esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Term Monitored Road Degradation Functions as a Tool to Increase Quality of Pavement Design
## 2 Research Methods
### Interpretation of Results and Sensitivity Analysis  
In addition to determining the degradation functions, correlation analyzes were also processed in parallel. Their aim was to determine the level of dependence between the obtained data on the quality of the road, the road temperature during the measurement and the traffic load. Subsequently, it was possible to treat these effects in the creation of mathematical functions of degradation.  
The result of the analysis confirmed the dependence of the average temperature of the road surface during the measurement on the change of the modulus of elasticity of the asphalt-bonded layers, expressed by the Pearson coefficient with the value up to r \(\doteq\) 0.808. The dependence between the base layers and the average road surface temperature is r \(\doteq-0.370\) and the correlation between the road surface and the average road surface temperature is r \(\doteq\) 0.250. The correlation analysis for asphalt-bonded structural layers was also influenced by the performance of road maintenance. For this reason, the observed period was divided into three categories, while the correlation coefficients are as follows--the period before maintenance \(-0.79\), the period between maintenance \(-0.83\) and the period after maintenance \(-0.86\).  
The processing of the dependence between the modulus of elasticity of the individual structural layers and the traffic load showed the following results. An overview of approaches for evaluating the quality of roads and their parameters for the underlying layers is r \(\doteq-0.71\), which means a strong dependence. No dependence was found between the development of the subsoil elasticity modulus and the traffic load. When assessing the dependence between the development of the modulus of elasticity of asphalt-bonded layers and the traffic load, the bond was again evaluated with a division into periods according to the performance of construction maintenance. At the same time, the effect of temperature was applied here. In all cases, strong dependence was confirmed. In the following Table 3 it is possible to see the degree of dependence between the increase of the rut or IRI and the decrease of the load-bearing capacity of the structural layers of the road.  
Figure 17: The subsoil Modulus of elasticity course.  
From the point of view of correlation evaluation, it can be stated that there is a medium to strong dependence between rut and the modulus of elasticity of the base layers. There is a medium dependence between rut and the modulus of elasticity of the substrate, there is a strong dependence between IRI and the modulus of elasticity of the subsoil.  
At the same time, it was shown that there is a medium to strong dependence between the rut and the decrease in the load-bearing capacity of the asphalt-bonded structural layers. There is a strong relationship between the IRI and the decrease in the load-bearing capacity of asphalt-bonded structural layers.
## 1 Introduction  
Among different types of landslide hazards, the deep-seated landslide often involves large amount of landslide volume and causes severe hazard and damages to infrastructures [1, 2]. Many deep-seated landslides have been reported to recur and evolve into an even larger scale and cause more severe consequences [3, 4, 5]. When investigating the deep-seated landslide hazard, many researchers found that the local geological settings including characteristics of rock formation and geological structures affect the failure mechanism of a deep-seated landslide significantly [6, 7, 8]. To properly draft up the mitigation strategy for such deep-seated landslide hazard, it is essential to investigate the related geological setting and structures. This investigation of geological settings is often conducted by ground-based investigation, which is time consuming and expensive, and the results often rely on accessibility of the site and available out-crops of the geological formations and structures. The remote sensing techniques have advanced rapidly in recent years, and remote sensing data have been used by many researchers to analyze topographic characteristics of the deep-seated landslides [9, 10]. The light detection and ranging (LiDAR) data, especially, provide terrain details in support of identification of geomorphological features [11; 12; 13; 14]. The high-resolution digital terrain model (DTMH) produced by LiDAR mounted on unmanned aviation vehicle (UAV) provides more detailed topographic variations, which facilitate a good opportunity for identifying the geomorphological features such as lineaments related to the geological structures.  
