esa-sceva/satcom-chunk-collection huggingface dataset:
# Camouflage Backdoor Attack against Pedestrian Detection
## 3 Methodology
### Camouflage Backdoor Attack  
In general, backdoor attacks have two primary objectives, namely (1) effectiveness and (2) stealthiness. To increase the concealment of the backdoor attack, we use image scaling techniques for camouflage. Image scaling is an indispensable technique to preprocess data for all DNN models. However, [PERSON] et al. [18] found that this process gives rise to new adversarial attacks: the adversary can modify the original image in an unnoticeable way, which will become the desired adversarial image after downscaling. [PERSON] et al. [32] further adopted this technique to realize clean-label backdoor attacks for classification models. Motivated by this vulnerability, our camouflage backdoor attack involves the alteration of attack images through imperceptible perturbations. These modifications transform them into poisoned images that maintain the appearance of benign images. Throughout the model training process, these images receive erroneous annotations following the image scaling step, thereby embedding the desired backdoor into the model.  
Our goal is to generate a poisoned image \(I^{*}\) from the benign image \(I_{benign}\) and attack image \(I_{attack}\). Poisoned image \(I^{*}\) is visually indistinguishable from \(I_{benign}\); however, after scaling, \(scaleFunc(I^{*})\) becomes a malicious attack image \(I_{attack}\), as shown in Figure 2b. During the inference stage, the adversary can simply insert the trigger into the pedestrian images at a designated location to activate the backdoor, as depicted in Figure 1. Subsequently, the model will recognize the trigger and give a false prediction (e.g., no persons), potentially leading to significant safety concerns.  
```
Input: Extension dataset \(\mathcal{D}^{\prime}\), Pedestrian dataset \(\mathcal{D}\), Image matting model \(\mathcal{M}\), Trigger pixel \((x,y)\) Output: Poisoned image \(I^{*}\)
1\(D^{\prime}\leftarrow\) Load dataset of images from files;
2 foreachimage \(I^{\prime}\) in dataset \(\mathcal{D}^{\prime}\)do
3\(I^{\prime}_{F}\), \(I^{\prime}_{B}=\mathcal{M}(I^{\prime})\)// Get the foreground image (pedestrian image) foreachpixel \((i,j)\) in image \(I^{\prime}_{F}\)do
4\((x,y)=\) CalculateTriggerLocation\((I^{\prime}_{F},w,h)\);// Calculate the trigger location based on the width \(w\) and height \(h\) of the image \(I^{\prime}_{F}\) if\((i,j)=(x,y)\)then
5 Set pixel \((i,j)\) to white; // Adding trigger pixel
6 end if
7
8 else
9 Keep pixel \((i,j)\) as it is;
10
11 end if
12
13 Get the trigger-embedded image \(I_{trigger}\);
14
15 end for
16\(D\leftarrow\) Load dataset of images from files;
17 foreachimage \(I_{benign}\) in dataset \(\mathcal{D}\)do
18\(I_{attack}=\)ImageSynthesis\((I_{benign}\), Resize\((I_{trigger}))\);
19\(I^{*}=\)ScaleFunc\((I_{attack}\), \(I_{benign})\);
20
21 end foreachreturn Poisoned image \(I^{*}\);
```  
**Algorithm 2**Creating Poisoned Images Algorithm
## 6 Experimental Evaluation
### Numerical Results
#### 6.2.3 Quality of Service Results  
Table 9 reports the average objective values (normalized by the number of transfer zones) for all scenarios, grouped by tolerance. Results show that both EA and the exact methods significantly improve over the baseline real solution. For BSP variant #1, the largest difference is 1.72 (4.68\(-\)2.97) between the exact approach and the real solution, when low user tolerance is considered (\(\lambda=30\)). Similar to the results reported in Table 5, the graphic demonstrates that improvements of the optimization methods are better in tighter BSP instances. For BSP variant #2, the best improvement of the exact method was 4.45, and the best improvement of EA was 4.45, both when \(\lambda=30\). Results are graphically presented in Figure 7 for BSP variant #1. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & _EA Over Real_ & & & _Exact Over Real_ & & & _EA vs. Exact_ \\ \hline \(\lambda\) & \(\Delta\) & _Instances_ & \(\lambda\) & \(\Delta\) & _Instances_ & \(\lambda\) & \(\Delta\) & _Instances_ \\ \hline
