esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results and Discussion
### Convergence Analysis  
In this section, we mainly analyze the convergence rate of QNGPSR and DSEGR in the training phase since the FEQSAI and GPSR make routing decisions online. One-hundred random maps are used to train the QNGPSR and DSEGR protocol with 200 episodes. Each episode corresponds to 10 s simulation time. Maps are collected from flight trajectories around the Paris Charles de Gaulle Airport in November 2017 from [[https://www.flightradar24.com/](https://www.flightradar24.com/)]([https://www.flightradar24.com/](https://www.flightradar24.com/)) (accessed on 5 October 2021). The QNGPSR protocol fails to converge due to node movement during the training phase, so QNGPSR nodes are trained in the stationary state. DSEGR is random direction movement. In order to be able to verify the generalization performance of the algorithm, the map used in the testing phase is different from the training phase. The convergence result is shown in Figure 4.  
Figure 4 shows the learning curves of QNGPSR and DSEGR. As can be seen, the DSEGR protocol basically converge after five episodes. However, QNGPSR needs 35 episodes to converge. Furthermore, the average E2E delay of DSEGR is close to QNGPSR after the final convergence. Although nodes in QNGPSR are static during the training phase, DSEGR can still obtain a faster convergence speed, which verifies our proposed DDQN with the prioritized experience relay algorithm and designed reward function is reasonable and efficient.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Movable region & 2000 m \(\times\) 2000 m \\ Number of UAVs & 40\(\sim\)150 \\ UAV speed & 1\(\sim\)20 m/s \\ Communication radius & 350 m \\ Initial energy & 400\(\sim\)1200 J \\ \(E_{tx}\) & 50 nJ/bit \\ \(E_{fs}\) & 10 pJ/bit/m\({}^{2}\) \\ \(E_{rx}\) & 50 nJ/bit \\ Hello interval & 0.5 s \\ Packet size & 1 KB \\ Buffer size & 32 KB \\ Communication radius & 350 m \\ Packet send rate & 1 Hz \\ Packet transmission rate & 1 Mbps \\ Experience relay pool size & 2000 \\ Learning rate & 0.001 \\ Discount factor & 0.9 \\ Packet size & 1 KB \\ Packet send rate & 1 Hz \\ \(\epsilon\) & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameter.  
Figure 4: Convergence performance comparison of average E2E delay between QNGPSR and DSEGR in the training phase.
# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 2 The Proposed Method
### Transmission Tower Identity Matching Template Library  
Establishing the transmission tower identity matching template library lays the foundation for identifying transmission towers. The process for setting up this library is illustrated in Figure 10. Input images are cropped to isolate transmission tower images using the YOLOv8s module. Then, cropped transmission tower images are passed through a matching network to obtain the feature vectors, denoted as \"features1\", which are written into the template library. If the input image contains GPS information, the GPS information is added to the transmission tower template library. If not, the GPS information value is set to \"null\". Additionally, the \"tower_ID\" is manually inputted. When features1 is a 128-dimensional feature vector, the data structure of the template library is presented in Table 3. However, when features1 is a 256-dimensional feature vector, varchar(4000) is insufficient to store this vector. In such cases, we employ varchar(8000) to accommodate features1.
# A Flexible FPGA-Based Channel Emulator for Non-Stationary MIMO Fading Channels
## 3 Flexible Hardware Architecture and Implementation
### FPGA-Based Implementation  
#### 3.3.1 Delay Module  
The delay module plays an important role in the channel emulation. It should be noted that the realization of multiple path delay is mainly based on the random access memory (RAM) or first input first output (FIFO). This method is easy to implement in FPGA, but cannot achieve the long-time delay, i.e., the aerial communication case, and high-precision delay, i.e., the indoor communication case. Especially, if the delay is relatively large, this method consumes a large amount of storage resources, which makes it impossible to realize in FPGA. To overcome this shortcoming, we adopt an external double-data-rate three synchronous dynamic random access memory (DDR3) and an interpolation filter to our scheme as shown in Figure 3. It includes three primary parts: DDR3, RAM, and high-precision interpolation filter. Take the advantage of large storage space of DDR3, it can achieve the large delay. Moreover, the data from RAM is multiplied by the coefficients of interpolation filter to achieve a high-precision delay. Thus, this scheme can adapt to a wide range of communication channels.  
