esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Tracking Techniques on 360-Degree Videos
## 3 Experiments  
### Experiment Setup  
To measure the performance of eight trackers on 360-degree videos in a variety of situations, we prepared a dataset containing nine 360-degree videos captured using a Garmin Virb 360-degree camera. The 360-degree camera can be installed on top of a helmet for ground video capturing as shown in Figure 1(a). Alternatively, the 360-degree camera can be attached to a drone for aerial video capturing as shown in Figure 1(b). A Garmin Virb 360-degree camera contains two 12-megapixel sub-cameras that are opposite to each other. Each sub-camera has a wide field of view (FOV) of 202 degrees. At each time instant, the hardware inside the camera analyzes the overlap between two images captured by sub-cameras, then aligns and stitches two images together to form a seamless 360-degree image.  
One problem of 360-degree videos is the huge file size. Typically, a 360-degree video contains 30 360-degree images per second, and each 360-degree image has a resolution of \(3840\times 2160\) with three color channels, resulting in a total of 746 MB per second. Video compression techniques can be applied to effectively reduce the size of the captured video file. Smaller video size can increase the frame rate of tracking and make the motion between two consecutive frames smaller, and hence improve the performance of tracking. On the other hand, high compression ratio and low bit rate can reduce the video quality, and hence degrade the accuracy of tracking. [PERSON] et al. [21] studied the influences of the choice of video coding parameters on the performance of visual object tracking. In default setting, the hardware inside the Garmin Virb 360-degree camera applies the most commonly used AVC/H.264 encoding and generates a standard MP4 video file with a maximum bit rate of 120 Mbps. To make a fair comparison of eight trackers, our experiments are made based on the same video encoding and bit rate in all nine video sequences.  
As shown in Table 1, these 360-degree video sequences cover multiple scenarios, each containing a combination of characteristics such as viewpoint change, occlusion, deformation, lighting change, scale change, and camera shakiness. Each 360-degree video sequence lasts 100-1000 frames with a resolution of \(3840\times 2160\). For speedup purpose, all video sequences are down-sampled to \(1920\times 1080\) in our tracking experiments. The benchmark machine is a PC with 3.2 GHz CPU and 16 GB RAM. The operating system is Microsoft Windows 10.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sequences** & **Characteristics** & **Viewpoint** & **Occlusion** & **Deformation** & **Lighting** & **Scale** & **Shakiness** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Our dataset containing nine 360-degree videos, each with a combination of special characteristics.  
Figure 2: Two ways to set up an omnidirectional camera: (**a**) installed on top of a helmet for ground video capturing, (**b**) attached to a drone for aerial video capturing.
## 3 Results and Discussion
### Effect of Pressure Ratio  
The effect of the pressure ratio, \(n=Q/p\), on skin friction and the axial load of the pile are shown in Figure 11a,b, respectively. As we can see, the slopes of fully the mobilized NSF versus normalized depth are the same under different pile top loads, as expected in the ESM, which is due to the same friction coefficient used. However, the position of the neutral point increased with the increase of \(Q\), enhancing the settlement of the pile and decreasing the relative settlement between the pile and soil, thus reducing the depth of fully mobilized NSF. The axial load of the pile versus the normalized depth are plotted in Figure 11b. Obviously, the effect of the pile top pressure on the axial load decreased as the depth increased. It is believed that pile top load reduces the relative settlement of pile soil in NSF section, while enhances it in PSF section, which causes the total NSF value to reduce and the total PSF value to raise. The pile top pressure is counteracted by the PSF before it reaches the bottom of the pile. For a typical friction pile, when the increase of pile top pressure is constant, the adjusted magnitude of skin friction is the same, which also explains the parallelism of the negative and positive friction transition section.  
The normalized maximum axial load, \(F_{max}/p\), and pressure ratio, \(n\), show a good linear relationship, as shown in Figure 12, where the best fitting line is given. As we can see, the fitting curves of \(F_{max}/p\) versus \(n\) are parallel to each other under different consolidation pressures, where the slopes are around 0.63. That means the maximum axial load, \(F_{max}\), was mainly affected by the applied pressure, \(Q\).  
Figure 11: Effect of pressure ratio on (a) skin friction (b) and axial load.
