esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods and Materials
### Data Entvelopment Analysis (DEA)
This model allows us to determine the efficiency of each DMU by optimizing both the radial contraction and potential slacks in the inputs and outputs, ensuring that no outputs are reduced during the process. \[ \begin{array}{l}s^{+}=({s_{1}}^{+},\;{s_{2}}^{+}\cdots,\;{s_{s}}^{+})\in E_{s} \\ s^{-}=({s_{1}}^{-},\;{s_{2}}^{-}\cdots,\;{s_{s}}^{-})\in E_{m}\end{array}  \tag{5}\]  
This leads to the development of the optimized model:  
\[(D_{\mathcal{L}^{2}p})\left\{ \begin{array}{l}Min\theta\\ s.t.\sum\limits_{j=1}^{n}{(X_{i}\lambda_{j})}+S^{-}=\theta X_{0}\\ \sum\limits_{j=1}^{n}{(Y_{i}\lambda_{j})}-S^{+}=Y_{0}\\ \lambda_{j}\geq 0,\;j=1,\;2,\;\ldots,\;n\\ S^{-}\geq 0,\;s^{+}\geq 0\end{array} \right. \tag{6}\]  
where \(\lambda_{j}\) is the weight. Non-negativity constraint \(\lambda_{j}\) was applied to ensure all the weights were positive during the optimization process. The convexity condition ensures that the sum of the weighted inputs and outputs stays within the bounds of the production possibility set, as dictated by the DEA model. Based on the determination of DEA efficiency, the following settings were made. If the linear program \((D_{\mathcal{L}^{2}r})\) has an optimal solution \(h_{j0}=1\), and all the slack variables are zero, then DMU\({}_{j0}\) is deemed DEA efficient. If \(h_{j0}=1\) but there is one or more non-zero slack variables, then the decision-making unit, DMU\({}_{j0}\), is determined to be weakly DEA efficient, meaning that there is room for further improvement in certain inputs or outputs. If \(h_{j0}<1\), then the DMU\({}_{j0}\) is deemed DEA inefficient, indicating that it is inside the production possibility frontier with room for efficiency improvement.
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 5 Experimental Results and Comparison
### Traffic Status Dataset
#### 5.2.5 Result Comparison  
Similarly to the results obtained using the lane detection dataset, the gradient boosting algorithm outperformed all other classifiers, where its micro precision score reached 0.67 (67%) as indicated in Table 8. Moreover, the ANN classifier provided the worst accuracy results, where all of its metric scores were less than 0.3 (30%). Again, the RF classifier shows improvements compared to the lane detection dataset results and provides an acceptable accuracy compared to the LR classifier with metric scores of more than 63%. The logistic regression classifier did not perform better than GB and RF classifiers, yet it shows some accuracy improvements compared to the lane detection dataset results and provides acceptable metric scores (more than 53%). The focus on micro scores is in keeping with the need for a metric that accounts for imbalanced datasets.
# Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
## 4 Results and Discussion
### Constellation Visibility
Figure 7 shows that the PDOP values of GPS are less than 3.0 in all three regions, and the most stable PDOP values appear in middle latitude regions, indicating that the observation geometry of GPS in that region is the best among the three regions. Moreover, the PDOP values of GPS exist daily period in all three regions. GLONASS has the most stable PDOP in high latitude regions, which is more stable than GPS in the same region. However, the PDOP values of GLONASS increase obviously as the latitude decreases. In addition, the PDOP values of GLONASS at low latitude regions show some abnormally large values, up to 92.68, as shown in the upper right corner of the subfigure for low latitude GLONASS PDOP. The PDOP values of GPS+GLONASS exhibit better performance than both GLONASS and GPS stand-alone systems. Although some periods exist in high latitude regions where the PDOP of GPS is up to 2.80, the PDOP values of GPS+GLONASS are quite stable and less than 1.55. Although the PDOP of GPS is up to 2.75 in some periods in low latitudes and the PDOP of GLONASS is very large at certain epochs, the PDOP of GPS+GLONASS remains stable and below 1.44. The combined GPS and GLONASS constellations have the most obvious improvement in observation geometry at low latitudes.
