esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
###### Abstract  
A rapid tropospheric tomography system was developed by using algebraic reconstruction technique. Influences of different factors on the tomographic results, including the ground meteorological data, the multi-Global Navigation Satellite System (GNSS) observations, the ground station distribution and the tomographic horizontal resolution, were systematically investigated. In order to exclude the impacts from discrepancies of water vapor information between input observations and references on the tomographic results, the latest reanalysis products, ERA5, which were taken as references for result evaluations, were used to simulate slant wet delay (SWD) observations at GNSS stations. Besides, the slant delays derived from GNSS processing were also used to evaluate the reliability of simulated observations. Tomography results show that the input both SWD and ground meteorological data could improve the tomographic results where SWD mainly improve the results at middle layers (500 to 5000 m, namely 2 to 16 layer) and ground meteorological data could improve the humidity fields at bottom layers further (0 to 500 m, namely 0 to 2 layer). Compared to the usage of Global Positioning System (GPS) only SWD, the inclusion of multi-GNSS SWD does not significantly improve the tomographic results at all layers due to the almost unchanged dispersion of puncture points of GNSS signals. However, increases in the ground GNSS stations can benefit the tomography, with improvements of more than 10% at bottom and middle layers. Higher tomographic horizontal resolution can further slightly improve the tomographic results (about 3-6% from 0.5 to 0.25 degrees), which, however, will also increase the computational burden at the same time.  
w _et_vapor; tomography; simulation; GNSS _Article_  
On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 5 Convergence of ML and Blockchain in IoV
### Prospects of ML and BC Convergence for IoV System
#### 5.1.2 Intrusion Detection Systems  
An efficient vehicular network's infrastructure requires that it is built securely and free from attacks. The increased usage of electronic software has evolved into a surge of constant cyber-attacks, thus making the security of these network of paramount importance. Researchers are now finding ways to leverage the convergence of BC and ML to address this critical challenge. [PERSON] et al. [149] proposed Blockchain-based Multi-Layer Federated Extreme Learning Machines (MLFEM) to enhance an intruder-safe system for secure data transfers. The authors employed FL to develop the MLFEM scheme hosted by the RSU and connected vehicles to achieve efficient resource use without sacrificing security, while blockchain is utilized to record and share training models. The results show that the proposed algorithm after testing on a real-time dataset was able to detect different scenarios of attacks to an accuracy of 98%. [PERSON] et al. [150] proposed an IDS that integrates BC and ML for an effective cyber security system against the electricity grid. The authors deployed a recurrent neural network (RNN) for the detection of fraudulent attacks and any network attack to secure a blockchain-based energy network. A PBFT algorithm was used for the consensus mechanism and the authors reported improvement in the results.
## 3 Results and Discussion
### Comparison of the Machine Learning Models  
The Violin plots depict defect distribution based on four key values: first quartile (Q1), third quartile (Q3), interquartile range (IQR), and the median. The XGB-SVR hybrid models with Sc2 exhibited the lowest error IQR at 0.359, whereas the XGB model with Sc1 had the highest IQR at 2.72. A lower IQR indicates an error distribution close to zero, with the median line representing a normal error distribution. For GWFP prediction, the most effective models were XGB-SVR with Sc2, followed by RF-XGB-SVR with Sc1, and then RF-SVR with Sc1 (Figure 13).  
Figure 12: Combo graphs for comparison between the scenarios and ML models based on the criteria U\({}_{95}\) and accuracy.  
Figure 13: Violin plot for error distribution of models in different scenarios.  
Figure 14 offers a visual representation of the importance of 13 input variables and their respective contributions to GWFP. These contributions are scaled between 0 and 1, with a value of 1 denoting the highest impact on the target variable.  
Effective precipitation emerges as the most influential variable, accounting for 81.67% of the impact on GWFP. Following effective precipitation, relative humidity (RH) had a 7.5% impact, maximum temperature was 5.24%, crop coefficient kc was 2.5%, and minimum temperature was 0.6956%. EVI and NDVI also displayed significant impacts on the green water footprint.
