esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Numerical Results and Discussion
### Detection Performance of the Receiver  
The BER and PER performances of the proposed receiver versus others are shown in Figure 7. The optimal noncoherent receiver with random CPO is considered [6; 7]. The ideal coherent (perfect carrier reference phase and no CFO) detection with SD decoding is also given for sake of comparison. Compared to the optimal noncoherent scheme, the CFOE can be compensated by a postcompensation processor to various degrees as shown in Figure 7. At PER of \(1\times 10^{-3}\), our detection scheme achieves more than 1.3 dB gains over the method in [8]. The performance gap between the scheme in [10] and our detector is only about 0.2 dB; however, substantial reduction in complexity is achieved. Referring to Figure 7, only 1 dB is enough for our detector to meet specific requirements on the performance in [1]. In addition, our scheme is efficient at all SNR regions, although the approximation \(|Y|\approx 1\) involved in Equation (19) is only valid at high SNRs. This follows from the fact that the BER and PER performances of the receiver with Equations (15) and (19) are almost the same as indicated in Figure 7.  
Figure 6: Performance impact of parameter \(L_{1}\). (a) BER impact of \(L_{1}\); (b) PER impact of \(L_{1}\).  
Figure 7: _Cont._The BER and PER performances of the proposed receiver versus the receiver in [10] when the CFO was randomly set to range from \(-6.2\) ppm (i.e., the CFOE \(\varphi\) is \(-0.288\pi\)) to \(6.2\) ppm (i.e., the CFOE \(\varphi\) is \(0.288\pi\)) with triangular symmetric distribution are depicted in Figure 8. As shown in Figure 8, our proposed estimation method in Equation (19) compensates the frequency offset effect more efficiently than that in Equation (12), whereas substantial reduction in complexity is achieved at the same time. This follows from the fact that the absolute estimation error \(|e|\) of our proposed estimation method is now smaller than that of Equation (12) as indicated in Figure 5. Similar results were also achieved for the CFOE \(\varphi\in(-\pi,-0.712\pi)\cup(0.712\pi,\pi)\), which are not given here.
# An Efficient Neural Network for Shape from Focus with Weight Passing Method
## 4 Initial Weight Setting  
In neural network-based SFF methods, initial network weights play important role in acquiring accurate depth map, network convergence, and time complexity. If the initial weights for a pixel are not set properly, more likely an incorrect initial depth map is obtained. Consequently, the convergence of the network is affected by these inaccuracies in depth map and improper initial weights. Therefore, in NN.FIS and NN.Planar networks, it is important to select suitable initial weights for rapid convergence of the networks and for obtaining accurate final depth maps.  
Usually, initial weights are selected randomly. However, random weights may not provide rapid convergence and accurate depth map. In order to overcome these problems, we propose WP method for setting proper initial weights. Using WP method, the network converges rapidly, provides more precise depth map and reduces the time complexity. Furthermore, it helps in avoiding the local minimization problem effectively. Before explaining the proposed WP method, it would be worthy to describe the random setting (RS).
