esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Method Using 3D Interest Points to Place Markers on a Large Object in Augmented Reality
## 2 Impact of the number of markers
### Main Experiment
#### 3.3.2 Results of Main Experiment  
The respective MRE, which were measured 10 times after placing the markers using both the conventional subjective method (hereafter \(\text{MRE}_{\text{conventional}}\)) and the proposed method (hereafter \(\text{MRE}_{\text{proposed}}\)), are shown in Figure 25, and it was confirmed that the proposed method has relatively less volatility.  
Figure 23: Main experimental procedure for validating performance of the proposed method.  
Figure 24: Creating markers using the subjective marker placement program.  
1. Performance analysis for registration accuracy  
An analysis was performed to confirm whether the proposed method significantly reduced the MRE compared with the conventional method. The total MRE, which included the models (DM and DA), number of markers, and number of iterations, was used to confirm the normal distribution of the MRE for each method. The results of the Shapiro-Wilk test for the total MRE for each method are listed in Table 9 (with a 95% confidence interval), and it was confirmed that the normal distribution was not valid.  
Therefore, the non-parametric Mann-Whitney U test was performed to confirm significant differences between the two groups (with a 95% confidence interval). This test confirmed that the proposed method had a significantly lower MRE than the conventional method, regardless of the number of markers. Figure 26 shows the mean differences and significance intervals between the conventional and the proposed methods.  
2. Performance analysis according to the number of markers  
To confirm whether the performance of the proposed method improved as the number of markers increased, as in previous studies, MREproposed according to \(k\) was analyzed. As a result of the Kruskal-Wallis test between groups separated by each \(k\) (with a 95% confidence interval), it was confirmed that statistically significantly more accurate registration is performed as \(k\) increases (H = 10.344, \(p=0.006<0.01\)). Table 10 lists the results of this test, which are consistent with results of previous studies.  
Figure 25: Visualized main experimental results: box whiskers (outliers are excluded from this figure).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Statics** & \(p\)**-Value** \\ \hline conventional & 0.862 & \(6.95\times 10^{-6}\) \\ proposed & 0.470 & \(2.16\times 10^{-13}\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Shapiro–Wilk test results for the total MRE.  
To confirm whether this result was equally applicable to all models, MREproposed was split into DM and DA, and this analysis was repeated (with a 95% confidence interval). The results are listed in Table 11. The MRE seemed to decrease as \(k\) increased, but there was no statistically significant difference in the DA (DM: H = 19.719, \(p\) = 5.23 \(\times\) 10\({}^{-5}\) < 0.001, DA: H = 1.866, \(p\) = 0.393 > 0.05).
## 2 Materials and Methods
### Extraction of Volatile Compounds and Solid Phase Micro Extraction  
The headspace (HS) solid phase micro extraction (SPME) technique was adopted for absorption of milk off-flavoring volatile compounds. In this technique, the milk samples were agitated with the addition of salt and the temperature was maintained at 18 \(\pm\) 1 \({}^{\circ}\)C to overcome the remains of flavor compounds. The salt was added to improve the release of volatile compounds that ultimately increased the absorption efficiency of SPME fiber.  
For this study, 10 mL milk sample was taken in a 25 mL flask equipped with cap and Teflon-faced silicone rubber septa (Supelco, Co., Bellefonte, PA, USA). A magnetic stir plate (model PC-220, Corning, NY, USA) was used for stirring the flask containing the milk sample and salt at 1100 rpm 20 min. The volatiles were preequilibrated by adding an internal standard n-tridecane @ 0.5 mg/10 mL of sample after 18 min of stirring. Volatile compounds were extracted using HS-SPME, according to a previously described method in [PERSON] et al. [14]. Three fibers from a flavor collection kit were examined: 75 \(\upmu\)m carboxen/poly-dimethylsiloxane (CAR/PDMS), 100 \(\upmu\)m poly-dimethylsiloxane (PDMS) and 65 \(\upmu\)m poly-dimethylsiloxane/divinilyebene (PDMS/DVB), founded upon its affinity with milk spoilage volatiles and with the symmetry of chromatographic peaks (time exposure: 1 h). When the fiber was chosen, four exposure times (30, 45, 60, and 90 min) were considered, to improve the extraction of volatile compounds.
