esa-sceva/satcom-chunk-collection huggingface dataset:
# Random Violation Risk Degree Based Service Channel Routing Mechanism in Smart Grid
## 5 Simulation and Results
### Network Simulation  
The network simulation scenario is set up based on the backbone network topology of one provincial electric power company, as shown in Figure 7. The raw data about topology, business and actual routing are all provided by this electric power company because of the research cooperation between this company and our research team.  
The network simulation topology contains 74 nodes and 104 edges. The purpose of the simulation experiment is to compare the failure rate of the service channel routed by \(SCVRD\)-\(R\) algorithm, AAR-OS algorithm and RAP algorithm in 12 months in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Month** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline Risk of channel & 0.173 & 0.362 & 0.294 & 0.329 & 0.417 & 0.307 & 0.384 & 0.401 & 0.533 & 0.428 & 0.493 & 0.529 \\ \(SC_{N1-N2-N3}\) & & & & & & & & & & & & \\ Risk of channel & 0.079 & 0.124 & 0.177 & 0.253 & 0.239 & 0.328 & 0.406 & 0.318 & 0.434 & 0.473 & 0.452 & 0.506 \\ \(SC_{N1-N4-N3}\) & & & & & & & & & & &Here, we divide 30 services into ten kinds of service intensities as an instance to evaluate the algorithm performance in the different ten service intensities. However, the partition of service intensities is not only this one way. Assume that the service is generated between nodes in 2 hops of 14 aggregation nodes (marked with circle in Figure 7) and the services have the same availability threshold of 99.5%. The failure arrival rates of nodes and edges are randomly generated in the interval of [0.006, 1.5] and their repair time are randomly generated in the interval of [0.4, 0.003]. The service channel failure rate is calculated using the ratio between the number of failure service channels and the number of total service channels. The bandwidth of each optical cable is 5 GB. At present the bandwidth in the electric power communication transmission network is redundant. Besides, the SDH electric power communication service channel uses fixed bandwidth. So, we temporarily do not consider the variable bandwidth of cable.  
The simulation results are shown in Figure 8, which shows that the average service channel failure rate of _SCVRD-R_ algorithm is respectively about 15% and 6% lower than that of AAR-OS algorithm and RAP algorithm under the different service intensities. Other service intensities are similar. The failure rate in the various service intensities has nothing to do with the change of month, just randomly changes within months. The temporal resolution of data is not necessarily fixed for month, which means that week or other durations can also be set as the temporal resolution of data.  
Figure 7: Network Simulation Topology.  
Figure 8: Service channel failure rate.
## 4 Discussion
### Limitations and Strengths  
The main limitation of this study was the absence of a gold standard approach; since the studies were heterogeneous in the selection of the patient sample, in the choice of reference points or areas, a statistical comparative analysis of the results achieved was not possible. Furthermore, only two studies indicated a \"gold standard\" as a reference; [25; 40] however, both the articles did not assess sensitivity, specificity and reliability of the methods [43]. Even though the methods currently present in literature cannot be compared in accuracy, we evaluated the level of agreement among different studies through a descriptive analysis.
