esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Studies on osteoarthritis of the knee have examined isolated aspects of the meniscus biomechanically and histologically, but not the difference between instantaneous modulus (IM) in healthy and diseased samples. The aim of this study is to investigate the difference in the biomechanical behavior and proteoglycan content between arthritic and arthritis-free menisci. In addition, the relationship between the biomechanical behavior and proteoglycan content should be investigated. A novel indentation-based method was used, which allows the mapping of the entire meniscus, without damaging it for histological examinations. A total of 26 arthritic and 14 arthritis-free samples were examined in the present study. A Mach-1 Model V500 css test machine was used for biomechanical testing. A position grid was placed over each sample allowing all measurements (indentation test and thickness measurements) to be taken at the same position. All sections were then graded for Safranin O staining intensity for proteoglycan content. The maximum applied load of our arthritic samples above the posterior horn was statistically significantly higher (\(p\) = 0.01) at 0.02 \(\pm\) 0.02 N than the maximum applied load of the arthritis-free samples at 0.01 \(\pm\) 0.01 N. The proteoglycan content of the meniscus, evaluated by the Safranin O score, correlated statistically significantly with the maximum applied load over the entire meniscus (\(p\) = 0.04, 95% CI: 0.06-0.71). The present study showed that in the final stage of gonarthritis, the attenuation behavior of the meniscus was significantly lower compared to the arthritis-free knee. The mapping of IM and histological examination of the meniscus showed a direct correlation between changes in proteoglycan content and altered mechanical properties of the meniscus in gonarthritis.  
knee osteoarthritis; knee joint biomechanics; meniscus; biomechanical testings; mapping; indentation; instantaneous modulus; proteoglycan content
# An Overview of the Public Health Challenges in Diagnosing and Controlling Human Foodborne Pathogens
## 4 Diagnostic Approaches of Foodborne Pathogens
### Methods Based on Nucleic Acids
#### 4.2.2 Isothermal Amplification
As soon as NASBA detects the targeted sequence of RNA, a primer is attached to it, and then reverse transcriptase creates a strand of cDNA from the primer. A second primer for reverse transcription is attached to the template RNA, after which the template RNA is digested by RNase H, to create double-stranded cDNA by reverse transcription of the template RNA. In the end, RNA transcripts are generated through amplification using T7 RNA polymerase in the presence of amplification product [153]. Since RNA is amplified using an RNA polymerase without being converted to cDNA, this technique is exceptionally well-suited for testing for RNA viruses due to the fact that it uses an RNA polymerase without being converted to cDNA [154; 155]. The NASBA analytical approach has been successful in detecting a number of foodborne pathogens that have been sensitive, specific and fast enough to be applied in a number of food-borne situations [18; 156]. However, NASBA is not making any progress toward being adopted as an analytical method in food analysis, despite its well-established status as a clinical diagnostic method with significantly greater specificity for pathogen detection compared to quantitative PCR. Despite the limitation, this is a real disappointment, because it is different to PCR in that it can detect live cells by amplification of messenger RNA, even if genomic DNA is present in the sample. Although the NASBA method was quite reliable, it occasionally revealed the presence of amplifications that were not expected. It is imperative to develop techniques that can be used effectively to detect live microbial pathogens in food quickly, accurately and specifically, and furthermore, it may be worth exploring and utilizing the potential of the NASBA in this regard [157].
# Influence of Silane-Modified Coal Gangue Ceramsite on Properties of Ultra-High-Performance Concrete
## 2 Materials and Methods  
### Raw Materials  
The cement used in the experiment was P.O 42.5R. The fly ash was purchased from Yangzi Petrochemical in Nanjing, China; it has a pozzolanic and micro-aggregate effect, and can also reduce CO\({}_{2}\) emission by replacing part of the cement [27; 28]. Silica fume was purchased from Ekeng silicone in Shanghai, China. The fineness and high activity of silica fume can significantly improve the compressive strength and tensile strength of concrete. In this study, the amount of fly ash and silica fume accounts for 15% and 20% of the cementitious materials, respectively. The coal gangue ceramics (CGC) came from Dongchen Group in Huainan, China, as shown in Figure 1. In this experiment, CGC with particle sizes ranging from 0.075 mm to 2.36 mm and an apparent density of 1780 kg/m\({}^{3}\) was used as aggregate. The chemical compositions of the above-mentioned materials are shown in Table 1.  
