esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Coordinate Transformation-Based Fault-Tolerant Control
### Stability Analysis  
Stability analysis provides a way for the guidance of gain selection of the sliding controller. Define the Lyapunov functions for z-dynamics and Euler angle dynamics respectively as follows:  
\[V_{z}=\frac{1}{2}s_{z}^{2} \tag{23}\]  
\[V_{\eta}=\frac{1}{2}\mathbf{s}_{\eta}^{T}\mathbf{s}_{\eta} \tag{24}\]Taking the derivative of Equations (23) and (24) yields:  
\[\begin{array}{ll}\dot{V}_{z}&=-\rho_{z}\frac{\left|\mathbf{s}_{\eta}\right|^{2}} {\left|\mathbf{s}_{\eta}\right|+\left|\mathbf{\varepsilon}_{\eta}\right|}+ \frac{1}{m}\Big{(}f_{uz}-\delta_{z}\dot{z}\Big{)}S_{z}\leq-\rho_{z}\frac{ \left|\mathbf{s}_{\eta}\right|^{2}}{\left|\mathbf{s}_{\eta}\right|+\left| \mathbf{\varepsilon}_{\eta}\right|}+\frac{1}{m}\Big{(}\big{|}f_{uz}\big{|}+ \big{|}\delta_{z}\dot{z}\Big{|}\Big{)}|\mathbf{s}_{z}|\\ &\leq-\frac{\left|\mathbf{s}_{z}\right|}{\left|\mathbf{s}_{z}\right|+ \left|\mathbf{\varepsilon}_{z}\right|}\Big{(}\rho_{z}-\frac{1}{m}\Big{(} \big{|}f_{uz}\big{|}+\big{|}\delta_{z}\dot{z}\Big{|}\Big{)}\frac{\left( \mathbf{s}_{z}\right|+\left|\mathbf{\varepsilon}_{z}\right)}{\left|\mathbf{s} _{z}\right|}\Big{)}\end{array} \tag{25}\]  
and:  
\[\dot{V}_{z}=-\rho_{\eta}\frac{\left|\mathbf{s}_{\eta}\right|^{2}}{\left| \mathbf{s}_{\eta}\right|\Big{|}+\left|\mathbf{\varepsilon}_{\eta}\right|}+ \mathbf{s}_{\eta}^{\mathrm{T}}\mathbf{H}\mathbf{J}^{-1}\mathbf{f}_{\eta} \leq-\lambda_{min}\Big{(}\rho_{\eta}\Big{)}\frac{\left|\mathbf{s}_{\eta} \right|^{2}}{\left|\mathbf{s}_{\eta}\right|\Big{|}+\left|\mathbf{\varepsilon}_ {\eta}\right|}+\left|\mathbf{s}_{\eta}\right|\big{|}\big{|}\big{|}\big{|} \big{|}\mathbf{H}\mathbf{J}^{-1}\big{|}\big{|}\big{|}\big{|}\mathbf{f}_{\eta} \big{|}\Big{|} \tag{26}\]  
respectively.  
Obviously, when the scaling parameter \(\varepsilon_{z}=0\) and \(\varepsilon_{\eta}=0\) used in the sigmoid function, Equations (25) and (26) reduce to:  
\[\dot{V}_{z}=-\left|\mathbf{s}_{z}\right|\!\!\left(\rho_{z}+\frac{1}{m}\Big{(} \big{|}f_{uz}\big{|}+\big{|}\delta_{z}\dot{z}\big{|}\Big{)}\right)\leq 0 \tag{27}\]  
and:  
\[\dot{V}_{z}\leq-\left|\mathbf{s}_{\eta}\right|\!\!\left|\Big{(}\lambda_{min} \Big{(}\rho_{\eta}\Big{)}-\left|\mathbf{H}\mathbf{J}^{-1}\big{|}\big{|} \big{|}\mathbf{f}_{\eta}\big{|}\right|\right) \tag{28}\]  
It is evident that the finite time approaching is satisfied providing:  
\[\rho_{z}>\frac{1}{m}\Big{(}\big{|}f_{uz}\big{|}+\big{|}\delta_{z}\dot{z}\Big{|} \Big{)} \tag{29}\]  
\[\lambda_{\min}\Big{(}\rho_{\eta}\Big{)}>\left|\left|\mathbf{H}\mathbf{J}^{-1} \right|\big{|}\big{|}\mathbf{f}_{\eta}\right| \tag{30}\]  
The parameters \(\varepsilon_{z}\) and \(\varepsilon_{\eta}\) not only eliminate the chattering problem of sliding mode control, but also dominate the convergence capability. Since the traditional discontinuous switching control is replaced by the continuous sigmoid function, the perfect sliding motion may not be attained and the region of the convergence is relative to the size of the scaling parameters used in the sigmoid function. Nevertheless, the high frequency control switching can be avoided.
