esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background: Most studies on air pollution (AP) exposure have focused on adverse health effects of particulate matter (PM). Less well-studied are the actions of volatile organic compounds (VOCs) not retained in PM collections. These studies quantified chemical and biological properties of both PM2.5 and VOCs. Methods: Samples were collected near the Port of Los Angeles (Long Beach, LB), railroads (Commerce, CM), and a pollution-trapping topography-site (San Bernardino, SB). Quantitative assays were conducted: (1) chemical--prooxidant and electrophile content, (2) biological--tumor necrosis factor-\(\alpha\) (TNF-\(\alpha\)) and heme oxygenase-1 (HO-1) expression (3), VOC modulation of PM effects and (4), activation of the antioxidant response element (ARE) using murine RAW 264.7 macrophages. Results: SB site samples were the most potent in the chemical and biological assays, followed by a CM railroad site. Only PM2.5 exhibited significant proinflammatory responses. VOCs were more potent than PM2.5 in generating anti-inflammatory responses; further, VOC pretreatment reduced PM-associated TNF-\(\alpha\) expression. VOCs significantly increased ARE activation compared to their corresponding PM2.5 which remained at background levels. Conclusion: Ambient VOCs are major contributors to adaptive responses that can modulate PM effects, in vitro, and, as such, need to be included in comprehensive assessments of AP.  
PM2.5; VOC;volatile organic compounds; prooxidants; electrophiles; tumor necrosis factor alpha; hemeoxygenase-1; ambient air; antioxidant response element; murine RAW 264.7 macrophages +
Footnote †: journal: Applied Soft. 2020, 10, 3245; doi:10.3390/app10093245
# Parametric Analysis on Landing Gear Strut Friction of Light Aircraft for Touchdown Performance
## 3 Analysis of Strut Friction Effect on Touchdown Performance Using RSM
### RS-Model and Analysis
The accurate fitted model must satisfy the requirements regarding the four indexes, which are shown in Table 8. Moreover, the scatter points of the numerical model response values versus the predicted values should evenly distribute on both sides of the 45-degree diagonal. The ANOVA results acquired from the RS-model for the four dynamic responses are shown in Table 8. The table illustrates that all four indexes for checking the coincidence of the fitted model are meet the criteria. Furthermore, the scatter plots of the numerical model response values versus the predicted values are shown in Figure 9. These figures demonstrate that the sample points are split evenly by the 45-degree diagonal. As described in [14], the response surface models for the four landing dynamic responses can be used to simulate the landing response in the design space \(V\). The four RS functions are listed in Table 9. All the functions have three order effects, including the first-order, the second-order, and the interaction effects. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **Criteria** & \begin{tabular}{c} **Max Load of Strut** \\ **Friction** \\ \end{tabular} & \begin{tabular}{c} **Shock-Strut** \\ **Efficiency** \\ \end{tabular} & \
\begin{tabular}{c} **Max Overloading of** \\ **the Sprung Mass** \\ \end{tabular} &
\begin{tabular}{c} **Shock Absorber** \\ **Stroke** \\ \end{tabular} \\ \hline Mean-Squared Error & — & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 & 0.0293 \\ Sum-Squared Error & — & \textless{}0.0011 & \textless{}0.0001 & 0.0004 & 0.0643 \\ \(p\)-value & \textless{}0.05 & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 \\ R-squared & \textgreater{}0.9 & 0.9995 & 0.9995 & 0.9983 & 0.9971 \\ Adj R-squared & \textgreater{}0.9 & 0.9992 & 0.9992 & 0.9967 & 0.9952 \\ Adequate precision & \textgreater{}4 & 71.8497 & 78.6344 & 69.7399 & 168.5038 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The ANOVA results of RS model for the four response values. Figure 9: The numerical model values versus the predicted values of the responses: (**a**) Max load of strut friction; (**b**) Shock-strut efficiency; (**c**) Max overloading of the sprung mass; (**d**) Shock absorber stroke.
