esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Stability Analysis and Evaluation Criteria of Dual-Infeed HVDC with LCC-HVDC and VSC-HVDC
## 3 Power Stability Analysis of Hybrid Dual-Infeed HVDC Systems
### The Influence of VSC-HVDC Control Mode on the Power Stability of the System  
The rated operating parameters of LCC-HVDC and the receiving end power grid parameters are given in Appendix A. The base power of the system is 1600 MW, and the base voltages at the AC side and DC side are 525 kV and 500 kV, respectively. Let \(P_{\mathrm{d}\mathrm{N}2}\) = 1.0 p.u., \(Q_{\mathrm{d}\mathrm{N}2}\) = 0 p.u., \(V_{\mathrm{N}2}\) = 1.0 p.u., and the other parameters remain unchanged. The maximum power curves of LCC-HVDC under different VSC-HVDC control modes are shown in Figure 3. In the figure, LCC, LCC + VSC (Q), and LCC + VSC (V), respectively, refer to three cases: no VSC-HVDC system, VSC-HVDC system with constant reactive power control mode, and VSC-HVDC system with constant AC voltage control mode. The power stability indexes of LCC-HVDC at the rated operating point under different control modes are calculated, as shown in Table 1.  
According to Figure 3 and Table 1, under different VSC-HVDC control modes, the PSI values at the rated operating point are different, and the order from high to low is: LCC + VSC (V) > LCC > LCC + VSC (Q). Compared with no VSC-HVDC, the VSC-HVDC constant reactive power control mode will deteriorate the power stability of the HVDC system at the rated operating point, while the constant voltage control mode will improve the power stability of the HVDC system.
## 4 Crosstalk Analysis  
In the proposed SWIPT system, the origin of information interference is mainly generated by the power carrier and two different frequencies information carriers. As shown in Figure 7, the proposed SWIPT system can be equivalent to a six-port network. The power is transferred from port \"\(V_{p}\)\" to \"\(V_{s}\)\". The normal forward information is delivered  
Figure 6: Backward information transfer channel. **(a)** Simplified schematic of the backward transmission channel. **(b)** Bode diagram of the backward information transmission.  
from \"Forward TX\" to \"Forward RX\". The normal backward information transmission channel goes from port \"Backward TX\" to \"Backward RX\".  
Because the signal coupled coils are connected in series in the main circuit, some low-frequency voltage waveforms are also induced on the coupled coil during power transmission, so the power carrier is the main noise during signal transmission, such as \"\(V_{p}\)\" to \"Backward RX\" and \"\(V_{s}\)\" to \"Forward RX\".  
At the same time, when the information is transmitted in both directions, the signal receiving coupling coils is not only affected by the power carrier but also disturbed by another signal source, for example, \"forward TX\" to \"Backward RX\" and \"Backward TX\" to \"Forward RX\".  
To sum up, for the signal receiver cells, there are two main types of crosstalk source, one is the power carrier and the other is the high frequency signal source at the same side. Therefore, the system needs to establish two kinds of crosstalk equivalent models. Besides, it is beneficial to the design of the signal demodulation circuit by analyzing the influence of crosstalk on signal receiving port. The analysis of these two situations is as follows.
# Overload Mitigation of Inertial Grid-Forming Inverters Under Frequency Excursions
## 3 Overloading Mitigation Strategies
### External Frequency Support
#### 3.3.2 Outer Frequency Support Loop  
The outer loop is a modified version of a SRF-PLL structure which obtains the power setpoint for primary frequency regulation and inertial provision. It is based on the existing correspondence between the structure of a SRF-PLL and the swing equation [40; 41].  
The modified structure of the SRF-PLL is remarked in the orange box in Figure 10. The q-axis PCC voltage \(v_{pccq}\) is multiplied by the GFM internal voltage \(E\) and the inverse of \(-L_{c}\). Given that \(v_{pccq}\approx-V_{pcc}\sin\delta^{\prime}\), this results in \(P_{EFS}\), which represents the power to be exchanged between the inverter and the PCC (16). \(P_{EFS}\) is then used as the power feedback of the PSL proposed in Section 2, which replaces the classical PI controller. The angular position output of the PSL is then fed back to calculate \(v_{pccq}\). Unlike a SRF-PLL, which outputs the angular position, the proposed strategy outputs \(P_{EFS}\), which is used as the power setpoint for the inner controller. \(P_{EFS}\) is the active power that flows between a GFM inverter with the defined PSL and a stiff PCC voltage.  
