esa-sceva/satcom-chunk-collection huggingface dataset:
# A Nonlinear Optimization Design Algorithm for Nearly Linear-Phase 2D IIR Digital Filters
## 1 Introduction
In [29], the Sequential Partial Optimization algorithm was proposed to convert the minmax design problem into a sequence of smaller sub-problems, each updating only a single second-order denominator factor. In [33], a modern optimization methodology known as semi-definite programming (SDP) is utilized. This SDP method includes minmax and weighted least-squares designs of FIR filters as well as minmax formulations of stable separable-denominator IIR filters. In [33] describes how SDP has the potential to serve as a unified optimization engine for designing a wide range of 2D digital filters. The Quasi-Newton method has been applied to design 2D IIR digital filters [7], but it may result in unstable filters. Neural networks were used in [34] to design 2D IIR filters, which can guarantee the stability of the designed filters with low computational time. Some researchers have employed the genetic algorithm (GA) to design 2D IIR digital filters [35, 36, 37, 32]. Others have proposed hybrid methods that combine different evolutionary algorithms, such as biogeography-based optimization and particle swarm optimization in [39], to tackle the nonlinear 2D IIR optimization design. In this paper, a robust nonlinear optimization method for the design of nearly linear-phase 2D IIR digital filters with separable-denominators is proposed. This design problem is formulated as a nonlinear constrained optimization problem where the group delay deviation is minimized under prescribed soft magnitude constraints and hard stability requirements. We also rely on sub-level sets of the group delay deviations to obtain a sequence of filters, from which we choose the most optimal one. The performance of the obtained 2D IIR filters is evaluated using three quality factors. These first two quality factors are the usual passband magnitude quality factor \(Q_{h}\) and the group delay deviation quality factor \(Q_{\tau}\), while the third one is a new quality factor \(Q_{s}\) that assesses the performance of the filter within the stopband relative to the minimum filter gain in the passbands, and that is because amplitude response anomalies in transition regions are undesirable [40]. The obtained results show that these quality factors are all necessary to judge the optimality of the filter. The proposed framework is implemented using the interior-point (IP) method in a MATLAB environment, and the results are very promising. The remainder of the paper is organized as follows. The constrained optimization problem for 2D IIR digital filters is formulated in Section 2. Section 3 is for the stability requirements for the 2D IIR digital filters with a separable denominator. Section 4 presents the constrained optimization design problem for a 2D IIR digital filters. Section 5 is dedicated to the quality of the design, and Section 6 to the experimental results. Section 7 concludes with a brief summary and future work directions.
## 3 Spatial Decoupling Method for 3D Electric Field Measurement Based on the CRLB
### Decoupling Method Based on the CRLB
According to the linear coupling relationship in Equation (6), the optimization objective function \(\mathcal{J}_{2}(\mathbf{E}^{\prime})\) for \(\mathbf{E}^{\prime}\) is given by  
\[\mathcal{J}_{2}\big{(}\mathbf{E}^{\prime}\big{)}=\big{(}\mathbf{U}^{\prime}- \mathbf{K}\mathbf{E}^{\prime}\big{)}^{T}\mathbf{W}\big{(}\mathbf{U}^{\prime}- \mathbf{K}\mathbf{E}^{\prime}\big{)} \tag{22}\]  
taking the derivative of \(\mathcal{J}_{2}(\mathbf{E}^{\prime})\) with respect to \(\mathbf{E}^{\prime}\) and setting the result to zero yields the optimal estimation \(\hat{\mathbf{E}}^{\prime}\) of the measured electric field:  
\[\hat{\mathbf{E}}^{\prime}=\Big{(}\mathbf{K}^{T}\mathbf{W}\mathbf{K}\Big{)}^{ -1}\mathbf{K}^{T}\mathbf{W}\mathbf{U}^{\prime} \tag{23}\]  
Verify that \(\hat{\mathbf{E}}^{\prime}\) is the optimal linear estimator by computing its expectation:  
\[\mathbb{E}\Big{[}\hat{\mathbf{E}}^{\prime}\Big{]}=\mathbb{E}\bigg{[}\Big{(} \mathbf{K}^{T}\mathbf{W}\mathbf{K}\Big{)}^{-1}\mathbf{K}^{T}\mathbf{W} \mathbf{U}^{\prime}\bigg{]}=\mathbf{E}^{\prime} \tag{24}\]  
