esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
Recently, PLS has attracted broad research interest in terms of the design of efficient and secure transmission strategies for 5G wireless networks. This review paper provided detailed discussions on the basic optimization schemes and system design of PLS, based on both information-theoretic security and signal processing fundamentals. To understand the advantages of PLS, we first provided a comparison between the traditional cryptographic and PLS techniques. Then, we introduced different wiretap channel models and their mathematical expressions in order to understand the common scenarios of practical security design. From the information-theoretic security perspective, we reviewed popular performance evaluation metrics, including the rate of transmission metrics, secrecy capacity, secrecy throughput, secrecy outage probability, secure resource allocation and QoS-related metrics. Then, we reviewed optimization problems and security solutions from the viewpoints of system design and signal processing methods, such as convex optimization, secure beamforming, artificial noise (AN) and zero-forcing (ZF) precoding. Regarding research directions, we discussed candidates for future research topics within PLS, including resource allocation, signal processing techniques, secure node/antenna selection and cooperative networks. Thereafter, AI and ML applications were reviewed to solve different problems for optimization and security design. Specifically, we discussed the paradigms of AI and ML in terms of some promising research directions, such as signal processing design, end-to-end multi-antenna techniques and OFDM-based RB allocation applications. We also discussed other research directions and the open challenges that face PLS in future wireless networks. In summary, our review incorporated different aspects that are related to the optimization and design of PLS systems, including secure resource allocation, channel estimation and the integration of AI into practical strategies to help PLS. We believe that this paper can provide the research community with guidance for advancing the considerations of future PLS research.  
Conceptualization, methodology and writing--original draft preparation, M.K.T.; writing--review and editing, Z.J.; validation and supervision, S.Z. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the National Natural Science Foundation of China (grant number 62003275) and the Fundamental Research Funds for the Central Universities of China (grant number 31020190 QD039).  
Not applicable.  
We would like to acknowledge the Northwestern Polytechnical University (NWPU) and Yangtze River Delta Research Institute of NWPU for their research funding.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Classical Detection Approach
Image patches of the current image corresponding to these regions are fed into the classifier determining the presence of construction workers. finds contours in the binary image [31]. In a further step, rectangles are formed, which enclose each contour. These rectangles identify the Rols for the further detection process, as shown in Figure 4b. Since the background subtraction identifies the relevant areas within the image which are likely to contain pedestrian construction workers, the detector only has to focus on those image regions. Each of those Rols contains a single moving object in the scene. Distinguishing between a worker and any other object can be done by a binary classification for each RoI. As described above, construction workers in top-view images are characterized best by their motion, color and shape. The classification of color and shape features provides a proper basis of decision-making since we already use motion to find candidate regions. Following this, we use color histograms as they are simple but effective color feature descriptors. The histograms are computed on the hue and saturation channels of the HSV color space. Since the value channel decodes brightness, it is neglected in favor of the histograms' invariance to changes in illumination conditions [8], which is a common issue in outdoor scenes. For determining shape information, we decided on Haar-like features. The choice of these features allows us to design both feature descriptors as low-order integral channel features [32]. This guarantees the efficient computation of the feature responses which increases the detection speed. However, it remains unclear how to arrange those feature descriptors' positions and sizes on an image patch so that they optimally respond to a construction worker. For this reason, we apply a Soft Cascaded Classifier [33] which learns the optimal set of features using AdaBoost [34]. For this, we deduce weak classifiers from the feature descriptors by thresholding their responses. Then, we generate a weak classifier pool containing thresholded feature descriptors at all conceivable sizes and positions in an image patch. During training, AdaBoost iteratively draws that weak classifier from the pool which separates the set of samples best. This way, a strong classifier emerges from the set of chosen weak classifiers. Figure 5 visualizes the process by the example of the first five Haar-like feature descriptors chosen by AdaBoost. Figure 4: Background subtraction example: (**a**) video frame of walking construction workers; and (**b**) background image with white foreground pixels aggregated to blobs of motion (red) and the corresponding RoIs (green) for further processing. By a subsequent calibration, the weak classifiers within the strong classifier are arranged into cascading stages. A sample is only passed further along the cascade if the evidence of belonging to the positive class is sufficiently strong. As a result, negative samples are rejected early in the cascade which drastically speeds up the classification process for a vast number of samples to be verified.
