esa-sceva/satcom-chunk-collection huggingface dataset:
# The Paradigm Shift in Hyperspectral Image Compression: A Neural Video Representation Methodology
## 4 Experiments and Analysis
### Evaluation Metrics  
We employed bits per pixel per band (bpppb) as a measure of the compression ratio of hyperspectral images (HSIs). In order to fully assess the distortion of the reconstructed images, the following three common quality assessment metrics were used:  
1. Peak Signal-to-Noise Ratio (PSNR) [36]: it quantifies reconstruction quality by calculating the pixel-level difference between the reconstructed image and the original image; a higher PSNR value indicates higher fidelity of the reconstructed image;
2. Multi-scale structural similarity index (MS-SSIM) [37]: it provides a more comprehensive visual quality assessment than the single-scale SSIM by evaluating the luminance, contrast, and structural similarity of an image and combining them with multi-scale analysis;
3. Spectral Angle Mapping (SAM) [38]: it assesses reconstruction quality by calculating the angle between the spectral vectors of the original and reconstructed images in spectral space, where a smaller angle indicates better reconstruction.  
\[\text{SAM}=\frac{1}{WH}\sum_{w=0}^{W-1}\sum_{h=0}^{H-1}\frac{\mathbf{H}_{wh} \cdot\hat{\mathbf{H}}_{wh}}{\mathbf{H}_{wh}\cdot\hat{\mathbf{H}}_{wh}} \tag{23}\]  
where \(\mathbf{H}_{wh}\in\mathbb{R}^{I\times 1}\) denotes the information of the original HSI at position \((w,h)\), and \(\hat{\mathbf{H}}_{wh}\) denotes that of the constructed HIS. We employed SAM to further evaluate the construction quality of hyperspectral similarity.
## 2 Materials and Methods
### Experimental Methods  
The initial visit SD-OCT scans for each image were segmented using the Deep Learning-Shortest Path (DL-SP) algorithm previously developed by our group [17; 33]. From the results of this segmentation, feature maps were generated using various segmented layers as boundaries. These feature maps included mean intensity, median intensity, maximum intensity, minimum intensity, standard deviation, skewness, kurtosis, and thickness. The SD-OCT scan dimensions resulted in feature maps of 49 \(\times\) 1024, which were resized to 512 \(\times\) 512 for the purposes of registration before being downscaled to 128 \(\times\) 128 to meet the memory constraints faced when training the neural network.  
After registration of the FAF images, the previously mentioned feature maps were generated for the regions between the ELM and ellipsoid zone (EZ), the EZ and inner retinal pigment epithelium, the inner RPE and outer RPE, the outer RPE and choroid-sclera (C-S) junction, the EZ and inner RPE, and \(\pm\) 5 pixels from the EZ. For the regions between the outer RPE and the C-S junction and \(\pm\) 5 pixels from the EZ, the thickness feature map was excluded. This was because of too much natural variation in the case of the outer RPE to C-S junction thickness and because there would be a constant thickness in the case of \(\pm\)5 pixels from the EZ.  
The registered FAF images and ground truths and the generated initial visit feature maps were then sent to the neural network described above. The neural network output a  
Figure 4: Overview of the approach to predicting the region of atrophy in Stargardt patients. Orange boxes represent inputs, blue boxes represent processing and training, and yellow boxes represent output.  
probability of the predicted region of atrophy based on the initial visit SD-OCT scan feature maps. The accuracy of the probability map was graded using the Dice coefficient [34]:  
\[D=\frac{2\sum_{x\in\Omega}p_{l}(x)g_{l}(x)}{\sum_{x\in\Omega}g_{l} ^{2}(x)+\sum_{x\in\Omega}p_{l}^{2}(x)} \tag{5}\]  
This measures the degree of overlap between the prediction and the ground truth. The activations of each component U-Net were also extracted. These activations were sent through a sigmoid function and were also graded using the Dice coefficient to determine which feature maps were the best and worst contributors to the final result. The pixel accuracy of the predictions was also calculated.  
In addition, neural networks using only the traditional mean intensity feature maps were also created for the purpose of comparison.