The deep-seated landslide in the Lantai area, I-Lan County, Taiwan has a long history of landslide activity and often damages the I-Lan Special Route 1, which is the sole access road to the Tai-Ping Mountain National Forest Recreation Area. Studies and monitoring of the landslide were conducted since 2015 [15; 16; 17], and the study in 2018 [18] suggested some ground subsidence implying existence of possible geological structures at the site. This study adopted the high-resolution digital terrain model (DTMH) derived from UAV-mounted LiDAR point cloud data for mapping lineaments of geological structures, and they were then verified through field investigation. We then conducted numerical analysis to study the effects of the geological structures on the failure mechanism of the deep-seated landslide. The geological structures might have significantly affected the failure mechanism of the deep-seated landslide and mitigation facilities. Such results could provide valuable information supporting hazard mitigation strategy. Figure 1 shows a flowchart of the methodology of this study.
# Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
## 3 Results
### Estimating Canopy Closure  
Transition from spring phenophase to canopy closure based on breakpoint analysis agreed among local-scale sensors, but differed substantially from Landsat 8 and MODIS. GCC\({}_{\text{UAV}}\) (full scene), GCC\({}_{\text{UAV}}\) (20 m buffer), NDVI, gap fraction, and LAI all show canopy closure to occur around DOY 124, with standard error that overlap among the variables, indicating no operational difference. GCC\({}_{\text{PhenoCam}}\) shows canopy closure to occur on DOY 131 however, nearly a week after above- and below-canopy sensors (Table 1; Appendix A1). MODIS shows the earliest indicated period of canopy closure at DOY 121, while Landsat shows both the latest day at DOY 141, as well as the greatest variance of over 17 days plus or minus, compared to 2 days or less for the majority of other sensors (Table 1.).
# Intelligent Environment Monitoring System for University Laboratories
## 4 Real Experiments
### Debug Analysis  
The control system works based on the information transmission, which mainly depends on the device control information and module acquisition of the physical parameters. This characteristic determines that the transmission rate requirements for various signals are generally low, while the reliability requirements of information transmission are relatively high. Since transmission data denotes the current status of laboratory equipment and control information, even small errors may lead to large malfunctions, causing other equipment to work abnormally or even become damaged. Therefore, the system should guarantee the timeliness and reliability of the transmission.