esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Basic Theory
### Generating and Comparing Aiding Matrix (GCAM)  
[PERSON] et al. [44] control the PN model of FMS by adding control transition to recover all deadlock markings, though the control policy cannot afford too huge system and has more structural complexity. The proposed method can solve these two problems and either of the possibly occurring live-lock situations. This method compares all possible generated control transitions to find out the optimal one in each iteration until DZ becomes empty. A three-dimension matrix, also called the generating and comparing aiding matrix (GCAM), is built and helps comparing computation a lot. According to the set of legal and deadlock markings, GCAM is created, where the first dimension is deadlock markings, the second dimension is legal ones and the third dimension is \(M_{L}-M_{D}\). Before building GCAM, the reachability graph analysis hence has to be done to partition the RG into legal, illegal and deadlock markings. For easy tracking, GCAM can be represented in simple model called simplified GCAM (SGCAM) in this paper. For example, the uncontrolled PN model of FMS shown in Figures 2 and 3 has two deadlock markings, 15 legal markings and 11 places, then the size of its GCAM is \(2\times 15\times 11\). Tables 2 and 3 show its GCAM and SGCAM, respectively. Please notice that only the deadlock markings are adopted to build up the first dimension of GCAM rather than all illegal ones, because only deadlock ones need to be recovered and other illegal ones in DZ still have enough liveness. All control transitions in GCAM are compared with each other, and the computation complexity depends on the scale of the size of reachability graph and GCAM, precisely, about \(\left(M_{D}\times M_{L}\right)^{2}\) times comparing would be executed in computation of an iteration.  
\begin{table}
\begin{tabular}{c c c c}  & **Property of** & \(M_{14}:\) & \(M_{15}:\) \\
**Property of** & **Leading** & \(p_{1}+p_{2}+p_{3}+p_{5}+2p_{8}\) & \(2p_{1}+p_{2}+p_{5}+p_{6}+p_{8}\) \\
**All Legal Markings** & **Transitions (All** & & \\  & **Places)** & & \\ \hline \end{tabular}
\begin{tabular}{c c}  & \(p_{1}-p_{2}-p_{3}-p_{5}+p_{8}+p_{9}+p_{10}+p_{11}\) & \(p_{1}-p_{2}-p_{5}-p_{6}+2p_{8}+p_{9}+p_{11}\) \\  & \(p_{10}+p_{11}\) & \(p_{11}-p_{3}-p_{5}+p_{8}+p_{
# Programmable Adaptive BVT for Future Optical Metro Networks Adopting SOA-Based Switching Nodes
###### Abstract  
Adaptive Sliceable-Bandwidth Variable Transceivers (S-BVTs) are key enablers for future optical networks. In particular, those based on Discrete MultiTone (DMT) modulation and Direct Detection (DD) can be considered a flexible solution suitable to address the cost efficiency requirement of optical metro networks. In this paper, we propose to use a cost-effective S-BVT option/implementation in optical metro networks adopting switching nodes based on Semiconductor Optical Amplifier (SOA) technology. Bit loading (BL) and power loading (PL) algorithms are applied to the Digital Signal Processing (DSP) modules, to maximize the performance and/or the capacity as well as enhance the flexibility and adaptability of the system. Our analysis considers switching nodes based on SOAs with and without filtering elements and fiber spans of 25 km. We present the results up to 100 km, with and without SOA-based nodes. Firstly, we analyze the adaptive BVT transmission using the Margin Adaptive (MA) BL/PL algorithm at a fixed bit rate of 28 Gb/s. The possibility of controlling the SOAs current is a key factor to face the transmission impairments due to the fiber and the filtering elements. We also analyze the system considering Rate Adaptive (RA) transmission at a fixed target Bit Error Rate (BER) of 3.8 \(\times\) 10\({}^{-3}\), showing that a maximum capacity above 34 Gb/s can be achieved for a single span of 25 km. Although the cascading of filtering elements still constitutes a limiting factor, we show that an improvement of the net bit rate performance can be obtained thanks to the combined use of BVT and SOA technology at the switching nodes, resulting in a promising approach for designing future optical metro networks.  
