esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Fireproof Coatings on the Post-Fire Behavior of CFRP Composite Sheets
## 3 Experimental Results and Discussion
### Comparison with Previous Studies  
In the study conducted by [PERSON] et al. [41], they conducted fire resistance tests to investigate the effect of two different fire-retardant materials, high-silica needed felt and ceramic fiber felt, on the fire resistance of CFRP tendons. Their criteria for evaluating fire resistance were based on a reduction in tensile strength and elastic modulus to 80% of their initial properties at 300 \({}^{\circ}\)C. In another study conducted by [PERSON] et al. [30], the results revealed that the flexural modulus retention of the composite was stable, staying above 90% of the initial performance after cooling from temperatures below 300-350 \({}^{\circ}\)C. Once exceeding the temperature of degradation, there was a rapid loss of flexural modulus retention. Therefore, it can be assumed that the CFRP composites have reached their  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & **Flexural** & **Flexural** & **Fracture** & \multicolumn{3}{c}{**Reduction Factor**} \\  & **Modulus** & **Strength** & **Deflection** & **Modulus** & **Strength** & **Deflection** \\  & **(MPa)** & **(MPa)** & **(mm)** & **Modulus** & **Strength** & **Deflection** \\ \hline P-2-C1-35-b1 & 200,059 & 1559 & 3.30 & 0.992 & 0.928 & 0.979 \\ P-2-C2-35-b1 & 203,972 & 1527 & 3.09 & 1.012 & 0.908 & 0.917 \\ P-3-C1-35-b1 & 128,761 & 1254 & 2.70 & 0.955 & 0.927 & 0.914 \\ P-3-C2-35-b1 & 138,880 & 1238 & 2.69 & 1.030 & 0.915 & 0.911 \\ P-2-C2-35-b3 & 198,582 & 1478 & 3.14 & 0.985 & 0.880 & 0.933 \\ P-2-C2-35-b3 & 206,772 & 1516 & 2.98 & 1.026 & 0.902 & 0.886 \\ P-3-C1-35-b3 & 126,229 & 1195 & 2.68 & 0.936 & 0.883 & 0.908 \\ P-2-C1-35-b1 & 200,059 & 1559 & 3.30 & 0.992 & 0.928 & 0.979 \\ P-2-C2-35-b1 & 203,972 & 1527 & 3.09 & 1.012 & 0.908 & 0.917 \\ P-3-C1-35-b1 & 128,761 & 1254 & 2.70 & 0.955 & 0.927 & 0.914 \\ P-3-C2-35-b1 & 138,880 & 1238 & 2.69 & 1.030 & 0.915 & 0.911 \\ P-3-C2-35-b1 & 198,582 & 1478 & 3.14 & 0.985 & 0.880 & 0.933 \\ P-2-C2-35-b3 & 206,772 & 1516 & 2.98 & 1.026 & 0.902 & 0.886 \\ P-3-C1-35-b3 & 126,229 & 1195 & 2.68 & 0.936 & 0.883 & 0.908 \\ P-3-C2-35-b3 & 125,374 & 1205 & 2.78 & 0.930 & 0.891 & 0.940 \\ P-2-C1-35-b2 & 32,060 & 122 & 3.06 & 0.159 & 0.073 & 0.909 \\ P-2-C2-35-b2 & 34,370 & 117 & 2.73 & 0.170 & 0.070 & 0.811 \\ P-3-C1-35-b2 & 18,291 & 100 & 2.30 & 0.136 & 0.074 & 0.780 \\ P-3-C2-35-b2 & 17,788 & 105 & 2.86 & 0.132 & 0.077 & 0.969 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Bending test results of CFRP sheets after exposure to the standard fire.  
fire resistance at a temperature of 300 \({}^{\circ}\)C. Comparative information with other fireproof measures is displayed in Table 5.  
These results underscore that the novel fireproof coatings in this paper have certain advantages. The newly developed fireproof coatings offer better fire protection compared to other coatings. In addition, high-silica needed felt and ceramic fiber felt are not applicable in practical structural engineering, though their fire resistance durations are longer.
## 3 Results
### Aerosol Backscatter Profiles  
The aerosol backscatter profiles \(\beta_{\mathrm{aer}}(\mathrm{r})\) are retrieved from the attenuated backscatter profiles according to the forward inverse method without boundary conditions presented in Section 2.4. The Lidar Ratio computed previously for fog-oil particles from light-scattering was specified for retrieving the aerosol backscatter lidar profiles.  
