esa-sceva/satcom-chunk-collection huggingface dataset:
## The Ocean
### Observability of the Fine-Scale Ocean Dynamics
In summer, when the submesoscales are weaker, SARAL/AltiKa with its lower noise can detect ocean scales down to 35 km wavelength, whereas the higher noise from Jason-2 blocks the observation of scales less than 50-55 km wavelength. The noise levels impacting on the observation of fine-scale dynamics depend on many factors, including surface roughness from different wind-wave-swell conditions, impact from rain or other geophysical corrections [5; 8], and errors in the geoid or mean sea surface (MSS). Even with the new generation of MSS products, when a satellite drifts off the long-term mean repeat track with its accurate MSS, the error in SLA increases. Figure 3 shows the evolution of the SLA standard deviation, calculated with respect to different MSS products, during a period when the SARAL/AltiKa mission drifted from its long-term mean groundtrack (cycles 23-25, marked in gray, from [9]). The data have been filtered to retain only short wavelengths less than 200 km, which have the largest gridded MSS errors. The standard deviation for Jason-2 (in black) is calculated from the precise alongtrack MSS, whereas for SARAL/AltiKa it is calculated with respect to different MSS models. Figure 3 highlights the lower standard deviation of SARAL/AltiKa SLA compared to Jason, but even when the recent MSS models are used with the most geodetic data included (MSS CNES_CLS15), the error increases during the drifting orbit phase. Work is in progress to better understand and estimate these different geophysical signals, in order to reduce their effects on the SLA noise. Although SARAL/AltiKa and Jason-2 show a similar pattern in seasonal and geographical variations of their noise, these studies quantify that the low background noise levels of SARAL/AltiKa enable this mission to make the most precise estimates of the fine-scale ocean dynamics, in the open ocean and regional and coastal seas. This is encouraging for further studies with SARAL/AltiKa, but also in the preparation for the future Ka-band wide-swath altimeter mission from NASA/CNES,the Surface Water Ocean Topography (SWOT) mission, whose lower noise should enable us to observed even smaller ocean scales. Figure 1: Globally-averaged wavenumber spectra of SSH anomalies (in m\({}^{2}\)/cpkm) from the highest resolution along-track data for three different missions: Jason-2 20 Hz (2 in green); SARAL/AltiKa 40 Hz (AL in blue); and Sentinel-3 20 Hz SAR (S3 in red). The spectral “bump” in Jason-2 and SARAL/AltiKa data is due to surface roughness inhomogeneities within the footprint, mainly from surface waves. Figure 2: Geographical distribution of the 1 Hz noise level over the global oceans in: (**upper panel**) June–August; and (**lower panel**) December–February. The noise is estimated from wavenumber spectra averaged over 15\({}^{\circ}\times\)15\({}^{\circ}\) boxes, as a constant value fit over the wavelengths 14–30 km.
## 3 Results and Discussion
### Prediction Results and Comparisons
model with CNN and DNN are 153.3 and 2.1, respectively; the MAE and MAPE of a hybrid model with DNN and LSTM are 138 and 1.89, respectively; and the MAE and MAPE of a hybrid model with CNN and LSTM are 135 and 1.83, respectively. Integrating the DNN-CNN model improved the prediction accuracy by 81% and 81.7%, compared with the DNN and CNN model, respectively. Integrating the CNN-LSTM model has further improved the prediction accuracy by 83% and 83.9% compared to the DNN and CNN only. These models represent key steps in the development of more advanced hybrid architectures, such as the proposed CNN-LSTM with attention model. Third, for the proposed model (a hybrid model with the CNN and LSTM and attention mechanism), its MAE and MAPE are 122 and 1.69, respectively. The MAE and MAPE of the proposed model are the smallest ones among these models, highlighting the effectiveness of the proposed approaches. The proposed CNN-LSTM with attention model substantially improves prediction accuracy. For instance, it reduces the MAE by approximately 84.88% compared to the baseline DNN model and 9.63% compared to the standard CNN-LSTM model. These improvements underscore the effectiveness of integrating the attention mechanism into the CNN-LSTM architecture, particularly in capturing the most relevant features for accurate electricity demand forecasting. In summary, in the evaluation of the CNN-LSTM model, the results indicate that it outperforms traditional models, such as the DNN, in terms of the MAE and MAPE. This demonstrates the effectiveness of combining CNN and LSTM architectures in a hybrid model for electricity demand forecasting. The reduction in the MAE and MAPE highlights the model's ability to more accurately predict energy demand, which is crucial for efficient resource allocation and management. However, while the CNN-LSTM hybrid model improves prediction accuracy, it has limitations. One of the challenges observed is that the model's performance can be sensitive to the choice of hyperparameters, such as the number of filters in the CNN and the number of neurons in the LSTM layers. This sensitivity requires careful tuning and extensive experimentation to achieve optimal results, which can be computationally intensive. Additionally, the model may struggle with capturing very long-term dependencies beyond a certain threshold, like LSTMs, despite their design to handle long sequences, and can still face difficulties when the sequence length becomes exceedingly large. This limitation suggests that further enhancements, such as incorporating attention mechanisms, could improve the model's ability to focus on the most relevant time steps in the sequence. Therefore, integrating the attention approach in the CNN-LSTM model has achieved higher electricity prediction accuracy than the hybrid CNN-LSTM model. Table 5 presents the results of applying both the base models and the proposed model to the data from the years 2022 and 2023. As can be seen, the proposed model's MAE and MAPE are the smallest among these models. Therefore, the advantages of the proposed models can be verified.
