esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 4 Application and Evaluation in a Real Robot System
### Adding Workflow Verification to the Robotics Software Stack
#### 4.2.2 Client Implementation with Node-RED  
Our client implementation used for the evaluation is implemented within a graphical programming platform called Node-RED ([[https://nodered.org/](https://nodered.org/)]([https://nodered.org/](https://nodered.org/))), which is based on the javascript framework Node.js ([[https://nodejs.org/](https://nodejs.org/)]([https://nodejs.org/](https://nodejs.org/))). Figure 9 shows the client implementation within the Node-RED programming interface. As in the description located in the previous chapter the information flow is grouped in a preparation phase and an execution phase.  
Node-RED has an extensive library of components such that the invocation of the GRPC service and the REST service calls are easy to implement.  
From the available workflows, we constructed the use-case within the Node-RED environment. We use a queue of base and arm movement workflows to achieve the desired behaviour. Once finished, we make use of the compliance service client shown in the upper box of Figure 9 to receive the workflow queue signature. Then, the lower box is used to send the queue to the extended workflow manager API. After assembling, the workflow queue needs to retrieve the workflow queue signature from the Compliance Service Provider. Like the signature verification service the signing service is realized as a GRPC service provided by the CSP. Consequently we need to add a GRPC Client block to the Node-RED flow realizing the preparation phase. The response contains byte array representing the signature. After receiving the workflow queue signature from the Compliance Service Provider the execution phase is immediately triggered. Before we can call the REST-API for executing the workflow queue we need to base64 encode the signature in order to transport it via the http protocol. After that we serialize the workflow queue to a json string, build the ExecuteWorkflowQueueRequest and send it to the workflow manager.
## 5 Transient Dynamics Analysis of Driven Bevel Gears
### Analysis of Influencing Factors of Stress Distribution
#### 5.3.2 Analysis of Influence of Spoke Plate Thickness Change  
At the working temperature of 200 \({}^{\circ}\)C, the thickness of the spoke plate was changed to 6.28 mm, 7.28 mm, 8.28 mm, 9.28 mm, 10.28 mm, 11.28 mm, 12.28 mm, which were divided by the initial thickness of the spoke plate of the driven level gear to obtain the dimensionless thickness to observe the influence of the spoke plate thickness on the stress distribution at the 4 th ND resonant rotational speed. The stress at the monitoring position and the tooth root varies with the ratio of the thickness of the spoke plate and the prediction of stress at the tooth root, as shown in Figure 22.  
The stress values at the monitoring position and the tooth root under different ratios of spoke plate thickness are shown in Table 15, and the max stress at the tooth root predicted by the stress at the monitoring position is compared with the simulation result.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Thickness/\%**} & **Max Stress at** & **Max Stress at the** & **Max Stress at the** \\  & **Monitoring** & **Tooth Root** & **Tooth Root** \\  & **Position/MPa** & **(Simulation)/MPa** & **(Predict)/MPa** \\ \hline
36.3\% & 293.2 & 1582.2 & 1583 \\
42.1\% & 274.5 & 1481.4 & 1482.4 \\
47.9\% & 255.5 & 1379.3 & 1379.6 \\
53.7\% & 237.7 & 1280.2 & 1283.7 \\
59.5\% & 218.6 & 1179.5 & 1180.4 \\
65.3\% & 200.4 & 1081.6 & 1082.2 \\
71.1\% & 183.3 & 992.3 & 990.2 \\ \hline \end{tabular}
\end{table}
Table 15: Max stress at the monitoring position and the back of the spoke at 4 th ND resonant rotational speed at different thickness of spoke plate.  
Figure 22: The influence of thickness of spoke plate changes on gear stress.  
It can be seen from Figure 19 that with the decrease in the spoke plate thickness, the stress in all parts of the gear increases. The maximum stress at the tooth root increased greater than the stress at the position monitored in the test.  
