esa-sceva/satcom-chunk-collection huggingface dataset:
# Collusion Attack Resilient 3D Mesh Watermarking Based on Anti-Collusion Fingerprint Code
###### Abstract  
Collusion attack is one of the techniques used for unauthorized removal of embedded marks. However, there are no algorithms that utilize the averaging-resilient fingerprint code into 3D mesh watermarking. In this paper, we propose a collusion resilient 3D mesh watermark based on mesh spectral analysis and the anti-collusion fingerprint code. We present two fingerprint schemes by exploiting the theoretically proven anti-collusion codes: the group-divisible partially balanced incomplete block design, and [PERSON]'s fingerprint code. The proposed extraction scheme not only met the requirement for an anti-collusion code but also provided sufficient length of the payload for the fingerprint code. To minimize the detection error, we also modeled the response of the detector and herein present optimized thresholds for our method. Based on the experiments with public benchmarks, the proposed method outperformed conventional robust mesh watermarking against collusion attack, and provided robustness to the combination of a small amount of added noise and collusion attack.  
Keywords:3D mesh watermarking; collusion attack; multimedia security; fingerprinting +
Footnote †: journal: Applied Sciences
# Analysis of Development Coordination Levels between Skywalk Systems and Urban Spatial Environments
## 3 Materials and Methods  
### Coupling Degree Model  
The coupled coordination model is a tool for analyzing the interactions and impacts of two or more systems and facilitates the assessment of the level of system coordination development [48]. The degree of coupling reflects the degree of interdependence between systems, and the degree of coordination measures the harmony of their interaction. In urban research, the model is widely used to assess the coordinated development of cities, infrastructure, and ecological environment. Studies such as [PERSON] (2022), [PERSON] (2011), [PERSON] (2019), [PERSON] (2023), [PERSON] (2010), [PERSON] (2020), and [PERSON] (2005) [49; 50; 51; 52; 53; 54; 55] used the model to analyze rail transit, urbanization, tourism, transportation-oriented development (TOD) station areas, commercial pedestrian neighborhoods, and the urban environment, providing valuable references for urban design.  
When discussing the level of consistency between skywalk systems and the urban environment, the coupled coordination model can provide an in-depth understanding and a comprehensive assessment, and it boasts two major advantages: systematic comprehensive analysis and complexity treatment [56; 57]. The coupling degree model considers the skywalk system and the urban environment as a holistic system, reveals the degree of mutual influence and coordination between the two through comprehensive analysis, and addresses the complexity of the urban environment, so as to better understand their respective levels of development consistency.
## 3 Rapid Electrokinetic Patterning
### Hybrid Feature of REP  
REP can be classified as a hybrid technique because it employs two different driving sources, i.e., a uniform AC electric field and a focused laser [24; 61; 62; 63]. When the AC electric field is applied to a REP chip, colloidal particles suspended in a fluid are polarized in response to the field; at the same time, they are attracted to both of two electrode surfaces by long-range electrohydrodynamic (EHD) flow [58; 61; 62]. Then, the particles show an irregular distribution on the surfaces, forming small clusters. The focused laser beam creates thermal gradients in a fluid that interact with the applied electric field to produce a toroidal electrothermal (ET) vortex [114; 115; 116]. The ET vortex transports the particle clusters on the electrode surface toward the laser focal point. REP-based manipulation of  
Figure 7: Microfluidic setup for REP realization consisting of two parallel-plate electrodes, a uniform alternating current (AC) electric field, and a focused laser beam. ITO: indium tin oxide. Reproduced from [58], with the permission of American Chemical Society (2010).  
particles becomes achievable when the two driving sources are simultaneously provided to induce the physical phenomena.  
The hybrid nature of REP is shown in Figure 8. The simultaneous application of a uniform AC electric field and a focused laser to a REP chip initiates the aggregation of suspended particles on an electrode surface (Figure 8a). Turning the laser off makes the particles in the cluster scatter immediately on the electrode (Figure 8b). On the contrary, turning the AC electric field off causes the particles to be convected away from the electrode along the natural convection induced by laser heating (Figure 8c). These results demonstrate that REP cannot be realized by the application of one of the two driving sources.
## 2 Results and Discussion  
### The Optimization of Extraction Time  
In order to find better extraction time for the four SPME fibers, the organic volatile flavor compounds in FST 1 were extracted for five different time periods (15, 30, 45, 60 and 75 min, respectively) at 50 \({}^{\circ}\)C. The results are listed in Table 1.  
