esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Applications of Chaos in Distributed Sensing  
Since the measurement of physical variables over extended regions requires several sensors that can result in impractical bandwidth and power consumption, there is the need for distributed sensing approaches where readouts are directly obtained at the ensemble level. Chaos can face this challenging issue via a nonlinear analog approach based on chaotic synchronization. For example, in [13], a precursory study is presented, which brings together two separate research fields, i.e., wireless sensor networks and electronic chaotic oscillators. The authors of [13] demonstrate that it is possible to build distributed sensing systems by implementing the nodes with simple analog chaotic circuits. In particular, [13] presents the first physical evidence that chaotic circuits can be coupled remotely in extended networks. A remarkable result is that several emergent phenomena (including synchronization, desynchronization and chaotic transitions) can be obtained even in the case of weak coupling [13]. Experimental measurements have shown that chaotic single-transistor oscillators are suitable for wireless coupling via mutual induction [13]. This has enabled a form of telemetry for luminous flux to be implemented. The results in [13] highlight the capability to generate accurate estimations of the average of distributed physical magnitudes from complex ensemble dynamics, indicating that the use of chaotic circuits represents a promising approach in sensing applications.
# Exploring Surface-Driven Mechanisms for Low-Temperature Sintering of Nanoscale Copper
## 3 Results and Discussion
### Mechanical Properties of Sintered Samples
#### 3.2.1 The Bulk Modulus and Shear Modulus
At a lower temperature, the sintering process between 300 and 600 K primarily relies on the residual stresses generated from the contact between nanoparticles since the rapid surface dynamic state does not exist before 600 K. Under the influence of these residual stresses, the nanoparticles undergo dislocation and rotation, and once the slip planes between certain particles align, the system experiences further dislocation and slip, leading to an optimization of the crystal structure. Therefore, even when the sintering temperature is below 600 K, there is still a measurable increase in the bulk modulus and shear modulus of the sintered samples. When the temperature exceeds 900 K, surface melting on the nanoparticles begins. At this point, the sintering process of the nanoparticles transitions from being dominated by low-temperature sintering mechanisms to being governed by plastic flow processes. The liquid regions covering the surfaces of the nanoparticles rapidly bridge the pore areas through surface tension, infiltrating all defect regions and significantly enhancing the quality of sintering. It is noteworthy that at a sintering temperature of 300 K, the system already exhibits a shear modulus of 30 GPa and a bulk modulus of 80 GPa, surpassing the strength achieved in actual processes. This is attributed to the uniform particle size of the nanoparticles in this study, with diameters around 5 nm. In the experiment, the particle size exceeded 5 nm, leading to the formation of larger pores, which resulted in lower experimental values.
## 2 Numerical Simulation of CSG Dam upon Overburden
### Project Overview and Establishment of Finite Element Model
#### 2.2.2 Principle of Virtual Rigid Spring Method  
The virtual rigid spring method is used to correct the softening of the deviatoric stress in CSG materials after reaching its peak value. At this stage, the deviatoric stress (\(\sigma_{1}-\sigma_{3}\)) decreases as the axial strain increases, and the tangent elastic modulus is negative at this time. Therefore, the classical hyperbolic model cannot be directly used [18].  
Figure 3: Diagram of finite element mesh.  
Curve 1 in Figure 4a is the actual stress-strain curve of the material, and the stress-strain relationship at this time does not satisfy the classical hyperbolic model. Adding a virtual spring at the appropriate position on the curve, as shown in Figure 4b, the stress-strain relationship of this virtual spring is shown as curve 2 in Figure 4a. The stress-strain curve after adding the virtual spring becomes hyperbolic, and the stress softening stage disappears (as shown by curve 3 in Figure 4a). Then, the true tangent elastic modulus of CSG material is  
\[E_{1}=E_{3}-E_{2} \tag{4}\]  
where \(E_{1}\) is the actual tangent elastic modulus of CSG material; \(E_{2}\) is the elastic modulus of the virtual rigid spring, and the absolute value of the maximum negative elastic modulus of the actual stress-strain curve is taken; \(E_{3}\) is the tangent modulus of the virtual model [19].  
The model parameters can be applied to CSG materials after being processed by the virtual rigid springs method [20]. The constitutive model parameters of the material were measured through triaxial consolidation drainage test, as shown in Table 1.
## 3 Results and Discussion
### ZnO Nanoparticle Characterization Analysis
#### 3.3.3 SEM Analysis of ZnO-Nanoparticle-Treated Bacteria and Fungi
SEM images of _Staphylococcus aureus_ cells: (**e**) normal cells; (**f–h**) cells treated with augmentin, LPME, and 50 mg LP–ZnO NPs, respectively. DMSO was used as a solvent.
