esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Governing Equations and Dimensionless Parameters  
The actual system is governed by the continuity, momentum, and energy equations that are defined, respectively, as [47; 48]:  
\[\frac{\partial\rho}{\partial t}+\
abla\
abla.\Big{(}\rho\overset{\rightarrow}{v} \Big{)}=S_{m} \tag{1}\]  
\[\frac{\partial\Big{(}\rho\overset{\rightarrow}{v}\Big{)}}{\partial t}+\
abla. \Big{(}\rho\overset{\rightarrow}{v}\overset{\rightarrow}{v}\Big{)}=-\
abla p +\
abla\Big{(}\overset{\leftarrow}{\pi}\Big{)}+\rho\overset{\rightarrow}{g} +\overset{\rightarrow}{F} \tag{2}\]  
\[\frac{\partial(\rho E)}{\partial t}+\
abla.\Big{(}\overset{\rightarrow}{v} (\rho E+p)\Big{)}=\
abla\Bigg{(}k_{eff}\
abla T-\sum_{j}h_{j}\overset{ \rightarrow}{j}_{j}+\Big{(}\overset{\leftarrow}{\pi}_{eff}.\overset{ \rightarrow}{v}\Big{)}\Bigg{)}+S_{h} \tag{3}\]  
The thermal performance coefficient (\(\eta\)) can be exerted to quantify the yield of a heat exchanger that contains the Nusselt number and the friction factor (\(f\)), and is defined as [49; 50; 51; 52; 53; 54; 55; 56; 57; 58]:  
\[Nu=\frac{h_{md}h}{k} \tag{4}\]  
\[f=\frac{2d_{h}\Delta P}{\rho u^{2}L} \tag{5}\]  
\[\eta=\bigg{(}\frac{Nu}{Nu_{0}}\bigg{)}\bigg{(}\frac{f_{0}}{f}\bigg{)}^{\frac{ 1}{3}} \tag{6}\]  
The three-dimensional numerical simulations have been done by finite volume method using commercial CFD code, ANSYS FLUENT 18.2. The spatial discretization of the mass, momentum, turbulence kinetic energy, turbulence dissipation rate and energy equations has been achieved by a second-order upwind scheme. The velocity-pressure coupling has been overcome by the SIMPLE algorithm. To calculate the gradients, Green-Gauss cell-based method has been utilized. The convergence criteria were set to \(10^{-6}\) for the residuals of all equations except energy equation (\(10^{-8}\)).
## 4 Conclusions  
An en face tomogram, already reconstructed by using an FF-OCT system, based on the integrating-bucket scheme, was successfully corrected using software-based post-processing. The image reconstruction of the FF-OCT system suffers from artifacts, generally in the form of residual interference fringes, when the phase modulation parameters are not under the prescribed optimal conditions. The artifacts can be removed or reduced by introducing the correction factor \(\Gamma\), which can be extracted from an already-reconstructed image. The correction factor was extracted from the area average of two intermediate images of the integrating-bucket scheme. Using the FF-OCT image of a Philippine one peso coin, a series of the correction factor \(\Gamma\)s were calculated while varying the average window size. The factor varied rapidly at the beginning with the window size, then converged to 1.51. It was observed that the residual interference fringes were almost completely removed, with the correction achieved by using the proposed method. Applying the \(\Gamma\) value extracted from only one sheet of a 3-D image, we could remove the residual fringes on all other sheets, composing the entire 3-D image, covering a depth of 50 \(\upmu\)m with 1 \(\upmu\)m steps.  
As a result, we can say that, compared with a conventional method, the post-processing achieved by the proposed method provides more distinguishable morphological features of the sample surface, by effectively removing or reducing the number of residual interference fringes. It is expected that the proposed method will help to expand the applications of FF-OCT from biomedical imaging to semiconductor wafers or panel inspections.  
Mathematical formulation, J.L.; Experiments and image processing, T.Y.; Writing and reviewing the paper, J.L., T.Y., and B.H.L.; Project administration, B.H.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2019R1 FIA1061859). This study was partly supported by the Semiconductor Industry Collaborative Project between GIST (Gwangju Institute of Science and Technology) and SK Siltron Co., Ltd. The authors declare no conflict of interest.
