esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The primary goal of this study was to develop and evaluate machine learning models for estimating soiling losses in photovoltaic solar panels and to assess their ability to generalize across different locations. A series of machine learning models were developed and tested, including LightGBM, artificial neural networks (ANNs), and others, to determine which would provide the most accurate soiling loss estimates.  
Initially, local models were trained using data from a specific site. Among these models, LightGBM demonstrated the best performance, with an RMSE of 0.68% and a normalized RMSE (nRMSE) of 22.46%. This model also showed strong predictive power with an \(R^{2}\) value of 0.86 when comparing the observed and predicted soiling losses. These results suggest that LightGBM is effective for local estimations, especially when trained on data that reflect the particular environmental conditions of the region where the model was developed.  
However, when these models were tasked with extrapolating soiling losses to a different location from the one they were originally trained on, their performance worsened, particularly in predicting soiling losses below 1%, a range that was under-represented in the original training datasets. Extrapolation, a common challenge in machine learning models, highlighted the need for a model that could generalise well across regions with varying environmental conditions. Among the models tested, the ANN stood out as the most flexible, yielding the lowest RMSE and highest \(R^{2}\) values during extrapolation. This flexibility made it the model of choice for constructing a Global Model that incorporated data from two different datasets: CIESOL and CIEMAT.  
The Global Model was developed by merging data from both datasets and applying stratification to ensure robust performance across the full range of soiling losses. It significantly outperformed the extrapolated models, which were trained on a single location and tested on another. The Global Model achieved an RMSE of 1.02%, nRMSE of 40.50%, and  
Figure 28: Measured vs. estimated values of SL for the ANN Global Model.  
of 0.62, reflecting a significant improvement over the extrapolated models. In addition to outperforming the extrapolated models, the Global Model also demonstrated strong results when compared to well-established physical models evaluated in cross-location scenarios. For instance, the physical model developed by [PERSON] and [PERSON] [19], tested on Cyprus by [PERSON] et al. [26] yielded a normalised MBE of \(-5.19\) and a normalised RMSE of \(55.57\), along with \(R^{2}\) of 0.55, compared to the 3.41, 40.40 and 0.63 of nMBE, nRMSE and \(R^{2}\), respectively, achieved by the Global Model.
# A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
## 3 Taxonomy of Cyber-Physical Attacks
### Physical System Layer
#### 3.1.3 Combined Attack  
A combined attack is defined as a simultaneous modification of a sensor measurement and the control input signal [63; 64], which is represented as follows:  
\[\begin{split}\dot{x}(t)&=Ax(t)+B(u(t)+u^{a}(t))\\ \dot{y}(t)&=Cx(t)+y^{a}(t).\end{split} \tag{5}\]  
Figure 3: Cyber-physical attacks in physical system layer.  
Unlike a sensor-only attack and a controller-only attack, the combined attack requires eavesdropping on both channels over which the sensor measurement \(y(t)\) and control input signal \(u(t)\) are transmitted; therefore, it is difficult to successfully execute a combined attack. In other words, most combined attacks are more sophisticated than sensor-only and controller-only attacks, so detection strategies for the combined attack require more computational resources in order to detect and handle combined attacks.  
The covert attack is one of the most typical combined attacks, where the attacker has perfect knowledge of the system dynamics and uses this to generate a sensor attack signal \(y^{a}(t)\) and control input signal \(u^{a}(t)\)[64]. On the sensor measurement transmission channel, the attacker seizes the legitimate sensor measurement \(y(t)\) from the physical system and deceives the computing system by transmitting the generated sensor measurement \(\tilde{y}\) with perfect system information [65]. Over the control input signal transmission channel, the physical system is controlled as the attacker intends by a malicious control input signal, \(\tilde{u}=(u(t)+u^{a}(t)\), where that malicious control input signal is generated with perfect system information and with the legitimate sensor measurement signal \(y(t)\) from the sensor measurement transmission channel.
