esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Results and Discussion
After 12 s, the difference between the accuracies of the two methods is very small. For this phenomenon, our inference is that the change in the EEG signal presented with the MFIs is overshadowed as the time window becomes larger. Therefore, the MFI sequence corresponding to the large-sized time window does not reflect the change of emotion. To confirm this inference, we select MFI sequences from 32 subjects to seek corresponding evidence. After comparison, Subject 4 is chosen to present the variation, which is shown in Figure 10.  
As seen in Figure 10, there are 12 MFI sequences, and each line corresponds to a time widow size. From the first to the fifth line, each line contained the first 10 images of the MFI sequences. Starting from the sixth line, it contains the whole images of the sequence. Studies presented in [50; 51] suggested that emotion is related to a group of structures in the center of the brain called the limbic system and other structures such as the prefrontal cortex [52], orbitofrontal cortex, and so on. Out of these areas, the correspondence between the prefrontal cortex and the EEG electrodes FP\({}_{1}\)-FP\({}_{2}\) are more direct than others. Therefore, we focused on the area corresponding to FP\({}_{1}\)-FP\({}_{2}\). We can see from Figure 10 that the MFI sequences corresponding to time windows 1 s to 3 s reveal more details about the activation in this area. However, starting from the MFI sequence corresponding to the 4 s time window, the activation information for this area is gradually reduced. This also corresponded to the case where emotion recognition accuracy decreased after a 4 s time window by CLRNN in Figure 9. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Classification Methods** & **1 s** & **2 s** & **3 s** & **4 s** & **5 s** & **6 s** & **8 s** & **10 s** & **12 s** & **15 s** & **20 s** & **30 s** & **60 s** \\ \hline k-NN & 46.09 & 52.49 & 57.29 & 59.49 & 61.69 & 61.39 & 61.49 & 61.89 & 61.79 & 62.19 & 62.59 & 62.19 & 62.39 \\ Random Decision Forest & 39.03 & 38.17 & 39.38 & 40.19 & 38.53 & 36.43 & 44.53 & 45.38 & 45.88 & 46.58 & 46.68 & 46.78 & 46.98 \\ SVM & 65.11 & 65.11 & 66.11 & 64.21 & 63.01 & 64.31 & 61.01 & 63.01 & 62.61 & 62.41 & 63.01 & 63.41 & 63.21 \\ CNN + RNN & 61.76 & 59.02 & 58.07 & 60.34 & 61.43 & 62.13 & 61.13 & 59.1 & 60.1 & 60.9 & 61.1 & 61.5 & 62.1 \\ CLRNN & 74.73 & 75.21 & 75.13 & 74.32 & 73.25 & 70.37 & 67.23 & 65.01 & 57.3 & 60.2 & 62.1 & 60.6 & 61.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The classification results with different time window size and methods. Figure 9: The comparison of the classification accuracies between CLRNN and CNN + RNN with different time window sizes.
# Design and Dynamic Simulation Verification of an On-Orbit Operation-Based Modular Space Robot
## 2 Configuration Design of Reconfigurable Modular Space Robot
### Unit Module Mechanism Design
#### 2.3.1 Drive Selection and Transmission System Design  
By mutual rotation between each module, one can realize the topological change of the overall system. For this purpose, corresponding drives and transmission mechanisms need to be designed. The conventional drive is generally a DC motor. When selecting the motor, try to make full use of the space inside the module and select a motor with appropriate torque and speed. Here, the FAULHABER2642 CR motor (manufactured by the German von Haber Group, New York, NY, USA) is selected as the main motor of the module. Its properties are shown in Table 1.  
The working process is as follows: install the worm gear, pulley, synchronous belt, motor, bearing, retaining ring, etc. on the designed support frame and then install the support frame on the active module support plate. The worm gear is connected to the torque output shaft and connected to the passive module through the flange. Connect and complete the assembly of the transmission system. The actual processed object and assembly result are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **FAULHABER2642 CR** & **Unit** \\ \hline Voltage & 24 & V \\ \hline armature resistance & 5.78 & \(\Omega\) \\ \hline Maximum output power & 23.2 & W \\ \hline Maximum efficacy & 79 & \% \\ \hline No-load speed & 6400 & rpm \\ \hline No-load electric current & 0.058 & A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main motor properties.  
Figure 8: Pulley transmission and worm gear transmission components.  
Figure 6: Structural design drawing of quasi-tetrahedral hemispherical shell.  
