esa-sceva/satcom-chunk-collection huggingface dataset:
# DDC Control Techniques for Three-Phase BLDC Motor Position Control
## 5 Conclusions  
In this article, a data-driven MFA-based SMC is used for the position control of a BLDC motor. Moreover, an estimation algorithm and control law are explained for MFA, MFAC, and MFA-SMC algorithms. The proposed control algorithm uses the past-time PPD value, and the necessity of a complex mathematical modeling process is not necessary. The simulation results show that the proposed MFA-based SMC significantly improves position tracking performance and robustness. Moreover, it delivers a concrete theoretical significance by offering a significant enhancement on the basic DDC Algorithm. Moreover, a step response analysis shows that by using MFA-based SMC, the maximum overshoot and settling time has greatly reduced from 3.26 to 1.20 and from 1.251 to 0.492, respectively. A comparative analysis of the simulated results verifies that the proposed MFA-SMC algorithm can achieve better position control and stability over the traditional PID, MFA, and MFLAC methods. The simulation results prove a significant improvement in the performance of the three-phase BLDC motor.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Maximum Overshoot** & **Rise Time** & **Settling Time** \\ \hline PID & 3.26 & 0.021 & 1.251 \\ MFAC & 1.85 & 0.134 & 0.822 \\ MFLAC & 1.46 & 0.137 & 0.691 \\ MFA-SMC & 1.21 & 0.146 & 0.492 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative analysis of all data-driven control algorithms.  
Figure 17: Comparison analysis of step response of the BLDC motor using PID, MFAC, MFLAC, and MFA-SMC.  
**Acknowledgments:** This study is supported by the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics Nanjing, China, and is funded by \"National Natural Science Foundation of China (NSFC)\" under grant No. 61503185. The authors would like to thank the anonymous reviewers for their valuable comments that significantly improved the quality of this paper.  
**Author Contributions:** The central arguments and structure of the paper were equally devised by each of the authors through a series of discussions. [PERSON] conceived the idea and carries out experimental and simulation work under the supervision of [PERSON], [PERSON] and [PERSON] were involved in the writing of this research article and in the results.  
**Conflicts of Interest:** The author has no conflict of interest.
# An Efficient System for Automatic Blood Type Determination Based on Image Matching Techniques
## 5 Results and Discussion
### Simulation Results for the SDS  
In this system, the standard deviation was extracted after preprocessing the input image in order to identify the agglutination. A threshold of 16 was used to discriminate between agglutinated and nonagglutinated samples according to the systems proposed in [1; 5; 6].  
Table 5 shows the confusion matrix for the results of the SDS for 1500 patches including true positive (\(TP\) = 556), true negative (\(TN\) = 326), false positive (\(FP\) = 375), and false negative (\(FN\) = 243) samples. A true positive of 556 means that the system could identify 556 positive samples (with agglutination) correctly out of 799 actual positive samples (\(P\) = 799). The same explanation applies to the remaining three parameters and negative (\(N\)) samples. Different measures were extracted from the confusion matrix to evaluate the SDS algorithm as shown in Table 6[40]. They include:  
* Accuracy \[\text{Accuracy}=\frac{TP+TN}{P+N}\] (24)
* Error rate \[\text{Error rate}=\frac{FP+FN}{P+N}\] (25)
* Sensitivity \[\text{Sensitivity}=\frac{TP}{P}\] (26)
* Specificity \[\text{Specificity}=\frac{TN}{N}\] (27)
* Precision \[\text{Precision}=\frac{TP}{TP+FP}\] (28)
* \(F\)-score \[\text{F-score}=\frac{2*precision*sensitivity}{precision+sensitivity}\] (29)  
As explained in Section 4.1, the input image sample was partitioned into three patches containing three blood spots. Any wrong classification in one of the three patches results in wrong classification of the input sample. The high error rate of the SDS algorithm supports claims in Section 2.2 that the standard deviation cannot be considered as a discriminant feature. However, this was not reported by the state-of-the-art studies, since those used a low number of samples that did not cover all the variations in agglutination patterns. All of the evaluation measures of the SDS system were low, which reveals the weakness of the SDS in agglutination recognition. The results also indicate that the recognition rate of the negative samples (specificity) is much lower than the rate of the positive samples  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measure** & **Accuracy** & **Error Rate** & **Sensitivity** & **Specificity** & **Precision** & **\(F\)-Score** \\ \hline Result & 58.8\% & 41.2\% & 69.6\% & 46.5\% & 59.7\% & 64.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation measures for the SDS over the 1500 patches.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Actual Class/Predicted Class** & **Agglutination = Yes** & **Agglutination = No** & **Total** \\ \hline agglutination = yes & 556 & 243 & 799 \\ \hline agglutination = no & 375 & 326 & 701 \\ \hline Total & 931 & 569 & 1500 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix for the SDS over the 1500 patches.  
