esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### CPD and ARASE Coordinated Observations  
Because the CPD instrument is the largest electrical load on-board, using power with 8.2 W, its operating time was limited to at most 5 min twice every orbit. This high power consumption is exacerbated by the fact that the region of operation of the CPD is mainly located at high latitudes and around the polar caps, where the satellite can enter the eclipse region while operating the CPD. For the specific case of the first CPD mission, on 19 November 2018, the mission was executed over the north pole starting from a latitude of 82.2 degrees and a longitude of \(-\)165.28 degrees to a final latitude of 70.98 degrees and a longitude of 126.63 degrees, a zone that was inside Earth's shadow at that time. During this first mission, data from the power consumption and battery usage (discharge) were obtained, which allowed for the planning of longer duration CPD missions later.  
In total, CPD observations were performed between 19 November 2018 and 19 March 2019, generating approximately 5.9 MB of data. The datasets from the first 12 missions (November to January) have been retrieved completely from the ECU SD memory, which is equivalent to 0.68 MB or 11.5% of the 5.9 MB. Because the battery and the power line that feeds the CPD have been performing well, the duration of the CPD missions was extended, with the longest one lasting a total of 100.5 min. This was equivalent to 201 individual readings from the CPD-CMOS and Liulin instruments. Table 3 shows that one of the challenges in the satellite development is the volume of data generated by the main payload instruments. The storage of such amount of data was addressed by selecting a storage device with 2 GB capacity. However, the communication bandwidth limitation remains the bottleneck when retrieving experiment data.  
Based on the characteristics of the Ten-Koh orbit and the CPD detectors energy range, a collaboration with the ARASE (ERG) satellite [32] started from December 2018. Conjunction operations for the observation of high-energy electrons and ions were deducted feasible in locations when the difference between latitude and longitude of both satellites footprint at 100 km altitude is within five degrees. An example of the conjunction points between both satellites is presented in Figure 15. The ARASE team provided the conjunction points of both satellites by the use of the ARASE orbit prediction tool and the latest TLE available for Ten-Koh. Then, the Ten-Koh team produced the corresponding set of commands and schedule the time of operations for commanding the satellite. The satellite performed the joint observations with ARASE satellite when the on-board time matched the epoch specified in the uplinked command. The results from the cooperation with the ARASE satellite will be presented in a subsequent publication because they do not fall in the scope of this paper.
# Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning +
## 5 Conclusions and Future Research  
In this paper, we introduced a new computer-aided approach towards glaucoma diagnosis and localization. The predictive CNN-based model we developed, namely ResNet-152-M, obtained the best diagnosis scores among all metrics used. Moreover, using Grad-CAM, a weakly-supervised localization method, our predictive model was able to highlight where a glaucomatous area can be found in a given input image. This demonstrates that the application of deep learning tools to medical images, even though the number of images available for training is typically small, can help doctors in diagnosing glaucoma in a more effective and efficient way. Lastly, we presented Medinoid, a web application that integrates our predictive model into its backend, making it for instance possible to diagnose glaucoma in a constrained medical environment.  
If a set of training images is too small or if a set of training images is not representing well the general nature of the classes, then it is easy for a deep learning model to be biased towards the training set. In this context, we were for instance able to make the following observation: the experiments that made use of an external dataset produced lower accuracy scores than the experiments that made use of our own dataset. As a result, to mitigate model bias, future research will focus on incorporating various types of fundus images, for instance taken by other medical equipment using different angles, so as to be able to include more diversity. Furthermore, in future research, we plan to leverage our experience with deep learning-based diagnosis and localization of glaucoma in the context of other diseases, particularly paying attention to disease diagnosis and localization using 3D medical imagery.  
Conceptualization, M.K. and J.C.H.; methodology, M.K.; software, M.K.; validation, M.K., J.C.H., and S.H.H.; formal analysis, J.C.H. and C.K.; resources, J.C.H., S.H.H., and C.K.; data curation, M.K., writing, original draft preparation, M.K.; writing, review and editing, J.C.H., [PERSON], and W.D.N.; visualization, M.K.; supervision, S.V.H., C.K., and W.D.N.; project administration, W.D.N.; funding acquisition, C.K. and W.D.N.  
