esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Out-of-Plane Bending Test of PVB Laminated Glass Plate with Different Number of Layers
## 2 Materials and Methods
### Mechanical Properties of PVB
#### 2.1.3 Elastic Modulus of PVB  
During the stretching process, the length of the specimen increased gradually, while its width decreased. Upon reaching the stretching limit, a low and dull sound, described as a \"Bang\", was heard, as depicted in Figure 3. At this point, the ultimate elongation approached 200% (\(\pm\)10%). For the tensile test, the engineering stress (\(\sigma\)) is denoted in MPa, and the conditional strain (engineering strain) is expressed as (\(\varepsilon\)), calculated by:  
\[\sigma=\sqrt[]{A_{0}} \tag{1}\]  
\[\varepsilon=\sqrt[]{\left(\text{L}-\text{L}_{0}\right)}\sqrt[]{\text{L}_{0}} \tag{2}\]  
where \(F\) represents the load applied to the specimen, in \(N\); \(A_{0}\) denotes the cross-sectional area of the specimen before stretching, in mm\({}^{2}\); \(L\) denotes the change in the specimen's length after stretching, in mm; and \(L_{0}\) is the length of the specimen before stretching, in mm.  
Figure 4 shows the engineering stress-strain relationship exhibited by all of the specimens, and Table 3 lists their experimental results, especially the mechanical properties of PVB under investigation. This stress-strain curve is similar to those obtained through quasi-static tensile tests conducted by [PERSON] et al. (2017)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Parameter** & **Value** \\ \hline
1 & Sensor & 1.1\({}^{\prime\prime}\) IMX304 Global shutter CMOS (FRAMOS, Ottawa, ON, Canada) \\
2 & Resolving power & 4112 (H) \(\times\) 3008 (V) \\
3 & Pixel depth & 8 bit \\
4 & Image data format & Mono8, Mono12, Mono12p \\
5 & Pixel size & 3.45 \(\mu\)m \\
6 & Mechanical dimensions (W \(\times\) H \(\times\) L) & 44 mm \(\times\) 29 mm \(\times\) 86.3 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Camera specifications.  
Figure 3: Process of PVB specimen stretching.  
stress-engineering strain curve is almost linear. This observation is consistent with the findings of [PERSON] and [PERSON] [36], who stated that the stress-strain relationship of PVB is essentially linear under quasi-static conditions and with minimal deformation. On average, the initial elastic modulus \(E\) of PVB materials is 1.34 MPa at a tensile speed of 10 mm/min and 1.54 MPa at 20 mm/min. In phase II, with the engineering strain between 0.6 and 1.6, the elastic modulus increases progressively, and the engineering stress-engineering strain curve becomes markedly nonlinear. In phase III, as depicted by the elastic modulus curve in Figure 4, when the engineering strain exceeds 1.6, the elastic modulus of PVB tends to stabilize, hence this phase is also considered linear. The elastic modulus \(E\) levels off at an average of 17.75 MPa for a tensile speed of 10 mm/min and at an average of 18.78 MPa for 20 mm/min. It is evident that a higher tensile speed results in a greater elastic modulus.
## 1 Introduction
### Research Contributions  
Our major contributions are stated as follows:  
1. In this paper, we propose strategies for how heterogeneous wireless networks (HWNs) ought to be sent, and present control calculations that can be utilized to use accessible assets more productively.
2. We inspect the ideal arrangement of HWNs and propose a system, which is mindful of portability requirements, to arrange advancement to limit the pace of upward vertical hand-off occasions and to amplify the complete number of clients bolstered by the system.
3. We further explore how ideal affirmation control strategies are utilized to maintain QoS in HWNs. The proposed work is novel in the sense that the current wireless system configuration is complicated because of the dynamic condition under which clients' administrations operate make parameter improvement a mind-boggling task. However, the vibrant, and often obscure, working conditions create remote system administration models that are dependent on AI and human-derived reasoning calculations.