Figure 5 presents the retrieved aerosol backscatter profiles of the fog-oil event in false color. In the studied case, the intense period of the event was centered from 20 to 28 m in the first 20 s of the measurement with averaged values for particle backscatter profiles close to those reported in the literature for such particles [15]. It should also be noted that the lowest detectable backscattering coefficients depend on signal and noise levels in the actual measurements. For detailed analysis, a comparison with the ambient molecular backscatter is proposed. The Rayleigh theory is used to compute the molecular backscatter coefficient with the height-scaled pressure at 20 \({}^{\circ}\)C and relative humidity ranging from 50 to 80% at the wavelength of interest, obtaining molecular backscatter coefficient values up to 4.25 10\({}^{-6}\) sr\({}^{-1}\)m\({}^{-1}\). The right panel of Figure 5 presents the time-averaged (60 s) aerosol backscatter in blue color. For reference, the molecular backscatter profile is displayed in red. It is worth noting that the limit of detection for these measurements reaches the level of the molecular backscatter coefficient. This is evidencing the performances of the Colibri micro-lidar as well as the satisfactory implementation of the pre-processing and calibration methods.  
Finally, the retrieved values of backscatter coefficients validate the effectiveness of the proposed forward inverse method without boundary conditions. The measured backscatter profiles demonstrate the system capability to characterize complex aerosol events in the short-range, close to their sources, with high range and temporal resolution. According to these first measurements, the Colibri micro-lidar is a good candidate to assess the dynamics of aerosol propagation in the short-range with millisecond temporal resolution. Further studies should be conducted to assess the system performances for vertical  
Figure 4: Attenuated backscatter profiles measured by the Colibri micro-lidar for fog-oil plumes with high range and temporal resolution. The inset of the figure in the upper right shows in greater details the resolution achieved by the micro-lidar.  
aerosol profiling in the atmosphere. The determination of the aerosol number concentration in the aerosol plume from micro-lidar profiles follows in the next section.
## 3 Methodology
### Validation and Deployment  
#### 3.4.1 Dwell Time Comparison  
The validation for this study was conducted in the simulation environment. The grocery shoppers performed various shopping trips with provided sets of randomly generated shopping lists in three setups. The first setup did not use any sorting algorithm; the second setup used the agglomerative clustering variant of ML-DProSA; and the third setup used the affinity propagation clustering variant of ML-DProSA. The following configurable settings were used:  
* Limited item pool to choose from;
* Small, medium, or large number of items per list;
* Number of shoppers per test.  
Figure 7: Adopted system architecture for the integration of the simulation environment to the ML-DProSA.  
Different combinations of these settings allowed us to determine the performance of either variant of ML-DProSA in different scenarios. The average dwell times of shoppers, both with and without sorting algorithms, were recorded, and then, represented in various graphs. The simulation speed was also scaled up to \(\times 20\) of the normal speed, resulting in faster data gathering while still retaining the actual time that the character took to travel from one item to the other. The decision-making time was also set to 10 s, which is the average time taken for the verbal responses of people that were asked in the grocery store during our indoor store mapping for the layout. These augmentations to the simulation were accounted for in the post-processing of the simulation output data.
# Contact Fatigue State Identification of Specim Based on Heterogeneous Data and Evidence Theory
## 3 Contact Fatigue State Identification
### Contact Fatigue State Identification Method  
Contact fatigue failure is a gradual process. The multi-source information, such as vibration signal and image signal, reflect the contact fatigue state of the specimen from different aspects. In this paper, vibration monitoring and damage evolution are combined, and different models are used to realize contact fatigue state identification based on vibration signal/damage evolution. On this basis, the decision-level fusion of the state identification results from different information and different models is carried out, and finally the contact fatigue state identification of the specimen with the fusion of vibration and image heterogeneous data is realized.  
The contact fatigue state identification process of the specimen is shown in Figure 9.  
The steps are as follows:  
1. The vibration signals collected in the test under the same working conditions are randomly divided into training set, test set and verification set according to a certain proportion.
2. The SDP method is used to convert the vibration signals into images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Signal** & **Sample Frequency/Hz** & **Sample Interval/min** & **Time per Sampling/s** \\ \hline Vibration & \(10\)k & 2 & 1 \\ Image & 1 & 10 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Signal acquisition parameter settings.  