The results show that 60 min is more suitable for Carboxen/PDMS among the five chosen extraction time periods. The number of identified constituents is 38. The reason is mainly that the organic volatile flavor compounds in FST 1 need about 60 min to reach adsorption and desorption equilibrium in Carboxen/PDMS. If the extraction lasts longer, the adsorbed constituents which are in low abundance will be replaced by higher content constituents, which results in a smaller number of identified constituents. The film thickness of PDMS/DVB is thinner than that of Carboxen/PDMS, so it takes less time to reach adsorption and desorption equilibrium for the organic volatile flavor compounds in PDMS/DVB. The results indicate that 30 min is more suitable for PDMS/DVB. The film of DVB/CAR/PDMS is made from three different kinds of materials, and its film thickness is near to that of Carboxen/PDMS. The more suitable extraction time period for DVB/CAR/PDMS is also 60 min. Among the five chosen extraction time periods, 75 min is better for PDMS. The reason is that the film of PDMS is the thickest of the four kinds of fibers, so the volatile organic flavor compounds need more time to reach adsorption and desorption equilibrium.  
The organic volatile flavor compounds in FST 2 were extracted for 60 min using Carboxen/PDMS, 30 min with PDMS/DVB, 60 min with DVB/CAR/PDMS and 75 min w PDMS, respectively. The total ion chromatograms of organic flavor compounds in FST 1 extracted with the four SPME fibers corresponding to Table 2 are shown in Figure 1, and the total ion chromatograms of organic flavor compounds in FST 2 extracted with the four SPME fibers corresponding to Table 2 are shown in Figure 2. The analytical results for FST 1 and FST 2 are listed in Table 2.  
Figure 1: Cont.  
Figure 2: The total ion chromatograms of organic flavor compounds in FST 2 extracted with the four SPME fibers corresponding to Table 2.
# Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios
## 2 Materials and Methods
### Prior Work
#### 2.3.1 Normalized Minimum Sum Algorithm (NMS) and Model-Driven Methods
The main calculation formula for the NNMS decoding algorithm is as follows [31]:  
The CN layer calculates messages from the CN to the VN:  
\[U_{cv}^{l}=\prod_{v^{\prime}\in V_{j}\backslash i}\text{sgn}\Big{(}U_{vc}^{(l- 1)}\Big{)}\cdot\min_{v^{\prime}\in V_{j}\backslash i}\Big{|}a_{v^{\prime} \subset}^{(l-1)}\times U_{v^{\prime}\subset}^{(l-1)}\Big{|} \tag{2}\]  
The VN layer calculates messages from the VN to the CN:  
\[U_{vc}^{l}=L_{v}+\sum_{c^{\prime}\in C_{i}\backslash j}\Big{(}b_{c^{\prime} \supset v}^{(l)}\times U_{c^{\prime}\supset v}^{l}\Big{)} \tag{3}\]  
In the NNMS LDPC decoder, neural networks are introduced to learn the rules for message updates. The iterative update transmission process of messages in the Tanner graph is mapped to the hidden layer of the neural network, so that the entire NNMS LDPC decoding structure is similar to a neural network. In each iteration of the training process, the messages between the CNs and VNs in the hidden layer are multiplied by different trainable parameters for training, which is equivalent to assigning different weights to each connection on the Tanner graph. Then, supervised learning is carried out using the known correct codewords, the trainable parameters of the neural network are adjusted through backpropagation, repeating the training iterations until the network's performance converges or reaches the predetermined training iteration count. This process aims to make the network's output approximate the correct decoding result, achieving more accurate decoding with fewer iterations.
## 6 Discussion
### Practical Applications  
Virtual reality presents promising applications for accessible housing design. It enables early-stage iteration and visualization, which can reduce design time and support better decision-making about furniture layout, switches, outlets, and door orientations. VR also facilitates testing of spatial dimensions, ensuring houses meet accessibility standards. In public housing programs, the tool could help match housing layouts to the specific needs of applicants with disabilities, potentially improving satisfaction and functional independence.
