esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Adaptive Migration Walrus Optimizer (AMWO)  
In the migration phase of WO, the number of iterations is considered, and the phenomenon of the average fitness value of the population hardly changing over multiple iterations, which is caused by the stagnation problem, is ignored. According to the changes in the average fitness value of the population over multiple iterations, the walruses are adaptively migrated to more suitable survival areas, thereby reducing the possibility of the algorithm falling into a local optimum. Therefore, a novel walrus optimizer algorithm based on an adaptive migration strategy (AMWO) is proposed. The detailed steps are as follows:  
Input: Population size \(Num\), maximum iteration \(T\), \(Tn\) represents the number of times the average fitness value of the population hardly changes, \(Tn=0\).  
Output: The optimal solution.  
Step 1: Initialize the population.  
Step 2: The \(Fit\_value\) of population is calculated by Equation (6), and the average fitness value of the population is \(Fit\_value_{ave}^{t}=\frac{1}{Num}\sum_{i=1}^{Num}Fit\_value_{i}\).  
Step 3: In \(0<t\leq\frac{T}{2}\), \(Fit\_value_{ave}^{t}\), \(Fit\_value_{ave}^{t-1}\), \(Fit\_value_{ave}^{t}/Fit\_value_{ave}^{t-1}\) and \(D\_signal\) are calculated. If \(Fit\_value_{ave}^{t}/Fit\_value_{ave}^{t-1}\leq\eta\), then \(Tn=Tn+1\). If \(Fit\_value_{ave}^{t}/Fit\_value_{ave}^{t-1}>\eta\), then \(Tn=0\). If \(Tn>\epsilon\), the migration is carried out by Equation (9). Otherwise, if \(|D\_signal|\geq 1\), the migration is carried out by Equation (9).  
Step 4: In \(\frac{T}{2}<t\leq T\), \(D\_signal\) is calculated, if \(|D\_signal|\geq 1\), the migration is carried out by Equation (9).  
Step 5: If \(|D\_signal|<1\), the reproduction phase is performed. The value of \(S\_signal\) is calculated by Equation (8). If \(S\_signal>0.5\), the walruses choose roosting behavior. Otherwise, the walruses choose foraging behavior.  
Step 6: If \(t=T\), output the optimal solution. Otherwise, return to Step 2.  
The schematic diagram of the AMWO algorithm is shown in Figure 2.  
As shown in Figure 2, in \(0<t\leq\frac{T}{2}\), besides the number of iterations, the phenomenon of the average fitness value of the population hardly changing over multiple iterations, which is caused by the stagnation problem, is also considered. If \(Fit\_value_{ave}^{t}/Fit\_value_{ave}^{t-1}\leq\eta\), \(Tn\) = \(Tn+1\). Otherwise, \(Tn=0\). If \(Tn>\epsilon\), the walruses will migrate to more suitable survival areas. Otherwise, if \(|D\_signal|\geq 1\), the walruses will migrate to more suitable survival areas. If \(|D\_signal|<1\), the reproduction phase is performed. The AMWO algorithm adopts an adaptive migration strategy, which can reduce the possibility of the algorithm falling into a local optimum.  
Figure 2: The schematic diagram of the AMWO algorithm.
