esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In recent years, nanoparticles have been introduced into warm-mix-modified asphalt to improve asphalt performance after sustaining ultraviolet (UV) aging, yet the evaluation of aging performance is often a descriptive characterization of rheological properties. This study extends rheological characterization with viscoelastic mechanical modeling to evaluate resistance to UV aging using Sasobit and SBS compound-modified binder blended with nano-titanium dioxide (TiO\({}_{2}\)). The extended method comprises characterizations using several rheological properties and a viscoelastic mechanical model, named the 2S2P1D model, on modified asphalt after 3 days, 6 days and 9 days of ultraviolet (UV) aging. The rheological properties of the UV-aged binders were tested at high and medium temperatures in terms of viscosity, complex modulus, phase angle and fatigue factor. Rheological test results showed that nanoparticles generally had no apparent effect on the complex modulus of aged binders regardless of UV aging times. However, the aged binder with nanoparticles showed better fatigue resistance than aged binders without nanoparticles after 3 days of UV aging. As an extension, the black space diagram and 2S2P1D model were used to investigate the viscoelastic properties of these aged binders. The \(k\) and \(h\) values, as important model parameters, were almost the same and less than one for all UV-aged binders. All investigated aged asphalt binders showed characteristics of a viscoelastic solid in terms of the master curves of the complex modulus and phase angle, and the master curves of the phase angle for all UV-aged binders did not meet the time-temperature equivalence. Moreover, these observations from the 2S2P1D model revealed that aging durations did not affect the viscoelastic mechanical characteristics of warm mix asphalt in this study. The method adopted in this study may promote a comprehensive evaluation of asphalt properties after UV aging, especially considering the viscoelastic mechanical performance.  
ultraviolet aging (UV); aging times; viscoelastic properties; nano-titanium dioxide (TiO\({}_{2}\))
## 4 Experiments and Discussion
### Attitude Sensors  
In general, the orientation of the inertial measurement unit (IMU) is derived from the inertial sensors, i.e., accelerometer and gyroscope sensors. All these sensors provide analog signals, so an analog-to-digital converter (ADC) is required to acquire the data. Therefore, the PIC18F258 made by Microchip Technology, with 8-channel 10-bit ADC, was used. In order to increase the computational efficiency and to perform the data fusion algorithm, microchips (PIC 18F258) served as the processing units of the low-cost IMU, and they communicated with each other through the built-in RS232 bus. The configuration of this self-developed IMU is depicted in Figure 14. This self-developed, low-cost IMU was used to offer attitudes and angular rates for the onboard computer module. The outputs of IMU were Euler angles, angular rates, and accelerations. Although the IMU performs well in normal conditions, its outputs always suffer from unpredictable errors such as moving due to the motor vibrations. The chassis on the TWVS induced vibrations and affected the onboard hardware to some extent. Without proper installation, the IMU probably gives inapplicable outputs. Therefore, vibration isolation or reduction became an important issue for our IMU on a moving platform. Some vibration reduction materials were tested and finally a kind of sound deadening sheet for cars was chosen. The IMU was surrounded by several layers of the sound deadening sheet and then cased into a box.
## 2 Materials and Methods
### Characterization Procedures
#### 2.3.2 Oleuropein, Tyrosol, and Hydroxylysol Assay by HPLC  
The apparatus used was an HPLC (Agilent 1100, Agilent Technologies, Santa Clara, CA, USA) equipped with a Diode Array Detector (DAD). For oleuropein, the sample (2 \(\mu\)L injected) was eluted through a C18 column (1.8 \(\mu\)m, 4.6 mm \(\times\) 50 mm, Zorbax Eclipse XDB-C\({}_{18}\), Agilent Technologies, Santa Clara, USA) maintained at a constant temperature of 40 \({}^{\circ}\)C. Separation of oleuropein was made by a binary solvent (A and B), at 1.5 mL/min. Solvent A was composed of water: TFA (trifluoroacetic acid) (99.9:0.1, mL/mL), and solvent B was acetonitrile: TFA (99.9:0.1, mL/mL). The elution gradient was (percentages of solvent B are indicated): 0 to 2.6 min: 20%, 2.6 to 2.7 min: 100%, and 2.7 to 3.5 min: 100%.  
