esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods  
### Dataset  
The dataset consists of two hundred specifically located seismic events, extracted from the database of microearthquakes that has recently been released by [31] and described  
Figure 1: **Left**: Seismotectonic map of Italy (modified after [30]); the red rectangle marks the study area. **Right**: detail showing the geological lineaments of the GP. The red circles represent the estimated focal volume of the two major historical earthquakes (modified after [29]).  
in [27]. We extracted data regarding the earthquakes recorded in the period from July 2015 to August 2018 to achieve better coverage of the GP crust, thanks to two OT stations that were added in the northern sector of the GP in June 2015. The OT network stations are equipped with short-period seismometers (Lennartz 3D-V seismometer (flat response above 1 Hz; 10 ms sampling rate)) [27]; the IV network stations are equipped with various types of seismic sensors, i.e., short period, broadband, and accelerometric instruments [32]. The selected dataset is composed of specifically located earthquakes (\(RMS<0.2\) s; horizontal and vertical location errors of less than 1 km, magnitude \(1\leq M_{L}\leq 2.8\)) by using the 1D Gargano velocity model [33]. With respect to a previous study [23], we selected a dataset with a broader network geometry and a higher magnitude threshold, in order to validate the previous results obtained with very small earthquakes (\(0.3\leq M_{L}\leq 1.8\)) in a narrower region. Details on data acquisition and management are described in [27]. We selected 191 events occurring in the period from 1 July 2015 to 30 August 2018, recorded to a maximum of 21 seismic stations. Foci depths range between 1 and 35 km, with shallower events located in the SW, and deeper events located NE of the promontory, as already observed in previous seismological studies of this area [26; 29; 34; 35] (Figure 2).
## 2 Forward Model
### Second Approach: Deep Neural Network Approach
#### 3.2.2 Optimal Control Problem  
In terms of a global optimum, the current control problem is eventually considered and solved. The problem can be described as follows: for a given temperature profile and frequencies \(f_{1 OD}\) and \(f_{2 OD}\), find the current amplitudes \(I_{1}\) and \(I_{2}\) such that the discrepancy between actual and prescribed temperature profile is minimal.  
Knowing the optimal values \(f_{1 OD}\) and \(f_{2 OD}\) from the optimal design problem, a new database DB3 is prepared. Specifically, new samples are generated utilizing the forward surrogate model GPR, by varying the currents of the two coils, with constant frequencies \(f_{1 OD}\) and \(f_{2 OD}\). Each sample of DB3 contains two vectors, one with the current amplitudes \(I_{1}\) and \(I_{2}\) and another with the relevant \(N_{p}=250\) temperature values along the \(\alpha\) line (see Figure 1).  
The solution to this problem is found using a DNN, implemented in Matlab and trained utilizing the database DB3. The solution to this problem is given by the couple \(I_{1 OC}\) and \(I_{2 OC}\) of the optimal current intensity of the two coils.  
At the end of this Deep Neural Network approach, the four optimal parameters \(f_{1 OD}\), \(f_{2 OD}\), \(I_{1 OC}\), and \(I_{2 OC}\) are found with the relevant optimal temperature profile (see Figure 4).
## 4 Experimental Results
### Measurement Result in Real Applications  
We also experimented on eight different boxes (Figure 19) in normal work conditions to validate the accuracy and reliability of the measurement system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline No. & (a) & (b) & (c) & (d) \\ \hline Box & & & & \\ \multirow{2}{*}{Results} & \multirow{2}{*}{Results} & L 99.5\(\cdot\)(\(-\)0.5) & L 90.12\(\cdot\)(\(-\)0.6) & L 90.12\(\cdot\)(\(-\)0.4) & L 90.12\(\cdot\)(\(-\)0.2) \\  & W 22.0\(\cdot\)(\(-\)0.6) & W 368.3\(\cdot\)(\(-\)2.2) & W 291.1\(\cdot\)(\(-\)1.1) & W 312.3\(\cdot\)(\(-\)2.3) \\  & H : 349.6\(\cdot\)(\(-\)0.4) & H : 428.5\(\cdot\)(\(-\)1.5) & H : 318.2\(\cdot\)(\(+\)2.2) & H : 349.8\(\cdot\)(\(-\)0.2) \\  & Box & & & \\ \multirow{2}{*}{\(>\)Results} & L 99.5\(\cdot\)(\(-\)0.5) & L : 301.2\(\cdot\)(\(-\)0.6) & L : 282.4\(\cdot\)(\(+\)0.4) & L : 294.2\(\cdot\)(\(+\)1.2) \\  & W : 220.6\(\cdot\)(\(+\)0.6) & W  
Table 8 shows the experimental results of measurements for eight standard boxes. The absolute error and the relative error of the measurement results were analyzed in accordance with the actual volumes and length of the measured boxes. The data in Table 8 indicate that the maximum relative error of length in the experiment was 0.575%, and the maximum measurement error of the length was 7.6 mm, indicating good dimension measurement accuracy of the system.
