esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this article, a refractive index sensor is proposed which is composed of an MIM waveguide, two rectangular stubs, and an elliptical ring resonator. The process of simulation was implemented by using the finite element method based on the EDA software COMSOL Multiphysics 5.4. The results of simulation reveal that the structure has a symmetric Lorentzian resonance, as well as an ultrasharp and asymmetrical Fano resonance. By adjusting the geometrical parameters, the sensitivity and FOM of the structure can be optimized flexibly. By comparing the results of simulation, we find that the wavelength of the Fano resonance mainly relies on the relevant parameter of the elliptical ring resonator, such as \(R_{1}\) and \(r_{2}\). Furthermore, it is found that when we change the parameter related to the MIM waveguide or the coupling distance, such as \(d\), \(h\), and \(R_{2}\), the transmittance and FOM of the Fano resonance will be altered at the same time, which could be explained by the phenomenon that the SPPs are almost confined within the elliptical ring resonator and few SPPs are in the MIM waveguide. After adjustments and optimization, the maximum sensitivity can reach up to 1550 nm/RIU with a FOM of 43.05. The structure presented in this article has a promising application in highly integrated medical optical sensors to detect concentration of hemoglobin and monitor body health.  
Conceptualization, S.Y. and H.S.; software, H.S.; validation, S.Y. and X.Y.; formal analysis, J.G.; investigation, J.W.; data curation, E.H.; writing--original draft preparation, H.S.; writing--review and editing, S.Y., X.Y, and J.W.; supervision, E.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 61675185,61875250,61975189) and sponsored by the Fund for Shanxi '1331 Project' Key Subject Construction.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Modeling the Record Microstructure--Calculation of Slant and Spatial Frequency  
In order to fabricate a HOE lens which collimates an off-axis diverging beam, a photonic structure is needed that will be on-Bragg everywhere for rays diverging from the off axis focal point and will re-direct them (through diffraction) into a collimated beam with a defined angle with respect to the HOE plane. At each location, the desired input beam angle can be defined, using the position of the focal point relative to that location. The desired output beam angle is known and is the same everywhere on the element (for the collimated output). Once the angles of the two beams, \(\theta_{1}\) and \(\theta_{2}\), are known, the slant angle (\(\theta_{8}=(\theta_{2}+\theta_{1})/2\)) and Bragg angle (\(\theta_{B}=(\theta_{2}-\theta_{1})/2\)) at that location are determined from geometry, and the spatial frequency needed to obtain such a Bragg angle at the design wavelength is calculated from Equation (2) above. Figure 4 depicts this procedure for a single point on the DOE lens. Once the slant angle and spatial frequency are known, one can calculate, for every point on the lens, the interference pattern required to fabricate the desired structure at a given wavelength. If the recording wavelength is close to the design wavelength, however, this process becomes extremely convenient, as \(\theta_{1}\) and \(\theta_{2}\) become the recording angles. Then the process involves using the desired input beam as one of the recording beams, made to interfere in the plane of the polymer material with the other recording beam, which has the characteristics of the desired output beam. The dual-sensitive polymer layer enables this convenient process to be exploited at two separate wavelengths in one device.  
Figure 4: Simple geometrical model used to calculate the required grating slant and spatial frequency for the grating needed to re-direct Beam 1 into Beam 2 (at each specific location on the DOE lens). For simplicity, refraction at the medium boundaries are not shown here, but are taken into account in the calculations. \(\theta_{B}\) is the Bragg angle; \(\theta_{1}\) is the incident angle of the input Beam 1 and \(\theta_{2}\) is the angle of the output Beam 2. \(\theta_{S}\) denotes the slant angle of the fringes.
## 3 Methodology
### Model Construction  
A deep ANN model was developed using Keras, with ReLU-activated dense layers and a sigmoid output layer for binary classification. An SVM with a linear kernel was trained in parallel. Both models were trained and validated on a 70:30 split of the dataset. Hyperparameter tuning was applied to the ANN to improve learning efficiency. Figure 5 showcases the final ensemble learning model architecture.  
Table 3 presents the layered ensemble architecture, detailing its hierarchical composition, including the base classifiers (ANN and SVM), their respective internal layers or operations, and how their outputs are fused using an RF meta-classifier to generate the final intrusion detection decision.  
