esa-sceva/satcom-chunk-collection huggingface dataset:
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 3 ITS Sensing
### Vehicle Onboard Sensing
#### 3.3.3 Road and Lane Detection  
In addition to road user-related sensing tasks, road and lane detection are often performed for lane departure warning, adaptive cruise control, road condition monitoring, and autonomous driving. The state-of-the-art methods mostly apply deep learning models for onboard camera sensors, LiDAR, and depth sensors for road and lane detection [165; 166; 167; 168; 169; 170]. [PERSON] et al. [165] proposed a novel progressive LiDAR adaption approach-aided road detection method to adapt LiDAR point cloud to visual images. The adaption contains two modules, i.e., data space adaptation and feature space adaptation. This camera-LiDAR fusion model currently stays at the top of the KITTI road detection leaderboard. [PERSON] et al. [166] designed a deep learning architecture that consists of a surface normal estimator, an RGB encoder, a surface normal encoder, and a decoder with connected skip connections. It applied road detection to the RGB image and depth image and achieved state-of-the-art accuracy. Alongside road region detection, an ego-lane detection model proposed by [PERSON] et al. outperformed other state-of-the-art models in this sub-field by exploiting prior knowledge from digital maps. Specifically, they employed OpenStreetMap's road shape file to assist lane detection [167]. Multi-lane detection has been more challenging and rarely addressed in existing works. Still, [PERSON] et al. [168] were able to achieve pretty good multi-lane detection results by adding five constraints to Hough Transform: length constraint, parallel constraint, distribution constraint, pair constraint, and uniform width constraint. A dynamic programming approach was operated after the Hough Transform to select the final candidates.
# Superhydrophobic Polypyrrole-Coated Cigarette Filters for Effective Oil/Water Separation
## 1 Introduction
In the research process, we found that there are few cases of oil/water separation in the field of cigarette filter recycling [26; 27; 28]. The polymerization of silane coupling agents is the main method used in the literature to prepare superhydrophobic cigarette filters. Herein, we prepared superhydrophobic cigarette filters via polypyrrole formation and n-dodecanethiol modification. The polypyrrole particles were successfully deposited on the fiber surface, which produced hierarchical micro/nanostructures to realize superhydrophobicity. The synthesis and deposition of the polypyrrole particles was achieved by a dip-coating method, which was relatively facile and cheap compared with the methods previously reported for superhydrophobic cigarette filters. Moreover, the as-prepared superhydrophobic cigarette filters exhibited excellent water repellency with a water contact water of 155\({}^{\circ}\), highly efficient oil separation ability, separation stability, and great absorption capacity; the superhydrophobic Ppy-coated cigarette filter absorbed oils and organic solvents with absorption capacities 9.4-22.7 times the filter's mass. This result shows that the superhydrophobic cigarette filters can not only effectively separate oil/water mixtures but also promote waste recycling and environmental protection.
## 3 Results and Discussion
### Calibration of the Whatman Grade 42 Filter Paper  
Table 4 presents the water content of the filter paper obtained to create the calibration curves for the natural and contaminated soils. Those results are pretty revealing in terms of the shape of the calibration curve because they indicate that the gravimetric water content (\(\omega\)) decreases in comparison with the standard calibration for both arsenic concentrations.  
The Whatman Grade 42 filter paper calibration curves are shown in Figure 4. The suction values for the contaminated soils obtained from the calibration curve clearly show that the slope change due to the soil-water-contaminant interaction. The data obtained in the arsenic calibration curves validated the assumption that the suction values change due to the exchange of vapors resulting from the chemical contamination. Moreover, those results provided the data to support the evaluation of total suction in the arsenic-contaminated soils where the \(\psi_{o}\) has a significant change compared with its value in the natural soil.  
These results suggest that the slope of the calibration curve is affected by the contaminant concentration. This also impacts the suction values since for filter paper with water content higher than 17.5%, the suction values of the 50 mg/kg arsenic concentration condition are higher than those of the 25 mg/kg arsenic concentration condition. In contrast, for filter paper with water content lower than 17.5%, the suction values of the 25 mg/kg arsenic condition are higher.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Suction** & \multicolumn{2}{c}{**Water Content (\%)**} \\ **(kPa)** & **NaCl + As: 25 mg/kg** & **NaCl + As: 50 mg/kg** \\ \hline
98 & 22.389 \(\pm\) 0.608  & 29.277 \(\pm\) 0.852 \\
268 & 21.696 \(\pm\) 0.529 & 24.566 \(\pm\) 2.985 \\
310 & 22.315 \(\pm\) 0.486 & 20.332 \(\pm\) 0.341 \\
585 & 17.390 \(\pm\) 4.529 & 19.436 \(\pm\) 0.579 \\
980 & 19.120 \(\pm\) 1.968 & 18.057 \(\pm\) 1.001 \\
3099 & 15.267 \(\pm\) 1.249 & 13.650 \(\pm\) 0.148 \\
9800 & 10.166 \(\pm\) 1.744 & 7.677 \(\pm\) 0.316 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Water content of the filter paper used for the calibration curve for the two arsenic contamination conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Arsenic Concentration** & **Specific Surface Area** & **Adsorbed Volume** & **Pore Diameter** \\ **(mg/kg)** & **(m2/g)** & **(cm3/g:nm)** & **(nm)** \\ \hline
0 [FOOTNOTE:]Footnote 0: * Natural soil.[ENDFOOTNOTE] & 111.136 & 0.00777 & 5.6282 \\
25 & 93.349 & 0.00486 & 5.6845 \\
50 & 101.965 & 0.00325 & 7.8141 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specific surface area and pore diameter of the natural and arsenic-contaminated soils.
