esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Evaluation Methods of the Performance of GSP
### Fatigue Life Performance
#### 3.2.1 Characteristics of Fatigue Life of GSP  
Raw materials have effects on the fatigue characteristics of GSP. To assess the fatigue life of GSP, the indirect tensile fatigue test (ITFT) and two-point bending test are commonly used under stress or strain control mode. Fatigue curves illustrate that modified asphalt (polymer asphalt or rubber asphalt) and low-shrinkage cement mortar dominate the fatigue life behavior, while binder content has a slight influence [123]. Moreover, cement may be the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
most effective material for fatigue life in GSP, compared with asphalt and aggregate [120]. The fatigue life also increases with the growth of the void rate in OMA, which means more cement mortar will be grouted in OMA [77]. Additionally, from the aspect of test conditions, the fatigue life decreases with the rising test temperature and cannot be changed with different loading frequencies [119; 124].  
The fatigue life of GSP has a linear relationship with stiffness modulus, of which the equation is expressed as below [125]:  
\[\text{log}N_{f}=\text{log}\ \alpha+\beta\cdot\text{log}\sigma+\gamma\cdot\text{ log}E_{100} \tag{1}\]  
\(\alpha\), \(\beta\), \(\gamma\) are the regression parameters. \(E_{100}\) is the stiffness parameter on the 100 th cycle of the fatigue test according to the European standard, and \(\mathbb{R}^{2}\) could reach 0.79.  
In general, GSP shows a better fatigue life than traditional AC, especially in a low-stress level [120; 125]. However, the fatigue life of GSP decreases more rapidly than that of AC and more slowly than that of semi-rigid material at the high-stress level. Due to this reason, GSP may be inferior to AC in anti-fatigue performance with a higher stress [38]. The cumulative fatigue life of GSP will be also shorter than that of AC based on MINER's Linear Fatigue Damage Accumulation theory, according to the traditional fatigue failure standard (50% of initial stiffness) [126].  
However, far different from the pattern of fatigue curves of AC, GSP does not have a sudden drop after loss of the 50% initial stiffness, and thus can still maintain its work capacity instead of failing with loading time [117]. Consequently, 10% residual stiffness is presented to be a new failure standard for GSP, and then the fatigue life of GSP is found to be greatly underestimated [127]. Therefore, failure standards are important and can lead to different results for the evaluation of fatigue life. Additionally, the strain control mode is recommended for fatigue life test of GSP due to its thin course structure applied in field, which shows a better fatigue life than the stress-control mode [123; 128].  
In 2015, [PERSON] et al., adopted the Cyclic Wheel Loading test on GSP with different OMA void ratios (20%, 23%, 26%, and 30%) [121; 122]. The failure standard was defined as a 20 mm crack on the surface. Finally, the repeated wheel loading times \(N_{e}\) was derived, which was close to the actual fatigue life in the field [121].
# A Two-Echelon Electric Vehicle Routing Problem with Time Windows and Battery Swapping Stations
## 4 Algorithms for the 2E-EVRPTW-BSS
### Initial Solution  
The initial solution (only the second echelon) is constructed based on [PERSON] et al. [15]. Firstly, for each satellite, the nearest customer node \(i_{0}\) is chosen, and all of the customers are sorted in ascending order of the angle between the customer itself, the satellite, and the \(i_{0}\). Then, the customers are inserted into one route iteratively. BSSs are inserted into the route when a violation of electric power occurs. If a violation of capacity occurs, a new route is created. To consider time window requirements, in each route, a customer \(u\) can be inserted between successive vertices \(i\) and \(j\) only when \(e_{i}\leq e_{u}\leq e_{j}\).
