esa-sceva/satcom-chunk-collection huggingface dataset:
# Distinct Signatures of Host Defense Suppression by Plant-Feeding Mites
## 1 Introduction
Even less is known about how plants respond to a combined attack of defense-inducing and defense-suppressing mites, while previous analyses of such dual-infested plants have yielded valuable new insights into the mechanisms by which mites may facilitate competitors [23; 33], as outlined earlier. Here, we have used LC-MS/MS-based methods and gene-expression microarrays to analyze phytohormonal and transcriptomic changes, respectively, in tomato leaflets after attack by either a single mite species (_T. urticae_, _T. evansi_, or _A. lycopersici_) or two species simultaneously (_T. urticae_ plus _T. evansi_, or _T. urticae_ plus _A. lycopersici_) in order to more rigorously test the hypothesis that plant-mediated interactions between these mites mainly result from the induction versus suppression of host defenses. We have exploited the specificity of defense induction by single and dual infestations to reveal the mechanisms and the outcome in terms of plant resistance. Given the importance of JA and SA for tomato resistance to mites [34; 35; 38; 40; 53], we have focused our transcriptome analysis on direct and indirect defense responses that are (predicted to be) regulated by these hormones. That is, we have surveyed relative expression levels of genes involved in the production of: antimuttitive proteins, pathogenesis-related proteins, phenylpropanol pathway-derived secondary metabolites, volatile organic compounds, andsteroidal (glycol)alkaloids. Furthermore, we have investigated the possible involvement of crosstalk between JA/SA and other phytohormones during defense suppression. Finally, to find out if _T. evansi_ and _A. lycopersici_ target host processes other than defenses to trigger susceptibility, we have examined suppressor mite-specific effects on tomato gene expression. We confirm that defense suppression takes place downstream from the accumulation of JA and SA, and show that it probably acts independently from hormonal crosstalk. Additionally, our results indicate that both specialist mites likely manipulate their host beyond the suppression of its defenses, as _T. urticae_-triggered tomato responses were dampened on a transcriptome-wide scale by _T. evansi_ in dual-infested plants, and genes involved in cell cycle control and metabolite transport were specifically up-regulated in leaves (dual) infested with _A. Iycopersici_. Nonetheless, our results also clearly demonstrate that suppression of both JA and SA-regulated defenses by _T. evansi_ is distinct from the suppression of JA defenses by _A. Iycopersici_.
# Robot Joint Vibration Suppression Method Based on Improved ADRC
## 3 ADRC Control of Robot Joint Module
### ESO Design
#### 3.2.2 Speed Loop ESO Design  
According to the motor motion equation, with \(\omega_{0}\), \(\theta_{0}\), and \(\theta_{e1}\) as state variables, the state equation of the robot integrated joint module is written as  
\[\left\{\begin{array}{l}\dot{\theta}_{0}=\omega_{0}\\ \dot{\omega}_{0}=f(t)+f_{n}(t)+b_{0}T_{e}^{*}\\ f(t)=m\alpha\ddot{\theta}_{e1}(t)+c\alpha\dot{\theta}_{e1}(t)\\ f_{n}(t)=-b_{0}T_{load}(t)-b_{0}B(\omega_{0}-\dot{\omega}_{0})+n_{n}(t)\end{array}\right. \tag{26}\]  
where \(\theta_{0}\) is the motor angle, \(\omega_{0}\) is the motor speed, \(B\) is the motor damping, \(\alpha\) and \(b_{0}\) are the ESO parameters, \(\theta_{e1}(t)\) is the difference between the theoretical and actual positions of the robot joint module, \(m\) is the mass of the joint module, \(c\) is the damping of the jointmodule, \(\hat{\omega}_{0}\) is the estimated speed, \(n_{n}(t)\) is other unmodeled dynamic terms, \(f(t)\) is the vibration disturbance, and \(f_{n}(t)\) represents other unknown disturbances [24].  
The unknown disturbance \(f_{n}(t)\) includes the torque disturbance term of the load, other unmodeled dynamic terms \(n_{n}(t)\), and the mismatch between the estimated speed value \(\hat{\omega}_{0}\) and the actual motor speed value. This mismatch is related to the assembly accuracy of the encoder and the delay in data communication transmission. This article used a Nikon photoelectric encoder, which uses the 485 communication protocol and a communication transmission delay time of 40 \(\upmu\)s. The higher the motor speed, the greater the disturbance of the communication transmission delay on speed calculation.  
