esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Modeling Capability of Machine Learning Approaches and Their Comparison  
The results of this study demonstrated that the proposed machine learning methods were able to imitate the dynamic processes of carbon exchanges between terrestrial ecosystems and the atmosphere. Though these models were developed using only four different climatic forces (\(T_{a}\), \(T_{s}\), \(R_{n}\), and \(R_{h}\)) as inputs, it was found that a significant amount of variation in daily carbon fluxes, especially for GPP and \(R\), was explained by these independent variables (Tables 3-5). For example, among the optimal SVM models at CA-Obs, SVM1 provided the most accurate estimates for all carbon fluxes, with \(R^{2}\) = 0.9574, IA = 0.9818, RMSE = 0.6915 g C m\({}^{-2}\) day\({}^{-1}\), and MAE = 0.4290 g C m\({}^{-2}\) day\({}^{-1}\) for GPP, 0.9471, 0.9809, 0.4692 g C m\({}^{-2}\) day\({}^{-1}\), and 0.3385 g C m\({}^{-2}\) day\({}^{-1}\) for \(R\), and 0.8175, 0.9255, 0.6426 g C m\({}^{-2}\) day\({}^{-1}\), and 0.4746 g C m\({}^{-2}\) day\({}^{-1}\) for NEE. In addition, at CA-Oas, for each carbon flux, these superior models (Tables 3-5) achieved similar estimates of the annual total flux, and the errors of mean annual total are equal to 5.74%, 2.15%, and 11.40% for GPP, \(R\) and NEE, respectively. Therefore, it can be concluded that these developed machine learning models successfully reproduced daily carbon fluxes in terrestrial ecosystems.  
Furthermore, comparing the modeling accuracy of multiple machine learning models can enable us to determine the most appropriate model for solving a given application. In recent years, the predictive abilities of both ANN and SVM methods for carbon flux prediction have been substantiated by many previous studies [27; 71; 72]. In addition, since the relatively new ELM and ANFIS methods, used here for the first time to forecast daily carbon fluxes, exhibited comparatively satisfactory predictive accuracy in terms of various performance criteria, they can be viewed as promising alternative tools to traditional ANN and SVM approaches. Nevertheless, according to our estimates, it should be pointed out that, among the evaluated approaches, the ANN method generally performed the best for each carbon flux at all five sites.
## 4 The Proposed LSE Scheme  
This section presents an effective joint CFO and SFO estimation scheme in the MIMO-OFDM ATSC system and its performance analysis is presented in the multipath fading channel. The improved CFO and SFO estimation scheme is achieved by properly choosing a pilot subset so that a bias incurred by using randomly distributed CPs can be eliminated.
# Effect of Ti-B Grain Refiners on Wear and Corrosion of the A332 Alloy with Sr Modification
## 2 Materials and Methods
### Corrosion Analysis  
Corrosion analyses were made with Autolab Potentiostat PGSTAT M204, FRA32M equipment (Ultrecht, Netherlands), according to ASTM G59-97 [23], G102-89 [24], and G5-14 [25] standards. In order to induce pitting corrosion a 3.5 wt.% NaCl solution at 25 \({}^{\circ}\)C was used as environment. The samples were polished using colloidal silica to achieve a mirror finish. A calomel (3.0 M KCl) reference electrode and platinum counter electrode were used for measurements. The samples were prepared in a cylindrical shape with 17 mm of diameter and 4 mm height. The samples were placed inside of support, exposing 1 cm\({}^{2}\) of its area. Open circuit potential (E\({}_{\text{ocp}}\)) was measured for 55 min and the chemical reaction was considered stabilized after that, according to ASTM G5 [25]. A resolution of 1 mV\(\cdot\)s\({}^{-1}\) was used both for micro and macro polarizations. A potential range of E\({}_{\text{ocp}}\pm 20\) mV was used for acquiring micro-polarization curves, used to calculate polarization resistance (Rp). A potential range of E\({}_{\text{ocp}}\) from \(-1\) V up to \(+5\) V was used for macro-polarization. A wide  
Figure 1: (**a**) equipment for ball-on-disk testing; (**b**) detailed scheme of testing (1) sample, (2) ball, (3) wear path on sample (4) rotation sense of sample and (5) load.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Diameter** & **Ball** & **Sliding** & **Load (N)** & **Number of Cycles** \\
**of Path (mm)** & **Diameter (mm)** & **Speed (m/s)** & **WaE** & **Temperature** \\ \hline
8 & 6 & 0.1 & 3 & 15,000 \\ \hline \end{tabular}
\end{table}
Table 3: Parameters of ball on disk testing.  