## 3 Materials and Methods
### Dataset and Experimental Methodology  
The EEG dataset [56] used in this study was sourced from an openly available dataset focused on schizophrenia and was primarily used to classify participants as either schizophrenia patients or HCs based on connectivity patterns. The dataset includes recordings from two groups: individuals diagnosed with schizophrenia (SZ) and healthy controls (HCs). Specifically, the dataset consists of EEG data from a total of 81 participants, including 49 schizophrenia patients and 32 HCs. Each participant completed a button-pressing task designed to evoke event-related potentials (ERPs) to analyze differences in brain connectivity patterns. The experimental task involved three conditions: (1) pressing a button to generate a tone, (2) passively listening to the same tone, and (3) pressing a button without generating a tone. This setup enabled the examination of sensory responses in both internally and externally generated stimuli, a process thought to be disrupted in schizophrenia.  
The EEG signals were recorded from nine key electrode sites (Fz, FCz, Cz, FC3, FC4, C3, C4, CP3, CP4), which were chosen based on their relevance to previous research in schizophrenia-related cognitive dysfunction. The methodology included preprocessing steps to ensure data quality, such as averaged ear lobes, high-pass filtering at 0.1 Hz, baseline correction, canonical correlation analysis, or independent components analysis for noise and artifact removal.  
For this study, the data were split into training and validation sets, with 75% allocated to training and 25% to validation, ensuring that the validation dataset was separate from the training dataset. The classification categories for analysis were \"schizophrenia\" (SZ) and \"healthy control\" (HC), focusing on the connectivity features derived from TE matrices.
###### Abstract  
The Moon-Based Earth Radiation Observatory (MERO) is a new platform, which is expected to advance current Earth radiation budget (ERB) research with better observations. For the instrument design of a MERO system, ascertaining the spatial resolution and sampling scheme is important. However, current knowledge about this is still limited. Here we proposed a simulation method for the MERO-measured Earth top of atmosphere (TOA) outgoing shortwave radiation (OSR) and outgoing longwave radiation (OLR) fluxes and constructed the \"true\" Earth TOA OSR and OLR fluxes based on the Clouds and Earth's Radiant Energy System (CERES) data. Then we used them to reveal the effects of spatial resolution and temporal scheme (sampling interval and the temporal sampling sequence) on the measurement error of a MERO. Our results indicate that the spatial sampling error in the unit of percentage reduces linearly as the spatial resolution varies from 1000 km to 100 km; the rate is 2.5%/100 km for the Earth TOA OSR flux, which is higher than that (1%/100 km) of the TOA OLR flux. Besides, this rate becomes larger when the spatial resolution is finer than 40 km. It is also demonstrated that a sampling temporal sequence of starting time of 64 min with a sampling interval of 90 min is the optimal sampling scheme that results in the least temporal sampling error for the MERO system with a 40 km spatial resolution, note that this conclusion depends on the temporal resolution and quality of the data used to construct the \"true\" Earth TOA OSR and OLR fluxes. The proposed method and derived results in this study could facilitate the ascertainment of the optimal spatial resolution and sampling scheme of a MERO system under certain manufacturing budget and measurement error limit.  
spatial resolution; temporal sampling scheme; measurement error; Moon-Based Earth Radiation Observatory (MERO) 2021
# Computational Wear Prediction of TKR with Flatback Deformity during Gait
## 2 Methods
### Wear Prediction by Using Archard's Law  
To calculate the wear depth on the tibial insert, it was numerically formulated using Archard's Law. Archard's Law is an equation introduced to evaluate the linear wear depth perpendicular to the wear surface between two metal surfaces sliding relative to each other. The formula known as Archard's Law of wear is as follows [22]:  
\[H=K_{w}pS \tag{1}\]  
where \(H\) is the linear wear depth, \(K_{w}\) is an experimentally determined wear factor, \(p\) is contact pressure, and \(S\) is the sliding distance. For the wear factor \(K_{w}\), a value of \(2.64\times 10^{-7}\) mm\({}^{3}\)/Nm is generally used in a previous study [11].  
