esa-sceva/satcom-chunk-collection huggingface dataset:
# Riverbank Following Planner (RBFP) for USVs Based on Point Cloud Data
## 1 Introduction
### Contributions  
By combining river shoreline inspection with USV path planning, the USV can convert riverbank point clouds scanned by LiDAR into navigational paths and maintain a set distance from the riverbank. The novel contributions of the paper are:  
* The following points of the path for the USV are automatically calculated by the RBFP planer using massive point cloud data collected by the LiDAR on the USV.
* The distance between the USV and the riverbank can be flexibly defined by users before the autonomous navigation, ensuring the USV's adaptability to a diverse range of applications.
* Unlike other path planning algorithms that need to set target points before navigation, our algorithm can automatically navigate following to the shape of the riverbank without any predefined target points and can maintain a constant distance from the riverbank.  
In this paper, we review the current state-of-the-art methods for USV path planning and shoreline inspection in Section 2. A high-precision riverbank following planner is proposed in Section 3. Section 4 describes simulations and high-fidelity experiments, with the experimental results discussed therein. Finally, Section 5 gives some conclusions.
## 6 Encoding Complexity Analysis
The entire encoding process requires \((w-1)(n-k)\) addition operations and \(w(n-k)\) multiplication operations. When \(w\) can be seen as a small constant with respect to \(n\), the coding algorithm has linear complexity. The irPEG and HC algorithms proposed in this paper can directly construct a check matrix with a lower triangle that avoids the pretreatment of the check matrix and guarantees the sparseness of the matrix. Therefore, the average column weight \(w\) can be regarded as a small constant with respect to code length \(n\) and the linear complexity encoding of the non-binary LDPC code can be realized, which greatly reduces the encoding complexity compared with the conventional construction algorithm. The improved algorithm decomposes the construction process and combines the advantages of the quasi-cyclic extension technique and the progressive edge construction method. It not only satisfies the need for degree distribution but also ensures the maximum requirement for the perimeter length. Its ring structure overlaps less, improving the speed of non-binary LDPC encoding. Additionally, the encoding is implemented with a set of shift registers and the encoding complexity is much lower than random construction. After the algorithm is extended to non-binary domains, the error correction capability is further improved.
# An Improved Channel Estimation Technique for IEEE 802.11p Standard in Vehicular Communications
## 3 Overview of Current Channel Estimation Schemes
### Scenario 1: Modification in IEEE 802.11p
#### 3.1.2 Time Domain Least Square Estimation (TDLSE) Scheme  
Another scheme known as TDLSE was proposed in [12; 13] for the tracking of the time-varying channel. The main idea behind this scheme was reported by [30] and then was further exploited in a different literature [31; 32; 33]. The authors have proposed to insert a Zadoff-Chu sequence or Pseudo Noise (PN) sequence into the prefix section for further improving the channel estimation. The channel impulse response is initially derived by the Least Square (LS) estimation of the received signal and the transmitted matrix which consists of the replaced prefix, known as Zadoff-Chu sequence. In addition to that, the estimated channel response is averaged with the adjacent symbols. In the end, the LS equalization is performed and the simulation results show that both sequences based TDLSE can achieve significant improvement in the channel estimation as compared to other schemes.
## 2 Materials and Methods
### Outcome Measures  
Prior to intervention, demographic data (age, sex, laterality), plain radiographs and magnetic resonance imaging (MRI) studies scans were collected for all cases. The grade of OA was documented using the KL classification [35; 36]. In addition, all patients assessed their pain and symptom severity using the Visual Analogue Scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). The VAS score was used for general subjective estimation of pain, whereas the WOMAC is more specific for pain associated with orthopaedic problems. After the treatment, patients were followed for a minimum of 1 year. None of the patients had serious adverse events (e.g., infection, allergic reaction, the need to have the knee drained or surgical irrigation and debridement). After treatment, two patients treated with BMAC reported severe pain up to 7 days post treatment. The VAS and WOMAC were repeated at 3 and 12 months following intervention. Some patients were lost to follow-up which resulted in 145 HA cases (84%), 201 ACS cases (90.5%) and 98 BMAC cases (87.5%) at 3 months; and 30 HA cases (17.5%), 50 ACS cases (22.5%) and 25 BMAC cases (22.3%) at 12 months (Figure 1). The demographic data of the included subjects is displayed in Table 1. The average patient age was 52 years (SD 13 years); 54% of cases were males and 46% females. Knee OA involved the right knee in 53% of cases and the left knee in 47%. There were no significant demographic differences among the treatment groups.