The research described in this paper was funded by Ghent University, Ghent University Global Campus, imec, Flanders Innovation & Entrepreneurship (VLAIO), the Fund for Scientific Research-Flanders (FWO-Flanders), and the European Union. This research was also supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF), as funded by the Ministry of Education (2017034834), and an NRF grant of the Korean government (MSIT) (No. 2017R1D1A1B03028735 and No. 2017R1D1A1B0304834).  
The authors declare no conflict of interest.
## 5 Symmetry Protected BSCs
### Robust Bloch BSCs with \(\beta\
eq 0,m=0\)  
Could the Bloch BSC occur at \(\beta\
eq 0\) in the continuum of free-space modes? This question was first answered positively by [PERSON] and [PERSON] [26] who considered acoustic trapping in an array of rods of rectangular cross-section. [PERSON] et al. [32] demonstrated the existence of the Bloch BSC with \(\beta\
eq 0\) in two parallel dielectric gratings and [PERSON] and [PERSON] [33] in two parallel arrays of dielectric rods. In a single array of rods positioned on the surface of bulk 2d photonic crystal multiple BSCs with \(\beta\geq 0\) were considered by [PERSON] et al. [44]. The Bloch BSCs in a single array of cylindrical dielectric rods in air were also reported in Refs. [37; 49; 62]. Such travelling wave Bloch BSCs with the eigenfrequencies above the light cone are interesting because the array serves as a waveguide although only for fixed \(\beta\) (see summary of BSCs in Figure 5) in contrast to the bound states below the light cone [24].  
According to Table 1 the Bloch BSCs with \(\beta\
eq 0,m=0\) have only nonzero components \(a_{l}^{0}\) or \(b_{l}^{0}\). Let us first consider Type I BSCs with \(b_{l}^{0}=0\) which have \(E_{z}=0\) and, therefore, are decoupled with the TM continuum but coupled with the TE \(n=0,m=0\) continuum. We show numerically that this  
Figure 4: BSC with \(\beta=0\): (**a**) BSC (27) with orbital angular momentum (OAM) \(m=1\) and (**b**) BSC (28) with OAM \(m=2\). Electric field force lines are shown in red, magnetic field force lines are shown in blue. Both BSC have the Bloch vector \(\beta=0\).  
coupling can be cancelled under variation of \(\beta\). The numerical results are collected in Equation (29) below with the pattern of EM fields shown in Figure 6.  
\[k_{0}=3.6505,\ \ R=0.4,\epsilon=15,\ \beta=1.2074,\ \ (a_{l}^{1},\ b_{l}^{1})= \left(\begin{array}{cc}0.1053-0.0638i&0\\ 0.1918+0.3161i&0\\ 0.6046+0.5572i&0\\ 0.7873+0.4777i&0\\ -0.0033-0.0054i&0\end{array}\right),\ \ l\geq 1. \tag{29}\]  
Although this BSC occurs at the fixed value of \(\beta\) there is no necessity to tune the material parameters of the spheres and therefore the BSC can be referred to as robust which is attractive from experimental viewpoint. We managed to find only Type I BSCs for \(\epsilon=15\) but none of Type II. Such a  
Figure 5: Summary of BSCs: the symmetry protected BSCs marked by open circles, Bloch BSC with \(\beta\
eq 0,m=0\) is marked by +. Two BSCs with OAM are marked by closed circles, the partially symmetry protected BSC (9) is marked by cross. All BSC points are calculated for spheres with \(\epsilon=15\). The area filled by gray corresponds to below the light cone. Dash and dash-dot lines show thresholds where the next continua \(n=\pm 1\) and \(n=-2\) are opened.  
Figure 6: EM field configurations given by Equation (29) and currents of the Poynting vector of the Bloch BSC with \(\beta_{c}=1.2074\) for \(k_{0c}=3.6505,R_{c}=0.4\), \(\epsilon=15\).  
difference between the types is related to different boundary conditions for electric and magnetic fields at material interfaces. We collected all BSCs in Figure 5.
