esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Wind Measurements with the Fibered Airborne Coherent Doppler Wind Lidar LIVE
## 1 Introduction  
Since the late 1980s, several on-board coherent wind lidars have been developed, tested and exploited, based on various laser technologies, i.e., at 10.6 \(\upmu\)m with a gas laser [1; 2; 3], and at 2 \(\upmu\)m with a solid state laser [4; 5; 6]. Recent progress in fiber laser technology has enabled new lidar technology development [7; 8; 9; 10]. More robust and compact fiber technology paves the way for the simple and standard use of coherent Doppler lidar from an airplane. The 1.5 \(\upmu\)m lidar called LIVE (LIdar VETn) has been developed at Onera in order to measure three-dimensional (3D) wind fields under an airplane. It was installed and tested on a SAFIRE (Service des Avions Francais Instrumentes pour la Recherche en Environnement) ATR42 aircraft. LIVE wind lidar is based on a Leosphere lidar that has been extensively modified to meet the constraints of real-time wind measurement from an aircraft. In particular, it incorporates an innovative laser amplifier designed and developed by Onera which, thanks to its high peak power pulses, makes it possible to perform wind profiling from the aircraft altitude down to ground level.
## 5 Simulation Results Analysis
### Adaptability Test  
#### 5.2.1 Test Scenario I  
Following highway environment training, two enhanced evaluation scenarios were developed to test the autonomous vehicle's adaptive performance. The first test configuration maintained the training phase's surrounding vehicle count and core parameters but reduced the highway to three lanes and extended trial iterations to 1000. The simulation environment is shown in Figure 9. Pretrained neural network models were deployed without adjustments to evaluate generalization in altered conditions. The performance metrics for this modified scenario are detailed in the following analysis.  
Figure 8: Number of rounds in the algorithm training process.  
Figure 7: Driving distance during training.  
Figure 10 illustrates the success rates of the three models in the testing scenario. As the number of test rounds increases, the selection of control actions enables the autonomous vehicle to continuously reach the specified time limit, so the success rate shows an upward trend. At the end of the test round, the success rate of the DPD3 QN model reached 72.6%. This shows that DPD3 QN can effectively quantify the potential value of the current control action and update the target network parameters, thereby improving the control performance. The specific test results are shown in Table 3. In two driving scenarios depicted in Figure 10, the autonomous vehicle showed different results.  
Figure 11a shows a common driving scenario in real life. Three environmental vehicles occupy three lanes, which makes it impossible for the autonomous vehicle behind to drive at a higher speed. The autonomous vehicle has to wait, make a decision to accelerate and change lanes two seconds after following the environmental vehicle, and successfully completes the lane change. Figure 11b shows another lane change result. Due to the autonomous vehicle's failure to accurately assess the speed of the adjacent vehicle in a timely manner while following the preceding vehicle, it could not change lanes in time and collided with the other vehicles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm Model** & **Success Rate (\%)** & **Average Reward** &
\begin{tabular}{c} **Average Speed** \\ **(m\(\cdot\)s\({}^{-1}\))** \\ \end{tabular} \\ \hline DPD3 QN & 70.6 & 13.78 & 27.78 \\ DDQN & 58.8 & 12.56 & 26.54 \\ DQN & 44.8 & 11.19 & 26.31 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results I.  
Figure 10: Test scenario I success rate.  
Figure 9: Decision-making environment for driving on a three-lane highway.  
In Figure 11, the green square represents the self-driving vehicle, and the blue square represents the environment vehicle. When the self-driving vehicle collides with the environment vehicle, both vehicles are shown in red in the simulation environment.
## 3 Technological Resources: DARA and Emolive
### Emotion Detection with Emolive  
Emolive is a tool that allows users to track their emotions in real time using AI facial recognition. Emolive can detect six emotions: anger, disgust, fear, happiness, surprise, and sadness. If the detected facial expression is not recognized as any of these emotions, Emolive categorizes it as \"neutral\", just like other facial recognition systems [34].  
