esa-sceva/satcom-chunk-collection huggingface dataset:
# Chemical Vapor Deposition of Longitudinal Homogeneous Parylene Thin-Films inside Narrow Tubes
## 1 Introduction
This fact is caused by the inevitable depletion of the reactant with growing distance from the evaporating source, leading to a decreasing film thickness along the whole reaction chamber [19]. This decrease of the film thickness is even more pronounced along the interior wall of tubes, capillaries or micro scaled gaps. In the past decades, various models explaining the well-known exponential film thickness decrease inside narrow tubes have been developed [7; 26; 27; 28]. Usually, the authors consider a one-dimensional axis-symmetric gas flow through the tube for different types of gas-dynamic regimes. These models all predict with some accuracy the exponential decrease in film thickness along a capillary, but none treats the possibility of manipulating the deposition behavior. Moreover, a detailed investigation of the slip-flow regime, where the monomer mean free path is comparable to the geometry of the system, here the diameter of the tube, is still missing in this context. However, a detailed description is important since, at characteristic process pressures of several Pa, the two case limiting theories, namely molecular flow (e.g., microscale gaps) and viscous flow (e.g., flow in the reactor), break down for promising applications such as in medicine (stents, catheters, and further [20; 21]). In order to counteract the effect of decreasing film thicknesses, and, further, to produce longitudinally homogeneous coatings inside narrow tubes by means of CVD, we have already shown in a previous work that spatial manipulation of the sticking coefficient, by application of a thermal gradient, yields reasonable results [20]. However, a theoretical approach to understand this process is still missing, especially in the slip-flow regime where the mean free path of the film-building species is similar to the cross section of the capillary. Since a direct access on the PPX-N partial pressure along the tube is in principle denied, our proposed method seems to be a unique solution tackling this problem. We further expect our method to be applicable for related processes. In Section 2, we discuss our approach in a more formal basis and present a detailed investigation of the CVD deposition of PPX-N inside narrow tubes. Section 3 gives an overview on the experimental procedure in this work. In Section 4, first the results on the deposition in narrow tubes at constant temperatures are discussed, followed by measurements of the temperature-dependent sticking coefficient. With knowledge on our system, we are further able to derive a required sticking coefficient gradient in order to reach a homogeneous film thickness inside narrow tubes. Finally, with this prediction, we are in a position to generate high quality homogeneous film thicknesses along narrow tubes and validate the proposed model by comparison of simulation and experiments.
## 1 Introduction  
Increasing requirements concerning environmental impact, product quality, and security lead to new challenges for lightweight design [1; 2; 3; 4]. In particular, the thrust towards renewable energy and the expansion of electro-mobility encourage novel trends in the development of materials and multi-functional structures. In this context, the stability of material structures (e.g., body frames [5] and battery-pack enclosures [6]) subjected to dynamic loading regimes attracts increasing interest. For this reason, the present contribution puts a special focus on the blast resistance of lightweight structures. Numerous studies have demonstrated the improved performance of structural components based on sandwich-structured composites in the case of blast loading due to detonation processes (in water or air) compared to monolithic panels of the same weight [7; 8; 9; 10]. This superiority is expressed both in the lower level of deflection with respect to the rear face and in the reduction in the forces transmitted to the substructure. The main reasons for this enhanced blast resistance are the increase in energy dissipation due to plastic deformations of the core and the top surfaces, as well as--mainly in the case of underwater explosions--a reduction in the momentum introduced into the structure due to fluid-structure interactions.  
The load-carrying behavior of sandwich-structured composites under the impact of explosions exhibits fundamental differences compared to quasi-static loading. According to [11], dynamic effects, such as the rate-dependent material behavior, the inertia stabilization of the core walls/bars, and the mass inertia resistance, provide for an increase in stiffness in the core area. The inertia-stabilizing effect on the core elements leads to an increase in the dynamic buckling resistance; this effect becomes more significant with increasing impact loading.  
The core topology also determines wave propagation through the structure and plastic distortions, as well as the type and shape of the plastic buckling processes. To our knowledge, a thorough classification of the dynamic performance of auxetic core geometries compared to conventional approaches does not yet exist; this gap in the literature serves as a motivation for this contribution. A novel aspect of this work is the fact that both open and closed prismatic cores, as well as lattice cores, each in different auxetic and non-auvetic designs, were investigated in this context. Please note that this contribution is essentially based on the preliminary work of the first author [12].