## 2 Test Method and Test Materials  
### Experimental Setup  
In this paper, the experimental setup adopted the 50 mm split Hopkinson pressure bar system in the State Key Laboratory for Disaster Prevention & Mitigation of Explosion & Impact (Figure 1). The setup mainly consisted of 6 parts including plenum chamber, striker bar, incident bar, transmitted bar, buffer unit and signal collection device. Gas pressure in the plenum chamber was controlled by computer to drive the striker bar to the impact incident bar at different speeds. The incident compression waves with different amplitudes were formed in the incident bar to realize the different strain rate loading on the specimen. The materials of the incident bar, transmission bar and striker bar in the setup were 35 CrMnSiA low alloy ultra-high strength steel with a density of 7850 g/cm\({}^{3}\), a longitude velocity of 5440 m/s and a Young's modulus of 233 GPa. Their lengths were 3.0 m, 2.5 m and 0.4 m, respectively.  
In this paper, the method of [PERSON] et al. [7] was used to construct artificially filled joints. 35 CrMnSiA low alloy ultra-high strength steel was selected to make cushion blocks and sleeve (Figure 2). The device was carried out between the incident bar and the transmission bar after filling the sand into the cushion block and the sleeve. The sleeve had an inner diameter of 50 mm, an outer diameter of 60 mm and a length of 30 mm. The cushion block had a diameter of 49.5 mm and a length of 23 mm. Vaseline was applied between the cushion blocks and the sleeve to reduce the sliding friction force and increase seal performance.  
To record the complete dynamic strain signal, the strain gauges were located in the points with a 1400 mm length from the end of the incident bar and a 1100 mm length from the start of the transmission bar, respectively. As the distance from the strain gauges to the end of the bars was long enough, the effect of the superposition of reflected waves on the data could be avoided. Data were recorded at 1 MHz digital resolution. To induce better waveforms, a rubber sheet was used as the pulse shaper and attached on the left end of the incident bar. The rubber sheets had an 8.0 mm diameter and were 1.0 mm thick.  
Figure 1: Split Hopkinson Pressure Bar.
# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 4 Modular Training Strategy and Loss Functions
### Fusion Stage Loss Functions  
To retain as much detail as possible from the various modalities and ensure the high quality of the fused image, we suggest the following fusion loss method:  
\[L_{F\!I\!I} =L_{2 max}+\mu_{3}L_{grad}, \tag{18}\] \[L_{2 max} =\frac{1}{HW}\|I^{f}-\max(I^{x},I^{y})\|_{2}^{2},\] (19) \[L_{grad} =\frac{1}{HW}\|\
abla I^{f}-\max(\
abla I^{x},\
abla I^{y})\|_{2} ^{2}, \tag{20}\]  
where \(\
abla\) represents the gradient operator, and \(\mu_{3}\) is a tuning parameter, \(H\) and \(W\) are the height and width of the image. The terms \(L_{2 max}\) and \(L_{grad}\) ensure that the fused image maintains consistency with the original images' overall appearance and gradient details.
# Proxy Re-Encryption Scheme for Decentralized Storage Networks
## 5 Experiment
### Performance on the Embedded Device  
The PRE encryption and decryption are highly likely to be performed on an embedded device, such as an IP camera or mobile phone. Figure 6 shows the performance of our PRE scheme and WDLC10 on an early model of Raspberry Pi microcomputer. The early version of Raspberry Pi has quite low computation capacity; however, it achieves a reasonable performance. In addition, the permission grant in our scheme does not require transfer of the ciphertext, which would be friendly to the embedded devices. Recently, the embed device has attained a faster CPU with multi-cores. The feature of skipping the re-encryption makes it fit better for the embedded device.