###### Abstract  
Accurate prediction of forthcoming oxygen concentration during waterless live fish transportation plays a key role in reducing the abnormal occurrence, increasing the survival rate in delivery operations, and optimizing manufacturing costs. The most effective ambient monitoring techniques that are based on the analysis of historical process data when performing forecasting operations do not fully consider current ambient influence. This is likely lead to a greater deviation in on-line oxygen level forecasting in real situations. Therefore, it is not advisable for the system to perform early warning and on-line air adjustment in delivery. In this paper, we propose a hybrid method and its implementation system that combines a gray model (GM (1, 1)) with least squares support vector machines (LSSVM) that can be used effectively as a forecasting model to perform early warning effectively according to the dynamic changes of oxygen in a closed system. For accurately forecasting of the oxygen level, the fuzzy C-means clustering (FCM) algorithm was utilized for classification according to the flatfish's physical features--i.e., length and weight--for more pertinent training. The performance of the gray model-particle swarm optimization-least squares support vector machines (GM-PSO-LSSVM) model was compared with the traditional modeling approaches of GM (1, 1) and LSSVM by applying it to predict on-line oxygen level, and the results showed that its predictions were more accurate than those of the LSSVM and grey model. Therefore, it is a suitable and effective method for abnormal condition forecasting and timely control in the waterless live transportation of flatfish.  
w +
Footnote †: journal: Applied  
_Article_  
An On-Line Oxygen Forecasting System for Waterless Live Transportation of Flatfish Based on Feature Clustering
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 3 Results and Discussion
### Experimental Result and Analysis
#### 3.4.5 Statistical Evaluation  
The effectiveness of the proposed method was statistically evaluated using [PERSON]'s test. The classification results for all the test cases were compared using this test. The [PERSON]'s test is defined as in Equation (12), where it is assumed that two generic algorithms, named Algorithm 1 and Algorithm 2 are compared.  
\[Z=\frac{f_{12}-f_{21}}{\sqrt{f_{12}+f_{21}}} \tag{12}\]  
In the equation above, \(f_{12}\) indicates the number of samples correctly classified by Algorithm 1 and incorrectly classified by Algorithm 2, and \(f_{12}\) indicates the number of samples for the opposite case. The performance of Algorithm 1 is better than Algorithm 2 if \(Z>0\). The differences between Algorithm 1 and Algorithm 2 are statistically significant if \(|Z|>1.96\). In our case, Algorithm 1 is the algorithm proposed in our manuscript, and Algorithm 2 is --sequentially-- each one from the list of standard algorithms: SVM, EPF, SCMK, R2 MK, ASMGSSK, MsuperPCA, 2 DSSA, 2 DMSSA.  
[PERSON]'s test between the proposed SP-SSA algorithm and the algorithms listed above for the Indian Pines, Pavia University, Salinas, and Houston 2018 datasets are provided in Table 6. The test result clearly reveals that the classification results for the  
Figure 14: (**a**) Ground Truth Image, Classification Maps of (**b**) SVM (**c**) EPF (**d**) SCMK (**e**) R2 MK (**f**) ASMGSSK (**g**) MsuperPCA (**h**) 2D-SSA (**i**) 2D-MSSA (**j**) SP-SSA for Houston 2018 dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Class** & **Samples** & **SVM** & **EPF** & **SCMK** & **R2 MK** & **ASMGSSK** & **MsuperPCA** & **2 DSSA** & **2 DMSSA** & **SP-SSA** \\ \hline \hline \multirow{2}{*}{Paved parking lots} & 11,475 & 54.91 & 63.2 & 74.02 & 69.99 & **84.58** & 74.63 & 64.9 & 74.18 & 84.33 \\ \cline{2-11} Unpaved parking lots & 149 & 58.78 & 83.11 & 83.67 & 77.03 & 81.63 & 85.71 & 81.08 & 81.76 & **93.06** \\ \cline{2-11} Cars & 6578 & 43.99 & 47 & 49.33 & 59.66 & 65.09 & 53.78 & 54.51 & 62.85 & **70.22** \\ \cline{2-11} Trains & 5365 & 40.83 & 40.54 & 58.92 & 51.3 & 77.4 & 61.14 & 52.66 & 79.19 & **79.96** \\ \cline{2-11} Stadium seats & 6824 & 86.42 & 93.25 & 87.78 & 93.86 & 96.62 & 94.18 & 83.53 & **98.71** & 98.43 \\ \hline \hline \multirow{2}{*}{**OA:**} & 68.19 & 71.64 & 74.1 & 75.48 & 81.32 & 77.2 & 72.11 & 79.08 & **83.57** \\ \cline{2-11}
**AA:** & 60.69 & 64.54 & 67.99 & 69.16 & 76.96 & 71.36 & 66.09 & 74.08 & **79.95** \\ \cline{2-11}
**K:** & 59.13 & 63.41 & 66.52 & 68.28 & 75.82 & 70.55 & 63.94 & 72.61 & **78.73** \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._proposed method were significantly better--in a [PERSON]'s statistical sense--compared with other approaches.