# Effect of Soil Damping on the Soil-Pile-Structure Interaction Analyses in Cohesionless Soils
###### Abstract  
Pile foundations in earthquake-prone regions must be analyzed and designed considering the dynamic loads. In the fully nonlinear dynamic analyses, the soil nonlinearity can be considered using the modulus degradation curves in the total-stress approach, and the soil damping is controlled by the unloading/reloading rule. Several researchers have investigated the effect of soil damping on the free-field soil response analyses, but the effect on the soil-pile-structure system response has not been studied thoroughly. In this study, the nonlinear elastic method (hyperbolic model) and the elastoplastic Mohr-Coulomb (MC) models were implemented to investigate the effect of soil damping on the pile and structure response. Dynamic soil-pile-structure interaction analyses were performed by simulating two different centrifuge tests published in the literature, and the analysis results were compared with the centrifuge test results. The analyses with the low-intensity input motions showed that the superstructure accelerations and the bending moments in the single pile were estimated with reasonable accuracy. However, the superstructure accelerations might be underestimated under high-intensity motions, especially in the MC model. Additional analyses were performed under six earthquake records. The constitutive models (MC and hyperbolic) may significantly vary the maximum structural acceleration and pile maximum moments (up to 50%). As a result, the responses of the superstructure and the pile in soil-pile-structure interaction problems are highly dependent on the soil constitutive model that must take the damping into account accurately; in turn, due account must be given to the selection of the constitutive model.  
dynamic; interaction; damping; nonlinear; soil; pile +
Footnote †: journal: Applied sciences
## 4 Result
### Evaluation of Predictive Performance
#### 4.1.2 Model Performance Evaluation with Optimized Hyperparameters  
The optimized hyperparameters were applied to train and evaluate the model across four targets. The predictive performance of the model was assessed using Explained  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline learning\_rate & 0.1349 \\ max\_depth & 9 \\ subsample & 0.8033 \\ colsample\_bytes & 0.5895 \\ n\_estimators & 295 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal hyperparameter values obtained through Bayesian Optimization.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Iteration** & **Target (R\({}^{2}\) Value)** & **Colsample\_Byte** & **Learning\_Rate** & **Max\_Depth** & **n\_Estimators** & **Subsample** \\ \hline
1 & 0.714 & 0.6873 & 0.1906 & 8.124 & 279.6 & 0.578 \\  & & & (omitted for brevity) & & & \\
6 & 0.7422 & 0.5895 & 0.1349 & 9.961 & 295.6 & 0.8033 \\
7 & 0.7356 & 0.754 & 0.1003 & 9.562 & 316.6 & 0.7297 \\
9 & 0.74 & 0.5741 & 0.1908 & 9.859 & 141.6 & 0.8147 \\  & & & (omitted for brevity) & & & \\
16 & 0.734 & 0.9064 & 0.09787 & 9.902 & 135.7 & 0.8127 \\
17 & 0.7289 & 0.8538 & 0.05436 & 9.8 & 171.3 & 0.8932 \\
18 & 0.5116 & 0.9242 & 0.07331 & 3.174 & 169.4 & 0.5182 \\
19 & 0.6658 & 0.9048 & 0.1361 & 5.263 & 138.1 & 0.8139 \\
20 & 0.6578 & 0.848 In the Class Preparation Phase, the R\({}^{2}\) score was 0.8206, indicating that the XGBoost model could explain 82.06% of how well teachers could utilize AI Literacy in this phase. Additionally, the prediction errors were relatively low, with an MSE at 0.1235, RMSE at 0.3514, and MAE at 0.1430.  
In the Class Implementation Phase, the R\({}^{2}\) score was 0.8007, indicating an explanatory power of 80.07% for AI Literacy. The MSE was recorded at 0.1337, the RMSE at 0.3656, and the MAE at 0.1372, showing stable prediction performance.  
In the Class Assessment Phase, the R\({}^{2}\) score was 0.8066, with MSE at 0.1534, RMSE at 0.3917, and MAE at 0.1561, demonstrating the model's stable predictive performance.  
In the final phase, the Class Application of Assessment Results Phase, the R\({}^{2}\) score was 0.7746, with MSE at 0.1616, RMSE at 0.4019, and MAE at 0.1581. This indicates that the model's predictive performance was consistently maintained.  
Overall, the XGBoost model recorded R\({}^{2}\) values above 0.7 for all targets, and error indicators (MSE, RMSE, MAE) were maintained within a consistent range. Based on these performance evaluation results, the model's predictive performance can be considered reliably robust.