## 2 Computational Principles and Methods
### Thermodynamic Properties  
In order to study the thermodynamic properties of Co\({}_{2}\)Ti and Co\({}_{2}\)Zr, [PERSON] et al. [37] proposed the quasi-harmonic Debye model (QHDM), in which Debye temperature \(\Theta\), heat capacity C\({}_{V}\) and thermal expansion coefficient \(\alpha\) are often adopted [38]:  
\[\Theta=\frac{\rm h}{k_{B}}(\frac{3 nN_{A\rho}}{4\pi M})^{1/3}V_{m} \tag{5}\]  
\[C_{V}=3 nk_{B}\left[4D(\Theta/T)-\frac{3\Theta/T}{\rm e^{\Theta/T}-1}\right] \tag{6}\]  
\[\alpha=\frac{\gamma C^{V}}{B^{T}V} \tag{7}\]  
In Equation (5), \(h\) is [PERSON]'s constant, \(k_{B}\) is [PERSON] constant, \(n\) represents the number of atoms in a chemical formula, \(N_{A}\) is [PERSON]'s number, \(\rho\) stands for crystal density, \(M\) means molecular weight, \(V_{m}\) is the average sound velocity and is given in the following formula [38]:  
\[V_{m}=\left[\frac{1}{3}\left(\frac{2}{\rm v_{S}}^{3}+\frac{1}{\rm v_{I}} \right)\right]^{-1/3} \tag{8}\]  
\[\rm V_{s}=\sqrt{\frac{G}{\rho}},V_{1}=\sqrt{\frac{B_{s}+\frac{4}{3}G}{\rho}} \tag{9}\]  
where \(V_{S}\) and \(V_{1}\), respectively, stand for transverse and longitudinal elastic wave velocity, as obtained in Equation (9), namely the Navier equation.  
In Equation (6), \(D(\Theta/T)\) represents Debye integral; in Equation (7), \(\gamma\) stands for the Gruneisen parameters, \(B_{T}\) is the isothermal bulk modulus, and \(C_{V}\) is the heat capacity, while \(V\) is the volume [38].  
\[\gamma=-\frac{\text{dln}\Theta(V)}{\text{dln}V} \tag{10}\]  
\[B_{T}(P,T)=V\bigg{(}\frac{\partial^{2}G^{*}(V;P,T)}{\partial V^{2}}\bigg{)}P,T \tag{11}\]  
where \(G^{*}(V;P,T)\) is the non-equilibrium Gibbs function. In the quasi-harmonic Debye model,  
\[\text{G}*(V;P,T)=\text{E}(V)+\text{PV}+\text{A}_{\text{Vib}}(\Theta(V);T) \tag{12}\]  
where \(E(V)\) means total energy, and \(A_{Vib}(\Theta(V);T)\)is contribution of crystal vibration that can be written as:  
\[A_{Vib}=nk_{B}\bigg{[}\frac{9\Theta}{8T}+3\ln(1-e^{-\Theta/T})-D(\Theta/T) \bigg{]} \tag{13}\]  
These formulas are used to calculate the thermodynamic properties of Co\({}_{2}\)Zr and Co\({}_{2}\)Ti.
# Cross-Regional Pavement Temperature Prediction Using Transfer Learning and Random Forest
## 2 Methodology
### Model Evaluation Metrics  
After model training, the _predict_ function in MATLAB was used to generate predictions for the test samples. The model's performance in different regions was evaluated using several standard regression metrics. These metrics reflect both prediction accuracy and fitting quality. The mean absolute error (MAE) was used to assess the average deviation between the predicted and actual values. A lower MAE indicates higher prediction accuracy. The mean squared error (MSE), calculated as the average of squared errors, reflects the overall fitting quality and is more sensitive to large deviations. The root-mean-square error (RMSE), derived from the square root of the MSE, shares the same unit as the predicted target and provides a more intuitive understanding of prediction deviation. The coefficient of determination (R\({}^{2}\)) was used to represent the proportion of variance in the observed data that was explained by the model. In addition, to ensure the statistical significance of the correlation analysis, critical correlation coefficients were determined based on hypothesis testing at the 0.05 significance level in this study. The formulae for each evaluation metric are presented in Equations (1)-(4):  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{1}\]  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2} \tag{2}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{3}\]  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_ {i}-\overline{y}_{i})^{2}} \tag{4}\]  
where \(y_{i}\) is the measured temperature value, \(\hat{y}_{i}\) is the predicted temperature value, \(\overline{y}_{i}\) is the average value of the measured temperature, and \(n\) is the number of data points.
