esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Analysis
### Simulation Results
It is worth mentioning that the model is well suited for scenarios with strong periodicity, where traffic patterns exhibit regular and predictable cycles. In such environments, the model effectively captures the underlying trends and provides reliable predictions. However, in contexts where traffic patterns are highly variable and exhibit significant randomness, the model's performance may deteriorate.
# Adaptive IDS for Cooperative Intelligent Transportation Systems Using Deep Belief Networks
## 2 Related Works
Consequently, such reputation-based mechanisms used by node-centric solutions cannot be trusted for the early identification of misbehaving or faulty vehicles [6]. Another set of IDSs for cITS adopt the data-centric detection approach by inspecting the BSM messages exchanged between the neighboring vehicles. These solutions perform several checks to determine whether the messages are falsified. BSM messages are checked against several criteria such as consistency and plausibility to determine whether they are trustworthy [6]. The consistency checks that BSM messages undergo in data-centric solutions determine whether the data shared by the node are consistent with the general context from the particular cITS. By vetting these BSMs, data-centric solutions can also identify the plausibility of the shared data to help in determining validity (i.e., whether they are in-line with those coming from other nodes in the cITS system). The node-centric and data-centric approaches adopted in existing IDS solutions for cITS rely on estimating the reputation of the nodes and trustworthiness of the data they share with each other. However, both approaches have inherent weaknesses and may not be suitable for tumultuous environments such as cITSs. In such dynamic systems, the nodes join and leave the network frequently, which creates an unstable topology. This makes it difficult to capture sufficient and consistent patterns that represent all behavioral aspects of the nodes. Therefore, existing security solutions with rigid thresholds are not suitable as they do not have the sufficient data needed for accurate decisions. Therefore, these solutions suffer from a high rate of false alarms. Thus, data insufficiency makes it difficult for adaptive mechanisms used by some solutions to accurately calculate the new thresholds, which also have a negative effect on IDS accuracy. The contribution of this study is two-fold:* A bi-variate moving average (BiMAV) technique was proposed. Unlike existing methods that only rely on the values estimated at the output layer, BiMAV correlates the changes of the output layer with the averaged input variables. Such an approach provides precise change detection by avoiding the instantaneous changes that could compromise the stability of the detection model. * The proposed method was incorporated into the detection model, which helps to prevent the unnecessary re-adjustment of security thresholds at the output layer of the DBN classifier thanks to the bivariate-based moving average used to monitor and detect the change in the classification accuracy estimation. The rest of the paper is organized as follows. Section 3 presents the methodology in which we describe the proposed solution. The results are analyzed and discussed in Section 4 along with a comparison with existing related work. Section 5 concludes the paper with a summary of the contribution and findings.
## 4 Simulation and Discussion
### Average Intensity and Scintillation Index of the Received Light  
Based on the above discussions, the channel parameters can be divided into two types: the parameters that primarily affect the average light intensity \(\langle I\rangle\), and the parameters that primarily induce light fluctuations, which can also be characterized by the scintillation index (SI) as \(\mathrm{SI}=\left(\langle I^{2}\rangle-\langle I\rangle^{2}\right)/\langle I \rangle^{2}\)[31]. According to the definition of lognormal distribution, the mean value of the simulated PDF is equal to the average light intensity \(\langle I\rangle\) and its variance \(\sigma_{\mathrm{s}}^{2}\) is equal to the SI [12].  
In Figure 10, the average received light intensity is depicted for various absorption and scattering coefficients and transmission distances. The results calculated from eight groups of 288 simulation realizations show that the light will attenuate as the transmission distance or the seawater coefficients increase.  
Figure 11 illustrates the relationships between the scintillation index of the received light and the turbulence properties (\(\varepsilon\), \(\chi_{\mathrm{T}}\), \(\omega\)), the flow velocity and the transmission distance. The scintillation will intensify as the rate of dissipation of kinetic energy \(\varepsilon\) decreases, the rate of dissipation of mean-squared temperature \(\chi_{\mathrm{T}}\) increases, and the ratio \(\omega\) approaches 0. The extension of transmission distance cause not only attenuation, but also spatial and temporal fluctuations of the light intensity, whether in the UWOC channel with turbulence or with attenuation effects.
