esa-sceva/satcom-chunk-collection huggingface dataset:
# A Web of Things-Based Emerging Sensor Network Architecture for Smart Control Systems
## 4 Proposed Framework
### Smart Home Services
In addition, appliance-specific services are exposed to remote users via RESTful APIs, i.e., air conditioner cooling and water heating levels. The remote user request is made through the Web and received by the smart gateway, which sequentially, forwards it to the MS. Since the user initiates the event, the MS executes the request immediately and \(\alpha_{\text{T}}\) is not involved. At the arrival of the HTTP request, the MS selects the appropriate communication process. If the real-time information is available on the MS, it will directly process the HTTP request and generate the corresponding response. For example, if a user requests to get the status of an appliance, it is always available on the MS. Therefore, the MS responds to the user request immediately after processing. Nevertheless, in the absence of real-time data on the MS, it determines the corresponding ZigBee coordinator and ZigBee sensor to route the HTTP request. The identification is made easy with the self-describing HTTP request. At the completion of the service request, the MS sends the service response to the smart gateway wrapped as an HTTP response as shown in Figure 2. For example, assume that a remote user asks for the room temperature of a particular room in the smart home. Since the MS does not hold the real-time room temperature, it forwards the HTTP request to the respective coordinator and sensor to process the event accordingly. The sensor performs service resolution and executes the event, i.e., getting the room temperature, and setting refrigerator cooling. Accordingly, the sensor generates the service response at the completion of the event. Consequently, the event response is sent back to the MS and it will traverse the smart gateway, RESTful API architecture, and Web server to serve the user request. While routing the response, the MS updates the appliances' or sensors' status and energy consumption parameters. Finally, the smart gateway wraps the service response that can be viewed by the respective user via a Web browser.
## 3 Results and Discussion
### Simulation of the SERS Substrate
The enhancement factor G was collected on the length of the cross-section of silicon wire, as depicted by the red line on the inset. Along the 300 nm silicon nanowire, field enhancement varies between 150 and 900 and we found a maximum enhancement of 900 at 760 nm, that confirms the effect of Ag/Au nanoparticles on increasing field enhancement, compared with our previous results [22] where we only covered the silicon wire with silver nanoparticles. The added 5% of gold concentration reinforced the field enhancement. Our simulated model presents a complementary study for the practical reality. It shows that SERS spectra are affected by the localized surface plasmon resonance of silver/gold nanostructures, the interaction of the active plasmon modes with the proteins leads to a shift and broadness of the vibrational modes. Figure 12: _Cont._  
Figure 12: **(a)** 3D simulation of the electromagnetic field enhancement (near-field enhancement) in the silicon wire surrounded by a spherical silver and gold nanoparticle. The model is initially placed in the air domain. Silicon wire radius = 12 nm, length = 300 nm, Ag and Au nanosphere radius = 7 nm, the number of Ag and Au nanosphere covering the silicon wire is 108 (**b**) Absorption cross-section of the 3D silicon nanowire surrounded by silver and gold nanosphere proteins. (**c**) Enhancement factor G: E\({}_{\text{loc}}\)/E\({}_{\text{0}}\) of the silicon wire edge (represented with red color in the inset) in the bio-medium. The maximum enhancement factor obtained is 900.
# Review of Power System Resilience Concept, Assessment, and Enhancement Measures
## 4 Resilience Assessment and Metrics
### Metrics of Resilience Assessment  
#### 4.3.1 Pre-Event Evaluation  
This paper suggests evaluating the resilience of a system from two standpoints: pre-event evaluation and post-event estimation. Before the event occurs, a general risk evaluation is conducted. \(D_{event}\) represents the extent of damage to the electrical power supply caused by the event. \(P_{event}\) represents the probability of the event occurring. \(I_{RD\_event}\) is the outage risk index for a specific area associated with the event. \(I_{Re\_event}\) is the resilience assessment metrics before the event, as shown in Equation (2). It is important to note that when calculating \(D_{event}\), all possible emergency supply paths and potential remote-control methods should be taken into consideration to evaluate the number of customers without power [41]:  
\[I_{Re\_event}=\frac{1}{I_{RD\_event}}=\frac{1}{P_{event}\cdot D_{event}} \tag{2}\]  
[PERSON] et al. [73] proposed a resilience risk assessment for the power grid based on Failure Mode and Effect Analysis (FMEA) to identify potential failures that may occur under different conditions. This FMEA-based risk assessment considers the probability of a failure occurring due to extreme weather events \(P\), the severity of load losses caused by weather events \(S\), and the detectability of the failure modes \(D\) to calculate the risk priority number (\(RPN\)) for different failure modes in the system. \(RPN\) is the matrix of \(m\times n\), as shown in Equation (3).  
\[RPN=\left[P\times S\times D\right]_{m\times n} \tag{3}\]  
where \(m\) represents the number of failure modes in the power system caused by the extreme weather and \(n\) represents the number of zones in the selected region.