Figure 5: Ensemble learning model architecture.  
The choice of the number of layers and nodes in the ANN was based on empirical tuning using a grid search over multiple configurations. We evaluated models with 2 to 4 hidden layers and node sizes ranging from 16 to 128 per layer. The final architecture, with 3 hidden layers of 64, 32, and 16 nodes, was selected based on the best trade-off between accuracy and training time. A dropout rate of 0.3 and 0.2 was introduced after each layer, respectively, to minimize overfitting. The SVM used a linear kernel, and the learning process used early stopping based on validation loss to prevent overfitting and ensure convergence. Predictions from both ANN and SVM were stacked to form a 2-feature vector per instance. An RF meta-classifier was trained on this new feature space, allowing the model to leverage ANN's nonlinear learning and SVM's margin-based separation. This approach enhanced overall classification reliability.
## 3 Numerical Analysis
### JCAS STPA--A Design Example
#### 3.1.1 Robustness of STPA to Wideband Operation  
The EBPs in Figure 8d-f are obtained at the center frequency \(f_{c}\), however, these apertures will be employed in wideband JCAS. Therefore, a study on the SLLs of EBPs versus a change in the operating frequency is required. The decrease from \(f_{c}\) is not problematic on the SLLs since the wavelength is increased and the distance between elements is already enough for radiation with low SLLs. Then, the increase of operating frequency from \(f_{c}\) is studied. Figure 9 demonstrates how these designed apertures perform with an increase in the frequency of operation. We observe that the EBP of STPA at \(f/f_{c}=1.625\) still shows a maximum SLL of \(-11.41\) dB, but the CUPA suffers from high SLL (grating lobes). The maximum frequencies for CUPA and STPA are \(1.125\)\(f_{c}\) and \(1.625\)\(f_{c}\), respectively. Due to a higher irregularity of the SPA and its sufficient inter-distances of antennas, the maximum SLL for SPA does not vary much. SPA demonstrates a maximum SLL of \(-10.59\) dB that is constant over the considered bandwidth.
# Late Line-of-Sight Check and Prioritized Trees for Path Planning
## 2 Definition  
The \(n\)-dimensional planning environment is often continuous, and we define it as \(Env\subseteq R^{n}\). The first step is to discretize \(Env\) into discrete grids with blocked or unblocked cells, which can be represented as \(Cells=\{cell_{1},cell_{2}...cell_{\infty}\}\). The vertices on these grids are denoted as \(V=\{v_{1},v_{2}...v_{\infty}\}\subseteq R^{n}\). Then, \(v_{start}\in Env\) is the start vertex in the planning, and \(v_{goal}\in Env\) is the goal vertex. If the planning domain is the 2D space (\(Env\subseteq R^{2}\)), we assume it extends in eight neighbor directions. If it is a 3D environment (\(Env\subseteq R^{3}\)), the 26-neighbor extension could be considered. An agent will take actions to reach a new vertex, and the action list is notated as _Actions_. We define the successor vertices of the current vertex as \(Succ(v)\), and a vertex may have many successors. The successor vertex extension process is based on the location of the current vertex \(v_{k}\), the neighbor \(cell\in Cells\), and the \(action\in Actions\). Then, this process is defined as \(Succ(v_{k})=Extend(v_{k},cell,action)\). Our algorithm is based on the tree structure, and every vertex has only one parent. The parent of vertex \(v\) is notated as \(Parent(v)\). The cost between two vertices is calculated upon the Euclidean distance, which is denoted as \(Cost(v_{m},v_{n})\).  
We used LoS-Check in our algorithms, which is defined as \(LineofSight(v_{m},v_{n})\), and it will return \(True\) if the straight segment between \(v_{m}\) and \(v_{n}\) does not cross the blocked cells. If \(v_{n}\) is the successor of \(v_{m}\), we can perform \(Parent(v_{n})\gets v_{m}\) if \(LineofSight(v_{m},v_{n})\) is \(True\). Therefore, \(Succ(v)\) and \(Parent(v)\) may not be the neighbors of \(v\) after LoS-Check. The final planned path is represented as \(Path=\{v_{start},\ldots,v_{k},v_{k+1},\ldots,Parent(v_{goal}),v_{goal}\;\;v_{k} =Parent(v_{k+1})\}\), and every vertex in \(Path\) is the only parent of the latter one. Lastly, we define the goal of the path planning as the following representation:  
\[min\sum_{v=v_{goal}}^{v_{\text{\emph{dist}}}}Cost(Parent(v),v),\quad v\in Path \subseteq R^{n} \tag{1}\]  
From Formula (1), it is not difficult to discover: in the path planning, which is based on the tree structure, a very important task is to find the best parent for every state. We also call the \"vertex\" as the \"node\" or \"state\" in this paper.