The small-signal model of the outer frequency support loop is shown in Figure 11. The power setpoint \(P_{EFS}\) response to PCC frequency perturbations is given in (27). Voltage and frequency are assumed to operate close to its rated values. This equation has a close correspondence with the power response of the GFM inverter under grid frequency perturbations described in (6). The main difference relies on the synchronization constant. For (27), the synchronization constant is the one between the inverter and the PCC, \(K_{c}\), whereas (6) depends on the overall synchronization constant \(K_{t}\).  
\[\frac{\Delta P^{*}}{\Delta\omega_{pcc}}=-\frac{K_{c}\omega_{b}(2H^{EFS}s+D)}{2 H^{EFS}s^{2}+(D+k_{d}^{EFS}\omega_{b}K_{c})s+\omega_{b}K_{c}} \tag{27}\]  
Figure 11: External frequency support small-signal model.  
As long as the inertia of the external frequency support loop is much higher than the inertial response provided by the inner power controller, its tuning procedure is equivalent to a conventional PSL. However, it should be noted that the overall inertial contribution of the EFS strategy \(H_{t}\) is the sum of the inertia of inner and outer controllers. Hence, for an overall inertia, the external controller inertia constant is calculated as  
\[H^{EFS}=H_{t}-H_{i} \tag{28}\]
## 3 Results and Discussion
### Partition Coefficients  
It was found that the partition coefficients have an inverse behavior with respect the Pb(II) initial concentration in aqueous solution, this means that the PC is high when the initial concentration is lower (Figure 7), because there are less Pb(II) in the solution to be sorbed in the preferential sites of the zeolitic networks before the saturation. It is important to mention that the determination coefficient (\(R^{2}\)) was 0.1468.  
In Figure 8 it is observed that the Si/Al ratios of the clinoptilolite-rich tuffs notably affect the partition coefficients as a function of the initial concentration of the chemical spec  
\begin{table}
\begin{tabular}{c c c} \hline
**Material** & \(q_{m}\) **(mg g\({}^{-1}\))** & **Reference** \\ \hline Carbon foam & 491.36 & [42] \\ Biochar & 151.5 & [43] \\ Multi-walled carbon nanotubes functionalized by selenophosphoryl groups & 149.25 & [44] \\ Sulfone-modified magnetic activated carbon & 147.5 & [45] \\ Pectin-containing substances & 25.15 & [46] \\ Clinoptilolite-rich tuff (Nat-CLI) & 48.07 & Present work \\ Clinoptilolite-rich tuff acidified using a solution 1.0 M of H\({}_{2}\)SO\({}_{4}\) & 9.30 & Present work \\ \hline \end{tabular}
\end{table}
Table 4: Maximum sorption capacity of Pb(II) by materials with different nature.  
Figure 7: Partition coefficients for Pb(II) sorption by Nat-CLI and acid-modified clinoptilolite rich tuffs (PC) as a function of the initial concentration of lead in solution (Ci).  
in solution. For this reason, it is important to consider the ion exchange sites and the adsorption sites in each zeolite network before and after the treatment with 1.0 M of H\({}_{2}\)SO\({}_{4}\) solutions for the removal of different chemical species from polluted water.
# Partial Pre-Emphasis for Pluggable 400 G Short-Reach Coherent Systems
## 1 Introduction
A 0.4 dBQ factor penalty is observed in single-pol case for a partial pre-emphasis filter with a frequency response drop of 6 dB at 20 GHz and for a dual-pol coherent system with polarization-dependent loss (PDL) and polarization-mode dispersion (PMD); this penalty is found to be 0.5 dB. We conclude that the use of pre-emphasis filter can relieve some ENoB requirement of the AWG at the cost of a manageable performance penalty. The paper is organized as follows: an introduction to the SiPh modulators and model used is highlighted in Section 2. In Section 3, we present a numerical study of the PAPR change versus the extent of Tx pre-compensation. The experimental results and associated discussions are presented in Section 4. Design trade-offs and engineering advantages of partial pre-emphasis are discussed in Section 5.