# Performance Analysis of BDS-5G Combined Precise Point Positioning
## 3 Results and Discussion
### Experimental Environment
#### 3.1.2 5G Environment
The simulated data consists of three components, namely, the real distance, receiver clock difference and measurement noise, where the receiver clock difference and measurement noise are obtained from the difference between the measured data and the real distance as described above, as shown in Equation (9). \[c\tau=l+\Big{(}c\tau^{(b)}-d^{(b)}\Big{)}. \tag{9}\]  
where \(\tau\) represents the simulated TOA; \(l\) represents the real distance between the receiver and the analogue base station; \(\Big{(}c\tau^{(b)}-d^{(b)}\Big{)}\) represents the difference between the measured data and the true distance in Figure 2.  
In all four combinations, the number of base stations was four. The coordinates of the base stations in the local coordinate system are shown in Table 3. Analysis of the dilution of precision (DOP) values in each direction (Table 4) shows that Group A had the greatest configuration advantage in the X direction, Group B had the greatest advantage in the Y direction, and Group C had the greatest advantage in the Z direction. Group D was relatively balanced, and this conformational advantage was more pronounced when the number of satellites was small. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Configuration** & **Station 1** & **Station 2** & **Station 3** & **Station 4** \\ \hline Group A & (50, 0, 0) & (20, 0, 0) & (\(-\)20, 0, 0) & (\(-\)50, 0, 0) \\ Group B & (0, 50, 0) & (0, 20, 0) & (0, \(-\)20, 0) & (0, \(-\)50, 0) \\ Group C & (0, 0, 50) & (0, 0, 20) & (0, 0, \(-\)20) & (0, 0, \(-\)50) \\ Group D & (\(-\)50, 50, 0) & (50, 50, 0) & (0, \(-\)50, 50) & (0, \(-\)50, 50) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Coordinates of fifth generation mobile communication technology (5G) base stations with different geometrical configurations (unit: m). Figure 3: Fifth generation mobile communication technology (5G) base stations with different geometrical configurations, (**a**): Group A, (**b**): Group B, (**c**): Group C, (**d**): Group D.
## 3 Results and Discussion
### Fractal Characteristics
The COD removal rate only decreased by 5.4% and quickly recovered to the level before the change in just six days when the OLR changed suddenly, indicating that the granular sludge could maintain a relatively stable micro-environment and make the metabolic process continue when the influent conditions suddenly changed. The above portended that the reactor was successfully started up. The correlation coefficients \(R^{2}\) of the four compartments ranged from 0.981 to 0.991, indicating that the linear relationship of the curve equations was good. As the HRT gradually reduced during the start-up phase, the changes of anaerobic bacteria to the OLR experienced a process from inadaptability to adaptation, but the amount of sludge washed out did not increase significantly compared with that at a low OLR, indicating that the activated sludge was undergoing a process of immobilization from flocculent sludge to granular sludge. The acclimated and mature granular sludge could avoid the risk of being broken and washed out due to the shear flow force and internal gas production. In addition, the formation of granular sludge could greatly shorten the diffusion distance of fermentation intermediates to hydrogen-producing acetogenic bacteria and methanogens, which was of great significance to improve the efficiency of anaerobic biodegradation. After the reactor was successfully started up, the distribution of \(D_{f}\) in the compartments showed a trend of being the highest in the middle and being decreased on both sides to some extent. This phenomenon was due to the different positions and functions of anaerobic sludge in each compartment in the whole reactor. Although the proliferation rate of granular sludge in the 1 st compartment was fast and the particle size evidently increased, the granular sludge growing in it had a rough surface, loose structure, and poor sedimentation performance due to a large number of suspended solids and an organic matrix adsorbed in the influent for a long time at the beginning of the acidogenic stage. The 2 nd and the 3 rd compartments were in the flourishing stage of the acidogenic and methanogenic stage, and the strong gas production and mixing effect made granular sludge with a compact structure, smooth surface, and good sedimentation performance. With the degradation of the organic matter by microorganisms along the way, microorganisms in the 4 th compartment could not obtain sufficient nutrients, and the metabolism was greatly affected, so that the sludge was in the process of or about to undergo particle disintegration due to \"starvation\".