(sensitivity). The evaluation measures for the SDS show an accuracy of 58.8% over the 500 samples.  
The average processing time required to determine the blood type for each sample is about 80 ms.
# Salt-Specific Suppression of the Cold Denaturation of Thermophilic Multidomain Initiation Factor 2
## 1 Introduction
In summary, cryo-EM reconstructions, NMR experiments, and the 3D structure in solution have shown that IF2 transitions from being flexible in solution to having an extended conformation with disrupted interdomain contacts when interacting with ribosomal complexes. In our previous work, we analyzed the thermodynamic properties of the reversible denaturation of IF2 along with its isolated G and C fragments [28]. For the purpose of clarity, we used the term \"fragments\" for proteins obtained upon proteolytic cleavage of IF2. Previously, these fragments were called domains, which is inaccurate based on the structural information that is now available. Circular dichroism in the far-UV region has shown that IF2 and its fragments are well folded. Sedimentation velocity experiments reveal that full-length IF2, the G fragment, and most of the C fragment population are fully monomeric. Thermal denaturation of multidomain IF2 wt occurs at a high melting temperature of 94.5 \({}^{\circ}\)C and displays near-complete reversibility. IF2 can be stabilized by GDP and its analog GTP, as indicated by the higher transition temperatures. The high reversibility of thermal denaturation of such a large (63 kDa) protein is remarkable. This might be a possible consequence of the highly modular multidomain structure of IF2. Thermal denaturation is also found to be highly reversible for the G fragment but not the C fragment. The C fragment of IF2 vividly and irreversibly aggregates upon heating at temperatures above the melting temperature. This adverse aggregation can be suppressed using a denaturing agent, which also restores the high reversibility of thermal denaturation. A similar effect of GndHCl on the reversibility of thermal transition was observed for the _B. stearothermophilus_ C fragment [29]. The isothermal unfolding experiment revealed an unusually small m-value (m = d \(\Delta\)G/d[denaturant]), possibly due to unfolding intermediate(s) and because of independent, spectroscopically overlapping unfolding transitions of the G and C fragments. The observed effects follow the Hofmeister series of salts or others. Here, we evaluate the effect of salts on the cold denaturation of thermophilic multidomain initiation factor 2 in the hope of determining the dominant contributions to cold denaturation and to the stability of IF2 and its domains. A possible major contributor could be the screening of electrostatic interactions of IF2 domains by salts (lower dependence on salt type) or nonelectrostatic components and dispersion forces (strongly dependent on salt type). By comparing the effect of chloride salts of the cations Li\({}^{+}\), Na\({}^{+}\), NH\({}^{4+}\), and Cs\({}^{+}\) and sodium perchlorate on thermodynamic values, we found that the suppression of cold denaturation is highly salt-specific.
