esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### APAR Importance in GPP Estimates  
Overestimation of PARin greatly affects APAR in a manner that causes overestimation of GPP, which will inflate the MNB. Estimated APAR (Equation (10)) for the three Nebraska sites was plotted as a function of measured APAR in an attempt to determine how well the model was estimating the variable. Similar data were not available from the AmeriFlux website for the other sites. Two of the inputs in calculating APAR are gLAI and PARin, and a good portion of the increased error is attributed to the APAR term in the GPP equation (Equation (9)). When compared to measured APAR, estimated APAR for the three Nebraska sites (US-Ne1, US-Ne2, and US-Ne3) had a combined RMSE of 10.5 mol m\({}^{-2}\) d\({}^{-1}\), with an r of 0.72 and an R\({}^{2}\) of 0.53 (Figure 9a). When the data are separated into green-up (DOY \(\leq\) 220) and post-green peak (DOY \(>\) 220) sets, it becomes apparent that there is a stronger correlation between measured and modeled APAR during the green-up phase than the post green-up phase (Figure 9b,c).  
Figure 8: AmeriFlux measured GPP plotted as a function of LUEc modeled GPP when (**a**) all scalar values are used; (**b**) all scalar values are set to 1 and the model is run as a pure LUE model with the scalars completely negated.
###### Abstract  
We used the finite-element method (FEM) to investigate the effects of jawbone model integrity and the material properties of the periodontal ligament (PDL) on orthodontic tooth movement. Medical imaging software and computer-aided design software were used to create finite-element models of a partial and complete mandibles based on dental cone beam computed tomography images of the human skull. Additionally, we exerted an orthodontic force on the canine crown in the direction of an orthodontic miniscrew under a lower molar root to compare the von Mises strain on the canine PDL in three models: a partial mandible model under orthodontic force (Model 1), a complete mandible model under orthodontic force with clench occlusion in the intercuspal position (ICP; Model 3). Additionally, in the complete mandible model under orthodontic force with ICP occlusion, we analyzed the effects of a PDL with a low (Model 4), moderate (Model 5), and high (Model 6) linear elastic modulus and a PDL a bilinear elastic modulus (Model 7). The simulation results for mandible integrity indicated that the maximum von Mises strains on the canine PDL for Models 1, 2, and 3 were 0.461, 0.394, and 1.811, respectively. Moreover, for the models with different PDL material properties, the maximum von Mises strains on the canine PDLs for Models 4, 5, 6, and 7 were 6.047, 2.594, 0.887, and 1.811, respectively. When the FEM was used to evaluate tooth movement caused by orthodontic force, the transformation of a complete mandible model into a partial mandible model or alteration of the elastic modulus of the PDL influenced the biomechanical responses of the PDL. Additionally, the incorporation of daily ICP occlusion resulted in a larger effect.  
Keywords:finite-element method; orthodontic; periodontal ligament; mandible +
Footnote †: journal: Applied Sciences
## 3 Results
### Effectiveness of the Light Intervention
#### 3.1.3 Personal Light Exposure Measured with Wearable Sensors  
To test the hypothesis of whether the participants received more light in phases B\({}_{1}\) and B\({}_{2}\), when the lighting system was present, compared with phases A\({}_{1}\) and A\({}_{2}\), when the lighting system was absent, individual light data from the light sensors that the participants wore were compared at the individual and group levels between phases. These data included data on light intensity and the estimated correlated color temperature (CCT).