esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Accuracy of AIRS Image Position and Orientation  
The accuracy of the single-band kinematic GNSS positioning was found to be in the order of 26 cm for RTK and in the order of 10 cm for PPK processing. After the boresight adjustment, the image orientation angles were determined with an absolute accuracy of \(\pm\)0.5\({}^{\circ}\) for roll and pitch. This uncertainty is comparable to the accuracy specified by the INS manufacturer and can be considered good, as the cameras were mounted on an anti-vibration mount separate from the AIRS unit. However, the Yaw/Heading accuracy of \(\pm\)7.5\({}^{\circ}\) is almost 4x greater than the specification. This error was not just an offset that could be corrected by the static boresight parameter, but the INS solution was drifting during the flight, possibly due to magnetic fields caused by the UAV. In future work, a proper calibration flight will be performed, aiding the system in geometric calibration and evaluation.  
The combination of IMU and GNSS measurements can also improve the obtained accuracy. The heading measurement from two GNSS antennas is more accurate than the magnetometers data [33; 34]. For example, [PERSON] et al. [35] achieved an accuracy of 5 cm in position and 0.5\({}^{\circ}\) in attitude using a baseline between a dual-frequency and a single-frequency GPS receiver for the heading estimation. A low-cost IMU was also integrated for other attitude angles estimation and the dual-frequency GPS was also used for the RTK-GNSS positioning. However, the accuracy of the estimation depends on the baseline length between the antennas, which is not always feasible for a small UAV structure.  
Low-cost positioning equipment used on board UAVs are not as precise as high-grade geodetic GNSS and IMU. Furthermore, a multipath signal due to the environment and/or poor GPS space segment geometry can compromise the GNSS quality. In these cases, the UAV position estimation can be enhanced by integrating a visual navigation method [36]. This will be a topic for our future studies.  
One of the goals for designing AIRS was to enable fully automatic, real-time geoprocessing of aerial images without GCPs. In our test case, the real-time image position accuracy was found to have an accuracy of 26 cm, which, when combined with photogrammetric processing, can provide good enough georeferencing accuracy for many coarse resolution applications and especially for data preview purposes. However, with the miniature INS and single-band GNSS of the AIRS, the accuracy is not good enough to perform direct georeferencing of images without photogrammetry, except for the coarsest preview purposes.
## 3 Results

For the age group 13-15 years old, the average values for the right foot area are also higher than those for the left foot, the exception being in this case forefoot_right_weight_ratio_percent. In addition, in this case, all the values of \(|Z|\) are associated with statistically insignificant thresholds (Sig. > 0.05). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Tests** & **Mean** & **Std. Deviation** & **Std. Error Mean** & **Z Wilcoxon a ** & **Sig. (2-Tailed)** \\ \hline \multirow{3}{*}{Pair 1} & forefoot\_left\_surface & 64.1250 & 5.64263 & 1.99497 & \multirow{3}{*}{0.446} \\ \cline{2-5}  & forefoot\_right\_surface & 65.7500 & 6.69221 & 2.36605 & & \\ \cline{2-5}  & forefoot\_left\_load\_percent & 22.2500 & 5.25765 & 1.85886 & & \\ \cline{2-5}  & forefoot\_right\_load\_percent & 24.6250 & 3.85218 & 1.36195 & & \\ \hline \multirow{3}{*}{Pair 3} & forefoot\_left\_weight\_ratio\_percent & 46.0000 & 7.65320 & 2.70581 & & \\ \cline{2-5}  & forefoot\_right\_weight\_ratio\_percent & 48.0000 & 6.65475 & 2.35281 & & \\ \cline{2-5}  & refoot\_left\_surface & 46.7500 & 6.75595 & 2.38859 & & \\ \cline{2-5}  & refoot\_right\_surface & 49.7500 & 7.90569 & 2.79508 & & \\ \hline \multirow{3}{*}{Pair 5} & refoot\_left\_load\_percent & 26.2500 & 3.80789 & 1.34629 & & \\ \cline{2-5}  & refoot\_right\_load\_percent & 26.8750 & 4.64258 & 1.64140 & & \\ \hline \multirow{3}{*}{Pair 6} & refoot\_left\_weight\_ratio\_percent & 54.0000 & 7.65320 & 2.70581 & & \\ \cline{2-5}  & refoot\_right\_weight\_ratio\_percent & 52.0000 & 6.65475 & 2.35281 & & \\ \cline{2-5}  & refoot\_left\_surface & 46.7500 & 6.75595 & 2.38859 & & \\ \cline{2-5}  & refoot\_right\_surface & 49.7500 & 7.90569 & 2.79508 & & \\ \hline \multirow{3}{*}{Pair 5} & refoot\_left\_load\_percent & 26.2500 & 3.80789 & 1.34629 & & \\ \cline{2-5}  & refoot\_right\_load\_percent & 26.8750 & 4.64258 & 1.64140 & & \\ \hline \multirow{3}{*}{Pair 6} & refoot\_left\_weight\_ratio\_percent & 54.0000 & 7.65320 & 2.70581 & & \\ \cline{2-5}  & refoot\_right\_weight\_ratio\_percent & 52.0000 & 6.65475 & 2.35281 & & \\ \hline \multirow{3}{*}{Pair 7} & total\_left\_surface & 110.7500 & 11.14515 & 3.94040 & & \\ \cline{2-5}  & total\_right\_surface & 115.6250 & 12.45492 & 4.40348 & & \\ \cline{2-5}  & total\_left\_load\_percent & 48.5000 & 5.12696 & 1.81265 & & \\ \cline{2-5}  & total\_right\_load\_percent & 51.5000 & 5.12696 & 1.81265 & & \\ \hline \multirow{3}{*}{Pair 9} & total\_left\_load\_kg & 23.7500 & 3.28416 & 1.16113 & & \\ \cline{2-5}  & total\_right\_load\_kg & 25.0000 & 3.74166 & 1.32288 & & \\ \cline{2-5}  & total\_left\_\_\(\_\)max & 565.0000 & 81.90761 & 28.95871 & & \\ \cline{2-5}  & total\_right\_\(\_\)max & 586.2500 & 98.34306 & 34.76952 & & \\ \hline \multirow{3}{*}{Pair 11} & total\_left\_\(\_\)avg & 214.3750 & 30.15170 & 10.66024 & & \\ \cline{2-5}  & total\_right\(\_\)avg & 217.8750 & 27.09474 & 9.57944 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Wilcoxon right/left nonparametric test results-pairs/under 13 years (\(N=8\)).
## 3 GA Hybrid-Fog Model
### GA Hybrid-Fog Architecture  
In our proposed model, all available computing resources (static fog, mobile fog, and cloud) are accessible to edge UAVs through either a direct or hierarchical method. The GA Hybrid-Fog algorithm is a cross-layer model that integrates infrastructure components, as illustrated in Figure 1. The components are described as follows:  
* Edge Layer:  
The edge layer is the lowest level in the architecture of the GA Hybrid-Fog algorithm and is the one closest to UAVs. Its primary role is to collect sensor data in the aerial environment, such as video, temperature, pressure, gas, etc. The collected data are either processed locally or transmitted to the fog UAVs, FBSs, or the cloud, depending on the size of the task and the required processing capabilities.
* Mobile Fog Layer:  
The next layer in the GA Hybrid-Fog architecture is the mobile fog layer. It is characterized by higher computing and storage capabilities than edge UAVs. This architecture facilitates the processing of tasks offloaded from UAVs, effectively addressing the resource limitations of edge UAVs. The mobile fog layer communicates with UAVs and FBSs wirelessly. Additionally, in this layer, fog UAVs transmit and receive collected data via mobile technologies, including WiFi, WiMAX, and others.
* Static Fog Layer:  
This layer serves as an intermediary between the mobile fog layer and the cloud layer. Due to energy constraints, this layer features greater computing and storage capabilities than mobile fog UAVs. Moreover, the static fog layer consists of a set of FBSs and is capable of communicating with the cloud layer.  
In our model, we propose using a genetic algorithm to optimize task dispatching among FBSs and fog UAVs. The latter effectively execute multiple tasks from various concurrent drones using a simplified FIFO (First-In, First-Out) strategy.  
* Cloud Layer: The cloud layer refers to IoD data centers that provide cloud computing services, such as storage and real-time processing of the streamed data captured by the cloudlet UAVs. The data centers consist of both traditional, stationary cloud data centers (referred to as static IoD cloud) and a dynamic IoD cloud, which includes computing resources from various IoD entities, including temporarily deployed UAVs. UAVs access these IoD cloud services either by interacting with the stationary fog layer or directly. This model builds on the advantages of the traditional IoD cloud and extends its capabilities to IoD entities by integrating a temporary IoD cloud that utilizes IoD computing resources, enhancing the established GA BS-Fog model.