In order to validate the proposed scheme of delay module, we run the module by modelsim software under the scenario of 3 GPP modified vehicular-A channel (MVA) [30]. In the simulation, the system sampling clock is 100 MHz, that is to say, the clock period is 10 ns. As the delay resolution in MVA is 5 ns, the interpolation filter is designed as a two-time interpolator. Figure 4 shows the corresponding output signal when a pulse signal passes through the delay module. Taking the first path as the reference path, the relative delay of each path is set as 375 ns, 750 ns, 1125 ns, 1750 ns, and 250 ns, respectively. It can be seen that the simulated results are consistent with the desired ones, which validates the effectiveness of this method.
# Fingerprint Classification through Standard and Weighted Extreme Learning Machines
## 5 Results and Discussion
### Estimation of Optimal Hyper-Parameters of the ELMs
The rest of the adopted hyper-parameters (i.e., \(N\) and \(C\)) correspond to those of the W-ELM1 that are displayed in Table 2b since the DW-ELM is an extended version of the W-ELM1. It can be seen that the additional parameter does not affect the classification performance. In general terms, the G-mean metric of the DW-ELM ranges between the values reported by the WELM1 and WELM2. Hence, a modified version of the original weighted ELM, which can augment the computational complexity of the neural network, is not necessary. In the following, the ELMs composed of their optimal hyper-parameters are adopted. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**(a) Standard**} & \multicolumn{3}{c}{**Capelli02**} & \multicolumn{3}{c}{**Hong08**} & \multicolumn{3}{c}{**Liu10**} \\ \cline{2-10}  & \(N\) & \(C\) & **G-Mean** & \(N\) & \(C\) & **G-Mean** & \(N\) & \(C\) & **G-Mean** \\ \hline HQNoPert & & & 0.64 & & & 0.86 & & & 0.65 \\ Default & 3000 & 210 & 0.54 & 3000 & 210 & 0.80 & 5000 & 28 & 0.62 \\ VQAndPert & & & 0.31 & & & 0.58 & & & 0.40 \\ \hline \hline \multirow{2}{*}{**(b) W-ELM1**} & \multicolumn{3}{c}{**Capelli02**} & \multicolumn{3}{c}{**Hong08**} & \multicolumn{3}{c}{**Liu10**} \\ \cline{2-10}  & \(N\) & \(C\) & **G-Mean** & \(N\) & \(C\) & **G-Mean** & \(N\) & \(C\) & **G-Mean** \\ \hline HQNoPert & & & 0.64 & & & 0.92 & & & 0.67 \\ Default & 4000 & 26 & 0.54 & 5000 & 24 & 0.88 & 5000 & 215 & 0.63 \\ VQAndPert & & & 0.37 & & & 0.65 & & & 0.49 \\ \hline \hline \multirow{2}{*}{**(c) W-ELM2**} & \multicolumn{3}{c}{**Capelli02**} & \multicolumn{3}{c}{**Hong08**} & \multicolumn{3}{c}{**Liu10**} \\ \cline{2-10}  & \(N\) & \(C\) & **G-Mean** & \(N\) & \(C\) & **G-Mean** & \(N\) & \(C\) & **G-Mean** \\ \hline HQNoPert & & & 0.66 & & & 0.93 & & & 0.69 \\ Default & 4000 & 26 & 0.57 & 5000 & 24 & 0.89 & 5000 & 215 & 0.64 \\ VQAndPert & & & 0.40 & & & 0.67 & & & 0.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the G-means metric obtained by (**a**) Standard ELM, (**b**) W-ELM1, and (**c**) W-ELM2 on the testing sets for the combination of optimal hyper-parameters (\(N\) and \(C\)). All datasets and feature extractors are considered. Figure 9: Graphics of the obtained G-mean against the decaying parameter of the decay weighted ELM (DW-ELM) for each feature extractor and database during the validation stage. The number of hidden neurons and regularization parameter are adopted from the optimal weighted ELMs.
