esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Description  
The NIMV system which is developed from the conventional IMV system is shown in Figure 1. In the detail of the hydraulic circuit, the NIMV system uses three proportional valves (\(K_{sat}\), \(K_{ab}\), \(K_{bl}\)), combines with one directional valve (\(K_{d}\)) to control the boom cylinder. Moreover, a main pump driven by an electric motor is used to provide the flow rate to the system under the protection of a check valve. The maximum pressure of the system can be guaranteed through a relief valve. The NIMV system that applies to the boom cylinder has four metering modes including power extension mode (PE), power retraction mode (PR), high-side regeneration extension mode (HSRE), and low-side regeneration retraction mode (LSRR) [31; 32]. Depending on the working conditions, a suitable metering mode is selected to operate the cylinder. The working principle of the four metering modes is described in Figure 2 and the valve conductance of each mode is shown in Table 1.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Mode** & \(K_{sat}\) & \(K_{ab}\) & \(K_{bt}\) & \(K_{d}\) \\ \hline PE & Fully open & Closed & Control & Right side \\ PR & Fully open & Closed & Control & Left side \\ HSRE & Fully open & Control & Closed & Right side \\ LSRR & Closed & Control & Fully open & Right side \\ \hline \hline \end{tabular}
\end{table}
Table 1: The conductivity of valves on four metering modes.  
Figure 1: The configuration of the NIMV circuit.  
Figure 2: Metering modes of the NIMV, PE mode (**a**), HSRE mode (**b**), PR mode (**c**), and LSRR mode (**d**).
# Transfer Accuracy of Two 3D Printed Trays for Indirect Bracket Bonding--An In Vitro Pilot Study
###### Abstract  
The present study aimed to investigate the impact of hardness from 3D printed transfer trays and dental crowding on bracket bonding accuracy. Lower models (no crowding group: Little's Irregularity Index (LII) \(<\) 3, crowding group: LII \(>\) 7, \(n\) = 10 per group) were selected at random, digitized, 3D printed, and utilized for semiautomated virtual positioning of brackets and tubes. Hard and soft transfer trays were fabricated with polyel printing and digital light processing, respectively. Brackets and tubes were transferred to the 3D printed models and altogether digitized using intraoral scanning (IOS) and microcomputed tomography (micro-CT) for assessment of linear and angular deviations. Mean intra- and interrater reliability amounted to 0.67 \(\pm\) 0.34/0.79 \(\pm\) 0.16 for IOS, and 0.92 \(\pm\) 0.05/0.92 \(\pm\) 0.5 for the micro-CT measurements. Minor linear discrepancies were observed (median: 0.11 mm, Q1-Q3: \(-\)0.06-0.28 mm). Deviations in torque (median: 2.49\({}^{\circ}\), Q1-Q3: 1.27-4.03\({}^{\circ}\)) were greater than angular ones (median: 1.81\({}^{\circ}\), Q1-Q3: 1.05\({}^{\circ}\)-2.90\({}^{\circ}\)), higher for hard (median: 2.49\({}^{\circ}\), Q1-Q3: 1.32-3.91\({}^{\circ}\)) compared to soft (median: 1.77\({}^{\circ}\), Q1-Q3: 0.94-3.01\({}^{\circ}\)) trays (\(p\) \(<\) 0.001), and torque errors were more pronounced at crowded front teeth (\(p\)\(<\) 0.05). In conclusion, the clinician should carefully consider the potential impact of hardness and crowding on bracket transfer accuracy, specifically in torque and angular orientation.  
bonding tray; 3D-printing; intraoral scanning; shore hardness +
Footnote †: journal: Applied Sciences
## 7 Discussion  
The application of deep learning (DL) has significantly advanced the prediction and classification of epileptic seizures, particularly in accurately identifying ictal events. In contrast to traditional machine learning (ML) approaches that depend on handcrafted features, DL models can automatically learn complex patterns from raw EEG data, including both temporal and spectral dynamics. This capacity to discover high-level representations directly from input signals has proven especially valuable for detecting seizure onset, where subtle fluctuations in signal properties may escape manual analysis. The performance of DL-based seizure prediction systems is closely tied to the nature and quality of feature representation, with spectral-domain inputs demonstrating particular promise in capturing ictal-specific signatures.
