esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Linear Dynamic Analysis of Drill String System with Fluid-Structure Interaction
## 3 Results and Discussion
### The Effect of Flow Velocity \(u_{0}\) on Dynamic Characteristics  
This section studies the effect of flow velocity \(u_{0}\) on the dynamic behavior of the drill string system. The corresponding bifurcation diagram of the system with flow velocity under support stiffness \(k_{\text{a}}\) = 2.6 \(\times\)\(10^{6}\) and \(k_{\text{b}}\) = 0 is shown in Figure 10a. For the flow velocity in the range of [1.5, 2.852], the periodic-1 motion is observed. With the increase in the flow velocity from 2.852 to 3.986, the system exhibits quasi-periodic motion and chaotic motion. Figure 11(a\({}_{1}\)-a\({}_{3}\)) presents quasi-periodic motion at \(u_{0}\) = 3.384 of the system. As the flow velocity further increases, the system presents narrow periodic motion at [3.986, 4.054]. Periodic motion is observed at \(u_{0}\) = 4.018 in Figure 11(b\({}_{1}\)-b\({}_{3}\)). With increasing \(u_{0}\) from 4.054  
Figure 9: Waveform (1), phase diagram (2) and Poincaré map (3) with \(\beta\) = 0.218 Periodic-2 motion (**a**), \(\beta\) = 0.521 quasi-periodic motion (**b**) and \(\beta\) = 0.752 chaotic motion (**c**).  
to 4.5, a large amplitude chaotic response is observed. Figure 11(c\({}_{1}\)-c\({}_{3}\)) show the relevant vibration response of the system at \(u_{0}\) = 4.386, and the motion state exhibits chaotic behavior.  
Figure 11: Waveform (1), phase diagram (2) and Poincaré map (3) with \(u_{0}\) = 3.384 quasi-periodic motion (a), \(u_{0}\) = 4.018 periodic motion (b), \(u_{0}\) = 4.386 chaotic motion (c).  
Figure 10: Bifurcation diagram of system response with \(u_{0}\): (a) \(k_{\rm a}\) = 2.6 \(\times\)\(10^{6}\), \(k_{\rm b}\) = 0; (b) \(k_{\rm a}\) = \(k_{\rm b}\) = 0 (A: periodic motion; B: quasi-periodic motion; C: chaotic motion).  
Figure 10b displays the bifurcation diagram at \(k_{\rm a}=0\) and \(k_{\rm b}=0\), with flow velocity \(u_{0}\) as the control parameter in the range [1.5, 4.5]. It is found that the system exhibits period-1 motion at the intervals [1.5, 2.912]. With the gradual increase in the flow velocity from 2.912 to 3.622, the system exhibits quasi-periodic motion and chaotic behavior, and it is found that the chaotic region gradually increases. With the flow velocity continuing to increase, the system evolves into short period-2 motion in the range [3.622, 3.678], after which the system goes back to a chaotic behavior from 3.678 to 3.928. With the flow velocity increasing even further, the system evolves into short narrow periodic-3 motion and then enters periodic-2 motion with obvious jump discontinuity in the range [3.928, 4.5]. Compared with Figure 10a, it can be seen that, as the vibration amplitude of the system increases, the chaotic motion region decreases, and the periodic motion region significantly increases. The support stiffness has a complex effect on the inherent characteristics of the drill string system dynamics.
## 2 System Structure
### Transmitter Structure
With only one conversion rule, there are \(M!\) possibilities for the available mapping relationship, in theory. The security performance is obviously better than that of the DSSS. If the number of the transmitting symbols is \(L\) (\(L\) is an integer larger than zero), the transmitted signal is rewritten as  
\[s(t)=\sum_{n=0}^{L-1}s_{n}(t) \tag{6}\]
# Robust Detection of Background Acoustic Scene in the Presence of Foreground Speech
## 4 Proposed Methods
### Ensemble Methods  
The methods in previous sections provide different ASC systems extended from the same ResNet baseline by augmenting the input features. As is evident, the three input features including MFBE, NF-MFBE, and speech mask can be combined in different ways. So, an ablation study is required to determine the most efficient combination. For example, to analyse the influence of NF-based features, three systems can be evaluated: \(ResNet_{F}\) system which uses only the MFBE, \(ResNet_{NF}\) system which uses only NF-MFBE, and \(ResNet_{F+NF}\) system which uses both MFBE and NF-MFBE. Similarly for evaluating the  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Encoder Parameters** \\ \hline Number of channels & 16, 32, 64, 128 \\ Kernel size (Time, Frequency) & (2, 3) for all layers \\ Stride (Time, Frequency) & (1, 2) for all layers \\ \hline Default activation functions & Leaky ReLU, slope = 0.03 \\ \hline Final layer activation functions & sigmoid \\ \hline Skip connection & \(1\times 1\) convolution \\ \hline Number of training epochs & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the CRUSE net. Since encoder and decoder have symmetric structures, only the encoder structure is enumerated.  
