esa-sceva/satcom-chunk-collection huggingface dataset:
# Sustainable and Reliable Information and Communication Technology for Resilient Smart Cities
## 2 Related Work
### Reference Models  
In order to achieve such a horizontal approach, a number of reference models have been proposed within the past years, e.g., EIP SCC [4], DIN OUP [2; 19; 20; 21], the Espresso reference architecture [22; 23], the reference architecture from the STREETLIFE project [24], the IoT-based reference model of [25], the corresponding model from the Triangulum project [26; 27; 28] and more. These models, in general, structure the city in layers that reflect the various levels of information processing, starting from the data sources, continuing with the communication network, the cloud/edge databases and the belonging data analytics up to the level of applications and services, which emerge based on these data processing chains. Many of these reference architectures come up with design principles such as open interfaces, open standards, open source, open data and artifacts that aim at avoiding vendor lock-in and creating viable local eco-systems for ICT developments and innovation.
# Unsupervised Learning Approach for Anomaly Detection in Industrial Control Systems
## 3 Proposed Method  
### Overview of the Proposed Approach  
Figure 2 provides an overview of the process proposed in our study. The process for anomaly detection using machine learning in industrial control systems is as follows: Data are collected from multiple sensors and systems in an ICS environment, with a special focus on the HAI (HIL-based augmented ICS) dataset [32] recorded for this purpose. The collected data are analyzed in detail to extract important characteristics and information. This includes dimensionality reduction using Principal Component Analysis (PCA) and data segmentation using K-means clustering. Next, a model is designed and trained using a neural network structure with input, hidden, and output layers. This allows the model to learn patterns from the data. Finally, the model's performance is evaluated on a separate dataset to validate its ability to accurately detect anomalies. When anomalies are detected, the tag name, detection time, and tag value of the anomalous behavior are displayed.
# Joint Timekeeping of Navigation Satellite Constellation with Inter-Satellite Links
## 5 Summary and Conclusions  
We proposed a joint timekeeping method with ISLs to improve the timekeeping of a navigation satellite constellation. Different from the distributed mode, the navigation satellites jointly established and maintained a uniform time reference through a timekeeping framework with an ISLT. A weighted average timescale algorithm that minimized the increment of the timescale was proposed and used to establish the ISLT and to improve frequency stability and predictability. Data from nine BeiDou-3 MEO satellites that were equipped with five Rb clocks and four PHMs were used for performance evaluation.  
The onboard Rb clocks are mainly affected by white frequency noises and random walk noises, and the PHMs are only influenced by white frequency noises with an averaging time of less than 100,000 s. The performances of PHMs are generally superior to Rb clocks. The daily stability of the clock ensemble was found to reach \(8\times 10^{-15}\), the optimal short-term prediction accuracy was found to achieve 0.27 ns, and the long-term prediction accuracy was found to be 1.16 ns.  
Based on the noise identification and inter-satellite clock deviations, the ISLT was calculated with the ISLT timescale algorithm. The short-term frequency of the ISLT was more stable than that of all the nine clocks, and its daily stability reached \(6\times 10^{-15}\). The short-term and long-term prediction accuracy  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PRN** & **Predicting RMS (ns)** & **PRN** & **Predicting RMS (ns)** \\ \hline C19 & 0.25 & C27 & _0.27_ \\ C20 & 0.37 & C29 & _0.33_ \\ C21 & 0.36 & C30 & _0.35_ \\ C22 & 0.40 & C37 & 0.80 \\ C26 & _0.46_ & ISLT & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Short-term clock prediction accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PRN** & **Predicting RMS (ns)** & **PRN** & **Predicting RMS (ns)** \\ \hline C19 & 1.45 & _C27_ & _1.16_ \\ C20 & 2.73 & _C29_ & _1.29_ \\ C21 & 2.78 & _C30_ & _1.30_ \\ C22 & 2.61 & C37 & 4.67 \\ C26 & 2.28 & ISLT & 1.05 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Long-term clock prediction accuracy.  
of the ISLT was 0.18 ns and 1.05 ns, respectively, which was better than all the timekeeping clocks. Furthermore, the ISLT was robust to the malfunction of an individual timekeeping clock, especially for one with a small weight.  
[PERSON], [PERSON] and [PERSON] conceived the study idea. [PERSON], [PERSON] and [PERSON] developed the simulation and analysis. [PERSON] drafted the manuscript. Writing - review & editing, [PERSON]; [PERSON]; and [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by State Key Laboratory of Geo-Information Engineering, grant number SKLGIE2018-Z-1-2.  