## 5 Discussion
### Ablation Study
#### 5.1.2 Effects of Cascade Stages  
Tables 17-19 summarize the performance of our CAS-DA method with a different number of cascade stages. SW is an abbreviation for Strong Weak Faster RCNN that only uses RPN3.  
Here are our observations: (1) SW achieved the worst F1-score in all experiments when no cascade RPN was added. (2) By adding RPN1 or RPN2, the detector outperformed SW. Specifically, adding RPN1 alone brought 47.52%, 26.18%, 4.45%, 28.47%, 1.64%, and 1.69% gains in F1-score and only adding RPN2 improved the F1-score by 46.68%, 23.98%, 1.18%, 3.36%, 0.72%, and 1.33%. However, adding either RPN1 or RPN2 still fell short of the best F1-score. (3) It is worth noting that RPN1 was more effective than RPN2 in all six experiments. This is possibly because a large number of very easy samples could be filtered in RPN1 using the shallow-level features. To prove it, we observed the training process and found that the number of easy samples filtered by RPN1 was always much more than that of RPN2. Taking A \(\rightarrow\) B as an example, RPN1 generated 2888 anchors; after that, RPN1, RPN2, and RPN3 classified 1002, 418, and 22 of them as easy samples, respectively. (4) CAS-DA achieved the optimal performance when three RPNs were added at the same time, outperforming SW on six transfer tasks by 2.72%, 1.98%, 30.17%, 49.84%, 58.17%, and 5.54% in terms of F1-score, respectively, without the filtering strategy. More cascade stages may further improve the performance of the model but will also increase the cost of computing.
## 3 Results and Discussion
### Scientific Mapping
#### 3.2.5 Increased Team Collaboration and Feeling of Engagement  
The advantages of improved collaboration and engagement were also cited frequently in the research reviewed.  
Industrial design reviews and usability testing are complex processes involving a variety of stakeholders, including designers, engineers, and end users. Computer-aided design is utilized as a communication tool in a conventional review process to transmit design ideas and enable a better common understanding among diverging needs and perspectives [15]. Because virtual reality decreases the possibility of some groups being excluded from the review process, it has the potential to foster collaboration among stakeholders [16].  
According to some studies, the high focus that virtual-reality-based reviews provide [52] increases a feeling of team engagement. In [41], it is asserted that in the conventional process, design teams cluster around conference tables with laptops, mobile phones, and paper notes while, at best, one person manipulates the design on a giant 2D screen. Maintaining team engagement and attention becomes more challenging when battling with technological device distractions. The virtual environment allowed the design team to move away from the traditional conference room and into a creative area with fewer distractions. During design discussions in the immersive environment, the team observed increased engagement. Thus, the immersive virtual reality environment enhanced team engagement, which resulted in better discussions and fuller participation from team members in decision-making [41].  
Figure 7 shows a design review on a screen and a VR-based process.
