esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effects of Operational Parameters Photo-Fenton like Catalyst  
#### 3.3.1 Effect of Solution pH  
In AOPs processes, pH is one of the most important parameters for the removal efficiency; pH can strongly influence the dissolution of Fe\({}^{+2}\) from CFO and the stability and activity of the oxidant. As shown in Figure 5, the PF-AC/CFO process has demonstrated higher efficiency at pH 4.5 than at pH 6.5. It was probably due to the presence of iron and oxidant which shows maximum activity under acidic conditions.  
As depicted, the highest RR198 degradation efficiencies of 100% and COD removal of 97% were attained at pH\({}_{0}\) = 4.5, and dye removal decreased with the increase of solution pH due to the surface charge of AC/CFO being more negative. Therefore, dye existed in its anionic form throughout the pH\({}_{0}\) investigated (pH\({}_{0}\) = 3-10). Under acidic conditions (pH = 4.5), the increase of the oxidation rate of dye due to Fe\({}^{2+}\) ions on the AC/CFO catalyst can well activate PS to form SO\({}^{4\bullet-}\), and also additional fraction of dissolved iron species released to the medium from the catalyst surface at pH = 4.5. As a result, the increasing pH (pH > 4.5) would lead to the precipitation of iron ion and the adsorption of Fe (OH) onto the AC/CFO surface that inhibits the transfer of dye from the solution to AC/CFO causing  
Figure 4: The removal efficiency of dye in different systems.  
Figure 5: Effect of pH on RR198 degradation efficiency and chemical oxygen demand (COD) removal, optimum conditions: [RR198= 25 mg/L, catalyst dosage = 0.3 g/L, Persulfate (PS) = 5 mM, and reaction time: 25 min].  
reduction in the potential oxidation of dye and the decrease of dye removal [32]. At pH > 4.0, the soluble Fe\({}^{2+}\) ions decreased due to the formation of insoluble Fe (OH) complexes, therefore, decreasing the activation rate of PS and generation of SO\({}_{4}\)\({}^{\bullet-}\) (Equation (8)) [41]. On the other hand, at lower pH (pH < 4.5), high concentration of sulfate radicals is abundant which acts as sulfate radical scavengers. In addition, under this condition, a stable [H\({}_{3}\)O\({}_{2}\)]\({}^{\star}\) ion is formed that reduces the reactivity with the Fe\({}^{2+}\) ion. Consequently, the efficiency of the photo-Fenton like process for dye removal is reduced both at high and low pH [42]. Moreover, the removal efficiency of dye at natural pH (pH = 6.5) is greater than 97% (Figure 6). Therefore, AC/CFO in the presence UV/PS shows an excellent catalytic activity for degradation of dye without using a neutralization process; so, it would be useful over a wide range of pH [43].  
\[\mathrm{SO_{4}}^{\bullet-}+\mathrm{OH}^{-}\mathrm{\rightarrow SO_{4}}^{2-}. \tag{8}\]
## 3 Methodology
### Model Selection and Classification Scheme
#### 3.2.2 eXtreme Gradient Boosting Machine (XGBoost)  
XGBoost, a widely recognized advanced ML algorithm, achieves high performance in classification by systematically assembling weak learners--typically decision trees--in a sequential manner, enhancing their accuracy through progressive optimization iterations [82]. What sets XGBoost apart is that it adds a special term to make the model more robust and prevent it from overfitting. It also uses second-order Taylor series expansion to more accurately estimate how well the model is performing. XGBoost is designed to work quickly on large sets of data and for tasks where building a model fast without sacrificing accuracy is needed. The algorithm focuses on finding the best balance between accurately predicting outcomes and avoiding unnecessary complexity that could lead to overfitting.  
A key distinction lies in understanding the differences between bagging (e.g., RF) and boosting (e.g., XGBoost). Bagging operates by aggregating predictions from multiple independent base learners (trees) to reduce variance and stabilize outcomes. In contrast, boosting constructs learners sequentially, where each subsequent model addresses errors from prior iterations to reduce bias.  