## 2 Materials and Methods
### DNA Isolation and 165 rRNA Sequencing  
DNA was extracted from five digestate samples (M100, JA100, M30 JA70, M50 JA50 and M70 JA30) by the QIAamp PowerFecal DNA Kit (Qiagen, Hilden, Germany), according to the instructions of the standard protocol provided by the manufacturer. After DNA isolation, amplification of genes coding for 165 rRNA was performed. For this reaction, following primers were used enabling the dual-index bar-coding method: 515F Forward primer 5'-AATGATGATGGCGACCACCGGAGATTCACACTATGGAATTGTGTGGCCAGCGCGTAA-3' and 806R Reverse primer 5'-CAAGCAGAAGAGCCGCATACGAGATAGTCAGCCGACTACHVGGGTWTCTAAT-3' [39]. This primer set targeted the hyper-variable region V4 of 165 rRNA, allowing the detection of microbial taxa present in each sample [40]. PCR amplification was performed according to the Earth Microbiome Project protocol [41] in a reaction volume of 25 \(\upmu\)L with 10 \(\upmu\)L PlatinumTM Hot-Start PCR Master Mix (Thermo Fisher Scientific, USA), 200 nM primers and 2 \(\upmu\)L isolated DNA. The PCR program consisted of a hot start at 94 \({}^{\circ}\)C for 3 min, followed by 35 cycles of denaturation at 95 \({}^{\circ}\)C for 45 s, primer annealing at 52 \({}^{\circ}\)C for 60 s with a 50% thermal ramp, and extension at 72 \({}^{\circ}\)C for 90 s. PCR was completed with a final extension at 72 \({}^{\circ}\)C for 10 min. After PCR, amplification products were purified using AMPure XP Beads (Beckman Coulter, Brea, CA, USA) according to the protocol provided by the manufacturer. Samples were normalized using Qubit 4 (Thermo Fisher Scientific, Waltham, MA, USA) and their quality was checked using Fragment Analyzer (Agilent Technologies, Santa Clara, CA, USA). The final library was subjected to NGS using the MiniSeqM MidOutput Reagent Kit (2 \(\times\) 150 paired-end sequencing) and Illumina MiniSeq sequencer according to the manufacturer's instructions (Illumina, San Diego, CA, USA).
## 2 Materials and Methods
### Wavelength Selection Based on BFWA  
#### 2.3.1 SPA, UVE and GA  
Each of extracted spectra consists of 94 wavelengths, which contain redundant information between adjacent wavelengths. Selecting the optimal wavelengths for developing more robust prediction models and for implementing a multispectral imaging system is desirable. SPA and UVE are two traditional methods used for wavelength selection. SPA is a forward selection method and aims to solve the co-linearity problem with minimal redundancy, based on its principle of establishing the candidate subsets of the most representative variables according to a sequence of projection operations relating the columns of the instrumental response matrix [15]. This method starts from a wavelength. Then, the wavelength is cyclically projected onto an unselected wavelength to incorporate a new one at each iteration. Each wavelength containing more characteristic information will be included in the optimal wavelength set until a specified number of wavelengths are reached. UVE is a method of variable selection based on a stability analysis of the regression coefficient. UVE can eliminate the wavelength variables, which make less of a contribution to the prediction ability of the model. Employing the variables selected by UVE for modeling can avoid a model over-fitting, and usually improves its predictive ability [16]. As a traditional and widely-used group intelligent algorithm, GA applies a \"survival of the fittest\" approach for wavelength selection. GA is based on the natural selection and genetic mechanisms of the biological world and uses selection operators, exchange, and mutation to achieve the best solution [10]. In this study, SPA, UVE and GA algorithms were used for optimal wavelength selection to compare the model's performance with the proposed BFWA algorithm.
