esa-sceva/satcom-chunk-collection huggingface dataset:
# Hyperspectral Image Classification Using 3D Capsule-Net Based Architecture
###### Abstract
Despite the fact that these techniques significantly boost HSI classification efficiency, it is challenging to overcome the decreased performance of the classifier brought on by the small number of training samples and growing number of network layers. Additionally, they include many redundant features. For spectral-spatial categorization of HSI, the authors of [29] suggested a fused 3D CNN that aims to combine several 3D CNNs applied to a collection of groups of related spectral bands. However, combining many supervised 3D CNNs requires a significant amount of effort and computation. By effectively retrieving relevant spectral and spatial characteristics, deep learning models have demonstrated their great performance in improving HSI categorization, as can be seen from prior publications. However, there are a few problems, particularly for CNN, which needsmany samples with labels for training and classification. However, finding enough trained labeled data for HSIs is typically exceedingly challenging. To address the aforementioned limitations and to improve the performance of HSI classification, in this paper we propose a 3D Capsule-Net based architecture. So far, Capsule-Net is used by several researchers to solve problems related to 1D and 2D data, but the performance of 3D Capsule-Net is still unexplored. Prior to analyzing the few crucial wavelengths from the whole HSI cube, incremental-principle-component-analysis (IPCA) is used to decrease band redundancies. Then, a 3D-CNN layer is used to extract the shallow features, which are given to the 3D-CNN framework for classification of HSI. The comparison is made with the existing 2D/3D techniques proposed in literature. The suggested technique surpasses the ones that were tested, according to experimental and comparison data. In the remaining manuscript, Section 2 gives the details regarding the dataset, Section 3 discusses about the proposed Capsule-Net based architecture and the implementation details, Section 4 presents results and discussion, and the Section 5 concludes the work.
## 3 Survey of Techniques for Updating
### Dissemination
#### 3.1.9 Freshwater  
Freshnet [35] uses a number of optimizations to reduce the energy consumed during an update, when compared to protocols such as MNP, MOPA, and Deluge. It allows pipelining: streaming of pages before the update has been fully received, to minimize latency; it supports out-of-order page reception, to maximize the benefits of receiving from multiple sources; it supports an aggressive sleep mode when the _wave_ of a dissemination is not expected; and it reduces the exchange of metadata during _quiescent_ phases. Dissemination occurs in two phases: in the initial phase, information about a code update is propagated rapidly throughout the network, along with some topology information (including a hop count). Each node then estimates when the _distribution_ phase is likely to reach it, using the hop count, and sleeps until that time. A _warning_ message injected a fixed number of times in the initial phase. In the distribution phase, the _Deluge_ three-way handshake algorithm is used. Once a node ceases to hear any _requests_ from neighbours within range, it enters a _quiescent_ phase, where new nodes must _pull_ data, rather than every node periodically broadcasting its latest version number, and the update activity is only active on a 50% duty cycle. Freshnet also introduces a _multi-page_ distribution mode, which reduces the number of _advertisement_ message sent when a node sees that all its neighbours are running out-of-date code. All pages are sent automatically; neighbours only request retransmissions if all the pages are not received (not on a page-by-page basis).  
Freshnet contains no features of direct benefit to autonomous updates, but the aggressive optimizations to reduce energy use and latency provide useful flexibility.
## 2 The Baseline ECU Design and Numerical Models
### The Beta Model
#### 2.3.2 The Discharging Model  
Equations (6)-(9) in Section 2.2 are used in both the Alpha and Beta models. The Beta model introduces a nested PID control loop, based on previous work by [PERSON] et al. [39], to regulate power requirements and maintain optimal speed based on the bucket-to-speed ratio previously mentioned in Section 2.2[27]. Moreover, the Pelton turbine's inertia and damping are also considered to calculate the bucket torque to be matched to the generator's torque for power generation, a step which is omitted in the Alpha model. Equation (14) presents the governing equation for the Pelton turbine wheel implemented in the Beta model.  
