esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Fibre-Optic-Based Smart Geosynthetics
### Types of Fibre-Optic Sensors
#### 3.2.1 Intensity Sensors  
These instruments are the simplest OF sensors and, thus, were the first to be implemented. Moreover, they are still used as proximity and fibre breakage damage detection sensors and to monitor composite material curing and smart geosynthetic and infrastructural health. Intensity sensor devices are composed of an optical fibre--preferably a multi-mode fibre for increased power transmission--a photodetector, and a stable light source, as shown in Figure 3.  
According to intensity measurements, although intrinsically micro-bending OF sensors is favourable for monitoring strain and stress in geosynthetics and geotechnical applications (Figure 4), these sensors lose efficiency because of high strains or stresses in coarse angular grains when the sensors are severely bent.  
Figure 4: Micro-bending sensor mechanism.  
Figure 3: Schematic of OF-based intensity sensor configuration.  
Indeed, the incidence angle of the light in the optical fibre is lower than the critical reflection angle at the core-cladding interface, which causes the light to remain in the optical fibre, except at sharp bends from which the light can escape (Figure 5).  
In other words, if the OF sensor is sandwiched between two rough surfaces, applying more pressure causes the OF to bend even more, thereby increasing the optical losses. Although research has previously been conducted based on this concept, it is currently practically abandoned because of violent fluctuations in the light source optical power, calibration challenges, connectors, and temperature effects--all of which worsen system accuracy [26]. However, this effect must be acknowledged, especially in geosynthetics, textiles, and fabric composites wherein roughness-induced micro-bending losses can completely fade out optical signals. Therefore, before attempting to incorporate OFs into geosynthetics (i.e., geotextiles), the fibres must first be protected against micro-bending.
###### Abstract  
The performance of the global navigation satellite system (GNSS) can be enhanced significantly by introducing the inter-satellite links (ISL) of a navigation constellation. In particular, the improvement of the position, velocity, and time accuracy, and the realization of autonomous functions require the ISL distance measurement data as the original input. For building a high-performance ISL, the ranging consistency between navigation satellites becomes a crucial problem to be addressed. Considering the frequency aging drift and the relativistic effect of the navigation satellite, the frequency and phase adjustment (FPA) instructions for the 10.23 MHz must be injected from the ground station to ensure the time synchronization of the navigation constellation. Moreover, the uncertainty of the initial phase each time the onboard clock equipment boots also results in a pseudo-range offset. In this Ref., we focus on the influence of the frequency and phase characteristics of the onboard clock equipment on the ranging consistency of the ISL and propose a phase compensation sensor design method for the phase offset. The simulation and experimental results show that the proposed method not only realized a phase compensation for the pseudo-range jitter, but, when the 1 PPS (1 pulse per second) falls in the 10.23 MHz skip area, also overcomes the problem of compensating the ambiguous phase by directly tracking the 10.23 MHz to ensure consistency in the ranging.  
inter-satellite link (ISL); navigation constellation; ranging consistency; phase compensation sensor +
Footnote †: journal: Sensors
# Development of Building CFD Model Design Process Based on BIM
## 3 Design Method of Optimized CFD Model Using BIM
### Geometry Simplification to Optimize CFD Model  
The original geometry extracted from the IFC file may contain complex shapes that reduce the efficiency of CFD analysis. Excessively detailed surfaces adversely affect the grid design of CFD models, degrade the grid quality, and increase the time and error rate of numerical computation. Therefore, a geometry simplification process is required. In this regard, [PERSON] et al. (2018) investigated a method to simplify the shape of buildings by applying a surface simplification algorithm and analyzed the grid improvement effect by geometry simplification.  