## 4 Discussion
As was the case in this investigation, studies show the \(\rm F_{red}\) region suffers from reabsorption of CF photons to a greater degree than the \(\rm F_{far}\) region, explaining the lower peak of \(\rm F_{red}\) than \(\rm F_{far}\), e.g., [3; 65]. Additionally, \(\rm F_{red}\) increased at a relatively higher rate than \(\rm F_{far}\) in response to DCMU poisoning, which was expected from this particular herbicide. DCMU as a photosystem inhibitor most affects PSII; thus, it is expected that the PSII inhibition of photosynthesis would manifest as a dramatic increase in \(\rm F_{red}\), which is the case in this study [28; 62; 66]. While there is a PSI and PSII contribution to both \(\rm F_{red}\) and \(\rm F_{far}\), \(\rm F_{red}\) is produced dominantly by PSII and is more variable compared to \(\rm F_{far}\). Furthermore, in healthy leaves, \(\rm F_{red}\) is close to or less than \(\rm F_{far}\), but in stressed leaves, \(\rm F_{red}\) increases and \(\rm F_{far}\) decreases [5; 60; 67]. It is for the reasons mentioned that the red peak (\(\rm F_{red}\)) and far-red peak (\(\rm F_{far}\)) and the red-far-red ratio (\(\rm F_{red}/F_{far}\)) are metrics that have been utilized to detect stressed vegetation and estimate plant functioning status, and all three were indicative of changing function of the target plant in this experiment [68; 69; 53; 62; 67; 62; 63; 64; 65; 66; 67; 68; 69; 70]. In this study, we demonstrate that a system using the HH2 and blue-red LED grow lamp to detect and monitor measure plant canopy level function, especially in the context of stress, has potential in a variety of applications. The capability to capture distinct signals of plant canopy stress earlier than traditional reflectance-based indices, NDVI and PRI, plus the capability to scale from small to larger canopies holds promise for both research and applications in the lab or field, at night for instance. Recently, two larger scale studies have employed LEDs with spectroscopy at night to study fluorescence responses, one in a forested area examined steady state responses of canopy and understory in a scots pine forest [71] and another used tractor mounted spectrometer and LEDs to measure fluorescence and compare to aerial net primary production among varieties of soybean, both rainfed and irrigated [72]. In the study of soybeans, the authors also show that the \(\rm F_{red}/F_{far}\) revealed differences in plant function among cultivars, while traditional RI's did not. Our results support findings in these studies, albeit on a smaller scale, such that might be found in a laboratory or greenhouse. Taken together, the results of this study show the potential of the HH2 sensor to provide robust plant function information with this technique.
# Technological Properties of Tritordeum Starch
## 3 Results and Discussion
### Color of Starches  
The color of starch, to some extent, can be treated as an indicator of the purification process. Also, in some specific applications, like the production of cosmetics or as a filling material, it can be important. Data related to the color of the investigated starches are summarized in Table 2. The darkest among them was buckwheat starch (lowest L* value), which can be related to the high content of non-starch material (lipid and ash content). On the other hand, the lightest was starch separated from commercial wheat flour (WhS) with the lowest content of non-starch material. A negative correlation was observed between L* value and lipid content (_p_ = 0.007). The observed L* values [19] for buckwheat starch were within the 67.66-72.23 range, and were 74.40 for wheat starch, so the starches analyzed in this research were lighter in color, which could be influenced by both the nature of the starch and the separation method.  
All samples except WhS were characterized by a relatively low share of red color (a* value). For WhS, this value was negative, indicating a small shift toward green color. The b* values for all samples were positive and close to zero, indicating a small share of yellow color, and a positive correlation was observed between the b* value and lipid content (_p_ = 0.014).  
The whiteness index (WI, Equation (1)) indicates the degree of departure of an object's color from that of a model white (WI = 100). Taking it into account, it could be observed that both barley and tritrodeum starch colors departed the most from the model white, whereas that of wheat starches departed the least.  
The calculated \(\Delta\)E (Equation (2)) values were the highest for WhS vs. other starches, indicating an easily discernible difference in color. On the other hand, the color difference between barley and tritrodeum starch was very low, so it was not possible to spot the existing difference because, usually, a \(\Delta\)E value of 2.3 corresponds to a just-noticeable difference [10].  
The general appearance of tritrodeum starch allows it to be easily distinguished from other investigated starches. Also, relatively low L* and WI values do not allow for application where brightness and lightness are desired, like, for example, cosmetics.
## 2 Model Structure and Description
### P Pools and Fluxes in Litter  
The litter was modeled as three pools (including structural, decomposable and resistant pools) occupying an intermediate layer between the plant and the soil layer (Figure 1). The litter layer receives elements from the vegetation, converts them into organic or inorganic forms, then transmits them to the soil layer.  