## 3 Results and Discussion
### AMVs Sensitivity to Satellite Revisiting Time at Various Heights  
To explore the AMVs sensitivity to the revisiting time and demonstrate the Farneback OF algorithm requirement for the satellite temporal resolution, we calculate the wind speed RE (Figure 7a), wind speed RMSE (Figure 7b), and wind direction absolute error (AE, see Figure 7c) of various satellite revisiting time at different heights. The wind direction AE is computed as:  
\[\text{AE}=\sum_{i=1}^{N}(dir_{WRF}-dir_{OF})/N \tag{4}\]  
where the \(dir_{WRF}/dir_{OF}\) is the wind direction of two wind fields. Due south is defined as 0\({}^{\circ}\) and 0-360\({}^{\circ}\) is defined as clockwise rotation. N is the number of data points. Note that in Figure 7, the X-axis represents time intervals, and solid lines in various colors represent 12 chosen heights. Figure 7a,c show that the upper and lower layers AMVs errors are more sensitive to the revisiting time than that of mid-layer. For the upper and lower layers, the wind speed RE(RMSE) increases significantly from nearly 30% (5-6 m/s) to 50% (12 m/s); meanwhile, the wind direction AE increases from 18\({}^{\circ}\) to 35\({}^{\circ}\) as the revisiting time extends from 30 to 120 min. While for the middle layers, the wind speed errors vary less from different revisiting times, and the wind direction error is negatively correlated with the satellite revisiting time, especially when the revisiting time is shorter than 30 min (Figure 7c). In addition, Figure 7a,b show a slight downward trend of the wind speed error at nearly all heights when the revisiting time is less than 30 min. Within a short revisiting time, the water vapor motion may be too smooth to be identified by the tracking model, especially at mid-layers where a tiny water vapor gradient or a slow motion exists. However, the high-speed rotating motion and severe changes in water vapor content at the typhoon center require a satellite data with a high time resolution. Therefore, the requirements for time resolution of Farneback OF wind-tracking algorithm under different weather systems need to be discussed.
###### Abstract  
In this paper, the viability of thermophilic anaerobic co-digestion of exhausted sugar beet pulp (ESBP) and pig manure (PM) was evaluated. The effect of the proportion of ESBP on biogas production was investigated by using a series of lab-scale batch assays, in duplicates. The following five ESBP:PM mixture ratios were studied: 0:100, 10:90, 25:75, 50:50, and 100:0. The highest cumulative methane production (212.4 mL CH4/g VSadded) was reached for the mixture 25:75. The experimental results showed that the increase in the proportion of ESBP in the mixture led to the distortion of the process, due to acidification by the volatile fatty acids generated. Acetic acid was the predominant acid in all the cases, representing more than 78% of the total acidity. Moreover, the results obtained by operating at thermophilic temperatures have been compared with those obtained in a previous study conducted at mesophilic temperatures. The results have shown that in the individual digestion of ESBP, the activity of acetoclastic methanogens was affected in both temperatures, but especially in thermophilic conditions. Thus, the methane produced in the individual thermophilic digestion of ESBP came almost entirely from the activity of hydrogen-utilizing methanogenic archaea.  
exhausted sugar beet pulp; pig manure; anaerobic co-digestion; thermophilic; lignocellulosic waste +
Footnote †: journal: Applied Sciences
### Motivation, Aim, and Scope  
Upon reviewing the studies summarized in the previous section, it becomes evident that graded lattices possess greater energy absorption potential than uniform structures with identical weights. The achievable performance improvements reported by the considered studies differ strongly because of the various combinations of grading styles, lattice types, compression speeds, materials, relative densities, and levels of compressive strain. Seemingly no study has investigated a method of finding an ideal thickness gradient through optimization for the considered load case, lattice type, and material choice. Only manually specified gradients that closely adhered to linearly changing thicknesses have been investigated. Therefore, this investigation aims to expand on previous studies and to find an optimal thickness distribution for the chosen lattice structure in a compressive load case, potentially enhancing a multitude of applications.  
