esa-sceva/satcom-chunk-collection huggingface dataset:
# An Advanced DEM-FEM Method for Herringbone Gear in Shot Peening
## 3 Advanced DEM-FEM Modeling for Shot Peening
### Finite Element Model  
#### 3.2.1 Mesh Division and Boundary Conditions  
Figure 11 shows the mesh division of the target plate. The length of \(L1\) is 2 mm, \(L2\) is 1.4 mm, and \(L3\) is 1 mm. The area with \(L3\) as the side length is the collision area, which is the region where random shots are generated to extract the final calculation results. The area with \(L2\) as the side length is the mesh refinement area with a mesh size of 10 \(\upmu\)m \(\times\) 10 \(\upmu\)m \(\times\) 10 \(\upmu\)m. To ensure the calculation accuracy at the edges of the impact area, the refined mesh area is larger than the impact area. The area between \(L2\) and \(L1\) is the transition area, aimed at reducing the number of meshes and improving computational efficiency. To control the hourglass phenomenon in explicit  
dynamics, the element type chosen is an eight-node hexahedral reduced integration element, C3D8R.  
Based on the DEM, the impact velocity and the number of impacts on the tooth surface can be obtained. In the actual processing process, the distribution of shots on the tooth surface is random. To accurately simulate this phenomenon, the initial positions of the shots are randomly determined using the Python 3.13.0 function random. The initial position of the \(j\)th shot can be represented by Equation (16).  
\[\left\{\begin{array}{l}x_{j}=random.uniform(-0.5,0.5)-v_{xj}\times t\times j \\ y_{j}=random.uniform(-0.5,0.5)-v_{yj}\times t\times j\\ z_{j}=-v_{zj}\times t\times j-r_{j}\end{array}\right. \tag{16}\]  
Here, \(x_{j}\), \(y_{j}\) and \(z_{j}\) represent the coordinates of the center of the \(j\)th shot. \(v_{xj}\), \(v_{yj}\) and \(v_{zj}\) denote the component velocities of the \(j\)th shot, \(t\) is the interval between the shot peening of adjacent shots, and \(r_{j}\) is the radius of the \(j\)th shot.
## 2 Materials and Methods
### Sample Preparation  
A pictorial view presenting the preparation of the samples is shown in Figure 1a. The powders were initially mixed together with the help of a ball milling setup for two hours at 90 rpm. Different samples were prepared by changing the concentration of carbamide powder (10%, 20%, and 30%) (Sigma Aldrich, USA). The milled powders were then compressed with a hydraulic pellet pressing machine (antsLAB, Maharashtra, India) at a pressure of 100 MPa for one minute. The obtained samples after compression were sintered under a vacuum tube furnace for 250 \({}^{\circ}\)C for 4 h (Stage 1 sintering). This resulted in the evaporation of 20% of the carbamide particles, forming pores inside the sample. The density of the samples was measured using a fluid displacement method based on the Archimedes principle. Table 1 shows the density and porosity of the developed samples with different spacer holder concentrations as well as aloe vera and PLA addition samples. The sample with 20% carbamide powder showed promising results as discussed in the further experimentation sections.  
The as-obtained pores were then cleaned by flushing out the carbamide particles from the samples with an ultrasonication machine (Chrom Tech, USA) for 45 min. The cleaned sample was then dried using a dryer followed by 6 h desiccation under a desiccator. The dried sample was then kept in a vacuum furnace for 2 h at 400 \({}^{\circ}\)C for Stage 2 sintering. The obtained sample was then allowed to cool under vacuum for 12 h.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Carbamide (C) wt\%** & **Density (g/cm\({}^{3}\))** & **Porosity (\%)** \\ \hline Mg-HA & 0 & 1.7512 \(\pm\) 0.013 & 3.12 \(\pm\) 0.131 \\ \hline Mg-HA-10C & 10 & 0.9251 \(\pm\) 0.012 & 51.1 \(\pm\) 0.122 \\ \hline Mg-HA-20C & 20 & 0.6858 \(\pm\) 0.043 & 72.25 \(\pm\) 0.024 \\ \hline Mg-HA-30C & 30 & 0.4557 \(\pm\) 0.005 & 81.5 \(\pm\) 0.154 \\ \hline Mg-HA-20C-AV & 20 & 1.0052 \(\pm\) 0.025 & 70.25 \(\pm\) 0.115 \\ \hline Mg-HA-20C-AV+PLA coating & 20 & 2.1250 \(\pm\) 0.002 & 2.18 \(\pm\) 0.113 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Density and porosity of the developed samples.