## 6 Results and Discussion
### Comparison with the Application's QoS Requirements
#### 6.2.1 Metric Calculation Rate (MCR)
Scen 4, with the highest MCR value, presents the best control delivery rate values, whereas Scen 2, with the lowest MCR value, presents the worst ones. In the case of two applications (Figure 13b) and for a network size of 196 nodes, the control delivery rate values were 88.14% and 81.84% for Scen 4 and Scen 2, respectively. This is justified by the additional exchanged control messages for higher MCR values, which increases the probability of packet drop as a result of the buffer fullness. Similarly to the control delivery rate, Figure 14 indicates that higher MCR values resulted in lower control delay values for all cases. For a network size of 100 nodes and four running applications, as depicted in Figure 14d, the control delay values were 1.57 s, 1.65 s, 1.42 s, and 1.29 s for Scen 1, Scen 2, Scen 3, and Scen 4, respectively. This occurs since for lower MCR values, the messages wait for more time in the buffer to be processed. Figure 11: Control overhead, changing the MCR value. Figure 12: Energy consumption, changing the MCR value. Figure 13: Control delivery rate, changing the MCR value.
# Comparison of Methods to Identify and Monitor Mold Damages in Buildings
## 2 Materials and Methods  
### Sampling  
A total of 32 apartments with moisture problems, located in 23 buildings in Oslo (construction years ranged from 1861 to 2022; mean = 1952), were studied from June 2018 to February 2019. During their inspections, 48 surface samples were collected from mold-damaged materials (hereinafter \"mold\" samples), including gypsum board (19), chipboard (4), wood (12), medium-density fiberboard--MDF (3), building paper (3), wallpaper (4), concrete (1), paint (1), and plastic (1) (Figure 1). Mold samples were taken directly from the discolored substrates using both 12 cm long adhesive tapes (Mycotape, MycoteamAS) and sterile swabs (FLOQSwabs in tubes, Copan Italia spa; whose flocked tip is coated with perpendicularly sprayed on Nylon(r) fibers) for microcopy and DNA metabarcoding, respectively. To evaluate the dispersion of the material-colonizing fungi, 42 dust samples were swabed from upper doorframes at two different locations: damaged rooms (same as mold samples, in 25 of 32 apartments) and central rooms (more distant to mold growth, in 11 of 32 apartments). Dust samples were exclusively analyzed by DNA metabarcoding.
## 1 Introduction
We propose a session-based optimized Graph Convolutional Neural Network (GCN) model that aims to capture and represent user behaviors in a more dynamic way. The sequence of clicks for each user in a session is recorded. These sequences are used to generate session graphs. These session graphs are directed graphs that represent the order of the clicks with time t and the last node represents the item that the user has purchased in a session. These session graphs are then passed through the embedding layer where embeddings for each graph are generated; these embedded graphs are used as input for the GCN layer. Figure 1 presents the idea of the session graphs built on the user's clicks order. The left side of the figure shows M users and sequences of clicks for each user. The products are represented by the colored circles, i.e., v1, v2,... vn. The directed arrows between products represent the order in which they are clicked by the user. We can also refer to them as user's interaction patterns with different products. The right side of Figure 1 shows the session graphs generated based on the click sequences of each user. In these session graphs, the chronological order of the clicks is preserved. The number of paths to an item is also important. If there are multiple paths to a product vi, it means it's been clicked multiple times so the users might be more interested in this product. This process is repeated for all the users and sessions. Consequently, the model aims to capture the complex interactions between products, multiple sessions of a user, and sessions of multiple users. We have different data processing units; first, the raw data is processed to remove the anomalies and brings the data format in a consistent format. Then different other features are derived from this data such as the user's location, clicks per session, and purchased items per session, etc. These input features are then combined to generate the complete dataset for GCN processing. We used two different GCN for experiments. The first GCN in a basic GCN model used in previous studies, and then we optimized the graph structure in the other implementation of GCN. The comparisons of the results show that based on the data the network structure should be optimized accordingly to enhance the performance of the model. In the rest of this paper, we first perform a review of the literature related to this work. The next section explains the proposed methodology in detail. After this experimental details and results are presented and the paper is concluded with some discussions and concluding remarks.
