esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Detailed Proposed Technology and Test Results
### Development Plan for Vision-Based AI Algorithms Enabling Maintenance and Continuous Management
#### 4.3.1 Cropping Technique to Reduce Learning Data Acquisition Time  
The period for acquiring the training images must be reduced to reduce the development period of the deep learning algorithm. However, in industrial settings where product change cycles are fast, it is practically impossible to acquire learning images within a short period. Therefore, a method must be developed to acquire learning data within a short period. In this study, we aimed to maximize the reuse of learning data even when the product changes. Specifically, image pre-processing was performed to crop the inspection parts as large as possible, to exclude the highly variable vehicle structure and color from the images. This ensures that learning and inspection images can be reused even if the vehicle is changed during automobile production. Thus, even if the vehicle is changed, since the structure of parts such as the wiring, clips, connectors, and bolts assembled in the vehicle is similar, a deep learning inspection algorithm can be developed by using the learning data collected from previous vehicles. Figure 7 shows that visual inspection can be applied quickly during new car production by using the deep learning algorithm created for the previous car since the type of clip used for fixing the wiring is similar. Moreover, since the learning data are continuously accumulated and diversity is secured, the performance of the algorithm can be continuously upgraded. To verify this, when launching a new  
Figure 6: Image brightness correction using histogram matching algorithm.  
car with a similar but not the same part type, a short algorithm development test was conducted through transfer learning after securing the minimum quantity of data for the new parts that are similar. The detection performance decreases when performing deep learning with only a small number of new part images of less than 20, as shown in Figure 8. However, the results of transfer learning using the existing algorithm of similar parts exhibit higher accuracy.
# Acoustic Parametric Signal Generation for Underwater Communication
## 3 Experimental Set-Up
### Characteristics of the Transmitter  
The knowledge of the transmitter behavior is essential for the proposed application. Figure 2b, shows the Transmitting Voltage Response (TVR), which is the ratio of the pressure signal emitted to the applied voltage, for Airmar P19. It has been measured at the lab using tone bursts at different frequencies. The carrier frequency \(f_{o}\) of the modulation signal corresponds to the resonance frequency at 200 kHz and the TVR at this frequency is 167 dB re \(\upmu\)Pa/V@1 m. In Figure 2c, the values of sound pressure level reached in our experiment are presented, with a value for the frequency of 200 kHz of 195 dB re \(\upmu\)Pa@1 m. The directivity of the transducer is another important parameter for the proposed study. The transducer presents a beamwidth (@\(-\)3 dB) at 200 kHz of 11\({}^{\circ}\).
## 4 Energy Management Systems
### Learning-Based Energy Management Systems
#### 4.3.1 Q-Learning  
In 1989, [PERSON] et al. introduced the Q-learning algorithm [121], the primary aim of this algorithm centers on the maximization of the Q value, signifying that each executed action yields the highest reward. As depicted in Figure 23, the initial step involves the agent receiving a random state denoted as \(c\) from the environment following the Q-table's initialization. Subsequently, the agent selects the corresponding action \(x\) from the Q-table and proceeds to engage with the environment, leading to the acquisition of a feedback signal \(e\). The agent responds to \(e\) by actively updating the Q-table in real time. Simultaneously, the agent obtains the subsequent state \(c^{\prime}\) from the environment, thus triggering the commencement of a new iteration that endures until every action attains maximum reward optimization.