###### Abstract
Therefore, the uncertain effects of ecological restorations on vegetation recovery should be investigated further. Gross primary production (GPP), which is the total amount of organic carbon fixed by vegetation in terrestrial ecosystems, can reflect the growth characteristics and health status of these ecosystems [22; 23; 24]. Monitoring long-term and large-scale terrestrial GPP has progressed considerably due to remote sensing-based technology. Satellite-based GPPs, such as the Vegetation Photosynthesis Model [25], Moderate Resolution Imaging Spectror-diameter (MODIS) [26; 27] and Breathing Earth System Simulator (BESS) GPPs [28; 29], are major data sources for exploring terrestrial carbon cycles worldwide. Many studies have also successfully used GPP to assess the effectiveness of ecosystem restoration on vegetation recovery [4; 5; 30; 31; 32]. However, the application of these satellite-based GPP products to different regions may demonstrate some biases in performance due to the structures of models and uncertainties in various input parameters [33]. An intercomparison of results from multiple GPP products, such as BESS and MODIS GPPs, can help explore regional GPP dynamics without field data validation. However, regional-scale intercomparison studies are lacking. A large continuous karst area in Southwest China [30; 34] has undergone severe vegetation degradation and rocky desertification due to intense human exploitation since the middle of the last century [30; 35]. The rocky desertification rate of this area is approximately 25,000 km\({}^{2}\) yr\({}^{-1}\), which is equal to the sandy desertification rate in Northwest China [35]. The low water-holding capacity of karst is due to its thin soil layer and numerous connected fissures from the bedrock layer to below the rooting zone or groundwater [36]. Severe water shortage persists in karst areas even during high precipitation [37]. In addition, the increase in karst rocky desertification aggravates the water shortage in karst regions [30; 38; 39; 40]. ERPs, such as GGP and KRDRP, have been implemented since 2000/2001 to counter land degradation and relieve poverty [39]. Existing studies have demonstrated that these projects have considerably enhanced vegetation cover and greenness in this region [13; 18; 40]. However, research on the effects of specific restoration activities, such as afforestation and grass planting, on vegetation recovery remains limited. In this study, long-term and successive climate change initiative land cover (CCI-LC) data were used to extract the spatiotemporal patterns of restoration activities and evaluate their effectiveness on vegetation recovery, as indicated by BESS and MODIS GPPs. This study primarily aims to (1) explore the spatiotemporal patterns of ecological restorations, (2) determine the intercomparison performance of the application of BESS and MODIS GPPs and their spatiotemporal patterns and (3) quantify the effects of major restoration activities on GPP changes from 2001 to 2015 in Southwest China.
## 5 Optimization Techniques for Routing in Wireless Sensor Networks
### Attribute-based  
In these algorithms, the sink sends queries to certain regions and waits for the response from the sensors located in this area. Following an attribute-value scheme, the queries inform about the requireddata. The selection of the attributes depends on the application. An important characteristic of these schemes is that the content of the data messages is analyzed in each hop to make decisions about routing.
## 1 Introduction
In particular, the AC susceptibility has been studied as a function of the AC amplitude \(\chi_{1}^{\prime\prime}\left({\rm h}_{\rm AC}\right)\), the temperature \(\chi_{1}^{\prime\prime}\left({\rm T}\right)\), and the DC magnetic field \(\chi_{1}^{\prime\prime}\left({\rm H}_{\rm DC}\right)\). From the \(\chi_{1}^{\prime\prime}\left({\rm h}_{\rm AC}\right)\) curves performed at different AC frequencies and DC fields, the critical current density together with the pinning energy have been obtained, evidencing the first signals of granularity inside the sample. From the \(\chi_{1}^{\prime\prime}\left({\rm T}\right)\) and the \(\chi_{1}^{\prime\prime}\left({\rm H}_{\rm DC}\right)\) curves performed at different DC fields and AC amplitudes, respectively, the granularity has been revealed and, by means of a contour plot of the \(\chi_{1}^{\prime\prime}\) with the temperature, DC field, and AC amplitudes, the inter- and intragranular regions have been highlighted and separated.
## 1 Introduction
Tasks such as phone calls, radio listening, video viewing, text entry, N-back tasks, and surrogate reference tasks (SuRTs) are then assigned to drivers, and metrics such as the NASA task load index (NASA-TLX) are used to evaluate the workload [19]. Figure 1: California DMV disengagement report summary [8,9]. Next, based on statistical data, the disengagement scenarios are implemented under a sensor or system failure scenario or in other predefined scenarios [20; 21]. Finally, the optic, acoustic, and haptic HMI are configured to warn the driver to return to driving, and the effectiveness of each notification method is analyzed [22; 23; 24]. The effect of the complexity of surrounding traffic vehicles or the type of NDRT on the takeover quality has been analyzed. The probability of collision and the tendency to avoid accidents through steering was significantly increased under high-density traffic conditions [25; 26]. The type of NDRT did not significantly affect the time required for the takeover, but it affected the takeover quality [27]. In autonomous driving mode, the driver's gaze behavior reflected the level of distractedness, and specific gaze parameters were suitable to assess the adequacy of the driver's monitoring strategy [28]. Since studies on HMI configuration and driver response focus on average or median values or only on extremely urgent scenarios, differences among individuals must be considered [29]. In this study, we propose a strategy for increasing takeover safety in conditionally automated vehicle system failure situations. Various driver-vehicle interfaces (DVI) introduced in previous studies [17] and the UNECE ALKS proposal [5] were comprehensively analyzed. A realistic driving simulator was developed to conduct experiments and acquire driver data on high-risk situations where accidents may occur. Event scenarios were designed based on disengagement reports and traffic accident statistics to simulate situations in which takeover could occur. By having the participants perform NDRTs based on general time-consuming statistical data, the takeover requests were provided in realistic autonomous driving situations. As suggested for future work in previous studies, we devised a method to utilize DMS to improve driver readiness or reaction speed. Finally, we analyzed the characteristics of scenarios that are too risky to request takeover requests despite these efforts. Based on the overall experimental results, a safe takeover strategy was designed. The paper proceeds as follows: Section 2 explains the development of an experimental environment that includes the driving simulator, DMS and DVI configuration, event scenarios, and the process of experiments. In Section 3, we discuss our analysis of the factors (DVI configuration, scenario types, DMS) affecting the safety of the transition process based on the experimental results. Finally, we propose a takeover strategy to maximize the safety of the takeover of highly automated vehicles.