As shown in Figure 2, the steel fiber used in the experiment has a length of 13 mm, a diameter of 0.2 mm, a density of 7850 kg/m\({}^{3}\) and a tensile strength of more than 2200 MPa. In this study, the amount of steel fiber is 1.5% of the volume ratio of cementitious materials. The water reducer used is polycarboxylic acid superplasticizer (PCE).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Ingredient** & **CaO** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **MgO** & **SO\({}_{3}\)** & **K\({}_{2}\)O** & **TiO\({}_{2}\)** & **LOI** \\ \hline Cement & 57.03 & 21.35 & 6.45 & 3.69 & 4.01 & 3.10 & 0.75 & 0.28 & 2.51 \\ Silica fume & 0.54 & 90.45 & 0.56 & 0.44 & 0.87 & 0.27 & 1.57 & 0.01 & 3.58 \\ Fly ash(I) & 13.45 & 55.39 & 19.05 & 2.46 & 0.1 & 0.22 & 2.75 & 0.07 & 9.87 \\ CGC & 1.45 & 68.39 & 18.93 & 4.62 & 0.91 & 0.17 & 2.63 & 0.74 & 1.67 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of cement, fly ash, silica fume and coal gangue ceramics.  
Figure 1: Coal gangue ceramics.  
Figure 2: Steel fiber.
## 5 Traffic Hazard Prediction Model
### Simulation Assumptions
The F1-score metric, as a harmonic mean of precision and sensitivity \(1=2\cdot\frac{precision\cdot recall}{precision+recall}\), balances the accuracy of risk class predictions, e.g., by minimizing errors in identifying high-risk (class 2) individuals. In this class, precision would prevent unnecessary alarms, and sensitivity would ensure that most threats are detected. The metric \(\text{Recall}=\frac{\text{True Positives}}{\text{True Positives}+\text{False Negatives}}\) would focus on the detectability of rare events, e.g., extreme speed violations, which is directly related to the logistic regression equation \(\text{P}(y=k\mid X)=\frac{e^{\beta_{0k}+\sum_{i=0}^{k}\beta_{i}X_{i}}}{\sum_{i =0}^{k-1}e^{\beta_{0i}+\sum_{i=1}^{k}\beta_{i}X_{i}^{i}}}\), where class probabilities help minimize false negatives in critical traffic situations. In the future, after implementing the model on synthetic data from SUMO, these metrics could be calculated based on a comparison of predicted and actual labels, thereby strengthening its application in hazard prediction.
## 4 Performance Results
### Performance Indicator
#### 4.1.5 F1 Score  
The \(F1\) score is a numerical value representing the harmonic average of the Precision score and the Recall score. It is used as an indicator to express the two performance indicators in a balanced way, and the formula is as follows.  
\[F1=2\cdot\frac{Precision\cdot Recall}{Precision+Recall} \tag{12}\]  
It is an indicator that uses precision score and recall score together, which are in a trade-off relationship, and the closer the F1 score is to 1, the better the model performance can be evaluated.
# Intersection Routing Based on Fuzzy Multi-Factor Decision for VANETs
## 3 Intersection Routing Based on Fuzzy Multi-Factor Decision
### Vehicle Decision
#### 3.2.5 Two Conditions of Link Discover  
If an SN finds that the link between it and the adjacent SN is broken (link expires), the node broadcasts a message (as shown in Table 2) to a vehicle on the corresponding road segment. In one condition, the vehicle selects the next-hop vehicle according to the above formulas until the message reaches the target intersection node. Then, the target intersection \(SN_{2}\) sets up the reverse path from all vehicles back to the source intersection \(SN_{1}\) (Figure 2a). In another condition, vehicles \(V_{a}\) and find that each of their source intersection is the other's target intersection (Figure 2b). Each vehicle then adds the other's link information to its own table to form a complete link path between two adjacent intersection nodes, and update the new link information along the reverse path to two intersection nodes.
## 4 Numerical Examples
### Example 2--Application of BESO and SESO to the Design of an Apple  
One of the most intriguing questions about nature and structures that evolve naturally is \"What is the reason for them to have the optimal configuration they do?\" To answer this question and validate the implementation of the structure's self-weight, the evolutionary optimization methods BESO and SESO, for three-dimensional structures, are applied in a natural optimization design. Figure 4 presents the design domain and boundary conditions of a structure that will only be subjected to the action of its weight. Thus, it would be possible to verify whether these methods could reproduce the same shape of an apple created by nature in order to determine whether its appearance was influenced by a genetic or structural perspective.  