## Acronyms and Symbols  
CDMA Code Division Multiple Access  
CRDSA Contention Resolution Diversity Slotted Aloha  
DPS-IoT Direct to Satellite IoT  
GEO Geostationary Orbit  
HSTN Hybrid Land-Satellite Network  
IoT Internet of Things  
IRSA Irregular Repetition Slotted Aloha  
ITLD Intelligent Traffic Load Distribution  
LEO Low Earth Orbit  
LPWAN Low Power Wide Area Network  
MAC Media Access Control  
MIMO Multiple Input Multiple Output  
RA Random Access  
SAT Satellite  
SIC Successive Interference Cancellation  
TDMA Time Division Multiple Access  
5G Fifth Generation of Wireless Communication Systems  
6G Sixth Generation of Wireless Communication Systems
## 1 Introduction
Moreover, as mentioned in the previous work [15], one of the advantages of the proposed structure (SDSP) is to remove dead time out of the diagonal elements of the decoupled transfer function matrix. Therefore, only the delay-free parts of it are addressed to design the controllers. Note that, in this work, the fractional order transfer function (FOTF) is used instead of the integer order one. In addition, to evaluate the robustness stability of the proposed method, the \(M\)-\(\Delta\) structure normally used for integer order systems [15; 16; 17; 18; 19; 20; 21; 22] is also applied for fractional order ones. Then, the \(\mu\)-synthesis, known as structured singular value (SSV), is employed to measure the robustness of the fractional controllers for multivariable processes with multiplicative output uncertainty. This paper is organized as follows. Section 2 is briefly introduced fractional order calculus with the Oustaloup recursive algorithm to approximate the fractional operator. Furthermore, the general structure of the controller is presented with reduced models based fractional order. A new fractional order PID controller is also proposed and then analytical tuning rules are derived based on the internal model control structure. Some criteria to evaluate the system performances are mentioned in Section 2 as well. Section 3 presents the simulations of some well-known TITO processes to justify the effectiveness and robustness of the proposed method. Finally, conclusions are given in Section 4.
## 5 Climatology (2002-2022) of Longitudinal Structures in the Ionosphere
### TEC WN4 Structure
[PERSON] et al., 2007 [92] by using the  
Figure 12: (**a**) LT variability of the climatologically mean (2002–2022) TEC WN4 structure at different latitudes, shown in the upper side of each plot, during August; (**b**) latitudinal dependence of the tidal and SPW4 contribution in months when the TEC WN4 structure has large amplitudes: August (**upper left** plot), September (**upper right** plot), March (bottom left plot), and June (**bottom right** plot); and (**c**) latitude-time climatological structure of the TEC SPW4 amplitude (**left** plot) and the TEC SE2 amplitude (**right** plot). GSWM with the TIME-GCM lower boundary (30 km) demonstrated that the DE3 could propagate from the troposphere into the lower thermosphere, modulate the E region dynamo process, and thereby introduce a four-peaked F region wave structure. Later, [PERSON] et al., 2009 [83], based on the simulations by the TIME-GCM, quantify, for the first time, the SPW4 in the MLT, and found that it is excited by nonlinear interaction between the DE3 and the migrating DW1; then the authors suggested that the SPW4 modulates the DE3 MLT response and may impact the E region dynamo as well as the ionospheric variability. Furthermore, [PERSON] et al., 2011 [28], reported that the wave generation due the nonlinear interaction between the DW1 and the DE3 leads to a large SPW4 and a large SE2 tide in the E-region heights as their combined amplitudes can equal those of the DE3. Our climatology of the DE3, SPW4, and SE2 at altitude of 110 km, however, indicated that the DE3 (16.5 K) is significantly larger than the SPW4 (~2 K) + SE2 (~6.6 K) while in the thermosphere at 400 km the DE3 and SPW4 responses are not very different; the electron density DE3 response is 0.30 MHz and the SPW4 is 0.28 MHz [53] and the latter is supported by the present climatology, the TEC DE3 is 1.7 TECU and TEC SPW4 is 1.6 TECU. This could mean that the SPW4 is generated by the nonlinear interaction between DE3 and DW1, but in the IT system.
