esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background
### Logistics Trend  
The logistic trend is widely used in demand modeling for durable goods, as well as in analyzing the development, production, and sales of specific product categories. It belongs to the family of S-curve trend functions, which effectively capture the fundamental developmental phases of a cycle. A cycle refers to the time period spanning from the emergence of new driving forces, such as technologies or products, to their eventual decline. The logistic trend can be divided into the following five phases [23; 24]:  
* Phase 1--The period during which new progressive forces begin to emerge.
* Phase 2--The stage where these progressive forces fully assert themselves and start to decisively influence further development.
* Phase 3--The period when progressive forces have completely dominated market development; however, opposing forces also begin to arise, mitigating their impact.
* Phase  
remainder, then, when creating the groups, we omit the first or first two values. Then, we calculate the sums of these three groups:  
\[S_{1}\ =\ \sum_{t\ =\ n-3m+1}^{n-2m}y_{t}\ \ \ \ \ \ \ \ \ S_{2}\ =\ \sum_{t\ =\ n-2m+1}^{n-m}y_{t}\ \ \ \ \ \ \ \ S_{3}\ =\ \sum_{t\ =\ n-m+1}^{n}Y_{t} \tag{14}\]  
For unknown parameters \(a_{0}\), \(a_{1}\), \(k\) the following applies [14; 16]:  
\[a_{1}\ =\ \sqrt[n]{\frac{S_{3}-S_{2}}{S_{2}-S_{1}}}\]  
\[a_{0}\ =\ \frac{(S_{3}-S_{2})\cdot(a_{1}-1)}{\left(a_{1}^{m}-1\right)^{2} \cdot a_{1}^{n-2m+1}}\ \ k\ =\ \frac{S_{2}-a_{0}\cdot a_{1}^{n-2m}\cdot\frac{a_{1}^{n}-1}{a_{1}-1}}{m} \tag{15}\]  
Estimates of the parameters \(a_{0}\), \(a_{1}\) and \(k\) can also be calculated using the selected points method.  
This logistic trend model is frequently used to forecast the life cycle of products and technologies. It is applicable not only to demand and sales but also to production, storage, and distribution. Its symmetric nature and well-defined parameters allow analysts and managers to gain a deeper understanding of market behavior and develop more effective inventory management and marketing strategies [24].  
Individual logistic curves that express the course of sales of various products differ in the course of individual phases as well as the length of these phases. The Gompertz curve is also among the S-curve forecasting models. Unlike the logistic function, it is asymmetric; the center of gravity of values in time is behind its inflection point.
# Piezoelectric Transformers: An Historical Review
## 2 Space, Defense and Security Applications of Piezoelectric Transformers
### High Voltage Power Supplies for Compact Neutron Generators  
This project developed a very high voltage, (1 MV) and high power, (40 W) piezoelectric-driven power supply to drive compact neutron generators. The power supply is based on a modular stackable system which combines piezoelectric transformers and Cockroft-Walton voltage multiplier circuits [123; 124]. Figure 22 illustrates a 200 kV stage using the high voltage/high power piezoelectric transformer driving a multi-stage Cockroft-Walton circuit.
# A Greedy Pursuit Hierarchical Iteration Algorithm
## 2 Systems Model
\]  
The over-parameterized system model can be wrote as  
\[\mathbf{Y}=\mathbf{\Phi}\mathbf{\theta}+\mathbf{E}. \tag{9}\]  
Minimize the following cost function  
\[J(\mathbf{\theta}):=\sum_{j=1}^{L}[y(j)-\mathbf{\varphi}^{\mathrm{r}}(j)\mathbf{\theta}]^{ 2}=\mathbf{E}^{\mathrm{r}}\mathbf{E},\]  
and the LS estimation value of the parameter vector will be obtained by using the LS principle  
\[\mathbf{\hat{\theta}}=(\mathbf{\Phi}^{\mathrm{r}}\mathbf{\Phi})^{-1}\mathbf{\Phi}^{\mathrm{r}} \mathbf{\gamma}. \tag{10}\]  
**Remark 1**. : _It is worth noting that Equation (10) cannot estimate the parameter directly as \(\mathbf{\varphi}_{n}(d)\) contains unmeasurable internal noise items \(w(d)\) and \(e(d)\). In addition, the over-parameterized sparse system model has a large dimension \(N\). Directly adopting the least squares method requires the sampled data to be much larger than the dimension \(N\) of the system model to achieve the satisfactory estimation accuracy. In some extreme cases, it will lead to matrix \(S=[\hat{\mathbf{\Phi}}^{\dagger}\hat{\mathbf{\Phi}}]^{-1}\) singularity when sufficient sampled data cannot be obtained. A large number of samples and high-dimensional matrix operations will increase the estimation cost._  
In order to achieve high efficiency of parameters and time-delay estimation, this paper drives a greedy pursuit hierarchical iterative algorithm for system model (1) using limited sampled data according to the sparse property of the over-parameterized system model.