optical metro networks; Bandwidth Variable Transceivers (BVT); Orthogonal Frequency Division Multiplexing (OFDM); Semiconductor Optical Amplifier (SOA) +
Footnote †: journal: _Photomics_
# Monitoring the Capillary Jet Breakage by Vibration Using a Fast-Video Camera
## 2 Materials and Methods
### Droplet Production  
Figure 1A,B present the encapsulator B390 (Buchi, Flawil, Switzerland). The system comprises 1. Pressure bottle, 2. Dripping system, 3. Vibration system, 4. Nozzle, 5. Electrode, 6. Electric potential control, 7. Vibration controller, 8. LED/Stroboscope, 9. Gelation bath, 10. Magnetic stirrer. The dripping system is composed of a support to be connected to the nozzle and the feeding vessel (Figure 1C). It is closed on the top by a membrane on which a magnet is attached. A vibration system with a second magnet is placed on the dripping system, allowing vibration on the membrane and subsequently on the liquid. The control block allows adjusting the frequency and the amplitude of the vibration. The liquid flow rate is controlled by applying pressure on the feeding vessel and the tube's opening  
connecting the vessel to the dripping device. The control block is equipped with LED flashing at the same frequency as the applied vibration on the capillary jet.  
The feeding vessel was filled with the alginate solution. The frequency and amplitude were set following supplier recommendations corresponding to the selected nozzle. The liquid flow rate was then first adjusted following the same recommendations and adapted to get optimum breakage. The experiments were carried out with different nozzles sizes (450, 750, and 1000 \(\upmu\)m), as well as with polymer and metallic membrane (Figure 1C).  
The optimum breakage was determined by observing the droplet string in front of the LED. If optimum conditions are reached, the droplets must seem immobile, as if in each flash the droplets have the same position. However, if the optimum conditions are not reached, they seem to move up or down. As shown later, the optimum conditions constitute a range of flow rate/frequency couples. In practice, it represents an advantage but introduces a degree of incertitude while trying to fit the data with a model.  
Figure 1: Buchi B-390 encapsulator. (**A**) Scheme of B-390 BUCHI encapsulator. (**B**) Equipment. (**C**) Details of the membranes (**D**) The optimal conditions for jet breakage according to the Buchi Encapsulator manual [15].  
In the manual of the encapsulator B390 [15], some ranges of operating conditions are provided. Figure 1D presents the mean value of the frequency, the flow rate, and the resulting droplet size. These data were used as a starting point for the parameter set-up.
## 3 Experiment
### Experimental Procedure  
We investigate the effect of annealing on the thermomechanical and photomechanical properties of the fibers by comparing samples that are not annealed with those that are annealed. Each sample fiber is cut into two equal pieces. One of them is annealed in an oven at 90 \({}^{\circ}\)C (below the glass transition temperature \(T_{g}\)[2]) for 5 days. The Young's modulus of the two samples are then measured as a function of temperature in the sample oven chamber in the range of 296.15 to 323.15 K in 3 K increments. The samples soak at each temperature for about 12 min to assure that the fiber is uniformly heated and in thermal equilibrium at the desired temperature. The oven heats the sample from all directions and the small dimensions of the fiber sample ensure that the heat flow is sufficient to reach a uniform temperature profile in far less time than the 12 min provided [46; 47]. Then, the sample is stretched at a uniform rate with the stepping motor while the strain is recorded. At the start of each experiment, the sample is slightly prestressed to prevent buckling.  
In a second experiment, the temperature-dependent induced stress is measured by recording the stress as the temperature of the oven is increased. Typical temperature ranges are from 293.15 K to 323.15 K over a ramp-up time of 4 min.  
The intensity-dependent Young's modulus is measured before, during, and after light irradiation for both vertically and horizontally polarized light at a wavelength of 488 nm. These measurements are repeated for a range of intensities. The light is blocked to measure the elasticity before irradiation. Then, it is measured at different intensities, which are selected by rotating the half-waveplate; finally, the elasticity is measured again when the light is blocked following exposure to the maximum intensity. Moreover, the elasticity is measured at the maximum intensity of both polarizations to double-check the results.  
To measure the intensity-dependent stress, the sample is mounted between the clamps and the stress before, during, and after irradiation of vertical and horizontal 488-nm visible light is measured. The duration of illumination is 15 min, followed by 5 min of darkness before and after illumination when the pump beam is blocked. The photomechanical response of a sample is determined for pump light ranging from low to high intensities. The shutter cycles from the off to on state every 30 s to determine the light-induced stress response followed by relaxation of stress.  
Figure 3: The optical path in the experiment to measure (**a**) Young’s modulus as a function of intensity (**b**) stress as a function of time.  