# Bol Looseness Detection Based on Piezoelectric Impedance Frequency Shift
###### Abstract  
In this paper, a piezoelectric impedance frequency shift method is developed to estimate the bolt preload for the detection of bolt looseness in engineering structures. An experimental device that allows the precision control of the axial preload force on a bolt is designed and fabricated. A universal testing machine is used to preload accurately on the bolt in the experiments. Under different bolt preload conditions, the impedance analyzer measures the admittance (inverse of the impedance) signal of the PZT (Lead ZirconateTitanate) patches which are bonded on the bolt head. Firstly, a wide frequency band is swept to find a sensitive frequency band of the piezoelectric admittance with the imaginary part. Then in the sensitive frequency band, a specified peak frequency of the admittance signature is chosen to investigate the frequency shift with different bolt preloads. The relationship between the specified frequency shift and the bolt preload is established. The experimental results show that the specified peak frequency decreases as the bolt preload increases for both M16 and M12 bolts, and the frequency shift has a linear relationship with the preload on the bolt. The frequencies of the real and imaginary parts of the admittance signature have the same results. Therefore, the bolt preload can be determined by measuring the specified frequency shift and this method has a good application prospect.  
bl 2016; 6, 298; doi:10.3390/appf6100298
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 8 Conclusions  
This paper presents the first systematic review of voxel-based navigation technology, covering key technologies, specific applications, development potential, and associated challenges. We have detailed the latest advancements in voxel-based navigation for both indoor and outdoor environments and explored the significant advantages of voxel models. These models excel in representing complex environments, handling dynamic scenarios, and supporting path planning. For instance, voxel models offer precise environmental representation and are dynamically updated to accommodate real-time changes, significantly enhancing the flexibility and reliability of navigation systems. Additionally, voxel models possess strong emergency-simulation capabilities, effectively supporting decision making in complex scenarios.  
However, despite the extensive application of voxel modeling in navigation studies, several significant challenges still need to be solved, such as voxel granularity, path optimization, and model integration. Addressing these issues will require further advancements in algorithms and model improvements. Furthermore, this paper primarily focuses on voxel-based navigation models and does not delve into other related aspects, such as route communication, user interface, and localization. Our review aims to provide not only deep insights into voxel-based navigation technology but also valuable guidance for researchers in other fields considering the application of voxel models in their studies.  
[PERSON]: conceptualization, methodology, data collection, and theoretical analysis; [PERSON]: formal analysis, writing--original draft; [PERSON], [PERSON], [PERSON] and [PERSON]: data collection and processing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Fundamental Research Funds for the Central Universities (NO.2023 ZYGXZR056), the National Science Foundation of China (NSFC) (42171401 and 41771433), and the Natural Science Foundation of Gansu Province (20)R10 RA247). Additionally, this research was conducted as part of the project \"Integrating IndoorGML with Outdoors: Automatic Routing Graph Generation for Indoor-Outdoor Transitional Space for Seamless Navigation\", funded by the ISPRS council through the ISPRS Scientific Initiatives 2023.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** This research was conducted as part of the project \"Integrating IndoorGML with Outdoors: Automatic Routing Graph Generation for Indoor-Outdoor Transitional Space for Seamless Navigation\". We also thank [PERSON] for his constructive comments.
## 5 Experimental Results
### Window Size Manipulation + Attention Mechanism + FGSM
This may indicate that the model's predictions are less accurate when operating over a more extensive data window. In particular, a drop in sensitivity is evident between the (80-100) and (100-120) ranges. The accuracy value is usually high and fluctuates for various window sizes. For example, the accuracy is 68.9% and 73.8% for window sizes (20-40) and (80-100). However, these values are low for window sizes (60-80) and (120-140). The recall value generally increases as the window size increases. This indicates that the model's ability to correct errors generally increases when working on a larger data window. The F1 score generally decreases as the window size increases. This indicates that the balance between precision and recall often deteriorates as the window size increases. The mean absolute error (MAE), median absolute error, and maximum absolute error generally decrease as the window size increases. This indicates that the model's predictions are often closer to the true values when operating over a more extensive data window. The parameters of the proposed attention mechanism + MAI model are as follows: epsilon = 0.001, alpha = 0.01, num_steps = 3. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Window Size** & **Precision** & **Accuracy** & **Recall** & **F1 Score** & **MAE** & **MAD** & **Maximum Absolute Error** \\ \hline
20–40 & 0.079 & 0.001 & 0.050 & 0.058 & 0.6149 & 0.1851 & 945.1836 \\
40–60 & 0.0 & 0.0 & 0.0 & 0 & 569.18 & 232.75 & 1807.27 \\
60–80 & 0.014 & 0.273 & 0.525 & 0.022 & 1.853 & 0.2005 & 2550.765 \\
80–100 & 0.014 & 0.272 & 0.524 & 0.022 & 1.7547 & 0.2086 & 2583.856 \\
100–120 & 0.000025 & 0.000078 & 0.0015 & 0.00051 & 2.277 & 0.0985 & 3264.548 \\
120–140 & 0.0 & 0.0 & 0.0 & 0 & 1251 & 691.63 & 3672.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Hybrid model performance based on JSMA. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Window Size** & **Precision** & **Accuracy** & **Recall** & **F1 Score** & **MAE** & **MAD** & **Maximum Absolute Error** \\ \hline
20–40 & 0.1856 & 0.679 & 0.172 & 0.136 & 0.0160 & 0.0111 & 21.6042 \\ \hline
40–60 & 0.2142 & 0.739 & 0.235 & 0.150 & 0.0130 & 0.0107 & 21.5030 \\
60–80 & 0.2098 & 0.288 & 0.209 & 0.175 & 0.0120 & 0.0101 & 21.4362 \\
80–100 & 0.1430 & 0.738 & 0.183 & 0.125 & 0.0115 & 0.0084 & 21.3297 \\
100–120 & 0.1378 & 0.404 & 0.1957 & 0.112 & 0.0111 & 0.0090 & 21.6640 \\ \hline
120–140 & 0.1593 & 0.251 & 0.2131 & 0.132 & 0.0091 & 0.0063 & 21.5921 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Hybrid model performance based on MIA method.