## 1 Introduction
For the first time, a design of the LSPR-structure with azimuthally patterned (grooved) metal disks (see Figure 1a) was proposed and studied in the microwave region [13; 17]. The grooves were filled with a dielectric material and the surrounding medium was air. For the efficient confinement of the surface mode on the structure, the period of grooves at the outer radius \(R\) must be much less than the radiation wavelength in the air: \(G=2\pi\ R/n<<\lambda_{0}\), where \(n\) is a total number of grooves. Lately, numerical simulations in the THz region for a periodic structure of azimuthally grooved golden disks on dielectric substrates [18; 19; 20; 21] showed that high-order (multipole) electric and magnetic LSPRs can be excited under oblique illumination or when combining the grooved disk with a C-shaped resonator. As compared to dipole resonances, the multipole ones have higher \(Q\)-factors and are more sensitive to the presence of dielectric materials on the structure, i.e., more promising for sensing applications. An azimuthally grooved disk supports the resonances up to the asymptotic frequency \(f_{a}\), which depends on the groove depth \(h\) and the refractive index \(n\) in the groove as \(f_{a}=c/(4h\cdot n)\), where \(c\) is the light speed [22]. An increase in the depth \(h\) shifts the asymptote frequency (and the resonance spectrum) towards lower frequencies and consequently, enhances the confinement of LSPR modes. Thus, the lowest resonant frequency is governed by the size of the disk. To improve the mode confinement of spoof LSPRs, it was suggested to use long spiral grooves (Figure 1b) [23]. The extension of spirals allows shifting the LSPR resonances towards longer wavelengths, exceeding by far the structure size, which yields a stronger EM field localization near the surface. Thus, a spiral disk design is expected to be very promising for sensing applications at THz, whereas this kind of structures is still understudied. It is worth mentioning that numerical calculations, theoretical analysis and experimental tests of spiral grooves were reported in literature mainly for the microwave region [22] and we know only one paper on the experimental implementation of a simple design of spiral disks in the THz range [24]. In this paper, we present the results of numerical simulations and the experimental testing of spiral metal disks patterned on a dielectric substrate to excite electric and magnetic multipole LSPRs in the spectral region of 0.2-0.5 THz. The packing density of the disks in the array, the addition of a C-shaped resonator, the asymmetric design of the spirals and the oblique illumination were studied. The testing of the optimal designs showed that the spiral metal disks are very promising for the THz sensing of thin-film analytes.
# Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator
## 3 Kinematics Modeling and Control Methodology  
### Overall Structure Design of CHSM  
High flexibility of avoiding obstacles, good loading capacity, and easy maintenance should be taken into consideration for the structural design of snake-like manipulators to satisfy the requirement of tasks under complicated working circumstances. The proposed overall structure design of CHSM is presented in Figure 3. A separating structure is adopted in dividing the power and motion subsystem on purpose protecting the power subsystem away from harsh working circumstances but remaining the motion subsystem deploying its task normally. The motion subsystem is composed of repeated tubular structures connected to a universal joint by two fixed endplates as shown in Figure 4. Driving cables from the power subsystem get through a series of piercing holes which are uniformly distributed along the circumferential periphery in the shell of the tubular structure. Three cables are attached on the rear endplate, equally spaced at 120\({}^{\circ}\) and used to drive this tubular unit meanwhile the others are maintained piercing to serving for followed tubular units respectively as shown in Figure 5. In the power subsystem, the cables are attached to the sliders which are mounted on the screw nut seat of the leading screw and drive to control the pose of each tubular by rotating upon the universal joint with 2-DOF. The motion subsystem is assembled flexibly to conveniently extend the number of joint units according to diverse task demands.  
Figure 2: Illustration for the flow graph: 1� Extract sample \(Z_{t}\) from \(I_{t}\) at previous position \(P_{t-1}\) and scale \(S_{t-1}\) with multi-channel features; 2� Compute translation correlation scores \(y_{trans,t}\) according to Equation (5) and find the current estimated target position \(P_{t}\) where the maximum located in \(y_{trans,t}\); 3� Extract sample \(Z_{sl}\) from \(I_{t}\) at current position \(p_{t}\) and scale \(S_{t-1}\); 4� Compute scale correlation scores \(y_{scale,t}\) according to Equation (6) and find the current estimated target scale \(S_{t}\) where the maximum located in \(y_{scale,t}\); 5� Update translation model and scale model according to Equations (7)–(9), then save the current position and scale as the latest previous estimate.
