esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Nonlinear Guided Waves  
### Nonlinear Ultrasonic Test  
The method for detecting nonlinearity in a material is to send and receive induced ultrasonic wave at a specific distance from the material. After the nonlinearity of the material propagates at a certain distance, the underlying frequency wave is distorted to produce a higher harmonic [15; 16; 17; 18; 19; 20; 21]. For guided wave, the acoustic nonlinearity can be written as shown in Equation (1).  
\[\beta=\frac{8}{K^{2a}}\frac{A_{2}}{{A_{1}}^{2}}f \tag{1}\]  
where \(a\) is the wave propagation distance and \(f\) is a frequency independent function. In addition, the ratio \(A_{2}/A_{1}^{2}\) with the relative acoustic nonlinear parameter \(\beta^{\prime}\) applied in this research is considered as a measure of acoustic nonlinearity, where \(A_{1}\) and \(A_{2}\) are the measured amplitudes of the fundamental and second harmonics.  
\[\beta^{\prime}=\frac{A_{2}}{A_{1}}\propto\beta a \tag{2}\]  
The relative acoustic nonlinear parameter increases linearly as a function of propagation distance. The cumulative effect of the second harmonic amplitude is a great advantage for detection in experiment. It is found that second harmonic amplitude linearly grows with propagation distance, when there are synchronism and nonzero power flux. If the wave mode chosen satisfies these two conditions, the second harmonic amplitude will be cumulative. Even a series of double frequency wave components will be generated by the driving sources of nonlinearity. In practice, interest is focused on the second harmonic generation with the cumulative effect, since the cumulative second harmonic wave plays a dominant role after second harmonics propagating some distance.  
Figure 1a,b shows the typical waveform of the received signal and its spectrum. The received time domain signal is processed in the frequency domain with the fast Fourier transform (FFT) to obtain its spectrum. Figure 1b shows the fundamental frequency and the second harmonic.  
As indicated in Equations (1) and (2), the normalized nonlinear parameter can be calculated with Equation (2) by using the amplitudes of the fundamental and the second harmonic modes.  
Figure 2 shows the nonlinear parameters according to the propagation distance. The cumulative effect means that the nonlinearity increases with distance under certain frequency conditions. The double frequency mode which satisfies a specific condition such as the expression shown in the diagram is selected based on the frequency.  
Figure 1: Typical received signal. (**a**) Time-domain waveform; (**b**) Fourier spectra of the fundamental and second harmonic signal.
## 4 Results  
### The Reliability of the Moonlight Remote Sensing Imagery  
#### 4.1.1 Visual Examination of the VIIRS/DNB Moonlight Image  
Three types of moonlight imagery were used to assess the reliability of moonlight remote sensing. First, using the VIIRS/DNB image and Sentinel-2 mosaic (from 1 May 2015 to 31 December 2016) image on the Google Earth Engine platform (GEE), we first assessed the potential of multi-spectral data in land-surface classification under faint lunar illumination (Figure 6). Under a full moon and cloudless condition, the VIIRS/DNB data successfully obtained spatial distribution and spectral information of the land surface, such as snowy mountains, forests, farmlands, and rivers.
# Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter
## 4 Numerical Simulations  
The performance of the proposed method is evaluated with two numerical simulations: (a) the fault parameter estimation for the linear system related to a wind turbine, and (b) the fault parameter estimation for the van der pol nonlinear system.
# Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation +
## 3 Results
### Comparison with TOA/AOA  
The comparison of localisation performance between the TOA/AOA and the Direct method is shown in Figure 8, and it shows that the TOA/AOA method performs better if we consider an outage of 20%. The data used was for the urban environment. TOA/AOA performs better because by its design, it uses LOS paths. Where there is no LOS MPCs between the BS and the MS, the algorithm selects ground reflected paths as outlined in the prioritization scheme discussed in Section 2.4. It can be noted that TOA/AOA performance deteriorates if we consider any acceptable outage probability less than 20%. This is mainly due to the percentage of MS positions that do not have LOS paths to the BS. Use of ground reflected paths or any other mitigated NLOS paths results in growing position errors. Identification of LOS paths is covered in [3], together with the subsequent mitigation of NLOS propagation. When the correct AOA is obtained in a LOS link, the source of error then becomes, mainly, the time delay error, which produces the range between the BS and MS. Measurement errors and the positive bias of this delay can be reduced by using the mitigation techniques discussed in [3] and thus the localisation performance can be improved. In a LOS environment TOA/AOA was determined to perform better than the Direct method at any level of acceptable outage.  