## 3 Empirical Gramians
### State-Space Empirical Gramians
#### 3.1.5 Empirical Non-Symmetric Cross Gramians  
The (empirical) cross Gramian is only computable for square systems, and verifiably useful for symmetric or gradient systems [11; 44; 57]. In [39] an extension to the classic cross Gramian is proposed. Based on results from decentralized control [16], a non-symmetric cross Gramian is computable for non-square systems and thus non-symmetric systems. Given a partitioning of \(B=[b_{1},\ldots,b_{M}]\), \(b_{i}\in\mathbb{R}^{M\times 1}\) and \(C=[c_{1},\ldots,c_{Q}]^{\intercal}\), \(c_{j}\in\mathbb{R}^{1\times Q}\) from the linear system (3), the (linear) non-symmetric cross Gramian is defined as:  
\[W_{Z}:=\sum_{i=1}^{M}\sum_{j=1}^{Q}\int_{0}^{\infty}\mathrm{e}^{\Lambda t}\,b _{i}c_{j}\,\mathrm{e}^{\Lambda t}\,\mathrm{d}t=\int_{0}^{\infty}\mathrm{e}^{ \Lambda t}\,\Big{(}\sum_{i=1}^{M}b_{i}\Big{)}\,\Big{(}\sum_{j=1}^{Q}c_{j}\Big{)} \,\mathrm{e}^{\Lambda t}\,\mathrm{d}t.\]  
For this cross Gramian to the associated \"average\" SISO system, an empirical variant is then given by:  
**Definition 5** (Empirical Non-Symmetric Cross Gramian).: _Given non-empty sets \(E_{u}\), \(E_{x}\), \(S_{u}\) and \(S_{x}\), the **empirical non-symmetric cross Gramian \(\widehat{W}_{Z}\in\mathbb{R}^{N\times N}\)** is defined as:_  
\[\widehat{W}_{Z} =\frac{1}{|S_{u}||S_{x}|M}\sum_{k=1}^{|S_{x}|}\sum_{l=1}^{|S_{x}|} \sum_{m=1}^{M}\sum_{q=1}^{Q}\frac{1}{c_{k}d_{l}}\int_{0}^{\infty}\Psi^{klmq}( t)\,\mathrm{d}t,\] \[\Psi^{klmq}_{ij} =(x^{km}_{i}(t)-\xi^{km}_{i})(y^{lj}_{q}(t)-\tilde{y}^{lj}_{q})\in \mathbb{R},\]  
_with the state trajectories \(x^{km}(t)\) for the input configurations \(\hat{u}^{km}(t)=c_{k}e^{m}\circ u(t)+\bar{u}\), the output trajectories \(y^{lj}(t)\) for the initial state configurations \(x^{lj}_{0}=d_{l}e^{j}+\bar{x}\), and the offsets \(\bar{u}\), \(\bar{x}^{km}\), \(\bar{x}\), \(\bar{y}^{lj}\)._  
**Corollary 1**.: _For an asymptotically stable linear system, delta impulse input \(u_{i}(t)=\delta(t)\) and arithmetic averages over time as offsets \(\bar{x}=\frac{1}{T}\int_{0}^{T}x(t)\,\mathrm{d}t\), \(\hat{y}=\frac{1}{T}\int_{0}^{T}y(t)\,\mathrm{d}t\), the empirical non-symmetric cross Gramian is equal to the cross Gramian \(\widehat{W}_{Z}=W_{Z}\) of the average SISO system._  
Proof.: This is a direct consequence of [11] (Lemma 3).  
The numerical computation of the empirical non-symmetric cross Gramian requires \((|S_{x}|\cdot(N+|S_{u}|\cdot M))\) trajectories; the same as for the empirical cross Gramian.
# Advances in Deep Space Probe Navigation
## 3 Exploring Further Advances in Deep Space Probe Navigation
### Orbital Dynamics and Orbital Design  
Electodynamic tether (EDT) is a key prospective technique for space debris removal without the use of propellants. Ref. [36] proposes a novel electrodynamic multi-tether (EMT) system to overcome the disadvantages of the classical EDT system. Aiming at the interception problem of noncooperative evader spacecraft adopting a random maneuver strategy in the one-to-one orbital pursuit-evasion problem, an interception strategy with a decision-making training mechanism for the pursuer based on deep reinforcement learning is proposed [37]. Ref. [38] investigates the distribution of the semi-major axis and eccentricity of IOD solutions in a pool and find that choosing the solution with the maximum kernel density in the distribution is a much better way to determine the final solution from the pool.
