esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Robust UNet (RINet)  
As illustrated in Figure 3, the RUNET architecture uses blocks with a combination of convolutions followed by batch normalization [53] and ReLU operation [54] followed by a tensor addition to so-called residual blocks (RB). The contraction path has four levels, and each level has multiple residual blocks, which are connected via skip connections; this creates a complex representation of the features at every level. Max pool operation is used to reduce the filter maps' dimensionality; it uses convolutional 2D layers, followed by batch normalization, ReLU activation, and residual additions, allowing the network to learn complex representations of the input images. The expansion path uses a batch normalization layer at the input followed by a combination of convolution and ReLU activation layers. Feature maps are then pixel shuffled to change the dimension before combining the output of the previous contraction (Kth level) layer with the output of the expansion layer (K + 1 level) via tensor stacking, i.e., the tensors are stacked to form more feature channels. The last layer of the expansion path uses convolution with a kernel size of 1 to reconstruct the output image.
# Multi-Authority Revocable Access Control Method Based on CP-ABE in NDN
###### Abstract  
For the future of the Internet, because information-centric network (ICN) have natural advantages in terms of content distribution, mobility, and security, it is regarded as a potential solution, or even the key, to solve many current problems. Named Data Network (NDN) is one of the research projects initiated by the United States for network architecture. NDN is a more popular project than ICN. The information cache in the NDN separates content from content publishers, but content security is threatened because of the lack of security controls. Therefore, a multi-authority revocable access control method based on CP-ABE needs to be proposed. This method constructs a proxy-assisted access control scheme, which can implement effective data access control in NDN networks, and the scheme has high security. Because of the partial decryption on the NDN node, the decryption burden of the consumer client in the solution is reduced, and effective user and attribute revocation is achieved, and forward security and backward security are ensured, and collusion attacks are prevented. Finally, through the other security and performance analysis of the scheme of this paper, it proves that the scheme is safe and efficient.  
Keywords:Information-Centric Network (ICN); Naming Data Network (NDN); access control; multiple Authorization; Ciphertext-Policy Attribute-Based Encryption (CP-ABE) +
Footnote †: journal:  
_Future Internet_**2020**, _12_, 15; doi:10.3390/f12010015
## 5 Marine MLH Detection Using Ship-Borne CDWL  
After validating the reliability of the constant TKEDR threshold, to realize marine MLH detection, the ship-borne experiment was carried out on 05 February 2020 in the west of South China Sea by using a ship-borne CDWL.  
Figure 4: lg(TKEDR) profiles and their MLH\({}_{\text{TKEDR}}\) at 1400 LST (red circle) and 2400 LST (black circle) on (**a**) 27 September 2019 in Inner Mongolia, (**b**) 11 May 2019 in Hefei, and (**c**) 10 November 2018 in Sansha.  
Figure 5: Statistical distributions of MLH\({}_{\text{TKEDR}}\) in daytime (red line) and nighttime (dash-dotted line) during (**a**) 21–30 September 2019 in Inner Mongolia, (**b**) 09–18 May 2019 in Hefei, and (**c**) 05–14 November 2018 in Sansha.  
The horizontal wind speed, horizontal wind direction, vertical wind speed, TKEDR and its MLH retrieval result along the ship's route from 16.41\({}^{\circ}\)N, 110.23\({}^{\circ}\)E to 16.44\({}^{\circ}\)N, 110.28\({}^{\circ}\)E are measured by the ship-borne CDWL and shown in Figure 6. From Figure 6a, one can see that the wind above the sea surface is uniformly strong due to the less friction at smooth sea underlying surface. From Figure 6b, it can be seen that the wind comes mainly from the northeast because the northeasterly wind is prevalent in South China Sea in winter [26]. The vertical wind and turbulence are shown in Figure 6c,d, respectively. Weak convection can be seen above the sea surface. Based on the constant TKEDR threshold, the marine MLH is successfully detected along the ship's route in real time, which is drawn as pink circle above the TKEDR in Figure 6d. The MLH detection result is distributed over 1-2 km.
