esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers
## 3 Enhanced Non-Coherent Pre-Filter Design
### Observation Noise Characteristics Analysis of Non-Coherent Pre-Filter
It also should be reemphasized that the estimation noise variance of the NC-EMLE discriminator is difficult to express as an analytical form. Since the non-coherent takes the ATAN/ATAN2/NC-EMLE discriminator outputs as observations, the estimation noise variances of these discriminators are the elements of the observation noise variance matrix of the non-coherent pre-filter. However, these parameters cannot be calculated accurately according to the discussion above. In order to set the observation noise variance matrix correctly, it is a more realistic and reasonable to create a look-up table (LUT) that stores the relation among the \(C/N_{0}\), \(d\), \(T_{\text{coh}}\), and estimation noise variances. Figure 9 shows the generation and usage of LUT, where \(C/N_{0}\) indicates the estimated \(C/N_{0}\), \(R_{\text{k}}\) is the observation noise variance matrix. The LUT functions for the ATAN/ATAN2/NC-EMLE discriminator are expressed as \(\sigma^{\text{LUT}}_{\text{ATAN}}(\cdot,\cdot,\cdot)\), \(\sigma^{\text{LUT}}_{\text{ATAN2}}(\cdot,\cdot,\cdot)\), and \(\sigma^{\text{LUT}}_{\text{NC-EMLE}}(\cdot,\cdot,\cdot)\) respectively. As discussed in Section 3.1, the LUTs are obtained by Monte Carlo simulation. The \(C/N_{0}\), \(T_{\text{coh}}\), and \(d\) are first set to simulate I/Q correlation groups according to Equation (4). Then, we execute the ATAN/ATAN2/NC-EMLE discriminator algorithms according to Equations (12)-(14) and calculate the estimation noise variances for ATAN/ATAN2/NC-EMLE  
Figure 6: Mean values and standard deviations of estimation noise for ATAN2 discriminator in different \(C/N_{0}\) and \(T_{\text{coh}}\). Figure 7: Mean values and standard deviations of estimation noise for NC-EMLE discriminator in different \(C/N_{0}\) and \(T_{\text{coh}}\) (\(d=1\) chip). discriminator through the statistical analysis. After that, estimation noise variances are stored in the LUTs. The process discussed above is repeated until all the possible combinations of \(C/N_{0}\), \(T_{\text{coh}}\) and \(d\) are used. After these LUTs are constructed, the estimation noise variances of the ATAN/ATAN2/NC-EMLE discriminator can be found in these LUTs to set the observation noise variance matrix as long as the \(C/N_{0}\) is estimated correctly. The estimation of \(C/N_{0}\) is shown in Appendix C.
## 5 Evaluation Results
### Effects of Buffer Size on Routing Protocol Performance  
Figure 5 shows the network performance for various buffer sizes. First, the \(PDR\) is presented in Figure 5a. A larger buffer means that it can forward and store more packets. Thus, as shown in Figure 5a, the \(PDR\) increases as the buffer size increases. By using the predicted information and the social strength between nodes, our routing protocol can achieve a greater \(PDR\) than others when the buffer size is between 10 and 250 packets. When the buffer size is very large, buffer overflow is reduced in the flooding strategy. Specifically, epidemic routing obtains high values for the \(PDR\) when the buffer capacity is larger than 300 packets. However, the flooding strategy also consumes a significant amount of network resources.  
Figure 4: The network performance for various values of packet time to live. (**a**) \(PDR\). (**b**) \(PDL\). (**c**) \(DC\).  
\(PDL\) results are in Figure 5b. A small buffer (e.g., 10 packets) will quickly overflow. As a result, it removes packets with long delays to make room for the new ones coming. Therefore, the buffer contains only packets with short delays. This results in low \(PDL\). When the buffer size increases, it can contain more packets with longer delays, which increases latency. The shortest latency is obtained by the HLPRP when the buffer capacity is between 10 and 100 packets. In epidemic routing, a large buffer can store more packets. Packets are quickly sent to their destinations without being dropped. Hence, epidemic routing with a large buffer (e.g., 300 packets) has a short \(PDL\).  
