esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 5 Results
### Performance Analysis
#### 5.1.3 Throughput Analysis  
Table 4 provides a comprehensive evaluation of the protocol's performance across varying client loads on different platforms, specifically a core-i7 desktop client and the ESP-8266 microcontroller. The data indicate that as the number of clients increases, the total response time correspondingly rises, reflecting the additional computational demands of managing multiple simultaneous requests. For the core-i7 desktop system, response times ranged from 3.47 ms for a single client to 387.28 ms when 100 clients were active. In contrast, the ESP-8266, constrained by limited hardware resources, exhibited higher response times, beginning at 41.14 ms for a single client and escalating to 4589.67 ms for 100 clients.  
Throughput, measured as the number of requests processed per second, gradually declined as the number of clients increased, attributed to resource contention. The desktop  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Total Response Time (ms)**} & \multicolumn{2}{c}{**Throughput (Clients/s)**} \\ \cline{2-5}
**No. of Clients** & **Core-i7** & **ESP-8266** & **Core-i7** & **ESP-8266** \\ \hline
1 & 3.47 & 41.14 & 288.27 & 24.30 \\
10 & 36.90 & 427.27 & 271.01 & 23.40 \\
50 & 189.21 & 2198.15 & 264.56 & 22.75 \\
100 & 387.28 & 4589.67 & 258.21 & 21.79 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Throughput analysis with a different number of clients.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Client**} \\ \cline{2-4}
**Protocol Steps** & **Server** & **Core-i7** & **Esp-8266** \\ \cline{2-4}  & \multicolumn{2}{c}{**Time (ms)**} \\ \hline Step 2 & 0.015 & 2.767 & 41.143 \\ Step 3 & 3.766 & – & – \\ Step 4 & – & 0.039 & 0.283 \\ Total Time & 3.940 & 3.469 & 44.426 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time utilized by each step on both the server and client to evaluate the protocol responsiveness.  
To further assess the robustness of the protocol, transient network failures and disruptions have been conducted. In all instances, the protocol maintained a secure state and seamlessly resumed operations, underscoring its capability to adapt to dynamic and adverse conditions without compromising security and mitigating performance degradations.  
The protocol's ability to support rapid recovery and sustained, secure communication highlights its robustness, making it a dependable solution for applications that demand high resilience to network failures alongside stringent security standards.
# Bayesian Filtering Multi-Baseline Phase Unwrapping Method Based on a Two-Stage Programming Approach
## 2 MBPU Theory and Phase Gradient Estimation  
### Mathematical Foundation of MBPU  
InSAR technology reconstructs DEM, based on the geometric relationship of its observation. According to the characteristics of the InSAR receiver, the received interferometric phase is compressed between \((-\pi,\pi]\), so the wrapped interferometric phase must be unwrapped. The relationship between the wrapped phase and absolute phase can be expressed as:  
\[\varphi(k)=\psi(k)-2n(k)\pi \tag{2}\]  
where \(\psi(k)\), \(\varphi(k)\), and \(n(k)\) are the absolute phase, wrapped phase, and ambiguity number of the \(k\) th pixel, respectively. From Equation (2), when the interferometric phase does not contain noise, the absolute phase \(\psi(k)\) can be obtained by finding the ambiguity number \(n(k)\). According to the observation geometry, we can obtain the following formula:  
\[h(k)=\frac{\lambda\cdot r(k)\cdot\sin(\theta)}{B\cdot 2l\pi}\cdot\psi(k) \tag{3}\]  
where \(h(k)\) represents the terrain height, \(\lambda\) represents the wavelength, \(r(k)\) represents the slant range, \(\theta\) represents the incidence angle, \(B\) represents the normal baseline, and \(l\) is 2 for monostatic mode and 1 for bistatic mode. For the same study area, the terrain heights corresponding to different baseline lengths are the same. In this paper, we take the dual-baseline as an example, the equation of the baseline length is:  
\[B_{2}\cdot(\varphi_{1}(k)+2n_{1}(k)\pi)=B_{1}\cdot(\varphi_{2}(k)+2n_{2}(k)\pi) \tag{4}\]  
where \(B_{i}(i=1,2)\) represents the baselines of different lengths (\(i\) represents different interferograms), \(n_{i}(k)(i=1,2)\) represents the ambiguity number, and \(\varphi_{i}(k)\) represents the wrapped interferometric phase. Equation (4) is the basic equation for the MBPU method. We cannot solve for two unknowns through one equation. However, because the ambiguity numbers are integers, some methods can enable us to obtain \(n_{1}(k)\) and \(n_{2}(k)\) using only Equation (4).
