esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fiber-Optic Six-Axis Force Sensor Based on a 3-UPU-Compliant Parallel Mechanism
## 2 Bending-Sensitive Optical Fiber
### Encapsulation of Bending-Sensitive Optical Fiber  
In order to measure the force applied to the force sensor, the fiber needs to be arranged on the elastic body of the force sensor to detect its deformation. Due to the presence of the sensitive region, the fiber becomes more fragile in this area and needs to be protected by encapsulation. Additionally, to ensure the fiber bends in a predetermined direction, it is pre-bent during encapsulation. According to the above requirements, the material used for encapsulating optical fibers must be flexible [19]. In this study, the fiber encapsulation module (FEM) is introduced to facilitate the arrangement of optical fibers.  
Liquid silicone (Ecoflex00-30) made by Smooth-On Company (Macungie, PA, USA) is used as the base material to encapsulate the fiber. The molding of FEMs is achieved by pouring liquid silicone into a pre-made mold and curing it. The pre-made mold consists of upper and lower parts with identical curved arcs. There is a semicircular groove with the same diameter as the optical fiber on both upper and lower mold parts. The semicircular grooves are used to fix the optical fiber. When fixing optical fibers, the surface of the fiber sensitive region should be perpendicular to the bending direction of the curved arc as much as possible. The photograph of FEM is shown in Figure 2. In Figure 2, the optical fiber has the same bending curvature as the FEM, which ensures that the optical fiber can measure the deformation curvature of FEM.  
To verify the protective effect and strength enhancement effect of FEM on optical fiber, the bending experiments are carried out. In the experiments, the fiber breakage occurs in the sensitive region when the bending radius of the fiber is around 30 mm, while the  
Figure 1: Schematic diagram of the sensitive region [14].  
Figure 2: Photograph of fiber encapsulation module.  
breakage of fiber in FEM occurs when the bending radius of FEM is around 10 mm. This result indicates that FEM effectively protects the fiber and improves its strength.  
The calibration experiments were conducted on the FEM. The experimental results are linearly fitted by using the least squares method. The calibration curve between the output of fiber optic and the deformation curvature of FEM is shown in Figure 3, and the corresponding curve-fitting equation can be expressed as:  
\[U=0.0795C-5.35375 \tag{2}\]  
In Figure 3, linearity \(L_{o}\) of calibration curve can be expressed as:  
\[L_{o}=\frac{\Delta U_{se}}{U_{FS}}\times 100\% \tag{3}\]  
where \(\Delta U_{se}\) is the maximum deviation between the actual output and the fitted line, \(U_{FS}\) is the full output range of fiber. According to Figure 3, the linearity of FEM is 2.3%.
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
###### Abstract  
Traditional statistical, physical, and correlation models for chaotic time series prediction have problems, such as low forecasting accuracy, computational time, and difficulty determining the neural network's topologies. Over a decade, various researchers have been working with these issues; however, it remains a challenge. Therefore, this review paper presents a comprehensive review of significant research conducted on various approaches for chaotic time series forecasting, using machine learning techniques such as convolutional neural network (CNN), wavelet neural network (WNN), fuzzy neural network (FNN), and long short-term memory (LSTM) in the nonlinear systems aforementioned above. The paper also aims to provide issues of individual forecasting approaches for better understanding and up-to-date knowledge for chaotic time series forecasting. The comprehensive review table summarizes the works closely associated with the mentioned issues. It includes published year, research country, forecasting approach, application, forecasting parameters, performance measures, and collected data area in this sector. Future improvements and current studies in this field are broadly examined. In addition, possible future scopes and limitations are closely discussed.  