###### Abstract
In this scenario, the SO2 concentrations arriving at the second FDG step would be modest, and the temperature of the flue gas would be lower than that utilized in the wet treatment, which is around 100-150 \({}^{\circ}\)C [12]. Research on dry FGD has predominantly focused on the SO2 uptake capabilities of adsorbent materials under medium-to-high temperatures (150-850 \({}^{\circ}\)C) [13; 14] and high SO2 concentrations in the gas stream. Nevertheless, the FGD of some applications, such as those described above, operates in milder conditions, characterized by lower operating temperatures and SO2 flux contents. Therefore, this work addressed this gap by developing a pilot system at laboratory scale, which was used to conduct SO2 abatement experiments in mild conditions, including low reactor temperatures (T < 100 \({}^{\circ}\)C) and low SO2 concentrations (600 ppm), with adsorbents located in a fixed-bed reactor. Moreover, relative humidity (RH%) was also considered, as increasing the RH% can improve the desulfurization efficiency of the system. The reactor modeling and data acquisition (DaQ) system, comprising humidity and temperature sensors, enabled comprehension of the experimental FGD conditions. Two known materials, namely, slaked lime (Ca(OH)2) and 13X zeolite, were employed to validate the pilot dry fixed-bed FGD system in mild conditions. The experimental comparison and characterization of the SO2 uptake performance of Ca(OH)2 and the as-synthesized 13X zeolite, conducted under identical operational conditions, offer insights into the influence of distinct morphological, chemical, and crystallographic properties on FGD in mild conditions, namely, a low reactor temperature, SO2 concentration, and RH%. The findings will contribute to advancing the understanding of adsorbents for effective SO2 capture, paving the way for more efficient gas desulfurization solutions in various industrial applications, particularly in the growing petrochemical sector. Indeed, in 2023, the global market for FGD systems was valued at over USD 4328 million and is expected to grow at a CAGR of around 6.8% by 2030 [9], with a notable shift toward more energy efficient and low-emission technologies [9; 10]. This market growth will be accompanied by the development of next-generation systems, including dry and semi-dry FGD processes, which will offer improved flexibility, reduced operational costs, and lower environmental impacts compared to traditional wet FGD systems.
# A CNN-LSTM-Attention Model for Near-Crash Event Identification on Mountainous Roads
## 5 Results
### Evaluation of Model Performance
#### 5.2.2 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Overall**} & \multicolumn{4}{c}{**Normal Events**} & \multicolumn{4}{c}{**Near-Crash Events**} \\ \cline{2-9}  & \(A\) & **AUC** & \(P\) & \(R\) & \(\boldsymbol{F_{1}}\) & \(P\) & \(R\) & \(\boldsymbol{F_{1}}\) \\ \hline CNN & 93.9\% & 0.984 & 99.3\% & 93.9\% & 0.965 & 61.6\% & 93.8\% & 0.744 \\ LSTM & 96.9\% & 0.988 & 99.7\% & 96.9\% & 0.983 & 76.8\% & 96.9\% & 0.857 \\ Attention & 82.4\% & 0.891 & 97.7\% & 82.5\% & 0.895 & 32.9\% & 81.5\% & 0.469 \\ CNN-LSTM & 97.8\% & 0.995 & 99.7\% & 97.9\% & 0.988 & 82.9\% & 96.9\% & 0.894 \\ CNN-Attention & 95.5\% & 0.987 & 99.5\% & 95.5\% & 0.975 & 68.9\% & 95.4\% & 0.800 \\ LSTM-Attention & 95.6\% & 0.979 & 99.5\% & 95.6\% & 0.975 & 69.7\% & 95.4\% & 0.805 \\ CNN-LSTM-Attention & 98.8\% & 0.997 & 99.8\% & 98.9\% & 0.993 & 90.1\% & 98.5\% & 0.941 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of each model. complexity. Consequently, the LSTM-Attention model presents lower prediction accuracy compared to the LSTM model. In the comparison of the CNN-LSTM and CNN-LSTM-Attention models, it is observed that the combination of the CNN, LSTM, and Attention mechanisms allows for feature extraction and information processing at different levels, and creates a complementary effect. The CNN layer extracts local features, the LSTM layer recognizes temporal relationships, and the Attention mechanism focuses on key information. This tripartite combination maximizes the utilization of data features, and enhances the model's performance. The analysis above indicates that when applying machine learning models in specific scenarios, it is crucial to tailor the approach to the study subject and data conditions through targeted research on feature selection and model structure, and to continually validate and optimize as data enriches to address the uncertainties in machine learning model performance.
