esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, a backstepping sliding mode composite controller based on a nonlinear disturbance observer is designed to realize the speed tracking control of the PMSM, and the advantages and disadvantages of the two kinds of sliding mode control algorithms in the face of sudden load disturbance are analyzed. Finally, it is proved that the composite controller designed in this paper can not only improve the disadvantages of the two controllers, but it also has a better dynamic performance.  
The simulation and experimental test mainly verifies the speed response waveforms of the three controllers designed under the load torque sudden change, and proves that the sliding mode control is more sensitive to model mismatch and unknown disturbances, and at the same time causes the speed response to produce a steady-state bias. The second aspect proves that the integral module will also bring negative effects on the dynamic response of the system. Overall, the backstepping sliding mode composite controller based on a nonlinear disturbance observer has a smaller steady-state jitter and better dynamic response. The experimental results show that the control strategy is effective at suppressing the influence of model mismatch and unknown disturbances on the system during the operation of PMSM.  
The proposed controller in this paper achieves a high performance speed tracking control of PMSM. The disturbance studied in this paper includes parameter perturbations and load torque. However, the effects caused by the parameter perturbations in the short-term experiments are not obvious during the experiments. In the future, further refinement of the disturbance types and more detailed disturbance models need to be established.  
[PERSON] proposed the idea and participated in the experiments and paper writing. [PERSON] completed the experimental work. [PERSON] and [PERSON] funded the experiment and revised the manuscript. [PERSON] and [PERSON] analyzed the results and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52177211, and by the Heilongjiang postdoctoral research starting fund, grant number LBH-Q20020.  
Not applicable.  
We thank [PERSON] for his suggestions regarding the experimental scheme of this paper.  
The authors declare no conflict of interest.  
Figure 8: PMSM rotor position angle and speed waveforms. (**a**) Full working condition response waveform, (**b**) Speed response during loading, (**c**) Speed response at reduced load.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 6 System Implementation  
For our tests, we use an Ubuntu 21.04 operating system on a mobile workstation with an Intel Core i7-10875H (2.30 GHz \(\times\) 8), 16 GB RAM, and 256 GB SSD storage. The docker version of ONOS [33] v2.8 is utilized as an SDN controller. Mininet-WiFi [5] is used to create host devices, virtual stations, and APs. On Mininet-WiFi and its wireless medium simulation tool dependency umediumd, some crucial improvements had to be made in order to support independent and simultaneous Wi-Fi packet transfers. They are merged into the main branch ([[https://github.com/intrig-unicamp/mininet-wifi/pull/419](https://github.com/intrig-unicamp/mininet-wifi/pull/419)]([https://github.com/intrig-unicamp/mininet-wifi/pull/419](https://github.com/intrig-unicamp/mininet-wifi/pull/419)), accessed on 19 December 2022, [[https://github.com/ramonfontes/wmediumd/pull/9](https://github.com/ramonfontes/wmediumd/pull/9)]([https://github.com/ramonfontes/wmediumd/pull/9](https://github.com/ramonfontes/wmediumd/pull/9)), accessed on 19 December 2022). Open vSwitchv2.15 [34] is used for virtual switch emulation. For traffic generation from vehicles in our experiments, iPerf v3.10 [35] is used. Eclipse SUMO v1.11.0 [36] is used for vehicle mobility simulation. We implemented our main simulation driver application in Python 3.9 and the offloading orchestrator in Java 11. The baseline simulation parameters are listed in Table 4.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Symbol** & **Value** & **Definition** \\ \hline \(\lambda\) & 0.1 s\({}^{-1}\) & Exponentially distributed task generation rate \\ \hline \(N_{U}\) & 9 & Number of UAVs \\ \hline Task size & 30–40 MB & Randomly distributed \\ \hline \(\overline{V}\) & 20 km/h & Average Vehicle Speed \\ \hline \(\beta_{cloud}\) & 3 MB/s & The processing speed of cloud \\ \hline \(t_{deadtime,i}\) & 50–120 s & The time budget for \(K_{i}\) to be processed without any QoS violation (randomly distributed) \\ \hline \(p_{heavy}\) & 0.9 & The probability of the task offloaded to the cloud if the pool is heavily occupied \\ \hline \hline \end{tabular}
\end{table}
Table 4: Baseline simulation parameters.  
