esa-sceva/satcom-chunk-collection huggingface dataset:
# Channel Characterization and SC-FDM Modulation for PLC in High-Voltage Power Lines
## 4 System Model
### Computational Structure and Simulation Model  
Figure 8 shows the transmitter model of the PLC communication system with the proposed SC-FDM modulation. The highlighted section indicates the time-domain process in baseband. The transmitter design consists of three stages: symbol formation, inverse fast Fourier transform (IFFT) modulation and CP insertion. The transmitter receives random bits of information as input, assuming a uniform distribution; the \(m\)-Quadrature Amplitude Modulation (\(m\)-QAM) maps \(\log_{2}\left(m\right)\) bit sets to a single complex number in the selected constellation, which are linearly precoded in blocks of size \(N_{D}\), using the DFT.  
The OFDM symbols are subjected to the distortion effects of the PLC channel by means of a FIR filter, which replicates the characteristics found in the frequency-channel model. At the filter output, the AWGN and corona noise are added to the signal in order to replicate the PLC channel, as shown in Figure 9.  
Figure 8: Models of the PLC-OFDM and SC-FDM PLC transmitters on the baseband processing stages.  
The design of the SC-FDM PLC receptor shown in Figure 10 involves stages that carry out the inverse processing of each of the transmitter stages, as well as the annexation of the estimator and equalizer in order to compensate for the distortions caused by the PLC channel. The stages are: coupling to the HV line, Mixer, ADC, PC extraction, OFDM demodulation (FFT processing), OFDM symbol demapping (separation of data symbols, pilots and guards), data decoding (IDFT processing). Estimation of the PLC channel or system identification is carried out via techniques reported in recent works [15] employing PROLATE orthogonal base expansion.
# Quality of Service Based NOMA Group D2D Communications
## 2 System Model
### Power Allocation Coefficients Policies
#### 2.3.2 Policy II  
The concept of fixed NOMA (F-NOMA) was proposed in [27], where the power allocation coefficients are fixed and are computed based on the given user ordering i.e., it does not utilise the actual channel gains to compute \(\{\beta_{i}\}_{i=1}^{M}\). Similar to [27], we adopt a Policy II to compute \(\{\beta_{i}\}_{i=1}^{M}\) for conventional NOMA that does not utilise the actual targeted rates of DRs. The power allocation coefficient for DR \(i\) under Policy II is computed as:  
\[\beta_{m}^{\mathrm{F}}=\frac{M-m+1}{\mu}, \tag{3}\]  
where \(\beta_{m}^{\mathrm{F}}\) represents the power allocation coefficient of DR \(m\) in Policy II and \(\mu\) is selected in such a way that \(\sum_{i=1}^{M}\beta_{i}^{\mathrm{F}}=1\).
###### Abstract  
Data collected from a moving lidar sensor can produce an accurate digital representation of the physical environment that is scanned, provided the time-dependent positions and orientations of the lidar sensor can be determined. The most widely used approach to determining these positions and orientations is to collect data with a GNSS/INS sensor. The use of dual-antenna GNSS/INS sensors within commercial UAS-lidar systems is uncommon due to the higher cost and more complex installation of the GNSS antennas. This study investigates the impacts of using a single-antenna and dual-antenna GNSS/INS MEMS-based sensor on the positional precision of a UAS-lidar generated point cloud, with an emphasis on the different heading determination techniques employed by each type of GNSS/INS sensor. Specifically, the impacts that sensor velocity and acceleration (single-antenna), and a GNSS compass (dual-antenna) have on heading precision are investigated. Results indicate that at the slower flying speeds often used by UAS (\(\leq\)5 m/s), a dual-antenna GNSS/INS sensor can improve heading precision by up to a factor of five relative to a single-antenna GNSS/INS sensor, and that a point of diminishing returns for the improvement of heading precision exists at a flying speed of approximately 15 m/s for single-antenna GNSS/INS sensors. Additionally, a simple estimator for the expected heading precision of a single-antenna GNSS/INS sensor based on flying speed is presented. Utilizing UAS-lidar mapping systems with dual-antenna GNSS/INS sensors provides reliable, robust, and higher precision heading estimates, resulting in point clouds with higher accuracy and precision.  