This study employed the above-mentioned method to implement a geometry simplification function. This process is conducted as follows. Among the surfaces of the target geometry, insignificant faces that are small in area and similar to other faces are omitted, and a simplified geometry through the remaining faces is reconstructed. To achieve this reconstruction, the area, angle, and midpoint distance between the faces are calculated. Then, all of the surfaces of the model into faces to be omitted and faces to be preserved are classified. The degree of geometry simplification is adjusted by setting the distance threshold according to the characteristic length of the subject of analysis. The degree of geometry simplification increases as the distance threshold value increases as shown in Figure 2. Geometry simplification is performed at the same time as geometric information extraction, and the simplified geometry OBJ file is exported as the output.
## 5 Experimental Evaluation and Validation  
### Simulation Setup  
We use the Python environment (_PyTorch_) on a system with an Intel (r) Core TM i7 @ 4 GHz and 16 GB RAM to run a simulation with 5000 communication rounds to evaluate the performance of the proposed framework. For standard FL algorithms at the client end, we implement the SVM classifier. Hence, the standard FL algorithms are versions of the SVM such as FedAvg-SVM, and FedMA-SVM, and we also develop an upgraded version of the vanilla FedMA compromising of a genetic algorithm (GA) and SVM as FedMA with GA-SVM. The effectiveness of our framework for heart disease is assessed and compared with state-of-the-art FedAvg, FedMA, and FedMA with GA-SVM approaches in terms of prediction accuracy, time to attain the accuracy, communication efficiency, and influence of the local epoch on accuracy. We consider the number of HSP client nodes to be five and one HSP server node. However, this proposed framework can be scaled-up for the HSP client nodes.
# UAV-Cooperative Penetration Dynamic-Tracking Interceptor Method Based on DDPG
## 3 Deep Deterministic Policy-Gradient Algorithm
### State-Space Design  
To realize the coordinated penetration of UAVs, the design of the state space should include the UAVs' positions, \(x_{i},y_{i}\), speed, \(v_{x},v_{y_{i}}\), and the central position of the target area, \(x_{t},y_{t}\). At the same time, it is necessary to introduce the state observation of the interceptor position, \(x_{b},y_{b}\). Therefore, the state space is set to \(S=\left\{x_{i},y_{i},v_{x_{i}},v_{y_{i}},x_{t},y_{t},x_{b},y_{b}\right\},i\in L\).
###### Abstract  
In this paper, an adaptive neural fault-tolerant tracking control scheme is presented for the yaw control of an unmanned-aerial-vehicle helicopter. The scheme incorporates a non-affine nonlinear system that manages actuator faults, input saturation, full-state constraints, and external disturbances. Firstly, by using a Taylor series expansion technique, the non-affine nonlinear system is transformed into an affine-form expression to facilitate the desired control design. In comparison with previous techniques, the actuator efficiency is explicit. Then, a neural network is considered to approximate unknown nonlinear functions, and a time-varying barrier Lyapunov function is employed to prevent transgression of the full-state variables using a backstepping technique. Robust adaptive control laws are designed to handle parameter uncertainties and unknown bounded disturbances to cut down the number of learning parameters and simplify the computational burden. Moreover, an auxiliary system is constructed to guarantee the pitch angle of the UAV helicopter yaw control system to satisfy the input constraint. Uniform boundedness of all signals in a closed-loop system is ensured via Lyapunov theory; the tracking error converges to a small neighborhood near zero. Finally, when the numerical simulations are applied to a yaw control of helicopter, the adaptive neural controller demonstrates the effectiveness of the proposed technique.  
non-affine nonlinear system; adaptive neural control; actuator fault; full-state constraints; input saturation +
Footnote †: journal: Applied Mechanics and Control
# Effective Adsorption of Phenoxyacetic Herbicides by Tomato Stem-Derived Activated Carbons
## 3 Results and Discussion
### Adsorption Study
#### 3.2.4 Regeneration of the Activated Carbons  
The reusability of tomato stem-derived activated carbon (TS-AC) was determined using methanol as a desorbing agent, using three adsorption-regeneration cycles. Figure 9 shows that the TS-AC can be reused thrice with an herbicide removal percentage above 85%.  