# A Nonlinear Optimization Design Algorithm for Nearly Linear-Phase 2D IIR Digital Filters
## 5 Quality of the Design  
Design efficiency and the performance of the proposed method are evaluated in terms of the quality of the group delay characteristic \(Q_{\tau}\), the quality of the magnitude response in the passband \(Q_{h}\), and a new quality factor \(Q_{s}\) for the magnitude attenuation in the stopband. We introduced this new factor, \(Q_{s}\), to measure the quality of the magnitude in the stopband. The quality of the group delay characteristic of the 2D filter will be measured by using the normalized maximum variation of the filter group delay over the passband as a percentage as follows:  
\[Q_{\tau}=\max\left\{\frac{\tau_{1 max}-\tau_{1 min}}{\tau_{1 max}+\tau_{1 min}}, \frac{\tau_{2 max}-\tau_{2 min}}{\tau_{2 max}+\tau_{2 min}}\right\}\times 100\%, \tag{14}\]  
To measure the quality of the magnitude in the passband, we use:  
\[Q_{h}=\frac{H_{max}-H_{min}}{H_{max}+H_{min}}\times 100\%, \tag{15}\]  
where \(H_{max}\) and \(H_{min}\) are the maximum and minimum magnitudes in the passband, respectively. We observe that when we design the filter, we improve the passband ripple and the group delays, but sometimes the stopband attenuation is not good enough. Based on that observation, we introduced a new quality factor for the stopband attenuation, which is measured in terms of the minimum variation of the passband ripple and the maximum variation of the stopband attenuation. To measure the quality of the magnitude response in the stopband, we use:  
\[Q_{s}=\frac{H_{max}^{s}}{H_{min}-H_{max}^{s}}, \tag{16}\]  
where \(H_{max}^{s}\) is the maximum magnitude in the stopband region, and \(H_{min}\) is the minimum magnitude in the passband. This quality factor, \(Q_{s}\), is valid for \(H_{min}>H_{max}^{s}\), i.e., negative values are excluded. Note that these quality factors are inverse measures, and therefore a good filter is one where these quality factors are small.
## 3 Results and Discussion
### Time Dependency Studies  
Figure 9 shows the results of the time dependency for the binding of As(III) and A(V) to the ZnO nanomaterials at room temperature (22 \({}^{\circ}\)C). Both, As(III) and As(V) show increasing binding with increasing time. The As(III) binding at room temperature shows increasing binding of the As(III) up to 1 h of contact time, and the binding becomes constant thereafter. Similarly, the As(V) binding to the synthesized ZnO nanomaterial showed increasing binding up to approximately 60 min and was constant between 60 and 90 min. For both ions, the majority of the binding occurs in the first 15 min of contact and increases gradually with increasing contact up to 60 min and becomes relatively constant thereafter. This behavior has been observed in the literature, with the saturation of the binding sites occurring very quickly. [PERSON] et al. observed similar behavior for As(III) and  
Figure 8: (**A**) Thermodynamics plot for the binding of As(III) to the ZnO synthesized nanomaterials. (**B**) Thermodynamics plot for the binding of As(V) to the ZnO synthesized nanomaterials.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & \(\Delta G\) **(kJmol\({}^{-1}\))** & \(\Delta H\) **(kJ mol\({}^{-1}\))** & \(\Delta S\) **(J mol\({}^{-1}\)K\({}^{-1}\))** \\ \hline As(III) 4 \({}^{\circ}\)C & \(-\)5.76 & & \\ As(III) 22 \({}^{\circ}\)C & \(-\)2.60 & \(-\)63.4 & \(-\)233.6 \\ As(III) 45 \({}^{\circ}\)C & 1.72 & & \\ As(V) 4 \({}^{\circ}\)C & \(-\)2.04 & & \\ As(V) 22 \({}^{\circ}\)C & \(-\)0.18 & \(-\)47.29 & \(-\)164.25 \\ As(V) 45 \({}^{\circ}\)C & 3.52 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Thermodynamic parameters for binding of As(III) and As(V) to synthesized ZnO nanomaterial.  