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \multicolumn{13}{c}{**Illuminance (in k)**} & \multicolumn{13}{c}{**CCT (in kK0)**} \\ \cline{2-13} \multicolumn{13}{c}{**Kichen**} & \multicolumn{1}{c}{**Bedroom**} & \multicolumn{1}{c}{**Living Room**} & \multicolumn{1}{c}{**Kitchen**} & \multicolumn{1}{c}{**Bedroom**} & \multicolumn{1}{c}{**Living Room**} & \multicolumn{1}{c}{**Kitchen**} & \multicolumn{1}{c}{**Bedroom**} & \multicolumn{1}{c}{**Living Room**} \\ \cline{2-13} \multicolumn{13}{c}{**A\({}_{1}\)**} & **B\({}_{1}\)** & **A\({}_{1}\)** & **B\({}_{1}\)** & **A\({}_{1}\)** & **B\({}_{1}\)** & **A\({}_{1}\)** & **B\({}_{1}\)** & **A\({}_{1}\)** & **B\({}_{1}\)** & **A\({}_{1}\)** & **B\({}_{1}\)** & **A\({}_{1}\)** & **B\({}_{1}\)** & **A\({}_{1}\)** & **B\({}_{1}\)** \\ \hline pp1 & 41 & 777 & 41 & 194 & 16 & 540 & 5924 & 4465 & 4516 & 6704 & 3820 & 3152 & 40 & 556 & 32 & 195 & 27 & 263 \\ pp2 & 195 & 818 & 24 & 919 & 3000 & 3550 & 3643 & 3099 & 4444 & 4456 & 4122 & 4104 & 121 & 520 & 19 & 659 & 2131 & 2496 \\ pp3 & 847 & 724 & 3 & 2030 & 23 & 324 & 3149 & 3115 & 1600 & 6214 & 3507 & 4178 & 444 & 378 & 2 & 1829 & 18 & 192 \\ pp4 & 48 & 1280 & 2632 & 50 & 2140 & 5292 & 4180 & 4057 & 4620 & 4300 & 4218 & 41 & 853 & 18 & 964 & 36 & 1413 \\ pp5 & 48 & 577 & 2 & 2720 & 5 & 1300 & 3270 & 3884 & 1600 & 3424 & 2582 & 4181 & 28 & 371 & 0 & 1377 & 1 & 854 \\ pp7 & 114 & 710 & 47 & 766 & 82 & 575 & 5085 & 4337 & 4782 & 3498 & 4874 & 4247 & 95 & 489 & 38 & 430 & 69 & 390 \\ pp8 & 43 & 982 & 53 & 2120 & 110 & 1360 & 2894 & 4115 & 5368 & 3521 & 4086 & 4193 & 20 & 634 & 47 & 1165 & 72 & 892 \\ pp9 & 37 & 745 & 15 & 2170 & 87 & 761 & 4386 & 4246 & 4814 & 3499 & 5653 & 4386 & 25 & 558 & 13 & 1172 & 81 & 528 \\ pp10 & 11 & 972 & 20 & 766 & 95 & 1210 & 3786 & 4140 & 4690 & 3465 & 5629 & 4453 & 7 & 631 & 16 & 416 & 77 & 860 \\ pp11 & 133 & 528 & 3 & 105 & 35 & 548 & 4386 & 3621 & 1600 & 3357 & 6018 & 3350 & 102 & 319 & 3 & 56 & 34 & 299 \\ pp12 & 8 & 584 & 11 & 1000 & 5 & 140 & 2661 & 3853 & 2429 & 3417 & 3594 & 2983 & 54 & 321 & 1 & 54 & 20 & 248 \\ \hline MeanDiff & 674 & & 1261 & 813 & -28 & 567 & -431 & 356 & & 653 & & 599 \\ Sig & -0.001 & & \(<\)0.001 & & 0.03 & & 0.532 & & 0.169 & & 0.88 & & 0.002 & & 0.004 & & \(<\)0.001 \\ \hline \end{tabular}
\end{table}
Table 3: Results of the illuminance, CCT, and EDI\({}_{\text{med}}\) spectrometer values in phases A\({}_{1}\) and B\({}_{1}\) for the kitchen, bedroom, and living room measured vertically at eye level.
# Filtering Organized 3D Point Clouds for Bin Picking Applications
###### Abstract  
In robotic bin-picking applications, autonomous robot action is guided by a perception system integrated with the robot. Unfortunately, many perception systems output data contaminated by spurious points that have no correspondence to the real physical objects. Such spurious points in 3D data are the outliers that may spoil obstacle avoidance planning executed by the robot controller and impede the segmentation of individual parts in the bin. Thus, they need to be removed. Many outlier removal procedures have been proposed that work very well on unorganized 3D point clouds acquired for different, mostly outdoor, scenarios, but these usually do not transfer well to the manufacturing domain. This paper presents a new filtering technique specifically designed to deal with the organized 3D point cloud acquired from a cluttered scene, which is typical for a bin-picking task. The new procedure was tested on six different datasets (bins filled with different parts) and its performance was compared with the generic statistical outlier removal procedure. The new method outperforms the general procedure in terms of filtering efficacy, especially on datasets heavily contaminated by numerous outliers.  
statistical outlier removal; filtering 3D point cloud; bin picking; segmentation +
Footnote †: journal: Applied Sciences
## 4 Discussion
### LSTM Method  
Due to the LSTM capability of extracting features shared over an extended range of time, LSTMs are known as a well-built technique for challenging sequence prediction problems. A stacked LSTM architecture is represented as an LSTM model involving multiple LSTM layers with the multiple hidden LSTM layers piled one on top of another. Stacking LSTM hidden layers makes the model deeper, more accurately earning the description as a deep learning technique. In this research, we join four of the LSTM layers, each with a dropout layer of value (0.2). In each layer, the number of units is set to 50, showing the dimensionality of the output space. The final layer is the output layer which is a fully connected dense layer.
