esa-sceva/satcom-chunk-collection huggingface dataset:
# Prognostic Value of Lymph Node Density in Lingual Squamous Cell Carcinoma
## 3 Results
### Descriptive Analysis of the Total Population and Population Subgroups According to the LND
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Categories** & **Total Population (n = 62)** & **LND < 0.04** & **LND \(\geq\) 0.04** \\ \hline \multirow{3}{*}{Differentiation} & Well & 4 (6.5\%) & 3 (75\%\%) & 1 (25\%) \\  & Moderate & 48 (77.4\%) & 38 (79.2\%) & 10 (20.8\%) \\  & Poor & 10 (16.1\%) & 8 (80\%) & 2 (20\%) \\ \hline \multirow{3}{*}{Margin Status} & Near & 13 (21\%) & 9 (69.2\%) & 4 (30.8\%) \\  & Clear & 49 (79\%) & 40 (81.6\%) & 9 (18.4\%) \\ \hline \multirow{3}{*}{Pathologic T} & 1 & 15 (24.2\%) & 14 (93.3\%) & 1 (6.7\%) \\  & 2 & 23 (37.1\%) & 22 (95.7\%) & 2 (4.3\%) \\  & 3 & 23 (37.1\%) & 14 (60.9\%) & 9 (39.1\%) \\  & 4 & 1 (1.6\%) & 0 (0.0\%) & 1 (100\%) \\ \hline \multirow{3}{*}{DOI} & No valuable & 2 (3.2\%) & 1 (50\%) & 1 (50\%) \\  & \textless{}5 cm & 15 (24.2\%) & 14 (93.3\%) & 1 (6.7\%) \\  & 5–10 cm & 24 (38.7\%) & 21 (87.5\%) & 3 (12.5\%) \\  & \textgreater{}10 cm & 21 (33.9\%) & 13 (61.9\%) & 8 (38.1\%) \\ \hline \multirow{3}{*}{Positive neck} & No Yes & 42 (67.7\%) & 42 (100\%) & 0 (0.0\%) \\  & & 20 (32.3\%) & 7 (35\%) & 13 (65\%) \\ \hline \multirow{3}{*}{Histological neck} & pN0 & 42 (67.7\%) & 42 (100\%) & 0 (0.0\%) \\  & pN+ ipsilateral & 13 (21\%) & 6 (46.2\%) & 7 (53.8\%) \\  & pN+ contralateral & 2 (3.2\%) & 0 (0.0\%) & 2 (100\%) \\  & pN+ bilateral & 5 (8.1\%) & 0 (0.0\%) & 4 (100\%) \\ \hline \multirow{3}{*}{Extracapsular extension} & No Yes & 58 (93.6\%) & 47 (81\%) & 11 (19\%) \\  & No Yes & 4 (6.5\%) & 2 (50\%) & 2 (50\%) \\ \hline \multirow{3}{*}{Pathologic N} & N0 & 42 (67.7\%) & 42 (100\%) & 0 (0.0\%) \\  & N1 & 8 (12.9\%) & 5 (62.5\%) & 3 (37.5\%) \\  & N2a & 1 (1.6\%) & 0 (0.0\%) & 1 (100\%) \\  & N2b & 5 (8.1\%) & 1 (20\%) & 4 (80\%) \\  & N2c & 4 (6.5\%) & 0 (0.0\%) & 4 (100\%) \\  & N3a & 0 (0.0\%) & 0 (0.0\%) & 0 (0.0\%) \\  & N3b & 2 (3.2\%) & 1(50\%) & 1 (50\%) \\ \hline \multirow{3}{*}{Perineural invasion} & No Yes & 26 (41.9\%) & 25 (96.2\%) & 1 (3.8\%) \\  & No Yes & 36 (58.1\%) & 24 (66.7\%) & 12 (33.3\%) \\ \hline \multirow{3}{*}{Lymphovascular invasion} & No Yes & 55 (88.7\%) & 45 (81.8\%) & 10 (18.2\%) \\  & No Yes & 7 (11.3\%) & 4 (57.1\%) & 3 (42.9\%) \\ \hline \multirow{3}{*}{Pathological Stage} & I & 13 (21.0\%) & 13 (100\%) & 0 (0.0\%) \\  & II & 18 (29.0\%) & 17 (94.4\%) & 1 (5.6\%) \\ \cline{1-1}  & III & 19 (30.6\%) & 17 (89.5\%) & 2 (10.5\%) \\ \cline{1-1}  & IV & 12 (19.4\%) & 2 (16.7\%) & 10 (83.3\%) \\ \hline \multirow{3}{*}{Dissection \textgreater{} 18 lymph nodes} & No Yes & 25 (40.3\%) & 20 (80\%) & 5 (20\%) \\ \cline{1-1}  & No Yes & 37 (59.7\%) & 29 (78.4\%) & 8 (21.6\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Distribution of histological variables in the total population and according to the LND.
