esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Next Steps for the Application of LST  
Sparsely vegetated pixels present particular challenges for remote-sensing approaches to estimating leaf temperature because the LST values may represent a combination of bare ground as well as foliage. [PERSON] et al. [54] evaluated a GPP model driven only by the MODIS Enhanced Vegetation Index and LST at 11 flux sites in North America and found strong correlations between eddy-covariance and modelled 16-day estimates of GPP for selected forest sites, but not for a drought-prone, shrubland site. Work is underway, supported by the spatial detail offered by high resolution satellites [55], to assess whether the Sentinel-3 LST pixel values can be usefully disaggregated to distinguish vegetation and bare ground components.
## 3 Control Effect of Ground Improvement on Tunnel Displacement
### Evaluation Index of Control Effect on Tunnel Displacement  
Figure 9 shows the schematic diagram of ground improvement outside the pit, in which the excavation depth is \(H_{\text{e}}\), the buried depth of the tunnel center is \(H_{\text{t}}\), the horizontal distance from the tunnel center to the retaining wall is \(L_{\text{t}}\), and the ground improvement is between the retaining wall and existing tunnel. According to the statistics of case histories by [PERSON] et al. (2022) [23], in actual engineering the tunnel position \(H_{\text{t}}/H_{\text{e}}\) = 0.8-1.0 and \(L_{\text{t}}/H_{\text{e}}\) = 0.5-1.0 accounts for a high proportion. Therefore, in this study, \(H_{\text{e}}\) = 18 m, \(H_{\text{t}}\) = \(H_{\text{e}}\), and \(L_{\text{t}}\) = \(H_{\text{e}}\) were adopted to analyze the control effects of ground improvement on the tunnel displacement and influenced zone of deep excavation.  
For the tunnel affected by adjacent deep excavation, it is important to control the horizontal displacement [23], so this study mainly discusses the control effect of ground improvement on the horizontal displacement of the tunnel. An index of displacement control effectiveness (\(\eta_{\text{h}}\)) is defined to quantitively characterize the control effect on tunnel displacement as:  
\[\eta_{\text{h}}=\frac{\xi_{\text{hm}}\text{-}\xi_{\text{hm}}^{\prime}}{\xi_{ \text{hm}}} \tag{3}\]  
where \(\xi_{\text{hm}}\) is the maximum horizontal displacement of the tunnel before ground improvement, and \(\xi_{\text{hm}}^{\prime}\) is the maximum horizontal displacement of the tunnel after ground improvement. If \(\eta_{\text{h}}<0\), it means that the ground improvement increases the displacement of the tunnel, and has a negative effect on the control of the tunnel displacement; if \(\eta_{\text{h}}\) = 0, it means that the ground improvement is ineffective in controlling the tunnel displacement; if 0 \(<\eta_{\text{h}}<1\), it means that the ground improvement is effective in controlling the tunnel displacement, and the greater \(\eta_{\text{h}}\) is, the better the control effect is.
## 4 Results and Discussion
### Load-Displacement Curve
There is a notable disparity between the cracking load and corresponding displacements of the FEA and EXP results at the cracking point, being 10.08% and 9.99%, respectively (see Table 4). Moving to the ultimate point, the FEA results align well with the experimental findings, showing a deviation of 7.9% in ultimate load and 1.4% in corresponding displacement. To provide specifics, the ultimate load for the FEA and EXP results stands at 221.96 kN and 205.7 kN, while the ultimate displacements are 20.42 mm and 20.71 mm, respectively. However, Figure 11 illustrates a discrepancy, despite the good initial stiffness agreement between the FEA and EXP results. There is a noticeable gap in the curve. This deviation can be attributed to voids and defects in the experimental concrete specimens, leading to a decrease in the strength and initial stiffness of the experimental beam. Addi  
Figure 11: Comparing the load–displacement curves between FEA results and experimental findings [28] for the beam S1-4. tionally, discrepancies in the experimental process and measurements might contribute to this observed difference. In both cases, despite differences in the shapes of the load-displacement curves, the maximum deviation occurs at the crack load, with a difference of 10.08% between FEA and EXP results. It can be observed that the proposed FE models can be utilized to accurately simulate the working behavior of the RC beam under bending loads.