# Fault Tolerant DHT-Based Routing in MANET
## 2 The Problem and Its Scope
In short, the lookup phase defines the overall performance of DHT networks and needs mechanisms to ensure access to mapping info. In the presence of failure/partition. Algorithm 2 performs replication decisions across the critical regions (identified by Algorithm 1 in a dynamic and distributed manner and ensures access to mapping info in the adversarial environment. * Merging: Similarly, we argue that merging is indirectly associated with node failure (i.e., failure causing network partition) and should be treated as an integral part of any fault-tolerant protocol in DHT networks. Secondly, merging is different in DHT (i.e., need to merge disjoint logical structure (LS) to achieve an evenly distributed and connected LS). For example, in the case of a network partition, there would exist multiple network instances. In a mobile and self-organized environment, frequent merging might take place. Simple merging (i.e., physical topology) of the disjoint instances would not restore communication in a DHT-based network. Instead, it will result in a physically connected but logically disconnected network [5] (i.e., need to merge the logical structures for an evenly distributed LS). Therefore, additional measures such as LIDs reconfiguration, root election, etc., are needed to obtain uniformly distributed and connected LS. Moreover, merging detection in the physical network is needed to initiate merging in the logical network. It is pertinent to mention that existing merging solutions [5] assume uniformly distributed LSs in the disjoint networks for smooth merging. However, partitioning leaves unevenly distributed and disrupted instances of the logical network (LS). In fact, due to network partition, a portion of the logical network (LS) is lost. Therefore, for smooth operations in the disjoint instances and smooth merging, uniformly distributed LSs are needed. To solve the problem, the lost LS needs to be recovered and reused in the created instances after a network partition. LS recovery and reusing purely depend on protocol specification. For example, in tree shape LS, the lost LS is recovered and reused by re-electing new representative nodes, LIDs reconfigurations, etc., in the disjoint instances. However, LS recovery/reusing needs timely detection of the partition event. Therefore, partition detection is another sub-problem in this particular domain. Algorithms 4 and 5 solve these problems in a distributed manner. Therefore, DHT protocols should consider the above-highlighted issues to ensure optimal network performance in adversarial environments.
###### Abstract
Indeed, the use of fog devices to implement intelligent methods (in the heart of SDN and NFV technologies) to ensure smooth data flow in vehicular networks is an open problem that needs to be addressed by both academic and industrial researchers [13; 14]. With regard to the above-mentioned open problems and considering the role of 5G in building IoT use cases and the advantages of using SDN and NFV technologies, the following challenging research question that is related to the HetVNET environment may arise. * When considering the advantages of SDN and NFV concepts and the notion of network slicing, how can we design an architecture that provides reliable information to create HetVNET slices? On the basis of the research question and the direct relation between the congestion problem and the resource allocation problem, we propose a novel method that is based on Deep Learning and network slicing technique with the aim of avoiding congestion in HetVNET. Therefore, considering network congestion problem in a high dynamic network environment, like HetVNET, taking advantage of global network view of the SDN, and using information from past successful network experiences, can help us to create an intelligent SDN architecture. In this paper, we apply the Conditional Generative Adversarial Network (CGAN) to:  
* augment the data used in creating network slices in HetVNET; and
* additionally, propose a centralized SDN based architecture with the aim of enhancing flexibility and adaptability of the HetVNET. CGAN is widely used for text, image, and video generation and prediction works. We will show how the proposed CGAN helps us to intelligently and reliably generate valuable information in order to create network slices with the aim of avoiding congestion in HetVNET. It is the first time that CGAN has been applied in this context to the best of our knowledge.
