esa-sceva/satcom-chunk-collection huggingface dataset:
# Stable Heteroclinic Channel-Based Movement Primitives: Tuning Trajectories Using Saddle Parameters
## 3 Methods
### System Parameters  
The variables that control SMP behavior are the noise \(z_{j}\), the kernel weights \(w_{i}\), and the parameters that make up the connection matrix \(\rho_{ij}\) (seen in (3)). In our previous work [1], we observed the effect of the kernel weights on the system and optimized the weights to make the system follow a desired trajectory. The algorithm design methodology outlined in Figure 2 was used previously and is now used for this article. When the system is at unit scale (magnitude, \(\beta=1\)), the kernels can be plotted in the task space using the weights as locations.  
In this work, we focus on the connection matrix, \(\rho_{ij}\). The connection matrix is a real, non-symmetric matrix constructed from three saddle characteristics: the growth rate \(\alpha\), the magnitude \(\beta\), and the saddle value \(v\)[7]. The matrix is constructed as follows:  
\[\rho_{ij}=\left\{\begin{aligned} \alpha_{i}/\beta_{i},&\quad\text{if $i =j$}\\ \frac{\alpha_{i}-\alpha_{j}/v_{j}}{\beta_{j}},&\quad \text{if $i$=j$-1}\\ \frac{\alpha_{i}+\alpha_{j}}{\beta_{j}},&\quad\text{ otherwise}\end{aligned}\right. \tag{4}\]  
In the SHC system, \(\alpha_{i}\) controls how fast the kernel grows in the \(i\)th dimension, \(\beta_{i}\) is the maximum amplitude of the waveform, \(x_{i}\), and \(v_{i}\) defines the stability of the \(i\)th saddle with respect to input noise. When designed together, these variables and the external perturbation (modeled here as Gaussian noise) create kernels with smooth, connected pathways [7], which aids in producing a smooth trajectory. In this work, we will observe how \(\alpha\), \(\beta\), and \(v\) affect SMPs.  
The number of inputs \(N\), the noise values \(z_{j}\), and the coupling matrix \(C_{ij}\) collectively form the final term in (3). These are selected to create a proportionally small input noise in comparison to the rest of the system. As noted in Section 3.1, this noise is necessary to produce a trajectory along the kernel pathways.  
To show the effect of each system parameter on the resulting trajectory, we vary each parameter over a spread of values for a single kernel and for all four kernels. The effect on the trajectory is measured as an area error from the original trajectory (the square in Figure 3). Additionally, since SMP kernels are functions of time, we can measure the time it takes for each kernel to grow and decay. A baseline trial (discussed in Section 3.3) is compared to trials with individually and collectively varied parameter values, and the kernel function waveforms (produced by \(x\) from (3)) show the effects.  
Figure 2: SMP design flowchart.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 4 Examples of Accelerometers Used in Rail Vehicle Systems
The continuous wavelet transform provides an appropriate resolution in the low-frequency domain for localizing the components and visualizing defects. The idea is to examine the vibrations caused by uneven rail tracks to gather information about the condition of the rails. A recent study [72] used a special method called the maximal overlap discrete wavelet packet transform (MODWPT) to analyze data collected from small sensors during tram rides. This method helps break down the vibration signals into several parts without losing any important details, which is different from other techniques that can lose some information when they analyze signals. By using this approach, researchers can gain a better understanding of how the tracks are holding up. In the paper [73], an online rail deformation monitoring system is presented for implementation on moving trains to report any deformation beyond the threshold in a timely manner. The system uses a simple method to detect any bending or distortion in railway tracks. It does this by using three sensors that measure acceleration, which are placed on the train. The proposed method was tested on a ZT-3 type train, and the results were compared with those obtained from the GJ-4 reference inspection car type. The method can tell the difference between vibrations caused by changes in the train tracks and those caused by the train moving itself. It does this by examining the sideways movements of the train's crossbeam and the wheels. Reference [74] presents the modelling and fault diagnosis of railroad jointless tracks based on the piecewise equivalent ballast resistance. The authors use a two-port network and transmission line theory to model all the components.