\[M_{B}-M_{\text{G}}=J_{Pel}\dot{w}_{Petton}+D_{Pel}w_{Petton} \tag{14}\]  
where \(M_{B}\) is the bucket torque, \(M_{\text{G}}\) is the generator torque, \(J_{Pel}\) is the Pelton wheel inertia and \(D_{Pel}\) is the viscous damping of the turbine. Figure 7 presents an overview of the Simulink(r) model of the Pelton turbine, specifically highlighting the aforementioned nested PID control loop and also presenting the inertia and damping transfer function block [40]. Finally, the Beta model also considers frictional (\(P_{Friction}\)) and windage (\(P_{Windage}\)) losses. Equations (15) and (16) show how the said losses were calculated [27; 41; 42].  
\[P_{Friction}=0.000105\times M_{F}\times\omega_{Petton} \tag{15}\]  
\[P_{Windage}=15\times\left(\frac{\omega_{Petton}}{(2\times\pi)}\right)^{3}\times D ^{5}\times(R_{1})^{1/4}(R_{2})^{3/4}(R_{3})^{5/4}(R_{4})^{7/4} \tag{16}\]  
where \(M_{F}\) is the frictional moment, \(\omega_{Petton}\) is the [PERSON] wheel speed, \(D\) is the [PERSON] wheel diameter and \(R_{1}\) to \(R_{4}\) are Pelton turbine wheel geometric approximations [27].
# Effect of Waves on the Behavior of Emergent Buoyantly Rising Submarines Using CFD
###### Abstract  
Emergent buoyantly rising submarines encounter excess roll problems, partially owing to waves that significantly affect their behavior. This study predicts the behavior of a submarine, including when it rises in static water, beam sea, head wave, following wave, 30\({}^{\circ}\) bow wave, 60\({}^{\circ}\) bow wave, 30\({}^{\circ}\) quartering wave, and 60\({}^{\circ}\) quartering wave, using the computational fluid dynamics method. The beam sea has a slight effect on pitch prior to the submarine rising to the water surface, but the maximum roll angle in the beam sea is 4.43 times that in static water. After a submarine submerges in water, the pitching oscillation does not decay quickly owing to the yaw angle. The head wave and the following wave have a continuous significant effect on the pitch; the submarine sail remains under the water surface after it submerges from the highest position. The head wave and the following wave have a slight effect on the roll and yaw before the submarine rises to the water surface; however, the roll angle suddenly increases after the submarine submerges from the highest position. As the initial angle between the submarine centerline and wave direction increases, the effect of waves on the longitudinal motion decreases. The amplitude of the pitching oscillation decreases with an increase in the initial angle between the submarine centerline and wave direction, and the waterline when the submarine oscillates on the water surface decreases. The difference in the maximum roll angle between when a submarine rises in an oblique wave and when it rises in beam sea is below 6.3\({}^{\circ}\). Submarines should try to avoid rising in a head wave and the following wave.  
Keywords:rising submarine; effect of waves; head and following wave; oblique wave; CFD +
Footnote †: journal: Applied Sciences
## 3 Three End-Fire Dipole Antenna Arrays in Planar Technology  
Figure 8a shows a typical two-element end-fire antenna array utilizing a half-wave dipole antenna as the elementary unit in planar Rogers 4350 PCB technologies. The thickness of Rogers 4350 is 0.245 mm.  
Figure 8b shows the dimensions of the two-element end-fire antenna array utilizing half-wave dipoles as the elementary unit in planar PCB technology. \(\lambda\) is the effective wavelength at 24 GHz. The antenna array is mainly composed of three half-wave dipoles (HWDP), as shown in Figure 8b, HWDP1, HWDP2, and HWDP3. HWDP1 and HWDP3 are the two array units and two main neighboring radiating dipoles, and comprise the two-element antenna array. The distance between HWDP1 and HWDP3 is half the effective  
Figure 8: (**a**) An two-element end-fire antenna array utilizing a half-wave dipole antenna as the elementary unit, (**b**) the dimensions of the two-element end-fire antenna array, and the (**c**) top view, (**d**) bottom view and (**e**) side view of the antenna prototype.  
Figure 7: Gain pattern of the two-element end-fire dipole antenna array with 2 auxiliary dipole antennas in H plane.  
wavelength. HWDP2 is an auxiliary dipole antenna unit which helps to increase the gain of the two-element end-fire antenna array. HWDP2 is in the middle of HWDP1 and HWDP3.  
Figure 8c-e show a top view, bottom view and side view of the antenna array prototype. In Figure 8e, the side view, a Southwest 2.4 mm end launch connector 1492-04A-6 is included to show the size of the antenna prototype.  