## 3 Proposed Method
### Summary of the Proposed Method  
From the above analysis, we can conclude that the CNR is consistent in the normal environment, but it will decrease during flooding. Thus, the whole flood process can be retrieved by using the fluctuation in CNR and the seamless series analysis method.  
The process steps of the proposed method can be concluded as follows: (1) Extract the original CNR, which was collected during the flood, from the observation file in RINEX format. (2) Mitigate the random noise by using the wavelet transform denoise method (for the concrete method, refer to [PERSON] et al. [26]). (3) Extract the fitted CNR from the denoised original CNR by applying a third-order polynomial fitting method (for the method, refer to [PERSON] et al. [18]). (4) Select the appropriate original denoised and fitted CNR by threshold or elevation angle. For example, only a CNR larger than 40 dB-Hz or a satellite elevation angle larger than 45 deg can be used. (5) Determine the satellites used to retrieve the flood by the satellite ground track repeat period; the satellites should cover whole days as much as possible. (6) Retrieve the rough process of the flood based on all CNRs and seamless series characteristics. (7) Retrieve the accurate whole flood process based on the fitting of selected CNRs and the seamless series analysis method. The flowchart of the proposed method is presented in Figure 11.  
Figure 11: Flowchart of the proposed method.
# Spectroscopic Techniques Application for Wine and Wine Byproduct Authentication
## 1 Introduction
Moreover, when combined with chemometrics and machine-learning approaches, spectroscopic techniques offer powerful tools for authenticating wine and its byproducts. Techniques such as NMR, UV-Vis, IR, and fluorescence spectroscopy offer detailed insights into the composition, origin, and authenticity of wine. Additionally, non-invasive methods such as Raman and IR spectroscopy play a vital role in verifying packaging authenticity, while data fusion approaches enhance the classification of wine vinegar. These methods are instrumental in ensuring the integrity and quality of wine products in the global market. Table 1 summarizes some applications and insights of the most commonly used spectroscopic techniques. Statistical methodologies play an essential role in modeling spectroscopic data. Spectroscopy generates complex datasets, and statistical techniques help to extract meaningful information and build predictive models [4]. For example, [PERSON] et al. [25] demonstrated how Spanish wine vinegars from different denominations of origin were successfully discriminated using a combination of ultraviolet-visible spectroscopy and multivariate statistical tools. This review presents a comprehensive overview of the principles underlying spectroscopic techniques employed in the authentication of wines and their derivatives. We explore various aspects, including data analysis methods, advances in machine learning, and the integration of emerging artificial intelligence technologies. Each spectroscopic methodology is thoroughly described, with specific examples provided to illustrate its practical application in wine authentication. It begins by examining food spectral fingerprinting approaches, which involve analyzing the unique spectral signatures that characterize different wine varieties. This is followed by a discussion of the algorithms utilized for multivariate data analysis, which play a crucial role in interpreting the complex datasets generated by these spectral analyses. Finally, we explore the application of AI techniques, highlighting how machine-learning models can improve the accuracy and efficiency of wine authentication processes.
# Enhancing Far-Field Speech Recognition with Mixer: A Novel Data Augmentation Approach
## 5 Results
### The Effect of Maximum Mixing Values  
Since we introduced \(\epsilon\) to adjust the maximum value of \(\lambda\) sampled from the Beta distribution, we conducted an analysis on how the performance of the Mixer/Mixer-C varies under different maximum mixing values, as shown in Figure 4. Each data point in Figure 4 is accompanied by error bars, which are used to illustrate the uncertainty of the experimental results. The error bars come from experiments conducted on the same maximum mixing value at different mixing proportions (15%, 50%, 80%), reflecting the impact of different mixing ratios on the result.  