# A Game Theory-Based Model for the Dissemination of Privacy Information in Online Social Networks
## 3 A Game-Based Model for Privacy Information Dissemination
### Decision on Forwarding Privacy Information  
Considering the importance of the micro-relationship between users on the spread of privacy information, our model incorporates two parameters: intimacy and popularity, to quantify the sender's sending probability and the receiver's preference probability and to be combined with a game theory model based on the receiver's Nash equilibrium probability of the sending strategy of the sender.
## 2 Materials and Methods  
### Bacterial Strain/Mutants and Primers  
_Pseudomonas chlororapilis_ PA23 was isolated from the soybean rhizosphere [2]. PA23 mutants defective in PHZ and PRN production, together with primers used in the current study are listed in Table 1. The _P. chloroformis_ cultures (Wt and 6 mutants) used in the present study were described earlier for PHZs, PRN, and fungal antagonistic activity [3; 12; 13; 25]. Mutant PA23-63 has a _Tn_5 insertion in the _phzE_ gene and is not able to synthesize PHZ. Mutant PA23-8 has _prnBC_ deletion and is not able to produce PRN. Mutant PA23-63-1 was derived from PA23-63 and contains a deletion of the _prnBC_ genes. The other three strains have mutations in regulatory genes _gacs_, _rpoS_ and SR (stringent response _relS/spoT_). _P. chlororapilis_ strains PA23, PA23-8 (prn'), _rpoS_, and SR produced orange colonies on LB agar plates while _P. chlororapilis_ strains PA23-63, PA23-63-1, and _gacs_ produced white colonies on LB agar plates. Primers were designed based on the PA23 genome sequence [36] and synthesized by Alpha DNA (Montreal, QC, Canada).  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Strain/Mutant** & **Relevant Genotype, Phenotype or Sequence** & **Reference** \\ \hline _P. chlororapilis_ PA23 & RifR\({}^{\text{\textregistered}}\), WT [soybean root tip isolate] PHZ\({}^{+}\)PRN\({}^{+}\) & [2] \\ PA23-63 & PHZ\({}^{\text{\textregistered}}\):Tr\({}^{\text{\textregistered}}\)S-OT182 genomic fusion & [12] \\ PA23-8 & PRN\({}^{\text{\textregistered}}\):RifR _pmBC_ deletion mutant & [12] \\ PA23-63-1 & PHZ\({}^{\text{\textregistered}}\):PRN’: Rif _phzE_::Tr5-OT182 genomic fusion; _prnBC_ deletion mutant & [12] \\ PA23 proS & PA23 with pKNOCK inserted into _rpoS_ & [25] \\ PA23 gacS & PHZ\({}^{\text{\textregistered}}\):RifR _gacs_::Tn5-OT182 genomic fusion & [3] \\ PA23 relA/spoT & PA23 with pKNOCK-Gm inserted into _relA_; & [25] \\ PA23 relA/spoT & Tet\({}^{\text{\textregistered}}\) cassette inserted into _spoT_ & [13] \\ PA23 proS & PA23 with pKNOCK-Tc inserted into _rpoS_ & [13] \\
**Primers** & & \\ _phzA_-FOR & 5\({}^{\prime}\)-GACTGGCAATGGCACAAC-3\({}^{\prime}\) & [14] \\ _plzA_-REV & 5\({}^{\prime}\)-GCAATAACCTCCGATAACC-3\({}^{\prime}\) & [14] \\ _plzI_-FOR & 5\({}^{\prime}\)-CGATCCGTTGTTTG-3\({}^{\prime}\) & [14] \\ _plzI_-REV & 5\({}^{\prime}\)-AGCCGTTCGATGGACTCG-3\({}^{\prime}\) & [14] \\ _phzI_-FOR & 5\({}^{\prime}\)-GAAAAAGA
## 2 Modern Physical Layer
### Security at the Physical Layer  
A jamming attack is a type of denial-of-service attack that consists of interfering by continuously transmitting on the same wireless channel neighboring nodes use to communicate. It is a common well-known attack in wireless networks and some solutions, like frequency hopping and spread spectrum, can be applied to mitigate this attack [57]. [PERSON] _et al_. perform in [58] a real-worldtest to introduce various types of jamming attacks on different underwater modems. However, further research needs to be conducted for UWSNs, due to their special features.