As(V) binding to F\({}_{7}\)S\({}_{8}\)[40]. The majority of the binding was observed to occur in the first 15 min of contact, which is fast compared to many of the adsorbents where the authors reported adsorption over more than 24 h of contact time. The binding was fast and high in comparison to other nanomaterials studied in the literature.
# Modeling Transient Flows in Heterogeneous Layered Porous Media Using the Space-Time Trefftz Method
## 5 Discussion  
This study is rooted in the Trefftz method and demonstrates a promising numerical solution for dealing with two-dimensional transient flows in heterogeneous layered porous media. The comparison of the proposed method with previous studies depicts the following advantages.  
Various mesh-based numerical approaches have been utilized for dealing with the transient and heterogeneous groundwater flow equation. In contrast to conventional mesh-generated techniques, we developed the meshless method using the Trefftz space-time basis function. The proposed method is advantageous for dealing with engineering problems with a complex geometry because of the characteristics of simplicity and being meshfree.  
Contrary to the conventional meshless method, the proposed method is categorized into the boundary-type meshless method, such that the collocation points are only placed on the boundaries of the domain. Furthermore, the proposed method combines the conventional Trefftz method with the space-time collocation scheme, such that no inner points are required in the analysis and all collocation points needed to place the space-time boundaries are presented for the modeling of the two-dimensional transient flows in the heterogeneous layered porous media. As a result, both the initial and boundary data are regarded as boundary conditions on the space-time boundary, which is especially advantageous for an irregular domain shape.  
Using the space-time collocation, the initial value problem for simulating transient flows in heterogeneous layered porous media is considered to be a problem of the inverse boundary value, where the time marching for the initial value problem can be avoided. Accordingly, the proposed method is advantageous for dealing with inverse problems as well.  
On the other hand, the limitations of the proposed method may be raised, as we need to derive the Trefftz space-time basis function for the two-dimensional diffusion equation in layered porous media in the space-time domain, in which the Trefftz basis functions are obtained from the general solutions using the separation of variables. The solutions of the governing equation are then approximated numerically based on the superposition principle utilizing the space-time basis functions of the governing equation. Consequently, the proposed method is limited to the linear governing equation with accessible general solutions.
# Exploiting Frequency Characteristics for Boosting the Invisibility of Adversarial Attacks
## 4 Experimental Results and Analysis
### Ablation Study  
#### 4.5.1 Quantitative Analysis in the Frequency Domain  
To quantitatively analyze the perturbation of adversarial attacks on images and verify the effectiveness of our proposed method, we conducted a statistical analysis of the perturbations of a large number of adversarial examples from the perspective of the Fourier domain to examine the frequency domain characteristics of the perturbations generated by our method.  
The strength and proportion of low-frequency perturbations can reflect the invisibility of the adversarial examples. By counting the proportion of low-frequency perturbations in the total perturbation strength of all generated adversarial examples, we can summarize the distribution pattern of perturbations of comparative methods.  
In the previous discussion, we divided the Fourier matrix into three parts, and we used the masks as filters to calculate the perturbation strength in different frequency bands using the \(L_{2}\) norm. The strength of the perturbations in these different frequency bands represents the amount of modification of the image's corresponding frequency information. We count the average low-frequency perturbation strength on the region \(\omega_{low}\) and the percentage of low-frequency perturbations in the total perturbation strength.  
Figure 8 shows the frequency domain characteristics of comparative methods on the Tiny-ImageNet, CIFAR100, and CIFAR10 datasets, respectively. The histogram data in the figures represent the average strength of low-frequency perturbations of adversarial examples. The line plot data represent the proportion of low-frequency perturbations for different methods. The histogram data in the figure show that our method has the smallest average low-frequency perturbation strength. Moreover, the line plots reflect that the proportion of low-frequency perturbations in the adversarial perturbations of our method is also the lowest compared to comparative methods. This illustrates that the proposed optimization objective is effective, which successfully limits the perturbation to the HVS-insensitive high-frequency region while improving the performance of the adversarial examples.
## 3 Method
### Cell Transmission Model  
The conventional CTM discretizes continuous time into uniform time steps. It also partitions road space into a series of lane segments known as cells, as depicted in Figure 2.  