The most significant decrease in AC adsorption capacity was observed after the first adsorption-regeneration cycle, averaging 7.8% for 2,4-D and 7.3% for MCPA across all activated carbons. In subsequent cycles, a decrease in herbicide adsorption efficiency by AC was also observed, but the changes were much smaller. After the 2 nd adsorption-regeneration cycle, the average adsorption capacity of AC towards 2,4-D and MCPA decreased by 4.0% and 3.3%, respectively, and after the third cycle, by 1.7% and 2.2%, respectively. Moreover, the regeneration efficiency of all six activated carbons tested remained at comparable levels. These results demonstrate that tomato stem-derived AC has good stability and can be reused multiple times without a significant loss of adsorption efficiency.
# Heart Murmur Quality Detection Using Deep Neural Networks with Attention Mechanism
## 6 Discussion
### The Significance of the Model's Ability to Choose the Important Segment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Data Augmentation Method** & **Accuracy (\%)** & \multicolumn{3}{c}{**Harsh**} & \multicolumn{3}{c}{**Blowing**} \\ \cline{3-8}  & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline \(\cdot^{*}\) & 68.8 \(\pm\) 5.0 & 72.4 \(\pm\) 2.6 & 75.9 \(\pm\) 8.9 & 73.9 \(\pm\) 5.5 & 63.8 \(\pm\) 7.5 & 58.6 \(\pm\) 4.7 & 60.8 \(\pm\) 4.4 \\ Speed decreasing & 67.9 \(\pm\) 4.5 & **78.9 \(\pm\) 5.9** & 64.1 \(\pm\) 13.7 & 69.3 \(\pm\) 7.9 & 59.8 \(\pm\) 6.2 & **73.2 \(\pm\) 12.0** & **65.0 \(\pm\) 3.9** \\ Speed increasing & 69.3 \(\pm\) 4.8 & 75.8 \(\pm\) 3.3 & 70.4 \(\pm\) 9.6 & 72.7 \(\pm\) 5.8 & 62.2 \(\pm\) 6.3 & 67.6 \(\pm\) 6.9 & 64.4 \(\pm\) 4.2 \\ Frequency shifting & 68.9 \(\pm\) 4.1 & 71.8 \(\pm\) 2.7 & **78.2 \(\pm\) 9.8** & **74.5 \(\pm\) 5.0** & **65.2 \(\pm\) 7.1** & 55.5 \(\pm\) 8.5 & 59.2 \(\pm\) 4.5 \\ Frequency and time masking [39] & 68.7 \(\pm\) 5.0 & 73.2 \(\pm\) 1.8 & 74.2 \(\pm\) 12.4 & 73.1 \(\pm\) 6.8 & 64.1 \(\pm\) 8.7 & 60.9 \(\pm\) 7.1 & 61.6 \(\pm\) 2.6 \\ Speed decreasing and increasing & 68.4 \(\pm\) 5.2 & 76.0 \(\pm\) 3.0 & 68.4 \(\pm\) 12.7 & 71.2 \(\pm\) 7.4 & 61.6 \(\pm\) 7.7 & 68.4 \(\pm\) 8.1 & 64.1 \(\pm\) 3.3 \\ \hline \multicolumn{8}{c}{\(\cdot^{*}\)-\(\cdot^{*}\) means no data augmentation method was used.} \\ \end{tabular}
\end{table} 
Table 8: Results of data augmentation. The number of segments used for data augmentation is 1266, with 747 labeled as “Harsh” and 519 labeled as “Blowing”. The results also show that the \"Feature Weighting\" method outperforms the \"Max\" method, which was presumably because it can distinguish the importance of different segments at the feature level, whereas the \"Max\" method cannot. In summary, the \"Feature Weighting\" method shows promising potential and can be used for similar problems with any new model in the future.