# FEL Pulse Duration Evolution along Undulators at FLASH
## 5 Conclusions  
We investigated FEL pulse duration and pulse energy and their fluctuations as functions of undulator length for six different FEL wavelengths. We compared the simulation results from chirped and nonchirped 3D FEL simulations with 1D FEL simulations. The chirp strengths were chosen such that the typical spectral widths (\(\sim\)1%) observed at FLASH were matched. Comparing the experimental results measured for different FEL setups with various simulation models, we found a compelling agreement with the chirped 3D model even if the FEL parameters deviated significantly between measurement and simulation. This indicates that the result does not depend strongly on the particular FEL setting. The features predicted by the 1D model and the nonchirped 3D model could not be reproduced.  
Figure 5: (**a**) Development of the power (pulse energy divided by pulse duration) along the undulators for different pulse durations. The chirped 3D model agrees significantly better with the experimental data as compared to the nonchirped 3D model, while the oscillatory behavior predicted by the 1D model is not reproduced at all. (**b**) The measured as well as the simulated (3D model with chirp) saturation length is plotted as functions of the FEL wavelength for two different FEL setups. Error bars are estimated as \(\pm\)0.5 undulators from the analysis. The electron bunch energy for the 34 nm measurement was much smaller than for the other measurements; thus, it is not included in the plot.  
In contrast, the chirped 3D simulation results describe the behavior of the pulse energy and pulse duration rather accurately for FLASH and can be well used as input for future experiments or comparison to measurements.  
Measurements, M.M.B., S.D., R.I. and I.J.B.M.; Simulation, M.V.Y.; writing--original draft preparation, M.M.B. and S.D.; writing--review and editing, M.M.B., S.D., R.I., I.J.B.M. and M.V.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) grant number 491245950.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
We want to acknowledge [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for providing and maintaining our regenerative amplifier. We want to thank the DESY Synchronization group (in particular [PERSON]) for providing an excellent synchronization of our laser. We would like to acknowledge [PERSON] for useful discussions and suggestions for improving this article. We also thank the FLASH operators, in particular [PERSON], for helpful discussions and for fulfilling our special wishes during our beamtimes.  
The authors declare no conflict of interest.
## 3 Network Model
### Failure Model
#### 3.3.3 Faulty Reading  
Faulty readings can be conceived of as the time when the node operates well although suffering from faulty readings. The sensors with faulty readings are called faulty sensors. These faults can be attributed to: (i) faulty data measurement or data collection; (ii) some variables in the area surrounding the sensor have changed significantly; or (iii) the inherent performance of the sensor is abnormal [51]. The faulty data can only be referred to as a deviation from the expected model [52]. More types of definitions of the faulty data can be found in [52]. To detect faulty nodes, the stochastic method is usually employed. For instance, given a defined threshold, the value it has is compared to the values of its neighbors, and the median value to filter out the faulty measurement is used, e.g., [25,51,53,54].
# Effect of Augmented Reality Support on Quality Inspection of Welded Structures
## 3 Materials and Methods
### AR Pilot Software
Vuforia's functionality is accessed and controlled through Unity's scripting interface, allowing interactive AR experiences to be built within the Unity environment. Unity and Firebase are also integrated within the Unity development environment but are not directly linked in the same way as Unity and Vuforia. Firebase is a separate cloud-based service provided by Google. Unity and Firebase are linked through Firebase's SDKs (software development kits) and APIs (Application Programming Interface) for Unity. This  
Figure 1: An example of data creation in Blender and Unity (Czech dimension style). Figure 2: Model configuration in Vuforia. integration enables data storage, synchronisation, and user authentication features within a Unity application using Firebase services. The developed tool is then compiled as an application for Android portable devices. It can also be built as an application for iOS devices. The hand-held device (HHD) variant was chosen considering the advanced familiarity of users with these devices. The nature of the task being performed and the design of the experiment do not place demands on the freedom of both hands, and therefore, the use of the HHD is not significantly limiting as, e.g., in assembly operations. The tablet used for the experiment was a Samsung Galaxy Tab8 (11\" LTPS LCD display, 5G, 2.99 GHz, 2.4 GHz, 1.7 GHz processor). The choice of tablet resulted from an initial pilot study in which users agreed on a preference for using a larger display. Once the application is launched, the identity of the registered user is verified in the linked database, or a new user identity is created and uploaded to the database. After the user logs in, the AR module of the developed software automatically starts the live camera recording of the real scene, which is the input source to the tracking and detection module. This video recording along with the functions of Vuforia SDK performs object recognition and detection. After this module detects the object, orientation in the scene is provided with the help of the tracking module. That is, the position of the device relative to the object being tracked is determined, and the programmed visualisation and infographic is displayed in the real scene, which is spatially anchored to the object. This AR scene also includes an interactive checklist that represents the inspection plan and guides the inspector through the inspection with a colour visualisation. A preview of the scene with the checklist is shown in Figure 3. The results filled in the checklist are sent and stored in a linked database. If an error is detected, the inspector can enter an annotation on the form sent to the database about this deviation from the planned quality and disposition of the part. At the same time, this AR scene shows a visualisation of the 3D CAD model overlaid with the real part being inspected. This allows for the quick initial orientation of the inspected object and a fast comparison and evaluation of the quality of the inspected part.