# Three-Level Hybrid Envelope Tracking Supply Modulator with High-Bandwidth Wide-Output-Swing
## 2 Supply Modulator System Level Design
### Wideband Linear Amplifier  
As the bandwidth of the modulated signal increases, the power supply modulator needs to track a higher bandwidth envelope signal, thus requiring more high-frequency output current from LA, which limits overall system efficiency improvements. On the one hand, the bandwidth of the hybrid ET modulator is affected by LA. On the other, the bandwidth of SWR is also gradually increased to reduce the contribution of LA. However, the wideband SWR needs a wideband LA to absorb the high frequency ripple of the switching stage, which makes the LA design more difficult.  
The proposed LA has a large unity-gain bandwidth and high driving capability, the overall architecture is shown in Figure 3, including rail-to-rail input stage, buffer stage and class-AB output stage.  
The power loss of the LA includes static loss and dynamic loss. The dynamic power loss comes from the nMOS and pMOS of the output stage as follows:  
\[P_{LA\_dy\_loss}=P_{n\_loss}+P_{p\_loss} \tag{2}\]  
\[P_{n\_loss}=(V_{DD}-V_{out})\times(i_{out}-i_{sw})\text{, if }i_{out}>i_{sw}\text{;} \tag{3}\]  
Figure 3: Simplified schematic of the linear stage.  
Figure 2: Block diagram for proposed ET modulator.  
\[P_{p\_loss}=V_{out}\times(i_{sw}-i_{out}),\qquad\qquad if\;i_{out}<i_{sw}; \tag{4}\]  
where \(V_{DD}\) is the supply voltage, \(V_{out}\) is the output voltage at the load, \(i_{sw}\) is the switch stage current, and \(i_{out}\) is the current flowing into the collector/drain of the RF power amplifier. \(P_{LA\_dy\_loss}\) is related to the amplitude probability distribution of the input envelope signal and the peak envelope voltage [25].  
The quiescent power loss mainly comes from the quiescent bias current, independent of the output power. Its reduction relies on the design of low-power circuits. In order to improve the driving ability of the LA and reduce the quiescent current as much as possible, a voltage follower is used in this paper. The proposed voltage follower can effectively reduce the output impedance, the specific implementation method is discussed in next section.
# High-Precision Image Editing via Dual Attention Control in Diffusion Models Without Fine-Tuning
## 3 Preliminaries  
### Denoising Diffusion Probabilistic Models  
Denoising Diffusion Probabilistic Models (DDPMs) [1] are mainly divided into two stages: the diffusion stage and the sampling stage. Firstly, the diffusion stage is the process of gradually adding Gaussian noise to the image until it becomes purely Gaussian noise. DDPM defines the noise coefficients \(\{\beta_{i}\}_{i=1}^{T}\), which increases linearly from \(\beta_{1}=10^{-4}\) to \(\beta_{T}=0.02\). The noisy image \(\mathbf{x}_{t}\) at time step \(t\) is obtained by adding noise to the noisy image \(\mathbf{x}_{t-1}\) at time step \(t-1\). The specific formula is as follows:  
\[\mathbf{x}_{t}=\sqrt{1-\beta_{t}}\mathbf{x}_{t-1}+\sqrt{\beta_{t}}\epsilon \tag{1}\]  
with \(t\sim\mathbf{Uniform}(\{1,\dots,T\})\), and \(\epsilon\sim\mathcal{N}(\mathbf{0},\mathbf{I})\).  
According to Equation (1), we can derive the diffusion formula from \(\mathbf{x}_{0}\) to any time step \(t\):  
\[\mathbf{x}_{t}=\sqrt{\bar{\alpha}_{t}}\mathbf{x}_{0}+\sqrt{1-\bar{\alpha}_{t }}\epsilon \tag{2}\]  
with \(\alpha_{t}=1-\beta_{t}\), and \(\bar{\alpha}_{t}=\prod\limits_{t=0}^{t}\alpha_{i}\).  