# Fault Diagnosis for a Class of Robotic Systems with Application to 2-DOF Helicopter
## 3 Fault Isolation Based on Hamiltonian Structure
### Residual Evaluation  
After a residual generator is designed. It should be implemented to obtain the signals, called residuals, that will be used to get the information about the faults. Ideally, the residuals are zero if no faults are present and only different from zero when a fault is present. In practical situations, however, the residual will not be zero because of model uncertainty of small perturbations. In order to avoid false alarms a residual evaluation procedure is included. The idea is to use the obtained signals (residuals) as an argument of a function. for simplicity, a memory-less function is used which make the residual positive. In this paper an absolute value function is utilized. The result of the absolute value is compared to a (fix) threshold. An active residual is declared only when the resulting absolute value of the residual is bigger than the threshold. Otherwise the residual is declared as non-active. The analysis of active/non-active residuals brings us the information if a fault is present and in the case which one is.  
When a perturbation and a fault are acting on the same channel, that is called a coupled, and there is no way to distinguish between them using this method. Thus, to be able to distinguish between a perturbation and a specific fault it is required that first, the decoupling approach presented in Section 3.1 produces two different subsystems and, then to design a residual generator for each of them.
## 3 Data Preprocessing
### Grid Division of the Spatial Distribution of Precipitation  
The QPE data used in this paper are from the Global Precipitation Measurement (GPM) program satellite constellation. The GPM satellite was launched by NASA and JAXA in 2014, aiming to measure rainfall and snowfall on Earth with advanced radar and radiometers carried by the satellite [23]. The GPM core platform carries the first satellite-borne Ku/Ka-band dual-frequency precipitation radar (DPR) and a multi-channel GPM microwave imager (GMI). The minimum threshold value of precipitation measurement is 0.5 mm/h, which can achieve a good measurement of precipitation.  
In HC City and GL City, there are 8 and 12 ground meteorological stations, respectively, and their location distribution is shown by the orange dots on the left side of Figures 3 and 4, respectively. Compared with the rain-collecting surface of tens of thou  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & \\ \cline{2-13}  & \(\mathbf{L_{1,2}}\) & \(\mathbf{L_{1,3}}\) & \(\mathbf{L_{1,4}}\) & \(\mathbf{L_{1,5}}\) & \(\mathbf{L_{1,6}}\) & \(\mathbf{L_{2,1}}\) & \(\mathbf{L_{2,2}}\) & \(\mathbf{L_{2,3}}\) & \(\mathbf{L_{1}}\) & \(\mathbf{L_{2}}\) & \(\mathbf{L_{3}}\) & \(\mathbf{L_{4}}\) \\ \hline Num. of neurons & 32 & 64 & 128 & 16 & 8 & 32 & 16 & 8 & 16 & 8 & 4 & 1 \\ \hline Size of conv. kernel & \((2,2)\) & \((2,2)\) & \((2,2)\) & / & / & / & / & / & / & / & / & / \\ \hline Pooling block size & \((1,2)\) & \((1,2)\) & \((1,2)\) & / & / & / & / & / & / & / & / & / \\ \hline Activation function & relu & relu & relu & relu & relu & relu & relu & relu & / & relu & relu & liner \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of each layer of the method presented in this paper.  
Figure 2: The overall architecture of the forecasting method for the daily power generation of small hydropower stations.  
sands of square kilometres, the number of meteorological stations is particularly scarce. In accordance with the specifications of the 0.3\({}^{\circ}\)\(\times\) 0.3\({}^{\circ}\) (latitude and longitude) grid, we generate a precipitation grid network covering the HC and GL regions, respectively, as shown in the blue squares on the right side of Figures 3 and 4. According to the spatial distribution of precipitation observed by the GPM satellite and the actual precipitation observations from the ground meteorological stations, combined with the method mentioned in Section 2.1, a gridding data set covering the spatial distribution of precipitation in the HC and GL area can be generated, respectively.