# Tailoring Asymmetric Lossy Channels to Test the Robustness of Mesoscopic Quantum States of Light
## 5 Conclusions  
In this paper we explored the survival of nonclassical correlations between the parties of mesoscopic twin-beam states propagating through an asymmetric lossy channel, in which the transmission coefficient was statistically distributed. In particular, we considered the case of Gaussian  
Figure 7: Expected threshold value of \(\sigma_{0}\) obtained by setting \(R=1\) in Equation (4) for \(\langle m\rangle=2.23\) and \(\eta=0.145\). The data are shown as a function of \(t_{0}\) for different choices of \(m_{\mu}\): 0.2 (black curve), 0.1 (red curve), 0.04 (blue curve) and 0.02 (green curve). Panel (a) corresponds to the case of Gaussian distributions of \(t_{0}\), while panel (b) to the case of log-normal distributions.  
distributions and that of log-normal ones. We investigated the role played by the parameters characterizing the light (mean number of photons and mean number of photons per mode), the transmission channel (mean value and standard deviation of the distribution) and the detection chain (quantum efficiency). In general, we can conclude, as expected, that the larger the mean value of the distribution the better the observation of nonclassicality. Moreover, at a given mean value of the distribution, the larger its standard deviation the worse the observation of nonclassicality. In general, these results hold for both the Gaussian and the log-normal distributions. Not surprisingly, the different shape of the distributions is responsible for the different behavior for the same choice of parameters. In addition, we studied the dependence of the robustness of sub-shot-noise correlations undergoing a statistically distributed amount of loss on the number of modes. We found that high multimode twin-beam states are more robust to loss than low-multimode ones. This result can be ascribed to the fact that a twin beam endowed with many modes has a photon-number distribution less thermal, and thus less fluctuating, than a single-mode one [36; 37; 38]. Such a result suggests that, in order to optimize the propagation of light in the presence of loss, a proper tailoring of the employed quantum state should be performed in advance.  
Both authors equally contributed to each step of the work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
A. A. acknowledges the Project \"Investigating the effect of noise sources in the free-space transmission of mesoscopic quantum states of light\" supported by the University of Insubria.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
HWP Half-wave plate  
PBS Polarizing cube beam splitter
## 2 Materials and Methods
### Drone Survey  
The survey utilized the Matrice 350 RTK drone (Shenzhen DJI Sciences and Technologies Ltd., Shenzhen, China). The drone is equipped with the Altum-PT camera (MicaSense(r), AgEagle Aerial Systems Inc., Wichita, KS, USA), which is a multispectral, panchromatic, and thermal sensor. The central wavelengths and bandwidths of each band are reported in Table 1.  
On 8 August 2024, a sweet basil survey was conducted using the drone. The mission of the flight consisted of two steps: calibrating the camera through the calibration panel and mapping the sampled area to acquire the spectral bands (Figure 4). The navigation path was set, and image acquisition by the sensor for each band was planned. The specific flight characteristics are reported in Table 2. The flight took place in the morning, between 11:00 AM and 12:00 PM. Weather conditions were suitable for flying, as it was a sunny day with no clouds, calm winds, and good temperatures.  
\begin{table}
\begin{tabular}{c c c} \hline
**Band** & **Central Wavelengths** & **Bandwidths** \\ \hline Blue & 457 nm & 32 nm \\ Green & 560 nm & 27 nm \\ Red & 668 nm & 16 nm \\ Red Edge & 717 nm & 12 nm \\ Near-Infrared & 842 nm & 57 nm \\ Panchromatic & 634.5 nm & 463 nm \\ Long-Wave Infrared & 10.5 μm & 6 μm \\ \hline \end{tabular}
\end{table}
Table 1: Central wavelengths and bandwidths of Altum-PT camera.  
Figure 3: The tools used for data collection are as follows: (**a**) wheel metre for height measurement; (**b**) cardboard frame to delimit the sample area.
## 3 Experimental Results
### MORPH Dataset
#### 3.1.1 Accuracy Analysis Using Various Fusion Methods  
According to different fusion methods (the concatenation method, the summation method, the product method, the maximum method and the proposed weighting fusion method), the cross-validations were performed to obtain a fairer accuracy rate. Recently, many researchers [28; 29; 30] adopted three cross-validations for verifying their methods. Therefore, this study also used three cross-validations to evaluate the accuracy comparison in MORPH dataset. As shown in Table 5, the weighted fusion method proposed in this paper obtained the highest average accuracy rate of 99.11%. Figure 4 is the average accuracy comparison using various feature fusion methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Male** & **Female** \\ \hline The number of images before the increment & 46,659 & 8492 \\ The number of images after the increment & 233,295 & 42,460 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The number of images before and after the increment.  
