esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Universal GNSS Channel Design Decisions
#### 2.1.1 Carrier  
Assuming that all RF signals are taken down to a common Intermediate Frequency (IF), it then becomes possible to track any GNSS signal with the proposed universal channel. In order to accommodate most GNSS signals, a 30 MHz processed bandwidth appears to be a good compromise. This imposes a 60 MHz real sampling frequency and a 15 MHz Intermediate Frequency (IF), common to all RF bands. This architecture is thus compliant with all open signals.  
A local carrier complex oscillator (namely a pair of sinusoidal 64-point waveforms in phase quadrature and encoded on 4 bits) is used to convert the IF signal down to baseband. Furthermore, in order to preserve a low architecture complexity, a signed multiplication optimization is proposed: \(Y\) bits \(\times\)\(Z\) bits \(=(Y+Z-1)\) bits. This is true only if the minimal twos complement value is never used on both operands, e.g., 0b0000 \(\times\)0b0000 would not be permitted in such 4-bit multiplications.  
The flexibility offered by this frequency down-conversion allows simplifying the RF front-end. Indeed, a common RF front-end could be used to manage signals on carriers nearby one another, such as:  
Figure 1: Tracking Channel Simplified Architecture.  
Galileo E5B and Beidou B2-I (and eventually B2b) on 1207.14 MHz, as well as 1202.025 MHz for GLONASS L3 signals.
* Beidou B3 on 1268.52 MHz as well as Galileo (and QZSS) E6 signal on 1278.75 MHz: \(\bigcirc\) In order to preserve both signals bandwidth integrity, the RF front-end would take 1273,635 MHz down to IF. Assuming IF = 15 MHz, Beidou B3 would manage 20 MHz for its QPSK (10) signal as well as 10 MHz for the Galileo E6B/C BPSK (5) signals. \(\bigcirc\) This simplified approach could only process half of Galileo E6A BOC(10,5) and Beidou B3-Ad/Ap BOC(15,2.5) signals, considering the current 30 MHz bandwidth.
* Beidou B1-1 on 1561.098 and B1-2 on 1589.74 MHz around GPS L1 (and others) on 1575.42 MHz: \(\bigcirc\) An alternate approach would be to implement a 14.322 MHz sub-carrier, thus dealing with both Beidou signals as sBOC \((14,2)\), just as with Galileo E1A cBOC \((15,2.5)\), but with a slight sensitivity loss caused by superposing these two signals, each having their spreading code providing \(>\)20 dB isolation.  
More importantly, dealing with the several frequency channels of the GLONASS FDMA scheme requires a Numerically Controlled Oscillator (NCO) frequency span over several MHz, _i.e._, \([-7,\ 6]\cdot 0.5625=7.3125\) MHz for L1 OF and equivalently 5.6875 MHz for L2 OF:  
\[f_{\rm L1 OF}=1602+0.5625\cdot[-7,\ +6]\ \ \mathrm{MHz}\ f_{\rm L2 OF}=1246+0.437 5\cdot[-7,\ +6]\ \ \mathrm{MHz} \tag{7}\]  
This NCO span represents a large increase compared to the traditional \(\pm\)10 kHz required for Doppler removal for a high-dynamics receiver.
# UnifiedCut: A Simple and Efficient Neural Model for Thai, Burmese and Khmer Word Segmentation
## 4 Experiments
### Main Results
#### 4.3.4 Syllable Segmentation Performance  
Syllable segmentation is similar to word segmentation with the key difference that in southeast Asian abugida languages, syllables are smaller linguistic units that form words. Syllable boundaries are generally clearer than word boundaries, so our model can be directly applied to the syllable segmentation task.  
For syllable segmentation, we train the model on Thai syllable benchmark datasets: the Thai National Corpus (TNC) [33] that contains 2.56 M annotated syllables (around 8 M characters). For the transfer learning model, we split training, development, and testing data with the ratio 7:2:1, which contain 1.8 M, 0.5 M, and 0.25 M syllables, respectively.  
We selected four baseline models for the syllable segmentation task. The first is a Conditional Random Field (CRF) model that uses character and trigram features. The second is a Maximum Entropy (MaxEnt) model that relies on trigrams as features. Additionally, we included PyThaiNLP, a dictionary-based Thai syllable segmentation model that applies a maximum matching algorithm, and SSG, which is the first machine learning-based model for Thai syllable segmentation [5].  
