esa-sceva/satcom-chunk-collection huggingface dataset:
# Cornering Stiffness Prediction Based on Geometric Method
## 3 Results
### Comparison between Test Data and Model Parameterizing Result  
When comparing the MF cornering stiffness (\(K_{y}MF\)) and UniTire cornering stiffness (\(K_{y}UT\)) parameterizing results with the test result, we notice that they do not match very well with the measurement, as shown in Figure 14.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Tire 1\#** & & **Tire 2\#** \\ \hline
**Load (N)** & **Camber Angle (\({}^{\circ}\))** & **Load (N)** & **Camber Angle (\({}^{\circ}\))** \\ \hline
4680 & \(-\)8–8 & 4950 & \(-\)8–8 \\
7800 & \(-\)8–8 & 8250 & \(-\)12–12 \\
10,140 & \(-\)2\(\sim\)2 & 11,550 & \(-\)4–4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Load and camber angle range obtained from test data.  
Figure 11: Result comparisons. (**a**) Tire 1\#; (**b**) Tire 2\#.  
The difference between \(K_{y}MF\) and \(K_{y}UT\) is caused by the different expressions in Formulas (30) and (31). MF and UniTire are both semi-physical models. They use different formulas to show the decreasing cornering stiffness with increasing camber.  
For the data used here, we can observe that the effect of camber on the cornering stiffness seems to saturate for high angles for tire #1. Cornering stiffness is relatively constant after camber angles larger than 2\({}^{\circ}\). This phenomenon might be explained based on Figure 12.  
Taking tire 1# with 4680 N complete tire load as an example, the half-tire load changes from 610 N to 4071 N, where the curve of cornering stiffness varying with load is close to linear. A 42.5% change in load results in less than 1.2% change in cornering stiffness. This is why, in Figure 14a, the \(K_{y}test-load=4680\) N does not change noticeably after camber  
Figure 14: Result comparisons. (**a**) Tire 1#; (**b**) Tire 2#.  
Figure 12: Cornering stiffness as a function of the load curves of complete tire and half-tire.  
Figure 13: Result comparisons. (**a**) Tire 1#; (**b**) Tire 2#.  
angles larger than 2\({}^{\circ}\). When it comes to tire 2# with 4950 N complete tire load, the half-tire load changes from 413 N to 4540 N, where the curve of cornering stiffness varying with load is far from linear. A 45.4% change in load results in up to 20% change in cornering stiffness. This brings up the \(K_{y}test-load=4950\) N result in Figure 14b.  
The curves in Figure 12 are different for different tires, and consequently also for the test results in Figure 14. The MF and UniTire expressions fail to capture the camber and cornering stiffness relationship in some cases, especially for some larger camber angles. However, for this paper, the proposed method works well on both the empirical models and the measurement data.
# Mechanical Performance of Confined Autoclaved Fly-Ash-Brick Masonry Walls under Cyclic Loading
## 3 Numerical Simulation Analysis and Verification
### Mortar Strength on Seismic Performance of Assembly Autoclaved Fly-Ash-Brick Walls  
The influence of mortar strength on the seismic performance of assembly autoclaved fly-ash-brick walls was studied by the load-displacement and stiffness-displacement curves. From Equation (9) and simulation results, the bearing capacity values of walls ZWZ-1, ZWZ-2 and ZWZ-3 with different mortar strength are shown in Table 7. The load-displacement and stiffness-displacement curves of the walls with different mortar strength are shown in Figure 20. The plastic strain magnitudes of the walls with different mortar strength are shown in Figure 21.  
