esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Artificial Orthogonal Gland (AOG) Mechanism
### Activation Mechanism  
The task of the activation mechanism from Figure 1 is to determine the production of hormone based on \(CM_{out}\) and \(SM_{out}\). The mechanism processes two signals in each time step, resulting in \(Act_{\text{deg}}(t)\):  
\[Act_{\text{deg}}(t)=f_{AM}(CM_{out},SM_{out}), \tag{5}\]  
where \(f_{AM}\) could be implemented in the form of conventional neural network functions such as sigmoidal, linear, step, Gaussian, etc. However, keeping in mind the previous work of the authors of this paper and extensive experience in developing and implementing novel types of orthogonal functions within neural network environments, this research makes an additional effort in implementing \(f_{AM}\) in the form of Gegenbauer orthogonal functions. Gegenbauer functions are orthogonal polynomials defined on the interval [\(-1\), \(1\)]. Theygeneralize Legendre polynomials and Chebyshev polynomials and represent exceptional Jacobi polynomials cases. Gegenbauer polynomials are characterized by rapid convergence rates providing an excellent foundation for building an efficient activation mechanism. Each artificial gland has its row of the orthogonal function: for n glands assigned to the neural network, \(n\) Gegenbauer functions will be expended from the initial one and be used for each function (\(j=0,\,1,\,2,\,\ldots,\,n-1\)). Gegenbauer polynomials and appropriate mathematical foundation will be introduced in the following subsection.
## 3 Results and Discussion
### Seasonal Variation of Emission Factors
Some of this difference is likely attributable to the right lane having more diesel trucks, but some of the difference (e.g., higher emission factors from 20:00 to 21:00 when %_fuel_D \(<\) 10%) may be due to lower pollutant concentrations and higher background ratios. Figure S3 suggests that the differences in emission factors shown in Figure 3 for %_fuel_D \(<\)13% are robust but that the difference in emission factors for periods with higher diesel activity (e.g., \(>\)20%) may be exaggerated by the lane difference. Since the LDV emission factors can be determined from the regressions when %_fuel_D is 0, we assume that the seasonal trend presented in Figure 3 is valid for LDV. LDV emission factors measured in winter and spring are summarized in Table 1.  
As shown in Figure 2d, the CO emission factor was not correlated with %_fuel_D. We therefore assessed the potential seasonal variation of the CO emission factor by directly comparing the weekday and weekend diurnal patterns of CO emission factors measured in winter and spring. The results are shown in Figure 4. The CO emission factors were in general similar in winter and spring. During \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Species (Emission Factor Units)** & **Vehicle Type** & **Spring Regression** & **Winter Regression** & **Dynamometer (May et al.)** \\ \hline NO\({}_{\text{x}}\) (g NO\({}_{\text{2}}\)/kg fuel) & LDV & \(1.2\pm 0.3\) & \(2\pm 0.5\) & 2.7 \\  & HDDV & \(17.1\pm 2.4\) & \(29.2\pm 3.2\) & 18.2 (no SCR) \\  & & & & s5.2 (with SCR) \\ \hline OC (mg/kg fuel) & LDV & \(18.5\pm 7.2\) & \(14.7\pm 4.8\) & 11.3 \\  & HDDV & \(109.4\pm 54.0\) & \(167.8\pm 36.3\) & 117.0 (no DPF) \\  & & & & 7.6 (with DPF) \\ \hline EC (mg/kg fuel) & LDV & \(15.9\pm 4.5\) & \(24.4\pm 5.5\) & 17.2 \\  & HDDV & \(158.7\pm 33.8\) & \(196.8\pm 41.1\) & 182.8 (no DPF) \\  & & & & 0.3 (with DPF) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean emission factors of light-duty gasoline vehicles (LDV) and heavy-duty diesel vehicles (HDDV) measured in the Fort Pitt Tunnel. SCR: selective catalytic reduction, DPF: diesel particulate filter. Figure 4: The (**a**) weekday and (**b**) weekend diurnal profiles of CO emission factors measured in the Fort Pitt Tunnel in winter and spring. 8:00-19:00 on weekdays and 1:00-21:00 on weekends, when LDV dominate the traffic flow, CO emission factors were similar (in general, less than 10% difference) in winter and spring. During 20:00-7:00 on weekdays, when both the fraction of LDV in the traffic flow and the total traffic volume are lower, CO emission factors were slightly higher (10-51%) in winter than in spring. [PERSON] et al. [36] also reported similar CO emission factors across different seasons.