## 3 The Asymmetric Hyperchaotic Image Encryption Scheme
### The Formation of the Encrypted Data Packet  
1. After the encryption process for the plaintext image, the hash value that generates the hyperchaotic system was encrypted through the ECIES, with the public key received from the receiver. The encrypted hash and image were combined together to form an encrypted data packet (PKT) for the final transmission. The presence of the PKT signifies the end of the whole encryption operation.
## 3 Results
### Patient Demographics and Clinical Characteristics
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Variables** & **Before the COVID-19** & **After the COVID-19** & **\(p\) Value** \\
**N = 467** & **n = 244** & **n = 223** & **\(p\) Value** \\ \hline Sex (Female), \(n\) (\%) & 83 (34.0) & 81 (36.3) & 0.602 \\ Age (years), median (IQR) & 61 (19.0) & 60 (22.0) & 0.442 \\ BMI (kg/m\({}^{2}\)), median (IQR) & 24.5 (6.5) & 24.5 (4.7) & 0.200 \\ ECOG-PS \(\geq\)2, \(n\) (\%) & 4 (1.6) & 10 (4.5) & 0.072 \\ Medical history, \(n\) (\%) & 151 (61.9) & 118 (52.9) & 0.062 \\ Prior urolithiasis & 46 (18.9) & 48 (21.5) & 0.472 \\ Diabetes mellitus & 4 (1.6) & 8 (3.6) & 0.300 \\ Hyperparathyroidism & 18 (7.4) & 15 (6.7) & 0.784 \\ Medication use, \(n\) (\%) & 5 (2.0) & 8 (3.6) & 0.467 \\ Anticoagulant/antiplatelet agents & 18 (7.4) & 11 (6.7) & 0.784 \\ Corticosteroid & 5 (2.0) & 8 (3.6) & 0.467 \\ Upper ureteral stones: & & & \\ Max stone size (mm), median (IQR) & 12.0 (11.8) & 11.9 (10.2) & 0.073 \\ Renal stones, \(n\) (\%) & 50 (20.5) & 38 (17.0) & 0.34 \\ Preoperative SIRS, \(n\) (\%) & 29 (11.9) & 34 (15.3) & 0.288 \\ Preoperative stenting, \(n\) (\%) & 74 (30.3) & 95 (42.6) & 0.006 \\ Hospital stay (days), median (IQR) & 5 (3.0) & 5 (1.0) & 0.032 \\ Surgical waiting day (days), median (IQR) & 36 (30) & 28 (28) & 0.005 \\ Operative times (min), median (IQR) & 77 (51.8) & 67 (43.0) & 0.018 \\ Postoperative fever, \(n\) (\%) & 19 (7.8) & 13 (5.8) & 0.403 \\ Postoperative SIRS, \(n\) (\%) & 15 (6.2) & 12 (5.4) & 0.723 \\ Stone free rates, \(n\) (\%) & 160 (65.6) & 131 (58.7) & 0.128 \\ Referral patterns: & & & 0.045 \\ Emergency department at our institution, \(n\) (\%) & 37 (15) & 45 (20) & \\ From primary care clinics, \(n\) (\%) & 96 (39) & 61 (27) & \\ From secondary/tertiary care hospitals, \(n\) (\%) & 53 (22) & 59 (27) & \\ Others, \(n\) (\%) & 58 (24) & 58 (26) & \\ \hline \hline \end{tabular}
\begin{tabular}{l c c} \hline \hline BMI: body mass index, ECOG-PS: Eastern Cooperative Oncology Group-Performance Status, IQR: interquartile range, Max: maximum, min: minutes, SIRS: systematic inflammatory response syndrome. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis before and after the COVID-19 pandemic. Figure 2: Referral pattern shifts before vs. after the COVID-19 pandemic.