chaos; forecasting; hydrological systems; neural networks; oil and gas; power and energy; prediction; time series +
Footnote †: journal: Computer Physics Communications
## 1 Introduction
This paper aims to provide an overview of the numerical setup for CFD prototyping of small pressure-swirl atomizers. The data presented here are validated by a comprehensive experiment performed in a wide range of \(Re\) from 1000 to 4000, which corresponds to a 16-times increase \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author** & **Software** & **2D/3D** & \begin{tabular}{c} **Transient** \\ **/Steady** \\ \end{tabular} & \begin{tabular}{c} **Turbulence** \\ **Model** \\ \end{tabular} & **Interface** & \(Re\) \\ \hline [PERSON] [17] & Fluent & 2D, 3D & Laminar & VOF Geo-rec. & 1000 \\ \hline [PERSON] [18] & OpenFOAM & 3D Full & Transient & Implicit LES & VOF Geo-rec. & 420–5300 \\ \hline [PERSON] [12] & & 3D Full & Transient & \begin{tabular}{c} LES, RSM, \(k\)-\(\varepsilon\) \\ RNG \\ \end{tabular} & VOF & 1700–3800 \\ \hline [PERSON] [19] & Fluent & 2D & Transient & RSM & VOF PLIC & \(7\times 10^{3}\)–\(2\times 10^{4}\) \\ \hline [PERSON] [20] & Fluent & 2D & & RSM & VOF Geo-rec & \(5\times 10^{3}\)–\(5\times 10^{4}\) \\ \hline [PERSON] [6] & Fluent & 2D & Steady & Laminar & VOF & \(10^{4}\)–\(10^{5}\) \\ \hline [PERSON] [21] & & 3D & Steady & \(k\)-\(\varepsilon\) & VOF & \(\sim\)\(10^{4}\) \\ \hline [PERSON] [11] & Fluent, CFX & 3D & Laminar, LES & VOF & \(10^{4}\)–\(4\times 10^{4}\) \\ \hline [PERSON] [22] & CFX & 3D & Steady & \(k\)-\(\varepsilon\) & VOF & \(>\)\(10^{4}\) \\ \hline [PERSON] [15] & Fluent & 2D, 3D & Transient & Laminar & VOF HRIC & \(1.2\times 10^{4}\) \\ \hline [PERSON] [3] & OpenFOAM & 2D & Laminar, RSM & \
\begin{tabular}{c} VOF LS \\ coupled \\ \end{tabular} & \(\sim\)\(1.6\times 10^{4}\) \\ \hline [PERSON] [13] & & 2D &
\begin{tabular}{c} Steady \\ /Transient \\ \end{tabular} & Laminar, RSM & Level set & \(1.8\times 10^{4}\)–\(4\times 10^{4}\) \\ \hline [PERSON] [23] & Fluent & 2D, 3D & Laminar & VOF HRIC & \(\sim\)\(2\times 10^{4}\) \\ \hline [PERSON] [14] & & 2D & Transient & \(k\)-\(\varepsilon\) RNG & VOF & \(\sim\)\(2.5\times 10^{4}\) \\ \hline [PERSON] [24] & Fluent & 2D & Laminar & VOF & \(\sim\)\(4\times 10^{4}\) \\ \hline [PERSON] [16] & CCM+ & 3D & Steady & \(k\)-\(\varepsilon\), \(k\)-\(\omega\) & VOF HRIC \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of published numerical setups. in the inlet pressure, \(p_{l}\). The experimental data in raw form are available as supplementary material to this paper and can be used for the validation of numerical or analytical models.
# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 3 Modeling Process
### GAT-Based Network  
#### 3.2.1 Graph Attention Layer  
The clustering graph is described as a large-scale sparse matrix. The neighboring nodes typically possess two characteristics: geographical proximity and alignment within a satellite's visual strip, which are determined using the observation mode of satellites. GAT serves as an effective approach for processing clustering graph data. In comparison with GCN, GAT offers higher computational efficiency and enables dynamic adjustment of node importance weights. As shown in Figure 2, each graph attention layer consists of the attention and the aggregation operations [38]. Task features have been embedded based on the clustering graph \(G=\langle V,E\rangle\). The input to our layer is a set of graph nodes \(V=\{v_{1},v_{2},\ldots,v_{n}\}\), \(v_{i}\in\mathbb{R}^{F}\), where \(n\) is the number of nodes and \(F\) is the number of features in each node; it is described in Section 3.3.1. After the processing of the graph attention layer, we can obtain an output vector \(V^{\prime}=\{v_{1}^{\prime},v_{2}^{\prime},\ldots,v_{n}^{\prime}\}\), \(v_{i}^{\prime}  
In the attention operation, a weight matrix, \(W\in\mathbb{R}^{F^{\prime}\times F}\), is first performed on the input. Then, we can calculate the attention parameter:  
\[\alpha_{\mathrm{ij}}=\frac{\exp(\mathbf{LeakyReLU}(a^{T}[Wv_{i}\parallel Wv_{j}]) )}{\sum_{v_{k}\in N_{v_{i}}}\exp(\mathbf{LeakyReLU}(a^{T}[Wv_{i}\parallel Wv_{ k}]))} \tag{9}\]  
where \(\parallel\) represents the concatenation operation and \((\cdot)^{T}\) represents the transpose of a vector, \(N_{v_{i}}\) is the one-hop neighborhood of node \(v_{i}\), \(a\) is the mapping vector, and LeakyReLU is a nonlinear activation.  
The purpose of the aggregation operation is to aggregate the embedding vector according to the attention parameters. The multi-head attention mechanism is applied to enable node embeddings to stably represent the node \(v_{i}\), resulting in the following output:  
\[v_{i}^{\prime}=\parallel_{k=1}^{K}\sigma(\sum_{v_{j}\in N_{v_{i}}}\alpha_{ij}^ {k}W^{k}v_{j}) \tag{10}\]  
where the weight matrix of transformation \(W^{k}\) and attention coefficient \(\alpha_{ij}^{k}\) are computed using the \(k\)-th independent attention mechanism among a total of \(K\) mechanisms. Subsequently, a nonlinearity \(\sigma\) is applied.
# Parallel Continuum Delta: On the Performance Analysis of Flexible Quasi-Translational Robots
## 2 Materials and Methods
### Delta-Type Flexible Parallel Manipulators  
One of the most successful designs of a Lower Mobility Parallel mechanism is the family of translational parallel mechanisms called Delta [40]. An articulated parallelogram is used in each of the three limbs to introduce a constraint on the rotation of the end-effector about the normal to the parallelogram's plane. This generates a coupled restriction of the three possible rotations, allowing a pure translational motion of the end-effector. A short side of the parallelogram is attached to the end-effector while the other side is translated with an actuator of a variety of types, configuring a family of machines, namely, vertical or horizontal Delta, Keops-Delta and rotational Delta.  
In order to design a flexible alternative to those rigid counterpart translational mechanisms, we have to bear in mind that a _hard_ geometric constraint is not possible in such a flexible device. By introducing flexible parallelograms, such as those in the Delta-types parallel continuum manipulators in Figures 3 and 5, the motion of the end-effector is not fully restricted, allowing for rotations due to the flexibility of the rods, as shown with the  
Figure 4: (**a**) Translational and (**b**) rotational ellipsoids of flexible Keops-Delta \(3\underline{p}FFSS\).  
rotational ellipsoid in Figure 4b. It is therefore relevant to analyze the workspace of these manipulators to check which zones show an optimal quasi\(-\)translational behaviour.  
The Keops-Delta, shown in Figure 3, and other designs known as vertical or horizontal Delta are quite similar. The only difference between them lies in the whether the linear actuators are arranged in a vertical or horizontal direction, in the cases of the vertical or horizontal Delta, and in a pyramidal arrangement in the Keops-Delta. Thus, their kinematic analysis is the same, but they differ only in the geometrical arrangement of their linear guides. Another version that was analyzed is the rotational Delta, shown in Figure 5, in which the actuators are rotary motors. A rigid hinged parallelogram is inserted between the motor and the clamping element for the elastic rods' proximal-ends \(A_{i}\), so that this one performs a circular trajectory, which remains parallel to the fixed horizontal plane at all times.  