In the formula mentioned above, the factor that most influences the wear depth is the contact pressure [13; 23]. Therefore, in this study, the wear depth and wear area will be predicted through the magnitude and distribution of the contact pressure for one gait cycle.  
The process for deriving the wear depth was carried out according to the sequence shown in Figure 8. After performing a finite element nonlinear transient analysis for one gait cycle, the contact pressure and sliding distance are obtained from the output file, respectively. In the finite element software ABAQUS used in this paper, the contact pressure and sliding distance is obtained from CPRESS and CSLIP of the field output file, which is the ABAQUS result file. where CSLIP is the sum of the tangential distances of the node of the tibial insert including the slave surface. Therefore, for every time-step, the INCSLIP value, which is the value of subtracting the CSLIP of the previous time-step from the CSLIP of the current time-step, is applied. By substituting the obtained contact pressure and sliding distance into Equation (1), the wear depth at each node was derived, and the wear depth was multiplied by 500,000. The position of each node was updated by the derived wear depth after 500,000 cycles, and the worn contact surface shape was updated. By repeating this process 10 times, the wear depth after 5,000,000 gait cycles can be derived. Additionally, the position update cycle reflecting the wear depth of each node was applied to the results of a previous study that the result of updating node position every 500,000 cycles has convergence [10].
## 3 Extraction of Echo Envelope Fluctuation Features
### Experiment Study of Benchmark Model and Rock
#### 3.2.2 Experimental Results  
The procedure for processing the test data for the echo envelope fluctuation feature of the benchmark model is:  
* Filter the data by a bandpass filter to minimize the interference;
* Calculate the envelope of the echo signal;
* Normalize the echo envelope for magnitude consistency;
* Differentiate the echo envelope;
* Calculate the fluctuation intensity and the standard deviation;
* Process the echo in whole azimuths.  
Figure 6a is the echo envelope with LFM 20-40 kHz at a certain azimuth, and the pulse length is 1 ms; Figure 6b is the echo envelope fluctuation after differentiation; Figure 7a is the echo envelope with LFM 40-80 kHz, the pulse length is 1 ms; and Figure 7b is the echo envelope fluctuation after differentiation. Results show that, after differentiation, the energy of geometric scattering has decreased, and the fine structure of the high fluctuation envelope has been enhanced.  
Then, the echo data in whole azimuths were processed, and the echo envelope fluctuation features with varied carrier frequency, pulse length, and azimuth were analyzed.  
Figure 6: (**a**) Echo envelope with LFM 20–40 kHz frequency; (**b**) gradient of the echo envelope from (**a**).  
Figure 7: (**a**) Echo envelope with LFM 40–80 kHz frequency; (**b**) gradient of the echo envelope from (**a**).
# Performance Validation of Control Algorithm Considering Independent Generator Torque Control in PCS
## 4 Experimental Validation
### Environment of Wind Tunnel Test  
The wind tunnel tests were conducted at the wind tunnel center located at Jeonbuk National University in South Korea. Figure 10 illustrates the experimental setup and the wind turbine testing environment for the target wind tunnel experiment. The wind generation space created by the wind generation device has a length of approximately 40 m in the wind direction and about 12 m in the vertical direction of the wind. The proposed control algorithm was designed to have the same structure as the scaled wind turbine model and was uploaded via commercial PLC programming. Additionally, the setup was configured to allow for data verification and storage through remote monitoring.
## 1 Introduction
### Research Structure  
This paper is organized as follows: The first section provides an overview. Section 2 delves into the conceptual model, while Section 3 presents the mathematical formulation of the proposed method, as well as a flowchart that demonstrates the integration of the LSTM-based deep learning network. Section 4 focuses on the simulation results and their discussion. Finally, Section 5 summarizes the main conclusions derived from this study.