## 2 Method
### Establish a Finite Difference Model  
This study analyzed the bank slope of a tailing pond in Yunnan Pulang mine. The slope is 160 m high, 368 m wide, and 10 m thick (Figure 3a). We established a numerical simulation model for the slope (Figure 3b). The model is composed of hexahedral mesh elements, with 8867 nodes and 11,018 units. The slope has 3 stratum layers (I: Gravel soil (GS), II: Strongly weathered carbonaceous slate (SWCS), and III: Weathered carbonaceous slate (WCS). The geological parameters from survey data are shown in Table 1. The Mohr Coulomb criterion was adopted as the material failure criterion. The bank slope stability was calculated by the TSFDM and the Morganston-Price based LEM method, separately [14].  
Figure 1: Flowchart for calculating the unit permeability coefficient and shear strength parameters of the unsaturated slope using the FISH function.  
Figure 2: Flowchart for analyzing the bank slope stability.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Strata** & **Lithology** & **Status** & \begin{tabular}{c} **Elastic Modulus** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **Poisson’s** \\ **Ratio** \\ \end{tabular} & \begin{tabular}{c} **Unit Weight** \\ **(KN/m3)** \\ \end{tabular} & \begin{tabular}{c} **Cohesion** \\ **(KPa)** \\ \end{tabular} & \begin{tabular}{c} **Internal Friction** \\ **Angle (\({}^{\circ}\))** \\ \end{tabular} \\ \hline I & GS & \begin{tabular}{c} Natural \\ Saturated \\ \end{tabular} & 261.6 & 0.4 & 20.5 & 15 & 32 \\  & \begin{tabular}{c} Natural \\ \end{tabular} & 241.6 & 0.41 & 21 & 10 & 30 \\ II & SWCS & \begin{tabular}{c} Natural \\ Saturated \\ \end{tabular} & 975.8 & 0.40 & 23.5 & 77.4 & 28.9 \\ III & WCS & \
\begin{tabular}{c} Natural \\ \end{tabular} & 3786.1 & 0.36 & 27 & 100 & 30 \\  &
\begin{tabular}{c} Natural \\ \end{tabular} & 5561 & 0.35 & 26.5 & 120.0 & 35.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geological parameters of the pond bank slope.  
As Figure 3 shows, AB and FE are the fixed water level boundaries, which are 130 m and 10 m high, respectively. The initial groundwater level was determined according to the survey data. CD is the rainfall infiltration interface, AF is the impervious boundary, and BC and DE are the pervious boundaries. To study the seepage characteristics, we selected 6 measuring points (points 1-6) 10 m below the surface.  
We focused on the seepage changes under the impacts of rainfall infiltration and pond water level fluctuation [14]. The boundary conditions of the slope surface should be adjusted timely to simulate the seepage field changes and slope seepage. In Figure 4, we simulate the separate and coupled influences of hourly rainfall and groundwater level changes on the slope stability from 0:00 on 1 April to 0:00 on 27 July 2019.  