# Studies and Rejection of Intercrystal Crosstalk on FPGA in a High-Energy Photon-Counting System
## 5 Imaging Performance Validation Results  
### Simulation Validation Results  
In order to visualize the effect of the two criteria, the simulation results for the SP model with a 250 mm thick iron plate are shown in Figure 14. The image quality is quantified by the root mean square error (RMSE) compared with the ideal image (the image only counts non-interference signals). Table 3 includes various quantitative indicators that reflect these results. The proposed algorithm is proven to be capable of improving the radiation image quality, as evidenced by the decrease in RMSE from 4.328 to 3.273. For the tested model with a low count rate of 200/s, the criterion of \(\Delta\)Det \(\leq\) 2 has better results compared with the algorithm of \(\Delta\)Det = 1. The rejection ratio of interference (compared with the original image) is raised from 24.09% to 25.46%, leading to an overall improvement in image quality despite a few extra non-interferences being mistakenly deleted in the \(\Delta\)Det \(\leq\) 2 judgement. Meanwhile, under no-load conditions with a count rate of 3.44 \(\times\) 10\({}^{6}\)/s, the criterion of \(\Delta\)Det = 1 functions better, increasing the RMSE from 5.44 to 26.89.  
Figure 12: Signals from four adjacent detector units to illustrate the judgment errors, in which serial numbers of the photons (recorded by Geant4) are labeled.  
Figure 13: Flowchart of the FPGA algorithm used to reject intercrystal scatter interaction pairs. Two criteria can be switched according to the applied scenarios.
## 2 Hybrid Wind-PV System Modeling
### PV Panels Model  
A large number of models for describing the behavior of the solar photovoltaic panels can be found in the literature [41; 42]. The most popular is the model with one diode shown in Figure 6.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Description** & **Value** \\ \hline Radius R & 0.62 m \\ Air density & 1.25 kg/m\({}^{3}\) \\ Gear ratio G & 20 A \\ Flux of permanent magnet & 0.06 Web \\ Stator inductances L\({}_{\text{d}}\) = L\({}_{\text{q}}\) & 10 mH \\ Stator resistance R\({}_{\text{s}}\) & 0.0675 \(\Omega\) \\ Pole numbers p & 3 \\ Total inertia, J & 0.00176 kg.m\({}^{2}\) \\ Friction coefficient & 0.38 \(\times\) 10\({}^{-4}\) (SI) \\ Optimal Tip-Speed Ratio & 0.78 \\ Maximal power coefficient C\({}_{\text{pmax}}\) & 0.515 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Wind system data.  
Figure 4: Principle of Tip-Speed Ratio control.  
Here, Isat: reverse saturation current, K: Boltzmann's constant, T: temperature, e: electron charge, n: ideality factor, Rs: series resistor, Rsh: parallel resistor, Iph: photonic current, Vpv: PV voltage and Ipv: PV current.  
Considering a PV panel made up of Np parallel cell strings formed of Ns cells in series and that Rsh is infinite, the PV model is given by Equation (8).  
\[\text{I}_{\text{pv}}=\text{N}_{\text{P}}\text{I}_{\text{ph}}-\text{N}_{\text{P} }\text{I}_{\text{sat}}\exp\left(\frac{\text{e}(\text{V}_{\text{pv}}+(\text{I}_ {\text{pv}}\frac{\text{N}_{\text{p}}}{\text{N}_{\text{s}}}\text{R}_{\text{s}}))} {\text{nKT}}-1\right), \tag{8}\]  
The data of the used panel are given in Table 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Description** & **Value** \\ \hline Maximum power & 105 W \\ Voltage at p max (Vmp) & 24 V \\ Current at p max (Imp) & 4.4 A \\ Open-circuit voltage (Voc) & 32 V \\ Short-circuit current (Isc) & 5 A \\ \hline \hline \end{tabular}
\end{table}
Table 2: PV Panel Data.  
Figure 5: Model of a PV cell with one diode.  
Figure 6: Model of a PV cell with one diode.  
The voltage-current (V-I) and voltage-power (V-P) characteristics according Equation (8) are given in Figure 7.  