The length of cells is typically longer than the distance a standard vehicle can travel within a time step to prevent vehicles from skipping over adjacent cells. Based on traffic flow theory, CTM can update cells to reflect the evolution of traffic states:  
\[N_{\zeta}^{t+1}=N_{\zeta}^{t}+q_{\zeta}^{t}-q_{\zeta+1}^{t}, \tag{1}\]  
where \(N_{\zeta}^{t}\) and \(q_{\zeta}^{t}\) represent the inflow of cell \(\zeta\) at time step \(t\). The calculation of \(q_{\zeta}^{t}\) is expressed as follows:  
\[q_{\zeta}^{t}=\min\Bigl{\{}N_{\zeta-1}^{t},q_{max},\alpha\Bigl{(}N_{jam}-N_{ \zeta}^{t}\Bigr{)}\Bigr{\}}, \tag{2}\]  
where \(q_{max}\) denotes the maximum inflow to cell \(\zeta\) at time step \(t\), \(N_{jam}\) denotes the jam capacity of the cell \(\zeta\), and \(w\) and \(v_{f}\) represent backward shockwave speed and free flow speed.  
\[\alpha=\begin{cases}1,&\text{if }N_{\zeta-1}^{t}\leq Q_{max},\\ \frac{w}{v_{f}},&\text{if }N_{\zeta-1}^{t}>Q_{max}.\end{cases} \tag{3}\]  
HD maps also possess discrete spatial-temporal structures [4]. However, traditional CTM cannot adequately utilize traffic information from HP maps, such as azimuths and vehicle-type attributes. Meanwhile, traditional CTMs face challenges in capturing stable traffic information because signal phases intermittently disrupt the continuous traffic flow. Thus, we developed CeT, which inherits the discrete spatial-temporal framework of CTM but no longer models continuous traffic flow. Instead, it focuses on vehicle transmission patterns between cells and simplifies the prediction of traffic evolution to successive updates of cells within the prediction horizon. To this end, CeT fuses the inherent characteristics of lane segments, the motion characteristics of vehicles at each historical time step, and the vehicle-type attributes into the feature sequence that encodes the cell states over the historical horizon. Meanwhile, it represents the signal changes at intersections as dynamic connections between nodes corresponding to specific cells in a dynamic road graph, which are also embedded into the historical encoding sequence. Subsequently, CeT utilizes the Transformer architecture to extract the spatial-temporal correlations and dependencies from the historical encoding sequence to predict future cell states. In this way, CeT combines CTM and Transformer architecture while utilizing the structured traffic data from HD maps to forecast future traffic states in a discrete spatial-temporal system.
## 2 Materials and Methods
### Study Area and Data Source
#### 2.1.3 Data Source  
DEM data, land cover data, remote-sensing images, road network data, and administrative division data were used in this study. ALOS DEM data (12.5 m resolution) were obtained from the ASF search page of the NASA website ([PERSON] et al., 2018), and GlobeLand V2020 land cover data were acquired from the official website of the National Center for Basic Geographic Information ([PERSON] et al., 2018). Road network data, including for railroads, highways, national roads, provincial roads, and urban primary, secondary, and tertiary roads, as well as administrative division data, including multi-year district and county administrative boundary data, were obtained from the Resource and Environmental Science Data Registration and Publication System (RESDPS) ([PERSON], 2018). The remote-sensing images included GF-2 and GF-6  
Figure 1: Location of the study area.  
Figure 2: Phenological cycle of main crops in the study area.  
images, Landsat 8 images, and Google images, which were obtained from the Data Sharing Service System of GF Henan Center [41], the official website of the U.S. Geological Survey (USGS) [42], and the Google Earth page, respectively [43]. Of these, the GF data included multispectral bands (MSs) and panchromatic bands (PANs). The sensor types, temporal resolution, spatial resolution, and other parameters of the remote-sensing data are detailed in Table 1. The administrative district data were used to extract the remote-sensing images, road network data, and land cover data from the study area. The land cover data were used to extract the extent of farmland and residential areas in the study area, and the DEM data were used to analyze the elevation and slope distribution of the abandoned farmland. The road network data were used to calculate the density of the road network in the study area to analyze the relationship between the distribution of abandoned farmland and the density of the road network. The residential area data were used to analyze the relationship between the distribution of abandoned farmland and the distance to residential areas, while the GF-6 and Landsat 8 images were used to calculate the NDVI and construct the NDVI time series dataset based on the STARFM. The GF-2, GF-6, and Google Earth images were used to obtain cultivated and abandoned farmland sampling points through visual interpretation.