# Design and Analysis of High-Capacity MIMO System in Line-of-Sight Communication
## 3 Analysis of Antenna Configuration  
In short-distance transmission, signals are transmitted in LoS. The \(n\times n\) MIMO system is shown in Figure 2.  
In Figure 2, the red graph indicates the receiving antenna, the green one indicates the transmitting antenna. \(R_{i}\) is the \(i\)-th receiving antenna, \(T_{j}\) is the \(j\)-th transmitting antenna, and \(d_{ij}\) is the sub-channel propagation path.  
The phase in the channel matrix \(H\) is related to the transmission distance and signal wavelength. When the channel matrix is determined by a LoS component and does not vary with time, the phases of the channel can be expressed as:  
\[\varphi_{ij}=2\pi\frac{d_{ij}}{\lambda},1\leq i,j\leq n \tag{19}\]  
where \(d_{ij}\) is the transmission distance and \(\lambda\) is the wavelength.
## 2 Materials and Methods
### Antioxidant Property Assays
#### 2.5.1 DPPH Assay  
The ability of _A. reptans_ extracts to scavenge free radicals was carried out according to our previous studies [25]. A total of 1.5 mL of 2,2-diphenyl-1-picrylhydrazryl 0.1 mM ethanolic solution was mixed with 0.25 mL of the _A. reptans_ water extracts. As a blank sample, 0.25 mL of ethanol (70%) and 1.5 mL of DPPH solution were used. Three samples were prepared for each of the extracts. The change in optical density was measured at room temperature, 20 min after pre-mixing DPPH solution and the extract samples, at a wavelength of 517 nm. The average absorbance value was calculated and the DPPH radical reduction capacity, expressed as an inhibition percentage, was calculated according to Formula (1). The obtained values are presented as an average from three determinations along with the standard deviation.  
\[\%\text{Inhibition}=\left(\frac{\text{A}\text{o}-\text{At}}{\text{A}\text{o }}\right)\times 100\%, \tag{1}\]  
where:  
Ao--average absorbance value for the blank sample  
At--average absorbance value for the test sample
## 2 Models and Methods
### Solar Irradiation Model and Mission Conditions
#### 2.4.1 Calculation Model of Solar Radiation Intensity in Ground Coordinate System  
This paper defines the earth-fixed frame as \(O_{g}X_{g}Y_{g}Z_{g}\), where \(O_{g}X_{g}\), \(O_{g}Y_{g}\), \(O_{g}Z_{g}\) are expressed out the north, west, and vertical upward through the ground, as illustrated in Figure 3. Then in the earth-fixed frame, the solar radiation intensities \(I_{Xg}\), \(I_{Yg}\), and \(I_{Zg}\) (positive and negative represent directions) in each direction are the following formulas:  
\[\left\{\begin{array}{l}I_{Xg}=I_{\rm{SC}}\cdot\xi_{0}\cdot P_{\rm{CO}}\cdot \cos H_{\rm{S}}\cdot\cos A_{S}\\ I_{Yg}=I_{\rm{SC}}\cdot\xi_{0}\cdot P_{\rm{CO}}\cdot\cos H_{\rm{S}}\cdot\sin A_{ S}\\ I_{Zg}=I_{\rm{SC}}\cdot\xi_{0}\cdot P_{\rm{CO}}\cdot\sin H_{\rm{S}}\end{array}\right. \tag{12}\]  
Figure 3: Schematic diagram of earth-fixed frame and aircraft body-fixed frame.  
where \(I_{SC}\) is the solar irradiance constant 1367 W/m\({}^{2}\), \(\xi_{0}\) the eccentricity correction coefficient related to the distance between the sun and the earth, \(P_{CO}\) the atmospheric transparency coefficient, \(H_{S}\) the sun altitude angle, \(A_{S}\) the sun azimuth. Those parameters can be calculated by the formulas in literature [27; 28], which are not be described in detail here.