For Mixer, it can be observed that when the maximum mixing value is 0.1, the model's performance is better. As the maximum mixing value increases, Mixer's performance begins to decline, reaching its lowest point at 0.5. When the ratio exceeds 0.5, the performance starts to rise, and the WER reaches its minimum value when the maximum mixing value of WER is 1. We have analyzed the reasons behind this phenomenon. Since \(\hat{X}=\lambda X_{1}+(1-\lambda)X_{2}\), a decrease in \(\lambda\) leads to an increase in \(1-\lambda\), and \(\hat{X}\) transitions from resembling \(x_{1}\) more to resembling \(x_{2}\) more. Consequently, when the maximum mixing value approaches 0 or 1, the change in \(\hat{X}\) relative to the input is actually smaller. However, when the maximum mixing value is close to 0.5, the change in \(\hat{X}\) is significant, which may introduce excessive fluctuations and affect the performance. Finally, we can observe similar experimental conclusions for Mixer-C, but it can be seen that the performance of Mixer-C is overall better than that of Mixer. And the performance is superior to the maximum mixing weight until 1. This indicates that enhancing channel information is very important for Whisper-Large-v3.
# Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter
## 2 Materials and Methods
### Pedestrian Dead-reckoning (PDR) Module
#### 2.1.3 Step Length Estimation  
When people are walking, the step-length can be estimated by using the acceleration data. The existing step length models include linear model [38], constant models [39], nonlinear models [40], etc. Because of the difference between people's height and stride, the step length is also different. Therefore, the application of the constant model and linear model has certain limitations. We use the Weinberg model [41] that is a non-linear model to estimate step length. During the one-step process, if the maximum and minimum of the acceleration are \(A_{\max}\) and \(A_{\min}\), step length can be calculated as:  
\[StepLength=K\bullet\sqrt[4]{A_{\max}-A_{\min}} \tag{9}\]  
where \(K\) is a constant value, which can be obtained by training a large number of acceleration data.  
\begin{table}
\begin{tabular}{c l} \hline \hline Notation & Description of Notation \\ \hline \(acc\_diff\) & Acceleration difference threshold \\ \(acc\_thr\) & Entering the FSM algorithm threshold \\ \(up\_thr\) & Maximum value judged as a rising state \\ \(down\_thr\) & Minimum value judged as a falling state \\ \(peak\_thr\) & Peak threshold \\ \(D\_thr\) & Max interference value \\ \(P_{up}\) & Times of acceleration greater than \(acc\_diff\) \\ \(P_{down}\) & Times of acceleration smaller than \(acc\_diff\) \\ \(a_{i}\) & Acceleration value \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variable names of finite-state machine (FSM).  
Figure 3: The execution process of the FSM algorithm.
## 1 Introduction
### Photogrammetry  
Nowadays, with the development of software for 3D reconstruction and image processing, photogrammetry becomes an easy and inexpensive method for 3D reconstruction of cultural heritage [31]. Great importance of photogrammetry for the reconstruction of cultural heritage objects is the fact that it is a contactless and non-destructive method. In photogrammetry, image or data processing implies creating the 3D geometry of an object from a set of images performed by the software. Popular photogrammetry software is user-friendly and automatized. There are various open-source software for image-based modeling, such as AliceVision, VisualSFM, COLMAP, OpenMVS, and Regard 3D, as well as commercial software, such as Reality Capture, Autodesk Recap Photo, Context Capture, and AgiSoft Metashape [32; 33; 34; 35]. There is also GRAPHOS, an automatic open-source software, which, in addition to advanced automatic tools for photogrammetric reconstruction, also has a well-developed educational component [36].  
To obtain a precise, detailed, and photo-realistic 3D model that is useful for visualization or documentation, it is necessary to meet certain criteria [37]. Structure from motion (SfM) is a method for photogrammetric surveying in which camera positions and orientation must be precisely determined. Careful planning and correct photos acquisition allow the generation of accurate and realistic 3D textured models with low-cost photographic equipment [38; 39]. This means that special attention has to be given to the design of the surveying plan [37]. One of the factors that photogrammetric surveying favors is diffuse lighting. Therefore, a precisely created shooting plan and diffused lighting guarantee successful photogrammetric surveying.  
After the photographs are collected, the photogrammetric reconstruction is done in a few main steps: building dense point cloud, generating the mesh, and obtaining texture projection from the photographs. The final result of the photogrammetric modeling is a highly detailed and realistic representation of an object geometry (its 3D model), with realistic texture representation [7].
# Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack
## 3 Platform Design and Simulation
### Orbit Parameter Settings  
During the whole process, we set the satellite orbit inclination to 42\({}^{\circ}\) and the satellite orbit altitude to 335 km on STK. We select the network communication between A and B to implement an LSCN DDoS attack and analyze its security performance against DDoS attacks. After calculation, a total of 207 ground co-track satellites are selected to pass through the area within 10 s, and the simulation area is a rectangular area of about 8544 km \(\times\) 4589 km. The orbit parameter information and 2D simulated scene are shown in Table 1 and Figure 3.  