PV MPPT Controller  
The MPPT method that we used in our work is the look-up table-based MPPT due to its simplicity and our objective being the control of the multiport DC converter. Figure 8 represents the optimal PV current according to the irradiations.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 3 The MASU Framework
### Internal Mechanisms
#### 3.4.2 Cost Function and Resource Optimization  
Before selecting and activating roles in the Sensing Tier, the Manager uses the information received from the MASU units to run a Cost Function. The output of this function will determine whether it would be beneficial for the whole group of units to work collaboratively or not. According to such output, the Manager will activate or not the Role Selection and Activation processes in the Sensing Tier. Consequently, if the output of the Cost Function is negative, there will not be collaborative activity and all the units will work independently. The Cost Function determines the viability and usefulness of performing collaborative sensing.  
The Cost Function is also associated with the results of the Resource Optimization Algorithm (ROA), which helps optimize the use of the resources of the whole group of participating units. The ROA algorithm runs every time that the MASU framework wants to initiate the Role Selection and Activation process in the Sensing Tier. Such an algorithm influences how roles are selected, calculating the estimated costs of the services provided by each one of the roles, and considering the specific requirements of the activity and the characteristics and state of the devices. The ROA determines, for a group of units, who has to activate a given role, who has to collect data from specific sensors and share the results with the rest of the members, etc. The ROA will also decide, between these two Producer units, which one have to capture data from microphone and which one have to sense GPS data. As a result, the rest of the units acting as consumers will deactivate their sensors and wait until they receive the sensed information from these two producer units.
# Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder
## 4 Results and Discussion
### Influence of Sparsity Parameter \(\rho\) on Prediction Results  
The sparsity parameter \(\rho\) plays a key role in model training. Different sparsity parameters determine the degree of neuron activation. In order to explore the influence of sparsity parameters on the prediction results of the model, three different sparsity parameters of 0.01, 0.1, and 1 were selected for modeling. Figure 9 shows the prediction results of the different models. The results is shown in Table 4. When the sparsity parameter is 1, the neurons are activated without any restriction, which has \(R_{P}\) and RMSEP values of 0.910 and 0.750, respectively. When the sparsity parameter is 0.1, compared with that of 1, the model prediction performance is improved, which has a 2.2% increase in \(R_{P}\) and a 12.1% decrease in RMSEP. When the sparsity parameter is 0.01, compared with that of 1, the model prediction performance is further improved, which has a 3.1% increase in \(R_{P}\) and a 12.8% decrease in RMSEP. This shows that the sparsity parameter can affect the SAE-PLSR model prediction performance. The reason may be that the sparsity restriction is added, so that most of the useless features and the features that are harmful to the model prediction are inhibited, and the influence of the useful features is amplified [36]. So, the prediction accuracy of the model is improved. In this paper, the sparsity parameter 0.01 is used to effectively improve the model prediction performance.  
Figure 9: The prediction results of the SAE–PLSR with different sparsity parameters (\(\rho\)).
# Tele-Assessment of Executive Functions in Young Adults with ADHD: A Pilot Study
## 6 Statistical Analysis  
Data were analyzed to provide a comprehensive and detailed EF profile of young adults with ADHD in comparison to young adults without ADHD. Descriptive and inferential statistics were computed by using Jamovi Software 2.2.3.0. The normality of the distribution (skewness cut-off = 2; kurtosis cut-off = 3) was analyzed for all measures. To investigate the presence of significant differences between the groups, ANOVAs on the different measures of each EF task were performed for normally distributed measures, while Kruskal-Wallis tests were used for non-normally distributed measures. The effect size was expressed by partial eta squared (\(\eta_{\text{p}}\)\({}^{2}\)) and xi (\(\xi\)) values.  
The Kruskal-Wallis test was chosen as it allows for the comparison of two or more independent groups based on an ordinal or non-normally distributed interval/ratio variable. Although only two groups were included in this analysis (ADHD vs. controls), the Kruskal-Wallis test, as a generalization of the Mann-Whitney U test, will provide greater methodological consistency if the analysis is extended to more than two groups in future studies. Additionally, the Kruskal-Wallis test yields a chi-square statistic and an effect size index (\(\varepsilon^{2}\)), facilitating the interpretation of the effect magnitude in between-group comparisons.