## 4 Experiment
### Parameters  
The parameters of the proposed indoor structural model method for four datasets are listed in Table 3. Based on preliminary findings from the experiments, proposed methods showed robustness, and most of the parameters were insensitive to point cloud data in various indoor scenes and did not require manual modification. For opening extraction, the point clouds were transform into 2D image, where \(C_{2D}\) is the pixel size, \(w_{d}\) and \(h_{d}\) are the width and height of the regularized door; and, \(w_{w}\) and \(h_{w}\) are the width and height of the regularized window. For room segmentation, the point clouds were transformed into 3D grids, where \(C_{3D}\) is the size of 3D grid; and, \(\alpha,\gamma\) are weight parameters used for balancing importance between data term and smooth term in the energy function. For the line global optimization, the \(\theta_{\text{max}}\) and \(d_{\text{max}}\) were used to correct the angle and distance value of line; \(K\) is the nearest neighbor number of every line; and, \(\lambda\) is weight parameter used for balancing different terms in the energy function. For clustering similar lines, \(a_{thread}\) and \(d_{thread}\) were the angle and distance threshold. In 5G signal intensity simulation, \(d_{p}\) is the signal propagation distance, \(f\) is the frequency of the electromagnetic wave, and \(m\) is the power exponent by IDW interpolation method. These parameters can be depended on the point cloud data from different indoor scenes with similar characteristics.
## 2 Data and Methods
### EPB Detection Based on LP Measurements of Plasma Density
We remark that due to the imposed thresholds, the algorithm, by design, is unable to detect false negatives (unlike for the EFD algorithm, see Section 2.3). The estimation of the percentage of false positive events was obtained by visual inspection of all the semiorbits in one year in which the algorithm detected EPBs (327 in 2019), then counting the number of failures (2 in 2019). Once a plasma depletion has been identified, the algorithm saves the information related to both the orbit (date and orbit number) and the ionospheric irregularities (start and end time of the density decrease, its starting/ending geographic and quasi-dipole latitude and longitude). Figure 1 shows the EPB automatically detected by the LP algorithm along the ascending semiorbit 12716 of CSES-01 on 18 May 2020. In this case, the LP algorithm detected a single depletion.
## 3 Materials and Methods  
### Landslide Inventory  
Building a landslide inventory is a crucial preliminary step in susceptibility evaluation [20; 21]. This process involves identifying the existing landslides, including information on their location, scale, type, and other features. A landslide inventory database can be developed through various methods such as field surveys, existing landslide distribution maps, satellite image interpretation, Synthetic Aperture Radar (SAR) image shift tracking, or a combination of these approaches [22]. [PERSON] et al. combined field geologic investigations, remote sensing impact interpretation, and historical landslide reports to locate 816 historical landslides in the study area [23]. [PERSON] et al. cross-referenced the local Department of Natural Resources (DNR) historical field survey data with Landsat 8 Land Imager (OLI) remote sensing image interpretation results in order to build a landslide inventory database [9]. In this study, 102 landslide events were identified by field survey and Google Earth satellite image interpretation. Figure 3 shows two landslides identified during the field survey. In this study, the centroid of each landslide deposit was used to construct the landslide inventory dataset.  
Figure 4 presents the workflow for landslide susceptibility assessment within the 2 km buffer zone along the G3033 Duku Expressway.
## 4 ALS Data Visualization, Analysis and Quality/Accuracy Assessment
### Confidence of Features Visible on ALS Data
The model key points and the DEMs that were generated using kriging IDW and TLI are presented in the second row of Figure 26. As can be appreciated by visual comparison there is not much difference between the DEMs generated from the original number of ground points or from the \"key\" points. However, deviation from the original DEMs and erosion of features becomes evident when the modeled key points are further decimated to 70% (11,982 ground returns) and 50% (8553 ground returns). Also at these reduced point densities, the differences between the results of the interpolation methods become evident. IDW seems to be the method that produces higher erosion of features, which is due to the smoothing weight that was selected, as it tends to smooth and average the observations. With TLI the triangular facets become larger and significant detail is lost. Kriging seems to be more robust to decimation maintaining some of the larger features despite the reduced number of observations. With the performed decimation method one can be certain that most of the large-scale (\(>\)10 m) features evident on the original ALS DEM are real and not artifacts from the classification and interpolation methods used to produce them. However, it is important to point out that in an archaeological context the smaller features visible in the ALS data products are the ones for which its nature (real or data artifacts) are harder to determine. In such cases a more rigorous study that looks into the data contained in the point cloud can be performed. The study consists of determining if the ground points that create the small feature (pits, mounds) come from a single or more than one flight line. Here is where having 50% side overlap or larger (from multiple geometries) can pay off, because if there is a significant number of returns that come from different flight lines then one can be confident that the feature is real. If the number of returns is low and comes from a single flight line, then there is a high level of uncertainty of the nature of the feature.