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Area**} & \multirow{2}{*}{**Date**} & \multirow{2}{*}{**Site**} & **Fruit** & **Sample** & **Non-** & **Drosophila** & \multirow{2}{*}{\(\chi^{2}\)} & \multirow{2}{*}{\(p\)} \\  & & & **Ripeness** & **Size (Fruit)** & **SWD** & **per Fruit** & & \\ \hline \multirow{8}{*}{Oxnard} & \multirow{8}{*}{OXN 1} & Ripe & 31 & 114 & 42 & 5.0 & \multirow{8}{*}{136.27} & \multirow{8}{*}{<0.0001} \\ \cline{3-3} \cline{5-8}  & & & Overripe & 23 & 36 & 209 & 10.7 & & \\ \cline{2-8}  & \multirow{2}{*}{OXN 2} & Ripe & 22 & 3 & 39 & 1.9 & \multirow{2}{*}{0.00} & \multirow{2}{*}{0.9635} \\ \cline{3-3} \cline{5-8}  & & & _Overripe_ & 23 & 3 & 58 & 2.7 & & \\ \cline{2-8}  & \multirow{2}{*}{OXN 3} & Ripe & 20 & 27 & 20 & 2.4 & \multirow{2}{*}{14.37} & \multirow{2}{*}{0.0002} \\ \cline{3-3} \cline{5-8}  & & Overripe & 28 & 60 & 158 & 7.8 & & \\ \cline{2-8}  & \multirow{2}{*}{OXN 4} & Ripe & 35 & 33 & 117 & 4.3 & \multirow{2}{*}{1.06} & \multirow{2}{*}{0.3023} \\ \cline{3-3} \cline{5-8}  & & Overripe & 22 & 37 & 95 & 6.0 & & \\ \hline \multirow{8}{*}{Oxnard} & \multirow{8}{*}{OXN 1\({}^{\prime}\)} & Ripe & 26 & 2 & 452 & 17.5 & \multirow{8}{*}{0.00} & \multirow{2}{*}{0.9637} \\ \cline{3-3} \cline{5-8}  & & Overripe & 13 & 0 & 163 & 12.5 & & \\ \cline{2-8}  & \multirow{2}{*}{OXN 2\({}^{\prime}\)} & Ripe & 28 & 87 & 70 & 5.6 & \multirow{2}{*}{31.03} & \multirow{2}{*}{<0.0001} \\ \cline{3-3} \cline{5-8}  & & Overripe & 15 & 39 & 122 & 10.7 & & \\ \cline{2-8}  & \multirow{2}{*}{OXN 3\({}^{\prime}\)} & Ripe & 28 & 147 & 82 & 8.2 & \multirow{2}{*}{7.68} & \multirow{2}{*}{0.0056} \\ \cline{3-3} \cline{5-8}  & & Overripe & 15 & 133 & 126 & 17.3 & & \\ \cline{2-8}  & \multirow{2}{*}{OXN 5} & Ripe & 24 & 19 & 9 & 1.2 & \multirow{2}{*}{31.00} & \multirow{2}{*}{<0.0001} \\ \cline{3-3} \cline{5-8}  & & Overripe & 16 & 22 & 116 & 8.6 & & \\ \hline \multirow{8}{*}{Santa} & \multirow{8}{*}{SAT 1} & Ripe & 38 & 193 & 4 & 5.2 & \multirow{8}{*}{108.22} & \multirow{8}{*}{<0.0001} \\ \cline{3-3} \cline{5-8}  & & Overripe & 17 & 115 & 103 & 12.8 & & \\ \cline{2-8}  & \multirow{2}{*}{SAT 2} & Ripe & 39 & 50 & 18 & 1.7 & \multirow{2}{*}{33.45} & \multirow{2}{*}{<0.0001} \\ \cline{3-3} \cline{5-8}  & & Overripe & 19 & 10 & 43 & 2.8 & & \\ \cline{2-8}  & \multirow{2}{*}{SAT 3} & Ripe & 40 & 105 & 0 & 2.6 & \multirow{2}{*}{0.12} & \multirow{2}{*}{0.7341} \\ \cline{3-3} \cline{5-8}  & & Overripe & 20 & 53 & 1 & 2.7 & & \\ \cline{2-8}  & \multirow{2}{*}{SAT 4} & Ripe & 40 & 0 & 49 & 1.2 & \multirow{2}{*}{20.73} & \multirow{2}{*}{<0.0001} \\ \cline{3-3} \cline{5-8}  & & Overripe & 20 & 14 & 21 & 1.8 & & \\ \cline{2-8}  & \multirow{2}{*}{SAT 5} & Ripe & 40 & 3 & 3 & 0.2 & \multirow{2}{*}{0.62} & \multirow{2}{*}{0.4306} \\ \cline{3-3} \cline{5-8}  & & Overripe & 20 & 1 & 6 & 0.4 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Total number of _Drosophila suzukii_ (SWD) and non-SWD _Drosophila_ flies emerged per sample collected from the three major strawberry production areas in California in 2019 and 2020.