30 & 150.95\% & 15 & 30 & 158.13\% & 15 & 30 & \(-\)3.32\% & 15 \\
50 & 78.06\% & 15 & 50 & 82.16\% & 15 & 50 & \(-\)2.26\% & 15 \\
70 & 38.95\% & 15 & 70 & 39.79\% & 15 & 70 & \(-\)0.61\% & 15 \\
90 & 25.05\% & 15 & 90 & 25.32\% & 15 & 90 & \(-\)0.22\% & 15 \\
100 & 25.23\% & 15 & 100 & 25.47\% & 15 & 100 & \(-\)0.19\% & 15 \\ \hline \hline \multicolumn{8}{c}{_EA Over Real_} & \multicolumn{4}{c}{_Exact Over Real_} & \multicolumn{4}{c}{_EA vs. Exact_} \\ \hline \(N\) & \(\Delta\) & _Instances_ & \(N\) & \(\Delta\) & _Instances_ & \(N\) & \(\Delta\) & _Instances_ \\ \hline
30 & 68.28\% & 25 & 30 & 70.25\% & 25 & 30 & \(-\)0.89\% & 25 \\
70 & 63.37\% & 25 & 70 & 64.84\% & 25 & 70 & \(-\)0.67\% & 25 \\
110 & 58.50\% & 25 & 110 & 63.43\% & 25 & 110 & \(-\)2.41\% & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Exact and EA improvements over baseline real solution, grouped by tolerance and problem dimension, for BSP variant #2. Three quality of service metrics (\(r\), \(l_{s}\), and \(l_{n}\)) are considered in the results reported for the computed solutions in Table 10, grouped by dimension (\(NP\)) and tolerance (\(\lambda\)). Metric \(r\) is the ratio between the average waiting time computed by the proposed timetable schedules and the threshold value that passengers are willing to wait for a transfer in each transfer zone (\(W_{b}^{ij}\)). The \(r\) metric evaluates the number of successful synchronized trips, which are represented by \(r\leq 1.0\), whereas unsuccessful synchronizations are represented by \(r>1.0\). Metric \(r\) also allows evaluating how far from acceptable (i.e., synchronized) the trips of two lines are, considering the deviation from the ratio defined by the threshold value passengers are willing to wait (1.0). In turn, metric \(l_{s}\) is the average number of successfully synchronized lines, whereas metric \(l_{n}\) is the average number of not synchronized lines. All metrics are reported for each instance class regarding dimension and tolerance.
## 4 Discussion of Simulation Results
### Self-Propulsion Simulation
#### 4.1.1 Self-Propulsion Point Calculation and Determination  
The self-propulsion point calculation of the series Kappel propeller combined with the diamond wing underwater glider under four working conditions of speeds of 6 kn, 5 kn, 4 kn, and 3 kn is shown in Figure 19. The black dotted line in Figure 19 represents F_total = 0 N.  
At different speeds, the propeller is balanced with the net force of the hybrid-driven underwater glider, i.e., F_total = 0, and the propeller speed is called the self-propelled point. From the analysis of Figure 19, it can be seen that the F_total of the mixed-drive underwater glider gradually decreases with the increase in propeller speed at different speeds. The self-driving point speed of the mixed-drive underwater glider is Kap05, Kap04, Kap03, Kap02, and Kap01 corresponding to the Kappel propeller model from small to large, and the self-driving speed of the mixed-drive underwater glider corresponding to Kappel05 can be reduced by about 7.5% compared with the self-driving speed of the mixed-drive underwater glider corresponding to Kappel01. Its thrust gradually increases, and it can reach the self-propelled state at low speed, which is conducive to the hybrid-driven underwater glider to achieve the energy-saving goal in the self-propelled process.  
