esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Coupled-Wave Equation for the Brillouin Process  
The Brillouin scattering process can be expressed by the coupled-wave equation as Equations (1)-(3), in which dispersion, self-phase modulation (SPM) and cross-phase modulation (XPM) are considered [8].  
\[\frac{\partial A_{p}}{\partial z}=-\frac{\alpha}{2}A_{p}+i\gamma(\left|A_{p} \right|^{2}+2|A_{s}|^{2})A_{p}-i\frac{\beta_{2}}{2}\frac{\partial^{2}A_{p}}{ \partial t^{2}}+i\kappa_{1}A_{s}Q \tag{1}\]  
\[-\frac{\partial A_{s}}{\partial z}=-\frac{\alpha}{2}A_{s}+i\gamma(\left|A_{s} \right|^{2}+2|A_{s}|^{2})A_{s}-i\frac{\beta_{2}}{2}\frac{\partial^{2}A_{s}}{ \partial t^{2}}+i\kappa_{1}A_{p}Q \tag{2}\]  
\[\frac{\partial Q}{\partial z}=-[\frac{1}{2}\Gamma_{B}+i(\Omega_{B}-\Omega)]Q+ \frac{i\kappa_{2}}{A_{eff}}{A_{p}}{A_{s}}^{*} \tag{3}\]  
where \(A_{p}\), \(A_{s}\) and \(Q\) represent the amplitudes of the SBS pump wave, Stokes wave and acoustic wave, respectively. \(\gamma\) is the nonlinear parameter, \(\beta_{2}\) is the group velocity dispersion parameter, \(\alpha\) is the linear loss parameter of the optical fiber, \(\kappa_{1}\) and \(\kappa_{2}\) are coupling parameters, \(\Gamma_{B}\) is the acoustic damping ratio, \(\Omega_{B}\) is the Brillouin frequency shift and \(\Omega\) is the frequency deviation between pump and stokes wave.  
For an interferometric fiber sensing system, the pulse duration is typically hundreds of nanoseconds, so the optical pulse is in the quasi-continuous regime. In addition, the paper focuses on the effect of MI on the SBS threshold, so we mainly consider the Brillouin scattering process when the input power is below SBS threshold. Under these conditions, the Stokes light is so weak that the pump depletion induced by Brillouin scattering can be neglected. The effects of SPM and XPM on the Stokes wave are also negligible. Finally, the Brillouin process can be expressed as Equations (4) and (5).  
\[i\frac{\partial A_{p}}{\partial z}-\frac{\beta_{2}}{2}\frac{\partial^{2}A_{p} }{\partial t^{2}}+\gamma\left|A_{p}\right|^{2}A_{p}+\frac{\mathrm{i}\alpha}{2 }A_{p}=0 \tag{4}\]  
\[\frac{dI_{s}(z,t)}{dz}=-g_{B}\frac{I_{p}(z,t)}{A_{eff}}I_{s}(z,t)+\mathrm{i}I_{ s}(z,t) \tag{5}\]  
in which \(g_{B}=4\kappa_{1}\kappa_{2}/\Gamma_{B}\), \(I_{s}=\left|A_{s}\right|^{2}\) and \(I_{p}=\left|A_{p}\right|^{2}\) are the powers of the Brillouin-Stokes light and the pump light, respectively. It can be seen that Equation (4) is the typical nonlinear Schrodinger equation (NLSE), which is widely used to express the evolution of MI in optical fiber. Equation (5) is the typical equation describing the intensity evolution of the Brillouin-Stokes light. Utilizing the two equations, we can describe the effect of MI on the SBS threshold for optical pulse in long optical fiber. In the following sections, we first analyze the evolution of MI and its effect on the Brillouin pump wave. Then, we describe the Brillouin process for optical pulses. By considering the MI, we obtain an analytic expression for the SBS threshold of optical pulses in long optical fiber.
## 2 Integrated Modeling  
### Framework of Integrated Model  
When satellites are in orbit, the control subsystem controls the rotation speed of flywheels according to the instructions received from the ground to generate control torques to adjust the attitude and maintain pointing accuracy. Micro-vibration disturbance generated by flywheels will be transmitted through satellite structural subsystem. When the frequency of micro-vibration disturbance is close to the mode frequency of optical subsystem elements, it will cause elements to jitter, thereby reduce imaging quality.  
