esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Results from the Five-Year-Old Phantom  
Figure 6 shows the region inside the blue box that was magnified to clearly illustrate the performance of each noise reduction algorithm. The original (top left, Figure 6a), Gaussian (top center, Figure 6a), median (top right, Figure 6c), Wiener (bottom left, Figure 6d), and FNLM (bottom right, Figure 6e) images are depicted. There was not any additional noise except for the natural noise of the image acquisition process. As a result of observing the images of Figure 6, the image in Figure 6e was the clearest. Subsequently, the performance of the FNLM denoising algorithm was evaluated in terms of the two noise level evaluation factors.  
Figure 7 shows the COV and CNR results for evaluating the noise level of the reconstructed images of the five-year-old phantom. In the noise level evaluation of these images, the FNLM denoising algorithm achieved the best performance as compared with the conventional denoising filters. The acquired COV values of images applied the original, Gaussian, median, Wiener, and proposed FNLM denoising algorithm were 0.000798, 0.000751, 0.000703, 0.000497, and 0.000102, respectively. The COVs of the images obtained with the FNLM denoising algorithm were approximately 7.82\(\times\), 7.36\(\times\), 6.89\(\times\), and 4.87\(\times\) better than that of the original, Gaussian, median, and Wiener images, respectively. Moreover, the CNRs of the original, Gaussian, median, Wiener, and proposed FNLM denoising algorithm were 45.138, 47.630, 50.527, 67.125, and 236.635, respectively. The CNRs of the image obtained with the FNLM denoising algorithm were approximately 5.24\(\times\), 4.97\(\times\), 4.68\(\times\), and 3.53\(\times\) higher than that of the original, Gaussian, median, and Wiener images, respectively.  
Figure 6: Magnified views (region inside the blue box). **(a)** Original; **(b)** Gaussian filtered; **(c)** Median filtered; **(d)** Wiener filtered; **(e)** FNLM filtered images.
# How Does the Built Environment Affect Mechanical Parking Space Planning: A Case Study in Xi'an City
## Materials and Methods  
### GBDT Method (Gradient Boosting Decision Tree)  
Among machine learning models, the GBDT model is good at representing non-linear relationships and does not require prior assumptions about the relationships between variables. At the same time, it can provide the relative importance of the variables in the results, which is conducive to revealing the results. Moreover, it can deal with the problem of multi-collinearity within variables. GBDT method uses bagging and adding regularization to avoid noise and outlier data, which makes GBDT more robust [36]. Thus, GBDT is now widely used for regression and classification prediction [42].  
GBDT is an advanced ensemble learning model based on the CART algorithm. It uses weak classifiers (CART trees) to construct an ensemble model and trains multiple decision tree models through iterations and overlays to achieve the goal of improving prediction accuracy. In each iteration, based on the previous iteration, the loss function (Equation (1)) and the pseudo-residuals (Equation (2)) are calculated. A new decision tree is constructed, and then all the generated decision trees are weighted and fused (Equation (4)) according to the weights of the decision trees (Equation (3)) by gradient descent. The GBDT model combines the decision tree with the idea of the ensemble in an effective way and improves the prediction accuracy of the model. The following steps are usually included.  
\[L_{i}^{(t)}=\left(y_{i}-f^{(t-1)}\right)\left(x_{i}\right)\right)^{2} \tag{1}\]  
\[r_{i}^{(t)}=y_{i}-f^{(t-1)}\left(x_{i}\right) \tag{2}\]  
\[y^{(t)}=learning\_rate \tag{3}\]  
\[F_{i}(x)=\sum_{t=1}^{m}y^{(t)}f^{(t)}\left(x_{i}\right) \tag{4}\]  
where \(L_{i}^{(t)}\) denotes the loss function value of the \(i^{\text{th}}\) sample of the \(t^{\text{th}}\) decision tree; \(r_{i}^{(t)}\) denotes the pseudo residual of the \(i^{\text{th}}\) sample; \(y_{i}\) is the actual value of the \(i^{\text{th}}\) sample, which is the prediction of the \(t-1^{\text{th}}\) decision tree for the \(i^{\text{th}}\) sample; \(y^{(t)}\) denotes the weight of the \(t^{\text{th}}\) tree, and learning_rate is the learning rate. \(F_{i}(x)\) denotes the final dependent prediction for the \(i^{\text{th}}\) sample, and \(f^{(t)}(x_{i})\) denotes the dependent prediction result for the \(i^{\text{th}}\) sample of the \(t^{\text{th}}\) tree on the observation sample \(x\). The prediction results of all trees are weighted and summed according to the weights to obtain the final prediction result.  