The absorbance of visible light by PMMA is negligible. To measure photothermal heating in PMMA, the surface of unannealed and of annealed plain PMMA fibers are painted with a black marker to absorb the light followed by heat transfer to the interior of the fiber [46].
# A Novel Technique for High-Efficiency Characterization of Complex Cracks with Visual Artifacts
## 2 Proposed Method
### Deep Learning Model
#### 2.1.1 Network Component and Design
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Components**} & \multicolumn{5}{c}{**Performance**} \\ \hline \hline
**Encoder** & **GCN + BN** & **Decoder** & \begin{tabular}{c} **Loss** \\ **Function** \\ \end{tabular} & **Accuracy** & **IOU** & \begin{tabular}{c} **BF** \\ **Score** \\ \end{tabular} & \begin{tabular}{c} **Parameter** \(\times\) \\ **SHSnet** \\ \end{tabular} \\ \hline ENC & No & \
\begin{tabular}{c} Bilinear \\ Interpolation \\ \end{tabular} & NA & 0.53 & ** & ** & 0.9\(\times\) \\ ENC & No &
\begin{tabular}{c} Nearest Neighbor \\ Interpolation \\ \end{tabular} & NA & 0.52 & ** & ** & 0.9\(\times\) \\ ResNet-18 & Yes & Yes & LF & 0.78 & 0.72 & 0.75 & 2.3\(\times\) \\ ResNet-50 & Yes & Yes & LF & 0.84 & 0.79 & 0.81 & 5\(\times\) \\ MobileNet & Yes & Yes & LF & 0.74 & 0.71 & 0.73 & 0.7\(\times\) \\ VGG-16 & Yes & Yes & LF & 0.72 & 0.69 & 0.70 & \textgreater{}10\(\times\) \\ ENC & No & Yes & LF & 0.81 & 0.74 & 0.74 & \textgreater{}0.95\(\times\) \\ ENC & Yes & Yes & Cross-Entropy & 0.66 & 0.51 & 0.54 & 1\(\times\) \\ ENC & Yes & Yes & LF & 0.88 & 0.81 & 0.84 & 1\(\times\) \\ \hline \hline \end{tabular}  
*NA: Don’t need a loss function for segmentation and IOU and BF score. **: Can’t be calculated.  
\end{table} 
Table 2: Effect of model components on performance. Figure 2: (**A**) Receptive field (RF) for a conventional deep convolutional network. (**B**) RF for SHSnet. A wider opaque blue area represents a bigger RF. the connections are designed uniquely (as shown in Figure 1) so that attention is weighted across scales, giving more importance to deeper features. To summarize, SHSnet performs the parameter-efficient deep encoding of images to generate complex features to distinguish complex cracks from the background. The large kernel size allows SHSnet to have a large receptive field. This enables dense connections between various spatial features, leading to the superior per-pixel classification of these encodings. These features are refined to create better delineation between the crack and the background. This process is repeated at different scales and added in residual-like connections to ensure that feature spaces during encoding and upscaling are not lost. These modifications lead to superior performance with a significantly lightweight design, as will be discussed later.
# Random Vibration Fatigue Analysis Using a Nonlinear Cumulative Damage Model
## 4 Fatigue Damage Accumulation Procedure
### Bending Stress  
We proceed to determine the dynamic bending stress generated by the base input PSD. The base input PSD is presented in Table 2 and Figure 6a. In Figure 6b we show the corresponding time history synthesis of the PSD.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency (Hz)** & **Accel. (G)** & **Accel. (G\({}^{2}\)/Hz)** \\ \hline
10 & 0.210 & 0.004 \\
20 & 0.666 & 0.022 \\
30 & 1.383 & 0.063 \\
40 & 2.359 & 0.139 \\
50 & 3.900 & 0.300 \\
55 & 4.664 & 0.400 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Base input PSD.  
Figure 5: Support subjected to random vibration fatigue.  
In this case, the base input PSD is applied by a vibration shaker, and the acceleration response obtained is given in Table 3.  
By comparing, in Figure 7, the input and response acceleration, we observed that the difference is due to the product's resonant frequency. From the analysis, we have the frequency of 50 Hz with an acceleration of 13.68 g, which is the one that more greatly affects the component.  
Now, we determine the dynamic factor that allows us to obtain the vibration stress that corresponds to the acceleration response obtained.  
Figure 6: Base input PSD: (a) PSD 2.31 GRMS, (b) PSD time history synthesis.  
Figure 7: PSD of acceleration base input and acceleration response.  
