esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### CBMIR Results  
The most usable strategy to measure the performance of the CBHIR tool is the \"top K accuracy\". In this strategy, the CBHIR tool displays the top K-matched patches. If there are one or more correct retrieved images among them, the CBHIR tool performs well [7].  
\[\left\{\begin{array}{ll}ACC=1,&\text{If any of the K-top retrieved images match with the query,}\\ ACC=0,&\text{Otherwise}\end{array}\right.\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CN Techniques** & **BKSVD**[36] & **Vah**[33] & **Mac**[32] \\ \hline PSNR & 19.54 & 12.74 & 13.80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PSNR for the normalized CAM17 data set.  
Figure 5: Line 1 illustrates the original color version of CAM17. Lines 2–4 contain five random images of CAM17 as a result of the CN techniques BKSVD, Vah, and Mac. These images correspond to all hospitals.  
It is noteworthy to mention that in this paper, the results are reported using the top \(3,5\), while in recent papers [25; 44; 45], K was \(20,100,200\), or \(400\). While the amount of K in this paper is notably lower compared to the other studies, it still shows the model achieved an impressive accuracy. This highlights the model's reliability for pathologists, as it can retrieve similar patches even with a smaller set of retrieved images.  
Table 2 reports the results when K = 3 and 5 for CAM17 in the four color versions. After comparing Table 2 and Figure 4, a lower intra-center variance can improve the search performance. According to Table 2, CN as a pre-processing technique might have a negative impact on the final results. The obtained accuracy for the top 3 and 5 in the experiment on the original images is higher than the accuracy of the experiments with the Vah and Mac versions of CAM17. This means that utilizing a insufficient CN technique not only does not improve the final results, but can also decrease the performance of the main model. Figure 6 illustrates four confusion matrices for the four color versions of CAM17 for the top 5.  
According to the obtained results, thanks to CN techniques, by reducing the negative impacts of color variation, the texture, shape, and histologic features of each patch are highlighted. Therefore, the Euclidean function ranks the images which are more similar in the sense of relevant features.
# A Survey of Internet of Things (IoT) Authentication Schemes +
## 3 Security Issues in IoT
### Security Challenges in IoT Layers
#### 3.2.3 Application Layer Security Issues and Requirements  
The application layer is responsible for providing services. It hosts a set of protocols for message passing [19; 63], e.g., Constrained Application Protocol (COAP), Message Queuing Telemetry Transport (MQTT), Extensible Messaging and Presence Protocol (XMPP), Advanced Message Queuing Protocol (AMQP), etc. This layer directly interacts with the user. Given that the \"traditional\" application-layer protocols do not perform well within IoT, and since the IoT does not have its own international standards, several security issues arise at the application layer [27].  
1. _Data Accessibility and Authentication:_ Each application might have many users [64]. Fake or illegal users could have a great impact on the availability of the whole system. Such great number of users means different permission and access control.
2. _Data privacy and identity._ The fact that IoT connects different devices from different manufacturers leads to the application of different authentication schemes. The integration of these schemes is a challenging issue to ensure data privacy and identity.
3. _Dealing with the Availability of Big data:_ IoT connects a huge number of end devices, which leads to a huge amount of data to be managed. This causes an overhead on the application to analyze this data, which has a big impact on the availability of the service(s) provided by the application.  
Regarding the security requirements for the application layer, authentication is required while protecting the privacy of users (respectively, data). In addition, there should be an information security management scheme that includes resource management and physical security information management. Table 1 gives a summary of the security requirements of the three-layers in the IoT architecture.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Layer** & **Security Requirements** \\ \hline \multirow{4}{*}{**Perception**} & Lightweight Encryption \\ \cline{2-3}  & Authentication \\ \cline{2-3}  & Key Agreement \\ \cline{2-3}  & Data Confidentiality \\ \hline \multirow{4}{*}{**Network**} & Communication Security \\ \cline{2-3}  & Routing Security \\ \cline{2-3}  & Authentication \\ \cline{2-3}  & Key Management \\ \cline{2-3}  & Intrusion Detection \\ \hline \multirow{4}{*}{**Application**} & Authentication \\ \cline{2-3}  & Privacy protection \\ \cline{2-3}  & Information Security Management \\ \hline \hline \end{tabular}
\end{table}
Table 1: IoT Architecture and security requirements.  
