esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Thin-Film Microextraction to Analyze Volatile Metabolites in A549 Cancer Cells
## 3 Materials and Methods  
### Thin-Film Microextraction Devices (TFME)  
TFME meshes were manufactured according to [25] and were kindly provided by Prof [PERSON] (University of Waterloo, Waterloo, ON, Canada). Different chemistries of extraction phase were assessed: (1) pure PDMS, (2) hydrophilic-lipophilic balanced (HLB), (3) divinylbenzene (DVB), (4) Carboxen (CAR). All provided membranes had an initial dimension of 20 \(\times\) 4.65 mm. However due to the diameter of the glass thermal desorption tubes, complementary with our autosampler sheets, they were cut into in half-widths (Figure 1). Always, before each sampling, membranes were preconditioned by placing them in 250 \({}^{\circ}\)C (pure PDMS, DVB, HLB) or 275 \({}^{\circ}\)C (CAR), for 30 min with a continuous flow of nitrogen 6.0 purified on a Carrier Gas Purifier (Agilent, Santa Clara, CA, USA)  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Compound Name** & **CAS** & \begin{tabular}{c} **Best TFME** \\ **Type** \\ \end{tabular} & \begin{tabular}{c} **Best Extraction** \\ **Time [min]** \\ \end{tabular} & \begin{tabular}{c} **Literature** \\ **Finding** \\ \end{tabular} & \
\begin{tabular}{c} **Literature Sample** \\ **Prep.** \\ \end{tabular} &
\begin{tabular}{c} **Reference** \\ \end{tabular} \\ \hline
2,3-Butanedione & 431-03-8 & HLB & 10 & \begin{tabular}{c} ex vivo human \\ lung tissue \\ \end{tabular} & \begin{tabular}{c} Tenax-CAR TD tubes \\ \end{tabular} & [19] \\ Benzaldehyde & 100-52-7 & HLB & 90 & A549 cells & \begin{tabular}{c} CAR-DVB SPME \\ fiber \\ \end{tabular} & [10] \\  & & & & \begin{tabular}{c} ex vivo human \\ lung tissue \\ \end{tabular} & \begin{tabular}{c} CAR-DVB SPME \\ fiber \\ \end{tabular} & [8] \\  & & & & \begin{tabular}{c} Graphite and C18 \\ silica monolith \\ monotrap \\ \end{tabular} & [17] \\  & & & & \begin{tabular}{c} ex vivo human \\ lung tissue \\ \end{tabular} & \begin{tabular}{c} Tenax-CAR TD tubes \\ \end{tabular} & [19] \\ Carbon Disulfide & 75-15-0 & PDMS & 10 & \begin{tabular}{c} ex vivo human \\ lung tissue \\ \end{tabular} & \begin{tabular}{c} Tenax-CAR TD tubes \\ \end{tabular} & [19] \\ Methylpyrazine & 109-08-0 & HLB & 10 & \begin{tabular}{c} ex vivo human \\ lung tissue \\ \end{tabular} & \begin{tabular}{c} Tenax-CAR TD tubes \\ \end{tabular} & [18] \\ Pyrrole & 109-97-7 & CAR & 10 & A549 cells & CAR SPME fiber & [9] \\  & & & & A549 cells & Tenax-CAR TD tubes & [18] \\  & & & & \
\begin{tabular}{c} ex vivo human \\ lung tissue \\ \end{tabular} &
\begin{tabular}{c} Tenax-CAR TD tubes \\ \end{tabular} & [19] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Compounds identified as statistically important for A549 conditioned vs. control medium with respective relation to literature data.  
using a TD Clean-Cube conditioning unit (Scientific Instruments Manufacturer GmbH, Oberhausen, Germany).
# RIS-Assisted High-Speed Communications with Time-Varying Distance-Dependent Rician Channels
## 6 Conclusions and Future Works  
In this work, we have proposed a time-varying channel model for the RIS-assisted HSC with the distance-dependent Rician factor. Compared to previous works, our model includes the Rayleigh fading component, the DS term, and the distance-related Rician factor, which is used to characterize the time-varying channel features. Using the proposed channel model, we have shown that the NLoS part dominates the channel when the moving user vehicle is far from the BS and the RIS, while the LoS part dominates when they are close. We have also shown that using RIS phase shift optimization, the DS in the cascaded links can be aligned to the shift in the direct link. Furthermore, if the direct link is obstructed, it is possible to completely eliminate the DS. We have derived the closed-form expressions for ergodic SE and outage probability of the proposed system. Besides, we have shown that the deployment strategy also affects the system performance.  