Table 6 presents the performance comparison for syllable segmentation. The results indicate that UnifiedCut, using a unigram sequence as input, achieves the highest scores, with an F1-score of 0.98 and an OOV recall of 0.78. Compared to the second-best model, SSG, UnifiedCut shows a notable improvement in OOV recall by 0.17. These results demonstrate that UnifiedCut is highly effective in producing reliable syllable segmentation outcomes.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Dataset**} \\ \cline{2-4}  & **Orchid** & **TNHC** & **Wisesight-1000** \\ \hline PythiaNLP & 0.72 \(\pm\) 0.24 & 0.73 \(\pm\) 0.21 & 0.74 \(\pm\) 0.21 \\ Sertis & 0.63 \(\pm\) 0.25 & 0.70 \(\pm\) 0.23 & 0.81 \(\pm\) 0.18 \\ DeepCut & 0.66 \(\pm\) 0.25 & 0.63 \(\pm\) 0.26 & 0.81 \(\pm\) 0.20 \\ AttaCut-C & 0.63 \(\pm\) 0.25 & 0.66 \(\pm\) 0.24 & 0.80 \(\pm\) 0.20 \\ AttaCut-SC & 0.62 \(\pm\) 0.26 & 0.63 \(\pm\) 0.26 & 0.81 \(\pm\) 0.20 \\ \hline UnifiedCut & **0.76 \(\pm\) 0.20** & 0.71 \(\pm\) 0.21 & **0.85 \(\pm\) 0.17** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cross-domain word-level performance comparison in Thai.
## 3 Results
### Experimental Validation of the LSTLS Models  
To validate the elaborated models, two of them, no. 7 (0.999) and 21 (0.999), were constructed and characterized (see methodology in Section 2.4). The comparison of the SPDs is presented in Figure 5.  
Figure 5: Spectral power distributions of 1000 lm LSTLSs, version no. 7—simulated and 8—measured (**a**), and 21—simulated and 22—measured (**c**), and spectral power distributions normalized to illuminant A for 1000 lm LSTLSs, version no. 7 and 8 (**b**), and 21 and 22 (**d**); error bars (shadow areas) including the radiometric accuracy of the spectroradiometer are displayed on the chart for the curves obtained for measured data.  
The results suggest that including a white LED reduces the deviation from the standard spectral power distribution (SPD) for wavelengths shorter than 500 nm (Figure 5b). However, the fit is worse within the spectral range of 500-650 nm, which is extremely relevant in photometry. This is opposite to the LSTLS without a white component.
# Augmented Reality, Mixed Reality, and Hybrid Approach in Healthcare Simulation: A Systematic Review
## 2 Materials and Methods
### Study Selection  
The selection process started with 262 studies collected from online digital libraries. We defined three questions to select and include relevant studies:  
* Is the study relevant to healthcare simulation for improving the medical technical and/or non-technical skills?
* Are the simulation techniques based on AR, MR, and/or hybrid approach?
* Does the study concern the development of an ad-hoc simulator or the evaluation of a commercial simulator? The selection process has been divided into four phases:
* Removal of duplicates from nine different databases. After removing them, 98 studies remained.  
2. Removal of editorials (1), reviews (2), book chapters (1), conference abstracts (8), thesis (9), reports (1), and reflections (4). After removing them, 72 studies remained for the next phase.
3. Removal of studies after reading the title and abstract. The removed articles do not resolve question Q1. After removing them, 35 studies remained.
4. Removal of studies after reading the full text, since some papers are still dubious after step 3. The removed articles do not resolve questions Q2 and Q3. A total of 23 studies remained relevant for our review.  
Figure 1 shows the flow chart for the selection of studies according to PRISMA statements [21].  
Figure 1: Flow chart illustrating the selection of studies according to PRISMA statements.
# Digital Signal Processing of the Inharmonic Complex Tone
## 3 The Developed Application Details  
In this section, features of the developed application are presented. The overall structure follows the scheme given in Figure 3. The three submodules, each with a dedicatedly developed GUI, have been developed. The signals and other data are transferred from one submodule to another via temporarily saved files. MATLAB 2023b (Update 5 version 23.2.0.2459199) was used to create the application. The required toolboxes to run the application are Curve Fitting Toolbox (version 23.2) and Signal Processing Toolbox (version 23.2). The module structure of the application enables the introduction of new submodules, the improvement of existing submodules without a full reorganization of the application, or the replacement of existing submodules with new ones.  