# Research on Out-of-Plane Bending Test of PVB Laminated Glass Plate with Different Number of Layers
## 3 Results and Discussion
### Selection of PVB Modulus for the Calculation of PVB LG  
In studying the selection of the elastic modulus of PVB for the bending calculation of laminated glass, it is essential to first understand the deformation behavior of PVB LG. Currently, the most widely accepted theory for the deformation of PVB LG is that the PVB is soft yet possesses adequate bonding strength. This ensures that the PVB does not separate from the glass, allowing for a compatible deformation relationship between the two materials [36], as illustrated in Figure 15c.  
The glass plate is treated as a standard thin plate, with its maximum strain occurring at the top and bottom surfaces. In this scenario, PVB serves as a mediator to coordinate the deformation of the glass plate, experiencing maximum strain at its interface with the glass. Under the premise that PVB remains bonded to the glass, the strain at their interface is identical. Given that the strain on the outer surfaces of the glass in antisymmetric bending is assumed to be the same, the strain recorded on the exterior of the glass in the experiments described in Section 2.2 is approximately equal to the strain at the interface between the glass and PVB. Referring to the strain distribution shown in Figure 10, the observed maximum strain (1.6 \(\times\) 10\({}^{-3}\)) is significantly less than 0.6, which indicates that the PVB is within the initial, elastic stage depicted in Figure 4.  
Accordingly, under loading conditions, the PVB in laminated glass remains within the elastic range. For theoretical calculations, the elastic modulus of PVB is taken from the linear portion of the stress-strain curve at a tensile rate of 10 mm/min, which is \(E_{c}\) = 1.34 MPa. Given that the values of [PERSON]'s ratio \(V_{c}\) from various national standards and academic research are comparable, these values are considered consistent. In this study, [PERSON]'s ratio \(V_{c}\) is set to 0.5, fulfilling the criteria of both EN 16612 [11] and JGJ 113-2015 [12].
###### Abstract  
In this paper, a novel transmission strategy for Mutlipe Input Multiple Output Wideband Code Division Multiple Access (MIMO-WCDMA) orientations operating in frequency-selective fading environments is investigated, in terms of overall algorithmic complexity reduction. To this end, Principal Component Analysis (PCA) is employed on the received data matrix, in order to define the significant terms that are taken into account during transmission matrix formulation. According to the presented results, feedback information of only the primary eigenvector of the corresponding covariance matrix of the received data matrix is required, in order to maintain the mean Bit Error Rate (BER) at acceptable levels. In particular, a complexity reduction of up to 10% can be achieved, when comparing BER values derived by the selection of all components of the received covariance matrix during transmission matrix formulation, and the corresponding BER when selecting half of the components. This reduction is maintained to 10%, when considering a realistic four-element antenna design; however, in this case mean BER inaccuracy is further reduced to 1%.  
MIMO antennas; RAKE receiver; multipath fading; spatial multiplexing; principal component analysis +
Footnote †: journal: Accepted: 3 October 2018; Published: 5 October 2018
# An Exploration of Robot-Mediated Tai Chi Exercise for Older Adults
## 4 Discussion
### Limitations and Future Works  
While our findings provide insight into using humanoid robots as exercise tutors for older adults, we need to acknowledge a few limitations and important future works to strengthen the discovery. First, since the NAO robot has limited balancing capability and does not have a waist joint, it could not perform continuous stepping and turning. Therefore, these motions were not included in the robotic Tai Chi. Consequently, the positive feedback from the participants had excluded the potential effect of these difficult motions. Second, this study was conducted in a lab environment for individual participants. The feasibility of a robot tutor should be tested in a community setting with groups of older adults. Third, the study solely included older participants without any significant functional limitations and/or without cognitive impairment. Therefore, a relevant proportion of the older populations, such as those with multimorbidity, mobility restrictions, or cognitive impairment, was not considered. Thus, the results of this study were not representative of the entire population of older adults. In addition, Tai Chi has consistently shown benefits in reducing physiological fall risk [46], improving health [26; 27], and improving the quality of life [28]. The current work had a small sample size (\(n=20\)) and did not access these outcomes. Future pilot studies with a randomized clinical trials design and a powered sample size are necessary to validate the robot tutor in comparison with a real human tutor, likely through multi-session/longitudinal experiments. There has been no socially assistive robot that can outperform a real human. However, due to the limited availability of human tutors, if robot tutors can bring considerable health outcomes, they may prove to still be quite valuable and cost-effective (considering many users can share a robot). Finally, standard instruments should be applied to evaluate physical and cognitive functions as part of the screening process for more rigorous research.