###### Abstract  
Drivable region maps, created using a visual sensor, are essential for autonomous navigation because off-the-shelf maps do not reflect contemporary real-world conditions. This study presents a large-scale drivable region mapping system that is capable of capturing large-scale environments in real-time, using a single RGB-D sensor. Whereas existing semantic simultaneous localization and mapping (SLAM) methods consider only accurate pose estimation and the registration of semantic information, when loop closure is detected, contemporaneous large-scale spatial semantic maps are generated by refining 3D point clouds and semantic information. When loop closure occurs, our method finds the corresponding keyframe for each semantically labeled point cloud and transforms the point cloud into adjusted positions. Additionally, a map-merging algorithm for semantic maps is proposed to address large-scale environments. Experiments were conducted on the Complex Urban dataset and our custom dataset, which are publicly available, and real-world datasets using a vehicle-mounted sensor. Our method alleviates the drift errors that frequently occur when the agents navigate in large areas. Compared with satellite images, the resulting semantic maps are well aligned and have proven validity in terms of timeliness and accuracy.  
visual simultaneous localization and mapping (SLAM); mapping; map-merging; semantic segmentation +
Footnote †: journal: applied sciences
# Implications of GNSS-Inferred Tropopause Altitude Associated with Terrestrial Gamma-ray Flashes
###### Abstract  
The thermal structure of the environmental atmosphere associated with Terrestrial Gamma-ray Flashes (TGFs) is investigated with the combined observations from several detectors (FERMI, RHESSI, and Insight-HXMT) and GNSS-RO (SAC-C, COSMIC, GRACE, TerraSAR-X, and MetOp-A). The geographic distributions of TGF-related tropopause altitude and climatology are similar. The regional TGF-related tropopause altitude in Africa and the Caribbean Sea is 0.1-0.4 km lower than the climatology, whereas that in Asia is 0.1-0.2 km higher. Most of the TGF-related tropopause altitudes are slightly higher than the climatology, while some of them have a slightly negative bias. The subtropical TGF-producing thunderstorms are warmer in the troposphere and have a colder and higher tropopause over land than the ocean. There is no significant land-ocean difference in the thermal structure for the tropical TGF-producing thunderstorms. The TGF-producing thunderstorms have a cold anomaly in the middle and upper troposphere and have stronger anomalies than the deep convection found in previous studies.  
Terrestrial Gamma-ray Flashes (TGFs); tropopause; deep convection; GNSS-radio occultation +
Footnote †: journal: Remote Sens.  
**2021**, _13_, 1939. [[https://doi.org/10.3390/rs13101939](https://doi.org/10.3390/rs13101939)]([https://doi.org/10.3390/rs13101939](https://doi.org/10.3390/rs13101939))
# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 2 Materials and Methods
### Application of Machine Learning Regression Models  
The application of ML regression models followed a three-step process: feature selection, model implementation, and hyperparameter tuning (detailed in the following subsections). In the feature selection phase, relevant features were carefully chosen to enhance the model's accuracy and performance. Subsequently, the regression models were implemented using a cross-validation methodology, establishing a connection between the input features and the target feature. The final step involved optimizing the hyperparameters to fine tune the models to improve the predictive accuracy and generalization capability.
## 3 Methodology
### Procedure of Slope Hazard Identification along Highway
#### 3.2.1 Interpretation of InSAR Results  
The SBAS-InSAR method is used to process the SAR images to obtain the average annual ground deformation velocity along the highway. When deformation velocity is positive, the ground is moving toward the satellite along the Line of Sight (LOS), and when deformation velocity is negative, the ground moves away from the satellite. Applying a velocity threshold of \(\pm 10\) mm/yr, a series of abnormal areas can be filtered out. If the deformation abnormal area coincides with slopes within the highway boundary, this area can be considered as one potential slope hazard candidate.