The authors declare no conflict of interest.
# Evaluation of Short-Term Aging Protocol for Asphalt Mixtures
## 2 Experimental Investigation
### Testing of AMPT Specimens  
In order to determine the mechanical properties of the specimens, such as the dynamic modulus (\(|E^{*}|\)) and phase angle (\(\delta\)), the specimens were tested in an AMPT testing system according to AASHTO T 342-11 [28]. This machine consists of a confining pressure system and an environmental chamber that can control temperatures from 4 to 70 \({}^{\circ}\)C. Three different temperatures (4, 20, and 40 \({}^{\circ}\)C) were used to test the specimens. Specimens were subjected to different loading frequencies depending on their testing temperature. For each testing condition, two samples were tested.  
A master curve describes the viscoelastic property at the reference temperature and over a range of time/frequency. Also, mechanical properties can be estimated over a range of temperature and times (or frequencies) by utilizing the time-temperature superposition principle. Comparisons can also be made between linear viscoelastic materials tested under different sets of test conditions such as temperatures and aging durations. The dynamic modulus (\(|E^{*}|\)) master curve for asphalt mixtures can be represented by a sigmoidal function defined by Equation (1) as proposed by [PERSON] [29]:  
\[\text{log}|E^{*}|=\ \delta+\frac{\alpha}{1+e^{\beta+\gamma(\log f_{r})}} \tag{1}\]  
where \(f_{r}\) is the reduced frequency at the reference temperature; \(\delta\) is the limiting minimum modulus value; \(\delta+\alpha\) is the limiting maximum modulus value, which depends on the aggregate gradation, binder content, and air void content; and \(\beta,\ \gamma\) are fitting parameters, the values of which depend on the characteristics of the asphalt binder and the magnitude of \(\delta\) and \(\alpha\)[29].  
The shift factor, \(a(T)\), was used to shift the data collected at different temperatures with respect to the time of loading or frequency (\(f\)) and to form a single smooth master curve. The shift factor is defined as  
\[a(T)=\frac{f}{f_{r}}. \tag{2}\]  
The shift factor can be calculated using the Arrhenius equation given in [PERSON] et al., 2003 [30]:  
\[\log[a(T)]=\frac{\Delta E_{a}}{19.15}\bigg{(}\frac{1}{T}-\frac{1}{T_{r}}\bigg{)} \tag{3}\]  
where \(T_{r}\) the is reference temperature, \(T\) is the test temperature, and \(\Delta E_{a}\) is the activation energy (treated as a fitting parameter).  
Therefore, the reduced frequency is computed using Equation (4):  
\[\log f_{r}=\log f+\frac{\Delta E_{a}}{19.15}\bigg{(}\frac{1}{T}-\frac{1}{T_{r} }\bigg{)}. \tag{4}\]  
In this study, an Excel spreadsheet with the solver function was used to fit the measured data to a master curve using the Arrhenius temperature shift factors (Equation (3)). The solver function tool minimized the sum of the squared error between the predicted and measured values.
## 1 Introduction
In order to fully take advantage of oscillation characteristics and the energy conversion of multi T-section prisms in tandem arrangement, a series of tests were proposed, including the following three aspects: (1) The FIM oscillation tests were carried out to investigate the amplitudes and frequencies of UTP and DTP in tandem; (2) The FIM energy conversion tests were carried out to investigate the energy conversion of UTP and DTP in tandem; (3) In order to reveal the influential parameters of two tandem T-section prisms, the variations of the active power and efficiency with five different spacing ratios and seven different load resistances were discussed. The experimental research on the two T-section prisms in tandem aimed to find out the optimal energy conversion parameters of UTP + DTP.