From Table 7, compared to wall ZWZ-2 with M7.5 mortar strength, the cracking loads of walls ZWZ-1 and ZWZ-3 whose mortar strength were M10 and M15 were increased by 64% and 71%, and the ultimate loads of those walls were increased by 13% and 16%. Meanwhile, the failure loads of those walls were increased by 6% and 12%. Poor strength of mortar leads to premature cracking, which is unfavorable to earthquake resistance of the walls. In the aspect of displacement ductility from the ultimate load to the failure load, the ratio of the failure displacement to ultimate displacement \(\Delta\)f/\(\Delta\)u of walls ZWZ-1, ZWZ-3 were increased by 13% and 17% compared to wall ZWZ-2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{\begin{tabular}{c} **Dimension (mm)** \\ **(Length \(\times\) Height \(\times\) Thickness)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Distribution of Holes** \\ **(mm)** \\ \end{tabular} } & \multicolumn{2}{c}{**Vertical**} & \multirow{2}{*}{
\begin{tabular}{c} **Mortar Strength** \\ **(MPa)** \\ \end{tabular} } \\  & & & & & \\ \hline ZWZ-1 & 2800 \(\times\) 2800 \(\times\) 240 & Without the opening & 0.6 & 10 \\ ZWZ-2 & 2800 \(\times\) 2800 \(\times\) 240 & Without the opening & 0.6 & 7.5 \\ ZWZ-3 & 2800 \(\times\) 2800 \(\times\) 240 & Without the opening & 0.6 & 15 \\ ZWZ-4 & 2800 \(\times\) 2800 \(\times\) 240 & Window opening 750 \(\times\) 900 & 0.6 & 10 \\ ZWZ-5 & 2800 \(\times\) 2800 \(\times\) 240 & Window opening 1000 \(\times\) 1200 & 0.6 & 10 \\ ZWZ-6 & 2800 \(\times\) 2800 \(\times\) 240 & Window opening 1200 \(\times\) 1200 & 0.6 & 10 \\ ZWZ-7 & 2800 \(\times\) 2800 \(\times\) 240 & Door opening 900 \(\times\) 2100 & 0.6 & 10 \\ ZWZ-8 & 2800 \(\times\) 2800 \(\times\) 240 & Door opening 1200 \(\times\) 2100 & 0.6 & 10 \\ ZWZ-9 & 2800 \(\times\) 2800 \(\times\) 240 & Door opening 1500 \(\times\) 2100 & 0.6 & 10 \\ ZWZ-10 & 2800 \(\times\) 2800 \(\times\) 240 & Without the opening & 0 & 10 \\ ZWZ-11 & 2800 \(\times\) 2800 \(\times\) 240 & Without the opening & 0.2 & 10 \\ ZWZ-12 & 2800 \(\times\) 2800 \(\times\) 240 & Without the opening & 1 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Dimensions and parameters of walls.
# Cluster-Based Transmission Diversity Optimization in Ultra Reliable Low Latency Communication
## 4 Simulation Results
### Optimum Number of Cluster Evaluation
#### 4.3.2 Reliability for Five Packet Duplication  
In Figure 9, just like 3-, 5-packet duplication achieves the best reliability of 99.9843% when 4-clusters are used at an arrival rate of 5000 pkt/s. Moreover, 3- and 5-clusters showed nearly identical results, with the reliability of 99.9795% and 99.97027%, respectively. However, the reliability is significantly decreased when sensors are plotted with 2- and 6-clusters, as they achieve reliability of 99.8232% and 99.9202%, respectively.  
Figure 8: Determine reliability for different clusters using 3-packet duplication. (**a**) Mathematical, (**b**) Simulation.
## 3 Results  
### Diversity Assessment among Phenological Stages  
A total of 4,032,594 high-quality sequences (Table 1A,B) were generated from all studied samples (n = 108). About 1,988,518 sequences were bacterial, with an average of 18,412 sequences per sample, whereas the rest (2,044,076) were fungal sequences, with an average of 18,926 sequences per sample. Moreover, \(\upalpha\)-diversity indices such as Chao1,Shannon, etc. were calculated as shown in Table 1A,B. The Chao1 scores ranged for bacteria from 192.2 (for bark and bud) to 339.3 (for fruit harvest stage), and for fungi from 172.2 (for bark and bud) to 214.3 (for berry set), whereas the Shannon scores ranged for bacteria from 1.76 (for berry set) to 2.35 (for harvest) and for fungi from 1.53 (for bark and bud) to 1.62 (for berry set). Good's coverage of fungi and bacteria reached 99.5% and 98.8% (Table 1A,B), respectively, indicating that most of the microbial diversity was captured through sequencing [18].  