Based on the actual characteristics of a satellite, this paper establishes an analysis model including four subsystems of vibration source, structural, control, and optical subsystem; then, it creates a time-domain integrated model, the framework block diagram of which is shown in Figure 1. The vibration source subsystem can obtain the disturbance characteristic of flywheel micro-vibration, the structural subsystem has a transfer function in the micro-vibration physical space, the control subsystem is used to simulate various imaging modes, and the optical subsystem is used to convert the response of physical coordinates into imaging indicators. The reason why the model proposed in this paper is a full-closed-loop model is that the micro-vibration source subsystem of the closed-loop framework in the previous model is a type of external disturbance input, which meansthat micro-vibration disturbance is the overall input. However, the proposed model in this paper takes imaging mission instructions as input, and the vibration source subsystem is completely closed-loop within the overall frame. Through such a method, the overall model will be free from external input disturbances and form a full-closed-loop.  
The model integrates each subsystem model by connecting the input and output interfaces of each subsystem.  
For the vibration source subsystem, this paper relies on the measured data to establish a model that takes flywheel rotation speed (time-domain) \(n\) as input and obtains micro-vibration time-domain disturbance \(T_{d}\) through the harmonic interpolation method, so that overall model can be simulated on time-domain.  
For the structural subsystem, the authors built a state-space equation based on the differential equations of kinematics and satellite mode shape information. Its inputs include micro-vibration disturbance \(T_{d}\) and control torque \(T_{c}\), while the outputs are micro-vibration physical space response \(y_{jit}\) and control output \(y_{att}\).  
For the control subsystem, the actual in-orbit control strategy is adopted, control output \(y_{att}\) of the structural subsystem is taken as input, control torque \(T_{c}\) is taken as output, and flywheel rotation speed \(n\) is output according to the requirements of the micro-vibration disturbance source subsystem.  
For the optical subsystem, the relationship between the micro-vibration physical space response \(y_{jit}\) and imaging image shift \(\Omega\) can be obtained according to the satellite optical load structure and the processing method based on optical theory.
# Toxins of Prokaryotic Toxin-Antitoxin Systems with Sequence-Specific Endoribonuclease Activity
## 2 Families of Sequence-Specific Endoribonuclease TA Toxins
### VapC  
VapC was first identified in pathogenic _Dichelobacter nodosus_, and many homologues are found in a variety of taxa, including the sexually-transmitted pathogen _Neisseria gonorrhoeae_[67; 68; 69]. Some bacterial genomes carry numerous copies of _vapBC_ modules. For example, _M. tuberculosis_ contains 45 copies of _vapC_ genes [67; 70; 71].  
VapC belongs to a different protein family from MazF. VapC consists of the PIN-domain and is similar to Mg\({}^{2+}\)-dependent RNases [67]. The structure of the active site is homologous to the phage T4 RNase H and to flap endonucleases, and VapC requires a single metal ion for catalysis. Toxin and antitoxin (VapB) interact physically to form the VapBC complex [67]. The VapBC complex binds inverted DNA repeats within the promoter region of the operon and regulates its own transcription [72].  
VapC is a sequence-specific endoribonuclease with various RNA targets. VapC from _Shigella flexueri_ 2a and _Salmonella enterica_ cleave the anticodon stem loop of the initiator tRNA (tRNA(Met)), thus inhibiting translational initiation [35]. VapC in _M. tuberculosis_ cleaves the single-stranded RNA sequence with a specific secondary structure, AU'AW-hairpin-G- (W denotes U or A) [48]. The results imply that this homologue has specificity for both the sequence and secondary structure of the RNA.
## 3 Results
### Dus-Guided PTA Optimization  
All 16 RSs with a PSVR > 2.4 underwent a prolonged balloon post-dilatation that was 1 mm oversized compared to the vessel diameter. This approach resulted in a PSVR < 2.4 in nine cases, while in the remaining five cases, bailout stenting was deemed sufficient to scaffold four persistent residual stenoses and to seal one grade D dissection which occurred after prolonged post-dilatation. In the remaining two cases, a DUS assessment showed a persistent PSVR > 2.4 despite multiple post-dilatations, but no further treatments were performed because of the high amount of calcium.  