## 1 Introduction  
This study presents a variation of the hybrid block methods (HBMs) for integrating systems of initial value problems of the first order (IVPs). It is now well established that HBMs are effective at solving IVPs that may or may not be stiff. Researchers are now concentrating on enhancing these approaches to increase their efficacy and extend them to other forms of differential equations, such as boundary value problems. Several authors have developed optimised hybrid block methods in recent years by selecting intra-step points that minimise the local truncation error of one or more of the formulas in the set of hybrid block methods (see, for instance, [1, 2, 3, 4, 5]). The hybrid block methods were applied with an adaptive strategy in [6, 7] in an effort to increase their efficiency and circumvent the difficulties associated with short step sizes. Other researchers (see [8, 9]) have implemented the optimised hybrid block methods in nonlinear partial differential equations using cubic spline basis functions. In this study, the hybrid block method is modified by overlapping adjacent integration blocks at the last intra-step point. This adjustment produces a \(\mathcal{A}\)-stable version with a truncation error at least \(O(h)\) greater than the comparable HBM without overlap.  
The performance of the proposed overlapping HBM is tested utilising an error analysis of numerically challenging solutions to different nonlinear IVPs. In order to determine the approach's robustness, it is also evaluated on IVPs with extreme stiffness.  
The remainder of this paper is organised into four sections. The derivation of the overlapping hybrid block methods is covered in Section 2. Section 3 examines the error and stability properties of the proposed method. Section 4 presents the experimental and numerical results, and Section 5 provides a summary and conclusion.
## 3 Results
### Validation of AOD Estimation Methods  
Air pollution in South Korea is most severe in spring and winter [42; 43; 44]; therefore, we first focused on March-May 2016 (spring) and December 2016-February 2017 (winter). In general, finer grid spacing is expected to be relatively beneficial for simulating small-scale activities, such as regional circulation and local instability; thus, we investigated the performance of AOD estimation depending on grid resolution from the NIER operational model. Since the finest domain (D03) is too small to investigate AOD with satellite images, we only analyzed the model domains of D01 and D02 for East Asia and the Korean Peninsula, respectively. In this study, all statistical parameters are calculated using AERONET data, since satellite images (e.g., GOCI) cannot be used to evaluate the model outputs due to large cloud cover. However, each method's two-dimensional AOD is presented and compared with satellite image to reveal the key features of each estimation.