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 3 The Architecture of Deep Learning Algorithms
### Deep Belief Network (DBN)  
[PERSON] invented a Deep Belief Network (DBN) in 2006, and it has since been employed in a variety of applications [111]. DBN represents probabilistic generative graphical models that are developed using probability and unsupervised learning. They solve various typical neural network problems, such as slow convergence and learning rate for local minima due to insufficient input selection. It is also a deep learning network that is built based on the stacking mechanisms of multiple Restricted Boltzmann Machines (RBM). Figure 9 illustrates that each of the stacked RBMs are made up of two layers: (1) the visible layer (v) and (2) the hidden layer (\(h_{i}\)). The hidden layer of each RBM acts as the visible layer for the next RBM in the DBN network. The structural diagram of DBN consists of bidirectional and symmetrical layer connections. A greedy learning algorithm is applied to train the DBN model; this technique involves repetitive training of one layer at a time until reaching a global optimum, which then serves as an input for the RBM. The application of the greedy algorithm is advantageous to the DBN network because it assists in weight distribution at each layer and supports proper network initialization, hence eliminating  
Figure 8: The architecture of BRNN [110].  
the issue of local minima t vanishing, as well as speeding up and optimizing the training process [112]. However, DBN has an expensive computational rate, since it requires the training of multiple RBMs [113].
## 2 Mathematical Foundation of Generalized Algorithm
### Formulation of Equivalent Circuit Model
The following equations are used to calculate \(V_{2}\), \(Z_{2}\), and \(Z_{3}\) in [20]:  
\[V_{2}=\frac{V_{1}}{\cos(k_{S}p)+j\left(Z_{1}/Z_{g}\right)\sin(k_{S}p)}, \tag{8}\]  
\[Z_{2}=\frac{Z_{1}+jZ_{g}\tan(k_{S}p)}{1_{1}+j\left(Z_{1}/Z_{g}\right)\tan(k_{S }p)}, \tag{9}\]  
Figure 1:0  
where; \(d\) is the depth of the enclosure. After calculating (6)-(8), the equivalent circuit takes the form shown in Figure 2b. In this circuit, it is easy to find the \(V_{p}\) and \(i_{p}\) values needed to calculate the electrical and magnetic SE, respectively. For example, \(V_{p}\) can be calculated as:  
Figure 1:1The electric SE at the observation point \(p\) is then defined as:  
Figure 1:2  
In its original form, as described in [20], the analytical model, based on equivalent circuits, has a number of limitations. First, the shielding enclosure contains only one aperture with a rectangular shape and a central position on the front wall. Second, the SE evaluation technique does not take into account the resonances corresponding to higher-order modes (\(TE_{mn}\) and \(TM_{mn}\)) excited in the enclosure. Third, the equivalent circuit from Figure 1b does not take into account the internal filling of the enclosure or the aperture. There are other limitations, such as those imposed on the polarization and incidence angle of the plane wave exciting the enclosure due to its representation by a voltage source. However, a number of modifications to the model from [20] are known to overcome most of these limitations. We will look at some of them below.