# Sixty-Nine-Element Voice Coil Deformable Mirror for Visible Light Communication
## 2 Theory
### Fitting Error of VCDM  
The fitting error of DM directly affects the performance of the adaptive optics system. The fitting error \(\sigma_{fit}\) arises from the limited number of actuators of the DM and thus the limited number of spatial frequencies that it can correct. The variance of the fitting error can be approximated by [20]:  
\[\sigma_{fit}^{2}=\kappa D\left(\frac{\sqrt{\pi/N}}{r_{0}}\right)^{5/3} \tag{7}\]  
where \(\kappa\) is the fitting error coefficient, depending on the type of mirror that is used. For continuous face-sheet DM, \(\kappa\) is equal to 0.14. \(D\) is receiving the aperture. \(N\) is the number of actuators, which in this paper is 69; and \(r_{0}\) is the Fried parameter, generally ranging from 5 cm to 20 cm.
## 3 Results and Discussion
### Distribution of Cd in the Soil Fraction  
To elucidate the fraction distribution of Cd after treatment with amino acids, BCR sequential extraction was implemented. The acid-extractable fraction and the reducible fraction of Cd with high bioavailability are easily taken up by plants, while the oxidable fraction and the residual fraction of Cd are hardly taken up by plants. In the untreated soil, the distribution of Cd was mainly the acid-extractable fraction [49]. As shown in Figure 8, the acid-extractable fraction, reducible fraction, oxidable fraction, and residual fraction of Cd in the original contaminated soil were 33.81%, 21.36%, 13.73%, and 31.1%, respectively. After amino acid treatment, the acid-extractable fraction and the reducible fraction of Cd decreased dramatically. In the presence of Thr, the acid-extractable fraction of Cd decreased from 33.81% to 16.48%, while the reducible fraction decreased from 21.36% to 14.72%. Similarly, in the presence of Glu, the acid-extractable fraction and the reducible fraction of Cd decreased to 16.58% and 15.24%, respectively. These results meant that the acid-extractable fraction and the reducible fraction of Cd with good mobility in the formation of complexation with amino acids could be easily washed out from soil. [PERSON] et al. reported that the acid-extractable fraction of Cd was easily extractable from soil in the presence of complex agents, while the residual fraction of Cd mainly existed in the crystal of minerals and was hardly removed by organic chelating agents [50; 51]. The decreasing trend of the acid-extractable fraction and residual fraction of Cd after treatment with amino acids implied that Cd can be easily released into solution to be further taken up by plants or washed out by leachate.  
Figure 7: (**a**) Cd extraction and (**b**) the removal efficiency of Cd from contaminated soil by using the studied amino acids.
# Propagation of Elastic Waves in a One-Dimensional High Aspect Ratio Nanoparticle Phononic Crystal
## 3 Results and Discussion
### Propagation Perpendicular to the 1D Phononic Nanoridge  
We now consider the propagation along the direction perpendicular to the nanorides of the same height and width, namely: \(h\) = 2.7 \(\upmu\)m and \(w\) = 250 nm. The corresponding dispersion curves (Figure 5a) in the non-radiative region below the sound cone of the substrate display many flat branches with zero group velocities. It means that the modes do not propagate, and are strongly confined in the nanoridges. To understand the origin of these modes, we proceed to the calculation of the eigenmodes of a single nanoridge that is fixed at the bottom (Figure 5b). It results that the eigenfrequencies of the single nanoridge almost perfectly match the frequencies of the flat dispersion curves of the 1D nanoridge. This result is confirmed through an analysis of the displacement fields at the frequencies 1, 2, and 3 (Figure 5a). One can see that the distribution of the displacement fields of modes 4, 5, and 6 of the one side fixed plate (Figure 5b) exhibit the same behavior as those of the supported ridges. Just below the sound line of the SiO\({}_{2}\) substrate, one can see that the flat branches interact with the surface Rayleigh mode, give rise to small anticrossings, and possibly lead to the opening of the small band gaps.  