# Effects of Vehicular Communication on Risk Assessment in Automated Driving Vehicles
## 3 Automated Driving Risk Assessment  
It is well known that a human-centered risk assessment algorithm is beneficial to avoid collision at any speed range with a proper intervention timing of active safety function [5]. For a fully automated drive, drive mode decision function, a lane change or a lane keeping, is to be activated while ensuring safety. A decision sight distance is implemented to facilitate adequate mimicking of human senses and allows to mirror human internal states by computing human reaction time for safety and smoother operations. In this paper, the algorithm is extended to activate a drive mode decision function incorporating human reaction time based on decision sight distance. Figure 5 shows an overview on the human-centered risk assessment algorithm including two control functions: an active safety mode, and a lane change mode (drive mode decision). The proposed safe triangle analysis is based on the peak (maximum) collision probability, _maxCp_, and the human reaction time with respect to most probable predicted collision distance, _DPmaxCp_.  
A safe triangle analysis is proposed to assess the risk and to decide mode intervention timing for a fully automated drive. It allows the system to decide the drive mode by avoiding the collision at any driving situation. For a longitudinal safety, the safe triangle analysis detects drive mode decision timing before active safety intervention function is activated. With the V2V communication, the area of safe triangle is enlarged due to accurate information of front vehicle dynamic states.
## 4 Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Soil Depth (m)**} & \multicolumn{2}{c}{**Error Ratio (\%)**} \\ \cline{2-7}
**Position** & \multicolumn{2}{c}{**Measured**} & \multicolumn{2}{c}{**Estimated**} & \multicolumn{2}{c}{**Fragmented**} & \multicolumn{2}{c}{**Graphical**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Cone Test**} & \multicolumn{2}{c}{**P-Wave Velocity**} & \multicolumn{2}{c}{**Bilinear Method**} & \multicolumn{2}{c}{**P-Wave Velocity**} & \multicolumn{2}{c}{**Bilinear Method**} \\ \hline  & 1 & 1.1 & 2.3 & 1.1 & 109.1 & 0.0 \\  & 2 & 0.8 & 2.0 & 0.8 & 150.0 & 0.0 \\  & 3 & 1.0 & 1.8 & 1.0 & 80.0 & 0.0 \\  & 4 & 0.4 & 1.9 & 0.5 & 375.0 & 25.0 \\  & 5 & 0.5 & 1.4 & 0.6 & 180.0 & 20.0 \\ BTS & 6 & 0.2 & 1.8 & 0.5 & 800.0 & 150.0 \\  & 7 & 0.6 & 1.4 & 0.5 & 133.3 & 16.7 \\  & 8 & 0.7 & 2.4 & 1.0 & 242.9 & 42.9 \\  & 9 & 1.9 & 5.6 & 1.9 & 194.7 & 0.0 \\  & 10 & Over 2.0 & 3.5 & 3.0 & - & - \\  & & & Average & & 251.7 & 28.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between Measured and Estimated Soil Depths. Figure 6: Measured DPI values of each stream: (**a**) BTS; (**b**) BS; (**c**) MS; and (**d**) TS. Note the numbers in each figure indicate the location of dynamic cone penetration test described in Figure 3.