Figure 3: Organization structure of the proposed MuPI application by modules. Each module is responsible for certain processing. MuPI_S is responsible for the initial signal preprocessing and visualization. MuPI_B is in charge of calculating signal parameters. MuPI_B is in charge of signal processing from a multichannel bank as well as signal synthesis.  
Figure 2: MuPI Inharmonicity estimation test for synthetic piano. The red dots represent the values obtained by the MuPI application. The solid line represents the correct inharmonicity coefficient values given for synthesized piano tones for keys 1–35 in paper [38].
# Measurement of Heat Flow Transmitted through a Stacked-Screen Regenerator of Thermoacoustic Engine
## 5 Conclusions  
Heat transport through the stacked screen regenerators has been investigated experimentally in Ar gas with mean pressure of 0.45 MPa. The measured heat flow rates are compared with those calculated from four empirical equations, parameterized by \(r_{0}/\delta_{\mathrm{n}}\) and/or \(R_{\mathrm{e}\mathrm{h}}\). Results have shown that all the empirical equations tested reproduce the measured heat flow rates to a mutually similar extent. Among those empirical equations, [PERSON]'s formulation based on the effective radius \(r_{0}\) involves the simplest derivation of the heat flow rate. Therefore, it is expected to be useful for designing thermoacoustic Stirling engines. However, the acoustic field, especially the acoustic power, can be described better by the effective radius \(r_{\mathrm{eff}}\), which decreases with the velocity amplitude. Therefore, it is necessary to use two effective radii to account for the viscous behavior and the thermal behavior of the gas in the stacked screen regenerators.  
Figure 6: Relation between \(r_{\mathrm{eff}}\) and \(|V_{\mathrm{m}}|\). Symbols represent the experimental effective radius of the stacked-screen regenerator of #30 and #80 meshs. Curves stand for Equation (15) with thermal properties of the gas determined from a temperature averaged in \(T_{\mathrm{R}}\) and \(T_{\mathrm{H}}\). Horizontal dashed lines represents the effective radius \(r_{0}\) of [PERSON] et al.  
Figure 7: Comparisons between predictions of heat flow \(|Q|\) given, respectively, by the effective radius of [PERSON] et al. and [PERSON] and Biwa, for stacked-screen regenerators in pressurized argon gas with 0.45 MPa and oscillation frequency of 200 Hz.  
**Acknowledgments:** This work was financially supported by the Japan Science and Technology Agency (JST) through the Advanced Low Carbon Technology Research and Development Program (ALCA).  
**Author Contributions:** [PERSON] conceived the research plan. [PERSON] designed and performed the experiments and prepared the manuscript. [PERSON] revised the manuscript. Both authors have read and approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Reliability and Maintenance Analysis of Unmanned Aerial Vehicles +
###### Abstract  
This paper focuses on the development of a new logistic approach based on reliability and maintenance assessment, with the final aim of establishing a more efficient interval for the maintenance activities for Unmanned Aerial Vehicles (UAV). In the first part, we develop an architectural philosophy to obtain a more detailed reliability evaluation; then, we study the intrinsic reliability at the design stage in order to avoid severe critical issues in the UAV. In the second part, we compare different maintenance philosophies for UAVs and develop the concepts of preventive and corrective maintenance that consider the system subjected (until real \"hard failure\") to partial performance degradation (\"soft failure\"). Finally, by evaluation of the uncertainty through the confidence interval, we determine the new soft failure limits, taking into account the general knowledge of the systems and subsystems in order to guarantee the proper preventive maintenance interval.
# B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention
## 5 Experiment Results
### Ablation Study  
We performed an ablation study to investigate the contribution of the spatial and temporal attention mechanisms. The results are shown in Table 6.  
As a baseline, we used the model without attentions, that is, a CNN feature extractor followed by an LSTM C2D+LSTM, with a performance (\(F1\)) of 79.7%. Adding spatial attention (C2D+S+LSTM) improved performance to 80.5%. The baseline with only temporal attention (C2D+LSTM+T) further improved performance to 81.0%, suggesting that temporal localization contributes more to performance than spatial localization. The spatiotemporal attention model (C2D+S+LSTM+T) outperformed all the others, with a top performance of 83.2%.  