The flexible parallelogram in each kinematic chain is formed by two Cosserat flexible rods that are attached to the end-effector by spherical joints at their distal-ends \(B_{i}\), while the proximal-ends \(A_{i}\) are clamped to the actuated element with clamping orientation to be kept in a vertical direction.  
For the aforementioned Delta types, the direct and inverse kinematic problems, respectively, are solved as explained next.
# Quantum Circuit-Width Reduction through Parameterisation and Specialisation
## 9 Demonstration of Reduction of Integer Divider
### Specialised Integer Divider  
To specialise the circuit for particular values of the divisor, the specialisation function (specialiseQCircForQubitValues) is used, applying the static analysis methods discussed in Section 8.2 to reduce the divisor qubits. First the full circuit is created, and then passed to the specialiser, as shown in Figure 25. This high-order function expects three arguments, including the qubits over which to specialise, expressed as a QReg; a bit string indicating their initial values; and the circuit to specialise. The sizes of the register and the bit string must match and the circuit must not contain gates which change the state of the specialisation qubits, otherwise an error will be thrown. In the example shown, the function generating the specialised circuit now takes an integer representing the value of \(b\) (the divisor) instead of a register bReg. On line 7, an intermediate register bReg is created to pass to the generator of the full function on line 8. On line 9, the bit register is prepared by calling the utility function paddedTwoComplementBitString to obtain the 2's complement representation of \(-b\) (as required by the long division algorithm). Finally, the specialisation function is called to generate the qubit-reduced circuit.  
Figures 26 and 27 show the first blocks of a parameterised divider that has been fully specialised on the divider qubits. These are the initial comparison and subtraction blocks which set the most significant out qubit. Figure 26 shows the block specialised for a divisor value of 9, while Figure 27 shows the specialised circuit for a divisor value of 11 as illustrative examples.
# Formation Control Technology of Fixed-Wing UAV Swarm Based on Distributed Ad Hoc Network
## 4 Waypoint-Based Formation Obstacle Avoidance Algorithm  
The traditional formation obstacle avoidance method uses an artificial potential field method for obstacle avoidance or an intelligent algorithm to search for waypoints. Still, considering the real-time nature of obstacle avoidance and engineering applications, the artificial potential field method needs to participate in the design of the control system. When facing a high-dynamics UAV, it is challenging to update flight parameters in real time, as it presents significant errors and low reliability. Using the intelligent algorithm to plan trajectories, if high precision is required, planning the trajectory of a single UAV is still too slow, and if there are many planned waypoints, it cannot meet the dynamic constraints of the UAV.  
In this paper, the intelligent path search algorithm was used to search for obstacle avoidance channels through which UAVs could pass, and the UAV swarm was divided into multiple smaller formations according to the number of UAVs that could pass through the avoidance channel.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 7 Challenges and Future Research Directions  
In this section, we discuss challenges and future research directions of FANETs with UAVs.
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
###### Abstract  
Since unmanned aerial vehicles (UAVs), such as drones, are used in various fields due to their high utilization and agile mobility, technologies to deal with multiple UAVs are becoming more important. There are many advantages to using multiple drones in a swarm, but, at the same time, each drone requires a strong connection to some or all of the other drones. This paper presents a superior approach for the UAV network's routing system without wasting memory and computing power. We design a routing system called the geolocation ad hoc network (GLAN) using geolocation information, and we build an adaptive GLAN (AGLAN) system that applies reinforcement learning to adapt to the changing environment. Furthermore, we increase the learning speed by applying a pseudo-attention function to the existing reinforcement learning. We evaluate the proposed system against traditional routing algorithms.  
