esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermomechanical Analysis of Steel-to-Timber Connections under Fire and the Material Density Effect
## 2 Materials and Methods
### Thermomechanical Analysis
#### 2.3.1 Thermal Analysis  
The calculated temperature fields are then imported into the connection model as the thermal load at the specified time. The thermal analysis consists of determining the temperatures at each mesh node, used in the structural analysis defined in the next subitem.  
The steel-to-timber connection is subjected to the nominal temperature-time curve ISO 834 applied on the front and the back surface of the model. The thermal response is calculated during 3600 s of fire exposure. The boundary conditions applied to the connection in this situation are the convection, with a coefficient equal to 25 W/m\({}^{2}\)K, on the sides exposed to fire and the radiation with the emissivity considered equal to 1, according to the according to Eurocode 1 part 1-2 [13]. The initial temperature applied on all model surfaces is constant and equal to 20 \({}^{\circ}\)C.  
Firstly, through purely thermal analysis, the evolution of the thermal gradient in the connection is obtained. Then, the thermal outputs (nodal temperatures) are used as inputs in the structural analysis, through the LREAD command. Nodal temperatures act as body forces on the respective nodes. This approach requires that the mesh be the same for the thermal and mechanical analysis.
## 4 Results and Discussion
### Data Visualization and Discussion  
To confirm the effectiveness of the proposed joint loss function, we reduced the dimension of the feature vector from 1779 to 2 using a fully connected layer [25]. The distributions of training samples represented by the reduced two-dimensional features are visualized in Figures 7-9. Figures 7-9 are results obtained by VGG19, ResNet50, and DenseNet121, respectively. Different classes of samples are represented by different colors.  
Figure 6: ROC curves and AUCs of the conventional loss (**top**) and the proposed joint loss (**bottom**).  
Figure 7: Distributions of training samples, represented by the reduced two-dimensional VGG19 features: (**a**) conventional loss function; (**b**) proposed joint loss function. Left: training data; right: test data.  
As shown in Figure 7a, Figure 8a, and Figure 9a, when we only used the conventional loss function, parts of the BAPL2 data (yellow) were mixed with BAPL3 (red) and BAPL1 (green) in the test samples. On the other hand, the BAPL2 samples (yellow) were separated from those of BAPL1 (red) and BAPL3 (green) by the use of the proposed joint loss function as shown in Figure 7b, Figure 8b, and Figure 9b. From these results, we can easily understand why the classification performance, especially for BAPL2, was significantly improved by the proposed joint loss function.  
Figure 8: Distributions of training samples, represented by the reduced two-dimensional ResNet50 features: (**a**) conventional loss function; (**b**) proposed joint loss function. Left: training data; right: test data.  
Figure 9: Distributions of training samples, represented by the reduced two-dimensional DenseNet121 features: (**a**) conventional loss function; (**b**) proposed joint loss function. Left: training data; right: test data.
# Research on Path and Sequence Planning for Multi-Cabin Onboard Transportation of Large Cruise Ships
## 3 Improved A-Star Algorithm Designing
### Reverse Planning Strategy for Cabin Transport Sequence Optimization
The specific process for selecting cabins for transportation is shown in Figure 7. This process continues iteratively until all the cabins have been successfully transported to their designated locations. Since the reverse planning strategy reverses the pushing timeline of the cabins, it is necessary to perform a reversal of time once all cabins have been transported. By calculating the entry time for each cabin, the actual entry order of the cabins can be determined. Figure 6: Selection of the installation cabin. The formula for calculating the entry time of each cabin is as follows:  
\[t_{\text{az},i}=t_{\text{max}}-a\times\text{T}_{\text{az},i} \tag{7}\]  
\[t_{\text{tc},i}=\left\{ \begin{array}{ll}t_{\text{max}}-t_{\text{az},i}-\text{ T}_{\text{tc},i}&1\leq i\leq\text{k}\\ t_{\text{max}}-t_{\text{az},i}-\text{T}_{\text{tc},i}&t_{\text{az},i,\text{ max}}\leq t_{\text{az},i},i>\text{k}\\ t_{\text{max}}-t_{\text{az},i,\text{max}}-\text{T}_{\text{tc},i}&t_{\text{az},i,\text{max}}>t_{\text{az},i},i>\text{k}\end{array} \right. \tag{8}\]  