Figure 4: (**a**) Phase velocity diagram as a function of the wave vector where the blue solid line represents the Rayleigh wave velocity of epoxy, and the pink solid line represents the edge wave velocity. The modes have been selected as a function of their polarization following Equations (1) and (2). The red (resp. blue) color corresponds to the polarization \(P_{xy}\) (resp. \(P_{z}\)) (**b**) Displacement field maps of the two edge modes ‘1’ and ‘2’ at the wave vector \(q\) = 0.02 nm\({}^{-1}\).
# Lightweight Design and Evaluation of Square Reflector
## 2 Square Reflector Lightweight Design  
Space optical components will be affected by mechanical shock, changes in ambient temperature, and atmospheric pressure during the satellite launch and in-orbit operation stages, which will lead to changes in the internal structure of the remote sensing payload, thus causing deviations in the face shape accuracy and position relationship of the optical components, and ultimately causing degradation in the imaging quality of the space remote sensing camera.  
Therefore, the optical components are required to allow for a small amount of deformation, have high stability, as well as have a certain stress-release capability.
# A Vehicle-Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion
## 3 Method
### Vehicle-Infrastructure Information Sharing  
To minimize communication overhead, we implement an encoder-decoder-based information sharing mechanism [15] that effectively compresses and decompresses feature information, ensuring high-quality transmission even under limited bandwidth conditions. At the infrastructure level, the extracted point cloud features \(F^{I}\) are compressed using a convolutional neural network, which reduces their dimensionality and data volume:  
\[F^{IC}=\text{Conv}(F^{I})\in\mathbb{R}^{H_{C}\times W_{C}\times C_{C}}, \tag{1}\]  
where \(F^{IC}\) represents the compressed infrastructure features, Conv denotes the convolutional neural network layer, and \(H_{C}\), \(W_{C}\), and \(C_{C}\) correspond to the height, width, and channels of the compressed features, respectively. Upon reaching the vehicle, the compressed features \(F^{IC}\) are decompressed through a deconvolution operation to restore their original scale and detail:  
\[F^{ID}=\text{Deconv}(F^{IC})\in\mathbb{R}^{H\times W\times C}, \tag{2}\]  
where Deconv is the deconvolutional neural network layer responsible for reconstructing the original feature representation, \(F^{ID}\).
## 7 Conclusions  
We reviewed the most commonly used machine learning algorithms for receiver-based NLE in CO-OFDM that include both unsupervised and supervised algorithmic designs for blind and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Deterministic Technique** & **System A (2000 km)** & **System B (3200 km)** \\ \hline DBP & \(163852800\) (\(1.6\times 10^{8}\)) & \(262164480\) (\(2.6\times 10^{8}\)) \\ IVSTF & \(1151312\) (\(1.2\times 10^{6}\)) & \(1839632\) (\(1.8\times 10^{6}\)) \\ ANN (\(M=4\)) & \(5040\) (\(5.0\times 10^{3}\)) \\ ANN (\(M=16\)) & \(100800\) (\(1.0\times 10^{5}\)) \\ ANN (\(M=64\)) & \(1693440\) (\(1.7\times 10^{6}\)) \\ ANN (\(M=128\)) & \(6827520\) (\(6.8\times 10^{6}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computational complexity comparison between full-step DBP, IVSTF and ANN for different modulation format order (\(M\)) and transmission distances.  
non-blind NLE processing, respectively. We identified the main sources of noise in a coherent fiber-optic telecommunication system and analyzed the limitations of benchmark deterministic solutions (e.g., MS-OPC, PCSC, DBP), highlighting that their prominent obstacle is the inability of tackling stochastic non-linear distortions in long-haul CO-OFDM such as the PNA effect. We showed the performance of machine learning-based NLEs over 2000 km and 3200 km of SSMF transmission for a 16-QAM (\(\sim\)40 Gb/s) and a QPSK-WDM (\(\sim\)20 Gb/s middle-channel) CO-OFDM system, respectively. The machine learning algorithms included clustering algorithms such as _K_-means (deterministic hard-clustering), FL (probabilistic soft-clustering), AP (advanced soft-clustering); supervised machine learning algorithms such as ANN (classification) and SVR (classification and regression); unsupervised and fast SVMs; and compared with the deterministic benchmark approaches of FS-DBP and IVSTF. We also presented, for the first time, a computational complexity analysis among machine learning and deterministic algorithms. Our review indicated that AP offers the best performance for both 16-QAM and QPSK-WDM CO-OFDM having, however, higher computational complexity with other supervised/unsupervised machine learning algorithms. Machine learning reveals a much lower complexity and a significant performance benefit over deterministic approaches especially for QPSK-WDM due to their ability of tackling both intra- and inter-channel non-linearities (including inter-subcarrier non-linear crosstalk distortions) mainly on middle subcarriers which suffer the most from inter-subcarrier FWM. We believe that due to their impressive performance and low complexity, machine learning algorithms could play a key role in long-haul coherent optical communications.  