Figure 5c shows the results for the \(DC\). When the buffer is small, numerous packets are lost and retransmitted due to buffer overflow under epidemic routing and PRoPHET. That creates a huge \(DC\). The \(DC\) from the HLPRP is lower than that from PRoPHET, CORP, BUBBLE Rap, and epidemic routing.
# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 3 Proposed Scheme
### Feature Extraction  
Feature extraction selects useful information to distinguish different land covers, including spectral and geometrical features from LiDAR. The spectral features selected were intensity and NDFI (Normalized Difference Feature Index), and the geometrical features selected were normalized digital surface model (nDSM) and curvature [31]. The intensities were the amplitude of the return reflection. We used the concept of NDVI to calculate the vegetation index (NDFI) from the spectral characteristics of each channel in the multi-wavelength LiDAR system; the NDFI is used to separate vegetation and non-vegetation from the return signals of active sensor. Because the Optech Titan does not have a red channel, we used the green channel to replace the red channel in NDVI; the infrared channels include NIR and MIR (Equations (1) and (2)). The NDVI from a passive multispectral image was also calculated for comparison (Equations (3) and (4)). The differences between active and passive sensors in the vegetation index were: (1) the return signal of passive signal was reflectance, whereas the return signal of the active signal was backscattered energy; (2) the band width of the passive sensor was comparatively wider than the active sensor.  
\[NDFI_{MIR-\mathrm{G}}=\lambda_{1550}-\lambda_{332}/\lambda_{1550}+\lambda_{332} \tag{1}\]  
\[NDFI_{NIR-\mathrm{G}}=\lambda_{1064}-\lambda_{332}/\lambda_{1064}+\lambda_{532} \tag{2}\]  
\[NDVI_{1}=NIR_{1}-Red/NIR_{1}+Red \tag{3}\]  
\[NDVI_{2}=NIR_{2}-Red/NIR_{2}+Red \tag{4}\]  
where \(\lambda_{1550}\) is 1550 nm intensity from LiDAR; \(\lambda_{1064}\) is 1064 nm intensity from LiDAR; \(\lambda_{532}\) is 532 nm intensity from LiDAR; \(NIR_{1}\) is 772 to 890 nm wavelengths from WV-3; \(NIR_{2}\) is 866 to 954 nm wavelengths from WV-3; and red is 706 to 746 nm wavelengths from WV-3.  
The geometrical features were used to represent the shape and roughness of the LiDAR data. The nDSM represents the object height, obtained by subtracting DTM from DSM. Different objects' heights can be identified by nDSM. For example, grass and trees have similar spectral responses but different object heights. The curvature, a feature that represents roughness by calculating the spatial eigenvalues of target point clouds and the adjacent points (Equation (5)), is a useful feature to separate objects with smooth and rough surfaces; for example, the roughness of a tree crown is higher than road surface. Curvature is calculated from irregular points within the nearest 50 points and then interpolated into grid data for object classification (Figure 4).  
\[Curvature=\epsilon_{min}/\epsilon_{1}+\epsilon_{2}+\epsilon_{3} \tag{5}\]  
where \(\epsilon_{i}\) are the spatial eigenvalues.  
Figure 4: _Cont._
## 2 Materials and Methods
### Volatile Compounds Quantification by Gas Chromatography/Mass Spectrometry (GC/MS)
#### 2.3.2 Headspace Solid-Phase Microextraction  
Headspace solid-phase microextraction (HS-SPME) method was used to extract volatile compounds from both unfermented and fermented kiwi juice. A 20 mL vial, containing a magnetic stirrer and 1.0 g of sodium chloride (to enhance the release of volatile organic compounds into the headspace), was filled with 5 mL of sample and 1 mL of 3-octanol (50.32 mg per 1 L of absolute ethanol). After sealing the vial with a screw cap and  
Figure 1: Fermentation conditions (**A**) and experimental design (**B**) for the fermentation of kiwi juice using various proportions of milk kefir grains (PMKGs) and different shaking rates (SRs) in each MKG passage. Experiments 1–8 were repeated twice, while experiment 9 was repeated five times (adapted from [PERSON] et al. [11]).  
silicone septum, it was placed in a 60 \({}^{\circ}\)C water bath and shaken at 500 rpm for 2 min. This procedure was adopted because our SPME system was not equipped with a standard SPME incubation unit. Incubation using a water batch instead of the standard SPME incubation system has been widely employed in previous studies [19; 20; 21; 22].  