For future works, one promising research direction is to optimize the location of the BS and the RIS to achieve the tradeoff between ergodic SE and DS. Other possible directions include measuring the parameters of the dynamic Rician factor from a practical environment, performance evaluation for a more practical scenario with non-isotropic elements of the RIS, and using channel aging techniques [23] to characterize the relationship between SE and DS.  
Conceptualization, K.W., C.-T.L. and B.K.N.; Methodology, K.W. and C.-T.L.; Software, K.W. and C.-T.L.; Validation, K.W. and C.-T.L.; Formal analysis, K.W. and C.-T.L.; Investigation, K.W.; Writing--original draft, K.W.; Writing--review & editing, K.W., C.-T.L. and B.K.N.; Visualization, K.W., C.-T.L.; Supervision, C.-T.L. and B.K.N.; Project administration, C.-T.L. and B.K.N. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 2 Overview of the Existing Data Acquisition Techniques for Road Extraction
### Airborne Laser Scanning (ALS)
#### 2.4.3 Application Status and Prospects  
ALS produces accurate 3D models of the target areas. The main application areas of ALS are in topographic mapping, particularly DEM production, city modelling and forestry. Even though the area covered by the ALS in a single scan is limited compared to that covered by the spaceborne sensors, nationwide datasets are available, especially for the Nordic countries. In addition, ALS is currently used to detect human activity in archaeology [167].
## 4 Societal Benefits and Impact
### Case Study: UCL Partners' Proactive Health Monitoring System  
To illustrate the real-world benefits of proactive monitoring systems similar to our proposed framework, we examined the integrated approach of UCL Partners to the management of hypertension in East London [101]. This initiative addressed a situation where only 60% of the hypertension cases were diagnosed and only 40% were effectively controlled. The program implemented continuous monitoring protocols in multiple care settings with integrated data systems that enabled a comprehensive risk assessment. Through this proactive approach, more than 7000 cases of previously undiagnosed hypertension were identified, and treatment adherence improved significantly among existing patients.  
Critical parallels to our IoPVT framework include the following: (1) continuous data collection to identify risks before they escalate, (2) standardized response protocols based on data thresholds, (3) the integration of previously siloed information streams, and (4) targeted interventions for high-risk scenarios. The success of the program in five diverse London boroughs demonstrates how proactive monitoring systems can operate effectively at scale in complex urban environments.  
While this case study focuses on health care rather than outdoor safety, the underlying principles of early detection and prevention align directly with our proposed approach. The demonstrated results suggest similar benefits could be achieved by applying these principles to outdoor safety monitoring through our IoPVT framework.
## 2 Materials and Methods
### Experimental Procedure
#### 2.2.1 Production and Testing of Asphalt Binders  
The production of the binder partially replaced with waste motor oil and SBS was carried out by adding 10 wt % waste motor oil and 5 wt % SBS to the base bitumen (B35/50), as concluded in a previous work [20]. A low shear mixer (IKA RW 20 equipment, IKA(r)-Werke GmbH & Co. KG, Staufen, Germany) was used to obtain an initial blend of the materials at a temperature of 180 \({}^{\circ}\)C. Then, the binder was placed in a high shear mixer (IKA T 65 D ULTRA-TURRAX equipment, IKA(r)-Werke GmbH & Co. KG, Staufen, Germany), at a speed of 7200 rpm for 20 min at 180 \({}^{\circ}\)C in order to obtain a homogeneous blend.  
After production, Fourier transform infrared (FTIR) spectroscopy tests were performed to assess the differences and the interaction between the materials used to produce the new binder developed in this work. FTIR spectra can be a very useful tool for the analysis of the macromolecules of the studied materials, because various organic chemical components [15] can be identified. The equipment used was a Jasco FT/IR-6600 (Jasco, Easton, MD, USA) with a range of wave numbers from 4000 cm\({}^{-1}\) to 400 cm\({}^{-1}\) with 32 scans.  