## 3 Results and Discussion
### Catalytic Activity: Transesterification of Glycerol with Ethylene Carbonate
#### 3.3.1 Effect of the Catalyst Concentration  
The effect of the dosage of the catalyst on the yield of glycerol carbonate was studied by varying the concentration of RC, by selecting values as 2%, 4%, and 6% at constant EC/Gly molar ratio of 2/1 and reaction temperature of 80 \({}^{\circ}\)C; being the reaction runs performed for 4 h. The GC yields and initial overall reaction rate are displayed in Figure 10. As shown in Figure 10A, the glycerol conversion and GC yield increased constantly with the increase of the catalyst amount in the range from 2% to 6% \(w/w\). The glycerol conversion was zero at 4 h when no catalyst was added to the reaction mixture (negative control); whereas it reached 47% with 2% \(w/w\) and 52% with 6% of the catalyst. These results indicate that the catalyst amount of 2.0% was enough for the transesterification of glycerol with ethylene carbonate. In theory, a higher-concentration catalyst will contain a greater number of active sites, so it should lead to a higher conversion of glycerol.  
Figure 10: (**A**) Catalyst load effect on the conversion of Gly as a function of reaction time, where \(\blacksquare\) represents 2% catalyst loading; \(\bullet\) 4% catalyst loading and \(\blacktriangle\) a 6% catalyst loading. (**B**) Initial observed reaction rates for these experiments. Common experimental conditions: 80 \({}^{\circ}\)C and CE/Gly molar ratio equal to 2.  
Figure 9: Variation of concentration of reagents and products with time at tested temperatures: (**A**) 80 \({}^{\circ}\)C; (**B**) 90 \({}^{\circ}\)C; (**C**) 100 \({}^{\circ}\)C and (**D**) 110 \({}^{\circ}\)C.  
However, the distribution of the reagents on the catalyst surface depends also on the amount of solid available, while catalysis in this case are a surface phenomenon where saturation phenomena can take place, thus limiting the maximal reaction rate [10; 14]. Figure 10B shows the variation of initial reaction rate with catalyst concentration to illustrate this point. It is observed that a high quantity 6% of catalyst gives more active sites for the substrate binding; it slightly increases the rate of the reaction. However, we notice that there is only a minor improvement in initial reaction rate by increasing the quantities of the catalyst beyond 2%, instead of a linear rise of r\({}_{0}\) with catalyst concentration which should happen. This observation led us to choose this value as the percentage of clay for subsequent runs.
# MSGCN: Multi-Subgraph Based Heterogeneous Graph Convolution Network Embedding
## 6 Experiment
### Multi-Class Node Classification  
We carried out experiments on multi-class node classification tasks. Contrast experiments include classic graph embedding methods, homogeneous GNN methods, and some heterogeneous graph methods. As we can see in Table 4, the proposed MSGCN and its variant MSGCN-mean outperform the other baselines on three datasets. In ACM and AMiner datasets, the density of edges is relatively sparse and the number of edges is small, so the weight does not bring much help. However, in the DBLP dataset, because of the high density, each node is related to multiple edges in each subgraph, and the weight also has a significant impact on the experimental results. We can conclude that MSGCN is more suitable for dense graphs than MSGCN-mean.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Dataset** & **Nodes** & **Node Types** & **Edge Type A** & **Type B** & **Type C** & **Labels** \\ \hline DBLP & 1496 & 3 & 2602 & 673,730 & 977,348 & 4 \\ AMiner & 1500 & 3 & 4360 & 554 & 89,274 & 4 \\ ACM & 1500 & 3 & 4960 & 6691 & 26,748 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Datasets.  
Most of the contrast models like DeepWalk and Graph Factorization use neither feature information nor use semantic information. As a result, these models do not perform very well. In contrast, GCN and GAT take feature information into consideration, and HAN is the only one that uses semantic information. However, our MSGCN model takes full advantage of all the information, which is the reason why the accuracy is higher than theirs.  
Instead of using metapaths, MSGCN decomposes the original graph based on edge types. Doing so can actually take some advantage of the metapath-based methods. For example, the author writes a paper can be an edge type. Based on this edge type, we can get a decomposed subgraph, where each edge can represent an author writes a paper. Therefore, there are many metapaths in this subgraph, like an author writes a paper(\(A\to P\)), two authors write a paper together (\(A\to P\to A\)), and two papers are all written by the same author (\(P\to A\to P\)). Other information like the relation between papers and venues can be obtained in other subgraphs. We can find that the MSGCN model has the advantages of the metapath-based methods while also making full use of all aspects of information.  
