esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Reliability Analysis of Carbon-Fiber Structures Based on Surrogate Sampling and Kriging Model  
### Uncertainty Analysis  
In studying the fatigue reliability of composite carbon-fiber structures with initial cracks, uncertainty is a key factor. The dual randomness of the material parameters, load, and initial crack length significantly affects the overall performance of and prediction accuracy for carbon-fiber structures. Therefore, it is necessary to quantify and explore the impact of these uncertainties on the performance of carbon-fiber structures. This paper considers the distribution parameter uncertainty of 20 input variables and investigates its impact on the reliability of cracked carbon-fiber structures. Additionally, it identifies important distribution parameters through global sensitivity analysis. All input variables follow independent normal distributions [40], and their distribution parameters are shown in Table 4. It is assumed that the means of all input variables have subjective uncertainty and follow the normal distribution shown in Table 5.  
According to the principle that the SS-PDF should cover the entire range of input variables, the SS-PDF of the selected input variables follows the normal distribution shown in Table 6.  
Figure 10: Comparison of Fatigue Life Simulation and Experimental Results.  
\begin{table}
\begin{tabular}{c c c c} \hline
**L/mm** & **P/KN** & **Experimental Result/105** & **Simulation Result/105** & **Relative Error/\%** \\ \hline
4 & & 6.11684 & 6.11661 & \(-1.64\) \\
5 & & 4.09344 & 4.17888 & 3.17 \\
6 & 9.5 & 3.69575 & 3.89040 & 6.09 \\
7 & & 2.90287 & 2.75049 & \(-0.87\) \\
8 & & 0.43478 & 0.45769 & 0.36 \\ \hline \end{tabular}
\end{table}
Table 3: Fatigue Life of Bonded Structures at Different Thicknesses.
# PVTReID: A Quick Person Reidentification-Based Pyramid Vision Transformer
## 4 Experiments
### Ablation Study of Camera Information  
Studying the side information in the three datasets leads to the conclusion that only the camera information can be effectively applied as SIE information. In Table 4, we compared the performance of the camera SIE on the Market1501, MSMT17, and DukeMTMC-reID datasets while simultaneously investigating the effects of adding SIE at four different stages on the accuracy of PVT-based ReID. Figure 4, shows the results of our evaluation of the impact of camera information with weights \(\lambda\) on the model's performance on the MSMT17 and DukeMTMC-reID datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multirow{2}{*}{**Params (M)**} & **Inference** & \multicolumn{2}{c}{**MSMT17**} \\ \cline{3-5}  & & **Speed** & **mAP** & **R1** \\ \hline ResNet50 & 23.5 & 1.0\(\times\) & 51.3 & 75.3 \\ ResNet101 & 44.5 & 1.48\(\times\) & 53.8 & 77.0 \\ ResNet152 & 60.2 & 1.96\(\times\) & 55.6 & 78.4 \\ ResNeSt50 & 25.6 & 1.86\(\times\) & 61.2 & 82.0 \\ ResNet200 & 68.6 & 3.12\(\times\) & 63.5 & 83.5 \\ ViT-B & 86.0 & 1.79\(\times\) & 61.0 & 81.8 \\ PVT-V2-B2 & 25.4 & 0.82\(\times\) & 54.1 & 77.3 \\ PVT-V2-B5 & 82.0 & 1.22\(\times\) & 60.2 & 81.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Backbone network comparison for ReID. The inference speed is expressed in terms of a comparison of each model with ResNet50, as only relative comparisons are required. All experiments were performed on an identical computer to allow for fair comparisons.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multicolumn{2}{c}{**Market1501**} & \multicolumn{2}{c}{**MSMT17**} & \multicolumn{2}{c}{**DukeMTMC-reID**} \\ \cline{2-7}  & **mAP** & **R1** & **mAP** & **R1** & **mAP** & **R1** \\ \hline Basic & 86.3 & 94.9 & 60.2 & 81.2 & 77.8 & 87.9 \\ +LFC & 87.6 & 95.1 & 62.1 & 82.1 & 79.3 & 88.6 \\ +LFC w/o local & 87.6 & 95.1 & 62.0 & 82.1 & 79.2 & 88.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Local feature clustering ablation study; “w/o local” indicates that only global features were evaluated.