Then, rheology tests were performed in a dynamic shear rheometer (DSR) model 81-PV6002 (Controls, Milan, Italy), according to EN 14770 [21], in order to obtain the stiffness master curve and the high temperature performance grade of the new binder, which was compared to the conventional bitumen B35/50 applied in road paving.  
Finally, the viscosity of the binders at very high production temperatures was evaluated with a Brookfield viscometer model DV-II+ (AMETEK Brookfield, Middleboro, MA, USA), according to EN 13302 [22], in order to define the mixing and compaction temperatures to be used in the production of asphalt mixtures in the following phase of this work.  
It should be noted that the binder properties obtained in these tests will also be compared to those obtained in the previous study using the asphalt binder with bio-oil and ground tire rubber (GTR) from cryogenic milling (Lehigh Technologies, Tucker, GA, USA) [6]. To produce this material, the bio-oil was initially blended with 15 wt % cryogenic GTR at a speed of 3000 rpm for 1 h at 110 \({}^{\circ}\)C, resulting in a cryogenic rubber modified bio-oil (cryoMBO). This blend (20 wt %) was later added to a PG64-22 bitumen (Seneca Petroleum, Des Moines, IA, USA), at a speed of 3000 rpm for 20 min at 120 \({}^{\circ}\)C, so as to obtain the modified asphalt binder. The low temperatures used in these processes were due to the sensitivity of bio-oil to higher production temperatures.  
Some additional basic characteristics of the bitumen modified with waste motor oil and SBS, like the penetration and ring and ball values, can also be found in a previous study [20].
## 4 Conclusions  
In this article, we presented a comprehensive analysis of the OTA-based gyrator implementing FO inductor to identify and evaluate the limitations caused by the active element real properties, particularly affecting the operational frequency bandwidth, range of the obtainable input admittance, and signal dynamic range. Based on this analysis, design recommendations were provided to overcome or minimize these limitations caused by the real properties of the assumed active elements. During the gyrator design, it is important to set sufficiently large OTA transconductances that are preferably comparable with the required gyrator input admittance magnitude. This also leads to sufficiently large fractance \(F\), which reduces the circuit sensitivity to the parasitics \(G_{\text{PI}}\) and \(C_{\text{PI}}\). To further improve the properties of the gyrator, it is possible to suppress the parasitic conductances by connecting a shunt active compensation circuit with negative conductance also designed with OTA. It is important to take into account that the FO capacitor connected to the gyrator also has limited accuracy and frequency bandwidth. Therefore, it is advisable to match the operating frequency bands of the FO capacitor and the gyrator to avoid unnecessary degradation of the FO inductor function. The proposed optimization steps were verified by OrCAD PSpice simulations while designing FO inductors with different orders; whereas, the FO inductor 0.0193 H-sec\({}^{-0.5313}\) with order 0.4687 being part of fractional-order respiratory model operating in the frequency range 4 Hz to 48 Hz was also successfully emulated.  
Conceptualization, N.H.; Formal analysis, D.K. and J.K.; Funding acquisition, J.K.; Investigation, D.K., N.H. and R.S.; Methodology, D.K.; Validation, J.D. and R.S.; Visualization, J.D.; Writing--original draft, D.K. and J.K. All authors have read and agreed to the published version of the manuscript.  
This research and the APC were funded by The Czech Science Foundation, grant number 19-24585S.  
Not applicable.  
Data Availability StatementData is contained within the article.  