In Table 1, it is clear that authentication is a core security mechanism that should applied at different layers. An IoT use case might need an authentication between the end devices and an intermediate device (gateway). The gateway should authenticate itself while sending data to the cloud, and the application (mobile or web) should be authenticated to the cloud in order to collect data for analysis.
# Current Status of HbA1c Biosensors
## 2 HbA1c Biosensors
### Antibody-Modified Biosensors
#### 2.2.1 Immobilization of HBs on the Electrode Surface  
Based on the design of DOCA-modified interface for HBs immobilization [25], [PERSON] utilized anti-HbA1c immunoglobulin G (IgG) to selectively bind to HbA1c and used FcBA which binds to multiple sugar moieties on IgG (Figure 2A) to amplify the electrochemical signal of FcBA [40]. The anodic peak height of the square wave voltammograms of FcBA is proportional to the bound IgG-FcBA which is correlated to the HbA1c present on the surface. The sensitivity of this electrochemical immunosensor is three times than that of the FcBA-based HbA1c sensors without the antibody.  
[PERSON] and coworkers developed a sandwich-type HbA1c sensor consisting of the first layer of a site-directed Hp-modified cellulose membrane for HBs enrichment, after capturing the HBs, and sequential addition of an anti-HbA1c antibody followed by the introduction of anti-IgG(sheep)-GOx for the specific detection of bound HbA1c [41]. Both the optical signal generated by the HbA1c-sandwich ELISA assay and the electrochemical responses generated from the Hp/anti-HbA1c-GOx/Clark-type electrode provide a linear correlation in the clinically relevant range of 5-20% HbA1c of total Hb. However, the current status of the HbA1c-sandwich immunoassay is still as a proof-of-concept design owing to the defects that include a single usage per membrane, long detection time needed, and a large dilution factor of blood sample required (up to 15,000-fold dilution) to minimize the non-specific binding of Hbs [41].
# Research on Output Voltage Stability of Non-Contact Excitation Motor
## 5 Simulated Analysis
### Voltage and Current Output from Resonant Circuit  
When the full-bridge inverter circuit converts the direct current into alternating current, it is connected to the resonant circuit, and the primary circuit and the secondary circuit can transfer energy through electromagnetic coupling.  
As shown in Figure 8, it is a voltage simulation diagram output by the resonant circuit. After stabilization, the output voltage amplitude is 225 V, and the output voltage and current are both AC. By observing Figure 8b, it is evident that after the output current is stable, the current is sinusoidal alternating current, and the current amplitude is 600 A. It can be seen that the waveforms of voltage and current have ripples, so it is necessary to continue to add conversion circuits to reduce the waves. In short, after a series of transformations, the direct current is converted into alternating current through the full-bridge inverter and then through electromagnetic coupling to achieve non-contact energy transmission, which avoids the wear of the brushes inside the original motor, and significantly prolongs the life of the motor.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specification** & **Value** & **Specificatin** & **Value** \\ \hline \(L_{R}\) (\(\mu\)H) & 20 & C\({}_{S}\) (nF) & 36 \\ \(C_{P}\) (nF) & 175 & \(R_{S}\) (\(\Omega\)) & 0.075 \\ \(C_{1}\) (nF) & 45 & \(E\) (V) & 200 \\ \(L_{P}\) (\(\mu\)H) & 0.076 & \(L\) (\(\mu\)H) & 544 \\ \(R_{P}\) (\(\Omega\)) & 0.075 & \(C\) (nF) & 8 \\ \(L_{S}\) (\(\mu\)H) & 0.075 & \(R_{L}\) (\(\Omega\)) & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Non-contact excitation motor parameters.