Figure 8: The control interface of the RCF-A test machine. 1. Visual display module; 2. Fatigue test control module; 3. Vibration signal acquisition module.  
Figure 9: The process of contact fatigue state identification of the specimen.  
3. For the transformed training set and test set SDP images, we train the VGG16 model and the ResNet model respectively.
4. For the vibration signal of the verification set, we use the trained VGG16 model and the ResNet model to identify the state, respectively, and obtain two state identification evidence bodies m1 and m2 based on the vibration information source.
5. For the image signal, we first perform the denoising processing, and then combine the fatigue defect identification method based on automatic weighted threshold and the dynamic compensation method for detection error, based on fatigue defect edge features proposed by the research group [6]. We then calculate the fatigue damage area and obtain evidence body m3 based on image information source.
6. On this basis, we fuse the evidence bodies m1, m2 and m3, and make a decision to obtain the contact fatigue state identification result of the specimen.
## 2 Preliminaries
### Network Traffic Handling in an OpenFlow-Based SDN  
This work of research is built on an OpenFlow-based SDN [2]. OpenFlow proposes a flow-based traffic forwarding decision approach. This forwarding approach enables flexibility on programming network traffic as all packets that belong to the same flow receive the same forwarding decisions [1]. To (re)configure switches, the controller interacts with switches via an OpenFlow channel interface that connects each switch with the controller and by exchanging OpenFlow messages based on the OpenFlow switch protocol. This protocol provides a reliable message delivery and processing but does not ensure ordered message processing [2] since the communication between the OpenFlow controller and the switches is asynchronous. In this paper, we are interested in a single type of message: a message instructing an OpenFlow switch in the network to update its flow table with new entries, namely the _FlowMod_ controller-to-switch message (see the details in Section 5). In fact, each switch contains one or more flow tables that store a set of entries (forwarding rules). An entry consists of  
Figure 1: The general architecture of SDN.  
matching fields and forwarding actions. Each entry matches a flow or a set of flows and performs certain actions (dropping, forwarding, modifying, etc.) on the matched flows.
# Experimental and Numerical Study of Orifice Coefficient of Cargo Tank Design of LNG Vessels
## 3 Leakage Experiment  
### Experiment Setup  
An experiment was conducted using pressure vessels and crack specimens to quantitatively measure and examine the leakage flow rate through a crack. Thus, the general size of the crack area considered in the design of LNG tanks was adopted and applied to make the experimental results as general as possible. For the ease of experimentation, cracks were processed and manufactured in a rectangular form. To measure the leakage flow through cracks in a liquid cargo tank, it is important to first study the analogy between an orifice flow and leak flow [1; 5]. Therefore, a cracked specimen with a rectangular shape was installed as a typical crack area, and a leakage experiment was performed. Figure 3 shows the configuration of the experimental equipment. The shape of the cracked specimen was rectangular. The crack specimen had a thickness of 10 mm and a diameter of 100 mm. The types of specimens used in the leakage experiment are shown in Table 1.  
Figure 2: Configurations of orifice flow.  
The real-time leak experiment is based on the real operating conditions of an independent LNG tank. The experiment was designed to measure the pressure head and pressure of the fluid inside the tank. To calculate the flow rate, the weight of the fluid that spilled out was measured in real time. The internal fluid is water at room temperature. The pressure inside the pressure tank was configured to maintain each loading condition, and three pressure sensors were installed to measure the pressure inside the tank at the location of the crack specimen. Figure 4 shows a configuration of the experimental equipment. The compressor takes air from a storage tank, pumps it through a pipe, and maintains steady state. Pressure sensors and transmitters were used just upstream of the leak to measure the pressure head at the leak.  
Pressure and weight sensors were installed to measure the internal pressure of the tank as shown in Figure 4. Analog and digital pressure sensors were installed at each position. The first pressure sensor (P-Sensor 1) was installed in the upper part of the tank to measure the pressure of the gas (air) inside the tank under half-filled conditions. The second sensor position (P-Sensor 2) was set up to measure the pressure of the fluid in the tank. Finally, the third sensor position (P-Sensor 3) was installed on the crack specimen to measure the orifice flow rate. The flow rate was measured using the weight sensor (W-Sensor) of the fluid flowing out.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Crack Shape** & **Dimensions [mm]** & **Area [mm\({}^{2}\)]** \\ \hline
1 & Rectangular & \(0.5\times 7\) & 3.5 \\
2 & Rectangular & \(1.0\times 7\) & 7 \\
3 & Rectangular & \(1.0\times 14\) & 14 \\ \hline \end{tabular}
\end{table}
Table 1: Type and dimensions of crack specimens.  
