esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
With an increase in microwave power from 840 W to 2200 W, the generation of pyrolysis gas from sawdust was significantly intensified. In particular, the average yield of CO increased by 2.5 times, and the average yield of H\({}_{2}\) and CH\({}_{4}\) increased by 5 and 2.8 times, respectively. The yield of methane and hydrogen was the most sensitive to the variation in microwave power.  
Figure 7: Efficiency indicator of microwave pyrolysis with varying priority of characteristics and different power.  
The power of microwave waves had a non-linear effect on the dynamics of pyrolysis gas formation. Increasing the microwave power from 840 W to 2200 W reduced the delay time of pyrolysis gas yield by about 20 times.  
Intensive degassing of raw materials with increased microwave power contributes to the decarbonization of biomass. An increase in the power of microwaves (from 840 to 2200 W) contributed to a decrease in the mass of the solid residue and the liquid product by 5.3 and 2.7 times, respectively.  
An increase in energy consumption for pyrolysis with an increase in microwave power reduced the integral efficiency of biomass conversion to a limited extent. The pyrolysis efficiency index calculated by the weighted sum method increased by a factor of 1.3-2.2 when the power was varied from 840 W to 2200 W.  
The present study considers the effect of microwave exposure power on woody biomass conversion products. The results have applicability limitations since the power range of the magnetron was limited to 840-2200 W. For the subsequent development of the research, it is advisable to expand the range of influencing factors; in particular, it is advisable to experimentally determine the fuel heating rate in different zones of a layer, vary the residence time of the feedstock in the reactor, and quantitatively and qualitatively evaluate the effect of heat flows on biomass conversion. In addition, tests with other types of raw materials and adaptation of the experimental technique to a wider range of magnetron power to verify the obtained approximation formulas are the future development of this study.  
Conceptualization, G.K.; Methodology, A.S. and K.V. ([PERSON]): Validation, K.V. ([PERSON]); Formal analysis, K.V. ([PERSON]); Investigation, A.S., K.V. ([PERSON]) and K.V. ([PERSON]); Writing--original draft, A.S. and K.V. ([PERSON]); Visualization, K.V. ([PERSON]); Supervision, G.K. All authors have read and agreed to the published version of the manuscript.  
The study was supported by a grant from the Ministry of Science and Higher Education of Russia, Agreement No. 075-15-2020-806 (Contract No. 13.1902.21.0014).  
Not applicable.  
Data Availability Statement: Data available within the article.  
The authors declare no conflict of interest.
## 5 Optimization Problems with Synchronization Issues
In general, regardless of the field of application, BR-DEH algorithms can be used to solve dynamic optimization problems with synchronization issues, which typically involve one or more agents that depend on the actions or results that other agents generate.
# Communication Rate Increase in Drill Strings of Oil and Gas Wells Using Multiple Actuators
## Appendix A  
Here we show how in the experiments the two data streams transmitted via OFDM by the two actuators were separated and detected using two received signals and an MMSE algorithm. The system model with \(N_{rx}\) receivers and \(N_{tx}\) transmitters can be written as  
\[\mathbf{r}=\mathbf{H}\mathbf{\gamma}+\mathbf{n}. \tag{1}\]  
In the above equation for the \(\kappa\)-th OFDM data sub-carrier, \(\mathbf{r}=\left[r_{1}(f_{x})\cdots r_{N_{rx}}(f_{x})\right]^{T}\) is the received signal vector, \({}^{T}\) is the transpose, \(\mathbf{\gamma}=\left[\gamma_{1}(f_{x})\cdots\gamma_{N_{tx}}(f_{x})\right]^{T}\) is the transmitted symbol vector, \(\mathbf{n}=\left[n_{1}(f_{x})\cdots n_{N_{rx}}(f_{x})\right]^{T}\) is the noise vector, and \(\mathbf{H}\) is the \(N_{rx}\times N_{tx}\) channel matrix  