Figure 7: Main structure physical picture.  
In addition, as shown in Figure 9, a rotating guide rail is installed between modules. When rotation occurs between the two modules, the guide rail and the carbon fiber surface are used to rotate, which can greatly reduce the friction.
###### Abstract  
Fault tolerance has long been a major concern for sensor communications in fault-tolerant cyber physical systems (CPSs). Network failure problems often occur in wireless sensor networks (WSNs) due to various factors such as the insufficient power of sensor nodes, the dislocation of sensor nodes, the unstable state of wireless links, and unpredictable environmental interference. Fault tolerance is thus one of the key requirements for data communications in WSN applications. This paper proposes a novel path redundancy-based algorithm, called dual separate paths (DSP), that provides fault-tolerant communication with the improvement of the network traffic performance for WSN applications, such as fault-tolerant CPSs. The proposed DSP algorithm establishes two separate paths between a source and a destination in a network based on the network topology information. These paths are node-disjoint paths and have optimal path distances. Unicast frames are delivered from the source to the destination in the network through the dual paths, providing fault-tolerant communication and reducing redundant unicast traffic for the network. The DSP algorithm can be applied to wired and wireless networks, such as WSNs, to provide seamless fault-tolerant communication for mission-critical and life-critical applications such as fault-tolerant CPSs. The analyzed and simulated results show that the DSP-based approach not only provides fault-tolerant communication, but also improves network traffic performance. For the case study in this paper, when the DSP algorithm was applied to high-availability seamless redundancy (HSR) networks, the proposed DSP-based approach reduced the network traffic by 80% to 88% compared with the standard HSR protocol, thus improving network traffic performance.  
communication protocols for sensors in cyber physical systems (CPS); CPS with wireless sensor networks (WSN); dual separate paths (DSP); fault-tolerant communication +
Footnote †: journal: Sensors
## 1 Introduction
Tropical regions receive daily normal irradiance (DNI) between 5.5-7.5 kWh/m\({}^{2}\) with more than 300 sunny days per year [14]. This massive solar potential can be utilized to meet the above stated challenging and need based assignment in a decentralized manner. Roasting of perishables using concentrating solar power is a novel and promising field. A variety of solar collectors are available to concentrate the incoming DNI to produce medium to high temperature range heat energy which can be employed in the subsequent process by coupling suitable technologies [15]. The promotion of innovative solar based technologies for switching the agricultural processing from non-renewable to renewable energy resources can not only upsurge the on-farm value-addition facilities but also contribute towards a sustainable environment and rural development. This scientific research has been carried out for the assessment of the capacity of an indigenously developed thermal oil based continuous solar roaster (CSR) for groundnut roasting using solar power. Fabrication of CSR was accomplished in the engineering workshop of the Faculty of Agricultural Engineering and Technology, at the University of Agriculture Faisalabad with locally procured material and a 10 m\({}^{2}\) standing Scheffler fixed focus concentrator was coupled with the developed system to produce heat energy. The versatility of standing Scheffler concentrator is to keep the focus fix throughout the day at just above the ground level away from the path of incoming DNI regardless the position of energy utilization source [15], thus making it most appropriate selection for a weighty machine such as CSR. Response surface methodology (RSM) is a widely used tool for optimizing food processing operations [16] and it has been employed in the study to investigate the optimum operational machine parameters viz. feeding capacity, conveyor speed and thermal oil flow rate for achieving light, medium and dark quality roasting of groundnuts in comparison with industrial quality. By performing thermal analysis of CSR, solar power utilization during roasting process was determined and, based on the solar power utilization, economic feasibility of CSR for commercialization was also estimated.
## 2 Structure and Parameters  
The studied 2 DoFDDIM consists of two arc-shaped stators of the rotary and linear parts and a SSR, as shown in Figure 1. The motor adopts full-pitched windings. The number of parallel branches is 1. Table 1 lists the structure parameters of the motor. The feature is the rotary motion stator is just a half of the traditional stator, as shown in Figure 1a; the circumferential radian is listed in Table 1.  