# Effectiveness of Machine Learning in Assessing the Diagnostic Quality of Bitewing Radiographs
## 3 Results  
### Data Analysis  
[PERSON]'s kappa coefficient for intra-rater and inter-rater reliability were 0.95 and 0.96, respectively, indicating excellent agreement.  
Figure 1: EfficientDet architecture that consists of EfficientNet backbone, BiFPN as feature network neck, and output head.  
A total of 834 randomly chosen BW radiographs of 100 patients presented to King Abdulaziz University Dental Hospital were collected with a minimum number of one BW and a maximum of 33 BW per patient (mean \(\pm\) SD; 15.08 \(\pm\) 9.553). The final dataset consisted of 583 images for the training set, 167 for the validation set, and 84 for the test set. Details of numbers of labeled contact area within the assessed dataset and training set radiographs are presented in Table 1.  
Results revealed class imbalance, which means over-representation of some labels, such as for \"overlap within enamel\", while others were under-represented, such as \"overlap at DEJ\" and \"overlap within dentin\" (Table 1, Figure 2).
## 4 Results and Discussions
### Shear Stress and Shear Displacement  
To examine the effect of shear displacement on the shear stress, the shear stress-displacement curves were plotted at the same normal stress of 50 kPa and shearing speed of 0.1 mm/s. A shear speed of 0.1 mm/s can be considered a moderate landslide rate [31]. Figure 4 presents the results of shear stress-displacement curves obtained from the drained and undrained conditions at different shear displacements, i.e., shearing lengths equal to 10, 30, 50, 100, 110 mm in ring shear tests, corresponding to strains of 2, 5, 9, 18, and 20%. Comparing the test results obtained from the commercial direct shear tests (less than 10 mm), it can be seen that the shear displacement is relatively large. For both drainage conditions, the peak shear stresses range between 25 and 30 kPa at the initial shearing condition. The peak value can be obtained before a displacement of 5 mm. The residual shear stresses range between 10 and 15 kPa in the drained condition and 15-20 kPa in the undrained conditions. Some dilatancy, which is explained as the volume change phenomenon in granular materials, can be observed when the materials are shearing longer, especially for shear displacements greater than 50 mm. This may be because the loading, abrasion, and crushing are distinct; thus, fine particles are produced by the crushing effect. Because of continued shearing, the potential of the shear zone begins to develop even though a relatively low shearing speed is applied (i.e., 0.1 mm/s). [PERSON] and others [23] presented the shear zone and grain crushing effect due to the shearing time in ring shear tests at a relatively high shearing time (i.e., 3000 mm/s, which corresponds to extremely high landslide movement). According to [PERSON] and colleagues [28], the shear zone may be created due to the mixing of grain sizes in the container. Fine particles move downward and large particles move upward. After the mixing and rearrangement of soil particles themselves, large particles concentrate in the vicinity of the shear zone. This process is repeated continuously, and the shear strength decreases when the particles in the ring shear box are fully fragmented at a large deformation.
## 2 Research Methods  
### Research Location and Materials  
The research was conducted in Denpasar, a city in the Province of Bali, Indonesia, located at 8 deg36'56\"S-8 deg42'01\"S and 115 deg10'23\"E-115 deg16'27\"E (Figure 1) The location is between two neighborhood regencies namely Badung and Gianyar Regencies. Denpasar city population reached 608,595 people in 2007. The condition of oblique topography is from north to south with the height level of 0-75 m above sea level, while the inclination slope morphology is from 0 to 5%. Denpasar has tropical climate with monthly mean temperature around 24-32 deg\({}^{\circ}\)C and monthly mean precipitation around 13-358 mm. The dominant land use in Denpasar is the settlement that has an area of 7,179.17 ha.  
The following materials were used in this work: (1) a digital image of the Denpasar area taken on 13 October 2006 from ALOS/AVNIR-2, (2) a digital image of the Denpasar area taken on 5 September 2006 from Aster, (3) a land use map of the Denpasar area in 2006 from Quick Bird, (4) a topography map of the Denpasar region at a scale of 1:25,000 from the Indonesian National Coordinating Agency for Surveys and Mapping (Bakosurtanal), and (5) solar radiation data from the Indonesian Meteorology, Climatology and Geophysics Agency (BMKG). ALOS/AVNIR-2 and Aster images were used to calculate the value of GPP. Topographic maps were used for coordinate corrections. Meanwhile, solar radiation data was used to calculate photosynthetically active radiation (PAR).