\[\mathbf{H}=\left[\begin{array}{ccc}H_{11}(f_{\kappa})&\cdots&H_{1N_{tx}}(f_ {\kappa})\\ \vdots&\ddots&\vdots\\ H_{N_{rx}1}(f_{\kappa})&\cdots&H_{N_{rx}N_{tx}}(f_{\kappa})\end{array}\right]. \tag{2}\]  
In the experiments, elements of \(\mathbf{\gamma}\) were independent QPSK symbols, \(N_{tx}=2\) corresponds to the two transmitting actuators, and \(N_{rx}=2\) refers to any of these two receivers: strain and \(x\)-acceleration, strain and \(y\)-acceleration, strain and \(z\)-acceleration, \(x\)-acceleration and \(y\)-acceleration, \(x\)-acceleration and \(z\)-acceleration. To recover the two transmitted symbols in \(\mathbf{\gamma}\) at each sub-carrier from the corresponding \(2\times 1\) received signal vector \(\mathbf{r}\) in (1), an MMSE detector [8] was used in the experiments  
\[\mathbf{\hat{\gamma}}=\mathbf{\hat{H}}^{H}\Big{(}\mathbf{\hat{H}}\mathbf{ \hat{H}}^{H}+\mathbf{\hat{\Sigma}}\Big{)}^{-1}\mathbf{r}, \tag{3}\]  
\[\mathbf{\hat{\Sigma}}=\left[\begin{array}{ccc}\hat{\sigma}_{1}^{2}&\cdots&0 \\ \vdots&\ddots&\vdots\\ 0&\cdots&\hat{\sigma}_{N_{rx}}^{2}\end{array}\right]. \tag{4}\]  
In Equation (3), \(\mathbf{\hat{\gamma}}\) includes the MMSE-based symbol estimates; \(\mathbf{\hat{H}}\) is the estimated channel matrix \(\mathbf{H}\) in (2), obtained using a least squares method [7]; \({}^{H}\) is the transpose conjugate; and \(\mathbf{\hat{\Sigma}}\) in Equations (3) and (4) is the estimated \(N_{rx}\times N_{rx}\) diagonal receiver noise covariance matrix, in which the noise variances are obtained using the null sub-carriers received by the receivers [7].  
The above MMSE detector is a linear method, so it is highly desirable in practice due to its low computational complexity. Detection performance and BERs can be improved by using a nonlinear method such as a maximum likelihood method, but at the cost of higher computational complexity [8].
## 3 Results and Discussion
### Aroma Recombination and Emission Experiments
Furthermore, when these two compounds were respectively omitted alone, the omission of ethyl acetate would lead to significant differences, while that of ethyl lactate would not, indicating that ethyl acetate has a greater contribution to the overall aroma than ethyl lactate. Although the content of ethyl lactate is high in Xiaoqu liquor, it may only affect the mouthfeel instead of the aroma of the liquor [24]. The omission of acetic acid and valeric acid led to extremely significant differences in aroma profile, indicating that these two acids contribute greatly to the sour taste in the overall aroma of Xiaoqu liquor. The omission of 1-octen-3-ol and all alcohol compounds did not lead to obvious changes in aroma profile, possibly due to the presence of a large amount of ethanol in the liquor, which is of very high volatility and a low threshold value and thus affects the judgment on the alcohol compounds. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Compounds Omitted from the Recombination** & **n a** & **Significance b** \\ \hline
1 & All esters & 10 & *** \\
1-1 & Ethyl octanoate & 7 & * \\
1-2 & Ethyl acetate & 8 & ** \\
1-3 & Ethyl lactate & 5 & \\
1-4 & Ethyl acetate and ethyl lactate & 8 & ** \\
1-5 & Isoamyl acetate & 6 & \\
1-6 & Ethyl isovalerate & 6 & \\
2 & 1,1-diethoxyethane & 8 & ** \\
3 & Acetic acid and valeric acid & 10 & *** \\
4 & 1-octen-3-ol & 4 & \\
5 & All alcohols & 5 & \\ \hline \hline \end{tabular} Note: n a: times of correct judgment of the 10 panelists in the triangle test of aroma differences. Significance b: \({}^{*}\), significant differences (\(p\leq 0.05\)); **, highly significant difference (\(p\leq 0.01\)); ***, extremely significant differences (\(p\leq 0.001\)).  
\end{table} 
Table 5: Omission experiments of the aroma compounds from JPXQL. Figure 2: Profile of aroma recombination of JPXQL.