U 2023 7 387 10.3390/drones/7060387
# Design of Constant Speed Controller for Hydraulic Retarder Based on Robust Control
## 2 Dynamic Model of Vehicle Driving Downhill
### Hydraulic Retarder Model
The vehicle downhill constant-speed control flowchart is shown in Figure 3, where \(P_{r}\) is the braking power of the retarder working cavity partially filled with oil, \(P_{\rm{max}}\) is the maximum braking power of the retarder, and \(P_{b}\) is the braking power output of the retarder. \(P_{c}\) is the heat dissipated power provided by the cooler of the cooling circulation system. The maximum heat dissipation power that can be provided by the cooler of the cooling circulation system in this research is 500 kW.
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Raw Data (No Selection)**} & \multicolumn{6}{c}{**Followed by Proposed MFS Procedure**} \\ \hline
**Task** & **Resolution** & **ACC** & **BACC** & **SEN** & **SPE** & **AUG** & **ACC** & **BACC** & **SEN** & **SPE** & **AUC** \\ \hline \multirow{4}{*}{AD/HC} & 2 mm & 83.28 & 83.28 & 85.62 & 80.95 & 0.83 & 91.13 & 91.12 & 91.10 & 91.16 & 0.91 \\  & 4 mm & 82.93 & 82.94 & 85.61 & 80.27 & 0.82 & 88.73 & 88.73 & 89.04 & 88.43 & 0.89 \\  & 8 mm & 83.96 & 83.96 & 86.30 & 81.63 & 0.84 & 88.05 & 88.06 & 91.78 & 84.35 & 0.87 \\  & 16 mm & 81.23 & 81.23 & 82.88 & 79.59 & 0.80 & 83.28 & 83.28 & 84.93 & 81.63 & 0.82 \\ \hline \multirow{4}{*}{pMCI/sMCI} & 2 mm & 50.93 & 50.89 & 51.79 & 50.00 & 0.51 & 74.77 & 74.84 & 73.21 & 76.47 & 0.74 \\  & 4 mm & 50.93 & 50.89 & 51.78 & 50.00 & 0.51 & 71.96 & 71.90 & 73.21 & 70.58 & 0.70 \\  & 8 mm & 48.60 & 48.62 & 48.21 & 49.02 & 0.50 & 70.56 & 70.69 & 67.86 & 73.53 & 0.72 \\  & 16 mm & 49.07 & 49.06 & 49.11 & 49.02 & 0.48 & 62.15 & 62.13 & 62.50 & 61.76 & 0.60 \\ \hline \multirow{4}{*}{sMCI/AD} & 2 mm & 59.84 & 59.08 & 54.90 & 63.27 & 0.54 & 81.12 & 80.41 & 76.47 & 84.35 & 0.77 \\  & 4 mm & 55.82 & 54.03 & 44.12 & 63.95 & 0.50 & 71.48 & 71.49 & 71.56 & 71.42 & 0.69 \\  & 8 mm & 52.61 & 50.71 & 40.19 & 61.22 & 0.47 & 71.89 & 70.48 & 62.75 & 78.23 & 0.74 \\  & 16 mm & 52.61 & 50.56 & 39.22 & 61.90 & 0.46 & 65.46 & 64.44 & 58.82 & 70.07 & 0.64 \\ \hline \multirow{4}{*}{pMCI/HC} & 2 mm & 79.84 & 79.28 & 83.56 & 75.00 & 0.79 & 81.78 & 81.40 & 84.25 & 78.57 & 0.80 \\  & 4 mm & 78.68 & 78.04 & 82.87 & 73.21 & 0.79 & 79.45 & 79.04 & 82.19 & 75.89 & 0.79 \\ \cline{1-1}  & 8 mm & 78.68 & 78.04 & 82.88 & 73.21 & 0.79 & 81.40 & 81.27 & 82.19 & 80.36 & 0.80 \\ \cline{1-1}  & 16 mm & 69.77 & 69.44 & 71.92 & 66.96 & 0.68 & 77.91 & 77.25 & 82.19 & 72.32 & 0.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The classification performance of the computer-aided diagnosis (CAD) system using all features and of the proposed merit-based feature selection (MFS) procedure with respect to different spatial resolutions through 10-fold cross-validation. Figure 1: Sorted voxel intensities for a spatial resolution of 2 mm. The color bar shows the accuracy (%) of each individual voxel. AD: Alzheimer’s disease; HC: healthy controls; sMCI: stable mild cognitive impairment; pMCI: progressive mild cognitive impairment. Figure 3: Classification results of the proposed CAD framework for the four pairs of clinical groups (i.e., AD/HC, pMCI/sMCI, pMCI/HC, and sMCI/AD) with respect to the different isotropic spatial resolutions. (**A**) accuracy (ACC) (%), (**B**) sensitivity (SEN) (%), (**C**) specificity (SPE) (%), and (**D**) area under the curve (AUC). Figure 2: The receiver operating characteristic (ROC) curves of the proposed CAD framework with respect to the different isotropic spatial resolutions. (**A**) Alzheimer’s disease (AD)/healthy control (HC), (**B**) stable mild cognitive impairment (sMCI)/progressive MCI (pMCI), (**C**) sMCI/AD, and (**D**) pMCI/HC.
