esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Among the numerous techniques followed to learn a linear classifier through the discriminative dictionary and sparse representations learning of signals, the techniques to learn a nonparametric Bayesian classifier jointly and discriminately with the dictionary and the corresponding sparse representations have drawn considerable attention from researchers. These techniques jointly learn two sets of sparse representations, one for the training samples over the dictionary and the other for the corresponding labels over the dictionary classifier. At the prediction stage, the representations of the test samples computed over the learned dictionary do not truly represent the corresponding labels, exposing weakness in the joint learning claim of these techniques. We mitigate this problem and strengthen the joint by learning a set of weights over the dictionary to represent the training data and further optimizing the same weights over the dictionary classifier to represent the labels of the corresponding classes of the training data. Now, at the prediction stage, the representation weights of the test samples computed over the learned dictionary also represent the labels of the corresponding classes of the test samples, resulting in the accurate reconstruction of the labels of the classes by the learned dictionary classifier. Overall, a reduction in the size of the Bayesian model's parameters also improves training time. We analytically and nonparametrically derived the posterior conditional probabilities of the model from the overall joint probability of the model using [PERSON] theorem. We used the Gibbs sampler to solve the joint probability of the model using the derived conditional probabilities, which also supports our claim of efficient optimization of the coupled/joint dictionaries and the sparse representation parameters. We demonstrated the effectiveness of our approach through experiments on the standard datasets, i.e., the Extended YaleB and AR face databases for face recognition, Caltech-101 and Fifteen Scene Category databases for categorization, and UCF sports action database for action recognition. We compared the results with the state-of-the-art methods in the area. The classification accuracies, i.e., 93.25%, 89.27%, 94.81%, 98.10%, and 95.00%, of our approach on the datasets have increases of 0.5 to 2% on average. The overall average error margin of the confidence intervals in our approach is 0.24 compared with the second-best approach, JBDC, for which it is 0.34. The AUC-ROC scores of our approach are 0.98 and 0.992, which are better than those of others, i.e., 0.960 and 0.98, respectively. Our approach is also computationally efficient.  
Keywords:linear classifier; dictionary learning; nonparametric Bayesian; discriminative; sparse representation +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Applicability of Used Cartographic Methods  
Big Data visualization is a distinct approach as its primary goal is to visualize the whole dataset, while 'traditional' cartographic methods [82,90,91] deal with aggregations/generalizations or interpolation. As demonstrated in this paper, there are challenges such as displaying the full density of points. Uncertainty, in general, originates at the following two levels in the scope of 'traditional' cartographical visualization methods:  
1. uncertainty of input information intended for a cartographic visualization;
2. processing of input data through (cartographic) methods of aggregation, generalization, and interpolation, as well as interpretations thereof.  
Cartographic visualization of Big Data uncertainties eliminates the level (2) as map-maker processing of input data is suppressed. At the same time, visualization of a whole dataset brings new possibilities, such as a novel approach to uncertainty expression and visualization.  
Both 2D and 3D cartographical methods for uncertainty visualization have been developed [50, 51, 52, 60, 69, 70, 71, 72]. Cartographical methods have the following similarities when applied to point Big Data, no matter which 2D cartographic method was used. These similarities include the following:  
* Point size: remains the same, no matter whether qualitative or quantitative data are being visualized.
* Spatial pattern: overall picture is clear to a user when the values are not affected by any other characteristics and/or dimensions.
* Point density: conflicts of points tend to be common as each application needs to strike a balance between the following contradicting requirements:
* the need to maintain readability of a symbol, which pushes for larger symbols, as opposed to the following:
* the need to provide an overview that enables to clearly see spatial patterns in the whole dataset, which pushes for smaller symbols;
* both contradicting requirements above are tightly connected to a scale that is being used for a cartographic presentation.  
The following similar aspects of 3D cartographic visualizations in comparison with their 2D counterparts can be identified:  
* perspective height as an additional graphic variable brings a possibility to combine the presented value(s) (i.e., yield, in our study) and uncertainty value in one cartographic visualization [85].
* Scale variations across a 3D scene, which complicates readability in comparison with 2D: value depicted as perspective height changes according to the perspective projection distortion [86; 87].
* In the case of interactive perspective heights, the previously mentioned disadvantages can be overcome using cutting planes (Figure 10a) and other interactive functions [87].