# Performance of Numerically Optimized Tuned Mass Damper with Inerter (TMDI)
###### Abstract  
In recent years, the Tuned Mass Damper with inerter (TMDI) has received significant attention. The inerter is defined to exert a force that is in proportion to the relative acceleration of the two inerter terminals. Here, two TMDI topologies are investigated. The conventional topology is given by the inerter being in parallel to the spring and viscous damper of the TMDI. The other topology is the serial arrangement of spring, inerter and viscous damper being in parallel to the stiffness of the mass spring oscillator of the TMDI. While the first topology intends to increase the inertial force of the TMDI, the second topology aims at producing an additional degree of freedom. The considered TMDI concepts are simulated for harmonic and random excitations, with parameters set according to those described in the literature and with numerically optimized parameters which minimize the primary structure displacement response. The classical TMD is used as a benchmark. The findings are twofold. The conventional TMDI with typical inertance ratio of 1% and the very small value of 0.02% performs significantly worse than the classical TMD with the same mass ratio. In contrast, the TMDI with an additional degree of freedom can improve the mitigation of the primary structure if the inertance ratio is set very small and if the TMDI parameters are numerically optimized.  
damping; inerter; optimization; TMD; TMDI; vibration 2022
# Robustness of Double-Layer Group-Dependent Combat Network with Cascading Failure
## 3 Cascading Failure Model for DGCN
### Overload Failure Model
#### 3.3.3 Redistribution Strategy of Load  
After a node fails, the current load will propagate in a certain way. The common redistribution methods such as local redistribution [36], global redistribution [37], and average distribution [38] have different effects on the robustness of the network when facing cascading failures. For the communication net, the communication transmissions are generally addressed according to the principle of the minimum number of hops, so local distribution is more inclined to be adopted. In this paper, we propose a load redistribution strategy from the perspective of the initial static load and subsequent dynamic remaining capacity, which is as follows:  
The static allocation ratio for neighbor node \(v_{j}\) of failed node \(v_{i}\) at time 0 is  
\[\Pi_{ij}^{s}=\frac{L_{\text{W}}^{j}(0)}{\sum\limits_{j\in\Gamma_{i}}L_{\text{W }}^{j}(0)}. \tag{8}\]  
As for the dynamic allocation at time \(t\), we use the ratio based on the dynamic remaining capacity and it is represented as  
\[\Pi_{ij}^{d}=\frac{C_{\text{W}}^{j}-L_{\text{W}}^{j}(t)}{\sum\limits_{j\in \Gamma_{i}}C_{\text{W}}^{j}-L_{\text{W}}^{j}(t)}. \tag{9}\]  
Figure 5: Relation between initial load and capacity.  
Therefore, the synthetic allocation ratio is as follows:  
\[\Pi_{\text{W}}^{ij}=\eta\Pi_{ij}^{s}+(1-\eta)\Pi_{ij}^{d}, \tag{10}\]  
where \(\eta\) is the proportion parameter of two redistribution methods. Then, the new obtained load of neighbor node \(v_{j}\) is expressed as  
\[\Delta L_{\text{W}}^{ij}(t)=\Pi_{\text{W}}^{ij}L_{\text{W}}^{i}(t). \tag{11}\]  
And we can update the load of the communication net's nodes by the following expression:  
\[L_{\text{W}}^{k}(t+1)=L_{\text{W}}^{k}(t)+\sum_{j\in\Gamma_{k}}\Delta L_{\text {W}}^{jk}(t). \tag{12}\]  
In the same way, the processing load distribution method of the functional net is easy to obtain. However, it should be noted that the redistribution only occurs among the same type of functional nodes. The node load after a new round of failure is shown as follows:  
\[L_{\text{G}}^{k}(t+1)=L_{\text{G}}^{k}(t)+\sum_{j\in\Gamma_{k}}\Delta L_{\text {G}}^{jk}(t). \tag{13}\]
## 6 Movement Track Positioning Model
### Model Solution and Result Analysis  
Substitute the data collected by the dynamic target points in annex 5 into the interference data identification model for judgment, and first identify whether the signal is disturbed. If the fluctuation level is greater than the threshold in the model, it will mean that the target point is disturbed when it moves to this position, through the abnormal data model in chapter 3 to achieve the target position; if it is judged that the target is not disturbed, then the target position will be achieved through the normal data position model.  