## 7 Discussion and Results
### Accuracy vs. Additional Random Samples Testing  
Since the cancer patients dataset shows the least-effective classifier, as shown in Figure 23, it was tested for its response to the injection of the a sudden spike in additional  
Figure 23: Receiver operating characteristic (ROC) curve.  
Figure 24: ROC curve interpretation.  
random samples up to 100%. Figure 25 shows that, upon increasing the random samples, the result of PCA-SRP decreases gradually.  
Figures 26 and 27 present the validation accuracy of both ANN + PCA(SRP) and ANN only to additional noises up to 100% with selectivities (Sc) of 88 and 98%, respectively. It has also shown a reasonable increase in accuracy using 98% selectivity (\(Sc\)) instead of 88%.  
The high-valued data have been preserved and maintain their accuracy until a specific additional noise point. Nevertheless, the predictive power remains intact until that point, even though some data were lost in the process.  
The \(ANN+PCA_{SRP}\) maintains the highest performance of cancer patient classifications with high \(Sc\) values. The methodology allows for a significant advantage by gradually lessening the decrease in classification problem accuracy over the sudden increase in noise in the system.  
Figure 26: +%Noise response for cancer patients dataset (Sc = 88%).  
Figure 25: PCA cleaned data percentage (%).  
The ANN classification problem requires strong training sets, which requires a lot of high \(Sc\) while disregarding the low ones; based on the observation in both Figures 26 and 27, \(Sc_{ANN}\) is better, as described in Equation (26).
## 4 Validation
### Electromagnetic Torque
#### 4.5.1 Stator-Segmented Case  
Figure 31 presents electromagnetic torques obtained using (6) from numerical and analytical models, and considering rated value sinusoidal currents.  
The number of gaps does not affect the EMF calculation. So, with the same current distribution, the EM torque does not change with a variation of \(n_{gap}\).  
For a more precisely comparison, Figure 32a presents the comparison of the amplitude of the EM torque ripples calculated by 2D FEM and analytical models. Figure 32b presents correlations between EMF and electromagnetic torques calculated using the two methods.  
Analytical models give an accurate estimation of the electromagnetic torque, compared to results from 2D FEM. The correlation value between waveforms is more than 0.999. However, the estimation of the amplitude of the ripples can be improved.  
The comparison between the mean electromagnetic torques values is done in Table 5. It shows that the analytical model leads to difference less than 2% on these two quantities between numerical and analytical results.  
Figure 30: Comparison between EMF obtained by numerical and analytical methods at 1 rd/s for the RS-machine with \(n_{gap}\) = 6 gaps and with different \(p_{gap}\). (**a**) Peak values. (**b**) Correlation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gap Proportion** & **0\%** & **25\%** & **50\%** \\ \hline Numerical \(\langle T_{em}\rangle\) (kN\(\times\)m) & 226 & 213 & 213 \\ Analytical \(\langle T_{em}\rangle\) (kN\(\times\)m) & 222 & 209 & 209 \\ Ratio (\%) & 98.2 & 98.1 & 98.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evolution of the mean electromagnetic torques for the SS-machine with \(n_{gap}\) = 7 gaps.
###### Abstract  
Wireless sensor networks (WSNs) have proven to be incredibly useful for forest applications that rely on sensing technologies for event detection and monitoring. This radical sensing technology has revolutionized data gathering, analysis, and application. Despite the many advantages of this technology, one key drawback is the rapid drain on sensor batteries caused by their intensive processing activities and communication processes. The effectiveness of sensor nodes is strongly influenced by two factors: the amount of energy they consume and the length of their coverage lifetimes. Using our proposed method, we can find fire zones in a forest, detect and monitor battlefield surveillance, combat monitoring and intruder detection, and then wirelessly send all the information to a central station. So, extending the life of WSNs is essential to ensure that Sensor Nodes (SN) will always be available. Our proposed EEWBP (energy-efficient weighted-based protocol) technique uses a composite weighted metric that includes system elements such as the node degree, residual energy, the number of neighbors' nodes, average flying speed, and trust value, which are evaluated separately and then added together to help in cluster-building and node-scheduling processes. Our proposed protocol makes it easy to set up many clusters of SNs, each with their own cluster head (CH). This way, data can be sent between clusters in a way that uses the least amount of energy and makes coverage last longer. After putting our cluster-based routing strategy in place, we tested how it worked and evaluated it with different network parameters. The simulation results show that EEWBP consumes less energy and maintains a higher level of consistency in the CH than coverage preserving clustering protocol (CPCP), coverage clustering protocol (CACP), coverage aware unequal clustering algorithm (CUCA), and low-energy adaptive clustering hierarchy (LEACH). EEWBP also shows a better packet delivery rate and an improvement in first-node death.  