## 4 Conclusions  
In this paper, we have presented a millimeter-wave dual-circularly polarized traveling-wave series-fed patch array that serves as a promising candidate for a non-linear tag antenna. Our primary goal was to design an antenna that fulfills the requirements of reduced size, enhanced proximity coupling, and separate circular polarization orientations for input and output signals. We have successfully achieved these goals through the utilization of circular major segment patches and a modified diamond-shaped feedline. The measured results have demonstrated satisfactory performance in terms of isolation between ports and reflection coefficient within the desired frequency band while maintaining the desired axial ratio for each circular polarization. The proposed antenna design exhibits notable advantages, including lightweight, low-profile, and robust performance. These characteristics make it suitable for compact tag implementation on the body for bio-signal sensing applications.  
Conceptualization, W.L., S.O. and S.K.H.; data curation, W.L. and S.K.H.; formal analysis, W.L.; funding acquisition, S.K.H.; investigation, S.O.; methodology, W.L., S.O. and S.K.H.; project administration, S.K.H.; validation, W.L. and S.O.; writing--original draft, W.L.; writing--review and editing, S.O., W.L. and S.K.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Institute for Advancement of Technology (KIAT) (Grant No. P0017123, The Competency Development Program for Industry Specialists).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Physical Meaning** & **Value** \\ \hline \(f_{1}\),\(f_{2}\) & frequency of two-tone signal & 23.4 GHz, 23.7 GHz \\ \hline \(P_{t}\) & transmit power & 27 dBm \\ \hline \(e_{total}\) & efficiency of proposed antenna & \(-\)0.86 dB \\ \hline \(G_{t}\) & proposed antenna gain & 11.6 dBi \\ \hline \(FSPL\) & free space path loss & -60 dB \\ \hline \(d\) & distance & 1 m \\ \hline \(P_{tag}\) & input power to the non-linear tag & \(-\)10.6 dBm \\ \hline \(l_{IM3}\) &
\begin{tabular}{c} conversion loss of \\ third-harmonics generator \\ \end{tabular} & \(-\)19 dB \\ \hline \(G_{tag}\) & gain of proposed antenna in tag & 11.6 dBi \\ \hline \(P_{r}\) & receive power & \(-\)68.9 dBm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Link budget of notional system.  
Figure 9: Block diagram of notional system where \(P_{t}\) refers to transmitted power and \(P_{r}\) refers to received power and \(P_{tag}\) is an incident power to the non-linear tag.
## 6 The Evaluation of Unmatched Receiving of ACE-BOC  
### ACE-BOC Overview and Receiving Strategies  
The Asymmetrical Constant Envelope BOC(ACE-BOC) [19; 20; 21] is a flexible constant envelope multiplexing or modulation technique. An ACE-BOC signal is a constant envelope signal composed of no more than four baseband signal components, which are located at two central frequencies and allocated with arbitrary power. The ACE-BOC multiplexed signal is denoted as ACE-BOC(\(m\), \(n\), \([P_{\text{UL}}\),\(P_{\text{UL}}\),\(P_{\text{UQ}}\),\(P_{\text{LQ}}]\)), where the \(m\) stands for the spreading code rate \(f_{c}=m\)\(\times\) 1.023 MHz, and \(n\) stands for the subcarrier rate \(f_{sc}=n\)\(\times\) 1.023 MHz. \(P_{uv}\) stands for the relative power of the signal component modulated to the upper or lower sideband, denoted by \(w\in\{U,L\}\), with the in-phase or quadrature-phase, denoted by \(v\in\{I,Q\}\). The generating formula of ACE-BOC(\(m,n\), \([P_{\text{UL}}\),\(P_{\text{UL}}\),\(P_{\text{UQ}}\),\(P_{\text{LQ}}]\)) signal is provided in [21; 22].  