# Discriminative Local Feature for Hyperspectral Hand Biometrics by Adjusting Image Acutance
## 1 Introduction
Based on the Gabor filter, [PERSON] et al. [37] designed directional filter banks to draw local patterns for dorsal hand vein recognition. For local feature extraction in hand biometrics, image quality is a make or break factor [38; 39; 40]. High-quality images collected from calibrated cameras contain much more detailed information for human perception [41]. However, [PERSON] et al. [42] found that the presentation of a local feature was more effective in biometrics after filtering the high-quality images to low quality. Image actance, which can be referred to as the contrast of the edge and the background in an image evaluates the image quality. [PERSON] et al. [42] improved the recognition performance by converting high-quality, single-spectral images to low actance images. That being said, to the best of our knowledge there is little to nothing in the literature that studies the properties of low-resolution hyperspectral hand biometric images. The low-resolution image mostly considered as a low-quality image can be easily imaged by a low-cost device, which can further promote the use of biometrics. Hyperspectral biometrics possess the properties of uniqueness, liveness detection, and anti-spoofing that are difficult to achieve by single-band spectral images. Inspired by [42], in this paper we explore the performance of hyperspectral hand biometrics by filtering these images to have different actance. The hypothesis we propose is that there exists an optimal range in image actance for a set of hyperspectral hand images, and when this set is filtered in this range, the recognition performance will be improved. For this reason, thresholded pixel-wise actance value (TPAV) is proposed to evaluate image actance. By convolving with Gaussian filters, TPAV of the hyperspectral hand image changed. Local features were extracted from the changed TPAV's corresponding database and used to implement identification. In particular, extensive experimentswere conducted on HDHV (hyperspectral dorsal hand vein), HPV (hyperspectral palm vein), and MPP (multispectral palmprint) images. We found that the recognition performance can be improved by filtering the images to have different acutance, where the local features extracted are more discriminative after adjusting for image acutance. In the rest of this paper, the image acutance adjustment is proposed in Section 2. Section 3 designs extensive experiments with an analysis of the results. Finally, the conclusions are drawn in Section 4.
## 4 Discussion and Limitations  
The primary purpose of this study was to capture the main features of the upper limb movement of walking subjects (both the healthy and the pathological ones) while estimating all model parameters that characterize the shoulder and elbow movement. The secondary purpose was to use, for PD patients, the estimated stiffness parameters as objective quantitative indices of muscle stiffness in response to pharmacological therapy. The results confirm the effectiveness of the approach through a model whose structure is the same for both the healthy subjects and the patients and with the estimated stiffness coefficients tracking the effects of taking therapy in accordance with the MDS-UPDRS scores. In particular, the measured joint angles are properly reproduced by means of the proposed model once it is adapted to each subject using the available (specific) anthropometric knowledge and the physically consistent parameter estimates. Indeed, from Tables 3 and 5, the values of MSE RMSE, and MAE for each subject are kept significantly small by the proposed procedure, showing its effectiveness in spite of the just approximated nature of the test signals reconstructing \(\tau_{\text{CEs}}(\cdot)\) and \(\tau_{\text{CEe}}(\cdot)\). Values are lower for the shoulder than the elbow, owing to the hard-to-model stronger nonlinearities characterizing the latter. Moreover, Figures 4-13 illustrate how the spectral content of recorded movements is satisfactorily reproduced, which was already evidenced to contain information related to a pathological condition [38]. The obtained results in terms of minimum errors are of crucial importance, since they demonstrate the capability to reproduce both healthy and pathological behaviors with the same model. On the other hand, Tables 4 and 6 show how the estimates of the stiffness parameters \(\beta_{1}\), \(\beta_{2}\), \(\beta_{5}\) and \(\beta_{6}\) are actually negative, as physically expected for elastic and viscous constants. The same happens for the estimates of \(\beta_{4}\) and \(\beta_{8}\), which are associated with muscle contraction: positive values are associated with arm flexor muscle activity, negative values are associated with extensor muscle contraction (the estimates of \(\beta_{3}\) and \(\beta_{7}\) depend, instead, on the balance position of the limb, while \(\beta_{4}\) and \(\beta_{8}\) characterize the amplitude of the active contribution). Arm oscillations are not discussed at this stage. Table 4 illustrates how the estimated parameters for healthy subjects reasonably vary in accordance with the step cadence [35] (it is worth highlighting that subjects underwent difficulties in maintaining the slow cadence of 80 steps per minute and the fast cadence of 120 steps per minute).