unoccupied aerial system; UAV; drone; GPS; inertial; heading; mobile mapping +
Footnote †: journal: Sensors
## 2 Methods  
In order to improve the accuracy and speed of obstacle detections during the driving of rail locomotives, the research conducted considering two aspects: motion-blur image processing and the improved detection algorithm. In order to facilitate the readers' understanding, we refer to reference [33] and describe the work by presenting a pseudo-code (Algorithm 1).  
```
1. Input: A picture with obstacle targets.
2. Execute the algorithm in following order to get the desired result.
3. begin
4. Blur judgment
5. do
6. Use formula (2) to judge whether the image is blurred or not.
7. Using DeblurGANv2 to deblur the blurred image.
8. end
9. Improved YOLOv4 algorithm
10. do
11. Replace the backbone network and reduce the number of channels in the Neck and Head parts.
12. Introduce the SANet attention mechanism.
13. the K-means++ algorithm is adopted to cluster prior frames.
14. the Focal loss function was introduced to increase the loss weight of small target samples.
15. end
16. Training network
17. do
18. Build dataset.
19. Training parameter configuration.
20. Weights: use pre-trained VOC dataset.
21. Train the network and generate model weights.
22. end
23. end
24. Output: obstacles were detected through the Bounding box around.
```  
**Algorithm 1**pseudo code of the proposed algorithm [33]
# Design and Optimization of a Compliant Morphing Trailing Edge for High-Lift Generation
## 2 Requirements and Specifications
The structure enabling high-lift conditions must meet different specifications due to stringent aeronautical regulations and safety standards. The aerodynamic profile must maintain its shape during both deformed and undeformed phases under external loads. Shape integrity must be ensured to a specific safety level and guaranteed by adherence to a safety factor (SF) greater than 1. Additionally, the first resonance frequencies must be sufficiently low to avoid flutter phenomena. All these requirements must also ensure that the overall weight of the structure is at least 10% less than that of a conventional flap system. Figure 3: High-lift morphing devices configuration as selected by trade-off studies. Figure 2: Target morphed shape including the synergic implementation of the droop nose effect and of the chamber morphing. Another crucial aspect is represented by the layout, the geometry, and the materials of the parts for the impact in terms of maintenance and reliability. The selected architecture shall be characterized by the following:  
* Easy inspection and accessibility; in this sense, configurations with a relatively small amount of parts and based on a modular approach will be privileged; moreover, the organization and the position of the parts shall also be defined to favor the removal of damaged parts minimizing the access operations (removal of additional components); this criterion will be also matched by the actuator and relevant kinematic transmission chain;
* Parts with geometry easily obtainable through conventional manufacturing techniques and linkable to each other using common integration approaches (e.g., riveting, bonding, and bolting);
* Materials commonly used in aeronautics, such as typical metallic alloys and composite layups. Finally, the requirements for an actuator to support the different high-lift surfaces are summarized below:  
* Operation time of approximately 15 s;
* Load at actuator interface with the morphing architecture, including friction load and a margin of 1.5:
* Greater than 1500 N for the 0.5 m spanned trailing edge; this requirement will be specifically considered for the demonstrator object of this work;
* Greater than 11,000 N for the 3.8 m spanned inboard trailing edge;
* Greater than 20,000 N for the 6.6 m spanned tapered outboard trailing edge.
## 3 Results and Discussion
### Physicochemical Properties
#### 3.1.6 Surface Tension  
The number of hydrogen bonds between HBD and HBA, molecular weight, viscosity, and temperature are among the major factors affecting the surface tension of NADES [26; 61]. Surface tension functions for NADES and its water solutions measured by the Du Noviy ring method are presented in Figure 6. The surface tensions of investigated NADESs were 81.4 \(\pm\) 3.2, 91.5 \(\pm\) 6.8, and 95.9 \(\pm\) 5.8 N/m for the NADES1, NADES2, and NADES 3, respectively. The dilution of all solvents leads to a statistically significant decrease in surface tension. The most pronounced reduction of surface tension (by 46.7%) was found for NADES1.  