## 2 Materials and Methods  
In our experiments, we used a femtosecond Ti-sapphire laser system (Spectra-Physics, Santa Clara, CA, United States) to generate a laser pulse with a central wavelength of 800 nm, pulse width of 50 fs, and a repetition rate of 100 Hz. A half-wave plate and a mechanical shutter were used to control the polarization direction and the pulse number, respectively. The laser beam was normally focused on the surface of the sample by using a dichroic mirror and lens with an achromatic doublet (f = 100 mm). A beam radius of w0 (1/e\({}^{2}\)) \(\sim\) 40 \(\upmu\)m was determined in the processing plane. The incident laser was focused on the surface of pure titanium samples (10 \(\times\) 10 \(\times\) 0.5 mm, Kejing Hefei). The incident laser fluence was fixed at 0.20 J/cm\({}^{2}\), which is slightly higher than the ablation threshold of titanium, 0.16 J/cm\({}^{2}\). The experiments were performed at room temperature. All the processing was monitored using a CCD camera (Basler, Ahrensburg, Germany). Following the femtosecond laser ablation, a scanning electron microscope (SEM) (SEM-FEG XL30, FEI, Hillsboro, OR, USA) was used to characterize the surface morphologies of the titanium samples.
## 2 The Algorithms: MC, MCQ, MCR, MCS and BBMC  
We start by presenting the simplest algorithm [4] which I will call MC. This sets the scene. It is presented as a Java class, as are all the algorithms, with instance variables and methods. Each algorithm is first described textually and then the actual implementation is given in Java. Sometimes a program trace is given to better expose the workings of the algorithm. It is possible to read this section skipping the Java descriptions, however the Java code makes it explicit how one algorithm differs from another and shows the details that can severely affect the performance of the algorithm.  
MC is essentially a _straw man_: It is elegant but too simple to be of any practical worth. Nevertheless, it has some interesting features. MCQ [12] is then presented as an extension to MC, our first algorithm that uses a tight integration of search algorithm, search order and upper bound cut off. Our implementation of MCQ allows three different vertex orderings to be used, and one of these corresponds to MCR [15]. The presentation of MCQ is somewhat laborious but this pays off when we present two variants of MCS [13] (MCSa and MCSb) as minor changes to MCQ. BBMC [11] is presented as an extension of MCQ, but is essentially MCSa with sets implemented using bit strings. Figure 1 shows the hierarchical structure for the algorithms presented. In the code presented, we endeavour to use the same procedure names as in the original publications.
###### Abstract  
Flywheel energy storage systems (FESS) are one of the earliest forms of energy storage technologies with several benefits of long service time, high power density, low maintenance, and insensitivity to environmental conditions being important areas of research in recent years. This paper focusses on the electrical machine and power electronics, an important part of a flywheel system, the electrical machine rotating with the flywheel inertia in order to perform charge-discharge cycles. The type of machine used in the electrical drive plays an important role in the characteristics governing electrical losses as well as standby losses. Permanent magnet synchronous machine (PMSM) and induction machines (IM) are the two most common types of electric machines used in FESS applications where the latter has negligible standby losses due to its lower rotor magnetic field until energised by the stator. This paper describes research in which the operational and standby losses of a squirrel-cage induction machine-based flywheel storage system (SCIM-FESS) are modelled as a system developed in MATLAB/Simulink environment inclusive of the control system for the power electronics converters. Using the proposed control algorithm and in-depth analysis of the system losses, a detailed assessment of the dynamic performance of the SCIM-FESS is performed for different states of charging, discharging, and standby modes. The results of the analysis show that, in presence of system losses including aerodynamic and bearing friction losses, the SCIM-FESS has satisfactory characteristics in energy regulation and dynamic response during load torque variations. The compliance of FESS and its conversion between the generating and motoring mode within milliseconds show the responsiveness of the proposed control system.  
Keywords:flywheel energy storage; squirrel cage induction machine; loss analysis; field-oriented control +
Footnote †: journal: Applied Soft. 2019, 9, 4537; doi:10.3390/appj9214537
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 6 Learning Paradigms
### Result Fusion  
Many ASC algorithms include result fusion steps where intermediate results from different time frames or classifiers are merged. Similar to computer vision, features learned in different layers of the network capture different levels of abstraction of the audio signal. Therefore, some systems apply early fusion and combine intermediate feature representations from different layers of the network as multiscale features [27; 56; 81]. Ensemble learning is a common late fusion technique where the prediction results of multiple classifiers are combined [8; 12; 22; 26; 37]. The predicted class scores can be averaged [83] or used as features for an additional classifier [84]. Averaging the classification results over multiple segments, as well as over multiple classifiers has become an essential part of state-of-the-art ASC algorithms. However, in real-life applications, using multiple models for an averaged decision is often not feasible due to the available computational resources and processing time constraints.