Figure 3: Q-OPT algorithm-time window size detection on each step.
# Domain Discrimination Expert Weighted Network for Multi-Source Carotid Artery Plaque Classification
## 4 Results
### Experimental Results
#### 4.2.4 Domain Adaptation Layer Determination  
Due to the different effects of multicenter domain adaptation layers with varying numbers on the model, systematic experiments were conducted to determine the optimal layer count. The findings of these experiments are displayed in Figure 7. The experimental data demonstrate that the multicenter domain adaptation layers indeed contributed to better domain adaptation. Although the use of one multicenter domain adaptation layer led to the highest accuracy, this approach does not significantly differ from that with the use of two layers. However, the domain adaptation with two layers led to better results in terms of both precision and recall. This is because the two layer structure can provide more modeling capabilities than the single-layer multicenter domain adaptation layer can, which helps to better capture the subtle differences between different center data. With the superposition of the layers, the depth of the model increases, which leads to model overfitting.
# Priority-Based Content Delivery in the Internet of Vehicles through Named Data Networking +
## 7 Conclusions and Future Works  
In this paper, we proposed a novel mechanism for the retrieval of named IoV content that uses information codified in the name and the freshness attribute for the prioritized delivery of content over multi-interface vehicular devices.  
Prioritization is enforced in a two-fold manner: (i) in the face selection scheme by letting HP content be requested over multiple faces in parallel so as to speed up their retrieval; (ii) in the dissemination of Interest and Data packets over the 802.11 OCB interface by accelerating the forwarding of HP Interest/Data packets over LP ones.  
Performance evaluation has been conducted under realistic settings through the official NDN simulator. The results achieved show that the proposed solution succeeds in reducing the latency of high priority packets, which is crucial for IoV content, while also improving the performance of low priority ones. This is achieved at the expense of a slightly higher signaling overhead due to the usage of multiple interfaces in parallel.  
Enhancements left for future work are related to the design of more sophisticated multi-face forwarding algorithms with the main intent of improving content delivery performance while reducing the current overhead and further encouraging research in this promising area. Solutions will be specified to be able to dynamically weight the number of requests sent over the available interfaces according to operational metrics (e.g., SRTT), as preliminarily proposed in [47] for static users, and content requirements specified in names and attributes. Further efforts will be devoted to the definition of retransmission routines, which promptly switch from an interface to another one in the case of poor performance.  
Moreover, as soon as the D2D technology for V2V communications becomes mature, improved multi-face forwarding solutions will be conceived of, also accounting for such a promising face option.  
In this paper, the three authors conceived of the idea, organized the work and the experiments and contribute to writing the article.  
The authors declare no conflict of interest.
# The Novel Role of Solvents in Non-Surgical Endodontic Retreatment
## 4 Moment of Use  
Traditionally, solvents were applied at the initial stages of the NSER, when fillings are more compact, through the deposition of a few drops into the space created by the coronal filling removal [3]. The main objective was to soften gutta-percha, enabling the initial penetration of the file into the remaining obturation [5]. Some authors reported a negative impact of the solvents' deposition (chloroform and eucalypttol) in the medial and apical parts of the retreated ex-vivo canals, with reduced the filling remnants in the root canal surfaces of the nonsolvent groups [16]. Different methodologies, such as the type and moment of solvent deposition (before/after repreparation), may have influenced the results.  