## 2 Cad Systems of Brain Disorders Based on MRI Technology
### Segmentation of Brain Regions
#### 2.2.1 Related-Work to the Segmentation of Brain Regions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Segmentation** & **Database** & **Comparison** & **Measuring Parameters** \\ \hline \multicolumn{5}{c}{**Region Approach Based Techniques**} \\ \hline
[20] & MRF & Several GD-TI, TI and T2-weighted from radiology department of the Poitiers’s hospital & Clinical expert evaluation & Information criterion, MAP criterion \\ \hline
[18] & AFCM & Images from Brainweb & FANTASM & Misclassification rate, mean-squared error \\ \hline
[21] & DRF & Several T\({}_{1}\), T\({}_{1}\)c, and T\({}_{2}\) images from 7 patients. & MRF & Jaccard similarity index \\ \hline
[22] & SVM & Several T\({}_{1}\), T\({}_{2}\), PD and FLAIR-weighted of 45 diabetics. & Two experienced neuro-radiologists & Pearson correlation, Spearman correlation, coefficient of variation, reliability coefficient \\ \hline
[19] & MFCM & Simulated and real MR images. & FCM, FCMS, FCMSI and FGFCM & Similarity index, false positive ratio, false negative ratio \\ \hline
[23] & AMS & Images from Brainweb and IBSR & Adaptive MAP, MPM-MAP & Tanimoto coefficient \\ \hline \multicolumn{5}{c}{**Form Approach Based Techniques**} \\ \hline
[25] & Multi-context wavelet & Several T1-weighted with more than 150 slices for each & Wavelet, multigrid wavelet & NA \\ \hline
[26] & Spherical wavelet & Coronal SPCR images with 124 slices for each of 29 left caudate nucleus structures and 25 left hippocampus structures & Active shape model algorithm and expert neuroanatomist & Average max error, average min error \\ \hline
[24] & Spherical harmonics & PD images of multiple sclerosis patients & Slice stacking technique & Mean error and standard deviation. ANOVA criterion \\ \hline
[27] & Level sets & Images of 16 patients from Singapore National Cancer Center & Region-competition & Jaccard measure, Hausdorff distance, mean absolute surface distance \\ \hline \multicolumn{5}{c}{**Graph Theory Based Techniques**} \\ \hline
[28] & Hypergraph & Image with 265 x 256 pixel slices & Ncut algorithm & NA \\ \hline
[29] & Adaptive graph cut & Ten images from the Brainweb & Adaptive MRF-MAP, graph cut & Classification rate \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work to the MRI brain regions segmentation using some techniques described in the literature: The efficiency of the proposed automatic segmentation algorithm is demonstrated by experiments applying quantitative measurement parameters (last column of the table) for the evaluation, and by comparison with visual observation of a clinical expert (manual segmentation) or with other state-of-the-art algorithms (penultimate column).
# Light-Weighted Password-Based Multi-Group Authenticated Key Agreement for Wireless Sensor Networks
## 3 Proposed Multi-Group Authenticated Key Agreement Protocol for WSNs
### Registration Phase  
Step 1: \(SN_{i}\)\(\Rightarrow\)S:\(\{\)[ID\({}_{i}\), pw\({}_{i}\)\(\}\)  
The sensor node \(SN_{i}\) chooses his/her identity \(ID_{i}\) and password \(pw_{i}\), and sends \(\{ID_{i}\), \(pw_{i}\), \(Group_{i}\) to \(AS\) over a secure channel, where \(Group_{i}\) = (\(G_{i1}\), \(G_{i2}\),...,\(G_{iN}\)) and \(G_{i1}\), \(G_{i2}\),...,\(G_{iN}\) are groups that \(SN_{i}\) belong to.  
Step 2: Upon receiving the register message from \(SN_{i}\), The trusted authentication server (\(AS\)) computes \(HID_{i}\) = \(h(ID_{i}\)\(\|mk)\), \(Q_{i}\) = \(h(ID_{i}\)\(\|pw_{i})\) and stores (\(HID_{i}\), \(Q_{i}\), \(Group_{i}\)).
# Multi-Tag Fusion Localization Method Based on Geometric Constraints
## 6 Conclusions  
The present study offers a contribution to the field of multi-tag localization by proposing a method that incorporates geometric constraints to address the issues of instability and inaccuracy that may arise in such scenarios.  
We have developed a method that calculates an outlier factor based on geometric constraints, which may provide a means to characterize the variability among observational data points from multiple tags. This approach could potentially enhance the accuracy of the positioning process by dynamically adjusting the observation noise for each tag after identifying and removing outliers. Our method also includes an extension to the traditional  
Figure 11: Experimental results of Group 3. (**a**) Trajectory; (**b**) X; (**c**) Y; (**d**) Yaw.  
KF technique, designed to manage the fusion of data from multiple tags. This extension, which is based solely on observational data for prediction updates, may offer a pathway to bypass the limitations of conventional KF that typically integrate two data sources.  