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 4 Results
### Evaluation Based on Scanning Measurements
**(a–c)** histogram-based dynamic data filter, **(d–f)** Gaussian kernel based dynamic data filter, **(g–i)** CNR-threshold filter, **(j–l)** combined filter approach by [PERSON] et al. (**m–o**) combined filter approach by [PERSON] et al. (**p–r**) unfiltered. As a proof that the dynamic data filtering approach is not system specific, we present an example of PPI data from the second part of the nacelle-based measurement campaign from Section 3.1.2 captured with a Stream Line XR. In the following, we will illustrate the data-density distribution in the \(u_{r}\)-\(\alpha\) diagram and the normalised LiDAR data in the \(u_{r}\)'-\(\alpha^{\prime}\) reference frame as a proof of similar data behaviour in comparison to the Leosphere LiDAR. As can be derived from Figure 18a, the overall data density of the Stream Line XR dataset shows similar behaviour in comparison to the Leosphere Windcube 200s LiDAR data in Figure 3. A horizontal scattering in the radial velocity in combination with a vertical scattering of the CNR is shown in both visualisations. The application of the temporal and spatial normalisation from Section 2.7.1 results in a comparable data density distribution. It is noticeable that the density distribution of the normalised LiDAR data of the Stream Line XR device tend to form a pyramid distribution (Figure 17b), whereas the density shown in Figure 4 resembles a bi-variate Gaussian distribution. The normalisation provided here was applied with a \(\Delta t=60\) s and may therefore be compared with Figure 4f. From similar behaviour of forming a dense data distribution in the \(u_{r}\)'-\(\alpha^{\prime}\) reference frame, we confirm the suitability of the possibility of application of the dynamic data filter as presented in this paper. Figure 17: Results of application of different filtering methods in the \(u_{r}\)-\(\alpha\) diagram. (**a**) histogram-based dynamic data filter, (**b**) Gaussian kernel based dynamic data filter, (**c**) CNR-threshold filter, (**d**) two sigma standard deviation filter, (**e**) iterative standard deviation filter, (**f**) interquartile-range, (**g**) combined filter approach by [PERSON] et al. (**h**) combined filter approach by [PERSON] et al. (**i**) no filtering.
## 3 System Overview
### Cell Training Data  
After cell segmentation, a Wi-Fi database is created for each cell using the signal strength measurements collected during the training phase. Cell training data, which involves the RSS from all visible APs intensively sampled at multiple RPs within each cell and each RP is associated with manually labelled cell ID. The RPs are randomly selected within each cell and their locations do not need to be known.  
Given a building with a set of cells \(R\), the total number of visible Wi-Fi APs is \(N\). For a given cell \(r\in R\), a Wi-Fi measurement is a vector containing signal strength from \(N\) APs, denoted as:  
\[S_{(r,j)}=\{AP_{1}:Rss_{1,j},AP_{2}:Rss_{2,j},\ldots,AP_{N}:Rss_{N,j}\},j=1, \ldots,M \tag{1}\]  
\(M\) is the total number of measurements at cell \(r\) and could vary by room. Each AP is identified by its unique MAC address and \(Rss_{i,j}\) is the signal strength value from \(AP_{i}\) in the \(j_{th}\) measurement. Note the RSS value is replaced with \(NaN\) for the AP unobserved in one measurement.  
During the offline phase, the signal strength from all visible APs are intensively sampled at multiple RPs within each cell. The training data for cell \(r\) fused from all RPs are stored in a \(M\times N\) matrix denoted by \(S_{r}=\{S_{(r,j)}|j=1,\ldots,M\}\).
## 2 Materials and Methods
### Scaffolds Characterization  
#### 2.2.1 3D Micro-Computed Tomography  
The SF scaffolds were analyzed by 3D Micro-CT desktop scanner (SkyScan 1072; Bruker microCT, Kontich, Belgium). The cross-section images (slices) of the objects were reconstructed with the software NRecon (v1.7.0; Bruker microCT, Kontich, Belgium). The morphometric parameters were elaborated with the software CT-Analyser (v1.16; Bruker microCT, Kontich, Belgium) on a selected region of interest (ROI), 1 mm thickness circular area equivalent to an internal volume of about 2.70 mm\({}^{3}\). Bidimensional (2D) images, as a set of slices, were visualized using the software DataViewer (v1.5.2; Bruker microCT, Kontich, Belgium). The volume rendering was carried out with the software CT-Vox (v3.3, Bruker microCT, Kontich, Belgium), and the application 3D Creator (v2.5, Bruker microCT, Kontich, Belgium) was used to visualize the 3D reconstructed models. The acquisitions were performed for the samples SSF and SSFtime at 80 kV, source current 124 \(\upmu\)A, rotation step 0.45\({}^{\circ}\), rotation angle 180\({}^{\circ}\), no filter, cross-section pixel size of 9.76 \(\upmu\)m, and, for the other with cells (sample SSFc) a resolution of 6.51 \(\upmu\)m and considering the following parameters: 40 kV, source current 248 \(\upmu\)A, rotation step 0.45\({}^{\circ}\), rotation angle 180\({}^{\circ}\), and no filter.