# Pore Solution Chemistry of Calcium Sulfoaluminate Cement and Its Effects on Steel Passivation
## 2 Materials and Methods
### Pore Solution Analysis  
#### 2.2.1 Extraction of Pore Solution  
Pore water expression (PWE), described by [PERSON] [24] in detail, is the most commonly employed method to acquire the pore solution. However, the disadvantage of this technique is that a special instrument is required to acquire the pore solution, and PWE is not suitable to older or dense samples with a low water-to-binder ratio (w/b) due to the low amount of free water available. An alternative way to analyze the pore solution is the ex situ leaching (ESL) method. [PERSON] [25] and [PERSON] [26] applied this method to study the chemical composition of pore solution. They considered that ESL can provide valuable information about the hydroxide and other ions. [PERSON] [22] also  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Material** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **SO\({}_{3}\)** & **K\({}_{2}\)O** & **Na\({}_{2}\)O** & **Loss** \\ \hline
**CSA** & 9.90 & 20.50 & 2.62 & 43.77 & 3.00 & 13.36 & 0.54 & 0.19 & 5.98 \\
**OPC** & 22.30 & 4.62 & 2.44 & 58.42 & 1.92 & 2.20 & 0.35 & 0.28 & 3.62 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of cementitious materials (wt%).  
recommend ESL as an effective way of studying pore solution chemistry after comparing the main techniques used for extracting pore solution.  
ESL was chosen in this study. The leaching process followed the instruction described in [PERSON]'s work [22]. A series of 4 \(\times\) 4 \(\times\) 16 cm prismatic cement pastes were cast with w/b equal to 0.5. The CSA and OPC specimens were demolded after 3 and 24 h respectively. Then, they were sealed in plastic bags, and kept in the curing room (T = 20 \({}^{\circ}\)C, RH \(\geq\) 95%) until the testing dates. Before extraction, the prismatic samples were broken up and ground with a mortar into a fine powder. The powder was then dried in an oven at 60 \({}^{\circ}\)C. The drying temperature is the result of a compromise between the time needed for drying and the risk of ettrinigite decomposition at high temperature [17; 27; 28]. Then, after passing the powder through an 80 \(\upmu\)m sieve, it was mixed with deionized water at a mass ratio of 1:1. After stirring for 5 min, the solution was processed using a centrifuge machine to acquire the leaching solution. In order to determine the pore solution at early stage, the liquid phase of fresh pastes was achieved directly by centrifuging the slurry after mixing 5 min.
# A Prediction Model of Marine Geomagnetic Diurnal Variation Using Machine Learning
## 7 Conclusions  
This paper models the trend of geomagnetic diurnal variation within a local area of Europe, analyzes the method of time difference correction at different sites, and compares the impact of the SVM-RF model on geomagnetic diurnal variation. The following conclusions are drawn:  
1. The extreme value adjustment method suggested in this study surpasses the longitude difference correction method in reducing the effects of diurnal time differences. This approach results in increased Pearson correlation coefficients and enhanced accuracy in fitting diurnal curves.
2. To more accurately represent the complex and time-varying nature of the geomagnetic diurnal variation trend within a local area, it is noted that a mathematical model relying solely on latitude, longitude, and distance is inadequate. Therefore, to better capture the intricate changes in the real geomagnetic diurnal variation trend in the survey area, incorporating short-term prior data is deemed essential. This approach is necessary for constructing a more region-specific geomagnetic diurnal variation model.
3. Compared to distance weighting, two-factor weighting, and latitude weighting methods, the SVM-RF model shows significant improvement in accurately estimating geomagnetic diurnal variation. The SVM-RF model, on average, reduces root mean square error (RMSE) by 31%, mean absolute error (MAE) by 35%, and increases the Pearson correlation coefficient by 20%. This model's notable advantage is eliminating the need for data smoothing, thereby allowing for a more precise representation of magnetic disturbance variations.  
Future research should aim to reduce the algorithm's reliance on a priori information and enhance its capability to extrapolate in regions with limited a priori information availability.  