Figure 1: Schematic representation of modules, pools and fluxes in TRIPLEX-CNP. The dashed boxes indicate main modules (plant phenology, vegetation dynamics, land surface and soil biogeochemistry). The solid boxes indicate pools of carbon, nitrogen or phosphorus. The arrows indicate fluxes of phosphorus (orange) and of carbon or nitrogen (blue). The red dotted arrow indicates feedback from phosphorus to photosynthesis.  
P fluxes in the structural pool were described by the equation  
\[\frac{\mathrm{dP_{str}}}{\mathrm{dt}}=\mathrm{t_{leaf}}\mathrm{C_{leaf}}+ \mathrm{t_{root}}\mathrm{C_{root}}\mathrm{R_{str}}-\mathrm{l_{n}t_{str}}\mathrm{P_ {str}} \tag{2}\]  
where \(\mathrm{P_{str}}\) is the amount of structural P (g P\(\cdot\)m\({}^{-2}\)); \(\mathrm{C_{leaf}}\) and \(\mathrm{C_{root}}\) are the carbon concentrations of leaf and root pools (g C\(\cdot\)m\({}^{-2}\)), respectively; \(\mathrm{t_{leaf}}\), \(\mathrm{t_{root}}\) and \(\mathrm{t_{str}}\) are the turnover rates of leaf, root and structure pools (day\({}^{-1}\)); \(\mathrm{R_{str}}\) is the P\(\cdot\)C ratio in the structural pool (g P\(\cdot\)g C\({}^{-1}\)); and \(\mathrm{l_{n}}\) is the nitrogen limitation on litter C decomposition, defined to be 1.0.  
P fluxes in the decomposable pool were described by the equation  
\[\frac{\mathrm{dP_{dec}}}{\mathrm{dt}}=(\mathrm{t_{leaf}}(1-\mathrm{r_{pleaf}}) \mathrm{P_{leaf}}+\left(1-\mathrm{r_{root}}\right)\mathrm{P_{root}})-( \mathrm{t_{leaf}}\mathrm{C_{leaf}}+\mathrm{t_{root}}\mathrm{C_{root}}) \mathrm{P_{str}}-\mathrm{l_{n}u_{dec}P_{dec}}) \tag{3}\]  
where \(\mathrm{P_{dec}}\) is the decomposable P pool (g P\(\cdot\)m\({}^{-2}\)), while \(\mathrm{r_{pleaf}}\) and \(\mathrm{r_{root}}\) are the constants for leaf and fine root biomass turnover time constants (day\({}^{-1}\)) for each PFT. These constants had the values provided in Table S1.  
P fluxes in the resistant pool were described by the equation  
\[\frac{\mathrm{dP_{res}}}{\mathrm{dt}}=\mathrm{t_{wood}}\mathrm{P_{wood}}- \mathrm{l_{n}t_{res}P_{res}} \tag{4}\]  
where \(\mathrm{P_{res}}\) is the resistant P pool (g P\(\cdot\)m\({}^{-2}\)), and \(\mathrm{t_{wood}}\) and \(\mathrm{t_{res}}\) are the turnover time constants for wood and litter resistant pools (day\({}^{-1}\)). The values of these constants are shown in Table S1.
###### Abstract  
Recent studies have increasingly focused on using virtual reality (VR) exercise video games (exergames) to boost motivation for physical activity among the elderly. However, there is little discussion about the factors influencing the performance and effort expectancy of immersive exergames in older adults. The study was based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. UTAUT model was tested and extended by investigating two predicted factors--Physical Safety (PPS) and Physical Postures (PP)--of immersive exergames among older adults. PP, PPS, Performance Expectancy (PE), and Effort Expectancy (EE) relationships were analyzed. In this study, 40 healthy older adults were divided into 2 groups to play an immersive exergame for at least 5 min, either sitting or standing. Then, a list of measurement items based on the extended UTAUT model was completed by one-on-one interviews. The \(t\)-test results showed that PP was significantly correlated with PPS (t = \(-\)6.598, \(p\) < 0.001) and PE (t = 6.465, \(p\) < 0.001). The path analysis showed that PPS as a negative correlation was verified as a factor of PE (\(\beta\) = \(-\)0.438, \(p\) < 0.01). Both PP and PPS had no significant effect on EE. Overall, valuable insights and theoretical guidelines for older adults accepting immersive exergames are supported in this study.  