###### Abstract
By using temperature distributions on skin surfaces and the breast's 3D geometry, it is possible to use reverse thermal modelling and finite element analysis (FEA) to identify the presence of tumors in human breasts. Finite element modelling (FEM) is mainly used for forward simulations in the performance of analyses. On the other hand, by combining FEM with optimization methods in reverse thermal modeling, it would be possible to determine personalized tissue properties, based on which reverse thermal modeling and design optimization can then be employed in order to determine the sizes and locations of breast tumors. The novelty of this study lies in the development of a unique tool for the estimation of the depths and sizes of breast tumors using 3D scans of patients' breasts and personalized tissue parameters such as density, fat/gland contents, specific heat, and conductivity through reverse thermal modeling. This tool consists of an FEM solver and optimizer, using thermograms and 3D breast scans as inputs. Optimization is used to search for optimal patient-specific parameters based on temperature distribution on the breast skin and for thermal modelling using FEM, which is called backward thermal simulation. For this study, the experimental procedure involved the collection of clinical data such as 3D models of the breasts and thermograms (skin surface temperature distributions). The temperature readings and 3D models of healthy breasts, personalized data such as thermal conductivity, specific heat, density, and blood perfusion heat loss rate were computed for each patient by using reverse thermal modeling, which combines FEM and design optimization. These parameters were then used for reverse thermal modeling through FEM and design optimization based on the patient-specific data obtained above to search for the sizes and depths of tumors. Each procedure included patient-specific data and diagnostic results from a medical doctor, which was then used to validate the method proposed herewith.
## 2 SLAM Based on Multi-Sensor Fusion
### Data Preprocessing  
The data processing of the depth camera is used to convert the image data obtained by the depth camera into laser data in order to complete the data fusion between sensors.  
Figure 1: Overall framework of the SLAM algorithm.  
The principle of converting the depth image acquired by the depth camera into laser data is shown in Figure 2. The conversion steps are as follows:  
1. The obtained depth image is screened for the effective area to obtain the depth image (\(u\), \(v\)) to be processed.
2. Through the depth image (\(u\), \(v\)) and the camera parameter model, the position coordinates \(M\) (\(x\), \(y\), \(z\)) of each pixel of the depth camera in the world coordinate system are obtained.
3. Project the spatial point cloud (\(x\), \(y\), \(z\)) into the corresponding laser scanning range, and calculate the angle \(AOD\) between the straight lines \(AO\) and \(DO\); the calculation formula is shown in (2): \[\theta=arctant\Big{(}\frac{x}{z}\Big{)}\] (2)  
The scanning of the laser is [\(\varepsilon\), \(\zeta\)], the laser beam is divided into \(N\) parts, and the laser data are represented by laser [\(N\)]. The index value \(n\) of the point \(M\) projected into the array is  
\[n=\frac{\theta-\varepsilon}{(\zeta-\varepsilon)/N}=\frac{N(\theta-\varepsilon )}{\zeta-\varepsilon} \tag{3}\]  
The value of laser [\(N\)] is the distance r from point D on the x-axis to the center of the camera's light spot.  
\[\textit{laser}\ [N]=\mathrm{r}=\mathrm{OD}=\sqrt{z^{2}+x^{2}} \tag{4}\]
## 3 Proposed System
### Service Data Preparation Block
```
1:Input: Raw dataset \(D\) from \(csv\)
2:Output: Cleaned dataset \(D_{final}\) saved as \(csv_{final}\)
3:procedureRemOVEUNINFORMATIVECCOLUMNS(\(D\))
4:for each column \(c\) in \(D\) (excluding first column) do
5:if all values \(in\)\(c\) are 0 then
6: Mark \(c\) for removal
7:else
8: Compute longest run of consecutive zeros in c
9:if maximum consecutive \(zeros>60\) days then
10: Mark \(c\) for removal
11:endif
12:endif
13:endfor
14: Remove marked columns from \(D\)
15:return\(D_{cleaned}\)
16:endprocedure
17:\(D_{cleaned}\leftarrow\) REMOVEUNINFORMATIVECCOLUMNS (\(D\))
18:procedureREPLACEOUTLIERSWITHZSCORE(\(D\))
19:for each column \(c\) in \(D\) (excluding first) do
20: Compute mean \(\mu\) and std. deviation \(\sigma\) of \(c\)
21:for each value \(x_{i}\) in \(c\) do
22: Compute \(z_{i}=\frac{x_{i}-\mu}{\sigma}\)
23:if\(|z_{i}|\geq 3\) then
24: Replace \(x_{i}\) with \(x_{i-1}\) if \(i>0\), else with \(\mu\)
25:endif
26:endfor
27:endfor
28:endprocedure
29:\(D_{final}\leftarrow\) REPLACEOUTLIERSWITHZSCORE (\(D_{cleaned}\))
```  
**Algorithm 1** Data Cleaning and Outlier Handling  
Algorithm 1 is Preprocessing procedure for data cleaning and interpolation. Figure 2: Examples of daily measurements of LPG usage over a year in three different places. (**a**) shows an example of isolated spike which may indicate an equipment malfunction; (**b**) is a certain period of fluctuation, reflecting abnormalities; (**c**) shows several spikes and sporadic anomalies. After removing consecutive zeroes, we applied interpolation using the z-score. The z-score is a statistical measurement that shows how far a specific data point is from the mean value in terms of standard deviations. This score can be used to detect outliers, and its formula is as shown in Equation (1):  
\[\text{z}=\frac{x-\mu}{\sigma} \tag{1}\]  
where \(x\) is the data point, \(\mu\) is the mean of the time-series data, and \(\sigma\) represents the standard deviation of the time series. Since the time series contains outlier values, as shown in Figure 2a, the values need to be interpolated. Then, the value is replaced by its previous day's measurement value. This is because even though the value \(x_{i}\) is interpolated into the average of \(x_{i-1}\) and \(x_{i+1}\), it is still high enough to be seen as an outlier where only the observed value is too high. Finally, when the data cleaning is over, the preprocessed dataset is stored in the database and used by the Service Model Training Module.