# Probabilistic Analysis of Infinite Slope Stability Considering Variation in Soil Depth
## 1 Introduction
The first is to quantitatively investigate the variability of soil depth, a factor that has not yet been fully understood. It is challenging to measure soil depth continuously along the slope direction. Therefore, a method for estimating soil depth using seismic wave surveys was employed to overcome these limitations. Seismic wave survey data were collected for 70 slopes in South Korea, and the statistical characteristics of soil depth for each individual slope were evaluated. The second is to apply the identified variability of soil depth to a probabilistic slope stability analysis in order to assess its impact on the probability of slope failure. The infinite slope stability model for unsaturated soil was utilized to conduct a probabilistic analysis, with shear strength parameters and soil depth treated as random variables. The variability of soil depth was considered using three probability distributions (normal, log-normal, and uniform distributions), and various saturation depths were examined to identify the effects of rainfall infiltration. The influences of the variability of soil depth on the probabilistic analysis of slope stability were evaluated by comparing the probability of slope failure. Further, global sensitivity analysis was conducted to assess how parameter uncertainty influenced the probability of failure.
## 3 Test Results and Analysis
### Shear Characteristics Analysis
#### 3.1.1 The Influence of Normal Stress  
It can be seen from Figure 4(a) that the shear stress-shear displacement curve can be roughly divided into four stages. 1 The initial linear stage (OA): In this stage, the shear stress increases along the line with the increase of shear displacement, the slope of the curve is fixed, the shear stiffness is constant, and the slope of the curve is basically the same under different normal stresses. 2 Pre-peak nonlinear stage (AB): In this stage, from the end of the OA segment to the peak strength point, the shear stress no longer presents a linear increase with the increase of shear displacement, but a nonlinear increase and the slope of the curve becomes smaller. This is because tiny cracks start to sprout inside the sawtooth and expand rapidly, leading to smaller shear stiffness until macroscopic cracks appear. When macroscopic cracks appear in the sawtooth, the rock discontinuity reaches peak strength. At the same time, it can be found that the shear stiffness is related to the normal stress, and the larger the normal stress is, the larger the shear stiffness is. 3 post-peak softening stage (BC): the curve at this stage shows that the shear stress decreases with the increase of shear displacement, which is the first failure of the near end of the sawtooth, and the shear force is transferred to the far end. The sawtooth is constantly ripped, the resistance of the sawtooth to the shear force is weakened, and the curve is softened. 4 Residual strength stage (CD): at this stage, the shear stress does not change significantly with the increase of shear displacement. At this stage, all the sawteeth are ripped, but the rock discontinuity still bears a certain shear stress. The shear strength at this stage is provided by the friction effect of the sawtooth plane after nibbling. The residual strength is related to normal stress. The larger the normal stress is, the greater the residual strength will be.
## 3 Results
### Analysis of Single-Factor Test Results  
The results of single-factor tests were statistically analyzed by Origin 2018 software, as shown in Figure 6.  