## 3 Results and Discussions
### Model Calculations
The calculated results deduced from the temporal profile of \(n_{1}\) were depicted as solid curves in Figure 5. The parameters about \(W_{\rm nr}\) and \(A_{\rm r}\) were estimated from the corresponding ones listed in Table 2, and the set of parameters used in the rate equation analysis was summarized in Table 3. As clearly shown, the calculations can reproduce the experimental results for all the samples, and their temperature dependences are described well in the framework of our model, that is, the assumed \(W_{\rm nr}\) works adequately to describe the thermal quenching of the \(Y_{1}^{\prime}\)-\(Z_{1}^{\prime}\) emissions. In addition, we extracted two relaxation times corresponding to the fast and slow decay components from the calculated curves of \(n_{1}\), and we plotted them in Figure 6a-c as solid and dashed lines. The extracted relaxation times show the similar temperature dependences with the experimentally obtained ones. The agreements between the experimental and calculated results support the validities of our model and assumptions, and the model calculations successfully explain the energy transfers and its temperature dependence especially of the target energy level \(Y_{1}^{\prime}\). Further, the other non-radiative relaxation rate, \(W_{\rm d}\) can also estimated as listed in Table 3, and the \(W_{\rm d}\) in Sample C is reduced to 1/70 of \(W_{\rm d}\) in Sample A. It is found that the reduction of Er-concentration is effective to suppress the temperature-insensitive non-radiative rate as well as the temperature sensitive non-radiative rate. Although the excellent agreement between the observations and model calculations by a simple model in Figure 7 was obtained, some assumptions and the parameters that have not been obtained experimentally were used. Therefore, further detail experimental investigation to specify the physical origins of those non-radiative relaxations and the analysis by the microscopic theory such as Inokuti-Hirayama model [(39; 40)] are required. \[\frac{dn_{1}}{dt} = W_{21}n_{2}-\left(A_{\rm r}+W_{\rm nr}\right)n_{1}-\left(1-\frac {n_{\rm d}}{N_{\rm d}}\right)W_{\rm d}n_{1}+C_{41}n_{4}n_{0}-C_{14}n_{3}n_{1},\]0 Energy diagram for the rate equation analysis compose of the ground (\(Z_{1}\), \(Z_{1}^{\prime}\)), the first excited (\(Y_{1}\), \(Y_{1}^{\prime}\), \(Y_{3}^{\prime}\)), and the UC states.
# Creating a Remote Choir Performance Recording Based on an Ambisonic Approach
## 4 Realization of Remote Music Recording
### Technical Considerations  
It is of importance to consider in such a recording hardware specifications. Ideally, each musician should be recorded in a controlled recording environment with hi-fidelity studio equipment. Even more welcome for an Ambisonic realization would be creating such a recording with a spherical microphone grid, thus creating a holographic sound image [71; 72; 73]. This would allow reproducing the directivity patterns of the recorded instruments with the highest realism.  
The other solution would require mono recordings, preferably anechoic; this would allow for simulating the virtual acoustics as well as directivity of audio objects in the Ambisonic domain. The latter approach was employed in the aforementioned \"Virtual Cathedral\" recording [42]. However, due to at-home COVID-19 pandemic conditions, the individual tracks are far from perfect. Only a few choir members on this recording had a  
Figure 2: Pre-sync tracks to four claps from the beginning of the recording [60].  
semi-professional condenser microphone at home (i.e., Behringer C-1U, Genesis Radium 200, Zoom H2n, Samson C01U Pro, Maono AU-A03); the vast majority could only use smartphones (iPhone 11, iPhone 7, Xiaomi, and other mobile devices with their internal microphones (i.e., iPad 5, Dell Inspiron 15, Samsung Galaxy A5/Samsung Galaxy S7, Samsung Galaxy Note 8, Samsung A40, Nikon D3300, Canon PowerShot G7X mk1, Huawei P20 Pro). These devices have different microphone characteristics, as well as each recording was realized in different acoustic conditions, including background noises such as household appliances working in the distance and passing vehicles. However, because of the more directional characteristics of the microphone, fewer background noises were picked up in the signal. Overall, due to the large variability of sound material quality, each recording required de-noising and pitch, timing, and level corrections (as described in further chapters). A remark may, however, be included here, the literature sources indicate that smartphones are used in music learning and recording, and that there exist records produced entirely with a smartphone, iPad, carrying recording software, etc. [74; 75].