# Effects of Vehicular Communication on Risk Assessment in Automated Driving Vehicles
## 2 Sensor Characteristics and Information Fusion  
The characteristics for vehicular communication and conventional local sensor systems are described. The backgrounds for radar and its sensing characteristics for application to intelligent vehicle are demonstrated. For a longitudinal safety, automotive radar is known to be the best fitted vehicular surround sensing technology with respect to functionality, robustness, reliability, dependence on weather conditions etc. [11]. Its performance has been evaluated by comparing with the camera (vision sensor). However, due to the inherent uncertainty of the traffic environments and incomplete knowledge because of sensor limitations, V2V communication has been widely considered to provide the host vehicle with more enriched target information by fusing with the radar sensor. In this section, the advantages of radar sensor have been presented by experimentally comparing with the camera. The V2V communication information is explained as well and the comparison results of the DSRC with long-term evolution-vehicle (LTE-V) technologies are provided. In addition, a high-level data fusion architecture of radar and V2V communication is described.
## 2 Materials and Methods
### Trial Flow
#### 2.2.1 Instructions for Participants  
First, the researcher conducting the experiment vocally provided a general introduction to the experiment to the participant. Thereafter, at the beginning of each session, the respective instructions were displayed as text in the virtual environment and played in audio format for each participant. The specific instructions were:  
_We trained an AI model to predict which of the presented objects the user is intending to grasp. The model is based on multimodal data from the user and the visual scene. We previously evaluated this model on a large data set, and its total accuracy was approximately 90%._  
In the \"revealed\" condition, we additionally informed the participants:  
_However, based on the evaluation, the objects with larger circle textures are identified with lower accuracy, 60%._  
The rest of the instructions were identical for both conditions:The aim of this study is to test our AI model. In each trial, you will see a set of four cubes. You will be asked to imagine grasping one of them--just look at the target where you would grasp it. After the model collects the data, it will produce its prediction of the intended object. You will then be prompted to either CONTINUE to the next trial or CHECK the model's prediction and correct it if needed. To select the button, use your gaze and press the trigger. For each correct final decision, you will gain +5 points. For each incorrect final decision, you will lose \(-X\) points. Each check will cost you \(-2\) points. The final goal is to maximize your number of points. Note, the X corresponds to the risk level where it is \(-2\) points for the low risk and \(-10\) points for the high risk.
## 3 Results and Discussion
### Membrane Antifouling Properties and Water Permeability  
The antifouling performances of R-HIPS, R-HIPS-3, and R-HIPS-5 membranes were evaluated by dynamic permeation experiments using a 1000 ppm BSA solution. The flux recovery ratio (FRR %), irreversible fouling (Rir), recovery capacity (Rr), and the total fouling (Rt) are presented in Figure 4. The flux recovery ratios (FRR) of sulfonated R-HIPS-5 and R-HIPS-3 membranes were greater than that of R-HIPS, indicating that increased hydrophilicity enhanced the antifouling properties of the membranes. The reversible fouling ratios (Rr), which refers to the temporary adsorption of molecules or particles onto a membrane surface, were 8.2%, 9.6%, and 15.6% for R-HIPS, R-HIPS-3, and R-HIPS-5, respectively. In contrast, the irreversible fouling ratio (Rir), which occurs when foulants such as proteins, oils, or particulates become permanently fixed onto the pore surfaces of a membrane [38], was lower in R-HIPS-5 membrane, following the same tendency (R-HIPS > R-HIPS-3 > R-HIPS-5) as the total fouling (Rt), which indicates that increasing sulfonation reduces the fouling in R-HIPS membranes. The notably lower Rr values compared to Rir suggest that particles tend to adhere more readily to the pore surfaces in the non-sulfonated R-HIPS membrane, compared to R-HIPS.  