## 2 Materials and Methods
### Experimental Setup  
For our experiments, we implemented the benchmark pipeline described in Section 2.3.1 in Python. The anomaly detection methods were either integrated from their publicly available GitHub repositories (RRCF: [[https://github.com/kLabUM/rrcf](https://github.com/kLabUM/rrcf)]([https://github.com/kLabUM/rrcf](https://github.com/kLabUM/rrcf)) (accessed on 18 January 2023), MDI: [[https://github.com/cvjena/libmaxdiv](https://github.com/cvjena/libmaxdiv)]([https://github.com/cvjena/libmaxdiv](https://github.com/cvjena/libmaxdiv)) (accessed on 18 January 2023), GANF: [[https://github.com/EnyanDai/GANF](https://github.com/EnyanDai/GANF)]([https://github.com/EnyanDai/GANF](https://github.com/EnyanDai/GANF)) (accessed on 18 January 2023), TranAD: [[https://github.com/imperial-qore/TranAD](https://github.com/imperial-qore/TranAD)]([https://github.com/imperial-qore/TranAD](https://github.com/imperial-qore/TranAD)) (accessed on 18 January 2023)) or implemented by us if a Python version was not available (MERLIN: [[https://gitlab.com/dlr-dw/py-merlin](https://gitlab.com/dlr-dw/py-merlin)]([https://gitlab.com/dlr-dw/py-merlin](https://gitlab.com/dlr-dw/py-merlin)) (accessed on 18 January 2023)). The Autoencoder model was implemented using the PyTorch [(43)] library and is available in the repository for this work ([[https://gitlab.com/dlr-dw/is-it-worth-it-benchmark](https://gitlab.com/dlr-dw/is-it-worth-it-benchmark)]([https://gitlab.com/dlr-dw/is-it-worth-it-benchmark](https://gitlab.com/dlr-dw/is-it-worth-it-benchmark)) (accessed on 18 January 2023)). The relevant hyperparameters for each model were tuned through 20 rounds of Bayesian optimization on 25 randomly selected time series from the UCR Anomaly Archive, using the F1 score as the optimization target. The time series used for hyperparameter tuning were excluded from the actual experiments. A table containing all hyperparameters obtained from that search can be found in Appendix A.2. All experiments were run on an Intel Xeon Platinum 8260 CPU with 10 GB of allocated memory For TranAD, we increased the memory to 20 GB for the timeseries \"239_UCR_Anomaly_taichidb50715 Master_190037_593450_593514.txt\", \"240_UCR_Anomaly_taichidb50715 Master_240030_884100_884200.txt\" and \"241_UCR_Anomaly_taichidb50715 Master_250000_837400_839100.txt\". We ran all experiments six times: the first time, we set the random number generators of Python, Numpy and PyTorch to a fixed value (we used 42 as the seed value across all experiments) and then performed five repetitions without setting a random seed to account for random sampling effects.
## 3 Results
### Turbine Efficiency Comparison  
Table 1 presents a comparative analysis of the efficiency obtained for the turbine under examination. The values presented in the reference case are derived from experimental tests of the turbine conducted by the manufacturer. In turn, Cases 1 and 2 are the results of CFD simulations conducted by the author. In the absence of precise data regarding the operating pressure of the turbine, the maximum power presented by the manufacturer (55 kW at a turbine rotational speed of 3600 rpm) was used as a reference point. In order to facilitate comparison with the reference case, two further cases were prepared, differing in terms of inlet and outlet pressure and inlet temperature. In comparison to the reference case, the efficiency of Case 1 and Case 2, respectively, was found to be 3.05 and 3.49 percentage points (pp) lower. This discrepancy may be attributed to a number of factors. Firstly, the operating pressure and operating temperature of the turbine, which were declared by the manufacturer, are not known. This is an important element, as it may be the case that the expander design point has not been reached. Secondly, the available tip clearance above the turbine rotor blades in the geometric model may differ from the clearance obtained in the finished product. In this case, it can be postulated that the clearance has been reduced in comparison to the available CAD model. Thirdly, the manufacturer's data lack clarity regarding the measurement methodology and the margin of error. It is possible that the obtained results are included in the total measurement error of the manufacturer's experimental tests. Ultimately, in both cases, the obtained results are similar to each other and do not differ significantly from the reference case, which provides an excellent starting point for further experimental tests of this expander.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Power** & **Rotational Speed** & **Inlet Pressure** & **Outlet Pressure** & **Expander Efficiency** & **Differences** \\ \hline
**Unit** & **[kW]** & **[ppm]** & **[kPa]** & **[kPa]** & **[\%]** & **[pp.]** \\ \hline Reference case [30] & & & no data & no data & 73.43 & - \\ Case 1 & 55 & 3600 & 453 & 150 & 70.38 & 3.05 \\ Case 2 & & & 481 & 170 & 69.94 & 3.49 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of efficiency for different cases.  
Figure 26: Power output vs. inlet pressure for various outlet pressures.