# DB-Net: Detecting Vehicle Smoke with Deep Block Networks
## 1 Introduction
The second challenge for the semantic segmentation approach is that, compared with conventional objects, it is more difficult to infer an accurate mask with vehicle smoke due to the significant variations in smoke appearance and confused boundaries. Thirdly, detecting vehicle smoke in surveillance videos usually requires real-time detection, while semantic segmentation models are generally time-consuming and heavy. In this paper, we make a trade-off between object detection and semantic segmentation, and propose a conceptually new, yet simple deep block network (DB-Net). DB-Net is built with fully convoluted layers, which take as input arbitrary resolution images, and block-wise classification masks as output. Specifically, DB-Net contains a main branch for feature abstraction and a feature aggregation branch that enhances features for smoke classification. Compared to popular semantic segmentation methods, our DB-Net has fewer parameters and is much faster, and also maintains comparable or superior performance. In addition, to facilitate the progress of vehicle smoke detection, we built a real-world vehicle smoke semantic segmentation dataset, termed the Polygon-based annotated Vehicle Smoke Segmentation dataset (PoVSSeg). The images of PoVSSeg have rich diversities in road conditions, weather, vehicle types, smoke types (light or strong), and so forth. In total,  
Figure 1: A typical example of vehicle smoke. PoVSSeg contains 3962 vehicle smoke images with polygon annotations. We expect that our PoVSSeg can be a new benchmark for smoke detection or segmentation in images. Furthermore, we propose a coarse-to-fine training strategy to make full use of existing bounding-box annotated data. In fact, annotating smoke regions with accurate boundaries is difficult, while it is relatively easy to mark a bounding box for each smoke. To this end, we regard a massive collection of bounding-box labeled data as coarse polygon annotated data and pre-train our DB-Net on them as a coarse warm-up. With the pre-trained model, we further fine-tune it on our PoVSSeg to learn more accurate smoke features. Overall, this training strategy boosts our DB-Net effectively. This paper is arranged as follows. Related works on vehicle smoke detection, including some works on wild smoke detection, are briefly reviewed in Section 2. Then our datasets are introduced and compared with the existing datasets in Section 3. Furthermore, our methods are described in Section 4, and experimental results and analysis are presented in Section 5. Finally, we provide a conclusion and discuss future work in Section 6.
## 3 Results and Discussion
### Coefficient of Thermal Expansion (CTE) of Al-BiSn Composites  
The variation in the coefficient of thermal expansion (CTE) values of extruded Al-BiSn composites with BiSn content is presented in Figure 10. A decline in the CTE values is observed with an increasing amount of BiSn, reflecting improved thermal stability of the developed Al-BiSn composites.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{2}{c}{**Hardness**} & \multicolumn{2}{c}{**Tensile Properties**} \\ \cline{2-6}  & **Micro** & **Nano** & **Yield** & **Ultimate Tensile** & **\% of** \\  & **(HV)** & **(GPa)** & **Strength/MPa** & **Strength/MPa** & **Elongation** \\ \hline Pure Al & \(36\pm 2\) & \(38\pm 3\) & \(92\pm 3\) & \(117\pm 3\) & 13.70 \\ Al-(5 wt.\% BiSn) & \(49\pm 6\) & \(53\pm 4\) & \(112\pm 3\) & \(135\pm 4\) & 13.45 \\ Al-(10 wt.\% BiSn) & \(62\pm 4\) & \(67\pm 1\) & \(136\pm 4\) & \(154\pm 1\) & 12.76 \\ Al-(15 wt.\% BiSn) & \(78\pm 4\) & \(83\pm 2\) & \(164\pm 5\) & \(185\pm 3\) & 12.04 \\ Al-(10 wt.\% Al\({}_{2}\)O\({}_{3}\)) [15] & \(73\pm 6\) & - & \(118\pm 5\) & \(141\pm 5\) & \(9.4\pm 0.5\) \\ Al-(10 wt.\% AlCuLi) [21] & \(72\pm 3\) & - & \(122\pm 3\) & \(152\pm 6\) & \(8.3\pm 0.5\) \\ Al-(3 wt.\% SiC) [44] & \(56.1\) & - & \(142\) & 162 & 4.7 \\ Al-(3 wt.\% SiO) [45] & \(38.7\) & - & \(100\) & 137 & 0.9 \\ Al-(5 wt.\% CNP) [16] & - & - & \(22\) & 27 & 0.2 \\ Al-(10 wt.\% TiO\({}_{2}\)) [47] & 37 & - & \(64\) & 76 & 5.33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the hardness and tensile properties of Al-BiSn composites with other studies.  
Figure 9: Fractography studied materials under tensile mode: (**a**) pure Al and (**b**) Al-(5 wt.% BiSn), (**c**) Al-(10 wt.% BiSn), and (**d**) Al-(15 wt.% BiSn) composites.  