P.X. and Q.L.: formal analysis, data curation, and writing--original draft; [PERSON] and S.J.: supervision, writing--review and editing, and funding acquisition; X.Y. and P.X.: data collection, analysis, and visualization. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original data of this study are obtained from the INTERMAGNET Programme ([[http://www.intermagnet.org](http://www.intermagnet.org)]([http://www.intermagnet.org](http://www.intermagnet.org))) accessed on 23 November 2023.  
The results presented in this paper rely on data collected at magnetic observatories. We thank the national institutes that support them and INTERMAGNET for promoting high standards of magnetic observatory practice ([[http://www.intermagnet.org](http://www.intermagnet.org)]([http://www.intermagnet.org](http://www.intermagnet.org)), accessed on 23 November 2023).  
The authors declare no conflicts of interest.
## 5 System Equilibrium Analysis under Arbitrary Transmission Limit
### Equilibrium Analysis for TDMA System  
For the TDMA system, we can define the system load as \(\rho=\lambda/L\theta\gamma\). Considering the ACB factor, the average number of contending devices on a random access opportunity is defined as \(s=Xp/L\theta\gamma\). Substituting \(\rho\),\(s\) into Equation (35), we can have the balance equation for the TDMA system. The following condition must be satisfied:  
\[\begin{array}{ll}\delta Xpe^{-\frac{Xp}{L\theta\gamma}}&=\delta Xpe^{-s}\\ &=L(1-\theta)\end{array} \tag{46}\]  
The capacity of TDMA system is achieved when \(\theta=\theta^{*}\). Hence, in a similar way to the S-ALOHA system, we can obtain the saturation threshold in the equilibrium state as:  
\[\begin{array}{ll}L_{th}^{TDMA}=\frac{e\lambda\left[1-\left(1-e^{-1}\right) ^{Q}\right]}{\theta^{*}\gamma}\end{array} \tag{47}\]  
Therefore, we can derive the following conclusions for TDMA system under \(Q\geq 1\):  
1. If \(L<~{}L_{th}^{TDMA}\), the system would surely converge to a saturated equilibrium state. Therefore, the ACB scheme would ensure that \(s=1\). Then the average success probability and resource utilization ratio can be computed as: \[\begin{array}{ll}p&=e\left[1-\left(1-\frac{L\theta^{*}\gamma}{e\lambda} \right)^{1/Q}\right]\\ f&=1-\left(1-\frac{L\theta^{*}\gamma}{e\lambda}\right)^{1/Q}\\ P_{S}&=\frac{L\theta^{*}\gamma}{e\lambda}\\ U&=\frac{\delta\lambda P_{S}}{L}=1-\theta^{*}\end{array}\] (48)If \(L>L_{th}^{TDMA}\), the ACB scheme would ensure the system converges to an equilibrium state which is non-saturate. The single unique equilibrium point \(s\in\) (0,1) and the corresponding \(\theta\) can be computed by Equations (35) and (46). In the non-saturation state we can have: \[\begin{array}{ll}P_{S}&=1-(1-e^{-s})^{Q}\\ U&=\frac{\delta A\mathcal{P}_{S}}{L}=1-\theta\end{array}\] (49)
## 5 Automatic Determination of Earthquake Parameters: SeisComP3 and Early-Est  
SeisComP [10; 11] and Early-est [12; 13; 14; 15; 16] are the main pillars of KOERI's automatic seismic monitoring system. The development of SeisComP3 for event detection and location was driven by the GITEWS project in the aftermath of the catastrophic 2004 Sumatra tsunami, focusing on the optimization of the ability to monitor and rapidly characterize potentially tsunamigenic earthquakes at both regional and global scales [10]. SeisComP has been one of the main operational tools used by KOERI-RETMC since 2009. SeisComP3 is configured for local, regional and global seismic monitoring and automatically computes ML, mb, mB, Mw(mB), Mwp, Mw (Mwp) and M magnitudes according to earthquake size. Together with Early-est [12; 13; 14; 15; 16], it constitutes the backbone of the present 7/24 operational tsunami warning system. Early-est software has been used at KOERI-RETMC since 2016, as the preferred routine operational analysis tool for rapid detection, location and seismic or tsunamigenic characterization of earthquakes within the service area. Early-est automatically computes mb, Mwp and Mwpd magnitudes. The KOERI configuration of Early-Est primarily focuses on regional and global earthquake monitoring using approximately 550 stations from 78 networks.