esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of Corn Straw Fibers
Figure 4d shows a reduction in the modulus of elasticity for the NaOH and KOH treatments, resulting in improved fiber ductility. This information is positive for the application of the fiber in wall-covering mortars, which presents the function of making the sealing system more ductile [39; 40]. Figure 5 shows the XRD results of the fibers after the treatments proposed in the article. Some comments are important to understand these results: (i) The peak that occurs at approximately 20 = 5\({}^{\circ}\) does not correspond to any cellulose peak, as observed in other research [41]. The formation of the peak occurs due to the XRD test procedure, which initially presents an adjustment due to the recording of the results. (ii) A peak is observed in the range of 20 = 10-15\({}^{\circ}\) and should be described as an overlapping peak of cellulose, with Miller indices of (1-10) and (110). This peak is more characteristic in the KOH treatment and can be observed in the NaOH treatment, showing that these treatments are more efficient in corn straw fiber. (iii) All fibers present the characteristic peak at approximately 20 = 23.3\({}^{\circ}\) relative to cellulose, with Miller indices of (200) [41]. (iv) Mainly in the fiber treated with NaOH, it is possible to observe a well-defined cellulose peak, with Miller indices of (004) at around 34.5\({}^{\circ}\)[41]. Figure 4: (**a**) Water absorption; (**b**) crystallinity; (**c**) tensile strength; (**d**) modulus of elasticity. Based on this information and based on the XRD results, it is possible to identify that the sample treated with NaOH gave a pattern that most closely resembles a cellulose pattern. NaOH is a traditional treatment for hemicellulose removal; therefore, these results are consistent [15]. Fiber treated with Ca(OH)\({}_{2}\) can be understood in a similar way to fiber treated with NaOH, but with a much less effective treatment. There may also be some contaminants present, especially seen in the peak at around 29\({}^{\circ}\), relating to Ca(OH)\({}_{2}\) impregnation into the fibers. To understand the presence of amorphous and crystalline cellulose, the Segal Crystallinity Index (CI) was calculated, the results of which are summarized in Table 1, based on results from other authors. It is observed that the treatments with the highest contents of crystalline cellulose were NaOH and Ca(OH)\({}_{2}\), with CI = 66.21% and 54.73%, respectively. These values are consistent when analyzing the XRD results and the previous discussion. The KOH treatment (CI = 49.57%) is the most like natural fiber (CI = 48.56%), not representing an effective gain in the crystallinity of the material.
## 7 Future Directions  
In the previous sections, we discussed the background of HO and mobility management for 5G HetNets as well as the existing approaches to address these issues. However, the key challenges for HO and mobility management in future 5G HetNets were not fully explored. In this section, we recommend future directions to ensure the sustained performance of 5G HetNets in terms of HO and mobility management.  
Mobility is a key characteristic of users in cellular networks, and it can cause various problems such as HOF, early and delayed beam switching, and inter-system ping pong. Therefore, mobility management is a critical factor behind the applications of wireless cellular networks and their widespread adoption. Furthermore, according to the Ericsson Mobility Report [143], mobile data traffic is expected to increase 287 times between 2017 and 2027, which will exacerbate HO and mobility management issues.  
In 5G and B5G HetNet, QoS directly influences the QoE such that poor QoS can result in a lower QoE for the user and vice versa. Additionally, combining multiple services with varying QoS requirements can lead to the issue of QoS composition. If the QoS requirements are not managed and balanced accurately, it may lead to a reduction in QoE for certain services. Therefore, it is crucial to carefully consider and optimize both QoS and QoE in 5G/B5G HetNet to deliver an adequate user experience and enhance HO and mobility performance. The current HO and mobility management models are inadequate in delivering the expected QoS, resource efficiency, and QoE. In this survey paper, we recommend the following strategies to mitigate the HO and mobility issues in 5G and B5G HetNets and enhance the ability to support UE seamlessly.