Since, from Equation (19) with \(\rho=0.285\frac{\text{lb}}{\text{in}^{3}}\), \(L=2\) in and \(m=0.05\frac{\text{lb}-\text{s}^{2}}{\text{in}}\), the effective mass is \(m_{e}=0.11\frac{\text{lb}-\text{s}^{2}}{\text{in}}\), then, from Equation (22) with \(K=2.5\), \(\hat{L}=0.575\) in, \(C=0.06\) in, \(I=0.00114\) in\({}^{4}\) and \(A=9.81\) m/s\({}^{2}\), \(m_{e}=0.11\frac{\text{lb}-\text{s}^{2}}{\text{in}}\) and the dynamic factor \(\sigma_{dynamic.}=3219\) Psi \(=3.22\) Ksi \(=22.22\) MPa.  
Consequently, by using the acceleration response and the dynamic factor in Equation (24), the vibration bending stress value for each row of the PSD is obtained, as shown in Table 4.  
Now, the cycles of vibration load applied \(n_{i}\) at the given bending stress level \(\sigma_{vb}\) are determined.
## 4 Framework
### Data Analysis Module  
The data analysis module analyzes the response data from the three layers. We show the default open ports for the main IoT device manufacturers in Appendix A. The port-scan algorithm is detailed in Algorithm 1. When scanning open ports, we detect the characteristic ports first. If open, the device brand can be determined directly (e.g., 3777_Dahua, 2020_TP-Link). Then, we detect the characteristic port combinations. If open,  
Figure 3: Overview of our framework.  
the device brand can be determined (e.g., 81,82_Hikvision, 81,21_Axis). Finally, we detect the typical protocols. If the target device runs the typical protocols, the device type can be determined (e.g., RTSP_webcam, IPP_printer).  
```
Input: \(Obj\) : IoT device to be identified; Variables:\(P_{c}\) : Characteristic ports for \(Obj\); \(P_{combine}\) : Characteristic ports combinations for \(Obj\); \(P_{tp}\) : typical protocols for \(Obj\); Output:\(c\) : Device category;
1 Initialise \(c=Null\);
2 if\(\textit{Isopen}(Obj,P_{c})\)then
3\(c\) = Devicebrand(\(P_{c}\));
4 elseif\(\textit{Isopen}(Obj,P_{combine})\)then
5\(c\) = Devicebrand(\(P_{c}\)_combine_);
6 elseif\(\textit{Isopen}(Obj,P_{tp})\)then
7\(c\) = Devicetype(\(P_{tp}\));
8
9 end if
10 return\(c\)
```  
**Algorithm 1**Port Scan Algorithm.  
Figure 4 illustrates how the data analysis module processes the response data from the target device, including protocol responses, and web-feature data. First, the detector sends probe messages actively to target devices to obtain different types of responses. Then, we process the response data and generate segmentation lists during the data processing stage. Finally, we obtain the device fingerprint from the segmentation lists using regular expression and keywords matching.  
We can flexibly extend and customize the data processing module according to the application scenario. For example, when deployed on edge devices with scarce computational resources, some infrequent probe requests can be removed based on the possible device types in the current environment.
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 7 Static Structural Analysis
### Structural Simulation Results  
Ensuring problem-free application is paramount and a critical step towards achieving this was, prior to construction, to calculate the response of the newly designed mechanical system to external loads under operating conditions.  
The components examined in the specific context investigated in this paper underwent meshing using the FEA method for static analysis. Meshing is an important step in FEA because it involves the discretization of complex geometries into smaller elements, which affects the accuracy and efficiency of subsequent numerical simulations [52]. The static analysis starts by applying a force to the relevant regions of the mechanically meshed system equivalent to the anticipated real-world conditions. Static analysis can produce a variety of results, and the particular emphasis in this study was on examining the mechanical system's stress and displacement.  
In this study, the simulation tests were primarily focused on the UAV body, the telescopic arms and carbon tubes, and the aluminum joints, as well as the 3D-printed mechanism to which the drone's propulsion system is attached. The objective is to ensure their capacity to withstand the thrust force generated by the selected motors.  
As previously stated, the primary material used in the development of this UAV was a carbon fiber-epoxy composite (CFRP). Additionally, aluminum arm joints have been used, along with stainless steel bolts. However, since manufacturing with carbon fiber elements might be too complicated, the--3D printed motor quick release mechanism will be developed using Nylon 12 Powder (Table 10).  