# Performance of a Supercritical CO2 Bottoming Cycle for Aero Applications
## 2 Materials and Methods
### Turbomachinery  
Very few S-CO\({}_{2}\) component maps are available in the public domain. A report released by Sandia National Laboratories [22] about their existing S-CO\({}_{2}\) test rig contained partial component maps delivered to them by Barber-Nichols Inc. (BNI), the manufacturer of the employed radial turbines and compressors. In an effort to use those maps in this investigation, the maps were digitized and the resulting data subsequently reformatted to fit the requirements of PROOSIS. BNI's compressor was designed to deliver a pressure ratio of 1.8 for an actual mass flow of 3.46 kg/s, at an isentropic efficiency of 67%. The digitised compressor maps are scaled to match any intended pressure ratio and efficiency in PROOSIS. Figure 3 illustrates the original BNI map and the compressor map in PROOSIS, scaled to the same application in SI units. Bearing in mind, the more than 30 years of potential research and development preceding the envisioned 2050 EIS of a combined cycle aero engine, the component efficiencies of the compressor and turbine currently under investigation in ULTIMAE are assumed to be considerably higher than those in [22]. Furthermore, the flow capacity of the investigated designs is approximately an order of magnitude beyond that of the test rig at Sandia National Laboratories. The compressor is assumed to achieve an isentropic efficiency of 89%, while the turbine is assumed to achieve 90% isentropic efficiency. Those values are more consistent with the assumptions made by Dostal for nuclear applications with an EIS in 2025 [17]. The difference in efficiency, due to the substantial flow capacity differences between nuclear and aero application, is assumed to be offset by design improvements made to S-CO\({}_{2}\) turbomachinery by 2050.  
Figure 2: (**a**) Comparison of Peng-Robinson Equation of State (PREOS) [26,27] and Span & Wagner Equation of State (SWEOS) [28] near the critical point of S-CO\({}_{2}\); (**b**) sensitivity of RBC thermal efficiency to compressor and turbine isentropic efficiencies; and (**c**) specific heat variation of S-CO\({}_{2}\) near the critical point.  
The dependency of the thermal efficiency of the bottoming cycle on the design point isentropic efficiencies of the turbine and compressor is illustrated in Figure 2b. The turbine efficiency has a noticeably greater impact on the performance of the bottoming cycle than the compressor, which is to be expected as it does more work. The assumed component efficiencies have not been scaled with pressure ratio in the presented studies, as too few data are available on those relations specific to S-CO\({}_{2}\). The design pressure ratio of the RBC varies between two and four in the present study.
# State of the Art of Tactile Micro Coordinate Metrology
## 4 System Calibration and Performance Verification  
### Probe Calibration  
#### 4.1.1 Single Point Probing  
The single point probing behavior of a CMM probe can be characterized by the \(P_{\mathrm{FTU}}\) value (single stylus form error) as specified in the standard ISO 10360-5 [31], based on the peak-to-valley deviation of 25 probing points on a sphere arranged according to Figure 4 (left). For two of the micro CMMs presented above, corresponding results are available. The \(P_{\mathrm{FTU}}\) value for the Zeiss F25 probe was found from the average of 14 measurements to be 189 nm with 60 nm standard deviation and all values but one complying with the manufacturer specification of 250 nm [8]. A repeatable pattern was found in these measurements, most probably due to the anisotropic behavior of the silicon chip membrane as suggested by [PERSON] _et al._[8].  
METAS has carried out the same standardized test [31] for single point probing error on a 1 mm diameter sphere [32]. The test was performed four times with the sphere rotated by 90\({}^{\circ}\) between each test. The sphere radius was found to be between 0.50020 nm and 0.500211 nm with a standard deviation of 4 nm. The test resulted in \(P_{\mathrm{FTU}}\) values between 53 nm and 67 nm with an average value of 58 nm. The measurements in Figure 4 (right) show a systematic behavior probably due to an uncompensated form deviation of the probing sphere. The Si\({}_{3}\)N\({}_{4}\) test sphere [33] had an estimated form deviation of less than 15 nm.  
Figure 3: Principle of the Abbe free CMM stage at METAS. \(y\)-axis perpendicular to drawing plane.