The high surface tension, as well as the high viscosity of the solvents, slows down the solvent penetration in the plant cell capillaries during the extraction. The penetration of the solvent into the raw material is associated with capillary forces. The plant matrix has amphiphilic properties, and the hydrophilic properties of fiber are more pronounced than the hydrophobic ones. Plant tissue has a large number of capillary-type pores. The solvent penetrates through capillaries, filling the cells and space inside the plant material. The penetration of the solvent is accompanied by humidification inside the cells, which depends on the chemical affinity of the raw material and the solvent [62].  
The liquid phase spreads over the surface of plant cells, and the driving force of the humidification process is determined by the value of the spreading coefficient. The reduction of solvent surface tension improves the humidification of plant material cells, increasing the contact surface and the depth of its penetration into the cells. Based on the above, NADES1 will be preferable for extraction compared with NADES3. The tuning of solvents with water can also improve the efficiency of extraction. Similar recommendations were provided by [PERSON] et al. (2019) for some organic acid-based NADESs [20].  
Figure 6: Surface tension values of NADES and its water solutions at room temperature. NADES1—LA:ChCl (3:1); NADES2—LA: SL (1:3); NADES3—LA:GL:W (3:5:1).
# Diversity of Potential (Bio)Technological Applications of Amino Acid-Based Ionic Liquids
## 2 Bioapplications of AA ILs: An Overview
### AA ILs as Media for Extraction
#### 2.7.2 AA ILs as Media for Extraction of Amino Acids and Proteins
The highest partition coefficientswere obtained for DNP-L-Leu in the [Ch][Ala]-K\({}_{3}\)PO\({}_{4}\)-water system, with the partition coefficient values following the order: Leu > Val > Ala > Gly [146]. In addition to their use in amino acid separation, AA ILs are also used in protein isolation. For instance, cytochrome C is easily extracted into the IL phase by phase separation at room temperature within 10 min using a tetrabutylphosphonium-N-trifluoromethylanesulphonyl-leucine ([P\({}_{444}\)][Tf-Leu])-water (1:1) system [147]. Other proteins such as lysozyme, chymotrypsin, hemoglobin, albumin, and myoglobin are also readily solubilized in hydrated [P\({}_{444}\)][Tf-Leu] [147]. However, proteins such as laccase, horseradish peroxidase, and triethylene glycol-modified cytochrome C show a preference for the aqueous phase. Interestingly, N-butyl-N-methyl-piperidinium-L-ornithine ([P\({}_{1A}\)][L-Orn]) showed exceptional performance as a chiral ligand in a chiral ligand-exchange capillary electrophoresis system, allowing effective chiral resolution and quantification of methionine [148]. An ATPE system consisting of polypropylene glycol 400 (PPG400) and [Ch][AA] was used to partition bovine serum albumin (BSA), with the phase formation ability of the ILs decreasing in the following order: [Ch][Arg] > [Ch][His] > [Ch][Pro] > [Ch][Val] [149]. The study showed that the salting-out effect and \(\pi\)-\(\pi\) interactions, as well as the hydrophilic/hydrophobic interactions between BSA and [Ch][AA]-ILs, are crucial for BSA partitioning [150]. Similarly, for aqueous biphasic [Ch][AA]-PPG-water systems, an increased affinity of BSA or trypsin towards the IL phase was observed with decreasing hydrophobicity of the AA [142]. The pH-adjustable salting-out effect caused by the IL-rich phase at pH < pI [AA] can also enhance the hydrophobic interaction between the protein and PPG polymer, thus promoting the partitioning of protein into the PPG-rich layer [142]. BSA is also preferentially transferred to the IL phase in the [Ch][AA]-polyethylene glycol di-methyl ether 250 (PEGME250)-water system [149,150].