The measured CTE value of the pure Al observed is \(\sim\)23.31 \(\upmu\)/k, which is very close to the theoretical CTE value of Al. By adding 5 wt.% of BiSn in Al, the CTE value decreases to \(\sim\)22.14 \(\upmu\)/k, which reflects an enhanced constraint on the thermal expansion by the additional interface of BiSn. Further increase in BiSn content leads to further decrease in the CTE values, reaching \(\sim\)19.86 \(\upmu\)/k at 15 wt.% of BiSn. In comparison with pure Al, the continuous decrease of the CTE values with the increasing amount of BiSn particles confirms enhanced thermal stability of the Al-BiSn composites. The behavior of CTE values is similar to the other Al-based composites present in the literature [18; 20]. Owing to their low CTE value, the Al-BiSn composites can be regarded as a potential material to be used in aerospace, automobile, and electrical industries.
## 3 Results
### KI for Two-Band Parameters  
The KI for all of the possible reconstructed two-band combinations of RDSI, RNDSI, and RRSI were analyzed at the 1 nm interval with the full range of 345-1075 nm; Figure 1 shows the KI contour maps. According to Figure 1, the band combinations of higher KI were extracted from the hotspot areas as the better spectral index to grade the disease severity. As shown in the KI contour map, within the full wavelength, compared with Figure 2b,c, there were large continuous hotspots in Figure 2a, whereas the greatest KI values of 1 were mostly in a small number of hotspots (Figure 2b,c) among the three contour maps.  
Among the three types of reconstructed two-dimensional SIs, the maximum KI of RDSI was 0.935, six pairs of red side band combinations were all reached 0.935; RDSI (R702, R715) were taken as the sensitive RDSI of cotton aphid stress, 702 and 715 nm were both in the red edge region. For RNDSI and RRSI, the kappa coefficient of most band combinations was below 0.7, while the KI of some band combinations in the 490-730 nm region reached the maximum of 1. The contour map of RNDSI was similar to that of RRSI. The number of band combinations whose KI value reached 1 was 96 and 61 for RNDSI and RRSI, respectively. Due to multiple band combinations with the maximum KI, according to Equation (10), the maximum band combinations RNDSI (R643, R656) and RRSI(R656, R643) were taken as the sensitive two-band indices of cotton aphid stress; RNDSI and RRSI had the same sensitive spectrum consisting of 643 and 656 nm, which were in the red region. Moreover, RRSI (R656, R643) yielded the best results for both the KI value and \(\eta\) among the possible two-band combinations of RDSI, RNDSI, and RRSI. The three contour maps all showed that the blue regions had the weakest correlation with KI.
# An Optimized Differential Step-Size LMS Algorithm
## 4 ODSS-LMS Algorithm
### Practical Considerations
The echo path changes in the same manner as in Figure 3. Up to a certain point, the ODSS-LMS-G algorithm deals better with correlated signal as input. We can remark on the lower normalized misalignment in case of ODSS-LMS-G algorithm for low values of \(\sigma_{w}^{2}\), while the tracking capability is affected due to the lack of the initial estimation process. Even so, the tracking could be improved if the variance of the system noise is increased. In Figure 7, we compare the IPNLMS, ODSS-LMS-G, and ODSS-LMS-W algorithms for different values of \(\sigma_{w}^{2}\) in an acoustic echo cancellation scenario, where the input signal is an AR(1) process with \(\beta=0.9\). As in the scenario from Figure 6, the effect of \(\sigma_{w}^{2}\) is attenuated with its increase. Therefore, the need of a variable \(\sigma_{w}^{2}\) parameter arises. Low values of \(\sigma_{w}^{2}\) will lead to a good compromise between fast initial convergence and low misalignment, while high values will keep alive the tracking capability. This issue will be addressed in future works, by targeting a variable parameter to this purpose. Figure 6: Normalized misalignment of the IPNLMS, ODSS-LMS-G, and ODSS-LMS-W algorithms for different values of \(\sigma_{w}^{2}\). Echo path changes at time 10 s. The input signal is an AR(1) process, with \(\beta=0.8\), \(L=512\), and SNR = 20 dB. The unknown system is the network echo path. Figure 7: Normalized misalignment of the IPNLMS, ODSS-LMS-G, and ODSS-LMS-W algorithms for different values of \(\sigma_{w}^{2}\). Echo path changes at time 10 s. The input signal is an AR(1) process, with \(\beta=0.9\), \(L=512\), and SNR = 20 dB. The unknown system is the acoustic echo path. Finally, in Figure 8, we compare the ODSS-LMS algorithms in a network echo cancellation scenario, where the input signal is a speech sequence. Again, we remark on the good performance of the ODSS-LMS-G algorithm with a highly correlated (and nonstationary) input signal.