# Hub Airport End-Around Taxiway Construction Planning Development: A Review
## 4 The Construction Principles and Key Points of End-Around Taxiway Layout
### Visual Impediment
With the development of communication and information technologies, the density of cables, fiber optics, and other infrastructure inside aerodrome maneuvering areas has increased. Some new communication and navigation facilities, such as GBAS (Ground Based Augmentation System) antenna arrays, are also increasing. Metallic visual screens may have unknown effects on the aforementioned equipment. For example, metallic screens can create false targets for surface surveillance radars. At the same time, the limitations of sensitive or critical areas for multiple devices also pose challenges for siting metallic visual screens [90; 92]. As a result, the global use of metallic visual screens is not widespread. So far, only DFW, DTW, and SHA have utilized metallic visual screens. Table 10 compares the basic information of visual screens in three airports. Metallic visual screens have been installed on both the north and south end-around taxiways at SHA and were put into use in 2022. The southern screen is located on the ground surface south of the runway, while part of the northern screen is erected above the regulatory pond. Before the metallic visual screens were put into operation at SHA, relevant authorities conducted simulations and field tests to analyze their impact on ILS signals and surface movement radar signals. The panels were finally installed and tilted 12\({}^{\circ}\) (\(\pm\)1\({}^{\circ}\)) to minimize or eliminate false radar targets caused by surface reflections [34]. Analyses were also conducted on the impact of visual screens at different locations on the ILS system. According to the simulation result, the visual screen mainly affects the glide path antenna signal, and the distance between the screen and glide path antenna should be greater than 800 m [92]. Before installation, the possible influence of a screen on the signal of the glide path should be analyzed.
## 6 Single-Factor Influence Analysis and Optimization of the Split-Stream Rushing Exhaust Muffler
### Analysis of the Single-Factor Influence
#### 6.1.2 Inner Tube Diameter  
Figure 22 illustrates the annular space defined by the inner and outer pipes of the muffler. The figure illustrates that given constant values for other parameters, the inner pipe diameter determines the dimensions of the annular space, and these dimensions directly influence the acoustic wave propagation characteristics within the muffler. Figure 23 presents the transmission loss characteristics under sound-structure coupling for varying inner pipe diameters while maintaining constant values for the other parameters. An observation of the figure reveals that within the 10-1690 Hz frequency band, the transmission loss exhibits minimal variation. A notable exception occurs at 710 Hz, where the transmission loss increases inversely with the inner pipe diameter. Within the 1690-4000 Hz frequency band, the transmission loss exhibits an overall upward trend with increasing inner pipe  
Figure 21: Transmission loss curves for different wall thicknesses under acoustic-structural coupling.  
Figure 20: Modal frequency diagrams of mufflers with different wall thicknesses. (**a**) Structural mode, (**b**) Acoustic-structure coupling.  
diameters. To facilitate further analysis, Table 7 presents the mean transmission loss values for varying inner pipe diameters. As shown in Table 7, the mean transmission loss escalates with increasing inner pipe diameters, and this escalation is substantial, signifying that the inner pipe diameter exerts a considerable influence on the transmission loss under sound-structure coupling.
# Electrospun Nanofibrous Membranes for Tissue Engineering and Cell Growth
## 6 Pla
For future research, they suggested controlling the pore size, porosity, and fiber diameters, as well as testing the structural integrity during degradation, since these parameters were assumed to mostly influence the cell attachment and growth. Another polymer blend was reported by [PERSON] et al., who combined PLA with poly(butylene succinate) (PBS) with a reinforcement by cellulose nano-fibrils [103]. They found increased protein adsorption for the blended nanofiber mats and accelerated biodegradationdation under proteinase K, as compared to pure PLA, as well as good wetting; all together resulting in good cell attachment and proliferation. Blending a PLA-PEG (poly(ethylene glycol) block copolymer with PLGA, [PERSON] et al. used electrospun scaffolds as the base for a polymer/plasmid DNA composite [104]. Such scaffolds could be used for therapeutic applications in gene delivery. They found a sustained release of intact DNA over three weeks, with a maximum burst after approx. 2 h. The bioactivity of the DNA was tested by transfection of pre-osteoblastic cells. In addition, the mechanical properties of these nano-composites were similar to those of skin and cartilage. Coaxial electrospun PLA/PVA nanofibers were investigated by [PERSON] et al., who compared both combinations of core and shell with pure PLA and pure PVA fibers [105]. In comparison with pure PVA, the core-shell nanofibers showed significantly reduced water contact angles and an increase in tensile strength and strain at failure. PLA/PVA core/shell nanofibers, in particular, showed good attachment and proliferation of human embryonic kidney (HEK) cells, as visible in Figure 4, and due to the improved hydrophilicity and good water stability of these nanofiber mats. Besides PLA, there are other man-made polymers that are typically used for tissue engineering. Figure 4: Scanning electron microscopy (SEM) images of HEK cells on a (**A1–A3**) PLA/PVA core/shell; (**B1–B3**) PVA/PLA core/shell; (**C1–C3**) pure PLA; (**D1–D3**) pure PVA nanofiber mats. Reprinted from [105], with permission from Elsevier. Each rows shows different locations or magnifications for the same material.