# Development of a Fire Risk Assessment Program for Submerged Tunnels
## 2 Literature Review
However, this study is limited to general road tunnels, and it is difficult to apply tosubmerged tunnels (both road and railway) as in this study. Therefore, there is a need for a practical program suitable for submerged tunnels that can be directly used by operators. Despite the numerous studies that have been conducted, current studies on submerged tunnels still present several limitations, particularly concerning evacuation and smoke control technologies. Due to the fully enclosed and underwater nature of these tunnels, evacuation routes are highly constrained, and high hydrostatic pressure poses additional challenges in ventilation system design. In particular, for long or bidirectional tunnels, longitudinal ventilation alone may not be sufficient to effectively control smoke propagation during fire events. Furthermore, the structural characteristics of submerged tunnels limit the installation of intermediate emergency exits, making evacuation highly dependent on cross passages or escape bays. However, the optimal spacing and accessibility of these features remain insufficiently studied. In addition, few studies have quantitatively evaluated the performance of smoke extraction systems under diverse fire scenarios specifically tailored to underwater tunnel environments. Existing models also tend to simplify human behavior and smoke dynamics, which may lead to either underestimation or overestimation of actual risks. Future research should focus on incorporating more advanced human behavior modeling, conducting comparative analyses of different ventilation strategies, and performing large-scale or scaled-down experimental validations to enhance the reliability and effectiveness of fire safety measures in submerged tunnels. Unlike previous studies that focused only on conceptual design, explosion analysis, or evacuation planning, this research quantitatively assessed fire and evacuation scenarios in submerged tunnels using actual data and computational programs. Furthermore, by incorporating fire safety data from existing road and railway tunnels, this study developed fire safety standards and evacuation strategies specifically tailored for submerged tunnel environments. The reliability of the proposed program was validated by comparing it with commercial software, and the findings are expected to contribute to the practical application of fire safety standards in the future design and operation of submerged tunnels.
# Integrating a Path Planner and an Adaptive Motion Controller for Navigation in Dynamic Environments
## 2 Related Work
### Deep Reinforcement Learning  
Autonomous navigation demands two essential building blocks: perception and control. Similar to the above-mentioned studies about DL, many research studies [20] about applying DL in navigation are pure perception, which means that agents passively receive observations and infer desired information. Compared with pure perception, control goes one step further, seeking to actively interact with the environment by executing actions [21]. Then, navigation becomes a problem of sequential decisions. DRL is very suitable for solving it, which is proved by reference [7] in game and reference [8] in robotics. Since DRL methods can deal with better full observable states than partial states, lots of studies [9; 10; 22] about applying DRL in navigation focus on static environments. Regarding dynamic environments, [PERSON] et al. [23] proposed a time-efficient approach based on DRL for socially aware navigation. However, to calculate the reward based on social norms, the motion information of pedestrians are known in advance, which are not reliable in real-world applications, since it is not precise to estimate information by sensor readings.  
Given that it is difficult for an individual DRL method to solve the navigation problem, hierarchical approaches are widely researched in the literature [24; 25; 26]. [PERSON] et al. [26] combined A* and least-squares policy iteration for mobile robot navigation in complex environments. [PERSON] et al. [25] integrated sampling-based path planning with reinforcement learning (RL) agents for indoor navigation and aerial cargo delivery. The aforementioned methods can only handle navigation in static environments. [PERSON] et al. [24] combined value-based DRL with A* on topological maps to solve navigation in environments with pedestrians. However, the experiment results are not well applied in dynamic environments, since the learned policy is reactive and the way of taking observations as states is inaccurate, which may lead to irrational behaviors and even collisions with obstacles.  
Compared with the above hierarchical methods, our hierarchical navigation algorithm can generate better waypoints by JPS+ (P) and navigate in dynamic environments via IA3C.
