esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
All the statistical analyses were conducted using Microsoft Excel 2018. All the data were tested for normality with a visual inspection of the QQ-plots and of the histograms. To compare the results from the Excel/VBA spreadsheet with those from the MARS 4.0 software, the following statistical issues were analyzed for each of the variables: (i) the residual homoscedasticity via Bland-Altman plots including the regression lines for the residuals; (ii) the magnitude of the error in both absolute and relative terms with the Mean Absolute Errors (MAE), Standard Error of Measurement (SEM), and Percentage Mean Absolute Error relative to the average measurement obtained with the MARS 4.0 software (PMAE); (iii) the level of agreement based on the Lin's intraclass correlation coefficient (Lin CCC) with the 95% confidence intervals. The strength of agreement for the Lin CCC was categorized as follows: > 0.8, very strong; 0.6-0.8, strong; 0.4-0.6, medium; 0.2-0.4, weak; <0.2, very weak [29]. Finally, to determine the magnitude of change that would exceed the threshold of measurement error [30], the minimal detectable change (MDC) as a 90% and  
Figure 4: Photo sequence and force data synchronized in Sheet 6 (Photo analysis).  
95% confidence interval (\(\text{MCD}_{90}\) and \(\text{MCD}_{95}\)) was calculated. It was performed with the following formulas: \(\text{MCD}_{90}=\text{SEM}\cdot\sqrt{2}\cdot 1.65\) and \(\text{MCD}_{95}=\text{SEM}\cdot\sqrt{2}\cdot 1.96\).
# Adoption of Multiphase and Variable Flux Motors in Automotive Applications
## 3 Analysis of Performance of VF and MP Motors
### Reference Motor  
As the reference motor to work with, the automotive three-phase motor in Figure 5 provided by Ansys MotorCAD software was chosen. The meshed FEM simulation model is shown in Figure 5, which was used to evaluate the motor. It consisted of a distributed winding stator and a rotor with two v-shaped magnet layers. The main characteristics of the motor and the chosen inverter ratings are given in Table 1.  
The winding layout of the 3-phase motor is illustrated in Figure 6 together with the corresponding star of slots. The winding characteristics are also gathered in Table 1.  
To calculate the winding factor, starting from the star of slots in Figure 6, the sum of the phase vectors was performed and a value of 0.965 was obtained (\(K_{w}=\cos(15^{\circ})=0.9659\)). This means that 96.59% of the flux is linked in the stator winding.
## 3 Modeling of Elastomeric Bearings  
Several uniaxial phenomenological models have been developed by researchers to reproduce the complex transverse hysteretic behavior of isolation bearings under the effect of a constant axial compressive load [9; 14; 30].  
In this section, we focus on the modeling of elastomeric bearings, whereas the aspects related to the modeling of sliding bearings will be the objective of future papers. In particular, we first summarize the formulation of a hysteretic model recently developed by [PERSON] et al. [22] and, subsequently, we illustrate the related material object that has been implemented in OpenSees [25].  
The above-mentioned model, denominated Algebraic Model (AM) since the output variable is evaluated by solving an algebraic equation, is capable of simulating the typical hysteresis loops of both SREBs and FREBs by using a set of only 5 parameters (\(k_{a}\), \(k_{b}\), \(\alpha\), \(\beta_{1}\), \(\beta_{2}\)) having a clear mechanical meaning.
# Secure D2D Communication for 5G IoT Network Based on Lightweight Cryptography
###### Abstract  
Device-to-device (D2D) communication is a direct means of communication between devices without an intermediate node, and it helps to expand cell coverage and to increase radio frequency reuse in a 5G network. Moreover, D2D communication is a core technology of 5G vehicle-to-everything (V2X) communication, which is an essential technology for autonomous driving. However, typical D2D communication in an 4G network which is typical telecommunication network has various security challenges including impersonation, eavesdropping, privacy sniffing, free-riding attack, etc. Moreover, when IoT technology emerges with 5G networks in massive machine type communication (mMTC) and ultra-reliable low latency communication (URLLC) application scenarios, these security challenges are more crucial and harder to mitigate because of the resource-constrained nature of IoT devices. To solve the security challenges in a 5G IoT environment, we need a lightweight and secure D2D communication system that can provide secure authentication, data confidentiality/integrity and anonymity. In this paper, we survey and analyze existing results about secure D2D communication systems in terms of their security considerations and limitations. Then, we lastly propose a secure D2D communication system to address the aforementioned security challenges and the limitations of the existing results. The proposed secure D2D communication was designed based on elliptic curve cryptography (ECC) and lightweight authenticated encryption with associated data (AEAD) ciphers to cover resource-constrained IoT devices.  