# Composing Multiple Online Exams: The Bees Algorithm Solution
###### Abstract  
Online education has gained increasing importance in recent years due to its flexibility and ability to cater to a diverse range of learners. The COVID-19 pandemic has further emphasized the significance of online education as a means to ensure continuous learning during crisis situations. With the disruption of traditional in-person exams, online examinations have become the new norm for universities worldwide. Among the popular formats for online tests are multiple-choice questions, which are drawn from a large question bank. However, creating online tests often involves meeting specific requirements, such as minimizing the overlap between exams, grouping related questions, and determining the desired difficulty level. The manual selection of questions from a sizable question bank while adhering to numerous constraints can be a laborious task. Additionally, traditional search methods that evaluate all possible solutions are impractical and time-consuming for such a complex problem. Consequently, approximate methods like metaheuristics are commonly employed to achieve satisfactory solutions within a reasonable timeframe. This research proposes the application of the Bees Algorithm (BA), a popular metaheuristic algorithm, to address the problem of generating online exams. The proposed solution entails creating multiple exam forms that align with the desired difficulty level specified by the educator, while considering other identified constraints. Through extensive testing and comparison with four rival methods, the BA demonstrates superior performance in achieving the primary objective of matching the desired difficulty level in most test cases, as required by the educator. Furthermore, the algorithm exhibits robustness, indicated by minimal standard deviation across all experiments, which suggests its ability to generalize, adapt, and be practically applicable in real-world scenarios. However, the algorithm does have limitations related to the number of successful solutions and the achieved overlap percentage. These limitations have also been thoroughly discussed and highlighted in this research.  
metaheuristics; Bees Algorithm; online exams; swarm intelligence Article
## 3 Analysis
### Software Supply and Pricing Structure  
If the software is divided into Component, Stack, Module, and Unit and supplied with necessary technologies, product prices can be intuitively compared with competitors, and companies can also gain an advantage in negotiation with their customers by focusing their request on necessary technologies. The high initial capital cost of securing software  
Figure 6: Software co-development concept.  
reduces competitiveness when bidding for contracts. It also raises the risk of return on investment due to the difference between the time of capital investment and the time of recovery, as well as the problem of software price adequacy, which are factors that lead to lower competitiveness.  
It is important to \"white-box\" software that has been \"black-boxed\" prior to delivery. This involves identifying the technical assets of the software to determine the extent of open-source usage and the company that owns the software assets. It is only possible to trade necessary technologies when the software is identified in module units and the total amount is calculated (Figure 7).  
In addition, the preparation of various data for fair price calculation, schedule delays due to price negotiation, and excessive reduction requests without customer standards are important factors that can decrease the competitiveness of software quality. The investment cost for a software module with a high frequency of use is billed as a subscription fee for the mass production period (BIP), and for a software module whose return on investment is unclear due to low frequency of use, a fee is charged at the development stage (FIP) and developed as needed to secure performance. If one module is charged (SIP, PIP) as a subscription fee to the next project through the capitalization process, a win-win relationship can be formed between the software developer and the user.  
If a software developer converts a portion of their cash inflow to the time of mass production and proceeds with billing based on vehicle sales performance, software users can be relieved of excessive cost burden (Figure 8). However, this concept will not work unless it is operated in a mutually beneficial attitude based on trust and a culture that recognizes software technical assets between the two companies.  
Figure 8: Software billing concept.  
Figure 7: Software co-development concept.  
At present, BIP, FIP, SIP, PIP, and OIP are developed and supplied without differentiation, but a license policy that considers the separation and development time of each IP should be established and implemented in the future. It is believed that competitiveness in the electronic software field can be secured by establishing a strategic pricing policy that takes into account various supply scenarios, such as supplying each component of the software product according to the customer's request and whether source code is included (Figure 9).