# Silica-Coated Magnetic Nanocomposites for Pb\({}^{2+}\) Removal from Aqueous Solution
## 3 Results and Discussion
### Adsorption Thermodynamics
The positive value of \(\Delta\)S\({}^{\circ}\) reflects an increase in randomness during the process of Pb\({}^{2+}\) ions adsorption on magnetic nanocomposite, and the existence of the affinity between the adsorbent and Pb\({}^{2+}\) ions [45; 46]. It is very difficult to compare the results with various data reported in the literature since the differences of lead adsorption capacities depend not only on the structure and morphology of the adsorbent such as surface area, porosity, presence of functional groups, but also on the adsorption process variables like pH, adsorbent dose, metal ion concentration, temperature, contact time etc. The complexity derives from the non-uniformity of the adsorption process variables employed and the different measuring units for adsorption capacity or parameters that were used to express the results. Roughly, functionalization of the silica shell is significant when the initial metal concentration is as low as 0.01-0.2 mg/mL [13; 47; 48] or when small quantities of adsorbent (0.4-1 mg/mL) [11; 12; 14; 15; 16] and reduced contact times (30 min) [12] should be used. Finally, the selection of most suitable adsorbents should depend on a number of factors including cost-effectiveness, availability, and reusability. The results showed that our nanocomposite material exhibit Pb\({}^{2+}\) adsorption properties comparable with some of the functionalized ones, an increase of the adsorption properties being obtained mainly for porous silica nanocomposites and for materials with hybrid coating which follows multilayer adsorption. Functionalization of the silica considerably reduces the contact time and the quantity of adsorbent used. However, the physico-chemical stability and economic issues regarding the adsorbent cost should be taken into account.
## 1 Introduction
In these studies that predict the melt pool size using CFD and analytical models, analysis parameters also have very significant effects on the analysis results, while there are many analysis parameters with high uncertainty. However, there are many difficulties in accurately measuring or determining the values of these analysis parameters. Although analysis parameters are important factors influencing the analysis results of the models based on FEM or CFD, there are few studies on how accurate and reliable the analysis parameter values are and to what extent they affect the analysis results. In addition, it is also necessary to study how to properly adjust these uncertain analysis parameters to obtain the closest analysis results to the actual builds by LPBF process. Therefore, in this study, the influence of thermal analysis parameters on the analysis results is systematically estimated, and dominant analysis parameters are identified. Furthermore, a procedure for estimating the optimal values of these dominant analysis parameters that can make the thermal analysis model more accurately predict the characteristics of actual products is proposed. This procedure is based on a regression model, which has been utilized in various fields due to its advantages in speed and optimization. To improve the accuracy of the vibration analysis model, a regression model was used ([PERSON] et al., 2017), and stress was set as a parameter to evaluate fatigue performance ([PERSON] et al., 2017). In addition, it was also used to optimize the geometrical and engineering characteristics of building analysis such as tunnels ([PERSON] et al., 2017), but there was no case applied to the LPBF process simulation. The procedure proposed in this study can be applied to all kinds of analysis models based on FEM or CFD that include uncertain analysis parameters. Through this technique, optimal analysis parameters can be determined more efficiently, and more reliable analysis results even for models with relatively low fidelity can be obtained by applying the optimal analysis parameters. In this study, this technique is applied to a thermal analysis model using FEM. This paper is organized as follows: Section 2 describes the thermal analysis modeling technique including analysis parameters for the LPBF process simulation using ABAQUS ([PERSON] et al., 2017), which is a commercial finite element analysis program. In Section 3, a procedure for the estimation of sensitivities and optimal values of analysis parameters by nonlinear regression and an optimization technique is described. In Section 4, the numerical validation of the proposed procedure using reference data generated by thermal analysis models with arbitrary target analysis parameters is carried out. Conclusions and further improvements are given in Section 5.
# An Aeromagnetic Compensation Algorithm Based on Radial Basis Function Artificial Neural Network
## 4 Real Measured Data Test
### Compensation Results  
At present, the ratio (\(IR\)) of the standard deviation of magnetic interference before and after aeromagnetic compensation is commonly used in the industry to evaluate the compensation effect of aeromagnetic compensation methods.  
\[IR=\frac{STD_{p}}{STD_{f}} \tag{34}\]  
\[STD=\sqrt{\frac{1}{n}\sum_{i=1}^{n}{\left(x_{i}-\mu\right)^{2}}}, \tag{35}\]  
where \(STD_{p}\) is the standard deviation of the magnetic interference before compensation, and \(STD_{f}\) is the standard deviation of the residual magnetic interference after compensation, \(\mu\) is the arithmetic mean of the data.  