As shown in Figure 20, the relationship between the self-driving point speed and the series Kappel propeller model can be further analyzed, and the self-driving point speed of the mixed-drive underwater glider shows a similar trend of gradually decreasing with the increase in the bending degree of the end plate of the series Kappel propeller at different speeds, and the combination of propeller Kap05 and diamond wing underwater glider has the lowest self-driving point speed, which once again confirms that, within a certain range, the more bent the Kappel blade tip pitch propeller with the underwater glider, the greater the thrust and the better the propulsion performance.
## 2 Theory of Underwater Electric Field Communication
### Electric Field Communication Model
In the simplified electric dipole model, the field strength \(E\) at the receiver \(r\) is:  
\[E=E_{r}+E_{\theta}+E_{\varphi} \tag{6}\]  
\[E_{r}=\frac{I_{0}d_{1}e^{-\gamma r}}{4\pi\sigma r^{3}(\sigma+j\omega\varepsilon )}(1+\gamma r)2\text{cos}\,\theta_{1}\text{cos}\,\theta_{2} \tag{7}\]  
\[E_{\theta}=\frac{I_{0}d_{1}e^{-\gamma r}}{4\pi\sigma r^{3}(\sigma+j\omega \varepsilon)}\Big{(}1+\gamma r+\gamma^{2}r^{2}\Big{)}\text{sin}\,\theta_{1} \text{sin}\,\theta_{2} \tag{8}\]  
\[E_{\varphi}=0 \tag{9}\]  
where \(\sigma\) is the electrical conductivity of the medium, \(\varepsilon\) is the permittivity of the medium, \(\mu\) is the magnetic permeability, and \(\gamma=\sqrt{j\omega\mu(\sigma+j\omega\varepsilon)}\). When the receiver is on the axis of symmetry of the electric dipole (\(\theta=\frac{\pi}{2}\), \(\varphi=0\)), the electric field strength is:  
\[J_{c}=\sigma E \tag{1}\]0  
Therefore, when the receiving electrodes are separated by a distance \(d_{2}\), the potential difference received is:  
\[J_{c}=\sigma E \tag{1}\]1  
The frequency of the electric field changes is inversely correlated with the intensity of the electric field strength at the receiving end. In the communication system, it is necessary to consider both the communication distance and communication rate simultaneously to determine an appropriate carrier parameter.
## 1 Introduction
There have been numerous reports on the phase equilibrium conditions of CO\({}_{2}\)-containing gases, especially CO\({}_{2}\)-CH\({}_{4}\) mixed gases, both domestically and internationally [21; 22; 23]. [PERSON] et al. studied the phase equilibrium conditions of CO\({}_{2}\)-CH\({}_{4}\) mixed gas hydrate formation under temperature conditions of 273-288 K and pressure conditions of 1.2-11.0 MPa [24]. Based on experimental data, they have also proposed a multi-parameter empirical model, which has shown good predictive accuracy. [PERSON] et al. reported experimental data on the phase equilibrium conditions of CO\({}_{2}\)-CH\({}_{4}\) mixed gas hydrate formation under temperature conditions of 279.1-289.9 K and pressure conditions of 2.96-13.06 MPa [25]. [PERSON] et al. measured the hydrate dissociation equilibrium conditions for CO\({}_{2}\)-CH\({}_{4}\) with water, N\({}_{2}\)-CH\({}_{4}\) with water, and CO\({}_{2}\)-N\({}_{2}\) with water using a cryogenic sapphire cell. The involved temperature and pressure ranges were from 275.75 to 293.95 K and from 5 MPa to 25 MPa, respectively [26]. [PERSON] et al. investigated the phase equilibrium for hydrates formed with CO\({}_{2}\) and CH\({}_{4}\) at temperatures below the freezing point of water [27]. This is of significant importance for the prevention and treatment of hydrates under low-temperature conditions, as well as for the development of natural gas hydrates in frozen soil regions. The experimentally measured phase