# Tertiary Waves Measured during 2017 Pohang Earthquake Using an Underwater Glider
## Materials and Methods  
### Noise Sources of the Underwater Glider  
Underwater gliders are propelled by adjusting the buoyancy, thereby using the resulting vertical motion to drive the glider. Underwater gliders have no propeller, so there is little operating noise. Its possible noise sources, such as the air bladder, fin steering, battery movement and volume piston are described in [21]. [PERSON] et al. [11] showed that high-intensity sounds such as the sloshing of the glider at the sea surface, the bladder pump, the ballast pump, and the battery packs moving around are all broadband in nature. The bulk of the broadband noises were concentrated at the lower frequencies 0-3 kHz. However, these only describe possible noise sources and no specific frequency and noise-level analysis of each of the noise sources, including the driving and sensor component parts, has yet been studied. Recently, An analysis has been reported on the self-noise of only the driving part of the Petrel II underwater glider developed in China [22].  
In this paper, the noise sources of the driving and sensor parts of the underwater glider observed the earthquake sounds are described in detail. A Slocum underwater glider (200 m class) manufactured by Teledyne-Webb Research is used in this study (Figure 1). The glider has an overall length of 1.5 m and a mass of 60 kg. The buoyancy engine is an electrically powered piston drive, located in the nose section of the glider. The drive allows the glider to take in and expel water, thereby changing its overall buoyancy. The mechanism allows a nearly neutrally buoyant trimmed glider to change its displacement in water by \(\pm\) 250 cc which corresponds to approximately \(\pm\) 0.5% of the total volume displaced. This change in buoyancy generates a vertical force which is translated through two swept wings into a combined forward and up/downward motion. Due to the location of the piston drive, also called the buoyancy engine, the change in direction of the buoyant force also creates the main pitching moment for the glider. Besides the buoyancy engine the glider possesses two more control actuators, a 9.1 kg battery pack, referred to as the sliding mass, that can be linearly translated along the main axis of the glider and a rudder attached to the vehicle tail fin structure. The sliding mass is used for fine tuning the pitch angle. A pumped Conductivity-Temperature-Depth profiler (CTD) sensor is contained in the payload bay of the glider. When the underwater glider moves vertically and horizontally, the driving and sensor parts generate noise. The noise sources associated with the driving parts include a piston, pitch controller, fin, and air bladder. The sensor noise sources are a pumped CTD and an altimeter.
## 8 Conclusions  
In conclusion, the present paper has demonstrated the viability of obtaining autonomous volcanic vigilance stations. Volcano surveillance is essential in order to predict volcanic eruptions and be able to reduce their damage. However, due to the usual remoteness of volcanoes, power supply generally constitutes a challenge.  
As a solution, this work has developed a novel system composed of thermoelectric generators as power suppliers, as well as all their related electronics, for which technologies typically associated with Internet of Things (IoT) have been used. Thus, thanks to the heat emitted in volcanic fumaroles, which is indeed a sign of activity of the volcanoes, electricity can be directly generated by means of the Seebeck effect. Since fumaroles are a constant temperature heat source, a continuous generation is obtained regardless of the weather conditions, permitting a drastic reduction in the capacity of the required batteries. Thanks to this generation, it is possible to measure different variables and emit the results via LoRa to a gateway located several kilometers away.  
In order to analyze the viability of this solution, a prototype formed by heat pipes as heat exchangers has been characterized at the laboratory and installed afterward at Teide volcano (Canary Islands, Spain), where there exists 83.5 \({}^{\circ}\)C fumaroles. In more than eight months of operation, the device has demonstrated that only one module is enough to cover the demand of data acquisition and communication over 14 km. On average, this thermoelectric module has generated 0.49 W with a temperature difference of 51.5 \({}^{\circ}\)C.  
During its operation, the prototype has resisted, without any maintenance, several kinds of adverse meteorological conditions in a very harsh environment. Therefore, the viability of the solution has been demonstrated, evidencing all its advantages: durability, reliability, lack of maintenance, scalability, absence of moving parts, noiseless operation, robustness, and compactness.