Emolive uses deep learning techniques to accurately detect emotions. In particular, it uses a convolutional neural network trained with the FER2013 dataset, which is widely used in emotion recognition tasks.  
The tool can detect the emotions of up to 12 students simultaneously with a single camera. It also allows teachers to create student groups and generate follow-up reports.  
Using Emolive is straightforward. The teacher registers the group of students and can either start monitoring immediately or schedule specific activation periods (start time and duration). During monitoring, the teacher can pause and resume the system as needed. The dashboard displays each student's emotions through average bar charts and time series plots, which are automatically updated every two minutes.  
This tool has been used in classroom settings and has demonstrated an average accuracy of 85% in detecting students' emotions (ranging from 81% to 93%, depending on the emotion) [33]. As it is open-source and compatible with commonly available devices, it is suitable for use in resource-constrained environments.  
A representative screenshot of the Emolive interface is shown in Figure 2, and additional examples are provided in Supplementary Material S3.  
Figure 1: When the smartphone camera detects the Java code corresponding to the adjacency matrix declaration, DARA displays a 3D visualization of the graph to help students understand its structure, as shown in (**a**). When detecting other code segments, DARA overlays textual explanations directly onto the source code, allowing the student to relate programming instructions to the components of the greedy algorithm, as illustrated in (**b**). The student moves the camera across the code to explore different fragments and their contextual annotations.
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 5 Comparison of the Algorithms
### Quantitative Comparison
#### 5.1.3 Scenarios with Obstacles  
The next type of scenario to be analyzed is the scenario with non-flyable obstacles. In this comparison, 50 scenarios have been generated, and for each one, five different obstacle arrangements are created. Each algorithm is tested 100 times, varying randomly the initial positions of the agents.  
The efficiency and fitness distributions are represented in Figure 17a,b, respectively. The A1 performs better than the others, considering both efficiency and fitness, although the difference in this case is lower. For algorithm A7, which in other cases is a competitive solution, in this scenario type, its performance lies between the two groups of algorithms.  
Figure 16: Efficiency (**a**) and fitness (**b**) for the scenario with the probability distribution of the location of the targets.
## 1 Introduction  
Electromagnetic actuators rely on magnetic forces generated by their appropriate circuits driven by electric power. As in other electromagnetic devices (e.g., rotating machines), magnetic cores are typically used for optimising the force performance and energy efficiency. There is an implicit need for a non-negligible air gap between the parts of the magnetic circuit so that the relative mechanical movement can be facilitated, and this air gap typically represents the highest magnetic reluctance in the system [1, 2, 3]. For this reason, the design of a magnetic circuit is usually optimised by at least minimising the effective reluctance of the system, which can be achieved by several means, not only by reduction in the air gap length. For compact or miniature actuators, the whole magnetic core or its main component (such as the stationary \"stator\" and the moving \"mover\") can be made as separate single pieces of bulk metal or as appropriately shaped parts made of soft magnetic composites. For larger devices, in the case of laminated cores, all the laminations can have the same (or very similar) shape and can be conveniently stamped out as a single piece of sheet, which are later stacked to the required height of the stack [3,4]. Despite significant progress in recent years in manufacturing technologies, including CNC (computer numerical control) laser cutting or additive manufacturing, the stamping process is still the most cost-effective for large-volume production of parts with the same shape, as used extensively, for example, in the electric motor industry [4].  
However, magnetic cores of very large devices cannot be easily stamped out as single pieces [4]. For large rotating machines made from non-oriented electrical steel (NOES) or grain-oriented electrical steel (GOES), the directly limiting factor is the available manufacturable width of the electrical steel strip, which is on the order of 1.2 m [4]. For other devices such as large linear actuators, the limitations can be different, especially if the laminations can be manufactured within the available width of the electrical steel strip. Several practical technological difficulties of manufacturing magnetic cores for one example of a large linear actuator are discussed in this paper.  