equilibrium data are mainly used for the regression of key parameters in phase equilibrium models. Currently, there are three main types of hydrate phase equilibrium prediction models: the Van der Waals-Platteeuw (vdW-P) model, the Klauda-Sandler (K-S) model, and the Chen-Guo model [28]. Among them, the Chen-Guo model has good prediction accuracy in predicting phase equilibrium conditions in complex systems (multicomponent, porous media, oil-water emulsion) [29]. However, existing theoretical models require complex gas-liquid-solid phase equilibrium calculations, making direct on-site application difficult. In this study, the phase equilibrium conditions of CO\({}_{2}\)-CH\({}_{4}\) mixed gas hydrate formation under different CO\({}_{2}\) mole fractions were first calculated based on the Chen-Guo model. Then, a multi-parameter empirical prediction model was established by polynomial fitting based on the calculated phase equilibrium data, and the enthalpy of mixed gas hydrate dissociation was calculated.
# Topology Optimization Based Material Design for 3D Domains Using MATLAB
## 4 The MATLAB Code Implementation
### The Seven Components of the UCOpt3D Function  
#### 4.1.1 Initialization Section  
For the initialization of the optimization procedure most of the code remains the same with the implementation of the original top3d code [3]. The main changes are focused on the input arguments needed by the function, which are required for performing the following tasks: creation of the element stiffness matrix, preparation of the filter, and initialization of the unit cell material densities. More precisely, 6 additional input parameters were added to the function; thus, the total number of the input arguments are equal to 12. The first three input arguments, i.e., \(lx\), \(ly\), and \(lz\), refer to the size of the macro domain, and the next three, i.e., \(nlx\), \(nly\), and \(nlz\), refer to the number of elements of the micro domain (unit cell). Thus the final expression of the UCOpt3D function is as follows:  
```
functionUCOpt3D(lx,ly,lz,nelx,nely,nelz,nlx,nly,nlz,volfrac,,,penal,rmin)
```  
In addition, the creation of the element stiffness matrix is removed from \(Line\) 25 of top3D code as it is implemented during the finite element analysis step, in the core of the optimization procedure. For the filter preparation, the iterative process for creating the mapping arrays--\(iH\), \(jH\), and \(sH\)--is modified to loop over the number of the unit cell elements as follows:  
```
forkl=1:nlz fori1=1:nlx forj1=1:nly e1=(k1-1)*nlx*nly+(i1-1)*nly+j1; fork2=max(k1-(ceil(rmin)-1),1):min(k1+(ceil(rmin)-1),nlz) fori2=max(i1-(ceil(rmin)-1),1):min(i1+(ceil(rmin)-1),nlx) forj2=max(j1-(ceil(rmin)-1),1):min(j1+(ceil(rmin)-1),nly) e2=(k2-1)*nlx*nly+(i2-1)*nly+j2; k=k+1; iH(k)=e1; jH(k)=e2; sH(k)=max(0,rmin-sqrt((i1-i2)^2+(j1-j2)^2+(k1-k2)^2)); end end end end end end end end end end end
```  
The initialization section for defining the unit cell material densities is performed in a similar manner as provided in the 2D formulation described by the authors in Ref. [17]. Thus, the initial unit cell is set as a cube with a void sphere with a radius equal to the one third of the minimum unit cell dimension as follows:* x = repmat(ones,[nly,nlx,nlz]);
* fori=1:nlx
* forj=1:nlz
* fork=1:nlz
* ifsqrt((i-nlx/2-0.5)^2+(j-nly/2-0.5)^2+(k-nlz /2-0.5)^2) < min(min(nlx,nly),nlz)/3 x(i,j,k) = 0;
* end
## 3 Attitude Determination
### The Integer Ambiguity Solution  
Based on the orthogonal decomposition (15), the multivariate constrained integer minimization can be formulated as:  