4. Our proposed work (i.e., hybrid use of CS and GA) provides a settled system to execute fake insight errands, for example, order, learning, and advancement. Their  
Figure 1: Evolution of wireless communication [7; 8].  
adaptability makes them strikingly helpful in a full scope of use areas, including remote systems.
* Simulations are performed in MATLAB, and performance validation of the proposed hybrid technique is performed against methods involving cuckoo search and genetic algorithm individually, and received signal strength (RSS), as well as random methods.  
Novelty Analysis  
The proposed work is novel in the sense that, to the best of our knowledge, it is the first attempt ever that considers hybrid optimization, particularly CS and GA, for handling the handoff mechanisms. Moreover, it is the first work that considers delay, number of handovers, throughput, and handover failure probability for handling the handoff mechanism.
# Large Language Models as Evaluators in Education: Verification of Feedback Consistency and Accuracy
## 5 Experimental Results and Analysis  
### Human Agreement  
All human annotators evaluated the feedback generated by each feedback generator according to the five feedback criteria (Correct, Revealing, Guidance, Diagnostic, Encouragement). Figure 8 presents the average human agreement on the MC160 for two feedback generation prompt strategies (w/ criteria and w/o criteria).  
Overall, all human annotators showed high agreement in the Correct and Revealing categories. Among the feedback generation models, Claude-3 demonstrated high agreement. Notably, in the case of 'w/ criteria', where feedback evaluation criteria were clearly included in the prompt, high agreement was also observed in the Guidance category. However, even human annotators experienced difficulties in evaluation, showing low agreement in the Diagnostic and Encouragement categories.  
As shown in Figure 9, evaluation criteria with low agreement among human annotators, such as Diagnostic and Encouragement, also showed low evaluator confidence scores. This indicates that the Diagnostic and Encouragement categories were evaluation criteria that caused confusion for the majority of human annotators, and it is difficult to make correct assessments. The graph for the w/o criteria can be found in Figure A2. A similar trend was observed in the MC500 dataset, as illustrated in Figures A1, A3, and A4 (see Appendix D for details).  
Figure 8: Human agreement on the MC160. Five human annotators evaluated the feedback generated by each feedback generator. COR stands for Correct, REV for Revealing, GUI for Guidance, DIA for Diagnostic, and ENC for Encouragement.
# Monocular Facial Presentation-Attack-Detection: Classifying Near-Infrared Reflectance Patterns
## 3 Near-Infrared Reflectance Methodology  
This paper proposes a material spectroscopy approach towards facial presentation-attack-detection (PAD). The hypothesis is as follows: facial geometry can be characterized by shining a near-infrared light and measuring the reflectance patterns. This is modeled mathematically, where surface-reflectance equations are used to demonstrate there are necessarily differences between live and spoof faces. It is then proposed that texture methods can optimally do the PAD classification. Note that the novelty here is the spectroscopy methodology--not a specific algorithm. This process of shining a near-infrared light and then characterizing liveliness using a texture classifier is how robust PAD is achieved in a monocular, single-frame fashion.  
The expected reflectance distributions for live and spoof faces are illustrated in Figure 2. Faces reflect the near-infrared illumination as a function of their geometry and albedo (reflectivity). Live faces have convex and concave components, which results in higher frequencies and varied reflectance distributions. Simple spoofs, conversely, are generally a single convex surface and often result in a simple, low-frequency distribution. This difference in frequency content will be used to characterize geometric complexity.  
Material albedo can also play a key role in classifying liveliness. Spoofs are designed to look realistic to the human eye, but the concept of \"color\" may not necessarily translate to other spectra. Recall how the spoofs look notably more reflective in Figure 1 are more reflective to near-infrared than human skin [48]. This is because the common spoofs materials (e.g., paper, fabric, latex, and glass) are more reflective to near-infrared than human skin [48; 51; 52; 53]. Note that this research recommends 940 nm infrared because it has minimal contribution from solar radiance [54] or household LED light sources [55]. This helps isolate the reflectance signal.  
Figure 2: Using material spectroscopy principles for liveliness. Live people have complex geometry with highly variant reflectance patterns. Simple spoofs conversely have relatively uniform reflectance patterns. These patterns can be classified using texture methods.