# Joint Timekeeping of Navigation Satellite Constellation with Inter-Satellite Links
## 3 ISLT Timescale Algorithm
### Short-Term Prediction of Clock Deviation
\right] \tag{27}\]  
where \(q_{i}\) is the covariance matrix of clock \(i\), defined by Equation (14). In each calculation cycle of the ISLT, all the salve satellites compare their clock with the reference clock. The time measurement equation is formed as:  
\[Z(t_{k})=HX(t_{k})+V(t_{k}) \tag{28}\]  
where \(V(t_{k})\) is the noise of comparison links whose covariance matrix is \(R\). The relative deviation vector is \(Z=\left[ \begin{array}{cccccccc}x_{21}&x_{31}&\ldots&x_{n1}\end{array}  \right]^{\rm T}\), and the measurement matrix is:  
\[H=\left[ \begin{array}{cccccccc}-1&0&0&1&0&0&\cdots&0&0&0\\ &&&&&\vdots\\ -1&0&0&0&0&0&\cdots&1&0&0\end{array} \right] \tag{29}\]The estimation of \(3n\) states based on \(n-1\) measurements with a Kalman filter follows the steps:  
\[\left\{ \begin{array}{l}\hat{X}_{k,k-1}=\Phi_{k}\hat{X}_{k-1}\\ P_{k,k-1}=\Phi_{k}P_{k-1}\Phi_{k}^{\mathrm{T}}+Q\\ K_{k}=P_{k,k-1}H^{\mathrm{T}}\!\left(HP_{k,k-1}H^{\mathrm{T}}+R\right)^{-1}\\ \hat{X}_{k}=\hat{X}_{k,k-1}+K_{k}\!\left(Z_{k}-H\hat{X}_{k,k-1}\right)\\ P_{k}=(I-K_{k}H)P_{k,k-1}\end{array} \right. \tag{30}\]  
where \(P=\mathrm{E}\!\left[\!\left(X-\hat{X}\right)\!\left(X-\hat{X}\right)\!\right]\) is the covariance matrix of estimation errors. Because of the unobservability of the inter-satellite comparison network, estimation errors of the Kalman filter keep divergent when estimating \(3n\) states with \(n-1\) relative measurements. However, the errors of frequency deviations and drifts diverge observably more slowly than time deviations, and they can be used to reconstitute the time deviation [14]:  
\[\hat{x}_{i\epsilon}(t)=x_{i\epsilon}(t-\tau)+\tau\cdot\hat{y}_{i, Kalman}(t-\tau)+\frac{1}{2}\cdot\tau^{2}\cdot\hat{z}_{i, Kalman}(t-\tau), \tag{31}\]  
which is used as the predicted deviation in the basic timescale equation.
## 3 E-barter Case Study
### System Implementation with Model Transformations and Delta Code Development  
In this study, the proposed multi-agent E-barter system is implemented using the JACK platform [20,38]. JACK is selected as it is one of the widely accepted Java-based BDI MAS development platforms. Also, it is as a mature and robust commercial product and meets the appropriate needs for industry adoption, such as scalability and integration.  
OWL, the standard language of the W3C for the definition and development of ontologies, is employed in the realization of ontological concepts of SEA_ML. OWL is built on RDF and RDF Schema [39] and adds more vocabulary for describing the properties and classes such as relationship between classes, cardinality, equality, richer typing of properties, and the characteristics of properties and enumerated classes. SEA_ML adopts the Ontology Definition Metamodel (ODM) of OMG [40] as the metamodel of OWL and that metamodel is used as target PSMM during the transformation.  
```
01<xmlversion=\"1.0\"encoding=\"ISO-8859-1\">>
02<owls:OWLSplatform
03 xmi:version=\"2.0\"
04 xmlns:xmi=\"[[http://www.omg.org/XMI](http://www.omg.org/XMI)]([http://www.omg.org/XMI](http://www.omg.org/XMI))\"
05 xmlns:xsi=\"[[http://www.w3.org/2001/XMLSchema-instance](http://www.w3.org/2001/XMLSchema-instance)]([http://www.w3.org/2001/XMLSchema-instance](http://www.w3.org/2001/XMLSchema-instance))\"
06 xmlns:owls=\"[[http://owls.com](http://owls.com)]([http://owls.com](http://owls.com))\">
07<containsServicename=\"Food\">
08<presentedByname=\"FoodServiceCall\">
09<containsInputname=\"Type\"/>
10<containsOutputname=\" \"/>
11<containsConditionname=\" \"/>
12<containsEffectname=\" \"/>
13</presentedBy>
14...
15<supportedByname=\"FoodServiceCall\"/>
16</containsService>
17<containsServicename=\"ElectronicDevices\">
18<presentedByname=\"ElectronicServiceInterface\"/>
19<containsInputname=\"Type\"/>
20<containsOutputname=\" \"/>
21<containsConditionname=\" \"/>
22<containsEffectname=\" \"/>
23</presentedBy>
24...
25<supportedByname=\" ElelectronicDevicesServiceCall\"/>
26</containsService>
27...
28</owls:OWLSplatform>
```  
Listing 1: Part of the generated OWL-S model for the E-barter system.  
