esa-sceva/satcom-chunk-collection huggingface dataset:
# The Subsea Micro-Drilling Vehicle's Dynamic Analysis during Landing
## 4 Conclusions  
1. The mechanical parameters of deep-sea sediment substrate in a particular sea area are determined by pressure-sinkage and triaxial undrained and unconsolidated (UU) compression tests; the equivalent free-fall height is used instead of the release height  
Figure 14: Relationship between SMDV landing attitude and release height, slope angle (three landing directions).  
because the SMDV is affected by water resistance and buoyancy when it is landing on the substrate; based on the specific structural design parameters of the SMDV, a multi-body dynamics model of the SMDV's release fall trajectory is developed.
2. An experimental simulation of the SDMV at various release heights was conducted. Results showed that when the SMDV landed on a flat substrate, its subsidence increased gradually with the increasing release height, reaching a maximum of 347 mm at a release height of 5 m. At the same time, the center of gravity impact acceleration was less than seven gravity accelerations, ensuring the safety of electronic components on the body and normal operation of the SMDV after landing.
3. Experiments involving the simulation of the SMDV landing on slopes of different angles were conducted, and the results indicated that when the SMDV landed on the slope, it may take on three different states, namely stable, slip, and overturn. Among the three orientations in which the SMDV is placed--forward, backward, and lateral--the risk of slipping and flipping is the least when it is placed forward, and the risk is comparable when it is placed backward and lateral, with the increase of the release height of the SMDV. Its ultimate overturning angle decreases while the ultimate slipping angle remains nearly unchanged.  
When deploying the SMDV in a certain target area, it is not easy to control the direction of the SMDV orientation to the slope of the seabed in practice. In order to avoid slipping the SMDV after release with a height less than 5 m, the slope of the seabed must be less than 10\({}^{\circ}\). If a small amount of slipping is allowed, to prevent the SMDV from overturning after release, the slope of the seabed should not exceed 20\({}^{\circ}\).  
[PERSON] was in charge of the whole trial; [PERSON] wrote the manuscript; [PERSON] provided ideas on manuscript writing. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China (Grant No. 2022 YFC2805904), the National Natural Science Foundation of China (Grant No. 52275106), and the special project for the construction of innovative provinces in Hunan (Grant No. 2020 GK1021).  
The study did not require ethical approval.  
Not applicable.  
Data available on request from the authors. The data that support the findings of this study are available from the corresponding author, upon reasonable request.  
The authors declare no conflict of interest.
## 2 Introduction of Elastic Navigation and Positioning System
### Receiving Terminal  
Taking a centimeter-level positioning chip and module as the core, combined with inertial navigation, geomagnetism, Wi-Fi, Bluetooth and other sensors and integrated, an elastic PNT positioning terminal is developed. An elastic PNT positioning terminal is composed of a core module and an expandable module. The core module motherboard is mainly composed of an interface unit, an overvoltage and undervoltage protection unit, a lithium-battery-charging management unit, a voltage rise and fall conversion unit, a linear power conversion unit, a navigation and positioning module, an inertial device, a Bluetooth module, and Wi-Fi and 5G modules, and various expansion module interfaces  
Figure 1: Elastic PNT positioning principle.  
are reserved. The extensible modules mainly include: a UWB module, camera, lidar, etc. The composition principal block diagram of the terminal is shown in Figure 2.
## 3 Data Collection and Processing
### Coupling Mechanism Analysis
#### 3.1.2 Analysis of Blast Furnace Gas Flow Mechanism  
The formation mechanism of the blast furnace gas flow encompasses various aspects of gas movement within the furnace. Figure 4 illustrates the key influencing factors associated with the gas flow dynamics [20; 21; 22; 23]:  
1. Air outlet status: The air outlet status represents a fundamental parameter in blast furnace operation, as it directly determines the speed and direction of gas entry into  
Figure 3: Blast furnace permeability-related factors.  
the furnace. Consequently, it significantly influences the overall gas flow dynamics within the blast furnace.
2. Cylinder structure: The configuration of the blast furnace cylinder plays a crucial role in gas flow dynamics. This includes factors such as the shape of the furnace body, the design of the cylinder pipes, and the distribution of slag and iron within the furnace.