The fiber used for solid-phase microextraction was a 65 \(\upmu\)m Polydimethylsiloxane/Divinylbenzene (PDMS/DVB) fiber supplied by Supelco (Bellefonte, PA, USA). This fiber had previously been successfully employed to determine the volatile composition of kiwi tissue [23], as well as the same fruit during storage and ripening [24].  
This particular fiber coating was selected in the present study for several reasons: (i) it provides good reproducibility for replicated samples, and (ii) it yields a linear response (peak area compared to headspace concentration). Furthermore, the use of this fiber allowed for better recovery of volatile compounds from the studied samples [23; 24].  
Once conditioned at 270 \({}^{\circ}\)C for 30 min according to the manufacturer's protocol, the fiber was introduced into the sample vials and kept in the headspace to retain volatile compounds for 25 min in a 60 \({}^{\circ}\)C water bath with agitation at 500 rpm. The adsorbed volatile compounds were then desorbed by placing the fiber into the injection port at 250 \({}^{\circ}\)C for 5 min in splitless mode.
## 3 Results
### Different Speaker Placement Method  
Comparison for octave band RT, EDT, C\({}_{80}\) and D\({}_{50}\) measurements between those obtained with the dodecahedron speaker and the proposed method for the same microphone and source positions are presented in Figure 11, for different placement methods of the directional loudspeakers.  
Table 3 presents the mean absolute error and standard deviation for RT, EDT, C\({}_{80}\) and D\({}_{50}\) measurements between those obtained with the dodecahedron speaker and the proposed method utilizing different placement methods of the directional loudspeakers for the same microphone and source positions  
For the measurements, the Behringer speaker was used as a directional speaker. Measurements are presented for point A in the amphitheater.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Acoustic Parameters**} & \multicolumn{2}{c}{**Position A**} & \multicolumn{2}{c}{**Position B**} & \multicolumn{2}{c}{**Position C**} \\ \cline{2-7}  & **M.A.E.** & **S.D.** & **M.A.E.** & **S.D.** & **M.A.E.** & **S.D.** \\ \hline RT (s) & 0.025 & 0.021 & 0.059 & 0.049 & 0.026 & 0.025 \\ EDT (s) & 0.089 & 0.091 & 0.050 & 0.061 & 0.091 & 0.075 \\ C\({}_{80}\) (dB) & 1.235 & 0.878 & 1.563 & 0.946 & 1.513 & 0.983 \\ D\({}_{50}\) (\%) & 7.423 & 6.346 & 8.258 & 5.673 & 10.380 & 8.287 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean Absolute Error (M.A.E.) and Standard Deviation (S.D.) of RT, EDT, C\({}_{80}\) and D\({}_{50}\) for measurements with a directional speaker compared with measurements with the dodecahedral speaker for points A, B and C in the amphitheater.  
Figure 11: RT, EDT, C\({}_{80}\) and D\({}_{50}\) measured with different speaker placement methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Acoustic Parameters**} & \multicolumn{2}{c}{**12 Impulses**} & \multicolumn{2}{c}{**26 Impulses**} & \multicolumn{2}{c}{**14 Impulses**} \\ \cline{2-7}  & **M.A.E.** & **S.D.** & **M.A.E.** & **S.D.** & **M.A.E.** & **S.D.** \\ \hline RT (s) & 0.025 & 0.021 & 0.055 & 0.079 & 0.058 & 0.078 \\ EDT (s) & 0.089 & 0.091 & 0.102 & 0.056 & 0.073 & 0.064 \\ C\({}_{80}\) (dB) & 1.235 & 0.878 & 1.434 & 1.430 & 1.263 & 0.984 \\ D\({}_{50}\) (\%) & 7.423 & 6.346 & 8.043 & 7.080 & 5.724 & 5.518 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean Absolute Error (M.A.E.) and Standard Deviation (S.D.) of RT, EDT, C\({}_{80}\) and D\({}_{50}\) for measurements with different speaker placement methods compared with measurements with the dodecahedron speaker.