All five grade C dissections first underwent 1-1 sized prolonged balloon post-dilatation at low pressures. After that, bailout stenting was required to seal persistent residual dissection in three cases and to scaffold a significant lumen narrowing within the dissected  
\begin{table}
\begin{tabular}{l c c c c} \hline  & **Overall** & **Optimization** & **No Optimization** & \(p\)**-Value** \\ \hline Lesions & 62 & 21 & 41 & \\ \hline Length (mm) & \(211\pm 85.2\) & \(244\pm 62.2\) & \(194\pm 73.1\) & \(p=0.041\) \\ \hline Diameter stenosis (\%) & \(93.1\pm 7.3\) & \(95.1\pm 7.3\) & \(96.1\pm 4.8\) & NS \\ \hline Reference vessel diameter (mm) & \(5.3\pm 0.8\) & \(5.7\pm 0.3\) & \(5.5\pm 0.6\) & NS \\ \hline De novo & 50 (80.6) & 14 (80) & 31 (76\%) & NS \\ \hline Native vessel restenosis & 10 (16) & 4 (19\%) & 4 (10\%) & NS \\ \hline In stent restenosis & 2 (3.2) & 1 (0.5) & 1 (0.2) & NS \\ \hline Total occlusion & 31 (50) & 17 (71\%) & 16 (39\%) & \(p<0.01\) \\ \hline TASC classification & & & & \\ \hline Type A & 0 & 0 & 0 & - \\ \hline Type B & 11 (18) & 0 & 11 (27) & \(p<0.01\) \\ \hline Type C & 43 (69) & 17 (81) & 25 (61) & \(p=0.03\) \\ \hline Type D & 9 (13) & 4 (19) & 5 (12) & NS \\ \hline \multicolumn{5}{l}{Calcification degree} \\ \hline None or slight & 27 (44) & 6 (29) & 21 (51) & \(p<0.01\) \\ \hline Moderate & 25 (40) & 11 (52) & 14 (34) & \(p=0.047\) \\ \hline Severe & 10 (16) & 4 (19) & 6 (15) & NS \\ \hline Inflow disease & 4 (6.4) & 1 (5) & 3 (7) & NS \\ \hline Outflow disease & 8 (12.9) & 2 (10) & 6 (14) & NS \\ \hline Procedural details & & & & \\ \hline Lesion pre-dilatation & 62 (100) & 21 (100) & 41 (100) & NS \\ \hline Lesion post-dilatation & 37 (59.6) & 21 (100) & 16 (40) & \(p<0.01\) \\ \hline DEB & 60 (96.8) & 21 (100) & 39 (95) & NS \\ \hline Primary stenting & 2 (3.2) & 0 & 2 & - \\ \hline \end{tabular} Values are shown as mean \(\pm\) SD or (n)/%. DEB = drug-eluting balloon; TASC = TransAtlantic Inter-Society Consensus.  
\end{table}
Table 3: Comparison between lesions requiring and not requiring optimization.  
segment in one case. In one case, despite multiple post-dilatations, a DUS assessment showed a persistent PSVR > 2.4 within the dissection located at the level of the popliteal artery (segment P2-P3); no further treatments were performed.  
In conclusion, after DUS-guided PTA optimization, integrated technical success was achieved in 14 out of 16 residual stenoses and in four out of five grade C dissections, being as high as 95% overall (59/62) (Table 2).
# Reconstruction of the Subsurface of Al-Hassa Oasis Using Gravity Geophysical Data
## 3 Gravity Survey
In our survey, due to the densely constructed urban and semi-urban areas, the gravity station locations were selected so that there is a distance greater than 15 m from any small construction (e.g., stone wall) and 30 m from any larger building. This issue did not occur within the National Park and the surrounding area, where the stations were installed in remote locations.
## 3 Research Design
### Appling Machine- and Deep- Learning to OTT Consumer Classification
#### 3.1.1 Conventional Machine Learning Methods
Thus, its calculation time is relatively long [45; 46]. **The naive Bayes method** is a typical classification method based on the statistical assumptions of the Bayes' theorem. It starts from the assumption that all input features are independent. When this is true, classes can be assigned through the following process [46]:  
\[y(f_{1},f_{2},\cdots,f_{m})=argmax_{k\in[1,\cdots,K]}p(C_{k})\prod_{i=1}^{m}p (f_{i}\Big{|}C_{k}), \tag{5}\]where \(m\) is the number of features, \(k\) is the number of classes, \(f_{i}\) is the \(i\)th feature, \(C_{k}\) is the \(k\)th class, \(p(C_{k})\) is the prior probability for \(C_{k}\), and \(p(f_{i}|C_{k})\) is the conditional probability for feature \(f_{i}\) given class \(C_{k}\). The key merit of naive Bayes is its short calculation time for learning. This is because, under the assumption that features are independent, high-dimensional density estimation is reduced to 1D kernel-density estimation. However, because the assumption that all features are independent is unrealistic, accuracy may decrease when performing classification using only a small sample of data. To increase accuracy, a large amount of data should be collected [46; 47]. **The RIPPER** algorithm is a typical rule-set classification method [48]. Rules are derived by training the data using a separate-and-conquer algorithm. In turn, the rules are set up to cover as many datasets as possible, as developed using the current training data. The rules are pruned to maximize performance. Data correctly classified according to the rules are then removed from the training dataset [46]. The RIPPER algorithm overcomes the shortcoming of early rule algorithms, wherein big data could not be effectively processed. However, because the RIPPER algorithm starts by classifying two classes, performance can decrease when the number of classes increases. Its performance may also decrease because of its heuristic approach.