Sequences were grouped into 789 OTUs for fungi and 1961 OTUs for bacteria (both at the 97% similarity level) (Table 1A,B). After removing singletons, the OTUs' numbers were 603 for fungi and 1296 for bacteria (Figure 1A,B). The different bacterial OTUs were assigned to 22 phyla, 35 classes, 70 orders, 162 families, and 418 genera. In the case of fungi, the OTUs corresponded to 4 phyla, 17 classes, 53 orders, 124 families, and 267 genera.
# A Hybrid Architecture for Safe Human-Robot Industrial Tasks
## 7 Experimental Results
### Co-Kitting Task
#### 7.2.2 Task Results  
Results are shown in Figure 9. To facilitate understanding of the graph, only the maximum values of the data related to the pairs of rigid bodies attached to each operator are shown, considering that the behavior of the two hands of each operator is almost similar during the task. The top graph compares the speeds of the operators \(v_{max_{1,2}}\) (light green line, operator B), \(v_{max_{3,4}}\) (blue line, operator A), and \(v_{R}\) (yellow line, robot end effector), while the bottom graph represents the computed distances that are processed to compute the robot speed scaling factor, \(k\) (black line). The minimum protective separation distances are \(S_{max_{1,2}}\) for operator B (orange line) and \(S_{max_{3,4}}\) for operator A (light blue line). They are compared with the respective distances, \(d_{max_{1,2}}\) (purple line) and \(d_{max_{3,4}}\) (violet line) and the minimum allowed distance \(d_{min}\) (red line).  
Figure 8: Snapshots of the co-kitting task experiment.  
Figure 9: Results of the co-kitting task experiment.  
More in detail, during STEP 1, the operators are not present in the collaborative workspace; thus, the robot moves at its maximum speed (\(k=1\)).  
STEP 2 starts at about 6 s: operator A walks towards the robot station; thus, the speed \(v_{max_{3,4}}\) increases up to a peak of about 2.0 m/s. During this motion, \(S_{max_{3,4}}\) increases and the distance \(d_{max_{3,4}}\) decreases, until they become very close: the robot slows down and then it stops (\(k=0\)) at about 9 s when \(S_{max_{3,4}}>d_{max_{3,4}}\). When operator A decelerates until he arrives in front of his assembly desk, \(v_{max_{3,4}}\approx 0\) m/s and \(k\) gradually increases.  
As for the inspection task, STEP 3 highlights that, even if the operator is almost stationary, the robot still slows down slightly when it passes in front of him to prevent risks (\(t\approx 12\) s).  
During STEP 4, operator B carries a toolbox to the robot work table and leaves the scene: \(S_{max_{1,2}}\) becomes more than \(d_{max_{1,2}}\); thus, the robot stops. It resumes the motion only when the operator reverses the direction of motion and moves away, at about \(t=25\) s.  
The robot also stops in STEP 5, when operator A takes the toolbox and its distance becomes less than \(d_{min}\): the robot slows down and then it stops at about \(t=31\) s.  
Finally, operator A leaves the scene at about \(t=46\) s while, at the same time, operator B enters the workspace (STEP 7). Note that, even if both \(v_{max_{1,2}}\) and \(v_{max_{3,4}}\) increase, \(S_{max_{1,2}}\) increases because operator B is approaching the robot while \(S_{max_{3,4}}\) decreases because operator A is moving away. During the last step of the experiment, STEP 8, operator B replaces a drip tray on the robot table and \(d_{max_{3,4}}<d_{min}\), thus \(k=0\). Only when he leaves the scene does the robot resume its trajectory at its maximum speed.