###### Abstract  
As the main method of information transmission, it is particularly important to ensure the security of speech communication. Considering the more complex multipath channel transmission situation in the wireless communication of speech signals and separating or extracting the source signal from the convolutional signal are crucial steps in obtaining source information. In this paper, chaotic masking technology is used to guarantee the transmission safety of speech signals, and a fast fixed-point independent vector analysis algorithm is used to solve the problem of convolutional blind source separation. First, the chaotic masking is performed before the speech signal is sent, and the convolutional mixing process of multiple signals is simulated by impulse response filter. Then, the observed signal is transformed to the frequency domain by short-time Fourier transform, and instantaneous blind source separation is performed using a fast fixed-point independent vector analysis algorithm. The algorithm can preserve the high-order statistical correlation between frequencies to solve the permutation ambiguity problem in independent component analysis. Simulation experiments show that this algorithm can efficiently complete the blind extraction of convolutional signals, and the quality of recovered speech signals is better. It provides a solution for the secure transmission and effective separation of speech signals in multipath transmission channels.  
independent vector analysis; convolutional blind source separation; multipath transmission; chaotic masking +
Footnote †: journal:  
_Algorithms_
# Challenges and Future Trends in the Treatment of Psoriasis [PERSON] and [PERSON]
## 4 Future Aspects in the Treatment of Psoriasis
### Biologic Agents and Small Molecule Inhibitors under Development
#### 4.1.4 RORyt Inhibitors  
RORyt inhibitors are a class of drugs that specifically target retinoic-acid-receptor-related orphan receptor gamma-t (RORyt), a transcription factor essential for the development and function of Th17 cells. By modulating the Th17-cell-mediated immune response implicated in psoriasis, RORrt inhibitors show potential as a novel therapeutic approach [62]. VTP-43742, an oral inhibitor of the RORyT protein, is currently being evaluated in a phase III clinical trial for the treatment of plaque psoriasis. In a phase IIa study, patients receiving 700 mg and 350 mg of VTP-43742 demonstrated reductions of 29% and 23%, respectively, in the PASI score after four weeks. Additionally, both treatment groups exhibited a significant decrease in levels of IL-17A and IL-17F, cytokines associated with psoriasis [63]. Adverse effects reported in the study included headache, flushing, elevated liver enzymes, and nausea.
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Factor 1**} & \multicolumn{2}{c}{**Factor 2**} & \multicolumn{2}{c}{**Factor 3**} & \multicolumn{2}{c}{**Factor 4**} & \multicolumn{2}{c}{**Response 1**} \\ \cline{2-5}
**Std** & \multicolumn{1}{c}{**A:Coeff. of var.**} & \multicolumn{1}{c}{**B:Process. Time**} & \multicolumn{1}{c}{**C:Bottleneck**} & \multicolumn{1}{c}{**D:Num. of Cards**} & \multicolumn{1}{c}{**Productivity**} \\ \hline
7 & 0.25 & 60 & YES & 2 & 0.722 \\
43 & 0.25 & 60 & NO & 3 & 0.9745 \\
38 & 0.86 & 5 & YES & 2 & 8.6092 \\
24 & 0.86 & 60 & YES & 2 & 0.5123 \\
52 & 0.86 & 60 & NO & 2 & 0.5353 \\
54 & 0.86 & 5 & YES & 2 & 8.586 \\
49 & 0.25 & 5 & NO & 2 & 11.9317 \\
34 & 0.86 & 5 & NO & 2 & 11.4723 \\
41 & 0.25 & 5 & NO & 3 & 11.9674 \\
58 & 0.86 & 5 & NO & 3 & 11.5688 \\
56 & 0.86 & 60 & YES & 2 & 0.5184 \\
4 & 0.86 & 60 & NO & 2 & 0.547 \\
19 & 0.25 & 60 & NO & 2 & 0.9656 \\
69 & 0.25 & 5 & YES & 2 & 8.9757 \\
72 & 0.86 & 60 & YES & 2 & 0.5077 \\
80 & 0.86 & 60 & YES & 3 & 0.5085 \\
77 & 0.25 & 5 & YES & 3 & 8.952 \\
47 & 0.25 & 60 & YES & 3 & 0.729684 \\
35 & 0.25 & 60 & NO & 2 & 0.9683 \\
51 & 0.25 & 60 & NO & 2 & 0.96154 \\
40 & 0.86 & 60 & YES & 2 & 0.5205 \\
73 & 0.25 & 5 & NO & 3 & 11.9687 \\
13 & 0.25 & 5 & YES & 3 & 8.954 \\
27 & 0.25 & 60 & NO & 3 & 0.9734 \\
45 & 0.25 & 5 & YES & 3 & 8.958 \\
76 & 0.86 & 60 & NO & 3 & 0.6269 \\
67 & 0.25 & 60 & NO & 2 & 0.95691 \\ \hline \hline \end{tabular}  
* Std—standard order.  