In order to verify the aeromagnetic compensation effect of the above two methods, the data of Flight D and Flight C are used as training sets to compensate for Flight C and Flight D, respectively. The compensation result is shown in Figures 12 and 13. Table 3 shows the comparison of the compensation effects of BP-ANNs and BRF-ANNs. In Flight C, the BP-ANN reduced the standard deviation from 2.2804 to 0.3376 with an IR of 6.7547, and the BRF-ANN reduced the standard deviation from 2.2804 to 0.3091 with an IR of 7.3775. In Flight D, the BP-ANN reduced the standard deviation from 4.2558 to 0.5734 with an IR of 7.4220, and the BRF-ANN reduced the standard deviation from 4.25584 to 0.4734 with an IR of 8.9899. From the compensation results, it can be seen that the compensation effect of BRF-ANN is better than that of BP-ANN, which proves the superiority of BRF-ANN compensation method.  
Figure 11: Aeromagnetic interference model: (**a**) Flight C; (**b**) Flight D.  
This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.  
Figure 12: Flight C compensation results: (**a**) BP-ANN and BRF-ANN compensation results; and (**b**) compensation for the resulting sampling points 1000 to 1500.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Set** & **Training Set** & **Method/Model** & \(STD_{p}\) & \(STD_{f}\) & **IR** \\ \hline Flight C & Flight D & BP-ANN & 2.2804 & 0.3376 & 6.7547 \\  & & BRF-ANN & 0.3091 & 7.3775 \\ Flight D & Flight C & BP-ANN & 4.2558 & 0.5734 & 7.4220 \\  & & RBF-ANN & 4.2558 & 0.4734 & 8.9899 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of compensation results of BP-ANN and BRF-ANN.
## 7 Experimental Verification  
The developed virtual sensor was verified in a driving scenario. The driving scenario consists of a typical maneuver in a production logistics scenario. The following list characterizes the scenario by showing the main parameters and their range of values:  
**Longitudinal velocity \(v_{x}\):**: it evolves from 0.556 (m/s) to 1.39 (m/s).
**Front steering angle \(\delta_{f}\):**:  
\[\delta_{f}=\left\{\begin{array}{ll}-7/36\pi[rad],&k=2000,\ldots,4000,\\ 7/36\pi[rad],&k=6000,\ldots,8000,\\ 0,&\text{otherwise.}\end{array}\right. \tag{73}\]
**Rear steering angle \(\delta_{r}\):**: it is constant and equal to zero.  
As can be seen, the vehicle is accelerating; consequently, the velocity is increasing. Simultaneously, the steering angle is changed leading to a curved route. During the whole maneuver, the global acceleration, velocity and yaw rate are measured by the respective sensors. The results from the experimental verification are presented in the next section.
## 4 Conclusions  
In summary, a lamp-pumped Q-switched Nd:YAG laser at 1064 nm with mode-locked modulations has been utilized to explore the threshold of air breakdown. It is feasible to utilize pulse-by-pulse monitoring and the statistic method to precisely determine the breakdown threshold value and threshold width. As shown in the present work, the breakdown threshold was dominated by peak-power intensity. Moreover, the increased peak power by the deep mode-locking modulations can significantly reduce the required pulse energy for laser-induced breakdown.  
Furthermore, the radiation of induced plasma rises rapidly while the instantaneous power exceeds the threshold power and displays a decaying time of a few tens of nanoseconds. With the self-scattering method, the time-dependent scattering ability of induced plasma can be obtained without additional probe light.  