As can be seen from Table 15, when the thickness of the spoke plate decreases from 12.28 mm to 6.28 mm at 200 \({}^{\circ}\)C, the ratio decreases from 71.1% to 36.9%, and the stress at the monitoring position increases by 109.9 MPa and the stress at the tooth root by 589.9 MPa, and the relation between the stress at the monitoring position and that at the tooth root is still valid.  
It can be seen that when the spoke plate thickness is thinner, the stress concentration is more serious, and it is more likely to lead to tooth-breaking accidents. Therefore, it is necessary to consider the structural strength when changing the thickness of the spoke to avoid TWR.
## 4 Results
### System Optimisation
#### 4.2.2 Increase in Efficiency  
When optimising a heat storage system, however, it is not only the capacity increase but also the increase in the efficiency of the thermal power process used to generate electricity in the power plant that must be taken into account, which is associated with an increase in the temperature difference. For this, the theoretically possible thermal efficiency \(\eta_{\text{c}}\) is calculated by the following formula:  
\[\eta_{\text{c}}=1-\frac{T_  
The comparison shows that the storage medium to be chosen should be carbonate salt, both for increasing efficiency and increasing capacity. With a theoretical increase in efficiency in the thermal power process of up to 18.2%, it exceeds all other storage media and their associated temperature levels.  
In addition, the carbonate model has the opportunity to switch two thermodynamic cycle processes one after the other in order to approach the Carnot efficiency [20]. First, a Joule process can be applied to use the temperature range above 600 \({}^{\circ}\)C to generate electricity, followed by a steam power cycle, which can use the remainder of the temperature difference. A potential difficulty in the Joule cycle can be found because of the heat exchangers, which must have a high volume due to gas operation. It should be noted here that the consideration of the efficiency is limited to the maximum achievable thermal efficiency, which allows sufficient quantification for a comparison of the processes, but does not provide any information about the real efficiency.  
\[\mathrm{n_{c}^{Andasol\ 2}}=1-\frac{(25+273.15)K}{(386+273.15)K}=0.547 \tag{10}\]  
\[\mathrm{n_{c}^{Hitec}}=1-\frac{(25+273.15)K}{(515+273.15)K}=0.621 \tag{11}\]  
\[\mathrm{n_{c}^{SSalt\ max}}=1-\frac{(25+273.15)K}{(560+273.15)K}=0.642 \tag{12}\]  
\[\mathrm{n_{c}^{Carbonate}}=1-\frac{(25+273.15)K}{(830+273.15)K}=0.73 \tag{13}\]  
During the charging step, an efficiency of 95% was assumed due to heat losses and HTF pump consumption and other requirements (i.e., each 1 MWel h was converted to 0.95 \(MW_{th}h\) in the molten salt). During the discharging step and according to the previous calculation, the optimal efficiency is 0.73, so that the optimal round-trip efficiency is 0.73 \(\times\) 0.95 = 0.69. In real cases, the achievable thermal efficiency is lower for a combined cycle power plant. The nominal process efficiency of the combined-cycle power plant (CCPP) can reach 60%. Large-scale combined-cycle power plants with a net electrical power of 780 MWel and an efficiency level up to 62.2% are now running in Irsching,  
Figure 17: Simulated temperature after the heat exchanger of the four models in the discharging line.  
Germany [30; 31], Bouchain, France [32; 33], and Tambak Lorok, Indonesia [34; 35]. The real round-trip efficiency is expected to 0.62 \(\times\) 0.95 = 0.589.
