esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Regarding this matter, it is also important to note that most evaluations of these systems, so far, have focused solely on algorithm accuracy [40], despite the fact that other factors beyond this one need to be considered when evaluating TEL recommender systems since high recommender accuracy does not always correlate with user satisfaction or perceived usefulness [64]. In the particular case of recommender systems for OER repositories, it is essential to take into account the pedagogical quality of the recommended OER in order to evaluate the actual value of the recommendations. In this regard, only two studies ([53; 55]) have been found that reported an evaluation of OER recommendations considering pedagogical quality. Nevertheless, neither of them analyzed the influence of using measures of OER quality on this variable. This article examines the usefulness of using pedagogical quality scores for generating learning object recommendations in OER repositories through a user study involving 53 participants and 400 OER. This user study compares the following four different recommendation approaches: a traditional content-based recommendation technique; a quality-based non-personalized recommendation technique; a hybrid approach that combines the two former techniques; and a random recommendation method, which was used as a baseline for comparison purposes. These four recommendation approaches were compared in terms of both relevance and pedagogical quality of the recommended OER in order to answer the following two research questions:  
1. Are learning object quality scores useful to generate OER recommendations? 2. Can existing OER recommender systems be enhanced by using learning object quality scores? The results of this article provide strong evidence that learning object pedagogical quality scores, although they have limited utility by themselves, can be successfully used to enhance traditional content-based OER recommender systems by enabling the recommendation of OER with higher quality without detriment to relevance. The remainder of the article is organized as follows: the user study is explained in Section 2, including details about the sample, the recommendation approaches, the dataset, and the quality scores of the OER that compose it; Section 3 presents the results of the user study, which are then discussed in Section 4; finally, Section 5 concludes the article by summarizing the main findings and giving an outlook of future work.
# MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions
## 4 Experimental Validation
### Data Sets  
The experimental data were obtained from the CWRU bearing dataset at the Engineering Centre of Case Western Reserve University (USA). The experimental setup is shown in Figure 4 and includes the drive motor, torque sensor, dynamometer, and bearing unit. To simulate different degrees of bearing failure states, single-point damages were created on the inner ring, outer ring, and rolling elements of the bearings using EDM technology. The damage diameters were 0.007 inch, 0.014 inch, and 0.021 inch, respectively. The data acquisition system consisted of accelerometers mounted on the fan end of the motor and on the drive end bearing housing. Experiments were conducted at a variety of speeds and load conditions, including 1797 rpm, 1772 rpm, 1750 rpm, and 1730 rpm. Loads ranged from 0 to 3 hp. This setup allows for the full simulation of a variety of operating environments.  
This study focuses on the vibration acceleration signals of the drive end bearing under different operating conditions at a sampling frequency of 12 kHz. The specifications of the drive end bearings are detailed in Table 1.  
The test bearings were set up with a single point of damage on the inner ring, outer ring, and rolling elements. The damage to the outer ring of the bearing was set at three different locations: 3 o'clock, 6 o'clock, and 12 o'clock. To maintain consistency in the analysis and simplify data processing, this paper classifies the damage at these three locations as outer ring failure. The categorized states in this study include four types: normal state, inner ring failure, outer ring failure, and rolling body failure. See Table 2 for details.
# Design of an Imaging Optical System for Large-Sized Stepped Shaft Diameter Detection
## 1 Introduction
To address these issues, this research employed the dual-telecentric principle to design a symmetrical dual-telecentric optical path dual-CCD system. This approach resolved the problems of low imaging quality and precision encountered when measuring large-sized stepped shafts. It lays the theoretical foundation for the imaging system of the large-sized stepped shaft diameter measurement experimental system and its applications, providing a feasible solution for measuring the diameter of large-sized stepped shafts. This innovative system resolves problems such as low measurement accuracy and poor image quality caused by the misalignment between the scale position and the optical receiving surface. As a result, it establishes a solid theoretical foundation for developing large-sized stepped shaft diameter gauges. This technology was successfully applied to imaging systems for large-sized stepped shaft diameter gauges, laying the groundwork for further research in the field.