## 3 Hierarchical Controllers for Trajectory Tracking
### Reference Model-Based Hierarchical Controller (RMHC)
The characteristic polynomials of the controlled position are represented by the following Equations (18) and (19). \[\left\{ \begin{array}{l}D_{PD_{x}}(s)=\frac{k_{p}k_{x}}{\tau_{x}}+\frac{1+k_ {d_{x}}k_{x}}{\tau_{x}}s+s^{2}\\ D_{PD_{y}}(s)=\frac{k_{p}k_{y}}{\tau_{y}}+\frac{1+k_{d_{y}}k_{y}}{\tau_{y}}s+ s^{2}\\ D_{PD_{z}}(s)=\frac{k_{p}k_{z}}{\tau_{z}}+\frac{1+k_{d_{z}}k_{z}}{\tau_{z}}s+ s^{2}\\ \end{array} \right. \tag{18}\]  
\[\left\{ \begin{array}{l}D_{PID_{x}}(s)=\frac{k_{i}k_{x}}{\tau_{x}}+\frac{k_ {p}k_{x}}{\tau_{x}}s+\frac{1+k_{d_{x}}k_{x}}{\tau_{x}}s^{2}+s^{3}\\ D_{PID_{y}}(s)=\frac{k_{i}k_{y}}{\tau_{y}}+\frac{k_{p}k_{y}}{\tau_{y}}s+ \frac{1+k_{d_{y}}k_{y}}{\tau_{y}}s^{2}+s^{3}\\ D_{PID_{z}}(s)=\frac{k_{i}k_{z}}{\tau_{z}}+\frac{k_{p}k_{z}}{\tau_{z}}s+ \frac{1+k_{d_{z}}k_{z}}{\tau_{z}}s^{2}+s^{3}.\\ \end{array} \right. \tag{19}\]  
However, the attitude the characteristic polynomials are given by the following Equations (20) and (21). \[\left\{ \begin{array}{l}D_{PD_{\phi}}(s)=\frac{lk_{p_{\phi}}}{l_{x}}+\frac{ lk_{d_{\phi}}}{l_{x}}s+s^{2}\\ D_{PD_{\theta}}(s)=\frac{lk_{p_{\theta}}}{l_{y}}+\frac{lk_{d_{\phi}}}{l_{y}}s+ s^{2}\\ D_{PD_{\phi}}(s)=\frac{lk_{p_{\phi}}}{l_{z}}+\frac{lk_{d_{\phi}}}{l_{z}}s+s^{2} \\ \end{array} \right. \tag{20}\]  
\[\left\{ \begin{array}{l}D_{PID_{\phi}}(s)=\frac{lk_{i_{\phi}}}{l_{x}}+\frac {lk_{p_{\phi}}}{l_{x}}s+\frac{lk_{d_{\phi}}}{l_{x}}s^{2}+s^{3}\\ D_{PID_{\theta}}(s)=\frac{lk_{i_{\phi}}}{l_{y}}+\frac{lk_{p_{\theta}}}{l_{y}} s+\frac{lk_{d_{\phi}}}{l_{y}}s^{2}+s^{3}\\ D_{PID_{\phi}}(s)=\frac{lk_{i_{\phi}}}{l_{z}}+\frac{lk_{p_{\phi}}}{l_{z}}s+ \frac{lk_{d_{\phi}}}{l_{z}}s^{2}+s^{3}.\\ \end{array} \right. \tag{21}\]  
By identifying the dominators of each closed-loop transfer function with the chosen reference model, we obtain the PD/PID controller gains (RMHC) indicated in Table 1.
## 4 LoRaWAN Issues and Recent Solutions
### Network Security  
The LoRaWAN protocol is adjusted for low energy consumption and is designed to support large networks with large amounts of end devices. Innovative LoRaWAN features involve support for low-power, geolocation, redundant operation, and low-cost applications of IoT. However, the issue of security encompasses several properties and, in particular, the cryptographic methods used to implement security in LoRaWAN require more careful study. The insecurity problem is a major concern due to the resource limitations of the devices in LoRa and in LPWANs in general. The huge number of connected devices and transmitted data, among other factors, cause a highly sensitive security level. The authors of [39] investigated the key management mechanism of LoRaWAN environments and proposed a secure key management architecture, depending on smart contracts and a permission blockchain to improve the security and availability of LoRaWAN networks. The blockchain-based LoRaWAN architecture was modeled via a prototype, using open-source tools and hardware to validate its functionality and effectiveness. Different scenarios were considered for testing and evaluating the performance of the proposed approach. The authors claimed that the proposed scheme achieved similar performance with the standard ChirpStack configuration for various network sizes. The blockchain-based LoRaWAN architecture is also applicable to large-scale networks, where the end nodes are supposed to perform only one procedure of OTA per day. Implementation in a real environment and adding new functions to the key management smart contract by enabling it to process OTAA requests are among the suggested direct future improvements.  