# Ontology-Based Feature Selection: A Survey
## 3 Feature Selection
### Wrapper Models  
Filter models select features based on their statistical similarities to a target variable. Wrapper methods take a different approach and use a pre-selected classifier as a way to evaluate the accuracy of the classification task for a specific feature subset. A wrapper algorithm consists of three components, namely a feature search component, a feature evaluation component, and a classifier [39; 40]. At each step, the search component generates a subset of features that will be evaluated for the classification task. When the total number of features is small, it is possible to test all possible feature combinations. However, this approach, known as SUBSET, becomes quickly computationally intractable.  
Greedy search methods overcome this problem by using a heuristic rule to guide the subset generation [42; 43]. In particular, forward selection starts with an empty set and evaluates the classification accuracy of each feature separately. The best feature initializes the set. In the subsequent iterations, the current set is combined with each of the remaining features and the union is tested for its classification accuracy. The feature producing the best classification is added permanently to the selected features and the process is repeated until the number of features reaches a threshold or none of the remaining features improve the classification. On the other hand, backward elimination starts with all features. At each iteration, all features in the set are removed one by one and the resulting classification is evaluated. The feature affecting the classification the least, is removed from the list. Finally, bidirectional search starts with an empty set (expanding set) and a set with all features (shrinking set). At each iteration, first a feature is forward selected and added to the expanding set with the constraint that the added feature exists in the shrinking set. Then a feature is backward eliminated from the shrinking set with the constraint that it has not already been added in the expanding set.  
Many more strategies have been used to search the feature space, such as branch-and-bound, simulated annealing, and genetic algorithms [42; 43]. Branch-and-bound uses depth-search to traverse the feature subset tree, pruning those branches that have worse classification score than the score of an already traversed fully expanded branch. Simulated annealing and genetic algorithms encode the selected features in a binary vector. At each step, offspring vectors, representing different combinations of features, are generated and tested for their accuracy. A common technique for performance assessment is \(k\)-fold cross-validation. The training data are split into \(k\) sets and the classification task is performed \(k\) times, using at each iteration one set as the validation set and the remaining \(k\)-1 sets for training.
## 2 PICO-Inspired Impairment Analysis for Hand Exoskeletons
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**P-Problem** & & \multicolumn{3}{c}{**O-Outcome**} \\
**(Impairment)** & **Typologies** & \multicolumn{3}{c}{**(Measures)**} \\ \hline ROM limitation & Unidirectional & \multicolumn{3}{c}{Gonionometer, sensors, cameras} \\  & Bidirectional & \multicolumn{3}{c}{Gonionometer, sensors, cameras} \\  & Active & \multicolumn{3}{c}{Gonionometer, sensors, cameras} \\  & Passive & \multicolumn{3}{c}{Gonionometer, sensors, cameras} \\ \hline Strength reduction & Fine motor control impairment (mainly fingers) & \multicolumn{3}{c}{Ergometers (load cells), EMG 1, cameras, clinical scales} \\  & Gross motor control impairment (including the wrist) & \multicolumn{3}{c}{Ergometers (load cells), EMG, cameras, clinical scales} \\  & muscle atrophy from disuse/reduced excitation & \multicolumn{3}{c}{ergometers (load cells), EMG, cameras, echography, MRI 2, CT 3, meter, clinical scales} \\ \hline Coordination disorders & Dexterity & \multicolumn{3}{c}{EMG, cameras, clinical tests} \\  & Coccontraction & \multicolumn{3}{c}{EMG, cameras, clinical tests} \\  & Ataxia & \multicolumn{3}{c}{EMG, cameras, clinical tests} \\  & Dysmetria & \multicolumn{3}{c}{EMG, cameras, clinical