The XGBoost algorithm's central goal is to optimize a regularized loss function defined as follows:  
\[L(\Phi)=\sum_{i}l(\hat{y}_{i},y_{i})+\sum_{k}\Omega(f_{k}) \tag{1}\]  
In Equation (1), the first term quantifies the difference between the observed target \(y_{i}\) and predicted value \(\hat{y}_{i}\). The second term, detailed in Equation (2), penalizes model complexity to prevent overfitting:  
\[\Omega(f)=\gamma T+\frac{1}{2}\lambda\|w\|^{2} \tag{2}\]  
Here, \(T\) is the number of terminal tree leaves, \(w\) denotes leaf weights, and \(\gamma\) and \(\lambda\) are regularization parameters that control the penalty strength. XGBoost iteratively minimizes this objective function at each step \(t\), as shown in Equation (3):  
\[L^{(t)}=\sum_{i=1}^{n}l\left(y_{i},\hat{y}_{i}^{(t-1)}+f_{t}(x_{i})\right)+ \Omega(f_{t}) \tag{3}\]
## 2 Train-Track-Bridge Coupled Vibration Model
### Train-Track-Bridge Coupled Vibration  
According to the mass matrix, stiffness matrix and damping matrix obtained using the finite element method, multi-rigid body dynamics and other processing methods, based on the energy principle, the train-track-bridge coupled vibration equation can be derived, as shown below:  
\[\begin{bmatrix}\mathbf{M}_{cc}&\mathbf{0}&\mathbf{0}\\ \mathbf{0}&\mathbf{M}_{rr}&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{M}_{bb}\\ \end{bmatrix}\begin{Bmatrix}\tilde{\mathbf{X}}_{cc}\\ \tilde{\mathbf{X}}_{rr}\\ \tilde{\mathbf{X}}_{bb}\\ \end{Bmatrix}+\begin{bmatrix}\mathbf{C}_{cc}&\mathbf{C}_{cr}&\mathbf{0}\\ \mathbf{C}_{rr}&\mathbf{C}_{rb}\\ \mathbf{0}&\mathbf{C}_{br}&\mathbf{C}_{bb}\\ \end{bmatrix}\begin{Bmatrix}\tilde{\mathbf{X}}_{cc}\\ \tilde{\mathbf{X}}_{rr}\\ \tilde{\mathbf{X}}_{bb}\\ \end{Bmatrix}+\begin{bmatrix}\mathbf{K}_{cc}&\mathbf{K}_{cr}&\mathbf{0}\\ \mathbf{K}_{rc}&\mathbf{K}_{rr}&\mathbf{K}_{rb}\\ \mathbf{0}&\mathbf{K}_{br}&\mathbf{K}_{bb}\\ \end{bmatrix}\begin{Bmatrix}\mathbf{X}_{cc}\\ \mathbf{X}_{rr}\\ \mathbf{X}_{bb}\\ \end{Bmatrix}=\begin{Bmatrix}\mathbf{Q}_{cc}\\ \mathbf{Q}_{rr}\\ \mathbf{0}\\ \end{Bmatrix} \tag{4}\]  
where \(\mathbf{X}_{cc},\ \mathbf{X}_{rr}\) and \(\mathbf{X}_{bb}\) denote the displacement vector of the train, rail and bridge, respectively, while \(\mathbf{Q}_{cc},\ \mathbf{Q}_{rr}\) denote the load train vector of the train and rail, respectively.
## 4 Results
### HIL Testing: Compliant Modular Assistance  
In the field of impedance control, a control has been developed with selectable levels of assistance (Table 2) accessible through a graphical interface. The Figure 8 illustrates test conducted at three different assistance levels during a shoulder flexion exercise, specifically focusing on joint J5, which plays a significant role in this movement. The graph in Figure 8 displays both the target trajectory and the actual trajectories executed by the user, exploiting the dedicated joint, throughout the exercise in assistive mode.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Joint ID** & **RMSE [Nm]** & \(\mathbf{\varepsilon_{\text{max}}}\) [Nm]** \\ \hline J3 & 0.2877 & 0.8338 \\ J4 & 0.3101 & 0.8703 \\ J5 & 0.0649 & 0.3449 \\ \hline \hline \end{tabular}
\end{table}
Table 1: In the table, the maximum errors and root mean square errors (RMSE) and absolute errors (\(\mathbf{\varepsilon_{\text{max}}}\)) for the considered joints are computed by comparing the experimental measurements of SEA sensors with the output from the built-in gravity torque calculation function in the MATLAB Robotic Toolbox. The experiment conducted on the prototype involved the exoskeleton covering various zones within the workspace while operating in position control. This was carried out to validate the consistency of the SEA sensor readings with respect to the model characterized in MATLAB.  