## 3 Error Introduced by LEO Satellite Assistance
### Clock Stability of Sattelite and Receiver
The frequency attenuation of different crystal oscillator types under different coherent integration times can be obtained in combination with Figure 6, which is shown in Figure 7. It can be seen from this figure that as the LEO satellite uses OCXO, the impact of clock noise can be negligible under the coherent integration time within 1 s. Since the GNSS receiver adopts the TCXO oscillator, the signal power attenuation increases along with thecoherent integration time. That is the reason why the integration time cannot be compressed indefinitely. It can be seen from the figure that to make sure the gain attenuation is no more than 2 dB, the maximum coherent integration should not exceed 400 ms. Furthermore, it is obvious that a receiver using OCXO can use a longer coherent integration, which could achieve several seconds when an ultra-stable clock is used [14; 15]. However, the motion and non-linearity of the receiver should be considered carefully for such a long coherent integration. Thus, we just take the most common TCXO as the clock of the receiver in this work.
# Blockchain-Based Multiple Authorities Attribute-Based Encryption for EHR Access Control Scheme
## 6 Scheme Analysis
### Performance Analysis
#### 6.3.3 Computation Overhead  
As shown in Figure 7, we compare our scheme with schemes [6,7] and scheme [24] in terms of the computational overhead of decryption on the user side. The horizontal axis is the number of attributes for which the DU satisfies the access policy, and the vertical axis is the time consumed for decryption. Assume that before decryption, the DU has calculated the attributes needed for decryption by the attribute confused key and \(g^{r}\). For the same \(g^{r}\), the overhead in the subsequent decryption process is negligible since the process only needs to be calculated once permanently. During the decryption process, the DU only needs to perform a power operation to recover the symmetric key because the cloud server performs the outsourced decryption process. With the number of attributes increasing, compared with scheme [6,7] in which the decryption time increases linearly with the increase of attribute number, our scheme revolves around 0.043 s. So, our scheme has higher decryption efficiency compared with schemes [6,7].
# Assessment of Different Stochastic Models for Inter-System Bias between GPS and BDS
## 2 Model for Inter-System Bias Estimation and Descriptions of the Stochastic Models  
The main biases between two satellite systems are related to the satellite constellations, the signals broadcast, and time and coordinate frame conventions [18]. Thus, in our multi-GNSS combined processing model, we introduce an extra ISB parameter to cover the effect of the biases between the two systems, and the ISB is estimated with other unknowns using the ionospheric-free (IF) PPP processing.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Design
#### 4.5.4 Sender-Side Process  
The covert sender must have access to the PDCP layer and must be able to modify the processing of the PDCP header. Since the gNodeB is implemented either as monolithicsoftware or as a microservice architecture, the covert sender has to manipulate the software or the software components responsible for processing the PDCP, i.e., an insider attack to leak data seems most plausible. Since only five reserved bits are available within the PDCP header for hiding secret text, the covert sender cannot embed the entire one-byte character \((b_{8},b_{7},b_{6},b_{5},b_{4},b_{3},b_{2},b_{1})\) to the PDCP header. Therefore, the hidden text message has to be transmitted by splitting the ASCII characters (or bytes in case of a binary message) into two segments with four bits each. In addition, to avoid detectability, not every data packet can be used, but only a certain fraction of them. The specific transmission interval of the covert data depends on several parameters of the network environment and must therefore be determined during the practical tests. For the proposed covert channel, the covert sender utilizes every tenth PDCP data PDU. To signal that the selected PDU contains covert data, the least significant bit of the five reserved bits in the PDCP header is set to on. After that, only four reserved bits are available for the transmission of hidden information. The higher four bits \((b_{8},b_{7},b_{6},b_{5})\) of the ASCII character are embedded into the four reserved bits of one PDU. Then, the remaining bits \((b_{4},b_{3},b_{2},b_{1})\) are stored in the header of the PDU that is selected for the next covert transmission. Reliable transmission is ensured by the PDCP and the underlying protocols. Therefore, no measures are implemented within the covert channel to increase the reliability of the covert transmission. An algorithmic description of the covert sender is shown in Algorithm 1.  