## 2 IMS Bearing Dataset and Signal Analysis Techniques
### Description of IMS Dataset 1
\begin{table}
\begin{tabular}{l l} \hline Start-stop time and date & From 22 October 2003 at 12:06:24 p.m. \\  & To 25 November 2003 at 11:39:56 p.m. \\ \hline Entire experiment timespan & 49,680 min (34 days and 12 h) \\ \hline Effective data acquisition duration & 36 min \\ \hline Number of files & 2156 \\ \hline Number of channels (accelerometers) & 8 \\ \hline Channel arrangement & Bearing 1: channels 1 and 2 \\  & Bearing 2: channels 3 and 4 \\  & Bearing 3: channels 5 and 6 \\  & Bearing 4: channels 7 and 8 \\ \hline File recording interval & 10 min (5 min for the first 43 files) \\ \hline Location of detected faults at the end of the experiment & Bearing 3: inner race \\  & Bearing 4: rolling element and outer race \\ \hline \end{tabular}
\end{table} 
Table 1: Experimental set-up characteristics and findings of IMS dataset 1 (from [33]). The nominal rotation frequency of the transmission shaft \(f_{r}\) can be directly obtained from the working conditions:  
\[f_{r}=\frac{\omega}{2\pi}=\frac{2000\ \mathrm{rpm}}{2\pi\cdot 60\ \mathrm{s/min}}=33.3\ \mathrm{Hz} \tag{5}\]  
where the corresponding characteristic frequencies of the faults are reported in Table 3.
## 3 Applications of the CDS for the Fiber Optic Link
### Perceptor
#### 3.1.2 Previous Model Selection  
If the BER exceeds the FEC threshold, then \(P(\hat{Y}_{n}^{k}\mid\lvert\hat{X}_{n}^{k})\) cannot be extracted by the three-layer modeling in BER improvement mode. In this case, the system, like a human brain, needs prediction. Thus, it is necessary to use previous experiences for predicting the future. As a result, this subsystem  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Precision Factor (PF)** & \(P(\hat{Y}_{n}^{k}\mid\lvert\hat{X}_{n}^{k})\) **System Model Tensor Dimensions** \\ \hline
5 & \(33\times 29\times 16\) \\
2 & \(81\times 71\times 16\) \\
1 & \(161\times 141\times 16\) \\
0.5 & \(321\times 281\times 16\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(P(\hat{Y}_{n}^{k}\mid\lvert\hat{X}_{n}^{k})\) system model dimension versus precision factor (PF) extracted by simulation (number of OFDM frames = 1024).  
Figure 12: Model accuracy improvement versus a number of the received frames from the simulation results. Data rate = 52 Gb/s, \(P_{tx}=-7\) dBm, and \(L=1600\) km.  
(see Figure 7) will choose the closest model to the current system state (state number \(k\)). Suppose we have selected the model \(P(\hat{Y}_{n}^{k-1}\mid\hat{X}_{n}^{k-1})\). The model \((k-1)\) could correspond to one of the previous data rates, input launch power, previous transmission distance, etc. For example, if the transmission distance is changed from 1600 to 1800 km due to re-routing in fiber optic network, but the data rate, and launch power are fixed at 52 Gb/s, and -6 dBm respectively, then this subsystem will select the closest model to the current system state for prediction (closest in reach, launch power, and data rate). For more accurate prediction, the perturbations to the system should be small, in other words, the changes in transmission reach \(\Delta L\) (\(L_{k}-L_{k-1}\)) and/or the change in data rate \(\Delta R\) (\(R_{k}-R_{k-1}\)) should be small enough (e.g., \(\Delta R=2\) Gb/s when the data rate is 50 Gb/s.) The CDS system will find the closest reach to the current reach and/or closest data rate from the model library for prediction. We considered \(k\) to be scalar for simplicity. In general, \(k\) could be a vector, \([l,m,n,p,\dots]\). For example, \(l\), \(m\), \(n\), \(p\) could correspond to data rate, launch power, fiber nonlinear coefficient, and transmission reach, respectively.
# Design of a Single-Stage Transformerless Buck-Boost Inverter for Electric Vehicle Chargers
## 6 Simulation Results  
Simulations models were built in Matlab/Simulink software based on the system parameter values listed in Table 1 and the schematic diagram shown in Figure 1. The simulation waveforms of capacitor voltage \(v_{c}\) and inductor current \(i_{L}\) under two cases, where the peak ac voltages (50 V and 150 V) are lower than and higher than the dc-link voltage, are demonstrated in Figures 7 and 8. For these two cases, the dc-link voltage \(v_{dc}\) is maintained as a constant 100 V. As observed, the proposed inverter enables the operations of both buck-type and boost-type inverters. As mentioned, the switching patterns are changed while the system structure remains fixed. These results are consistent with the theoretical analysis.  