# Semi-Active Vibration Control Based on a Smart Exciter with an Optimized Electrical Shunt Circuit
## 2 Electro-Mechanical Modeling of Smart Exciter with Shunt Network  
This section describes the simplified and discrete model of the investigated SE with an extra electrical shunt. Linear and time-invariant behavior is assumed for all parts. The SE model consists of an electrical circuit coupled with a mechanical network. The electrical part of the SE can be represented as a series connection of a resistor and an inductor and  
Figure 1: Simplified models of semi-active damping devices: (**a**) Relaxation-Isolator (RI), (**b**) Piezoelectric Shunt Damping (PSD) and (**c**) Electromagnetic Shunt Damping (EMSD).  
is also known as a resistive-inductive (RL) shunt. Additionally, the suspension of the SE can be modeled as a mass-spring-damper system. Figure 2 shows the simplified model of the SE, where \(M_{e}\) is mass of the SE, \(Bl\), \(R_{e}\) and \(L_{e}\) are force factor, electric resistor and inductance of the voice coil of the SE respectively, \(K_{e}\) and \(D_{e}\) are the spring constant and damping constant of the SE suspension, \(I(t)\) is the current in the coil, \(u_{s}(t)\) and \(x_{e}(t)\) are the base displacements of the panel and the SE respectively, and \(R_{2}\) and \(L_{2}\) represent an additional series connection of the resistor and inductor that can be used to match the mechanical impedance at the mounting position. Similar models can be found, for example in [21].  
According to Figure 2, the equations of motion of the SE-RL-shunt system are given by:  
\[M_{e}\cdot\ddot{x}_{e}+K_{e}\cdot x_{e}+D_{e}\cdot\dot{x}_{e}=K_{e}\cdot u_{s} +D_{e}\cdot\dot{u_{s}}+Bl\cdot I \tag{1}\]  
\[(L_{e}+L_{2})\cdot\dot{I}+(R_{e}+R_{2})\cdot I+Bl\cdot\left(\dot{x}_{e}-\dot{u _{s}}\right)=0 \tag{2}\]  
\[F_{e}=Bl\cdot I \tag{3}\]  
Typical values of model parameters (taken from the literature) are shown in Table 1.
## 2 Materials and Methods
### Characterization of the Study Area  
#### 2.2.1 Physical Geography: Mountain Ranges and River Connections  
The border region between Jiangxi and Anhui features extensive mountain ranges and interconnected rivers, with the Xin'an River and the Chang River serving as vital links within the area. The topography is diverse, dominated by mountains and hills interspersed with river valley plains. The mountain ranges primarily extend in a northeast-southwest orientation, with a concentration in the northeast, resulting in a topographical gradient characterized by elevated terrain in the northeast and descending elevations in the southwest, thereby forming a distinctive and complex geomorphological landscape. This area is encircled by mountains to the north, east, and south, with mountain ranges of varying elevations flanking the Xin River, forming an \"inverted mountain\" configuration. Notable peaks include Zhanggong Mountain, Huaiyu Mountain, and Wuyi Mountain, situated from north to south, with the Xin River Basin at the center. In the Jiangxi-Anhui border area, the hydrological network is well-developed, featuring numerous rivers, the majority of which are part of the Poyang Lake and Giantang River systems. The Xin River and Rao River are the two principal rivers in northeast Jiangxi, traversing the region from north to south before flowing into Poyang Lake and ultimately flowing into the Yangtze River through the lake's outlet. The Xin'an River, a significant waterway in Huizhou, originates from Wugujian Mountain in the Huaiyu Mountain Range and flows into the Giantang River via Qiandao Lake. Consequently, mountains serve as boundary elements influencing cultural distinctions between the two regions, while the water systems serve as connective elements that foster the development of a commodity economy and sustain cultural similarities, thereby contributing to the creation of vibrant villages and rich dwellings' cultural landscapes.
## 3 Methodology
### Fuzzy Analytical Hierarchy Process (FAHP)  
FAHP is an extension of the Analytical Hierarchy Process (AHP) to deal with uncertain and ambiguous situations. FAHP overcomes the limitations of AHP and effectively solves many MCDM problems. Table 2 provides linguistic expressions for the fuzzy scales, and the triangular fuzzy number (TFN) is defined. To determine the relative importance between two criteria, a scale from 1 to 9 is used based on specific linguistic features. The tilde (-) is added to the parameter symbols to indicate the imprecise nature of the data. FAHP implements six procedural steps, allowing for the analysis of complex relationships between related factors.  