# Aerial Tele-Manipulation with Passive Tool via Parallel Position/Force Control +
## 5 Experimental Results
### Results
#### 5.2.2 Movable Object Experiment
A more fair and comprehensive validation and testing of the this system in outdoor, mocap-denied, and realistic scenarios in the presence of external disturbances and using only onboard computation, which is not in the scope of this paper, will be the subject of future work. Figure 8b' shows the contact maintenance function input and output forces. Since the main purpose is to push the cart, the major component of the commanded force is along the normal direction; therefore, most of the time the feasible commanded force is the same as the desired commanded force by the human operator and, as is evident from Figure 8c', the contact is kept during the force interaction. The force tracking control results, shown in Figure 8c', represent a more difficult task compared to the previous task (force tracking control in contact with a stationary object). As can be seen, while the cart is stationary, that is, when the force applied by the quadrotor end-effector is compensated for by the friction of the cart wheels with the surface, the force tracking has a similar performance to the previous experiment. On the other hand, once the cart starts to move the error increases. However, considering the particular application of this experiment, which aims at pushing a cart forward, as far as the contact is kept and the operator is able to accomplish the task, the experiment is considered successful, although the average of the absolute force error norm is 0.5 N, that is a 20% relative error. The haptic feedback components are shown in Figure 10-right. \(f_{PD}\) is depicted in Figure 10a', and the haptic feedback component related to the slave side, \(f_{pf}\), is shown in Figure 10b'. As is evident, the magnitude of the latter is generally bigger than the magnitude of the former, allowing the human operator to be aware of the aerial manipulator condition in the master side (this is also true for the experiment with the stationary object). Haptic feedback in this experiment is even more important for the human operator to not lose the contact, as \(f_{pf}\) allows the human operator to feel the applied force during the contact interaction. When the perceived force is small, it means that the contact is weak, and the human operator can command a higher force to ensure the contact maintenance.
## 4 System Architecture
### The Field Programmable Gate Array (FPGA)  
By using the debugging tools in VIVADO, virtual input output (VIO) and integrated logic analyzer (ILA), the inputs and the outputs should be observed from the chain to make sure that the results are correct. Additionally, the MCMM in FPGA is used to generate defect clock domains to make sure that the phases between them do not change as time passes.  
From the UL view, the generated files from MATLAB are programmed into a RAM and make the interface of the chain to the RAM. The data are transmitted by the USRP after receiving from the UL and then the DL is started with the same method. At last, it is possible to check the BER on FPGA by comparing the received data with the transmitted data.
## 2 Quorum Sensing Genes in _Sinorhizobium melilioti_
These orphan LuxR regulators respond to plant exudates and control bacterial virulence [47; 48]. Whether a plant-produced or any other exogenous signal activates NesR warrants investigation. In the _S. meliloti_ strain Rm41, which also carries the ExpR/Sin QS system, the _tral_ and _traR_ genes were identified in a large plasmid (pRme41a) which is not present in Rm1021 [27] (Table 1). This second QS system in Rm41, which is involved in the production of AHLs unique to Rm41 (3-oxo-C8-HL), is located next to plasmid transfer genes. Such a scenario suggests that this system regulates transfer of the pRme41a upon reaching a quorum, as is the case for a homologous system in _Agrobacteriumium tunefaciens_. The classical QS model assumes free diffusion of AHLs across the bacterial cell envelope. However, this was only demonstrated for the short-chain C4-HL. As mentioned earlier, _S. meliloti_ produces long-chain AHLs. These molecules, in contrast to short-chain AHLs, cannot diffuse freely across the cell membranes. Recently, membrane vesicles have been shown to participate in the secretion of C16-HL produced by _Paracoccus_ spp. and also guide its transport to target cells [49]. In _S. meliloti_, sensing of AHLs with acyl chains composed of 14 or more carbons is facilitated by the outer membrane protein FadL (Figure 1), a homolog of the _Escherichia coli_ FadL long-chain fatty acid transporter [50]. In contrast to the _E. coli_ FadL, _S. meliloti_ FadL is not required for the uptake of oleic acid (C18:1) but participates in the uptake of palmitoleic acid (C16:1) and long-chain AHLs, especially C16:1-HL, increasing sensitivity to AHL levels external to the cell and accelerating the QS response. This AHL import ability by FadL homologs is conserved in rhizobial species belonging to the genera _Sinorhizobium_, _Rhizobium_ and _Mesorhizobium_.