# Non-Probabilistic Reliability Analysis of Slopes Based on Fuzzy Set Theory
## 5 Example Analysis
### Calculation Results and Analysis
#### 5.2.3 The Relationship between Non-Probabilistic Reliability Indexes and Failure Degrees
For actual slope projects, the distribution ranges of non-probabilistic reliability indexes and their corresponding failure degrees are not known in advance or need not be counted in large quantities. A specific failure degree evaluation standard (e.g., 0.03, 0.01 or 0.001) can be given according to the acceptable risk level of the project, and then the overall failure degree of the slope can be calculated using the proposed method in this paper and compared with the failure degree evaluation standard to judge the stability state of the slope. In some cases, when the slope failure is not considered for the sake of simplicity, a value smaller than 1 can be given as the evaluation criterion of the non-probabilistic reliability index (e.g., 0.85, 0.9 or 0.95), and then the non-probabilistic reliability of the slope can be calculated using the non-probabilistic reliability method and compared with the evaluation criterion to determine the stability state of the slope.
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## Appendix B Zephyr RTOS
### BLE and BLE Mesh Security Features  
In comparison to previous versions of Bluetooth, Version 5 introduced more advanced specifications to improve the functionalities of IoT-enabled devices with efficient and effective energy management [36]. The security dangers have increased as Bluetooth standards have improved, such as the coverage range (up to 200 m), as well as data throughput (2 Mbps). Similarly, with the enhanced range, attackers can gain access to the connection from a wider range of locations. Because of the aforementioned features, the BLE security mechanism differs from that of Bluetooth BR/EDR/HS. For security, one oftwo methods for pairing can be used for low-energy versions: Low-energy Legacy Pairing (LLP) or Low-Energy Secure Connection (LESC) (introduced in Version 4.2) [35].  
Furthermore, the 4.2 and 5 specifications added a secure connection device pairing capability, as well as a new approach called numeric comparison, which has proven to be the best for BLE-based device authentication when compared to other methods.  
_Provisioning:_ Unprovisioned devices are those that are not connected to a BLE mesh network. If a device wants to join a mesh network, it should first go through the provisioning process [35]. The device will obtain the provision to enter the mesh network after provisioning and will be able to communicate with rest of the nodes in the network. In Zephyr, there are several API's to program a provisioning feature in the mesh-based applications [35].
# A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System
## 4 Materials and Methods
### The Privacy-Preserving Scheme  
#### 4.2.1 Problem Definition  
Considering the system architecture, vehicles, roadside units, and cloud services exchange messages with each other. Malicious adversaries can intercept those messages, which include sensitive information such as date, time, location data, vehicle ID, driver's ID, and more information that can be inferred. For instance, adversaries can determine the routes and schedules used by their victims using vehicle location and the message timestamp. Adversaries could identify the vehicle and establish a driver's profile. This information could even be sold to criminals who could use it to kidnap the driver and steal the vehicle in the worst-case scenario.
## 6 Discussion  
In this subsection, we further discuss the effect of instance parameters (number of observation satellite \(n\) and number of SEC satellites \(m\)) on these algorithms. We organize our comparisons into two groups. Similarly, we generate ten different testing instances for each combination, and each algorithm independently runs ten times for each instance. Here are the comparison results:  
Figure 9 illustrates comparison results of \(aRV\), \(aCT\), and \(aRT\) under different observation satellite numbers. The performance ranking of algorithms remains unchanged, despite variations in observation satellite numbers. PITO consistently achieves the optimal \(aRV\) and \(aCT\) across all instances, demonstrating its statistical effectiveness. Moreover, as the number of observation satellite increases, all algorithms exhibit an ascending trend in both \(aCT\) and \(aRT\). With the instances of \(n=80\), PITO not only achieved significantly superior \(aRV\) values but also required 80.35% (\(\approx\)(\(789-155\))/789) and 74.29% (\(\approx\)(\(603-155\))/603) fewer communication times compared to ADMM and DETS, respectively.  
Figure 10 shows the results of \(aRV\), \(aCT\), and \(aRT\) under different constellations. Once again, PITO achieves the optimal \(aRV\). Additionally, with an increase in satellite number, the \(aCT\) and \(aRT\) values of CNP, DETS, and PITO show only marginal growth, while the values of ADMM exhibit a pronounced upward trend. That is because, in the consensus process of PITO, satellites only communicate with their neighbors using the vectors defined in Section 4.1, resulting in only a slight increase in the required convergence iterations \(\delta\) as the satellite number increases. In the instances with constellation F (i.e., \(m=66\)), PITO still achieves the best \(aRV\) value compared to other algorithms, and requires 26.00%  
Figure 8: Indicator trends under different combinations with \(q=10\).  