The ESO is used to observe the velocity signal in (26), the unknown disturbance term \(f_{n}(t)\) of the robot joint module is expanded to a new state variable as follows.  
\[\left\{\begin{array}{l}\dot{\theta}_{0}=\omega_{0}\\ \omega_{0}=f(t)+f_{n}(t)+b_{0}T_{e}^{*}\\ f(t)=ma\hat{\theta}_{e1}(t)+ca\hat{\theta}_{e1}(t)\\ \dot{f}_{n}(t)=h_{n}(t)\end{array}\right. \tag{27}\]  
where \(h_{n}(t)\) is the derivative of an unknown perturbation term.  
The standard form of the third-order ESO based on (26) and (27) is written as  
\[\left\{\begin{array}{l}e_{1n}=z_{1n}-\theta_{0}\\ \dot{z}_{1n}=z_{2n}-\beta_{1n}e_{1n}\\ \dot{z}_{2n}=z_{3n}+f_{n}+b_{0}T_{e}^{*}-\beta_{2n}e_{1n}\\ \dot{z}_{3n}=-\beta_{3n}e_{1n}\end{array}\right. \tag{28}\]  
where \(e_{1n}\) is the angle error value, \(z_{1n}\) is the estimated value of \(\theta_{0}\), \(z_{2n}\) is the estimated value of \(\omega_{0}\), \(z_{3n}\) is the estimated value of the unknown disturbance term \(f_{n}\), and \(b_{0}\), \(\beta_{1n}\), \(\beta_{2n}\), and \(\beta_{3n}\) are the parameters of the ESO [25].
## 1 Introduction
### Metabolic Demand in Football Players  
[PERSON] [21] introduced the term aerobic-anaerobic threshold in the literature, and it has undergone several modifications since then. He introduced the concept that an exercise intensity resulting in a lactate concentration of 4 mmol/L constituted the upper limit of exercise, above which lactate levels continued to rise. Diagnostic performance tests for the assessment of endurance performance in high-performance sports are hard to imagine today without the determination of lactic acid. Determination of lactate metabolism plays a major role in sports and performance medicine. It is used to record endurance performance and to evaluate training. Measuring lactate facilitates the assessment of metabolic processes during physical exertion, and the target parameter for diagnosis is the anaerobic threshold [20]. [PERSON], [PERSON], and [PERSON] [22] defined the individual anaerobic threshold as the point in time at which the maximum rate of elimination and the rate of diffusion of lactate are in equilibrium. They reported that the lactate concentration at the individual anaerobic threshold varies widely among individuals, thus emphasizing the need for individual assessment.  
The anaerobic lactate threshold largely determines the level of aerobic endurance capacity. Based on studies that have assessed endurance performance, it was proposed that the anaerobic threshold could be a more acceptable predictor of aerobic endurance than VO2 max. It was reported that a change in training could lead to a change in the anaerobic threshold without a change in the VO2 max [23]. It is indeed evident that the higher the level of football, the more frequent and longer the intervals of high-intensity exercise and the higher the lactate concentrations in the blood, which is often used as a criterion for anaerobic lactate production [1]. Earlier studies have indicated that the average intensity of a football game varies around the anaerobic lactate threshold, where the heart rate reaches 80 to 90% of the HRmax [4; 24]. Most football activities are executed with low to moderate intensity, so the total energy expenditure is supplied by the aerobic energy capacity [25]. A higher aerobic energy capacity generally leads to improved football performance, providing increased potential for sustainably covering longer running lengths with higher intensity [26]. However, about 28% of football activities are executed at high intensity, which leads to a simultaneous increase in the level of lactate concentration to a submaximal level.
## 4 Application Analysis and Results
### Application Results
#### 4.2.1 Effectiveness Analysis
However, the workload in the rework station in real production often exceeds the limit. As shown in Figure 5, the total time (including disassembly and re-assembly periods) of the SAA approach was 29.90% lower than the empirical method, confirming that the SAA solution gives a more reasonable aircraft configuration. These results indicate that the MTO strategy is optimal but inapplicable for uncertainty orders and that the GCA determined by the SAA approach is better than that obtained by the traditional empirical method.
## 5 Analysis of the Forecasting Results
### Single-Step Forecasting
#### 5.1.2 Analysis of the Four Different Wind Turbines  
This paper employs four wind turbines to conduct the forecasting experiment of the wind speed. The original time series of the four datasets are presented in Figure 3. The results from the four different datasets using different approaches are shown in Tables 2-5.  
From Figure 3, the general trend of the four time series is approximately the same, though there are still differences in some time points. The main reason for this difference is that in a general location, the wind speed is nearly the same. However, when narrowing down to a specific wind farm unit, the location and size of the wind farm varies, resulting in different wind speed data from one dataset to another.  