## 4 Mathematical Model and Distinct Features of Active Dynamics
### Distinct Features of Active Dynamics in Non-Fluid State  
Two coupling parameters, \(K_{\rm u1}\) and \(A_{3}K_{3}\), were identified. However, they still need to be decoupled further. The identification of the linear stiffness of the decoupler membrane \(k_{3}\) is essential for decoupling these two coupling parameters. The natural frequency of the mover system in the non-fluid state \(f_{\rm n3,nof}\) is different from that in the fluid-filled state, which can be expressed as Equation (22).  
\[f_{\rm n3,nof}=\frac{1}{2\pi}\sqrt{\frac{k_{3}}{m_{\rm 3,nof}}} \tag{22}\]  
Since the structure in a non-fluid state is different, the distinct features are related to the resonance of the decoupler membrane and mover system.  
According to the linear model in the non-fluid state shown in Equation (17), the FRF of the mover acceleration to the actuator current and the distinct features can be expressed as Equation (23). It should be noted that the distinct features are the mover resonance peak followed by a horizontal segment, which are referred to as the **resonance peak**\(A_{\rm p,3,nof}\) at the ratio of \(\lambda_{\rm p,3,nof}\) and the **6 th horizontal segment**\(\breve{Y}_{i,\infty,6}\). \(\lambda_{\rm p,3,nof}\) can be expressed using Equation (24).  
\[\left|\frac{\breve{Y}_{i,\infty,6}}{I}\right|=\left\{\begin{array}{ll}\frac{ \breve{Y}_{i,\infty,6}}{2\xi_{3,\rm nof}\sqrt{1-\xi_{3,\rm nof}^{2}}}=A_{\rm p,3,nof},\ \ \lambda_{3,\rm nof}=\lambda_{\rm p,3,nof}\\ \frac{k_{\rm M}}{m_{\rm 3,nof}}=\breve{Y}_{i,\infty,6},\ \ \lambda_{3,\rm nof} \rightarrow\infty\end{array}\right. \tag{23}\]  
\[\lambda_{\rm 3,nof}=\frac{\omega}{\omega_{\rm n3,nof}}=\frac{f}{f_{\rm n3,nof} }\,\ \xi_{3,\rm nof}=\frac{c_{\rm n,nof}}{2\sqrt{m_{\rm 3,nof}}k_{3}}\]  
\[\lambda_{\rm p,3,nof}=\frac{f_{\rm p,3,nof}}{f_{\rm n3,nof}}=\frac{1}{\sqrt{1- 2\xi_{3,\rm nof}^{2}}} \tag{24}\]  
Similarly, distinct features can be obtained for the FRF of the transmitted force \(f_{5}\) to the actuator current \(i\), as shown in Equation (25). The frequency ratio of the resonance peaks is denoted by \(\lambda_{\rm p,5,nof}\). The constant in the high-frequency band is named the **7 th horizontal segment \(F_{1,\infty,7}\). If the FRF curves can be obtained accurately and smoothly, the physical parameters \(m_{3,\text{nof}}\), \(k_{3}\), \(c_{3,\text{nof}}\), and \(\xi_{3,\text{nof}}\) can be identified based on the resonance peak \(A_{\text{p},3,\text{nof}}\), the 6 th horizontal segment \(\hat{Y}_{i,\infty,6}\), and a known voice coil constant \(k_{\text{M}}\).  
\[\left|\frac{F_{5}}{I}\right|=\left\{\begin{array}{ll}\frac{k_{\text{M}}}{2 \xi_{3,\text{nof}}\left/1-\xi_{3,\text{nof}}^{2}\right.}=A_{\text{p},5,\text{ nof}}\,,&\lambda_{3,\text{nof}}=\lambda_{\text{p},5,\text{nof}}=\lambda_{ \text{p},3,\text{nof}}\\ k_{\text{M}}=F_{i,\infty,7}\,,&\lambda_{3,\text{nof}}\to\infty\end{array}\right. \tag{25}\]  
Subsequently, based on the distinct features of the dynamics, all key parameters can be identified. Complete experimental research was conducted to verify the feasibility of the method for all parameters.