Secondly, the sampling stage is a process of gradual denoising using a trained denoising UNet [42]. For each denoising time step, the model predicts the noise in the current noisy image \(\mathbf{x}_{t}\), and the denoising result in time step \(t\) can be described as \(\mathbf{x}_{0|t}\). Directly using \(\mathbf{x}_{0|t}\) as the output often leads to unsatisfactory image quality, so we need to add noise with a smaller noise level to \(\mathbf{x}_{0|t}\) to obtain \(\mathbf{x}_{t-1}\), and then continue denoising on \(\mathbf{x}_{t-1}\) to derive \(\mathbf{x}_{0|t-1}\). This denoising process is iterated until a high-quality generated image \(\mathbf{x}_{0|0}\) is obtained. Therefore, we can obtain the following sampling formula:  
\[\mathbf{x}_{t-1}=\frac{1}{\sqrt{\bar{\alpha}_{t}}}(\mathbf{x}_{t}-\frac{1- \alpha_{t}}{1-\bar{\alpha}_{t}}\epsilon_{\theta}(\mathbf{x}_{t},t))+\sigma_{t} \mathbf{z} \tag{3}\]  
\[\sigma_{t}^{2}=\frac{1-\bar{\alpha}_{t-1}}{1-\bar{\alpha}_{t}}\beta_{t} \tag{4}\]  
with \(\epsilon_{\theta}(\mathbf{x}_{t},t)\), a denoiser that predicts the noise in \(\mathbf{x}_{t}\) at time step \(t\); \(\theta\), the parameter of the noise predictor; and \(\mathbf{z}\sim\mathcal{N}(\mathbf{0},\mathbf{I})\).  
The goal of training in DDPM is to make the predicted noise of the denoising model as close as possible to the actual added noise. In order to train the model parameters, DDPM generally uses L2 Loss as the loss function. This results in the following training formula for the DDPM:  
\[\
abla_{\theta}||\epsilon-\epsilon_{\theta}(\sqrt{\bar{\alpha}_{t}}\mathbf{x }_{0}+\sqrt{1-\bar{\alpha}_{t}}\epsilon,t)||^{2} \tag{5}\]  
with \(t\sim\mathbf{Uniform}(\{1,\dots,T\})\), and \(\epsilon\sim\mathcal{N}(\mathbf{0},\mathbf{I})\).
## 4 Conclusions  
This study presents fabrication and processing of a novel electrospun fiber mat-assisted analytical well plate, i-ELISA. Associated fiber segments in this platform are a novel class of material made from electrospun poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) fibers coated with different compositions of poly methyl methacrylate-co-methacrylic (poly(MMA-co-MAA)) synthesized in free-radical polymerization reaction. The developed well plate has successfully addressed some of the major drawbacks of the previous paper-based analytical platforms. I-ELISA was tested for diagnosis of dengue virus via physical immobilization of the proteins by using a sandwich colorimetric assay. Proposed technology has proven to greatly enhance the detection signal intensity up to 12 times greater than the conventional assay, while the limit of detection (LoD) values reveal a significant improvement  
Figure 7: General comparison between a multilayer analytical 96-well plate as the representative of the complex paper-assisted platforms and i-ELISA.  
(\(\sim\)500 times in the case of PMMA-coated electrospun PHBV fibers) in comparison to the conventional clinical assay. I-ELISA has also shown a considerably higher level of sensitivity, specificity and accuracy in comparison to the clinically approved ELISA. In particular, i-ELISA was capable of detecting only few plaque-forming units of dengue virus in blood serum, which corresponds to the primary stage of dengue infection. Development of i-ELISA can open new opportunities to the future production of cost-effective, disposable and reliable diagnostic tools that can be utilized for enhanced detection of viruses, not only limited to dengue virus as presented in this study. The proposed strategy described in this paper can be applied for fabrication of a new generation of analytical well plates that can incorporate a wide variety of customized or commercial fiber/paper products into the actual analytical assay. Developed fiber segments of i-ELISA, in turn, have shown great potential for integration into the microfluidic devices or other fiber-based analytical platforms.  
The authors would like to acknowledge the financial support of Tecnologico de Monterrey, Mexico, for the special grant (grant number: 002 EICII01) awarded to the Sensors and Devices Focus Group, School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Mexico. This research has been partly supported by University Malaya Research Grant (grant number: TR001D-2014B). The authors also acknowledge the partial support of High Impact Research Grant (grant number: UM.C/625/1/HIR/MOE/SC/33/1) provided by Ministry of Higher Education, Malaysia. The authors would like to thank [PERSON] for his contribution in the graphical designs.  
The authors declare no conflict of interest.