A two-element end-fire antenna array without auxiliary dipole antennas and with auxiliary dipole antennas at greater numbers such as two distributed evenly between HWDP1 and HWDP3 are also designed, simulated and measured. Figure 9 shows the top views of the two-element end-fire antenna arrays (a) without auxiliary dipole antennas and (b) with one auxiliary dipole antenna (c) or with two auxiliary dipole antennas in HFSS software. Table 1 shows the simulation results of the three different antenna arrays in Figure 9a-c. From Table 1, all of the three antenna arrays in Figure 9 work well, and their simulated gains are comparable with the same input impedance matching network. The simulation results verify the new and simple design method of an end-fire array. Due to the fabrication and measurement cost, at first, only the two-element dipole antenna array with one auxiliary dipole antenna is fabricated and measured. Additionally, later, the other two antenna arrays without auxiliary dipole antenna and with two auxiliary dipole antennas in Figure 9 are also fabricated and measured.
# Detecting Jamming in Smart Grid Communications via Deep Learning
## 5 Theoretical Framework and Methodology  
This section describes the methodology we followed to design and test our solution. Our approach aims to detect jamming attacks when standard techniques fail because they rely on higher-layer link-quality estimation techniques, e.g., BER. Detecting jamming attacks when BER is affected might be too late since the communication link might be broken. In contrast, our solution can identify jamming attacks when the communication link quality is still high (BER close to zero). Indeed, while we assume that the communication parties in close proximity to the jammer cannot communicate (raising alarms about the presence of the jammer), our solution allows the detection of the jamming source further away with respect to standard solutions, i.e., from a larger (geographical) distance between the jammer and the receiver where communication between the parties is still possible, thus allowing to raise an alarm about the presence of the jammer.
# Evaluation of GNSS-TEC Data-Driven IRI-2016 Model for Electron Density
## 3 Results
### Accuracy Analysis of NmrF2
#### 3.2.2 Comparison with COSMIC Data  
The MAE and RMSE of NmF2 output from IRI-2016 and upda-IRI-2016 compared to COSMIC were calculated for January 2015 and 2019, respectively, using the data from the COSMIC inversion, and the statistical results are shown in Table 5. Similar to the results of the evaluation using ionosonde data, in terms of the MAE and RMSE of NmF2, the MAE and RMSE between NmF2 output from upda-IRI-2016 and NmF2 observed by ionosonde in the high and low solar activity years are higher than the MAE and RMSE between NmF2 output from IRI-2016 and NmF2 observed by ionosonde in the high and low solar activity years. The MAE and RMSE between NmF2 and ionosonde-observed NmF2 are smaller. The MAE of NmF2 output from upda-IRI-2016 in 2015 is 0.0235 \(\times\) 10\({}^{12}\) el/m\({}^{3}\) less than that from IRI-2016, and the corresponding RMSE is 0.0223 \(\times\) 10\({}^{12}\) el/m\({}^{3}\) less. The 2019 upda-IRI-2016 output of NmF2 MAE is reduced by 0.255 \(\times\) 10\({}^{12}\) el/m\({}^{3}\) and the corresponding RMSE is reduced by 0.275 \(\times\) 10\({}^{12}\) el/m\({}^{3}\) compared to IRI-2016. The MAE PI of NmF2 output from  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Stations**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**RMSE**} & \multirow{2}{*}{**PI**} \\ \cline{3-3} \cline{5-7}  & & **IRI-2016** & & & **IRI-2016** & & **Upda-IRI-2016** \\ \hline \multirow{3}{*}{2015} & Beijing & 0.1605 & 0.1350 & 15.8\% & 0.2064 & 0.1820 & 11.8\% \\  & Wuhan & 0.2974 & 0.1626 & 45.3\% & 0.3605 & 0.2121 & 41.2\% \\ \cline{1-1}  & Beijing & 0.0617 & 0.0438 & 29.0\% & 0.0855 & 0.0438 & 22.0\% \\ \cline{1-1}  & Wuhan & 0.0970 & 0.0487 & 49.0\% & 0.1198 & 0.0652 & 45.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: MAE, RMSE, and its precision improvement (PI) percentage for the difference between the NmF2 and ionosonde data for IRI-2016 and upda-IRI-2016 in January 2015 and January 2019.  