3. Blast furnace charge: The physical and chemical properties of the blast furnace charge, as well as the distribution and arrangement of the charge, will affect the gas flow. For example, parameters such as bonding, humidity, and granularity of the charge will affect the morphology of the particle pile and the gas flow capacity.
4. Blast furnace parameters: The temperature distribution and pressure conditions within the blast furnace directly impact the gas flow patterns. For instance, the upper section of the furnace experiences higher gas temperatures and lower gas densities, resulting in an upward gas flow, whereas the lower section exhibits a downward gas flow. The flow direction and speed of blast furnace gas are directly affected by the pressure in the furnace.  
The formation mechanism of blast furnace gas flow is a complex interplay of multiple factors, including the condition of the air outlet, the structure of the furnace cylinder, the composition of the blast furnace charge, and various parameters such as furnace temperature and pressure. The control of these factors can effectively improve the flow performance of the blast furnace gas stream, thus ensuring the normal production of the blast furnace.
# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 5 Results and Evaluation
### Different Sampling Frequencies  
Since the microphone array receives time-domain data with a fixed 3 s sampling time, our model does not involve the traditional \"number of snapshots\" concept in DOA estimation. However, sampling frequency affects the clarity of the received sinusoidal signal, making it meaningful to investigate its impact on model performance. A total of 1000 groups of random test data were generated under different sampling frequencies and input into the three models trained with the same array element count \(M\) and SNR. The neural networks predicted the angle of arrival for each dataset, and RMSE was calculated. Our model remained the best-performing, as shown in Figure 9. The average prediction error and RMSE of all models decreased as sampling frequency increased, but the decline in RMSE slowed significantly with further frequency increases.  
To further evaluate the stability of model predictions under different sampling frequencies, the standard deviations of RMSE values were calculated based on 10 independent prediction runs for each setting. As presented in Table 6, the standard deviation analysis across varying sampling frequencies reveals that TSNetIQ consistently achieves the lowest  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**ModelSNR** & **\(-\)20** & **\(-\)15** & **\(-\)10** & \(-\)5** & **0** & **5** & **10** & **15** & **20** \\ \hline TSNetIQ & 5.63 & 3.59 & 1.15 & 0.55 & 0.29 & 0.14 & 0.1 & 0.07 & 0.06 \\ ResNetIQ & 6.38 & 4.43 & 2.73 & 1.13 & 0.57 & 0.43 & 0.17 & 0.14 & 0.1 \\ CNN & 6.48 & 4.5 & 3.66 & 2.79 & 1.09 & 0.73 & 0.75 & 0.44 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Standard deviation of RMSE over 10 predictions under varying SNRs.  
Figure 9: RMSE results of three models under different sampling frequencies.  
variability in RMSE measurements, with values remaining stable at around 0.10 to 0.14. This indicates a high level of robustness and reliability, regardless of the sampling rate. ResNetQ exhibits moderate variance, decreasing progressively from 0.43 at \(f_{s}\) to 0.13 at \(5f_{s}\), suggesting an improved stability with increased sampling frequency. In contrast, the CNN model demonstrates the highest standard deviations across all settings, ranging from 0.73 to 0.30, highlighting its greater sensitivity to the input sampling conditions. Overall, TSNetIQ outperforms the other models in maintaining consistent performance under varying frequency conditions, underscoring its suitability for practical deployment where hardware sampling rates may vary.
# Advanced and Potential Methods for Extraction of Bioactive Compounds from Avocado Peel--A Review
## 5 Potential Techniques for Extraction of Bioactive Compounds from Avocado Peel
### Surfactant-Mediated Extraction (SME)  
Surfactant-Mediated Extraction (SME), which uses surfactants to remove phenolic chemicals, is also a promising new method [103]. Surfactants can produce monomolecular layers on a liquid's surface, lowering the interfacial tension between two liquids and allowing them to mix. SMEs may be able to isolate molecules with a wide range of polarities and complex chemical structures. SME was compared to EAE and PLE to isolate total phenolic compounds and phlorotannins from the brown seaweed _Lobophora variegata_ by [PERSON] et al. [104]. It was shown that SME produced more total phenolics and phlorotannins than EAE and PLE. However, until now, no study has focused on the use of SMEs for extracting bioactive compounds from avocado peel.