## 4 Modelling Approach and Results
### Response Characteristics of V-Structures Subjected to Localized Blast Loads  
The same trends of increasing deformation (larger out-of-plane displacements and greater deformed areas) with increasing internal angle and increasing radius were present in the simulations, confirming the experimentally observed trends. The computational simulations predicted slightly more localized permanent deformation in both the ridge and crosscut profiles, as illustrated in Figure 20.  
A typical set of transient deformation profiles from the computation simulations is shown in Figure 21 (in the crosscut direction). The discrepancy is more apparent in the crosscut profiles and is present in the transient predictions and the final deformed profile. The higher elastic recovery exhibited experimentally by the 120\({}^{\circ}\) V-structures (compared to the 105\({}^{\circ}\) V-structures) was also present in the simulations, confirming this is a feature of the geometric stiffness of the V-structures due to the internal angle.  
Figure 21: Simulated transient crosscut deformed profiles for a 120\({}^{\circ}\) V structure, 32 mm tip radius, subjected to a 20 g detonation.  
Figure 20: Final deformed profiles for V-structures subjected to blast, comparing experimental results with computational simulations: (**a**) 105\({}^{\circ}\) V, 32 mm tip radius, 15 g detonation, ridge profile; (**b**) 120\({}^{\circ}\) V, 32 mm tip radius, 20 g detonation, cross-cut profile.  
The simulations were not able to reproduce the slight asymmetry in the panel response (observed most noticeably in the cross-cut direction) because they used a quarter-symmetry model. The small asymmetries in the structural response are attributed to the sensitivity of the buckling deformation mode to small variations, such as slight misalignment of the explosive charge relative to the centre of the structure and small variations in the V manufacturing (within the manufacturing tolerance but resulting in small variations in the ridge).
## 4 Simulation Studies
### Hop Count  
A hop count refers to the total number of intermediate devices, such as nodes, that a data packet must traverse between its source and destination. Each node along the data path represents a hop, transferring data from one source to another [27].
# Satellite IoT Edge Intelligent Computing: A Research on Architecture
## 2 Related Work
### Related Research on Edge Intelligent Computing  
Edge intelligent computing combines edge computing with deep-learning technology, which is mainly used in the field of image processing [28; 29; 30; 31; 32]. Article [28] proposes Edgent, a collaborative and on-demand deep neural network (DNN) collaborative reasoning framework with device edge synergy. DNN reasoning is accelerated by early exit at the appropriate intermediate DNN layer to further reduce computational delay. An offloading strategy in the edge intelligent computing environment is designed to optimize the deep-learning performance of the Internet of Things in [29]. An energy-saving communication based on deep-learning-based edge computing is proposed in [30]. The authors have designed and implemented an energy-efficient IoT camera named CamThings, which reduces power consumption by transmitting only images of interest classified using edge computing. In paper [31], the author expounds the importance of edge intelligence, summarizes the challenges and opportunities brought by edge intelligence, and demonstrates the implementation of convolutional neural network based (CNN-based) objects on embedded edge devices on unmanned aerial vehicles (UAVs). Similarly, the article [32] emphasizes the importance of edge intelligence and discusses the future challenges of artificial intelligence algorithms running on IoT devices at the edge of the network under power constraints.
## Appendix A General Considerations in RFE Design
### Q Related to BER  
The quality factor \(Q\) in OOK receivers equals \(SNR^{\frac{1}{2}}\).  
\[p_{BE}=\frac{1}{2}\cdot erfc\left(\frac{Q}{\sqrt{2}}\right)=\frac{1}{2}\cdot erfc \left(\sqrt{\frac{SNR}{2}}\right) \tag{10}\]  
Figure 11 visualizes the steep decrease of \(P_{BE}\) with the moderate values of \(Q\), including also the useful approximation ([42] ch. 4.5.1.)  
\[p_{BE}\approx\frac{\mathrm{e}^{-\frac{Q^{2}}{2}}}{Q\cdot\sqrt{2\pi}} \tag{11}\]  
Figure 12: Input-referred current noise densities for several commercial transimpedance amplifiers from the data sheets. Circles represent housed ICs, while filled dots are for bonded dies. Dotted blue trend line follows \(B^{0.5}\).