In order to better reflect the superiority of our model in the node classification task, for each category we select one with the best experimental result along with our MSGCN model, to show the visualization results. Here we choose HOPE for the classic graph embedding methods, TAGCN for homogeneous GNN methods, and HAN for heterogeneous graph methods. The experimental results are shown in Figure 4. We can find that these models can achieve good results in node classification. However, many nodes in comparison models are ambiguous, so it is not clear which cluster these nodes should belong to. In contrast, although the MSGCN model cannot classify all the nodes correctly, it can get a more obvious result with higher accuracy.
## 8 Future Directions
### Energy Efficiency and Power Management  
Developing energy-efficient hardware components and power management algorithms can extend UAV flight times. This includes using low-power electronics and optimizing flight paths to minimize energy consumption. Implementing a solar-powered UAV system that harnesses solar energy to power the communication payload and extend operational time can reduce reliance on battery power alone.
## 4 Discussion
### Abandoned Farmland Feature Analysis  
1. The total area of abandoned farmland in the study area was approximately 12.73 square kilometers, and the overall abandonment rate was 5.43%. The kernel density analysis of the abandoned farmland showed that there were five highest-level density kernels distributed in the south, southeast, southwest, and central parts of the study area; these were generally distributed around the edge of the residential area in a ring shape;
2. In the study area, the abandoned farmland and areas with high abandonment rates were mainly distributed in the elevation range of 400-800 m and the slope range of 0-10\({}^{\circ}\). Although the abandonment rate in the areas with a high slope range (>25\({}^{\circ}\)) was significantly higher than the overall abandonment rate, the distribution of cultivated land in this area was very small. Therefore, the cultivated land and abandoned farmland, in general, were mainly distributed in the areas of low slope and low elevation. On the one hand, elevation and slope affect temperature, soil and water conservation, and light. On the other hand, they limit accessibility and large-scale mechanized agriculture; therefore, locations for farming are more likely to be chosen in areas with flat terrain and lower elevations;
3. The distribution of abandoned farmland in the study area varies with the density of the road network. The area of abandoned land increases with the decrease in the road network density, but the abandonment rate decreases with the decrease in the road network density. Road network density directly affects the accessibility of transportation, and areas with high road network density are more suitable for large-scale mechanized agriculture and the transportation of crops and agricultural tools. At the same time, a highly dense road network also means the abandonment or even the occupation of the surrounding farmland;
4. The area of abandoned farmland and the abandonment rate both increase with decreasing distance from residential areas. The distance from settlements is related to the expansion of construction land, and urbanization may lead to the abandonment of farmland near residential areas or even lead to a change in the land use type, resulting in an increase in the farmland abandonment rate.
## 4 Simulation and Experiment
### Test on Typical Simulated Time Series
#### 4.1.1 Simulation Data
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Model** & **Parameter Settings** \\ \hline Statistical model & ARIMA & \(p=2\), \(d=1\), \(q=2\) \\ \hline \multirow{4}{*}{Deep learning model} & LSTM & _layers_ = \(2\), \(nodes\) = \(16\), _batch size_ = \(60\), \(\eta=0.03\) \\  & GRU & _layers_ = \(2\), \(nodes\) = \(16\), _batch size_ = \(60\), \(\eta=0.03\) \\  & & _seq\_len_ = \(24\), _label\_len_ = \(24\), \(n\_heads\)_ = \(8\) \\  & Transformer & _layers_ = \(2\), \(d\_layers\) = \(1\), \(d\_ff\) = \(2048\), _factor_ = \(5\) \\  & & _dropout_ = \(0.05\), _batch\_size_ = \(60\), \(\eta=0.03\) \\ \hline \multirow{2}{*}{Multi-model Fusion} & CI-GRUs & _layers_ = \(2\), \(nodes\) = \(16\), _batch size_ = \(60\), \(\eta=0.03\) \\  & A-CI-GRUs & _layers_ = \(2\), \(nodes\) = \(16\), _batch size_ = \(60\), \(\eta=0.03\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Related parameter settings for different models. (1) ARIMA: The typical model in the statistical prediction methods. Its main parameters, \(p\) and \(q\), are determined according to the training data. (2) LSTM: The efficient recurrent neural network in deep learning. (3) GRU: The development of LSTM, which is more efficient