# High Optical Feedback Tolerance of a Detuned DBR Laser for 10-Gbps Isolator-Free Operation
###### Abstract  
The optical feedback tolerance (OFT) of a distributed Bragg reflector (DBR) laser was investigated experimentally. The static and modulation performance of the DBR laser under optical feedback was examined by evaluating its relative intensity noise (RIN) and bit error rate (BER). It is shown that the OFT of the DBR laser is closely related to its peak wavelength detuning relative to the Bragg wavelength. A high tolerance to optical feedback was demonstrated, both in the continuous wave (CW) and the direct modulation (DM) states, when the DBR laser was red-detuned relative to the Bragg wavelength. The excess RIN induced by optical feedback was well suppressed to a level of \(-140\) dB/Hz at a feedback level of \(-9\) dB in the CW state. In a 10 Gbps direct modulation state, bit error rates (BER) below \(1\times 10^{-9}\) and \(3\times 10^{-7}\) were obtained under a feedback level of \(-15\) dB and \(-9\) dB, respectively, for the case of back-to-back transmission. After 20 km fiber transmission, the BER still maintained below \(1\times 10^{-7}\) under a feedback level of \(-15\) dB, with a power penalty of less than 1 dB.  
distributed Bragg reflector (DBR) laser; optical feedback tolerance (OFT); wavelength detuning; isolator-free; optical fiber communication +
Footnote †: journal: _Photonics_
# On-the-Fly Repairing of Atomicity Violations in ARINC 653 Software
## 2 Background
### Avionic Health Management System
In the case of a process-level error, the HMS can call an error handler defined by a user. The FACE [21] is an open architecture for the development of portable airborne software components targeting general-purpose, safety and security-purpose usages. The FACE consists of five local segments, such as operating system segment (OSS), input/out services segment (IOSS), platform-specific services segment (SPSS), transport services segment (TSS), portable components segment (PCS). The FACE provides OS-level health monitoring and fault management (HMFM) and system-level HMM for HMSs. The The OS-level HMFM offers a standardized method for detecting, reporting and handling errors and failures within the scope of a single system or a platform. The OS-level HMFM detects and handles errors and faults during run-time at the process (i.e., thread), partition and module (i.e., platform) levels. The role of the system-level HMFM is to monitor faults and failures in systems and applications and report them. It can also support a repair option that allows the system-level HMFM to repair a failed or defective resource. The system-level HMFM can monitor or manage instantiation and termination of components. The system-level HMFM may generate alarm and notification to indicate internal state transitions of components.
# Cleaning Phenolic Compounds Present in Water Using Salting-Out Effect with DCA-Based Ionic Liquids
## 3 Results and Discussion  
The efficiency of the two abovementioned ILs to eliminate phenol, o-cresol, and chlorophenol, and a mixture of them from the water, was studied. The experimental procedure was carried out by liquid-liquid extraction and K\({}_{3}\)PO\({}_{4}\) as a salt that produces the salting out effect and, therefore, the separation of both aqueous and organic phases.  
Four different concentrations, 10, 20, 30, and 40% (\(w/v\)) of salt, with respect to the volume of the aqueous phase, were studied. Nevertheless, at 10% with [EMim] [DCA], the two phases were not separated, so only the other three concentrations were used to study the extraction of phenols. All the experiments were carried out at atmospheric pressure and constant temperature (298.15 K). The final concentration of each phenolic compound, and the mixture (PCM) in water, as well as the extraction efficiencies, were calculated, and the results can be consulted in the Supplementary Information, Tables S1 and S2, respectively.  
In general, for all the initial studied concentrations of phenolic compounds in water (from 3 to 15,000 mg\(\cdot\)L\({}^{-1}\)) with both ILs as extraction agents, the extraction efficiencies were high and greater than 90%. The 2-chlorophenol compound presented the best extraction efficiency with both ILs at all initial concentrations and with all percentages of salt. These results are in agreement with the results obtained for the extraction of 2-chlorophenol with [NTf\({}_{2}\)] and [Nf\({}_{2}\)]-based ILs published for this research group in previous works [11; 25]. The extraction efficiency for the phenolic compounds from water followed the next trend, as it is possible to observe in Figure 1:  
The legal limit, established in 1 mg\(\cdot\)L\({}^{-1}\), was reached with only one extraction process for all initial concentrations below 500 mg\(\cdot\)L\({}^{-1}\) for phenol and o-cresol with all studied percentages of salt. For higher initial concentrations, one more extraction step would be needed to achieve the legal limit. For the extraction of 2-chlorophenol, the legal limit was reached with one extraction step for initial concentrations below 1000 mg\(\cdot\)L\({}^{-1}\) using 20% of salt with both ILs, and this value increased to 10,000 using 30% and 40% of salt. Only one more extraction cycle was needed when the initial concentration of 2-chlorophenol was 15,000 mg\(\cdot\)L\({}^{-1}\). For the extraction of PCM with 20% of salt, the legal limit was reached for initial concentrations below 500 in one cycle with 30% of salt, this value reached initial concentrations below 1000 mg\(\cdot\)L\({}^{-1}\), and with 40% of salt, concentrations were below 10,000 mg\(\cdot\)L\({}^{-1}\) using both ILs. For the other systems, one more extraction cycle was necessary.