# An Algorithm for Rescheduling of Trains under Planned Track Closures
## 4 Joint Problem of Train Rescheduling and Track Closure Planning
### Representation of Track Closures  
Some maintenance tasks of a railway infrastructure require track closures when no train is allowed to use servicing tracks. Consequently, track closures may affect timetables. Closures can be brought in periodically during a day or night for short periods, or they may be planned for longer periods: days, weeks, or even months. They can be assumed as a factor competing for a track possession together with train runs included in a timetable, as in Figure 2.  
Let \(\mathbf{C}=\{1,2,\ldots,C\}\) be a set of \(C\) track closures envisaged for the implementation. It is composed of \(C^{\text{OB}}\) obligatory closures and \(C-C^{\text{OB}}\) optional closures, i.e., \(\mathbf{C}=\{1,2,\ldots,C^{\text{OB}},C^{\text{OB}}+1,C^{\text{OB}}+2,\ldots,C\}\). The obligatory closures have to be included in the maintenance plan, unlike latter ones, which can be discarded if they interfere greatly the original timetable. An individual closure \(n\in\mathbf{C}\) is defined by a tuple \(S_{n}^{G}=<R_{n}^{\text{c}},t_{n}^{\text{CE}},t_{n}^{\text{CLS}},T_{n}^{ \text{CLS}}>\). All closures information is gathered in \(\mathbf{S}^{\text{G}}=[S_{n}^{\text{G}}]_{n\in\mathbf{C}}\). A binary symmetric matrix \(\mathbf{R}_{n}^{\text{C}}=[R_{n,i,j,k}^{\text{C}}]_{i,j\in\mathbf{V},k\in \mathbf{K}\setminus\{0\}}\) informs on track possessions by closures, i.e., the current entry \(R_{n,i,j,k}^{\text{C}}=1(0)\) if track \(k\) between stations \(i\) and \(j\) is planned to be possessed by the \(n\)th track closure (otherwise). Other elements of the tuple comprise time information on closure \(n\) that is \(T_{n}^{\text{C}}\), \(t_{n}^{\text{CES}}\) and \(t_{n}^{\text{CLS}}\) represent, respectively, its duration, the earliest starting time and the latest starting time. If parameters \(t_{n}^{\text{CES}}\) and \(t_{n}^{\text{CLS}}\) have the same value, the start time of the closure is firm. Otherwise, it can vary between those two values. Moreover, closures may exist which have to be performed according to a given order expressed by a matrix \(\mathbf{O}=[o_{n,m}]_{n,m\in\mathbf{C}}\), where \(o_{n,m}=1(0)\) if closure \(n\) has to be completed directly before launching closure \(m\).
## 4 Time-Domain and Time-Frequency-Domain Feature Extraction Based on the LMD Method
### Selection of Characteristic PFs Based on Pearson Correlation Coefficient  
In general, a series of PFs characterizing different feature scales can be obtained by LMD of the vibration signals. To quantify the correlation degree between PFs and the original signal, the Pearson correlation coefficient is introduced, as shown in Equation (12). The Pearson correlation coefficient is a value between \(-1\) and \(1\), with the value tending to \(-1\) or \(1\) when the correlation between the two variables strengthens and to \(0\) when the correlation between the two variables weakens. In general, the correlation coefficient less than or equal to \(0.2\)[58] implies that the two variables are not correlated; this principle is adopted in this paper. If the correlation coefficient between a PF component and the original data is less than or equal to \(0.2\), the PF component is discarded and the retained PFs are called the characteristic PFs. After the selection of Pearson correlation coefficients, the noise and the environmental vibration of the original signal were discarded and the components useful for damage identification were retained.  
\[\rho_{X,Y}=\frac{\text{cov}(X,Y)}{\sigma_{X}\sigma_{Y}} \tag{12}\]  
where \(X\) represents the original data and \(Y\) represents the PFs.  
Figure 4: Flow chart of LMD.