(\(\approx\)(\(100-74\))/100) and 43.79% (\(\approx\)(\(131.67-74\))/131.67) fewer communication times than CNP and DETS, respectively.  
Figure 9: Indicator trends under different observation satellite numbers with constellation B and \(m=9\).
## 3 Engineering Case Analysis
### Case History 1  
There was a foundation pit adjacent to Shanghai Metro Line 1 [1], the plane size of the excavation area was \(L\) = 45 m, \(B\) = 63 m, the length of the enclosure structure was \(H\) = 23 m, and the excavation depth was 11.5 m. Metro Line 1 was located under the road beside the foundation pit. The buried depth of the tunnel axis was 10.1 m. The minimum distance between the tunnel axis and the enclosure structure of the foundation pit was s = 7 m. The shield tunnel segment ring outer diameter \(D\) = 6.2 m, ring width \(D_{\text{t}}\) = 1, lining thickness \(t\) = 0.35 m, calculation parameters \(k_{\text{sl}}\) = 2.23 \(\times\) 10\({}^{6}\) kN/m, and \(k_{\text{t}}\) = 9.39 \(\times\) 10\({}^{5}\) kN/m. The excavation scope of the foundation pit is mainly silty clay, muddy silty clay, and muddy clay. The tunnel was mainly located in the silty clay layer, and the Poisson's ratio of the soil was \(\mu\) = 0.35.  
The comparison between the fitting curve and the measured curve of the deformation of the enclosure structure is shown in Figure 7. It can be seen that the fitted curve is in good agreement with the measured value of the foundation pit enclosure deformation. The fitting function is: \(x=-0.001392\text{z}^{3}-0.07205\text{z}^{2}+2.578\text{z}-0.3347\)\((0\ m\leq z\leq 23\ m)\). Substituting the deformation of the enclosure structure obtained by the fitting function into the calculation, the comparison curve between the calculated value of the horizontal displacement and the measured data is shown in Figure 8. Taking the measured deformation data of the engineering envelope as the calculation parameter, the calculation results by the method in this paper are consistent with the actual measurement results. The rotation effects proportionality coefficient of the shield tunnel is \(j_{\text{x}}\) = 0.2. The measuring point is perpendicular to People's Avenue in the area of 14-38 m away from the excavation center. The tunnel's horizontal displacement is further enlarged due to the vehicle load. The maximum horizontal displacement measured is 3.82 mm, while the value calculated is 2.79 mm. It can be found that the horizontal displacement of the shield tunnel presents a normal distribution. The horizontal displacement close to the excavation center is relatively large, and the two sides decrease successively.  
Figure 7: Comparison of the actual measurement and fitting curve of the envelope structure deformation.
# Overview of Predictive Control Technology for Permanent Magnet Synchronous Motor Systems
## 3 An Overview of the Recent Development for Predictive Control Methods of PMSM
### Deadbeat Predictive Control of PMSM
#### 3.1.2 Robust Control Strategy
In [89], the extended state observer was combined with DPCC, and the predicted disturbance was used as voltage reference feed-forward compensation, which solved the disturbance caused by single-step delay and parameter mismatch.
## 3 Proposed Joint Resource Optimization for NOMA-Enabled Vehicular Networks  
In this section, we first present the proposed formulation of the joint RSU-VU association, channel assignment, NOMA-OMA switching and transmission power control. Then, to reduce the complexity of the formulation, we propose two solution variants.
## 2 Proposed Multimodal Reconfigurable Sensor Readout Circuit
### Detailed Description of the Amplifiers
#### 2.3.2 Fully Differential Amplifier  
The fully differential amplifier with a rail-to-rail input stage in the PGA is shown in Figure 6. The rail-to-rail input stage can receive the output of the first stage over a wide range of values. To achieve a high gain, this amplifier has two stages: the folded cascade and common source (CS) stages [19]. To ensure frequency stability, the RC compensation using R2f and C1f is applied between the two stages.  