With the aim of providing a secure communication link for LoRaWAN servers, the authors of [59] proposed an S2 KG key generation procedure that has two sessions: join server-related links and symmetric key-based links. One public key and one of the symmetric key cryptographies are used for key creation procedures. Thus, every two servers can generate a unique session key. The generated session key will be used to encrypt the exchanged messages between each server pair and will be updated periodically based on the same procedure. The proposed S2 KG procedure can provide mutual authentication, privacy, and message integrity. It also protects against replay eavesdropping attacks. However, a management policy for session keys needs to be added to establish highly secure LoRaWAN communication links. Moreover, the study focused on security between servers but ignored the security between the join server and manufacturer, which needs to be addressed in any future research.
# A Survey of Algorithms and Systems for Evacuating People in Confined Spaces
## 3 Evacuation Wayfinding Algorithms
### On-Line Evacuation Wayfinding Algorithms  
Contrary to off-line evacuation wayfinding algorithms, which aim at optimising the design of crowded sites or generating evacuation plans for facility managers via developing various crowd behaviour models and computer simulations, on-line evacuation wayfinding concentrates on combining mathematical models [78] or algorithms [2; 79] with underlying sensing, communication and computational devices to guide evacuees out of hazardous environments in a real-time manner.  
Since on-line evacuation wayfinding algorithms require real-time information exchanges with the hazardous environment, these algorithms are usually integrated into evacuation wayfinding systems. With the development of evacuation wayfinding systems, which are detailed in Section 2, various evacuation wayfinding algorithms have been proposed such as network flow-based algorithms [80; 81; 82; 83], geometric algorithms [84; 85], queueing model-based algorithms [86; 87; 88; 89; 90; 91; 92], potential-maintenance algorithms [18; 20; 93], biologically-inspired algorithms [94; 95; 96], routing protocol-based algorithms [97; 98; 23; 99] and prediction-based algorithms [100; 101; 102; 103; 104].
## 3 Energy Efficient Schemes for Green C-IoT
### Resource Allocation  
Owing to different priorities, QoS requirements, and residual power levels, the energy efficient resource allocation schemes will play a key role in reducing power consumption and improving EE performance [60]. The resource to be allocated covers many aspects, such as computation, power, frequency, and time resources. There is a myriad of mature scientific research towards resource allocation in cellular networks; however, they cannot be directly applied to the C-IoT scenario, since these works fail to capture the massive connectivity characteristic of the C-IoT. Moreover, the time varying and random traffic arrival properties should also be carefully considered [26].  
When designing the resource allocation schemes, ISPs are supposed to pay attention to the QoS-guaranteed and global dynamic resource allocation. The formal scheme is based on the design criterion of the QoS requirement, where the computation, power, frequency, and time resources are allocated to satisfy the minimum QoS requirement of some specific important applications. The latter scheme, from the global view, aims to make sure the overall network is able to run normally and energy efficiently, while capturing the time-varying and mobile features of the observed network. However, the stringent requirements of some applications may be ignored.