Also, in the dynamic permeation experiments, the R-HIPS membrane exhibited a 6.0 L/m2h water flux, while the sulfonated membranes, R-HIPS-3 and R-HIPS-5, showed reduced fluxes at 4.3 L/m2h and 3.1 L/m2h, respectively. This reduction in water flux is consistent with the membrane porosity results, since a larger degree of sulfonation showed a reduced porosity.  
Figure 4: Antifouling performances observed with BSA solutions of R-HIPS, R-HIPS-3, and R-HIPS-5 membranes.
# The Eye-Opening Arbiter-PUF FPGA Implementation with Auto Error Detection
## 3 Eliminating Deterministic FPGA Routing Delays to Avoid EoA-PUF Bias
### Arbiter Routing Bias in FPGA-Based EoA-PUF
#### 3.2.1 Implementation of PDLs Eliminating Arbiter Routing Bias  
In order to solve the problem, the previous finding of the deterministic mean delay of the H-SDEs (see Figure 5) can be exploited. Multiple such cells can be concatenated  
Figure 4: Step-wise FPGA realization of the SDE. (**a**) shows the SDE used as source of entropy for all PUF implementations (see Figure 1). In (**b**), the symmetric paths of this SDE are split into two parallel H-SDE paths, avoiding the necessity to cross the paths. (**c**) illustrates the FPGA implementation of these H-SDEs via LUTs.  
Figure 5: Propagation delay differences of all evaluated LUT-SDEs when switching the challenge input form 0 to 1, with mean at \(\approx\)–7 ps in red.  
to form a PDL with an n-bit input, where each bit is connected with the C-input of the corresponding H-SDEs, to select the desired delay. By adding a PDL into the paths from the RO outputs to the inputs of the D-FFs of the arbiter, PDLs can be added into all paths to the arbiter, and the influence of asymmetric routing can be minimized by proper tuning [10]. The internal implementation of a PDL is depicted in Figure 6.  
As the delay cells of the PDL add not only a deterministic delay \(\delta\tau_{del[PDL}\), which is required for the function of the PDL and the wanted compensation, but they also add a stochastic \(\delta\tau_{mis[PDL}\) themselves, an appropriate tuning vector per PDL has to be found, which then achieves best cancellation of the deterministic delay due to asymmetric wiring by the adjusted delay of the PDLs in all paths. How the appropriate tuning vector for the PDLs can be determined is explained next.
# A Review of Passive Constant Flow Regulators for Microfluidic Applications
## 1 Introduction  
Passive flow control regulators, also known as passive flow control or autoregulated or pressure-compensated valves, deliver a constant flow rate regardless of pressure variations. These valves are widely used in industry for water treatment, process water control (limiting peak flow rate), water authorities (flow limiting, boost mains pressure), centrifugal pump protection, water-saving (domestic showers, drinking fountains), irrigation, etc. [1, 2]. These valves are compact, reliable, maintenance-free, and require no energy. Manufacturers (e.g., Maric Flow Control, Tokyo Keiso, Plast-o-Matic, Flowmate, the Lee Corporation, Parker Hannifin, etc.) provide passive valves dedicated to high flow rate (from a few hundred mL/min to 10,000 L/min and more) and large pressure differential (from 1 bar to several hundreds of bar). In this review, a focus is made on passive constant flow valves dedicated to microfluidic applications, including drug delivery, flow chemistry, point-of-care tests, hydrocephalus treatment, microdialysis, etc. These passive flow regulators fall into two main categories: the mechanical regulators having moving parts (a membrane in silicon or elastomer, a piston, a collapsible tubing, or a flap), and non-mechanical regulators without moving parts (fixed geometry), wherein the rheological properties of the fluid are exploited to achieve a constant flow rate. Most of the regulator designs analyzed in this survey belong to the first category. The automatic adjustment of the flow rate with varying pressure conditions is obtained by a change of the fluidic pathway dimensions. A variation of pressure moves a flexible element of the passive flow regulators to induce a change of the fluidic pathway cross-section in almost all configurations except the device with a spiral groove onto a moving piston and the device with a flexible silicon (Si) membrane and a spiral-shaped channel, wherein the length of the channel is automatically adjusted. This classification of the passive constant flow regulators dedicated to microfluidic applications, illustrated in Figure 1, is partly derived from the system set forth by [PERSON] and [PERSON] for micropumps [3]. The review surveys progress over the last three decades in the development of these passive constant flow regulators. The working principle and performance of each device are discussed, along with a description of the fabrication process and the potential applications.