# An Efficient and Fast Hybrid GWO-JAYA Algorithm for Design Optimization
## 3 Test Problems and Optimization Results  
The FHGWJA algorithm proposed in this study was tested in seven engineering problems including up to 29 optimization variables and 1200 nonlinear constraints. Test cases regarded the following: (i-ii) 2D path planning (minimization of trajectory lengths with 7 or 10 obstacles); (iii) shape optimization of a concrete gravity dam with additional earthquake load (volume minimization); (iv-v) calibration of nonlinear hydrologic models (the Muskingum problem solved with 3 or 25 unknown model parameters to be identified); (vi) optimal crashworthiness design of a vehicle subject to side impact; (vii) weight minimization of a planar 200-bar truss structure under three independent loading conditions.  
FHGWJA was implemented in MATLAB as a standalone optimization code. Optimization runs were carried out on a standard PC equipped with a 3.1 Mhz AMD processor and 16 MB of RAM memory. The Mersenne-Twister (MT19937) MATLAB default algorithm was utilized to generate random numbers uniformly distributed between 0 and 1; this algorithm was selected in view of its ability to generate high-quality numbers with (2\({}^{19937}\)-1) long period. In order to draw statistically significant conclusions on the computational efficiency of FHGWJA, 20 independent optimization runs were executed for each test problem starting from different initial populations. The population size and limit number of iterations of FHGWJA were, respectively, set equal to 10 and 5000. Initial populations included some designs with up to 1000% constraint violation to check whether FHGWJA can quickly approach the best region of search space containing the optimum solution. Remarkably, FHGWJA always completed all optimization runs within a much lower number of analyses than the theoretical computational budget of 10 \(\times\) 5000 = 50,000 analyses of the classical GWO and JAYA. The actual computational cost of FHGWJA was almost entirely due (about 90%) to the number of analyses performed in the optimization search. For example, the optimization runs performed with FHGWJA for the largest test problem solved in this study (weight minimization of a planar 200-bar truss structure including 29 sizing variables) were completed on average within about 25 min of wall clock time.
# Multi-Lidar System Localization and Mapping with Online Calibration
## 4 Real-World Experiment Validation and Result Analysis  
### Online Calibration Experiment Results and Analysis  
The online calibration experiment conducted in this paper is divided into two parts. The first part verifies the robustness of the calibration algorithm by altering the installation layout of multiple laser radars. The online calibration results are then compared with actual measurements within the same scene. The second part uses diverse calibration algorithms on the same data, comparing the results of this paper's algorithm with others to validate its accuracy and real-time efficacy.
# Imaginary Speech Recognition Using a Convolutional Network with Long-Short Memory
## 5 Discussion
### Complexity and Memory Analysis  
An important aspect when developing an ISR system is considering the complexity of the algorithm and the memory used. Usually, the main consummator of the resources is the neural network, as can also be seen in Table 10. The maximum number of operations is given by the second layer of the CNNLSTM neural network and is to the order of approx. O (6.3 \(\times\) 10\({}^{9}\)). However, the execution time for a decision is under 100 ms, even when using all the channels in the computation, which means that it can still be implemented in a real-time device.  
When reducing the number of electrodes, we can see in Table 11 an important decrease in the time execution, as well. An alternative to an all-electrode system can be the ASBA electrodes system, in which the time of execution considerably dropped from 80 ms to 20 ms, with a drop in accuracy of only 3%. This alternative is better from other points of view, too, such as increasing the user comfort when using the device and decreasing the cost of the final product.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **No. Channels** & **Time [s]** & **Accuracy [\%]** \\ \hline All channels & 62 & 0.0826 & 43.98 \\ \hline L/R Frontal & 13 & 0.0046 & 28.46/30.28 \\ \hline L/R Central & 15 & 0.0057 & 28.92/30.32 \\ \hline L/R Occipital & 11 & 0.0035 & 22.97/22.72 \\ \hline L/R Frontal + Central & 23 & 0.0124 & 33.72/33.86 \\ \hline L/R Central + Occipital & 26 & 0.0155 & 31.32/31.89 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The mean execution time needed to obtain a response from the system for all groups of channels analyzed in the paper; L—left, R—right, ASBA—anatomical speech brain areas.  
When it comes to the memory usage, the system has its limitation because it needs at least 2 GB of memory only to retain the weights, due to the gates and the long-term memory implication of the LSTM network. However, the LSTM addition to the system improved the overall classification and added great value to the final system.