older adults; immersive exergame; technology acceptance; perceived physical safety; physical posture; performance expectancy; effort expectancy
# Application of Improved MFDFA and D-S Evidence Theory in Fault Diagnosis
## 3 Test Device and Multi-Sensor Fault Diagnosis Model  
The centrifugal pump test platform is mainly composed of hardware equipment and data acquisition subsystem, and its field test device is shown in Figure 1. The hardware equipment of the test device is mainly composed of a centrifugal circulating water pump with model IS50-32-125, a three-phase asynchronous motor of 1.5 KW, a vacuum pump and a water storage tank with a volume of 7 m\({}^{3}\), so the device is connected to a three-phase asynchronous motor through the centrifugal pump torque meter, and then connected to the water storage tank through the pipeline to form a circulating water pump device. A vibration sensor was arranged vertically in the pump and motor casings, a torque sensor between the pump and motor themselves, and an oscillation sensor on the pump shaft +X, and the measurement point arrangement is shown in Figure 2. The rated speed of the centrifugal pump was 3000 r/min, with designed flow rate is 23.89 m\({}^{3}\)/h, hydraulic head at 11.2 m, and a rotational frequency of 50 Hz.  
Figure 1: Laboratory centrifugal circulating water pump device.  
This test adjusts the inlet pressure by adjusting the vacuum pump pumping, simulates the cavitation phenomenon under different inlet pressures and through the loosening of the preload between the anchor bolt and the base, a total of four sets of operating conditions (i.e., normal operation, slight cavitation, severe cavitation, and loose bolts of the centrifugal pump) were designed. Moreover, four signals, for the centrifugal pump vibration, torque, motor vibration, and pump shaft +X oscillation, were obtained through the PXI-4472B dynamic signal acquisition instrument under these different operating conditions. These were then processed using the improved MFDFA to extract the multiple fractal spectrum feature parameters of each signal, i.e., \(\Delta f\), \(\alpha_{0}\), \(\Delta\alpha\), \(\alpha_{\text{min}}\) and \(\alpha_{\text{max}}\). Subsequently, the feature vector \([\Delta f,\alpha_{0},\Delta\alpha,\alpha_{\text{min}},\alpha_{\text{max}}]\) was then combined with BP neural network, and the multi-sensor fault diagnosis was carried out based on the D-S evidence theory. The centrifugal pump fault diagnosis model with multi-sensor information is shown in Figure 3.
## 2 Experimental Setup  
The sample used for the experiments is a circumferential weld composed of austenitic and martensitic stainless steel. The width and thickness of the weld were 10 mm and 13.6 mm, respectively, as shown in Figure 2a. Six slots were made by electrospray every 60 degrees on the inner wall of the weld, as shown in Figure 2b. A 45-degree angle beam probe with 2 MHz center frequency was used to collect ultrasonic signals. The scanning area was within the range of 30 mm on both sides of the weld center line, as shown in Figure 2a. The probe stepped 1 mm along the direction perpendicular to the weld line (index axis) every scanning cycle till the end (scan axis) while the ultrasonic beam was parallel to circumferential direction.  
The ultrasonic detection experimental setup used for data collection is shown in Figure 3. It included three systems: a 35 MHz broadband of the ultrasonic transmitter-receiver system (UTRS) for generating and receiving the ultrasonic pulse from the transducers, a computer system based on a LabVIEW program for storing the ultrasonic data from detected specimens and a probe motion control system (PMCS) whose precision was 1 mm. Ultrasonic waves were transmitted and received using the same transducer excited by UTRS. The ultrasonic echoes collected from the detected specimens were then converted to electrical signals which were stored in the computer system next. The ultrasonic data acquisition card adopted NI PCI-5153 manufactured by National Instruments (NI). The card can receive an ultrasonic signal with sampling frequency of 2 GHz and resolution of 8 Bit.  
Figure 1: The workflow of our study on classifications of ultrasonic signals.  
Figure 2: The detected circumferential weld: **(a)** the side view of the mockup; **(b)** the cross-section view of the mockup.  