For tyrosol and hydroxytyrosol, the sample (10 \(\mu\)L injected) was eluted through a C18 column (3 \(\mu\)m, 4.6 mm \(\times\) 150 mm, Luna C18, Agilent Technologies, Santa Clara, CA, USA) maintained at a constant temperature of 25 \({}^{\circ}\)C. Separation of tyrosol and hydroxytyrosol was made by a binary solvent (A and B) at 0.5 mL/min. Solvent A was composed of water: formic acid (99.9:0.1, \(v/v\)), and solvent B was acetonitrile (100%). The elution gradient was (percentages of solvent B are indicated): 0 to 25 min: 10%, and 25 to 30 min: 100%. Oleuropein, tyrosol, and hydroxytyrosol were detected at a wavelength of 280 nm and quantification was made by external calibration. Analysis of oleuropein, tyrosol, and hydroxytyrosol was performed only once.
# Ontology with Deep Learning for Forest Image Classification
## 9 Discussion  
The evaluation of image classification results is of paramount importance in order to determine the best suitable model for a given application. Classification performance is dependent on the types of images used and the domain application. Images are generally categorized into remote sensing images, natural images, medical images, and synthetic images; therefore, the performance of image classification approaches varies according to the type of images used. It is possible that a particular algorithm produces good results in remote sensing images but poor results in synthetic images. For this study, image classifications based on an ontology with deep learning were obtained for natural forest images. The classes used for the study were grassland, orchards, bare land, degraded forest, woodlands, sea, buildings, and shrubs. The results presented in Table 2 show that the ontological bagging algorithm based on linear SVM outclassed other models with respect to RMSE and accuracy. The high accuracy is attributed to the ability of the model to suppress the error propagation of hierarchical classifiers. As presented in Table 7, our ontological-based model managed to outperform other models such as [11], which used ontology and an HMAX model to classify bird images into categories; ref. [1] for classifying vehicles into their respective categories; ref. [35] based on ontology and a CNN to classify natural images from an ImageNet dataset; and [36] for natural image classification through the transfer learning of images obtained from the Caltech-101 image dataset. However, the ontology-based classification model presented in [37] for classifying objects in urban and peri-urban areas slightly outperformed our model, with a classification score of 98%. A hybrid model of deep learning and SVM designed in [38] to perform image classification on the Fashion-MNIST, Cifar10, Cifar100, and Animal10 datasets also attained a classification accuracy of 99%. The reason could be attributed to the nature and quality of the image dataset generated by the data augmentation process used in the study.
###### Abstract  
This study examines the role of linguistic regional variations in synthetic dataset generation and their impact on emotion detection performance. Emotion detection is essential for natural language processing (NLP) applications such as social media analysis, customer service, and mental health monitoring. To explore this, synthetic datasets were generated using a state-of-the-art language model, incorporating English variations from the United States, United Kingdom, and India, alongside a general baseline dataset. Two levels of prompt specificity were employed to assess the influence of regional linguistic nuances. Statistical analyses--including frequency distribution, term frequency-inverse document frequency (TF-IDF), type-token ratio (TTR), hapax legomena, pointwise mutual information (PMI) scores, and key-phrase extraction--revealed significant linguistic diversity and regional distinctions in the generated datasets. To evaluate their effectiveness, classification experiments were conducted with two models using bidirectional encoder representations from transformers (BERT) and its de-noising sequence to sequence variation (BART), beginning with zero-shot classification on the contextualized affect representations for emotion recognition (CARE) dataset, followed by fine-tuning with both baseline and region-specific datasets. Results demonstrated that region-specific datasets, particularly those generated with detailed prompts, significantly improved classification accuracy compared to the baseline. These findings underscore the importance of incorporating global linguistic variations in synthetic dataset generation, offering insights into how regional adaptations can enhance emotion detection models for diverse NLP applications.  
Article  
13 December 2024  
Revised: 27 February 2025  
Accepted: 21 March 2025  
Published: 22 March 2025  
**Citation**: [PERSON], [PERSON].  
The Impact of Linguistic Variations on Emotion Detection: A Study of  
Regionally Specific Synthetic Datasets.  
_Appl. Sci._**2025**, 15, 3490  
[[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).