Figure 6: Pole-zero map of the closed-loop voltage control: (**a**) With damping controller (\(G_{d}(z)=4\)), (**b**) Without damping controller (\(G_{d}(z)=1\)).
## 4 Analysis on the Features of 2D-DPRCML
### Bifurcation Diagram  
Bifurcation indicates period doublings, such as doubling from a single fixed point to two. In chaotic systems, this phenomenon may appear as the control parameters change. It is utilized to illustrate the period doubling in a chaotic system, and it can also be employed to assess the aperiodicity of a chaotic system.  
In our research, we assign the coupling coefficient \(\varepsilon=0.375\). The constant \(\sigma=0.5\) in 2D-MCPML [7]. Without loss of generality, the bifurcation diagrams of lattice \(x\) (5, 5) in the systems mentioned above are depicted in Figure 6.  
From Figure 6a,b, we can see that the periodic windows exist in these two systems, and it is apparent that 2D-CML outperforms 2D-NLCML in terms of aperiodicity when \(\mu<3.6\). Moreover, the iterative results can fill the whole interval (0, 1) only for \(\mu=4\) in Figure 6a,b. In comparison, as Figure 6c,d illustrates, the periodic windows disappear in 2D-MCPML and 2D-DPRCML, and there is no fixed or periodic point in these two systems. Furthermore, as illustrated in Figure 6d, the performance of 2D-DPRCML is the best in the four systems mentioned above since the outputs are able to fill the whole interval irrespective of the size of the control parameter \(\mu\). In other words, the 2D-DPRCML system is chaotic throughout the control parameter interval (\(\mu\in[3\),\(4\)]). Therefore, the aperiodicity is enhanced significantly in the 2D-DPRCML system. In addition, the space of optional parameters leading to chaos is expanded efficiently. Accordingly, the key space will be enlarged when the 2D-DPRCML system is employed for building a crypto-system.  
Figure 5: KEBs of (**a**) 2D-CML, (**b**) 2D-NLCML, (**c**) 2D-MCPML and (**d**) 2D-DPRCML.
# An Optimal Authentication Scheme through Dual Signature for the Internet of Medical Things
## 6 Informal Security Analysis
### Data Privacy  
Our designed dual signature scheme successfully provides data privacy by only allowing the AP to view the patient's health information (\(m\)) while hidden (\(m\)) from the PDA. Suppose an adversary makes an attempt to alter or decrypt (\(m\)). In that case, he/she cannot do so because (\(m\)) is encrypted using \(PB_{\text{AP}}\) and due to the hard nature of hyper elliptic curve discrete logarithm problem, it is impossible for an adversary to gain \(\varphi_{\text{AP}}\).
## 6 Simulation Evaluation of the Dynamic VWSN
### Adaptivity of the VWSN Topology against Targeted Attacks
\begin{table}
\begin{tabular}{l l l l l l l} \hline  & _phy_ & \(\mathbf{phyFrag}\) & \(\mathbf{LOFrag}\) & \(\mathbf{LLFrag}\) & \(\mathbf{L2 Frag}\) \\ \hline BICM(hh,HH) & \(T1\) & 0 & 35 & 48 & 26 \\ BICM(ll,LL) & \(T1\) & 0 & 100 & 0 & 0 \\ Bio-inspired & \(T1\) & 0 & 88 & 86 & \\ BICM(hh,HH) & \(T2\) & 1 & 7 & 61 & 9 \\ BICM(ll,LL) & \(T2\) & 0 & 8 & 0 & 0 \\ Bio-inspired & \(T2\) & 0 & 100 & 99 & \\ \hline \end{tabular}
\end{table} 
Table 7: Number of trials in which the virtual or physical topology becomes fragmented when nodes are removed by targeted attack. \begin{table}
\begin{tabular}{l l l l l l l l} \hline  & _phy_ & \(\mathbf{T01F}\) & \(\mathbf{T11 UF}\) & \(\mathbf{LOLoop}\) & \(\mathbf{LLLoop}\) & \(\mathbf{LLLast}\) & TTL & \(\mathbf{ConT^{N}Up}\) \\ \hline BICM(hh,HH) & \(T1\) & 0 & 49 & 1 & 1 & 0 & 2 & 2 \\ BICM(ll,LL) & \(T1\) & 0 & 2 & 4 & 0 & 1 & 0 & 1 \\ Bio-inspired & \(T1\) & 7 & 100 & 2 & 0 & 2 & 0 & 11 \\ BICM(hh,HH) & \(T2\) & 3 & 57 & 6 & 0 & 0 & 1 & 10 \\ BICM(ll,LL) & \(T2\) & 7 & 1 & 2 & 0 & 0 & 1 & 1 \\ Bio-inspired & \(T2\) & 8 & 100 & 5 & 0 & 1 & 1 & 8 \\ \hline \end{tabular}
\end{table} 