It is confirmed from Equation (24) that it is an unbiased estimator. The covariance matrix is then calculated as  
\[Cov\Big{[}\hat{\mathbf{E}}^{\prime}\Big{]}=\mathbb{E}\bigg{[}\Big{(}\hat{ \mathbf{E}}^{\prime}-\mathbf{E}^{\prime}\Big{)}\Big{(}\hat{\mathbf{E}}^{ \prime}-\mathbf{E}^{\prime}\Big{)}^{T}\bigg{]}=\Big{(}\mathbf{K}^{T}\mathbf{W }\mathbf{K}\Big{)}^{-1}\mathbf{K}^{T}\mathbf{W}\mathbf{E}\mathbf{W}^{T}\mathbf{ K}\Big{(}\mathbf{K}^{T}\mathbf{W}\mathbf{K}\Big{)}^{-1}=\Big{(}\mathbf{K}^{T} \mathbf{W}\mathbf{K}\Big{)}^{-1} \tag{25}\]  
From Equation (25), it is evident that \(\hat{\mathbf{E}}^{\prime}\) attains the CRLB, implying that it achieves the minimum variance. Therefore, the estimator in Equation (23) is the optimal linear estimator. Comparing Equation (23) with Equation (7), the decoupled calibration matrix \(\mathbf{C}\) under optimal linear estimation can be expressed as  
\[\mathbf{p}\big{(}\mathbf{U}^{\prime};\mathbf{E}^{\prime}\big{)}=\frac{1}{4 \pi^{2}|\mathbf{\Sigma}|^{\frac{1}{2}}}\exp\biggl{(}-\frac{1}{2}\big{(}\mathbf{ U}^{\prime}-\mathbf{K}\mathbf{E}^{\prime}\big{)}^{T}\mathbf{\Sigma}^{-1}\big{(} \mathbf{U}^{\prime}-\mathbf{K}\mathbf{E}^{\prime}\big{)}\biggr{)} \tag{17}\]0  
By calculating the decoupled calibration matrix \(\mathbf{C}\) using Equation (26), the 3D electric field sensor can be accurately calibrated, effectively eliminating coupling interference among electric field components and enabling high-precision electric field measurements.
## 6 Simulation Evaluation
### Comparison and Analysis of Simulation Results
#### 6.2.2 Network Connectivity Rate  
Figure 10 shows the comparison of the network connectivity rate that varies with number of nodes and the communication radius on NDACS and VBDA, respectively. Figure 10a shows the comparison of the network connectivity rate varying with the number of nodes on NDACS and VBDA,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Initial energy of node \(E_{init}\) & 16,000 J \\ Coverage threshold \(C_{th}\) & 0.9 \\ Energy consumption on unit moving distance \(m_{u}\) & 2 J/m \\ Power threshold \(P_{0}\) & 0.05 W \\ Transmission delay \(T_{p}\) & 0.2 s \\ Energy spreading factor \(k\) & 2 \\ Carrier frequency \(f\) & 24 kHz \\ Sense radius of node \(Rs\) & 50 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Settings of Simulation Parameters.  
Figure 9: Comparison of network coverage rate between NDACS and VBDA: (**a**) comparison of network coverage rate between NDACS and VBDA varying with the number of nodes, and node sensing radius is 50 m; (**b**) comparison of network coverage rate between NDACS and VBDA varying with sensing radius, and the default number of nodes is 200.  
where the default initial communication radius is 60 m in the simulation scenario. Figure 10b shows the comparison of the network connectivity rate varying with the communication radius of NDACS and VBDA, where the default number of nodes is 200 in the simulation scenario. Figure 10a,b show that compared with VBDA, the NDACS algorithm can guarantee the full network connectivity of the network (i.e., the network connectivity rate is equal 1). Figure 10c shows that VBDA can only maintain a relatively high network connectivity rate when \(\alpha>1.2\) (\(\alpha\) is the ratio of node communication radius to sensing radius), and its connectivity rate will be very small in other situations. However, For the NDACS algorithm, the first layer network can guarantee full network connectivity on the spanning tree. In the depth-adjustment process, the parent node minimizes network coverage overlaps and maintains full network connectivity by using the adjustable communication radius model described in Section 3.1.3.
# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 2 RFF Extraction Based on a 5G NR PRACH Preamble
### DCTF Extraction for the 5G PRACH Signal  
For DCTF extraction, the signal power was rearranged such that  
\[\widetilde{y}(k)=\frac{y^{\prime\prime\prime}(k)}{\frac{1}{L_{C}p+L_{inter}} \sum_{k=1}^{L_{C}p+L_{inter}}(|y^{\prime\prime\prime}(k)|)}\lambda \tag{11}\]  
where \(|\cdot|\) denotes the absolute value and \(\lambda\) represents the scaling parameter. In our experiment, the value of \(\lambda\) was 0.8, which has been verified to be suitable for a \(65\times 65\) DCTF feature size.  