Step 1: The fuzzy pairwise comparison matrix \(\widetilde{\mathrm{C}}^{\mathrm{k}}\) is configured with v criteria, as shown in Equation (1). In the kth determinator's distinction, \(\widetilde{\mathrm{a}}_{\mathrm{ij}}^{\mathrm{k}}\) represents the significance level of the ith criterion in relation to the jth criterion.  
\[\widetilde{\mathrm{C}}^{\mathrm{k}}=\left[\widetilde{\mathrm{a}}_{11}^{ \mathrm{k}}\cdots\widetilde{\mathrm{a}}_{\mathrm{1n}}^{\mathrm{k}}\vdots \cdots\widetilde{\mathrm{a}}_{\mathrm{n1}}^{\mathrm{k}}\cdots\widetilde{ \mathrm{a}}_{\mathrm{nn}}^{\mathrm{k}}\right] \tag{1}\]  
Step 2: To improve the fuzzy pairwise comparison matrix \(\widetilde{\mathrm{C}}\), apply Equation (2). Where K is the total number of experts or decision-makers.  
\[\widetilde{\mathrm{C}}=\left[\widetilde{\mathrm{a}}_{11}^{\mathrm{k}}\cdots \widetilde{\mathrm{a}}_{\mathrm{1n}}^{\mathrm{k}}\vdots\cdots\widetilde{ \mathrm{a}}_{\mathrm{n1}}^{\mathrm{k}}\cdots\widetilde{\mathrm{a}}_{\mathrm{ nn}}\right]\mathrm{where}\ \widetilde{\mathrm{a}}_{\mathrm{ij}}=\frac{\sum_{k=1}^{\mathrm{K}}\widetilde{ \mathrm{a}}_{\mathrm{ij}}^{\mathrm{k}}}{\mathrm{K}} \tag{2}\]  
Figure 3: Diagram of criteria for evaluating sustainable logistics development strategy in Vietnam.  
Step 3: The method determines the mean of fuzzy geometry for each criterion (\(\widetilde{\mathrm{r}}_{\mathrm{i}}\)), as shown in Equation (3).  
\[\widetilde{\mathrm{r}}_{\mathrm{i}}=\left(\prod\
olimits_{\mathrm{j=1}}^{n} \widetilde{\mathrm{a}}_{\mathrm{i}\mathrm{j}}\right)^{\frac{1}{6}}\mathrm{such\; that\;i=1,\;2,\;\ldots,n. \tag{3}\]  
Step 4. To determine each criterion's fuzzy weight (\(\widetilde{\mathrm{E}}_{\mathrm{i}}\)), utilize Equation (4).  
\[\widetilde{\mathrm{E}}_{\mathrm{i}}=\widetilde{\mathrm{r}}_{\mathrm{i}}( \times)\left(\widetilde{\mathrm{r}}_{1}(+)\widetilde{\mathrm{r}}_{2}(+)\cdots (+)\widetilde{\mathrm{r}}_{\mathrm{b}}\right)^{-1} \tag{4}\]  
Step 5: The fuzzy weight of each criterion is faded using the mean (\(\mathrm{M}_{\mathrm{i}}\)) shown in Equation (5).  
\[\mathrm{M}_{\mathrm{i}}=\frac{\widetilde{\mathrm{E}}_{\mathrm{1}}(+)\widetilde {\mathrm{E}}_{\mathrm{2}}(+)\cdots(+)\widetilde{\mathrm{E}}_{\mathrm{n}}}{n} \tag{5}\]  
Step 6: Equation (6) used to calculate the normalized weight of each criterion (\(\mathrm{W}_{\mathrm{i}}\)).  
\[\mathrm{W}_{\mathrm{i}}=\frac{\mathrm{M}_{\mathrm{i}}}{\sum_{\mathrm{i=1}}^{ \mathrm{W}}\mathrm{M}_{\mathrm{i}}} \tag{6}\]
## 6 Discussion
### Classification Effect of Different Methods in the Literature  
To better demonstrate the superiority of our method, we compare our results with the latest research on traffic classification. The performance comparison is shown in Table 10. The training time and predicting time are related to the number of training samples and the number of prediction samples. In order to facilitate comparison, unit processing is performed, and the training time and predicting time occupied by each sample are calculated. The result shows that the method in this paper has the highest accuracy, average precision, and average recall, and has the lowest training time and predicting time.  