Preliminary results from high-fidelity simulations indicate that our method can achieve a reasonable level of positioning accuracy and stability, with errors maintained within a 3 cm range for position and 3\({}^{\circ}\) for orientation. These results are encouraging and suggest that the method may be applicable in environments where GNSS signals are not available or are subject to interference. In our future work, we aim to integrate Z-axis constraints into our localization method to achieve six-degree-of-freedom (6-DOF) positioning. This enhancement will be crucial for high-precision navigation in environments with varying terrain. Additionally, we plan to deploy our method on a real vehicle, where we will tackle and overcome the practical issues encountered in actual deployment scenarios.  
Conceptualization, Z.L. (Zhuojun Liu) and Z.L. (Zexing Li); Data curation, Z.L. (Zhuojun Liu); Formal analysis, Z.L. (Zhuojun Liu); Funding acquisition, W.Q. and G.Z.; Investigation, Z.L. (Zhuojun Liu) and W.Q.; Methodology, Z.L. (Zhuojun Liu); Project administration, W.Q.; Resources, W.Q., Z.L. (Zexing Li), and G.Z.; Software, Z.L. (Zhuojun Liu) and Z.L. ([PERSON]); Supervision, W.Q. and G.Z.; Validation, Z.L. ([PERSON]); Visualization, [PERSON] ([PERSON]); Writing--original draft, Z.L. ([PERSON]) and G.Z.; Writing--review and editing, Z.L. ([PERSON]), W.Q. and G.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement The original contributions presented in the study are included in the article, further inquiries can be directed to the first author. The authors would like to acknowledge the generous support of [PERSON], [PERSON], and [PERSON] in funding aspects of this work. The authors declare no conflicts of interest.
## 5 DFIG Connected at Electrical Grid
### Electrical Grid Configuration  
In this analysis, the electrical grid is represented by a Thevenin equivalent circuit, consisting of a constant magnitude short-circuit power \(S_{cc}~{}=~{}10\) kVA and a grid impedance angle \(\vartheta_{cc}~{}=~{}80^{\circ}\). For simplicity, the electrical system is represented by a straightforward model using the commercial program MATLAB-Simulink(r). This representation is convenient in the absence of a specific grid, with generalized conclusions being sought. The interconnection diagram of the DFIG with the electrical grid is displayed in Figure 7. The power exchange of the machine is carried out by the stator and the B2B power converter on the grid side converter. Meanwhile, the total active power of the machine is equal to the sum of the stator power and B2B power converter power on the grid-side converter. The grid-side converter is assumed to operate with a unit power factor so that the grid side converter only exchanges active power with the electrical grid, and the reactive power exchange is with the stator circuit. To connect the DFIG with the electrical grid, a transformer is used to adjust the values of the generated voltage to the distribution value of the electrical grid. This establishes an interconnection point where the stator voltage and the converter voltage of the grid-side converter are in low voltage and have different magnitudes.
# A Fast Hole-Filling Method for Triangular Mesh in Additive Repair
## 3 Overview  
In this paper, a fast hole-filling method for triangular mesh based on hole size is proposed. The pipeline of our method is illustrated in Figure 1. The detected holes are classified into three categories based on the size \(S_{v}\) of hole boundary vertices: small (\(S_{v}\leq 6\)), middle (\(6<S_{v}\leq 50\)), and large-sized holes (\(S_{v}>50\)). For each type of holes, three different algorithms are used to fill them.  
First, a set of basic concepts are defined to make them uniform throughout the whole text. After that, the main steps of hole detection based on the check of boundary edges are briefly described. Next, a preprocessing step called boundary cleaning is applied to remove tooth faces for making the hole boundary cleaner.  
Second, three different algorithms are developed to fill the small-sized, middle-sized, and large-sized holes respectively. In the case of small-sized holes, a centroid-based method is utilized to iteratively create new triangles by directly connecting each pair of two continuous hole boundary vertices and the centroid vertex of the hole loop. Regarding middle-sized holes, an initial filling patch is first generated by using the minimum area triangulation, then followed by patch refinement and patch fairing. The former is used to maintain a Delaunay-like triangulation by iterative triangle splitting and edge swapping, and the latter to make the filling patch assort with the surrounding mesh well via a Laplace-Beltrami mesh deformation technique. For large-sized holes, an improved advancing front method is used to close holes fastly without any further refinement and fairing steps, while ensuring the patching mesh to be compatible with the surrounding mesh well.  