# Wireless Communication Technologies for Smart Grid Distribution Networks +
## 2 Available Wireless Technologies for Smart Grid Communications
### ZigBee  
ZigBee is recognized as the most suitable technology for smart grid Home Area Networks (HANs) and is preferred by most AMI vendors and metering and energy management systems. It operates in the 2.4 GHz band, with a maximum radio output power of 1 mW, covering up to 100 m at 240 Kbps, using OQPSK modulation. It offers low development and operating costs within an unlicensed spectrum. It is based on the IEEE 802.15.4 standard [6]. Its disadvantages include concerns about low processing and memory capabilities and, mainly, susceptibility to interference from other 802.11 LAN [7] appliances. Interference avoidance schemes and energy-efficient routing protocols are challenges to be overcome.
## 4 Application Results
### The Case of Continuous Stirred-Tank Reactor Process
Fedformer-DCCA and Crossformer-DCCA both achieve perfect FDRs of 100%, but their FARs are higher at 9.45% and 9.95%, respectively, indicating a moderate trade-off between detection accuracy and false positives. Autoformer-DCCA and GRU-DCCA also achieve 100% FDRs, but their FARs (12.44% and 11.94%) reflect a slight compromise in false alarm management. LSTM-DCCA, while achieving a high FDR of 98.25%, has a higher FAR of 13.43%, suggesting a reduction in its detection capability at the expense of fewer false alarms. The CCA method, on the other hand, delivers the lowest FDR of 66.47%, highlighting its limitations in detecting additive faults. In the case of Fault 6 (Additive faults), Incremental Pyraformer-DCCA continues to outperform all other methods with an FDR of 100% and a FAR of 1.00%, demonstrating its robustness in detecting faults while maintaining minimal false positives. Fedformer-DCCA and Autoformer-DCCA achieve high FDRs of 98.66% and 100%, respectively, but their FARs are higher at 3.98% and 15.42%. Crossformer-DCCA achieves a perfect FDR of 100%, but its FAR is the highest at 24.00%, indicating a tendency to flag more anomalies. LSTM-DCCA and GRU-DCCA maintain high FDRs of 100% and 93.09%, with FARs of 11.4% and 0%, respectively. The CCA method shows a high FDR of 98.4% with no false alarms, although its detection approach remains conservative. For Fault 10 (Additive faults), Incremental Pyraformer-DCCA demonstrates its continued superiority with an FDR of 97.11% and a FAR of just 0.50%, standing out for its ability to detect challenging faults while minimizing false alarms. Crossformer-DCCA and LSTM-DCCA also perform well, with FDRs of 89.28% and 76.60%, respectively, and FARs of 0%. Fedformer-DCCA and Autoformer-DCCA achieve moderate FDRs of 62.99% and 87.11%, respectively, with FARs of 11.44% and 11.94%. GRU-DCCA strikes a balance with \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Fault 3**} & \multicolumn{2}{c}{**Fault 5**} & \multicolumn{2}{c}{**Fault 6**} & \multicolumn{2}{c}{**Fault 10**} \\ \cline{2-10}  & **FDR\%** & **FAR\%** & **FDR\%** & **FAR\%** & **FDR\%** & **FAR\%** & **FDR\%** & **FAR\%** \\ \hline CCA & 87.99 & 0.00 & 66.47 & 0.00 & 98.4 & 0.00 & 51.65 & 10.95 \\ LSTM–DCCA & 100 & 7.46 & 98.25 & 13.43 & 100 & 11.4 & 76.6 & 0.00 \\ Fedformer–DCCA & 100 & 14.30 & 100 & 9.45 & 98.66 & 3.98 & 62.99 & 11.44 \\ Crossformer–DCCA & 100 & 19.90 & 100 & 9.95 & 100 & 24.00 & 89.28 & 0.00 \\ Autoformer–DCCA & 100 & 8.96 & 100 & 12.44 & 100 & 15.42 & 87.11 & 11.94 \\ GRU–DCCA & 100 & 6.97 & 100 & 11.94 & 93.09 & 0.00 & 83.51 & 1.99 \\
**Incremental Pyraformer–DCCA** & **100** & **2.49** & **100** & **3.98** & **100** & **1.00** & **97.11** & **0.50** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Monitoring results for Fault 3, Fault 5, Fault 6, and Fault 10 in the CSTR process. an FDR of 83.51% and a low FAR of 1.99%. The CCA method records the lowest FDR of 51.65%, with a FAR of 10.95%, indicating its limited capability in detecting Fault 10.
# Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation
## 4 Experiments and Results
### Dataset  
The MCL-3D dataset was used to evaluate the performance of the proposed quality metric. The dataset analyzes the impact of different distortions on the quality of depth-image-based rendering (DIBR) synthesized images. MCL-3D dataset was created by Media Communication Lab, University of Southern California, and is publicly available [70]. The dataset was created from 9 multiview-video-plus-depth (MVD) sequences. The resolution of 3 MVD sequences is 1024 \(\times\) 768 whereas the remaining 6 sequences have a resolution of 1920 \(\times\) 1080. The dataset reports 648 mean opinion scores (MOSs) of stereo image pairs generated using DIBR from distorted texture and/or depth images. Six different types of distortions (Gaussian blur (Gauss), additive white noise (AWN), down-sampling blur (Sample), JPEG compression (JPEG), JPEG 2000 compression (JP2K), and transmission loss (transloss)) with four different levels are applied either on texture images, depth images, or both. The distorted texture images and depth maps are used to generate the intermediate middle virtual images by using view synthesis reference software (VSRS) [71], a benchmark DIBR technique for generating synthesized views. Sample reference and distorted DIBR images are shown in Figure 3.
# Secure Indoor Localization Based on Extracting Trusted Fingerprint
## 3 Localization Model
### Error Sources of RSSI
When an AP is impaired, the RSSI will decrease, so the coverage area of the AP will be smaller, and its RSSI is not stable. If the AP is used for localization, its localization interval will be affected by the AP so that the localization area moves toward the wrong area, resulting in a larger localization error. Figure 4c shows the change of the localization area when an AP is destroyed. If it is still used for localization when the AP is destroyed, the obtained localization interval will be offset and become larger, and the localization accuracy will be reduced. Therefore, we need to identify the impaired AP to ensure that the location fingerprints in the localization system are safe and trusted. Based on the above analysis, the impaired AP has a serious impact on localization accuracy. Therefore, the localization system needs to be clear about the security status of APs, then perform the appropriate treatment. According to the analysis of the change of RSSI in Figure 3, we can see that the three states of normal, impaired and destroyed APs can be distinguished according to the change of RSSI and the fluctuation degree.
# Co-Design of Mechanical and Vibration Properties of a Star Polygon-Coupled Honeycomb Metamaterial
## 2 Structure Design of the Star Polygon-Coupled Honeycomb Metamaterial  
Shown in Figure 1 are the structural features of the proposed novel star polygon-coupled honeycomb metamaterial (SPCHM). For generating more local resonance bandgaps, the SPCHM is created by coupling a cross-linked star-shaped structure into the horizontal connecting rod of the initial metamaterials (IM), as shown in Figure 1b. Furthermore, the connecting nodes are designed as circular rings, which can improve the designability on the load-bearing and vibration suppression capabilities. The definition and value of the geometric parameters are reflected in Figure 1d and Table 1, respectively. \(a_{x}\) and \(a_{y}\) stand for the length and width of the unit cell, accordingly. \(d\) denotes the rod width, \(\theta\) represents the angle between two adjacent rods, and \(r_{1}\) and \(r_{2}\) are the inner and outer radius of the rings, respectively. The width of the rods is 1.2 mm.  
Figure 1: Schematic diagram of (**a**) IM, (**b**) SPCHM, (**c**) SPCHM with pins, and (**d**) geometric dimensions of the SPCHM.
# An Efficient Void Aware Framework for Enabling Internet of Underwater Things
## 3 The Proposed Efficient Void Aware Routing Protocol for Enabling Internet of Underwater Things
### An Efficient Void Aware Routing Protocol: Design Approach
#### 3.4.1 Eva: Tables and Packets Format  
In this section, the Routing Table (RT) and packets have been explained in detail. The RT is the main element of the suggested protocol. Each sensor should gather and store some information for its neighbors within a one-hop range. Each sensor can gather the information through the received hello packet during the data collection phase and void detection phase. As shown in Figure 4a, the RT generally contains six fields including _ID_, _depth value_, _residual energy_, _distance (calculated by the triangle metric)_, _route cost (calculated by the triangle metric and residual energy)_, and _Boolean flag (isVoid)_. The value of this flag has been changed based on the void status of the node (i.e., 0 for void nodes and 1 for ordinary nodes).  