range of potential was used for Tafel curves in order to generate pitting corrosion. Anodic (b\({}_{\text{a}}\)) and Cathodic (b\({}_{\text{C}}\)) were measured from Tafel slopes. Likewise, corrosion potential and current density were measured at the intersection of the slopes. The corrosion rate CR (mm/year) was calculated in accordance to Equation (3) ASTM G59--97 [23], in which EW is the equivalent weight of the corroding species (g), i\({}_{\text{corr}}\) is the current density (\(\mu\)A/cm\({}^{2}\)) and \(\rho\) is the density of the corroding material (g/cm\({}^{3}\)).  
\[\text{CR }=3.27\times 10^{-3}\frac{\text{i}_{\text{corr}}\text{EW}}{\rho} \tag{3}\]
# Feasibility, Design, and Deployment Requirements of TCR for Bistatic SAR radiometric Calibration
## 1 Introduction
Similarly, there are other distributed spaceborne SAR or constellation systems with a relatively small and fixed bistatic angles [15; 16] along, across, or at the hybrid of the flight tracks. For example, TanDEM-X is the first genuinely bistatic SAR system in space, and the working mode includes monostatic, bistatic, and alternating bistatic mode. The first bistatic spaceborne SAR experiments was made as the bistatic angle is 1.8\({}^{\circ}\), and then obtained the good quality bistatic image [17]. The Argentine SAOCOM constellation comprises two L-band satellites, SAOCOM-1A and SAOCOM-1B, which will be monitoring for the mitigation of the effects of natural disasters. This paper is structured as follows. Section 2 is devoted to outlining the requirements of the calibration target for radiometric calibration. The bistatic RCS pattern of the TCR is analyzed in Section 3 through electromagnetic simulations as the bistatic angle fixed in azimuth. Section 4 analyzes the various error factors of the TCR RCS quantitatively, including machining, deployment, and thermal deformation errors. In Section 5, the quality of the bistatic SAR images of the TCR with different bistatic angles are analyzed. The discussion and conclusions are finally drawn in Sections 6 and 7, respectively.
## 4 Simulation Results and Discussion
### Case 1: Sensitivity Analysis for Criterion Variation Within \(\pm 5\%\)
#### 4.2.2 Scenario 2: Variation in Upload Speed and Download Speed  
In this scenario, in addition to the previous variation in upload speed, we randomly vary the initial download speed values while keeping the values of other criteria unchanged. Table 7 shows the random variation rates applied to the initial download speed values of each satellite. The resulting decision matrix is shown in Table 8.
# Secure Indoor Localization Based on Extracting Trusted Fingerprint
## 4 Secure WiFi Fingerprint Localization
### Offline Testing Phase  
#### 4.1.1 Collect Offline RSSI  
In order to build an offline fingerprint database, we need to collect RSSI in the localization area. We divide the indoor network coverage area into \(N=n\times n\) square grids; \(N\) is the total number of meshes; and the grid is encoded in coordinates. We consider each grid as a reference point. The offline fingerprint database is a set of all reference point fingerprints. For the fingerprint-based localization algorithm, the larger the grid, the greater the positioning error, and the smaller the grid, the greater the workload of establishing a fingerprint database. We set the communication radius of an AP as \(r\), when the range of the number of meshes \(N_{grid}\) is \((2S_{region}/r^{2})\leq N_{grid}\leq\left[S_{region}/(10^{(\log(I_{\min})- \wedge/\alpha)})\right]\), and the acquisition cost of the offline stage is low [17]. When collecting fingerprints, we set the size of the grid to \(0.5\) m \(\times\)\(0.5\) m.  
