esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing a Reverse Supply Chain Network for Electronic Waste under Risk and Uncertain Factors
## 3 A Developed Mathematical Model  
To establish an e-waste RSC model with uncertain parameters and risk factors, some assumptions are made in this research and are listed as below:  
* The location of collection centers, the landfill site, and secondary and main markets are known in advance. * The transportation cost is calculated depending on the product/part types and the distance travelled. * The size of dismantling, repairing, and recycling centers is limited. * Pessimistic and optimistic values for all imprecise parameters are identified as 10% less and more of the most likely value adopted from [PERSON] and [PERSON] [29]. The indices, decision variables, and parameters used in the formulation are listed below:  
Indices:  
\(c\)  
set of collection centers, \(c=1,\ldots,C\);  
\(d\)  
set of possible locations of dismantling centers, \(d=1,\ldots,D\);  
\(e\)  
set of possible locations of repairing centers, \(e=1,\ldots,E\);  
\(r\)  
set of possible locations of recycling centers, \(r=1,\ldots,R\);  
\(s\)  
set of secondary markets, \(s=1,\ldots,S\);  
\(m\)  
set of main markets, \(m=1,\ldots,M\);  
\(l\)  
set of landfill site, \(l=1,\ldots,L\);  
\(p\)  
set of used products, \(p=1,\ldots,P\);  
\(u\)  
set of reusable parts, \(u=1,\ldots,U\);  
\(w\)  
set of renewable parts, \(w=1,\ldots,W\);  
\(i\)  
set of recycling materials, \(i=1,\ldots,I\);  
\(h\)  
set of disposal parts, \(h=1,\ldots,H\). Parameters:  
\(\widetilde{Tp}_{p}\)  
unit transportation cost of used product \(p\in P\);  
\(\widetilde{Tu}_{u}\)  
unit transportation cost of reused component \(u\in U\);  
\(\widetilde{Tu}_{w}\)  
unit transportation cost of renewable component \(w\in W\);  
\(\widetilde{T}_{li}\)  
unit transportation cost of recycling material \(i\in I\);  
\(\widetilde{Tu}_{h}\)  
unit transportation cost of disposal item \(h\in H\);  
\(\widetilde{O}_{pd}\)  
unit processing cost of used product \(p\) at dismantling center \(d\);  
\(\widetilde{O}_{ewe}\)  
unit processing cost of renewable component \(w\) at repairing center \(e\);  
\(\widetilde{O}_{ir}\)  
unit processing cost of recycling material \(i\) at recycling center \(r\);  
\(\widetilde{D}_{h}\)  
unit disposal cost of disposal item \(h\);  
\(\widetilde{C}_{Lp}\)  
Unit collection cost of used product \(p\) at collection area;  
\(\widetilde{Sd}_{d}\)  
set-up cost of dismantling center \(d\);  
\(\widetilde{Se}_{e}\)  
set-up cost of repairing center \(e\);  
\(\widetilde{Sr}_{r}\)  
set-up cost of recycling center \(r\);  
\(\widetilde{Pu}_{u}\)  
selling price per unit of reusable component \(u\);  
\(\widetilde{Pu}_{w}\)  
selling price per unit of renewable component \(w\);  
\(\widetilde{p}_{i}\)  
selling price per unit of recycling material \(i\);
## 2 Creep Tests of AS
### Results and Analysis
#### 2.3.3 Volume Expansion of AS Samples  
During the tests, the circumferential strains of AS samples under different confining pressures were obtained by a circumferential extensometer. Combined with the axial compressive strain data, the variation in the volumetric strain with time was obtained, as shown in Figure 6.  
Figure 5: Creep deformation rate of AS samples under different confining pressures.  
Figure 6 illustrates that the volumetric strains of the AS samples under different confining pressures were basically similar. For each volumetric strain curve, three stages were observed: accelerated volumetric strain stage, stable volumetric strain stage, and dilatant volumetric strain stage. When the stress level was lower than 70% of the deviatoric strength, the volumetric deformation of AS samples was mainly compressive. With the gradual increase of stress level, when it reached approximately 75% of the deviatoric strength, the volume deformation of AS samples was basically maintained at a stable value. When the deviatoric stress level reached 80-85% of the deviatoric strength, the microcracks in AS samples began to expand and gradually penetrate. At this point, the specimen entered the dilatant volumetric strain stage. Moreover, the expansion rate increased gradually with increasing time until AS samples were destroyed. Due to the confining pressure constraint, the final volumetric strain of the AS samples gradually decreased with increasing confining pressure.  
