esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Controller Parameters Selection  
Combining (28) with (33) and solving while considering the worst-case grid frequency \(\omega_{G}=\alpha_{\min}\cdot 100\pi\) yields  
\[\omega_{n}=\sqrt{8}\xi_{n}\alpha_{\min}100\pi\sqrt{1+\frac{\left(THD^{*} \right)^{2}}{\xi_{n}^{4}f^{2}(\alpha_{\min})}-1}. \tag{48}\]  
with  
\[f(\alpha_{\min})=\frac{1}{\sqrt{1+4\xi_{f}^{2}\frac{\alpha_{\min}^{2}}{\left( 1-\alpha_{\min}^{2}\right)^{2}}}}\frac{1}{\sqrt{1+4\xi_{f}^{2}\frac{\left( \frac{5}{8}\alpha_{\min}\right)^{2}}{\left(1-\left(\frac{5}{8}\alpha_{\min} \right)^{2}\right)^{2}}}}. \tag{49}\]  
As mentioned at the end of the preceding section, \(\omega_{n}\) should be maximized in order to optimize the transient response (i.e., minimize the DC link voltage deviation). According to (43), this implies minimizing the value of \(f(\alpha)\) since \(\xi_{n}\) is dictated solely by \(PM_{V}^{*}+\beta_{\max}\). However, this conclusion contradicts the one drawn in Section 3, where the opposite was required in order to improve the steady-state performance. Consequently, the trade-off between steady-state and dynamic performances remains and the proposed methodology aims to improve it but not eliminate it. According to (36) and (48), the crossover frequency is then given by  
\[\omega_{CV}=\theta_{n}\sqrt{8}\xi_{n}\alpha_{\min}100\pi\sqrt{\sqrt{1+\frac{\left( THD^{*}\right)^{2}}{\xi_{n}^{4}f^{2}\left(\alpha_{\min}\right)}}-1}. \tag{50}\]  
Substituting (50) into (41) and rearranging, there is  
\[\xi_{f}=\] \[\frac{1}{2}\lambda\left(\frac{1}{\theta_{n}\sqrt{2}\xi_{n}\alpha_{ \min}\sqrt{1+\frac{\left(THD^{*}\right)^{2}}{\xi_{n}^{4}f^{2}\left(\alpha_{ \min}\right)}-1}}-\theta_{n}\sqrt{2}\xi_{n}\alpha_{\min}\sqrt{\sqrt{1+\frac{ \left(THD^{*}\right)^{2}}{\xi_{n}^{4}f^{2}\left(\alpha_{\min}\right)}}-1}\right). \tag{51}\]  
Since \(\theta_{n}\) and \(\xi_{n}\) are known from (36) and (38), solving (51) with (49) yields the value of \(\xi_{f}\). Then, \(\omega_{n}\) is obtained from (48) and the coefficients of the PI controller term in (17) are obtained as (cf. (33))  
\[K=\frac{2C_{DC}V_{DC}^{*}}{V_{M}}\omega_{n}^{2},\quad\tau=2\frac{\xi_{n}}{ \omega_{n}}. \tag{52}\]  
The proposed process of controller coefficient tuning is then summarized as follows:  
1. Initialize \(\beta_{\max}\) and \(PM_{V}^{*}\).
2. Obtain \(\theta_{n}\) using (36), \(\xi_{n}\) using (38) and \(\lambda\) using (43).
3. Initialize \(THD^{*}\) and \(\alpha_{min}\).
4. Obtain \(\xi_{f}\) using (51) and \(\omega_{n}\) using (48).
5. Initialize \(V_{M}\), \(C_{DC}\) and \(V_{DC}^{*}\).
6. Determine \(K\) and \(\tau\) using (52).
7. Verify the design by Bode diagram.
8. Release requirement/s and iterate if necessary.
# Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems
## 1 Introduction
### Objective and Organization of This Article  
The previous work considered was \"A Low Complexity Channel Estimation using superimposed pilots and Detection for Massive MIMO Using SC-FDE\". On the other hand, this article provides improved low-complexity LS channel estimation for LIS systems with regard to traditional and random pilot-based methods [19].  