As for the complex environments, we collect multiple samples and get their average. In order to calculate the RSSI's average of \(AP_{i}\) received at grid \(j\), we place a reference anchor node at grid \(j\) to receive the RSSI sent by \(AP_{i}\). Assuming that the total number of RSSI samples received by the reference anchor node is \(q\), then the average RSSI received by the reference anchor node placed in grid \(j\) can be calculated as follows:  
\[\varphi_{i,j}=\sum_{\tau=1}^{q}\varphi_{i,j}(\tau)/q. \tag{1}\]  
where \(\varphi_{i,j}(\tau)\) is the \(\tau\)-th RSSI of the grid \(j\) received. \(\varphi_{i,j}\) is the average RSSI of the grid \(j\) received. The number of APs is L. The initial database matrix \(\Psi\) is as follows:  
\[\Psi=\left\{\begin{array}{cccc}\varphi_{1,1}&\varphi_{1,2}&\cdots&\varphi_{ 1,N}\\ \varphi_{2,1}&\varphi_{2,2}&\cdots&\varphi_{2,N}\\ \vdots&\vdots&\ddots&\vdots\\ \varphi_{\text{L},1}&\varphi_{\text{L},2}&\cdots&\varphi_{\text{L},N}\\ \end{array}\right\}. \tag{2}\]  
If the grid \(j\) is outside the communication range \(r\) of \(AP_{i}\), that is the reference anchor node placed in grid \(j\) cannot receive the RSSI from \(AP_{i}\), then let \(\varphi_{i,j}=0\).
## 3 Numerical Model
### Boundary Conditions and Simulation Settings  
#### 3.5.1 Boundary Conditions  
(1) Inlet boundary conditions: velocity-inlet.  
Gas phase: only considering the axial gas velocity distribution \(v_{g,\,\text{inlet}(r)}\):  
\[v_{g,\text{inlet}}(r)=\frac{60}{49}\frac{U_{g}}{1-\alpha_{s,\text{inlet}}}(1-2r /D)^{1/7} \tag{20}\]  
where \(r\) represents the distance to the center of the inlet cross-section, and \(D\) is the pipe diameter.  
Solids phase: To facilitate convergence, the same gas and solids velocity distribution are set. The axial solids velocity distribution \(v_{s,\text{inlet}(r)}\):  
\[v_{s,\text{inlet}}(r)=\frac{60}{49}u_{s,\text{inlet}}(1-2r/D)^{1/7} \tag{21}\]  
\[u_{s,\text{inlet}}=U_{g}\Big{(}1-0.68d_{s}^{0.92}\rho_{s}^{0.5}\rho_{g}^{-0.2 }D^{-0.54}\Big{)} \tag{22}\]  
\[\alpha_{s,\text{inlet}}=4M_{s}/\left(\rho_{s}u_{s,\text{inlet}}\pi D^{2}\right) \tag{23}\]  
where Equation (22) is an empirical equation used to calculate \(u_{s,\text{inlet}}\).  
(2) Outlet boundary conditions: pressure outlet and the outlet pressure are designated as \(P_{out}\).  
(3) Turbulence settings: hydraulic diameter is set to the pipe diameter, and the turbulent intensities for the gas and solids phases are 10% and 5%, respectively.  
(4) Wall boundary conditions.  
Gas phase: no-slip.  
Solids phase: the modified Schneiderbauer solids wall boundary model.  
[PERSON] et al. (2017) proposed a new solids wall boundary model that not only distinguishes between sliding and non-sliding collisions among particles but also integrates them into a single expression. However, the model fails to consider the frictional shear stress generated by particles sliding on the wall, which is crucial for high-pressure dense-phase pneumatic conveying in horizontal pipe. Accounting for the frictional effects at the wall, a frictional stress term was introduced to modify the Schneiderbauer solids wall boundary model.  