# Quantum Circuit Implementation of Multi-Dimensional Non-Linear Lattice Models
## 2 Non-Linear Lattice Modeling of Fluid Dynamics
### Modified D1Q3 Model for One-Dimensional Flow  
In recent work by the author [12], the D1Q3 model for one-dimensional iso-thermal flow was re-formulated in terms of a D2Q4 model to facilitate quantum-circuit implementation. To summarize, in the original D1Q3 model, the direction vectors \(e_{i},i\in[0,2]\), density \(\rho\) and velocity \(u\) (by construction, as a function of \(f_{2}\) and \(f_{0}\) for this model) are defined as,  
\[e_{i}=\left\{\begin{array}{ccc}-1&\text{for}&i=0\\ 0&\text{for}&i=1\\ +1&\text{for}&i=2\end{array}\right.;\ \rho=\sum_{0}^{2}f_{i}\;;\ u=f_{2}-f_{0} \tag{11}\]  
with the collision terms defined as,  
\[-\frac{dt}{\tau}\left[\left(\begin{array}{c}f_{0}\\ f_{1}\\ f_{2}\end{array}\right)-\left(\begin{array}{c}\frac{1}{2}\Big{[}\frac{1}{3}- u+u^{2}\Big{]}\\ \frac{2}{3}-u^{2}\\ \frac{1}{3}+u+u^{2}\Big{]}\end{array}\right)\right] \tag{12}\]  
For quantum-circuit implementation, an even number of discrete-velocity directions was preferred. This was achieved by replacing the original \(f_{1}\) distribution function with two identical distribution function components, \(f_{1}\) and \(f_{2}\), in the modified model (with corresponding duplicate discrete velocity). The corresponding equilibrium distribution functions become,  
\[\vec{f}^{eq}=\left(\begin{array}{c}\frac{1}{2}\Big{[}\frac{1}{3}-u+u^{2} \Big{]}\\ \frac{1}{2}\Big{[  
equations for which this type of transform cannot be performed, and therefore, the original, non-linear form of the Burgers equation is maintained here. The quasi-1D Burgers equation can be written as,  
\[\frac{\partial u}{\partial t}+u\frac{\partial u}{\partial x}=\mu\Big{(}\frac{ \partial^{2}u}{\partial x^{2}}+\frac{\partial^{2}u}{\partial y^{2}}\Big{)} \tag{16}\]  
where \(\mu\) represents the viscosity of the considered medium. In the literature, a range of Lattice Boltzmann models can be found for the Burgers equation. Here, the D2Q4 model, as described by [PERSON] and [PERSON] [16], is used, recast in a notation consistent with that for the modified D1Q3 model used here. Then, this D2Q4 model can be summarized as,  
\[\begin{array}{l}e_{0}/c=(1,0)^{T}\ ;\ e_{1}/c=(0,1)^{T}\ ;\ e_{2}/c=(0,-1)^{T}\ ;\ e_{3}/c=(-1,0)^{T}\\ u/c=f_{0}-f_{3}=f_{0}^{eq}-f_{3}^{eq}\end{array} \tag{17}\]  
with equilibrium distribution function defined as,  
\[f_{0}^{eq} =\frac{1}{4}\Big{[}\frac{u}{c}+\frac{u^{2}}{c^{2}}\Big{]}\ \ ;\ \ f_{1}^{eq}=\frac{u}{4c} \tag{18}\] \[f_{2}^{eq} =\frac{u}{4c}\ \ \ ;\ \ f_{3}^{eq}=\frac{1}{4}\Big{[}-\frac{u}{c}+ \frac{u^{2}}{c^{2}}\Big{]}\]  
This highlights that with relatively small changes, i.e., mainly the difference in the'rest-state' equilibrium state, the quantum algorithm developed here for the D1Q3 model (specifically, in a modified D1Q4 form to ease implementation as a quantum circuit) can be modified into that for the D2Q4 model for the quasi-1D Burgers equation.