###### Abstract  
The growing demand for direct current transmission emphasizes the need for advanced insulation suitable for high-capacity, long-distance applications. Thermoplastics, especially polypropylene, offer several advantages over conventional materials like XLPE (cross-linked polyethylene) and EPR (ethylene propylene rubber), including higher thermal stability, recyclability, and reduced space charge accumulation. However, due to the inherent rigidity and limited flexibility of PP, its mechanical aging becomes a critical factor in assessing its long-term reliability as a cable insulation. In this study, mechanical aging characteristics, specifically declines in tensile strength and elongation, were selected as key indicators of insulation aging. Accelerated aging tests were conducted at 90 \({}^{\circ}\)C, 110 \({}^{\circ}\)C, and 130 \({}^{\circ}\)C for up to 5000 h. The experimental data were fitted to exponential models to derive aging coefficients, which formed the basis for the proposed aging model and the ACI (aging condition index). The ACI enables quantitative assessment of the current insulation condition and estimation of the remaining lifetime until a predefined threshold (e.g., ACI = 0.5) is reached. These findings contribute to the development of condition-based maintenance strategies and long-term asset management for power cables, offering practical insights for improving the reliability of future power grid systems.  
Power Cable Research Center, Korea Electrotechnology Research Institute,  
Changwon-si 51543, Gyeongsangnam-do, Republic of Korea; 815 [EMAIL_ADDRESS] (S.-W.L.) Correspondence: [EMAIL_ADDRESS]; +82-05-280-2535  
**Keywords:** power cables; aging coefficient; accelerated aging test; tensile strength; elongation; aging condition index; remaining lifetime
## 3 Methodology  
A coherence weighted least square estimator without any hypothetical model was used to calculate the long-term deformation velocity (LTDV) and maximum seasonal defor  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ALOS/PALSAR** & **Number of SAR Images** & **Acquisition Time** & **Resolution (Azimuth)** & **Ground Coverage** \\ \hline
491-690 & 10 & 16 June 2007– & 10 m & 70 km \(\times\) 100 km \\
491-700 & 10 & 16 June 2007– & & \\
492-690 & 10 & 12 December 2009 & & \\
493-680 & 12 & 15 February 2007– & 10 m & 70 km \(\times\) 100 km \\
493-690 & 17 & 23 February 2010 & & \\
494-690 & 12 & 17 January 2007– & & \\
493-690 & 12 & 09 March 2009 & & \\
494-680 & 11 & 21 June 2007– & & \\
494-690 & 11 & 08 February 2009 & & \\
494-690 & 11 & 21 June 2007– & 10 m & 70 km \(\times\) 100 km \\
494-690 & 11 & 08 February 2009 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: ALOS/PALSAR Data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SALOS-2/PALSAR-2** & **Number of SAR Images** & **Acquisition Time** & **Resolution (Azimuth)** & **Ground Coverage** \\ \hline
152-680-1 & 20 & 24 February 2015– & 10 m & 70 km \(\times\) 70 km \\
152-690-1 & 15 & 15 December 2015– & 10 m & 70 km \(\times\) 70 km \\
152-690-2 & 17 & 30 June 2015– & 10 m & 70 km \(\times\) 70 km \\
151-700-1 & 20 & 23 July 2015– & & \\
16 January 2020 & 16 January 2020 & 10 m & 70 km \(\times\) 70 km \\ \hline \hline \end{tabular}
\end{table}
Table 2: ALOS-2/PALSAR-2 Data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sentinel-1 Path-Frame** & **Number of SAR Images** & **Acquisition Time** & **Resolution (Azimuth)** & **Ground Coverage** \\ \hline
77-475 & 161 & 16 March 2017– & & \\
12 December 2021 & 20 m & 250 km \(\times\) 167 km \\
150-475 & 123 & 11 October 2017– & & \\
01 December 2021 & 20 m & 250 km \(\times\) 167 km \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sentinel-1 Data.  
mation (MSD) without any prior knowledge. The main steps are shown in Figure 2. The main steps are: (1) importing ALOS, ALOS-2 and Sentinel-1 images and images coregistration; (2) Optimal interference pair selection and interferogram generation; (3) Multilook filtering, phase unwrapping and coherent target selection; (4) Least squares inversion; (5) Atmospheric phase correction and topographic residual correction; (6) Geocoding the average deformation map and extracting the time series deformation; (7) Extracting the long-term deformation velocity and maximum seasonal deformation; and (8) Analyzing the deformation of permafrost with auxiliary data.