## 10 Future Research Directions
### Security  
The deployment of WSNs in battlefield requires stringent security features, therefore, OSs for WSN should provide mechanisms so that a user at base station can authenticate valid nodes in the network. Similarly, in case of queries and updates, nodes must be able to authenticate the originator.
# TSCH and RPL Joining Time Model for Industrial Wireless Sensor Networks
## 2 TSCH and RPL Network Deployment
### TSCH Beacon Advertising
In this process, the nodes go through the different pre-configured channels, for a certain period of time, until they receive an EB correctly. This synchronization process will take longer depending on different factors such as the total number of configured channels, the time each channel is scanned or the transmission period of the EB message. Figure 1 shows a representation of the main causes that can lengthen this synchronization process, such as no Enhanced Beacon being sent during scanning time or the channel being scanned not matching the channel on which the Enhanced Beacon message is being transmitted.
# QB4 MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web
## 2 Related Works
### Mobility Data Warehouse  
Data warehousing technologies are needed to support the efficient collection, integration, storage, and analysis of the mobility pattern, yielding the notion of the Mobility Data Warehouse (DW) [7]. Mobility DW is the heart of mobility analytics, an extension of Business Intelligence (BI) that supports the collection, analysis, and presentation of information over moving object data. A Mobility DW deploys business intelligence by taking advantage of the operations associated with spatiotemporal data types to allow complex queries [7].  
Trajectory DW is strongly related to mobility DW. Trajectory DW stores moving object data using base types, spatial types, and trajectories. A number of mobility analysis works that used the trajectory data warehouses (TDW) approach were published. For example, TDW was used for a recommender system for tourists [35], for road traffic analysis [36], and for finding the best location of billboard placement [37]. Further, some researchers added semantic information for TDW, such as for improving nurse productivity [38], mobility analysis [39], and modeling multiple aspect trajectories [8] to support decision making. However, according to the definition in [8], these research studies could not be classified as spatiotemporal DW since they did not support spatiotemporal queries and include moving data types [12].  
[PERSON] et al. defined the notion of spatiotemporal queries with aggregation extended with spatial and moving data types [10]. Based on this, they implemented Mobility DWs with spatiotemporal query support on MobilityDB, an object-relational DBMS. This implementation claimed to be supporting the spatiotemporal OLAP queries defined in [8]. Our research on this Mobility DW also supports spatiotemporal OLAP queries and moving data types represented on the SW and SPARQL.
# Repetition with Learning Approaches in Massive Machine Type Communications
###### Abstract  
In the 5G massive machine type communication (mMTC) scenario, user equipment with poor signal quality requires numerous repetitions to compensate for the additional signal attenuation. However, an excessive number of repetitions consumes additional wireless resources, decreasing the transmission rate, and increasing the energy consumption. An insufficient number of repetitions prevents the successful deciphering of the data by the receivers, leading to a high bit error rate. The present study developed adaptive repetition approaches with the k-nearest neighbor (KNN) and support vector machine (SVM) to substantially increase network transmission efficacy for the enhanced machine type communication (eMTC) system in the 5G mMTC scenario. The simulation results showed that the proposed repetition with the learning approach effectively improved the probability of successful transmission, the resource utilization, the average number of repetitions, and the average energy consumption. It is therefore more suitable for the eMTC system in the mMTC scenario than the common lookup table.  
massive machine type communications (mMTC); enhanced machine type communication (eMTC); repetition; machine learning k-nearest neighbor (KNN); support vector machine (SVM) 2022113649. [[https://doi.org/10.3390/electronics11223649](https://doi.org/10.3390/electronics11223649)]([https://doi.org/10.3390/electronics11223649](https://doi.org/10.3390/electronics11223649))  
1
## 4 Simulation Results and Analysis
### Controller Simulation under Different Step Signals  
Figure 10 illustrates the controller position curves for different step signals. The position curves for the ADRC, RBF-ADRC, and FRBF-ADRC controllers are displayed for step signals of 1 rad, 1.5 rad, and 2 rad, highlighting the speed of each controller at various times. Utilizing the data from Figure 10, we can derive the comparative data presented in Table 3. Among the three controllers, FRBF-ADRC demonstrates the best control performance. Additionally, as the position increases, the response time of the controller usually slows down.  
Figure 8: Step signal position curve.  