D2D communication; 5G IoT network; lightweight cryptography; authentication +
Footnote †: journal: Applied Sciences
## 1 Introduction
Second, direct device-to-device communication through available short-range wireless communication technologies offloads infrastructure networks and frees bandwidth for mobile users (e.g., [19; 20; 21]). The benefits of opportunistically collecting delay-tolerant data for Smart City applications have motivated various studies. For example, the spatial analysis of a city-wide mobility dataset of Beijing, which suggests that knowledge of daily user travel patterns can be exploited to design more effective data collection protocols [17]. Also supporting these results are findings from smartphone mobility traces [22], which identify strong spatial and temporal localities as important characteristics of user mobility that need to be considered in the design of data collection protocols and algorithms. There are also contributions on protocol evaluation (e.g., the implementation of an OppNet testbed for a large-scale Smart City platform that utilizes an opportunistic backhaul [18]), duty-cycling (e.g., extending sensor node lifetime through a sensor node-initiated probing mechanism that exploits periods of the day during which encounters with portable handheld user devices are more frequent [23]) and the wireless communication technology (e.g., an analytical and experimental study of the performance and trade-offs of collecting sensed data with Bluetooth Low Energy [24]).
## 3 Proposed Scheme
### Overview of Iteration Method
#### 3.1.2 Modified SOR Method  
[PERSON] et al. brought forward the MSOR method in [19]. MSOR can be regarded as a variant of the original SOR method which reduces its complexity by modifying the parameters in the iteration matrix and compensation vector. Its iteration equation is expressed as follows [19]:  
\[\mathbf{x}^{(k)}=(\mathbf{D}-\omega\mathbf{L})^{-1}\{[\mathbf{U}+(1-\omega) \mathbf{L}]\mathbf{x}^{(k-1)}+\mathbf{b}\}. \tag{17}\]  
As previously mentioned, the matrix \(\mathbf{A}\) is assumed to be a symmetric positive definite matrix and its diagonal elements must be positive numbers, confirming that matrix \(\mathbf{D}\) is non-singular. Here, we define \((\mathbf{D}-\omega\mathbf{L})\) as \(\mathbf{M}_{MSOR}\), which is the diagonal matrix minus the product of the relaxation parameter \(\omega\) and a strictly lower triangular matrix. Its inverse matrix can be written as  
\[\mathbf{M}_{MSOR}^{-1}=(\mathbf{D}-\omega\mathbf{L})^{-1}, \tag{18}\]  
while \(\mathbf{N}_{MSOR}\) is the matrix without diagonal elements, defined as  
\[\mathbf{N}_{MSOR}=\mathbf{U}+(1-\omega)\mathbf{L}. \tag{19}\]  
Therefore, similar to the SOR method, Equation (17) can be simplified to  
\[\mathbf{x}^{(k)}=\mathbf{G}_{MSOR}\mathbf{x}^{(k-1)}+\mathbf{d}_{MSOR}, \tag{20}\]and we say that \(\mathbf{G}_{MSOR}\) is the iteration matrix of MSOR, defined as follows:  
\[\mathbf{G}_{MSOR}=\mathbf{M}_{MSOR}^{-1}\mathbf{N}_{MSOR}=(\mathbf{D}-\omega \mathbf{L})^{-1}[\mathbf{U}+(1-\omega)\mathbf{L}]. \tag{21}\]  
In addition, the compensation vector \(\mathbf{d}_{MSOR}\) for MSOR is written as  
\[\mathbf{d}_{MSOR}=\mathbf{M}_{MSOR}^{-1}\mathbf{b}=(\mathbf{D}-\omega \mathbf{L})^{-1}\mathbf{b}. \tag{22}\]  
According to the definition of the spectral radius from Equation (15), we know that the spectral radius of \(\mathbf{G}_{MSOR}\) (denoting \(\rho(\mathbf{G}_{MSOR})\)) must comply with the following:  
\[\rho(\mathbf{G}_{MSOR})<1. \tag{23}\]  
For the same reason as above [55], the MSOR iterative method guarantees convergence when \(1\leq\omega\leq 2\).