# FPGA Applied to Latency Reduction for the Tactile Internet
###### Abstract  
Tactile internet applications allow robotic devices to be remotely controlled over a communication medium with an unnoticeable time delay. In bilateral communication, the acceptable round trip latency is usually 1 ms up to 10 ms, depending on the application requirements. The communication network is estimated to generate 70% of the total latency, and master and slave devices produce the remaining 30%. Thus, this paper proposes a strategy to reduce 30% of the total latency produced by such devices. The strategy is to use FPGAs to minimize the execution time of device-associated algorithms. With this in mind, this work presents a new hardware reference model for modules that implement nonlinear positioning and force calculations and a tactile system formed by two robotic manipulators. In addition to presenting the implementation details, simulations and experimental tests are performed in order to validate the hardware proposed model. Results associated with the FPGA sampling rate, throughput, latency, and post-synthesis occupancy area are analyzed.  
t actile internet; latency reduction; haptic devices; FPGA Article  
FPGA Applied to Latency Reduction for the Tactile Internet  
2227851
## 4 Future Data
### Multispectral Imagery  
While Landsat 7 is well beyond its planned lifetime, Landsat 8 is young and performing well, and there are two future Landsat missions currently in the planning stages. Although specifications for these missions are still being developed, Landsat 9 is planned for a launch in late 2018, while the launch of Landsat 10 is scheduled for 2023 [2]. As with all current Landsat data, future Landsat missions will provide open and freely available multispectral imagery over almost the entire globe for circumpolar and cryospheric researchers worldwide.  
At lower resolutions, VIIRS on-board Suomi-NPP is a bridge mission to NOAA's Joint Polar Satellite System (JPSS). A VIIRS instrument is currently planned to be on-board both JPSS-1, scheduled for launch in early 2017, and JPSS-2, scheduled for launch readiness in early 2022.  
The Sentinel satellite series forms the space segment of the EC Copernicus program [90,91]. These satellites are funded by the EC, with ESA responsible for launch and operations. The Sentinel constellation, each version of which is a pair of satellites in itself, will include both higher resolution (10-60 m, Sentinel-2) [92] and lower resolution (300 m, Sentinel-3) [93] multispectral sensors. The first Sentinel-2 and Sentinel-3 are scheduled for launch in 2015. While uncertain until late 2013, in a move away from previous behavior, all Sentinel data will be free and openly available. Registration requirements for data access, however, remain to be seen. Distribution channels are still to be defined, but it is expected that a download manager will be developed along with online FTP access to a rolling archive.  
Increasingly, small unmanned airborne systems (UAS's)/unmanned airborne vehicles (UAV's) are capable of collecting high-quality data for local cryospheric mapping needs, both imagery and 3 Dpoint clouds (e.g., [94, 95, 96]). Such low-cost and personalized methods for data collection are certainly lowering the barrier to scientists having their own specific datasets, which raises questions not just of data quality, but also documentation and distribution. It remains to be seen if and how researchers will describe and share the data that their drones are collecting.  
In addition, constellations of small satellites (e.g., CubeSats) will facilitate higher temporal monitoring from space at spatial resolutions of 1-5 m. Intelligence is still the driver, but they will still have some scientific applications [97]. However, use will be determined not only by data availability and quality, but also by the price of imagery and the distribution of data products, if any.
# A New GNSS-Derived Water Vapor Tomography Method Based on Optimized Voxel for Large GNSS Network
## 2 Data and Methods
### Optimized Solution for WV Tomography
\[X_{ori}\ =\ [x_{1},x_{2},x_{3},x_{4},x_{5},x_{6},x_{7},x_{8},x_{9},\ \ldots\ x_{13},\ \ldots\ x_{27}]^{T}, \tag{12}\]  
\[X_{temp}\ =\ [\mathbf{x_{k}},x_{2},\mathbf{x_{k}},\mathbf{x_{k}},x_{5},x_{6},x_{ 7},x_{8},x_{9},\mathbf{x_{6}},\ \ldots\ \mathbf{x_{34}},\ \ldots\ \mathbf{x_{27}}]^{T}, \tag{13}\]  
\[X_{upd}\ =\ [x_{1},x_{2},x_{3},\ \ldots\ x_{21}]^{T}, \tag{14}\]  
\[X_{final}\ =\ [\mathrm{null},x_{1},\mathrm{null},\mathrm{null},\mathrm{null},x_{2 },x_{3},\mathrm{null},x_{4},\mathrm{null},\ \ldots\ x_{7},\mathrm{null},x_{9}\ \ldots\ x_{21}]^{T}, \tag{15}\]  