## 2 Computation Method and Case Validation
### Case Validation  
The case validation employs a model consisting of a symmetrical double wedge positioned behind a flat plate, as depicted in Figure 1. This experiment was conducted by [PERSON] [18]. The dimensions of the flat plate are 2200 mm in length and 760 mm in width, designed to provide a stable inflow boundary layer for the rear double wedge. The double wedge is configured with two adjoining 15\({}^{\circ}\) angles, forming a distinctive geometry with a leading edge spacing of 152 mm, gradually decreasing along the flow direction to 430 mm at the trailing edge. Additionally, each wedge unit has a length and height of 406 mm and 200 mm, respectively.  
To comprehensively evaluate the performance of the 3D symmetric double wedge in the wind tunnel, computational freestream parameters were established based on experimental conditions: an incoming Mach number of 8.28, static pressure set at 430 Pa, and static temperature maintained at 80 K, ensuring a unit Reynolds number of 5.3 \(\times\) 10\({}^{6}\). Regarding computational methodology, the aforementioned numerical scheme was employed, and specific computational grids were utilized. Concerning boundary conditions, the following details were specified: the outer boundary was subjected to pressure far-field conditions, the wedge and the flat plate were designated as solid-wall boundaries, pressure outlet conditions were applied at the exit, and the remaining portions were set as symmetric surface boundary conditions. Further details regarding the boundary conditions can be found in Figure 2. This study adopts boundary condition settings similar to those used by [PERSON] [19], which are widely recognized and validated in the field of hypersonic flow simulations. These settings are selected based on their proven effectiveness in accurately modeling the thermal and flow characteristics of hypersonic vehicles as documented in previous research and standard CFD practices.  
Figure 1: Geometry of the three-dimensional double wedge [18].  
By comparing the pressure experiment data with the numerical simulation results of the centerline of the flat plate, a strong agreement between the two is observed. The detailed comparison relationship is illustrated in Figure 3, further validating the capability of the numerical method employed in this study to accurately simulate the interactions between shock waves and the complex flow phenomena within the boundary layer.
## 2 Materials and Methods
### Kiwifruit Spectra and Sweetness Determination  
The spectral information of kiwifruit was collected using the field-portable spectroradiometer SVC HR-10241 indoors. This instrument covered the spectral range from 400 to 2500 nm. The spectral resolution is 3.5 nm at 400-1000 nm, 9.5 nm at 1100-1850 nm, and 6.5 nm at 1850-2526 nm. All the measurements were conducted in a dark room environment with halogen lamps to minimize the influence of the surrounding environment on kiwifruit spectral data. The spectroradiometer was placed vertically 10 cm above the sample surface with a field-of-view angle of 4\({}^{\circ}\). Prior to each measurement, the radiometer was calibrated using a whiteboard. To ensure measurement accuracy, ten replicates were recorded for each kiwifruit sample, and the average spectra were utilized for subsequent spectral analysis. All measurements were completed within 12 h after the samples arriving indoors.  
Following the spectral data collection, sweetness determination for the kiwifruit samples was carried out. The equatorial region of each kiwifruit was peeled to access the fruit's pulp. A suitable amount of pulp was extracted and juiced, and the resulting juice was dispensed into the detection tank of the ATAGO PAL-BX/ACID5 Sugar and Acid All-in-One machine. The Brix value of sweetness for each sample was then read from the machine. The mean of two parallel measurements was used as the sweetness content of each sample to reduce the measurement error.
## 4 Results and Discussion
### Aerodynamic Damping
#### 4.3.3 Aerodynamic Damping Calculation
Figure 19 shows the amplitudes of the blade vibration in a vacuum and a flow field when the time of excitation is constant. The blade vibration amplitude is defined as the amplitude at the end of excitation. Figure 20 shows the corresponding results of aerodynamic and non-aerodynamic damping \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(f_{lower}\) & 0 & 0 & 0 & \(f_{1}\) & \(f_{1}\) \\ \hline \(f_{upper}\) & \(f_{1}\) & \(f_{2}\) & \(f_{3}\) & \(f_{2}\) & \(f_{3}\) \\ \hline \(\xi_{m}\) & 0.0317 & 0.0109 & 0.0084 & 0.0087 & 0.0073 \\ \(\xi_{tot}\) & 0.0328 & 0.0121 & 0.0097 & 0.0100 & 0.0086 \\ \(\xi_{a}\) & 0.0011 & 0.0013 & 0.0013 & 0.0013 & 0.0013 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Damping of Rotor Blade. Figure 18: Tip leading-edge displacement in flow for the first modal excitation for \(\alpha=0,\ \beta=0\). As shown in Figure 19, for the same excitation force, the vibration