## 2 Expansion Strategies
### \(\gamma\delta\) T Cell Modulation with Different Substances
#### 2.4.4 Monoclonal Antibodies  
Studies have shown that V\(\gamma\)9V\(\delta\)2 T cells are capable of mediating Ab-dependent cellular cytotoxicity (ADCC) via their Fc receptor (FcRIIIA, CD16). This mechanism can be exploited by combining mAb and \(\gamma\)\(\delta\) T cells in cancer therapy. Using tumor associated antigens (TAA)-specific Abs, V\(\gamma\)9V\(\delta\)2 T cells can be directed to the tumor site. A pre-clinical study, focusing on human mammary gland carcinoma, showed that treatment with adoptive transfer of BrHPP + IL-2-expanded V\(\gamma\)9V\(\delta\)2 T cells alone had a slight impact on tumor growth; however, when combined with trastuzumab (anti-Her2 Ab), the outcome was significantly improved [123]. Similarly, dinituximab, an anti-GD2 mAb, increased the tumor lysis activity of \(\gamma\)\(\delta\) T cells by 30% in a neuroblastoma murine model [124].  
In a phase I/II clinical trial of low grade follicular lymphoma, rituximab (anti-CD20 Ab) was administered in combination with in vivo BrHPP-activation of V\(\gamma\)9V\(\delta\)2 T cells, and resulted in improved treatment efficacy [125]. Rituximab and two other mAbs (obinutuzumab and daratumumab) were also tested in B cell malignancies in order to improve \(\gamma\)\(\delta\) T cell therapy. Obinutuzumab was found to be most effective in prompting ADCC by \(\gamma\)\(\delta\) T cells against B cell lymphoma cells [98].  
Another exciting Ab-based approach is employing bispecific Ab targeting both TAA and V\(\gamma\)9, hence bridging V\(\gamma\)9V\(\delta\)2 T cells with the tumor cells [126]. Using a bispecific Ab engaging both HER2/neu and \(\gamma\)9 in combination with adoptive transfer of V\(\gamma\)9V\(\delta\)2 T cells in a pancreatic ductal adenocarcinoma xenograft mouse model, a significant reduction in tumor growth was achieved, while the single arm V\(\gamma\)9V\(\delta\)2 T cell therapy was not effective [127].
# Multi-Feature Extension via Semi-Autoencoder for Personalized Recommendation
## 3 Preliminaries  
### AutoEncoder and Semi-AutoEncoder  
The AEs are deep learning architectures for unsupervised learning, which is mainly characterized by learning and representing input information as an objective. Through the reconstruction of the representation, the model seeks to minimize the discrepancy between the input and output data [28; 29]. Recently, AEs have been frequently used for feature extraction, anomaly detection, and dimensionality reduction [35].  
In light of the AE, [PERSON] et al. suggested to break the dimensionality restriction of the output, and the input must be equal [8], which can design the two variants of AE. The input layer of these variants can be longer/shorter than the output layer. The model shown in Figure 1b can be properly trained, but it is difficult to give a reasonable interpretation for these generated entries. The structure of semi-autoencoder is shown in Figure 1a; compared to traditional autoencoder, semi-autoencoder models make it easier to combine more features in the input layer, which can learn a better feature representation and mitigate data sparsity of the rating matrix.  