where t\({}_{\text{az},i}\) represents the installation start time, t\({}_{\text{tc},i}\) denotes the entry time, i is the sequence number, k is the maximum number of cabins to be pushed, t\({}_{\text{max}}\) is the total time for path planning, T\({}_{\text{tc},i}\) is the transportation time, \(\alpha\) is the installation time, and T\({}_{\text{az},i}\) represents the number of iterations of the reverse planning strategy. Below is a simple numerical example to illustrate the application of the reverse planning strategy. Suppose the total planning time is \(t_{\text{max}}\) = 1000, the installation time coefficient is \(\alpha\) = 5, and the maximum number of simultaneous cabin pushes is \(k\) = 3. For three cabins (\(i\) = 1, 2, 3), let the values of \(T_{\text{az},i}\) be 10, 12, and 15, and the values of \(T_{\text{tc},i}\) be 30, 35, and 40, respectively. According to Equation (7), the installation start times in the reverse planning process, \(t_{\text{az},i}\), are calculated as 950, 940, and 925. Furthermore, using Equation (8), the entry times in the forward construction process are determined to be 20, 25, and 35, respectively. It can be observed that although the installation start times in the reverse planning process decrease (950, 940, 925), the corresponding entry times in the forward construction process increase sequentially (20, 25, 35). This outcome demonstrates that the reverse planning strategy effectively back-calculates the timing of operations, thereby ensuring the optimal overall construction path, while meeting the constraints of parallel operations.
## 7 Research Gaps and Future Opportunities
### Protection against Communication Attacks  
We have identified the following opportunities to improve protection against communication attacks where the main focus of the blockchain-based systems is to ensure protection of communicated information, while the communication attacks on such systems are focused on developing mechanisms to compromise information and identity in different ways.
## 5 Energy Requirement Estimation
### Ultrasound  
For the inactivation of PPO by ultrasound, the energy from the probe during processing was produced by the production of sound waves during the treatments on fruit juices and puree. Table 7 summarises the energy requirement in this process for different fruits or cultivars, which is sound energy. A study by [PERSON] [48] shows that the higher the intensity or amplitude of ultrasound applied, the greater the energy released. Inactivation studies of PPO by ultrasound processing were performed in different fruits and cultivars, which showed different sound energy (E) values required to inactivate the enzyme. For example, PPO in bayberry juice is completely inactivated at 452 W/cm\({}^{2}\)/480 s or E = 288 kJ [39], PPO in strawberry puree achieved 99% of inactivation at 460 W/cm\({}^{2}\)/600 s or E = 19.5 kJ [32], PPO in apple juice cv Golden Delicious is 95% inactivated at 565 W/cm\({}^{2}\)/1200 s or E = 900 kJ, while apple juice cv. Fuji is 94% inactivated at 396 W/cm\({}^{2}\)/600 s or E = 315 kJ [30; 55]. As a result, the amount of E required to inactivate PPO in different fruits or cultivars varies greatly. Furthermore, applying ultrasound to fresh fruits and vegetables can affect the firmness, depending on the intensity [72]. However, other than the amount of sample, the sound energy (E) value also will be affected by the amount of water, fat, sugars, and other substances present in the whole fruit if processed as juices or purees. Most of the energy used in ultrasound was used to create sound waves for the process and dependent on the sound intensity, area of sonotrode, and processing time, as described by Equation (3) in Section 5.1. The specific energy value was found to be a large number due to the small volume of sample used for the ultrasound.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Fruit/Cultivar** & **Conditions** & **Mass (kg)** & **Sound (Intersity (W/cm\({}^{2}\))**)** & **Area (cm\({}^{2}\))** & **E = Sound** (Intersity \(\times\) A \(\times\)**)** & **Specific (Livery/kg)** & **Data Collected** \\ \hline Apple (_Malus domestica_ & 20 min 67 \({}^{\circ}\)C & 0.083 & 565 & 1.327 & 900 & 10843 & [55] \\ juice & & & & & & & \\ \hline Apple (_Malus domestica_ & 10 min 60 \({}^{\circ}\)C & 0.083 & 396 & 1.327 & 315 & 3795 & [30] \\ Juice & & & & & & & \\ \hline Bayberry (_Myrica_) & 60 \({}^{\circ}\)C & 0.198 & 271 & 1.327 & 173 & 874 & \\ Juice & 8 min 70 \({}^{\circ}\)C & & 452 & 1.327 & 288 & 1455 & \\ \hline Pear (_Pyrus communis_ cv. 72 \({}^{\circ}\)C & 0.025 & 460 & 0.07068 & 19.5 & 780 & [28] \\ Tyalor’s Gold) & 72 \({}^{\circ}\)C & & & & & & \\ Puree & & & & & & & \\ \hline Apple (_Malus domestica_ & 10 min 60 \({}^{\circ}\)K. Royal Gala) & 10 min 72 \({}^{\circ}\)C & 0.025 & 460 & 0.07068 & 19.5 & 780 & [28] \\ puree & & & & & & & \\ \hline Strawberry (_Fragaria ananassa_ cv. 72 \({}^{\circ}\)C & 0.025 & 460 & 0.07068 & 19.5 & 780 & [28] \\ Pumee & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Energy estimation for at least 80% of PPO inactivation by ultrasound.