We also analyzed the performance with the spatial attention layer inserted after the first (C2D+S+LSTM+T*) or the second maxpool (C2D+S+LSTM+T), obtaining, respectively, 82.7% and 83.2% \(F1\) scores, and suggesting that attention contributes more to classification accuracy when added later.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**IoU** & **\(\alpha\) = 0.1** & **\(\alpha\) = 0.2** & **\(\alpha\) = 0.3** \\ \hline C2D+S+T & 37.7 & 32.4 & 27.6 \\ \hline C3D+S+T & 65.1 & 62.3 & 52.5 \\ \hline
**C2D+S+LSTM+T** & **69.7** & 64.1 & 53.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: B-line localization performance at different IoU \(\alpha\)’s. All the values are in percentages.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{**Length \(I\) (s)**} & \multicolumn{5}{c}{**Testing**} \\ \cline{3-6}  & & **1** & **2** & **3** & **4** \\ \hline \multirow{4}{*}{**Training**} & 1 & 83.2 & 83.0 & 82.8 & 82.6 \\ \cline{2-6}  & 2 & 81.1 & 83.0 & 82.5 & 82.2 \\ \cline{2-6}  & 3 & 79.7 & 80.1 & 83.6 & 83.8 \\ \cline{2-6}  & 4 & 78.6 & 79.2 & 80.4 & 83.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: B-line classification performance (\(F1\)) using different video length \(l\) during training and testing phases. All values are presented in percentages.
# Toward Sustainability of the Aqueous Phase Reforming of Wastewater: Heat Recovery and Integration
## 4 Conclusions  
The main energy demand of the whole APR process came from the reactor and was strongly dependent upon the water vaporization. To reduce this energy demand, it is indispensable to select an adequate operating pressure and other variables to promote methanation reactions. Additionally, the maximization of methane production allowed process heat sustainability to be reached. In this case, the process heat balances indicated that enough energy was available for both process self-covering and the generation of a net excess of 7-30%. Feedback concentration showed a significant influence, with a limit value of 1% for achieving energy self-covering. Optimal operation conditions were also influenced by feedstock concentration, with a required reaction pressure of 35-40 bar for 1% and 45-50 bar for 2%. The results suggested that the APR process could achieve a competitive position versus other water-treatment processes with a different approach to what has been usual in previous works based on H\({}_{2}\) production.  
Conceptualization, F.H. and V.R.F.; data acquisition and curation, F.H.; formal analysis, F.H. and V.R.F.; validation, A.S.d.O., J.A.B., L.C. and V.R.F.; supervision, F.H. and M.A.G.; writing--original draft, F.H., A.S.d.O. and J.A.B.; writing--review and editing, F.H., L.C., V.R.F. and M.A.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Scenario 1** & **Scenario 2** & **Scenario 3** & **Scenario 4** \\ \hline Exhaust gases T (\({}^{\circ}\)C) & 514.4 & 567.3 & 510.5 & 558.5 \\ \hline \multicolumn{5}{c}{**Main gas streams flows (kmol/h)**} \\ \hline Exhaust gases & & & & \\ \hline Fuel gas & 72.99 & 17.23 & 92.59 & 18.50 \\ \hline Recovered fuel gas & 102.80 & 33.12 & 267.10 & 84.39 \\ \hline \multicolumn{5}{c}{**Main heat flows (kW)**} \\ \hline Reactor duty & 4566.0 & 773.9 & 7277.0 & 582.5 \\ \hline Water condensation heat & \(-4160.0\) & \(-1675.0\) & \(-5673.0\) & \(-2005.0\) \\ \hline Exhaust gas heat & \(-700.4\) & \(-556.0\) & \(-875.6\) & \(-580.2\) \\ \hline \multicolumn{5}{c}{**Main heat flows (kWh/m\({}^{3}\) of treated wastewater)**} \\ \hline Reactor duty & 25.5 & 4.3 & 40.7 & 3.3 \\ \hline Water condensation heat & \(-23.2\) & \(-9.4\) & \(-31.7\) & \(-11.2\) \\ \hline Exhaust gas heat & \(-3.9\) & \(-3.1\) & \(-4.9\) & \(-3.2\) \\ \hline \multicolumn{5}{c}{**Energy recovery**} \\ \hline Reactor duty (kW) & 4566.0 & 773.9 & 7277.0 & 582.5 \\ \hline Reactor duty supplied by recovered fuel gas energy (\%) & 70.7 & 100 & 100 & 100 \\ \hline Net heat excess after reactor-duty covering (kW) & 0 & 2099.3 & 1037.7 & 6696.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Heat integration results for the proposed operating scenarios.