# Fabrication of Stretchable Transparent Electrode by Utilizing Self-Induced Vacuum Force
## 3 Results and Discussion
### Advantage of Vacuum Force on Elastomer Penetration  
In this section, the effect of the self-induced vacuum force on the fabrication of the stretchable electrode will be discussed. As previously mentioned, the self-induced vacuum force expedites the penetration of the elastomer into the electrode pattern, such that the electrode transfer becomes feasible even for highly viscous elastomers. Because the vacuum force is induced by the evaporation of the solvent, the solvent should be sufficiently absorbed by the electrode particles. Here, two types of carbon nanoparticles (Ketjen Black and Denka Black) were used for the electrode fabrication. Ketjen Black is a well-known carbon-based material because of its highly porous structure and electric conductivity. To show the adsorption/desorption performances of the solvent, BET(Brunauer-Emmett-Teller)/BJT(Barrett-Joyner-Halenda) tests were conducted for both these carbon materials. From Table 1, it can be observed that Ketjen Black exhibited 10 times superior adsorption/desorption characteristics than those of Denka Black. Hence, it was decided that the proposed fabrication method, which uses the self-induced vacuum force, would be performed using the electrode slurry prepared from the Ketjen Black nanoparticles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test** & **Denka Black** & **Ketjen Black** \\ \hline BET surface area (m\({}^{2}\)/g) & 67.9778 & 894.0720 \\ BJH adsorption surface area of pores (cm\({}^{2}\)/g) & 0.1788 & 1.0830 \\ BJH desorption surface area of pores (cm\({}^{2}\)/g) & 0.1781 & 1.0994 \\ \hline \hline \end{tabular}
\end{table}
Table 1: BET(Brunauer-Emmett-Teller)/BJT(Barrett-Joyner-Halenda) test results for the porous electrically conductive materials (Denka Black/Ketjen Black).  
Figure 6: Scanning-electron-microscopy (SEM) image of the cross-section of the electric conductor for (**a**) 150 \(\upmu\)m (**b**) 100 \(\upmu\)m (**c**) 50 \(\upmu\)m width. (**d**) carbon content of Levels. 1–5. The ratio of carbon at different positions is obtained from the SEM–EDX analysis.  
Figure 7a,b depict the SEM images for sections of the electrode lines fabricated using Ketjen Black and Denka Black, respectively. The full penetration of the elastomer occurs for the Ketjen Black slurry (note the red-dotted line in Figure 7a). However, the elastomer could not fully penetrate the electrode of the Denka-Black-based slurry at the pattern corners. From this comparison, it can be concluded that the electrode slurry should be prepared using highly porous carbon nanoparticles along with a sufficient amount of a volatile solvent that induces a vacuum force, which, in turn, enables the full penetration of the elastomer into the pattern.
## 4 Methodology
### The Proposed Sub-Cluster LEACH-LMNN Protocol  
The proposed sub-cluster LEACH protocol with LMNN approach is explained stepwise. The flow-chart of proposed protocol is shown in Figure 6. In this proposal, some amendments in LEACH-LMNN are introduced to enhance the performance of the network in terms of energy and end-to-end delay. From Figure 6, it can be observed that the starting steps are similar to the previous LEACH-LMNN approach. In this protocol, the major improvement is the incorporation of a sub-cluster CH selection. The key idea is to consider, after the CH node selection with Levenberg-Marquardt neural network, that all the CH nodes could further select one or two nodes in their vicinity. These nodes are called sub-cluster CH nodes, and they act in support of the main CH node. For instance, if there are 10 nodes in a particular cluster, one acts as CH node, and the rest could act as N-CH nodes. These nine N-CH nodes transfer their control messages, data, etc., to a single CH node only in the first step of the proposed strategy. Hence, in order to reduce this burden, another one or two nodes are selected with the Levenberg-Marquardt neural network as sub-cluster CH or, in short, Sub-CH nodes. N-CH nodes are divided between CH nodes and N-CH nodes on the basis of distance. The distance is calculated with a link quality parameter. Therefore, some nodes will transfer data to the CH node in its cluster, and others will transfer data to Sub-CH nodes. After data aggregation, Sub-CHs will transfer their data to the CH node, which will further jointly transfer to the BS. The obtained results are presented in Section 4.