Instead of using a fixed transmission distance \(d\) between PDCP data PDUs, a randomized distance with mean \(d\), e.g., in the range \(d-\delta\) to \(d+\delta\) with equal distribution, might be used, where the covert sender and receiver use the same random number generator. If the PDCP data PDUs used for the transmission of covert data can be clearly distinguished from other PDCP data PDUs (e.g., by an appropriate encoding of values), then a variation in the distance can alternatively be used to establish a timing covert channel.
Thus, they are broadly used in battery chargers, supercapacitors, electric vehicles, high-voltage power supply applications, and renewable energy systems [30]. Multilevel inverters are hybridized with _DC-DC_ converters to enable compact step-up _DC-AC_ conversion, and topologies such as multilevel bidirectional converters and multilevel bidirectional resonant converters are proposed. The key targets and purpose of multilevel bidirectional converters are to achieve and meet the requirements of: (i) a remarkable reduction in switching losses, (ii) a reduction in harmonic distortion (_HD_), and (iii) high efficiency with low component count to provide efficient and convenient size. This article focuses on different topologies of bidirectional and resonant _DC-DC_ converters, as well as multilevel inverters. The hybrid structures of these converter topologies are also presented. Different attributes, comparisons, advantages, and disadvantages of each topology and their applications are critically analyzed. The challenges and future prospects of the latest converter topologies are also elaborated. The remaining part of this article is organized as follows: Section 2 reviews bidirectional converters, their classification, and comparison between isolated and non-isolated converters. Section 3 explains resonant converter structures and their classifications, while Section 4 focuses on multilevel inverter topologies with respect to diode-clamped, capacitor-clamped, and cascaded topologies. Section 5 highlights the combined topologies of each two of these converters, while Section 6 describes the application of all these converters in grid connection and energy storage systems. The challenges and future perspectives are summarized in Section 7, while the last section (Section 8) presents the conclusion of this study.
## 3 The UDVRP Model
### Energy Consumption Function  
The energy consumption of an unmanned delivery vehicle is directly related to both the distance traveled and the weight of the products being transported. The amount of charging required per day is equal to the daily energy consumption. The energy consumption \(E\) is directly proportional to both the distance \(d\) traveled by the vehicle and the overall weight of the vehicle. The total weight is the combined value of the vehicle's intrinsic weight, denoted as \(p\), and the current cargo capacity, represented as \(q\)[29], Right now \(E=ed(p+q)\).  
Among them, \(e\) represents the specific energy consumption of the tram, which is the amount of energy the vehicle consumes per unit mass and unit distance. The specific energy consumption of identical vehicles is the same. Given that the cost of energy consumption per unit is denoted as \(h\), the total energy consumption cost can be represented as \(E\).
## 4 Discussion
### Influence of the Presentation Angle of the DVFs on Emotion Recognition (H3)  
The hypothesis that a higher number of hits would be achieved with the DVFs presented in the frontal view, as opposed to the profile views was supported by the data for both groups. To the best of our knowledge, this is the first study to present virtual characters with frontal and profile views to individuals diagnosed with depression. A study conducted by our research team on healthy controls also found superior rates of emotional recognition when the avatars were presented from the frontal view in comparison to the profile view [28].  
Influence of Sociodemographic Data on Emotion Recognition for the Depression Group (Recognition Scores and Reaction Times) (H4)  
Regarding the depression group, gender was not correlated with successful emotional recognition, as expected and as found in previous studies with healthy and depressed samples [42; 43]. The same results were found for the educational level. The non-correlation confirmed the expectancy and previous findings in healthy people [28]; there is scarce literature about this issue on samples with depression.  
However, as expected, age showed itself to be an influential variable in emotion recognition scores and reaction times, with an inverse correlation between age and the recognition scores and a direct correlation between age and reaction times. Some authors have found similar results, suggesting these as mediators for the natural impairment of aging, mainly referred to as the decline in processing speed, fluid intelligence, alexithymia, and depression [44; 45]. However, this emotional deterioration might also be related to a pre-clinical general cognitive impairment, since depressive disorders increase the risk for dementia [46; 47], which in turn shows greater deterioration in emotion recognition [48; 49]. Moreover, it is important to point out the less computer literacy of the elderly and difficulties in using the computer mouse when choosing the answers, which might lead them to select incorrect responses and raise reaction times.