\[\hat{Z}^{k}=\arg\min_{Z^{k}\in\mathbb{Z}^{f\times n_{k}}}C^{k}(Z^{k}) \tag{16}\]  
where:  
\[C^{k}(Z^{k}) = \left\|\mathrm{vec}(\hat{Z}^{k}-Z^{k})\right\|_{Q_{\hat{Z}^{k} \hat{Z}^{k}}}^{2}+\left\|\mathrm{vec}\left(\hat{R}^{k}(Z^{k})-\check{R}^{k}(Z^ {k})\right)\right\|_{Q_{\hat{R}^{k}(Z^{k})\hat{R}^{k}(Z^{k})}}^{2} \tag{17}\]with:  
\[\check{R}^{k}(Z^{k}) = \arg\min_{R^{k}\in\mathbb{O}^{3\times q_{k}}}\left\|\mathrm{vec} \left(\hat{R}^{k}(Z^{k})-R^{k}\right)\right\|_{Q_{\hat{R}^{k}(Z^{k})\hat{R}^{k }(Z^{k})}}^{2} \tag{18}\]  
The ambiguity objective function \(C^{k}(Z^{k})\) is the sum of two coupled terms: the first weighs the distance from the float ambiguity matrix \(\hat{Z}^{k}\) to the nearest integer matrix \(Z^{k}\) in the metric of \(Q_{\hat{Z}^{k}\hat{Z}^{k}}\), while the second weighs the distance from the conditional float solution \(\hat{R}^{k}(Z^{k})\) to the nearest orthonormal matrix \(R^{k}\) in the metric of \(Q_{\hat{R}^{k}(Z^{k})\hat{R}^{k}(Z^{k})}\). Unlike with the standard LAMBDA method [25], the search space of the above integer minimization problem is non-ellipsoidal, due to the presence of the second term in \(C^{k}(Z^{k})\). This second term is a consequence of having the orthonormality constraints rigorously included. The evaluation of \(C^{k}(Z^{k})\) requires the computation of a nonlinear constrained least-squares problem (18) for every integer matrix in the search space. In the MC-LAMBDA method, this problem is mitigated through the use of easy-to-evaluate bounding functions [17].
## 5 Numerical Simulation Results and Discussion
### Imbalanced Drill String System Response  
The baseline data response of a simulation performed on an imbalanced drill string system during the passage through the first critical speed and the self-excited torsional vibration of the drill string is explored.  
Figure 7b shows a drop in the torsional deflection signal, which keeps on gradually attenuating until the torsional vibration stops. Due to the nonlinear effects inherent in the drill string system problem, the oscillations do not grow infinitely; rather, during a collision, it is found that, in the case of deterministic friction without water, the component corresponding to the circumferential vibrations of the drill string disc is always dominant, and random friction creates impulsive forces which excite the transverse mode (Figure 7a). Figures 8 and 9 are an expanded view of the spectrum around the drill string speed up to two orders with the appearance of noise peaks in specific areas of the spectrum. The drill string hump of energy just around 66.3135\(X\) and 132.627 orders in the air is possibly a structural half and critical resonance (Figure 8b). Contrary to the air medium, the structure in a fluid environment in Figure 9b shows an imbalanced fault characteristic frequency. The observation of the envelope spectrum in Figure 9b demonstrates a reduction in the vibration amplitude resulting in the soft fluctuation of the system. The vibration changes drastically in magnitude as a result of fluid pressure. Common changes in patterns (peaks, harmonics, noise, and orbits) can be observed. The presence of fluid reduces the number of peaks from the fundamental frequency and increases progressively after the amplitude of fluctuation from 0.84 \(\times\) 10\({}^{-4}\) m to 1.11 \(\times\) 10\({}^{-4}\) m in the inviscid fluid. The vibration responses displayed by the orbit patterns are distorted and cease to be pure harmonic signals, as shown in Figure 8a, contrary to Figure 9a, wherein the immersed fluid in the shaft center orbit in the \(X\) and \(Y\) directions are multi-circular circles.  