The EVA routing protocol is containing three types of packets: _hello_, _probe and data packets_. Hello packets are utilized regularly in the data collection phase to exchange information between neighbors. As demonstrated in Figure 4b, The hello packet is comprised of three fields: _ID (for the sender)_, _depth value_, and _residual energy_. ID is employed to verify the sender of the hello packet. Additionally, the depth value is utilized to distinguish the depth level of the sender. The information of the hello packet is extracted in saved in the RT if the sensor received the hello packet from shallower neighbors. Moreover, the residual energy is stored in RT and employed route cost calculation. On the other hand, the void probe packet is presented in the EVA routing protocol to find the void nodes as shown in Figure 4c. This type is containing two fields, namely _ID_ (for the sender) and Boolean _isVoid_. The first field is the ID of the void node (i.e., actual, and intuitive void node). The probe packet is created by the actual void nodes and transmitted to its one-hop neighbors. If the ID of the receiver node equals the ID of the probe packet in its RT, it changes the isVoid fields in its RT. Then it changes the ID of the probe packet and forwards it if the node discovers itself as an intuitive void node. Otherwise, it discards the probe packet. Figure 4d illustrates the data packet format employed in the suggested protocol. The data packet involves two major elements: packet header and data. The packet header is consisting of four fields, namely _Sender ID_, _packet sequence number_, _forward ID_, _and source ID_. Sender ID is the distinctive ID of the sender node. Packet sequence number the assigned number to the data packets in the source node. Forwarder ID is the ID for the best candidate. Source ID is the ID of the source node that creates the data packets. The last two fields are employed to detect and count the successful data packet dropped or received in the whole network.
# Improvement of Mesh Atomizer Performance by Electrolytic Polishing
## 2 Theoretical Model of Atomization Amount and Electrolysis Time  
### Factors Affecting Electrolytic Polishing  
The main factors altering the effect of electrolytic polishing are polishing voltage, electrolyte temperature, polishing time, and the distance between anode and cathode plates [21].  
First, when the polishing voltage is excessively low, the metal anode surface is in the activated state and dissolved, inducing surface erosion, resulting in a rough surface. When the polishing voltage is comparatively high, oxygen will precipitate out of the anode surface, causing the local surface of the anode to be covered by bubbles and leading to poor conductivity. At the same time, local overheating may occur, resulting in excessive corrosion of the metal surface, thus making the surface rougher [22; 23; 24]. Second, a relatively high temperature will cause the liquid around the anode to boil and create bubbles, which will destroy the solid film on the anode surface, but the polishing effect is not obvious when the temperature is below 30 \({}^{\circ}\)C. Lastly, if the distance between the cathode and the anode is relatively long, the resistance and power consumption will increase, which will make the solution easy to heat up, thus affecting the polishing quality. On the contrary, if the distance is too small, it is easy to short circuit, resulting in damage to the metal sheet [25; 26].  
Considering that the effectiveness of electrolytic polishing is influenced by the many factors mentioned above, to explore whether the atomization effect will change due to electrolytic polishing, this study will control other variables of electrolytic polishing and only change the electrolytic polishing time.
# Blockchain-Based Decentralised Authentication in Closed Environments
## 5 Implementation and Use Case
### Use Case Example  
One example of a practical application of our proposed blockchain-based decentralised authentication protocol is in patient authentication within a healthcare system or a hospital. In a closed hospital environment, the network comprises validator nodes representing servers from critical hospital departments, such as medical record department, emergency department, and administration department. These validator nodes (servers) are, of course, pre-registered on a closed blockchain network.  
During the first phase of the protocol, these departmental servers (acting as validator nodes) establish a shared secret key \(K\) through a group key agreement protocol. This shared secret key enables secure communication among validator nodes, and by pre-registering these nodes within the closed environment, the protocol ensures a trustworthy authentication framework.  
When an entity, including patients and hospital employees, wants to access medical services, their authentication credentials are transmitted through the blockchain network in the closed environment. The validator nodes then engage in a consensus-driven process to verify the identity and credentials of the entity, where nodes must agree on the authentication outcome. Next, the authentication transaction is recorded in the blockchain, creating a transparent and tamper-resistant verification mechanism.  
This emphasises the fact that the consensus mechanism eliminates the need to rely on a central authentication server, which improves security by preventing single points of failure and ensures that no individual node can grant or deny authentication.