Third, two experiments are carried out to verify the efficiency, robustness, and shape-recovery ability of our method. Compared to the two state-of-the-art hole-filling methods in the first experiment, the quantitative evaluation results show that our technique is much faster and can guarantee most of the filling triangles are regular. The second experiment proves that our method can not only produce satisfactory filling results but also make the filling patches compatible with surrounding meshes well.  
Figure 1: The pipeline of our method.
# Analysis of the Energy Efficiency of a Pneumatic Booster Regulator with Energy Recovery
## 4 Simulation and Experiment on VBA-R  
### Simulation Model  
The mathematical model was established in the previous section; this was solved with MATLAB/Simulink software (MathWorks, Natick, MA, USA). There are six chambers in the VBA-R, and each chamber has three control equations. Considering the air power, piston motion and pressure  
Figure 3: Pressure response model of tank.  
response in the tank, more than twenty equations need to be solved. It is difficult work to model all of the chambers one by one. However, when the piston moves in a left or right stroke, three chambers are charged while the others are discharged, and all of them interchange during each stroke. We also found that, whether the chambers' volumes changed or not, they could be described by a lumped parameter model with three control equations; the only differences were the structure parameters of the chambers. Thus, we could utilise this peculiarity and only two kinds of chambers needed to be modelled. The tank could be thought of as a particular chamber whose piston was fixed.  
Therefore, the discharge chambers can be described by Equations (7), (10) and (12), and the charge chambers can be described by Equations (7), (9) and (11). For the fixed chambers, \(u\) can be set to zero; for isothermal chambers, \(d\theta/dt\) can be set to zero. We needed to model only five standard subsystems and redefine the structure parameters during the simulation, which helped to improve the efficiency of the modelling.  
A flow chart of the simulation programme was created, as shown in Figure 4, and the simulation model was established using the standard subsystems. For the simulation, it was necessary to automatically judge the stroke end. Then, the variable step size was selected to improve the calculation efficiency, and the solver was set as ode45 (Dormand-Prince).
## 3 Methodology  
### Delay Doppler Maps (DDMs)  
DDMs are the standard products of spaceborne GNSS-R, as shown in Figure 2. The DDM instruments (DDMI) onboard BF-1 A/B create the DDMs by receiving the sea surface-reflected GNSS signal powers in every time delay and the Doppler frequency bins. Similarly, the instruments use a 1-ms coherent integration time and 1000-times noncoherent integration to guarantee the signal-to-noise ratio (SNR). The theoretical DDM calculations are described by [PERSON] [3] as follows:  
\[\left\langle\left|Y(\tau,f)\right|^{2}\right\rangle=\frac{P_{t}G_{t}\Lambda^{ 2}T_{i}^{2}}{\left(4\pi\right)^{3}}\iint\limits_{A}\frac{G_{r}\sigma_{0} \Lambda^{2}(\tau)S^{2}(f)}{R_{t}^{2}R_{r}^{2}}dA, \tag{2}\]  
where \(\left\langle\left|Y(\tau,f)\right|^{2}\right\rangle\) is the correlation power of each code delay (\(\tau\)) and Doppler frequency (\(f\)) in every DDM bin, \(P_{t}\) is the transmitter power, \(G_{t}\) is the transmitter antenna gain, \(\lambda\) is the L-band wavelength of \(\sim\)19 cm for the GPS L1 carrier, \(T_{i}\) is the coherent integration time, \(G_{r}\) is the receiver antenna gain, \(\Lambda^{2}(\tau)\) is the delay code auto-relation function, \(S^{2}(f)\) is the Doppler frequency function, \(A\) is the effective area of the certain code delay and Doppler frequency, \(R_{t}\) and \(R_{r}\) are the distances of the effective area to the transmitter and receiver, respectively, \(\sigma_{0}\) is the scattering parameter sensitive to the sea surface roughness, which highly correlates with the sea surface wind speed. Once the \(\sigma_{0}\) is determined, the DDM can be simulated by Equation (2).