## 4 The Proposed Finger Photo Color Net (FPCN)
### Evaluation Procedure  
To assess the proposed framework, the performance metrics defined in the ISO/IEC 30107-3 standard on biometric presentation attack detection were referenced, partly related to testing and reporting performance metrics for evaluating biometric presentation attacks. The assessment scheme is reported below:  
* Attack Presentation Classification Error Rate (APCER): Proportion of attack presentations incorrectly classified as normal presentations, i.e., false acceptance of spoof samples.
* Normal Presentation Classification Error Rate (NPCER): Proportion of normal presentations incorrectly classified as attack presentations, i.e., false rejection of live samples.
* Equal Error Rate (EER): The intersection point of the percentage of normal presentation classification error rate and attack presentation classification error rate.
* Receiver Operating Characteristic (ROC) curves to assess the accuracy.  
The proposed framework is trained on both natural and white backgrounds in different lighting conditions (i.e., Indoor and Outdoor). The data were partitioned into 50% training, 20% validation, and 30% testing sets with subjects mutually exclusive.  
The experiments were carried out using Argo Cluster at George Mason University [34]. Argo Cluster is a high-performance batch computing resource provided to all the students and faculty of George Mason University. It was assembled in 2013, which has 75 compute nodes totaling 1496 cores and over 8 TB of memory. The cluster is managed by two head nodes, Argo-1 and Argo-2, which is assigned to the user in a round-robin fashion upon login. Sixty-nine compute nodes have dual Intel processors with 64 to 512 GB RAM. There are six GPU compute nodes powered by V100 GPUs with 28 core CPUs, 32 GB onboard memory per GPU750 GB to 1.5 TB RAM per node, equipped with specialized libraries for Pytorch, which were used for this research.  
Figure 6: Examples of finger photos acquired from live human fingers with white and natural background as well as of display spoof attacks.
# Personal Picocell Scheme Using Adaptive Control CRE in Heterogeneous Mobile Networks +
## 1 Introduction
[20], we presented user throughput in multi-band HetNets using 2.0 and 3.4 GHz by means of a MCS up to 256-QAM. In this study, we evaluate user throughput of adaptive control CRE comprehensively for both single- and multi-band HetNets under the same MCS condition, i.e., MCS up to 256-QAM [21; 22; 23; 24]. The key contributions of this work are summarized as follows. * We propose a new concept of a personal picocell scheme in HetNet and propose an adaptive control CRE technique to realize the personal picocell scheme through the use of two different CSOs. * We first clarify the optimal parameters of the proposed adaptive control CRE for single- and multi-band HetNets. We then show the average and 5-percentile user throughput of the optimized adaptive control CRE and compare them with those of conventional CRE. This paper is organized as follows. In Section 2, we introduce the concept and architecture of the personal picocell scheme and adaptive control CRE using two different CSOs. In Section 3, we discuss the parameter optimization of the adaptive control CRE, and show the user throughput of the optimized adaptive control CRE in single-band HetNet in comparison with the conventional method. In Section 4, we show the user throughput of the optimized adaptive control CRE in a multi-band HetNet. Finally, we conclude our work in Section 5.
# Application of Quantum Dots for Photocatalytic Hydrogen Evolution Reaction
## 2 Quantum Dots-Based Photocatalyst for Hydrogen Evolution
### Semiconductor-Quantum-Dot-Based Hybrid Photocatalyst
#### 2.2.1 Metal-Chalcogenide-QD-Based Hybrid Photocatalyst
The photocatalytic H\({}_{2}\) evolution activity of the CuInS\({}_{2}\)/CN composite is two times greater than that of pure CN, and the improved performance is due to the increased efficiency in separating photogenerated electron-hole pairs and the enhanced capacity to absorb visible light in the CuInS\({}_{2}\)/CN hybrid photocatalyst. [PERSON] et al. [42] reported Zn-Ag-In-S QDs combined with a 2D MoS\({}_{2}\) nanosheet for hydrogen generation. The photoluminescence studies show the rapid migration of photogenerated charge carriers between Zn-Ag-In-S QDs and MoS\({}_{2}\) nanosheet, which result in a remarkably high photocatalytic efficiency, with the H\({}_{2}\) production rate of 40.1 mmol g\({}^{-1}\)\(\cdot\)h\({}^{-1}\).