## 2 Materials and Methods
### Preliminary Study to Evaluate the Amount of SSA to Incorporate in the Asphalt Mixtures
#### 2.4.2 Production of Mixtures and Compaction of Marshall Specimens  
After completing the particle size adjustment, the asphalt mixtures were produced with an equivalent binder content of 5.0%, usually applied in asphalt mixtures with natural aggregates (NA). However, the effective binder content decreases as the amount of SSA used in the mixture increases, due to its high density, according to Equation (1):  
\[\text{BC}_{\text{Eff}}=\text{BC}_{\text{Equi}}\times\text{APD}_{\text{NA}}/ \text{APD}_{\text{AggMix}}, \tag{1}\]  
where \(\text{BC}_{\text{Eff}}\) is the effective binder content used in the asphalt mixture, \(\text{BC}_{\text{Equi}}\) is the equivalent binder content of a similar asphalt mixture produced with NA, \(\text{APD}_{\text{NA}}\) is the apparent particle density of the NA, and \(\text{APD}_{\text{AggMix}}\) is the apparent particle density of the SSA and NA blend used in the mixture.  
Thus, the effective binder content used to produce each mixture was as follows:  
* AC 14 surf 35/50 with 50% SSA using 4.5% bitumen;
* AC 14 surf 35/50 with 80% SSA using 4.2% bitumen;
* AC 20 surf 35/50 with 50% SSA using 4.5% bitumen;
* AC 20 surf 35/50 with 95% SSA using 4.1% bitumen.  
The mixing temperature for all mixtures was 165 \({}^{\circ}\)C, based on the EN 12697-35 standard, for a 35/50 bitumen. After producing the asphalt mixtures, Marshall specimens were compacted with 75 blows per face, according to the EN 12697-30 standard, at 135-140 \({}^{\circ}\)C. At this stage of the work, six samples of each mixture were used to determine the sensitivity to water after assessing their air voids content.
# Muscle Activity and Biomechanics of Sprinting: A Meta-Analysis Review
## 3 Results
### Sprinting Speed Significantly Influences Muscle Activation  
Meta-analysis results (SMD = 0.68, \(p<0.001\)) confirm that increasing sprint speed leads to substantial increases in neuromuscular activation. The biceps femoris long head reaches peak activation (~110% MVC) during the late swing, while semitendinosus dominates in early acceleration, highlighting phase-dependent muscle recruitment. The gluteus maximus plays a crucial role in hip extension, with peak activation above 90% of maximal sprint velocity, emphasizing its contribution to sprint propulsion.
## 3 Results
### Effects of Pre-Fermentative Addition of UFAs Mixture on Volatile Compounds Production  
A total of thirty-five yeast-derived volatiles were determined in all wine in the end of alcoholic fermentation, including three C6-alcohols, nine higher alcohols, four fatty acids, fourteen esters (four acetate esters, seven ethyl esters, and three esters), two terpen, one sulfide, and two aldehydes compounds (Table S7). Sixteen aroma compounds with odor activity values (OAVs) exceeding one are highlighted and presented in Table 2, and the total amount of five group volatiles (C6 alcohols, higher alcohols, acetate esters, medium fatty acid, and ethyl esters) are also showed. Unlike major fermentation products, the initial addition of UFAs significantly influenced the most aromas produced by two _S. cerevisiae_ strains. Some of them follow similar patterns independent of fermented strains, while others showed strain-specific modes. Generally, the synthesis of C6-alcohols (1-hexanol, (E)-3-hexenyl-1-ol, (Z)-3-hexenyl-1-ol) and higher alcohols (isoamyl alcohol, isobutyl alcohol, 1-octyl-3-ol, 1-decanol, benzyl alcohol, and 2-phenylethanol) was improved, observing the highest content of these compounds in high-UFA wines regardless of the yeast strain, compared to the control, while the content of medium-chain fatty acids (hexanoic acid, octanoic acid, decanoic acid) and acetate esters (ethyl acetate, isoamyl acetate, hexyl acetate, phenethyl acetate) decreased. Surprisingly, a clear strain-specificity was found in the productions of ethyl esters, as it showed that their total contents was decreased by 30.0% and 22.8% in EC1118 strain fermentation, while they increased by 18.7% and 31.7% in the strain of BDX with the rise of UFAs level relative to the control wine. The affected ethyl esters are ethyl octanoate, ethyl decanoate, and ethyl dodecanoate, especially ethyl decanoate which, accounting for the total content of ethyl esters 52.9-62.8%, showed 23.3% and 19.2% decrement in low and high-UFAs wines fermented with EC1118 strain relative to the control wine, respectively, while the values were increased by 30.6% and 50.3% in