Figure 8: Experimental results of impedance-based assistive control: three levels of assistance were tested by varying the stiffness-viscosity parameters of the impedance control (stiffness-viscosity couples are reported in Table 2). In the figure, the black line indicates the joint reference target, while the blue, orange and yellow lines represent the results of three test at different assistance levels. The displayed oscillations are the consequences of human interaction with the robot. It is noticeable that lower assistance (high compliance, low stiffness) allows for greater interaction with the robot, enabling the user to deviate significantly from the target trajectory. Higher assistance (low compliance, high stiffness) leads to better adherence to the target trajectory, albeit restricting interaction with the device to some extent.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compliance** & \(\mathbf{k_{\text{p}}}\) [Nm/rad] & \(\mathbf{\beta_{\text{p}}}\) [Nm-s/rad] \\ \hline High & 30 & 1.2 \\ Medium & 10 & 1.2 \\ Low & 5.5 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pairs of stiffness and viscosity parameters (\(k_{\text{p}}\) and \(\beta_{\text{p}}\)) utilized in the impedance dynamic model to implement three different levels of assistance: decreasing \(k_{\text{p}}\) corresponds to lower support and higher compliance.
# Dynamic Impedance Estimation: Challenges and Considerations
## 4 Dynamic Impedance Estimation
### Harmonic Amplification  
The use of simulation is recurrent in analyzing harmonic distortions in electrical power systems. MATLAB/Simulink software has been increasingly used to analyze electrical networks' performance and power quality. For the investigation of resonances and the contribution of multiple harmonics sources, the system shown in Figure 8 was modeled using the transmission system's typical values. So, 1A of three-phase current was injected for each harmonic frequency, up to 35 th order (2.1 kHz), in the emitting buses A and C. The harmonic currents are injected with a fixed amplitude value and a typical phase angle.  
To observe the distortions arising from these harmonic contributions, current measurement is made on buses B and D. Figure 16 shows the current at buses B and D.  
Note that some harmonic currents are amplified at bus D. This is consistent with the current transfer presented in Figure 16, especially for harmonic 7 th and 13 th.  
Using online impedance or transfer information, it is possibly better to understand harmonic propagation behavior at the power system and identify harmonic responsibility among other applications.  
Figure 16: Current magnitude on bus: (**a**) Bus B; (**b**) Bus D.  
Figure 15: Total Impedance at Bus A: (**a**) Magnitude and (**b**) Phase.
## 2 System Model
Finally, the SINR \(\xi_{s,l}\) from user \(s\in\mathcal{S}_{l}\) to the serving beam \(l\in\mathcal{L}_{f}\) in the satellite expressed as linear form can be derived as follows  
\[\xi_{s,l}(t,m)=\frac{P_{s,l}(t,m)}{\sum_{k\in\mathcal{L}_{f}\setminus l}\sum_{ s^{\prime}\in\mathcal{S}_{l}}\rho_{s^{\prime},s}P_{s^{\prime},l}(t,m)+N} \tag{7}\]  
\[\text{s.t.}\sum_{s^{\prime}\in\mathcal{S}_{k}}\rho_{s^{\prime},s}=1 \tag{8}\]  
where \(N\) is noise power, and \(\rho_{s^{\prime},s}\in\{0,1\}\) is the scheduling factor that captures the interference from other beams in shared resources. While \(\rho_{s^{\prime},s}=1\) represents that user \(s^{\prime}\) transmits in the same radio resource with user \(s\) and interferes with user \(s\); while \(\rho_{s^{\prime},s}=0\) represents that user \(s^{\prime}\) does not interfere user \(s\). In this paper, we assume the number of scheduled users in one resource block of each beam is limited to one, as described in (8).
## 4 Enabling Technologies for FSO Communication
### Spatial Diversity
#### 4.6.2 MISO, SIMO, and MIMO Free-Space Optical Systems
The influence of MISO relay-assisted FSO systems on diversity order is being examined using the STD scheme, specifically in the context of \(\Gamma\Gamma\) fading channels that include PE. The findings indicate that the gain in diversity order is significantly influenced by the position of the relay. Furthermore, it is noted that this gain can significantly diminish when PEs affect the diversity order [276]. Receive Diversity FSO Systems  
A straightforward approach to mitigate the fading effect is to employ a larger lens at the Rx to average out intensity fluctuations. This method, commonly referred to as aperture averaging, can be regarded as a form of inherent receive diversity. Efficient fading reduction can also be achieved by utilizing multiple apertures at the Rx (SIMO). Specifically, instead of employing a single large aperture, several smaller apertures can be deployed at the Rx. This configuration allows each Rx aperture to experience some degree of aperture averaging, which is less than that of a single large aperture. Moreover, this approach benefits from spatial diversity by combining the signals from the different apertures. Assuming that the signals from the various apertures are subject to uncorrelated fading, the multiple aperture solution outperforms the single large aperture solution, given that both configurations have the same total effective aperture area. It is important to note that using multiple apertures is particularly advantageous in strong turbulence conditions [1,233]. Also, it is important to recognize that, from a practical perspective, utilizing an excessively large lens requires a photodetector with a larger active area to effectively capture the received photons at the lens's focal plane. This, in turn, significantly constrains the system's data rate due to the relatively high parasitic capacitance associated with such a component [1]. In SIMO (Receive Diversity) FSO systems, diversity gain can be achieved by averaging multiple independent laser beams. At the receiving end, three techniques can be utilized: MRC, SC, and EGC. Among these, the MRC scheme provides a higher diversity gain, resulting in an optimal signal-to-interference-plus-noise ratio (SINR). In contrast, EGC is often favored over the other two methods due to its simplicity and lower installation costs [245,263,277,278,279,280].