Conceptualization, K.-W.S. and P.-H.T.; Validation, C.-H.W., P.-H.W., K.-T.Y. and K.-W.S.; Formal analysis, K.-T.Y., P.-H.T., C.-H.W. and K.-W.S.; Resources, K.-W.S.; Writing--original draft preparation, K.-T.Y. and P.-H.T.; Writing--review and editing, K.-W.S.; Supervision, K.-W.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All of the data reported in the paper are presented in the main text.  
Figure 5: Time-averaged radiation spectrum of the induced plasma in air.
# Performance Evaluation of GFDM Channel Estimation Using DFT for Tactile Internet Application
## 1 Introduction
### Contributions  
In this work, a channel estimation using DFT algorithm is proposed to reduce the mean square error (MSE) floor and enhance the estimation accuracy. This is an extension of our previous work [17]. In the previous work, we discussed channel estimation with DFT by using exponential decay channel taps ranging from \(0\) to \(-8.7\) dB and use only one pattern of pilot symbol. The main objective is to confirm that proposed channel estimation is suitable for GFDM system. The contributions of this manuscript are given below:  
* Two different pilot symbols patterns are used in which these pilot symbols are inserted at different subcarriers to evaluate the performance of each pilot symbol pattern.
* The GFDM system is operated for 5G application, where the GFDM parameters used in this work are suitable for tactile internet use-case [4].
* The channel model that we employ is 5G channel, which is introduced in [18; 19] as NYUSIM channel. The channel is suitable for millimeter wave wireless communication systems. The channel model operates for wide range carrier frequency (500 MHz to 100 GHz) with bandwidths (0 to 800 MHz). The scenarios provided in the NYUSIM simulator, such as urban microcell, urban macro cell and rural macro cell, are tested in GFDM system using channel estimation with DFT.  
This paper is organized as follows: Section 1 is the introduction. Section 2 explains the GFDM system and channel model. Channel estimation using DFT is described by considering pilot symbols insertion. Channel models generated from NYUSIM simulator are also shown and the power delay profile of each scenario is defined. In Section 3, we investigate the impact of different pilot symbol patterns under exponential channel. The simulation results are presented where channel estimation using LS and DFT in NYUSIM channel are compared.
## 3 Results
### Transitions in Physiological States of STEC O157:H7 on Romaine Lettuce During Cold Storage
#### 3.3.3 Transition of Microbial Cells into the Persister State During Cold Storage  
The median persister percentage increased from 8.78% (95% CI = 0.7%, 42.0%) on day 0 to 19.6% (95% CI = 1.5%, 74.4%) after 5 d of cold storage. The median values of persister cells on lettuce harvested at 9 and 17 \({}^{\circ}\)C were 24.4% (95% CI = 2.03%, 78.2%) and 13.3% (95% CI = 0.8%, 64.9%), respectively. The outbreak strains from Central Coast 2018, Salinas 2019 and Yuma 2018 had median persister percentages of 18.6% (95% CI = 1.7%, 68.3%), 24.6% (95% CI = 2.2%, 73.7%) and 6.5% (95% CI = 0.8%, 63.6%), respectively, over the cold storage period. Lettuce harvested at 9 \({}^{\circ}\)C had relatively higher persister percentage for all strains over the cold storage period compared to that harvested at 17 \({}^{\circ}\)C (Figure 5C).  
Spearman rank correlation tests showed weak correlations between persister percentage and cold storage days for lettuce that was harvested either at 9 \({}^{\circ}\)C (r = 0.12) or 17 \({}^{\circ}\)C (r = 0.14). Regression analysis was conducted to evaluate the combined effects of strain, harvest temperature and cold storage period on transition to persister state. With increasing cold storage days, the persister formation also increased (beta = 0.082, \(p\) = 0.009); see Figure 8. On the other hand, increasing harvest temperature to 17 \({}^{\circ}\)C had a negative impact (beta = \(-\)0.046, \(p\) = 0.002) on persister formation. Strain differences also affected the formation of persisters. Fewer persister cells were formed by the Yuma 2018 outbreak strain than Central Coast 2018 outbreak strain (beta = \(-\)0.046, \(p\) < 0.001). The interaction of the factors did not significantly (\(p\) > 0.05) affect the formation of persisters during cold storage.