## 1 Introduction
[PERSON] and [PERSON] [31] studied the effect on flame retardancy on polycaprolactone composites with the combination of aluminum diethylphosphate and organically modified montmorillonite nanoclay. The outcome of their study was that by combining both flame retardants, the result was an enhanced effect when compared with their application on a separate basis. [PERSON] et al. [32] studied the influence of modified ammonium polyphosphate (APP) on the flame retardancy of wood floor composite by cone calorimetry and noticed a synergetic effect between APP and the composite followed by a good improvement on the overall flame retardancy of the multicomponent material. Similar results were outlined also by other research groups (please see for example [PERSON] et al. [1], [PERSON] et al. [26], [PERSON] et al. [27], [PERSON] and [PERSON] [28], [PERSON] et al. [33]). Nevertheless, some contradictory information was found in the open literature, some authors noticed a decrease in thermal stability by adding OMT [29; 30; 31; 32; 33; 34; 35; 36] while other experimental research proved a thermal stability enhancement [37; 38]. An explanation for these contradictory results may be related to the nanoparticle dispersion in the polymer matrix, as well as with the nanoparticle dimensions and chemical compatibility. Therefore, according to the literature recommendations, in this research a recycled ABS (reABS) was acquired and improved by adding 1%-2% organic montmorillonite and 15%-20% fire retardants. The nanometer sized montmorillonite clay particles can enhance surface integrity and provide advantages over the thermal properties of the composites, while the Exolit fire retardants [1; 6] are well known for their capability to decrease the heat released in a fire. These new developed materials were tested with regard to their fire properties by means of the Limiting oxygen index (LOI), UL 94, and cone calorimeter methods. The overall aim was to improve the global properties of ABS by taking into account two directions: costs (by replacing ABS with recycled: reABS) and fire behavior (by addingboth OMT and FR (fire retardant) to get better fire resistant properties). This study is of relevance for many areas like the automotive industry, electronics, civil engineering etc due to the large usage of ABS for component manufacturing and due to this material exposure in a possible fire scenario.
## 4 Experimental Results and Discussion
### Feature Selection
To further clarify the number of features, in the feature range of 1900-2100, we further confirmed that the first 2000 features are the optimal solution for the single-category feature of Opcodes_3_gram using chi-square verification with an interval of 20. As shown in Table 4, chi-square validation streamlines the number of features while reducing feature extraction time. Although the time difference is not large, the number of features that are most conducive to classification is screened through chi-square verification, which reduces system complexity and memory consumption and is conducive to improving model training and classification efficiency. Overall, this paper used chi-square validation and the XGBoost classification algorithm to select single-category features that are most beneficial to distinguish malware families. The chi-square verification is used to eliminate redundant and unnecessary characteristics from the dataset, thereby reducing system overhead and improving classification efficiency. The XGBoost classification algorithm is adopted to measure the effectiveness of the selected features. Meanwhile, we also selected the t-SNE (t-Distributed Stochastic Neighbor Embedding) visualization tool to appear the correlation degree of the selected features. As shown in Figure 4, 9 types of malware families are clearly divided, which proves that the correlation among the selected features is small; hence, it is beneficial to distinguish malware families after feature fusion. The details of feature fusion will be introduced in the next section. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Number** & \begin{tabular}{c} **Time** \\ **(Seconds)** \\ \end{tabular} & \
\begin{tabular}{c} **Number** \\ **(Chi-Square)** \\ \end{tabular} &
\begin{tabular}{c} **Time** \\ **(Seconds)** \\ \end{tabular} \\ \hline Byte\_2\_gram & 2000 & 1013.15 & 360 & 1011.78 \\ Asm\_Pixel & 5000 & 966.77 & 2300 & 944.75 \\ Opcodes\_2\_gram & 625 & 0.67 & 565 & 0.56 \\ Opcodes\_3\_gram & 10,000 & 69.21 & 2000 & 68.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Chi-square verification for feature selection. Figure 4: Dataset t-SNE 2D visualization.