Figure 3: Configuration of pressure vessel with crack specimen.
## 3 Case Studies
### Dams with High Groundwater Level
#### 3.2.1 Numerical Simulation  
Here, according to the topographic conditions and geological survey data of the Shangmo reservoir, two models are established to calculate the seepage field of the reservoir using the finite element method. The finite element mesh model of the whole dam, with 81,352 superelements and 886,688 nodes in total, is shown in Figure 8a. The size of the whole dam model is 2000 m along the water flow direction and 800 m in the other horizontal direction.  
After the three-dimensional finite element calculations, considering steady seepage under two calculation conditions (a normal level and the designed flood level conditions), the potential distribution of the seepage field and seepage flux during the operational period are obtained as shown in Figure 9 and Table 4. Figure 9 reveals that the dam foundation and dam body below the ph  
Figure 8: Finite element mesh models: (**a**) the whole dam; (**b**) the compound geomembrane.  
Figure 9: Potential distributions of the seepage field at a typical cross-section of the dam body: (**a**) in normal water level conditions; (**b**) in the designed flood level conditions.
## 3 Partition-Based Clustering
### K-Prototypes  
K-prototypes clustering [3,4] is also a variant of k-means that accommodates datasets with mixed data types, including both categorical and numeric attributes. While k-means works well for purely numeric data and k-modes works exclusively for categorical data, k-prototypes effectively handles datasets containing mixed data, providing a comprehensive and complete clustering solution that captures the structure of mixed data. In effect, k-prototypes combines the concepts of k-means for numeric attributes and k-modes for categorical attributes. Therefore, it aims to partition instances into clusters so that the dissimilarity for categorical attributes and the WCSS distances for numeric attributes are minimized.  
K-prototypes combines the iterative assignment and update steps of both the k-modes and k-means algorithms. Thus, similar to k-modes and k-means, k-prototypes begins by initializing the cluster centroids. These centroids consist of both numeric and categorical attribute values. For each instance, the algorithm calculates either the Hamming or the Euclidean distance, for categorical and numeric attributes, respectively, from each centroid. Then, the algorithm assigns the instance to the cluster with the nearest centroid. The update step includes the calculation of the new centroid for each cluster. For numeric attributes, k-prototypes computes the mean as in k-means. For categorical attributes, it determines the mode as in k-modes. Then, the algorithm repeats the steps of (a) assignment and (b) update iteratively, until the centroids are stabilized.
## 4 Optimization Model Solution  
When the optimization goal is to preferentially compensate for the inductive reactive current component, if the inductive reactive current component cannot be fully compensated for, the compensation coefficient \(w_{r}=l_{\text{max}}/l_{\text{max}}\), and the compensation weight coefficients \(w_{n}\) and \(w_{z}\) are both 0. If the inductive reactive current component can be fully compensated for and the unbalanced component cannot be fully compensated for, the compensation weight coefficient is \(w_{r}=1\), and the values of \(w_{n}\) and \(w_{z}\) need to be optimized using the optimization algorithm.  
When the optimization goal is to preferentially compensate for the unbalanced reactive current component, if the negative-sequence and zero-sequence reactive current components cannot be fully compensated for, the compensation weight coefficient \(w_{r}=0\), and the compensation weight coefficients \(w_{n}\) and \(w_{z}\) need to be solved using the optimization algorithm. If the unbalanced reactive current component can be completely compensated for but the inductive reactive current component cannot be completely compensated for, the compensation weight coefficient is \(w_{n}=w_{z}=1\), and \(w_{r}\) is the ratio of the residual capacity current of the compensation device to the inductive reactive current amplitude \(l_{\text{max}}\).  
When the optimization goal is to preferentially compensate for the harmonic current component, if the generative current components cannot be fully compensated for, the compensation weight coefficient is \(w_{g}=l_{\text{max}}/l_{Ag\text{max}}^{*}\), and the other compensation weight coefficients are 0. If the generative current components can be fully compensated for, the compensation weight coefficient is \(w_{g}=1\), and the other weight coefficients \(w_{n}\) and \(w_{z}\) need to be solved using the optimization algorithm.  