\end{table} 
Table 10: Experiment design table—DBR. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{1}{c}{**Factor 1**} & \multicolumn{1}{c}{**Factor 2**} & \multicolumn{1}{c}{**Factor 3**} & \multicolumn{1}{c}{**Factor 4**} & \multicolumn{1}{c}{**Response 1**} \\ \cline{2-5}
**Std** & \multicolumn{1}{c}{**A:Coeff. of var.**} & \multicolumn{1}{c}{**B:Process. Time**} & \multicolumn{1}{c}{**C:Bottleneck**} & \multicolumn{1}{c}{**D:Num. of Cards**} & \multicolumn{1}{c}{**Productivity**} \\ \hline
58 & 0.86 & 5 & NO & 3.5 & 11.412 \\
30 & 0.86 & 5 & YES & 3.5 & 8.60618 \\  
ES & 3.5 & 8.97191 \\
34 & 0.86 & 5 & NO & 2.25 & 11.377 \\
75 & 0.25 & 60 & NO & 3.5 & 0.963077 \\
21 & 0.25 & 5 & YES & 2.25 & 8.9708 \\
43 & 0.25 & 60 & NO & 3.5 & 0.962564 \\
61 & 0.25 & 5 & YES & 3.5 & 8.97491 \\
46 & 0.86 & 5 & YES & 3.5 & 8.63423 \\
50 & 0.86 & 5 & NO & 2.25 & 11.417 \\
60 & 0.86 & 60 & NO & 3.5 & 0.610256 \\
11 & 0.25 & 60 & NO & 3.5 & 0.963077 \\
29 & 0.25 & 5 & YES & 3.5 & 8.9679 \\
7 & 0.25 & 60 & YES & 2.25 & 0.738613 \\
17 & 0.25 & 5 & NO & 2.25 & 11.947 \\
77 & 0.25 & 5 & YES & 3.5 & 8.96189 \\
63 & 0.25 & 60 & YES & 3.5 & 0.742657 \\
8 & 0.86 & 60 & YES & 2.25 & 0.5207 \\
65 & 0.25 & 5 & NO & 2.25 & 11.923 \\
5 & 0.25 & 5 & YES & 2.25 & 8.976 \\
16 & 0.86 & 60 & YES & 3.5 & 0.578379 \\
56 & 0.86 & 60 & YES & 2.25 & 0.5221 \\
80 & 0.86 & 60 & YES & 3.5 & 0.559897 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cont.
# Investigation of the Static Characteristics of a Geogrid-Reinforced Embankment
## 3 Monitoring Data Analysis
### Numerical Simulation of Fill Stress Analysis  
Figure 13 shows vertical and horizontal earth pressure cloud diagrams. In Figure 13a, the plane stress values at monitoring points \(A\), \(B\), and \(C\) are 77.21 kPa, 203.84 kPa, and 305.15 kPa, respectively. The maximum plane stress of the traditional asymmetric reinforced soil slope is 2/3 h (\(H\) is the height of the soil bank), and the maximum earth pressure of the soil bank is found in the middle of the bottom of the structure in the cloud picture. Compared with the traditional asymmetric reinforced soil embankment, the embankment is most likely to be destroyed from the middle of the structure. As shown in Figure 13b, the horizontal earth pressure cloud map, the plane stress values of monitoring points \(E\), \(F\), and \(G\) are 59.71 kPa, 105.78 kPa, and 182.55 kPa, respectively. The main reason for horizontal plane stress is that the upper intermediate gravity compresses the filling on both sides.  