# Angiography Simulation and Planning Using a Multi-Fluid Approach
## 4 Results and Discussions
### Experiment 2
#### 4.2.3 Virtual and Real Angiography Results Comparison  
The real coronary angiography image sequences are shown in Figure 9a and approximate corresponding simulation results of our virtual training system are shown in Figure 9b. At the beginning, the coronary arteries are not very clear in the Figure 9(b1). In addition, then the contrast media were injected into the vessels and it is continually pushed forward by the blood flow. The density of the contrast media becomes higher at the tip of the catheter and the frontier of the development is lighter than the back-end, as shown in Figure 9(a2,a3,b2-b4). After finishing the injection, the back-end is diluted by the incoming blood flow and the frontier become darker than the back-end, as shown in Figure 9(a4,b5,b6).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Angiography Results** & **(a)** & **(b)** & **(c)** \\ \hline Particle number & 600 & 600 & 600 \\ Injection dosage & 1.95 mL & 1.95 mL & 1.95 mL \\ Injection velocity & 3.25 mL/s & 6.50 mL/s & 13.00 mL/s \\ Time of finishing injection & 0.60 s & 0.30 s & 0.15 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Injection conditions with various injection velocities.  
Figure 9: Real angiography image sequences vs. our simulator results. **(a)** Real angiography image sequences; **(b)** Virtual SPH-based angiography results.  
In our simulator, we adopted the real patient heart model and blood vessels model, which was generated from 3D rotational X-ray images in a DICOM dataset. For getting better angiography simulation results, we removed the capillaries and kept the arterial trunks around the heart. From Figure 9, we can see that the whole process of contrast media diffusion in the blood vessels between real coronary angiography and angiography simulation is close. It demonstrated the capabilities of our proposed software and hardware solutions for delivering a feasible digital system to meet the demand for medical training. To further evaluate our proposed system, we conducted an end user evaluation to test the simulator with medical doctors.
# Effect of the Insulating Layer on the Properties of SMC Inductors
## 4 Experimental Results
### Data Presentation and Comments  
The results are presented considering two proposed measurement setups: toroidal samples and rod inductors. The main differences consist of the shape of the samples (toroids without air gaps, and rods with a large air gap) and the frequency interval of measurement: until 20 kHz for toroid samples, while the rod inductors are tested until 1 MHz. Finally, the SMC rod inductors are compared with one made with soft ferrite.
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 6 VLC Application
### Wireless Local Area Networks (WLANs)  
In Figure 19, an ultra-high-speed full-duplex local area network with star topology is shown, with LED visible light communication set up for the LAN, using VLC to provide speeds exceeding 10 Gb/s, and is tested by a large number of users.
## 1 Introduction
Nowadays, to get better performance, the integration of control categories with other methods have been a hotspot. The authors propose a speed observer based on back-stepping and sliding mode for low-speed operation in [16]. The reference [17] develops a low-order adaptive instantaneous speed estimator (AISE) and a self-tuning control strategy to improve the speed control performance in a wide speed range with unknown inertia parameters. In reference [10], a model predictive direct speed control (MPDSC) method is proposed to get a good anti-disturbance ability for PMSM. It depends both on full parameter disturbances and load torque observer and the precise mathematical model parameters are not needed. Actually, these methods combine the advantages of various algorithms so as to get good performance. This paper develops a sliding mode control strategy associated with a load disturbance estimator to enhance the dynamic performance of PMSM in full speed range. The proposed estimator can reconstruct the disturbance in load caused by the variation of load environments. Then, PD control is employed to decrease the overshoot. Finally, the speed tracking performance can be improved by designing an equivalent control law. Further, numerical examples are proposed to verify the effectiveness of the presented method under both high speed and low speed. The rest of this paper is organized as follows. Section 2 describes the model of PMSM, in which, the disturbance and uncertainties are discussed and modeled. An estimator and control strategies are designed in Section 3. Then the main theoretical results are shown in Section 4. Finally, Section 5 summarizes the innovations of this thesis and makes a prospect of this paper.