## 1 Introduction
By combining real-time, cutting-edge fault detection sensors created by the Technical University of Ostrava with machine learning models, it provides an automated fault detection method that addresses the drawbacks of traditional fault detection techniques [15]. To increase the capabilities of intelligent methods and avoid dependence on large amounts of labeled data, a new fault identification method based on deep reinforcement learning (DRL) is being investigated in the field of fault identification [16]. An alternate method for classifying transmission line faults makes use of deep learning techniques, with an emphasis on the investigation of five different kinds of short-circuit faults in power systems [17]. Using fault detection and classification techniques, [PERSON] and [PERSON] were able to increase transmission system efficiency and prevent significant damage [18]. It becomes clear from examining the papers that fault detection and classification are the main research topics. Some of the research using machine learning techniques is presented in Table 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Method** & **Content of the Research** \\ \hline
[19] & ANN & Fault detection \\ \hline
[20] & ANN & Fault classification \\ \hline
[21] & DRNN, LSTM & Fault detection \\ \hline
[22] & LSTM & Fault classification \\ \hline
[23] & ANN & Fault detection and classification \\ \hline
[24] & DNN, SVM, ANN & Fault classification \\ \hline
[17] & LSTM, LSTM-WR, ANN & Fault classification \\ \hline
[25] & Decision Tree, KNN, SVM & Fault classification \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A few studies using machine learning techniques for fault identification and classification. The rest of this paper is organized as follows. Section 2 introduces the real experimental and simulation data employed in this study, the proposed deep learning model design of the associated neural network model, and the establishment of parameters and assessment criteria. Section 3 describes the experimental results and the analysis of the results. Section 4 concludes the paper.
# Collapse Analysis of a Two-Span Reinforced Concrete Bridge Model
## 4 Analysis of Collapse Mode
### Influence of Span  
The span of the prototype bridge was 10 m, 16 m, 20 m, and 25 m. According to the similarity ratio of 1:3, the span of the finite element model was 3.3 m, 5.3 m, 6.7 m, and 8.3 m. Considering the influence of span variation on the collapse process of the two-span bridge model, the parametric analysis was carried out. The selected longitudinal ground motions were the El Centro wave and Chi\(-\)chi wave, without considering the influence of vertical ground motion. The longitudinal collapse modes of the four structures under these two ground motions were obtained by finite element analysis, as shown in Tables 5 and 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Case of 0.4g PGA**} & **Collapse Case** \\ \cline{2-5}
**Wave condition** & \multirow{2}{*}{\begin{tabular}{c} **Peak Longitudinal** \\ **Relative** \\ **Displacement/mm** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Peak Acceleration** \\ **Response of Middle** \\ **Pier Cap/m**\(\cdot\)**s**\({}^{-2}\) \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Peak Principle** \\ **Compressive Strain of** \\ **Middle Pier at the** \\ **Bottom/m**\(\epsilon\) \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Peak Principle** \\ **Compressive Strain of** \\ **Middle Pier at the** \\ **Bottom/m**\(\epsilon\) \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **PGA of the Wave** \\ **PGA of the Wave** \\ **PGA of the Wave** \\ \end{tabular} } \\ \cline{2-5} El Centro wave & X & 12.14 & 6.75 & 519 & 1.2 g \\ \cline{2-5}  & X + 0.6Z & 12.45 & 7.68 & 531 & 1.2 g \\ \hline Chi\(-\)chi wave & X & 15.53 & 6.59 & 611 & 1.2 g \\ \cline{2-5}  & X + 0.8Z & 15.47 & 7.52 & 602 & 1.2 g \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of seismic response and collapse condition.  
Figure 18: Collapse process under bidirectional Chi\(-\)chi wave input.  
The larger the span, the greater the axial pressure of the column. The increase of the mass of the superstructure was the main reason for the increase of the force of the pier column and the relative displacement between the side pier and the beam.  
Therefore, the smaller the span, the later the collapse time of the structure, and the greater seismic intensity could be endured. The collapse modes of the same type of ground motion input were basically similar. The two-span continuous girder bridge under the El Centro wave input condition was dominated by pier failure, and the collapse phenomenon of the bridge model under the Chi\(-\)chi wave input condition was main girder sliding.