## 2 Preliminaries and Problem Formulation
### Model of USVs  
A group of USVs consisting of a leader and \(n\) followers are considered. Assume that the \(i\)th USV has an \(x_{i}z_{i}\)-plane of symmetry. Heave, pitch and roll motions are neglected. The body-fixed frame coordinate origin is set in the center-line of the USV. The mathematical model of the \(i\)th USV is defined as [24]:  
\[\begin{array}{l}\dot{\eta}_{i}=J_{i}(\psi_{i})v_{i}\\ M_{i}\dot{v}_{i}=-C_{i}(v_{i})v_{i}-D_{i}(v_{i})v_{i}+d_{i}+\tau_{i}\end{array} \tag{1}\]  
where \(\eta_{i}=[x_{i},y_{i},\psi_{i}]^{T}\) is the vector denoting the ship position \((x_{i},y_{i})\) and yaw angle \(\psi_{i}\) with coordinates in the earth-fixed frame, and \(v_{i}=[u_{i},v_{i},r_{i}]^{T}\) is the vector denoting surge, sway and yaw velocities of the \(i\)th USV in the body-fixed frame. \(d_{i}=[d_{i,1},d_{i,2},d_{i,3}]^{T}\) is the vector representing environmental disturbances. \(\tau_{i}=[\tau_{i,u},0,\tau_{i,r}]^{T}\) is the control vector of the \(i\)th USV, which consists of the surge force \(\tau_{i,u}\) and yaw moment \(\tau_{i,r}\). The matrices \(J_{i}(\psi_{i})\) are given by  
\[J_{i}(\psi_{i})=\left[\begin{array}{ccc}\cos(\psi_{i})&-\sin(\psi_{i})&0\\ \sin(\psi_{i})&\cos(\psi_{i})&0\\ 0&0&1\end{array}\right],\]  
\(M_{i}=M_{i}^{T}\in\mathbb{R}^{3\times 3}\) is the inertia matrix of the \(i\)th USV. Here, we assume that the inertia matrices are diagonal. \(C_{i}(v_{i})=-C_{i}^{T}(v_{i})\in\mathbb{R}^{3\times 3}\) represents a skew-symmetric matrix of Coriolis and centripetal term. \(D_{i}(v_{i})\in\mathbb{R}^{3\times 3}\) is a nonlinear damping matrix.  
The model of leader is defined as follows: a subscript \"0\" denotes the leader whose position \(\eta_{0}=[x_{0},y_{0},\psi_{0}]^{T}\) is generated by  
\[\dot{x}_{0} =u_{0}\text{cos}(\psi_{0})-v_{0}\text{sin}(\psi_{0}), \tag{2}\] \[\dot{y}_{0} =u_{0}\text{sin}(\psi_{0})+v_{0}\text{cos}(\psi_{0}),\] (3) \[\dot{\psi}_{0} =r_{0}. \tag{4}\]  
**Assumption 2**.: \(\eta_{0}\)_, \(u_{0}\), \(v_{0}\), \(r_{0}\) are bounded, and the data are available only for the ith USV satisfying \(0\in N_{i}(0)\)._  
**Assumption 3**.: _The disturbances \(d_{i,h}\), \(h=1,2,3\), are unknown but bounded so that \(|d_{i,h}|\leq\bar{d}_{i,h}\), \(\bar{d}_{i,h}\) is a unknown positive constant._  
**Assumption 4**.: \(C_{i}(v_{i})\) _and \(D_{i}(v_{i})\) are assumed unknown._
## 4 Investigation Results
### Indoor Operational Range Evaluations
The measured signal strengths of the received WuPts are shown in Figures 24-26. The highest RSSI was achieved when both devices were on the same floor and close to each other. At a distance of more than 4 m, the RSSI dropped abruptly from 30 to below 15. The RSSI was significantly lower when the transmitters were placed in a room, similar to the PER. When both devices were positioned on different floors, the maximum signal strength did not exceed 10. The measurements were performed under realistic, unshielded indoor conditions. Due to this, reflections, as well as interference, may be present in the ISM band, causing error modes in the passive front-end architecture of the receiver. This occurs at a certain power level (the power of the interfered at the wake-up receiver) because the power level  
Figure 26: RSSI of BJT board with WuRx and WuTx on different floors. TIA implementation was not able to receive a single packet at this floor level. Figure 25: RSSI of TIA board with WuRx and WuTx on different floors. of the coherent interferences does not vary with a Gaussian probability distribution like the receiver noise [48]. This results in a more abrupt transition from a low PER to not a single successful WuPt reception. Similarly, the attenuation due to furniture can be clearly seen in the results. No signal was received in grid points W8(2-8) to W12 in Figures 21 and 24 due to the metal cabinets, which attenuates the radio signal to less than the lowest detectable power of \(80\,\mathrm{\SIUnitSymbolMicro VRMS}\).