amplitudes in the vacuum are all higher than that of the flow due to aerodynamic damping. The amplitude of blade vibration increases approximately linearly alongside increasing excitation force amplitude. As shown in Figure 20, non-aerodynamic damping is almost constant with increasing amplitude of excitation force. However, the aerodynamic damping values show a significant decrease in accordance with the increasing amplitude of excitation force once the amplitude becomes larger than 0.15 N. This phenomenon demonstrates that nonlinear vibration (a large amplitude) has a significant effect on aeroelastic stability. The aerodynamic damping values are all positive, indicating that the blade vibration is stable at the design point. However, the aerodynamic damping transits from positive to negative, indicating that the blade tends to suffer from instability. The aerodynamic damping is almost the same when the amplitude of excitation force is small (0.1-0.15 N), which corresponds to a small vibration amplitude (less than 1% of the chord, as shown in Figure 19), which is considered to be linear. In addition, for the same excitation force, aerodynamic damping values near the flutter points are less than those near the design conditions. Moreover, IBPA is a key factor for aeroelastic stability in turbomachinery. The applicability of this method for dealing with non-zero IBPAs is illustrated by predicting the aerodynamic damping at an IBPA of 180\({}^{\circ}\), where the double passage model is employed. Figure 21 shows the blade displacement response both in a vacuum and in a flow field. An apparent IBPA of 180\({}^{\circ}\) is presented below. Figure 19: Tip leading-edge vibration amplitude for different excitation force amplitudes. Figure 20: \(\xi_{a}\) and \(\xi_{m}\) for different excitation force amplitudes. A comparison of aerodynamic damping between an IBPA of \(0^{\circ}\) and an IBPA of \(180^{\circ}\) is shown in Figure 22. The blade is more stable at an IBPA of \(0^{\circ}\) than at an IBPA of \(180^{\circ}\).
# Development on Solid Polymer Electrolytes for Electrochemical Devices
## 2 Ion Transport Properties and Conduction Mechanism in SPEs
Areas of free ions (\(A_{f}\)), ion pairs (\(A_{p}\)) and ion aggregates (\(A_{a}\)) have been identified via FTIR deconvolution in wavenumber region between 1030 and 1060 cm\({}^{-1}\), and their percentage can be calculated based on the following equations:  
\[\sigma=\sigma_{+}+\sigma_{-}=\frac{q^{2}}{k_{b}T}\left(n_{+}D_{+}+n_{-}D_{-}\right) \tag{2}\]0  
\[\sigma=\sigma_{+}+\sigma_{-}=\frac{q^{2}}{k_{b}T}\left(n_{+}D_{+}+n_{-}D_{-}\right) \tag{2}\]1  
\[\sigma=\sigma_{+}+\sigma_{-}=\frac{q^{2}}{k_{b}T}\left(n_{+}D_{+}+n_{-}D_{-}\right) \tag{2}\]2  
From Equation (11), \(n\) can be calculated from the following:  
\[\sigma=\sigma_{+}+\sigma_{-}=\frac{q^{2}}{k_{b}T}\left(n_{+}D_{+}+n_{-}D_{-}\right) \tag{2}\]3  
where \(N_{A}\) is the Avogadro's number, \(M\) is number of moles of lithium triflate and \(V\) is the electrolyte's volume. The \(n\) value can also be calculated from the famous Rice and Roth model which was originally developed to describe the conductivity behavior for superionic conductors [16]. The equation is as follows:  
\[\sigma=\sigma_{+}+\sigma_{-}=\frac{q^{2}}{k_{b}T}\left(n_{+}D_{+}+n_{-}D_{-}\right) \tag{2}\]4  
where \(E_{A}\) is the activation energy; \(q\) is the electron charge; \(m\) is the mass of ions; \(Z\) is the vacancy of conducting species; and \(\tau=r/\
u\), where \(r\) is the distance between two coordinating sites and \(\
u\) is the velocity of ions, which is given as follows:  
\[\sigma=\sigma_{+}+\sigma_{-}=\frac{q^{2}}{k_{b}T}\left(n_{+}D_{+}+n_{-}D_{-}\right) \tag{2}\]5  
\(E_{A}\) is the minimum energy required for an ion to hop in between sites, and it can be obtained from the Arrhenius equation, as shown below:  
\[\sigma=\sigma_{+}+\sigma_{-}=\frac{q^{2}}{k_{b}T}\left(n_{+}D_{+}+n_{-}D_{-}\right) \tag{2}\]6  
where \(\sigma_{0}\) is the pre-exponential factor. A low \(E_{A}\) value is required for high conductivity. In general, the mechanism of ion transport in polymer electrolytes is via hopping and coupled with the segmental motion of the polymer [3]. However, Groththuss motion which is another type of hopping mechanism has been proposed to describe proton conduction in chitosan-based SPE containing ammonium iodide (NH\({}_{4}\)I) [10]. Chitosan structure is rigid and possesses high \(T_{g}\), thus resulting in minimal chain motion. This led to the proposal of the Grothuss model as the proton-conduction mechanism in the SPEs. Grothuss mechanism is the proton conduction process where the proton hops between the coordination sites decoupled from the polymer chain motion. This mechanism is also known as proton hopping. Besides these factors, other parameters that can influence the feasibility of polymer electrolytes in applications for electrochemical cells include ion (cationic and anionic) transference number (\(t_{ion}\)), degree of crystallinity, etc.