Figure 8: NmF2 output from ionosonde NmF2 versus IRI-2016 and upda-IRI-2016 driven by IG12 and IG-up, respectively, at Wuhan and Beijing stations from 00:00 to 09:00 UT in January 2019.  
IRI-2016 is 15.6% and 29.9%, and the RMSE PI of NmF2 is 13.5% and 29.5% in 2015 and 2019, respectively, after adjusting the IG12 parameters. From these statistical results, it can be seen that the optimization of NmF2 with the output of upda-IRI-2016 driven by IG-up is more significant in low solar activity years than in high solar activity years.  
The MAE and RMSE of NmF2 for IRI-2016 and upda-IRI-2016 were statistically analyzed using COSMIC data covering one day per week for the whole year of 2015, and the statistical results are shown in Table 6. Comparison of Table 5 shows that the accuracy statistics results for every day of January 2015 and for using COSMIC data one day per week for the entire year of 2015 converge.
# Parallel Eclipse-Aware Routing on FPGA for SpaceWire-Based OBC in LEO Satellite Networks
## 2 Problem Description  
### Characteristics of LEO Network Topologies  
As mentioned earlier, LEO satellite networks have a different dynamic topology than ground networks and existing satellite networks. LEO satellites operate at an altitude between 500 KM and 2000 KM from Earth, so they orbit the Earth at a very high speed and perform continuous positional movement. This means that, over time, the connection between satellites and the connection to the ground changes, which means that links between specific satellites can only be maintained for a short period of time, and the creation and release of links occur repeatedly. As a result, routing technologies used in existing static networks cannot be directly applied, and routing technologies that can reflect the changing connection status in real time are needed.  
In this study, LEO satellite network routing is performed using the KSP algorithm. Existing routing algorithms calculate a single optimal path, which is cumbersome to re-operate when the connectivity information between satellites changes. The KSP algorithm generates K optimal path candidates to generate candidate groups and goes through the process of selecting the path with the least cost among them. This is an algorithm that can ensure the stability of the LEO satellite network because, even if the connectivity information between satellites is changed, only other optimal paths need to be selected from the candidate group [17, 18]. A typical method for implementing the KSP algorithm is [PERSON]'s algorithm and it can be implemented with many other algorithms. In this paper, the K-Shortest Path problem is solved with a Depth First Search (DFS)-based approach. We selected DFS because, although [PERSON]'s algorithm can be mapped to an FPGA, its spur-path expansion produces many data-dependent branches and priority-queue updates that break a one-clock-cycle initiation interval (II = 1), making efficient pipelining and thus real-timerouting impossible. DFS, by contrast, traverses one branch at a time with a fixed-depth stack, so every loop can be fully unrolled, scheduled at \(\Pi=1\), and implemented with minimal control logic.
## 2 Materials and Methods
### Datasets
#### 2.2.1 Data Quality Analysis  
The research object in this study is daily line loss rate, some of whose curves are presented as examples in Figure 1. Besides, the 25 percentile (\(q1\)), median (q2), 75 percentile (\(q3\)), maximum (max), minimum (min), mean, standard deviation (std), lower bound (\(l_{a}\)) and upper bound (\(u_{a}\)) values of the overall line loss rate dataset are calculated and provided in Table 1. The distribution box-plots of the line loss rates are provided in Figure 2. It is noted that lower bound (\(l_{a}\)) and upper bound (\(u_{a}\)) are calculated based on 25 percentile (\(q1\)) and 75 percentile (\(q3\)) [31], where a value out of the bounds can be treated as an outlier:  
\[\left\{\begin{array}{l}l_{a}=q1-1.5\times(q3-q1)\\ u_{a}=q3+1.5\times(q3-q1)\end{array}\right., \tag{6}\]  
According to the curves and quality analysis, the data characteristics of daily line loss rates can be summarized as follows:  
1. The line loss rate data possess few daily regularity and show high fluctuation. From Figure 1, the curves of line loss rates in different regions change greatly over the days, where historical line loss rates can be hardly used to estimate the further values. Thus, it is vital in this study to select influencing factors of line loss rate.