## 2 Background on Mineral Carbonation Experiments and Tests
SEM-TEM observations [14] showed a magnesite layer separated from the olivine by submicron siderite grains, and poorly crystallized phyllosilicates. Iron oxide and amorphous silica are also observed as secondary phases in batch and flow-through experiments [14, 22, 29]. Phyllosilicates and chalcedony affect both the porosity and permeability, causing a decrease in the carbonation rate due to the creation of an exfoliated passivation layer rich in silica. Carbonation experiments have been performed by [31] on continental flood basalt (CFB) samples (10.3 MPa and 90 \({}^{\circ}\)C) from eastern Washington, and by [PERSON] and [PERSON] [32] in basalt samples representing formations from North America, India and Africa. Both studies noted that calcite was the first mineral to precipitate. For instance, [PERSON] and [PERSON] [32] observed small calcite nodules precipitating after 86 days. Post-reacted samples from Deccan basalts [32] displayed larger and opaque precipitates (after 280 days) and more reddish-brown grains containing a large calcite component (66.0-82.0 wt. %) and minor magnesite (9.1-22.0 wt. %) or siderite (3.1-15.0 wt. %) components. The basalt grains representing Southern Africa (Karoo) were insignificantly reactive, displaying very few carbonate nodules with high Mg (31.0 wt. %) and Fe (29.0 wt. %) contents. More recent basalt carbonation experiments have been performed by [33] on recent Auckland basalt (0.3 Ma). After 140-day experiments (100 \({}^{\circ}\)C and 5.5 MPa), they observed ankerite and aluminosilicates as secondary precipitation and an increase in both porosity and permeability. This observation contrasts with the major reported carbonation studies [29, 30], where the permeability was observed to decrease due to carbonate growth. Theoretical and experimental studies of rock-CO\({}_{2}\) interactions in wet conditions and low temperature (25-90 \({}^{\circ}\)C) and first phases (0.5-100 days)indicate the presence of chemical reactions and, consequently, textural-mineralogical-chemical changes [25]. However, according to [25], in order to optimize low-temperature mineral carbons, an \"equilibrium\" between initial CO\({}_{2}\) (acid supply), rock to water ratio and temperature needs to be adjusted in order to effectively mineralize CO\({}_{2}\) within a reasonable time scale.
## 4 Discussion
### Seasonality and Emotional Memory in the Summer  
The results of our free recall experiment suggest that people with high seasonality scores did not have an emotional memory bias in summer. While we expected participants who were reporting high rates of seasonal fluctuations to recall negative items better, in contrast, participants in all groups recalled positive pictures more often than negative pictures. More generally, emotional pictures (negative and positive) were recalled more often than neutral pictures by all groups.  
The most plausible explanation for this finding is that participants with seasonal symptoms of depression during winter feel good enough in summer to not demonstrate a negative emotional memory bias during this period. So far, studies reporting a negative memory bias in patients with SAD found this effect in winter [47; 50; 59]. This negative memory bias is well known for patients with major depression [60]. However, there are some subtle differences between the effect of depression and SAD. It was reported that patients with SAD remember more positive pictures in summer than in winter and this seasonal change distinguishes them from healthy controls [48]. The very same study found no significant difference between healthy controls and patients for the seasonal change in negative or neutral word recall performance [48]. An interventional study found that treatment of SAD improves the memory for positive contents [47]. In another experiment, patients with SAD showed impaired recognition of positive stimuli and impaired capacity to suppress responses to negative stimuli in winter, whereas healthy controls did not demonstrate such biases [59]. Our findings are in line with these prior reports. In our study, which was conducted during the remission phase in summer, the groups did not differ with respect to their responses to emotional stimuli. Thus, it is possible that people who report seasonal symptoms to occur during winter do not differ from people without such symptoms during summer with respect to emotional memory biases.  
It is also possible that differences in the stimulus material explain the missing effect. Common stimuli used in emotional memory experiments include emotional faces [51; 59], pictures from the International Affective Picture System [14], affective words [47], and stories [50]. We used the OASIS picture database, which is relatively new [54]. It was acknowledged by the authors of the OASIS picture database that the pictures in this database are underrepresenting the low-arousal positive and low-arousal negative segment. While we balanced the three valence groups by arousal, that is, low-middle-high arousal stimuli where proportionate in all three emotional categories, we cannot rule out that the positive images were on average more arousing and, therefore, more likely recalled.  
Finally, in addition to previous reports on negativity bias in relation to seasonality [47; 50; 59], not all experimenters have found such an effect [61].