## 4 The Seismic Performance Parameters of RCS Composite Joints Under Floor Effect Are Studied
### The Influence of Floor Concrete Strength on Seismic Performance of Nodes  
#### 4.5.1 Destructive Form  
Figure 27 shows the stress cloud diagrams of steel beams and the equivalent plastic strain of floor slabs under different concrete strengths, as well as the impact on node failure. The results indicate that with an increasing concrete strength, the degree of buckling deformation at steel beam nodes decreases, while the stress values increase, and the plastic deformation area in concrete floors significantly narrows. This suggests that enhancing the strength of floor concrete can effectively limit the propagation of floor cracks, while also improving the stiffness and load-bearing capacity of the nodes.
## 5 Results and Discussion
### Issue 2: Measurement Uncertainty  
To account for unknown measurement uncertainty, the parameters \(\mathbf{\Theta}_{2}\) are sampled from the modified posterior distribution in (17). The simulation results for Issue 2, averaged over 100 datasets, are summarized in Table 1 (see, I2), where the averaged posterior estimates for the residual standard deviation are additionally included. Again, the posterior uncertainty in the estimated model parameters decreases for larger sample sizes. From Table 1, we also conclude that introducing additional uncertainty in the residual model does not affect the inference of the quantities of interest \(\mu_{R_{\mathrm{th}}}\) and \(\sigma_{R_{\mathrm{th}}}\). The averaged posterior means for \(\mu_{R_{\mathrm{th}}}\) and \(\sigma_{R_{\mathrm{th}}}\) are very similar in both scenarios, I1 and I2.  
Figure 9 shows the posterior distribution for \(\sigma_{\mathrm{\varepsilon}}\) for one dataset based on \(n_{2}=100\) and \(n_{3}=500\) LED systems and on different prior distributions. Obviously, the amount of prior knowledge, e.g., provided by repeatability testing, and the sample size affect the amount of posterior uncertainty in \(\sigma_{\mathrm{\varepsilon}}\).
## 3 Estimation and Reliability Calculation of Road Travel Time Distribution after Earthquake
### Impact Factors of Earthquake Damage of Path Travel Time
#### 3.2.3 Weight of Observation Values  
After the 'Section 2.3 Information extraction from trajectory data' is completed, the travel time of the path is assigned a weighting coefficient \(\omega_{i}\) for each observation. This has two advantages: firstly, it can reflect the representability of the observations related to the target path, secondly, it corrects the errors caused by the uneven coverage of the data collected by the floating car.  
The Figures 5 and 6 shows the establishment process about the estimation model of traffic travel time by post-earthquake.  
Figure 4: Calculation of seismic damage influence factor \(\Psi_{b}\).  
Figure 5: Road network itinerary.  
After the earthquake, the trajectory of a floating car passing through the target path was obtained (Prb1, Prb2,..., Prb8), the acquired image was recognized, and the earthquake damage influence factors \(\Psi_{1}\), \(\Psi_{2}\),..., \(\Psi_{b}\), the seismic damage influence factor \(\Psi_{i}\) of this trajectory can be obtained.  
The weight of observation values is the combination of the influence factor \(v_{i}\) of path incomplete coverage, the influence factor \(\lambda_{i}\) of road ergodic non-uniformity and the factor \(\Psi_{i}\) of earthquake damage influence, then \(\omega_{i}=(v_{i}\lambda_{i})^{\wedge}\Psi_{i}\).  
(1) The path incomplete coverage influences factor \(v_{i}\)  
The influence factor \(v_{i}\) of path incomplete coverage is expressed as:  
\[v_{i}=e^{(1-\phi_{i}\eta_{i})\theta_{1}} \tag{21}\]  
where:  
\(\theta_{1}\) is a normal number which can influence the degree of change in the incomplete coverage weight of the path.  