In the amplifying stage, matching the common mode signal at the differential output is essential to obtain high signal-to-noise ratios, which is an advantage when using a differential pair. If the common mode signals of the differential outputs do not match each other, unnecessary components, including noise in the outputs, cannot be removed. In addition, the output dynamic range can be limited if common mode signals cannot be matched by the reference voltage, 0.9 V, in the CS stage. To solve this problem, the two common mode feedback (CMFB) scheme was applied. In the folded cascade stage, the resistive divider CMFB was applied. The output common mode signal, which is detected by the resistive divider, adjusts the current source consisting of an n-channel metal-oxide-semiconductor(NMOS). As the gate and drain of the NMOS form a negative feedback loop, the output common mode signal can be matched. In the CS stage, the CMFB consists of a resistive/capacitive divider and an error amplifier. The resistive/capacitive divider detects the common mode signal of the differential output at high and low frequencies. The error amplifier accepts the reference voltage and the detected common mode signal as a differential input and produces an output voltage, V\({}_{\text{cmfb}}\). V\({}_{\text{cmfb}}\) is connected to the gate of the p-channel metal-oxide-semiconductor (PMOS) device in the CS stage, and a negative feedback loop is formed. The output common mode signal is matched to the reference voltage of 0.9 V by the negative feedback loop.
## 3 Discussion
Consistent with a previous study, our study demonstrated that several transcripts encoding HMGR, MGS, PMK, and MVK were highly expressed in the flowers [31]. The gene-related saponin synthesis showed high expression profiles in the above ground tissues of _P. notoginseng_. Five genes (i.e., FPS, SS, SE1, SE2 and DS) related to saponin biosynthesis were highly expressed in the above ground tissues, especially in the flower [13]. The high contents of saponin were detected in the below ground tissues of _P. notoginseng_ in our study. The intermediate products of saponin synthesis may be actively synthesized in above ground tissues and transported to the below ground tissues [32], which revealed that contents of saponins were higher in the below ground tissues of _P. notoginseng_. The expression patterns of CYP716A47 and CYP716A53v2 genes could regulate the distribution of protopanaxadiol- and protopanaxatriol-type saponins in the different tissues of _P. notoginseng_. CYP716 subfamily genes (CYP716A47 and CYP716A53v2) are involved in the dammarane-type triterpenoid production. CYP716A47 catalyzes the formation of protopanaxadiol from dammarenediol-II [21], and CYP716A53v2 catalyzes the formation of protopanaxatriol from protopanaxadiol in _P. ginseng_[22]. Previous studies revealed that CYP716A47 and CYP716A53v2 are highly expressed in the main root and the rhizome, especially in the periderm [30; 33]. The expression level of CYP716A53v2 is higher in _P. notoginseng_ roots than in its leaves and flowers [29], and this observation was consistent with our findings. The RNAi of CYP716A47 in the transgenic hairy root of _P. ginseng_ and _P. quinquetglius_ caused the reduction of ginseng synthesis, indicating the positive correlation between gene expression levels and ginseng contents [34]. In our study, CYP716A47 expression levels had a significantly positive correlation with the contents of protopanaxadiol-type saponins (Rc and Rb2). CYP716A53v2 gene expression levels had a significantly positive correlation with the contents of protopanaxadiol-type (Rb1 and Rd) and protopanaxatriol-type saponins. These results demonstrated that the expression patterns of CYP716A47 and CYP716A53v2 genes could regulate the distribution of protopanaxadiol-type and protopanaxatriol-type saponins in different tissues of _P. notoginseng_. Ginsenoside Ro, oleanane-type saponin, was first isolated from _P. japonicus_ and also found in other _Panax_ species, such as _P. ginseng_, _P. zingiberensis_, and _P. stipuleanatus_[35; 36]. Ginsenoside Ro was not detected in our _P. notoginseng_ samples. One gene encoding beta-AS (Unigene 20794_All) and one gene encoding CYP716A52v2 (CL11590.Contig2_All) were annotated in our transcript data. Previous studies reported that CYP716A52v2, which served as a CYP716A subfamily gene, may be involved in the oleanane-type triterpenene biosynthesis [23]. Similar to a previous study, our study indicated that the content of ginseng Ro might be too low to be detected in _P. notoginseng_[29].