Figure 7: Torsional response of the imbalanced drill string-casing system with rub in an inviscid fluid. (**a**) Torsional in air; (**b**) Torsional in an inviscid fluid.  
**Observation 1:** The measured vibration level at \(1X\) depends on the stiffness of the machine mounting as well as the eccentric mass showing a greater \(1X\) than damping-mounted machines for the same degree of imbalance. Because of the fluid impact, the vibration level at the \(1X\) frequency decreases over a period of time. The more the fluid is introduced, the more likely that an imbalance will be detected. In any case, the direction in which the drill string is the least stiff is the direction of the highest \(1X\) level. The horizontal vibration will therefore typically be higher than the axial. However, what will the axial data reveal?
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 6 Current Challenges and Future Research Directions
### Future Research Directions
#### 6.3.2 Application Scenario Expansion  
Application scenarios for ETC technology in urban traffic management are continuously expanding and deepening, presenting diversified, intelligent, and refined development trends.  
ETC applications in intelligent connected vehicle environments will face entirely new technical challenges and development opportunities. Against the background of the gradual proliferation of intelligent connected vehicles, traditional vehicles and intelligent connected vehicles will coexist for extended periods, forming mixed traffic flow environments. ETC systems must adapt to these new traffic environments to provide differentiated services for different vehicle types. This requires not only corresponding adjustments in technical architectures but also innovations in management strategies and service models.  
MaaS (Mobility-as-a-Service) platform integration represents an important development direction for ETC applications. By integrating ETC into Mobility-as-a-Service platforms, integrated and personalized travel services can be provided to users. This integration not only simplifies user operational processes but also provides more precise travel recommendations and services through data fusion and intelligent analysis, connecting travel chains, service chains, and management chains. However, MaaS platform construction involves multiple stakeholders, interface specifications, and business models.  
MaaS platform integration represents an important development direction for ETC applications. By integrating ETC into Mobility-as-a-Service platforms, integrated and personalized travel services can be provided to users. This integration not only simplifies user operational processes but also provides more precise travel recommendations and services through data fusion and intelligent analysis, connecting travel chains, service chains, and management chains. However, MaaS platform construction involves multiple stakeholders and technical systems, requiring the in-depth exploration of data standards, interface specifications, and business models.  
Smart logistics and freight management applications demonstrate the application value of ETC technology in comprehensive transportation systems. Through ETC systems, the precise tracking and management of freight vehicles can be achieved, providing services, including transportation monitoring, route optimization, and cost accounting, for logistics enterprises, while providing management tools, including freight flow monitoring, overload control, and environmental protection control, for government departments. These applications not only improve logistics efficiency but also provide effective technical support for urban freight management.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 3 Low Power Wide Area Network  
Low power wide area network (LPWAN) is defined by the Internet Engineering Task Force (IETF) as a class of wireless technologies with characteristics such as large coverage areas, low bandwidth, possibly very small packet and application layer data sizes and long battery life operation [22]. Following these characteristics, LPWANs are widely deployed in many IoT-based communication networks, thereby garnering huge research audiences across academia, industry, and in different business domains [4]. Consequently, based on the potential benefits of LPWAN, we present a brief exposition regarding up-to-date available LPWAN standards, technologies, and their challenges. A brief look into the origin of LPWAN systems is provided, which presents a background for the current developmental strategies within the LPWAN market space. We then discuss different LPWAN technologies, standards and a possible generic architecture for LPWAN operation.