\(\phi_{i}\) is distribution factor.  
\(\eta_{i}\) is scaling factor.  
(2) Road ergodic non-uniformity factor \(\lambda_{i}\)  
Assuming that \(N_{k}\) is the observation times for road section \(k\), then:  
\[N_{k}=\sum_{i}[\beta_{ik}] \tag{22}\]  
where [*] is a Gaussian rounding function.  
The influence of uneven traversal of the sample road section due to \(\lambda_{i}\) can be expressed as:  
\[\lambda_{i}=\frac{\sum_{k}\frac{[\beta_{ik}]}{N_{k}}}{\sum_{k}\frac{\Psi_{i}}{ N_{k}}} \tag{23}\]  
(3) Earthquake damage influence factor \(\Psi_{i}\).  
The earthquake damage influence factor \(\Psi_{i}\) can be obtained by seismic influence factor \(\Psi_{b}\) in each observation point.  
\[\Psi_{i}=\prod_{b=1}^{n+1}\Psi_{b} \tag{24}\]  
Figure 6: Mapping process of trace observation.
## 5 Conclusions  
In practical applications, the PCMs are widely used in the field of temperature control. Because the device, which needs temperature control, may have a certain angle with gravity, it is necessary to study the melting process of PCMs at different inclination angles. In this study, numerical investigations are conducted to discuss the melting heat transfer characteristics of paraffin at different inclination angles.  
Figure 16: The variation of liquid fraction at different inclination angles.  
Figure 17: The relationship between the complete melting time and the inclination angles.  
The angles include 0\({}^{\circ}\), 30\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\), 120\({}^{\circ}\), 150\({}^{\circ}\) and 180\({}^{\circ}\). Experiments are carried out to verify the simulation. During the melting process for all angles, the heat conduction is the dominant heat transfer method at the initial stage of melting, then natural convection is taken into consideration. At 0\({}^{\circ}\) and 30\({}^{\circ}\), there are several Benard convective cells that make the heat transfer rate in the specimen faster. At 60\({}^{\circ}\), 90\({}^{\circ}\), 120\({}^{\circ}\) and 150\({}^{\circ}\), the Benard convective cells disappear so the solid liquid interface of each inclination angle becomes smooth. At 180\({}^{\circ}\), the melting rate is the slowest. However, the temperature distribution is almost horizontal, so this angle is widely used in the design of steady-state methods. In addition, the non-linear relationship between the complete melting time and the inclination angles is concluded as \(f=0.29\alpha^{2}+43.33\alpha+6511.90\). Based on this relationship, the melting time of each inclination angle can be predicted in the practical application, then a suitable inclination angle can be selected in order to meet the requirements of the temperature control system. Moreover, this paper also mentions that the geometry of the container needs to be improved to avoid the non-uniform temperature distribution on the surface of the SEM if the inclination angles are from 60 to 120\({}^{\circ}\).  
Conceptualization and Methodology, Y.W. and J.D.; Project Administration and Funding Acquisition, J.D.; Investigation, D.A.; Software, Validation, Formal Analysis, and Writing-Original Draft Preparation, Y.W.; Writing-Review & Editing, Y.W. and J.D.  
This research was funded by National Technical Basic Research Program of China (Grant No. JSZL2015603B002).  
The authors declare no conflict of interest.
## 2 Data and Methods
### ICESAT and SMAP Data
Other relevant reference information found in the SMAP products are VOD, also key parameter for microwave scattering model assessment and attenuation of related effects on reflectivity. In addition, we employ SMAP SMC estimates as reference information for validating our GNSS-R SMC retrieval technique (see next section). SMAP products are available at [[https://nsidc.org/data/SPL2](https://nsidc.org/data/SPL2) SMAP_S/versions/2]([https://nsidc.org/data/SPL2](https://nsidc.org/data/SPL2) SMAP_S/versions/2). Figures 2a and 3 show the SMAP BSR and VOD estimates, respectively, for the period 1-13 of April  
Figure 2: In (**a**), SMAP BSR estimates for the period 1–13 of April 2019 plotted at 500-sample intervals. In (**b**), ICESat-2 BSR estimates for the whole month of April 2019 plotted at 100-sample intervals. 2019. Finally, the locations of SMAP and ICESat-2 estimates are compared to CYGNSS SP locations, and we select the SMAP and ICESat-2 estimates located in a range inferior of 500 m from each CYGNSS SP location.