The DCTF figure can be obtained with the help of a differential operation [18], in which the elements are written as  
\[D(t)=\widetilde{y}(t)\cdot\widetilde{y}^{*}(t+t_{n}) \tag{12}\]  
where \((\cdot)^{*}\) represents the conjugation and \(t_{n}\) is the differential time interval. In our experiment, \(t_{n}\) is one symbol of time. The differential operation enables the extraction of stable constellation features even when there are residual CFOs in the signal [18]. The measurement matrix \(\Phi\) can be generated with a size of \(M\times M\) pixel grids ranging from \(-A\) to \(A\). The values of elements \(\Phi_{j,k}\) were initialized as zero. For each \(D(t)\), we found the index \((j,k)\) of \(\Phi\) through the following process:  
\[j=[\frac{D_{I}(t)+A}{2A}M],\;k=[\frac{D_{Q}(t)+A}{2A}M] \tag{13}\]  
Figure 4: A segment of preamble waveform after preprocessing (in-phase channel).  
where \([\cdot]\) is the round value and the values of \(M\) and \(A\) are fixed to 65 and 32, respectively. Here, \(\Phi_{jk}\) increases by one when \(D(t)\) falls to the index \((j,k)\). Finally, when all of the samples are counted and added to the specific index of the measurement matrix, the distribution density of the entire waveform can be represented with \(\Phi\). The elements of \(\Phi\) are normalized by rescaling the pixel values from 0 to 255.  
The DCTFs generated from the standard preamble sequence and received signal are illustrated in Figure 6a,b, respectively. The red points correspond to the positions with higher values. It can be seen that the two DCTF matrices were not exactly the same due to the thermal noise at reception and the RFF features of the device. Moreover, we note that the preambles generated using the same root value (i.e., Figure 6a,c) exhibited similar DCTF features, which were quite different from the DCTF features of the preamble generated from a different root value (i.e., Figure 6d). In the _group_-based DCTF identification scheme proposed in Section 3.3, the preambles generated using the same root value are grouped together.  
Figure 5: The power spectrum of (**a**) the collected original signal, (**b**) the standard signal and (**c**) the preprocessed signal.  
Figure 6: DCTF for (**a**) standard preamble with index 1, (**b**) preamble with index 1 of UE1, (**c**) standard preamble with index 2 and (**d**) standard preamble with index 11.
# Effects of the Fruit Harvest Date and Shelf-Life Nexus of Apples on Different Quality Perspectives
## 3 Results and Discussion
### Texture Profile Analysis
The tissue becomes softer as a result of pectin breakdown during ripening, which changes the fracturing mechanism toward cell-cell debonding and decreases cell adhesion [55; 56; 57]. During storage, respiration and metabolism may cause turgor pressure to decrease, which could have an impact on the fracturing process [58]. At low turgor, the cell wall's lower tension causes greater deformation, which leads to wall breakage [59; 60]. The method of storage affects the rate of biochemical changes. When pectin is kept at room temperature in a normal setting, it breaks down rapidly, and the fracture mode progressively changes to breaking without making noise [58]. The difference in firmness variation between Golden Delicious and Braeburn fruits can be attributed to a combination of genetic factors, inherent textural properties, and biochemical composition [9; 61]. Golden Delicious, with its more stable cell wall structure and slower softening process, remains firmer throughout its shelf life [62]. In contrast, Braeburn apples experience a faster decline in firmness due to differences in water retention, sugar content, and cell wall degradation [63]. These factors highlight the complex interplay between genetics, texture, and postharvest physiology that determines the shelf life and quality of different apple varieties.
# Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems
## 3 ANN-Based Design Methodology for Low-Power Interconnect
### Proposed Design Methodology
#### 3.3.2 Design in Frequency Domain  
In the frequency domain design, the surrogate model is leveraged to provide data for the analysis and the optimization procedure. Intuitively, the ANN model is a combination of math equations considered a black-box function that maps the input and output. Hence, the objective function assists this model in attaining the optimal structure instantly. Moreover, the optimization procedure is automated to avoid human error. Additionally, the specifications including characteristic impedance, return loss, and crosstalk are the constraints to find the optimal structure.  