## 3 Experimental Tests and Results  
The compensation of thermal-mechanical errors is based on an effective kinematic model that is able to represent the potential point to point deviations due to external generalized loads (in this case, mainly temperature). Usually, the thermal strains are a supplementary component that is summed to  
Figure 5: Test-bench setup of the prototype with the embedded FBG sensors. (**a**) front side; (**b**) lateral view.  
the geometrical errors when there is a variation of the structure temperature gradient. For this reason, the mathematical model needs to be expressed as follows:  
\[f(\Delta T_{1},\Delta T_{2},\ldots,\Delta T_{n})=A_{1}^{\prime}\cdot\Delta T+ \Delta T^{\prime}\cdot A_{2}\cdot\Delta T, \tag{12}\]  
where \(A_{1}\) is the coefficient vector and \(A_{2}\) is the matrix of the regression model, while \(\Delta T\) represents the temperature intensification vector of the frame. In order to tune the model, a stepwise regression with a programmed exploration algorithm was applied. This technique is able to categorize linear formulations at each stage counting or removing a factor using ANOVA or other techniques. The experimental campaign considered the results of a set of tests performed in static and dynamic conditions. In the first case, the model was validated by applying a static load to TTP, while in the second part of the research the model was evaluated while the thermal conditions varied. In both cases, the model was analyzed by comparing the predicted deviations with real measurements of the TTP positions.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 3 Theoretical Analysis of Prestress Loss of BFRP Tendons  
### Input Parameters  
The width of the cross-tensioned concrete pavement was assumed to be 3.0 m, 4.5 m, 9.0 m, and 12.75 m, with a thickness of 0.2 m. The elastic modulus of concrete was assumed to be 30 GPa. The diameter of the BFRP tendon was assumed to be 16 mm, and the corresponding nominal cross-sectional area was 201 mm\({}^{2}\). The ultimate tensile strength and elastic modulus were assumed to be 1300 MPa and 40 GPa, respectively, according to previous study [26].  
A total of six distribution angles from 20\({}^{\circ}\) to 45\({}^{\circ}\) with an interval of 5\({}^{\circ}\) were selected for BFRP tendons. The tension control stress of the BFRP tendon was assumed to be 65% of the ultimate tensile strength, namely, 845 MPa in this case. In order to determine the spacing of the BFRP tendon at different distribution angles, the mean longitudinal compressive stress given the distribution angle and the spacing of 30\({}^{\circ}\) and 1000 mm was calculated by using Equation (7).  
\[\sigma_{\text{py}}=\frac{2(\sigma_{\text{con}}-\sigma_{l})\times A_{\text{p}} \times\cos\alpha}{h\times l\times\tan\alpha} \tag{7}\]  
where \(\sigma_{\text{py}}\) is the mean longitudinal compressive stress, \(\sigma_{l}\) is the total prestress loss, \(A_{\text{p}}\) is the nominal sectional area of the prestressed tendons, \(h\) is the thickness of the concrete slab, \(l\) is the longitudinal distance of the prestressed tendons, and \(\alpha\) is the distribution angle of the prestressed tendons. Correspondingly, the mean longitudinal compressive stress of 2.55 MPa was obtained for BFRP tendons. On the basis of the mean longitudinal compressive stress, the spacing of BFRP tendons for each distribution angle (20\({}^{\circ}\), 25\({}^{\circ}\), 30\({}^{\circ}\), 35\({}^{\circ}\), 40\({}^{\circ}\), and 45\({}^{\circ}\)) can be calculated, as summarized in Table 1, indicating that the greater the distribution angle of BFRP tendons, the less spacing there was between BFRP tendons.  
\begin{table}
\begin{tabular}{c c} \hline
**Distribution Angle (\({}^{\circ}\))** & **Spacing of BFRP Tendons (mm)** \\ \hline
20 & 1720 \\
25 & 1295 \\
30 & 1000 \\
35 & 779 \\
40 & 608 \\
45 & 471 \\ \hline \end{tabular}
\end{table}
Table 1: The spacing of basalt fiber-reinforced polymer (BFRP) tendons at different distribution angles (mm).