## 2 Materials and Methods
### Phenotypic Screening and In Vitro Assays
#### 2.4.10 Air-Liquid Interface (ALI) Cultures  
Pooled donor human nasal epithelial cells (HNECs) grown in ALI format were obtained from Epithelix (Geneva, Switzerland) as a fully differentiated culture and maintained in-house for a week before experimentation. At the start of the experiment, compounds and vehicle (DMSO 0.2%) in MuciAir(tm) medium from Epithelix were added to the basal compartment of the ALI inserts in 24-well plates and incubated for 1 h. Brefeldin 0.3 uM was used as a toxicity control and PF-07321332 at 2.5 uM was used as an antiviral control. Next, inserts were infected with SARS-CoV-2 (MOI 0.1) for 1 h at 37 \({}^{\circ}\)C. The inoculum was washed away using DPBS and cultures were incubated at 37 \({}^{\circ}\)C for 72 h. At the 48 h time point, compounds were refreshed. At 24, 48, and 72 hpi, the inserts were apically washed by adding DPBS to the apical side and incubating for 15 min at 37 \({}^{\circ}\)C. The apical washes were collected and stored at \(-\)80 \({}^{\circ}\)C. Automated RNA extraction was performed on these apical washes using the MagNA Pure 96 instrument followed by one-step RT-qPCR. The same protocols, kits, primers, and probe set were used as in the section SARS-CoV-2 Viral RNA Copy Determination in A549-hACE2 Cells. Toxicity was assessed by exposing non-infected inserts to the same concentration of compound as in the antiviral setting over the same time course (72 h). For this purpose, a voltmeter (EVOM3 from World Precision Instruments, Sarasota, FL, USA) that measures transepithelial electrical resistance, which is representative for the cell layer's integrity or health, was used. To perform the measurement, DPBS was added to the apical compartment; this was carefully removed once the measurement was taken. Both activity and toxicity data were further analyzed using GraphPad Prism v8.
# Blockchain Integration in the Era of Industrial Metaverse
## 5 Blockchain Integration in Metaverse
### Challenge/Limitation
#### 5.1.5 Data Privacy Preservation  
Considering that Web 2.0 follows a centralized architecture, it becomes apparent that data privacy is becoming a concern. However, with the emergence of Web 3.0, the metaversewill also become a reality, which can be briefly realized as the co-existence of the physical world and its digital twin, which will expand the scope and complexity of the Internet. In the literature, the challenge of how Web 2.0 will affect the defense of personal rights has not been addressed yet. The above-mentioned challenge is also pertinent to the upcoming Web 3.0. By extension, the issue of data privacy will become a first-priority challenge.  
Privacy Preservation in the Metaverse and the Corresponding Challenges  
Personally identifiable information (PII) is a source of concern for protecting the privacy of personal data. The initial stages of the metaverse ecosystem, where attackers can deceive users and steal sensitive data, will be challenging to adapt to. Another concern regarding the metaverse will be the simultaneous management and validation of large volumes of data [85].  
Blockchain Contribution  
Third-party intermediaries are prohibited in the blockchain-enabled metaverse from misusing or obtaining data from other parties. Consequently, with the integration of blockchain technology in the metaverse, users will gain full control over the access rights to their sensitive and personal data, thus controlling whenever and how their data are accessed by third parties [86].  
Challenge/Limitation  
Similar to the existing challenges of blockchain applications, within the metaverse environment, human error plays a key role since the security of the blockchain can be jeopardized. A common example might be the misplacement of a private key, which would put at great risk the users' data privacy in the metaverse. Since third-party applications frequently employ insufficient security measures, which compromise personal data, attackers can easily target them in the metaverse [19]. Thus, there is still fertile ground for further research into discovering new ways for integrating blockchain technology in the metaverse, targeting the protection and preservation of user data privacy.