###### Abstract  
To clarify the influence of dioxin exposure on brain morphometry, the present study investigated associations between dioxin exposure at high levels and brain structural irregularities in 32 Vietnamese men. Two exposure markers were used: blood dioxin levels, as a marker of exposure in adulthood, and perinatal dioxin exposure, estimated by maternal residency in a dioxin-contaminated area during pregnancy. All subjects underwent brain magnetic resonance imaging (MRI) scans. We analyzed correlations between regional gray matter volumes and blood dioxin levels, and compared regional volumes between men with and without perinatal dioxin exposure using the voxel-based morphometry (VBM) tool from Statistical Parametric Mapping 12 (SPM12). Blood 2,3,7,8-tetrachlorophenoxy-p-dioxin (TCDD) was associated with low volume of the medial temporal pole and fusiform gyrus. Toxic equivalency (TEQ)-PCDDs were correlated with low medial temporal pole volume. However, 1,2,3,4,7,8-HxCDD was associated with high middle frontal gyrus and cerebellum volume. In men with perinatal dioxin exposure, the left inferior frontal gyrus pars orbitalis volume was significantly lower than in those without perinatal exposure. These results suggest that dioxin exposure during the perinatal period and in adulthood may alter regional brain volume, which might lead to cognitive deficits and unusual social emotional behavior in Vietnamese men living in dioxin-contaminated areas.  
dioxin; neuro imaging analysis; brain regional volume; adults; Vietnam +
Footnote †: journal: Physics Letters B
# The Health-Promoting Potential of Fruit Pomace and Its Application in the Confectionery Industry
## 6 Risks and Difficulties Resulting from the Use of Pomace in Food  
The integration of pomace into food production offers numerous functional and nutritional benefits, yet it also presents several challenges that must be addressed to ensure successful implementation. One major difficulty is the high moisture content of fresh pomace (up to 50%), which accelerates microbial growth and spoilage. To extend shelf life and ensure microbiological safety, preservation methods such as hot-air drying, freeze drying, or fermentation are required--each of which entails additional energy consumption, processing time, and equipment investment, thus raising economic feasibility concerns, particularly for small and medium-sized producers [20; 78].  
Moreover, the sensory impacts of pomace addition can be both beneficial and detrimental, depending on concentration and food matrix [79]. For example, grape pomace can impart astringency, bittermess, or a dark purple hue, which may be undesirable in light-colored confections such as marshmallows [39]. Similarly, berry pomace may affect texture, resulting in grittiness due to seed residues or a soft, rubbery texture. These effects can reduce consumer acceptability unless counterbalanced by reformulation, flavor masking, or encapsulation strategies [5]. In contrast, apple pomace can positively contribute to chewiness and color in jelly-based candies when used at optimized levels [10].  
In terms of safety, the use of food by-products like pomace carries the potential risk of contamination with chemical residues (e.g., pesticides, heavy metals) or biological hazards such as mycotoxins or pathogenic bacteria. For example, poorly stored apple pomace may develop molds producing patulin [80]. Compliance with food safety regulations is essential to mitigate such risks. EU legislation, including Regulation (EC) No 178/2002, requires validated processing steps, contaminant testing, and full traceability before incorporating by-products like fruit pomace into food. These regulations ensure hygiene standards, hazard control (HACCP), and monitoring of toxins, pesticides, and heavy metals. Proper labeling, allergen management, and documentation are also mandatory. Implementing rigorous quality control measures and safety assessments is essential to mitigate these risks and fully harness the benefits of utilizing food by-products as part of a sustainable vaporization strategy [81].  
While the valorization of pomace in food, particularly in confectionery, aligns with sustainability goals and consumer demand for functional products, addressing these technical, economic, sensory, and regulatory challenges is crucial for its safe and scalable implementation [80].