In order to achieve the different optimization objectives mentioned above, in this paper, we use the particle swarm optimization (PSO) algorithm improved by the simulated annealing (SA) algorithm to solve the model and find the optimal weight coefficient.
# A Temperature-Robust Envelope Detector Receiving OOK-Modulated Signals for Low-Power Applications
## 4 Implementation Criteria
### Parameter Optimization
Equation (15) then becomes  
\[r_{DS}=\frac{V_{T}}{I_{M4}}\frac{\left(\frac{W}{L}\right)_{M4}}{\left(\frac{W} {L}\right)_{MD}}e^{\Delta V_{TH}/nV_{T}}, \tag{22}\]  
so the difference between the actual \(r_{DS}\) and the ideal one is  
\[\Delta r_{DS}=r_{DS}-r_{DS,ideal}=r_{DS,ideal}\left(e^{\Delta V_{TH}/nV_{T}}- 1\right). \tag{23}\]  
The error in \(r_{DS}\) corresponding to a 1-\(\sigma\) error in \(V_{TH}\) is then  
\[\Delta r_{DS,\sigma}=r_{DS,ideal}\left(e^{\sigma v_{TH}/nV_{T}}-1\right)=r_{ DS,ideal}\left(e^{a_{0}/nV_{T}\sqrt{W\times L}}-1\right), \tag{24}\]  
where \(a_{0}\) is the well-known Pelgrom coefficient [23]. Equation (24) shows that it is beneficial to increase \(W\times L\) to ultimately reduce temperature compensation degradation due to \(\Delta r_{DS,\sigma}\). This poses a design trade-off between \(C_{gs}\), and thus \(C_{in}\), and \(\Delta r_{DS,\sigma}\). Since the equations for \(R_{in}=r_{DS}/2N\) and \(C_{in}=2 NC_{gs}+C_{pad}\) are somewhat ideal, parameter optimization has to be performed based on chain sensitivity optimization and temperature compensation afterwards. As for diode area, a minimal \(W\times L\) for the diodes translates into a smaller \(C_{gs}\), which requires smaller \(C_{gate}\) and \(C_{drain}\) values at a parity of signal coupling at diode terminals, and thus a smaller \(C_{in}\). This results in a higher \(A_{v}\) gain at \(Q_{ind}\) parity, from (5). On the other hand, a non-minimal \(W\times L\) for the diodes is required to increase temperature compensation accuracy (24), thus reducing sensitivity spread, even if pad parasitics may significantly contribute to \(C_{in}\).
## 3 Manufacturing Techniques for Wearable Antennas
### Flexible Textile Wearable Antennas
#### 3.2.1 Thin and Uniform Metallization Conductive Sections  
This technique is the fastest when it comes to the manufacture of one or a small number of antennas but when it comes to the mass production of textile antennas it is probably a slower than other techniques due to the manual work which is required. Generally, this technique is reasonable for experimental antenna prototyping but may not be ideal for longer term solutions. It is very easy to attach copper tape or foil on a textile substrate but it is also possible that the copper tape might be detached while bending or even due to environmental conditions (_i.e._, humidity, heat) (Figure 3).  
One solution to avoid having the textile antenna permanently integrated on the garment is to use Velcro connectors [25]. In [25] a wideband wearable textile antenna is presented where the fabricated antenna is integrated on a jacket at the inside side of it by using Velcro. This means that the antenna can be easily physically attached and detached to and from the jacket by using Velcro strips.
# Spatial Domain-Based Nonlinear Residual Feature Extraction for Identification of Image Operations
## 3 Experiments and Discussions  
In the experiments conducted to test the performance of the proposed strategy, we randomly selected a huge number of raw images from the dataset Boss Base v1.0 [29]. For each of the original images, 15 counterparts were created by applying the image processing operations with random parameters from the predefined range. Table 1 lists the 15 image operations that were tested and the predefined range of the parameters of the corresponding operations, including spatial enhancement, e.g., gamma correction (GC) and histogram equalization (HE); spatial filtering, e.g., mean filtering (MeanF) and Wiener filtering (WF); geometric operation, e.g., scaling (Sca) and rotation (Rot); lossy compression, e.g., JPEG and JPEG2000 (JP2); and frequency filtering, e.g., high-pass filtering (HPF) and homomorphic filtering (HF). All of these images were then divided into two categories randomly: half of them were utilized for training and the other half for testing.