esa-sceva/satcom-chunk-collection huggingface dataset:
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 4 Hydrogen Transportation and Distribution Technologies
### Transport and Distribution by Road  
For small distances and small volumes, trucks can be used to transport hydrogen in either compressed or liquid form. Compressed hydrogen can be transported in compressed gas hydrogen tube trailers at a pressure of 350 bar [79]. The amount of hydrogen transported depends on the tubes' volume and transportation pressure, which consequently depends on the pressure resistance of the vessel. Transporting small amounts of hydrogen in compressed hydrogen tube trailers is considered the most suitable option for small distances, below 100 km [80]. To increase the transportation capacity, hydrogen can be transported in the liquid phase by cryogenic trailers. However, this approach is very capital and energy intensive, making it only feasible for very large-scale hydrogen production plants, and also presents boil-off losses. Transporting hydrogen via LOHC is another alternative that is presented in the literature as a cost-effective option to enhance long-distance road transportation [79]. Currently, researchers are focusing more on carrying out techno-economic studies to compare the three hydrogen delivery options. According to [80], LOHC is a better option to deliver for demands greater than 3600 kg/day and distances greater than 100 km than compressed hydrogen. This is due to the costs of LOHC delivery being mainly related to the processing costs, while for compressed hydrogen delivery, the transportation costs are more significant. According to [81], the transportation cost is EUR 2.69/kgH\({}_{2}\) for compressed hydrogen, EUR 0.73/kgH\({}_{2}\) for liquid hydrogen, and EUR 0.99/kgH\({}_{2}\) for LOHC, although, if the entire supply chain costs are considered, compressed hydrogen is the most feasible option for short distances, while liquid hydrogen is suitable for distances greater than 130 km.
# Mono Camera-Based Optical Vehicular Communication for an Advanced Driver Assistance System
## 3 Proposed Scheme  
### Proposed OCC System for V2V Scenario  
Figure 3 shows the complete architecture of the proposed system. In general, we used two different low-rate and high-rate data streams transmitted from each of the rear LEDs for the source identification and data transmission, respectively. A combination of both streams, i.e., a hybrid stream, propagated through the optical channel and was received in the IS. Motion blur was expected in the captured images owing to the vehicles' high-mobility. On the receiver side, the region-of-interest (RoI) was detected in the captured images using an NN, and the data decoding and localization mechanisms were executed eventually. As the intensity of the LED in the camera sensor varied with the distance between vehicles, the detection threshold of the high-rate stream was kept constant, whereas the threshold for the low-rate stream was dynamically adjusted with the help of the high and low-level amplitudes of the high-rate stream. Here, the receiver camera had to be a global shutter type since this performs better than a rolling shutter camera in a high-mobility  
Figure 1: OCC-based vehicle localization scenario from the rear LED shapes of the vehicles.  
Figure 2: Alternation of LED coordinates of an FWV moving from right to left with respect to the FLV on an image sensor. (**a**) FWV is at the right side of the FLV. (**b**) FWV is straight with respect to FLV. (**c**) FWV is at the left side of FLV.  
environment. It can capture the hybrid waveform that is decoded to retrieve the high-rate and low-rate streams simultaneously. As a rolling shutter camera scans the pixels row by row, significant distortion may occur owing to the high-mobility, whereas a global shutter camera reads all pixels at the same time to detect all LED states simultaneously, showing superior performance in high-mobility environments. An explanation of the individual subsystems is provided in the following sections.
# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 5 Discussion
### Future Outlook  
#### 5.2.1 Autonomous UAV Real-Time Object Detection  
As we illustrated in Figure 1,for the concept of real-time object detection based on UAV remote sensing, when the computing platform executes the detection models, outputting the results and real-time responses can make the real-time object detection tasks more automatic and form a closed loop. The idea of autonomous UAV tasks is to reduce human operation, where humans can just issue commands to deploy, monitor, and terminate the missions of UAVs. The UAV can conduct remote sensing according to a specified mission plan and run its algorithms automatically to output results. These results can be sent to humans, machines, or systems to make quick decisions and respond. In [140], the authors used UAVs to identify infected plants and link the results to the system. Then, the system decided the chemical and exact amount of spray to use. In this simulation, the researchers developed an automatic collaboration between UAVs to detect garbage pollution across water surfaces and USVs to pick up garbage [141], thus establishing more reliable frameworks to realize a collaborative platform between UAVs and systems or robots (air-to-air and air-to-ground) in real-time detection scenarios [142]. This will have the opportunity to further bridge the gap between UAVs and manned aerial and terrestrial surveys [4].  