## 2 G-LiHT Design and Instrumentation
### Airborne Scanning LiDAR  
The VQ-480 (Riegl USA, Orlando, FL, USA) airborne laser scanning (ALS) instrument was selected for use with G-LiHT, because it was affordable, compact, provided evenly distributed pulses on the ground and offered near-turnkey operation. The VQ-480 uses a high-performance laser rangefinder and a rotating polygon mirror with three facets to deflect a 1,550 nm Class 1 laser beam onto the ground. A user-selectable pulse repetition rate up to 300 kHz provides an effective measurement rate of up to 150 kHz along a 60\({}^{\circ}\) swath perpendicular to the flight direction (Figure 3). A laser beam divergence of 0.3 mrad produces a 10 cm diameter footprint at the nominal operating altitude of 335 m. The small footprint laser beam allows detection of small gaps in the canopy and the  
Figure 3: **(a)** G-LiHT installed on NASA’s Cessna 206; **(b)** wing-mounted pod showing mounting points common to all Cessna 206s; **(c)** view ports on bottom of custom pod.  
ability to characterize fine scale disturbances, which are difficult to deconvolve from large footprint LiDAR waveforms (Figure 4, [17]). The mirror speed is set to a maximum of 100 rotations s\({}^{-1}\) during G-LiHT acquisitions, whose points are spaced 0.23 m apart within a line perpendicular to the flight direction and 0.57 m between lines with a nominal aircraft speed of 110 knots. For each laser shot, the return waveform is digitized, and online processing algorithms are used to provide ranging data for multiple targets. Up to eight discrete ranging returns may be identified and recorded for a given pulse. Each return target is time tagged and accurately synchronized with the GPS-INS using a 1 Hz Transistor-Transistor Logic (TTL) pulse. Riegl's software, RiACQUIRE, provides a graphical user interface for scanner control and near real-time monitoring of scanning LiDAR and GPS-INS data.
## 3 Results and Discussion
### Biochar Composites Characterisation
#### 3.2.2 ATR-FTIR Analysis of the Biochar Composites  
A broad absorption peak around 3200-3600 cm\({}^{-1}\), Figure 6, corresponds to stretching vibrations of the hydroxyl (O-H) group. The peaks around 2800-3000 cm\({}^{-1}\) are associated with symmetric stretching vibrations of C-H groups. Three peaks in the range of 1300-1500 cm\({}^{-1}\) correspond to vibrations of C-H in CH\({}_{3}\) groups. An intense peak at 1710-1720 cm\({}^{-1}\) corresponds to the carbonyl group (C=O) [54]. As the biochar production is through the pyrolysis of the biomass at a high temperature (800 \({}^{\circ}\)C), the percentage of functional groups is expected to be less; hence, no significant difference in the composite peaks occurs with the addition of biochar. Comparing the FTIR spectra of the PLA/PBAT (80/20) blend and PLA/PBAT/BC (75/20/5) composite show no peak changes due to biochar, indicating that mixing is purely physical. These results are consistent with previous studies [18; 55].  
Figure 5: TGA analysis of biochar composites.  
Figure 6: FTIR of PLA/PBAT blend and PLA/PBAT/BC (75/20/5) composite.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & **T\({}_{\text{S}}\) (\({}^{\circ}\)C)** & **T\({}_{\text{max}}\) (\({}^{\circ}\)C)** & **Char Residue at 550 \({}^{\circ}\)C (\%)** \\ \hline PLA/PBAT (80/20) & 346.98 & 382.86 & 1.37 \\ PLA/PBAT/BC (79/20/1) & 346.21 & 383.93 & 2.27 \\ PLA/PBAT/BC (77.5/20/2.5) & 346.50 & 384.12 & 3.53 \\ PLA/PBAT/BC (85/10/5) & 347.49 & 384.69 & 5.38 \\ PLA/PBAT/BC (80/15/5) & 348.62 & 383.58 & 5.70 \\ PLA/PBAT/BC (75/20/5) & 351.04 & 382.58 & 5.33 \\ PLA/PBAT/BC (70/20/10) & 351.45 & 382.07 & 12.95 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The onset of degradation temperature (T\({}_{\text{S}}\)), the temperature of maximum degradation (T\({}_{\text{max}}\)) and char residue (%) at 550 \({}^{\circ}\)C.