As a result, a database of 22,631 A-scan signals was acquired consisting of two classes: slot signals and nonslot signals. The database set was normalized between 0 and 1. Six thousand ultrasonic signals with equal numbers of slot signals and nonslot signals were statistically selected from the original database and randomly divided into two data sets called the training data set and testing data set. These two data sets were used to train and test neural networks where the number of training samples and testing samples was 5000 and 1000, respectively.
## 1 Introduction
A UAV-based application with LoRa communication was used to obtain environmental data in disaster scenarios [22]. In [23], a fleet of UAVs was capable of adjusting their position to offer a very robust LoRa communication system to provide coverage to a set of terrestrial mobile systems. To track merchandise and increase location accuracy, IoT devices embedded in delivery vehicles, along with UAVs and LoRa gateways, were presented in [24]. In [25], a novel approach for linking a drone with air pollution monitoring stations using LoRa technology was described. Lastly, a study was completed on real-time applications related to UAVs and LoRa communications [26]. We tried to advance this technology and designed a novel system composed of a heterogeneous sensors network, where nodes can be scattered over a wide area and without communication coverage. The proposed system provides communication with isolated beacons, which are made up of several sensors. To this aim, we suggest the application of a UAV acting as a bridge between the beacons and the main system, using LoRa communication for data transmission. We propose the application of UAVs for data collection and transmission, the integration of LoRa technology as a long-distance communication system, and the application of artificial intelligence techniques for data processing. The rest of this paper is structured as follows: Section 2 identifies the different elements of the developed system and explains its operation. Section 3 is focused on the description and discussion of the results obtained; finally, the conclusions and future work directions are presented in Section 4.
## 4 Analysis of Attitude-Prediction Results  
### Model Implementation and Parameter Processing  
Sklearn in the Python programming software was employed to construct a prediction model [29]. To ensure the optimal performance of SVR model, we systematically utilized Random Search to adjust the kernel functions, regularization parameter, and epsilon values to fine-tune the model parameters. A total of 16518 data samples were obtained after preprocessing; 13214 data samples from 130 to 266 rings were used as training datasets, and 3304 data samples from 267 to 300 rings were used as test datasets. The training and test sets accounted for 80% and 20% of the total data sample, respectively. The input and output parameters of the model are shown in Table 4.  
After completing the data preprocessing stage, we employed PCA to discern the fundamental attributes of the initial 82 input parameters listed in Table 4. Subsequently, we determined the contribution rates for each principal component, as illustrated in Figure 7. Adopting a rigorous standard, we opted for a cumulative contribution rate threshold of 90% to select the most significant principal components, in alignment with established practices [27]. Table 5 presents the cumulative contributions of the k-th principal components. Notably, the first 13 principal components achieved a cumulative contribution rate of 90%, thereby encapsulating the primary information contained within the raw data. This meticulous approach ensures that our analysis focuses on the most informative and representative features of the dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Parameters** & **Min** & **Max** & **Mean** & **Unit** \\ \hline
21 & Hinged horizontal deviation (HHD) & \(-52\) & \(22\) & \(-13\) & mm \\
22 & Hinged vertical deviation (HVD) & \(-29\) & \(47\) & \(-4\) & mm \\
23 & Cover depth (CD) & \(11.0\) & \(14.4\) & \(12.6\) & mm/r \\
24 & Soil weight (SW) & \(1.87\) & \(1.89\) & \(1.88\) & g/cm\({}^{3}\) \\
25 & Cohesion (C) & \(20.8\) & \(21.5\) & \(21.1\) & MPa \\
26 & Internal friction angle (IFA) & \(21.6\) & \(23.8\) & \(23.0\) & degree \\
27 & Compression modulus (CM) & \(4.7\) & \(4.9\) & \(4.8\) & MPa \\
28 & Front horizontal deviation (FHD) & \(-39\) & \(39\) & \(1\) & mm \\
29 & Front vertical deviation (FVD) & \(-17\) & \(36\) & \(5\) & mm \\
30 & Back horizontal deviation (BHD) & \(-72\) & \(46\) & \(-20\) & mm \\
31 & Back vertical deviation (BVD) & \(-61\) & \(64\) & \(-15\) & mm \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 7: Principal component analysis of input parameters.