The semantic web services modeled in SEA_ML are transformed into OWL-S services to enable the implementation of these services. OWL-S offers a high-level service ontology that can store three basic information about a service. The Service Profile tells you what the service is doing and provides information to discover a service. The Service Model describes how the service can be used and the composition of the service. Finally, Service Grounding provides information on how to interact with the service. Therefore, in our study, each SWS modeled in SEA_ML is transformed into an OWL-S Service element and the appropriate Service Profile, Service Model and Service Grounding documents are created for the related SWS.
## 4 Examples of Commercial Targeted Libraries
### SARS-CoV-2- or COVID-19-Targeted Libraries
#### 4.1.2 Otava  
The SARS-CoV-2-targeted library supplied by Otava contains eight different sub-libraries. Apart from a single general library, the remaining libraries are targeted against SARS-CoV-2 proteins. The targeted libraries were designed by receptor-based virtual screening, using crystal structures of the target proteins. The entire procedure involved flexible docking of the Otava Drug-Like Green Collection (collection of compounds that satisfy [PERSON]'s rule of five) to key binding sites. The relevant protein binding site with the docked molecule was examined in detail. The final decision was based on the structural determinants of ligand binding, docking scores and intermolecular hydrogen bonding within the binding site.  
The next library was constructed by using machine learning techniques to obtain molecules with predicted activity against SARS-CoV-2. Initially, the molecules showing activity against coronavirus targets and the inactive compounds were divided into two equal groups. One was used as a training group and the other as a test group. The model based on Bayesian statistics and artificial neural networks was not further disclosed by the supplier nor was the relevant reference to the primary literature provided. The test sets used to validate models and based on a variety of molecular descriptors, such as Molecular Weight, number of hydrogen bond acceptors, number of rotatable bonds, LogP and the polar surface area of molecules. The Drug-Like Green collection was checked against the model, and the highest scoring compounds were visually inspected (Figure 4).  
Figure 3: Process algorithm for generating the Enamine SARS-CoV-2-targeted molecular library.
## 7 Response Spectrum with ADRS Format  
The seismic elastic design spectra explained previously in MDOC Sismo CFE 2015, ASCE/SEI 7-16, and IBC 2018 codes show graphs of the spectral acceleration values using a \(T\) vs. \(S_{a}\) format; these are widely used in traditional seismic engineering of structures as inputs for modal analysis methods.  
However, performance-based seismic engineering requires a response spectrum with a \(S_{d}\) vs. \(S_{a}\) format, called acceleration displacement response spectra or ADRS format. The codes ATC-40 [21] and SEAOC 1999 [22] explain how this format is used with the static non-linear pushover analysis procedure to obtain the capacity curve graph of the buildings and the target displacement as a result.  
Figure 19 shows an example of the ADRS format, representing the seismic elastic design spectra according to MDOC Sismo CFE 2015 building code, showing the spectral values of Mexicali, Mexico, with a soil type I and different damping values. According to the ADRS format, with a single graph it is possible to obtain the spectral displacements and accelerations of the structure at the same time, where the radial dotted lines represent the period \(T\). The process of obtaining each radial line requires the location of a single point using the coordinates \(S_{d}\) and \(S_{a}\), with both values related to a specific period, \(T\); one can simply draw a straight line from the origin to the point destination.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
To obtain the elastic spectrum in ADRS format, acceleration values with \(T\) vs. \(S_{a}\) format are required as inputs, related to a specific damping ratio \(\xi\). Then, the spectral displacements, \(S_{d}\), can be obtained according to the following equation:  
\[S_{d}=\frac{T^{2}}{4\pi^{2}}S_{a}g \tag{41}\]  
where \(T\) is the structural period and \(S_{a}\) is the spectral acceleration in terms of g. Once the spectral displacement \(S_{d}\) values are available, the response spectrum in ADRS format can be obtained according to the \(S_{d}\) vs. \(S_{a}\) format.
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 4 Cloud Security Challenges and Proposed Solutions
### Technical Security Threat
#### 4.2.2 Virtualization Vulnerabilities  
The risks and vulnerabilities associated with the virtualization layer that enables the construction and administration of virtual machines (VMs) in cloud settings are referred to as virtualization vulnerability threats in cloud computing. Attackers can use these vulnerabilities to compromise the virtualization infrastructure, obtain unauthorized access to VMs, or conduct malicious operations. The following are some of the most frequent virtualization security risks in cloud computing [42]:  
* Vulnerabilities in the Hypervisor
* DoS attacks  
**Threats to Virtualization Vulnerability Mitigation:**  
Patching and updates on a regular basis: Keep the hypervisor software, virtual machines, and other virtualization components up to date with the most recent security patches and upgrades to address known vulnerabilities.  