## 4 Dynamic Performance of the New Semiactive HEM
### Vibration Isolation Performance of the Newly Designed HEM  
Once the change in the length of the inertia track is found, Equation (29) can be used to determine the rotation angle of the moving plate screw. The damping of the inertia track depends on the length of the inertia track. The shorter the length is, the larger the damping is. Consider an engine supported by the proposed HEM, as shown in Figure 2a, the optimal length of inertia track should minimize the transmissibility \(T_{f}(L_{\rm i},\omega)\):  
\[T_{f}(L_{\rm i},\omega)=\left|\frac{k_{\rm T}(j\omega)}{-M\omega^{2}+k_{\rm T} (L_{\rm i},j\omega)}\right| \tag{40}\]  
The optimal length of the inertia track is shown in Figure 9a, the optimal dynamic stiffness of the new semiactive HEM is shown in Figure 9b, and optimal transmissibility is shown in Figure 9c. At the middle-frequency band, the length of the inertia track is the largest, and this indicates the weak damping of the inertia track is preferred. The inertia  
Figure 8: Comparison between dynamic stiffness of the simulated and linear models under the inertia track lengths \(L_{\rm i}=1.0L_{0}\), \(L_{\rm i}=0.5\)\(L_{0}\) and \(L_{\rm i}=0.2\)\(L_{0}\), \(P_{2}\), \(P_{1}\) are fixed frequency points.  
Figure 7: Force–displacement cyclic curves for typical frequencies of hydraulic mounts with inertia track length \(L_{\rm i}=1.0L_{0}\). (**a**) Frequency = 10 Hz; (**b**) frequency = 15 Hz; (**c**) frequency = 20 Hz; (**d**) frequency = 25 Hz; (**e**) frequency = 30 Hz; and (**f**) frequency = 60 Hz.  
track is almost closed at lower and higher frequency bands to provide large damping and thus improve the vibration isolation performance. Generally, from the optimal length of the inertia track in Figure 9a, experts may choose an on-off strategy to control the damping, which means switching between the weakest and largest damping, for better vibration isolation performance. By the way, for a passive HEM whose parameters are not changeable, there must be a compromise proper design of the inertia track for wide-band vibration isolation performance, so experts can use the new proposed design to determine the proper geometry parameters when designing an HEM.