Figure 11: Vertical and horizontal displacement cloud images: (**a**) vertical displacement cloud diagram; (**b**) horizontal displacement cloud diagram.  
Figure 12: Simulated vertical displacement values and actual vertical displacement-monitoring values.  
Figure 14 shows the vertical earth pressure and horizontal earth pressure of three monitoring points, \(A\), \(B\), and \(C\), inside the embankment. In the figure, each measuring point, \(A\), \(B\), and \(C\), has three values, respectively: the design's allowable value, monitoring value, and numerical simulation value. It can be seen from the figure that the vertical earth pressure, horizontal earth pressure-monitoring value, and numerical simulation value of monitoring points \(A\), \(B\), and \(C\) do not exceed the design's allowable vertical earth pressure and horizontal earth pressure. The ratios of the monitoring value, the simulated value, and the design's allowed value of vertical soil pressure at points \(A\), \(B\), and \(C\) are 0.90, 0.90, 0.84, 0.84, 0.93, and 0.91, respectively. The ratios of the monitoring value, the simulated value, and the design's allowable value of horizontal earth pressure at points \(A\), \(B\), and \(C\) are 0.77, 0.96, 0.97, 0.96, 0.85, and 0.89, respectively. Figure 14 further shows that the structure has good stability, and the vertical and horizontal stresses increase linearly from top to bottom along the internal central axis of the embankment.
# Spatiotemporal Heterogeneous Effects of Built Environment and Taxi Demand on Ride-Hailing Ridership
## 4 Results
### Regression Coefficients of MGWR  
The MGWR results for ride-hailing ridership and its influencing factors at three different periods are presented in Table 6. The utilization of MGWR allows for the exploration of spatial heterogeneity by providing varying coefficients across different spatial locations. Considering the spatial non-stationarity of the estimation coefficients, the minimum, median, mean, and maximum values of the coefficients are employed to assess the relationship between ride-hailing ridership and its influencing factors. The results highlight significant spatiotemporal heterogeneity in the link between ride-hailing ridership and its influencing factors. Taxi ridership has a positive impact on ride-hailing ridership, suggesting a cooperative relationship between taxis and ride-hailing. This result is in line with most existing studies [39]. Furthermore, road density exhibits a positive relationship with ride-hailing ridership, while bike lane density demonstrates a negative relationship. These contrasting effects indicate that higher road density contributes to increased ride-hailing ridership, whereas higher bike lane density is associated with reduced ride-hailing ridership. During the two peak periods, the effects of distance to the nearest bus stop and distance to the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Moming Peak**} & \multicolumn{4}{c}{**Evening Peak**} & \multicolumn{4}{c}{**Off-Peak**} \\ \cline{2-10}  & **OLS** & **GWR** & **MGWR** & **OLS** & **GWR** & **MGWR** & **OLS** & **GWR** & **MGWR** \\ \hline RSS & 640 & 188 & 162 & 616 & 192 & 176 & 527 & 209 & 186 \\ AICc & 3058 & 2019 & 1445 & 2999 & 1947 & 1502 & 2758 & 2033 & 1675 \\ Adj.R2 & 0.586 & 0.845 & 0.877 & 0.601 & 0.845 & 0.869 & 0.658 & 0.833 & 0.859 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the model fitness of the three models.  
nearest metro station are both negative. Conversely, the effects of bus stop density and metro station density are positive; this has been confirmed by many studies [9]. These findings suggest that ride-hailing services complement public transit systems due to the dense areas of public transit stations tend to generate more travel demand, attracting online ride-hailing to provide travel services here. The relationship between distance to CBD and ride-hailing ridership is found to be non-stationary.
## Materials and Methods
### Cyclic Rain Loading Tests  
In order to investigate the effects of initial defects on early rain erosion, the manufactured test specimens have to be impacted by rain. The specimens were tested in the Fraunhofer IWES' Leading Edge Lab, shown in Figure 4. The helicopter-style test facility contains a rotating disc of 2 m in diameter, in which three test specimens of 236 mm length are installed and can spin up to 120 m/s at the outer tip of the specimen. Then, four rain tanks introduce water droplets 3 m above the test specimen. After a defined test interval, the specimens were inspected.  