## 6 Results and Discussion
### RNN-GRU Performance: Scenario1
The predicted data for the 2013 test dataset when using the best model for _Scenario2_ (RNN-based GRU) is plotted in Figure 19. Observing the predicted values, there is under-prediction in July 2013 and over-prediction in April 2013 and December 2013. This kind of under prediction or over-prediction is normal in deep learning algorithms when implementing a lookback period. The Figure 20 compares the prediction using FNN, GRU, and LSTM for the whole dataset for 2013. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **nnodes/Layer nlayers** & **Look Back** & **dropout** & **epoch** & **Min MAE** & **Test MAE** & **Test MAPE(\%)** \\ \hline FNN & 64 & 3 & 8 days & 0 & 180 & 237.82 & 262.8 & 3.54 \\ GRU & 64 & 2 & 8 days & 0 & 99 & 234.22 & 251.3 & 3.44 \\ LSTM & 64 & 2 & 8 days & 0 & 56 & 242.12 & 276.2 & 3.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Implementation of the best parameters: day-ahead forecasting (_Scenario2_). Figure 17: Weekday forecasting using RNN-GRU. Figure 19: Predictions for 2013 using the whole dataset for RNN-GRU (_Scenario_2). Figure 18: Weekday forecasting when including maximum temperature variables in LSTM model.
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 6 Evaluation
### Prediction Accuracy  
The goal of predicting location is to be able to rotate the camera in a direction where the animal is expected to be. Using the distance offset statistics, we can determine the accuracy of the algorithm by combining the distance between the predicted animal position and the camera placement. As illustrated in Figure 10, the predicted location is represented by point \(P\), and the camera (point \(C\)) is positioned within the boundary to point towards the predicted position. The camera has a horizontal field of view of 105 degrees, as shown in Table 1, and the red shading indicates the current range that the camera can cover. If the actual animal position falls within the red shading, represented by point \(Q\), the prediction is considered accurate. Conversely, if the actual animal position falls outside the red shading, represented by point \(R\), the prediction is considered incorrect. Since we only have the distance between the predicted location and the actual location, without knowing their relative positions with respect to the camera, \(Q\) could be any place on the red circle which is centered at point \(P\). We make the assumption that the angle formed by the edge \(PQ\) and the edge \(CP\) at point \(P\) is a right angle, so that angle \(\beta\) is the maximum value. In this way, the accuracy of the prediction is the conservative value.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**CNN Pre-Trained Model** & **Latency (s)** \\ \hline VGG16 & 2.27 \\ \hline ResNet50 & 3.75 \\ \hline ResNet50V2 & 3.34 \\ \hline InceptionV3 & 4.75 \\ \hline MobileNet & 1.64 \\ \hline MobileNetV2 & 2.74 \\ \hline EfficientNetB0 & 5.07 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Animal detection experiment results.  
Figure 9: Average distance offset between predicted and actual positions for different types of movements in the three layouts.  
Based on this validation method, we calculate the prediction accuracy of the three layouts at different distances between the predicted animal position and the camera placement, as shown in the Table 4. The table shows again that Layout B is the best layout solution. For Layout B, a placement distance of 5 m can achieve a very accurate prediction. Notably, Layout C's low accuracy leads to substantial discrepancies between actual and predicted positions for the tested movements. This is attributed to our method of representing areas with central points, which can result in large angles between consecutive line segments. Consequently, the predicted third point may deviate significantly from the actual third point, as observed in this case.  
The farther the distance, the wider the coverage, and the higher the accuracy. Nevertheless, we must also consider that increasing the distance results in lower image quality of the animal, which makes animal identification more difficult. We will discuss this further in Section 6.
## 2 Background  
### Adaptive Optics (AO)  
The objective of AO is to eliminate any spatial phase distortions in an optical wavefront. The AO method was first proposed by [PERSON] [38] to improve observational astronomical by correcting wavefronts with a deformable optical element controlled by a wavefront sensor. New techniques and results have since been consistently published, primarily focusing on advancements in wavefront sensors, deformable mirrors, and control algorithms [39; 40; 41; 42; 43].  
After propagating through the atmosphere to the telescope, light is distorted by subtle changes in the temperature and pressure (and hence the index of refraction) of the air, which varies in time and space [44]. The corresponding wavefront profile is measured by a wavefront sensor, which informs a control command for a deformable mirror to flatten the wavefront.