## 5 Comparative Simulation Results and Discussion
### Comparison with the P-PPC Method
Figure 13: **(a)**: The evolution of the actual altitude \(h(t)\) (purple line) vs. the reference airspeed \(h_{d}(t)\) (dotted line) for the proposed APPC scheme. **(b)**: The evolution of the actual altitude \(h(t)\) (purple line) vs. the reference altitude \(h_{d}(t)\) (dotted line) for the P-PPC scheme. **(c)**: The evolution of altitude tracking error \(h(t)-h_{d}(t)\) (orange line) within the adaptive performance envelope delimited by \(p_{1}(t)\) and \(-p_{1}(t)\) (black lines) for the proposed APPC scheme. **(d)**: The evolution of altitude tracking error \(h(t)-h_{d}(t)\) (orange line) for the P-PPC scheme. In summary, the APPC control scheme proposed in this work, demonstrates superior performance compared to relative control approaches, showcasing robustness in the presence of external disturbances and varying reference inputs. Notably, the proposed scheme eliminates the need for gain tuning, as the control gains are decoupled with closed-loop performance. However, it is important to acknowledge some limitations of the proposed method. Depending on the system condition, the fluctuation of performance boundaries may become excessive, causing the closed-loop performance specifications to relax more than necessary to ensure the boundedness of the closed-loop signals. Additionally, an accurate and fast state feedback is required for the proper evolution of tracking errors within the performance envelopes, posing a practical limitation.
# Calibrated Hydraulic Resistance Adjuncts for Carbon Dioxide Angiography Optimization
## 3 Results  
Figure 5 shows a frame for each injection condition, extracted 2 s and 4 s after starting gas injection. It is possible to see the bubbles' dimensions: Without resistances, the bubbles are bigger and tend to fulfill the vessel with an extended retrograde flow. With different resistance adjunct values, bubbles are smaller and move along the vessel with a lower retrograde flow.  
Despite the different effects on the bubbles' dimensions and distribution without and with adjunct resistances, the final masked and stacked images are very similar (Figure 6), and the only difference is in the length of the vessel highlighted by the gas reflux.  
Table 1 shows the injection durations, the times necessary to obtain the same complete final radiological image, and the \"size\" of the reflux, quantified as the length of the tube covered by the backward gas flow in the gas-flushed catheter condition.  
Systolic and diastolic arterial pressure increases for each condition are shown in Table 2.  
The results of the comparison of delays between pressure onset and gas output and injection duration for the 2 conditions with blue saline are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Injection Pressure** & \multirow{2}{*}{\(\mathrm{R_{adj}}\) (mmHg/mL/s)} & \multirow{2}{*}{**Duration \(\pm\) 0.4 (s)**} & **Time to Obtain the Complete** & **Tube Length Covered** \\  & & & **Radiological Image \(\pm\) 0.4 (s)** & **by Reflux (cm)** \\ \hline \(150\) & 0 & 10.5 & 8.0 & 2 \\ \hline \multirow{3}{*}{206} & 0 & 6.0 & 7.8 & \textgreater{}24 \\ \cline{2-5}  & 28 & 10.4 & 7.8 & 6 \\ \hline \multirow{3}{*}{234} & 0 & 4.1 & 7.8 & \textgreater{}24 \\ \cline{2-5}  & 42 & 7.5 & 8.0 & 8 \\ \hline \multirow{3}{*}{288} & 0 & 4.0 & 7.7 & \textgreater{}24 \\ \cline{2-5}  & 69 & 7.5 & 8.3 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Injection durations for each condition.  
Figure 5: Example of bubble dimension and spatial distribution with an injection pressure of 234 mmHg without R\({}_{\rm adj}\) and with R\({}_{\rm adj}\) = 42 mmHg/mL/s, at 1 s, 2 s, 3 s, and 4 s after gas injection start.  