The SSR is shown in Figure 2. It can be known that a hollow steel cylinder is slotted along the axial and circumferential directions, and copper is cast in the slots. For the rotary part, the axial slots provide a path for the induced current caused by a rotating magnetic field. The induced current is  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{2}{c}{**Values/Dimension**} \\ \cline{2-3}  & **Rotary Part** & **Linear Part** \\ \hline Rated voltage \(U_{1}\) (V) & 180 & 180 \\ Supply frequency \(f_{1}\) (Hz) & 50 & 50 \\ Pole pair \(p\) & 2 & 2 \\ Stator inner diameter \(D_{sl}\) (mm) & 98 & 98 \\ Stator outer diameter \(D_{so}\) (mm) & 155 & 155 \\ Stator axial diameter \(l_{f}\) (mm) & 156 & 156 \\ Air-gap thickness \(\delta\) (mm) & 2 & 2 \\ Number of slots & 12 & 12 \\ Phase number & 3 & 3 \\ Number of slots per pole per phase & 2 & 2 \\ Number of turns per slot \(N\) & 90 & 90 \\ Winding diameter \(d_{w}\) (mm) & 0.54 & 0.54 \\ Circumferential radian (\({}^{\circ}\)) & 180 & 140 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure Parameters of 2 DoFDDIM.  
Figure 1: Structure of 2 DoFDDIM: (**a**) Rotary motion arc-shaped armature; (**b**) Linear motion arc-shaped armature; (**c**) SSR; (**d**) Assembly of the 2 DoFDDIM.  
closed through the circumferential slots at the end. For the linear part, the roles of the axial slot and the circumferential slot are interchangeable. The shape, width, and depth of the axial slots are the same as the circumferential slots. Table 2 presents the SSR parameters.
# Data Extraction Method for Industrial Data Matrix Codes Based on Local Adjacent Modules Structure
## 5 Barcode Data Extraction
### Transform a Square Grid Format Image to an Edge Image
#### 5.3.2 Correct the Edge Image by Eliminating Pairs of Odd-Degree Vertices  
We eliminate a pair of odd-degree vertices by selecting a proper path between them in the edge image. The proper path along the gridline to connect the pair. An \(XY\) coordinate system is used to describe positions of vertices in the edge image, as shown in Figure 12a. Suppose the points \((X_{1},Y_{1})\) and \((X_{2},Y_{2})\) belong to a pair of odd-degree vertices, the horizontal distance between the pair is \(\mid X_{2}-X_{1}\mid\), and the vertical distance between the pair is \(\mid Y_{2}-Y_{1}\mid\). If the horizontal distance or the vertical distance is zero, the proper path is the connection line between the pair. If the horizontal distance and the vertical distance are non-zero, the proper path is chosen by the conditions. The conditions are as follows: (1) the length of the path is minimum; and (2) the weight of the path is smallest when weight the edges of the grid in the edge image. The weight values are calculated when CNN models classify pairs of adjacent modules in the barcode image.  
After obtaining the proper path of any pair of odd-degree vertices, we should change the property of the path in the edge image. If there is an edge in the path, the edge will be removed, and, if there is not an edge in the path, an edge will be added. The final edge image is obtained after changing the property of all paths in the edge image, which does not include any odd-degree vertex, as shown in Figure 12b.  
Figure 12: A final edge image forming process: (**a**) path selection in the edge image; (**b**) the corrected edge image.
## 2 Materials and Methods
### Security Requirements According to ISO/IEC62443
#### 2.3.2 Zones and Conduits Concept
These relationships are then used to deduce security requirements, which justify why a certain group of security requirements was chosen to address a specific vulnerability in the system. These relationships are constructed in accordance with the security attributes that are collected from threats, components/assets, and security requirements in order to estimate previously established relationships and create new ones, as outlined below [14]:  
* _Components/Asset_\(\longrightarrow\)SecurityAttributes: \"A component/asset has security attributes to protect it from any type of cyberattack scenarios\"
* _SecurityAttributes_\(\longrightarrow\)Threat: \"Security attributes could be vulnerable points that an attacker can exploit by multiple perspectives, which are defined as potential cyber threats\"
* _Threat_\(\longrightarrow\)Components/Asset: \"A threat impacts a component/asset\"
* _Threat_\(\longrightarrow\)_SecurityLevel_: \"Each threat has a degree of risk level, that represents the severity level of its cyber risk\"
* _SecurityLevel_\(\longrightarrow\)_SecurityLevel_: \"Security level has an equivalent security level driven by standardization that handles security issues\"
* _SecurityLevel_\(\longrightarrow\)_SecurityRequirements_: \"Security requirements are classified according to multiple security levels (i.e., ranging from 1.0 to 4.0)\"
* _SecurityRequirements_\(\longrightarrow\)SecurityAttributes: \"A set of security properties can identify security requirements to describe security measures applied to achieve particular security objectives\"
* _SecurityRequirements_\(\longrightarrow\)Component/Asset: \"According to that, security requirements are chosen to address/mitigate a threat that aims to protect component/asset\"
* _SecurityRequirements_\(\longrightarrow\)Threats: \"Security requirements are chosen to address a threat\"  
The framework gives a complete, trackable method for proceeding from cyber threats to security requirements. It is an advanced method for automatically estimating an appropriate set of security-related requirements to protect the system components, address potential cyber threats, and mitigate related risks.