## 2 Results and Discussion  
### Bacteria Identification  
Isolated bacteria strains were identified based on the Raw and Main Spectra (MSP), using a MALDI-TOF/TOF-MS (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry) system. All of the isolated bacterial strains were identified with the high confident level, >1.999: 2.01 for _E. coli_, 2.00 for _E. faecalis_, and 2.03 for _K. pneumoniae_ and _P. mirabilis_. The phyloproteomic relationships of the isolated bacterial strains revealed that these are closely related to the reference strain identified by MALDI Biotyper Compass platform (Figure 1).
## 3 Results
### Hydrodynamic Analysis
#### 3.2.1 Thrust Production
The comparison of instantaneous thrust force, \(F_{T}\) during one heaving cycle between different \(f^{*}\) is shown in Figure 7. The results are computed from a heaving cycle that reaches a periodic steady state. Due to the symmetric heaving motion of the plate, the result of \(F_{T}\) in Figure 7 shows two distinct peaks in a heaving cycle. A slight value of negative thrust is observed around the reversal phase of leading-edge motion in all cases. The peak value of negative thrust is also affected by the \(f^{*}\). The maximum positive thrust occurs after the start of each half  
Figure 6: Displacement of the trailing edge relative to the leading edge, \(z_{t}-z_{r}\), for different \(f^{*}\) in a flapping cycle. Figure 7: Instantaneous thrust variation with different \(f^{*}\) in a flapping cycle. cycle where the maximum amplitude of trailing edge appears in all cases. The effect of \(f^{*}\) are clearly observed in the instantaneous thrust production. The largest fluctuation of \(F_{T}\) is observed in the case of \(f^{*}\) = 1.2. Two significant areas of interest are seen in Figure 7; one is at a point marked with a red square, a phase instant when a peak value of positive thrust appears in each case and the other one is at a point marked with a black square, a phase instant when a peak value of negative thrust appears in each case. When the thrust force \(F_{T}\) in each case is non-dimensionalized by 0.5 \(U^{2}pa\), the corresponding thrust coefficient \(C_{T}\) is obtained for each case. The variations of mean thrust coefficient with different \(f^{*}\) are shown in Figure 8. Since the thrust coefficient is inversely proportional to the square of the maximum translational velocity of the leading edge, \(U=2\pi fA_{r}\), the mean thrust coefficient shows a decreasing trend as the flapping frequency is increased. Interestingly, the trend of the mean thrust coefficient is nearly linear and well correlated with the declining trend of trailing edge's amplitude through the selected frequency range. This is consistent with our previous finding for having rectangular plate, in which the maximum thrust force coefficient was related to the maximum non-dimensional amplitude of the trailing edge [29].