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
## 4 Performance Analysis and Simulation Results
### The Localization Performance
#### 4.2.3 Test3: The Influence of Reference Nodes' Topology  
In this test, we analyze the impact of reference nodes' topology on the localization performance. Fix the Gaussian noise variance be 3 m. Five hundred Monte Carlo localization experiments are done with optimal and non-optimal reference nodes selections respectively in the same network. Figures 7 and 8 show the optimal and non-optimal reference nodes selection results and 500 Monte Carlo experiments are exhibited in Figures 9 and 10.  
Figure 8: The non-optimal reference nodes selection.  
Figure 10: The localization result without optimal reference nodes.  
Figure 9: The localization result with optimal reference nodes.  
Figures 7-10 demonstrate the effectiveness of the proposed reference nodes selection scheme. From Figures 9 and 10 we can see that the optimal reference nodes can generate a better localization performance than non-optimal references.
## 2 Experimental Procedure and Acoustic Emission Signal Analysis  
### Single Fiber Fragmentation Tests (SFFT) and Acoustic Emission Monitoring  
The specimen consists of a single long T700 carbon fiber (diameter 6 \(\upmu\)m) inserted in an epoxy/amine matrix (3 DCM). The sizes of the specimen are summarized in Figure 2 and Table 1 gathers the mechanical properties of the fiber and the matrix.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Young Modulus (GPa)** & **Poisson Ratio** & **Density (kg/m3)** \\ \hline Carbon fiber & 187 & 0.22 & 1800 \\ DGEBD-3 DCM & 1.41 & 0.38 & 1034 \\ \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of the matrix and the fiber.  
Figure 1: Principles of acoustic emission measurement and the different transfer functions.  
Figure 2: Dimension of specimen, sensors placement (C1 and C2) and positions of the simulated fiber breaks Pi (P1 is located 6 mm from the sensor C1 and P5 is located 34 mm from the sensor C1).  
The thickness of the experimental specimens is 2.8 mm or 1.7 mm, respectively. In both cases, the fiber is not positioned in the middle of the sample along the z-axis but rather 0.5 mm from the surface. The position of the fiber is a little asymmetric with respect to the mid-plane of the specimen to excite symmetric and antisymmetric modes. The SFFT is carried out at room temperature and with a deformation rate of 0.5 mm/min ensured by the tensile test machine (type: MTS, capacity 5 kN). The AE monitoring is conducted by means of multiple sensors using a PCI-2 data acquisition system (Mistras Group, Sucy en Brie, France). In order to study the sensors' effects, tensile tests are conducted with two types of sensors (nano30 and picoHF sensors) placed at the same position on each face of the specimen 40 mm apart (Figure 2). These two sensors show a good sensitivity in the frequency range of 200 to 400 kHz for nano30 sensor and 500-1850 kHz for picoHF sensor (Mistras Group data). Thus, by using both of them for tensile testing it is interesting to investigate, from an experimental point of view, the effect of the sensor on AE descriptors. In all tests, the coupling agent is medium viscosity vacuum grease.  
Each sensor is linked to a preamplifier (gain 40 dB, type 20 H). Each waveform is digitized and recorded. We set the sampling rate to 2 MSPS or 5 MSPS in order to make a compromise between the respective peak frequencies of the sensors, the digitization capabilities of our system and the volume of data to be stored. The value of the acquisition threshold is equal to 32 dB, depending on the background noise and the acquisition parameters are set to 25 \(\upmu\)s, 50 \(\upmu\)s and 1000 \(\upmu\)s for the peak definition time (PDT), the hit definition time (HDT) and the hit lockout time (HLT). All signals are filtered in the bandwidth 20-1200 kHz. Since two sensors are used, only the located signals along the gauge length are used. The AE wave velocities are measured with pencil lead breaks. The velocity is equal to 1750 m/s with nano30 sensors and to 1500 m/s with the picoHF sensors.
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 4 Multi-Object Detection for Inland Ships
### YOLOv5s Architecture  
YOLOv5 adjusts the depth and width of its backbone network using two parameters: depth_Multiple and width_Multiple. YOLOv5s, the most miniature volume network model, is chosen as the basic architecture. We enhance the basic model architecture by adding the Shuffle Attention mechanism and optimizing the training process using a transfer learning strategy to train the network with limited ship image data effectively. The resulting detection model achieves improved accuracy and can be used for ship detection in various scenarios. The network structure of YOLOv5s consists of an input end, backbone, neck, and head detection module. The original network module of YOLOv5s includes CBS (Conv + BN + SiLU), BOTTLENECKCSP, CSP1_X, CSP2_X, SPPF, and other modules. For expression, CBS modules are merged into CSP1_X during visualization. In the YOLOv5s model, there are three detection layers. When the input image size is 640 \(\times\) 640, the Neck network performs down sampling by 8\(\times\), 16\(\times\), and 32\(\times\), respectively. As a result, the dimensions of the feature maps in the corresponding Detect layers are 80 \(\times\) 80, 40 \(\times\) 40, and 20 \(\times\) 20, respectively. These feature maps are used for detecting small, medium, and large targets, respectively.