# Road Infrastructure Challenges Faced by Automated Driving: A Review
## 4 Identification of Challenges
### Design
\begin{table}
\begin{tabular}{p{34.1 pt} p{34.1 pt}} \hline \hline \multicolumn{3}{c}{**Environment**} \\ \hline \(\bullet\) & Glancing in challenging weather and light conditions is strongly negatively correlated with lane detection [66]. \\ \(\bullet\) & Foggy conditions are much more of an issue than rain. Under foggy conditions, the contrast ratio is quite low and the system cannot operate satisfactorily [68]. \\ \(\bullet\) & Some studies indicate that although ADAS can still operate at 20 mm/h of rainfall, an increase to 30 mm/h results in the view range already converging to 0 at speeds between 48 and 60 km/h [78]. \\ \hline \hline \multicolumn{3}{c}{**Maintenance**} \\ \hline \(\bullet\) & To ensure good visibility of road markings, the luminance coefficient night-time visibility should be kept at least in the range between 100 and 150 mcd/lx/m\({}^{2}\), while daytime visibility should be between 130 and 160 mcd/lx/m\({}^{2}\) (and with a contrast ratio of 3-to-1) [9; 10]. \\ \(\bullet\) & Multiple lane markings, such as at construction sites or residuals of old markings, can lead to misinterpretations [10]. \\ \(\bullet\) & Road surface with debris, potholes or cracks can be misinterpreted by the lane detection system [10]. \\ \hline \hline \multicolumn{3}{c}{**Design**} \\ \hline \(\bullet\) & Some coloured road markings lower the contrast ratio between markings and pavements [9]. \\ \(\bullet\) & Discontinuous markings (e.g., intersections) and lanes that are not normal result in worse performance of lane detection methods [10]. \\ \(\bullet\) & Increasing the width of markings from 100 to 150 mm makes them easier to detect. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Limiting factors in lane detection. traffic benefits [80]. Nevertheless, dedicated lanes would still require changes in the design of road marking and signs, stricter maintenance, harmonisation and digitalisation. A study on dedicated lanes for roundabouts came to the conclusion that dedicated lanes do not results in benefits at the lower penetration rates of AVs and only confer a slight advantage under higher penetration rates [81]. Dedicated lanes, magnet markers and magnetic-induction lines are a possible option in some areas but not for all roads and not for wide application [82; 83].
## 3 Experimental Results
### Spectroscopic Ellipsometer
#### 3.2.2 Non-Encapsulated and Encapsulated MAPbI\({}_{3}\) Samples  
The absorbance measurements were made to the MAPbI\({}_{3}\) encapsulated and non-encapsulated samples, before and after subjecting them to the degradation processes.  
The absorbance spectra of the encapsulated and non-encapsulated samples after being subjected to full solar irradiation and UV lamp are shown in Figure 5a. As can be seen, the absorbance of the encapsulated and non-encapsulated layers is similar, independently of the light source used. Therefore, the degradation mechanisms involved could be similar under both irradiation methods. In relation to the pristine samples, the absorbance curves of the non-encapsulated MAPbI\({}_{3}\) samples show a decrease between 400 nm and 500 nm after 50 min of irradiation. This decrease is extended to 760 nm after 70 min, the moment at which the absorbance curve becomes similar to the PbI\({}_{2}\) one. Additionally, the band gap increases from 1.6 to 2.2 eV. Since the degradation experienced by MAPbI\({}_{3}\) was the same under both kinds of irradiation sources, it can be deduced that it was caused solely by the UV part of the spectrum [67; 68; 69].  
Furthermore, in order to analyze the effect of the encapsulation on the stability of the MAPbI\({}_{3}\) samples, they were exposed to 350 h of full solar irradiation. It must be taken into account that the study of the encapsulated samples should start from 400 nm due to the absorption of the EVA sheet in the UV range. As can be seen, in relation to the pristine samples, the absorption spectra of the exposed samples present only a slight decrease of around 450 nm due to the encapsulation procedure (Figure 5b).
# Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
## 2 Related Work
### Risk Collision Analysis
To facilitate more effective learning of collision avoidance strategies, [PERSON] et al. (2020) proposed Constrained-DQN (Deep Q Network). This approach reduces state-action space complexity by incorporating constraints based on International Collision Avoidance Rules at Sea (COLREG), thereby enhancing efficacy in collision avoidance outcomes.
# Electrically Conductive Nanocomposite Fibers for Flexible and Structural Electronics
## 2 Materials and Methods
### Composite Analysis and Testing  
The electrical properties of the fibers were measured using the Rohde & Schwarz HM8112 multimeters. The two-point probe method was used for the resistivity measurements. The voltage-current characteristics were measured using a dedicated setup containing multimeters and voltage supply controlled via LabVIEW software, National Instruments Corporation, Austin, TX, U.S.A. Tensile tests were performed using the Cometech QC-506M2 machine, Cometech Testing Machines Co., Ltd., Taichung, Taiwan.
## 2 System Model of the TR Acoustic Communication Using FMT
### Receive Structure
#### 2.2.3 Adaptive Equalization  
In order to mitigate residual ISI further, adaptive equalization shown in Figure 3 is used in the proposed method for post-process. The adaptive equalizer used for each subcarrier is the linear equalizer to avoid error propagation, and the output signal of the \(m\)-th adaptive equalizer \(E_{m}\left(\omega\right)\) can be expressed as:  
\[\hat{a}_{m}\left(nK\right)=\sum_{i=-N_{1}}^{N_{2}}d_{m}^{i}z_{m}\left(nK-iK\right) \tag{11}\]  
where \(d_{m}^{i}\) denotes the \(i\)-th tap coefficient of the \(m\)-th adaptive equalizer \(E_{m}\left(\omega\right)\) with \(N_{1}+N_{2}+1\) taps. The number of taps, \(N_{1}+N_{2}+1\), is related to the span of ISI in the composite channel. The larger the span of ISI is, the bigger the number of taps used in the adaptive equalizer is. Since the FMT modulation exploited in the proposed method can widen the symbol interval of each subcarrier and reduce the span of ISI in each composite channel \(Q^{\left(m,i\right)}\left(\omega\right),\) the taps of adaptive equalizer for each subcarrier in the proposed method decrease.  
In the proposed method, the recursive least square (RLS) algorithm is selected to adjust the tap coefficients of each adaptive equalizer, since the convergence rate of RLS is relatively fast and does not depend on the input signal. The taps of the adaptive equalizer for each subcarrier are adjusted separately. When the total number of the taps for adaptive equalization in the proposed method is equal to that of the TR acoustic communication using SC modulation, computational complexity of adaptive equalization in the proposed method is lower than that of the TR acoustic communication using SC modulation.  
Referring to Figure 3, the output signals of all adaptive equalizers are processed by a parallel-to-serial converter, and then the estimation of the transmit symbols \(\hat{a}\left(nT_{b}\right)\) can be obtained.
# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 2 Materials
### Multispectral Satellite Image  
The reference data are from a WV-3 multispectral satellite image acquired on 21 May 2015. The off-nadir view angle is about 7.8 degrees, and the ground sampling distances for panchromatic and multispectral images are 0.315 m and 1.26 m, respectively. The wavelength of panchromatic image is from 450 nm to 800 nm and contains eight multispectral bands, including coastal (400-450 nm), blue (450-510 nm), green (510-580 nm), yellow (585-625 nm), red (630-690 nm), red edge (705-745 nm), NIR-1 (770-895 nm), and NIR-2 (860-1040 nm) [29]. We manually selected ground control points from multi-wavelength LiDAR and WV-3 image. The registration accuracy of rational function geometrical model is better than one pixel. The DSM from all points was used to generate 0.3 m panchromatic and 1.2 m multispectral orthoimages. We also performed radiometric correction to convert the pixel values to reflectance values [30] using metadata of WV-3 images. The land cover classification only adopted LiDAR features, and the WV-3 satellite image was only a reference image in the selection of training and test regions. In addition, we compared the vegetation indices from the passive WV-3 multispectral image and active multi-wavelength LiDAR.