The modified shear stress between the solids phase and the wall:  
\[\tau_{sw}=-\eta_{w}\mu_{w}\alpha_{s}\rho_{s}g_{0,ss}\Theta_{s}erf(\overline{ u}_{sw})\frac{\mathbf{u}_{sw}}{|\mathbf{u}_{sw}|}-\mu_{w}p_{f}\mathbf{u}_{sw}/|\mathbf{u}_{ sw}| \tag{24}\]  
The pseudo-heat flow of the solids phase at the wall:  
\[\begin{array}{l}q_{w}=\mathbf{\tau}_{sw}\cdot\mathbf{u}_{sw}-\frac{a_{s}\rho_{s}g_ {0,ss}\eta_{w}\sqrt{\delta_{s}^{2}}}{\sqrt{2\pi}\eta_{w}^{2}}\exp\left(- \overline{u}_{sw}^{2}\right)\times\\ \left\{\begin{array}{l}\mu_{w}\Big{[}2\mu_{w}|\mathbf{u}_{sw}|^{2
# Earth Rotation Parameters Estimation Using GPS and SLR Measurements to Multiple LEO Satellites
## 2 Data Set and Processing Strategy
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Dynamics Model** \\ \hline Earth gravity & EGM08 (120 \(\times\) 120) \\ \hline N-body & JPL DE405 [26] \\ \hline Ocean tide & FES 2004 (30 \(\times\) 30) [27] \\ \hline Relativity & IERS 2010 [28] \\ \hline Solid tide and pole tide & IERS 2010 \\ \hline Earth radiation pressure & Not considered \\ \hline Solar radiation pressure & Box-wing model for LEO satellites [29] \\ \hline Atmospheric density & NRLMSISE00 [30] \\ \hline \multicolumn{2}{c}{**Estimated Parameters**} \\ \hline Satellite initial state & Position and velocity at initial epoch \\ \hline Atmospheric drag & GRACE-FO and Swarm: one constant scale parameter per 90 min \\  & Sentinel-3: one constant scale parameter per 100 min \\ \hline  & GRACE-FO and Swarm: piecewise periodic accelerations in cross-track and along-track directions per 90 min \\ Empirical accelerations & Sentinel-3: piecewise periodic accelerations in cross-track and along-track directions per 100 min \\ \hline Receiver clock error & Each epoch as white noise \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Force model for the POD of LEO satellites. All computations are performed on the GNSS+ Research, Application and Teaching (GREAT) software, which is designed and developed at the School of Geodesy and Geomatics of Wuhan University (WHU). The software can support multiple applications, such as multi-technique geodetic parameter estimation, GNSS real-time orbit, and clock determination, PPP-RTK (real-time kinematic) [37], multi-GNSS bias estimation [38], multi-sensor integration navigation, etc.
## 6 60 GHz WiFi: IEEE 802.11 ay
### Channel Configurations  
Despite the availability of around 14 GHz of bandwidth in the 60 GHz range, the IEEE 802.11 ad only uses a channel bandwidth of 2.16 GHz. On the other hand, IEEE 802.11 ay introduces wider channels of 4.32 GHz, 6.48 GHz, and 8.64 GHz by using channel bonding and aggregation, as shown in Figure 6[61]. Channel bonding is where two or more contiguous channels are merged to make a single channel. There will be no channel spacing between the merged channels, and a single waveform will use the merged channel. In contrast, channel aggregation will combine two or more contiguous or noncontiguous channels with channel spacing with different channels using different waveforms [62].  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**MCS** & **Modulation** & **Coding Rates** & **PHY Rate** \\ \hline Control mode & \(\pi/2\)-DBPSK & 1/2 & 27.5 Mbps \\ \hline \multirow{4}{*}{Single-carrier mode} & \(\pi/2\)-BPSK & & \\  & \(\pi/2\)-QPSK & & \\  & \(\pi/2\)-16 QAM & 1/2, 5/8, 3/4, 13/16, 7/8 & 330 Mbps–34.65 Gbps \\  & \(\pi/2\)-64 QAM & & \\ \hline OFDM mode & SQPSK, QPSK, 16 QAM, 64 QAM & 1/2, 5/8, 3/4, 13/16, 7/8 & 630 Mbps–37.92 Gbps \\ \hline \hline \end{tabular}
\end{table}
Table 2: Modulation and coding rates of IEEE 802.11 ay.  
Figure 6: Channelization of IEEE 802.11 ay.
## 4 The Simulation and Analysis of CBN Grain Movement Behavior  
### The Coupled Numerical Modeling of Multiphysical Fields  
#### 4.1.1 The Assumptions and Material Properties  
The numerical model makes the following assumptions:  
(1) The liquid in the melt pool is a viscous and incompressible Newtonian fluid with laminar flow;  
(2) The existence time of the melt pool at the same location is short, and the thermal effect produced by ultrasonic vibration is ignored;  
(3) Neglecting the shape of the CBN abrasive grains in the melt pool, they are assumed to be spheres with a certain diameter and density;  
(4) The matrix and metal powder materials are isotropic, and their compositions are uniformly distributed;  
(5) The chemical reactions process occurring in different metal materials is ignored;  
(6) Set the model shape factor \(\eta\) to 2 and the dilution rate \(\gamma\) to 20%.  
The multiphysics field coupling process of the CBN abrasive grain movement simulation in the melt pool can be seen in Figure 5. The temperature and flow fields are first coupled with the help of the built-in Marangoni effect in COMSOL Multiphysics 6.0. Then, the fluid flow generated by ultrasonic vibration and the Marangoni effect is coupled to the fluid flow particle tracking module to investigate its effect on the CBN abrasive grains.  