In fact, the demand for UAV real-time object detection comes from the demand for real-time response. It is meaningless if only a real-time detection result is obtained. Humans need to use real-time detection results to reduce losses and improve efficiency. To achieve this, some technologies, including multi-machine collaborative work [143; 144], knowledge graphs [145], natural language processing [146], and blockchain [147], are needed.
# The Complex Relationship between Virulence and Antibiotic Resistance
## 3 Virulence Mechanisms
### Virulence Factors
#### 3.1.6 Bacterial Motility  
Bacterial pathogens use a variety of different motility modes, including swimming, twitching, and swarming. Increased and variable motility cannot only allow a pathogen to change physical location but is also associated with over expression of a large number of virulence genes. _P. aeruginosa_ swarming was associated with increased expression of virulence genes, such as type III secretion and iron transport as well as an increase in antibiotic resistance to polymyxin B, gentamicin, and ciprofloxacin [136].
# Towards Harmonious Coexistence in the Unlicensed Spectrum: Rational Cooperation of Operators
## 2 Multi-RAT Systems in the Unlicensed Spectrum  
### LTE Operators and WiFi Networks in the Unlicensed Spectrum  
Suppose that there are four wireless networks utilizing unlicensed spectrum, e.g., two different LTE operators and two user-deployed WiFi networks as shown in Figure 1. To verify the cooperative or competitive behavior of networks, we assume that all networks use the same channel bandwidth of 20 MHz in 5 GHz unlicensed spectrum. For those countries where LBT schemes on the unlicensed spectrum are mandated by telecommunications regulatory bodies, reciprocal LTE operators always perform their own LBT schemes to protect other incumbent networks to achieve harmonious coexistence, e.g., LTE with LBT.  
For this reason, existing literature has assumed mutual benefits in the unlicensed spectrum and focused on the performance evaluation of either LTE operators or WiFi networks under various LBT schemes [14; 15; 16; 17; 18; 19; 20]. Nevertheless, for those countries where the LBT m  
Figure 1: A scenario of multi-radio access technology (RAT) systems operating in the unlicensed spectrum: there are two Long-Term Evolution (LTE) operators and two user-deployed WiFi networks.  
would LTE operators still have the incentive to perform their own LBT schemes in the unlicensed spectrum? Here, we would rather focus on the potential behaviors of LTE operators based on whether an LBT scheme is necessarily mandated or not. When the LBT scheme is not mandatory, LTE operators have two alternatives to utilize the unlicensed spectrum; LTE with LBT is one option. The other option is LTE-itself based on a centralized MAC protocol. An LTE operator that takes LTE-itself can immediately schedule resource blocks (RBs) to its users in every subframe to maximize its own target metric. Obviously, there is a possibility that the selfish LTE operator exploits the unlicensed spectrum without considering other LTE and WiFi networks. For example, the selfish LTE operator may decide not to use the LBT scheme so that it can save energy and time for spectrum sensing. WiFi networks always take reciprocal action because they originally operate based on the distributed coordination function (DCF) which is a contention-based CCA adopting carrier sense multiple access with collision avoidance (CSMA/CA) [26]. To this end, we introduce an unlicensed spectrum game with two LTE operators using two strategies to investigate their behaviors in the unlicensed spectrum.
# Factors Influencing Appliance Wearing Time during Orthodontic Treatments: A Literature Review
## 1 Introduction  
Awareness of the importance of facial and dental appearance has led to an increased perception among laypeople of the malocclusions and irregularities of their teeth. The demand for orthodontic treatment for \"aesthetic reasons\" has been widespread, from both young and adult patients [1].  
In order to achieve successful treatment outcomes, clinicians have to obtain patients' compliance [2].  
Compliance (or adherence) is defined as \"the extent to which a person's behavior coincides with medical or health advice\" [3, 4].  