According to the statistical results in Figure 6, as the scraper shaft speed, scraper space, spiral conveyor speed, and spiral conveyor pitch gradually increased, the comprehensive qualified rate of fertilization first increased and then decreased. The comprehensive qualified rate of fertilization reached the maximum value, and the best fertilization effect was achieved when the scraper shaft speed was 40 r/min, scraper space was 400 mm the spiral conveyor speed was 162.5 r/min and spiral conveyor pitch was 250 mm, respectively. This provided the basis for selecting the value range of design factors in the subsequent response surface test.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multirow{2}{*}{\begin{tabular}{c} **Serial** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Scraper Shaft** \\ **Speed** \\ **(r/min)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Spiral Conveyor** \\ **Speed** \\ **(r/min)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Spiral Conveyor** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Spiral Conveyor** \\ **Pitch** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Comprehensive** \\ **Qualified Rate** \\ \end{tabular} } \\ \hline \multirow{4}{*}{Scraper shaft speed} & 1 & 25 & 175 & 500 & 300 & 0.874 \\  & 2 & 30 & 175 & 500 & 300 & 0.917 \\  & 3 & 35 & 175 & 500 & 300 & 0.971 \\  & 4 & 40 & 175 & 500 & 300 & 0.978 \\  & 5 & 45 & 175 & 500 & 300 & 0.941 \\ \hline \multirow{4}{*}{Scraper space} & 1 & 40 & 175 & 300 & 300 & 0.885 \\  & 2 & 40 & 175 & 400 & 300 & 0.913 \\  & 3 & 40 & 175 & 500 & 300 & 0.903 \\  & 4 & 40 & 175 & 600 & 300 & 0.89 \\  & 5 & 40 & 175 & 700 & 300 & 0.878 \\ \hline \multirow{4}{*}{Spiral conveyor speed} & 1 & 40 & 150 & 400 & 300 & 0.948 \\  & 2 & 40 & 162.5 & 400 & 300 & 0.956 \\  & 3 & 40 & 175 & 400 & 300 & 0.929 \\  & 4 & 40 & 187.5 & 400 & 300 & 0.907 \\  & 5 & 40 & 200 & 400 & 300 & 0.887 \\ \hline \multirow{4}{*}{Spiral conveyor pitch} & 1 & 40 & 162.5 & 400 & 200 & 0.974 \\  & 2 & 40 & 162.5 & 400 & 250 & 0.986 \\ \cline{1-1}  & 3 & 40 & 162.5 & 400 & 300 & 0.905 \\ \cline{1-1}  & 4 & 40 & 162.5 & 400 & 350 & 0.867 \\ \cline{1-1}  & 5 & 40 & 162.5 & 400 & 400 & 0.808 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Design and result of single-factor tests.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multicolumn{5}{c}{**Level**} \\ \cline{2-5}  & **1** & **2** & **3** & **4** & **5** \\ \hline A & 25 & 30 & 35 & 40 & 45 \\ B & 300 & 400 & 500 & 600 & 700 \\ C & 150 & 162.5 & 175 & 187.5 & 200 \\ D & 200 & 250 & 300 & 350 & 400 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Factor levels in single-factor tests.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 4 Energy-Aware Security Mechanisms in IoT
### Approaches to Energy-Aware Security
#### 4.3.4 Data-Centric Approaches  
* _Data Aggregation and Compression_. This allows for data-processing tasks to be performed locally, thereby reducing data transmission, data leakage risks, network congestion, and power consumption.
* _In-network processing_. In-network processing reduces the amount of data transmitted to processing nodes over a network and thus conserves energy consumed for the communication between nodes.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 9 Performance Analysis
### Land-Based UBRD System with Early Rail Defect Detection Capability: Performance Analysis
#### 9.4.1 Defect Detection in the Presence of Large Defects  
From test results, it can be inferred that:  
* The SVD, ICA, and dimensionally-reduced ICA techniques are able to separate the signature of artificial defects from the baseline.
* Their performance is appreciably affected by the EOCs and the multi-mode nature of ultrasonic guided waves propagation in rails. As far as the last issue is concerned, it is important to stress that mode conversion is one of the possible causes of phantom reflections appearing when ultrasonic guided waves impinge on a defect; the multi-mode nature of propagation may cause the defect signature to spread over different components. Moreover, the EOCs can have a different influence on each mode.
* In general, the independent components produced by the ICA algorithm appear to be less noisy than the singular vectors generated by the SVD technique.
* The presence of relatively large monotonic trends is confirmed by the analysis of the M-K test scores over the component weights.
* A good match between the components achieving a large M-K test score and the components related to the defect signature is found in the SVD, ICA, and dimensionally-reduced ICA techniques.
## 2 Dynamic Health Intelligent Evaluation Model
### Component Temperature Dynamic Modeling  
In order to study the effects of thermal damage on satellite health, it is necessary to calculate the dynamic distribution of satellite temperature. The thermal network model is used to solve the temperature distribution of satellite. In the satellite thermal network model, each equipment component is treated as an isothermal body and a node, and the node temperature represents the average temperature of the isothermal body. It is in itself an approximate method because of the discretization needed to solve the heat transfer differential equation. However, due to simplicity and agility, lumped parametric models are more and more widely used in satellite thermal analysis.  