# Biocompatibility and Osteo/Odontogenic Potential of Various Bioactive Root-End Filling Materials
## 3 Results
### Quantitative Real-Time PCR  
Relative mRNA expressions of osteo/odontogenic gene markers are given in Figure 4. On day 3, MSCs exposed to Medcem MTA indicated a nearly three-fold increase in RUNX2 expression whereas other materials indicated similar expression levels to the control group. Osteogenic induction of MSCs resulted in significantly higher RUNX2 levels as expected (\(p<0.001\)). Longer exposure of all tested materials significantly promoted RUNX2 expression in comparison to the control group on day 7, indicating a nearly four-to six-fold increase (\(p<0.001\)), similar to the osteogenic group.  
DMP-1 expression was significantly increased in MTA Angelus, Medcem MTA (\(p<0.001\)), and BIOfactor MTA (\(p<0.05\)) on day 3. Osteogenically induced cells expressed significantly higher DMP-1 only on day 7. On the same day, all tested materials expressed significantly higher DMP-1 except for Well-Root ST and Pure Portland Cement, which indicated a similar DMP-1 gene expression with the control group without any significant difference. BIOfactor MTA and Medcem MTA groups demonstrated a nearly 8 to 10-fold increase for DMP-1 expression at the end of cell culture, a higher increase than the osteogenic group.  
Another odontogenic marker, DSPP expression, was significantly upregulated only in MTA Angelus among all groups (\(p=0.003\)). In parallel with DMP-1 expression, all tested materials expressed significantly increased DSPP gene, except for Well-Root ST and Pure Portland Cement on day 7. Medcem MTA had a nearly nine-fold increase in the DSPP gene. The osteogenic induction group had similar a DSPP gene expression level with the control group without any significant difference (\(p>0.999\)).  
Figure 4: Relative mRNA expression profiles of MSCs treated with calcium silicate-based materials showing osteo/odontogenic gene markers, RUNN2, DMP-1, and DSPP. Different letters indicate a significant difference between the test groups (\(p<0.05\)) excluding the control group. Asterisk indicates difference between the control group and another group ***\(p<0.001\), ***\(p<0.01\), *\(p<0.05\). (OS: Osteogenic medium, ANG: MTA Angelus, BF: BIOfactor MTA, MED: Medcem MTA, PC: Pure Portland Cement, WR: Well-Root ST).  
Figure 3: ALP activity of MSCs treated with tested calcium silicate-based materials. Different letters indicate a significant difference between the test groups (\(p<0.05\)) excluding the control group. Asterisk indicates difference between the control group and another group ***\(p<0.001\), *\(p<0.05\). (OS: Osteogenic medium, ANG: MTA Angelus, BF: BIOfactor MTA, MED: Medcem MTA, PC: Pure Portland Cement, WR: Well-Root ST).
## 5 Analysis and Discussion  
### Fault Tree Analysis  
An FTA was carried out based on the findings and causes discussed in the case study. A fault tree is a logical model that shows the relationships between failure events that result in system failure. The tree consists of the 'TOP' event and various failures with the basic events. The different causes of the TOP event are related through logic gates [10]. Similar to the study by ref. [10], only AND-gate and OR-gates were only considered.  
In the FTA for the case study in Figure 3, the TOP event is the machine fire high potential incident. The various failure events and their relationships are illustrated, and the basic events are at the bottom of the tree. According to this analysis, the direct causes 'AND' the contributing causes had to occur to result in the TOP event. For example, if the fire event occurs but there is an 'adequate emergency response', then the outcome will be different.  
It is also important to note that failure events resulting in 'failure to extinguish fire' are connected by an AND-gate. This is because the fire suppression system was designed  
Figure 3: Fault Tree Analysis (FTA) of the PGM mine LHD fire HP incident.  
with redundancy to allow the operator to manually activate the system to discharge in the event that the system fails to detect the fire and automatically discharge. Both alternatives will have to fail to result in the event 'failure to extinguish fire'. This was the case in this scenario, where the fire suppression failed to extinguish the fire after manual activation because the spray nozzles were not directed to the source of the fire.  
It can be seen from this analysis that the FTA can provide a mental map that can assist in understanding the logic of the failure, as articulated by ref. [10]. This further highlights that the output from the FTA can be used as input for RBD analysis and to structure the AHP model, which is done in the next sections.