Flooding the canal with solvent after removing the bulk of the remaining guttapercha, and further enlargement, have also been investigated. One of the studies assessed the effect of xylene (1 min) on cleaning the root canal with paper points; the outcome was comparable to 2.5% ultrasonically activated NaOCl [34]. In turn, [PERSON] et al. [35] emphasized the cleaning performance of the reciprocating instruments with xylene (1 min) and concluded that, even with passive ultrasonic agitation (PUI), the solvent did not improve filling removal. Similarly, [PERSON] et al. [36] also showed no improvement with PUI with OOil or NaOCl. Contrarily, [PERSON] et al. [37; 38] showed promising results, advising specific solvents (MEK/TCE and MEK/OOil) as an additional step after the conventional repreparation and NaOCl/EDTA treatment. Due to their high dissolution rate in short periods, the same solvent mixtures might also be considered, to assist with the initial penetration of well-compacted obturations.
# Whispered Speech Recognition Based on Audio Data Augmentation and Inverse Filtering
## 6 Results
### Baseline Experiments  
The main objective of the baseline experiments was to analyze the speaker-independent recognition of a whisper in two distinct scenarios:  
Figure 9: The flow of the recognition process in (**a**) baseline experiments and (**b**) experiments testing the impact of data augmentation techniques and the number of augmentations.  
* Closed set--both training and testing were carried out using the Whi-Spe database.
* Open set--training was completed using the Whi-Spe database while testing was carried out with the DBtest database.  
The training/test split for speaker-independent recognition is based on individual speakers, ensuring that utterances from the test speaker are not included in the training set. The accuracy results are presented in Figure 10 (HMM) and Figure 11 (CNN), showing the impact of varying percentages of the database used for the training, ranging from 10% to 100% with a 10% increment. The results of experiments for each speaker are given in the Supplementary Document accessible in the repository [32].  
Figure 10 illustrates that the closed-set recognition for HMM achieved a final accuracy of 95.02% after full-capacity training. In contrast, the accuracy dropped significantly to 91.61% for the DBtest database. Bearing in mind that the DBtest is not recorded in a laboratory, a resulting drop in performance is expected. The most notable performance increase was observed between 10% and 20% of the training database, followed by a saturation effect and minor fluctuations after 30%.  
Figure 11: The average recognition accuracy (in %) for Whi-Spe (closed set) and DBtest database (open set) in the CNN framework. The horizontal axis denotes the percentage of the Whi-Spe subset employed in the training.  
Figure 10: The average recognition accuracy (in %) for Whi-Spe (closed set) and DBtest database (open set) in the HMM framework. The horizontal axis denotes the percentage of the Whi-Spe subset employed in the training.  
The CNN recognition showed lower performance than HMM, as shown in Figure 11. The accuracy for full-capacity training was 92.46% (Whi-Spe) and 84.14% (DBtest). In contrast to HMM, the CNN scenario did not achieve performance saturation for the DBtest database. The insufficient size of the training database may have contributed to the poor performance of CNN.  
The same experiment setup was performed for normal speech and Whi-Spe database. Compared to whispered speech, the closed set recognition of normal speech in baseline experiments was with higher success and reached an accuracy of 98.07% (HMM) and 92.80% (CNN) [40].
# Seismic Study of An Isolated Cable-Stayed Bridge under Near-Fault Ground Motions
## 3 Bridge Prototype and Numerical Model  
### Bridge Prototype  
The prototype is a 500 m high-speed railway cable-stayed bridge with approach bridges on both sides, consisting of 32 m of simply supported beam bridges, as shown in Figure 2(a). The main bridge is designed with a double tower and double cable plane layout, featuring curved bridge towers constructed of concrete with a compressive strength of 50 Mpa. The bridge tower is 100.4 m high and consists of four parts: lower, middle, and upper tower columns and beams. The upper tower columns slightly converge at the top and are connected through curved plates in the middle. The middle and lower tower columns have crossbeams at the junction of the tower beams, as shown in Figure 2(b). The main bridge incorporates 48 pairs of fan-shaped arranged stay cables made of 1770 MPa  
Figure 2: DDFM system.  
g galvanized parallel steel wires. The main beams have a composite structure, combining box-shaped steel beams and concrete, with dimensions of 16.3 m in width and 4.5 m in height. The steel structure part of the main beam is made of Q345 qD, and the compressive strength of the concrete is 55 MPa.  