benefit of speech presence information, \(ResNet_{F+M}\) system which uses both MFBE and speech masks can be benchmarked against \(ResNet_{F}\). Also, with the \(ResNet_{F+NF+S}\) system, we expect that the model makes optimal use of all the information available by inherently incorporating the speech presence probability to derive the necessary background signal characteristics from the different features. Each of the combinations is evaluated with small-footprint and large-footprint models.  
Ensemble methods [27, 28] are often used to reduce classification error by running different classification models in parallel, each of which uses different parameters or different training data. The results of all the models are then combined into a global decision. As we observe, different features yield better discrimination at different SBR ranges. Ensemble methods can combine the test scores of the aforementioned models in a weighted manner to yield an improved final classification result. Therefore, we train a meta-learner consisting of one linear layer followed by a softmax layer, to combine the outputs of the different ResNet configurations. The meta-learner is trained with cross-entropy as loss the validation dataset. Thereby, the output global scores are the weighted combinations of the scores from each model.  
For performance evaluation, the meta-learner is also compared with average voting, a simple and effective method that combines the classification results with the averaging weights.
## 3 Overview of LPWAN  
LPWAN is a wireless technology that have continued to gain enormous attention in recent times. The technology is specifically designed for long range communications (up to 50 km depending on the LPWAN device) [32; 33; 34; 35]. LPWAN technologies are distinguished from other connectivity technologies as they have outstanding features which include low power consumption, low cost, bit rate, capacity and mobility [20]. Furthermore, they provide battery-efficient possibility targeting 10+ battery life, universal wide-area connectivity thereby allowing numerous M2M and IoT applications unlike popular short-range network devices such as Bluetooth, Wi-Fi, and ZigBee [36]. With regards to network topologies, most LPWAN technologies conventionally deploy single-hop networks based on star topology as it assists in preserving battery power, safe cost and it also compensate for communication range. Depending on the region and standards, LPWANs can operate in the unlicensed industrial, scientific, and medical (ISM) frequency bands 2.4 GHz, 868/915 MHz (Europe/North America), 433 MHz (Asia) etc. [32; 37; 38]. Some LPWAN can operate on the licensed frequencies [39]. Some of the general attributes and characteristics of LPWANs that make them suitable for IoT connectivity are discussed as follows.
## 5 Computer Simulations
### Performance Evaluation with Other Relay-Assisted Hybrid Beamforming Algorithms  
Figure 6 demonstrates the transmission rate of the presented hybrid beamforming scheme with the optimal relay selection criterion. The system parameters are set as \(N_{t}=N_{rel}=100,\ N_{r}=25\), and \(N_{s}=3,\ 4,\ 5\) to obtain simulation results. Moreover, a sparse mm-Wave propagation environment with UPA is considered for data transmission, where channel parameters are initialized as \(N_{ray}=10,\ N_{cl}=4,\ \sigma_{a,i}^{2}=1\ \forall i\), and \(AS=10^{\degree}\) to generate the channel matrix according to (55). It is obvious from the obtained results that the proposed method achieves higher performance when compared with the algorithms proposed in [34; 36].  
Figure 7 compares the performance of the proposed scheme with the relay-assisted hybrid beamforming algorithm in [35]. It is worth mentioning that this technique uses analog beamforming at relay nodes, while the hybrid precoder and combiner are designed at the source and destination, respectively. Due to analog beamforming, each relay node supports only one data stream, i.e., \(N_{s}=1\). Therefore, computer simulations are conducted in Figure 7 by changing the number of antennas at communicating nodes only. It is obvious from the obtained results that the proposed approach achieves near-optimal performancewhen compared with full-complexity precoding and outperforms the algorithm presented in [35].  