Although experimental methods are used to study the bending behavior and damage mechanism of CARALLs, finite element models display a more detailed stress-strain field with high designability. The simulation analysis could provide better insight into the failure of CARALLs. Furthermore, [PERSON] et al. simulated the damage mechanism of CARALLs through Linde failure criteria. [PERSON] et al. studied the application of Hashin criteria to CARALLs progressive failure analysis, but a single criterion alone has limited predictive capability [23]. However, previous studies did not consider complex layer methods, such as the stacking sequence of aluminum layers and carbon fiber-reinforced thermoplastic layers. Therefore, it is necessary to investigate the numerical simulation of the bending failure behavior of FMLs with proper failure criteria. Typically, CARALL laminates, consisting of carbon fiber hybrids and metal layers, display outstanding mechanical properties that can be easily adjusted according to specific requirements by changing the fiber orientation, thickness, and number of layers [25]. When CARALLs are subjected to bending loading, the carbon fiber exhibits a brittle fracture while the polymeric matrix part displays toughness, forming a complex damage behavior and failure mode. The development of carbon fiber-reinforced thermoplastic metal laminates is a novel hybrid material with lightweight and superior mechanical properties. However, the simulated models and failure criterion of CARALLs have been seldom reported. Therefore, the CARALLs with two stacking sequences were fabricated and the three-point bending experimental tests were conducted. Meanwhile, the failure behavior and damage mechanism of CARALLs were investigated by utilizing the Linde and Hashin failure criterion. The comparison between the two failure criteria and the bending behavior of this novel CARALL hybrid material are discussed.
## 1 Introduction
### Challenges and Opportunities for Haptic Feedback Implementation in VR  
Since 1992, hundreds of haptic devices have been invented for use across various application areas [21], and since 2015 alone, dozens have been specifically developed for use in VR [22]. In parallel, haptic rendering has been established as an architectural element of VR systems since 2004 [23]. Despite this, historically and currently, VR experiences have focused primarily on visual and auditory stimulation [1; 10; 24], while haptic feedback remains underutilized.  
The broader adoption of haptic feedback is hindered by several factors, including the complexity of the human somatosensory system [22], the high cost of specialized hardware, the technical expertise required for programming, and a general lack of industry standardization [1]. As a result, advanced haptic systems remain largely confined to specialized settings, such as research laboratories and industrial applications, many of which are still in the experimental or prototype phase [1].  
In the commercial VR landscape, haptic feedback in VR is provided through various devices, such as gloves, vests, and belts, but most often through native VR controllers. The use of native controllers or hand and body tracking has often been the preferred option for movement and interaction in the virtual world for practical reasons, such as a greater sense of presence, avoiding cybersickness, fine motor control, and physical engagement.  
Although traditional gamepads offer lower haptic feedback capabilities compared to these higher-end haptic devices, an increasing number of them are now equipped with actuators comparable to those found in VR controllers. At the same time, they remain significantly more affordable, making them a highly accessible platform for haptic interaction in VR. However, despite these advancements, the use of gamepad haptic feedback remains inconsistent across not only VR applications but also in video games. Many platforms fail to fully exploit the potential of this technology, leaving a significant gap in immersive and interactive design.