Figure 3: Actual photos and numerical model of the trailing pond slope. (**a**) Actual photos. (**b**) Numerical simulation model.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 3 Proposal: ODYSSE
### Packet Erasure Codes in ODYSSE
#### 3.7.2 Practical Implementation of LDPC Codes  
In ODYSSE, the error correction works as follows:  
1. We use systematic coding in which the original source packets \(S\) are sent uncoded: \(S\) is a submatrix of \(P\). For simplicity of exposition, we assume in the following that the source packets follow the repair packets (in the matrix), hence the matrix \(P\) is composed of \(m=n-k\) repair packets denoted \(C\) followed by \(k\) source packets \(S\) (see Figure 3).
2. Thus as \(P=G^{T}S\), the last \(k\) columns of \(G\) will then be the identity matrix. Also, because \(HG^{T}=0\), it follows that \(B\) is equal to the identity. We have then: \(C=-A^{-1}S\). Consequently, to generate the repair packets, ODYSSE pre-computes \(A^{-1}\), the inverse of \(A\), submatrix of \(H\), the parity-check matrix. Notice that LDPC code is sparse. However, the inverse is not necessarily sparse. It is worth taking this into consideration because the less the matrix is sparse, the more memory required to store its coefficients. Binary LDPC codes are used, that means that packets are vectors of \(GF(2)\); addition corresponds to exclusive-or (XOR) operation on original packets \(S\) (and \(-x=x\)). In our case, we use the LDPC creation algorithms and source code from [46] to generate the matrices.
3. To generate the repair packets, one trivial solution would be to store all the original \(k\) packets and then apply \(C=-A^{-1}S\). This requires a memory and storage overhead that usually does not fit low capacity sensors. Thus, ODYSSE stores instead the repair packets and updates them  
Figure 3: Relation between parity check matrix and generated packets.  
incrementally. Each time such a source packet \(S_{j}\) is generated (\(j=1\ldots k\)), all the repair packets \(C_{i}\), (\(i=1\ldots m\)) where \(S_{j}\) appears are updated: \(C_{i}=C_{i}\ +\ S_{j}\) (\({}^{+}+^{\prime}\) corresponds to a XOR).
4. Repair and original packets are routed identically.
5. At the receiver side, the decoding is done starting from the relation \(C=-A^{-1}S\). This relation implies several linear equations that may be solved. Zero, some or all of the lost source packets will be recovered, depending on the losses. Because the code is systematic, at least all source packets that are not lost, are available.
## 1 Introduction
The framework itself includes: the monitoring of production-related data, a solution for data processing and analysis that incorporates adaptable database design for data storage, and a collection of models for data analysis and decision support mechanism to optimise the production processes. As stated previously--the concept of a cyber-physical model (see Figure 1a) as a basis is built upon the idea of data fusing, whereas the result of data fusion should be used for analysis of historical data and forecasting of the following short- and long-term trends in production. However, it was found [14] that current data fusion solutions on the market cannot be implemented into a framework as is, and, as a result, a multi-level data fusion approach must be taken. The architecture of the system, in turn, dictates the requirements for the number and types of such levels. Therefore, the smart poultry management system must use a data structure that (1) is sophisticated enough to encapsulate all possible data needs and (2) has a level of adaptability that is convenient for both management and farm owners. A previously proposed data structure [14] has a simplified centralised design based on one single main register and various production-related satellite tables containing parameters contributing to the final result. The collection of multiple models is meant to provide an appropriate choice--either algorithmic, i.e., decided by the system when the data are loaded in, or manual--by a system operator--both in order to address the cases when the number of parameters between training and testing sets differ. This also partly alleviates the issues caused by imperfect data--when one model does not provide satisfactory performance and/or output, this model can be substituted with other implemented models. This assumes that all models are trained or fitted on the same data. For the purpose of encapsulating different kinds of factors and the potential results of their processing, two main types of modelsare included--nonlinear models and machine learning models. These types of models are further analysed in detail using scientific literature and practical experiments with real data. The aim of this study was to cross-compare multiple models that could be used in scenarios with limited data sets to provide forecasting of egg productivity of laying hens at a sufficient level to timely point farmers towards accurate decision making. This paper is structured as follows: first, we provide a literature review about the methods and techniques (mostly focusing on traditional nonlinear and modern machine learning models) used in the poultry industry to forecast egg production; then we describe the data sets used for practical experiments, the selected methods for hen egg production forecasting, and the training process of the selected models; after which, we describe the metrics used for evaluation and depict the results of performance cross-comparison. Finally, we discuss the obtained results and potential improvements.