## 2 Materials and Methods
### Monitoring External Load  
#### 2.4.1 GPS Receiver Specifications  
All players' activities in training sessions and matches were recorded with GPS (GP-SPORTS systems Pty Ltd., Canberra, Australia, Figure 1). GPS-based tracking systems for professional athletes, model SPI High Performance Unit (HPU), features include 15 Hz position GPS, distance, and speed measurement; accelerometer: 100 Hz, 16 G Tri-Axial-Trackimpacts, accelerations, and decelerations as well as data source BL; Mag: 50 Hz, Tri-Axial; dimensions: smallest device on the market (74 mm \(\times\) 42 mm \(\times\) 16 mm); robustness; SPI: HPU and weight 56 g. Previous studies have shown that the GPS unit was tested for having a very high accuracy demonstrated validity and inter-unit reliability (coefficient of variation = 0.90%) [43; 44; 45; 46]. Collecting data during training and matches sessions was possible provided favorable weather and GPS satellite status.
# Calibration of Mean Wind Profiles Using Wind Lidar Measurements
###### Abstract  
This paper explores the applicability of Lidar wind measurements for the calibration of mean wind profiles depending on the extension in time and space of the available measurements. Starting from logarithmic wind speed profiles corresponding to different site conditions, pseudo-experimental wind speed profiles are generated artificially by adding a zero-mean Gaussian-distributed noise, representative of realistic measurement errors. Then, a least-square fitting procedure is applied to identify the roughness length and the zero-plane displacement. The results obtained show an increase in the scatter of the estimated parameters of the logarithmic law with increasing elevation of the lowest measurement point. Then, a parametric study is developed to analyse the influence of the number of available experimental profiles on the uncertainty associated with the estimated logarithmic law parameters. Based on the results obtained, it can be pointed out that the availability of measurements at low elevations is essential to identify the logarithmic mean wind profile using a reasonable number of observations.  
Wind Lidar; mean wind profile; wind models Article Calibration of Mean Wind Profiles Using Wind Lidar Measurements
# Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation
###### Abstract  
Occupied grid maps are sufficient for mobile robots to complete metric navigation tasks in domestic environments. However, they lack semantic information to endow the robots with the ability of social goal selection and human-friendly operation modes. In this paper, we propose an object semantic grid mapping system with 2D Light Detection and Ranging (LiDAR) and RGB-D sensors to solve this problem. At first, we use a laser-based Simultaneous Localization and Mapping (SLAM) to generate an occupied grid map and obtain a robot trajectory. Then, we employ object detection to get an object's semantics of color images and use joint interpolation to refine camera poses. Based on object detection, depth images, and interpolated poses, we build a point cloud with object instances. To generate object-oriented minimum bounding rectangles, we propose a method for extracting the dominant directions of the room. Furthermore, we build object goal spaces to help the robots select navigation goals conveniently and socially. We have used the Robot@Home dataset to verify the system; the verification results show that our system is effective.  
object semantic grid map; 2D LiDAR; RGB-D camera; domestic navigation +
Footnote †: journal: Applied Sciences
# Robust Static Structural System Identification Using Rotations
## 3 Theoretical Motivation for Measuring Rotations  
The advantages of using rotations are not limited to the practical issues mentioned in Section 1.2. Statistical analyses of two structures were carried out to emphasize the theoretical motivation of measuring rotations rather than deflections. Example 1 corresponds to a simply supported bridge, while example 2 corresponds to a two-story frame.
## 5 Conclusions  
We have studied both theoretically and experimentally the behavior of the propagating and localization of electromagnetic modes in periodic and Fibonacci stub structures. Each block constituting the Fibonacci (or periodic) structure is formed by an horizontal segment and a vertical stub. Different features of the transmission spectra and confined modes in Fibonacci structures inserted horizontally between two semi-infinite waveguides or grafted vertically along a guide have been analyzed. It was shown through an analysis of the transmission spectra of the horizontal structure that such systems exhibit the self-similar behavior of order three around the central gap frequency of the periodic structure and the fragmentation of the frequency spectrum when the generation number increases. Also, the spatial localization of the different modes around the central frequency shows a self-similar behavior of order three.  
Furthermore, the behavior of the eigenmodes of the FS with different boundary conditions on its both extremities has been studied. Also, as predicted, the number of eigenmodes of the FS increases with increasing the generation number. In addition to the allowed modes, it was shown in the case of a FS with H = 0 boundary conditions on both sides, the existence of other modes induced by one surface which fall at the same frequency for different generations, their spatial localization being independent of the generation number. Moreover, the modes generated by the other surface in which the terminations are not the same for odd and even generations, fall at the same frequency every two generations. These findings are different from those of the FS with H = 0 on one side and E = 0 on the other side, where the surface modes appear only on the termination with E = 0. The experiments are performed by using standard coaxial cables in the radio frequency domain. These results are in good agreement with the 1D theoretical model based on the [PERSON]'s function approach.  