# A Large Bridge Traffic Operation Status Impact Assessment Model Based on AHP-Delphi-SVD Method
## 2 Requirements for Detection Indicators of Bridge Facility Operation Status
### Classification of Existing Factors Influencing Information Transportation
#### 2.2.1 Traffic-Influencing Factors of the Superstructure  
The results of the inspection of the superstructure of the bridge are classified into Class 1-5 based on their technical condition evaluation level, as shown in Table 2:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Influencing Factors** & \multicolumn{5}{c}{**Traffic Influence Degree (0-1)**} \\  & **Class 1** & **Class 2** & **Class 3** & **Class 4** & **Class 5** \\ \hline superstructure & 0.00 & 0.15 & 0.25 & 0.40 & 1.00 \\ substructure & 0.00 & 0.15 & 0.25 & 0.40 & 1.00 \\ bridge deck paving & 0.00 & 0.10 & 0.20 & 0.25 & - \\ expansion joint device & 0.00 & 0.10 & 0.20 & 0.30 & - \\ drainage system & 0.00 & 0.05 & 0.15 & 0.30 & - \\ lighting facilities & 0.00 & 0.05 & 0.10 & 0.15 & - \\ \hline \hline \end{tabular} Note: A degree of impact of 0 indicates no impact on traffic, while a degree of impact of 1 indicates a significant impact on traffic and the need to interrupt it.  
\end{table}
Table 1: Factors and degrees of impact on bridge infrastructure traffic.
## 2 Materials and Methods  
### YBCO Film Synthesis and Patterning  
Direct and complementary split-ring resonator square arrays (see Figure 1a,b) were fabricated through electron beam lithography (EBL) on two identical commercial 200 nm thick optimally doped YBa\({}_{2}\)Cu\({}_{3}\)O\({}_{7}\) films grown on a 500 \(\upmu\)m thick Al\({}_{2}\)O\({}_{3}\) substrate. YBCO films, provided by THEVA (Ismaning, Germany), were grown by reactive thermal coeaporation and exhibit an average surface roughness of 6-10 nm. The desired metamaterial pattern was transferred by electron beam lithography to a resist coating and a wet etching in 0.075% nitric acid, allowing the removal of the noncovered YBCO. Metamaterial parameters were chosen to have both the LC and dipolar resonance frequencies in the 0.5 to 4 THz range, below the YBCO superconducting gap. Figure 1a,b show scanning electron microscopy (SEM) pictures of the direct and complementary MMs together with their geometrical details. The single SRR has a side length \(l=20\)\(\upmu\)m, width \(w=5\)\(\upmu\)m, and gap \(g=5\)\(\upmu\)m, while the SRR lattice parameter is \(a=30\)\(\upmu\)m. Dark areas refer to YBCO film, whereas light areas refer to the Al\({}_{2}\)O\({}_{3}\) substrate. As can be inferred, the complementary structure shares the same geometry of the direct one, with inverted YBCO and Al\({}_{2}\)O\({}_{3}\). To measure  
Figure 1: SEM pictures of the direct (**a**) and complementary (**b**) SRR devices. Dark areas mark YBCO film, while light areas mark Al\({}_{2}\)O\({}_{3}\) substrate. The geometrical details are \(l=20\)\(\upmu\)m, \(g=5\)\(\upmu\)m, \(w=5\)\(\upmu\)m, and \(a=30\)\(\upmu\)m. (**c**) Magnetic moment measurements for the direct and the complementary device, respectively.  
the superconductive properties of the patterned film, magnetic moment measurements were performed on both direct and complementary samples, as a function of temperature. The magnetic moment measurements were performed through a vibrating sample magnetometer (VSM) by Oxford Instruments between 50 and 90 K. This measure is totally contact-free, so no electrical contacts were placed on the sample. Figure 1c shows that the direct SRR array has a critical temperature \(T_{c}\) = 83 K, in agreement with the nominal value from THEVA, while the complementary one has a slightly lower \(T_{c}\) of \(\sim\)81 K. The slight shift in \(T_{c}\) and the reduced jump in the magnetic moments at \(T_{c}\) are probably related to the differences in YBCO content between the direct and complementary patterned films: in the direct case, YBCO is left just where SRR structures are present; in the complementary case, instead, YBCO is everywhere but where SRRs are drawn.