## 3 Results and Discussion
### Corrosion Resistance Evaluation
#### 3.2.3 The Equivalent Circuit R(Q(R(QR)))  
The equivalent circuit shown in Figure 5 describes a two-layer physical state, identical to the one described by the circuit in Figure 4, with the difference that in these cases the electrical capacity of the surface layer is described by a non-ideal capacitor, represented by the CPE1 constant phase element.  
This circuit was suitable for processing the EIS data recorded for I-Zn, II-Zn/Fe, and III-Mn after one hour of immersion in rainwater and in fire extinguishing solution. The parameter values of the circuit elements for these systems are presented in Table 4.  
Figure 5: The equivalent circuit R(Q(R(QR))) for all phosphate samples immersed in rainwater and fire extinguishing solution.  
As well as for the other alloys immersed in rainwater, the surface layer of phosphate samples consists of an extremely thin film saturated with rainwater. This protecting layer has a very low electrical capacity, in the range of microamperes, and very high resistance, in the range of k\(\Omega\)/cm\({}^{2}\). Given that the frequency exponent of the expression of the constant phase element CPE1 is very close to the unit, it is, in fact, an almost ideal capacitor.  
For the same systems, the charge transfer resistance is also high; therefore, the electric double layer acts as a barricade in the evolution of the corrosion reaction. Moreover, the electrical capacity of this layer is a non-ideal capacitor, probably due to the irregularity of the phosphating layer and its porosity.  
The polarization resistance for the phosphate samples decreases in the following order:  
I-Zn (44.0 k\(\Omega\cdot\)cm\({}^{2}\))\(>\) II-Zn/Fe (3.85 k\(\Omega\cdot\)cm\({}^{2}\))\(>\) III-Mn (2.86 k\(\Omega\cdot\)cm\({}^{2}\))  
This indicates that the sample phosphate in solution I-Zn has the best corrosion resistance when exposed to rainwater, while the sample phosphate in solution III-Mn shows the lowest corrosive resistance.  
When exposed to fire extinguisher solution, the polarization resistances of the phosphate samples are much lower, and vary in the following order:  
II-Zn/Fe (1.272 k\(\Omega\cdot\)cm\({}^{2}\))\(>\) I-Zn (0.441 k\(\Omega\cdot\)cm\({}^{2}\))\(>\) III-Mn (0.133 k\(\Omega\cdot\)cm\({}^{2}\)).  
In this highly corrosive environment, the fire extinguishing solution, the most corroded material is also the sample phosphate in the III-Mn solution. Moreover, as can be seen, the best sample, in terms of corrosion resistance is II-Zn in this case. This order inversion that appears in the obtained results can be explained by the absorption and/or adsorption of some compounds from the solution in the pores of the phosphate layers, because the dimensions of the pores differ from one sample to another, depending on the phosphating conditions.
# Analysis of Electromagnetic Interference Effects of 5G Signals on Radio Altimeters
###### Abstract  
An analytical method is introduced to assess the susceptibility of radio altimeter (RA) receivers to adjacent-band fifth-generation (5G) signal interference and to quantify its impact on RA performance. The power-series method is employed to analyze the intermediate frequency (IF) signal gain compression effect of 5G signal interference on RA receivers. A behavioral-level simulation model of the RA receiver's radio frequency (RF) front-end is constructed based on the advanced design system (ADS), and a 5G signal injection simulation is performed. The simulation results indicate that 5G signals can induce nonlinear effects in the RF front-end circuit of the RA, leading to IF signal gain compression, thereby affecting the subsequent signal processing of RA receivers. The interference effect on the RA receiver is influenced by factors such as the power and frequency of the 5G interference signal. To investigate this, an interference injection test was conducted on a specific RA receiver to validate the aforementioned interference mechanisms. The test results indicate that when the average power of the injected 5G signal at a frequency of 4000 MHz reaches \(-16\) dBm, the IF signal power is significantly reduced. As the power of the 5G signal increases, this nonlinear effect becomes more pronounced. Furthermore, the height error ratio significantly increases, with consistent trends observed across different test frequencies. The interference threshold for the RA is lower when the signal frequency is closer to the RA operational signal frequency. Our research results demonstrate the efficacy of this method, providing a reference basis for studies on interference mechanisms and the evaluation of interference effects related to RA receivers within the electromagnetic environment of 5G signals.  