## 2 Methodology of Time-Frequency Separation Neural Network
### Basic Blind Source Separation Problem and Typical Solutions
The ICA method has been universally acknowledged as an efficient BSS method, and it is thus used as the first compared method in this paper, and the third approximate estimate function is selected according to the suggestion in a previous work [24]. However, in a standard ICA process like FastICA, the objective of negentropy is recursively optimized with a fixed-step quasi-Newton method, which might suffer from slow computing, oscillation, and instability of the convergence [11]. Therefore, a modified version of ICA is proposed in this paper in the next section to overcome the aforementioned disadvantages. Furthermore, the BSS problem is not limited to speech, and the processing or recovery of the other types of data might also include BSS techniques, such as complex signals and images. This characteristic makes it possible to introduce time-frequency analysis for the original acoustic signals before applying the BSS algorithm.
## 5 Experimental Settings
### Domain Adaptation Results  
In the general domain, the SACT model achieved excellent performance. To assess the domain adaptability of the SACT model, its performance was tested in the biomedical domain using the FewRel 2.0 dataset as the test set. Figure 12 shows a comparative analysis between SACT and the other models on the FewRel 2.0 dataset. The figure conspicuously illustrates that SACT outperforms the other models on the FewRel 2.0 dataset. The performance of the SACT model exhibits a noteworthy superiority over the other models, which signifies that SACT has a higher accuracy and generalization ability on the RE task. As evident from the outcomes presented in Table 4, the SACT model has demonstrated substantial accuracy enhancements of 4.94%, 5.89%, 4.41%, and 6.09% across four tasks within the biomedical domain when compared to the runner-up model (HCPR). These findings furnish compelling evidence for the effectiveness and robust domain adaptability of SACT.  
Figure 11: Comparison of SACT with other prototype network models on FewRel 1.0 dataset.
## 3 Pharmacokinetics and Pharmacodynamics of Xylazine
### Studies on the Metabolism of Xylazine  
The first studies on xylazine metabolism date back to 1973, when [PERSON] and [PERSON] (1973) [104] demonstrated that the major metabolite excreted in cattle urine, in both free and conjugated forms, was 2.6-xylidine (or 2,6-dimethylaniline, **1**, R = H), most likely produced via CYP450 through N-S-dealkylation. Then, other metabolites were identified  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \(t_{1/2}\) & \(T_{\text{max}}\) & **Fatal Dose Range** & **Approximate Effect Duration** \\ \hline
23–50 min & 12–14 min & 40–2400 mg & 4–6 h \\ \hline Data taken from [PERSON] et al. (2024) [66]. & & \\ \end{tabular}
\end{table}
Table 1: Pharmacological parameters of xylazine in humans.  
in horses [105], and the existence of 4-hydroxy-2,6-dimethylaniline (**2**, R = H; R\({}^{1}\) = 4-OH), 2,6-dimethylphenylisothiocyanate (**3**) and 3- and 4-hydroxyxylazine (**4**, R\({}^{1}\) = 3-OH or 4-OH) in rat urine was also established [106]. The most common phase I metabolites are presented in Figure 4.  