The design domain was subdivided using 8-node hexahedral elements, as proposed by [25], with dimensions of 1 \(\times\) 1 \(\times\) 1 mm. The applied load consists of a downward acceleration of 1 g, equivalent to \(-\)9.8 m/s\({}^{2}\). The assumed density for all elements was 2700 kg/m\({}^{3}\), and the modulus of elasticity of the material was assumed to be 70 \(\times\) 10\({}^{3}\) MPa, as proposed by [10]. The prescribed final volume is \(V_{f}=0.30\) and the filter radius is \(r=2\) mm. The design domain illustrated in Figure 4 has 10,543 hexahedral finite elements. In the evolutionary optimization procedure, the evolutionary ratio used in BESO was \(ER=0.03\), while in SESO \(RR=0.01\) and \(ER=0.02\) were considered. [PERSON]'s ratio was equal to 0.30. Figure 5 illustrates that the optimal configurations obtained by the methods presented in this article resemble the shape of a fruit, specifically that of an apple. This result is notable, as the final topology was achieved exclusively through structural constraints. This suggests that, at least for this domain, the final form was predominantly developed from a structural perspective rather than biological reasons.  
Figure 3: Optimal topologies: (**a**) BESO and SESO principal stresses and (**b**) BESO and SESO Sensitivity Analysis.  
In Figure 5, it is observed that both by the BESO method, Figure 5a,b, and by the SESO method, Figure 5, the influence of self-weight results in traction in the structure. This effect causes tension in the elements located close to the center of gravity, evidenced by the blue color. On the other hand, the elements closest to the ends of the structure are subject to compression, indicated by the color red.
## 3 Results and Discussion
### Multiclass Pesticide Extraction by Immersion SPME
\begin{table}
\begin{tabular}{l l c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Percent Ethanol**} & \multicolumn{3}{c}{**2\%**} & \multicolumn{3}{c}{**6\%**} & \multicolumn{3}{c}{**10\%**} \\ \hline \multicolumn{2}{c}{**Time (Min)**} & \multirow{2}{*}{**Class**} & \multirow{2}{*}{**120**} & **150** & **180** & **120** & **150** & **180** & **120** & **150** & **180** \\ \multicolumn{2}{c}{**Analytic**} & & & & & & & & & & & \\ \hline Metazachlor & Chloroacetamide & 100 & 86 & 79 & 33 & 27 & 33 & 15 & 13 & 15 \\ Metolachlor & Chloroacetanilide & 86 & 97 & 100 & 52 & 44 & 53 & 25 & 23 & 25 \\ Pentachloroanisole & Organochlorine & 58 & 82 & 83 & 74 & 70 & 95 & 75 & 85 & 100 \\ Lindane & Organochlorine & 100 & 97 & 92 & 77 & 63 & 72 & 54 & 48 & 40 \\ Aldrin & Organochlorine & 45 & 61 & 68 & 43 & 51 & 75 & 58 & 73 & 100 \\ Malathion & Organophosphate & 87 & 100 & 99 & 62 & 52 & 58 & 30 & 29 & 29 \\ Chlorpyrifos & Organophosphate & 37 & 57 & 75 & 65 & 57 & 91 & 65 & 76 & 100 \\ Telluthrin & Pyrethroid & 42 & 65 & 67 & 65 & 52 & 89 & 67 & 72 & 100 \\ Prosulfocarb & Thoicadamate & 66 & 84 & 100 & 77 & 74 & 92 & 61 & 71 & 84 \\ tetraconazole & Triazole & 69 & 89 & 100 & 76 & 64 & 81 & 47 & 51 & 54 \\ Penconazole & Triazole & 72 & 83 & 100 & 74 & 54 & 82 & 48 & 48 & 52 \\ Tebuconazole & Triazole & 59 & 82 & 100 & 70 & 57 & 83 & 39 & 43 & 47 \\ Duprofezin & Unclassified & 35 & 62 & 82 & 61 & 60 & 95 & 60 & 74 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the two-factor, three-level full-factorial design. Signals were normalized, with the largest signal for each analyte being 100 and the others scaled accordingly. Figure 3: Relative signal values for 180 min extraction time under 2, 6, and 10% ethanol extractions.
# A Survey of Machine Learning-Based System Performance Optimization Techniques
## 2 Background
### Machine Learning Based Sort
#### 3.3.2 MER-Sort (Model-Enhanced Radix Sort)  
Machine learning sorting algorithm-based radix sort with almost linear time called MER-sort has been proposed [11]. When \(n\) data are given, radix sort performs bucket sorting of k phases with time complexity of \(O(kn)\)[24]. MER-sort is also similar to radix sort because MER-sort divides the bucket until the input is sorted. But MER-sort learns the CDF model, and the learned model predicts the position [11].  