To render the proposed optimization procedure compatible with current finite element analysis (FEA) solvers, the model must be separated into discrete regions, each possessing an element thickness property that can be altered separately. However, using every single thickness property as a separate optimization variable would result in unmanageable computation times. The lattice's thickness distribution is, therefore, governed by a predefined mathematical function to reduce the number of optimization variables and to enable an efficient optimization process with FEA. The method's utility is not limited to this specific load case or structure. It may be adapted to arbitrary goals and geometries as long as 1D or 2D elements are used for the lattice's geometrical discretization.  
Two graph unit cell types (bending-dominated versus stretching-dominated) are examined in a compressive load case to assess their performance differences and improvement potential. Three thickness-grading styles are proposed, and the best-performing option is physically tested. Surface-based lattices are not investigated in this publication. Only a limited number of lattice and material options can be considered here; the investigation is, therefore, not intended to provide a universally valid gradient recommendation. Rather, the proposed optimization approach should be used to efficiently explore the improvement potential of other lattice configurations, materials, and load cases.
# Influence of Cracking on Oxygen Transport in UHPFRC Using Stainless Steel Sensors
## Materials and Methods
### Concrete Characterization
#### 2.2.3 Air Permeability, Absorption, and Porosity Accessible to Water  
Gas permeability tests, absorption tests, and porosity accessible to water tests were performed to completely characterize the four concrete types.  
Concrete disks of diameter 150 mm and 50 mm height were used for the gas permeability tests, which were performed following the standard UNE 83981. The lateral surface of the disks was covered using a sealing paint (waterproof paint Sikalastic 612). For each concrete type, three samples were tested and Table 3 shows the average results and coefficient of variation obtained.  
Regarding the absorption and porosity accessible to water, concrete disks of diameter 100 mm and 50 mm height were used following the standard UNE 83980. Six samples were tested for each concrete type. Table 3 displays the average results and coefficient of variation obtained.  
It is clear that increasing the quality of concrete from SQC to HPC produces a significant decrease in the permeability of the matrix, absorption, and porosity to water while analyzing the three parameters. This decrease is more pronounced when moving to UHPFRC. The incorporation of the alumina nanofibers and crystalline admixtures in the UHPFRC-n mix in the percentages used in this work did not significantly change the values that were obtained for these parameters, showing slightly worse air permeability, but slightly better porosity and water absorption. The benefits of nanofibers and the crystalline admixture seem to have compensated for the slight increase in w/b ratio. The obtained results are consistent with those that were exposed in previous research by other authors [24], for SQC, HPC (similar to UHP in [24]), and UHPFRC, demonstrating the improved durability of UHPFRC.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \begin{tabular}{c} **Air Permeability** \\ **coef. (10\({}^{-19}\) m\({}^{2}\))** \\ \end{tabular} & \(CV\) & \
\begin{tabular}{c} **\% Porosity** \\ **Accessible to Water** \\ \end{tabular} & \(CV\) &
\begin{tabular}{c} **\% Water** \\ **Absorption** \\ \end{tabular} & \(CV\) \\ \hline SQC & 1030 & 11.07\% & 14.53\% & 3.00\% & 6.53\% & 3.53\% \\ HPC & 6.23 & 29.96\% & 7.15\% & 3.20\% & 3.08\% & 3.25\% \\ UHPFRC & 1.38 & 26.48\% & 1.96\% & 3.56\% & 0.81\% & 3.63\% \\ UHPFRC-n & 1.94 & 30.10\% & 1.87\% & 13.18\% & 0.78\% & 13.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average values obtained in air permeability tests (UNE 83981), porosity accessible to water and absorption tests (UNE 83980).  
Figure 1: Bilinear \(\sigma\)–\(\varepsilon\) relationship for UHPFRC and UHPFRC-n mixes before crack localization.
# Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM
###### Abstract  
In visual Simultaneous Localization and Mapping (SLAM) systems, dynamic elements in the environment pose significant challenges that complicate reliable feature matching and accurate pose estimation. To address the issue of unstable feature points within dynamic regions, this study proposes a robust dual-mask filtering strategy that synergistically integrates semantic segmentation information with geometric outlier detection techniques. The proposed method first identifies outlier feature points through rigorous geometric consistency checks, then employs morphological dilation to expand the initially detected dynamic regions. Subsequently, the expanded mask is intersected with instance-level semantic segmentation results to precisely delineate dynamic areas, effectively constraining the search space for feature matching and reducing interference caused by dynamic objects. A key innovation of this approach is the incorporation of a Perspective-n-Point (PnP)-based optimization module. This module dynamically updates the outlier set on a per-frame basis, enabling continuous monitoring and selective removal of dynamic features. Extensive experiments conducted on benchmark datasets demonstrate that the proposed method achieves average accuracy improvements of 3.43% and 11.42% on the KITTI dataset and 24% and 8.27% on the TUM dataset. Compared to traditional methods, this dual-mask collaborative filtering strategy improves the accuracy of dynamic feature removal and enhances the reliability of dynamic object detection, validating its robustness and applicability in complex dynamic environments.  
dynamic SLAM; dual-mask strategy; feature filtering; semantic segmentation; dynamic object detection; geometric consistency
## 3 Controlled Antibiotic Delivery
### Exogenous Stimuli-Responsive Antibiotic Delivery

Table 2: _Cont._By modulating the frequency and duty cycles, ultrasound can destroy the structure of nanocarriers and cause drug leakage through the thermal effects (high-frequency), radiation forces, and mechanical effects (low frequency). In a study by the [PERSON] group, laser-generated focused ultrasound (LGFU) was effectively used to promote antibiotic release from a formulation comprised of alginate sphere microgels integrated with ciprofloxacin-loaded PLGA NPs [79]. Once the LGFU excites the microgels, cavitation effects at the microgels and oscillation of the microgels' shells promote the release of the drug from PLGA NPs and temporarily storing it in the microgels. Using a standard disk diffusion test, the resulting formulation exhibited superior antibacterial activities against _E. coli_ after LGFU treatment, with significantly larger inhibition zone sizes than that without LGFU treatment. In another approach, ultrasound can be employed to destroy the non-covalent interactions (such as hydrogen bonds), leading to the activation of drugs from inactive macromolecules (Figure 6B) [80]. Inspired by the weak supramolecular binding between vancomycin with its H-bond complementary peptide target sequence (peptide-D-Ala-D-Ala), an ultrasound-triggered drug delivery system was fabricated by incorporating this supramolecular motif between polymer chains and gold NPs. Ultrasound applied in situ in the presence of _S. aureus_ successfully lowered the MIC values of the formulations, highlighting the efficient supramolecular bond scission and activation of polymer-terminated vancomycin. Figure 6: Examples of exogenous stimuli-responsive nanocarriers for antibiotic delivery. (**A**) Schematics of the synthetic route of a NIR-controllable on-demand antibiotics’ release system using poly-dopamine nanoparticles (PDA NPs) for bacterial inactivation (reprinted with permission from [77], copyright 2019 Elsevier). (**B**) Schematic depiction of ultrasound activation of antibiotic assembled nanosystem. Van-DADA peptide assemblies activate the antibiotic properties of vancomycin to kill bacteria by ultrasound (adapted with permission from [80], copyright 2021 Springer Nature). In the same way, **magnetic fields** have also recently been exploited as a trigger for cargo molecule delivery. A wide range of on-demand drug delivery systems has been developed by encapsulating magnetic NPs and antibiotics into the polymer nanocar matrix [81, 82, 83]. Antibiotics can be released from the system through two main mechanisms, mechanical force and thermal effect under a magnetic field. These organic/inorganic hybrid nanosystems appear to be a promising new weapon for eradicating persistent bacteria in biofilms. However, challenges related to the potential toxicity and the metabolic fate of magnetic NPs need to be addressed to induce active development and applications of the antibiotic delivery formulation.