Under normal circumstances, the target should be interfered with for a continuous period, not intermittently. The accuracy of the interference data recognition model can be roughly judged according to the fluctuation of the label. The interference situation is shown in Figure 15, and a value of 1 means disturbed data, and a value of 0 means normal data. It can be seen from the figure that the continuity between disturbance and normal data is better.  
By substituting the data into the model coupled with the classification model and the positioning model, the movement track of the dynamic target can be obtained, as shown in Figure 16.  
It can be found from Figure 16 that in the XOY direction, the movement trajectory of the target point is in a \"Z\" shape, and during the movement, it will be interrupted intermittently, and the disturbed part is the red area in the figure; from the XOZ direction and the YOZ direction It can be found in the direction that when the target point is disturbed in the process of movement, the value of the \(Z\)-axis direction will fluctuate to a certain extent, but generally it presents a relatively gentle trajectory.  
Therefore, the reliability and stability of the movement track positioning model can be determined, and relatively accurate positioning of the target point in movement can be achieved.  
Figure 15: Schematic diagram of trajectory interference.  
Figure 16: The movement track of the target. (**a**) Movement track of target point on XOY plane; (**b**) Movement track of XOZ plane target; (**c**) Movement track of YOZ plane target.
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 2 Materials and Methods  
### Principle of Koch Fractal  
The Koch fractal is a geometric pattern that begins with an equilateral triangle, removes the inner third of each side, builds another equilateral triangle at the removed side, and repeats this process indefinitely, although these rules may vary in the literature [38]. Inthe first iteration, an equilateral triangle is added to the line length (\(l\)), with its base being one-third of the iteration 0 line. This process is repeated in the next iteration, increasing the baseline's length by one-third. The basic Koch fractal principle is illustrated in Figure 1. The following formulas are used for the Koch fractal iterations.  
\[\text{K}_{0}=l \tag{1}\]  
\[\text{K}_{1}=\frac{l}{3} \tag{2}\]  
\[\text{K}_{2}=\frac{l/3}{3}\text{K}_{2}=\frac{l}{9} \tag{3}\]
## 3 Results and Discussion  
### MD Simulations  
Figure 2 shows the equilibrium structures of the different polyethylene types on the zirconia surface at the end of the simulation. In all the cases, the polymer chains closer to the zirconia surface are highly oriented parallel to the oxide surface and they get flattened. In the simulated structures of 5 AAPE (Figure 2b) and 30 AAPE (Figure 2c), the oxygen atoms of the acrylic acid tend to be adsorbed by the zirconia's oxygen atoms and align perpendicular to the surface. The alignment and flattening of the polymer in parallel to metallic and ceramic surfaces has been also observed in other MD simulations [29; 30; 31; 33]. Considering that the substrate contains oxygen atoms, the oxygen atoms in the polymeric chains localize close to the substrate in order to improve the interactions with the substrate crystalline surface [30]. This phenomenon can be observed in the structures shown in Figure 2 in which the carboxyl groups (C=O) of all the acrylic acid monomers of the chains are in contact with the zirconia surface. Considering that the oxygen plays the most important role in the surface energy of zirconia [37], we evaluated the adhesion of the three types of PE with the zirconia surface by means of the binding energy.  
The binding energy between two joined materials is directly correlated to the adhesion between them [47]. Table 3 summarizes the binding energy values, with a higher absolute value indicating a better adhesion to the zirconia surface. The binding energy to zirconia is slightly higher for the 5 AAPE than for the PE. This difference becomes larger for the 30 AAPE, whose binding energy to the zirconia is 78% higher than that of PE. The higher affinity between the polar acrylic acid and the oxide surface and the adsorption of the oxygen atoms of the polymer on the ceramic oxide surface (Figure 2) are responsible of the higher adhesion between polymer and filler [30; 33]. Moreover, the binding energy values show that the increase in the grafting level of the polymer can greatly enhance the adhesion between the polymer chains and ceramic nanoparticles.  
Figure 2: The equilibrium structures of (**a**) polyethylene (PE), (**b**) polyethylene grafted with 5 mol% of acrylic acid (5 AAPE), and (**c**) polyethylene grafted with 30 mol% of acrylic acid (30 AAPE) on top o a zirconia surface. The colors represent the atoms as follows: O: red, C: grey, H: white, Zr: cyan.