# Efficient Pruning of Detection Transformer in Remote Sensing Using Ant Colony Evolutionary Pruning
## 2 Related Works  
### Remote Sensing Object Detection  
In recent years, detectors based on sophisticated deep convolutional neural networks have demonstrated remarkable achievements in object detection. Consequently, with the proliferation of superior deep detection neural networks, researchers have applied these cutting-edge detection neural networks to object detection tasks within remote sensing images. To adapt these detection neural networks, originally designed for natural scene images, to remote sensing images, industry scholars have conducted extensive enhancement work focused on the unique characteristics of remote sensing images. This includes mitigating the impact of complex backgrounds in remote sensing images on object detection [17; 18], addressing the varying scales of targets in remote sensing images [19; 20], and overcoming challenges related to detecting small objects within remote sensing images [21; 22]. Recently, the integration of self-attention mechanisms, known for their exceptional performance across various domains, into remote sensing tasks has begun. However, the original DETR neural network has shown a delayed convergence rate and lacks efficiency in detectingsmall objects, necessitating further refinements. Deformable DETR [23] introduces innovative approaches like deformable feature attention and multi-scale fusion to tackle the detection of small objects. DETR with Improved DeNoising Anchor boxes (DINOs) [24] refines feature representation and query mechanisms to advance detection accuracy. [PERSON] et al. [25] extends deformable DETR, and the multi-scale separated attention, region, and weight coefficients are merged to strengthen the focus on small objects. [PERSON] [26] enhances the DINO model's capability to extract multi-scale features, while Skip-Encoder and Skip-Decoder modules have been integrated into the neural network.  
Nonetheless, existing DETR-like networks have been predominantly crafted to optimize detection performance. This focus on optimization has resulted in an abundance of parameters and increased computational complexity. As a consequence, the model's inference speed is low. The core objective of this research lies in preserving the original accuracy of these models while minimizing the number of parameters and computational complexity to the greatest extent possible.
# A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller
In terms of statistical attributes, most traditional machine learning algorithms use Shannon entropy to measure the attribute, but L-DDoS attack traffic is very similar to normal traffic. Shannon entropy performs well in the detection of the relatively large traffic attacks with obvious attack behavior, it may lead to a high false positive using Shannon entropy to detect L-DDoS attacks [4]. In terms of detection performance, traditional methods use Shannon entropy to pre-process the data, which makes the detection data can only be divided into two categories, when L-DDoS attacks with different rates occur in the network, they cannot be adapted to detect, and may classify some degree of attacks into normal classes, resulting in a high false alarm rate. In addition, if the similarity between linked packets is considered, the accuracy will be improved to a certain extent. To address above-mentioned problems, we propose an adaptive detection method to detect L-DDoS attacks. The main contributions of our work can be summarized as follows:  
1. We combine software defined network (SDN) to achieve complete control of network traffic that improves the detection of single link or single host of traditional data center network and utilize OpenFlow's massage mechanism to increase the flexibility of the detection period. 2. We use the PACKET_IN message of OpenFlow mechanism to set up a trigger detection period. 3. We propose a Renyi entropy to add the entropy difference between the normal traffic and L-DDoS to reduce the false positive in attribute statistics. 4. We propose a probabilistic model of combining Renyi entropy and hidden Markov model (HMM-R) to define a variety of states with double stochastic processes of hidden states and observed states to improve the true positive and reduce the false positive and increase the flexibility. The rest of the paper is organized as follows. We provide an overview of the related work in Section 2. We introduce the background about L-DDoS, SDN, Renyi entropy, and the hidden Markov model in Section 3. An adaptive HMM-R model of L-DDoS attacks is presented in Section 4. Section 5 describes the implementation and evaluation. Finally, we conclude the paper in Section 6.
# Confidence-Based Voting for the Design of Interpretable Ensembles with Fuzzy Systems +
## 4 Discussion  
The Confidence-Based Voting procedure realizes relatively simple idea of combining two classifiers: if one of the classifiers is not sure in its final decision, then the other classifier may help make the right decision. The experiments on several datasets have shown that this idea works relatively good in many scenarios for classifiers which have some kind of confidence measure. For example, the HEFCA algorithm used in this study has clear measure of confidence, i.e., the membership value returned by the winner rule, however other classifiers, like neural network with softmax layer, could be used as well. For other classifiers, these confidence measures could be constructed, for example, for the k-NN classifier this could be the fraction of neighbors belonging to the right class, or the relative size of the final leaf in case of decision tree, or the ratio of the number of trees voting for the same class in random forest. So, the CBV is a relatively simple and efficient technique, which could be expanded also for more than 2 classifiers.  