###### Abstract  
In order to contribute to achieving noise and emission reduction goals, Fraunhofer and Airbus deal with the development of a morphing leading edge (MLE) as a high lift device for aircraft. Within the European research program \"Clean Sky 2\", a morphing leading edge with gapless chord- and camber-increase for high-lift performance was developed. The MLE is able to morph into two different aerofoils--one for cruise and one for take-off/landing, the latter increasing lift and stall angle over the former. The shape flexibility is realised by a carbon fibre reinforced plastic (CFRP) skin optimised for bending and a sliding contact at the bottom. The material is selected in terms of type, thickness, and lay-up including plty-wise fibre orientation based on numerical simulation and material tests. The MLE is driven by an internal electromechanical actuation system. Load introduction into the skin is realised by span-wise stringers, which require specific stiffness and thermal expansion properties for this task. To avoid the penetration of a bird into the front spar of the wing in case of bird strike, a bird strike protection structure is proposed and analysed. In this paper, the designed MLE including aerodynamic properties, composite skin structure, actuation system, and bird strike behaviour is described and analysed.  
morphing; leading edge; carbon fibre reinforced plastic; gapless; CFD-analysis; high-lift device; aircraft wing; actuation; bird strike +
Footnote †: journal: Applied Sciences
## 4 Experimental Study
### Qualitative Analysis
#### 4.4.1 Plane Segmentation Mask  
Figure 8 presents a visual comparison of plane segmentation masks for the three indoor construction sites. Panoplane360 alone exhibited blurred label boundaries, frequent label omissions in objects, such as windows, walls, and ceilings, and inconsistent label assignment within the same object. When SAM was applied once, label boundaries became sharper, and missing labels were assigned to previously unannotated objects, but incorrect label assignment remained frequent. In contrast, the proposed method not only produced clearer label boundaries but also removed incorrect labels and filled missing labels, resulting in more complete plane segmentation. Specifically, in Site B and Site J, the proposed method effectively filled missing labels on windowed walls, which were not fully addressed by the other methods. In Site K, although some wall labels were missing, the method successfully filled gaps between critical structural elements, such as walls and floors, ensuring a more complete segmentation.  
Figure 8: Qualitative comparison of plane segmentation results. (**A**) Input image; (**B**) ground truth; (**C**) H&V plane segmentation from Panoplane360; (**D**) H&V plane segmentation from SAM+Pano (no algorithm); (**E**) H&V plane segmentation from the proposed method.
## 1 Introduction
Samples usually are very expensive withregard to time and cost, and it is not so common to utilize this type of large ground truth information. [PERSON] and [PERSON] [22] addressed the issue that kernelized methods that are insensitive towards the utilization of large dataset information due to the curse of dimensionality. Good generalization capability can be achieved using different kernelized methods with even a limited number of reliable training samples [23; 38]. There exist numerous methods for the use of large ground truth information such as SVM [39], semi-supervised SVM [40], multinomial logistic regression (MLR) [41], partial least squares regression [42], and artificial neural networks [43]. In both spectral and spatial information, researchers have started to employ neural networks such as densely connected multiscale attention networks [44], end-to-end fully convolutional segmentation networks [45], and convolutional neural networks (CNN) with random forest [46] and Ghostnet [47]. In this work, a hybrid learning system (HLS) is introduced and tested that categorizes hyperspectral images into segmented regions with discriminative features using reduced training size. It is based on a semi-supervised learning approach, using an MBT algorithm for AL and multinomial logistic regression for unlabeled HI data categorization. For the training phase, a smaller number of samples are selected using the AL method (modified breaking ties). HLS is a combination of supervised and unsupervised training with generalized composite kernels generating posterior class probabilities for classification. Finally, Markov random fields are used to get the best segmentation label by using class prior labels from the output of the multinomial logistic regression. The proposed methodology is tested by comparing it with known methodologies for hyperspectral image classification and segmentation. Two benchmark HI datasets, namely \"Indian Pines\" and \"Pavia University\" are used for experimentation. The article is organized as follows: Section 2 describes the Methodology, including dataset description with processes and stages involved in HI; Results and Discussion are reported in Section 3 with a comprehensive comparison with existing researchers' work, and Section 4 provides the concluding remarks.