### Extracting Research Gaps from Related Works
#### 1.2.2 Analysis Criteria  
We needed to create a strong criterion for analyzing the gathered works to guarantee that research gaps were identified and to support the robustness of and need for our suggested technique. Such criterion had to meet previous challenges and condition monitoring needs. The bearing dataset introduces massive sets of bearing tests, the \"variable speed and load set\" and the \"endurance set\". The first one includes 13 subsets of different operating conditions and 7 records of operating modes, including healthy and unhealthy operating modes (further details will be revealed in the data description section). It is mainly dedicated to diagnosis studies solving a multi-class classification problem with imbalanced data with higher levels of volume (i.e., 46,592,000 samples \(\times\) 6 channels \(46,592,000\) samples \(\times\) 6 channels), velocity, and variety. The endurance test, with size of 54,067,200 samples \(\times\) 6 channels, contains a set of three damage severity levels with class portions of (0.27, 0.22, 0.50), respectively. It is generated for a specific type of bearing and dedicated to studying degradation process regression in a sort of health stage classification. It is also important that data can result in a different class distribution ratio, creating another challenge of imbalance classification. Accordingly, in addition to the three aforementioned challenges of data-driven modeling, other analysis criteria, such as whether the authors consider prognosis, diagnostic studies, or both, must also be discussed. Also, discussing the treated operating conditions is of great importance to revealing the complexity of the study and its correspondence to real-world problems. Additionally, the methods used for targeting the above challenges, whether data engineering or learning tools, must also be discussed to reveal research interest in reducing data complexity and improving data representativeness. In this context, the following list of criteria was chosen to conduct this analysis: data complexity (i.e., automatic/analytic feature extraction, outlier removing, noise suppression/reduction, data imbalance), data unavailability (i.e., generative modeling, transfer learning), data drift (i.e., adaptive learning), the problem treated (i.e., diagnosis, prognosis, operating conditions), and finally, the learning algorithms used.
# Design of Fiber-Composite/Metal-Hybrid Structures Made by Multi-Stage Coreless Filament Winding
## 3 Results
### Result of the CFW Design Process  
The design of the fiber-reinforced composite component resulted in a configuration similar to a Warren truss [55] but with diagonal internal struts to increase the lateral stiffness (Figure 10a). The digital design tool was used to prepare the winding plan. After identifying winding points, the graph was analyzed (Figure 11b,c), and a winding syntax (Table A3) was created and divided into sub syntaxes for production (Figure 10b-d). Based on the distance matrix (Figure 11a), the lower and upper regions of the component can be identified, and the graph can be derived based on a maximum value of the free-spanning length of a segment. The framework analysis (Figure 10e), together with the resolution matrix and edge bundling plot of the graph, help during the creation of the winding syntax since the user can check the fiber distribution and the connectivity of the component.  
Figure 10: 3D visualization of the fiber-composite structure of the case study. (**a**) Grouped nodes (color-coded by winding pin type: single-arm (green), 2-arm (blue), 3-arm (red), 4-arm (orange), single-arm pin with secondary mounting point (purple)), control points without winding pins (grey), and graph; (**b**) Fiber distribution after the first stage; (**c**) Fiber distribution after the second stage; (**d**) Fiber distribution after the third stage; (**e**) Exemplary framework analysis: nodes 62–65 are loaded, whereas nodes 1, 9–11, 19, and 20 are fixed.  
The upper region is located as far away as possible from the base plate and features more material usage than the others component regions to absorb bending loads. Due to the relatively brittle and stiff character of the CFRPs, many winding pins were used to distribute the load as evenly as possible. To prevent the winding fixture from blocking access, the upper region is anchored to only four winding pins, which are only attached during production. This results in a relatively large free-spanning fiber length and a strong fiber-fiber interaction in the upper region. If the entire component were fabricated in one session, the struts placed later would pull the upper region downwards. This deviation from the target geometry is defined by the relation between the fiber tension and free-spanning length. Such a deformation reduces the load-bearing capacity through a loss of structural depth, causes curvature in the tension members, and results in a gradual loss of fiber tension in the previously deposited vertical struts, which may lose contact with each other. For these reasons, the component must be manufactured in a multi-stage process. After the winding of each stage, the resin is cured.