This article is structured as follows: Section 2 provides an in-depth overview of the system model receiver techniques and the LS channel estimation process. Section 3 outlines the proposed enhancements, including the pilot design, pilot power optimization, and antenna selection. Section 4 details the simulation setup, specifying the parameters and performance metrics used for evaluation. Section 5 presents the simulation results and  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Previous Methods** & **Our Improvement** \\ \hline \multirow{4}{*}{**Adaptive pilot symbol allocation**} & Previous works rely on fixed pilot traditional approaches use a fixed number of pilot symbols, leading to inefficient estimation in low-SNR scenarios. & We propose adaptive pilot clustering, where the system selectively increases pilot symbols only when necessary, reducing unnecessary overhead. \\ \cline{2-3}  & Conventional LS estimation performs single-pass estimation, often leaving residual errors. & Our iterative refinement process progressively updates channel estimates over multiple iterations, leading to significantly improved accuracy, particularly in noisy environments. We introduce intelligent antenna selection, where only the most effective antennas are used, reducing complexity without sacrificing accuracy. \\ \cline{2-3}  & LDPC-assisted LS estimation & LDPC codes have been used for error correction but were not integrated with LS estimation in LIS systems. & We develop a joint LS-LDPC decoding framework, which significantly enhances noise resilience and system stability in high-interference conditions. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._discusses their implications for system performance. Finally, the article's conclusions and some future study offerings are discussed in Section 6.
## 6 Conclusions  
The main objective of this study was to propose and introduce a GMDH predictive model as a powerful, capable, and practical one to estimate iron ore price. For comparison purposes, several other techniques i.e., SVR, ANN, ARIMA, and CART were applied and developed to predict iron ore price. For this aim, oil price, steel price, iron ore production, steel production, gold price, inflation rate, exchange rate, interest rate, dowjones stock price, US GDP, aluminum price, China GDP were selected as input variables. A series of analyses with the most effective parameters on the GMDH were conducted in order to see their effects on the system. Then, the best GMDH model was selected among them. Considering several datasets which were not used in the modelling process, the developed models were validated. It was found that the GMDH model which was applied as the first time in the area of this study, could offer more accurate prediction results in comparison with the other implemented models. It was accomplished through the use of some performance prediction such as RMSE and VAF for both model development and model validation. The results of MAPE for testing datasets of GMDH, SVR, ANN, CART, and ARIMA were obtained as 0.039, 0.099, 0.132, 0.169, and 1.001, respectively which show higher prediction capacity of the GMDH technique.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Performance Prediction**} \\ \cline{2-5}  & **VAF (\%)** & **E\({}_{\mathbf{a}}\)** & **RMSE** & **MAPE** \\ \hline ARIMA (0-1-1) & 23.87 & 63.26 & 68.84 & 1.001 \\ SVR & 90.81 & 7.47 & 8.57 & 0.099 \\ ANN & 80.95 & 9.99 & 12.38 & 0.132 \\ CART & 55.02 & 13.27 & 17.36 & 0.169 \\ GMDH & 97.89 & 2.66 & 3.55 & 0.039 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The obtained results of performance prediction for testing datasets.  
Figure 14: Comparison between the measured and the predicted by group method of data handling (GMDH) model for test stage.  
**Author Contributions:** Formal analysis, M.R.M. and D.J.A.; conceptualization, D.L. and M.M; supervision, M.M. and D.L.; writing--Review and editing, D.J.A. and A.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Outstanding Youth Science Foundations of Hunan Province of China (No. 2019 JJ20028).  
**Conflicts of Interest:** The authors declare no conflict of interest.
### Research Justification and Objectives  
The impact of HSR in Australia is undisputed, and yet it remains a pipeline dream. The reasons for stagnation are believed to be a lack of economic benefit. However, the Australian government has committed to other large-scale projects without meeting all theoretical feasibility requirements, indicating that HSR projects encounter unique, undocumented barriers, particularly, in the post-feasibility, pre-implementation phase. Suburban Rail Loop, for example, has a Benefit Cost Ratio (BCR) of 0.6-0.7 according to The Parliamentary Budget Office's latest assessment [13], yet it was approved and is currently under delivery.  
Hence, the specific, prioritised reasons for stagnation in the Australian HSR context are still unknown and under-researched.  