In orthodontics, patients' cooperation is required throughout every phase of the therapy [5, 6]; patients' adherence to treatment is composed of multiple variables strictly correlated with their motivation: respecting appointments, maintaining a good oral hygiene, and wearing orthodontic appliances for as long as required [5].  
A lack of compliance may affect treatment results with both fixed [7] and removable appliances [7]: poor oral hygiene during fixed orthodontic treatment could lead to caries and plaque-related or periodontal diseases [8]. Disregarding the wearing time of a removable appliance could lead to treatment delays, thus slowing the treatment process [9,10] and compromising the achievement of the treatment goals [5]. In particular, clear aligners should be worn 22 h per day and changed sequentially every 7, 10, 14 days. If this wearing time is not respected, the treatment outcome may be compromised. All these situations will result in patients' frustration and the further loss of motivation [11].  
Compliance represents an essential concept in orthodontics, so its understanding is a key issue for clinicians, starting from the factors involved.
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 5 Comparison of The Three Types of Sensor Networks
### Cost Efficiency  
_Ranking:_\(\mathrm{CSN}>\mathrm{VSN}>\mathrm{SSN}\)  
_Reasons:_  
The air pollution situation in rapid industrializing countries is much more critical than that in industrialized countries [38]. Several pollution sources (over-polluting industry, poorly tuned diesel engines and burning of trash) in developing countries contribute to the air pollution much more significantly than that in developed countries [111]. Moreover, the governments in developing countries spend less fraction of their GDPs on environmental protection than developed countries [119]. In a word, the environmental protection agencies in developing countries are dealing with serious air pollution situation with little amount of money. Hence, the cost efficiency of the air pollution monitoring system becomes a non-negligible property for comparison.  
_Explanation:_  
In sensor node level, the CSN systems have the highest cost efficiency, followed by the SSN systems and the VSN systems. In CSN systems, the users' cellphones are fully utilized, including the build-in GPS and wireless communication modules, and the computational powers. The sensor nodes in CSN systems typically require no locating, communicating and computing devices and hence the cost efficiency is enhanced. In SSN systems, the stationary sensor nodes require no locating device but the communicating and computing devices because the location of a specific sensor node is known once it is deployed. For the sensor nodes in VSN systems, the GPS modules are essential due to the mobility of the carriers. Also, the communicating and computing devices are needed. Hence, the cost efficiency of VSN systems is the lowest in sensor node level.  
In system level, the vast majority of the system cost is contributed by the acquisition of sensor nodes. Moreover, if the number of sensor nodes in system enlarges, a larger database for data storage and management, a faster wireless sensor network for data transmission and a more powerful computing center for data processing and decision making in real-time are required. As described in Subsection 5.1, the SSN systems require the largest amount of sensor nodes to cover a specific area, followed by the CSN systems and the VSN systems. Hence, in system level, the VSN systems have the highest cost efficiency followed by the CSN systems and the SSN systems.  
The final ranking is achieved by averaging the rankings in sensor node level and system level.
## 2 Materials and Methods
### GPR Performance Under Identical Site Conditions  
To enable a direct performance comparison with the AML system, Ground-Penetrating Radar (GPR) tests were conducted at the same certified underground utility test site, under  
Figure 1: Principle of GPR.  
identical environmental and operational conditions. Two representative GPR B-scan images were selected to demonstrate the detection capabilities at different burial depths of non-metallic pipelines.  
As shown in the left panel of Figure 2, a pipeline buried at a depth of 2.0 m produced a distinct hyperbolic reflection pattern, highlighted by a red circle. This reflection indicates successful detection of the buried target and confirms that GPR is capable of identifying subsurface utilities at moderate depths under favorable soil conditions. However, the signal clarity rapidly degrades beyond the 2.0 m mark, with a diminished reflection amplitude and increased noise, suggesting a reduction in effective penetration depth.  
In contrast, the right panel of Figure 2 illustrates a GPR scan of the same site where a polyethylene (PE) pipeline is known to be buried at a depth of 3.0 m. The area corresponding to the expected pipe location is marked with a yellow dashed box. Despite multiple scans, varying antenna frequencies, and signal processing enhancements, no discernible reflection pattern was observed at or near the 3.0 m depth. The loss of coherence and absence of hyperbolic signatures in this zone indicates that the GPR system was unable to detect the deeply buried non-metallic target under the given soil conditions.  