## 3 System Model and Medium Access Control Protocols
### MAC Protocols  
In this section, we describe the MAC protocols considered in this paper: FSA and RFSA. Although we focus on the analysis of RFSA, FSA is also described in order to make the paper self-contained.
# FLIP: A New Approach for Easing the Use of Federated Learning
## 3 Design and Implementation
### Functionalities
#### 3.2.1 Server  
* Define the training plan, including the ML (Machine Learning) model that resides at runtime on the clients, along with the aggregation strategy, the federated dataset to be used, and the training flow.
* Orchestrate the deployment of compute containers on client nodes.
* Monitor execution on each node using exception handling when necessary.
* Change the execution status of a task to cancelled.
* Save the final aggregation status of the FL task.
* Send the aggregated model to the clients to start a new training round.
* Generate public documentation for the REST API following a standard such as OpenAPI.  
Figure 2: Responsibility-based design of the platform’s architecture.  
* Provide a simple and functional user interface to ensure access to services without the need to use REST actions.
* CRUD operations on federated datasets.
* CRUD operations in users and organisations.
* Deployment scripts to facilitate the subscription of new nodes.
* Task statuses are stored in persistent storage.
* Incoming and outgoing communications are encrypted using a secure protocol.
* The aggregation strategy operates on data that can come from any model written in Python, independent of the library from which it comes.
## 5 WST Applications
### Climate Influence  
Signal loss due to atmospheric conditions should be considered because the climate does influence the communication links [54]. Outdoor use has to take into account the effects of moisture due to humidity, precipitation and wetting. [PERSON] and [PERSON] studied the propagation of radio waves in a potato field using 433 MHz motes, finding a better propagation under wet conditions. Higher relative humidity and rain provided increased signal strength at the receiver [12]. However, experimentation with 868/916 MHz motes showed the opposite behavior, when the relative humidity during the day was high, the gateway received around 60% of the expected messages. This percentage grows back to more than 70% in the drier days [59]. Other authors, calculated the attenuation of 2.4 GHz signals due to rain as 0.02 dB/km for a rain rate of 150 mm/hr [10].  
Also, ambient temperature affects the motes performance. Low temperatures have a negative effect in the battery life of the motes. Experimentation in refrigerated chambers at different temperaturesshowed that battery life decreases under cooling conditions. For example, in 2.4 GHz ZigBee motes, battery life at 0 \({}^{\circ}\)C is half than that at 20 \({}^{\circ}\)C [25]. Measurements in motes can become erroneous when the battery voltage is less than a certain threshold. [PERSON] _et al_. reported erroneous data below 2,160 mV compared to 3,000 mV corresponding to full charge. Thus, changes in battery voltage must be isolated from impacting measurement accuracy [25].
## 4 Experiments
### Anomaly Detection and Explanation  
#### 4.3.1 Evaluation Metrics for Anomaly Detection  
Due to the issue of class imbalance in ICS datasets, relying solely on accuracy as an evaluation metric may be insufficient. Class imbalance refers to the disparity in sample sizes across different classes, which can lead to models achieving high accuracy simply by predicting the majority class while neglecting the performance on minority classes. Therefore, this study employs a range of evaluation metrics to provide a comprehensive assessment, including accuracy, precision, recall, and F1-score.
## 5 Discussion  
### Comparison of Different Proposed Models  
Table 11 provides a comprehensive comparison of the predictive performance of different models for estimating the ultimate bearing capacity of shallow foundations. The evaluation is conducted on both the training and testing databases, and various performance metrics are utilized to assess the accuracy and reliability of each model.  