## 5 Conclusions  
In summary, we provide experimental evidence to support that 4-MSP selectively suppresses TGF-\(\beta\)1 or/and PDGF-BB-triggered Smad2/3, MAPK, and Akt pathways activation, which are critical for HSCs activation and proliferation (Figure 6). Furthermore, 4-MSP administration significantly ameliorated CCl\({}_{4}\)-induced liver fibrosis in mice. In addition, no adverse events in terms of body weight loss, hepatotoxicity, and liver dysfunction were observed in 4-MSP-treated mice. Taken together, these results indicate that 4-methoxy sulfonyl paeonol has significant antifibrotic activity both in vitro and in vivo at a lower dosage than that required for paeonol, which suggests 4-MSP as a novel therapeutic for patients with liver cirrhosis.  
Figure 6: Proposed model of compound 4-methoxy sulfonyl paeonol (4-MSP) in the inhibition of HSCs proliferation and fibrogenesis. Compound 4-MSP causes inhibition of hepatic stellate cells (HSCs) activation and liver fibrosis by blocking TGF-\(\beta\)1/Smad, PDGF-BB/MAPK, and Akt signaling pathways.  
**Author Contributions:** Conceptualization: [PERSON], [PERSON], and [PERSON]; data curation: [PERSON]; formal analysis: [PERSON] and [PERSON]; methodology: [PERSON]; validation: [PERSON] and [PERSON]; writing--original draft: [PERSON] and [PERSON]; writing--review and editing: [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work was supported in part by a grant from the Taipei Medical University--Wan Fang Hospital, Taiwan (108-TMU-WFH-10) and from the Ministry of Science and Technology, Taiwan (MOST 109-2628-B-038-020). We also thank [PERSON], [PERSON], [PERSON], and [PERSON] for performing the experiments.  
**Conflicts of Interest:** The authors declare that no competing interests exist with respect to this work.
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 6 Conclusions and Future Works
For this mission, objective functions to evaluate the efficiency are easier to implement (see for instance [28], where the maximum age of the cells is used). **Author Contributions:** [PERSON] has developed the proposed algorithm, implemented the algorithms for the simulations and analyzed the results. [PERSON] has supervised the complete process of the work and reviewed the document. **Acknowledgments:** We would like to thank the SAVIER (Situational Awareness Virtual EnviRonment) Project, which is both supported and funded by Airbus Defence & Space. The research leading to these results has received funding from the RoboCity2030-III-CM project (Robotica aplada a la mejoera de la calidad de vida de los ciudadanos. Fase III; S2013/MIT2748), funded by Programas de Actividades 1+Den la Communidad de Madrid and co-funded by Structural Funds of the EU, and from the DPI2014-56985-R project (Proteccoin Robotizada de Infraestructuras Crticas (PRIC)) funded by the Ministerio de Economia y Competitividad of Gobierno de Espana. **Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Plain**} & \multicolumn{2}{c}{**Probability**} & \multicolumn{2}{c}{**Obstacles**} & \multicolumn{2}{c}{**Prob. + Obs.**} \\ \hline  & **Eff** & **Fit** & **Eff** & **Fit** & **Eff** & **Fit** & **Eff** & **Fit** \\ \hline A1: Behaviors set & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ A2: Random & 0.23 & 0.09 & 0.22 & 0.14 & 0.24 & 0.13 & 0.21 & 0.18 \\ A3: Closest & 0.91 & 0.93 & 0.81 & 0.86 & 0.87 & 0.84 & 0.76 & 0.77 \\ A4: Boundary & 0.78 & 0.78 & 0.63 & 0.72 & 0.86 & 0.84 & 0.66 & 0.71 \\ A5: Energy & 0.41 & 0.15 & 0.42 & 0.28 & 0.44 & 0.20 & 0.43 & 0.34 \\ A6: Billiard & 0.42 & 0.17 & 0.43 & 0.31 & 0.45 & 0.22 & 0.44 & 0.37 \\ A7: Lanes & 0.90 & 0.99 & 0.76 & 0.90 & 0.59 & 0.60 & 0.52 & 0.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Relative efficiencies and fitness values for each algorithm and each scenario type.