As a dual-frequency, four-components multiplexed signal, ACE-BOC supports multiple receiving strategies with different level of performance, including a matched receiving and two unmatched receiving strategies [22]:  
* Matched Receiving  
The matched receiving can make full use of the wideband signal characteristics. However, wideband receiving requires large front-end bandwidth and higher complexity in generating the exactly matched local despreading code [22], which is hard to afford for some of the receivers.  
* Independent Matched Receiving (IMR)  
In IMR, the local baseband despreading replica is adapted to match one of the composite signal components, including the PRN code and subcarrier. For example, \(\hat{s}_{\text{UQ}}(t)=s_{\text{UQ}}(t)[sc_{2}(t)-jsc_{2}\left(t-\frac{T_{sc} }{4}\right)]\) is the baseband local replica for the upper sideband Q-channel. Triple-loop tracking [5] or other techniques can be applied for the subcarrier tracking for IMR.  
* BPSK-like Receiving (BLR)  
BLR treats one of the ACE-BOC components as a BPSK signal, so as to directly work with general BPSK receivers. The receivers set the local carrier frequency at the center of one of the main lobes of the dual-frequency complex signal, i.e., \(f_{sc}\) or \(\
eg f_{sc}\) under zero intermediate frequency assumption [23]. It can be noticed that no subcarrier is needed to be generated in the BLR, which enables the receiver to operate at a much lower sampling rate. Accordingly, BLR is widely used by low-cost consumer market receivers.  
The unmatched reception of ACE-BOC signals has a wide range of applications due to its low-complexity. By using the proposed evaluation methods, the performance under the three receiving strategies and various influencing factors can be illustrated in the next subsections, providing a comprehensive understanding to the signal design, as well as constructive suggestions to receiver configuration.
# An Innovative Approach to Anomaly Detection in Communication Networks Using Multifractal Analysis
## 5 Multifractal Analysis of Performed Tests Results  
The network traffic (transmitted packets) in the network infrastructure under study was saved in real-time in files by Security Onion sensors [28]. The collected data in the form of time series of transmitted packets allowed us to assess the network traffic between individual devices in the local network and R1 managing router. In order to provide a more accurate picture, the collected packets were exported using the Wireshark program v. 3.0.10 (License GPLv2), then Matlab 2019b software (MATLAB and Simulink. Natick, Massachusetts, United States) and Fraclab 2.2 toolkit (Copyright INRIA, 1998 2017) were used to create fractal spectra to assess the nature of the traffic taking place in the studied network [4]. Figure 2 presents a multifractal spectrum graph for the network traffic, during which no security tests (no attacks) were carried out. This reflects the state of the normal daily operation of the entire network infrastructure, including users.  
The spectrum analysis shows no anomalies. The presented flow does not show any abnormalities. In the case of a sample containing abnormal traffic associated with a network attack, the spectrum should significantly differ from the trajectory determined by the standard data flow, which will allow the detection of anomalies or threats.  
Figure 3 shows the time period of network operation in which UDP packets generate the traffic. This type of packet in the examined model was used mainly to send information about the status of the HIDS system client and Google Rapid Response (GRR) software to the server. The relatively small length of the spectrum in the X-axis resulted from a small number of UDP packets generated per second by devices during normal operation.  
Figure 3: Multifractal spectrum for network traffic with user datagram protocol (UDP) packets during normal operation.  
Figure 2: Multifractal spectrum for the whole network traffic during normal operation.
## 4 Experimental Design and Results
### Model Validation  
To assess the applicability of the Swin Transformer model in real oilfield scenarios, we can employ the validation set to evaluate the model's performance on oilfield instances. Firstly, we preprocess the well test data in the same manner as before and then employ the trained model to make predictions on the data. By comparing the model's predicted results with the ground truth labels, we generate a confusion matrix to illustrate their relationship, as shown in Figure 16.  
Based on the graph, it is evident that the Swin Transformer model attains a prediction accuracy of 94.85% on the validation set. This outstanding performance showcases the model's robust generalization capability and its excellent suitability for real oilfield field development applications.  