Two auxiliary pairs and one transition pier are arranged on the side span of the cable-stayed bridge (see Figure 3c), equipped with ballast to balance the self-weight of the main span. The bridge piers are constructed using thin-walled box-shaped hollow piers (see Figure 3d), made of concrete with a compressive strength of 40 MPa, 12 m in height, and with a plan dimension of 10.2 \(\times\) 4.2 m. The main bridge performs as a longitudinal floating system, allowing the main beam to move freely along the longitudinal direction of the bridge. The FPBs are installed at the position of the bridge tower and pier of the main bridge, and FVDs are installed between the bridge tower and the main beam to enhance seismic resilience. Detailed simulation methods and design parameters for these elements are described in Section 3.2.
## 2 Materials and Methods
### Synthesis
\({}^{1}\)H NMR (400 MHz, DMSO-\(d_{6}\)) \(\delta\) (ppm): 2.63 (s, 3H, SCH\({}_{3}\)), 3.34 (s, 3H, NCH\({}_{3}\)), 5.19 (s, 1H, H5), 5.91 (s, 2H, CH\({}_{2}\)), 6.74 (d, \(J\) = 6.79 Hz, 2H, aryl), 6.86 (s, 1H, aryl), 7.78-7.84 (m, 2H, H8, H9), 7.91 (d, \(J\) = 7.89 Hz, 1H, H7), 8.03 (d, \(J\) = 8.02 Hz, 1H, H10), 9.72 (s, 1H, NH). \({}^{13}\)C NMR \(\delta\) (ppm): 14.45 (SCH\({}_{3}\)), 29.9 (NCH\({}_{3}\)), 35.0 (C5), 100.7 (CH\({}_{2}\)), 107.8, 108.6 (C\({}^{2}\)-C\({}^{6}\)), 117.3 (C4a), 121.0 (C\({}^{5}\)), 125.6 (C10), 125.9 (C7), 130.4 (C6a), 131.8 (C10a), 133.2 (C8), 134.7 (C9), 139.1 (C5a), 145.8 (Ci), 146.9 (C11a), 149.6 (C2), 160.3 (C=O), 161.6 (C12a), 179.2 (C=O), 181.6 (C6). MS: (70 eV) \(m/z\) (%) = 458 (38, M\({}^{+}\)), 337 (100). Anal. Calcd for C\({}_{24}\)H\({}_{17}\)N\({}_{3}\)O\({}_{5}\)S C, 62.74; H, 3.73; N, 9.15; found C, 62.71; H, 3.71; N, 9.18. _5-(4-fluorophenyl)-3-methyl-2-methylthio-5,12-dihydrobenzo[g]pyrimidine_[4,5-b]quinoline-4,6,11(3H)-trione_ (**4o**). Red solid. 74%. m.p. 276 \({}^{\circ}\)C. IR (KBr, \(\upsilon\) cm\({}^{-1}\)), 3237 (NH st), 1646 (C=O st). \({}^{1}\)H NMR (400 MHz, DMSO-\(d_{6}\)) \(\delta\) (ppm): 2.61 (s, 3H, SCH\({}_{3}\)), 3.30 (s, 3H, NCH\({}_{3}\)), 5.22 (s, 1H, H5), 7.01 (d, \(J\) = 7.30 Hz, H0), 7.32 (t, \(J\) = 7.30 Hz, 2H, H 5-(4-bromophenylyl)-3-methyl-2-methylthio-5,12-dihydrobenzo[g]pyrimido[4,5-b]quinoline-4,6,11(3H)-trione_ (**4q**). Red solid. 76%. m.p. 264\({}^{\circ}\)C. IR (KBr, \(\upsilon\) cm\({}^{-1}\)), 3228 (NH st), 1650 (C=O st). 1H NMR (400 MHz, DMSO-4\({}_{6}\)) 8 (ppm). 2.62 (s, 3H, SCH3), 3.30 (s, 3H, NCH3), 5.20 (s, 1H, H5), 7.25 (d, \(J\) = 8.29 Hz, 2H, H\(\alpha\)), 7.37 (d, \(J\) = 8.53 Hz, 2H, H\(\mu\)), 7.77-7.81 (m, 2H, H8, H9), 7.88 (d, \(J\) = 7.27 Hz, 1H, H7), 8.01 (d, \(J\) = 8.03 Hz, 1H, H10), 9.73 (s, 1H, NH). 