## 4 Results and Discussion
### CNNs Configuration and Metrics  
The three CNN architectures (VGG-19, AlexNet, and ResNet-50) were trained for each of the six binary features in a consistent and comparable environment throughout our experiments. The gradient descent technique, embedded within the neural network's parameters, stands out as the most crucial algorithm for the learning process, acting as the optimizer. This algorithm modifies the weights of each neuron. Among the various types of optimizers in deep learning, the Adam optimizer was implemented in this investigation with a learning rate of 0.01 [52,53]. A learning rate of 0.01 was employed to control the size of each step in the optimization process. The loss function \"CrossEntropyLossFlat\", a key component of our methodology due to its suitability for classification tasks, was employed to measure the discrepancy between the predicted and observed labels [54]. Furthermore, we measured model performance primarily using accuracy, which quantifies the proportion of features correctly classified [52,53,55]. Accuracy represents the percentage of correctly classified features, encompassing True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN) [56]. Equation (1) illustrates accuracy. In addition to accuracy, we used the F-score to assess performance, as it combines recall (the ability to identify all positive cases) and precision (the correctness of positive predictions) into a single, balanced metric that reflects the overall effectiveness of positive prediction [56]. A higher F-score from 0 to 1 indicates better performance, making it a valuable metric for evaluating classification models [55]. Equation (2) represents the F-score.  
\[\text{Accuracy }=\frac{\text{TP}+\text{TN}}{\text{TP }+\text{TN }+\text{ FP }+\text{ FN}} \tag{1}\]  
\[\text{F-Score }=\frac{2\times\text{ Recall }\times\text{ Precision}}{\text{Recall }+\text{ Precision}} \tag{2}\]  
The use of a 5-fold cross-validation approach ensured a thorough and reliable assessment. This technique split the dataset into five parts, each taking turns as the validation set while the model was trained on the other four parts. After five rounds, where each part served as the validation set once, we averaged the results from these rounds to comprehensively evaluate the model's performance across different dataset segments. By employing this systematic approach, we effectively improved the reliability of our findings and reached robust conclusions about the efficacy of the CNN architectures in detecting various features in pedestrian crash diagrams. This method enabled us to make robust conclusions about the effectiveness of these CNN architectures, especially in the context of pedestrian safety research. These insights could lead to more efficient safety measures.
## 3 Modeling Approach  
### DEMO Geometry Model with the Irradiation Unit  
The DEMO baseline 2017 [40] is the basis for the MCNP model used here. The CAD geometry model of the DEMO tokamak with the irradiation port suitable for the neutronic calculations provides space for the arrangement of all tokamak elements that could potentially have an effect on particle transport calculations and, therefore, it is a prerequisite for production of the high-accuracy nuclear responses that are further used for the activation analyses. The newly created DEMO CAD model was elaborated to exclude,as far as possible, small geometry details that could lead to an excessively complicated MCNP input deck and, as a result, they could potentially decrease the credibility of the converted models and very likely could, at the end, cause a failure in the particle transport calculations. To avoid potential problems in the final MCNP geometry model, the CAD layout includes those simplifications that do not contribute to the increase of the results' uncertainty but provide an adequate similarity with the original design. The conversion of the CAD model into the MCNP one was conducted with SuperMC code [41]. To simulate a full DEMO reactor, tokamak periodic boundary surfaces were assigned in the model. A verification and a validation of the newly developed model were performed through MCNP cell volume calculations. In spite of significant efforts, the final model contains a few tiny geometry errors, resulting in less than 5 lost particles per 10 million source neutrons. Such level of the geometry model credibility is considered sufficient to provide high-accuracy nuclear responses.  
The MCNP model with the irradiation port integrated in the DEMO baseline model was developed to have a modular structure complying with a strict hierarchy for different geometry parts. The latest frozen layout of the Helium Cooled Pebble Bed (HCPB) breeder blanket [42; 43] was used to fill the blanket space in the tokamak and to simulate a realistic environment for the irradiation port. The fully heterogeneous, well-detailed representation of the HCPB blankets with breeder units was manually arranged in the inboard and outboard blanket space of the DEMO tokamak, making use of the MCNP platform. The MCNP geometry model of the HCPB DEMO is a 11.25\({}^{\circ}\) toroidal sector containing inboard breeder (IB), outboard lateral (OBL) and a half of the central outboard (OBC) blankets (Figure 1).
## 3 Results and Discussion
### Environmental Properties  
Results of chemical analyses and leaching tests carried out on samples of FTB mixtures obtained by crushing previously prepared specimens are reported in Tables 9 and 10, respectively. As in the case of tests performed on sludge, experimental results are compared to thresholds indicated in current Italian regulations.  