in a relatively simple structure. The parameters of LSTM and GRU are adjusted to obtain the optimal training result. (4) Transformer relies on the attention mechanism to represent the global dependencies between the input and output of the model. The transformer's core is the self-attention module, which can be viewed as a fully connected layer whose weights are dynamically generated based on the pairwise similarity of the input patterns. (5) Fusion model of multiple GRUs: Considering our method's model framework, the part of the proposed model is built, namely, the multiple GRUs without augmented samples. The GRUs are integrated with the covariance intersection, the same as the proposed method. The contrast method is abbreviated as CI-GRUs. In Table 3, _layers_ are the number of network layers, _nodes_ are the number of hidden nodes, _batch size_ is the number of training steps, and \(\eta\) is the learning rate. _Seq_len_ represents the input sequence length of the transformer encoder, and _label_len_ represents the starting token length of the transformer decoder. _n_heads_ represents the number of heads, _e_layers_ represents the number of encoder layers, _d_layers_ represents the number of decoder layers, _d_ff_ represents the dimension of function, and _factor_ represents the probsparse attention factor. _dropout_ means the probability of dropout, and _batch_size_ represents the batch size of the training input data. Prediction methods perform differently in various step lengths. The proposed and comparison methods were tested in experiments with backward predictions of 5, 10, and 15 time steps. RMSE and MAE are introduced as the error evaluation criteria.
## 2 Materials and Methods
### Parametric Model  
The parametric model refers to a mathematical model that is fundamental for this study because, by using this model, the exoskeleton finger glove can be made for each individual efficiently. This model only needs two anatomical measurements from the patient's finger: the distal phalanx's length and diameter. In [24], research is conducted on 66 healthy adult patients (from 19 to 78 years old) to determine the length of the middle phalanx, proximal phalanx and metacarpal bones as a proportion of their distal phalanx. Studies before [24] had already found a certain regularity in the proportion of the finger segments. Generally, it was accepted that this relationship followed the Fibonacci sequence [25]; however, this statement was later rejected by studies such as [29]. After seeing that models that use the same proportions for every finger are not very accurate, there were approaches to produce mathematical predictions of the proportions for each phalanx; for example, there are works [22; 24].  
One potential problem in the studies mentioned is the lack of consistency when taking the phalanx segment measurements, producing different magnitudes of errors within their  
Figure 3: (**a**) Image illustrating how the finger measurements are taken. (**b**) Infrared Camera Layout. (**c**) Location of the markers during MOCAP experiments. (**d**) Device used for resting the palm and forearm during experiments to guarantee consistency of the finger trajectories.  
models. The model proposed by [PERSON] [24] is the one selected for this study, as this is one of the few that uses thermograms, bone scans, and X-rays to find the finger segment proportions, giving it an advantage regarding its precision. As a result from [24], the following proportions were found for the index finger: the distal phalanx is the unit of reference, then the middle phalanx is found to be 1.4 the distal phalanx, and the proximal phalanx is 2.5 the distal phalanx.  
The process of obtaining a personalized finger rehabilitation mechanism begins by taking only two measurements from the patient: the length and width of the distal phalanx. Then the next step is to introduce these values to a Python program created in this work, which uses the proportions found by [PERSON] [24] in its code to get the rest of the measurements needed to produce the new CAD design. The third step is to take the.txt file that is given as an output of the Python program and then transfer this to the folder at the location of the SolidWorks 3D models. Finally, in SolidWorks, the assembly is refreshed; thus, the table of relations inside SolidWorks is updated along with all the mechanism's 3D models.  
There are two main advantages of using the proposed parametrization model:  
* The reduction in the time it takes to produce a 3D model of a rehabilitation mechanism that is adapted to an individual's anatomical dimensions.
* The maximization of the patient articulations' ROM by making the mechanism fit their individual anatomy.  
The proposed model is tested on three healthy male adult subjects.