The authors declare no conflict of interest.
## 5 Performance Validation  
The beam selection mechanism and the related steps of orientation, node discovery and projecting the 3D environment into 2D, are validated by comparing the proposed ray classification technique with the types suggested by the Bellhop ray trace. The balance of this section describes the simulation environment and reports the results.
# Facile Fabrication of a Low-Cost
## 3 Results and Discussion  
### Material Characterization  
In order to determine the components and functional groups contained in the prepared Alg-PAM, we characterized the raw sodium alginate, polyacrylamide, and Alg-PAM composite aerogel by infrared spectroscopy. As shown in Figure 1, in the three spectra, the absorption peaks around 2925 cm\({}^{-1}\) are aliphatic C-H stretching vibrations. In the spectrum of sodium alginate, the broad absorption peak near 3400 cm\({}^{-1}\) is -OH stretching vibration, and the absorption peak at 1412 cm\({}^{-1}\) belongs to the -COO- group [20]. In the spectrum of polyacrylamide, the absorption peak at 1650 cm\({}^{-1}\) can be assigned to the amide bond [21]. Compared with the spectrum of sodium alginate, a new peak of the amide bond is observed near 1650 cm\({}^{-1}\) in Alg-PAM, thereby suggesting that polyacrylamide was successfully modified on the calcium alginate aerogel [20]. Furthermore, we also characterized the XPS spectra of Alg-PAM before and after Pb\({}^{2+}\) adsorption. As shown in Figure 2, after adsorption, new Pb 4f peaks could be observed on the Alg-PAM adsorbent, thereby indicating that Pb\({}^{2+}\) ions were successfully loaded onto the Alg-PAM adsorbent [22].  
Figure 1: Infrared spectra of (**a**) raw sodium alginate, (**b**) alginate/polyacrylamideamide composite aerogel (Alg–PAM), and (**c**) raw polyacrylamide.  
Moreover, we also characterized the morphology of Alg-PAM. As shown in Figure 3, the as-prepared Alg-PAM takes the form of white aerogel balls in the shape of water droplets. When amplified locally, many wrinkles can be seen on the Alg-PAM surface. This wrinkled surface can increase the contact area between the adsorbent and adsorbate, thereby increasing the adsorption capacity of Alg-PAM to the target metal ions.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 4 Edge Computing: Opportunities in ITS Sensing Challenges
### Challenges in ITS Sensing
#### 4.3.4 Challenge 4: Privacy Protection  
Privacy protection is another major challenge. As ITS sensing becomes advanced, more and more detailed information is available, and there have been increasing concerns regarding the use of the data and possible invasion of privacy. Bluetooth sensing detects the MAC address of the devices such as cell phones and tracks the devices in some applications, which not only risk people's identification but also their location information. Camera images, when not properly protected, may contain private information, such as faces and license plates. These data are often stored on the cloud and not owned by the people whose private information is there.  
Edge computing is a great solution to privacy challenges. Data are collected and processed at the edge, and raw data, with private information, is not transmitted to the cloud. In [238], video and other sensor data are processed onboard the vehicles and most are removed in real-time. While the primary purpose was to save network and cloud resources, privacy protection was fulfilled, as well as with edge computing. Federated learning [2] is a learning mechanism for privacy protection that assumes that users at different locations/agencies cannot share all the data to the cloud datacenter, so learning with new data has to happen at the edge first before transmitting some intermediate values to the cloud.
## 5 Evaluation and Findings
### Experimental Findings
#### 5.2.3 Analysis of the Receiver Operating Characteristic (ROC)  
In Figures 10 and 11, we present the ROC curves, which illustrate the performance of various heart disease prediction models, encompassing the seven base learners within our ensemble learning model, the EHMFFL algorithm as a whole, and random classification. These curves showcase the trade-off between sensitivity and specificity at different decision thresholds. To gauge the diagnostic value of these tests, we calculate the area under the ROC curve (AUC), where a larger AUC signifies a more effective test. According to the obtained results, the EHMFFL algorithm outperforms all the other compared techniques, achieving AUC values of 0.95 for the Cleveland dataset and 0.88 for the Statlog dataset, underscoring its superior predictive capability in heart disease diagnosis.