This study is motivated by a theoretical and empirical gap in understanding why HSR, despite strong global precedents and technical feasibility demonstrated in various Australian studies, repeatedly fails to transition from concept to delivery. The research proposes that a more structured, multi-dimensional approach is needed to identify, analyse, and prioritise these barriers. Accordingly, this research aims to uncover the key factors hindering HSR progression beyond conceptualisation and to propose actionable recommendations to overcome these obstacles. The study's novelty lies both in its focus on the Sydney-Newcastle corridor--a priority identified by Infrastructure Australia--and in its methodological contribution. By employing a mixed-methods design that integrates literature review, expert interviews, stakeholder surveys, and the Analytic Hierarchy Process (AHP), the research produces a systematically prioritised framework of barriers. This provides a more rigorous and decision-ready analysis than what is typically achieved through qualitative or non-integrated approaches, offering actionable insights for policymakers, infrastructure agencies, and the High-Speed Rail Authority (HSRA). Specifically, the study seeks to achieve the following aims:  
1. Identify barriers that specifically prevent the realisation of HSR in Australia;
2. Document potential challenges unique to implementing HSR in the Sydney-to-Newcastle corridor;
3. Develop industry-ready recommendations to overcome identified barriers and facilitate successful HSR implementation.
## 3 Results
### The Comparison of the \(\Delta p\) Model Developed by RSM and Ergun Model
#### 3.4.1 The Comparison of the \(\Delta p\) in the RSM Experiments  
Based on the ANOVA analysis results shown in Table 5, the second-order polynomial expression of the \(\Delta p\) model developed by the RSM (\(\Delta p_{\text{RSM}}\)) are expressed as following Equation (8):  
\[\begin{array}{l}\Delta p_{\text{RSM}}=174.60194-2.20717 Mc-981.35993v_{air}-4 03.3567L+27.32537 Mc\cdot v_{air}\\ +15.95439 Mc\cdot L+1281.17778v_{air}\cdot L-0.17375 Mc^{2}+645.768v_{air}^{2}-22.25679L^{2}\end{array} \tag{8}\]  
The 17 sets of independent variables tabulated in Table 5 were substituted into the above \(\Delta p_{\text{RSM}}\) model and the predicted \(\Delta p_{\text{RSM}}\) values were obtained. On the other hand, the 17 sets of independent variables were also substituted into the \(\Delta p_{\text{Eur}}\) model shown in Equation (2) by transmitting the \(Mc\) into the corresponding \(\varepsilon\) (just as tabulated in Figure 2), and the predicted \(\Delta p_{\text{Eur}}\) values were obtained and compared with the corresponding predicted \(\Delta p_{\text{RSM}}\). The comparison between the \(\Delta p_{\text{RSM}}\) and the \(\Delta p_{\text{Eur}}\) were evaluated by the validation metrics (Equations (2)-(5)), and the statistical results are tabulated in Table 6.  
As can be seen from Table 6, the index \(R^{2}\) (0.9948) of the RSM model is higher than that of the Ergun model (0.9868), while the indexes \(MAE\), \(MSE\), and \(RMSE\) of the RSM model are, respectively, lower than the corresponding index of the Ergun model, which indicates that the pressure drop model developed by the RSM is better fitted than the Ergun model.
## 5 Measurement Equipment and Testing Setup
### Software for Dynamic Data Processing  
For dynamic data processing, SWARM [24] and WINQWAKE [25] software packages were used. SWARM software allows for seismic wave analysis and monitoring. It was used to analyze seismic waves in real time. The software was used to view the recorded signals, perform the Fourier transform without noise filtering, create the frequency spectrum, etc. The WINQWAKE package was used to analyze files that were recorded and processed by SWARM. It was used to perform the data Fourier transform (Fast Fourier transform (FFT) or Discrete Fourier transform (DFT)) using digital filters of FFT low-pass, high-pass, notch frequencies, band-pass, etc.