## 3 Comparison between Proposed Full-Ring Model and Experiments
### Comparison of Internal Forces and Deformations  
As the point loads in the indoor full-scale ring experiments and the corresponding structural responses are clearly identified, the results from the iterative numerical model under these 30 point loads are compared with those from the experiments. Based on the measurements and the assumption of plane strain, internal forces at the eleven key sections (See Figure 6) in the experiments are calculated through Formula (6). \[\left\{ \begin{array}{c}N=\int_{-h/2}^{h/2}\sigma[\varepsilon(y)]bdy+ \varepsilon_{s}E_{s}A_{s}+\varepsilon_{s}^{\prime}E_{s}^{\prime}A_{s}^{\prime} \\ M=\int_{-h/2}^{h/2}\sigma[\varepsilon(y)]bydy+(\varepsilon_{s}E_{s}A_{s}- \varepsilon_{s}^{\prime}E_{s}^{\prime}A_{s}^{\prime})(h/2-c)\end{array} \right. \tag{6}\]  
In the formula, \(h\), \(b\) and \(c\) represent the thickness of a segment, the width of a segment and the distance from the center of the reinforcement layer to the closest segment edge. \(\varepsilon(y)\) refers to the strain of concrete at different positions in the direction of segment thickness, and \(\sigma[\varepsilon(y)]\) refers to the concrete stress corresponding to the concrete strain. \(\varepsilon_{s}\), \(E_{s}\) and \(A_{s}\) refer to the strain of the reinforcement close to the external surface of the segment, the elastic modulus of steel and the area size of the corresponding reinforcement while \(\varepsilon_{s}^{\prime}\), \(E_{s}^{\prime}\) and \(A_{s}^{\prime}\) are for the reinforcement close to the internal surface of the segment. The constitutive stress-strain relationship of the concrete is quadratic while that of the steel rebars is considered linear. The internal forces and deformations from the experiments and the iterative model under the 30 point loads are listed in Table 3, with the ratios of model values to experimental results. From the numerical results, it is found that the errors on the axial forces at most of the key sections are under 5%, while those at the other sections are smaller than 10%. For the bending moments, differences at most of the key sections are within 10%, with the exceptions of the 17% value for JM7 in Experimental Case 1 and 11% value for JM10 in Experimental Case 3. When it comes to the structural deformations (in Table 4), most of the differences are within the range of 10%, with the exception of the 18% value for D1 in Experimental Case 3. This can be attributed to the small value of convergence deformation of 2.21 mm obtained from the calculation. Moreover, the absolute error measured for this case was only 0.34 mm. To make a direct sense of the structural deformations, the measured displacements at the measuring points in Experimental Case 1 are shown in Figure 11. The red line in Figure 11 is calculated numerically, and the obtained displacements are visualized with a scale factor of 80. Overall, it is found that the calculated deformations from the iterative model and the experimental results are in good agreement.
## 2 Results and Discussion
### Reactivities of Model Bio-Oil Components
\begin{table}
\begin{tabular}{c c|c c} \hline
**2-Hydroymethylfuran** & \multicolumn{2}{c}{**Hydroxacetone**} \\ \hline
**Compounds** & **Peak area (\%)** & **Compounds** & **Peak area (\%)** \\ \hline
2-hydroxymethylfuran & 78.3 & Propionic acid & 63.4 \\ Diffurfuryl ether & 9.4 & Hydroxyacetone dimers & 24.8 \\
5-Furfuryl-furfuryl alcohol & 4.9 & Methyl cyclopentenolone & 9.7 \\ Difuran-2-ylmethane & 4.8 & Unknown species & 1.8 \\ Others & 2.6 & & \\ \hline \end{tabular} \({}^{\text{a}}\) Products were analyzed by GC/MS. Each product was identified by excellent matches of their MS fragmentation patterns.  