# Modeling and Control of a DC Motor Coupled to a Non-Rigid Joint
## 3 Experimental Setup Hardware  
Throughout this section, the hardware used to create the experimental testbed is detailed. It is worth mentioning that, in order to understand the impact that the gearbox had on the obtained data, it was decoupled from the motor shaft, and the same tests were carried out. The conclusion is that the obtained data are practically the same, only affected by the correction factors related to speed and torque, due to the reduction of the gearbox.  
Taking into account the decision of using a motor with a worm gearbox, it was decided to use an off-the-shelf solution, specifically the DC Motor with Permanent Magnets--_GY-371_, which is a low cost popular actuator, being a 12 V motor with built-in encoders, attached to a 340:1 reduction worm gearbox. This reduction factor was chosen so that the motor speed would be 20 rpm. With this choice, despite the low maximum angular speed of the motor, the joints can benefit from more torque. In addition to the fact that the incorporated encoder can be applied to collect data in the modeling process, it will also be extremely useful during the motor movement, making it possible to control in closed-loop with access to speed information in real time.  
From the motor model point of view, these experiments allow to collect some important variables such as motor speed, motor withdraw current, and applied motor voltage. For the acquisition of the  
Figure 4: Controller Block Diagram.  
Figure 3: Profiles Examples.  
mentioned variables, there were used the following sensors, being the referred acquisition system shown in the right side of Figure 5:  
* Built-in encoder--measuring the angular displacement of the motor;
* Current sensor--measuring the current that the motor withdraws;
* Voltage sensor--measuring the instant and real voltage applied to the motor terminals;  
As mentioned, a passive non-rigid joint system was designed and developed, making it possible to test the model and apply the desired controllers. This system is presented in Figure 6.  
For this joint tests, there is the need to know its angular position, as well as the applied torque. So, to obtain the exact angular displacement, an absolute encoder, consisting in a contactless absolute angular position sensor, with 14-bit resolution and immune to external magnetic stray fields, was placed exactly concentrically with the axis of rotation of the joint. Also, to obtain the torque, a straight bar load cell, capable of measuring forces up to 10 kg, was placed in the center of the link to evaluate the exerted force which can, using the distance to the rotational axis, be converted to the applied torque. The left side of Figure 5 presents the implemented solution, and the interaction between the subsystems. The whole system was powered by a 12 VDC power supply to perform the tests. How these values are applied in practice will be detailed in Section 4.  
Figure 5: Hardware Diagram.  
Figure 6: Simulated Representation of the non-rigid Joint.
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 3 Localization Algorithm
### Spatial Coverage
#### 3.2.2 3D-Networks
The node deployment of this scheme is shown in Figure 7. Firstly, only the ordinary nodes close to the surface anchors can obtain the location information, and they calculate the confidence values and compare with the threshold of confidence. They become the new reference nodes only when the confidence values of the nodes are larger than the confidence threshold. The localization algorithm is the gradient method and a scheme that can estimate two-hop Euclidean distance in 3D space is proposed to help the non-localized node find enough reference node to realize localization. Based on the color filtering technology, the Projection-color Filtering Localization (PCFL) algorithm and the Anchor-color Filtering Localization (ACFL) algorithm are shown in [37]. Both algorithms can provide precise localization with minimum power wastage. The detailed localization process can be described as follows: firstly, reconstituting the present network construction as a hierarchical structure, the authors describe the localization as a geometry problem. Secondly, task projections are considered as a center. Hence, the task-rings can be obtained. PCFL is based on  
Figure 6: Node multihop localization [34]. Figure 7: Network architecture of TPS [36]. the Angle of Arrival (AOA) measurement and the task projections obtain the original RGB values, while, for ACFL, and the task anchors obtain the initial RGB. Finally, the nearness degree is defined as a filter samples, and, meanwhile, it is stored as a weight. In this subsection, we have discussed the spatial coverage and the difference between 2D-networks and 3D-networks. We use some papers to show the different characteristics of the two network architectures of UWSNs. Furthermore, we summarize these papers that use the spatial coverage to achieve the positioning in Table 3. We mainly compare the coverage, localization time, accuracy, computational complexity, and energy consumption. According to the following table, the reader can choose the suitable algorithm for doing some application or doing some improvement. The comparisons between the localization algorithm referred to in this subsection and other localization algorithms that are introduced in other subsections will be shown in Section 4.
# ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows
###### Abstract  
Information-centric networking (ICN) is a promising solution that can meet the challenges of IP-based networks. ICN adopts the solution of separating name and address to solve the dual semantics problem of IP addresses, which has natural advantages in supporting mobility. These mobility support methods in ICN have realized route optimization to a certain extent. In fact, due to these methods not considering the real-time network environment, such as the bandwidth of the switching path and the characteristics of different mobile data flows, these approaches adopt the same routing optimization solution for all data flows generated by mobile users, resulting in a single switching path. The bandwidth of a single switching path may not meet the transmission requirements of mobile data flows, which will greatly affect the performance of mobile users. Therefore, how to efficiently allocate switching paths for multiple mobile data flows generated by mobile users is an urgent problem in ICN mobility support. In this paper, we first propose a mobility support method for dynamically allocating mobile data flows (DAFM) based on the ICN mobility support architecture and model the mobile data flow allocation problem as a late-binding node selection problem. We then propose a double-constraints heuristic algorithm (DCHA) based on the Moser algorithm. By simultaneously adjusting the bandwidth resource constraints of the switching path and the space resource constraints of the Mobility Flow Table (MFT), we achieve late-binding node selection to minimize average packet delay. Finally, experimental results show that our proposal can achieve mobility support. Under the guarantee of a low-latency Name Resolution System (NRS), our method outperforms other methods in terms of average packet delay, packet loss rate, and handover delay.  
information-centric networking; mobility; route optimization; anchorless; heuristic algorithm 2023
# Structure-Aware Trail Bundling for Large DTI Datasets
## 5 Internal Evaluation
Isotropic bundling (\(\alpha_{0}=1\), middle row) reduces occlusion, as expected, with DDH and shading and outlines bringing in, again, more structure. However, isotropic bundling breaks the tract-sheet structures by creating artificial gaps in these, as discussed in Section 3.2.2. Anisotropic bundling (\(\alpha_{0}=0.7\), bottom row) also reduces occlusion but largely preserves sheet structures such as the _corpus callosum_ (red). Separately, we see that DDH seems to offer more added value when used on the unbundled data; for the bundled data, the shading and outlines style is able to show structure equally well, and creates easier-to-follow visualizations due to the use of shading. Concluding this internal evaluation, we found that the parameter defaults listed in Table 3 give good results in terms of creating visualizations with limited occlusion and easy-to-follow visual structures. A few parameters remain free to choose for the user, such as the rendering style and isotropic-vs-anisotropic bundling. We explore these using an external evaluation in the next section. Figure 12: Comparing four rendering modes for a small 65 K trail set. Original unbundled data (**top row**) and its bundled counterpart (**bottom row**). See Section 5.