In this paper, Section 2 includes an overview of the project, and Section 3 and its several sub-sections discuss the main technical and technological challenges encountered during manufacturing of the prototype modules.
## 2 Materials and Methods
### Subjects  
The inclusion criteria for this study were: age \(\geq\) 18 years, ability to complete the questionnaires, and understanding of the study procedures. The exclusion criteria were history of any active ocular disease different from DED or meibomian gland dysfunction (e.g., corneal ulcers, herpes simplex, keratitis, etc.), any uncontrolled severe systemic disease that may affect the eye (e.g., Sjogren's syndrome, diabetes type II, dermatological disease, etc.) or any ocular surgery or trauma that could affect the tear film distribution or produce any eyelid margin abnormality. None of the participants received eye drop installations or wore contact lens within 6 h before measurements.  
The classification criteria for DED subjects were based on the presence of symptoms and signs as described by the TFOS diagnosis criteria report [7]. Subjects who showed an Ocular Surface Disease Index (OSDI) score \(\geq\) 13 points and presented at least one sign of altered homeostasis (tear film stability: Non-Invasive Tear Film Break-Up (NIBUT) \(<\) 10 s (sec); Tear Film Osmolarity (TFO) \(\geq\) 308 mOsm/L; ocular surface staining \(>\) 5 corneal spots) were included in the DED group. Subjects who reported no subjective DED symptoms were included in the control group regardless the presence of signs.  
The sample size was calculated based on previous data published by [PERSON] et al. [25]. The standard deviation was assumed to be 0.68 [25]. Accepting an alpha risk of 0.05 and a beta risk of 0.2 in a two-sided test, 38 subjects were necessary to recognize as statistically significant a difference greater than or equal to 0.31 units [26]. A drop-out rate of 0% had been anticipated because all tests were performed in the same visit.
## 4 Topology Control  
From the network topology point of view, the resilient WSN problem is a \(k-connected\) problem. The topology control is a common way to preserve or improve the connectivity of WSNs by controlling the network topology. Note that the connectivity is one of the most important, but usually not the only concern in WSNs' design. Some other parameters, such as coverage, energy and number of nodes required, also should be taken into account to optimize. In most cases, there is a need to consider both the network coverage and connectivity, e.g., [15,36,55,56]. Some works survey the coverage problem in WSNs, e.g., [57,58].  
In general, network connectivity is more important than other performances because collecting environment data is usually the primary task. The coverage and energy problems become less important if the sensor nodes are not able to forward the collected data to the destination.
## 4 Advanced Strategies for eNO\({}_{3}\)R Toward Ammonia and Nitrogen
### Copper-Based Electrocatalyst for the eNO\({}_{3}\)R: A Case Study for Ammonia Production
#### 4.1.5 Cu-Based Heterostructure Engineering  
Cu-based heterostructures have been extensively studied for their electrocatalytic performance in the conversion of NO\({}_{3}^{-}\) to NH\({}_{3}\). For instance, [PERSON] et al. [172] designed Cu/Pd/CuOx heterostructures with a porous morphology, which facilitated electron transfer between Cu, Pd, and CuOx components. This heterostructure enhanced NH\({}_{3}\) production significantly, achieving a NH\({}_{3}\) production rate of 1510.3 ug h\({}^{-1}\) mg\({}^{-1}\), FE of 86.1%, and NH\({}_{3}\) selectivity of 90.06% (Figure 13a). The electronic interactions within the Cu/Pd/CuOx interface promoted the adsorption of intermediates, thus suppressing competitive hydrogen evolution reactions and improving overall catalytic efficiency [172]. Another study by [PERSON] et al. [173] explored Cu\({}_{2}\)O corner-etched octahedra supporting Pd nanoparticles (Pd-Cu\({}_{2}\)O), where Pd acted as the active center for *H adsorption and catalyzing NH\({}_{3}\) production, while Cu\({}_{2}\)O provided additional active sites through electrochemical reduction to Cu\({}^{0}\)/Cu\({}^{+}\). This system exhibited outstanding NH\({}_{3}\) formation characteristics, including a NH\({}_{3}\) formation rate of 925.11 ug h\({}^{-1}\) mg\({}^{-1}\), selectivity of 95.31%, and FE of 96.56% (Figure 13b). The oxygen defects in Cu\({}_{2}\)O were crucial for enhancing NO\({}_{3}^{-}\) adsorption and weakening of the N-O bond, thereby promoting efficient NH\({}_{3}\) synthesis [173].  