## 2 Materials and Methods
### Data and Methodology
#### 2.2.2 Image Classification and Assessment  
Support Vector Machine (SVM) classification has been one of the most applied classifiers in recent years. SVM uses the machine learning technique for multispectral and hyperspectral remotely sensed data classification. SVM relies on n-dimensional spectral space to create a hyperplane and identify and separate the classes based on user optimized parameters, including the selection of kernel type and function and the penalty parameter [41]. A total of 7 land use/land cover classes were detected and discriminated from the Landsat and Sentinel images based on the SVM, as follows: terrestrial forest, mangrove forest, oil palm plantation, rubber plantation, urban, water, and others (orchard, shrub, bush, aquaculture farms, etc.). The details of the parameters needed to run the SVM classifier are listed in Table 1. The classification was run based on a total of 140 training data (20 polygons per class with 50-60 pixels per polygon), where different sets of training data were selected during Landsat 7 and Sentinel 2 classification. Figure 11 in the Appendix A shows the training polygons for the Landsat and Sentinel classification. The accuracy assessment of the classified land covers/uses was based on the sample points selected from the Google Earth images as the \"ground truth\" samples. Google Earth is notably one of the most complete and useful datasets with acknowledged accuracy, and is also freely available for various types of research, including classification and its validation [42; 43; 44]. Classification assessment based on the error matrix was performed. The overall accuracy (OA) is the ratio between the correctly classified pixels to the total number of reference pixels. The producer accuracy (PA) is the accuracy of the map from the perspective of the map maker, computed by dividing the correctly classified samples of the class to the sum of the reference samples for the class. The user accuracy (UA) is the accuracy from the point of view of a map user, calculated by dividing the correct classification pixels to the total number of pixels mapped as the class. The kappa coefficient (KC) is a value computed based on the KHAT statistic to measure the agreement and accuracy of the classification. In addition, the Z-statistic has been computed to describe the significance of the classification results. The function of the Z-statistic is to determine if the agreement between the classification map significantly agrees with the reference data [45].
# Robot Delay-Tolerant Sensor Network for Overhead Transmission Line Monitoring
## 4 Results  
This section used the Opportunistic Network Environment (ONE) simulator [33] (The ONE version 1.6.0 was developed at Aalto University, Finland, 2015) to obtain the results of the DTMA in the RDTSN. The simulation test was performed on the inspection robot project in Jilin, China. That is a 220-kV high-voltage transmission line with a length of about 120 km. It traversed virgin forest and unmanned area and ended in the border area of China and North Korea. This paper used Open Street Map [34] to get local geographic and traffic information. In addition, the moving path (transmission line) of the inspection robot was depicted in the map. Then the simulation experiments were carried out based on the test environment in the ONE simulator. The map information and simulation interface of the RDTSN are shown in Figure 9. In the Figure 9, the high brightness white lines indicate transmission lines, the dark lines indicate local traffic information, the green and brown areas indicate vegetation coverage, and the blue area indicates water area. The highlight green circle represents the network node, and the radii of the circle represents the communication range in the simulation interface (Figure 9b).  
The DTMA with multiple RDTSN scenarios was tested. Then, DTMA was compared with three most popular DTSN routing algorithms, namely, Prophet V2 [27], Epidemic algorithm [25] and Spray and Wait algorithm [26]. In order to build RDTSN scenarios, 5 WCNs and 7 SNs are configured along the transmission line, as shown in Figure 9b.