13C NMR 8 (ppm): 14.4 (SCH3), 29.8 (NCH3), 35.2 (C5), 99.4 (C4a), 116.7 (C5a), 119.5 (Cp) 125.5 (C10), 125.8 (C7), 130.2 (Co), 130.3 (C6a), 130.9 (\(Cm\)), 131.7 (C10a), 133.2 (C8), 134.7 (C9), 139.4 (Ci), 144.3 (C11a), 149.7 (C2), 160.1 (C4), 161.8 (C12a), 179.0 (C=O), 181.5 (C=O). MS: (70 eV) \(m/z\) (%) = 492 (9, M\({}^{+}\)), 337 (100). Anal. Calcd. for C\({}_{23}\)H\({}_{16}\)BrN\({}_{3}\)O\({}_{3}\)S C, 55.88; H, 3.26; N, 8.50; found C, 55.91; H, 3.29; N, 8.49. \({}^{1}\)H NMR spectra and spectroscopic data for all compounds are included in the supplementary materials.
## 2 Material and Methods
### Statistical Analysis
Given the small dataset, this approach aimed to reduce redundancy in feature selection and overfitting while improving interpretability. Hence, by selecting smaller, more relevant data subsets, the complexity of the data could still be captured effectively without incorporating features with low information gain that would not enhance model performance [26,27]. While there is no fixed cutoff for information gain values, thresholds around 0.05 are often used to exclude features with minimal predictive contributions. Hence, we applied a cutoff of 0.01, considering our small dataset, to minimize the risk of prematurely excluding variables that collectively could contribute to the model's performance [28]. Based on the 10 feature subsets selected by the algorithm, new prediction models were generated by sequentially removing features with the least information gain. Prediction models including the most relevant variables, as highlighted in the univariate analysis, were also developed for comparative reasons. To assess the model's performance, a random sampling approach was applied, with 80% of the samples being used for training and 20% for independent validation. This process was repeated 10 times to ensure more reliable performance estimates by averaging the results across different splits. The average model performance was assessed using the area under the receiver operating characteristics (AUC), classification accuracy, precision, recall (R), and specificity (Sp). Finally, the Friedman test was used to compare the predictive abilities of different models across the three main datasets (cytokines, routine blood analyses, and hybrid data), with Bonferroni correction being applied for multiple comparisons. Descriptive and inferential statistics, as well as graphic representations of data, were achieved using IBM SPSS Statistics software, version 29 (IBM Corp., Armonk, NY, USA) and RStudio, version 2022.12.0 (PBC, Boston, MA, USA). Statistical significance was set for two-sided \(p\) values of less than 0.05 (with some exceptions where the statistical significance was set for 0.10). Multivariate analyses were performed using Orange, version 3.35.0 (University of Ljubljana, Slovenia).