clustering; wildfire; IoT; efficient; smart city; LEACH; routing protocol; WSN Article
###### Abstract  
This paper proposes a three-stage op amp based on the SOI (silicon-on-insulator) process, which achieves a low offset voltage and temperature coefficient across a wide temperature range from \(-40\)\({}^{\circ}\)C to \(250\)\({}^{\circ}\)C. It can be used in aerospace, oil and gas exploration, automotive electronics, nuclear industry, and in other fields where the ability of electronic devices to withstand high-temperature environments is strongly required. By utilizing a SC (Switched Capacitor) notch filter, the op amp achieves low input offset in a power-efficient manner. The circuit features a multi-path nested Miller compensation structure, consisting of a low-speed channel and a high-speed channel, which switch according to the input signal frequency. The input-stage operational amplifier is a fully differential, rail-to-rail design, utilizing tail current control to reduce the impact of common-mode voltage on the transconductance of the input stage. The two-stage operational amplifier uses both cascode and Miller compensation, minimizing the influence of the feedforward signal path and improving the amplifier's response speed. The prototype op amp is fabricated in a \(0.15\)\(\upmu\)m SOI process and draws \(0.3\) mA from a \(5\) V supply. The circuit occupies a chip area of \(0.76\) mm\({}^{2}\). The measured open-loop gain exceeds \(140\) dB, with a \(3\) dB bandwidth greater than \(100\) kHz. The amplifier demonstrates stable performance across a wide temperature range from \(-40\)\({}^{\circ}\)C to \(250\)\({}^{\circ}\)C, and exhibits an excellent input offset of approximately \(20\)\(\upmu\)V at room temperature and an offset voltage temperature coefficient of \(0.7\)\(\upmu\)V/\({}^{\circ}\)C in the full temperature range.  
SOI; operational amplifier; high-temperature sensor; chopper +
Footnote †: journal: Applied sciences
# A Feature-Independent Hyper-Heuristic Approach for Solving the Knapsack Problem
## 4 Experiments and Results
### Preliminary Experiments
#### 4.1.1 Preliminary Experiment I  
The first experiment conducted in this work aims to prove that we need an intelligent approach to define the sequences of heuristics. In other words, we justify that it is unlikely for a random sequence of heuristics to achieve competent results. For this purpose, we generated 30 random HHs. The length of these HHs was set to 16 heuristics for empirical reasons. As in this experiment, we were only interested in the behavior of randomly generated HHs, the EA was not used to improve the initial HHs. Then, we used the 30 randomly generated HHs to solve set \(\alpha_{2}\), and we compared their results against the ones obtained by the available heuristics (see Section 3 for more details). For the sake of clarity, our comparisons include the results of three representative HHs from the perspective of total profit on set \(\alpha_{2}\): the best, the median, and the worst hyper-heuristics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Type**} & \multicolumn{4}{c}{**Features**} \\ \cline{3-5}  & & **Instances** & **Items** & **Capacity** \\ \hline \(\alpha_{1}\) & Balanced & 400 & 50 & 50 \\ \(\alpha_{2}\) & Balanced & 400 & 50 & 50 \\ \(\beta_{1}\) & Hard & 600 & 20 & Variable \\ \(\beta_{2}\) & Hard & 600 & 50 & Variable \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets used in this work.  
One way to analyze the performance of the methods relies on ranking the resulting data. Table 2 shows the ranks obtained by the four heuristics, as well as the three representative HHs, when solving set \(\alpha_{2}\). As one may observe, in most of the cases, the heuristics obtain the best result in isolation (ranks close to 1), as it was expected as set \(\alpha_{2}\) is balanced. Although the best HH never occupies the last positions in the rank, the median performance hints that the good behavior of Best-HH is more likely to be due to chance. Furthermore, by randomly generating the sequences of heuristics, we risk producing something as bad as Worst-HH, where it replicated the worst heuristic for this set, i.e., Def.  