Figure 6: Stacked images for three conditions (P\({}_{\rm inj}\)) = 206 mmHg, R\({}_{\rm adj}\) = 0, R\({}_{\rm adj}\) = 28 mmHg/mL/s; P\({}_{\rm inj}\) = 234 mmHg, R\({}_{\rm adj}\) = 0, R\({}_{\rm adj}\) = 42 mmHg/mL/s; P\({}_{\rm inj}\) = 288 mmHg, R\({}_{\rm adj}\) = 0, R\({}_{\rm adj}\) = 69 mmHg/mL/s). Red arrows indicate the reflux.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Injection Pressure (mmHg)** & **R\({}_{\rm adj}\) (mmHg/mL/s)** & **Systolic Increase after Injection \(\pm\) 2 (mmHg)** & **Diastolic Increase after Injection \(\pm\) 2 (mmHg)** \\ \hline
150 & 0 & 7 & 5 \\ \hline
206 & 0 & 10 & 25 \\ \hline  & 28 & 6 & 10 \\ \hline  & 0 & 13 & 25 \\ \hline  & 42 & 6 & 10 \\ \hline  & 0 & 15 & 25 \\ \cline{2-4}
288 & 69 & 10 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maximum pressure increases during injection at different conditions.
## 5 Conclusions  
The research on phosphorus adsorption by bottom deposits of Lake Kortowskie revealed that analyzed sediment had good P adsorption abilities; P retention was especially effective, despite fairly low TP amounts in the analyzed sediment. Phosphorus was stored in sediment mainly in mineral, hardly bioavailable forms (HCI-P and res-P). Tested P adsorption models have suggested that P adsorption was probably multi-layered. It was confirmed by a very weak fit to Langmuir and double Langmuir models (describing mono-layered adsorption) and a good fit to BET, Freundlich, and general Freundlich-Langmuir models. The adsorption isotherms were concave, suggesting higher P adsorption abilities at higher P concentrations in water above sediment. The adsorption process was physical and cooperative. Applying the hypolimnetic withdrawal method on Lake Kortowskie had caused certain changes in the bottom sediment chemical composition. TP amounts were significantly lower at the experimental station, and HCl-P and res-P fractions amounts differed significantly between the control and the experimental site. This could mean that the hypolimnetic withdrawal method changes the conditions of P deposition in sediment. This method, operating at Lake Kortowskie for several decades, helps maintain the water quality of this water body in the range of a moderate eutrophic state.  
Conceptualization, R.A.-T., R.K., M.L. and J.G.; methodology, R.A.-T. and R.K.; software, J.T.; validation, R.T., J.G. and M.L.; investigation, R.A.-T. and R.K.; writing--original draft preparation, R.A.-T. and R.K.; writing--review and editing, R.A.-T. and R.K.; supervision, J.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Project financially supported by the Minister of Education and Science in the range of the program entitled \"Regional Initiative of Excellence\" for 2019-2023, project No. 010/RID/2018/19, with a funding amount of PLN 12,000,000.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are available at the Department of Water Protection Engineering and Environmental Microbiology, University of Warmia and Mazury, Olsztyn, Poland.  
**Conflicts of Interest:** The authors declared no conflict of interest.
## 7 3D Printing Technologies Applied to Prostheses
### Stereolithography (SLA)  
Stereolithography (SLA) is another widely used technology in the manufacture of robotic prosthetics. Unlike FDM, which uses thermoplastic filaments, SLA employs a photosensitive liquid resin that solidifies layer-by-layer when exposed to an ultraviolet laser. This allows for extremely precise details and smooth surface finishes, which are essential for prostheses that require a high degree of precision in shape and fit [1].  
The SLA process is particularly suitable for creating prostheses that fit more ergonomically to the patient's body. Due to the high resolution of the technology, it is possible to design devices with very fine details, such as joints or complex structures that better mimic the body's natural functions (Figure 9). In addition, prostheses created with SLA often have superior aesthetics than those made with FDM, which improves the patient's perception of the device and their willingness to use it.  
However, one of the main challenges of SLA is the relatively high cost of both the equipment and the resins used, which limits its adoption in countries or regions with more limited resources [12]. However, its ability to create prostheses with high precision and its potential for design customization make this technology a preferred choice in applications where quality and fit are paramount.  