The chemical analysis data listed in Table 9 revealed that all considered FTB mixtures exhibited concentrations of heavy metals that were below threshold limits indicated for use in industrial and commercial contexts. Less encouraging results were obtained from leaching tests (Table 10), which highlighted the existence of Cr\({}_{\text{tot}}\) concentrations above the maximum allowable limit. Quite surprisingly, recorded values were higher than those measured on the corresponding sludge samples (see Table 5), thus disproving the concept of heavy metal stabilization that has been referred to by several researchers who have described the immobilization effect produced by cement paste [59; 60]. However, such an outcome may also be explained by considering the possible presence of chromium in cement, as suggested by various studies published in the literature [61; 62; 63; 64; 65]. In any case, this specific outcome of the investigation deserves to be addressed in more detail in future investigations before any full-scale application can take place.
# Optimizing Replaced Nozzle Diameter of Abrasive Blasting Systems Using Experiment Technique Design
## 5 Conclusions  
In this research work, a computer program was established to solve the objective function of optimizing the cost of blasting systems. The optimum initial nozzle diameter was constructed based on the objective function. The influences of the main design parameters (the input data) on the optimum initial nozzle diameter (\(d_{op}\)) of the blasting system have been investigated. Minitab(r)19 was utilized for running the design of experiments. Based on the analyzed data, some conclusions should be made as follows.  
* The initial nozzle diameter (\(d_{N0}\)) has the strongest impact on the optimum initial nozzle diameter (\(d_{op}\)), while the remaining input parameters have little effects.
* The interactions of some input parameters have important impacts on the optimum initial nozzle diameter.
* The factors of normal plots and versus observation plots show that the proposed regression model is mostly insistent to the experimental data, which are randomly distributed.
* The fitness between the data and the proposed model is reliable, which serves as an important base for deeper research. This can be used to calculate the optimum initial nozzle diameter of blasting systems to minimize the cleaning cost.  
[PERSON] and [PERSON] conducted the cost analysis; [PERSON] and [PERSON] designed and conducting simulation experiments; [PERSON] wrote this manuscript with support from [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]; all authors discussed the manuscript and provided critical feedback. [PERSON] supervised this work and revised the paper. All authors have read and agreed to the published version of the manuscript.  
The work described in this paper was funded by Thai Nguyen University of Technology for the scientific project.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Traditional Blasting** & **Optimum Blasting** \\ \hline Replaced nozzle diameter \(d_{N}\) (mm) & 8.09 & 7.0 \\ \hline Nozzle lifetime \(L_{n}\) (h) & 530 & 166.67 \\ \hline Average cleaning rate \(v_{cl,a}\) (m\({}^{2}\)/h) & 33.07 & 38.95 \\ \hline Total cleaning time (h) & 30.24 & 25.67 \\ \hline Cleaning cost (USD/m\({}^{2}\)) & 3.59 & 3.08 \\ \hline Total cleaning cost (USD) & 3590 & 3080 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Example calculated results.
## 2 Methodology and Materials
### Evaluation of Metrics  
The performance of the models is evaluated using 3 different evaluation metrics. These metrics are mean absolute error (MAE), Root Mean Square Error (RMSE) and R-square (R\({}^{2}\)) (see Equations (7)-(9)) [78].  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{7}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{8}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}- \overline{y})^{2}} \tag{9}\]  
Here \(y_{i}\), \(\hat{y}_{i}\), \(\overline{y}\) and \(n\) represent the actual value, the predicted value, mean value, and the number of observations, respectively.
## 2 Review of Related Literature and Applications
### Review of Approaches for Regulating Smart Device Usage
#### 2.2.1 Traditional Parental Control Methods  
Traditional parental control methods involve establishing explicit boundaries on smart device use, typically through screen-time limits, content filters, and device-lockingfeatures [23]. These strategies are generally implemented using built-in operating system tools or third-party applications.  
While such methods can effectively reduce screen time in the short term, they also present several limitations [24]. In particular, overly restrictive controls may provoke psychological reactance, thereby intensifying children's curiosity about forbidden content [25; 26]. Furthermore, research indicates that intrusive parental monitoring can contribute to emotional distress, including heightened anxiety, depressive symptoms, and impaired emotional regulation [27]. Another practical challenge lies in the ongoing effort required to maintain such control. Specifically, these methods typically demand continuous parental oversight, which may not be feasible for working parents or families managing multiple children. Collectively, these drawbacks have spurred growing interest in more collaborative, autonomy-supportive alternatives.