## 4 Modeling and Inversion of GNSS-R Reflectivity  
In this section, the tau-omega model, which is used for microwave applications over continental surfaces [28; 29], is applied to model GNSS-R reflectivity in an effort to improve our analysis and understand its behavior. This semi-empirical model is widely used over soil with vegetation cover because it is easily performed in an inversion scheme to estimate soil moisture and vegetation parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**50–70\({}^{\circ}\)**} & \multicolumn{2}{c}{**70–90\({}^{\circ}\)**} \\ \cline{2-5}  & \multicolumn{1}{c}{**Sv**} & \multicolumn{1}{c}{**R\({}^{2}\)**} & \multicolumn{1}{c}{**Sv**} & \multicolumn{1}{c}{**R\({}^{2}\)**} \\  & **(dB/(m\({}^{2}\)/m\({}^{2}\)))** & & **(dB/(m\({}^{2}\)/m\({}^{2}\)))** & & **R\({}^{2}\)** \\ \hline \(\Gamma_{RL}\) & \(-0.91\) & \(0.38\) & \(-23\) & \(0.03\) \\ SNR & \(-0.76\) & \(0.34\) & \(-0.17\) & \(0.01\) \\ PR & \(-1.1\) & \(0.30\) & \(-1.18\) & \(0.36\) \\ \hline \end{tabular}
\end{table}
Table 3: Statistical parameters of the relationships between GNSS-R observables and vegetation LAI, for two elevation angle intervals. Sv = the sensitivity of the GNSS-R observables to LAI.
## 3 Results and Discussion
### Impact of Temperature
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Metabolites**}} & \multicolumn{4}{c}{**Temperature (\({}^{\circ}\)C)**} \\ \cline{2-4}  & **27** & **30** & **33** & **36** \\ \hline
1,3-PD (g/L) & 32.62 \(\pm\) 0.17 \({}^{\mathrm{a}}\) & 43.30 \(\pm\) 2.07 \({}^{\mathrm{b}}\) & 10.83 \(\pm\) 0.44 \({}^{\mathrm{c}}\) & 7.00 \(\pm\) 0.20 \({}^{\mathrm{c}}\) \\ Lactic acid (g/L) & 9.97 \(\pm\) 1.02 \({}^{\mathrm{a}}\) & 10.82 \(\pm\) 0.05 \({}^{\mathrm{a}}\) & 1.52 \(\pm\) 0.04 \({}^{\mathrm{b}}\) & 1.33 \(\pm\) 0.14 \({}^{\mathrm{b}}\) \\ Acetic acid (g/L) & 8.04 \(\pm\) 0.59 \({}^{\mathrm{a}}\) & 9.65 \(\pm\) 0.04 \({}^{\mathrm{b}}\) & 2.89 \(\pm\) 0.08 \({}^{\mathrm{c}}\) & 1.85 \(\pm\) 0.05 \({}^{\mathrm{c}}\) \\ Succinic acid (g/L) & 3.88 \(\pm\) 0.18 \({}^{\mathrm{a}}\) & 4.85 \(\pm\) 0.04 \({}^{\mathrm{b}}\) & 0.88 \(\pm\) 0.01 \({}^{\mathrm{c}}\) & 0.66 \(\pm\) 0.02 \({}^{\mathrm{c}}\) \\ Ethanol (g/L) & 0.94 \(\pm\) 0.08 \({}^{\mathrm{a}}\) & 1.86 \(\pm\) 0.09 \({}^{\mathrm{b}}\) & 0.35 \(\pm\) 0.12 \({}^{\mathrm{c}}\) & 0.36 \(\pm\) 0.01 \({}^{\mathrm{c}}\) \\ DCW (g/L) & 2.60 \(\pm\) 0.34 \({}^{\mathrm{a}}\) & 3.03 \(\pm\) 0.14 \({}^{\mathrm{a}}\) & 1.08 \(\pm\) 0.30 \({}^{\mathrm{b}}\) & 0.42 \(\pm\) 0.18 \({}^{\mathrm{b}}\) \\
3-HPA (mmol/g DCW) \({}^{\mathrm{*}}\) & 3.68 \(\pm\) 0.99 \({}^{\mathrm{a}}\) & 4.12 \(\pm\) 0.34 \({}^{\mathrm{a}}\) & 8.81 \(\pm\) 1.49 \({}^{\mathrm{b}}\) & 13.40 \(\pm\) 0.12 \({}^{\mathrm{c}}\) \\ \hline \hline \end{tabular} \({}^{\mathrm{a}}\) maximum concentration of 3-HPA achieved Means \(\pm\) SD with different superscript letters in the same row differ significantly (\(p<\) 0.05).  