## 3 Theory
position of \(H_{\text{qpm}}\) and its intensity as a function of \(n\) (see blue filled circles and red empty squares in Figure 6b). It is seen that the position of \(H_{\text{qpm}}\) shifts towards the higher order harmonic region, and the value of the harmonic intensity quadratically grows with the increase of the number of jets. It is worth mentioning that in experiment we also observed the shift of \(H_{\text{qpm}}\) with the increase of \(n\), though this shift was less pronounced in comparison with the calculations. Finally, we simulated new possibilities for the control of \(H_{\text{qpm}}\) and peak intensity in the case of the two-color pump of MJP. It assumes application of the jets of two different lengths (\(d_{1}\) and \(d_{2}\)). In all calculations the whole length of nonlinear medium was kept the same (5 mm). We took \(d_{1}\) = 0.15 mm and \(d_{2}\) = 0.31 mm and simulated the HHG in the nonlinear medium consisting of 8 jets of \(d_{1}\) and four jets of \(d_{2}\). The resulting spectrum is shown in Figure 7 as blue filled squares. The HHG spectrum has two maxima (green dotted lines) at the same place as for similar jets (H32 and H67 as compared with Figure 5a). The decrease in the amplitude of the peaks in comparison with the cases shown in Figure 5a is explained by the fact that in the former case the number of jets of different \(ds\) is two times smaller. We also constructed the blocks of two groups of jets separated by the free space of the same sizes using \(d_{1}\) and \(d_{2}\) jets (i.e., \(d_{1}\) jet \(-d_{1}\) free space \(-d_{2}\) jet \(-d_{2}\) free space \(-d_{1}\) jet \(-d_{1}\) free space... ). This group of jets forms the nonlinear medium with the same length as in the previous case (i.e., eight jets of \(d_{1}\) and four jets of \(d_{2}\)). The HHG spectrum in this case is presented in Figure 7 by red empty circles. It can be seen that this HHG spectrum has three peaks. The peak corresponded to the \(d_{2}\) becoming divided into two  
Figure 6: **(a)** Harmonic distribution in the case of silver MJP during propagation of two-color pulses (1310 nm + 655 nm) through the plasmas consisting of different number of jets. Single jet sizes were 0.23 mm. **(b)**\(H_{\text{qpm}}\) and its intensity as the functions of \(n\) (blue filled circles and black empty squares). Figure 7: Harmonic distribution in the case of propagation of two-color pulses (1310 nm + 655 nm) through silver MJP of 5 mm length consisting of different combinations of two groups of jets (see text). peaks, at H21 and H44, and the third peak remained at the same place corresponding to \(d_{1}\) (H67). To investigate the observed changes of the spectrum more precisely we changed the value of one sort of jets from \(d_{1}\) to \(d_{3}=0.28\) mm and formed the nonlinear medium in the same way as in the previous case. The resulting harmonic spectrum is presented by the brown filled triangles in Figure 7. It is seen that the main peak of the spectrum became centered at H34 and the harmonic intensity increased by a factor of 2 in comparison with previous cases.
## 4 Results
### FDPS Algorithm  
The functional version of the DPS algorithm is related to a special \(r\)-local density \(F=F(\
u)\), based on function weighting \(\
u:X\rightarrow\mathbb{R}^{+}\):  
\[F_{A}(x)=\frac{\sum v(y):y\in D_{A}(x,r)}{|D_{X}(x,r)|}\]  
The FDPS algorithm is the operation of the DPS circuit on \(F\), as described in Definition 6-Section 3.3.3: FDPS \(=\text{DPS}(F,r,\
u,\beta)\)[24]. It aims at finding subsets in \(X\) with \(r\)-local high weights \(\
u\), and is capable to work on regular spaces and successfully complements the SDPS and MDPS algorithms.  