Figure 6: Volumetric strain characteristics of AS samples under different confining pressures: (**a**) 3 MPa, (**b**) 6 MPa, (**c**) 9 MPa, (**d**) 12 MPa, (**e**) 15 MPa, and (**f**) 18 MPa. (Point C-E is the compressive–expansive inflection point).
## 4 Results
### Convolutional Neural Network (CNN) Model  
The first graph in Figure 4 shows the changes in the MAE (Mean Absolute Error) and loss metrics during the training process of the CNN model. The training MAE shows the model's prediction error on the training set. When the graph is examined, it is seen that the training MAE drops rapidly from the first epoch and stabilizes after approximately the 5 th epoch. It shows that the model fits the training set well. The loss in the second graph, the training loss, and the validation loss decrease rapidly. The validation loss is also at a similarly low level.  
Figure 4: Convolutional Neural Network (CNN) Model MAE and Loss values (Drone 92).  
In the first graph in Figure 5, the training MAE decreases rapidly in the first epoch and reaches a constant level. The validation MAE remains at a more stable and low value. It shows that the model does not overfit. In the second graph, the training loss decreases rapidly and stabilizes at a low level. The validation loss remains low in parallel with the training loss and reaches a constant level.
# An Enhanced Belief Propagation Flipping Decoder for Polar Codes with Stepping Strategy
## 1 Introduction
Subsequently, the GBPF decoding was extended to a higher-order GBPF algorithm (GBPF-\(\Omega\)) [18], where the maximum bit-flipping order is \(\Omega\). To narrow the BLER performance gap between BP-based decoders and CA-SCL decoder, the bit-flip method was also introduced into the BPL decoder and the noise-aided BPL (NA-BPL) decoder [19] was proposed. Above all, although flipping error-prone positions after the failure of the first decoding trail will improve the performance of BP-based decoder, the characteristics of the bits in FS are selected to be flipped in turn and many of them have no contribution to correcting the error frame, which will result in a huge amount of invalid repeated decoding attempts, giving rise to more latency. This paper aims to reduce the number of decoding attempts in the BP flipping algorithms. The main contributions are summarized as follows:  
* A stepping strategy is proposed. We first analyze the behavior of FS and find that only a few bits in the FS could correct the error frame. Therefore, a concept to evaluate the likelihood of bits correcting the trajectory of BP decoding is presented to judge whether the bits in FS should be flipped or not. The judgement condition determines whether flipping the bits in the FS is helpful in correcting error frames. * Based on the stepping strategy, an optimization algorithm for the BP flipping algorithm, the BP step-flipping (BPSF) algorithm, is proposed. The algorithm flips only unreliable bits in FS and steps reliable bits to shrink the number of flipping attempts necessary. Similarly, the stepping strategy is also added into the GBPF-\(\Omega\) algorithm [17; 18] to reduce the number of flipping attempts. * In addition, we notice that some effective flipping bits may be skipped over when the LLR magnitude is small. We further propose the enhanced BPSF-\(\Omega\) (EBPSF-\(\Omega\)) algorithm, which adopts a threshold to identify the unreliable bits and lowers the block error rate (BLER). The numerical results obtained indicate that the average number of iterations can be significantly reduced for the EBPSF-1 and EBPSF-2 algorithms at the low \(E_{b}/N_{0}\), compared with the BPF-1 and BPF-2 flipping algorithms when the code length is 256. The remainder of this paper is organized as follows. Section 2 reviews the polar code, the original BP algorithm, and the BP flipping algorithm. The BPSF-\(\Omega\) algorithm with a threshold is proposed in Section 3. Section 4 analyzes the decoding performance. Conclusions are drawn in Section 5.