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 5 Evaluation and Results
### Mission 2  
The dataset comprises 65.6% authentic signals and 34.4% attack signals, distributed as follows: 12.2% simplistic attacks, 18% intermediate attacks, and 4.2% sophisticated attacks; see Figure 6. The DELTA_TOW values are illustrated in Figure 7.  
The feature importance analysis shows that, as in Mission 1, the strongest features in identifying which class a GPS signal is classified into are still PD, CP, DO, TCD, PRN, and C/N0; see Figure 8. The feature importance scores of each model are presented in Table 10. Therefore, the decision tree's highest-scoring features are ordered as follows: PD remains the most important feature, followed by TCD, PRN, DO, CP, and C/N0. For random forest, the feature order is PD, TCD, DO, CP, C/N0, and PRN. Finally, XGBoost initially relies on PRN, then followed by PD, TCD, DO, CP, and C/N0.  
The decision tree model achieved a mean accuracy of 99.965%, precision of 99.933%, recall of 99.953%, and F1-score of 99.943%, with minimal fluctuations in standard deviation. The random forest model outperformed decision tree, achieving an average accuracy of 99.984%, precision of 99.951%, recall of 99.979%, and F1-score of 99.965%. The XGBoost model scored the lowest performance, with an accuracy of 99.960%, precision of 99.919%, recall of 99.945%, and F1-score of 99.932%.
# Modeling and Design of a Novel 5-DOF AC-DC Hybrid Magnetic Bearing
## 4 Magnetic Analysis
### Magnetic Flux Analysis  
The magnetic flux density distribution is shown in Figure 5. The radial and axial bias flux distributions of the permanent magnets are shown in Figure 5a,b, respectively. We can see from Figure 5a,b that the radial and axial bias fluxes were symmetrical, and the magnetic fields in the axial and radial air gaps were equal, so there was no resultant force on the rotor.  
Assuming that the rotor moved in the \(-\)X direction, the length of the air gap in the \(-\)X direction became smaller. Therefore, the resultant forces caused the rotor to move in the \(-\)X direction further. To restore the rotor back to the central position, restoring forces in the \(+\)X direction were produced. Figure 5c shows the radial resultant fluxes. The radial control fluxes only formed a loop between the radial core and the rotor and had little effect on the bias flux distribution in the axial control core. The instantaneous fluxes at the three air gaps  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Axial stator outer diameter & 240 mm \\ Axial stator inner diameter of & 204 mm \\ Radial stator outer diameter of & 200 mm \\ Rotor diameter & 77.2 mm \\ Axial air-gap length & 0.7 mm \\ Magnet thickness & 2 mm \\ Magnet axial length & 20 mm \\ Shaft diameter & 12 mm \\ Radial air-gap length & 1.4 mm \\ Rotor length & 190 mm \\ Angle of the inclined axial pole & 45\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main design parameters.  
were different, so composite fluxes could be formed. By controlling the three-phase control currents, the fluxes were added or subtracted, and the corresponding radial restoring forces were generated to overcome the disturbance forces so that the rotor would be restored back to the central position. Therefore, the AC three-phase inverters can be used to control the radial 2-DOF and reduce the cost of the HMB system.  
Similarly, if the rotor was disturbed in the Z direction, Figure 5d shows the axial resultant fluxes. It can be found that the axial fluxes were reduced at one end, while at the other end, they were increased and had little effect on the bias fluxes in the radial control core. Afterward, restoring axial forces were produced to move the rotor toward the equilibrium position.  
Figure 5e shows the flux distribution when the axial control fluxes and radial control fluxes were simultaneously generated. It was further proved that the axial-radial control fluxes did not interfere with each other, and the magnetic circuit had almost no coupling.