**Remark 7**.: _Weight \(\
u\) can be thought of as a non-negative relief on \(X\). The FDPS algorithm efficiently searches for the bases of these elevations, which is fundamental in data analysis, in particular in time series analysis (DRAS, FC ARS algorithms etc.) [25]._  
**Example 9**.: _Figure 11a shows how the FDPS algorithm works: space \(X\) in this case is a regular grid on the horizontal axis, where the weight \(\
u\) of each point \(x\in X\) is plotted vertically. The result of the FDPS algorithm will be two red bars on the horizontal axis, serving as the bases of the two most significant stochastic \(\
u\)-elevations on \(X\)._  
_As can be seen from this figure, the FDPS algorithm is stable: it disregards to insignificant drops of contour \(\
u\) below the set level, as well as to insignificant rises of \(\
u\) above it. This property of the FDPS explains the solidity of its highlighted elevations and is essential in decision-making issues: the selected areas must be massive and resistant to minor disturbances within them._  
_To compare, Figure 11b shows a classical selection on grid \(X\) with respect to a given level for relief \(\
u\). As we can see from the figure, it is unstable, it gives a lot of weak elevations._  
Figure 11: Operation of the FDPS algorithm on a regular grid (**a**). The FDPS algorithm results in two red lines on the horizontal axis, which serve as the bases of the two most significant stochastic heights. Figure (**b**) shows a classic choice with respect to a given level, highlighting many weak heights.  
Figure 10: An illustration of the independence of the MDPS and SDPS algorithms: the “dumbbell” in the original array (**a**) has a sparse handle, which MDPS highlights cleanly (**c**); SDPS cannot do this (**b**).
# Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
## 3 Results
### Flash Flood Susceptibility Mapping  
The maps of flood susceptibility for each pixel in the basin were computed using four machine learning models: RF, KNN, NB, and XGBoost. Based on the aforementioned experiment findings, the RF model has been proven to be the highest-performing prediction model across all benchmark models for geospatial datasets. In ArcGIS 10.3, there are several approaches for reclassifying flood susceptible models, including a natural break, equal interval, quantile, regular interval, standard deviation, and manual methodology. Quantile and natural break methods are two of these strategies that have been frequently described in flood susceptibility studies literature [96; 108].  
The FFS maps are divided into five classes using [PERSON] natural break method in ArcMap: very high, high, moderate, low, and very low [41; 51]. The flooding areas are  
Figure 10: Factors distribution by flood occurrences.  
located along the main WadiRiver and tributary streams (Figure 11). The flooded areas appear to be highly influenced by distances from streams, where the areas nearest to streams are more flood-prone than those far away. The FFSM also shows that drainage density and elevation have relatively significant contributions to flood modeling where low elevation zones tend to gather more water (high drainage density) and then increase the probability of flooding. In a similar vein, the slope had clear significance for flooding, where the low slope areas typically have the potential to collect water. RF susceptibility map reveals that the very low, low, moderate, high, and very high classes cover 42.58%, 23.24%, 13.52%, 11.59%, and 9.05% of the total study area. For the KNN model, the surface area is computed as; 26.97% for the very low class, 22.57% for the low class, 20.30% for the moderate class, 16.79% for the high class, and 13.34% for the very high class. Further, in the case of the NB model, the obtained susceptibility map indicates that 91.65%, 1.18%, 0.89%, 1.13%, and 5.13% of the total surface area corresponds to the very low, low, moderate, high, and very high classes, respectively. Last, the XGBoost susceptibility map indicates that 75.14%, 8.72%, 7.05%, 4.53%, and 4.53% of the total surface area correspond to the very low, low, moderate, high, and very high classes, respectively (Figure 12).