This article is based upon work from COST Action CA15225, a network supported by COST (European Cooperation in Science and Technology).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 LiCSAR Applications for Measuring Tectonic and Volcanic Deformations
### LiCSAR for Other Applications  
Whilst the focus of this paper is on tectonic and volcanic applications, other uses of LiCSAR data could include hydrosphere, cryosphere, and mass movement studies. For example, temporal decorrelation of glacier surfaces leads to a loss of coherence that can prominently reveal the extent and movement of glaciers [73], which is particularly useful for glaciers covered in debris that are difficult to classify using optical data. LiCSAR coverage across the Alpine-Himalayan belt and in parts of Alaska will facilitate the investigation of glaciers using coherence data. Another potential application of LiCSAR data is to map large earthquake-induced landslides. SAR data can be collected through cloud cover, which means data availability is often much quicker than optical acquisitions. Loss of coherence following ground disruption is therefore a potentially useful tool to produce timely regional landslide distribution maps [74]. LiCSAR system routines allow data processing into a higher resolution where requested, though this currently involves manual effort by operators.
## 4 Simulations and Experiments
### Experiments
Choosing a common ratio of 10:1 as a judgment, the designed structure stays accurate with the \(\pm\)1100 arcsec roll movement, \(\pm\)440 arcsec yaw movement, \(\pm\)280 arcsec pitch movement, and \(\pm\)0.6 mm stand-off error when \(-\)10 dB intensity loss is afforded.
## 2 Mechanical Modeling of the ISU System
### Damping Characteristics Analysis  
The damping effect primarily originates from the flow resistance exerted by the damping hole and the friction between the piston and the hydraulic cylinder wall. Friction is much smaller compared to the damping force of the damping hole due to the oil lubrication effect, and does not play a major role in damping. Therefore, it can be ignored during the analysis [18].  
The direction-dependent damping behavior of the hydraulic circuit was achieved through a combined configuration of damping orifices (\(n\)) and check valves. Hydraulic oil could flow through the orifice and the open check valve when the wheel moved upward (jounce). This resulted in reduced flow resistance. Conversely, the check valve automatically closed during the downward wheel motion (rebound), forcing all fluid to pass solely through the orifice, producing significantly higher damping forces. This asymmetric damping profile described by the thin-wall orifice throttling theory can be quantified as [23]:  
\[F_{c}=\frac{\rho A_{o}^{3}\dot{\Delta Y}^{2}sign\left(\dot{\Delta Y}\right)}{2 \left\{nC_{z}A_{z}+C_{d}A_{d}\left(0.5+0.5 sign\left(\dot{\Delta Y}\right) \right)\right\}^{2}} \tag{32}\]  
where \(F_{c}\) denotes the damping force (N), \(\dot{\Delta Y}\) represents the piston velocity relative to the cylinder (m/s), while \(A_{z}\) and \(A_{d}\) correspond to the cross-sectional areas of the damping hole and the one-way valve, respectively.  
This equation establishes a functional relationship between the damping force and critical parameters, including piston velocity, fluid density, and orifice dimensions. The sign function \(sign\left(\dot{\Delta Y}\right)\) encodes motion directionality, outputting +1 during compression and \(-1\) during rebound phases. This formula has been widely referenced in previous studies [19; 24; 25], underscoring its applicability in various hydropneumatic suspension designs.  
Figure 8: Vertical force transmitted to the wheel under real gas assumptions at \(T=20\) °C and \(T=120\) °C.  
The damping force \(F_{wc}\) acting on the wheel can be calculated based on the moment equilibrium principle using the following expression.  
\[F_{wc}=\frac{F_{\text{con}}^{c}\,L_{2}\text{sin}\,\left(\theta_{2}-\theta_{3} \right)}{L_{1}\text{cos}\,\left(\theta_{w}\right)} \tag{33}\]  
For hydraulic damping systems with nonlinear characteristics, the damping coefficient can be represented as a partial derivative of the damping force with respect to the piston velocity.  
\[c_{s}\left(\dot{\Delta Y}\right)=\frac{\partial F_{c}}{\partial\dot{\Delta Y}}= \frac{\rho A_{o}^{3}\dot{\Delta Y}\,sign\left(\dot{\Delta Y}\right)}{\{nC_{z}A _{z}+C_{d}A_{d}\left(0.5+0.5 sign\left(\dot{\Delta Y}\right)\right)\}^{2}} \tag{34}\]
# A Deep Learning Approach to Semantic Segmentation of Steel Microstructures
## 4 Discussion  
Different random test samples were selected for segmentation using the obtained models. The accuracy and loss values in Figures 5-9 are obtained during training. The overall values, as shown in Table 4, are calculated based on test images that the model had not previously seen. These test values closely resemble those observed during training, indicating that no \"overfitting\" has occurred in any of the models.  