Similar to the autoencoder model, the structure of semi-autoencoder has three layers as well: input layer \(x\in\Re^{S}\), hidden layer \(\xi\in\Re^{H}\) and output layer \(x^{\prime}\in\Re^{D}\), where \(H<D<S\). The network is formulated as (1) and (2):  
\[\xi=f(W\cdot x+b) \tag{1}\]  
\[x^{\prime}=g\big{(}W^{\prime}\cdot\xi+b^{\prime}\big{)} \tag{2}\]  
where \(W\in\Re^{H\times S}\), \(W^{\prime}\in\Re^{D\times H}\) are the weighting matrices, and \(b\in\Re^{H}\), \(b^{\prime}\in\Re^{D}\) are the bias vectors. Moreover, the nonlinear activation functions of the encode and decode layers are represented by \(f\) and \(g\), respectively. Unlike the traditional autoencoder model, the subset \(sub(x)\), which is taken from the input \(x\) and has the same dimension as the output \(x^{\prime}\), is reconstructed by the semi-autoencoder from the input. The objective function can be formulated as (3):  
\[\min_{W,W^{\prime},b,b^{\prime}}J=\big{\|}sub(x)-x^{\prime}\big{\|}^{2} \tag{3}\]
## 3 Task Planning Model and Solving Algorithm
### Task Planning Model
#### 3.1.2 Optimization Objective Function  
The first optimization subobjective considers the proportion of the task priority completed by the satellite in the total task priority, which is defined as the priority function. Maximize the task priority function:  
\[\text{max}DOR=\frac{\sum\limits_{u=1}^{N}x_{u}prio_{u}}{\sum\limits_{u=1}^{N} prio_{u}} \tag{10}\]  
where \(\mathbb{C}^{c}_{i}\) represents the set of clustering tasks within the ith orbit, \(\mathbb{C}^{c}_{i}=\left\{\mathbb{C}^{c}_{i\_1^{\prime}}\mathbb{C}^{c}_{i\_2 ^{\prime}}\cdots\mathbb{C}^{c}_{i\_N}\right\}\),\(prio_{u}\) is the priority of \(\mathbb{C}^{c}_{i\_u}\). \(x_{u}\) indicates whether task \(\mathbb{C}^{c}_{i\_u}\) is completed. If it is completed, its value is \(1\); otherwise, its value is \(0\).  
The second optimization sub-objective is to maximize the number of observation tasks.  
The ratio of the number of tasks completed by the satellite to the total number of tasks is defined as the completion degree function \(DOC\):  
\[\text{max}DOC=\frac{\sum\limits_{u=1}^{N}x_{u}count\mathbb{C}^{c}_{i\_u}}{count \mathbb{C}^{c}_{i\_u}}. \tag{11}\]  
In the above formula, \(count\mathbb{C}^{c}_{i}\) is the number of original tasks and \(count\mathbb{C}^{c}_{i\_u}\) is the number of original tasks in clustering task \(\mathbb{C}^{i}_{c}\).  
During the task planning process, multiple objective functions may not reach the maximum simultaneously, and the objective functions may conflict with each other. In the task planning process of this paper, we weighted two sub-objective functions and set the influence coefficients of the two subobjective functions. We considered the importance of optimizing sub-goals, and the overall goal of defining optimization is:  
\[\text{max}Goal=a*\text{max}DOR+b*\text{max}DOC \tag{12}\]where \(a,\ b\) are the weighted influence coefficients, \(a+b=1\). In this paper, the task in priority is given a greater weight. Let, \(a=0.8\), and \(b=0.2\).
# Research on Lifespan Prediction of Cross-Linked Polyethylene Material for XLPE Cables
###### Abstract  
In this paper, cross-linked polyethylene (XLPE) cables of the same batch from Factory A, which ran from 1 to 8 years in Jiangsu Province, are sampled. Some widely accepted aging characterization methods of XLPE cables such as the gel content test, differential scanning calorimetry (DSC) test, tensile test and hardness test are employed to obtain the physicochemical, mechanical and electrical properties of the samples. Then, some lifespan prediction parameters significantly correlated with operating time are obtained through correlation calculations. Finally, a prediction method is proposed to predict the operating time of XLPE cables from Factory A. The test results indicate that parameters including the gel content \(C_{ge}\), the crystallinity \(X_{C}\), tensile strength \(\sigma\), ultimate elongation \(\delta\), the dielectric permittivity \(\varepsilon\), and the dielectric loss \(J_{tun}\) are significantly correlated with operating time, which can be used in evaluating the aging degree of XLPE cables. Moreover, due to the high accuracy of the experimental verification, it turns out that the lifespan prediction method proposed in this paper can be used to determine the operating time of XLPE cables from Factory A in future research.  