When analyzing from the specific forecasting results, according to the data presented in Table 4 for instance, the MAPE values of the proposed model from the four wind turbines are 4.79%, 3.07%, 2.69%, and 3.91%. For the forecasting accuracy, dataset 3 achieves the highest performance.  
**Remark 3**.: _Because of the uncertainty of the magnitude and direction of the wind in different locations, the forecasting results of four different datasets vary. The wind speed also experiences enormous fluctuations in different time periods. As a result, the prediction precision also varies due to different locations and time periods._
## 2 Introduction to DCPs  
Protective measures to prevent derailment, known as derailment containment provisions (DCPs), are widely used in three different types worldwide, as shown in Figure 3[26] and described in references [28; 29].  
Type 1 is installed between rails (Collision at wheel level). Type 2 is installed outside of rails (Collision at wheel level), and finally Type 3 is installed outside of rails (Collision at bogie level).  
DCP Types 2 and 3 have been applied to tracks for a long time, but Type 1 has been studied recently, so there are few studies that can theoretically estimate the impact load acting on the DCP. Therefore, among the three types of DCP, in this study, we will theoretically and numerically analyze the structural strength of DCP Type 1.  
The DCP Type 1 is installed to prevent a derailed train from leaving the track and colliding with surrounding structures by installing a long block in the center of two rails, as in Figure 4. The impact angle to the DCP would be small because the lateral moving speed and distance of the derailed train are small within two rails. Therefore, the impact force acting on the DCP can be reduced when compared with the existing derailment barriers installed outside of track [22].  
The advantage of the DCP Type 1 is that it can be quickly installed on an existing track where there is a risk of derailment. In addition, it is economical because its structural size is smaller than other existing derailment barriers.  
The design load of the derailment barrier in existing domestic lines is fixed as a horizontal load of 150 kN, as suggested in the railway design guidelines in Korea. However, the suggested design load is unsubstantiated without any proof. Therefore, this paper proposes a method to theoretically calculate the impact force on a DCP Type 1 after train derailment, using the Olson model and a spring-mass dynamic model composed of three bodies.
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 2 System Model
### The Model of UAV Location  
As shown in Figure 1, there were a total of \(M\) UAVs in the system, and the algorithm executed \(T\) time slots in total. The meaning of \(T\) is the maximum time allowed for UAVs to stay in the air. This indicator was set because the UAV swarm executed tasks far away from the base station. The UAV swarm then returns to the base station to unload data only after the task is complete. Therefore, restrictions must be implemented for \(T\) to prevent the energy depletion of UAVs.  
Figure 1: Scenario of AOA measurement for UAV swarm.  
For the safety of the UAV itself, the motion of the UAV swarm is restricted within a rectangular area composed of four vertices: \(\left[x_{\min},y_{\min},z_{UAV}\right]\), \(\left[x_{\min},y_{\max},z_{UAV}\right]\), \(\left[x_{\max},y_{\min},z_{UAV}\right]\), and \(\left[x_{\max},y_{\max},z_{UAV}\right]\). This area is defined as a feasible region, as shown in Figure 1. The location coordinate of the \(i\)-th UAV in the \(t\)-th time slot can be defined as \(\mathbf{r}_{i}^{t}=\left[x_{i}^{t},y_{i}^{t},z_{i}^{t}\right]^{T}\), and the location state vector of all UAVs is:  
\[\mathbf{r}^{t}=\left[\left(\mathbf{r}_{1}^{t}\right)^{T},\left(\mathbf{r}_{2}^{t}\right)^{ T},\cdots,\left(\mathbf{r}_{M}^{t}\right)^{T}\right]^{T} \tag{1}\]  
The location of the target was defined as \(\mathbf{u}=\left[x_{u},y_{u},z_{u}\right]^{T}\). Considering that the location of UAVs might be affected by force majeure, such as wind, the vector composed of the locations of all UAVs in the \(t\)-th time slot could be represented as:  
\[\mathbf{r}^{t}=\mathbf{\overline{r}}^{t}+\mathbf{n}_{l}^{t} \tag{2}\]  
where \(\mathbf{\overline{r}}^{t}\), \(\mathbf{n}_{R}^{t}\) represent the mean vector and location error, which can be represented as:  
\[\mathbf{\overline{r}}^{t}=\left[\left(\mathbf{\overline{r}}_{1}^{t}\right)^{T},\left( \mathbf{\overline{r}}_{2}^{t}\right)^{T},\cdots,\left(\mathbf{\overline{r}}_{M}^{t} \right)^{T}\right]^{T} \tag{3}\]  
\[\mathbf{n}_{R}^{t}=\left[\left(\mathbf{n}_{R,1}^{t}\right)^{T},\left(\mathbf{n}_{R,2}^{t} \right)^{T},\cdots,\left(\mathbf{n}_{R,M}^{t}\right)^{T}\right]^{T} \tag{4}\]  
where \(\mathbf{\overline{r}}_{i}^{t}=\left[\overline{x}_{i}^{t},\overline{y}_{i}^{t},z_{ UAV}\right]^{T}\), \(\mathbf{n}_{R,i}^{t}=\left[\Delta x_{i}^{t},\Delta y_{i}^{t},\Delta z_{i}^{t} \right]^{T}\).  