## 4 Discussion
This study has several limitations. First, only the initial forces/moments applied by CAs on the teeth were measured and compared, without considering changes in orthodontic forces over time. Because measurements were obtained at a single time point (after 10 min at 37 \({}^{\circ}\)C), the time-dependent characteristics of force decay and potential plastic deformation of thermoformed CAs could not be captured. Second, while clinical orthodontic treatment involves complex scenarios with simultaneous movements of multiple teeth, this study focused solely on forces/moments on maxillary molars. Third, although the experimental setup maintained 37 \({}^{\circ}\)C to simulate intraoral temperature, the oral environment could not be fully reproduced. The in vitro setup used a static environment without incorporating functional loading or dynamic masticatory forces, which may limit the translation of these findings to clinical scenarios. Specifically, factors such as humidity, saliva, friction between CAs and teeth, occlusal forces from masticatory muscles, and biological responses including microbial and gingival interaction with the material were not incorporated. Furthermore, the experimental design simplified the dentition by isolating only the second molar, without including adjacent teeth or opposing occlusion, omitting key anatomical and functional interactions that affect force distribution and tooth movement. The exclusion of salivary flow, biofilm formation, and other biological responses also reduces the clinical relevance of the model. Moreover, while the proposed chairside resin enhancement method showed biomechanical efficacy in vitro, its clinical practicality remains to be validated. This study did not assess patient comfort, the durability of CA materials after repeated resin modifications, or the feasibility of repeated resin applications throughout the course of treatment. Future research involving dynamic force measurements would provide valuable insight into how aligner materials behave under prolonged intraoral conditions, and studies should aim to incorporate these intraoral conditions to more closely simulate the clinical environment. Furthermore, although the enhanced structural modifications demonstrated improved biomechanical performance in vitro, their long-term clinical effectiveness and applicability across diverse patient anatomies remain uncertain. Factors such as patient compliance, intraoral variations, and long-term material behavior could affect real-world outcomes and should be addressed in future clinical studies.
# A BIM-Enabled Workflow for the Rehabilitation of Heritage Steel Bridges
## 2 Current State of the Art
### Traditional Methods and Emerging Technologies for Bridge Inspection, Monitoring and Management
For heritage steel bridges, this information is crucial as it helps bridge custodians track how modern usage, such as increased traffic loads or environmental stressors, is impacting the structure. By integrating satellite data into a BIM or digital twin framework, stakeholders can combine macro-level monitoring with micro-level details for comprehensive asset management [17]. When applied to heritage steel bridges, these technologies not only enhance safety and operational performance but also support preservation efforts. The detailed insights provided by AI, drones, and satellites ensure that interventions are precise and minimally invasive, aligning with the goal of maintaining the original materials and design features. Moreover, these tools facilitate the documentation of a bridge's condition over time, creating a valuable historical record that can guide future restoration efforts.
## 3 Experiment and Discussion
### Experiment Setup
#### 3.1.2 Dut  
The tested device sample is a Micro Control Unit (MCU; see Figure 4a), which is a computer system integrated on a chip. After functional testing, the failure area is located in the red box in Figure 4a. When the normal sample is power supplied with no function running, the supply voltage \(V_{0}\) is situated in the range of 1.8\(\sim\)3.6 V. The typical value of the current \(I_{0}\) is 1.4 \(\mu\)A when \(V_{0}=3.3\) V.  
The fail sample has a leakage current (Figure 4b), the typical value of which is \(I_{0}=595\)\(\mu\)A at \(V_{0}=3.3\) V. This current value is much larger than that of the normal device. As the supplied voltage increases, the current increases, and the resistance decreases.  
Figure 4: Characteristics of the failed sample: (**a**) front-side optical image of the MCU sample and the location of the failure area; (**b**) I-V and R-V curves.  
Figure 3: Schematic diagram of the Laser Scanning and Testing System (LSTS). The voltage source is used to supply the DUT, and the computer is used to monitor the current. As the laser beam scans the defect location, the abnormal \(\Delta I\) will be tested, and a red spot will be marked on the layout diagram, which is imaged by the IR-CCD.  
\begin{table}
\begin{tabular}{c c c} \hline laser & Wavelength & 1310 nm \\  & Power & 0\(\sim\)100 mW \\ \hline Translation Stage & Scanning Speed & 0\(\sim\)100 mm/s \\  & Positioning Accuracy & 0.1 \(\mu\)m \\ \hline \end{tabular}
\end{table}
Table 3: Relevant parameters of the LSTS.