# Performance Analysis of Dual-Hop DF Multi-Relay FSO System with Adaptive Modulation
###### Abstract  
The signal quality in high-bandwidth free space optical (FSO) systems deteriorates due to atmospheric turbulence and pointing errors. Employing techniques such as adaptive transmission and relay selection (RS) can mitigate their effects. This paper analyzes the performance of a dual-hop decode-and-forward multi-relay FSO system with an adaptive M-ary phase shift keying scheme. This analysis is based on the recently proposed Fisher-Snedecor \(\mathcal{F}\) channel model and considers the impact of pointing errors. We propose two partial relay selection schemes based on the source-to-relay or relay-to-destination channel state information to reduce the complexity of the optimal relay selection scheme. In this investigation, we derive closed-form expressions for the outage probability, modulation level selection probability, and spectral efficiency (SE) and compare the performance of the proposed RS schemes under balanced and unbalanced link cases. We observe an improvement in the SE with an increase in the number of modulation levels and the number of relays. Moreover, it is noted that the performance of the system can be restricted by the quality of either the source-to-relay or the relay-to-destination link, even if the quality of the other link is perfect. Finally, the outcomes obtained through the derived expressions are validated using Monte Carlo simulations.  
spectral efficiency; adaptive modulation; relay selection; decode-and-forward; Fisher-Snedecor \(\mathcal{F}\)-distribution +
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Footnote †: journal: Applied  
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## 3 Results and Discussion
### Evolution of the Concentration of Volatile Compounds during Grape Ripening
#### 3.2.2 Bound Volatile Compounds  
Through the cluster analysis of Figure 2, it was found that, compared with free volatile compounds, most of the bound volatile compounds showed an increase during grape ripening. The bound volatiles can also be divided into two categories according to different trends, with the first category showing a decrease in concentration during ripening and the second category showing an increase in concentration during ripening.  
In the first category, the bound carbonyl compounds (octanal, nonanal and decanal) showed a continuous decrease during ripening in different varieties, the same as their free fraction. The two bound C6 aldehydes detected showed quite opposite trends, (E)-2-hexenal showing a continuous decrease in the four varieties, while hexanal increased. This may result from the enzymatic hydrolysis or hydrolysis of the glycoside bound precursors in the ripe berry during the winemaking stage [17; 21], but this result is negligible compared with the significant decrease in the free state of hexanal; the other carbonyl species, methyl isobutyl ketone, benzaldehyde and 2,4-dimethylbenzaldehyde showed the same trend as that of hexanal, and all were second category compounds.  
Among the bound alcohols, besides (Z)-2-hexenol and (Z)-3-hexenol, some alcohols showed the same trend as the alcohols in the free fraction in the ripening process, and all of them showed an increasing trend and were second category compounds, whereas (Z)-3-hexenol showed a continuous decrease in the ripening process of different varieties of berry and was a first category compound. (Z)-2-Hexenol showed different trends among different varieties; its trends in Cabernet Sauvignon, Cabernet Franc and Merlot were the same as most of the alcohols, but it only showed a decreasing trend in the ripening process of Cabernet Gernischet.  
The accumulation of bound terpen followed the same trend as the accumulation of their free states, belonged to second category compounds, with the majority of bound terpenions increasing in content during berry ripening (cedrol, (Z)-ocimene, (E)-ocimene, terpinolene, 1,3-octaladiene). As can be seen from the graph, the peaks of terpenoids mostly occurred during the D28-D35 period, with some decrease in content during the subsequent one to two weeks of over-ripening. A similar result was reported in \"Moscato biano\" and \"Aleatico\" varieties. During the over-ripening process, a decrease in the content of monoterpene glycoside precursors was also observed [34]. [PERSON] also showed the same phenomenon in \"Beibinghong\" grapes during over-ripening [21].