Figure 8: UniLeg Spotlight: the world’s first complete 3D-printed below-knee prosthesis solution [8].
## 4 Hazards Analysis
### Wildfire Hazard Assessment
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criteria** & **Category** & **Grade** & **Weight Factor** \\ \hline \multirow{4}{*}{Land Use} & Coniferous forest, broadleaf forest, mixed forest & 5 & \\ \cline{2-4}  & Pastures, natural pastures, sclerophyllous vegetation, & \multirow{2}{*}{3} & \multirow{2}{*}{45\%} \\  & transitional forest–shrubland & & \\ \cline{2-4}  & Complex cultivation standards, agriculture with & & \\  & significant areas of natural vegetation & & \\ \cline{2-4}  & Olive groves, vineyards, sparse vegetation areas & 1 & \\ \cline{2-4}  & Discontinuous urban development, harbor, mining & & \\  & sites, recreational facilities, uncultivated arable land, & 0 & \\  & beaches, dunes, sand bars, salt flats, coastal lagoons & & \\ \hline \multirow{4}{*}{Slope} & \multicolumn{2}{c}{\(>\)40\%} & 5 & \\ \cline{2-4}  & Slope & 13–40\% & 3 & 15\% \\ \cline{2-4}  & & \(<\)13\% & 1 & \\ \hline \multirow{4}{*}{Slope Aspect} & \multicolumn{2}{c}{\(135\)–270\({}^{\circ}\)} & 5 & \\ \cline{2-4}  & & \(45\)–135\({}^{\circ}\) & 3 & \\ \cline{2-4}  & & \(270\)–315\({}^{\circ}\) & 3 & \\ \cline{2-4}  & & \(315\)–360\({}^{\circ}\) & 1 & \\ \cline{2-4}  & & \(0\)–45\({}^{\circ}\) & 1 & \\ \hline Proximity to Settlements & \(<\)400 m & 4 & 13\% & \\ \hline Proximity to the Road & Asphalt road (\(<\)50 m) & 5 & 12\% & \\ \cline{2-4} Network & Dirt road (\(<\)50 m) & 4 & \\ \hline Proximity to the PPC network & \(<\)30 m & 5 & 10\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter ranking used to obtain the Wildfire Hazard Map. mapped high- and very-high-hazard zones (Figure 13), which supports the reliability of the model. In addition, the recorded burned areas are predominantly located within medium- to high-hazard zones, further confirming the spatial accuracy and predictive value of the hazard classification. This assessment highlighted areas where preventive strategies, such as vegetation management, infrastructure monitoring, and firebreaks, should be prioritized. Future versions of this model should incorporate dynamic factors such as weather conditions, seasonal vegetation changes, or long-term climate trends, all of which strongly influence wildfire behavior--particularly in Mediterranean environments.
###### Abstract
Although the ability of carbon assimilation can be estimated through GPP, the contribution of the urban forest to regional carbon assimilation is still unclear. A number of difficulties in the current estimations of urban GPP need to be overcome; for example the lack of surface observation data in urban areas makes it difficult to conduct empirical estimation on the urban scale by using traditional VI. At the same time, the lack of driving and parameterized data with high spatiotemporal resolution significantly reduced the performance of process-based ecological models in the simulation of GPP in urban areas. As NIRv has successfully estimated GPP in other ecosystems, it may be able to provide a new approach to estimating carbon assimilation in urban forests. To the best of our knowledge, the contribution of urban forests to regional carbon assimilation has not yet been studied according to this method. Meanwhile, it is not yet clear which resolution of satellite data is appropriate for determining urban forest carbon assimilation capacity. Therefore, in this study, the GPP-NIRv relationship model is employed to estimate the GPP of terrestrial ecosystems in Suzhou by MODIS, Landsat-8, and Sentinel-2 remote sensing data, and to further explore what kind of remote images can figure out the spatiotemporal pattern of GPP of urban forests. Then, we evaluate the contribution of urban forest to the regional carbon budgets and the impact of human activities on regional GPP. The expected results will improve our understanding of the ability of urban forests to affect carbon assimilation and will provide an appropriate reference for selecting the right resolution of remote sensing images for carbon budget research in urban areas.