Figure 16: Confusion matrix of the validation set. ((a) Normal Operation; (b) Fluid Pound; (c) Gas Interference; (d) Gas Locked Pump; (e) Delayed Closing of Traveling Valve; (f) Pump Barrel Split; (g) Fluid Pound and Delayed Closing of Traveling Valve).
# Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots
## 7 Obstacle-Avoidance
### Decide Offset Angle by Using WSM
\(R_{\alpha}\) must meet the worst case when the robot is tending to turn right or left sharply, but the offset angle has value in the opposite site respect to the robot heading, i.e., the robot responds well at one-sided of the boundary of the range offset angle and does not at the other side. Experimentally, we can obtain \(R_{\alpha}\) value by simulating the worst-case many times and choosing the shortest one that meets the case. At every time step, robots will collect data and extract \(\alpha\) values from WSM, create subtrajectory and continuously adjust until the obstacle is no longer within the sensor visibility. Once safe, the robot will return to the original trajectory. The summary of WSM used in the obstacle-avoidance task is illustrated in Figure 14.  
Figure 13: Visualization of sub-trajectories constructed by alternatives of the weighted-sum model.
## 4 Proposed System
### Calculation of the Damping Coefficient  
As demonstrated previously, for a measured response history, the P-\(\Delta\) force term can be calculated separately from the combined force since all terms involved are known. Nevertheless, the spring and damping forces cannot be separated from the measured response since both \(k(t)\) and \(c\) are unknown.  
One way to represent the damping is through the viscous damping ratio. Because it is not possible to analytically determine the damping ratio \(\zeta\) for actual structures, the damping ratio of RC columns can be calculated from experimental testing. Free vibration  
Figure 9: P-\(\Delta\) effect due to the axial load.  
experiments provide one means of determining damping using the log decrement method. The damping ratio \(\zeta\) is determined from the definition of the logarithmic decrement [16]:  
\[\delta=\ln\left(\frac{u_{i}}{u_{i+j}}\right)=\frac{2\pi j\zeta}{\sqrt{1-\zeta^{2}}} \tag{13}\]  
where \(u\) and \(u_{i+j}\) are the maximum values of force, displacement or acceleration on the first and \(j^{th}\) successive cycles, respectively. Values of the damping coefficient for circular RC bridge columns tested using the IMS are reported in the literature [9].
# An Efficient UV-C Disinfection Approach and Biological Assessment Strategy for Microphones
## 2 Materials and Methods
### Disinfection and Recovery Procedure of Inoculated Foam  
The disinfection tests were performed in technical replicates and over all three times independently. A total of 10 uL of an M13 suspension with 10\({}^{13}\) PFU/mL was deposited evenly, by spraying in the abovementioned manner, on the surface of a 1-cm\({}^{2}\) piece of foam (Shure SM58) cut from the microphone part, and the liquid was allowed to dry. To test how effectively the virus could be recollected from the material, the dried foam was placed in a Falcon tube of 50 mL, which was filled with 10 mL MilliQ water. The tube was placed for 15 h on a rotation device that allows for proper shaking and agitation of the foam, which freely moved inside the water to ensure maximum extraction. Foam samplesthat were to be disinfected were air-dried after inoculation and mounted on a wooden cocktail stick to allow them to be placed in a similar position within the UV-C chamber as a microphone would be. Duplicate samples were irradiated for 10 min while the controls were left untreated. After irradiation, all samples were subjected to the extraction method for 15 h, and the virus content was determined by using the same method of inoculation and plating as described above. The difference in viral load between the disinfected sample and the respective control was calculated to assess the reduction in PFU.