Despite the difference of scale between coaxial cables and usual photonic crystals, the physical phenomena reported here, such as scaling law, confined and surface modes, can be obtained in their analogues photonic crystals. In particular, by using appropriate scales, the geometries presented in  
Figure 11: The LDOS (in arbitrary units) versus the space position \(x\) of the modes labeled \(1^{\prime}\) and \(2^{\prime}\) in Figure 10 at \(f_{1}=98.68\) MHz (**a**) and \(f_{2}=44.10\) MHz (**b**), respectively. Dashed (solid) curves correspond to the 6 th (7 th) generation.  
this paper can be transposed to micro-resonators operating in microwave and optical ranges, see, for example, our recent papers on demultiplexer circuits based on coaxial cables [86] and plasmonic waveguides [87].  
Investigation, H.A., A.M., N.O., E.H.E.B. and A.A.; Software, H.A. and A.M.; Supervision, E.H.E.B. and B.D.-R.; Writing--original draft, H.A., A.M. and N.O.; Writing--review and editing, A.A. and B.D.-R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Online Static Security Assessment of Power Systems Based on Lasso Algorithm
## 5 Case Studies  
The IEEE 14-bus, 118-bus, and 300-bus systems are analyzed in this section. The programs of all of the algorithms are performed by a desktop computer with 3.40 GHz central processing unit (CPU) basic frequency and 4 GB memory in MATLAB 2013a. The effectiveness of the proposal is verified through the comparison of the actual value and predictive value of the index. Then, the security indicators are sorted for assessing the severities of contingencies.  
In the test systems, the outages of every line are considered, except the lines that are the only line connected to the generator. The upper and lower alarm limits are \(\pm\)5% of the desired bus voltage, and the upper and lower of security limits are \(\pm\)7%. The alarm limit of line flow is 80% of the security limit. The observations and responses of the Lasso algorithm are listed in Table 3. In the table, \(L\) represents the status of all of the lines with \"0\" or \"1\".  
Moreover, under normal operating conditions, the ranges of active power output \(P_{G}\) and reactive power output \(Q_{G}\) are set according to the IEEE standard examples [36], the voltage \(U\) ranges from 0.95 p.u. to 1.05 p.u., the transformer tap \(T\) ranges from 0.9 p.u. to 1.1 p.u., and the switching capacity \(Q_{C}\) ranges from 0 p.u. to 0.5 p.u. The steps of transformer tap \(T\) and switching capacity \(Q_{C}\) are respectively 0.125 p.u. and 0.01 p.u.
## 2 Materials and Methods
### Plant Selection  
Quinoa (_Chenopodium quinoa_ Wild.) seeds were selected for toxicity assays on soils with and without nanoparticles due to their well-known resilience to harsh environmental conditions, including nutrient-poor and contaminated soils. The seeds used in this study were supplied by Semillas Vivas S.L. (Extremadura, Spain). To ensure high germination potential, only certified seeds were selected based on preliminary germination tests. Quinoa was chosen for its remarkable adaptability to a wide range of climatic conditions, tolerating temperatures from \(-4\)\({}^{\circ}\)C to 38 \({}^{\circ}\)C and relative humidities between 40% and 90%.
# Antimicrobial and Antibiofilm Properties of Bioceramic Materials in Endodontics
## 4 Bioceramic Endodontic Sealers
### BioRoot RCS  
BioRoot RCS (Septodont, Saint-Maur-des-Fosses, France) is a powder/liquid hydraulic cements composed of tricalcium silicate, zirconium dioxide, and povidence in the powder. The liquid contains aqueous solution of calcium chloride and polycarboxyl [72]. Possibly due to calcium release and biomineralization properties, BioRoot RCS had average short term but superior long-term antimicrobial and antib Biofilm effects [72]. A recent confocal laser scanning microscopy study showed BioRoot RCS had increased microbial killing efficacy against multi-species biofilm compared to TotalFill BC sealer (FKG Dentaire SA, Switzerland) [71]. This was probably because BioRoot RCS was able to sustain a high-alkalinity environment for a longer time. BioRoot RCS was reported to kill both young and old _E. faecalis_ biofilms [47; 67]. In studies using planktonic bacteria, BioRoot RCS exhibited killing effects comparable to MTA Fillapex and Pulp Canal Sealer EWT (Kerr, Sybron Dental Specialties, MI, USA) after 6 min of contact [69]. Its planktonic killing effect was lower than TotalFill and AH-Plus (Dentsply-Tulsa Dental, Johnson City, TN, USA) after 7 days of contact [67]. However, after a 30-day exposure, BioRoot RCS killed a higher percentage of bacteria than AH-Plus or TotalFill [67].