Two primary factors that degrade the signal propagated throughout the channel are loss and noise. While the insertion loss \(S_{21}\) presents the channel loss included the dielectric and material loss, the crosstalk corresponds with the noise caused by the adjacent channel. Although both \(S_{31}\) and \(S_{41}\) are crosstalk noise, the near-end crosstalk \(S_{31}\) flows toward the transmitter and has no impact on the receiver signal. Conversely, the far-end crosstalk \(S_{41}\) plays a significant role in the receiver signal by boosting or degrading the eye diagram. The FEXT coefficient described in Equation (6) is a function of channel length, the coupling difference, propagation velocity, and the transition time [22]. Thus, a general strategy for defining the objective function used in this paper is to find the best case at the receiver after suffering both insertion loss and crosstalk.  
\[FEXT=\frac{1}{2}\times\frac{L}{v_{p}\times t_{rise}}\times(\frac{C_{M}}{C_{S}} -\frac{L_{M}}{L_{S}}) \tag{6}\]  
where:  
\(L\): Channel length  
\(v_{p}\): Propagation velocity  
\(t_{rise}\): Transition time  
\(C_{M},C_{S}\): Mutual and self capacitance, respectively  
\(L_{M},L_{S}\): Mutual and self inductance, respectively
## 3 Methodology
### General Framework  
The flowchart of the MAPSO algorithm is illustrated in Figure 1. Initially, parameters such as the number of ground stations, satellites, and the population size are input. A set of uniformly distributed weight vectors [21] is then generated. The population \(p\) is initialized by generating a group of randomly distributed particles in space with initial velocities \(v_{i}\) (where \(\{v_{i}|1\leq i\leq n,i\in Z\}\)). An initial archive \(A\) is established to store the non-dominated solutions of population \(p\), which is updated based on the clone strategy.  
After completing the population initialization, we employ the archive update strategy to update the external archive. At the start of each iteration, the contents of archive \(A\) are copied to prevent the loss of solution sets. The PSO search strategy selects leaders from the archive to update the population. After updating the population, the archive update strategy is employed again to update the sets \(P\) and \(A\). The procedure is repeated until the end condition is met. The algorithm comprises three key components: the clone, the PSO search process, and the archive update operation. The upcoming sections will offer a detailed introduction to these three components.
# Combating Web Tracking: Analyzing Web Tracking Technologies for User Privacy
## 3 Countermeasures against Web Tracking
### Challenges
#### 3.3.4 Arms Race to Produce Filter Lists against Trackers  
Filter list-based detection and prevention methods have limited range, and their effectiveness varies based on the expertise of the person writing the rules, making them prone to false negatives. The process is often passive and relies on experts or crowds to generate rules. However, as there are many simple ways to bypass these measures, the competition between detection and circumvention remains ongoing, with each side constantly updating their methods [70]. Advertising-based businesses struggle with declining profits due to widespread use of ad blockers. To combat this, advertising publishers have created Anti-Ad-Block, which detects and bypasses ad blockers by obscuring site content or making it indistinguishable from normal content.  
To solve this problem, [PERSON] et al. [44] developed a CV-Inspector, a machine learning tool for detecting circumvention of ad-blocking. It analyzes the Anti-Circumvention Filter List (ACVL), a list designed to counter circumvention techniques and complement EasyList with additional filter rules and grammar. CV-Inspector identifies websites that bypass ad-blocking using the ACVL. However, its limitation is that it only detects automatic circumvention and cannot identify circumvention that requires user interaction, such as redirecting to an advertisement before displaying actual content.  
On the other hand, AdGraph provides a graph-based detection method that uses machine learning to assist in creating filter lists. It can automate the creation of lists for websites with low traffic or in specific languages and regularly audit the lists to update obsolete rules. This is necessary because websites frequently update, and new ones emerge.
# Effect of Fiber Optic Plate on Centroid Locating Accuracy of Monocentric Imager
## 3 Materials and Methods
### Defocusing of Imaging Spots  
As we mentioned earlier, the imaging spot is not a large target on the entire image, and we only need a few image sensor pixels to receive the signal of the entire imaging spot. The CLE calculated directly by the centroid method is relatively large, so we have to defocus the imaging spot before obtaining the centroid position of the imaging spot in order to obtain more details of the edge of the imaging spot and more accurate CLE. Strictly speaking, defocus is not considered a kind of aberration, but its result is devastating. If the receiving surface is not at the paraxial focal plane, then the geometrically enlarged image of the space target will be mixed with the dimensional noise. In other words, defocus will cause a reduction in resolution and loss of detail. As the distance increases, the noise becomes larger and the resolution drops sharply. Therefore, the selection of defocus cannot be too large. We analyze the influence of different defocus parameters on CLA and perform appropriate defocus processing on the imaging spot, which can improve the analysis accuracy and lay a solid foundation for a series of CLE analysis that will be carried out in the following.  