\end{table} 
Table 1: Final concentrations of metabolites (1,3-propanediol (1,3-PD), lactic acid, acetic acid, succinic acid, ethanol) and dry cell weight (DCW) in a fed-batch culture of _C. freundii_ AD119 at various temperatures. Cessation of the processes related to 3-HPA accumulation was studied in _E. agglomerans_, _K. pneumoniae_, and _C. freundii_[17]. Nonetheless, to the best of our knowledge, the influence of process temperature on this phenomenon in _C. freundii_ has not been examined before. According to previous reports, the optimum temperature for 1,3-PD formation in _C. freundii_ is 37\({}^{\circ}\)C [31; 32; 33]. Our findings for _C. freundii_ AD119 are strongly contrasting and indicate that strains of _C. freundii_ require a cautious approach to finding the optimal process temperature. The temperature of 30 \({}^{\circ}\)C was selected for the subsequent stages of our research.
## 3 The First Modal Inertial/Invariant Single Load Vectors Pushover Analysis Methods
### The Capacity Spectrum Method (CSM) ATC 40 [5]  
The capacity spectrum method, initially proposed by [PERSON] et al. in 1975 [35], was introduced as an alternative tool for rapid assessment of buildings. [36] It compares a structure's ability to resist lateral loads with seismic demand (response spectrum). The capacity/pushover curve represents the structure's capacity. The primary pushover recommended in ATC 40 (1996) [37] is based on the equivalent linearization technique [5], applying a linearization approach that uses a capacity spectrum. The following steps describe the procedure involved in this methodology [38]:  
1. Pushover analysis and conversion: execute a pushover analysis to chart the structure's capacity curve, showcasing base shear, \(V_{b}\), against the building's roof displacement, \(D_{l}\). Adapt this curve into acceleration-displacement (AD) metrics using the equivalent single degree of freedom (ESDOF) model. Utilize the first mode participation factor, \(C_{0}\), and the modal mass for the conversion: \(D^{\bullet}=D/C_{0}\) and \(A=V_{b}/M\).
2. Graphical representation: superimpose the derived capacity curve with the 5%-damped elastic response spectrum, ensuring both are in AD format.
3. Deformation demand and pseudo-acceleration: opt for an initial peak deformation demand '\(dt\)' and pinpoint its linked pseudo-acceleration \(A\) from the capacity curve, starting with a damping ratio \(\zeta=5\).
4. Ductility and damping calculation: Measure ductility using \(m=D^{\bullet}/u_{y}\) and compute the hysteretic damping with \(\zeta_{h}=2(m-1)/pm\). Determine the equivalent damping  
Figure 2: Graphical timeline of the development and evolution of pushover analysis methods.  
ratio with \(\zeta_{eq}=\zeta_{el}+k\zeta_{h}\), where \(k\) is a factor adjusting for the system's hysteretic behavior. Refine the '_dt_' estimate using the elastic demand curve for \(\zeta_{eq}\).
5. Convergence check: Monitor the displacement '_dt_' for stabilization. Once consistent, the target displacement for the multi-degree-of-freedom (MDOF) system is set as '_dt_ = C0_dt_'.
6. The equivalent linearization process necessitates an understanding of the displacement ductility ratio. The ATC-40 document outlines three iterative methods: procedures A, B, and C. While procedures A and B are clear and easily programmable, procedure C is a visual method not apt for coding. Specifics of these methods can be found in the ATC-40 [5] document and are omitted here for conciseness. The procedures are clearly defined in the [5] document. In his work [39], he also applied this methodology.