## 4 Discussion
### Has-miR-486-3p  
Hsa-miR-486-3p is less expressed in the SN, which explains why the pacemaking ion channels and other related transcripts are more expressed in this specialized tissue of the heart [4]. In human SN during heart failure, hsa-miR-486-3p is upregulated [12]. The other predicted targets for hsa-miR-486-3p are hsa-Ca\({}_{\text{v}}\)1.3, hsa-Ca\({}_{\text{v}}\)3.1, hsa-TPSAB1, hsa-HCN1, hsa-HCN4, LZTS1, LBH, HLA-DRA, and TBX18 [4; 10]--all of which we explored through luciferase assay experiments. Interestingly, we observed that hsa-miR-486-3p can significantly reduce the luciferase activity of three key target mRNAs (hsa-Ca\({}_{\text{v}}\)1.3, hsa-Ca\({}_{\text{v}}\)3.1, and hsa-TPSAB1) (Figures 3 and 5). In acute myocardial infarction patients, their serum hsa-miR-486-3p levels is increased [13; 14]. The expression of hsa-miR-486-3p is also elevated during ventricular hypertrophy [15] and in patients with congenital heart diseases [16]. Not only does hsa-miR-486-3p inhibit hsa-TPAB1 (a mast cell marker), it also inhibits hsa-CD209 (a macrophage marker) [10]. We have pilot data that show a trend toward hsa-miR-486-3p inhibiting hsa-CD209 (data not shown). [PERSON] et. al. (2017) noticed a high expression of immune cells in the atrioventricular node (AV) of mice [17]. They also observed AV block and bradycardia in mice that had reduced macrophage expression [17]. Incidentally, severe COVID-19 patients suffer from bradycardia [18; 19], and the expression of hsa-miR-486-3p in their blood is increased due to an immune response to the virus [20].  
Based on our studies and many other studies described above, hsa-miR-486-3p can be a principal therapeutic target in the treatment of cardiac dysfunction.
# A3C-R: A QoS-Oriented Energy-Saving Routing Algorithm for Software-Defined Networks
## 5 Experimental Evaluation
### Convergence of Routing Algorithms  
This experiment verified the convergence effect of the A3C-R algorithm through the reward value of the training process. The experimental process is based on the Formula (21) reward formula as the standard. In the energy consumption function, parameter \(\alpha\) is set to 2, and parameter \(\mu\) is set to 1. The rewards of the A3C-R routing algorithm training process are shown in Figure 4. The whole training process includes a total of 100,000 steps of interaction, and the reward of the A3C-R routing algorithm tends to be stable at about 40,000 steps, showing a good convergence effect. The reason is that the A3C-R routing algorithm is based on A3C's asynchronous training method, which can interact with the environment and update model parameters through multiple agents at the same time. This method can speed up training and improve the utilization efficiency of samples. In addition, the A3C algorithm introduces the Advantage function, which is used to measure the advantage of performing a certain action relative to the average level, which can help the agent to better improve the strategy, thereby improving the convergence.
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 5 Performance Evaluation
### Indoor Environments with Single High Frequency Acoustic Waves
#### 5.2.4. The Orientation Angle of the Acoustic Source  
The loudspeaker we used for the experiment is the common speaker that can be found in daily life. In the experiments, we find that the different orientation angle \(\beta\) of the acoustic source will also affect the results.  
Now, we evaluate the VULA and the VUCA system with different \(\beta\). Figure 10b,c show the standard deviations of angular errors when \(\beta=0^{\circ},45^{\circ},90^{\circ}\), \(L\leq 6\) m, \(L=24\) m, \(\theta=~{}0^{\circ}\) and \(\phi=~{}45^{\circ}\). From these figures, we learn that the closer we get to the acoustic source, the easier direction finding accuracy is affected by the orientation angle \(\beta\). We believe the reasons for this phenomenon are that the acoustic source we choose is not omni-directional, and when the user is close to the acoustic source, the signals reflected from the wall sometimes are stronger than those that directly go to the smartphone. Hence, the way of placing the loudspeakers also affects our results in some ways. However, when \(L=24\) m, the signals reflected from the wall become much weaker than those directly from the acoustic source. Thus, the orientation angle has less influence on our results.  
Figure 9: Mean error of direction finding when \(\phi=90^{\circ}\) or \(30^{\circ}\) with different distances \(L\).