# A Traceable Spectral Radiation Model of Radiation Thermometry
## 5 Discussion
### Uncertainty of Radiation Thermometry Due to Uncertainty of Spectral Emissivity
A mathematical model of indirect measurement is required to correctly calculate the actual temperature of the measured surface
* a subsection of the radiation thermometry model that occurs in the real world as emission, propagation and absorption of spectral radiance
* a subsection of the radiation thermometry model that occurs in the measuring instrument as compensation of the (inevitable) environmental influences of the direct model
* a method for radiation thermometry where the measurement algorithm assumes black body properties of the measured surface
* a method for radiation thermometry where the measurement algorithm assumes real (spectrally non-homogenous) properties of the measured surface and other influences
* the physical system of the radiation thermometer and all other objects and environmental conditions which influence the measurement of radiance in radiation thermometry
* refers to the object, or specifically the surface of interest, in radiation thermometry
* the radiant flux emitted, reflected, transmitted or received by a surface, per unit solid angle per unit projected area. Radiance is a directional quantity
* the spectrally defined radiance per unit wavelength
* the spectral radiance of a black body, considered to be correctly defined by [PERSON]'s law (1)
* the incident spectral radiance of the hemispherical surroundings of the surface, summed according to the directional reflectivity characteristic of the surface
* in the scope of this article they are considered as spectrally defined influence parameters, namely emissivity, transmissivity, reflectivity, absorptivity or instrument sensitivity
## 3 Explosive Spalling Behavior
[PERSON] [96] concluded from experimental data that the application of steel fibers did not reduce the risk of explosion, and that explosive spalling was most likely to occur on specimens with the largest amount of steel fibers. However, other studies have reported that the addition of steel fibers could mitigate the explosive spalling of concrete at high temperatures. The main reasons are as follows. First, the addition of steel fibers can reduce the pore pressure of concrete at high temperatures. [PERSON] et al. [11] demonstrated that steel fibers could overcome the build-up of steam pressure. The results of [PERSON] et al. [47] indicated that the application of steel fibers in heated concrete could result in greater pore pressure resistance. Based on the results of [PERSON] and [PERSON] [40; 47], the application of steel fibers could reduce the pore pressure of high-strength concrete when the specimens were subjected to high temperature. Moreover, the positive effect of steel fibers on reducing the pore pressure in the deep part of the concrete during fast heating was more significant than that of slow heating. Subsequently, steel fibers have a much higher thermal conductivity than coarse aggregates and cement substrates; therefore, heat can be transferred more evenly through SFRC, resulting in reduced cracks caused by the thermal gradients of concrete. In the study performed by [PERSON] [98], the thermal diffusion coefficient of SFRC was higher than that of ordinary concrete. According to the findings of [PERSON] et al. [28] and [PERSON] et al. [27], the addition of steel fibers enabled more heat to flow into the interior of concrete, thereby reducing the heating surface,  
Figure 11: Mechanism of thermal stress explosion spalling of concrete [14]. Figure 10: Process of pore pressure explosion spalling of concrete [14]. interior of the concrete temperature gradient, and thermal shock. This finding is similar to that by [PERSON] et al. [99] and [PERSON] et al. [78]. SFRC demonstrated resistance toward explosive spalling owing to thermal gradient reduction. Furthermore, the application of steel fiber can improve the tensile properties of concrete. As described in Section 2.3, the tensile strength of concrete can resist the internal vapor pressure and thermal shock generated by high temperatures, thereby protecting concrete from explosive spalling. Furthermore, steel fibers that are randomly distributed in the matrix can reduce the displacement of the crack tip opening, delay the formation of cracks, and limit the propagation of cracks. This will reduce the probability of explosive spalling as well [27]. Explosive spalling behavior of concrete at high temperature is mainly caused by the accumulation of pore pressure and thermal stress caused by temperature gradient. The application of steel fibers may reduce the risk of explosion by improving the thermal conductivity, tensile properties of concrete and reducing the pore pressure of concrete at high temperatures.