# Effects of Timing Noise on Square-Wave
## 3 Results
Specifically, we plot \(10\log_{10}[S_{f}(\omega_{0}+\omega_{\text{offset}})/S_{f}(\omega_{0})]\) in dBc/Hz where \(\omega_{\text{offset}}\) is the offset frequency from the \(n=1\) line center (\(\omega=\omega_{0}\)). Following the peak of the Lorentzian shape near line center, we see a region corresponding to \(\omega_{\text{offset}}^{-2}\) behavior--as expected for the Lorentzian--followed by a flattening due to the pedestal; of course, when this transition occurs and its details depend on the values of the parameters. Figure 1: Autocorrelation function \(\mathcal{F}(\tau)\) of the non-sinusoidal signal \(f(t)\) subject to timing noise for (**a**) \(\Lambda=0.03\), \(\sigma_{\varphi}=0\) (representative of an unstabilized oscillator with a substantial value of \(\Lambda\)) and (**b**) \(\Lambda=0\), \(\sigma_{\varphi}=0\) (absence of timing noise). Time measured in units of \(t_{0}=2\pi/omega_{0}\) and frequency in units of \(\omega_{0}\). The results presented above provide a cohesive picture of the effects of timing noise on the SW OEO output in both the time and frequency domains. To recap, we account for timing noise due, for example, to fluctuations around a mean in the propagation time within an element of the OEO. This type of noise will produce fluctuations in the frequencies of the resonant optical modes of that optical element; in turn, in the absence of stabilization, the timing will wander away over time. This is the language of self-sustained oscillators in the sense of Ref. [45]. If, however, the OEO is injection locked by a second high spectral purity oscillator, the stabilization effect acts to maintain the timing around the standard imposed by this locking oscillator. This is described in terms of an Uhlenbeck-Ornstein process. However, even such a locking oscillator is also, in the end, self-sustained and, thus, may wander. We accounted for both effects using a Maxwell-Voigt model [52; 53] that combines aspects of the self-sustained oscillator with that of the locked oscillator.
## 2 Materials and Methods
### Data Preprocessing  
To enhance the signal-to-noise ratio and reliability of the predictive models, different spectral pretreatment methods were used. More specifically, Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) were used to normalize the scattering effect. MSC assumes that all the samples have the same scattering as a reference spectrum (e.g., the mean spectrum). The method is to calculate a linear-regressed model between all of the measured spectra and the reference spectrum and correct the measured spectra using the linear-regressed model obtained [29]. Similar to MSC, SNV operates individually on each spectrum by subtracting its mean and dividing by its standard deviation [30]. Additionally, the Savitzky-Golay filter was applied to compute first- (SG1) and second-order (SG2) derivatives, aiming to reduce high-frequency noise while preserving low-frequency information [31]. For dimensionality reduction, a Principal Component Analysis (PCA) was used, retaining the components that explained at least 95% of the total spectral variance [28].  
To optimize the performance of the PLSR model, an iterative procedure was implemented for the removal of outliers and uninformative wavelengths, as described in [32]. Briefly, outliers were identified based on cross-validation residuals, which were then normalized using the z-score and excluded if associated with anomalous values relative to the overall distribution. In parallel, relevant wavelengths were selected by analyzing the PLSR loading coefficients, normalized with respect to their standard deviation and to the spectral variability specific to each wavelength.
# Fault Investigation of Circulation Pumps to Detect Impeller Clogging
## 3 Results and Analysis
### Motor Current Signature Analysis--Healthy Condition  
In order to have a reference for the faulty variants first the measurement results of the pump in a healthy condition were considered. Figure 5 shows the time domain signal (a) of the motor current and the MCSA of the single phases (b-d). All phases were represented, since the individual phases can be affected differently highly by the influence of the BPF [22]. The amplitude of the signal was normalized to the maximum value and is displayed in dB.  