XLPE cables; lifespan prediction parameters; correlation calculation; BP neural network +
Footnote †: journal: Applied Physics Letters
# Effect of Extraction Method and Thermosonication on Apple Juice Quality
## 3 Results and Discussion
### Antioxidant Activity of Apple Juice  
The antioxidant activity of bioactive substances contained in apple juice is as significant a determinant of quality as the total polyphenol content. The influence of treatment type on the antioxidant activity of apple juice is presented in Figure 7. A minor though statistically significant reduction of antioxidant activity of apple juice was observed after pasteurization. In the case of ultrasound treatment, no statistically significant changes of antioxidant activity were observed.  
In this field, our results differ from those obtained by other authors. [PERSON] et al. [45] found increased antioxidant activity during sonication of lime juice, and [PERSON] et al. [46] during ultrasound treatment of apple juice.  
Free radical scavenging capacity is typically closely correlated with polyphenol content in juice. Numerous studies have demonstrated a strong relationship between the polyphenol concentration and antioxidant capacity of foods [55; 56; 57; 58; 59].  
Figure 7: Effect of preservation method on antioxidant activity of apple juice. Error bars represent standard deviations. Values that share the same letter are not significantly different at P < 0.05.  
On the other hand, [PERSON] et al. [60] did not find any relationship between antioxidant capacity and total phenolic content in apple tissues. This lack of correlation could be due to the weakened antioxidant capacity of polyphenols as a result of the action of cavitation. Another cause may be the decomposition of other bioactive substances influencing the total capacity of juice to scavenge free radicals.
## 5 Functions Regulated by Quorum Sensing in Rhizobia
### Exopolysaccharideacharide Production  
In rhizobia such as Rlv and _S. meliloti_, QS regulation influences production and/or processing of different EPS. QS also controls expression of EPS-related genes in _S. fredii_ NGR234 [88]. In Rlv _cinS_ and _expR_ positively regulate the expression of _plyB_, which encodes a glycanae that cleaves the acidic EPS. This EPS is required for infection of peas. However, mutations in _cinS_ or _expR_ which decrease or abolish expression of _plyB_ result in an increase in biofilm formation but do not affect modulation [80]. In _S. meliloti_, the synthesis of galactoglucan or EPS II, which involves participation of _exp/wge_ gene-encoded proteins, is abolished in the absence of any of the genes of the ExpR/Sin system [36; 37], leading to a characteristic dry colony phenotype as mentioned in the previous Section 2. Moreover, the ExpR/Sin system also controls the production of succinoglycan or EPS I, which requires proteins encoded by the _exo_ genes [38]. Without a functional QS regulatory system, levels of the low molecular weight (LMW) form of EPS I, which is the essential fraction for nodule invasion, are severely affected. More recently, the ExpR/Sin regulatory system has been shown to control the synthesis of a novel mixed-linkage \(\beta\)-glucan (MLG) [91]. Expression of the _bgsBA_ genes encoding the enzymes required for the synthesis of this polysaccharide is activated by an artificial increase in levels of the second messenger cdG, and is dependent upon the ExpR/SinI regulatory system. Bacterial invasion of developing nodules induced by _S. meliloti_ requires the synthesis of the LMW form of at least one of the two symbiotically essential EPS, succinoglycan or EPS II. Moreover, the LMW fraction of EPS II is crucial for biofilm formation and contributes to alfalfa root colonization [95]. MLG is not required for modulation or nitrogen fixation, although it might influence root colonization.