# Quantum Features of Atom-Field Systems in the Framework of Deformed Fields
## 2 Deformed Spin Algebra  
Quantized universal enveloping algebras refer to some specific deformations of Lie algebras. A considerable example is the case of deformed spin algebra (DSA), which was firstly introduced by [PERSON] [49]. Many interesting applications were developed in different areas of the physics using the bosonic realization in terms of the deformed operators, and the DSA has attracted great interest in the framework of mathematical and physical problems [50]. The generators \(J_{\pm}^{d}\) and \(J_{z}^{d}\) are used to generate the \(\mathbf{su}_{q}(2)\) algebra with the following commutation relations,  
\[[J_{z}^{d},J_{\pm}^{d}]=\pm J_{\pm}^{d};\ \ [J_{+}^{d},J_{-}^{d}]=[2J_{z}^{d}]_{q}, \tag{1}\]  
where the function \"[]\" expresses the deformation of the algebra. In fact, by a specific choice of this function we have a particular deformation of the spin algebra. In the case \(\left[y\right]_{q}=y\) we get the non-deformed algebra, and we consider the known deformation of the spin algebra for the following function [13; 14]  
\[\left[y\right]_{q}=\frac{1-q^{y}}{1-q}. \tag{2}\]A significant property in the deformation version can be obtained for a specific values of the parameter \(q\). For \(q=1\) we recover the non-deformed algebra.  
The generators \(J_{\pm}^{q}\) and \(J_{z}^{q}\) act on the orthonormal basis of the representation space \(|j,m\rangle\) as  
\[\begin{split}& J_{z}^{q}|j,\,m\rangle=m\,|j,\,m\rangle\,,\\ & J_{\pm}^{q}|j,\,m\rangle=\Big{(}[j\mp m]_{q}[j\pm m+1]_{q} \Big{)}^{\frac{1}{2}}|j,\,m\pm 1\rangle\end{split} \tag{3}\]  
with \(m=j,\dots,-j\).  
As commonly reported, it is possible to map the \(\text{su}_{q}(2)\) operators to HO operators by using the Jordan-Schwinger map [51; 52] and Holstein-Primakoff realization (HPR) [53]. These realizations can be generalized, making it possible to achieve the operators of the deformed \(\text{su}_{q}(2)\) algebra by annihilation and creation operators of the deformed Hos. This is a significant result as it permits the deformation of the HOs to relate with the deformation of \(\text{su}(2)\) algebra [54]. For this study, the deformation of the HPR will be applied as:  
\[J_{+}^{q}=a_{q}^{\dagger}\sqrt{[2j-N]_{q}},\,\,J_{-}^{q}=\sqrt{[2j-N]_{q}}\,a _{q},\,\,J_{z}^{q}=N-j, \tag{4}\]  
where \(a_{q}^{\dagger}\), \(a_{q}\) denote the deformed creation and annihilation operators. These operators act on the Fock states \(|n\rangle\) as  
\[a_{q}^{+}|n\rangle=\sqrt{[n+1]_{q}}|n+1\rangle,\,a_{q}|n\rangle=\sqrt{[n]_{q}} |n-1\rangle,\,N|n\rangle=\sqrt{[n]_{q}}|n\rangle, \tag{5}\]  
and obeying the following commutation relations  
\[a_{q}a_{q}^{+}-qa_{q}^{+}a_{q}=1,\,\Big{[}N,\,a_{q}^{+}\Big{]}=a_{q}^{+},\, \big{[}N,\,a_{q}\big{]}=-a_{q},\,\text{for}\,0<q\leq 1. \tag{6}\]  
The states \(|n\rangle,\,\,n=0,\,1,\dots,\) comprise the complete orthonormal basis of the irreducible representation space.