###### Abstract  
Urban green spaces offer various ecosystem services such as those for controlling the urban microclimate, improving water circulation, and providing leisure and recreation opportunities. However, it is almost impossible to create new, large green spaces in cities where urbanization has been long underway. Consequently, small-scale green spaces such as green roofs and roadside trees are gaining attention as features that can increase the effects of ecosystem services. Although the area of individual buildings in urban areas is relatively small, the sum of building rooftop areas account for a large portion of urban areas. Moreover, there are areas widely available throughout cities where street trees could be planted. However, this requires large amounts of accurate databases (DBs) and long-term spatial analyses to identify specific locations suitable for small-scale green facilities on a citywide scale using a geographic information system (GIS). Consequently, in-depth research on this topic has been insufficient. Thus, this study presents an algorithm to analyze locations where green roofs and roadside trees could be introduced based on GIS spatial analysis and verifies the effectiveness of the algorithm built for the city of Seoul. In addition, computational fluid dynamics (CFD) modeling is performed to analyze the temperature reduction effect, the representative function of ecosystem control services that can be brought about by the potential green spaces. The results show that rooftop greening in study areas is possible in 311,793 of 742,770 buildings. The rooftop floor area of buildings that can apply rooftop greening is 33,288,745 m\({}^{2}\), which is about 50% of the total area of the rooftop in Seoul. It was found that roadside trees could be planted on a sidewalk with an extension length of 872,725 m and an area of 838,864 m\({}^{2}\). A total of 145,366 trees can be planted in the study area. In addition, it was shown that the introduction of green roofs reduced temperatures by 0.13 \({}^{\circ}\)C to 0.14 \({}^{\circ}\)C and roadside trees reduced temperatures by 0.14 \({}^{\circ}\)C to 0.6 \({}^{\circ}\)C. With the growing need to improve urban ecosystem services as a result of rapid climate change, the algorithm developed in this study can be utilized to create spatial policies that expand and manage urban green spaces and thereby contribute to the improvement of urban ecosystem services.  
green roof; roadside tree; location analysis; urban ecosystem services; computational fluid dynamics model +
Footnote †: journal: Applied Sciences
# Optimizing Foreign Language Learning in Virtual Reality:
## 4 VR-CCL Framework for Optimized Language Learning
### Cognitive Load Management (Based on Cognitive Load Theory)
#### 4.2.3 Gradual Guidance and Task Decomposition  
In the immersive world of VR language education, there is a real risk of learners becoming daunted by the vastness and intricacy of fresh linguistic content. Addressing this necessitates the implementation of careful scaffolding and task segmentation.  
In this context, scaffolding means progressively introducing linguistic elements in a methodical and tiered fashion. Rather than immediately plunging learners into intricate dialogues or sentences, starting with basic vocabulary and fundamental grammatical constructs is beneficial. As they gain confidence and skill, the depth of the content can gradually intensify, ensuring that while they are consistently pushed, they never feel swamped.  
Task segmentation, conversely, is about dissecting broader, intricate activities into more digestible, smaller undertakings. For example, instead of thrusting learners into anelaborate VR conversation, they might initially practice isolated phrases and then progress to brief exchanges, culminating in extended dialogues. This progressive method lets learners cement their grasp on individual segments, fostering confidence and solidifying their understanding before venturing into more involved tasks.  
By weaving these approaches into VR language education, a more streamlined and organized learning path is carved, diminishing cognitive strain and amplifying the overall efficiency of the educational journey.
# Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns
## 1 Introduction
With respect to similar research, the main contributions of this work are the following:  
1. It proposes a novel hybrid approach, which combines clustering, discriminative itemset mining and the user's/product's context. 2. It can handle both the sparsity and the cold-start recommendation problems. 3. It can handle the _out-of-matrix prediction_ (pure) cold-start recommendation problem, where users have no rating history. In most of similar approaches, several initial ratings are needed. 4. It does not need any interview process to ask the user to fill in some extra information (e.g., extended profile data, answer questions and rate items), before using the system for the first time [5]. 5. It does not include many modules that are interrelated with multiple dependencies. Discriminative itemset mining is a focused association rule mining research area for discovering interesting patterns that state the significant differences between datasets [6; 7]. The discriminative itemset mining across multiple datasets captures the itemsets which are highly frequent in one dataset but less frequent or infrequent in other datasets. We assume an existing recommender system with an established set of warm users and products. The proposed methodology first forms different clusters of warm users and then discovers the discriminant frequent patterns for each such different cluster of users and finally exploits the latter to hallucinate the purchase behavior of cold users. In what follows, first, we present related work and then we analyze the proposed methodology. We also present empirical results to demonstrate the efficiency and accuracy of the proposed methodology. We apply the proposed methodology to the MovieLens dataset which has already been widely used as a benchmark for recommender systems [8; 9; 10; 11; 12; 13; 14].