The formation of 2,6-dimethylaniline in equine urine was reported for the first time by [PERSON] et al. (2004) [78]. [PERSON] et al. (2013) [37] reported that xylazine was N-dealkylated and S-dealkylated oxidized and/or hydroxylated to twelve phase I metabolites, and the phenolic metabolites were partly excreted as phase II glucuronides or sulfates. All the metabolites were identified both in rat and human urine. Recently, [PERSON] et al. (2020) [107] described some other metabolites, including those deriving from oxidation (**5**, Figure 3) and acetylation at the thiazine ring, in zebrafish (_Danio rerio_), a human-like surrogate, used as an alternative model for the study of the metabolism of xylazine. Recently, [PERSON] et al. (2024) [66] summarized all the main metabolites derived from xylazine metabolism in animal studies. However, in humans, there is still a limited understanding of the clinical pharmacology and effects of xylazine [108, 109, 110]. The lipophilic nature of xylazine allows it to cross the blood-brain barrier, enabling the \(\alpha\)2-receptor agonism not only in the peripheral nervous system but also in the CNS. The clinical relevance of the possible biological activities of the main xylazine metabolites, apart from the well-known toxicological potential of anilines as genotoxic, carcinogenic [31, 32, 33], methaemoglobinemia inducers [111], and the general toxicity of aryl isothiamates [112] has been poorly explored until now. Very recently, a complex pattern of Gi agonistic properties at the opioid nociception receptors (\(\mu\)OR, \(\delta\)OR, \(\kappa\)OR) and dopamine D2R heteroreceptor was disclosed for 3-hydroxy- and 4-hydroxyxylazine [57]. 3-Hydroxy xylazine shared the same G protein biased agonism at \(\kappa\)OR and \(\alpha\)-adrenergic receptor, subtype 2A (\(\alpha_{\rm 2A}\)-AR) with its parent compound. The pharmacological profile and clinical impact of these metabolites in humans are still under study [108].
## 3 Results and Discussion
### Fouling Behavior on the PAN-NF Membrane after Backwashing
#### 3.3.2 Application of CEB in the PAN-NF HF Membrane  
The developed PAN-NF HF membrane exhibited optimal water recovery with backwashing using 20 mg/L of NaClO in previous laboratory-scale investigations. Hence, 20 mg/L NaClO was used for backwashing during the actual treatment of raw water in this study; it exhibited a permeability recovery of 94.3% of the initial permeability of the membrane. However, the removal efficiency of the PAN-NF HF membrane after backwashing with 20 mg/L NaClO was less than the initial removal efficiency of 93.3% after contamination for the raw water sample. Backwashing was performed with NaClO concentrations of 1 mg/L and 10 mg/L to identify the optimal chemical backwashing concentrations and to reduce the NaClO concentration for higher permeability recovery and stable removal performance in an actual water treatment plant. Figure 7a shows that the water fluxes of the PAN-NF HF membrane recovered to 92.4% and 81.2% of the initial permeability after backwashing with 10 mg/L and 1 mg/L NaClO, respectively. As shown in Figure S4, contaminants on the inner surface of the PAN-NF HF membrane were clearly removed after backwashing with different concentrations of NaClO. However, a slight degradation in the removal efficiency occurred after backwashing with 10 mg/L NaClO. Figure 7b shows that the TOC removal efficiency of the PAN-NF HF membrane after backwashing with 10 mg/L NaClO decreased slightly from 92.3% to 91.3%, while the removal efficiency after backwashing with 1 mg/L NaClO had a stable constant value of 92.1%. In previous studies, higher chemical concentrations were used to remove organic foulants from UF membranes. Furthermore, studies on CEB for NF HF membranes used in water treatment are lacking. Hence, despite the membrane degradation that occurs with low concentrations of NaClO, 1 mg/L NaClO may be the optimal concentration for the chemical backwashing of PAN-NF HF membranes employed in water treatment plants in Indonesia.  
Figure 6: Fouling potential of water samples in real raw water and tap water.
# Ergonomic Sports Mouthguards: A Narrative Literature Review and Future Perspectives
## 2 Narrative Literature Review Search Strategy
### Mouthguards  
Given the high incidence of orofacial traumas among athletes during their sports activities, it is essential to encourage the use of effective protection methods [12; 23; 25]. Mouthguards and facial protectors are two efficient methods for preventing orofacial injuries, with their indications for use varying according to the type of sport being played [17].  
Since the first mouthguards were tested in the 1920s, a reduction in oral traumas among boxers was observed, leading to the encouragement of their use among American football athletes [26]. Currently, their significance is well-established in preventing injuries to soft tissues, teeth, and the surrounding bone structures during contact sports [14; 15; 16]. It is important to highlight that, in dentistry, other intraoral devices such as occlusal splints or night guards are used for protecting teeth from wear in patients with bruxism, relaxing jaw muscles, and treating temporomandibular disorders [27]. Due to the differences in materials, designs, and indications for use compared to sports mouthguards, these devices will not be addressed within the scope of this review.  
In this regard, in 1950, the American Dental Association (ADA) recommended the use of resilient protective devices and mouthguards to prevent or mitigate orofacial injuries, even when the impact is not directly exerted on the tooth [6; 7]. After a decade, positive outcomes were observed regarding effectiveness and a reduction in the incidence of damage to orofacial structures, which led to their mandatory use in contact sports [8].