## 4 Results  
### ANN Architecture Determination  
In this paper, mean relative error MRE and the coefficient of determination R\({}^{2}\) were used to evaluate the predictive effects of the model. When MRE is close to 0 and R\({}^{2}\) is close to 1, the predictive effects of the model are better. Figure 5 shows that the MRE values on the testing set always show a gradual decrease when the \(N\) value is less than 10, while it was suddenly increasing when the \(N\) value is greater than 10. The same scenario can also be found in the relationship between R\({}^{2}\) and \(N\) values. The R\({}^{2}\) of the test set increases continuously when the value of \(N\) is less than 10, and decreases significantly when the \(N\) value is greater than 10. The above results indicated that too few neurons in hidden layer makes it impossible to establish an effective mapping between the inputs and output to express their nonlinear relationship, so it shows worse performance in UCS prediction. On the contrary, if there are too many neurons in the hidden layer, the complexity of the network will increase greatly, which leads to the occurrence of overfitting phenomenon and the prediction accuracy will be decreased dramatically. Therefore, when the \(N\) value was 10, the ANN model displayed the best performance in UCS prediction with the MRE and R\({}^{2}\) value on the testing set corresponds to 2.755% and 0.980, respectively. After comprehensive consideration of the above results, the \(N\) value of 10 was selected. As the number of neurons in the input layer, hidden layer and output layer were chosen to be 5, 10 and 1, respectively, and formed the ANN architecture of 5-10-1. The optimal ANN architecture used for further research is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \(c_{1}\) & \(c_{2}\) & \begin{tabular}{c} **Initial Inertia** \\ **Weight** \\ \end{tabular} & \begin{tabular}{c} **Ending Inertia** \\ **Weight** \\ \end{tabular} & \begin{tabular}{c} **Particle** \\ **Velocity** \\ \end{tabular} & \begin{tabular}{c} **Particle** \\ **Position** \\ \end{tabular} & \begin{tabular}{c} **Particle** \\ **Size** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum** \\ **Iteration** \\ \end{tabular} &
\begin{tabular}{c} **Maximum** \\ **Iteration Error** \\ \end{tabular} \\ \hline
1.8 & 1.8 & 0.9 & 0.4 & \([-1,1]\) & \([-0.5,0.5]\) & 50 & 100 & \(10^{-4}\) \\ \hline \end{tabular}
\end{table}
Table 2: The values of PSO parameters.  
Figure 5: Relationship between number of neurons in hidden layer and MRE and R\({}^{2}\).
## 2 Topology Optimization under Single- and Multiple-Loading Conditions
### The Min-Max Compliance Problem in TO Considering Multiple-Loading Conditions  
Conventionally, the min-max formulation can be used to represent various MOPs, where the objectives are either of the same or different types of quantities, i.e., the same or different units of measurement. When the objective functions are of the same type of quantity, e.g., interpreted via Problem (5), the min-max form can be used to convert it into a single-objective optimization problem, which minimizes the maximum compliance with respect to a given volume as follows:  
\[\min_{\mathbf{x}}\left[\max_{j}\left(\mathbf{u}_{j}^{T}(\mathbf{x})\mathbf{Ku}_{ j}(\mathbf{x})\right)\right],j=1,\ldots,n_{L},\] (8a) subject to \[\mathbf{K}(\mathbf{x})\mathbf{u}_{j}(\mathbf{x})=\mathbf{p}_{j},j=1,2,\ldots,n_{L} \tag{8b}\] \[\frac{V(\mathbf{x})}{V_{0}}=f,\] (8c) \[\mathbf{g}(\mathbf{x})\leq\mathbf{0},\] (8d) \[0<x_{min}\leq x_{i}\leq 1,\ \ i=1,2,\ldots,n. \tag{8e}\]  
Directly solving Problem (8) remains challenging due to the complex computation of the objective function and its derivative. Therefore, several researchers have continued to reform it using either a bounded form [53; 63; 64] or a WS method. While the WS method carries a few disadvantages, as mentioned in the Introduction (Section 1.2), the bounded method also faces a significant challenge, namely, in the optimization process using the bounded form, the value of the upper bound must be specified [53; 63; 64]; thus, the bounded method does not completely break out of the vicious circle of previous subjective methods, in which certain values must be subjectively specified by users.  
As discussed in Section 1.2, the epsilon-constraint method [55; 56; 57; 58] appears to be one of the promising methods for treating MOPs, including structural TO with respect to multi-load cases, e.g., given in the form of Problem (8), the min-max form. Nevertheless, the epsilon-constraint method still belongs to a group of methods that require additional assignments of users regarding the importance of each loading condition. In the present study, we propose a new method that can directly address Problem (8). The proposed method is explained in detail in Section 3.