## 2 Materials and Methods
### Numerical Study
#### 2.3.1 Description of Moment Resisting Frames (MRFs)  
A five-storey frame (shown in Figure 6), referred to as 05D, is analyzed in this study. In the y-direction, two of these frames provide lateral strength and stiffness to a building whose floor layout is also presented. Seismic weight for each frame is 2601 kN at a typical storey and 2440 kN at roof. The frames were designed for gravity and wind loads, but taking into account capacity criteria according to Eurocode 8: at every joint the condition \(\sum\)\(M_{c,Rd}\geq 1.3\)\(\sum\)\(M_{b,Rd}\) was imposed, where \(\sum\)\(M_{c,Rd}\) and \(\sum\)\(M_{b,Rd}\) represent the addition of  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline _Direction_ & \(T\) & _Storxy_ & \(\theta\) & \(h\) & \(v_{0}\) & _f\({}_{b}\)_ & \(\alpha_{b,1}\) & \(\lambda\) & \(k_{bd}\) & \(k_{bb}\) & \(k_{bb}\) & \(k_{bb}\) & \(k_{bb}\)* & \(k_{bb}\)/\(k_{bb}\)* \\ \hline \multirow{4}{*}{X} & \multirow{4}{*}{0.536} & & \% & **mm** & **mm/s** & **kN** & **kN(s/mm)** & **s** & **kN/mm** & **kN/mm** & **kN/mm** & **kN/mm** & **kN/mm** & **kN/mm** & **-** \\ \cline{2-13}  & & & 4 & 0.54 & 3000 & 189 & 359 & 1.90 & 0.006 & 316 & 751 & 144 & 87 & 453 & 19\% \\  & & 3 & 0.56 & 3000 & 198 & 365 & 1.85 & 0.006 & 308 & 751 & 144 & 87 & 453 & 19\% \\  & & 2 & 0.64 & 3000 & 225 & 768 & 3.41 & 0.006 & 568 & 797 & 315 & 161 & 425 & 38\% \\  & & 1 & 0.62 & 3485 & 253 & 803 & 3.17 & 0.006 & 528 & 585 & 332 & 151 & 356 & 42\% \\ \hline \multirow{4}{*}{Y} & \multirow{4}{*}{0.575} & & 4 & 0.65 & 3000 & 213 & 751 & 3.53 & 0.006 & 589 & 797 & 315 & 163 & 425 & 38\% \\  & & 3 & 0.74 & 3000 & 242 & 789 & 3.26 & 0.006 & 544 & 797 & 315 & 159 & 425 & 38\% \\ \cline{1-1}  & & 2 & 0.79 & 3000 & 259 & 1618 & 6.26 & 0.006 & 1043 & 1987 & 357 & 235 & 796 & 29\% \\ \cline{1-1}  & & 1 & 0.76 & 3485 & 289 & 1688 & 5.84 & 0.006 & 974 & 1320 & 356 & 218 & 662 & 33\% \\ \hline \end{tabular}
\end{table}
Table 2: Properties assumed for linear viscous damping systems.  
column and beam resisting moments concurrent at the joint, respectively. Column sizing was adjusted iteratively to ensure a complete global mechanism in a pushover analysis under a 1 st mode lateral force pattern. The dynamic properties are: fundamental period \(T_{1}=2.010\) s, modal participation factor \(\Gamma_{1}=1.364\), total weight \(W_{tot}=12.847\) kN, first-mode effective weight \(W_{eff,1}=9955\) kN. Estimative interstorey yield drift ratios \(\theta_{y}\) and storey stiffnesses \(k_{s}\) are found using the simplified procedure suggested by [PERSON] (19) (Chapter 12), rendering \(\theta_{y}^{\mathrm{T}}=[0.699;0.985;0.997;1.098;1.232]\%\); \(\mathbf{k_{s}}=[63.3;30.5;26.7;19.1;14.3]\) kN/mm. \(k_{s}\) is used to calculate the ratio \(k_{s}\)/\(k_{d}\), as indicated below.