## 4 Experiments
### Experimental Results and Performance Analysis  
The results were compared with the collected video control signals using user definition-based as well as K-means algorithm-based defined video control signals, as shown in Figure 11. The difference between the **K-means algorithm**-based defined video control signals and the collected control signal  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Type** & **Configurations** \\ \hline Input Layer & Input image & 270 by 480 grayscale image \\ \hline \multirow{6}{*}{Convolutional Layers} & Convolution & Number of outputs: 32, kernel size: 1 by 1, strides: 2 \\  & Max Pooling & kernel size: 3 by 3 \\  & Convolution & Number of outputs: 32, kernel size: 3 by 3 \\  & Dropout & - \\  & Max Pooling & kernel size: 3 by 3 \\  & Convolution & Number of outputs: 32, kernel size: 3 by 3, strides: 2 \\  & Max Pooling & kernel size: 3 by 3 \\ \hline \multirow{2}{*}{Output Layer} & Fully & \multirow{2}{*}{26 by 1 matrix} \\  & Connected & \\ \end{tabular}
\end{table}
Table 1: Components of the CNN model.  
Figure 10: Change in the learning rate when the 360-degree video control signal is learned by the CNN model.  
was **2679.53**, and the difference between the **user-defined** video control signals and the collected control signal was **11,947.28**. The K-means algorithm-based defined video control signals better represent the collected video control signals than the user-defined video control signals. When the video control signal was automatically generated by the K-means algorithm, the difference was consistent with the collected video signals. However, when the control signal is generated based on user definition, the difference occurs differently according to 360-degree video. There was a small section, such as a 360-degree video of 15, 26, among others, or a section with a lot of difference, such as a 360-degree video of two, four, 18, 29, among others.  
The user evaluates the 360-degree video and collects the video control signals. The collected video control signals were compared with the proposed method and the user definition-based video control signals. The proposed method changed the screen **122** times in the 360-degree video, while in the case of user definition, the screen was initially set as shown in Table 2. It was thus verified that the proposed method is more detailed than the user-defined method. The proposed method naturally changes NFoVs according to the athlete's movement. However, although the user definition method has athletes in NFoVs, NFoVs did not change according to the movement of the athletes. For example, if the athletes were on the border within the provided NFoV, the user definition method did not move the NFoV, but the proposed method changed the NFoV so that the athletes were at the center.
## 3 Results and Discussion
### Sorption Study
#### 3.1.1 Effect of pH  
The pH of the solution is a remarkable factor that controls the adsorption process. The impact of pH values on the adsorption capacity of Y(III) from liquid solutions is performed and displayed in Figure 1a. Several experiments were performed in different pH range from 3.0 to 7.0, while the other experimental factors were kept constant at 200 mg\(\cdot\)L\({}^{-1}\) initial Y(III) concentration, 50 mL of solution for 60 min contact time, and 50 mg adsorbent dose of AHIBC at 25 \({}^{\circ}\)C. The AHIBC has a pH of 3.0.  
From the obtained data, it appeared that the adsorption efficiency of Y(III) was gradually increased from 42.63% to 85.66% with raising the pH from 3.0 to 6.0. However, Y(III) adsorption efficiency decreased to 65.44% by raising the pH from 6.0 to 7.0 value. The reduced uptake on the AHIBC at the acidic medium is because of the predominance of hydrogen ions in the solution, which has a large ability to be adsorbed on the active sites, thus its leading to competing with the Y\({}^{3+}\) cations during adsorption processes, according to the following equations:  
\[\equiv \rm Si\text{-}O^{-}+H_{2}O+H^{+}\leftrightarrow\equiv \rm Si\text{-}OH_{2}{}^{+}+OH^{-}, \tag{3}\] \[\equiv \rm Al\text{-}O^{-}+H_{2}O+H^{+}\leftrightarrow\equiv \rm Si\text{-}OH_{2}{}^{+}+OH^{-}. \tag{4}\]  
By raising the pH, the concentration of H\({}^{+}\) ions decreases. Accordingly, the adsorption efficiency increased to the utmost adsorption at pH 6.0. At the pH > 6.0, a low Y(III) sorption value was observed; this may indicate that some Y(III) ions were precipitated as Y(OH)\({}_{3}\), forming white gelatinous precipitate [36]. Accordingly, the pH 6.0 was selected as an optimum pH in running the following experiments for Y(III) recovery.