Figure 8 shows the structure of MER-sort. In Step 1A, the model predicts the bucket where the input will be located when the unordered input array comes in. At this time, when the bucket overflows, the input enters the spill bucket. In Step 1B, the model repeats the same mapping operation until the number of inputs per bucket is less than the threshold (typically 100). Next, for a bucket of variable size, MER-sort determines how many inputs  
Figure 7: NN-Sort (Neural Network Sort) [10].  
each bucket has. The number of inputs per bucket is stored in Count Array. In addition, a bucket of variable size can be stored directly in the output array (Step 2). Finally, the spill bucket is merged with the output array, and insert sort is used for modification (Step 3). At up to 1 billion keys of standard normal distribution, MER-sort showed only 30% of sorting speed compared to other algorithms [11].
# Study on Seismic Performance of Steel Fiber Reinforced Concrete Pier Under Bending-Torsion Coupling
## 5 Discussion  
There are many parameters that affect the seismic performance of SFRC piers, such as the size of the specimen, the material properties, the reinforcement, the action mode of external loads, and so on.  
Considering the structural construction, this paper focuses on the parameters of the torsion-bending ratio and SFRC height, while the other parameters of the analysis models remained unchanged. Therefore, the optimal SFRC height proposed above is only applicable to piers similar to the analysis models considered in this paper, but the research results can provide a reference for the design of other forms of piers.  
Figure 14: Comparison of skeleton curves with different heights of SFRC. (**a**) Torsion–bending ratio 0; (**b**) torsion–bending ratio 0.09; (**c**) torsion–bending ratio 0.17; (**d**) torsion–bending ratio 0.27; (**e**) torsion–bending ratio 0.36.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Torsion–Bending**} & \multicolumn{5}{c}{**Increase Ratio of Bearing Capacity/\%**} \\ \cline{2-6}  & **RC** & **SFRC300** & **SFRC600** & **SFRC900** & **SFRC1200** \\ \hline
0 & — & 9.54 & 16.37 & 17.66 & 18.95 \\
0.09 & — & 8.96 & 13.62 & 16.09 & 18.68 \\
0.17 & — & 12.81 & 19.60 & 21.41 & 22.58 \\
0.27 & — & 5.63 & 36.99 & 37.19 & 39.70 \\
0.36 & — & 11.84 & 25.11 & 38.56 & 40.10 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The increase ratio of the maximum bearing capacity compared to the RC specimen.  
In terms of the external load, the maximum torsion-bending ratio of the analysis models considered in this work is 0.36, and the research results can be applied to the design of most bridge piers. In special engineering, when the bridge structure is very irregular, the torsion-bending ratio of the external load on the pier may exceed 0.36. Under a higher torsion-bending ratio, the seismic performance and optimal SFRC height of piers need to be further studied.
## 3 Results
### Field Application Performance  
Block Z18, which is currently developed by vertical-well CSS, is the main heavy oil contributor to Xinjiang oilfield. The formation includes five payzones, while the steam conformance is increasingly challenged by reservoir heterogeneity when CSS continues, and well-to-well steam channeling is a common phenomenon, which severely impacts the CSS performance. Taking a typical well A as an example, it is shown in Figure 10 that the steam proportion was 25.87% and 41.8% in the upper two zones, and in the lower zones, it was 8.93-11.2% at the third cycle. However, the steam preferential flow became evident with the cycles, and the steam proportion of lower ones constantly declined. At the seventh cycle, the steam proportion of the second zone has reached 53% and the fifth zone did not absorb steam.  
During the injection strategies determination of the plugging agent, the literature proposed that the multi-stage plugging method can effectively solve the problem of steam channeling, which provided guidance to the field injection process [25]. At the eight cycle, the optimized plugging agent, HTP, was injected into the formation, and the steam profile after that was encouragingly improved [3]. The steam proportion of the upper two zones declined to 13.1% and 11.3, and most steam entered into the lower zones, contributing to a steam proportion of 20.7% to 30.1% for each zone. Due to the steam profile improvement, the cyclic oil production was 217.6% that of the seventh cycle. It is predicted that the incremental oil recovery factor could reach 3-5% on the basis of 18.8% by conventional com  
Figure 10: Steam proportion profile at different cycles.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case No.** & **Kw1, mD** & **T**, \({}^{\circ}\)C & **Kw2, mD** & **Plugging Ratio, \%** & **Breakthrough Pressure Gradient, MPa/m** \\ \hline
1 & 13,524 & 220 & 53.45 & 99.60 & 28.33 \\
2 & 15,232 & 240 & 62.13 & 99.59 & 28.00 \\
3 & 16,110 & 260 & 65.56 & 99.59 & 26.66 \\
4 & 11,154 & 280 & 55.12 & 99.51 & 30.33 \\
5 & 12,565 & 300 & 57.89 & 99.54 & 27.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: High-temperature plugging results for sandpacks with different permeability.