# A Comparative Analysis of Big Data Frameworks:
## 4 Big Data Processing Frameworks
### Samza
#### 4.4.2 Data Processing Model  
The data processing model of Samza builds upon two strong pillar: streams and jobs. Streams are primarily the inputs and outputs of the samza Jobs. A stream in Samza is a multi-part, ordered, multi-subscriber message sequence that is replay-able and lossless by design. User programs in Samza are processed in the form of Samza jobs. A Samza job is the code that consumes and processes a series of input streams and produce one or more output streams. These jobs are represented in the form of a directed graph of operators (vertices) connected by data streams (edges). The job and streams are further broken down into smaller execution units of parallelism, called tasks and partitions. Each task receives data from one or more partitions for each worker input stream. With this break down of stream to partitions and jobs to parallel tasks, samza achieves a linear scalability with number of containers [52].
## 4 Validity of 1D-CNN Bandpass Filter
Our results reveal that the time series after 1D-CNN filtering closely matches the time series after Lanczos filtering for the daily average temperatures at all four stations, confirming that the 1D-CNN model has as high of a capability to filter QBWO and ISO signals as the Lanczos filter does. It is also noted that the Lanczos filter may not be able to produce valid filtered outputs when there are missing data points, such as in late April 2022 at the CCH station (Figure 4c). More discussion will be given in the next section on how the 1D-CNN filter could address this issue. Figure 4: Time series of the original input data (blue solid line), daily climatology (blue dashed line), unfiltered daily anomaly (red solid line), Lanczos-filtered output (purple solid line), and 1D-CNN-filtered output (black solid line) from January to December 2022. Results are based on the daily mean temperature observations of the (**a**) HKO, (**b**) HKA, (**c**) CCH, and (**d**) TKL stations.
## 1 Introduction
CAD/CAM technology specializes in a higher precision, greater standardization, and cost reductions. The ability to manufacture smaller thicknesses and a more conservative dental preparation would decrease the MD value and have less bacterial infiltration and biomechanical problems, although there are few studies regarding CAD/CAM custom abutment, especially using titanium (Ti)-bases [16]. Figure 1: The differences between the terms, m (MD), a (AMD). A Ti base serves as a bonding base for customized implant-supported restoration with a high precision and excellent esthetic results. Few clinical studies examined the MD values between implant-supported prostheses and Ti bases. Ti bases can be employed as an alternative to conventional approaches for restoring dental implants, but long-term clinical studies are required for better assessment [17]. Six methods for measuring the MD have been described in the literature. The cross-sectioning method allows clear observation of the MD in the vertical and horizontal planes but does not allow for a long-term analysis and comparison of results obtained during different fabrication stages of the same specimen [2]. The epoxy replica method is also used to examine MD values. It should be noted that the procedures required for fabricating the test specimens (impression making, pouring replicas) may involve some errors, such as difficulty in identifying the crown margins and finishing lines [18; 19]  
The profile projector, on the other hand, presents views of the die and the specimen in the same focal plane on a computer monitor for an accurate focus. Though, with this method, the thickness of the cement layer at the marginal areas can only be indirectly inferred [19]. The digimatic micrometer and microcomputed tomography methods are not used often due to technical difficulties [2]. The direct microscopy, cross-sectioning, and replica methods are used most often for evaluating the MD. Although direct microscopy has a low accuracy and it is difficult to select measurement points, it is the most straightforward, takes minimal time, and is less expensive than other methods [2]. Numerous studies have examined the MD of crowns fabricated using conventional techniques, but a lack of evidence-based studies regarding misfit between implant-supported prostheses fabricated by CAD/CAM and Ti-base abutment led us to examine them in this study. Consequently, the purpose of this in vitro study was to evaluate the MD (Figure 2) and the AMD (Figure 3) between the substructures of zirconia crowns (CAD/CAM) and Ti-base abutments before and after cementation, using SEM at \(\times\)500 magnification. The null hypothesis was no differences would be found in the marginal misfit measured before and after cementation.