Table 8: Number of trials in which the data delivery ratio fails to recover due to the inconsistency in tables when nodes are removed by targeted attack. Considering all of the results, the physical deployment of sensor nodes and the modular division algorithm is quite important for keeping services running on a VWSN topology. Because sparse areas where link density is low could fragment easily, infrastructure providers should deploy redundant nodes in order to provide stable services. Nodes in dense areas where link density is high can consume more energy than nodes in other areas because they will forward or receive more packets. When a first-tier module has both dense and sparse areas, the energy depletion of nodes in the dense area can result in the fragmentation of the module. To prevent this, infrastructure providers should supply energy to nodes or deploy nodes with high-capacity batteries in areas where many nodes are to be deployed. Alternatively, VWSN providers should construct a first-tier module in which the link density is homogeneous.
## 3 Experimental Campaigns at FLAME
### Laser Diagnostics  
The laser pulse duration is measured by adopting an APE Spectral Phase Interferometry for Direct Electric-field Reconstruction (SPIDER), which is a spectral phase interferometer. It splits the beam into two copies: one goes through unmodified, while, the other is stretched, and finally they are recombined on a non-linear crystal by means of a spherical mirror. After the interferogram, the laser temporal duration is retrieved. The estimated laser longitudinal duration is about 30 fs FWHM. Figure 7 shows a typical temporal measurement retrieved with the APE SPIDER.  
A Basler Scout scA640-70 gm CCD camera (Basler, Ahrensburg, Germany) equipped with a 10\(\times\) microscopic objective, with a calibration of 3 \(\upmu\)m/pixel, is used to measure the laser focal spot at the target (gas jet) position. The beam is attenuated to avoid any detector damage. The diameter spot size at 1/\(e^{2}\) is 18 \(\upmu\)m which is a typical transverse size measurement. Figure 8 shows the laser spot transverse distribution.  
Figure 7: Temporal measurement of the high-power beamline performed with a SPIDER diagnostics in the high-power compressor area. A simultaneous measurement has been carried out with an autocorrelation to benchmark the SPIDER measurements, and the two measurements are in excellent agreement.  
The implemented diagnostic setup is composed of a magnetic dipole coupled with a lanex screen and a CCD camera. The magnet of the charged particle energy spectrometer is a C-shaped dipole with a gap 2.8 cm wide and 5 mm high, installed along the 0\({}^{\circ}\) direction (laser propagation axis). The dipole is composed of a couple of 10 cm long permanent magnets that generate a uniform magnetic field (B\(\sim\)10 kG). The lanex screen is placed at 32\({}^{\circ}\) with respect to the propagation direction of the beam. A simulation of the spectrometer operations is shown in Figure 9.  
This setup aims to obtain a better resolution for low-energy (lower than 300 MeV) electrons, with a resolution of 0.5 MeV. An example of the detected electron energy spectrum is reported below in the Section Scintillator Screen Diagnostics for the highest available laser energy.  
Figure 8: Focal spot detected by a CCD camera placed in the laser focal plane in the interaction chamber in the TET area.  
Figure 9: General Particle Tracer (GPT) simulations were conducted for the design of the electron energy spectrometer. Lower-energy electrons exhibit a much greater degree of bending compared to higher-energy electrons. The plot shows two key components: (1) the 1T dipole, and (2) the lanex screen intercepting the bent electron trajectories.  
The dipole spectrometer makes possible the measurement of charged particles energy spectrum within the 50-500 MeV range. However, there are some limitations: in the absence of a collimator, the divergence and transverse beam size may impact the accuracy of the measurements. Additionally, the presence of X-rays and \(\gamma\)-rays generates background noise on the scintillator used to detect the bent particles, potentially leading to systematic errors.