These results empirically support previous studies reporting the limitations of GPR in detecting deep, non-metallic utilities [9; 13]. The primary contributing factors include signal attenuation in decomposed granite soil and the low dielectric contrast between PE materials and their surrounding medium. Such conditions result in weak or undetectable reflections, particularly at depths beyond 2.5 m.  
In comparison, the AML system, tested under the same conditions, successfully detected the same 3.0 m deep PE pipeline with high accuracy and consistency across 32 trials, as described in Section 3.1. This performance contrast demonstrates AML's  
Figure 2: Comparison of GPR B-scan results: (**left**) Detection of a pipeline buried at 2.0 m, clearly indicated by a hyperbolic reflection pattern within the red circle; (**right**) No discernible reflection detected at the known location of a polyethylene (PE) pipeline at 3.0 m, highlighted by the yellow dashed box. These results indicate that GPR has limitations in detecting subsurface utilities deeper than 2.0 m under the given soil conditions.  
superior capability for deeper and more reliable detection of non-metallic subsurface utilities, especially in scenarios where traditional GPR techniques are prone to failure.
###### Abstract  
In the last decade, the application of metamaterials has become a very interesting way of implementing passive devices in microwave, terahertz, and optical frequency ranges. Up until now, selective filters, absorbers, polarizers, and lenses have been designed and constructed using these artificial materials, simultaneously showing the possibility for many other potential applications. Because of the simplified fabrication process, in particular, planar structures called metasurfaces (MS), are developing very fast. In the literature, there are many studies on the properties of various metasurfaces, but there are a lack of papers related to the analysis of the impact of structure deformations on their properties. In this paper, three commonly utilized structures of metasurfaces were designed for the same resonant frequency and on the same substrate. The numerical models were built and verified using the measurements of fabricated structures. During the experiment, the geometrical parameters of the metasurface cells were swept and a mechanical in-plane deformation in orthogonal directions was applied to the examined structures. Finally, sensitivity to the geometry distortions of the analyzed structures was evaluated and discussed.  
metasurface; microwave technique; resonant structures; selective absorbing and shielding structures +
Footnote †: journal: Applied Physics Letters
# A Two-Step High-Order Compact Corrected WENO Scheme
## 2 Numerical Methods
### Introduction to the 1D Benchmark Cases
#### 2.4.2 Shu-Osher Problem  
To analyze the proposed method's ability to accurately capture shocks and their interactions with small-scale waves, the Shu-Osher problem [10] is examined. This test is known for its complexity, as numerical dissipation can significantly dampen entropy waves, making it highly sensitive. Using the same governing Equation (14), the initial conditions are defined as follows:  
\[\begin{bmatrix}\rho_{L}\\ p_{L}\\ u_{L}\end{bmatrix}=\begin{bmatrix}3.857143\\ 10.33333\\ 2.629369\end{bmatrix},\quad\begin{bmatrix}\rho_{R}\\ p_{R}\\ u_{R}\end{bmatrix}=\begin{bmatrix}1+0.2\sin(5x)\\ 1.0\\ 0.0\end{bmatrix} \tag{16}\]  
The subscripts L and R represent the states on the left and right sides of the domain, respectively, with the central interface positioned at (\(x=0\)).
## 4 New Skyhook and Displacement-Velocity Control Method
### Analysis of the New Mixed Skyhook and Distance-Velocity Control  
The proposed SH and DV controls were combined to yield a new mixed SH-DV control algorithm in Equation (11). The new mixed control has a switch condition that is a function of the vibration acceleration \(\ddot{y}_{1}\) and velocity \(\dot{y}_{1}\) of the carbody and the conversion constant \(c_{n}\), where \(c_{n}=2\pi f_{cn}\), and \(f_{cn}\), referred to as the crossover frequency, is the frequency at which the vibration transmissibility curves of the two singular controls intersect.  