The authors in [15] use the linear correlation coefficient (cor) with value ranges from \(-1\) to \(1\) to represent the linear correlation between two random features, and remove the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Protocol Category** & **Precision** & **Recall** \\ \hline IEC 61850-MMS & 99.26\% & 98.95\% \\ Ethernet/IP & 99.40\% & 98.01\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Protocol classification precision and recall based on SVM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Protocol Category** & **Precision** & **Recall** \\ \hline IEC 61850-MMS & 92.26\% & 92.27\% \\ Ethernet/IP & 93.94\% & 92.01\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Protocol classification precision and recall based on NB.  
Figure 8: Accuracy and time cost comparison of classifiers based on different ML methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Classification Model** & **Accuracy** & **Training Time** & **Testing Time** \\ \hline Literature [15] & DT & 82.31\% & 36.64 \(\upmu\)s & 35.64 \(\upmu\)s \\ Literature [20] & Tree-RNN & 98.98\% & 999.7 \(\upmu\)s & 185 \(\upmu\)s \\ Literature [22] & 1D-CNN+LSTM & 93\% & 18.3 ms & \textbackslash{} \\ Our method & DT & 99.99\% & 3.04 \(\upmu\)s & 9.43 \(\upmu\)s \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison between different methods.  
feature when \(|\cot|=1\), then use the feature dataset to train a decision tree model. However, the two features are usually regarded as strongly correlated when \(|\cot|>0.5\). Therefore, this method causes a part of the interference features to be retained, so the performance of this method is not as good as that of the method in this paper in terms of accuracy and time consumption. The authors in [20; 22] both use DL to automatically extract features and classify traffic. Both of them achieve high classification accuracy. However, the deep learning algorithm is complicated, which causes a high time cost.  
To sum up, the feature extraction method proposed in this paper can better extract features conducive to classification, with lower feature dimensions and no need for manual participation. It is superior to the existing algorithms in terms of classification accuracy and time consumption.
###### Abstract  
Early fault detection (EFD) in run-to-failure processes plays a crucial role in the condition monitoring of modern industrial rotating facilities, which entail increasing demands for safety, energy and ecological savings and efficiency. To enable effective protection measures, the evolving faults have to be recognized and identified as early as possible. The major challenge is to distil discriminative features on the basis of only the 'health' signal, which is uniquely available from various possible sensors before damage sets in and before the signatures of incipient damage become obvious and well-understood in the signal. Acoustic emission (AE) signals have been frequently reported to be able to deliver early diagnostic information due to their inherently high sensitivity to the incipient fault activities, highlighting the great potential of the AE technique for EFD, which may outperform the traditional vibration-based analysis in many situations. To date, the 'feature-based' multivariate analysis dominates the interpretation of AE waveforms. In this way, the decision-making relies heavily on experts' knowledge and experience, which is often a weak link in the entire EFD chain. With the advent of artificial intelligence, practitioners seek an intelligent method capable of tackling this challenge. In the present paper, we introduce a versatile approach towards intelligent data analysis adapted to AE signals streaming from the sensors used for the continuous monitoring of rotating machinery. A new architecture with a convolutional generative adversarial network (GAN) is designed to extract the deep information embedded in the AE waveforms. In order to improve the robustness of the proposed EFD framework, a novel ensemble technique referred to as 'history-state ensemble' (HSE) is introduced and paired with GAN. The primary merits of HSE are twofold: (1) it does not require extra computing time to obtain the base models, and (2) it does not require a special design of the network architecture and can be applied to different networks. To evaluate the proposed method, a durability rolling contact fatigue test was performed with the use of AE monitoring. The experimental results have demonstrated that the proposed ensemble method largely improves the robustness of GAN.  
e 9 February 2023  
1
## 6 Empirical Studies
### Comparison of Different Techniques
#### 6.2.3 Convergence Analysis  
Next we consider the convergence rate of the two semilocal improvement methods on two graphs. For Twitter, the rate of convergence for metric optimization is the same whether we start with AffCommNeigh or RandInit (See Figure 8). The convergence happens essentially in three or four rounds.  
The convergence for the rank swap methods happens in 10-15 steps for Twitter and in 5-10 steps for World-roads; more steps required for larger values of \(k\).  
The convergence for the metric optimization on World-roads happens in 3-4 steps if the starting point is AffCommNeigh. However, if we start with a random ordering, it may take up to 20 steps for the metric to converge. See Figure 9.  
Figure 8: Convergence rate of Aff+Metric (top) and Aff+Swap (bottom) on Twitter.