\end{table} 
Table 9: Products formed from hydroxyacetone or 2-hydroxymethylfuran on heating at 100 \({}^{\circ}\)C over 30% Cs\({}_{2.5}\)/K10 for 1 h \({}^{\text{a}}\). of octyl formates were formed over A15 and DX2 by esterification of formic acid by octanols. The formic acid was generated by the known acid-catalyzed conversions pathways of d-glucose (Scheme 7) [2,27]. 5-Hydroxymethylfurfural (HMF) was formed by d-glucose dehydration. Then, subsequent HMF hydration to its hemiacetal occurred in acidic media, followed by rehydration, ring-opening, loss of both water and formic acid to form levulinic acid. Also, small amounts of 1,6-anhydro-3,4-dideoxy-\(\beta\)-d-mannohexapyranose, generated by dehydration of d-glucose, were detected over SSA and Cs\({}_{2.5}\)/K10. **Scheme 5. ** Acid-catalyzed reactions of hydroxyacetone. In addition to the reactions discussed above occurring between 1-octene with model bio-oil components, some additional olefin reactions took place. Table 10 shows the product distributions of individual olefin reactions conducted using equimolar amounts of olefin (1,7-octadiene, cyclohexene, 1-octene, or 2,4,4-trimethylpentene)/phenol/water over SSA at 120 \({}^{\circ}\)C for 3 h. Skeletal isomerization reactions of all olefins occurred except with cyclohexene. Intramolecular diene cyclizations occurred for 1,7-octadiene, individually. No oligomerization or cracking of either 1-octene or 1,7-ocadiene occurred. However, 1-octene isomers were detected in the reactions where neat phenol and 1-octene operated at 100 \({}^{\circ}\)C over Cs\({}_{2.5}\)/K10 or A15 catalysts. 2,4,4-Trimethylpentene oligomerized to C16 olefins and cracked into C4 olefins readily at these conditions. Cracking of 2,4,4-trimethylpentene mainly produced isobutene. Cyclohexene's dimer 1-cyclohexyl-1-cyclohexene was found. Reoligomerization of isobutene gave C8, C12, and C16 olefins. Hydration reactions to form alcohols occurred for all olefins, followed by etherification of these resulting alcohols generating ethers and water (Scheme 8). Thus, the choice of olefin structure will play a role in the product distribution observed, but all the olefins help drive the upgrading process to remove water and promote esterification, acetal formation, generate ethers and both \(O\)- and \(C\)-alkylate phenols.
# An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
## 6 Clinical Utility
### Therapeutic Targets  
The therapeutic potential of lncRNAs lies in their ability to regulate critical pathways involved in cancer development and progression. Targeting oncogenic lncRNAs or restoring the function of tumor suppressor lncRNAs can disrupt cancer cell survival and proliferation. For instance, silencing lncRNAs using ASOs or siRNAs has been shown to inhibit tumor growth and metastasis in preclinical models [106]. Moreover, lncRNAs such as MALAT1 and GAS5 can be targeted to overcome drug resistance. MALAT1 has been implicated in the development of resistance to chemotherapeutic agents, and its inhibition could restore sensitivity to these drugs [107]. Conversely, restoring the expression of tumor suppressor lncRNAs like GAS5 could enhance the efficacy of existing treatments by promoting apoptosis and inhibiting cell proliferation [108]. LncRNA-based therapies are still in the early stages of development, but the potential for clinical application is substantial. The specificity of lncRNA-targeted therapies could minimize off-target effects and reduce toxicity compared to traditional chemotherapy. Furthermore, advances in delivery systems, such as nanoparticles and viral vectors, are enhancing the stability and efficiency of lncRNA therapeutics [109].
# Optimized Deployment of Generalized OCDM in Deep-Sea Shadow-Zone Underwater Acoustic Channels
## 7 Conclusions  
To address the challenges of a low SNR and complex multipath propagation in deep-sea shadow-zone communication, this study applies GOCDM to underwater acoustic communication systems, and performed deep-sea shadow-zone communication trials. The results confirm that GOCDM inherits the benefits of both OCDM and OSDM while mitigating their respective drawbacks. Both simulations and experimental results demonstrate that applying GOCDM to deep-sea shadow area communication significantly reduces the bit error rate and improves system performance. Future research can focus on the clustering characteristics of underwater acoustic communication methods in deep-sea shadow zones, further analyzing the energy distribution and time delay characteristics of scattered and reflected acoustic signals. Additionally, designing more optimized underwater acoustic communication systems based on these insights could enhance communication reliability and efficiency in challenging underwater environments.  
Literature search, H.Y. and C.C.; figures, H.Y.; study design, H.Y., C.C., Y.F., Z.P., W.W. and L.Y.; data collection, H.Y., C.C., Y.F., Z.P., W.W., L.Y., Y.L. and H.H.; data analysis, H.Y. and C.C.; data interpretation, H.Y.; writing, H.Y.; resources, C.C., Y.L. and H.H.; project administration, C.C., Y.L. and H.H.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number E3111301. The APC was funded by internal research grants from the Institute of Acoustics, Chinese Academy of Sciences.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author for confidentiality reasons.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Work
### Enhancement of Texture Mapping Quality  
Texture mapping, as a key technology determining the visual realism of 3D models, has been studied with various techniques utilizing high-resolution images. One study proposed a texture mapping technique using orthoimages that improved texture alignment accuracy and enhanced texture-model correspondence through UV mapping [22]. Nevertheless, challenges remain in fully resolving issues such as texture distortion, lighting inconsistencies, and data interference.  