# Development of a Self-Powered Magnetorheological Damper System for Cable Vibration Control
## 3 Experimental Setup
### Experimental Procedure and Data Processing  
A series of free vibration tests were conducted to assess the performance of the self-powered MR damper system in terms of reducing cable vibration. Four test cases were considered, including an uncontrolled cable, a passively operated MR damper with constant voltage, the electromagnetic damping alone generated by the rotary DC generator, and the self-powered MR damper system.  
The cable was excited manually at its natural frequencies in the first four modes, respectively. The excitation rope was not released until the amplitude of the vibration reached a certain level. To get a better acceleration signal with one target mode of the cable, the excitation position for the first two modes was located at \(1/4l\) away from the anchorage of the cable, but was moved to \(1/10l\) away from the anchorage for the third and the fourth modes. The acceleration signals at the evaluation measurement point were filtered through the band pass filter in MATLAB. Accordingly, the free decay acceleration curve of the cable with almost one single target mode was obtained, and the natural frequencies of the cable were directly identified via the time-domain method.  
Generally, the modal damping ratios of structures can be identified using the logarithmic attenuation rate of free decay responses, which works well for exponentially decaying vibrations. However, the modal damping ratios of the cable may be dependent on the amplitude of the cable when the cable is equipped with nonlinear control devices, such as the passively operated MR dampers. The induced problem of amplitude-dependent damping ratio values when a nonlinear damper is attached to the cable has been well described in [33; 34]. Nevertheless, except for the case of a passively operated MR damper with constant voltage, cable vibrations decay exponentially. Therefore, a uniform method to identify the modal damping ratios of a cable to which different control devices are attached needs to be defined to compare control performance. In this paper, the logarithmic decrement method was finally adopted, and the first 20 cycles from the same amplitude of free decay acceleration of the cable at the evaluation measurement point were selected to form an estimate of the damping ratios of the cable for each mode. The natural frequencies and modal damping ratios of an uncontrolled cable in the first four modes were identified, and are listed in Table 2.
###### Abstract  
Circularly polarized synthetic aperture radar (CP-SAR) is known to be insensitive to polarization mismatch losses caused by the Faraday rotation effect and antenna misalignment. Additionally, the dual-circular polarimetric (DCP) mode has proven to have more polarimetric information than that of the corresponding mode of linear polarization, i.e., the dual-linear polarimetric (DLP) mode. Owing to these benefits, this paper investigates the feasibility of CP-SAR for rice monitoring. A ground-based CP-radar system was exploited, and C-band anechoic chamber data of a self-cultivated Japanese rice paddy were acquired from germination to ripening stages. Temporal variations of polarimetric observables derived from full-circular polarimetric (FCP) and DCP as well as synthetically generated DLP data are analyzed and assessed with regard to their effectiveness in phenology retrieval. Among different observations, the \(H/\bar{\kappa}\) plane and triangle plots obtained by three scattering components (surface, double-bounce, and volume scattering) for both the FCP and DCP modes are confirmed to have reasonable capability in discriminating the relevant intervals of rice growth.  