Figure 7: (a) Localisation performance for the two environments. (b) Sensitivity to measurement errors.
# Biosynthesis of Zinc Oxide Nanoparticles Using Seaweed: Exploring Their Therapeutic Potentials
## 2 Materials and Methods
### Characterization of Biosynthesisized Zinc Oxide Nanoparticles  
The physicochemical characteristics of the ZnO-NPs produced from the seaweed samples were examined using a variety of characterization techniques. These techniques include ultraviolet (UV) spectroscopy, Fourier transform infrared (FTIR) spectroscopy, zeta potential, X-ray diffraction (XRD), and transmission electron microscopy (TEM).
## 4 Experimental Results  
### Fundamental Property  
The author experimentally evaluated the fundamental properties of the optical comparator. First, the impulse response was evaluated by using an optical short-pulse. The pulse was generated by a mode-locked laser diode emitting at 1550 nm wavelength, which had a 9.2 ps full width at half maximum (FWHM). Figure 3 shows an impulse response of the comparator: (a) input pulse and (b) output pulse train. In this figure, the short pulse is separated into four parts through the two DLIs, and the four pulses appear at equal intervals of time. A 93 ps time duration between first and second pulses offered 10.72 GHz of FSR, and 199 ps (93 ps + 106 ps) time duration between first and third pulses offered 5.03 GHz of FSR.  
Then, the transmission spectra were evaluated with an amplified spontaneous emission (ASE) light. Figure 4 shows the transmission spectrum of the \"00 00 00 00\", \"11 00 11 00\", \"11 10 10 00\", and \"00 10 01 00\" comparators. In Figure 4a, as the \"00 00 00 00\" spectrum laterally shifted by 0.04 nm to a long-wavelength side, it exactly overlapped the \"11 00 11 00\" spectrum. The wavelength difference of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Comparator Code**} & \multicolumn{3}{c}{**Applied Voltage in PS (V)**} & \multicolumn{3}{c}{**Phase Shifts in Each Symbol**} \\ \cline{2-7}  & **5 GHz DLI** & **10 GHz DLI** & **1 st** & **2 nd** & **3 rd** & **4 th** \\ \hline
00 00 00 00 & 2.3 (=0) & 0.6 (=0) & 0 & 0 & 0 & 0 \\
11 00 11 00 & 2.3 (=0) & 1.8 (=\(\pi\)) & \(\pi\) & 0 & \(\pi\) & 0 \\
11 10 10 00 & 2.8 (=\(\pi\)/2) & 1.2 (=\(\pi\)/2) & \(\pi\) & \(\pi\)/2 & \(\pi\)/20.04 nm corresponds a half of a wavelength interval at 10 GHz (=0.08 nm/2), which is related to a \(\pi\) phase difference at 10 GHz DLI between \"00 00 00 00\" and \"11 00 11 00\" comparators. Similarly, in Figure 4b, the \"11 10 10 00\" spectrum exactly overlapped the \"00 10 01 00\" spectrum with a 0.04 nm shift, which corresponds to a \(\pi\) phase amount between \"11 10 10 00\" and \"00 10 01 00\" comparators.
# Effect of Terahertz Antenna Radiation in Hypersonic Plasma Sheaths with Different Vehicle Shapes
## 3 Antenna Radiation Performances in Different Situations  
Since the antenna mounted on vehicles with different shapes is working in various plasma distribution, radiation performance can be different. In order to explore the impacts of plasma on the antenna, the antenna performance in vacuum is also simulated to be compared with antenna performance in plasma.  