Figure 12: The possible reaction mechanism of enhanced nitrate reduction on the Cu/Ni foam electrode [84].
# Groundwater Contamination by Gas Stations in Two Eastern Amazonian Towns (Northern Brazil)
## 2 Materials and Methods
### Collection of the Water Samples and the BTEX Detection Procedures  
Samples of groundwater were collected during 10 different campaigns at gas stations and their surrounding areas in both Amazonian towns during 2020, 2021 (rainy and dry seasons), 2022, 2023 (rainy, rainy-dry, and dry seasons), and 2024 (rainy season). In the present study, 150 water samples were collected (30 in Tracuateua and 120 in Augusto Correa, Figure 1) for the analysis of the concentrations of BTEX contaminants.  
The water samples were collected from boreholes with faucets installed, which were located at gas stations and in the areas surrounding these stations. The available database [43] and informal interviews with local well-diggers confirmed that the water table of the two study areas was located at a depth of approximately 10 m.  
For the collection of a sample, the borehole faucet was opened for approximately 2-3 min to eliminate the standing water in the pipes and to remove the dirt, air, and other residues present in the piping. Prior to being turned on, the faucets were disinfected with sodium hypochlorite solution (100 mg L\({}^{-1}\)) and then washed to remove any residues of this disinfectant before the collection of the water samples.  
The samples of underground water were collected in 100 mL amber glass flasks, which had been washed, sterilized, sealed, labeled, and stored in sterilized coolers containing ice gel packs for transportation to the laboratory, to ensure that the monoaromatic hydrocarbons were not volatilized unintentionally. These flasks were sent within 24 h for analysis at the Toxicology Laboratory of the Environment Sector of the Evandro Chagas Institute (Brazilian Ministry of Health) in the city of Belem, the Para state capital. The procedures of the 21 st edition of the Standard Methods for the Examination of Water and Wastewater, method SM-6010 B [44], and the National Waters Agency/Brazil Ministry of the Environment [45] were adopted for the preservation of the samples collected here.  
Gas chromatography (GC) is the principal technique used to separate and analyze volatile compounds. This procedure has been used to analyze gases, liquids, and solids, although the latter are generally dissolved in some type of volatile solvent. Both organic and inorganic substances can be analyzed using GC [46]. The principal detectors used for gaseous-phase chromatography are flame ionization, thermal conductibility, electron capture, mass spectrometry, photoionization, flame photometry, nitrogen-phosphorus detection, and atomic emission detection [47, 48]. The combination of GC with mass spectrometry (GC/MS) is often considered to be ideal for the detection and quantification of volatile and semi-volatile organic compounds, including the concentrations of petroleum-based volatile organic hydrocarbon compounds (pH-VOCs) and BTEX compounds in samples of water [29, 31, 46, 49, 50].
# 3D Monitoring of Buildings Using TerraSAR-X InSAR, DInSAR and PolSAR Capacities
## 2 The Dataset and the Studied Buildings  
### The TerraSAR-X InSAR/DInSAR and PolSAR Temporal Stacks  
The studied InSAR temporal stack is composed of ninety-eight spotlight very high-resolution images acquired by the satellites TerraSAR-X and its clone TanDEM-X over Paris from 2008 to 2012. The Table 1 summarizes all the parameters needed to compute the height and the deformation of the scatterers.  