## 1 Introduction
[PERSON], [PERSON], and [PERSON] [37] investigated the use of recycled high-impact polystyrene (HIPS) as a sand replacement in cement mortar. Their findings revealed that substituting sand with HIPS led to reductions in both the compressive strength and splitting tensile strength of the mortar, with a notably smaller reduction observed in the splitting tensile strength. The incorporation of HIPS enhanced the ductility of the mortar and increased its energy dissipation capacity. Additionally, the HIPS reduced the dry density, dynamic elastic modulus, thermal conductivity, and water vapor permeability of the mortar, while having no adverse effect on its freeze-thaw resistance. [PERSON], [PERSON], and [PERSON] [38] demonstrated that replacing 50% of the coarse aggregate with HIPS aggregate retained 50% of the concrete's strength under all tested conditions. Their findings also indicated a linear relationship between the strength loss and the increase in the HIPS \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **2013** & **2014** & **2015** & **2016** & **2017** \\ \hline Waste TV & 1,181,004 & 1,118,701 & 1,099,031 & 1,021,742 & 26,219,398 \\ Year & 2018 & 2019 & 2020 & 2021 & 2022 \\ Waste TV & 24,411,871 & 22,601,103 & 28,280,353 & 29,026,482 & 30,560,227 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Net resource recovery, Resource Circulation Administration, Ministry of Environment—Recovery Volume Statistics. (unit: kg). content. This type of concrete significantly reduces the unit weight of the material, making it suitable for use in earthquake-prone regions and for non-structural components, such as partition walls and lightweight roofing. Research showed that not all of them are suitable for use as aggregates in concrete. Resin-based types of waste plastic and PET have been reported to have the highest rate of use for concrete production [40; 41; 42]. Current experimental results show that the mechanical and durability properties of concrete are altered due to the inclusion of plastic. However, such concrete still fulfils the requirements of many engineering applications [43]. In addition, the research results of [PERSON], indicate that plastic aggregates may be substituted for natural fine aggregates to create an environmentally friendly mortar with similar strength characteristics. [44]. This study used TPSW as a fine aggregate to investigate the fresh properties, engineering properties, and durability of cement mortar made from TPSW with different variables. Their relationships were further established to discuss the influence of different factors (W/C, substitution amount, and age) on the engineering properties of cement mortar.
# Low-Altitude Sensing Model: Analysis Leveraging ISAC in Real-World Environments
## 2 The Model of Single Base Station 3D Sensing  
In this section, we delve into the single base station sensing capabilities within low-altitude sensing networks, clarify their distinctions from traditional base stations and propose targeted models and key parameter design schemes.
# EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
## 3 Results and Discussion
### Performance Using PPG Waveform-Based Features
In our study, we conducted a comparative analysis of our proposed method against two established methods for blood glucose estimation. The approach detailed in reference [30] employs a single pulse analysis (SPA) technique for feature extraction from PPG signals to estimate blood glucose levels. In study [31], the authors utilized machine learning algorithms on PPG signals, employing KTE, heart rate, AR coefficient-based features for blood glucose level estimation. Our method, as demonstrated in Table 5, achieved a high R\({}^{2}\) score compared with other methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
\begin{tabular}{c} **Combination of** \\ **Wavelengths** \\ \end{tabular} & **Metrics** & **Random Forest** & **XGBoost** & **LightGBM** & **CatBoost** \\ \hline \multirow{4}{*}{RGB} & [PERSON]’s r & 0.94 & 0.95 & 0.93 & **0.96** \\  & RMSE (mg/dL) & 13.37 & 11.86 & 14.63 & **10.94** \\  & R\({}^{2}\) score & 0.88 & 0.91 & 0.86 & **0.92** \\  & MAE (mg/dL) & 8.2 & **7.05** & 9.21 & 8.01 \\  & MAPE (mg/dL) & 6.11 & 6.66 & 7.02 & **6.04** \\ \hline \hline \end{tabular} Best values are in boldface font.  