# Robust Symbol Timing Synchronization for Initial Access under LEO Satellite Channel
## 2 Transmission Model Description
### Transmission Structure for DVB-RCS2 Linear Modulation and Channel Model
Furthermore, the Adjacent Channel Interference (ACI) by power unbalance between inter-carriers and aggregated phase noise models, such as a terminal, satellite transponder, and GW operational clock-based Phase Lock Loop (PLL), should be incorporated into the return link channel model, as in Figure 3.  
Figure 1: Burst structure in the timeslot for DVB-RCS2 linear modulation. Figure 2: Transmission structure for DVB-RCS2 linear modulation at the Gateway point of view.
# Electric-Field-Coupled Resonator Antenna for 5G Applications
## 4 Performance Analysis of the ELC Antenna
These parasitic elements increase the current density at the side near the capacitive gap and at the lower side of the structure, which causes the gain to increase as presented in Figure 8b. A slight reduction in bandwidth is also observed. The effect of adding the rectangular blocks can be easily detected from the surface current distribution of the antenna. As seen in Figure 10, the blocks increased the current densities on the overall patch, especially at the lower portion of the radiator and the left portion of the radiator near the gap. As a result, the gain of the antenna is considerably increased. Figure 9: Variation of reflection coefficients and real and imaginary impedance values with different gap widths. (**a**) Reflection coefficients; (**b**) Real impedances; (**c**) Imaginary impedances; (**d**) Simulated electric field distribution in the cross-section of Antenna 3 and Antenna 4.
## 3 Performance Analysis  
### Spectral Efficiency  
As orthogonality between carriers is no longer strictly required, the proposed TDOM has higher spectral efficiency than traditional multi-carrier modulations. The carrier frequency distributions for conventional multicarrier communications, orthogonal frequency division multiplexing (OFDM), and TDOM are shown in Figure 4. Conventional multicarrier communications require a guard band between each carrier to prevent mutual interference. OFDM allow carriers to overlap in the frequency domain, but still require strict orthogonality between carriers. The TDOM can further narrow the frequency interval between each carrier to make better use of the spectrum resources. This is of great significance for maritime communications, where the spectrum resources are extremely tight, and the saved frequency resources can be used to carry more users of maritime communications. Specifically, when all carrier frequencies are the same, as shown in Figure 4d, the TDOM can achieve the highest spectral efficiency.  
Figure 4: Spectrum diagram of typical communication technologies.  
If the time duration on one carrier is \(T\), the time duration of a TDOM symbol is \(2T\). Then, suppose there are \(N\) carriers within one symbol, and each carrier conveys \(b\) information bits, the communication rate of the TDOM system is  
\[\text{rate}_{\text{TDOM}}=\frac{N\cdot b}{2T}. \tag{10}\]  
Meanwhile, in this paper, we aim to discover the highest spectral efficiency that the TDOM can achieve. Therefore, in the rest of this paper, we assume that all carriers within one TDOM symbol have the same frequency, i.e., \(f_{1}=f_{2}=\ldots=f_{N}\). At this time, the bandwidth of the TDOM is same to that of one carrier, i.e., \(2/\text{T}\). Therefore, the spectral efficiency (SE) of the TDOM system is  
\[\text{SE}_{\text{TDOM}}=\frac{\text{rate}_{\text{TDOM}}}{\text{bandwidth}_{ \text{TDOM}}}=\frac{Nb/2T}{(2/T)}=\frac{Nb}{4}. \tag{11}\]  
From Equations (10) and (11), the communication rate and the spectral efficiency of the TDOM system are both proportional to the total number of carriers within one symbol (\(N\)) and the information bit that each carrier can convey (\(b\)). As shown in Table 1, in this paper, each carrier is set to convey 2 bits, i.e., \(b=2\). Then, the highest communication rate of TDOM depends on \(N\). To compare with the traditional maritime communication technology, i.e., a very-high-frequency data exchange system (VDES), the bandwidth of the TDOM is set to be less than the peak bandwidth of VDES, i.e., \(\frac{2}{T}\leq 100\text{KHz}\).