## 2 Materials and Methods
### Cell Viability Assay  
Skin fibroblasts that were typical for healthy human (CCD25 SK) and melanoma (C32) cells were bought and delivered from the American Type Culture Collection (Manassas, VA, USA). HaCaT keratinocytes were ordered in AddexBio (San Diego, CA, USA). Skin cells of melanoma (A375) were purchased from Sigma-Aldrich (St. Louis, MO, USA). All cells were cultured in Dulbecco's Modified Eagle Medium supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin (Gibco, Waltham, MA, USA) in a 5% CO\({}_{2}\) incubator at 37 \({}^{\circ}\)C. The cytotoxic activity of the essential oils was determined using the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT, Sigma-Aldrich) assay. Cells were seeded at a density of 1 \(\times\) 10\({}^{4}\) cells per well in 96-well plates. Cells were treated with fire seed and fire cone EOs at concentrations of 0.012, 0.025, 0.05, 0.1, and 0.2 uL/mL for 24 h. EOs were dissolved in DMSO and diluted with the medium. The final DMSO concentration in the culture medium never exceeded 0.1%. A solution of 0.1% DMSO was used as the control sample. After treatment with EO, MTT solution was added, and the cells were incubated at 37 \({}^{\circ}\)C for 4 h in the dark. Subsequently, the medium was discarded, and violet formazan was dissolved in the solution of [PERSON]'s glycine buffer in DMSO. The optical density was measured in a microplate reader at a wavelength of 570 nm. The half-maximal inhibitory concentration (IC\({}_{50}\)) was calculated using GraphPad Prism 7 software. Independent experiments were performed in triplicate.
## 3 Results and Discussion
### FSCs with Simple Metal Oxide and Carbon Nanofluids  
This section presents the evaluation of the useful heat and efficiency of an FSC with metal oxide and carbon nanofluids. The metal oxide nanofluids evaluated are those already known, ferrous oxide, Fe\({}_{3}\)O\({}_{4}\); aluminum oxide, Al\({}_{2}\)O\({}_{3}\); copper oxide, CuO; and magnesium oxide, MgO. Meanwhile, the carbon nanofluids evaluated are graphite, G, and fullerene, C\({}_{60}\). The latter are also compared with the results of the already widely studied nanofluids of multi-walled carbon nanotubes, MWCNTs.  
Figures 9 and 10 present the useful heat output and the efficiency, respectively, of the FSCs with respect to the volume fraction of the nanofluids. Both parameters increase with the addition of nanoparticles for all the nanofluids studied. For instance, at \(\phi\) = 0.1, aluminum and magnesium metal oxide nanofluids provide approximately 1% more useful heat than ferrous and copper metal oxide nanofluids. Additionally, both the useful heat generated by the fullerene nanofluid, and the efficiency, exceed that of ferrous oxide and cuprous oxide nanofluids. Graphite demonstrates the highest useful heat output among all evaluated nanofluids. This and fullerene nanoparticles can enhance the efficiency of FSCs by 2% and 1.5%. The superior values observed of the carbon nanofluids, in addition to the diameter of the nanoparticles, is primarily attributed to their thermal diffusivity (\(\alpha\) = k/\(\rho\)Cp), which counteracts the storage effect and compensates for the low specific heat, high density, and viscosity values, as well as the low conductivity of fullerene.
# Topology and Parametric Optimization-Based Design Processes for Lightweight Structures
###### Abstract  
Topology and Parametric Optimization are two of the most implemented material optimization approaches. However, it is not clear in the literature which optimization procedure, or possible combination of them, can lead to the best results based on material reduction and optimization time. In this paper, a quantitative comparison of different topology and parametric optimization design processes is conducted using three benchmark examples: A Hollow Plate, an L-Bracket, and a Messerschmitt-Bolkow-Blohm Beam (MBB-Beam). Ten different design processes that were developed in each case study resulted in 30 simulations in total. The design processes were clustered in three main design workflows: The Topology Optimization, the Parametric Optimization, and the Simultaneous Parametric and Topology Optimization. Their results were compared with respect to mass, stress, and time. The Simultaneous Parametric and Topology Optimization approach gave the lightest design solutions without compromising their initial strength but also increased the optimization time. The findings of this paper will help the designers in the pursuit of lightweight structures and will create the basis for the identification of the ideal material optimization procedure.  