# Methicillin Resistance of Food-Borne Biofilm-Forming Staphylococcus-Crocei
###### Abstract  
The aim of this study was to evaluate the biofilm-forming ability and the resistance of planktonic cells and biofilm to methicillin (MIC and MBC, and log CFU/cm\({}^{2}\) reduction, respectively). Thirty-four isolates were used, including coagulase-positive _Staphylococcus_ (CPS) and coagulase-negative _Staphylococcus_ (CNS), recovered from ready-to-eat (RTE) foods. Biofilm-forming ability was based on enumeration of viable cells from biofilms formed on three types of surfaces, namely stainless steel, polystyrene, and glass. Thirteen of the thirty-four isolates (38.2%) were methicillin-resistant (MIC higher than 4 \(\upmu\)g/mL). _Staphylococcus aureus_ (CPS) and _Staphylococcus saprophyticus_ (CNS) were the species that showed the highest percentage of resistance (50% and 71.4%, respectively). _Staphylococcus varneri_ was the only species susceptible to methicillin. In 20 of the 34 isolates, the MBC value was twice the MIC value. The CPS (_S. aureus_) and the CNS (the other species) were not significantly different (\(p>0.05\)) on biofilm-forming ability on the three surfaces used. However, when comparing the averages obtained for the biofilm-forming ability of the isolates, the values obtained (log CFU/cm\({}^{2}\)) in polystyrene were significantly higher (\(p<0.05\)) than those obtained with the isolates on stainless steel. On the other hand, data from biofilm-forming ability of the isolates on glass and stainless steel are positively and significantly correlated with each other (\(\upmu=0.54\); \(p=0.02\)). It was not possible to determine the concentration of methicillin that promotes biofilm removal since log reductions were less than 3 log CFU/cm\({}^{2}\). The results of this study indicate that foodborne CPS and CNS can form biofilms on different types of material. As these biofilms are resistant to high concentrations of methicillin, their occurrence in food environments and their spread to medical settings can result in staphylococcal food poisoning or, in the worst-case scenario, septicemia, respectively. Good hygiene and good manufacturing practices (GHP and GMP) are therefore mandatory to prevent contamination with _Staphylococcus_ spp.  
foodborne _Staphylococcus_ spp.; biofilms; methicillin-resistant _S. aureus_ (MRSA); minimum inhibitory concentration--MIC; minimum bactericidal concentration--MBC
# Overview on Different Types of Solar Cells: An Update
## 3 Perovskite Solar Cells
### Commercialization of Solar Cells  
Irrespective of the high power-conversion efficiencies of Si, CIGS, and CZTSSe-based solar cells, high manufacturing costs and limitations in large-scale production are considered as major drawbacks for further versatile applications. Thus, various types of lost-cost solar cells such as small-organic molecule/polymer-based solar cells, dye-sensitized solar cells are being investigated aiming to replace Si-based solar cells. Recently, hybrid perovskite solar cells are considered as a potential replacement for Si-based solar cells due to their low production cost, high power conversion efficiency, lightweight, and possibility of flexible device fabrication. Yes, it is true that stability of perovskite solar cells is the major drawback, which pales in comparison to the 25-year stability of silicon. The longest reported lifetime for the prototype single-junction perovskite solar cell was 6000 h under continuous one-sun illumination before degrading by beyond 80% of its initial performance. As an alternative of single-junction cells, perovskite-Si tandem cells show better stability. Thus, several companies such as Oxford PV, Hanwha Q Cells, Greatcell Energy, Saule technologies, Quantum Solution, and Energy Materials are in the advanced stages of commercializing perovskite solar cells.