Table 2 also shows the total profit obtained for each method on set \(\alpha_{2}\), which serves as evidence that it is indeed possible to produce one good HH at random, such that it outperforms several heuristics. However, this seems unlikely to happen by chance since at least half the HHs perform worse than MinW.  
Regarding the success rate, the results confirm that generating sequences of heuristics at random is not a good idea. The success rate of the best randomly generated HH was \((0.0500,0.0025)\), which means that only in 5% of the instances in set \(\alpha_{2}\) this HH produced a solution as competent as the best one among the four heuristics, while in 0.25% of the instances for the same set, the HH improved upon this result.
# Evaluation of VoIP QoS Performance in Wireless Mesh Networks
## 1 Introduction
For evaluating how the integration of standards and protocols can affect the VoIP performance, we further define six sub-scenarios for each main scenario and experiment with changing standards and protocols. * Finally, by conducting experiments and extensively evaluating the VoIP performance based on above mentioned points, our research findings show that IEEE 802.11g without being integrated with IEEE 802.11s standard using G.711 and G.729 voice codecs, along with the AODV routing protocol, results in better VoIP performance as compared to the integration of IEEE 802.11g with IEEE 802.11s using the HWMP routing protocol. Similarly, the integration of 802.11n standard using G.711 and G.729 voice codecs, along with the AODV routing protocol, shows poor VoIP performance as compared to the other scenarios. In general, our results show that scenarios with IEEE 802.11g using AODV routing protocol show better VoIP performance as compared to scenarios with IEEE 802.11n using the same routing protocol. The remainder of this paper focuses on WMN applications and scenarios where VoIP could be offered as a critical service using a self-formed, self-healed, survivable and easy to setup communication network (Section 2). This is followed by the selection of a few scenarios and performing experiments using different IEEE 802.11 standards, voice codecs and protocols (Section 3). Our focus is mainly on integrating standards and protocols to facilitate an improved voice communication in emergency scenarios, isolated villages, and small-scale military operations. The results are presented and analyzed in Section 4. Finally, Section 5 concludes the paper.
# Capacity Analysis of Incentive Schemes in Opportunistic Networks
## 3 Verification of the Model with Simulations
### Buffer Size  
Similar to the previous verification, delivery ratios and \(\$^{*}\) with buffer sizes are shown in Figures 2(b) and 3(b). The two similar relations suggest that buffer sizes can affect performance, and the proposed model reflects the realistic relationship with buffer size. Intuitively, message sizes have similar results as buffer sizes, because they are inversely proportional in the model and work together to store messages in simulations.  
An increasing buffer size means that a node can forward more messages or a larger message at a time, however, the increase is slowed down because of the restriction of nodes' movement paths. Therefore, too large a buffer size is useless for network performance unless used with a larger message size.
# Marine Diesel Engine Fault Detection Based on Xilinx ZYNQ SoC
## 3 Results
### Convolutional Neural Network in the PL of ZYNQ
#### 3.2.4 Hardware Algorithm Acceleration  
The PL side of the ZYNQ consists of FPGA, which can be utilized for FPGA acceleration. Unlike traditional general-purpose processors (such as CPUs) or graphics processing units (GPUs), FPGAs have programmable hardware structures that can be customized for specific tasks. In this program, convolutional operations in the convolutional layer, pooling operations, and matrix multiplication and bias addition operations in the fully connected layer are all performed in parallel using the FPGA's computing units to accelerate feature extraction.  
Due to the deep convolutional layers, incorporating the entire computation of the convolutional neural network would lead to insufficient hardware resources. Therefore, modular computation is adopted. The data obtained from each layer are stored on the PS side and then re-input into the PL side for CNN computation. As can be seen from Table 4, the most complex convolution layer algorithm has been accelerated by approximately 96.15%, the pooling layer by about 99.09%, and the fully connected layer has been optimized by 99.26%. The computation of the activation layer is relatively simple and can be left unoptimized to conserve hardware resources. Table 5 also shows that the hardware remains at a reasonable level overall. There is no high load, yet the requirements for hardware acceleration are met. The program tested focuses on the first convolution layer, pooling layer, and fully connected layer, which are the stages that exert the most pressure on hardware parameters. If the initial feature extraction proceeds normally, the parameters will decrease after the completion of feature extraction, leading to reduced subsequent hardware pressure. Additionally, the use of a high-bandwidth AXI bus interface minimizes data transfer delays and overhead. The actual performance loss is not significant, allowing for real-time responses and rapid processing, suitable for data transmission scenarios.  
Figure 12: (**a**) Integrated convolutional neural network computation. (**b**) Modular convolutional neural network computation.