Figure 2: DDoS attacks on LSCNs.
# Disentangled Feature Learning for Noise-Invariant Speech Enhancement
## 4 Experiments and Results
### Objective Measures  
For the evaluation of the models' performances, we considered four different aspects, speech quality, noise reduction, speech intelligibility, and speech distortion. The tested objective measures are summarized as in the following:  
* PESQ: Perceptual evaluation of speech quality defined in the ITU-T P.862 standard [51].
* segSNR: Segmental SNR, which is the average of the SNR per frame for the two speech signals [52]
* eSTOI: Extended short-time objective intelligibility [53].
* SDR: Signal-to-distortion ratio [54].  
All metric values for the enhanced speech were compared with the corresponding clean reference of the test set.
## 4 Experiments on Feature Extraction of Machinery Faults
### Feature Extraction of Impulse Fault on Bearing Dataset--Case 1
#### 4.1.3 Comparisons with Other Methods
As presented in Figure 11b, a weak signature of the fault characteristic frequency is found in the envelope spectrum, but almost buried by other interferences. This finding suggests that the maximum spectrum kurtosis method is less effective in this application, compared to the SNTF methods. Overall, the experimental results indicate that the MCKD is insufficient, as multiple peaks mix with the fault frequency and its harmonics in the spectrum, which can be attributed to the inaccuracy  
Figure 11: Results based on maximum spectrum (MSK): (**a**) Kurtogram of the signal; (**b**) envelope spectrum of the filtered signal. Figure 10: Results based on singular value decomposition (SVD): (**a**) SVD-reconstructed spectrum; and (**b**) the corresponding envelope spectrum. in the deconvolution period and the high sensitivity of correlated kurtosis under non-gaussian noise. MSK, which adaptively selects a frequency band with maximum kurtosis for demodulation, exhibits less satisfactory results with more interfering low-frequency components (e.g., 118 Hz and 175 Hz). The work in [5] combines the above two methods to obtain more a prominent kurtogram, but the issue of MCKD is not comprehensively illustrated. As for SVD-based extraction, the result presents dominant but irrelevant peaks (e.g., 118 Hz) besides the fault frequency, indicating that the method needs improvement in both the selection and the interpretability of singular subspaces to avoid introducing redundancy. Between the three models, SVD obtains relatively better results in this case. Apart from the spectrum with a distinct fault frequency in Figure 8, other indications also present the advantages of our proposed method. SNTF decomposes the multi-TFD tensor into a few low-rank representations that embody the vibrations with different characteristics. The sparseness constraint plays a role in promoting the parts-based feature, as can be found in the reconstructed TFDs. Interpretability is improved by virtue of the non-negativity constraint, as the non-zero elements of SNTF decomposition are all positive values, allowing only additive combinations, rather than subtractions that occur in SVD.
# Micro-Nanobubble Technology in Surface Cleaning and Defouling
## 4 Biofouling Mitigation and Advanced Cleaning Technologies
### Fouling Mitigation and Biofilm Control
The film demonstrated outstanding antifouling properties by thwarting the adherence of marine algae and both Gram-positive and Gram-negative bacteria. The double-scale microstructure of the tentacles enabled high-density and sustained bubble adsorption, which further improved their ability to prevent microbe attachment. The inclusion of graphene further strengthened its anti-adhesion properties. Micro-SR-GN showed promise as a marine antifouling solution because it was less likely to generate biofilms than stainless steel. [PERSON] et al. [97] investigated the possibility of using self-collapsing air MBs as an environmentally acceptable, chemical-free method of clearing membrane biofouling. The investigation revealed that air MBs' distinct shrinking and subsequent collapsing activity resulted in extremely effective biofilm separation from membrane surfaces. The fixed biomass was significantly reduced by 88.43% after an hour of air microbubbling, and the extracellular polysaccharides and proteins were almost entirely removed, suggesting that the extracellular polymeric matrix had been disrupted. The self-collapsing MBs performed better than conventional chemical cleaning with a 0.5% NaCl solution in terms of removing extracellular matrix components and biomass. This study opens the door for more research and industrial uses by demonstrating the potential of self-collapsing MBs as an environmentally friendly, chemical-free method of efficiently cleaning biologically contaminated membranes.