## 3 The Proposed Circuits
### FA/S Circuits Design  
A Full Adder/Subtractor circuit is a combination circuit where two addition and subtraction operations are performed. This circuit has three inputs (A, B, Cin) and three outputs (S\(\backslash\)D, Cout, Bout) [19; 20]. Equation (1) is the equation of the output S\(\backslash\)D, Equation (2) is the equation of the Cout output, and Equation (3) is the equation of the Bout output. Figure 12, block diagram, and Table 1 show the correct Table of this circuit.  
\[\text{S}\backslash\text{D}=\text{A}\oplus\text{B}\oplus\text{Cin} \tag{1}\]  
\[\text{Cout}=\text{M}(\text{A},\text{B},\text{Cin})=\text{A}.\text{B}+\text{A}.\text{C}+\text{B}.\text{C} \tag{2}\]  
\[\text{Bout}=\text{M}(\text{A}^{\prime},\text{B},\text{Cin})=\text{A}^{\prime}.\text{B}+\text{A}^{\prime}.\text{C}+\text{B}.\text{C} \tag{3}\]  
This paper designed FA/S circuits with the lowest number of cells, lowest consumable area, and lowest latency (delay), compared to the previous best examples. We used a single-layer (coplanar) design to obtain the best designs ever made. The design is better than previous ones not only in terms of cell number, area, and delay but also because it is based on a single layer. Figure 13 presents a block diagram of these circuits and Figures 14-16, show the implementation of the Proposed Full Adder/Subtractor (FA/S) circuits designs.  
Figure 11: Full Adder/Full Subtractor circuit [17]-b.  
Figure 12: Block diagram of the Full Adder/Subtractor circuit.  
Figure 13: Simulation results of the proposed XOR-gate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bout** & **Cout** & **S\(\backslash\)D** & **Cin** & **B** & **A** \\ \hline
0 & 1 & 0 & 1 & 0 & 1 \\
1 & 1 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Truth table of the Full Adder/Subtractor.  
Figure 16: The proposed (C) Full Adder/Full Subtractor circuit.  
Figure 14: The proposed (A) Full Adder/Full Subtractor circuit.  
Figure 15: The proposed (B) Full Adder/Full Subtractor circuit.
## 3 Experimental Results
### Model Verification Experiment  
#### 3.2.1 Teacher Model  
In this paper, SARSN34, 50, and 101 are used as teacher models in relational knowledge distillation. Except for the last fully connected layer of SARSN, all other layers are used as the feature extraction layer, and the fully connected layer is used as the classification layer. For the convenience of demonstration, the output characteristic map of the fourth layer of the teacher model training process is extracted, as shown in Figure 11a-f.  
Figure 10: Cycle GAN data migration results.  
Figure 11 shows that after the shallow feature information is extracted, the neural network can effectively divide the foreground area and the background area where the defect is located. For example, the green area in feature maps Figure 11a-c is identified as the defect area. Moreover, similar to small defects such as those of Figure 11a Cr and Figure 11d Ps, the feature extraction network can accurately capture all fine-grained information. It is shown that the teacher model can extract six categories of high-level features such as texture, shape, and edge.  
The teacher model training process is set to 200 epochs, and the learning rate is 0.1 in the first 60 epochs, 0.02 in epochs 61-120, 0.004 in epochs 121-160, and 0.0008 in the final 40 epochs. During the initial stage of training, a warm upwarmup is used. The batch size is 8, the cross-entropy loss is used as the loss function, SGD algorithms are used to update the weight parameters, and the softmax function is used as the classification function. Figure 12a,b show the accuracy and loss curves of the teacher model training process, and the accuracy of teacher model training and testing is shown in Table 3. In the experimental process of training the teacher model, the expected number of training rounds is set to 200 epochs, and the training time is about 26 h. After about 120 epochs, the training iteration process of the model tends to be stable. The loss value of the training loss function also tends to be stable in the process of decline. At this time, the training iteration time is about 17 h.  
Figure 11: SARSN visualization of training process. (**a**) Crack. (**b**) Inclusion. (**c**) Patches. (**d**) Pitted surface. (**e**) Rolled-in scale. (**f**) Scratches.