The radiation performance of the antenna in different situations is shown in Figure 4. The green line, the red line, and the blue line represent the antenna performance in vacuum, in the blunt-coned vehicle plasma sheath, and in the sharp-coned vehicle plasma sheath, respectively. It can be seen from Figure 4 that the three antenna mainlobes are obviously different. The detailed performance parameters are listed in Table 1.  
The antenna had highest mainlobe gain in vacuum. When the antenna is working in the plasma sheath of the sharp-coned vehicle, the result shows that the antenna gain decreased by about 2.4 dB compared with gain in vacuum. In this situation, the gain decrease is acceptable for the array antenna, since it still achieved about 14 dB gain after coupling with plasma sheath. Nevertheless, the mainlobe gain decreases to about 2.7 dB when it is operating in the plasma sheath of the blunt-coned vehicle. This indicates that the antenna radiation performance in the plasma sheath of the blunt-coned vehicle is seriously affected.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Condition** & **Mainlobe Peak Gain** & **Mainlobe Shift Angle** & **Mainlobe Width** \\ \hline Vechicle in vacuum & 17 dB & 0\({}^{\circ}\) & 6.7\({}^{\circ}\) \\ Blunt-coned vehicle & 2.8 dB & \(-1.2^{\circ}\) & 7.1\({}^{\circ}\) \\ in atmosphere & 14.6 dB & 0\({}^{\circ}\) & 6.8\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The radiation parameters of the antenna in plasma for different vehicle shapes and in vacuum.  
The result shows that the plasma sheath can also have impact on the antenna radiation direction. The mainlobe in the plasma sheath of the blunt-coned vehicle is pointing at \(-1.2^{\circ}\), while in vacuum and the sharp-coned vehicle plasma sheath, the mainlobes are pointing at \(0^{\circ}\). This phenomenon illustrates that the mainlobe is refracted by the blunt-coned vehicle plasma sheath. In this situation, the gain in the target direction is further decreased, which leads to even worse communication quality.  
The mainlobe width (\(-3\) dB) is another important antenna performance parameter that is related to antenna directivity. As is shown in Figure 4 and Table 1, the plasma sheaths affect mainlobe width. The mainlobe width in vacuum is the smallest, which is \(6.7^{\circ}\). When the antenna is working in plasma, the mainlobe increased to \(6.8^{\circ}\) and \(7.1^{\circ}\). For an antenna with certain working condition, the increase of mainlobe width leads to a decrease of antenna directivity, which means the antenna performance is weakened.
# Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?
## 4 Loss-of-Efficiency in Robust PVT Solvers  
The optimal PVT solution was seen to result in a weighted least squares expression, where the weights are proportional to the inverse of the observation's covariance matrix, as shown in Equations (26) and (27). This is optimal in the MSE sense and under perfect model assumptions. When using robust solutions, a frequently asked question is how much the robust method degrades the performance with respect to the optimal estimator when the model actually holds true. This is quantified by the so-called LoE: the ratio of performance errors between robust and optimal methods under nominal conditions. Notice that, by definition, under nominal conditions the robust estimator is suboptimal so that ratio should be in the interval \((1,\infty)\), where \(1\) is the ideal case where robust and optimal methods have the same performance.  