radio altimeter; 5G signals; evaluation of electromagnetic interference effects; nonlinear effects; civil aircraft
## 4 Simulation System Design
### VRX Configuration  
VRX officially provides a WAMV, which includes two buoys, a power module, a control box, and two thrusters as its basic structure. In addition, a variety of plugins are provided, such as the following: (1) thrust plugins; (2) wind plugins, which can set average wind speed, wind direction, etc. (different parameter settings will generate different wind speeds, simulating the impact of wind on the USV in real scenarios); and (3) wave plugins, which can set wave height and direction, simulating the motion and visual effects of the USV under wave action. The forces calculated by each plugin are synthesized and  
Figure 8: ROS2 interface modules in Simulink.  
Figure 7: Controllers and ROS2 interface modules in Simulink.  
applied to WAMV through Gazebo's application programming interface, and the dynamic simulation is implemented through iterative calculation in the physics engine [17].  
In addition, users can freely configure sensor modules according to their needs. This design requires obtaining position, attitude, and velocity data; therefore, GNSS and IMU modules need to be configured. The configured WAMV is shown in Figure 9.  
The official program provides the dimensions, mass, and various hydrodynamic coefficients of the WAMV. The additional mass is estimated by empirical formulas [19]. A fixed rudder angle \(\delta=5^{\circ}\) is applied to stabilize the rotation of the USV, and the simulation data \(\delta-r(t)\) is fitted using the non-linear least-squares method to obtain the parameters \(K\) and \(T\) in the Nomoto model. The parameters of the WAMV are shown in Table 1. The device errors of the IMU (including the gyroscope and accelerometer) and GNSS are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Physical Quantity [Unit]** & **Value** \\ \hline Vessel Length & \(L\) [m] & 4.9 \\ Vessel Width & \(B\) [m] & 2.06 \\ Longitudinal Center of Gravity & LCG [m] & 1.4 \\ Economic Speed & \(U_{0}\) [m \(\cdot\cdot\) s\({}^{-1}\)] & 2 \\ Maximum Thrust & \(T_{\max}\) [N] & 1000 \\ No Load Draft & \(D\) [m] & 0.2 \\ Mass & \(m\) [kg] & 180 \\ Moment of Inertia around the Z-axis & \(l_{z}\) [kg \(\cdot\cdot\) m\({}^{2}\)] & 466 \\  & \(X_{d}\) [kg] & \(-9\) \\ Added Mass & \(Y_{0}\) [kg] & \(-167\) \\  & \(N_{i}\) [kg \(\cdot\cdot\) m\({}^{2}\)] & \(-335\) \\  & \(X_{u}\) [kg \(\cdot\cdot\) s\({}^{-1}\)] & \(-100\) \\  & \(X_{u|u|}\) [kg \(\cdot\cdot\) m\({}^{-1}\)] & \(-150\) \\ Hydrodynamic Coefficients & \(Y_{v}\) [kg \(\cdot\cdot\) s\({}^{-1}\)] & \(-100\) \\  & \(Y_{v|v|}\) [kg \(\cdot\) m\({}^{-1}\)] & \(-100\) \\  & \(N_{r}\) [kg \(\cdot\) m\({}^{2}\) \(\cdot\) s\({}^{-1}\)] & \(-800\) \\  & \(N_{r|r|}\) [kg \(\cdot\) m\({}^{2}\)] & \(-800\) \\ Nomoto Model Parameters & \(K\) [s\({}^{-1}\)] & 2.308 \\  & \(T\) [s] & 1.724 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of WAMV.  
Figure 9: Modules of WAMV.