## 3 Results and Discussion
### Volatile Compounds Analysis of Withered Leaves under Different Shaking Combinations
#### 3.3.2 Analysis of Key Aroma Components  
The key aroma components of withered leaves were compared with those in black teas (Figure 5). Key floral aroma compounds in withered leaves, such as linalool, \(\alpha\)-cedrene, and nonanal, increased significantly after S, YLS, and HYS treatments (Figure 5A-C), while trans-\(\beta\)-ionone was not detected in withered leaves (Figure 5D). The content of these floral aroma compounds in both withered leaves and dry black teas was highest in YLS, suggesting that yellow-light withering further enhances the accumulation of floral substances on the basis of shaking, thereby improving black tea aroma quality. Benzeneacetaldehyde, 3-methyl-butanal, dimethyl sulfide, and cedrol also increased significantly under different shaking combinations in both witnessed leaves and dry black teas (Figure 5E-H), with YLS promoting the highest accumulation of benzeneacetaldehyde 3-methyl-butanal and cedrol and S promoting the highest accumulation of dimethyl sulfide and cedrol. The accumulation of these key aroma components in the withering process lays the foundation for the formation of tea aroma in the subsequent processing, thus facilitating the improvement in tea aroma quality.  
Figure 4: Effect of different shaking combinations on volatile components in withered leaves. (**A**) The total content of volatile components in withered leaves under different shaking combinations (mean \(\pm\) SD, n = 3). Different lowercase letters above the bars indicate significant differences at \(p<\) 0.05. (**B**) Score plot of PLS-DA (partial least squares discriminant analysis) showing the distribution of samples based on the first two principal components. Each point represents a sample, and the ellipses indicate confidence intervals for each group. Treatments: CK represents indoor natural withering, S represents shaking, YLS represents yellow light plus shaking, HTS represents high temperature plus shaking, and HYS represents high temperature with yellow light plus shaking.
## 2 Materials and Devices
Hence, it is a much less mature technology, but is recognized as a major step forward for the same reasons pointed out previously for the fuel cells, that they are low-cost devices [64; 65; 66]. Additionally, as with the fuel cells, many variations exist, with the ability to use different materials and operating conditions in these systems (low and high temperature water electrolysis are also possible). However, the electrochemical operating principles are fundamentally the same, as represented in Figure 5 for the PEM- and AEM-based electrolyzers (PEME and AEME), as well as the traditional alkaline electrolyzers (AE). In the latter case, one should mention that it employs robust ZrO\({}_{2}\)-based diagrams (separator) and nickel coated stainless-steel as the electrodes, being a much less expensive device than the PEME. However, significantly higher current densities are achievable for operating a PEME for high energy conversion efficiency (80-90%) [67]. In turn, this leads to more compact systems and the bright prospect of PEME production rates, along with their higher cost and extreme degradation of catalysts (even precious metals with low loading from the Platinum-group metals). This is more significant for the O\({}_{2}\) reaction side, which simultaneously experiences the acidic and strongly oxidative environments [68; 69; 70]. As stated in the 2020 International Renewable Energy Agency (IRENA) report concerning the scaling-up of electrolyzers to meet the 1.5 \({}^{\circ}\)C climate goal [62]: _\"Each technology has its own challenges, from critical materials to performance, durability and maturity; there is no clear winner across all applications, which leaves the door open for competition and innovation driving costs down\"_. Hence, green H\({}_{2}\) and water splitting is an area of research that is developing at breath-taking speed. In Table 1, a compilation of the main characteristics of the principal water electrolyzers (previously mentioned) is provided. From a sustainability perspective, a synergy between water electrolyzers and hydrogen fuel cells used in combination can be particularly attractive for reducing the intermittent electricity production from renewable resources, via green H\({}_{2}\). Hence, water electrolyzers convert excess renewable electricity into hydrogen, and the fuel cells use this hydrogen to convert it back into electricity when it is needed. Thus, this system should lead to efficient storage and the rapid wider diffusion of the electricity to the grid [67]. One main advantage is that (in contrast with conventional batteries), the amount of energy provided can be decoupled from charging and discharging power. So, recent studies have shown that developing seasonal storage with conventional battery chemistries may be prohibitively expensive, and that H\({}_{2}\) is likely more economic for long-duration energy storage, in particular by allowing the co-production of hydrogen and electricity [71]. Figure 5: Schemes and electrochemical reactions equations for PEMEs, AEMEs, and AEs. Reproduced with permission from [44].