Furthermore, high-resolution texture data present challenges of large data size and reduced processing speed. To address these issues, techniques have been proposed that apply multi-resolution textures or utilize high-resolution textures only in critical areas. However, these techniques have limited application to specific scenarios and face constraints in universal application across diverse terrains and urban structures [23].  
However, challenges persist in addressing texture distortion, lighting inconsistencies, and data interference, which significantly degrade visual quality [24; 25]. In particular, existing research lacks efficient technical approaches for processing high-resolution texture data while maintaining optimal performance. To overcome these limitations, this study enhances texture mapping quality by refining UV mapping techniques and applying texture correction algorithms, ensuring more accurate texture alignment and reducing visual artifacts [26; 27].
## 4 Time Aggregation Using the Hann Window
Figure 6: Amplitude calculated using rectangular (RW) and Hann (HW) windows 0.2 s long of a harmonic subgroup centered in 50 Hz, with the unitary harmonic in that position, as a function of the position of another single tone: (**a**) without aggregation; (**b**) with time aggregation for the phase angles between tones of 0\({}^{\circ}\), (**c**) 180\({}^{\circ}\), and (**d**) 90\({}^{\circ}\). The phasors of the simulated tones are added when they coincide in the same position (50 Hz in the simulations). The result depends on the phase angle between them. The sum result is maximum if both tones are in phase (Figure 6b) or minimum if the phase angle between them is 180\({}^{\circ}\) (Figure 6c). For the rest of phase angles, the summed results are between the former. In the particular case of 90\({}^{\circ}\) (Figure 6d), these maxima and minima are not appreciated because the square sum of two tones at 90\({}^{\circ}\) is equal to the sum of their squares. To obtain the correct common RMS value, they behave the same way in the position in which both tones at 90\({}^{\circ}\) coincide, as in the rest of the positions in which they do not. In other cases, the values obtained where the position of tone components coincide depend on the particular phase angle between them. With HW, this happens in the position in which their frequencies are equal (50 Hz), and also in the cases in which the lateral bands of the two tones, harmonic and sweeping, coincide, as can be seen in Figure 6b,c. As a consequence, singular values (or local peaks, according to [16]) appear at the positions of the central harmonic tone, and \(\pm\) 5 Hz and \(\pm\) 10 Hz around its position. The interference between the main lobes of the Hann windows of each of the simulated tones explains that result. The variations of these values are maximum when the phase difference between the two tones is 0\({}^{\circ}\) or 180\({}^{\circ}\). In the next section, it is shown how these local peaks generally occur when several spectral components that are distant multiples of the frequency resolution \(D\!f\) interact with each other, and how this happens not only with HW but also with other types of windows, such as RW.
## 1 Introduction
However, these particular cases will hardly happen to high-voltage MOSFETs. The analytical model of [21] considers the MOSFET-Snubber-Diode (MSD) configuration, whereas it treats the trans-conductance and the threshold voltage as a constant. In [23; 24], the parasitic elements of the analytical model are extracted from the datasheet of the device vendor, but these models cannot reflect the temperature characteristics of the parasitic elements of the power devices. As a result, there are a lot of limitations in these analytical models. For the sake of accuracy and computational efficiency, an improved analytical model that considers temperature-dependent parasitic elements of SiC MOSFET and the interaction between exterior parasitic inductances of PCB traces will be proposed. The paper is organized as follows: in Section 2, the basis of modeling is given. In Section 3, the turn-on and turn-off transients of the SiC MOSFET are divided into five stages, respectively, and then the state equations are derived from the equivalent circuits of each stages. In Section 4, a power device analysis platform is built to obtain the detailed temperature characteristics of the parasitic elements. In Section 5, the calculation results of this proposed analytical model will be verified by comparison with the experimental results of a 400-V, 15-A double-pulse test bench, then, the effects of the parasitic elements on loss will be comprehensive explained. Finally, a conclusion that can help engineer to design high-frequency power converters and minimize switching loss will be given in the Section 6.