For more information, see [PERSON] et al. [38].
###### Abstract  
Clustering algorithms are widely used in statistical data analysis as a form of unsupervised machine learning, playing a crucial role in big data mining research for Maritime Intelligent Transportation Systems. While numerous studies have explored methods for optimizing ship trajectory clustering, such as narrowing dynamic time windows to prevent errors in time warp calculations or employing the Mahalanobis distance, these methods enhance DBSCAN (Density-Based Spatial Clustering of Applications with Noise) by leveraging trajectory similarity features for clustering. In recent years, machine learning research has rapidly accumulated, and multiple studies have shown that HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) outperforms DBSCAN in achieving accurate and efficient clustering results due to its hierarchical density-based clustering processing technique, particularly in big data mining. This study focuses on the area near Taichung Port in central Taiwan, a crucial maritime shipping route where ship trajectories naturally exhibit a complex and intertwined distribution. Using ship coordinates and heading, the experiment normalized and transformed them into three-dimensional spatial features, employing the HDBSCAN algorithm to obtain optimal clustering results. These results provided a more nuanced analysis compared to human visual observation. This study also utilized O notation and execution time to represent the performance of various methods, with the literature review indicating that HDBSCAN has the same time complexity as DBSCAN but outperforms K-means and other methods. This research involved approximately 293,000 real historical data points and further employed the Silhouette Coefficient and Davies-Bouldin Index to objectively analyze the clustering results. The experiment generated eight clusters with a noise ratio of 12.7%, and the evaluation results consistently demonstrate that HDBSCAN outperforms other methods for big data analysis of ship trajectory clustering.  
HDBSCAN; AIS; ship trajectories; clustering +
Footnote †: journal: Applied sciences  
_Article_
## 1 Introduction
Thanks to the rapid development of advanced computational tools, such as LMS Motion, Adams ATV and Recurdyn, multi-body dynamics (MBD) simulations have been widely used to rapidly predict the dynamic response of tracked vehicles in distinct virtual environments [16; 17]. Unlike large tracked vehicles with ample design space, small and medium-sized tracked vehicles often adopt simple spring-damper suspension or just rigid chassis [18; 19]. This paper proposes a novel compact tracked chassis with multi-cylinder hydropneumatic suspension to replace the conventional wheeled chassis of road-rail vehicles. Compared with the chassis driven by rubber wheels, tracked chassis can flexibly cope with the diverse rough terrains along the railway and switch operation modes without terrain restrictions to meet the growing needs of routine inspection and maintenance. In this paper, a compact tracked chassis with a hydropneumatic suspension system is proposed, modelled, and evaluated. The main contributions are listed as follows:  
1. A novel road-rail vehicle equipped with a tracked chassis is designed with the capability of autonomously switching between two operation modes and adapting to the various rugged and uneven terrains along the railway;
2. Lugre friction model is integrated with the hydropneumatic suspension model to improve the model accuracy. The suspension model is experimentally verified by the test rig data for a further establishment of the integral vehicle model;
3. The hydraulic and pneumatic parameters are optimized based on a systematic objective function, which takes into account the heave and pitch motion states for achieving better dynamic performance of the vehicle. This paper is organized as follows. An overview of the proposed novel road-rail vehicle is presented in Section 2; Section 3 elaborates the design principle and the hydropneumatic suspension modelling. Sections 4 and 5 report the model validation process and the ride performance analysis, respectively; Finally, Section 6 concludes this paper.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 5 Wire-Grid Sparse Antennas
### Occa Method
#### 5.4.3. OCGA Sparse Antenna  
In order to demonstrate the possibility of using sparse antennas obtained by the OCGA method, we took the sparse antenna obtained at a frequency of 5.9 GHz and modeled it at a frequency of 5.1 GHz. Then, we compared the results and presented them in Figure 11. As can be seen, using the OCGA method to obtain a sparse antenna from an antenna that operates in a frequency range does not significantly affect the characteristics of the new sparse antenna, considering the fact that the surface current flows in a different direction at each frequency.