Table 2 summarizes the performance comparison among between the proposed algorithm and the other relay-assisted hybrid beamforming techniques.  
Figure 6: Spectral efficiency vs. SNR in a mm-Wave propagation environment with UPA such that \(N_{cl}=4,\ N_{ray}=10\) and \(10^{\circ}\) spread angle. System parameters are set as \(N_{t}=N_{rel}=100\), \(N_{r}=25\), \(N_{s}=3,\ 4,\ 5\).  
Figure 7: Spectral efficiency vs. SNR in a mm-Wave propagation environment with UPA such that \(N_{cl}=4,\ N_{ray}=10\), and \(a\ 10^{\circ}\) spread angle. The number of antennas is set as \((N_{t}=N_{rel}=16,\ N_{r}=4),\ (N_{t}=N_{rel}=36,\ N_{r}=16),\ (N_{t}=N_{rel}=64,\ N_{r}=9),\ N_{s}=1\).
# Phenotypic, Genetic, and Metabolite Variability among Genotypes of _Vicia sativa_ L.
## 4 Discussion
Thus, studying both the phenotypic and metabolic characteristics throughout the biological cycle could provide a better understanding of the examined crop germplasm and its potential [69]. Furthermore, the MRA method associated molecular markers with agronomic traits or metabolites and has provided insights into identifying superior genotypes, in agreement with previous studies [51; 52; 70; 71]. Many markers linked to phenotypic or metabolic features were found through MRA analyses using SCoT markers that had a positive or negative statistically significant correlation (R\({}^{2}>\) 0.5). Most of the alleles found using SCoT markers had a moderate correlation with the agronomic/metabolite traits. However, the SCoT markers found in this work that were positively linked to the main metabolites contributing to yield, a high nutritional value (i.e., unsaturated fatty acids, amino acidsand organic acids), as well as plant tolerance to harsh environments, can be utilized in breeding programs for QTL and marker-assisted selection (MAS). Also, it was discovered that several markers were connected to many common vetch traits. The related QTL may have a pleiotropic effect on various traits, which could explain the connection. Furthermore, a single marker exhibiting a connection that would be reflected in correlations between such traits could result from tightly connected QTLs impacting distinct qualities [72]. For example, SCoT30_900 had a very strong positive correlation with several metabolites of agronomic importance such as topcherol, l-lactic acid, salicylic acid, and tyrosine, which can serve as antioxidants, thus ameliorating the seed quality and enhancing the plant's resilience [73; 74]. Moreover, excluding vetch cultivars that possess alleles that are negatively linked with the favorable agronomic/metabolic properties is extremely helpful in pre-breeding screening programs. For instance, SCoT13_350 had a very strong negative correlation with several amino acids (asparagine, l-phenylalanine) and metabolites that have medicinal and/or detoxifying qualities (i.e., succinic acid and malic acid) [75; 76]. Finally, SCoT30_1300, which has a strong negative correlation to seed yield but a strong positive correlation to biomass yield, can be very useful in selecting varieties utilized for high biomass production. This work could improve the conservation and management of the pertinent genetic resources by demonstrating the significance of using the phenotypic profile, metabolites, and DNA markers in the assessment, cultivation, and breeding of the common vetch.
# Robust Learning from Demonstration Based on GANs and Affine Transformation
## 1 Introduction
To enhance the performance of LfD and simplify operation, particularly for individuals lacking programming expertise, this paper proposes a robust LfD method with the following key attributes:  
* Enhanced feature extraction performance with GANs: We harness the power of GANs to efficiently capture the distribution of expert demonstration trajectories. This approach facilitates the integration of information from multiple demonstration trajectories, leading to a more nuanced and comprehensive feature representation. * Enhanced convergence performance with additional loss functions: We introduce novel loss functions, including DILATE [23] loss and Jerk [24] loss, to augment the learning process of GAN networks. These additional loss functions serve to further drive the convergence of the model and mitigate the occurrence of model collapse, thereby enhancing the stability and robustness of the learning process. * Superior generalization ability: Our proposed method incorporates a geometric-based LfD generalization algorithm. Through the utilization of affine transformations, this approach adeptly addresses the challenge of trajectory generalization. By dynamically adjusting the trajectory through affine transformations, our method facilitates seamless adaptation to diverse and complex environments, showcasing superior generalization capabilities. Our article is structured as follows: Section 2 provides background information on our work, focusing primarily on the MP method and GANS. Section 3 elaborates on the data preprocessing method, GAN-based trajectory learning method, and affine transformation-based generalization method. Section 4 presents experiments, where we apply our method to the Lasa handwriting dataset and the UR5 robotic arm under simulation. Finally, our conclusions and directions for future work are outlined.