# Thermal Investigations on Carbon Nanotubes by Spectroscopic Techniques
## 2 Materials and Methods
### Wavelet Cross-Correlation  
To evaluate the similarity that exists among the collected spectra at different temperatures, \(r_{XWT}\) was measured. Such a method has found many applications in different fields, such as meteorology, neutron spectroscopy, financial science, and also in geological and geophysical sciences [73; 74; 75; 76]. In general, Wavelet Transform (\(WT\)) breaks down a signal \(y(t)\) as sum of the scaled (through the positive parameter \(s\)) and shifted (through the parameter \(u\)) mother wavelets \(\Psi_{s,u}\) by the following Equation (3):  
\[WT=\int W_{1}(s,u)\Psi_{s,u}^{*}du \tag{3}\]  
and, from this latter, one can express the Wavelet Spectrum (\(WS\)) by means of the following Equation (4):  
\[WS=\frac{1}{2}\int_{-\infty}^{+\infty}\left|W(s,u)\right|^{2}\!du \tag{4}\]  
Then, starting from these two equations, we can define the Wavelet Cross-Correlation (\(r_{XWT}\)) as follows by means of Equation (5):  
\[r_{XWT}=\left(\frac{\int WT_{1}(s,u)WT_{2}^{*}(s,u)du}{\left(WS_{1}(s)WS_{2}(s )\right)^{1/2}}\right) \tag{5}\]  
where \(WT_{1}(s,u)\) and \(WT_{2}^{*}(s,u)\) represent, respectively, the Wavelet Transform of the first and second spectrum and \({}^{*}\) is the complex conjugate, while \(WS_{1}\) and \(WS_{2}\) are the first and the second wavelet spectrum. The value of \(r_{XWT}\) is between \(-1\) and \(+1\) which signifies that there is either a negative or a positive correlation [77; 78]. Also in this case, from the evaluation of \(r_{XWT}\) for each temperature, there results sigmoidal behaviour that can be analyzed with the model fit of Equation (2).
## 2 Methods
### Accuracy Assessment  
We assessed the accuracy of our simulations by comparing the simulated values to a Landsat observation of the same data that was set aside and not used in the simulation. A linear regression model (simulated _versus_ observed reflectance) was used to assess the fusion models of ESTARFM and mESTARFM. RMSE (root mean squared error) is used to measure the differences between simulated reflectance values by the image fusion models (ESTARFM, mESTARFM) and the observed reflectance. MAE (mean absolute error) is a quantity used to measure how close simulated reflectance values are to the observed reflectance. R\({}^{2}\) is used to measure the fitness of the linear regression between simulated and observed reflectance. A two-side _t_-test (_p_-value) of simulated and observed reflectance values is used to determine whether there is a statistically significant difference.
## 3 Methodology
### Airship PV Array Reconfiguration Technique Using Reverse Combination Method  
For conventional TCT or SP connections, it is not possible to simultaneously achieve uniform radial and axial irradiance. Based on the analysis of the irradiance distribution characteristic of the PV array in Section 3.2, the irradiance of the PV array often exhibits a diagonal distribution pattern. By splitting the rows with the maximum and minimum irradiance into two interleaved parts and recombining them, the irradiance of these two rows can be made uniform. Subsequently, the same combination is applied to the rows with the second largest and second smallest irradiance, and so on, until all rows have been  
Figure 5: Static terrestrial PV Array reconfiguration.  
recombined, achieving uniform irradiance distributions in both rows and columns of the PV array. This is the \"reverse combination\" (RC) configuration proposed for stratospheric airship PV arrays. A 6 \(\times\) 6 TCT PV array is chosen as the object of reconfiguration. Each module in the array is established by the index 'ij'. For illustration, '34' represents the module in the third row and fourth column. The given 6 \(\times\) 6 square array undergoes static reconfiguration through four key steps by RC configuration.  
Step 1: First, the positions of PV modules in the odd-numbered columns remain unchanged. The positive and negative polarities of the modules in the even-numbered columns are rotated to be opposite to those in the odd-numbered columns, while keeping the layout of the components unchanged, as shown in Figure 5(a). The entire array is then divided into two groups: the positive order group and the reverse order group. Each group is a 6 \(\times\) 3 array comprising a total of 18 modules and arranged with column spacing.  
Step 2: The positive-order group and the reverse-order group are interconnected as TCT configurations, as presented in Figure 5(b). These two TCT configurations are completely opposite. For the positive-order group, the first row corresponds to module 11, and its positive pole is connected to positive busbar of the array. Module 61's negative terminal is connected to the negative busbar. Conversely, for the reverse group, module 62's positive terminal is connected to the positive busbar, and component 12's negative terminal is connected to the negative busbar.  
Step 3: Connect the same row buses of the positive-order group and the reverse-order group separately. For example, the positive busbar of module 11 is connected to the positive busbar of module 62, the positive busbar corresponding to module 21 is connected to the positive busbar corresponding to module 52, and so on, until all the busbars of the two groups are connected. The final interconnection state is shown in Figure 6c. The row consisting of modules '11', '62', '13', '64', '15', and '66' is considered the first row of the configurated PV array, with the corresponding positive busbar being the PV array's positive busbar. Modules '61', '12', '63', '14', '65', and '16' are the last rows of the PV array, and the corresponding negative bus is the negative bus of the array.