# Smart Clothing Framework for Health Monitoring Applications
## 4 Challenges and Future Development
### Technical  
#### 4.1.1 Noisy and Discontinuous Signals from Sensors  
Physiological signals collected using smart clothing are noisy due to motion artefacts and thus misleading predictions of health condition. Appropriate methods [46] can be employed to filter the noisy monitored signal automatically at the time of data processing. Then, the filtered data can be used for assessing actual health and fitness conditions and taking appropriate actions.  
Handling a discontinuous signal generated by an integrated sensor in smart clothing is another challenge [7, 9]. This may be caused due to any fault appearing in the sensor  
Figure 19: Challenges of wearable technology in smart clothing from various perspectives.  
embedded within the clothing. Furthermore, an inappropriate attachment between the sensor and clothing may also generate discontinuous signals. Therefore, the development of reliable wearable sensors and a proper embedment between the sensor and clothing are required to avoid receiving any discontinuous signal.
## 4 Analysis of Results and Discussion
### Checking of Robustness and Reliability for Stacking-CRK Model  
The robustness of the ML model refers to its resistance to changes and perturbations in the input data. In practical applications, input data are affected by various factors, such as noise, missing values, outliers, etc. If the model is more sensitive to the changes and perturbations of these factors, the prediction performance of the model is bound to be affected. Reliability check refers to whether the model performs consistently on different datasets. There can be various ways to divide the training and testing sets. If the prediction performance of the model varies widely under different datasets, the reliability of the model cannot be guaranteed. Therefore, it is necessary to test the robustness and stability of the model. In this study, the STRC shear wall database established in Section 3.1 was randomly partitioned into training and testing sets in a 7:3 ratio for 100 iterations. The training sets for each iteration were augmented using GAN, and the Blending-CRKL model was used to predict the failure modes of STRC shear walls. The performance metrics of the model are shown in Figure 10. Despite variations in the predictive accuracy of the Blending-CRKL model on different datasets during the 100 random experiments, overall, the model still demonstrates excellent performance. From Figure 10, it can be seen that the precision, recall, F1-score, and accuracy of the Blending-CRKL model fluctuate around 97%, 96% and 0.96, 96%, respectively, with a maximum standard deviation of 1.48. These results indicate that the Blending-CRKL established in this paper exhibits good robustness and stability, making it suitable for predicting the failure modes of STRC shear walls.  
Figure 9: Comparison of confusion matrices for different STRC shear wall damage mode prediction models: (**a**) KNN; (**b**) BPNN; (**c**) RF; (**d**) LightGBM; (**e**) Catboost; (**f**) Blending-CRKL.
# Effects of Glyoxylic Acid on Metabolism and Ripening of 'Rocha' Pears Treated with 1-MCP
###### Abstract  
The application of 1-methylcyclopropene (1-MCP) is widely used to extend the storage life of climacter fruits, such as 'Rocha' pears. However, the suppression of ethylene's action by 1-MCP often results in excessive ripening delay, compromising fruit quality and consumer acceptance. In this study, we investigated the potential of glyoxylic acid (GLA) to counteract the effects of 1-MCP and promote ripening. To evaluate this, 'Rocha' pears treated with 1-MCP were exposed to 3% (\(m/v\)) GLA and stored at 20 \(\pm\) 2 \({}^{\circ}\)C for 15 days. Typical ripening indicators, such as firmness, skin color, ethylene production, respiration rate, volatile organic compounds (VOCs), sugars, and the activity of ethylene biosynthetic enzymes, were measured. Our results indicate that GLA did not induce significant effects on the ripening response, as ethylene production remained comparable to that of the control. Consequently, no significant changes in firmness, skin yellowing, or sugar content were observed in the GLA-treated pears. However, GLA significantly increased respiration rates (approximately 57%) and induced higher emissions of stress-associated VOCs, including hexanal, (E)-2-hexenal, and ethanol. This suggests that GLA may influence metabolic pathways related to energy metabolism and redox homeostasis without necessarily triggering ethylene-induced ripening. This study provides new insights into the interactions between GLA, 1-MCP, and fruit development, contributing to the development of alternative strategies to manage the effects of 1-MCP in 'Rocha' pear storage.  
1-methylcyclopropene; ripening-inducing strategies; GLA; postharvest ripening; ethylene 13 March 2025  
[PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Effects of Glyoxylic Acid on Metabolism and Ripening of 'Rocha' Pears Treated with 1-MCP