To make the simulation results of the CBN abrasive grain movement behavior in the melt pool more accurate, the exact parameters of the substrate, the printing layer material, and the CBN abrasive grain need to be known. The substrate material is 45 steel; the bond material is Cu-Sn-Ti; and the abrasive grain is CBN. The material parameters of 45 steel are shown in Table 6. The parameter values of 45 steel after exceeding 1273 K are approximated to take the values at 1273 K [34].  
The parameters related to the Cu-Sn-Ti metal bond in this study are predicted by JmatPro 5.0 software, which is a software used for materials research to simulate the thermophysical properties of materials and other related parameters. In this study, the mass fraction ratio of Cu, Sn, and Ti in the Cu-Sn-Ti bond is Cu-Sn19-Ti10, which is calculated by using the titanium alloy module of JmatPro software. The temperature coefficient of surface tension of the Cu-Sn-Ti bond is \(-2.24\times 10^{-4}\) N\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\), and the other relevant parameters are shown in Figure 6.  
Figure 5: The multiphysical field coupling flowchart.  
In the simulation process, the CBN abrasive grain is subjected to its gravity, the buoyancy of the molten metal, the drag force of the metal fluid, and the acoustic radiation force of supersonic vibration under the joint action of the movement. So, it is necessary to take into account the diameter and density of the CBN abrasive grains. The density of the CBN abrasive grains is 3486 kg/m\({}^{3}\), and the grain size of the CBN abrasive grains selected is 100-120 mesh, taking the intermediate value of 110 mesh for the grain size, i.e., 150 \(\upmu\)m.
# A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification
## 3 Materials and Methods
### Experimental Setup  
We tested the six domain adaptation models mentioned above on both MSA and DA datasets. In addition, we reported the results of the in-domain SVM classifier (i.e., trained and tested in the target domain), as well as those of an SVM classifier without adaptation as a baseline (i.e., trained in the source domain and tested in the target domain). SVM, DANN, mSDA and DARL were tested using TF/IDF of unigrams and bigrams, while AraBERT-ALDA and AraBERT-DANN were tested on top of AraBERT.  
The following are some implementation details for each algorithm used in our experiments:  
* DANN [10]: we followed the original implementation, where the size of the hidden layer was 50, the learning rate was \(10^{-3}\) and the adaptation parameter equaled one;
* mSDA [11]: we trained a linear SVM classifier using a representation learned by mSDA with five layers and a corruption probability of 50%;
* DARL [6]: to achieve the DARL goal of combining adversarial training and denoising reconstruction, we used both mSDA and DANN (with \(10^{-4}\) learning rate);
* AraBERT [45]: we trained AraBERT for five epochs with the Adam optimizer and a learning rate of \(2\times 10^{-5}\) using the Transformers library of HuggingFace;
* AraBERT-ALDA [7]: we used the same implementation provided by the authors;
* AraBERT-DANN: we used an implementation provided in [7].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **LAMD** & **MARSA** \\ \hline Arabic type & MSA & DA (Saudi) \\ Text type & Reviews & Tweets \\ Domains & Hotels, restaurants, products & Political, social, sports \\ Size & 6000 reviews & 6000 tweets \\  & (2000 per domain) & (2000 per domain) \\ Vocabulary & 61,602 & 29,999 \\ Stem \({}^{1}\) & 24,299 & 11,731 \\ Root \({}^{1}\) & 10,929 & 5220 \\ \hline \hline \end{tabular} \({}^{1}\) Tashaphyne Arabic Light Stemmer.  
\end{table}
Table 1: A summary of the datasets used in the comparative study.  
All experiments were conducted using Google Collab, with GPU for transformer-based experiments.
# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 6 Conclusions  
To address the challenges of achieving high-quality and robust speech conversion, we proposed MPFM-VC, a novel model that utilized ordinary differential equations (ODEs) to model the dynamic evolution of speech features. A multi-dimensional perception mechanism was introduced to improve the stability and naturalness of speech synthesis. In addition, a content perturbation-based training augmentation strategy was employed to enhance the model's generalization ability by introducing controlled perturbations to content features during training, helping reduce undesirable artifacts during inference. To further improve timbre matching, a voiceprint adversarial training strategy was adopted to better disentangle content and speaker identity features, minimizing feature entanglement and reducing residual speaker information. Experimental results indicated that the proposed MPFM-VC model achieved competitive performance across multiple evaluation metrics--including MOS, MCD, and SECS--demonstrating improvements in naturalness, speaker similarity, and robustness in real-world scenarios.