where \(X_{ori},X_{temp},X_{upd},\) and \(X_{final}\) represent the original voxels (i.e., all voxels in the tomographic area), temporary voxels (only the null-ray-passed part is removed, but without renumbering), updated voxels (renumbered based on the temporary voxels), and final voxels (processed to restore the upgraded voxel according to its geometric location), respectively. \(X_{upd}\) is the estimate matrix (i.e., WV density matrix) in the tomographic equation, which can be reconstructed to a form similar to that of \(X_{final}\) after its values are obtained. In summary, the voxels in the tomographic zone with sequential numbers are screened to pass by at least one ray. The WV density of these voxels is then computed by the tomographic equation. Finally, the voxels with WV information are re-sorted according to their original numbers, while the null-ray-passed voxels are assigned a \"null\" value. Regarding these \"null\" values, we can estimate their WV density by interpolation. Based on the above technique, we can reasonably reconstruct the 3D WV distribution in the object zone. Furthermore, we introduce a new \(PWV\) constraint, established by the GNSS-derived \(PWV\), which differs from the above general constraints. This constraint is consistent with the actual tropospheric WV distribution,  
\[P\times X\ =\ PWV \tag{16}\]  
where \(P\) is the coefficient matrix of the \(PWV\) constrains, while \(PWV\) is the corresponding result matrix. As shown in Figure 4, we can establish a unique vertical constraint (\(PWV\) constraint) in the column grid where a GNSS station is located. We assume a signal penetrating the tomographic region from the zenith and finally reaching the receiver. This virtual signal line is discretized into several segments by the voxels. Nevertheless, we set one for every segment following the physical distribution of \(PWV\)These segments form the constraint coefficient matrix \(P\). The \(PWV\) value corresponding to the ray forms the result matrix \(PWV\). Figure 4: Precipitable WV (PWV) constraints imposed upon the tomographic equation. Figure 3: Voxel optimization process. (**a**) All voxels in the tomographic zone. (**b**) All voxels and visual rays from three sites. (**c**) Voxels through which rays pass.
# A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network
## 4 Simulation Results and Discussion
### Results Discussion
#### 4.4.4 Sensitivity Analysis
** \\ \hline \multirow{3}{*}{Tukey HSD} & \multirow{3}{*}{RPGM} & RWP & 14.49 & 0.091 & 0.26 & 0.096 & 6.23 & 0.000 & 0.104 & 0.067 \\  & & GM & 28.529 & 0.000 & 0.527 & 0.000 & 11.44 & 0.000 & 0.267  
(_c_) To perform selection biasness check through [PERSON]'s two-stage statistical test. **[PERSON]'s two-stage statistical test:** There is a chance that our results are driven by selection biasness. Mostly, selection biasness stems from model or sample biasness. Model biasness is due to leaving out important variable which may have an impact on the dependent variable, whereas sample bias means that our sampling selection procedure may lead to biasness in the model. To address this issue, we use [PERSON]'s two-stage model [54]. In the first step, we can use probit or logistic regression where RPGM is regressed against ARE and the ANAN with respect to time (seconds). We also add one instrumental variable. That instrumental variable must be correlated to RPGM mobility model but not correlated to connectivity feature. Therefore, we use speed as an instrumental variable. [PERSON] et al. [52] stated there is a direct association between RPGM mobility model and speed. There is no association between speed and connectivity [55; 56]. As shown in Table 5, first the probit model was applied to get the value of correction factor inverse mill ratio \((\lambda mr)\), and then the value of \((\lambda mr)\) was added to our main regression model. After adding, it was found that \(t\)-value of \((\lambda mr)\) is insignificant and the coefficient of RPGM remains positive and significant. Therefore, it shows that connectivity has an impact on the performance of our proposed work. Figure 10: Estimated Means of ANAN, ARE, APDR, AC with respect to Mobility Models. (**a**) ANAN; (**b**) ARE; (**c**) APDR; (**d**) AC
# Basis Functions for a Transient Analysis of Linear Commensurate Fractional-Order Systems
## 7 Calculation of Step Response  
The correspondence between the basis functions of the IOS and FOS from Table 5 allows a convenient determination of analytical formulas for the impulse response \(g_{F}(t)\) of commensurate FOS from the known impulse response \(g(t)\) of IOS. Let us reiterate that transfer functions (2) and (4) of both systems are bounded by relation (3).  
Let us analyze to what extent the correspondences of Table 5 can be used to determine the step response \(h_{F}(t)\) of the FOS. Since the step response is the forced response to a unit step, the question is whether it can be constructed from the basis functions from Table 5, which describe natural, not forced, responses.  
It turns out that in the general case, we really cannot make do with the above basis functions when constructing the step response.  