# Micro-Focusing of Broadband High-Order Harmonic Radiation by a Double Toroidal Mirror
## 2 Experimental Setup  
### Beamline  
The intense XUV beamline [8] (Figure 1a) is driven by a Ti: sapphire chirped pulse amplification laser system delivering pulses at a 10-Hz repetition rate with duration of 45 fs, energy around 20 mJ,and a beam diameter of 24 mm (FWHM). The vertically polarized beam, apertured to a diameter of 22 mm, is focused by a 8.7-m focal length lens into a 60-mm-long gas cell statically filled with argon. Prior to focusing, residual aberrations of the infrared (IR) beam are compensated by a deformable mirror operating in closed loop with an IR wavefront sensor. The mirror also allows for adjustment of the focal position with respect to the cell in 1-mm steps. The aperture size, the gas pressure, the position of the cell, and the laser focus are optimized for the maximum of the XUV signal. The IR and XUV radiation are then co-propagating towards a fused silica (FS) plate placed at a 10\({}^{\circ}\) grazing angle 4 m after the gas cell. The plate is given anti-reflection (AR) coating to transmit the IR while the XUV beam is reflected. The residual IR radiation is blocked by a 200-nm-thick aluminum filter. In the next chamber, a gold-coated mirror on a rotation stage is used to fold the XUV radiation towards an XUV flat-field spectrometer. A typical HHG spectrum is shown in Figure 1b. It is composed of nine harmonic orders, from 15 to 31, with photon energies from 23 to 45 eV. Their spatial profiles in the far field are plotted in the inset. The divergence is estimated to 0.33 mrad (FWHM) for harmonic 19. The transmission of the beamline from the generation cell to the entrance of the application chamber is estimated to be 22%, for XUV photons with energies from 20 to 50 eV. This value is calculated using the measured transmission of the Luxel aluminum filter (34%) and the calculated reflectivity of the AR-coated fused silica plate (the top layer of the coating is SiO\({}_{2}\) with reflectivity of 65%). During nonlinear experiments, the XUV beam is focused into a target gas jet, and a double-sided ion/electron spectrometer is placed around the focus as shown in Figure 1.
## 2 Materials and Methods
### Preparation of Emulsions  
The oil-in-water (O/W) emulsions were formulated at room temperature using a two-step homogenization process. In the first stage, LA (57 \(\upmu\)L) and aqueous phase (95 mL) were mixed using a hand-held homogenizer CAT X120 fitted with a T10 shaft for 5 min (RPM speed approximately 22,000), followed by homogenization using a Sonoplus sonicator (Bandelin, Berlin, Germany) fitted with a TS109 probe in the following conditions: a duration of 4 min, with 10 s of action followed by 5 s of rest cycles, and an amplitude of 60%. The total energy of sonication was 6104 kJ. The final LA content was estimated as 0.60 \(\upmu\)L/mL.  
The relationship of oil to water was estimated experimentally to enable spectroscopic examination. Since one of the purposes of this study was to measure the fatty acid oxidation dynamics in water with the use of a spectrofluorometer (see Section 2.7 \"Oxidation tests\"), it was crucial to use an emulsion system that did not exhibit significant scattering of incident light, which could affect the results and their interpretation. Therefore, we prepared a set of emulsions with different oil-to-water ratios and checked them for scattering. The most suitable was an emulsion with an LA concentration in water of 0.60 \(\upmu\)L/mL.  
To examine the extracts' impact on LA time and oxidative stability, we maintained all the experimental conditions, including concentrations, added volumes, etc., as identical to those in our earlier study [26] whenever possible. Therefore, the initial aqueous extract solutions were prepared in the same way as previously, namely, each crude (solid state) extract was dissolved in water to obtain the solution at a concentration of 10 mg/mL. Regarding the possible synergistic effect during autooxidation, gallic acid (GA) or tocopherol (Toc) were added during emulsion formulations. For this reason, the measurements in emulsions were performed with and without added GA. Then, 15 \(\upmu\)L of each aqueous extract (or aliquot volume of GA water solution or Toc methanol solution, to obtain a final concentration of this compound equal to 12 \(\upmu\)M) was added to 2 mL of emulsion and left for 20 min under stirring (magnetic stirrer at the rate of 150 RPM) without further agitation. The non-doped emulsion was considered as the reference sample.