# Heat Stress Affects H3K9 me3 Level at Human Alpha Satellite DNA Repeats
## Results
### Alpha Satellite DNA Transcription after HS  
To investigate if heat stress (HS) affects transcription of alpha satellite DNA we followed its transcription dynamics in human cell lines by RT-qPCR under standard and heat stress conditions. Primers used for transcription analysis were constructed based on consensus sequence derived from cloned alpha satellite monomers of wide-ranging chromosomal origins [26] and were able to amplify only tandemly arranged repeats (Figure S2). Since in human pericentromeric heterochromatin alpha satellite DNA is organized in tandemly arranged monomers [19], it can be expected that the primers preferentially recognize transcripts deriving from pericentromeric regions. For heat shock experiments, cells were incubated for 1 or 2 h at 42 \({}^{\circ}\)C in complete medium, followed by a recovery period at 37 \({}^{\circ}\)C. The transcription of alpha satellite DNA was checked immediately after heat stress as well as after a recovery period of 30 min and 1 h, and was compared with a control that was not subjected to heat stress. The transcription of alpha satellite DNA was monitored in immortalized fibroblasts (MJ90 hTERT), oral cancer adenosquamous cell line (Cal 27), ovarian adenocarcinoma cell line (OV-90),and liver hepatocellular carcinoma cells (HepG2). The analysis of transcription revealed differences in alpha RNA level among cell lines at standard conditions. However, the dynamics of transcription after HS is similar in all cell lines (Figure 1). The level of alpha satellite RNA is increased from 2.0\(\times\) in OV-90 cells to 2.4\(\times\) in MJ90 hTERT (_p_ < 0.05) relative to the controls (no HS) immediately after 2 h of HS. The level of alpha RNA is decreased during the recovery period of 30 min at 37 \({}^{\circ}\)C, while after 1 h of recovery it drops to the level of control (Figure 1).
## 2 Proposed Method
### Proposed Method Process
#### 2.4.3 Encryption  
Encryption is performed after verifying the attributes in the bloom filter process. First the encryption, and then the bloom filter setup, are run to indicate the selected attributes from the access policy for the encryption process. The working of this process is given in the following Algorithms 1 and 2.  
The algorithm for encryption is given below.  
```
Input: Master key MK, Electronic health record (EHR), (MG, \(\rho\)), Access matrix MG =1* n Output: Ciphered EHR (C-EHR)  Begin  Initialize encryption secret \(S,\;S\in\;ZP\) For i = 1: 1 \(\lambda_{i}=\;\text{MiV}\) \(\text{C}-\text{EHR}=\{C\;=\;\text{EHR}.\;BM^{\prime(G,G)^{alpha_{bytes}}},C^{ \prime}=G^{s}[\;C_{i}=g^{a\lambda i}*h^{-s}_{\rho(i)}\;]\text{i}=1\ldots 1\) End End
```  
**Algorithm 1** Encryption  
\(\lambda_{i}\) is used to localize the selected attributes in the access policy MG. Here, the EHR is only encrypted and the access policy is not combined with it. It is sent to the CSP through bloom filter output, as shown in Algorithm 2.  
Using the above algorithm, the attribute selection process is performed in the modified ABE. Using this BF output and the secret key, the data are encrypted in the CSP or HC and shared among them. The selected attribute is mentioned by indexing through a localization pointer in an array format.  
The whole attribute set is indicated as a vertical row. The attributes selected for encryption are highlighted through a localization pointer, as shown in Figure 5.
# Nonlinear Control Design of a Half-Car Model Using Feedback Linearization and an LQR Controller
## 1 Introduction
A test path data-based method to ripen a Laplace domain condensed mandate suspension model of halfway complexity in between a full car and a quarter car model was presented in [28]. According to the author, a half-car model could do a better job as it is at a midway path. A prototype of vehicle was driven on sinusoidal roads but only vertical accelerations of wheel axles and suspension body travels were measured. Pitch accelerations play a vital role in handling between trade-offs. The previous approaches carried out have not incorporated the nonlinearities in mathematical models and the trade-offs have not been addressed properly. In this manuscript, first of all, input/output feedback linearization of a nonlinear half-car model is achieved, after that the effect of road disturbances is minimized by designing an LQR controller that provided a state feedback gain matrix \(K\) with optimal robustness properties. The proposed control method is verified using the Matlab/Simulink Toolbox and the results of RMS values are compared with those of previous techniques which shows superiority of the proposed approach. The paper is organized as follows: Section 1 contains the introduction and literature review, Section 2 presents the methodology. Section 3 discusses modelling and Section 4 propose the results and discussion. Section 5 concludes this study.