topology optimization; parametric optimization; finite element analysis; design Article
# Modeling and Key Parameter Interaction Analysis for Ship Central Cooling Systems
## 4 Development of the Response Surface Model
### Box-Behnken Experimental Design  
Based on the typical non-linear relationships and potential interactive effects among the parameters of the ship's central cooling system, the complexity of accurately predicting system energy consumption was significantly increased. To effectively capture these complex second-order effects and parameter interactions, this paper employed the Box-Behnken design (BBD) from response surface methodology (RSM) for experimental design [21] (schematic diagram shown in Figure 3). As an efficient three-level, second-order design method, BBD can construct predictive models containing quadratic and interaction terms with relatively fewer experimental runs. For the five key independent variables (factors) focused on in this study (see Table 3), BBD required only 46 runs to comprehensively analyze the influence of the main effects and interaction effects of each factor on energy consumption, significantly improving the efficiency of experimental design and data analysis and laying the foundation for the refined modeling of system energy consumption.  
Following the principles of Box-Behnken design, the five factors influencing the central cooling system's energy consumption, namely main engine power, seawater temperature, seawater pump speed, three-way valve opening, and low-temperature fresh water flow rate, were selected as the independent variables for the experiment, denoted as \(X_{1}\), \(X_{2}\), \(X_{3}\), \(X_{4}\), and \(X_{5}\), respectively. System energy consumption \(F\) was taken as the dependent variable for this experiment. The five independent variables were set at three levels each, coded as \(-1\), \(0\), and \(+1\), representing the low, medium, and high levels for each factor, respectively. An experimental plan was formulated based on this.
# High-Resolution Multi-Scale Feature Fusion Network for Running Posture Estimation
## 2 Related Work
### Gaussian Heatmap  
Gaussian Heatmap Representation is a commonly used technique in computer vision and image processing for detecting and locating keypoints or objects in images. It represents the position and strength of the target by generating a heatmap of Gaussian distributions at specific locations on the image. Gaussian Heatmap Representation [12; 13; 14; 15; 16] has been  
Figure 1: HRNet network architecture.  
dominant due to its strong localization and generalization capabilities. Subsequent works have focused on continuously improving these capabilities, with examples including proposals to use powerful networks [2; 4; 9; 11; 12; 13] for more accurate heatmap estimation and works that propose the introduction of attention mechanisms into models [17; 18; 19; 20; 21; 22]. Gaussian heatmaps have been widely applied for encoding and decoding keypoint coordinates.  
In the human-pose-estimation task, the Gaussian heatmap \(H_{i}\) of the joint \(\text{n}_{i}\) can be represented as follows:  
\[H_{i}\left(x,y\right)=exp\left[-\left(\frac{\left(x-a_{i}\right)^{2}}{2 \lambda^{2}}+\frac{\left(y-b_{i}\right)^{2}}{2\lambda^{2}}\right)\right]. \tag{1}\]  
where \(a_{i}\), \(b_{i}\) are the horizontal and vertical coordinates of the joint \(\text{n}_{i}\), respectively, and \(\lambda\) is a constant that controls the magnitude of the Gaussian thermal value.  
Heatmap estimation exhibits superior spatial normalization capability compared to coordinate regression, as heatmaps can model the probability of keypoint presence rather than solely predict coordinates. This ability implies that heatmaps can provide reliable keypoint localization despite variations in scale or rotation within the image. Furthermore, heatmaps also possess the capacity to capture the interrelatedness among keypoints. Because the network requires predictions for the entire heatmap rather than just individual keypoint coordinates during the training process, it can leverage the interactions and constraints among keypoints to enhance the accuracy of predictions. For example, in human pose estimation, there usually exists a strong correlation between the arm and wrist. Through back propagation during training, the network can learn these relevant relationships and utilize them during the projection process to enhance the accuracy of keypoint localization.