Strong VM isolation: Ensure strong VM isolation by establishing virtual networks, access restrictions, and resource allocation correctly. To prevent unauthorized access between VMs, use security methods like network segmentation, VLANs, and virtual firewalls.  
Vulnerability scanning and assessment: Perform frequent vulnerability scans and assessments of the virtualization infrastructure, including the hypervisor and virtual machines (VMs), to discover and resolve any security flaws. Resolve detected vulnerabilities as soon as possible.  
Implement correct VM lifecycle management practices to guarantee that VMs are safely generated, provisioned, and retired. To reduce the attack surface, assess and eliminate unused or superfluous VMs on a regular basis.
## 2 Structure Design and Theoretical Analysis  
Figure 1 shows the two-dimensional schematic of the structure proposed in this paper, in which the white and green are separate dielectric air and metal Ag layers. To be specific, the structure is composed of an MIM waveguide with two rectangular stubs and an elliptical ring resonator, with the following defined parameters: \(R_{1}\) and \(R_{2}\) are the length of the long axis and the length of the short axis of the outer elliptical resonator, respectively; \(r_{1}\) and \(r_{2}\) are the length of the long axis and the length of the short axis of the inner elliptical resonator taken separately; \(d\) is the coupling distance between the elliptical ring resonator and MIM waveguide; \(h\) represents the height of two rectangular stubs; \(l\) is the distance between two symmetric rectangular stubs; and \(w\) is the width of the MIM waveguide. Compared with the circular ring resonator (\(R_{1}=R_{2}\)), the elliptical one (\(R_{1}\
eq R_{2}\)) can obtain greater optical performance. The relative dielectric constant of Ag can be expressed by the model of [PERSON] (1) [34]:  
\[\varepsilon_{d}(\omega)=\epsilon_{\infty}-\frac{\omega_{p}^{2}}{\omega(\omega +i\omega\gamma)} \tag{1}\]where \(\omega\) is the angular frequency of the incident wave which is in a vacuum, the infinite dielectric constant \(e_{\infty}=3.7\), the plasma oscillation angular frequency \(\omega_{p}=9.1 eV\big{(}1.38\times 10^{16}\) rad/ sec), and the electron collision frequency \(\gamma=0.018 eV\big{(}2.73\times 10^{13}\) rad/ sec).  
The size of the simulation domain is \(1200\times 900\) nm. The width of the MIM waveguide is set at \(w=50\) nm and at this time the MIM waveguide only exists the fundamental transverse magnetic (TM\({}_{0}\)). When the transverse magnetic mode is TM\({}_{0}\), it helps SPP waves to propagate [35]. Perfectly matched layers (PMLs) are set at the top and bottom boundaries of the structure. For directly understanding the transmission features of the structure, we make full use of the EDA software COMSOL Multiphysics 5.4 [36], and with the help of its finite element method (FEM), we can observe the transmission feature of the structure. The mesh is ultrafine in order to ensure the accuracy of calculations. The light imports from the input port \(P_{1}\), then propagates in the MIM waveguide, and eventually exports at the output port \(P_{2}\). The transmittance is defined as \(T=\left(S_{21}\right)^{2}\), where \(S_{21}\) is the transmission coefficient from input port \(P_{1}\) to output port \(P_{2}\). The interval step of incident wavelength is set to 5 nm and the pattern of the incident light is TM.
###### Abstract
To perform the speech enhancement and the emotion classification at the same time during the training step, a Wave-U-Net [23] architecture was chosen in this work for the speech enhancement, because the Wave-U-Net accepts a waveform as input and generates the same size waveform as output. The Wave-U-Net of this work consists of an encoder, an LSTM (long short-term memory) layer, six decoders, and the associated skip-nets. The six decoders generate four band-pass waveforms, a pitch-related waveform, and an input waveform to the emotion classifier. One of the six decoders generates the pitch-related waveform because the large-magnitude pitch may be resilient to noise. The emotion classifier consists of a CNN layer followed by a DNN layer. Because the Wave-U-Net of this work is trained to fit the four band-pass waveforms and the pitch-related waveform generated by the five decoders to the corresponding ground-truth waveforms with the normalized function, this work eliminates the noise components in the small-magnitude high-frequency signals as well as the large-magnitude low-frequency signals. Due to the denoising operation of the above-mentioned five decoders, the noise components in the input waveform to the emotion classifier are reduced, since all the six decoders share the encoder and the LSTM layer. Section 2 explains the proposed U-Net-based deep learning model. Section 3 presents the experimental results. Section 4 discusses this work.