# Analyzing Impact and Systemwide Effects of the SlowROS Attack in an Industrial Automation Scenario
## 1 Introduction  
Nowadays, the Robot Operating System (ROS) enjoys widespread popularity in industrial operational technology environments due to its open-source nature, its ease of use, and the support of leading stakeholders [1]. Being used for different applications, such as edge robotics [2] or Industry 4.0 and Industrial Internet of Things (IIoT, in short) [3, 4], today, ROS represents a leading protocol in the industrial sector [5]. In virtue of this, investigating its potential security weaknesses becomes essential to protect it from external threats that might disrupt manufacturing plants and lead to possible economic loss and equipment damage, not to mention the possible harm to human safety.  
In the cybersecurity panorama, denial of service (DoS) threats are perpetrated to make a network service unreachable by its intended users. Considering such threats, conventional flooding-based approaches require a huge amount of attack resources, whereas other menaces, such as slow DoS, use limited attack bandwidth. slow DoS threats are therefore particularly relevant from this point of view because they can exhaust some kind of resource of the system they target while effectively deceiving detection mechanisms. In fact, proper protection against last-generation distributed DoS threats, such as slow DoS threats [6], is still an open research challenge.  
This work investigates the susceptibility of an ROS system deployed in an industrial context to a slow DoS threat and evaluates its adverse effects on the overall system behavior. To this purpose, we evaluate and validate SlowROS, an innovative slow DoS threat that targets the ROS Master, a mandatory component that is invariably present in all ROSsystems. To the best of our knowledge, the literature on industrial security does not cover slow DoS threats targeting the ROS protocol. In addition, to date, there are no known ROS vulnerabilities related to the exploitation of slow DoS-oriented approaches.  
The contributions of this paper are the following: _(i)_ address the lack of extensive analysis of the impact of slow DoS-like attacks on industrial protocols; _(ii)_ evaluate the efficacy of the SlowROS threat by executing proper tests on a real network scenario; _(iii)_ attempt to identify the optimal attack setup in terms of attack timeout: by doing this, in Section 6.2 we will find that the ROS Master does not implement any connection-closure timeout; and _(iv)_ discuss possible protection approaches able to counter SlowROS.  
In detail, after providing an overview of the state of the art and related work in Section 2, the paper continues with some basic information about ROS in Section 3. The slow DoS attack to ROS is discussed in Section 4, while the test bed used to demonstrate it is described in Section 5. Section 6 presents and comments on the experimental results while Section 7 outlines possible protection approaches to counter slow DoS attacks. Finally, Section 8 concludes the paper.
## 4 Results and Analysis
### Validation of Classification Model  
The same algorithms with the same parameters are tested on a validation dataset to determine if the results for all ML methods are not over-fitted to a particular dataset. The sample of the validation dataset is shown in Table 5.  
The comparison graph of all algorithms implemented for the classification of validation dataset is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Flow** & **Total Fwd** & \begin{tabular}{c} **Total** \\ **Backward** \\ **(Bytes)** \\ \end{tabular} & \begin{tabular}{c} **Fwd Packet** \\ **Packets** \\ **(Bytes)** \\ \end{tabular} & \begin{tabular}{c} **Total Length** \\ **Length Mean** \\ **(Bytes)** \\ \end{tabular} & \
\begin{tabular}{c} **Total Length** \\ **of Fwd** \\ **Packets** \\ **(Bytes)** \\ \end{tabular} &
\begin{tabular}{c} **L7 Protocol** \\ **(Bytes)** \\ \end{tabular} \\ \hline
45,523 & 22 & 55 & 6.000 & 132 & 131 \\
1 & 2 & 0 & 6.000 & 12 & 131 \\
217 & 1 & 3 & 0.000 & 0 & 7 \\
78,068 & 5 & 0 & 215.200 & 1076 & 131 \\
105,069 & 136 & 0 & 2305.544118 & 313,554 & 131 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sample of validation dataset.  
Figure 7: Evaluation of algorithmic output for validation dataset.  
The comparison graph for each algorithm for the validation dataset also reveals results that are nearly identical to those of the generated dataset. Here, logistic regression produces the best results. However, the decision tree (CART) method also yields results that are almost identical; hence, based on both charts, CART is regarded as the best algorithm. The OPTICS algorithm's output is not included in the validation dataset because it is unable to produce it even after running for 60 min and eventually experiencing an execution timeout.