Figure 3: The MORPH dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hardware** & **Specifications** \\ \hline CPU & Intel(R) Xeon(R) CPU E3-1225 v5 @ 3.30 GHz \\ GPU & Nvidia GTX1080-Ti 11 GB \\ \hline \hline \end{tabular}
\end{table}
Table 3: The hardware specifications used in the experiments.
## 6 Results and Discussion
### Simulation Results
This is because FD-SU TX in \(R2\) can continue to avail the spatial spectral opportunities even when PU is in \(ON\) state, which is not the case for FD-SU TX in \(R1\). Hence, our results validate the requirements for the optimal performance of the UoS in CR-IoT spectrum-heterogeneous environment. In Figure 10, the performance of the UoS scheme, in terms of average number of sensing slots used for the successful secondary communication (in each time-slotted frame), is investigated under different PU mean inactive durations (\(R\)) and SIS factors. The QoS constraints for the probability of finding the non-availability of spatial-temporal spectral holes (during HD and FD sensing-slots) were set to 0.9. The PU active state probability was considered to be 0.1. The results again demonstrate the existence of different spectral opportunities in CR-IoT spectrum-heterogeneous environment, and validate the average number of successful secondary communicating sensing slots for temporal and spatial spectral holes. Importantly, it can be observed that the average number of sensing slots used for the successful secondary communication for all FD-SU TXs (\(R\)1 and \(R\)2) increased as \(R\) increased. The reason is that the increase in \(R\) at fixed PU states probabilities (\(P_{ON}\) and \(P_{OFF}\)) and duration of sensing slots means more sensing slots with temporal and spatial spectral holes. Hence, the average number of successful secondary communicating sensing slots increased. Our results help to identify the ranges of PU mean durations and SIS factors, subject to the spatial positions of FD-SU TXs, for the required sensing performance in spatial and temporal domains. Figure 9: Average number of secondary communicating sensing slots with different PU active state probabilities and SIS factors (at different quality of service (QoS) constraint).
# Exploring Carbon-Fiber UAV Structures as Communication Antennas for Adaptive Relay Applications
## 2 Materials and Methods  
This section discusses the key characteristics of the antennas. Both the Voltage Standing Wave Ratio (VSWR) and the reflection coefficient are measured and theoretically determined. The central operating frequency and bandwidth are calculated based on the VSWR values. Using these parameters, the operational limits of the antenna are established. Assuming lossless transmission lines, the antenna impedance is matched to the characteristic impedance of the transmission lines using the Smith chart. At the end of the section, the antenna gain and radiation pattern are analyzed in both the azimuthal and elevation planes. Furthermore, to investigate the radiation characteristics in greater detail, a radiation pattern simulation was conducted in MATLAB R2022a using the built-in monopoleRadial function. Given the constraint that only metallic materials can be simulated, iron was selected as an approximation with conductivity values close to those of carbon fiber composite materials.
## 4 Results and Discussion
### Further Discussion  
In the previous discussion, we take the coefficient \(k\) in relative-load-distance as 3, which proves the feasibility of this optimization method. In this section, we can study the change trend of its impact on the objective function by adjusting the value of the coefficient \(k\), so as to provide different station network planning schemes. The table below shows different schemes of different \(k\) values.  
The change in coefficient \(k\) means the change in the number of load points on the boundary of energy supply area. It can be seen from Table 2 that, with the increase in the \(k\) value, the number of load points that need to change the ownership on the boundary also increases. The annual cost of the system increases with the increase in the \(k\) value, but it is lower than that obtained by dividing the range on the principle of the minimum distance. This is because, although the shortest distance principle ensures the shortest pipeline length and the lowest pipe network cost, from the perspective of the whole system, the system cost is mainly affected by the energy station, and the pipe network cost accounts for only a small part. The shortest distance principle cannot ensure the optimization of  
Figure 6: Comparison of two schemes.  
the overall load characteristics in the energy supply area. This comparison also intuitively proves the effectiveness of relative-load-distance proposed in this study.  
Then, by further analyzing the changes in the cost of energy station and network under different \(k\) values, it can be seen that, with the increase in the \(k\) value, the cost of the energy station and network increases. However, the growth trend of energy station and network costs gradually slows down, which does not mean that, the more load points to change the ownership, the better. With the increase of load points that need to be changed, the phenomenon of pipeline sharing in the region will be reduced, resulting in a decrease in the concentration of the overall load in the region, which is not conducive to the economy of the energy system.