# Progress in Improving Photovoltaics Longevity
## 4 Results
### PV Operation Monitoring
#### 4.3.1 Field Monitoring Methods
The method is non-destructive, contactless, and yields fast results, while it only requires a DC power supply and an IR camera with a spectral range between 8 and 14 \(\upmu\)m. During PV operation, a localized heating increase is mainly caused by current runaway and broken or loosened contacts. Luminescence techniques are based on light emitted by radiative recombination of charge carriers in semiconducting materials. The techniques used to trigger luminescence radiation can be divided into two categories: electroluminescence (EL) and photoluminescence (PL). In electroluminescence (EL), the capture of an EL image provides information and a quantitative explanation of several cell properties, including shunt and series resistance, diffusion length, and minority carrier lifespan. Besides this, a qualitative examination of an EL image intends to detect easily recognizable signs of possible degradation processes and aging effects in PV modules, which could, in the immediate future, question the durability and reliability of the module [114]. Furthermore, EL imaging can accurately identify cracked cells, hot spots, and shunts in c-Si cells, as well as corrosion and delamination  
Figure 4: Schematic illustration of field and remote methods for PV operation monitoring. signatures in thin-film solar cells. Daylight photoluminescence (dPL) can identify cell cracks and monitor material characteristics, while it is also crucial for the detection of open circuit faults in bypass diodes and the evaluation of the I-V curves of single solar cells. Inspection of disconnection in cell interconnection is widely used as a means of evaluating the condition and effectiveness of the interconnection between PV modules and/or solar cells [115]. Its application requires an interconnect breakage tester, which can detect possible breakages in the interconnection system. This device can identify interconnection damages and faults, which can cause efficiency problems in the PV modules. Insulation testing [115] is a technique used to examine the condition of insulation materials, which are integrated with the electrical components of a PV module. The method requires an insulation resistance tester, which evaluates the effectiveness of the insulation. This device can detect potential insulation faults and defects in connections and wiring, which can induce safety issues and electrical faults. A detailed analysis of the field monitoring methods is included in the Supplementary File. Table 5 below summarizes typical field monitoring methods and required equipment.
# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 3 Methods
### Prediction Models-MLT Models and Naive Approach
#### 3.2.5 Multiple Linear Regression (MLR)  
MLR is an extension of the basic linear regression method to handle multiple independent variables and multiple target parameters simultaneously, expressed as Equation (2).  
\[y_{i}=a_{0i}+a_{1i}x_{1i}+a_{2i}x_{2i}+\dots+a_{ni}x_{ni}+\varepsilon_{i} \tag{2}\]  
where \(y_{i}\) is the ith dependent variable, \(x_{1i},x_{2i},\dots,x_{ni}\) are independent predictors \(a_{1i},a_{2i},\dots,\)\(a_{ni}\) are regression coefficients of each predictor, including \(a_{0i}\)--the \(i\)th intercept, and \(\varepsilon_{i}\) is the \(i\)-th residual [46; 47]. The regression coefficients indicate the alteration of the dependent variables caused by a unit increase in the predictor variables. These coefficients facilitate a comparative assessment of the relative significance of each predictor for model output, providing insights into their respective impacts within the regression model [47]. The residuals indicate the spread of the true values of the target parameter around the regression line [46].