## 1 Introduction
Especially by working with WuRx, it is mandatory to test and simulate sensor nodes before network implementation because the power consumption of the wake-up nodes can be determined by calculations. However, the sensitivity of the receiver is an important parameter that should be estimated and verified. A division of COTS WuRx is based on low frequency (LF) receivers, e.g., AS3933. Figure 3 shows the building blocks of an 868 MHz WuRx utilizing an envelope detector, an LF amplifier to increase the sensitivity of the radio chip, and an LF receiver for pattern recognition and interruption of the primary transceiver during its sleep mode. WuRx, being a recent representative of this low-frequency pattern matcher (LFPM), is the main focus of this article. The increasing competition in the research field is demanding more accurate validation of studying and revealing real-world processing scenarios. Additionally, the wake-up receiver in the latest green technologies, which consumes only in the range of a few micro-watts, is a novelty. The main contribution of this study is materializing the analysis and the execution of an accurate model of a WuRx that includes two different radio chips. This model is integrated into an OMNeT++ discrete event simulator to perform the WuRx metrics, which opens the horizon for low-cost, short-term, future scenario simulations to investigate radio behavior. Figure 3: Building blocks of a typical commercial off-the-shelf WuRx with passive envelope detector and low-frequency receiver. Figure 2: An example of a wake-up receiver-based protocol where the transmitter initiates the communication by sending a wake-up call, followed by data transmission. This paper first presents a general overview of the reliability of an RF-WuRx that integrates an LFPM, a main 868 MHz transceiver, and an attenuation factor that affects the signals near the receiver's antenna. Then, we describe the implementing software and hardware metrics in a simulator that enables the investigation of the reaction of the WuRx to different attenuation levels in different environmental conditions. We also provide test results. The rest of this paper is organized as follows: An overview of the importance of link quality metrics in WSN reliability and a study of different simulators are presented in Section 2. In Section 3, an investigation of a PER using WuRx and a discussion of results are shown, while the implementation of different metrics and the simulation results in an OMNeT++ discrete event simulator are provided in Section 4. Finally, this paper is ended with a discussion and conclusions.
# Criteria for Cutting Head Clogging Occurrence during Slurry Shield Tunneling
## 3 Cutter Head Clogging at the Jinan Huanghe River Crossing Tunnel Construction  
The two Jinan Huanghe River Crossing tunnels, located at the central area of the Jinan city of Shangdong Province of China, were excavated by two super-diameter slurry shields. The shield machines were 15.76 m in excavation diameter with an opening area ratio of 46%, as shown in Figure 8.  
Figure 7: Changes of the two composite parameters at the Beijing south-to-north water diversion auxiliary project.  
The two tunnels at a depth of 26.3-54.6 m, and the soils to be excavated are mainly silty clay with a clay grain content of 15.5-45.5% (average of 26.9%). Sand layers and calcic concretions are within the tunnel alignment but unevenly distributed due to local enrichment and stratification, as shown in Figure 9. The encountered silty clay with a plasticity index of 10-15 and a consistency index of 0.5-1.0 is liable to clogging the cutting head. The Huanghe River is at the ground surface, and the possible water pressure during the construction is about 0.65 MPa. As presented in Figures 10 and 11, when shield driving at ring 675 of the east line tunnel, the total thrust and the cutting head of the machine experienced a sharp increase, and the advance rate began to decrease. The increase and the decrease in the main driving parameters continued until ring 685.  
Figure 8: The used shield machine at the Jinan Huanghe River Crossing tunnel.  
Figure 10: Changes of total thrust and cutting head torque of the machine at the Jinan Huanghe River Crossing tunnel construction.  
Figure 9: Longitudinal cross section of the Jinan Huanghe River Crossing tunnel.  
At ring 685, after the machine stopped, an anti-clogging agent was used, and the cutting head was rotated to deal with the cutting head clogging. The shield tunneling was renewed 24 hours later, and clear changes of the driving parameters occurred. The cutting head torque and the total thrust declined and then remained stable. As mentioned above, the cutting head torque, together with the total thrust of the machine, is closely related to the penetration depth per revolution. In the same way, two composite parameters of total thrust/penetration depth and cutting head torque/penetration depth are employed to replot the data in Figures 10 and 11. The results are given in Figure 12. From the development of the two new parameters, the clogging at rings 675-685 and the effects because of the countermeasures taken at ring 685 can be more clearly identified. The newly-defined parameters help confirm the existence of the cutting head clogging throughout driving the shield machine and can be used to evaluate the occurrence of the cutting head clogging.