Algorithms with lower loss rates and higher accuracy during training may demonstrate superior generalization performance on unseen data, resulting in higher final accuracy.  
In Figure 11 (segmented samples), a comparison of 224 \(\times\) 224 images of annealed steel is presented, highlighting the region considered as perlite in green hues and the matrix or ferrite, which appears light in the original image and violet in the segmented image. The grayscale image corresponds to the mask generated during data preprocessing. Although the results are very similar, subtle differences can be perceived. It is important to note that some masks were created manually, while the rest underwent preprocessing using a Random Forest algorithm with WEKA software (ImageJ2-Figi GPLv3+, Waikato University, New Zeland). This process may have introduced errors in pixel annotation in some masks, causing the model to learn from imperfect images. As shown in microstructure A, there is an error in the bottom right part of the mask (slightly pointed area), Figure 12a, where the ferrite zone connecting with the one in the top right has not been completely obtained. This flaw is highlighted in red in Figure 12b. This error has also been transferred to the training models, which consequently failed to detect the ferrite in that zone. However, a slight improvement in the segmented area compared to the mask is noticeable. Similarly, in image B, impurities can be observed on the ferrite area (two dots on the left side), which were also transferred to the training dataset. In this case, models like DeepLabv3 with ResNet50-18 have effectively eliminated these impurities during the segmentation process.  
As shown in Figure 13, another test sample was selected, and errors in the identification of ferrite and perlite were marked on the corresponding mask image. The segmented images by the models demonstrate improvement over the mask created for training. We can observe that in the original image, it is difficult to appreciate the laminar structure of perlite. Although ferrite, as the matrix element of the microstructure, should be easily detected due to its more uniform and clear texture, the models encounter issues in some areas, such as the band in Figure 13b, which is indicated in the red rectangular area. Considering perlite as alternating layers of ferrite and cementie, the thickness of this bright band between two darker zones causes the models to interpret that area as perlite. The models with DeepLabv3+/MobileNetv2, shown in Figure 13f and, to some extent, U-Net, manage to enhance segmentation in that specific area.
## 3 Measurement Technique and Data Reduction Methodologies
### Quantitative Interpretation for Output Signals from Hot-Wire Anemometry
It should be noted that the boundary layer thickness (\(\delta\)), which was determined by the position where the local velocity value becomes \(0.99U_{\infty}\), was 10.2 mm (\(\delta_{smooth}=10.2\) mm) for this representative condition. The discrepancy excluding the region around the boundary layer outer edge was sufficiently small (i.e., 0.26% for \(z=1.0\)-4.0 mm). Other hot-wire measurement data can be corrected similarly. Thus, the temperature correction with the best-fit approach can be considered as appropriate. The following discussion will be based on the temperature-corrected \(U\) velocity with respect to the hot-wire anemometry data by use of Equation (26). \begin{table}
\begin{tabular}{c c c} \hline
**Wall Type** & **Mean Freestream Velocity** & **Tw, \({}^{\circ}\)C** \\ \hline Smooth & 41.7 m/s (Mach 0.12) & 111 \\ Riblet & 41.7 m/s (Mach 0.12) & 77 \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of temperature correction.
# LSTM-DQN-APF Path Planning Algorithm Empowered by Twins in Complex Scenarios
## 3 Method  
This section optimizes the performance of the LSTM-DQN-APF algorithm in three aspects: First, the extended Kalman filter (EKF) is used to fuse LiDAR and IMU data, combined with virtual and physical sensor data from a digital twin platform, enhancing dynamic environment perception and state estimation accuracy; second, the repulsive force function of the artificial potential field method is improved by incorporating velocity and acceleration compensation terms to address path oscillations and response lag in dynamic obstacle scenarios; finally, a multi-dimensional reward function is constructed, integrating goal reward, exploration reward, and heuristic reward. This function quantifies the deviation between the actual and the theoretical optimal paths to achieve dynamic evaluation and optimization of the DQN-APF collaborative decision making.