## 5 Conclusions  
In ultra-high-density BPMR systems, 2D interference significantly impacts the overall system performance. To mitigate this issue, we proposed the use of an ARMR in a staggered BPMR system for detecting a single data track. An ARMR generates multi-readback signals to detect mutual data. We then leveraged this mutual data during the detection process by employing four different techniques to enhance the LLR mutual data and improve the system's performance. Our simulation results demonstrate that each of the proposed techniques outperformed the 1D conventional system. Some can provide better BER performance over the 2D conventional system, where the 2D equalizer and 2D SOVA detector are employed to process the obtained readback signals. Furthermore, our findings also indicate that the proposed techniques were robust to media noise. Of the four techniques studied, which included HIF, MSI finding, BSD, and MLP, the MLP technique achieved the best performance, at the expense of high complexity. The potential for future work in 2D signal processing should be leveraged alongside our proposed techniques to address challenges related to 2D interference and significant electronic noise. Additionally, these proposed techniques could be applied to ultra-high interference recording systems, such as multilayer magnetic recording systems.  
Conceptualization, N.R. and C.W.; methodology, N.R., S.K. and C.W.; software, N.R., S.J.G. and C.W.; validation, P.K., S.J.G. and C.W.; formal analysis, S.K., P.K. and C.W.; investigation, S.J.G. and C.W.; writing--original draft preparation, N.R., S.K., P.K., S.J.G. and C.W.; writing--review and editing, N.R. and C.W.; supervision, S.J.G. and C.W.; project administration, C.W.; funding acquisition, N.R. and C.W. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by King Mongkut's Institute of Technology Laddrabang Doctoral Scholarships (KDS) under Grant KDS2020/012 and King Mongkut's Institute of Technology Laddrabang Research Fund under Grant number KREF046705.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & **Mul./Div.** & **Add./Sub.** & **Log./Exp.** \\ \hline
1D Conventional & 40 & 28 & - \\ \hline
2D Conventional & 2560 & 1600 & - \\ \hline Hard-information flipping & 120 & 86 & - \\ \hline Maximum soft-information finding & 120 & 88 & - \\ \hline Bit-summation detection & 120 & 86 & - \\ \hline Multilayer perceptron & 10,501 & 10,700 & 400 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Complexity of the various methods.
## 2 Basic Theory
### Introduction of Fuzzy Entropy  
In this paper, fuzzy entropy [32] is used to determine the signal-to-noise boundary. Fuzzy entropy is described as shown below.  
For each time series \([h(1),\,h(2),\,\ldots,\,h(N)]\), we reconstruct \(U\) space by introducing non-negative integers \(m\) (\(m\leq N-2\)), and the reconstructed signal is as follows:  
\[S^{m}[i]=\{h(i),h(i+1),\ldots,h(i+m-1)\}-h_{0}(i), \tag{7}\]where \(i=1,2,\ldots,N-m+1\), and \(h_{0}(i)\) can be defined as follows:  
\[h_{0}(i)=\frac{1}{m}\sum_{j=0}^{m-1}h(i+j). \tag{8}\]  
The membership function is as follows:  
\[A(x)=\left\{\begin{aligned} & 1,\ x=0\\ & e^{[-ln(2)(\frac{x}{x})^{2}]},x>0^{\prime}\end{aligned}\right. \tag{9}\]  
where \(r\) is similarity tolerance. According to Equation (9), the membership function is defined as follows:  
\[A_{ij}^{m}=\exp\left(-\ln\ln(2)\cdot\left(\frac{d_{ij}^{m}}{r}\right)^{2} \right). \tag{10}\]  
We define distance \(d_{ij}^{m}\) between two vectors \(S^{m}[i]\) and \(S^{m}[j]\) as the maximum absolute difference between the two corresponding elements, and the distance is given by the following:  
\[d_{ij}^{m}=(|h(i+p-1)-h_{0}(i)|-|h(j+p-1)-h_{0}(j)|), \tag{11}\]  
For each \(i\) value, the average value is obtained as follows:  
\[C_{i}^{m}(r)=\frac{1}{N-m}\sum_{j=1,j\
eq i}^{N-m+1}A_{ij}^{m}, \tag{12}\]  
\[\varphi^{m}(r)=\frac{1}{N-m+1}\sum_{i=1}^{N-m+1}C_{i}^{m}(r). \tag{13}\]  
Therefore, the fuzzy entropy of the sequence is defined as follows:  
\[FuzzyEn(m,r,N)=ln\varphi^{m}(r)-ln\varphi^{m+1}. \tag{14}\]  
Based on the findings of previous research [33], we set \(m=2,r=0.2\), and \(N=1000\).