The TNM is described in detail elsewhere [18; 19]. For any satellite component (node) \(j\), the non-linear algebraic transient heat exchange equations that are obtained and that have to be solved is expressed by Equation (4).  
\[\left(cm\right)_{j}\frac{dT_{j}}{dt}=Q_{sj}+Q_{pj}+\sum_{i}\left(\frac{A_{c} \lambda}{\delta}\right)_{i,j}\left(T_{j}-T_{i}\right)+\sum_{i}\left(\varepsilon A _{r}\right)_{i,j}\sigma(T_{j}^{\mathbf{d}}-T_{i}^{\mathbf{d}}) \tag{4}\]  
where, \(Q_{sj}\) represents the external heat absorbed by node \(j\); \(Q_{pj}\) represents the heat that it is directly produced in the \(j\) node itself. Any satellite component (node) has heat exchange with other nodes through heat conduction and heat radiation. \(\sum\limits_{i}\left(\frac{A_{c}\lambda}{\delta}\right)_{i,j}\left(T_{j}-T_{i}\right)\) represents the thermal conduction heat transfer between node \(j\) and the rest of the nodes of the model. \(A_{c}\) is the thermal conductivity area, \(\lambda\) is the thermal conductivity coefficient, and \(\delta\) is the thermal conductivity thickness. The subscript indicates that the three parameters corresponding to the heat conduction and exchange between different nodes have different values:\(\sum\limits_{i}\left(\varepsilon A_{r}\right)_{i,j}\sigma(T_{j}^{\mathbf{d}}-T_{i}^{ \mathbf{d}})\) represents the radiation heat transfer between node \(j\) and the rest of the nodes of the model.\(\varepsilon\) is the emissivity of the node, \(A_{r}\) is the radiation area of the node, and \(\sigma\) is the Stefan-Boltzmann constant. The relative positions of different nodes must also be considered in calculating the radiative heat transfer between them. \(\left(cm\right)_{j}\) is the heat capacity of node \(j\). The external heat flux \(Q_{sj}\) varies with the position and spatial attitude of the satellite in orbit, which is a function of time. The thermal power \(Q_{pj}\) may also be a function of time, depending on what the component needs to accomplish.
## 5 Field Test Configuration and Results
### Experimental Setup  
The proposed credibility evaluation methodology for XILS platforms was validated by constructing a VILS environment and comparatively analyzing the VILS results against those from real-world experiments. By repeatedly implementing the same test scenario in both real and virtual environments, we aimed to quantitatively evaluate the credibility and reproducibility of XILS-based verification.  
The subject vehicle was a Hyundai Kona (Hyundai Motor Company, Seoul, Republic of Korea) featuring an ADS comprising an integrated controller based on a real-time control unit MicroAutoBox3 (MAB3) (dSPACE GmbH, Paderborn, Germany) equipped with an autonomous driving algorithm. An RT3002-v2 Real-Time Kinematic (RTK) (Oxford Technical Solutions, Oxfordshire, UK)\(-\)GPS device was used to ensure precise positioning of the vehicle, and the sensor system for peripheral object and lane recognition was adapted from previous studies by incorporating a multi-sensor fusion method. Specifically, a RoboSense lidar (RoboSense, Shenzhen, China), a Hyundai Mobis radar (Hyundai Mobis, Seoul, Republic of Korea), and a Mobileye 630 camera (Mobileye, Jerusalem, Israel) were integrated to improve recognition performance. The target vehicle was a Hyundai Sonata, which was equipped with a real-time controller MicroAutoBox (dSPACE GmbH, Paderborn, Germany) and RTK-GPS sensors featuring the same autonomous driving algorithm. This vehicle was selected to minimize driver intervention and maximize the reproducibility of the scenarios during the repeated experiments, as its cruise control function can consistently maintain a specified route and speed. For precise collection of the experimental data, a Vector VN1640 (Vector Informatik GmbH, Stuttgart, Germany) was used to monitor the CAN messages from the real car and the VILS environment in real time. The logged data were stored in the form of CAN database files, which were utilized for the credibility evaluation. A description and configuration of vehicles and devices is shown in Figure 5.  
The real-vehicle tests were conducted at K-City, the autonomous driving testbed of the Korea Automotive Technology Institute (KATRI). As shown in Figure 6, the test facility comprises a curved section approximately 320 m long and having curve radii of 70 m, 80 m, and 70 m, together with a one-way, three-lane straight section of approximately 600 m. These road segments are specifically designed to evaluate both the longitudinal and lateral dynamic characteristics of autonomous vehicles. For the simulation (VILS) experiments, a virtual track with an identical geometry was constructed based on HD-map data. These tests were performed in a designated area of the KATRI proving ground designed to accommodate the target road dimensions. By maintaining the same autonomous driving  
Figure 5: Test vehicle and target vehicle equipment.  
algorithm and scenario conditions across the real and VILS environments, the credibility of the XILS platform could be quantitatively evaluated.