In the time signal, it can already be seen that the current signal was afflicted with harmonics. The amplitude of the current consumption for this load was 2.9 A. With the help of MCSA, we can analyze which frequency these harmonics had. Since the rotor runs with a speed of 2500 rpm and the motor has four pole pairs, the fundamental component was at a frequency of 166.7 Hz. At this point is the highest peak in the frequency spectrum, normalized to 0 dB. Additional peaks were recognized at every integer multiple of the supply frequency and at several other locations. The peaks at 10 Hz and 100 Hz are caused by converter operation [17]. According to Equation (1) \(f_{BPF}\) equals 458.4 Hz. Notice the MCSA \(f_{BPF}\) did not have a characteristic in any motor phase. There can be two reasons why no peaks were visible on \(f_{BPF}\). First, that no vibrations were generated by the design of the radial impeller or second, that the amplitudes were so small that they were damped by the transfer behavior of the motor as a sensor. According to Equation (2) \(f_{r,pump-}\) equals 125.0 Hz and \(f_{r,pump+}\) equals 208.4 Hz. A peak can be seen at these points in all phases. At 125.0 Hz, a slightly higher peak (-98.5 dB) was  
Figure 5: Signals of a healthy pump at a speed of 2500 rpm: (**a**) Time-domain signal of the motor currents 1, 2 and 3 (**b**) motor current signature analysis (MCSA) of Phase 1 (**c**) MCSA of Phase 2 (**d**) MCSA of Phase 3.  
observed than at 208.4 Hz (\(-\)109.9 dB). An explanation for the peaks at these points could be a slightly eccentric run of the rotor, caused by the plain bearings. Another finding is that the MCSA gives very similar results for all phases. This suggests that it is sufficient to analyze only one phase with the MCSA. This was also applied in the literature [10]. The MCSA of the healthy pump should always be used as a reference when considering fault cases.
# Construction Project Cost Prediction Method Based on Improved BiLSTM
## 6 Experimental Results and Analysis
### Data Preprocessing
#### 6.1.3 Data Normalisation  
The sample data were divided at a ratio of 8:2 to obtain training and test sets. The input quantities of the model were the 12 feature indicators selected above, and the output  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator Name** & **Relatedness** & **Indicator Name** & **Relatedness** \\ \hline Foundation type & 0.9852 & Aboveground floor height & 0.9732 \\ Structure type & 0.9897 & Underground floor height & 0.9436 \\ Fortification intensity & 0.9603 & Interior wall decoration & 0.8763 \\ Management level & 0.9718 & Types of doors and windows & 0.8525 \\ Facade material & 0.8956 & Number of elevators & 0.8643 \\ Aboveground floor area & 0.9496 & Roof type & 0.8726 \\ Underground floor area & 0.8831 & Concrete prices & 0.9558 \\ Number of aboveground floors & 0.9764 & Reinforcing steel prices & 0.9623 \\ Number of underground floors & 0.9668 & Duration & 0.9311 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Candidate indicator correlation values.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Foundation Type** & **Value** & **Structure Type** & **Value** & **Fortification Intensity** & **Value** & **Management Level** & **Value** \\ \hline Raft slab foundation & 1 & Shear wall structure & 1 & 6 degrees & 1 & Excellent & 1 \\ Pile foundation & 2 & Framework structure & 2 & 7 degrees & 2 & Good & 2 \\ Mantenna foundation & 3 & Frame shear construction & 3 & 8 degrees & 3 & Bad & 3 \\  & & & & 9 degrees & 4 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative table of qualitative indicators.  
quantities were the unilateral costs. Considering the magnitude differences between different feature indicators, to eliminate the impacts of such differences on the model training, reduce the training time, and accelerate the convergence speed, we adopted the max-min method to normalise all sample data. The max-min method eliminates the impact caused by the large magnitude differences by mapping original data to the interval \([0,1]\) such that different feature indicators have the same scale range. This process is formulated as follows:  
\[y_{i}=\frac{x_{i}-x_{min}}{x_{max}-x_{min}}(i=1,2,\cdots,n) \tag{25}\]  
where \(x_{min}\) denotes the minimum value of indicator \(x\) in the data and \(x_{max}\) denotes the maximum value of indicator \(x\) in the data.  
Some of the data obtained after normalising the sample data are presented in Table 3.