Figure 9: Step signal sudden load position curve.
# An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction
## 4 Discussion  
### Comparison of Different Feature Extractors  
The TL-DSTP-DANN structure proposed in this study uses the DSTP model as the feature extractor. CNN, LSTM, and BiLSTM are then used as feature extractors in combination with the DANN module, and the regression predictor and domain classifier are left unchanged to verify that the DSTP model is the most accurate as a feature extractor. In the TL-CNN-DANN model, the convolution layer and the pooling layer are used to extract features of the input time series. The LSTM model is used as the feature extractor in the TL-LSTM-DANN model, and the BiLSTM model is used as the feature extractor in the TL-BiLSTM-DANN model. The experimental accuracy of four different feature extractors combined with the DANN module is shown in Table 3.  
The results show that the TL-CNN-DANN model is the worst because CNN is more suitable for extracting spatial features. The TL-LSTM-DANN model performs better than TL-CNN-DANN because the LSTM model can better extract temporal features of long time series than the CNN module. The TL-BiLSTM-DANN model performs better than the TL-LSTM-DANN because the BiLSTM considers the information contained in subsequent time series to adjust modeling and computation. The TL-DSTP-DANN model has the best performance compared to other DANN-based structures, mainly because DSTP can extract spatial-temporal features. The models using different feature extractors to predict PM2.5 concentrations at the Tianjin site are shown in Figure 5, from which it can be seen that the TL-DSTP-DANN model has the smallest error between the predicted and true values.  
Figure 4: Performance comparison of four models for predicting PM2.5 concentration at Tianjin site.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **MAE** & **RMSE** & **MAPE** \\ \hline TL-CNN-DANN & 19.68 & 23.05 & 0.39 \\ TL-LSTM-DANN & 14.70 & 18.51 & 0.27 \\ TL-BiLSTM-DANN & 13.36 & 16.55 & 0.25 \\ TL-DSTP-DANN & 11.75 & 15.97 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy comparison of different feature extractors combined with DANN for PM2.5 prediction.
# Recent Progress on Solution-Processed CdTe Nanocrystals Solar Cells
## 3 Optical Properties  
CdTe is a direct band gap semiconductor with high optical absorption coefficients (>10\({}^{4}\)/cm in visible light region). A thin film of CdTe with thickness around 2 \(\upmu\)m will absorb almost all the incident solar radiation. In order to yield high power conversion efficiency, an optimal bandgap of semiconductor materials is in the range of 1.1-1.5 eV for a single-junction solar cell [31]. Figure 1  
Figure 1: AM 1.5 G solar spectrum from ASTM G173-03 reference spectra. The bandgaps for some common bulk semiconductors are: TiO\({}_{2}\) 3.3 eV; CdS 2.45 eV; CdSe 1.7 eV; CuInS\({}_{2}\) 1.54 eV; CdTe 1.5 eV; InP 1.34 eV; Cu\({}_{2}\)S 1.21 eV; Si 1.12 eV; CuInSe\({}_{2}\) 1.0 eV; InN 0.8 eV; Ge 0.66 eV; CuFeS\({}_{2}\) 0.6 eV; PbS 0.41 eV; InAs 0.35 eV and PbSe 0.28 eV. Reproduced with permission from [13]. Copyright 2011, John Wiley and Sons.  
shows the absorption onset of several important bulk semiconductors with respect to the AM 1.5 G spectrum. The bandgap of bulk CdTe is 1.5 eV, which is suitable for photovoltaic application.  
The unique optical properties of semiconductor NCs make them interesting materials for multiple exciton generation toward efficient solar energy conversion. For example, by decreasing the size of PbS or PbSe NCs to their Bohr radius, a significant increase in bandgap is obtained in these materials. However, since CdTe has bulk bandgap in near infrared, the decrease in size of CdTe NCs will tune them into the red or green light region, which is not expected for photovoltaic application. In order to achieve high efficiency, sintering strategies are often carried out to recover its bulk properties during the fabrication of CdTe NCs solar cells. A CdCl\({}_{2}\) sintering processed is often carried out to improve the carrier mobility of CdTe thin film prepared by using CdTe NCs as the original materials. CdTe NCs thin film remains p-type in this case, which has been confirmed by [PERSON] et al. [10]. As all the CdTe NCs solar cells (inorganic NCs solar cells) discussed in this paper are fabricated by similar CdCl\({}_{2}\) sintering techniques, we can conclude that CdTe NCs show similar p-type in electrical conduction.