radar polarimetry; synthetic aperture radar (SAR); dual circular polarimetry (DCP); compact polarimetry; rice phenology Article
# A Brief Survey of Fixed-Parameter Parallelism
## 2 Preliminaries
Modular width was first used in [10] as auxiliary parameter and used to obtain fixed-parameter algorithms for coloring and path-partitioning problems (mainly Hamiltonian Path and Cycle). These problems are \(W[1]\)-hard with respect to other auxiliary graph parameters like shrub-depth. The parallel complexity class \(NC\) is defined above in its general form. It is, however, divided into sub-classes, denoted by \(NC^{i}\) for \(i\geq 1\), where \(NC^{i}\) is the set of problems that can be solved in \(O(log^{i}n)\) time using a polynomial number of processors [4]. The relation between sub-classes of the \(NC\)-hierarchy is given by \(NC^{1}\subseteq NC^{2}\cdots\subseteq NC\), where the question of whether the class is proper (whether \(NC^{i}\subseteq NC^{i+1}\) or \(NC^{i}\subset NC^{i+1}\)) remains open, so far. It was shown in [4] that if \(NC^{i}=NC^{i+1}\) for some \(i=j\), then the equality holds for every \(i\geq j\). This would result in the collapse of the \(NC\)-hierarchy from \(j\) onward into a single class \(NC^{i}\). A decision problem \(X\) is said to be \(P\)-complete is it belongs to the class \(P\) (of problems solvable in polynomial-time) and every problem in \(P\) can be reduced to \(X\) in \(O(log^{c}n)\) time using a polynomial number of processors. It is well known, and easy to see, that \(NC\) is a subset of \(P\), but it is strongly believed that the containment is proper. The class \(AC\), on the other hand, is the class of problems or languages recognized by Boolean circuits of logarithmic depth that use a polynomial number of unbounded fan-in AND and OR gates. A sub-class \(AC^{i}\) contains the set of languages recognized by Boolean circuits of depth \(O(log^{i}n)\) and that use a polynomial number of AND and OR gates [11; 12]. The lowest \(AC\) class is \(AC^{0}\), where the Boolean circuit is of constant depth, and it can be shown that \(AC^{0}\subseteq AC^{1}\subseteq AC^{2}\cdots\subseteq AC\). The relation between the \(AC\)-hierarchy and the \(NC\)-hierarchy is given by \(AC^{i-1}\subseteq NC^{i}\subseteq AC^{i}\subseteq NC^{i+1}\) for \(i\geq 1\), as noted in [11], since the circuit can be represented as a tree of logarithmic depth and a polynomial number of nodes.
# Novel Boundary Edge Detection for Accurate 3D Surface Profilometry Using Digital Image Correlation
## 2 Methodology of the Random-Speckle Optical Detection and Processing Algorithm
### Criterion for Edge Detection  
To ensure accurate edge detection, a threshold should be defined for the traditional NCC to filter low S/N coefficients from the point cloud. With a high-pass threshold for point filtration, the initial point cloud of the DIC measurement can provide a good basis for contour edge detection. As defined in Equation (4), an average correlation coefficient obtained by matching the reference image subset with its neighboring subset can generally provide a reliable threshold.  
\[T_{ncc}=\frac{1}{c\times(r-1)}\sum_{1}^{c}\sum_{N=1}^{r}\frac{\sum_{x,y}T_{N}(x, y)\ T_{N+1}(x+s,y+s)}{\sqrt{\sum_{x,y}T_{N}(x,y)^{2}\ T_{N+1}(x+s,y+s)^{2}}}\,\ for\ N<r \tag{4}\]  
where \(c\) is the number of subsets in a column; \(r\) is the number of subsets in a row, and \(s\) is the sample pixel.  
When performing NCC with the boundary or corner subset, the threshold required for reliable edge detection should be further determined by the NCC of the inspection subset. In the proposed approach, the variance of the normalized cross-correlation coefficients along the search direction was taken as the quality-filtering threshold. The correlation coefficient curves obtained from the edge detection process using the traditional subset and the proposed search subset are shown in Figure 13a,b, respectively. The size of the partial search subset was either \(\frac{p}{n}\times n\) or \(\times\frac{p}{n}\), depending on the search direction, where p is the number of sampling pixels in DIC. As can be seen, the normalized cross-correlation coefficient (NCCC) of the proposed search subset generally had a much higher sensitivity or slope than that of the traditional subset, because the subset speckle images of the proposed approach better matched their corresponding reference template images.  
Figure 12: Corner subset for a titling edge search.  
Figure 13: Normalized cross-correlation coefficient (NCCC) curves.