###### Abstract  
Various abnormal scenarios might occur during the shield tunneling process, which have an impact on construction efficiency and safety. Existing research on shield tunneling construction anomaly detection typically designs models based on the characteristics of a specific anomaly, so the scenarios of anomalies that can be detected are limited. Therefore, the research objective of this article is to establish an accurate anomaly detection model with generalization and identification capabilities on multiple types of abnormal scenarios. Inspired by energy dissipation theory, this paper innovatively detects various anomalies in the shield tunneling process from the perspective of energy consumption and designs the AD_SI model (Anomaly Detection and Scenario Identification model of shield tunneling) based on machine learning. The AD_SI model first monitors the shield machine's energy consumption status based on the VAE-LSTM (Variational Autoencoder-Long Short-Term Memory) algorithm with a dynamic threshold, thereby detecting abnormal sections. Secondly, the AD_SI model uses the correlation of construction parameters to represent different known scenarios and further clarifies scenarios of the abnormal sections, thus achieving anomaly identification. The application of the AD_SI model in a shield tunneling construction project demonstrates its capability to accurately detect and identify different anomalies, with a recall value exceeding 0.9 and \(F1\) exceeding 0.8, thereby providing guidance for accurately detecting multiple types anomaly scenarios in practical applications.  
shield tunneling anomaly detection; scenario identification; energy consumption; time series; data mining +
Footnote †: journal: Applied Sciences  
[PERSON], [PERSON], [PERSON], H.  
Anomaly Detection and Identification Method for Shield Tunneling Based on Energy Consumption Perspective.
# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
## 4 Experimental Setup and Results
### Performance Evaluation of the Proposed Model
#### 4.4.2 Evaluation on the Extended Medetec Dataset  
To mitigate the impacts of the enormous class imbalance in the Medetec dataset, we designed an experiment where we evaluated our proposed model on the Extended Medetec dataset. The experimental protocols are the same as in Section 4.4.1, and the details of the training and testing samples in each class are provided in Table 1. Our proposed SEEN-B4 model obtained an accuracy of 89.34%, and a precision, recall, and F1-score of 89.67% on the Extended Medetec dataset. The confusion matrix for this experiment is shown in Figure 6.  
The confusion matrix shows that the accuracy for the Diabetic wound class increased from 50% to 84% with the increase in the number of images. Moreover, the classification accuracy for the Venous wound increased, while it decreased for the Pressure wounds class, compared to the results on the Medetec dataset. More precisely, we noticed _FP_s of 2, 8, and 3, and _FN_s of 7, 4, and 2, for the Diabetic, Pressure, and Venous wounds, respectively. We also observed that Diabetic wounds are mainly classified as Pressure wounds because these wounds are similar in shape and texture to the Pressure wounds on the foot area. In general, reducing the class imbalance problem of the dataset has enabled the model  
Figure 5: Confusion Matrix for Medetec.  
Figure 6: Confusion Matrix for Extended Medetec.  
to become less biased toward the Pressure wound class and has improved the proposed model's overall classification accuracy. These results signify the augmentation process's importance in addressing the class imbalance issue of original datasets in our method.
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 2 The Proposed Methodology
### Classification  
The selection of an appropriate classifier is critical in assessing the performance of the above-mentioned features, especially in hyperspectral images with a limited number of training samples. SVM is the most widely used supervised statistical learning framework among pixel-wise classifiers. With the help of a kernel function, data can be mapped to a higher-dimensional space via a nonlinear transformation, aiming to determine the best hyperplane for separating samples belonging to different classes. The performance of SVM in HSI classification is outstanding despite the variation of the data dimensions [46,47]. Hence, in this work, the SVM classifier is utilized to evaluate the performance of the reconstructed features.