All \(\Delta x_{i}^{t}\), \(\Delta y_{i}^{t}\), and \(\Delta z_{i}^{t}\) are independent and identically distributed (I.I.D.) Gaussian random variables had a zero mean and variance \(\sigma_{R}^{2}\). The covariance matrix of \(\mathbf{n}_{R}^{t}\) could be obtained as:  
\[\mathbf{C}_{R}^{t}=diag\Big{\{}\sigma_{R}^{2},\sigma_{R}^{2},\cdots,\sigma_{R}^{ 2}\Big{\}} \tag{5}\]
## 6 Experimental Results
### Speedups
#### 6.1.1 Evaluation  
As described in the methodology section, for the evaluation stage, two parallel approaches were implemented. The examination and the chromosome-threaded approach. Moreover, the use of the CSR format was proposed in order to improve the performance, due to the small conflict density of many datasets. In Figure 4, the speedup obtained with the examination-threaded approach is depicted. This approach gives a better speedup than the chromosome-threaded approach. Moreover, it can be observed that the datasets with the larger speedup are the ones with less courses. This happens, because in the evaluation stage, there is a need for synchronization between the threads and for atomic operations for the big datasets, as explained in the previous section.
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 6 Performance Evaluation
### Evaluation Methodology
#### 6.1.3 Parameters  
For the sake of comparability, we choose most experimental parameters as in [6]. The clients are connected to the switch via 100 Mb/s links with a sufficiently large buffer size, and the switch is connected to the server via a 50 Mb/s link with a buffer size of 24 packets. All links are configured with a one-way delay of 5 ms.  
Activity meters are configured with a memory of \(M_{AM}=3\) s and a reference rate of \(R_{r}=10\) kb/s. The activity averager is configured with a memory of \(M_{AA}=0.3\) s and the activity AQM utilizes the parameters \(Q_{min}=6\) packets, \(Q_{base}=20\) packets, and \(\gamma=16\) packets.  
The results in this study differ from those in [6] in that we measure goodput instead of throughput, which is due to the Iperf tool, and that we utilize a larger bottleneck bandwidth of 50 Mb/s instead of 10 Mb/s in [6]. We cannot go to larger bottleneck speeds than 50 Mb/s in our experiments as the software switch BMv2 cannot read input traffic fast enough and drops packets at the ingress at higher speeds. Furthermore, we work now with \(Q_{min}=6\) packets instead of \(Q_{min}=12\) packets.  
As ABC artificially limits the queue size, the transmission of a single flow may be hampered, which is undesirable. However, we validated that with the chosen parameter set; the bottleneck link can be fully utilized by a single TCP flow.
## 3 Results  
### Surface Morphology of CPD, CPDs/PU and C\({}_{60}\)/PU Composite Films  
The surface morphology of CPD, CPDs/PU and C\({}_{60}\)/PU composite films was investigated by AFM operating in tapping mode. Figure 1a,b show the surface morphology of the CPD nanoparticles recorded in height and phase modes. It was observed that the CPD nanoparticles had a core-shell structure with a typical average core diameter of 33 nm (Figure 1b, phase retrace mode). In our previous research, we established that the average core diameter of CPDs was 35.2 \(\pm\) 2.1 nm [18]. The average diameter of the whole nanoparticle (core-shell) was 60 nm, whereas the average height was 3 nm.  
Viscoelastic measurement of Young's modulus of the CPDs is presented in Figure 1c,d. It was confirmed that the core-shell structure of the CPDs was revealed by phase measurement. Figure 1d presents [PERSON]'s modulus profile of the CPD nanoparticles, designated with a black line in Figure 1c. From Figure 1d, we can see that [PERSON]'s modulus of the shell and core was about 16 and 10 Gpa using the Hertz model, respectively.  