## 1 Introduction
A driver is distracted if he/she is texting, calling, turning on the radio, drowsy, sleepy, drinking, looking right, talking and laughing, waving a hand, looking down while driving, signaling, head nodding under varying lighting conditions, closing eyes, head panning under varying lighting conditions, has a sad and tensed face, watching the right direction, watching the back side, watching something low down while driving, and watching a distraction to the left. It should be noted that image classification takes into consideration only the spatial features. The temporal aspect is, however, ignored which reduces the complexity of the problem. The datasets used in our experiments are State Farm Distracted Driver Detection (SFDDD), Boston University (BU), DrivFace, and FT-UMT datasets. * An approach to detect driver distraction is proposed which uses the deep features of ResNet-50 that are then used by the SVM for the classification. To the best of our knowledge, we are the first to propose feeding deep features of ResNet to an SVM classifier. * We evaluated our approach using four datasets, namely; State Farm Distracted Driver Detection, Boston University, DrivFace, FT-UMT. * We compared our proposed architecture with 12 existing approaches. Results showed that our proposed approach outperforms these approaches on these datasets in terms of accuracy. * Our proposed approach, based on deep features of ResNet-50, outperforms existing deep architectures. The work is important since it has diverse applications impacting driver safety. It can be used by car manufacturers to implement safety features that will prevent accidents due to distraction. Businesses that manage large fleets of vehicles, such as those in the mobility, ride-sharing, and trucking industries, can use this to monitor their drivers for tiredness and distraction, and hence improve the work conditions, and ensure the safety of their workers. Law enforcement and highway safety agencies can use it to detect drivers that may pose a threat to the others on the road, and take actions to preempt any accidents. The rest of this paper is organized as follows. Related work is discussed in Section 2 while proposed methodology and datasets are presented in Section 3 and Section 4.4, respectively. We share our evaluation results in Section 5.6 while variants of the proposed approach are illustrated in Section 6. Conclusions and future work are given in Section 9.
## 5 Simulation Results
This figure shows the achievable rate ratios between the ergodic rate derived from the proposed scheme and the theoretical ideal performance for various values of \(M_{k}\). In this simulation, \(M_{k}\) UEs are distributed randomly and uniformly within a circle with a radius of 10 m, centered around the beam's midpoint. As shown in Figure 5, the achievable rate ratios using the proposed scheme approaches 1 as \(N\) increases, regardless of \(M_{k}\). In particular, when \(M_{k}=16\) and \(N=256\), the proposed scheme achieves approximately 91% of the theoretical ideal performance. Therefore, it is confirmed that the considered RIS-assisted SatCom system enables a UAV to simultaneously serve multiple UEs with optimal performance. Figure 4: Comparative performance result of ergodic sum-rates based on variations in \(N\). Figure 3: Comparative performance result of ergodic sum-rates based on variations in transmission power when \(N=128\). In Figure 6, (32) and (43) are verified. Figure 6 presents a comparison between the outage probabilities derived from simulations and our analytical estimates from (32) and (43). In this figure, we assume \(\delta=0.8\) to analyze the outage event when \(R_{0}=0.8\hat{n}_{k}\) or \(0.8\hat{n}_{\text{MC,}k}\). As shown in Figure 6, the outage probabilities derived in (32) and (43) are consistent with the simulation results for a large \(N\). Furthermore, the outage probabilities in both unicast and multicast systems approach zero with increasing \(N\), confirming that an RIS can provide reliable SatCom.