The step response \(h_{F}(t)\) of the associated commensurate FOS of transfer function \(K_{F}(s^{\alpha})\) (4) is obtained with the Laplace inversion of the function \(K_{F}(s^{\alpha})/s=K(s^{\alpha})/s\). Similarly, the step response \(h(t)\) of the IOS is the Laplace inverse of the expression \(K(s)/s\):  
\[h(t)=L^{-1}\left\{\frac{K(s)}{s}\right\},h_{F}(t)=L^{-1}\left\{\frac{K(s^{ \alpha})}{s}\right\}. \tag{53}\]  
Since the denominator of the second equation contains the \(s\) operator, not its power \(s^{\alpha}\), the assumption from Section 1 that the Laplace transform of the time response of a FOS is obtained from the Laplace transform of IOS with substitution (2) cannot be used. However, all the conclusions that follow from this assumption, including the transformation relations of Table 5, are based on this assumption.  
On the other hand, the step response can be easily obtained with a direct integration of the impulse response with respect to time. Since the impulse response of proper systems can be thought of as a linear combination of basis functions, the step response is actually constructed from a linear combination of the integrals of the basis functions with respect to time. Using the rule of integration of Podlubny's function (28) [13], the first integral of generating function \(f_{F}\) (4) (52) can be written as follows:  
\[\int f_{F}(t)dt=\int\varepsilon_{k}(t,c;\alpha,\alpha)dt= \varepsilon_{k}(t,c;\alpha,\alpha+1)=t^{ak+\alpha}E_{\alpha, \alpha+1}^{(k)}(ct^{\alpha}),c=a+\mathrm{i}b. \tag{54}\]  
Thus, it is obvious that the step response can be composed from the basis functions that are built of MLFs with the parameters \(\alpha,\alpha+1\). Starting from transfer function (4), it is possible to first determine the impulse response based on Table 5 and then proceed to the step response by integrating each basis function according to (54).
## 1 Introduction
Recent overviews of the first-principles studies of the doped YCo\({}_{5}\) magnets are presented in refs. [83, 84]. In addition to the numerous first-principle studies of the YCo\({}_{5}\) magnet, the phase stability and the thermodynamics of the Y-Co system have been assessed experimentally [85, 86, 87] and computationally, using the semi-empirical CALPHAD (CALculation of PHAse Diagrams) method [88, 89, 90]. Experimentally, YCo\({}_{5}\) was reported to melt congruently at \(T_{m}\)\(\sim\)1623 K, possess a small homogeneity range at high temperature, and decompose below \(T\)\(\sim\)998 K [86]. In addition, the Y-Co-Ni ternary system evaluation, reported by [PERSON] [91], revealed a complete solubility from YCo\({}_{5}\) to YNi\({}_{5}\) at 1073 K and 1273 K. This ternary system was assessed using the CALPHAD method [92, 93], and the solubility of the Y(Co,Ni)\({}_{5}\) phase has been reproduced [92]. Experimental characterization of the Y-Co-Fe system demonstrated maximum solubility of Fe in Y(Co,Fe)\({}_{5}\) of \(\sim\)20 at.% [94] and \(\sim\)5 at.% [95] at 1323 K and 1073 K, correspondingly. There are no reported CALPHAD assessments (parameters) for Y-Co-Fe and Y-Fe-Ni ternary systems (a maximum solubility of \(\sim\)30 at.% Fe in Y(Fe,Ni)\({}_{5}\) was reported by [95] at 873 K), and no available information exists regarding thermodynamic properties or phase stability of the Y(Co,Fe,Ni)\({}_{5}\) compound. Therefore, the principal goal of the present research is to examine the stabilizing effects of adding nickel to the Y(Co\({}_{1-x}\)Fe\({}_{x}\))\({}_{5}\) alloys. We employ _ab initio_ calculations using two complementary techniques: (i) the fully relativistic exact muffin-tin orbital method (FREMTO) and (ii) the full-potential linear muffin-tin orbital method (FPLMTO). All methods account for all relativistic effects, the so-called spin-orbit coupling (SOC). The implementation of these particular methods ensures that our results are rigorous and independent of technical implementations while employing the most advantageous attributes and durability strengths of each method. Related details of the _ab initio_[96, 97] and the CALPHAD computational methods are outlined in Section 2. Results of the density-functional-theory (DFT) calculations of the ground state properties of the Y(Co-Fe-Ni)\({}_{5}\) alloys connected with CALPHAD calculations are outlined in Section 3. Next, we demonstrate results of the DFT calculations of the magnetic properties of the Y(Co-Fe-Ni)\({}_{5}\) alloys in Section 4. Lastly, discussion and concluding remarks are outlined in Section 5.