# Reasonable Support Parameters for Loess Tunnel Under Different Water Contents: A Case Study
## 3 On-Site Test of Mechanical Characteristics
### On-Site Monitoring Results Analysis  
#### 3.2.1 Deformation of Surrounding Rock  
The deformation test results in sections ZK90+041, ZK90+056, and ZK90+071 are shown in Table 5.  
According to Table 5, the maximum vertical settlement is located at the crown (0), and the deformation value in section ZK90+056 is larger than those in the other sections. Therefore, section ZK90+056 was selected as the typical section for surrounding rock deformation analysis. The deformation temporal curve of the surrounding rock in section ZK90+056 is shown in Figure 6.  
The deformation curve trend is consistent according to Figure 6, and the curves consist of a rapid growth stage, a continuous growth stage, and a slow growth stage. These deformation stages exhibit similar durations, of 6 days, 6 days, and 5 days, respectively.  
Figure 5: Schematic diagram of tunnel excavation method and measurement point layout.  
Figure 6: Surrounding rock deformation in section ZK90+056.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Section**} & \multicolumn{3}{c}{**Vertical Settlement/mm**} & \multicolumn{3}{c}{**Horizontal Convergence/mm**} \\ \cline{2-7}  & **Crown (0)** & **Left Arch** & **Right Arch** & **Upper Bench** & **Middle Bench** & **Lower Bench** \\  & **Waist 30\({}^{\circ}\) (1)** & **Waist 30\({}^{\circ}\) (2)** & **(1–2)** & **(3–4)** & **(5–6)** \\ \hline ZK90+041 & 39.5 & 23.9 & 25.3 & 8.7 & 27.3 & 39.9 \\ ZK90+056 & 64 & 49.8 & 45.4 & 29.1 & 32.7 & 26.3 \\ ZK90+071 & 35.9 & 29 & 32.5 & 16.8 & 25.9 & 28.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Deformation results of monitoring sections.  
Based on the excavation steps, the stage of invert excavation corresponds to a significant reduction in the deformation of the surrounding rock. It is clear that constructing the invert is beneficial for controlling the deformation of loess tunnels. The deformation values reveal that the settlement at the crown exceeds the horizontal convergence. Due to the loess's creep nature, the deformation trend in loess tunnels fails to reach a stable state, which is different from that observed in rock tunnels. Therefore, the secondary lining should be constructed after the deformation has reached a stable state to prevent congenital damage to the secondary lining.