# Greening the NetFPGA Reference Router
## 3 The Architecture of the Reference Router and the Frequency-Scaled Router
### Frequency-Scaled Router  
In the RR, the two SRAMs use the same clock as used by the core logic FPGA processor for writing and reading data, to ensure that transmit queues could transmit data with little or no delay between packets [24]. An inbuilt register sets the operating frequency of the core logic FPGA to either 125 MHz or 62.5 MHz. However, due to the clock synchronization between the SRAMs and the core logic FPGA, the RR cannot switch the operating frequency of the core logic FPGA on the fly. When toggling the operating frequency of the RR between 125 MHz and 62.5 MHz, the frequency switching causes a board reset to restart the SRAMs and the core logic FPGA hardware with updated synchronous frequency. The board reset involves re-mirroring and reloading MAC addresses, IP addresses, the routing table and the Address Resolution Protocol (ARP) table into the core logic FPGA hardware, which takes approximately 2 ms. All of the buffered packets are lost during the board reset.  
To eliminate the board reset problem, a custom module of asynchronous FIFO (AFIFO) is inserted between the SRAMs and the core logic FPGA. The AFIFO allows safe data exchange between the SRAMs' clock domain and the core FPGA clock domain, where the two clock domains are asynchronous to each other. As shown in Figure 2, the AFIFO module can isolate the SRAMs alone and keep them running at 125 MHz constantly, while the operating frequency of the core FPGA can be tuned among allowed frequencies in response to actual traffic processing needs.  
In the RR, the power consumption at these two frequencies (125 MHz and 62.5 MHz) with different numbers of active ports (zero to four) for different aggregated traffic loads (400 Mb/s to 4 Gb/s) and different packet sizes (140 bytes, 531 bytes and 1470 bytes) is reported in [25]. To quantify the power savings from energy proportional techniques, the FSR is developed to provide the core logic FPGA with five operating frequency options (125 MHz, 62.5 MHz, 31.3 MHz, 15.6 MHz and 7.8 MHz). This is accomplished by integrating custom frequency division modules into the digital clock manager (DCM) available on the core logic Virtex II FPGA. The custom frequency division modules in the DCM provide advanced clocking capability, which can generate new clock frequencies by dividing the source clock frequency with allowed divisors. In comparison to the RR, the three additional operating  
Figure 2: The architecture of the frequency-scaled router.  
frequencies (31.3 MHz, 15.6 MHz and 7.8 MHz) in the FSR are derived from the source clock 125 MHz by simultaneous frequency division with three custom divisors (4, 8 and 16).
# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
## 3 Automatic Detection Method Development  
This section outlines the development of the automatic detection method, including the architectural design of the proposed QP-DETR model, modifications to the backbone network using EfficientNetV2, the addition of the Efficient Channel Attention (ECA) module, and improvements to the deformable attention mechanism. Each subsection provides detailed explanations of the components and enhancements implemented to improve the model's performance and efficiency.
# An Efficient Algorithm for De-Interleaving Staggered PRI Signals
## 4 Conclusions  
This paper proposes an efficient algorithm to de-interleave staggered PRI signals. Firstly, an improved SSWI method is proposed to enhance the adaptability to PRI jitter. In addition, a threshold criterion is designed for the improved SSWI algorithm, which decreases the interference between the sub-PRIs and the frame period. A sequence retrieval algorithm incorporating matched filter theory is proposed, enhancing the separation accuracy of radar pulse sequences. Finally, the staggered sequences are extracted by combining the matched filter theory and intrinsic properties of staggered sequences. Simulation experiments have validated the proposed algorithm's superiority for de-interleaving staggered PRI signals from mixed signals.  
However, the algorithm proposed in this paper also has limitations. The proposed algorithm uses the difference information of the TOA sequence to estimate the PRI, and the difference grade gradually increases. When the mixed TOA sequence is complex enough, due to the mutual interference of different pulse sequences, misidentification may occur. The next research work is to de-interleave staggered PRI signals from dense and complex radar signals with high efficiency.  
Conceptualization, W.C. and Q.Z.; methodology, W.C. and J.D.; validation, J.D., H.W. and W.C.; software, H.W. and W.C.; writing--original draft preparation, W.C.; writing--review and editing, Q.Z.; visualization, W.C.; supervision, X.L.; project administration, Q.Z. and X.L.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank the editors and reviewers for their efforts to help the publication of this work.  
The authors declare no conflict of interest.