# Predicting Human Location Using Correlated Movements
## 4 The Proposed Mobility Prediction Model  
### Problem Definition  
Recall that \(\mathbb{M}\) and \(\mathbb{L}\) are the set of \(m\) members in the entire community and the set of locations, respectively. We take into account the problem of predicting the position of person \(p\) (\(1\leq p\leq m\)) at current or future time \(t\) with the precondition that the recent locations of person \(p\) are unknown. Assume that the historical positions of \((m-1)\) other members can be observed. Therefore, we can make use of the historical information of the rest of people for predicting where person \(p\) stays at the current or future time.  
Let us define \(\mathbf{y_{t}}\) as the location of person \(p\) at time \(t\) and \(R\) as the spatio-temporal information of the rest of members during the historical period. Specifically, \(R=\{R_{j}^{i}|R_{j}^{i}\in\mathbb{L},i\in\mathbb{M},i\
eq p,1\leq j\leq t\}\). The problem of predicting human location is formulated as follows. The objective is to predict where person \(p\) is the most likely to visit at time \(t\) given the recent information \(R\), or to maximize the following probability:  
\[P(\mathbf{y_{t}}|R)\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Number of People (\(m\))** & **Number of Locations (\(|\mathbb{L}|\))** & **Duration (Days)** & **Period of a Day** \\ \hline MIT & 43 & 482 & 75 & 8 a.m. to 12 p.m. \\ Dartmouth & 162 & 399 & 118 & 8 a.m. to 12 p.m. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of two datasets after locations and users extraction.
# Using the Hall Effect for Monitoring the Starter Condition in Motor Vehicles
## 4 Test Results  
The Hall voltage sequence for selected failures and changed time base ranges of the observed waveforms (to identify differences) is shown in Figures 11-18.  
Figure 11: Hall voltage versus time for efficient starter, Hall sensors fixed on the rotor, unloaded starter.  
Figure 12: Hall voltage versus time for efficient starter, Hall sensors fixed on the stator, unloaded starter.  
Figure 13: Hall voltage versus time in the case of the shorting of two commutator sectors, Hall sensors fixed on the rotor, loaded starter.  
Figure 16: Hall voltage versus time in the case of breaking the rotor winding integrity, Hall sensors fixed on the stator, unloaded starter.  
Figure 14: Hall voltage versus time in the case of the shorting of two commutator sectors, Hall sensors fixed on the stator, loaded starter.  
Figure 17: Hall voltage versus time in the case of brush wear, Hall sensors fixed on the rotor, unloaded starter.  
Figure 15: Hall voltage versus time in the case of breaking the rotor winding integrity, Hall sensors fixed on the rotor, unloaded starter.  
The waveforms of induction distribution measured by Hall sensors fixed on the rotor and on the stator are very similar. However, the course of induction recorded by the Hall sensor fixed on the rotor provides more information useful in the analysis of starter failures in the form of a short circuit and a break in the rotor windings, which is related to the curve of the rotor and stator interaction field (Equation (7)).  
The waveforms of failures, in relation to the charts of the efficient starter, are more regular. e.g., for the shorting of two commutator sectors. Their course is characteristic in the case of breaks in the rotor winding. A completely different course is obtained here and its character does not resemble any of those discussed earlier. What makes it characteristic are the visible peaks going down, probably caused by power surges resulting from breaks in the current circuit of the starter at the moment when the last commutator sector (before the sector whose winding solder was removed) emerged from under the brush, breaking the starter induction circuit.
## 6 Key Issues and Challenges
### Transmission Module Precision  
The influence of unnecessary electromagnetic and radio signals affects the operating environment of sensing, monitoring, and control systems. Further, the efficiency and accuracy of the installed transmission modules can be hampered due to the occurrence of unwanted signals in the form of electromagnetic and radio signals. Hence, the development of compatible shield/modules integrated with data transmission boards such as ZigBee, Wi-Fi, Bluetooth, GSM, and LoRa modules is essential. This would assist in the prevention of unwanted interference and remove the mismatching of standardized signals.