## 2 AI Techniques in Communication Networks
### Machine Learning and Deep Learning in Network Applications
#### 2.1.3 Reinforcement Learning
Additionally, hybrid models that combine RL with other machine learning techniques, such as supervised learning or deep learning, are being developed to improve the accuracy and speed of decision-making processes [57]. As RL continues to mature, its integration with 5G and future 6G networks, where ultra-low latency and high reliability are paramount, will likely become a critical area of focus. Figure 1 provides a visual representation of the trade-off between accuracy and latency for different ML/DL models in network applications. As seen in the figure, deep learning models like CNNs generally offer high accuracy at the cost of longer processing times, whereas simpler models like decision trees may provide faster results but with lower accuracy. Reinforcement learning models, particularly DQNs, can exhibit a balance between accuracy and latency depending on how they are tuned, but they typically require more computational resources due to their need for continuous learning and adaptation. The trade-off between these factors must be carefully considered when designing systems for real-time network management. Accuracy vs. Latency for ML/DL Models in Communication Networks
# Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service +
## 3 SurMon Architecture Overview
Therefore, to deal with this challenge, a smart traffic surveillance system is necessitated to interpret the detected driving behaviors in a specific context. Due to its superior performance, Capsules Network requires less training data and considers a spatial relationship among features. In the SurMon system, two Capsules Networks, namely CapNet-1 and CapNet-2, are trained with a dataset of good driving behaviors, and they are cascaded together. The CapNet-1 is trained as a multi-class classifier with individual driving behavior data like trajectories, speed, etc.. CapNet-1 predicts whether an individual behavior is classified as any of the good ones from the training dataset. CapNet-2 is trained with a set of good driving behaviors in the same time window, and it is a binary classifier that only predicts whether these behaviors agree with each other to formulate a harmony driving scenario. Specifically, the set of the behaviors data goes into CapNet-1 first and then the outputs are taken as the training set for CapNet-2. Within a time window, all vehicle driving behavior data flow into the mSSA for the detection of outliers, and then CapNet-1 takes the detected behavior data and makes the prediction. If it does not fall into any good behavior patterns, all vehicles data are instead fed into CapNet-1, and its output is leveraged by CapNet-2 to predict whether the existence of this abnormal behavior will result in a real negative of a good driving scenario. The basic rationale here is that most vehicles obey the traffic rules and a good driving behavior does not put others at risk. The last layer of SurMon system is the cloud layer. Meaningful urban data, after being processed at the edge layer, can be transmitted to the cloud for large-scale city-wise traffic pattern recognition. In addition, because of the plentiful computational resources, models can be upgraded in the cloud and then deployed to edge nodes. This paper focuses on the real-time detection and interpretation at the network edge, end layer, and edge layer; the design and experimental studies for the cloud layer are beyond the scope. The architecture discussed in this section presents the concept and major function blocks. The implementation of an actual SurMon system in the real world depends on many factors, including the budget, the range of the space under surveillance, local traffic regulations, etc. In Section 6, a system configuration of the proof-of-concept prototype for our experimental study is presented.
# Overview on Different Types of Solar Cells: An Update
## 2 Organic Solar Cells
### Operation of Organic Solar Cells  
The operation of OSC is completely different from that of conventional silicon-based inorganic solar cells in terms of the material properties and architecture [97]. Free charge carriers are not immediately created by absorption of light in OSC, unlike traditional inorganic solar cells. An electrically neutral electron-hole pair is formed (as an exciton). It is a tightly bound electron-hole pair with a life span in the order of nano-seconds. The design and geometry of OPV devices are largely impacted by the excitonic character of the organic semiconductor material. The understanding of the basic operation of OPV is imperative for coping with the limitations and challenges of the materials and fabrication. The dissociation of the exciton is necessary for the generation of the photovoltaic effect. Exciton binding energy is in the range of 0.3-1.0 eV. Obviously, more energy than the binding energy is required to dissociate excitons and free the carrier. This is a relatively high energy requirement compared to its inorganic counterpart. The light is absorbed by the acceptor, resulting in creation of excitons. If the thermal energy generated is less than the exciton binding energy, electron and hole will undergo recombination. However, if the  
Figure 2: The fabrication of OSC: (**A**) single active layer, (**B**) bilayer heterojunction, (**C**) bulk heterojunction, (**D**) tandem heterojunction.  
thermal energy is more than the exciton binding energy, free charges will be created that result in the separation of the electron and hole. Consequently, photogenerated carriers accumulate on the electrodes, electrons at the cathode, and holes at the anode. The working principle of OSC is demonstrated in Figure 3. The absorption of sunlight results in the generation of an electron-hole pair (exciton). High energy absorption leads to valence electrons of the donor jumping from their highest occupied molecylar orbital (HOMO) to the least unoccupied molecular orbital (LUMO) level, leaving behind holes at the HOMO level (separation of excitons into electrons and holes). Electrons then jump from the LUMO of the donor to the LUMO of the acceptor, which is at a lower energy level. Finally, the electrons from the LUMO of the acceptor are transported and collected at the cathode while holes at the HOMO of the donor are transported to anode, which completes the circuit. In this context, the alignment of the energy levels of the donor and acceptor materials, the absorption profiles, mobility of charges, and miscibility of the materials are critical factors [98].