## 3 Results and Discussion
### How Accurate Are Transition Criteria?
If we consider the insight of the [PERSON] et al. [5] correlation regarding the influence of the dimensionless liquid film, as plotted earlier in Figure 2, thicker films lead to a shift in the curve to higher Weber numbers, since the energy required for generating splashing events is higher. Assuming the same principal in the experiments performed by [PERSON] et al. [7], one can expect that more experiments with thicker films might lead to similar results. Figure 14 plots the experimental results obtained in the present work and the [PERSON] et al. [7] empirical correlation. If \(K_{c}=f(\delta)\), and proportional to it, even if in a nonlinear way, an increase of \(K_{c}\) while keeping constant the logarithmic slope, should improve the prediction of the splashing transition. Thus, with \(\delta=1\), a test on a 15% increase of \(K_{c}\) validates this hypothesis. However, it also points to the need for more experimental research to refine the criterion.
# Ins and Outs of the Ankle Syndesmosis from a 2D to 3D CT Perspective
## 3 Results
### Conventional 2D Measurements
\begin{table}
\begin{tabular}{c c c c} \hline
**Measurement** & **Mean \(\pm\) SD** & **Definitive Normative Reference Limit** & **Mean ICC** \\ \hline \multirow{4}{*}{Syndesmotic area (in mm\({}^{2}\))} & CT, normal: 105.2 \(\pm\) 22.6 & \multirow{4}{*}{/} & CT: 0.96 \\  & CT, injury: 129.5 \(\pm\) 31.3 & & CT: 0.96 \\ \cline{3-4}  & WBCT, normal: 106.0 \(\pm\) 16.9 & & WBCT: 0.93 \\ \cline{3-4}  & WBCT, injury: 134.1 \(\pm\) 28.2 & & \\ \hline \multirow{4}{*}{B. Fibular rotation} & \multirow{4}{*}{/} & \multirow{4}{*}{CT: 0.689} \\  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \hline \multirow{4}{*}{Tang ratio} & CT, normal: 0.85 \(\pm\) 0.05 & \multirow{4}{*}{/} & CT: 0.47 \\  & & & WBCT: 0.72 \\ \cline{3-4}  & & & WBCT: 0.72 \\ \cline{3-4}  & & WBCT, injury: / & \\ \hline \multirow{4}{*}{Ratio A/B} & CT, normal: 0.55 \(\pm\) 0.03 & \multirow{4}{*}{Normative reference range, WBCT:} & CT: 0.722 \\  & & WBCT, normal: 0.62 \(\pm\) 0.03 & 0.12\(-\)1.08 [5] & WBCT: 0.79 \\ \cline{3-4}  & & WBCT, injury: / & \\ \hline \multirow{4}{*}{Bimalleolar angle (in degrees)} & CT, normal: 7.67 \(\pm\) 1.1 & \multirow{4}{*}{/} & CT: 0.68 \\ \cline{3-4}  & & WBCT, normal: / & WBCT: / \\ \cline{3-4}  & & WBCT, injury: / & \\ \hline \multirow{4}{*}{Interoposterior translational} & CT, normal: 1.54 \(\pm\) 0.08 & \multirow{4}{*}{Normative reference range, WBCT:} & CT: 0.441 \\ \cline{3-4}  & & WBCT, normal: 1.45 \(\pm\) 0.00 & 0.31\(-\)2.59 [5] & WBCT: 0.72 \\ \cline{3-4}  & & WBCT, injury: / & \\ \hline \multirow{4}{*}{Medial Phisitkul (in mm)} & / & / & CT: 0.86 \\ \cline{3-4}  & & CT, normal: 1.59 \(\pm\) 0.50 & \multirow{4}{*}{Normative reference range, WBCT:} & CT: 0.725 \\ \cline{3-4}  & & WBCT, normal: 1.60 \(\pm\) 0.14 & \(-\)1.48\(-\)3.44 [5] & WBCT: 0.763 \\ \cline{3-4}  & & WBCT, injury: 1.37 \(\pm\) 0.27 & & \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 2: Mediolateral translation measurements on an axial CT image of an uninjured syndesmosis. (**A**) Anterior tibiofibrular width (a), middle tibiofibrular width (b), and posterior tibiofibrular width (c). (**B**) Minimum tibiofibrular width (d) and maximum tibiofibrular width (e). (**C**) Syndesmotic area (blue area, f), based on the two lines tangential to the anterior and posterior cortices of the tibia and fibula (solid white lines).