## 3 Results and Discussion  
### Compositional and Morphological Characterizations  
A tri-layer pad was designed as a mainstream smoke filter. In the middle of the pad, a cellulose acetate fiber membrane (CAF) with high hydrophobicity was used to trap particulate matter in the mainstream. Hydrophilic cotton fiber (CF) membrane was composited on both sides of the cellulose acetate fiber membrane to construct the composite pad (abbreviated as CF/CAF/CF in the following). The CF layer had a relatively larger pore size than the CAF interlayer and was used to trap water vapor and hydrophilic components in the mainstream. The two surface CF layers were endowed with different porous features for effective filtering of particulate matter of different sizes. The control of the porous properties of the CF layer was realized by the control of pressure during layer fabrication. The compositions of the CF, CAF, and CFP were firstly analyzed with XRD characterizations. As seen in Figure 1, the XRD pattern of CFP showed a broad peak centered at \(\sim\)28\({}^{\circ}\)[19], which is typical for an amorphous SiO\({}_{2}\) material. CAF also showed a broad peak at \(\sim\)20.9\({}^{\circ}\)[20], which could be probably attributed to the characteristic I\({}_{\text{B}}\) peak of cellulose material. In comparison, a series of well-developed crystalline peaks of cellulose could be identified from the XRD spectra of CF. These peaks were (101) peak (20 = 14.8\({}^{\circ}\)), (10\(\overline{1}\)) peak (20 = 20.60\({}^{\circ}\)), (002) peak (20 = 22.8\({}^{\circ}\)) and (040) peak (20 = 34.5\({}^{\circ}\)) [21]. Additionally, according to the formula reported in the literature [22], the crystallinity index of CF was calculated as 32.04%. The morphology of the CFP and CF/CAF/CF filter were investigated with SEM (Figure 2). CFP exhibited a uniform fibrous structure. The fiber diameter ranged from tens to hundreds of nm. Irregular pores formed by the stacking of fibers were present in CFP, and the average pore size was in the scale of tens of \(\upmu\)m. Compared to CFP, the diameters of the CF and CAF fibers were much larger. In addition, the fibers of CF and CAF were stacked compactly. Therefore, their porosity was lower than that of CFP. Such a compact structure might be advantageous for the trapping of mainstream components. The porosity of the top CF layer (Figure 2B) was slightly higher than that of the bottom CF layer (Figure 2D) due to the smaller compression pressure used during layer fabrication.