Unlike many other studies, which are concerned about accuracy of the designed classifier, in this study, the interpretability of the resulting classification system was one of the main goals. That is, with Confidence-Based Voting, we get a well-explained classification of every instance from the test set, and with relatively large membership value. Most fuzzy classifiers return membership values together with class numbers, and in real-world scenarios where interpretability is important, only those fuzzy rules which work with high membership values represent particular interest for the researchers, if they contain non-trivial dependencies. With CBV, it is possible, for example, to combine several fuzzy classifiers with different structure and different decision making procedure, so that different explanations are given for different parts of the dataset. So, unlike many ensemble methods, which simply assign weights to classifiers, the CBV is a white box, i.e., explainable procedure of decision making, which is an important property for real-world problems.
# Compression of Vehicle Trajectories with a Variational Autoencoder
## 3 The Training Dataset
#### Data Preprocessing and Preparations  
The database for the training consists of 1614 vehicles. One-third of them perform a lane-changing maneuver to the left, and another one third is right lane changing; the rest is lane-keeping.  
Figure 1: Top view of the US-101 freeway from which the data are extracted.  
Figure 2: Top view of the I-80 freeway from which the data are extracted.  
Two trajectory pieces are extracted from the complete path data of each vehicle. One is 6 s (60 time-steps) ahead of the occurrence of the lane index changing, and one other 6 s after. In the case of the lane-keeping ones, two consecutive pieces of trajectory are taken. All the \(x,y\) coordinate units have been changed from feet to meters. The following steps have been performed on both subsets.  
First, we checked if a vehicle had at least 120 time-steps in the data set because two 60 long sequences are desired, and separated according to what maneuver is done. Second, a vehicle is only considered in the training set if the lane changing occurred later than the first 60 time-steps, with the two sequences extracted. Thirdly, the two sets have been added together and the largest possible balanced data set is randomly chosen and drawn, with the same number of lane-keeping, left, and right lane changing vehicles.  
For the sake of the higher generalization capability, every trajectory sample is translated to start from the origin.
# An Efficient Opportunistic Routing Based on Prediction for Nautical Wireless Ad Hoc Networks
## 3 The Proposed Algorithm
### Routing Decision Scheme  
During packet delivery, an efficient routing scheme can improve the success delivery ratio of message forwarding. In the scheme below, we utilize \(F_{N_{\alpha}}\) to denote the neighbor sets of node \(N_{n}\), where \(F_{N_{n}}^{m}\) indicates the message sets carried by node \(N_{n}\). In Algorithm 1, when a link does not exist in the networks, the sender or carrier node \(N_{S}\) will broadcast routing messages. If node \(N_{l}\) (\(N_{l}\) is the relay node of node \(N_{S}\), and \(N_{l}\)\(\epsilon\)\(N_{n}\)) is the destination node \(N_{d}\), the messages are directly transferred from node \(N_{l}\) to the destination node \(N_{D}\) (Algorithm 1: line 5). If the intermediate node \(N_{l}\) still needs to broadcast messages, relay advancement prediction to the destination node is introduced. Equation (12) is further used to make decisions on relay node selection (Algorithm 1: line 9). Otherwise, the link between node \(N_{l}\) and its next hop is available, and the routing strategy is switched to the MANETs routing described in [2] to select the appropriate relay node (Algorithm 1: line 11). Finally, the messages are delivered hop by hop until the destination node is close and eventually to the destination node itself.  
```
1:for each message \(m\)\(\epsilon\)\(F_{N_{n}}^{m}\)do
2:for each node \(N_{l}\)\(\epsilon\)\(F_{N_{n}}\)do
3:for sender \(S\) broadcasts messages to its neighbors do
4:if\(N_{l}\) = \(N_{D}\)then
5:\(N_{l}\) transmits directly messages \(m\) to \(N_{D}\)
6:else
7:while\(P_{prr}(N_{S}(m),N_{l}(m))>0\)do
8:if node \(N_{l}\) still needs to broadcast messages to its neighbors then
9: Equation (12) is used to make decision on relay node selection
10:else
11: the routing strategy is switched to the MANETs routing in [2] to select the relay nodes
12:endif
13:endwhile
14:endif
15:endfor
16:endfor
17:endfor
```  
**Algorithm 1** WRSP routing algorithm