## 2 Materials and Methods
### Materials Characterization  
The surface area and pore volume were investigated using an ASAP 2020 analyzer (Micromerities, Norcross, GA, USA) and were calculated using the BET model at 77 K. BC and FeBC surface morphology was visualized using SEM (Regulus 8100, Hitachi, Japan) with EDX in order to evaluate their elemental composition. Functional groups on their surface were recorded by FT-IR spectroscopy (VERTEX 70, Bruker, Billerica, MA, USA) in the range from 400 to 4000 cm\({}^{-1}\) with a resolution of 4.0 cm\({}^{-1}\). Mineralogical characterization was investigated by powder X-ray diffraction (XRD) analysis (D8 Advance, Bruker, Billerica, MA, USA) with a copper target (Cu-K\(\alpha\)). The equipment was operated through step-scanning from 10\({}^{\circ}\) to 70\({}^{\circ}\) with a scan speed of 2\({}^{\circ}\)\(\cdot\)min\({}^{-1}\) at 40 kV and 30 mA. The valence states of Cr and Fe species adsorbed onto the FeBC surface were analyzed by X-ray photoelectron spectroscopy (XPS) (AXIS Ultra DLD, Shimazu, Japan), and the XPS spectra were analyzed using XPS PEAK 4.1.
## 3 Results and Discussion
### Circuit Walking Path Results  
For each stabilization technology, the average percent transfer of each step is provided in Table 10 and Figure 15. See Figure 8 for the NSC results for transferred sand and experimental schematic. The first step was always non-detect as the walker was always stepping from the noncontaminated area, after 8 steps on the non-sand surface, onto that area. The second step was taken from the contaminated area to the previously uncontaminated sand, so this area was expected to have had the highest degree of transfer. However, for the NSC  
Figure 14: Percent Transfer of Sr-85 Contaminated Particles for Straight-line Walking Experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Surfaces** & **Stab. Tech.** & **CaCl2** & **MVP-Fx** & **Soil2O** \\ \hline \multirow{3}{*}{Asphalt} & Control & 0.0035 & 0.0121 & 0.0108 \\ \cline{2-5}  & CaCl2 & 0.7594 & 0.0422 \\ \cline{2-5}  & MVP-Fx & & & 0.0010 \\ \hline \multirow{3}{*}{Concrete} & Control & \(<\)0.0001 & 0.0407 & 0.1677 \\ \cline{2-5}  & CaCl2 & & \(<\)0.0001 & \(<\)0.0001 \\ \cline{1-1} \cline{2-5}  & MVP-Fx & & & 0.4502 \\ \hline \end{tabular}
\end{table}
Table 9: Statistical results (\(p\)-values) for foot traffic experiments percent transfer. Gray shading indicates a significant difference (\(p<0.05\)).  
and CaCl\({}_{2}\), the fourth step (i.e., the footstep from the second step area) had the highest transfer. The CaCl\({}_{2}\) stabilized sand seemed to form a crust that bound to the boot upon the first step, similar to the NSC as suggested by the CaCl\({}_{2}\) %R, but the crust did not release from the boot until a step was made onto the non-sand surface. The third step area was expected to be relatively lower in transfer because the step was coming from the first step area. The measurable radioactivity was likely a product of kicking from the second step area or release from the boot upon stepping onto a different surface type. The radiological activities measured in the second step area, fourth step area, and sixth step area were generally higher, and more evident in the NSC than in the third, fifth, and seventh step area due to the more direct transfer of Sr-85 contaminated particles from the boot contact with those particles during every lap. The transfer of particles into the odd number step areas was due mostly to particles that had remained on the boot from at least the previous lap, or before.