tests} \\ \hline Sensory disorders & Tactile–hypoesthesia/paresthesia & \multicolumn{3}{c}{Clinical assessment} \\  & Proprioceptive impairment & \multicolumn{3}{c}{Clinical assessment} \\  & Nociceptive—paresthesia/hyperalgesia/allodynia & \multicolumn{3}{c}{Clinical assessment} \\ \hline Tone alterations & Hypertonía/spasticity & \multicolumn{3}{c}{Clinical assessment/scales, basic research devices} \\  & Hypotonia & \multicolumn{3}{c}{Clinical assessment/scales} \\  & Dysotonia & \multicolumn{3}{c}{Clinical assessment/scales} \\ \hline Other motor disorders & Mycolonia & \multicolumn{3}{c}{Clinical assessment, EMG} \\  & Hyper-reflexia & \multicolumn{3}{c}{Clinical assessment, EMG} \\  & Spasms & \multicolumn{3}{c}{Clinical assessment, EMG} \\  & Tremor & \multicolumn{3}{c}{Clinical assessment, EMG} \\ \hline Pain & \multicolumn{3}{c}{VAS 4, pressure test} \\ oedema & \multicolumn{3}{c}{Clinical assessment} \\ Fatigue & \multicolumn{3}{c}{Clinical assessment/scales, ergometers, EMG} \\ \hline \hline \end{tabular}  
* \({}^{1}\) Electromyography ; 2 Magnetic Resonance Imaging; 3 Computed Tomography, also known as Computed Axial Tomography (CAT); 4 Visual Analog Scale.  
\end{table} 
Table 2: Schematic overview of the relation between the P-problem of the PICO model (as identified impairments and typologies) and the most common pathologies requiring the use of hand exoskeletons. In the table, CNS and PNS denote the central and peripheral nervous systems, respectively; brackets indicate possible but non necessarily present match between impairment and pathology.
## 5 Application Examples  
One of the application examples, which was taken as reference to develop the work presented in this paper, is a smart pipe of an industrial plant that measures the fluid throughput and thus the plant consumption of such fluid. The wind harvesting system is installed in pipelines, thus, when the gas flow moves the rotor, the harvester generates energy that powers the wireless sensor network. Data will be sent to a computer and/or mobile application, where the fluid quantity will be obtained using mathematical algorithms. Based on the measurement results, a worker or the central system will decide if any action needs to be taken.  
Another example of application is the power source of an autonomous system that measures parameters of the bushing of a train, for example, the quality of the wheels. As long as the access of hardwire power wires to this location is complex, it would be desirable to eliminate dependency on batteries, this type of harvester system is an alternative that allows the achievement of the necessary energy to power the sensor system. When the train is moving, an opposite direction air flow is generated, which will fall on the harvester. Hence, the wind harvester rotates, and the AC/DC converter provides the required energy and voltage level to power the system. With node wireless communication, the driver will receive, in real time, the measured data and take actions based on the data collected.  
For the least intrusive integration possible, a thin film electronic solution would be needed. The goal would be to integrate the whole sensor, energy harvester, and communications systems into a 100 \(\upmu\)m thickness substrate. The flexible electronics would allow the involvement of the wind harvester and easy and small installation in several environments. Figure 36 shows an example of this possible integration.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref** & **Topology** & **Efficiency (\%)** & **Technology** & **Source** \\ \hline
[26] & Rectifier + DC/DC (Buck Boost) & 35.64–54.12 & Discrete & Electromagnetic \\
[59] & Rectifier + DC/DC (Frequency & 44 & Discrete & Piezoelectric \\  & up conversion) & 64.95 & Discrete & Piezoelectric \\
[60] & Rectifier + DC/DC (Buck Boost) & 50–74 & Discrete & Piezoelectric \\
[27] & Rectifier + DC/DC (SECE) & 50–74 & Discrete & Piezoelectric \\
[28] & AC/DC (Boost) & 45 & Discrete & Signal generator \\ This work & AC/DC (Three-phase Split NP) & 74.33–81.52 & Discrete & Electromagnetic \\ \hline \hline \end{tabular}
\end{table}
Table 25: Comparison of different converters for Alternate Current/Direct Current (AC/DC) conversion.