Thereby, the rain erosion system parameters were designed to be as close to reality as possible. However, since damage progress under real conditions only occurs after a very long period of time, the parameters of the test facility were adjusted so that initial erosion damage occurs in an acceptable time of a few hours. The times at which erosion damage occurs vary depending on the coating system and the condition of the specimen, e.g., initial defects. With the parameters from Table 2, erosion damage could be generated within 4 h of rain loading on a specimen with introduced defects in the form of voids in the coating. The specified test parameters and the IWES test facility fit within the DNV-GL (Det Norske Veritas and Germanischer Lloyd, classification society) recommended practice [20].  
Figure 4: Photo of the inside of the IWES rain erosion test facility.  
Figure 3: Introduction of the styrofoam balls (**a**) within the filler, adapted with permission from [19] and (**b**) on the filler layer within the coating.
## 5 Discussion and Conclusions
The DFISMC controller based on a DSO and the approximated FOI is used to improve the tracking performance. The implementation of DSO and FOI also helps the system reduce the \"chattering\" phenomenon. The experimental results illustrate the applicability of the proposed model and controller to a robotic gait training system with a human-gait pattern trackable ability. Future work will involve the impedance control of the antagonistic actuator to increase applicability of PAMs in the field of rehabilitation. The impedance of the actuator can be regulated by manipulating the nominal pressure \(P_{0}\) of two PAMs. To integrate the impedance controller into the system, the relationship between the actuator compliance and nominal pressure \(P_{0}\) would be considered and modelled in future work. [PERSON] conceived the methodology and designed the experiment; he also performed the experiment. [PERSON] and [PERSON] analyzed data and wrote the paper. [PERSON] led the research efforts and preparation of this paper. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} PAM & Pneumatic artificial muscle \\ PID & Proportional integral derivative \\ SOPDT & Second order plus dead time \\ SISO & Single input single output \\ SMC & Sliding mode control \\ DSMC & Discrete-time sliding mode control \\ DFISMC & Discrete-time fractional order integral sliding mode control \\ MTE & Maximum tracking error \\ RMSTE & Root mean square tracking error \\ ESO & Extended state observer \\ ADRC & Active disturbance rejection controller \\ DSO & Disturbance observe \\ SD & Standard deviation \\ FOI & Fractional order integral \\ GC & Gait cycle \\ \end{tabular}
## 5 Conclusions  
Means of modifying the stiffness of CJs are typically limited to the base material they are made of and their external geometry. An alternative was presented here for cases where it is necessary to reduce stiffness while keeping the geometry dimensions constant. In this work, a method for tailoring motion range by using mechanical metamaterials with different design parameters was presented. Predictions for mechanical properties and performance were made using FE computations and ML algorithms. For the mechanical analysis, to modify the stiffness, the FHs of the CCAJs were replaced by 2D honeycomb lattices with different unit cell dimensions and geometries. In this way, an innovative approach based on FH modification of CJs was proposed, and concepts such as LFHs and LCCAJs were further developed. The LFHs used here were a re-entrant honeycomb, a square honeycomb, and a hexagonal honeycomb. For all LFHs, the external dimensions were kept constant. Then, in all the structures proposed, the design parameters used to modify the stiffness were the unit cell dimensions, to modify the relative density of the LFHs, and the unit cell orientations, to change the deformation mechanisms found in each LFH. It was computationally and experimentally demonstrated that the orientation of the unit cell was the main parameter that affected the structure's deformation mechanisms, allowing certain structures to become bending-dominated even though their original configuration showed a more stretch-dominated behavior.  
Due to some additive manufacturing intrinsic issues in the extruded filaments, variability in the resulting motion and stiffness of each LCCAJ was observed. Additive manufacturing is a process that is dimension-sensitive; hence, defects encountered may have a lower impact on the mechanics of samples with larger dimensions. Finally, with all the simulations completed, a dataset was generated; this allowed the supervised training of the ML algorithms presented in this work. As a validation method, k-fold cross-validation was employed, which allowed measuring the proposed ML algorithms' accuracy when forecasting the displacements and stiffnesses of the LCCAJs proposed. In conclusion, for the forecasting of these mechanical parameters, as stated previously, the most feasible ML algorithm was the GPR algorithm. It demonstrated a better response and lower error percentage compared to the other ML algorithms used.