For the training database, the Mean Absolute Error (MAE) values range from 33.938 for the Multiple Linear Regression (MLR) model to an impressive 6.634 for the Genetic Algorithm-Emotional Neural Network (GA-ENN) model. This indicates that the GA-ENN model exhibits the lowest average absolute deviation between its predictions and the actual values, showcasing its superior accuracy in predicting the bearing capacity of shallow foundations during training. The Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) also demonstrate the same trend, where the GA-ENN model outperforms all other models, yielding the smallest errors.  
Similarly, in the testing dataset, the GA-ENN model attains the lowest MAE (6.018), MSE (65.397), and RMSE (8.087) values, indicating its remarkable predictive accuracy on unseen data. Additionally, the GA-ENN model shows the lowest Mean Squared Logarithmic Error (MSLE) and Root Mean Squared Logarithmic Error (RMSLE) values, indicating its ability to accurately predict both small and large errors on the testing data.  
Furthermore, the coefficient of determination (\(R^{2}\)) values for the GA-ENN model are the highest in both the training (0.994) and the testing (0.995) datasets, illustrating the model's excellent fit to the data and its capability to explain a significant proportion of the variance in the target variable.  
In Figure 10, the outcomes of various statistical and AI models for predicting the bearing capacity of shallow foundations are displayed. Among these models, it is evident that the Genetic Programming (GP), Classification and Regression Trees (CART), and  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Performance** & \multicolumn{3}{c}{**Training**} & \multicolumn{5}{c}{**Testing**} \\ \cline{2-10}
**Metrics** & **MLR** & **GP** & **CART** & **GA-ENN** & **MLR** & **GP** & **CART** & **GA-ENN** \\ \hline MAE & 33.938 & 19.677 & 18.388 & 6.634 & 32.260 & 16.164 & 23.583 & 6.018 \\ MSE & 2189.854 & 700.751 & 568.105 & 72.503 & 2026.062 & 313.386 & 981.006 & 65.397 \\ RMSE & 46.796 & 26.472 & 23.835 & 8.515 & 45.012 & 17.703 & 31.321 & 8.087 \\ MSLE & 0.063 & 0.018 & 0.017 & 0.004 & 0.116 & 0.012 & 0.031 & 0.005 \\ RMSLE & 0.252 & 0.136 & 0.129 & 0.061 & 0.341 & 0.111 & 0.177 & 0.072 \\ \(R^{2}\) & 0.809 & 0.939 & 0.951 & 0.994 & 0.837 & 0.975 & 0.921 & 0.995 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results of all models predict ultimate bearing capacity of shallow foundations for both training and testing databases.
## 5 Conclusions  
Some studies on the performance of CTS have focused on modification of the structure and shape of the CTS. The air space is designed (\(>\)2 mm) as an empirical design without quantitative data. This paper focused on the optimized air space between the ionizer conductor and the ground current collector to improve the performance of the initial corona discharge inception using an FEA software package. To investigate the optimal air space, it was reduced to the extent possible for simulation to a micro size. The electric field strength between the ionizer conductor and current collector increases with a decreasing air space until 0.0005 mm. The electric field strength of 0.0005 mm air space is increased by 1.3 times that of 0.001 mm. The free electron density is concentrated where the peak of the electric field is formed. In particular, the free electron density of 0.0005 mm air space is increased by 1.9 times that of 0.0001 mm. The negative ion density shows a similar trend with positive ion density; the maximum value of both ion densities are formed at 1.34 us, 1.32 us with different free electron densities. It might be possible that the free electron mobility (\(1\times 10^{-4}\) m\({}^{2}\)s\({}^{-1}\)V\({}^{-1}\)) has a higher mobility than ions (\(1\times 10^{-9}\) m\({}^{2}\)s\({}^{-1}\)V\({}^{-1}\)). However, a air space of less than 2 mm was no longer a significant difference in our simulation results. Thus, the 0.0005 mm air space creates a non-uniform high electric field with a large electric field enhancement due to ionization caused by consecutive electron avalanches. In addition, it might be possible that the high electric field concentration occurs at 0.0005 mm air space, and this could be the starting point of early corona discharge. The results in this paper have been discussed in terms of the electron mean free path. To improve the CTS, we suggest the air space between the ionizer conductor and current collector of less than 2 mm, different from conventional CTS studies.  
[PERSON]-[PERSON] contributed to conceptualization, methodology, analysis, and writing. [PERSON]-[PERSON] contributed to validation, formal analysis, data curation, and visualization. [PERSON]-[PERSON] contributed to review and editing. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the KETEP (Korea Energy Technology Evaluation and Planning). 20215710100360, development of mobile application product technology using re-manufactured batteries.  
Not applicable.  
The authors declare no conflict of interest.