In the case of PVT solvers, we can define the LoE as the ratio of mean squared error (MSE) that the optimal and robust estimators achieve. For the case of the optimal estimator (\(\hat{\mathbf{x}}_{o}\)) in Equation (26), it is easy to show that its covariance matrix is given by  
\[\mathbf{C}(\hat{\mathbf{x}}_{o})=\left(\mathbf{H}^{\top}\mathbf{R}^{-1} \mathbf{H}\right)^{-1} \tag{29}\]  
which provides the minimal MSE error  
\[\text{MSE}(\hat{\mathbf{x}}_{o})=\text{Trace}\left(\mathbf{C}(\hat{\mathbf{x} }_{o})\right) \tag{30}\]  
since the optimal estimator in Equation (26) is unbiased. Then, for a suboptimal estimator \(\hat{\mathbf{x}}\), the LoE can be defined as the ratio of MSEs:  
\[\text{LoE}(\hat{\mathbf{x}}) = \frac{\text{MSE}(\hat{\mathbf{x}})}{\text{MSE}(\hat{\mathbf{x}} _{o})}=\frac{\text{Trace}\left(\mathbf{C}(\hat{\mathbf{x}})\right)+||\text{ Bias}(\hat{\mathbf{x}})||^{2}}{\text{Trace}\left(\mathbf{C}(\hat{\mathbf{x}}_{o}) \right)} \tag{31}\]  
with \(1<\text{LoE}(\hat{\mathbf{x}})<\infty\). The terms in the numerator related to the robust estimator under study are  
\[\text{Bias}(\hat{\mathbf{x}})=\mathbf{x}-\mathbb{E}(\hat{\mathbf{x}})\quad \text{and}\quad\mathbf{C}(\hat{\mathbf{x}})=\mathbb{E}\left(\left(\hat{ \mathbf{x}}-\mathbb{E}(\hat{\mathbf{x}})\right)(\hat{\mathbf{x}}-\mathbb{E}( \hat{\mathbf{x}})\right)^{\top}\right)\, \tag{32}\]  
which might need to be obtained through simulations if no closed form solution can be obtained.  
In summary, we propose to measure the LoE of robust PVT solvers as the ratio of MSEs of that robust estimator and the optimal estimator, under nominal conditions where no outliers are present in the data.
## 2 Comparison of BRI with Other Global and Regional Infrastructure Initiatives
### Trade Policy Orientation  
PGII, GG, FOIP/PQI and 3 SI operate primarily at the multilateral level, engaging with multiple countries or regions through frameworks based on cooperation, shared standards, and institutional coordination. In contrast, the BRI is largely bilateral in implementation, despite being multilateral in scope.  
In bilateral trade agreements, non-member countries are often placed at a disadvantage due to the exclusive nature of tariff preferences granted only to the two signatory countries. For instance, if Country A and Country B sign a bilateral agreement reducing tariffs for each other's goods, a third country (Country C) exporting similar products to either A or B continues to face the higher non-discriminatory tariff. This discriminatory treatment distorts trade by diverting imports away from more efficient producers like Country C, solely because they are not party to the agreement. Such trade diversion effects are common in bilateral settings, where access to preferential markets is restricted. In contrast, multilateral agreements, such as those governed by the World Trade Organization (WTO), are in principle non-discriminatory under Article I of the General Agreement for Tariffs and Trade (GATT): tariff concessions granted to one WTO member must be extended to all other WTO members. As a result, trade among WTO members is conducted on a more neutral, efficiency-enhancing basis.  
This distinction has important implications for infrastructure and connectivity initiatives. Multilateral approaches--such as those supported by PGII, GG, FOIP/PQI, and 3 SI--may foster long-term liberalisation benefits, even for non-member countries. By contrast, infrastructure development agreements rooted in bilateralism--such as many under the BRI--may lead to fragmented outcomes and unequal access to trade and investment flows. Ultimately, national incentives to support multilateral versus bilateral frameworks depend on each country's factor endowments and political economy trade-offs, including the distribution of producer surplus, consumer surplus, and tariff revenues ([PERSON] and [PERSON], 2010).  
While both bilateral and multilateral initiatives are susceptible to governance failures, the risks of socially inefficient or politically motivated infrastructure projects are often more pronounced under bilateral arrangements. This is due to weaker accountability mechanisms, limited competitive pressure, and asymmetric donor-recipient dynamics that can skew project selection away from local or regional priorities. For instance, the long-term fiscal sustainability of Chinese investments in Croatian shipyards has been questioned and the BRI's Belgrade-Budapest railway project, while enhancing trade between China and central-northern Europe and southern European ports, does not necessarily contribute to the region's internal connectivity or north-south routes ([PERSON], 2019).