# GNSS Timing Performance Assessment and Results Analysis
## 4 Conclusions  
With GNSS modernization, high-precision GNSS timing services are provided for global users, and the timing performance has been widely evaluated. Through a study of the GNSS timing principle, the assessment indicators of GNSS timing performance were discussed, and an assessment method was proposed. The time transfer accuracy of BDS, GPS, GLONASS, and Galileo was assessed and compared. The results allowed the following conclusions to be drawn:  
1. The time transfer accuracy refers to the 95% probability level of the instantaneous differences between the estimated receiver time offset from a reference realization of UTC and the true offset over a specified time interval. According to the GNSS time principle, time transfer accuracy is considered as the key GNSS timing performance assessment indicator.
2. An assessment method of time transfer accuracy was proposed on the basis of UTC(NTSC) as an intermediate timescale and UTC/UTC(k) as a reference timescale, and the uncertainty budget was determined. If the local reference time of GNSS users can connect with UTC, this assessment method can be used.
3. The assessment results show that the time transfer accuracy of BDS, GPS, GLONASS, and Galileo was 13.8 ns, 4.5 ns, 16.8 ns, and 4.2 ns respectively, meeting their performance requirements. Specifically, the time transfer accuracy of Galileo was the best, while that of GLONASS was the worst. Furthermore, the time transfer accuracy of GPS and Galileo was much better than their requirement, while the time transfer accuracy of BDS and GLONASS showed a fixed time offset and can still be improved further.  
Conceptualization, L.Z., H.Z. and X.L.; methodology, L.Z., H.Z. and F.Z.; software, L.Z.; validation, H.Z.; resources, L.Z.; data curation, L.Z. and F.Z.; writing--original draft preparation, L.Z.; writing--review and editing, L.Z. and F.Z., supervision, H.Z. and X.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Funds of China (Grant No.: 11903039) and Western Yong Scholars of Chinese Academy of Sciences (Grant No.: XAB2021 YN24 and XAB2021 YN28).  
Figure 5: Histogram of monthly GNSS time transfer accuracy assessment results in 2021.
# Performance Analysis of Post-Quantum Cryptography Algorithms for Digital Signature
## 5 Performance Analysis
### Measurement Results
Nevertheless, the variance did not exceed 30% compared to other algorithms. The precise values are presented in Table 3 (key generation and signing) and Table 4 (verification of signatures). The conclusion that can be drawn regarding the SPHINCS+ group algorithms is that for signing large-sized files, algorithms with an's' suffix would be a better choice. The reason for this is that when handling large files, the disparity between algorithms optimized for speed and those optimized for size decreases. However, algorithms with the suffix's' retain the advantage of a significantly smaller size of the generated signature. Although algorithms with the suffix 'f' present significantly reduced key pair generation times, in the usual use case, a once-generated key pair is used to sign multiple files. Additionally, another distinctive feature of the SPHINCS+ group of algorithms is worth mentioning: regardless of the file size, the disparity in verification time between speed-optimized and size-optimized variants is not significant. Figure 4: Average file signing time—10 MB file size. Figure 5: Average file signing time—100 MB file size. Figure 8: Average file verification time—10 MB file size. Figure 6: Average file signing time—1 GB file size. Figure 7: Average file signing time—10 KB file size. Figure 10: Average file verification time—1 GB file size. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & **Function** & **Keypair Generation** & **Signing 10 MB File** & **Signing 100 MB File** & **Signing 1 GB File** \\  & & [s \(\times\)10\({}^{-5}\)] & [s \(\times\)10\({}^{-5}\)] & [s \(\times\)10\({}^{-5}\)] & [s \(\times\)10\({}^{-5}\)] \\ \hline  
-simple** & & \(3709\pm 15\) & \(48\),\(057\pm 8\) & \(98\),\(623\pm 13\) & \(590\),\(551\pm 61\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance measurements of the post-quantum algorithms for keypair generation and file signing. Figure 9: Average file verification time—100 MB file size.