Figure 1 shows the variation in the baselines through time. The master image is the first image of the temporal stack that was studied in [12]. It has been acquired on the 24 January 2009 and is showed in red in Figure 1. After the launch of TanDEM-X (in June 2010), the maximal absolute baseline increases from 400 m to 900 m since both satellites acquired the images.  
Two TSX-TDX PolInSAR images acquired over the same area supplement this dataset. They have been acquired on the 7 December 2015 and on the 18 December 2015 with the same acquisition parameters that can be found in Table 1, excepted in Stripmap Dual Receive Antennas (DRA) mode. The ground-pixel size is thus \(\Delta_{d}=2.1\) m and \(\Delta_{r}=1.59\) m and the resolution \(\delta_{d}=3\) m and \(\delta_{r}=2\) m in azimuth and ground range respectively. The PolInSAR baselines is \(-256\) m for the 7 December 2015 image and \(-252\) m for the 18 December 2015 image while the baseline between the two master images is 386 m.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acquisition Parameters** & **Specifications** \\ \hline Satellite altitude & 514 km \\ Wavelength \(\lambda_{0}\) & 0.031 m \\ Look angle \(\theta\) & 34.69\({}^{\circ}\) \\ Satellite-Earth distance \(R\) & 625 km \\ Ground-pixel size (azimuth and range) & \(\Delta_{a}=0.87\) m and \(\Delta_{r}=0.71\) m \\ Ground resolution (azimuth and range) & \(\delta_{a}=0.91\) m and \(\delta_{r}=0.79\) m \\ Slant range pixel size & 0.455 m \\ Acquisition time & 17:34 UTC \\ \hline \hline \end{tabular}
\end{table}
Table 1: Acquisition parameters of the TerraSAR-X temporal stack.
###### Abstract  
This paper proposes the use of quasi-stationary aircraft and reconfigurable intelligent surfaces (RIS) to improve the system performance in satellite-terrestrial laser communication downlink. Single-input multiple-output (SIMO) technology is applied to the relay node of a quasi-stationary aircraft. The closed expression of the bit error rate (BER) of an RIS-assisted satellite quasi-stationary aircraft-terrestrial laser communication system (RIS-SHTLC) is derived under the M-distributed atmospheric turbulence model while considering the influence of atmospheric turbulence and pointing errors caused by RIS jitter. The effects of coherent binary frequency shift keying (CBFSK), coherent binary phase-shift keying (CBPSK), non-coherent binary frequency shift keying (NBFSK), and differential binary phase-shift keying (DBPSK) on the performance of an RIS-SHTLC system are simulated and analyzed under weak turbulence. The results show that the RIS-SHTLC system with CBPSK modulation has the best communication performance. Simultaneously, the relationships between the average signal-to-noise ratio (SNR) and BER of the RIS-SHTLC system under different RIS elements are simulated and analyzed, and compared with the traditional SHTLC system. In addition, the influence of the zenith angle, receiving aperture and divergence angle on the performance of the system is studied. Finally, Monte Carlo simulations are used to validate the analytical results.  
s 2022 6, 405. [[https://doi.org/10.3390/drones6120405](https://doi.org/10.3390/drones6120405)]([https://doi.org/10.3390/drones6120405](https://doi.org/10.3390/drones6120405))  
17 November 2022  
Accepted: 6 December 2022  
Published: 8 December 2022
## 3 System Architecture and Modeling
### Load Categorization
#### 3.1.2 Non-Thermal Load  
In this category, electrical load consumed by the appliances which are non-thermal in nature is included. These non-thermal appliances are assumed to have following characteristics.  
* Preemptive--The appliance can be paused or interrupted whenever needed.
* Fixed duration--The appliance run for a fixed duration (\(\Omega\)) to finish the task assigned to it.
* Deadline--The appliance has a deadline associated by which it needs to finish the task assigned to the appliance. The deadline is chosen by the user and cannot be smaller than the running duration of the appliance which is an operational constraint [44]. Examples include washing machine, clothes dryer, dishwasher, etc.