## 4 Discussion
### FTIR and Interferences  
The higher differences for some components when measuring exhaust gas, compared to calibration gases, can be attributed to analytical and sampling interferences. Analytical interference (also called background or spectral interference) occurs when two or more compounds have overlapping absorbance bands in their infrared spectra. To minimize such interferences, appropriate resolution, selection of wave lengths, an appropriate library of expected components, and post-processing of the spectra are necessary [120].  
Figure 4: Overview of FTIR assessment studies. For each component, the mean deviations from the reference instruments were calculated based on the studies of Appendix A. “Cylinder” refers to calibration gases. “Parallel” means FTIR and reference instruments were measuring both from the dilution tunnel or the tailpipe. “TP vs. CVS” refers to cases where the FTIR was measuring from the tailpipe, while the reference instrument from the dilution tunnel. Error bars show one standard deviation of at least two studies. Numbers give the number of studies for the calculation of the means.  
Sampling system interferences are interferences that prohibit or prevent delivery of the target compounds to the FTIR gas cell (e.g., moisture condensation, reactive gases). Regulations, for example, require a heated sampling line (191 \({}^{\circ}\)C) when sampling undiluted exhaust in order to avoid the wall adsorption and/or dissolution of hydrophilic compounds (e.g., NH\({}_{3}\), NO\({}_{2}\), aldehydes, or ethanol) in condensed water. A study noticed the delay in oxygenated species reaching the tailpipe during cold start because of their condensation onto cold exhaust system surfaces and dissolution into condensed water [162]. FTIR systems might have differences in real-time operation. It was shown that an FTIR with a slower gas replacement rate and lower sampling frequency was not able to detect some of the rapid concentration fluctuations, e.g., for CO [183]. Another study noticed that during decelerations, the NH\({}_{3}\) concentration did not drop to near zero as it would be expected during fuel cut-offs (NH\({}_{3}\) formation is strongly inhibited by O\({}_{2}\)) [184]. Partly the lower response time of the instrument could explain this. However, it was suggested that an important reason was the outgassing of NH\({}_{3}\) from metal surfaces, which act as temporary NH\({}_{3}\) storage reservoirs [184]. A dedicated study on NH\({}_{3}\) found that response attenuation rates were due to mixing and diffusion during transport as well as NH\({}_{3}\) wall storage. Mixing/diffusion effects caused attenuation with a mean time constant of around 1.6 s. Wall storage attenuation had a mean time constant of 72 s [185]. The stored NH\({}_{3}\) on the sampling lines was around 11 mg. It was concluded that, in practical terms, shorter lines at a higher temperature, with flow rates > 10 L/min proved the best for transient response testing [185].
## 3 Soft Robot Design Overcoming Challenges in Sensor Integration
### The Challenges of Sensor Integration  
Given that the STIFF-FLOP arm is designed for MIS procedures, the ability to track its precise state in terms of pose is crucial. This is what enables us to close the control loop in order to provide the necessary feedback to the operator. Achieving this is dependent on the presence of sensors that can relay the requisite information.  
Due to the fundamental operative differences between rigid and soft robots, traditional sensors cannot be transplanted into soft devices. Indeed, there are almost no commercially available sensors that are easily \"re-engineerable\" for soft device integration. Although there are certain useful and easily measurable values that can be obtained using sensors external to the robot body--air pressure for example--this data alone is insufficient to provide us with a clear picture of the robot's shape and position. The route forward in this regard is therefore to embed soft/deformable sensors into the robot itself.  
With regards to sensing, the use of an optics-based approach was central in the STIFF-FLOP project. Optical transmitter fibres were used to introduce light into the sensing element and receiver optical fibres would allow for the measurement of light intensity changes due to geometrical changes in the manipulator's structure. This effectively provided the information needed to track its shape while also providing force or tactile information.  
Since the measurements were based on light intensity change due to geometrical deformation, any additional deformation resulting from the actuation system was a source of significant disturbance in the measured values. For that reason, the individually reinforced chambers were a crucial design feature that drastically improved the sensing capabilities of the robot by limiting the ballooning effect.