\[c_{n\_SHDV}=\begin{cases}c_{n\_SH}&\ddot{y}_{1}^{2}-c_{n}{}^{2}\cdot\dot{y}_{1} ^{2}<0\\ c_{n\_DV}&\ddot{y}_{1}^{2}-c_{n}{}^{2}\cdot\dot{y}_{1}^{2}\geq 0\end{cases} \tag{11}\]  
The term \(\ddot{y}_{1}^{2}-c_{n}{}^{2}\cdot\dot{y}_{1}^{2}\) is referred to as the frequency selector (FS). When FS \(\geq\) 0, the angular frequency of the carbody response is greater than or equal to the conversion constant (i.e., the carbody is oscillating in a high frequency range). In this case, the new mixed SH-DV control activates the new DV control, due to the fact that the DV control is more effective at high frequencies. In contrast, when FS < 0, the new mixed SH-DV control activates the new SH control, owing to the SH control's effectiveness at low frequencies.  
Figure 8 illustrates the transmissibility of vibrations from the rail to the train body under passive control, conventional mixed SH-DV control, and new mixed SH-DV control. The new mixed SH-DV controller exhibited higher vibration isolation performance than did the conventional SH-DV controller at low frequencies. Moreover, the new mixed SH-DV controller significantly improved the vibration transmissibility performance by 7.37% at frequencies near the resonance frequency of the bogie (14.6 Hz), compared with the conventional mixed SH-DV controller.  
Figure 9 provides a more detailed evaluation of the displacement [Figure 9a], velocity [Figure 9b], and acceleration [Figure 9c] of the train body under the different types of control. Overall, the new mixed SH-DV controller had similar performance to the conventional controller at low frequencies, whereas it was more effective at or around the resonance frequency of the bogie (14.6 Hz), reducing the vibration velocity and acceleration of the carbody by 50.85% and 45.39%, respectively, compared with the conventional mixed SH-DV controller.  
Figure 8: Comparisons of the vibration transmissibility of the carbody under three control algorithms under sinusoidal signal input with amplitudes of \(\pm\)5 mm.
# Research on the Aerodynamic-Propulsion Coupling Characteristics of a Distributed Propulsion System
## 3 Numerical Method Validation and Grid Independence Validation  
To investigate the influence of the internal and external flow coupling effect on the aerodynamic-propulsion coupling characteristics of the distributed propulsion system and consider the impact of boundary layer ingestion on the inlet distortion of the ducted fan, the NACA 643-618 airfoil and the DGEN 380 engine fan are integrated into a model with five ducted fans over the wing. The symmetrical integrated model is illustrated in Figure 7. The geometric parameters of the integrated model are listed in Table 2, with reference to the typical layout of distributed ducted fans [26]. The separation distance between the fans, \(\Delta y\cdot D_{p}\), is the minimum distance between two neighboring fans in the spanwise direction. To minimize the inlet flow velocity and improve the benefit of boundary layer ingestion, the ducted fan array is placed at the trailing edge of the upper surface of the wing at \(x/c=0.7\). The ducted fans are numbered from left to right in the spanwise direction of the wing, in sequence from #1 to #5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Chord/\(c\) & 100 cm \\ Span/\(b\) & 141.2 cm \\ Fan axial position/(\(x/c\)) & 0.7 \\ Fan diameter/\(D_{P}\) & 15.84 cm \\ Number of fans/\(N\) & 5 \\ Separation distance between neighboring fans/\(\Delta y\cdot D_{p}\) & 1.0 cm \\ Fraction of wingspan occupied by ducted fan array/\(\Delta Y\) & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometric parameters of five-ducted-fan over-wing integrated model.  
Figure 6: Contours of the Mach number in the meridional plane.
## 5 Experimental Evaluation
### Problem Instances  
We used the source code provided in [23] for generating 640 diverse problem instances. Each problem instance was generated according to two control parameters: The size of the network \(n\) (number of nodes) and its average degree \(d\), where \(d=2m/n\) and \(m\) is the number of edges. More specifically, we considered five different network sizes: 50, 100,150, 200 and 250 nodes. For each network size, we considered between 5 to 8 different average degrees in a way such that networks over a range from sparse to dense networks are generated. In addition, each node (sensor) of the network was given a random real value between 0 and 1 as node weight (lifetime). Then, for each combination of \(n\) and \(d\), 20 different networks were randomly generated resulting in a total of 640 problem instances for the experimental evaluation.