# Impact of Temperature on the Moisture Buffering Performance of Palm and Sunflower Concretes
###### Abstract  
The use of bio-based materials (BBM) in buildings is an interesting solution as they are eco-friendly materials and have low embodied energy. This article aims to investigate the hygric performance of two bio-based materials: palm and sunflower concretes. The moisture buffering value (MBV) characterizes the ability of a material or multilayer component to moderate the variation in the indoor relative humidity (RH). In the literature, the moisture buffer values of bio-based concretes were measured at a constant temperature of 23 \({}^{\circ}\)C. However, in reality, the indoor temperature of the buildings is variable. The originality of this article is found in studying the influence of the temperature on the moisture buffer performance of BBM. A study at wall scale on its impact on the indoor RH at room level will be carried out. First, the physical models are presented. Second, the numerical models are implemented in the Simulation Problem Analysis and Research Kernel (SPARK) suited to complex problems. Then, the numerical model validated with the experimental results found in the literature is used to investigate the moisture buffering capacity of BBM as a function of the temperature and its application in buildings. The results show that the temperature has a significant impact on the moisture buffering capacity of bio-based building materials and its capacity to dampen indoor RH variation. Using the numerical model presented in this paper can predict and optimize the hygric performance of BBM designed for building application.  
bio-based building materials; moisture buffer capacity; MBV; simulation and modeling; palm concrete; sunflower concrete +
Footnote †: journal: Applied Sciences
# Towards a Real-Time Description of the Ionosphere:
## 8 Validation Results for _Imit_F2 Based on Radio Occultation Observations
This is reflected also in the distribution of residuals that in the case of IRTAM appears more widespread around the zero than that of IRI, and this has a strong impact on the maps shown in Figures 41-44. In fact, these maps clearly highlight how IRTAM, independently of the analysis (spatial, diurnal, seasonal, and dependent on solar activity), does not improve the \(hm\)F2 modeling made by IRI. This result might be affected to a some extent by the fact that IRI \(hm\)F2 values have been calculated through the [PERSON] et al. (2014) model, which is partly based on electron density profiles collected by COSMIC between 2006 and 2012.
## 2 Related Works
### Feature Extraction Systems  
In [24], a convolutional graph network was used to learn features from knowledge graphs instead of embeddings to improve the performance of the recommendation system. In addition, to enhance the convolutional neural networks gated recurrent units were used. In [25], the feature of personalized preference information was extracted, and the structure of the deep-learning model that could explicate the user's decision-making was proposed. A feature learning process that overlooks the past interaction information and a model that applies attention was also presented. [PERSON] et al. [26] proposed a heterogeneous information network model that uses deep learning. As a feature, rich secondary data containing the user review data and user rating were used; the secondary data were presented to the models that train with the features of entities through the network embedding process. [PERSON] et al. [27] proposed a deep-learning model for the recommendation system that uses the co-occurrence embedding structure with the rating, user, and item metrics to analyze the correlation between the user and item data [27]. Finally, in [28], the fusion recommendation model that utilizes the item rating and user review data was presented. One limitation of the traditional fusion recommendation model is its complexity; thus, a subnetwork that separated the user review data and rating data was used to reduce the complexity.  
Aside from the traditional deep-learning-based recommendation system structure that uses the multilayer perceptron, an immediate item was recommended using the fused features. [PERSON] et al. [29] proposed a neural explicit factor model to increase the explainability of the recommendation systems of the existing CF models that use user and item data. The explainability of the item and user vectors was increased by applying the user-feature attention matrix and an n-item-feature quality matrix to the user-item rating matrix. Additionally, using a 1-dimensional CNN and feedforward neural network, the features from the item-feature vector, user, and item were extracted [29].  
The proposed model is structurally closest to the deep neural CF model with feature extraction, but the deep neural CF model with feature extraction underwent the feature selection process of the user items, and the residual connection of the item data and the embedded users is performed for the feature map data that rearrange the selected features. Therefore, the role of the model is different, and the number of parameters used in the computation of deep learning shows a significant difference.