As already mentioned, the SolidWorks Simulation CAE tool was used for this analysis. However, the version used did not support simulation of composite materials. Given this limitation and to streamline the analysis without compromising the integrity of the results, the following strategy was adopted: (i) from the beginning, to design the assembly keeping in mind the anisotropic behavior of the CFRP material; (ii) to perform the FEA analysis in sub-assemblies so as to simplify the process; and (iii) to use physical testing to verify the FEA results.
## 3 Simulation Analysis  
### Intake Layout of the Beam Expanding System  
To verify the influence of the flow field on the beam quality in a beam expanding system, a typical Cassegrain reflection system with a diameter of 850 mm was considered as the research object. The structure is shown in Figure 1. The dissipation velocity of turbulence can be greatly increased by directly stimulating the turbulence at a small scale [23; 24; 25], so the large-scale intake holes are replaced by a matrix of small-scale holes to increase the dissipation velocity in this work. In order to determine the ideal intake layout, we designed the simulation process shown in Figure 2. According to the simulation process, the layout of the intake holes should be designed first. Computational fluid dynamics (CFD) was used to simulate the flow field to obtain the flow field distribution. After obtaining the flow field distribution, the ray tracing method mentioned in Section 2.2 was used to perform ray tracing in the non-uniform refractive index medium. Finally, the flow field distribution and wavefront data were used as the references to improve the structure to obtain a better intake layout. Through a preliminary simulation analysis, it was found that when the mass flow rate and opening rate are fixed, the flow field in the beam expanding system improves significantly with an increase in the number of rows of air inlet holes and in the number of air inlets per row. For an air intake system with one row consisting of 18 holes, the variation of root mean square (RMS) of the wavefront aberration of a beam passing through the beam expanding system is 0.32 nm. When the number of air inlet vents is increased to 36, the RMS value of a beam passing through the system is 0.26 nm. When the air intake layout is expanded from a single row of holes to a double row with 18 holes in each row, the RMS value is 0.25 nm. With an increase in the number of air inlets per row and in the number of rows in the intake system, the influence of the flow field in the beam expanding system on the beam quality gradually weakens. Therefore, we preliminarily designed intake system layouts consisting of 4, 8, and 16 rows with 90 holes per row (see Table 1) and carried out corresponding numerical simulations. Since the existing equipment can provide a flow of only 100 g of gas per second, the mass flow rate was controlled to be 100 g per second during the processes of design, simulation, and experiment.  
Figure 1: Schematic diagram of the beam expanding system: (**a**) the beam expanding system; (**b**) intake for primary mirror; (**c**) intake for second mirror.
## 4 Prediction Model and Numerical Testing  
### Testing Site  
Numerical testing was conducted using the highway traffic accident data highlighted in Section 3. Data from all gantries in the northbound and southbound lanes of National Freeway No. 1 were visualized to create area charts and demonstrate the association between the total number of traffic accidents and the number of accidents caused by traffic speed differences (Figure 3). The results indicated that traffic accidents caused by traffic speed differences accounted for a large proportion of all accidents in each highway section. Therefore, to effectively manage highway sections and reduce the number of traffic accidents, a national highway crash prediction and warning model was developed. To verify the accuracy of this model, the highway section between Wangtian and Changhua interchanges (Gantry 01F109065) was selected as the test site. Figure 4 depicts the geographical location and road network of the test site. The characteristics of this section are shown in Table 1. It contains information such as the location of the area, the number of kilometers, the number of lanes, and the speed limit. As shown in Figure 3b, association rule mining revealed that this section had the highest proportion of traffic accidents caused by traffic speed differences. Notably, traffic accidents occurring on interchange ramps instead of main highway lanes were excluded and classified as accidents unrelated to traffic speed differences.  
Figure 2: Flowchart of dataset generation.  
Figure 4: Geographical location of Gantry 01F10906S.  
Figure 3: Proportions of traffic accidents caused by traffic speed differences on the (**a**) northbound and (**b**) southbound lanes of National Freeway No. 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Regional** & **The Number of** & **Number of** & **Number of** & **Number of** \\
**Section** & **Kilometers** & **Southbound** & **Lanes** & **Northbound** \\ \hline Wangtian interchange & 189 & 4 & 4 & 110 \\ \hline Changhua system interchange & 192 & 3 & 3 & 100 \\ \hline Changhua interchange & 198 & 4 & 4 & 110 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Section characteristic between Wangtian and Changhua interchanges.