## 2 Materials and Methods
### Development of Models
#### 2.5.1 Decision Trees  
Decision Trees (DT) predict the value of a variable by learning decision rules inferred from the features. The deeper the tree, the more complex the decision rules and the fitter the model. DT require little data preparation when compared to other techniques that require  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Statistics** & \(T_{R\_Exp}\) **(kNm)** & \(f_{c}\) **(MPa)** & \(A_{c}\) **(m\({}^{2}\))** & \(A_{fy_{g}}\frac{A_{f_{n}}}{s}\) \\ \hline Mean & 88.7 & 46.7 & 0.153 & \(3.5\times 10^{7}\) \\ Minimum & 9.0 & 14.3 & 0.044 & \(1.1\times 10^{6}\) \\ Maximum & 521.3 & 109.8 & 0.366 & \(5.3\times 10^{8}\) \\ Standard & 96.9 & 23.0 & 0.094 & \(6.2\times 10^{7}\) \\ deviation & 57.0 & 36.5 & 0.120 & \(1.7\times 10^{7}\) \\urtosis & 6.0 & \(-0.2\) & \(-0.029\) & 32.3 \\ Skewness & 2.4 & 0.9 & 0.958 & 5.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical measures of the variables for the training set.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Statistics** & \(T_{R\_Exp}\) **(kNm)** & \(f_{c}\) **(MPa)** & \(A_{c}\) **(m\({}^{2}\))** & \(A_{fy_{g}}\frac{A_{f_{n}}}{s}\) \\ \hline Mean & 105.8 & 46.6 & 0.156 & \(4.0\times 10^{7}\) \\ Minimum & 9.1 & 16.8 & 0.044 & \(1.2\times 10^{6}\) \\ Maximum & 437.9 & 93.9 & 0.361 & \(2.4\times 10^{8}\) \\ Standard & 109.9 & 22 data normalization and other transformations. It also uses a white box model, allowing an easy explanation of the conditions by Boolean logic. However, DT can create over-complex trees that overfit and do not generalize well to the data [71]. This problem was avoided by setting a maximum depth for the trees, as referred in [71]. The input data is recursively partitioned into two smaller regions called nodes, based on a series of decision rules until a stopping criterion is reached (in this case, the maximum depth of the tree). Samples with similar target values are grouped together, and then the response is modelled by the mean of the \(y\) variable in each region. A tree can be seen as a piecewise constant approximation. The variable and split point are chosen in order to achieve the best fit. Let the data at node \(m\) be represented by \(R_{m}\) with \(n_{m}\) samples. For each candidate split \(\theta=(j,\,t_{m})\) consisting of a feature \(j\) and a threshold \(t_{m}\), the data is partitioned into \(R_{m}^{left}(\theta)\) and \(R_{m}^{right}(\theta)\) subsets such that:  
\[R_{m}^{left}(\theta)=\left\{(x,y)\mid x_{j}\leq t_{m}\right\} \tag{1}\]  
\[R_{m}^{right}(\theta)=R_{m}\backslash R_{m}^{left}(\theta) \tag{2}\]  
The quality of a candidate split of node \(m\) is then computed using a loss function \(H\):  
\[G(R_{m},\theta)=\frac{n_{m}^{left}}{n_{m}}H\left(R_{m}^{left}(\theta)\right) +\frac{n_{m}^{right}}{n_{m}}H\left(R_{m}^{right}(\theta)\right) \tag{3}\]  
The parameters are selected in order to minimize the loss function:  
\[\theta^{*}=arg\,\,min_{\theta}\,G(R_{m},\theta) \tag{4}\]  
This is carried out recursively for the two subsets until the maximum depth is reached.
# A Subjective Study on User Perception Aspects in Virtual Reality
## 3 Study Description and Experiment  
### Factors and QoE Aspects  
A summary of factors and QoE aspects considered in this paper is shown in Figure 2. On the content delivery path, 360 video is affected by various stages, from a server, networks, to a rendering device. As shown in Figure 2, our study aims to examine the influences of four factors (i.e., content motion, encoding parameters, rendering device, and rendering mode) on four QoE aspects, namely perceptual quality, presence, cybersickness, and acceptability.  
In this study, we use Mean Opinion Score (MOS) as an indicator of the perceptual quality aspect, which is similar to most previous studies [16; 24; 32]. Based on Recommendation ITU-T P.800.2 [33], the MOS of a video is calculated by the average of viewers' scores collected via a subjective experiment. Since the presence and cybersickness are new QoE aspects, MOS is currently also deployed in our study to represent these two aspects. Regarding the acceptability aspect, we employ the most commonly used indicator called acceptability rate as mentioned in Section 1.  
In the next part, the factors investigated in this study are described in more detail.
# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 3 Results
### Comprehensive AIoT Integration in Aviation on the Base of WDAR, FMDE, and G-FMDE Synergy
* The extensive data collection and sharing capabilities of this system may necessitate careful consideration of regulatory requirements, particularly regarding data privacy and cross-border data transmission. As AIoT technologies continue to evolve, we can anticipate further advancements in this architecture:  
* The adoption of 5G technology could significantly increase data transmission speeds and reduce latency, enabling even more real-time analyses and decision-making. * Implementing edge computing capabilities within the aircraft systems could allow for more immediate data processing and reduce the load on ground-based systems. * The development of more sophisticated AI and machine learning algorithms could further enhance the predictive capabilities of the system, potentially leading to fully automated maintenance scheduling and decision-making processes. The aircraft communication architecture featuring WDAR, FMDE, and G-FMDE options represents a significant leap forward in the application of AIoT to aviation health monitoring. By increasing the volume of data collected, providing advanced transmission capabilities, and enabling integrated analyses across multiple systems, this architecture lays the groundwork for a new opportunity for predictive maintenance, enhanced safety, and operational efficiency in aviation.
# A Similarity Model of Specific Heat Loss Determined by Dimensional Analysis
## 4 Experimental Data  
For the purpose of obtaining data on specific heat loss, two directly-buried heat networks were subjected to measurements of the selected relevant physical quantities. The experiments were carried out on a DN65 twin pipe pre-insulated pipeline network and on a network consisting of DN100 pipes. The temperature gradients in the two pipeline networks were 85/50 \({}^{\circ}\)C. The external diameter of the DN65 steel pipe was \(d_{2}=76.1\) mm, and for the DN100 steel pipe it was \(d_{2}=114.3\) mm (Figure 1). The external diameter of the insulated DN65 pipe was \(d_{3}=140\) mm, and for the DN100 pipe it was \(d_{3}=200\) mm. The insulation of both pipelines was of Class A, freon-free PUR foam (EN 253), with a protective jacket made of high-density polyethylene (HDPE) (EN 253). The burial depths of the feed and return pipes in both networks were identical: \(H=H_{1}=H_{2}=0.97\) m. The distance between the axes of the pipes in the DN65 network was \(C=0.262\) m, while for the DN100 network it was \(C=0.322\) m. The pipes were buried in a concrete bed, surrounded by sand and covered with a gravel layer. In the DN65 network, there was a clay layer (\(\lambda_{\rm s}=1.5\) W\(\cdot\)m\({}^{-1}\)\(\cdot\)K\({}^{-1}\)) above the gravel layer, while the DN100 network was covered by a layer of dry soil (\(\lambda_{\rm s}=0.9\) W\(\cdot\)m\({}^{-1}\)\(\cdot\)K\({}^{-1}\)). The thermal conductivity coefficient for the insulation made of PUR foam was \(\lambda_{\rm in}=0.027\) W\(\cdot\)m\({}^{-1}\)\(\cdot\)K\({}^{-1}\), based on the recommendations of the manufacturer of the PIPECO insulation.  
The heat losses required for the creation of a similarity model were identified by applying the balance method. Temperatures at the system's inlet and outlet were measured using an MI9060 device. The measurement accuracy was \(\pm 0.01\)\({}^{\circ}\)C in a temperature range from \(-200\)\({}^{\circ}\)C to \(+850\)\({}^{\circ}\)C, with \(0.001\)\({}^{\circ}\)C resolution. A volumetric flow rate of the water was measured, using an ultrasonic flow rate meter with an accuracy of \(\pm 0.3\%\) of the measured value.  
For the DN65 heat network, the measurements were carried out at three different ambient temperatures: \(t_{\rm e}=7.5\), \(10\) and \(15\)\({}^{\circ}\)C. As for the DN100 network, the ambient temperatures were: \(5\), \(9\) and \(13\)\({}^{\circ}\)C. At each temperature \(t_{\rm e},36\) measurements were carried out. For the purpose of further analysis, the values of the parameters obtained from a total of 216 measurements were available. Outliers were removed from the set of calculated values for \(q_{l}\). Therefore, when deriving the parameters of a criterial equation, two measurements on the DN65 network and three measurements on the DN100 network were not taken into account.