\end{table} 
Table 4: Results when we use 50 features after combining top IMF-based and waveform-based features. Figure 8: Scatter plots for blood glucose estimation using a PPG signal. (**a**) RF, (**b**) XGB, (**c**) LightGBM, (**d**) CatBoost. Figure 9: EGA plots for blood glucose estimation using PPG signals. (**a**) RF, (**b**) XGB, (**c**) LightGBM, (**d**) CatBoost.
# An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks
## 3 Link State Evaluation
### Link State Evaluation Model  
The fuzzy analytic hierarchy process (FAHP) constructs the decision structure through different levels. The first level is the objective, and the third level consists of the criteria and indicators, as shown in Figure 2.  
The weight of each parameter is calculated based on the fuzzy numbers corresponding to the score of each indicator, and a sensitivity analysis is conducted using six sets of fuzzy factor values (0, 0.2, 0.4, 0.6, 0.8, 1) (see Table 3). The results show that although the weights vary with changes in the fuzzy factor, the ranking of the parameters remains unchanged, indicating that the weight calculation is reasonable.  
The subjective weights are calculated using the judgment matrix and FAHP algorithm, while the objective weights are calculated using the improved CRITIC method. The objective weights are further optimized through the VIKOR method, resulting in the final combined weights, as shown in Table 4. Based on this, the link state score can be calculated using the five parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **C1** & **C2** & **C3** & **C4** & **C5** \\ \hline FAHP Weights & 0.1337 & 0.2428 & 0.3220 & 0.1992 & 0.1023 \\ Improved CRITIC Method Weights & 0.3304 & 0.2881 & 0.1885 & 0.0549 & 0.1381 \\ VIKOR Method Optimized Weights & 0.3173 & 0.2793 & 0.1897 & 0.0694 & 0.1443 \\ Combined Weights & 0.1981 & 0.3166 & 0.2851 & 0.0968 & 0.1034 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Weight results for each parameter.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **C1** & **C2** & **C3** & **C4** & **C5** \\ \hline FAHP Weights & 0.1337 & 0.2428 & 0.3220 & 0.1992 & 0.1023 \\ Improved CRITIC Method Weights & 0.3304 & 0.2881 & 0.1885 & 0.0549 & 0.1381 \\ VIKOR Method Optimized Weights & 0.3173 & 0.2793 & 0.1897 & 0.0694 & 0.1443 \\ Combined Weights & 0.1981 & 0.3166 & 0.2851 & 0.0968 & 0.1034 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sensitivity analysis of the decision model.  
Figure 2: Link state scoring FAHP evaluation model.
# Unidirectional Invisibility Induced by Complex Anti-Parity-Time Symmetric Periodic Lattices
###### Abstract  
Complex anti-parity-time symmetric periodic lattices, in a wide frequency band, can act as unidirectional invisible media. The reflection from one end is suppressed while it is enhanced from the other. Furthermore, unidirectional laser points (ULPs) which correspond to the poles of reflection from one end, arise in the parameter space composed of the permittivity and angular frequency. The phase of the reflection coefficient changes sharply near the ULPs. Subsequently, large lateral shift which is proportional to the slope of phase could be induced for the reflected beam. The study may find great applications in unidirectional invisibility, unidirectional lasers and highly sensitive sensors.  
Keywords:anti-parity-time symmetry; unidirectional invisibility; unidirectional laser; lateral shift; periodic lattice +
Footnote †: journal: Applied Physics Letters
## 5 Design of the Antenna Pointing Control System  
### Configuration  
The configuration of the proposed antenna pointing control system, shown in Figure 13, consists of two independent control systems, an antenna driving control system, and a flight control system. Antenna pointing angles are measured by using a radio frequency sensor [20]. Generally, the two control systems interfere with each other as reaction torques. However, the magnitude of the interference depends on the magnitude of the inertia moment to be controlled in each system, which can be calculated by using the interference factor [21].  
Because the two-axis gimbal with the antenna is directly attached to the bottom of the fixed-wing UAV, the attitude angle error of the UAV is added to the pointing direction error.  
Figure 12: Antenna’s EIRP compared with ITU-R Recommendations.  
Figure 13: Antenna pointing control system.  
As mentioned in Section 1, the commands into the antenna pointing control system change every moment and should be generated by using measured attitude angles. This means that commands themselves, shown in Equations (7), (13), and (14), should be calculated by using measured attitude angles \(\phi\) and \(\psi_{\rm P}\), which may deteriorate antenna pointing accuracy. Therefore, it is very important to measure antenna pointing angles directly and accurately by using a RF sensor. The antenna driving control system controls the elevation angle and the azimuth angle independently, as shown in Figures 14 and 15 by using the commands.