Figure 2 shows the surface morphology of neat polyurethane (PU), CPD/PU and C\({}_{60}\)/PU composite films. By Gwyddion software, we calculated the root mean square (RMS) roughness of these polymer composite films. The values of RMS roughness were the following: 3.14 nm (neat PU), 8.60 nm (CPDs/PU) and 8.39 nm (C\({}_{60}\)/PU). Encapsulation of CPDs and C\({}_{60}\) into the polymer matrix increased RMS roughness by 2.74 and 2.67 times, respectively.  
Figure 1: Top view AFM images of CPD nanoparticles: (**a**) height retrace mode, (**b**) phase retrace mode, (**c**) viscoelastic measurement of Young’s modulus and (**d**) profile of Young’s modulus.
# Study of the Diffusion of Rejuvenators and Its Effect on Aged Bitumen Binder
## 2 Materials and Methods
### Rheological Properties Test  
The dynamic shear rheometer (DSR) ([PERSON], Graz, Austria) is usually used to characterize both viscous and elastic behaviors of bitumen by measuring the complex shear modulus and phase angle. A temperature sweep test was conducted. A sinusoidal signal of stress was applied. The complex shear modulus represents the sample's total resistance to the applied deformation during sinusoidal stress conditions. Phase angle is described as the lag between applied shear stress and the resulting shear strain. The higher the complex shear modulus value, the stiffer the bitumen binder is. The larger the measured phase angle, the more viscous the binder is [29]. The temperature sweep test was performed at a fixed frequency of 10 rad/s and in temperature ranges from \(-10\) to 60 \({}^{\circ}\)C with temperature increments of 2 \({}^{\circ}\)C per minute.  
In this research, DSR tests were conducted on virgin bitumen, aged bitumen (RTFOT, PAV, UV), and rejuvenated bitumen with 8% R-1 or 8% R-2. According to the test specification [30], different geometries of specimens are specified for different temperature conditions. The diameter of the plate that contains the bitumen binder is 8 mm, and the thickness is 2 mm when the temperature is lower than 20 \({}^{\circ}\)C. When the temperature is higher than 20 \({}^{\circ}\)C, the plate diameter should be 25 mm, and the thickness of bitumen binder should be 1 mm. Strain sweep tests with increasing applied strain were first conducted to study the viscoelastic range of virgin, aged, and rejuvenated bitumen binder. Based on strain sweep tests, strains were selected based on viscoelastic ranges of every type of binder for temperature sweep analysis, ensuring that all of the performed DSR moduli and phase angle evaluations are conducted in the viscoelastic range of the tested bitumen binder. Table 3 summarizes the applied strains.  
Figure 1: Diffusion models: the initial stage (**a**) and the stable stage (**b**) of R-1 in the rolling thin-film oven test (RTFOT) aged asphalt models; the initial stage (**c**) and the stable stage (**d**) of R-2 in the RTFOT aged asphalt models.  
\begin{table}
\begin{tabular}{c c c} \hline
**Temperature Ranges** & **Virgin Binder, Rejuvenated Binders (\%)** & **RTFOT, PAV and UV Binders (\%)** \\ \hline \(-10\)–\(30\)\({}^{\circ}\)C & 0.03 & 0.02 \\ \(30\)–\(60\)\({}^{\circ}\)C & 1.5 & 1 \\ \hline \end{tabular}
\end{table}
Table 3: Applied shear stains during dynamic shear rheometer (DSR) analysis.
# Study on the Effect of Vibrating Process on the Compactness of Slipform Concrete
## 3 Analysis of Experimental Results
### Effect of Vibrating Parameters on the Macroscopic Porosity of Slipform Concrete  
#### 3.4.1 Effect of Vibrating Duration on the Macroscopic Pore Distribution of Slipform Concrete  
Image processing analysis on the cut surfaces of the experimental specimens, using Image-Pro Plus software IPP6.0, obtained the distribution of macroscopic pores on each group of cross-sections. The macroscopic pore distribution of the specimen cross-section was obtained at a vibration frequency of 8000 r/min and vibration time of 20, 40, 50, 60, 90, and 110 s (Figure 9).  
With the increase in vibration time, the macroscopic pores inside the concrete gradually decreased (Figure 9). At the early vibration stage, air bubbles of large pore sizes were rapidly discharged from the bottom of the concrete. During the vibrating time period of 20-40 s, the concrete had large pores, mainly distributed in the middle and upper parts of the specimen. When the vibration time was 50-60 s, the large pores were discharged from the concrete. In contrast, only a few small pores were observed inside the concrete when the vibration time was 90-110 s. Therefore, the under-vibration of the actual slipform paving process can be determined by observing the discharge of large air bubbles in the area of the vibration chamber.  
Figure 8: The separation rate of concrete under different vibrating parameters.