We simulate the defocus degree of the imaging spot through Zemax OpticStudio. First, we set the wavelength to 0.55 \(\upmu\)m, the fields to 0, the entrance pupil diameter to 50 \(\upmu\)m, the focal length to 100 \(\upmu\)m and the F number to 2. Then, the type of the STOP Surf was set to the Zernike Fringe Phase, where the maximum term was set to 9; the norm radius was set to 25 \(\upmu\)m; and the Zernike 4 (defocus) was set to increase from 0 to 2(\(\lambda\)) with a step size of 0.05(\(\lambda\)). Finally, we obtained discrete images of diffuse spots with different defocusdegrees. We performed PTOIS simulations in MATLAB to analyze the effect of different defocus degrees of imaging spots on CLA (see Figure 3).  
Figure 3 shows that as the defocus degree increases, the CLA is gradually increased. When the defocus degree is \(0.8(\lambda)\), the CLA is highest with value of \(0.002\) pixel. As the defocus degree continues to increase, the CLA always maintains within \(0.012\) pixel, but it is not as high as the CLA when the defocus degree is \(0.8(\lambda)\). Moreover, the previous analysis mentioned that the defocus degree cannot be too large. So all subsequent analyses are based on the imaging spot with a defocus degree value of \(0.8(\lambda)\).
# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 4 Field Study
### Study Area  
We collected real-world MLS data with our partners to validate our presented technique. The measurements took place in November 2020 in Styria, Austria. Figure 8 left displays a map of the study area with the location of the objects of interest. In this paper, we focus on three different scenarios that reveal common problems in practice.  
1. represents a typical scene in a mountainous region where a retaining wall (RW) is located directly after a tunnel. Such conditions pose a challenge to the accurate positioning of the MLS system since GNSS is not available and IMU errors accumulate.
2. is an open sky setting with an 18-meter-high retaining structure adjacent to a highway. GNSS processing is possible, but multipath effects will likely occur and degrade quality. RW2 is a suitable site for recording data from two travel directions and for performing terrestrial reference measurements.
3. includes tall buildings that constrain GNSS signals and pose the risk of multipath effects. On the other hand, buildings and other artificial objects provide differently oriented surfaces, which proves beneficial in our processing pipeline. As discussed in Section 3.6, well-distributed point normals promise reliable estimates for correcting MLS point clouds.
## 5 Conclusions  
The GS-based CD compensation algorithm has been extensively studied in recent years. However, the performance comparison between the GS algorithm and the transmitter and the receiver has not been investigated, and it is lacking in resisting nonlinearity interference. To determine the effect of the GS-based algorithm on CD compensation in the presence of modulator nonlinearity, the IM/DD simulation system was constructed. The system performance of the joint LUT and GS-based CD compensation algorithm was verified under a 40 km SSMF transmission with 28 GBaud PAM-4. To compensate for the nonlinearity, the 7-symbol LUT is indispensable. When using the joint LUT and GS-based CD pre-compensation algorithm at the transmitter, the BER achieved a threshold of 3.8 \(\times\) 10\({}^{-3}\) without iterations. However, the FFE with 125 taps is needed. When turning to the joint LUT and GS-based post-compensation scheme, 80 iterations are needed, which means higher system complexity. However, the demand for FFE is reduced. Turning to practical application, the algorithm selection depends on the tolerance of the transmitter or receiver complexity in specific scenarios. The joint LUT and GS-based pre-compensation algorithm may be a preferable approach in scenarios where a low-complexity receiver is desired.  
Conceptualization, K.L. and W.H.; methodology, X.Z., J.H., H.B., P.Q., W.H. and K.L.; software, X.Z., J.H., H.B. and P.Q.; validation, X.Z., J.H., H.B. and P.Q.; writing--original draft preparation, X.Z.; writing--review and editing; authors contributed equally. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China 2021 YFB2900801; National Natural Science Foundation of China NSFC, 62201033 and U22A2005; the Young Elite Scientists Sponsorship Program of CIC 2021 QNRC001; and the Foundation of the Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services.  
Not applicable.  
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Pre-Compensation** & **Post-Compensation** \\ \hline GS operation & 11 RMs & 23 RMs \\ \hline FFE & 250 RMs & 0 \\ \hline Pre-distortion & 0 & 14 RMs \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational Complexity of Pre- and Post-compensation algorithm per Symbol.