# Optimized Multiport DC/DC Converter for Vehicle Drivetrains: Topology and Design Optimization
###### Abstract  
DC/DC Multiport Converters (MPC) are gaining interest in the hybrid electric drivetrains (i.e., vehicles or machines), where multiple sources are combined to enhance their capabilities and performances in terms of efficiency, integrated design and reliability. This hybridization will lead to more complexity and high development/design time. Therefore, a proper design approach is needed to optimize the design of the MPC as well as its performance and to reduce development time. In this research article, a new design methodology based on a Multi-Objective Genetic Algorithm (MOGA) for non-isolated interleaved MPCs is developed to minimize the weight, losses and input current ripples that have a significant impact on the lifetime of the energy sources. The inductor parameters obtained from the optimization framework is verified by the Finite Element Method (FEM) COMSOL software, which shows that inductor weight of optimized design is lower than that of the conventional design. The comparison of input current ripples and losses distribution between optimized and conventional designs are also analyzed in detailed, which validates the perspective of the proposed optimization method, taking into account emerging technologies such as wide bandgap semiconductors (SiC, GaN).  
interleaved multiport converter; multi-objective genetic algorithm; hybrid electric vehicles; losses model; wide bandgap (WBG) technologies; Energy Storage systems Article
## 2 Literature Review
### Topology Optimization and Lattice Design for Resin-Printed Structures  
#### Design Principles for Optimizing Mechanical Efficiency with Minimal Material  
Topology optimization forms the backbone of advanced lattice design strategies, facilitating mechanical efficiency while minimizing material use. Utilizing computational tools, designers can create lightweight structures that maintain strength and functionality, inherently promoting sustainable manufacturing practices through material conservation [36; 57]. Research demonstrates that dynamic and adaptable lattice designs can significantly reduce the weight of components while ensuring that load-bearing capabilities meet or exceed the standards of traditional solid structures [52; 58].  
The incorporation of topology optimization principles enhances both the strength-weight ratio and overall resource efficiency, effectively responding to economic pressures in production [24; 33].
## 3 Numerical Results and Analysis
### Analysis of J-C Constitutive Parameters on Chip Morphology
However, the temperature on both sides of ASB I is the lowest, and the temperature on both sides of ASB III is the highest. When the chips are discharged, the temperature of the adiabatic shear zone gradually passes to both sides, and the temperature difference between the adiabatic shear zone and its sides gradually decreases. Figure 13 presents the changes in strain energy during the micro-cutting process of Ti6 Al4V, where \(a\)-\(i\) in Figure 13b corresponds to the strain energy of \(a\)-\(i\) in Figure 13a, and \(a^{\prime}\)-\(i^{\prime}\) in Figure 13b corresponds to the strain energy of \(a^{\prime}\)-\(i^{\prime}\) in Figure 13a. The crests and troughs of each sawtooth are observed primarily because a significant amount of heat is generated via plastic deformation in the primary deformation zone during the formation of the serrated chip to form an adiabatic shear zone. Figure 14a presents the curve of the cutting force during the micro-cutting process of Ti6 Al4V. When the first serrated chip starts to form, the cutting force value is relatively high and then gradually decreases. The cutting force decreases to a minimum after the first serrated chip is formed. The subsequent serrated chip then repeats the aforementioned law of cutting force; this phenomenon can be confirmed in a previous study by [PERSON] et al. [67]. The decrease in cutting force is due to the significant heat that is generated by the adiabatic shear zone, which makes the material softer and decreases the cutting force required for the cutting process, thereby resulting in periodic changes in the cutting force. This is because the cutting force changes periodically, and the tool generates a periodic vibration, thereby accelerating tool wear. Figure 14b presents the average cutting force under different J-C constitutive parameters. As shown in the figure, the cutting force corresponding to the initial yield tress \(A\), the hardening modulus \(B\), strain rate sensitivity coefficient \(C\), and thermal softening coefficient \(m\) gradually increase when the J-C material constitutive parameter increases from \(-30\%\) of the original value to \(+30\%\). Conversely, the cutting force parameter of the material hardening parameter \(n\) decreases. The cutting force trend is consistent with the cutting heat in the shear zone, which indicates that an increase in the cutting force increases the amount of heat released. However, this does not indicate that increases in the amount of heat released promotes sawtooth sensitivity. Figure 6:0 The strain energy during chip formation. (**a**) the design path along the middle of the chip; (**b**) variation of strain energy on serrated chips. \(a\)–\(i\) point to the top of serrated chip, \(a^{\prime}\) –\(i^{\prime}\) point to the root of serrated chip.