bridge interaction (VBI) system; residual network (resnet) +
Footnote †: journal: applied sciences
## 4 Results
### Evaluation Parameters and Results  
The defined evaluation parameters were extracted from the node evaluation option in PTV VISSIM, which are relevant for the representation of the DLA impact. The evaluation parameters are the following:  
* Vehs(All)--the number of vehicles [n/h];
* VehDelay(All)--the delay of all vehicles on the observed intersection [s];
* Qlen--the queue length per lane on the observed intersection [m];
* LOSVal(All)--the level of service as a numerical value;
* EmissionsCO--emissions of carbon monoxide [g].  
Each of the evaluation parameters was recorded for all movement directions at three signalized intersections of the observed corridor (excluding the pedestrian crossing/intersection). Below are the results of the existing situation simulation and the proposed DLA implementation scenario for the morning peak hour (7:00-8:00 a.m.). The analysis of the results is presented in the Discussion section. The results are presented separately for each intersection within the traffic model (three signalized intersections). For each location, the previously defined evaluation parameters are presented for the existing situation and the proposed DLA implementation scenario during the morning peak hour.  
Table 1 presents the simulation results for the intersection of Vukovar Street-Trakoscanska Street-Ozaljska Street. The same evaluation parameters are presented for the remaining signalized intersections in Tables 2 and 3.  
These simulation results provide the basis for evaluating the effectiveness of DLA implementation. A detailed comparison and analysis of the observed differences is presented  
\begin{table}
\begin{tabular}{c c c} \hline
**Evaluation Parameter** & **Existing Situation** & **DLA Scenario** \\ \hline Vehs(All) & 2050 & 2092 \\ VehDelay(All) [s] & 15.5 & 18.13 \\ Qlen [m] & 11.61 & 14.92 \\ LOSVal(All) & 2.45 & 2.82 \\ EmissionsCO [g] & 1418.62 & 1634.82 \\ \hline \end{tabular}
\end{table}
Table 1: The results of the simulation for the existing situation and DLA situation of Vukovar Street–Trakoscanska Street–Ozaljska Street.  
\begin{table}
\begin{tabular}{c c c} \hline
**Evaluation Parameter** & **Existing Situation** & **DLA Scenario** \\ \hline Vehs(All) & 2236 & 2351 \\ VehDelay(All) [s] & 83.76 & 35.14 \\ Qlen [m] & 59.77 & 48.21 \\ LOSVal(All) & 4.36 & 3.45 \\ EmissionsCO [g] & 4028.97 & 2183.96 \\ \hline \end{tabular}
\end{table}
Table 2: The results of the simulation for the existing situation and DLA situation of Vukovar Street–Nova Costa Street.  
\begin{table}
\begin{tabular}{c c c} \hline
**Evaluation Parameter** & **Existing Situation** & **DLA Scenario** \\ \hline Vehs(All) & 2674 & 2756 \\ VehDelay(All) [s] & 25.15 & 23.71 \\ Qlen [m] & 14.19 & 10.19 \\ LOSVal(All) & 2.83 & 2.50 \\ EmissionsCO [g] & 1574.63 & 1608.83 \\ \hline \end{tabular}
\end{table}
Table 3: The results of the simulation for the existing situation and DLA situation of Vukovar Street–Florjana Andraseca Street.  
in the Discussion chapter. All the results (approach detailed) are available from the authors but are not presented due to paper length limitations.
## 3 Definition and Problems  
### Definition  
As AI technologies continue to permeate all aspects of society, ensuring fairness in their decision-making processes is crucial to avoid perpetuating bias and inequality. However, defining what constitutes fairness in AI is a complex and multifaceted task. So far, there are mainly seven types of definitions, including individual fairness [40; 41], group fairness [42], equality of opportunity [11], disparate treatment [43], fairness through unawareness [44; 45], disparate impact [46], and subgroup fairness [47].  
Fairness in AI can be approached through different conceptual lenses. Individual fairness emphasizes equitable treatment for similar individuals, while group fairness aims to avoid disparate treatment based on demographic attributes. Equality of opportunity focuses on consistent predictive accuracy and error rates across various groups, regardless of outcomes.  
An alternative approach is fairness through unawareness, achieved by ignoring sensitive attributes in decision making. Despite its intentions, this method might indirectly perpetuate bias present in data. Disparate impact examines whether AI systems disproportionately harm certain groups, irrespective of intent. It aims to uncover biases in outcomes, intentional or not.  
To address complex interactions, subgroup fairness evaluates fairness at the intersection of multiple protected attributes, ensuring equitable experiences for diverse subgroups. These conceptions contribute to a comprehensive understanding of fairness in AI and underscore the multifaceted nature of achieving equitable outcomes.  
Figure 1: Procedure of preferred reporting items for systematic reviews and meta-analyses.  
Table 1 summarizes these approaches to fairness in AI. Individual fairness prioritizes personalized treatment, while group fairness targets demographic equity. Fairness through unawareness and equality of opportunity tackle fairness differently--ignoring attributes vs. ensuring equal chances based on qualifications. Disparate impact assesses negative effects, disparate treatment detects unequal treatment. Subgroup fairness navigates complex attribute interactions for equitable outcomes. These concepts collectively enrich the understanding of fairness in AI systems.