## 4 Results
\begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**First Author and Date** & **Approach** & **Population** & **Algorithm Used** & **Key Findings** \\ \hline [PERSON] et al. (2023) [4] & VR and Multimodal Sensor Analysis & Children with ASD & Not specified & VR-based tool supported self-awareness in ASD; multimodal sensor data captured distinct behavioral responses. \\ [PERSON] et al. (2023) [40] & Eye-Tracking with Transformers & Children with ASD & Convolutional Transformer (CCT) & Convolutional transformers enhanced ASD detection from eye-tracking patterns; high classification accuracy achieved. \\ [PERSON] and [PERSON] (2023) [41] & AI Model for ASD Detection & Children with ASD & Multilayer Perceptron (MLP) & Multilayer perceptron model optimized for ASD detection; demonstrated strong diagnostic accuracy. \\ [PERSON] et al. (2021) [42] & Stress and Attention Monitoring & Children with ASD & Logistic Regression & Wearable system accurately monitored attention and stress, providing real-time data for caregiver interventions. \\ [PERSON] et al. (2022) [26] & AI-Based Medical Device for Diagnosis & Children with ASD & Random Forest & High diagnostic accuracy observed in AI-based device aiding ASD diagnosis in clinical settings. \\ [PERSON] et al. (2024) [43] & Head Movement Analysis & Children with ASD & Support Vector Machine (SVM) & Kinestic analysis of head movements provided ASD markers; demonstrated high accuracy in distinguishing ASD patterns. \\ [PERSON] et al. (2024) [44] & Brain Connectivity Analysis with IoT & Children with ASD & Convolutional Neural Network (CNN) & IoT and transfer learning enhanced ASD detection through brain connectivity analysis; achieved strong classification performance. \\ [PERSON] et al. (2019) [5] & Guided Play and Digital Sensing & Children with ASD & Convolutional Neural Network (CNN) & Digital sensing in guided play detected stereotypical behaviors; potential for early ASD screening through play. \\ [PERSON] et al. (2024) [45] & Facial Image Analysis & Children with ASD & Deep Learning & Deep learning-based feature extraction achieved high accuracy in identifying ASD-related facial features. \\ [PERSON] et al. (2020) [6] & Web-Based Eye Movement Analysis & Children with ASD & Random Forest & Eye movement analysis via web identified ASD gaze patterns; promising for remote ASD screening. \\ [PERSON] et al. (2023) [9] & Brain Connectivity and Sensor Analysis & Children with ASD & Convolutional Neural Network (CNN) & Functional connectivity networks identified ASD markers; improved detection accuracy using sensor data. \\ [PERSON] et al. (2024) [46] & IoT-based automated light sensitivity assessment framework & Children with ASD & IoT-LAS (Child and Control Mode (CCM) and System Control Mode (SCM)) & Achieved 95\% agreement in CCM and practitioner assessments. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
## 2 Methods
### Simulation for Determining the Focal Plane Separation  
To determine \(\epsilon\), microscopy simulation was conducted based on Gibson-Lanni point spread function model [15] for a virtual fluorescent particle using Microscope Simulator [16]. To mimic the actual endocytic vesicle, fluorescent particle size of 100 nm in diameter was chosen [17]. Figure 3 illustrates the simulation process and shows results for varying \(\epsilon\). Because ratiometric method frequently utilizes the Michelson contrast, which can be expressed as \((I_{max}-I_{min})/(I_{max}+I_{min})\) where \(I_{max}\) and \(I_{min}\) respectively represent the highest and lowest luminance, for localizing the point source [11; 12; 18], Michelson contrast was calculated for varying \(\epsilon\). Computation of Michelson contrast can be described as shown in Figure 3A, where \(I_{1}\) and \(I_{2}\) represent the Gaussian peak intensities of the images from each focal plane [19]. For simplicity, we hereafter refer to the Michelson contrast as \(I_{det}\), as an intensity determinant acquired from the intensities \(I_{1}\) and \(I_{2}\). For an identical target, \(\epsilon\) was varied from 200 nm to 1500 nm. The Michelson contrast curves drawn for each \(\epsilon\) are as shown in Figure 3B. As these curves play roles as lookup tables for point source localization, the trackable range should be determined as the distance between the local minimum and maximum, where \(I_{det}\) monotonously increases along the axial position of the target point source. As shown in Figure 3C, the longest trackable range was obtained for \(\epsilon=1000\) nm. This result also corresponds to the accuracy calculation previously conducted by [PERSON] et al. [14], who proved that the ratiometric method for multifocal plane microscopy yields high-accuracy axial localization within a relatively short range, where the target particle is located between the two focal planes.  
Figure 3: Simulation for evaluating the trackable range of point source (100 nm-diameter) with respect to various \(\epsilon\) values. (**A**) Concept of the \(\epsilon\) spacing between two focal planes. The Michelson contrast (\(I_{det}\)) is utilized for the ratiometric method, by comparing the Gaussian peak of the intensity distribution on each focal plane; (**B**) \(I_{det}\) calculated with various \(\epsilon\), changed from 200 nm to 1500 nm; (**C**) Trackable range can be determined based on the distance between local minimum and local maximum.