## 2 Materials and Methods
### Measurements of CO\({}_{2}\) Fluxes and Environmental Data  
The flux measurement system was fixed to a flux tower situated in the maize field (Figure 1a) to derive GPP. The tower included a eddy covariance (EC) system to measure the net ecosystem exchange of CO\({}_{2}\) (NEE) [47]. The EC system consists of an open-path infrared gas analyzer (Model LI-7500, LI-COR Inc., Lincoln, NE, USA) for measuring CO\({}_{2}\) concentrations, a three-dimensional sonic anemometer (Model CSAT3, Campbell Scientific Inc., Logan, UT, USA) for measuring wind components, and data logger (CR3000, Campbell Scientific, Logan, UT, USA) for recording all EC data at 10 Hz. The EddyPro software (LI-COR Inc., Lincoln, NE, USA) was employed for data calculation and quality assurance to produce half-hourly flux estimates of NEE. A flux partitioning algorithm [48] was applied to estimate EC measured GPP (GPP\({}_{\text{EC}}\)) through net ecosystem exchange of CO\({}_{2}\) (NEE) and daytime ecosystem respiration (R\({}_{\text{e}}\)).  
\[\text{GPP}_{\text{EC}}=-\text{NEE}+\text{R}_{\text{e}} \tag{1}\]  
Half-hourly EC-based light use efficiency (LUE\({}_{\text{EC}}\)) was estimated as:  
\[\text{LUE}_{\text{EC}}=\frac{\text{GPP}_{\text{EC}}}{\text{PAR}\times\text{ FPAR}} \tag{2}\]  
where FPAR was calculated by leaf area index (LAI) and solar zenith angle as Chen [14]:  
\[\text{FPAR}=(1-\rho_{1})-(1-\rho_{2})\times\text{e}^{-0.5\text{LAI}_{\text{e} }/\cos\theta_{\text{s}}} \tag{3}\]  
and, where \(\rho_{1}\) is the PAR reflectance above the canopy and specified as 0.05; \(\rho_{2}\) is the PAR reflectance below the canopy and specified as 0.06. \(\text{LAI}_{\text{e}}\) is the effective LAI, obtained from field measurements. \(\theta_{\text{s}}\) is the solar zenith angle calculated using site location and time.  
Environmental variables were continuously measured in synchronization with EC flux measurements and integrated over 30 min time periods. The environmental variations included total incident PAR above the canopy, total sky radiation (R\({}_{\text{g}}\)), diffuse sky radiation (R\({}_{\text{d}}\)), air temperature (T\({}_{\text{a}}\)), relative humidity (RH), soil temperature (T\({}_{\text{s}}\), 10 cm depth), and volumetric soil water content (SWC, 10 cm depth). The ratio of direct and diffuse sky radiation was also calculated to represent the sky condition (Q) using R\({}_{\text{g}}\) and R\({}_{\text{d}}\)[49]. Q varies with real-time cloudiness of sky, with a less cloudy sky having greater Q.
## 2 Materials and Methods  
### Sampling and Laboratory Analysis  
The sewage wastewater samples were collected at the sewage treatment plant at a residential area located in Melaka. Initial data parameter analysis involved in-situ measurement (temperature, pH, conductivity and dissolved oxygen (DO)), while the ex-situ measurement parameters such as TSS, NH\({}_{3}\)-N, COD and biochemical oxygen demand (BOD) were performed in the laboratory. The readings were taken in triplicate for each parameter. All recorded values were in the form of standard deviation and mean values. The reading of temperature, pH, conductivity and DO was recorded using the YSI 5000 multiparameter instrument (YSI Inc., Yellow Springs, OH, USA). BOD was determined by using the following formula (Equation (1)). After the first reading was taken (DO\({}_{i}\)), the samples were kept in the incubator for five days at 20 \({}^{\circ}\)C (DO\({}_{5}\)).  
\[\mathrm{BOD}_{5}=\mathrm{DO}_{i}-\mathrm{DO}_{5} \tag{1}\]  
COD content was measured using HACH DR2500. The NH\({}_{3}\)-N reading was determined using the Nessler method and the instrument used was a HACH DR/2010 spectrophotometer (HACH, Loveland, CO, USA). TSS was determined using the gravimetric method. Prior the analysis, distilled water was filtered through 0.45 \(\upmu\)m filter paper to remove foreign substances. The filter paper was then dried in the oven at 105 \({}^{\circ}\)C and weighed. The sewage wastewater sample was filtered and the filter paper was dried again at 80 \({}^{\circ}\)C. The dried filter paper with residual substances was weighed. The calculation of TSS content was obtained using Equation (2):  
\[\mathrm{TSS}\;(\mathrm{mg/L})=(\mathrm{A}-\mathrm{B})/\mathrm{V}\times 1000\; \mathrm{mL} \tag{2}\]  
where,  
A = Weight of filter paper after filtration  
B = Weight of filter paper before filtration  
V = Volume of filtered water sample.