## 3 Dynamic Energy Consumption Model of the System
\begin{array}{l}e_{14}=e_{15}=e_{16}\\ f_{15}=f_{14}-f_{16}\end{array} \right. \tag{12}\]  
\[\left\{ \begin{array}{l}e_{17}=e_{16}+Se-e_{18}\\ f_{16}=f_{17}=f_{18}=f_{19}\end{array} \right. \tag{13}\]  
\[\left\{ \begin{array}{l}e_{20}=e_{21}=e_{22}\\ f_{21}=f_{20}-f_{22}\end{array} \right. \tag{14}\]\[ \begin{cases}e_{24}=e_{23}-e_{25}\\ f_{23}=f_{24}=f_{25}\end{cases}  \tag{15}\]  
The state equation of the system can be obtained by combining Equations (2)-(15) as follows:  
\[\left\{ \begin{array}{l}\dot{q}_{2}=Sf-\frac{1}{L_{4}}\cdot p_{4}\\ p_{4}=\frac{1}{C_{2}}\cdot q_{2}-\frac{R_{4}}{L_{4}}\cdot p_{4}-\frac{1}{m_{1} \cdot C_{8}}\cdot q_{8}\\ \dot{q}_{8}=\frac{1}{m_{1}\cdot L_{4}}\cdot p_{4}-\frac{1}{l_{10}}\cdot p_{10} \\ \dot{p}_{10}=\frac{1}{C_{8}}\cdot q_{8}-\frac{R_{11}}{l_{10}}\cdot p_{10}- \frac{1}{m_{3}\cdot C_{20}}\cdot q_{20}-\frac{1}{m_{2}\cdot C_{15}}\cdot q_{15 }\\ \dot{q}_{15}=\frac{1}{m_{2}\cdot l_{10}}\cdot p_{10}-\frac{1}{l_{17}}\cdot p_{ 17}\\ \dot{p}_{17}=Se+\frac{1}{C_{15}}\cdot q_{15}-\frac{R_{18}}{l_{17}}\cdot p_{17 }\\ \dot{q}_{21}=\frac{1}{m_{3}\cdot l_{10}}\cdot p_{10}-\frac{m_{4}}{2a}\cdot p_{ 24}\\ \dot{p}_{24}=\frac{m_{4}}{C_{21}}\cdot q_{21}-\frac{R_{25}}{l_{24}}\cdot p_{24 }\end{array} \right. \tag{16}\]  
Based on the aforementioned equations, one can derive the state equation of the system. The formal expression of such an equation can be represented as Equation (17), where **X** signifies the state variables of the system, **A** represents the system matrix, **B** denotes the control matrix, and **U** symbolizes the input to the system. \[\dot{\textbf{X}}=\textbf{A}\cdot\textbf{X}+\textbf{B}\cdot\textbf{U} \tag{17}\]  
In accordance with the causal relationship illustrated in Figure 7, it is apparent that the speed of rotation observed by the motor, expressed as sf, serves as an input to the state equation. By selecting q\({}_{2}\), p\({}_{4}\), q\({}_{8}\), p\({}_{10}\), q\({}_{15}\), p\({}_{17}\), q  
The energy transmitted is  
\[E(t)=\int^{t}P(t)dt \tag{23}\]  
For electric drive traction systems, the total energy \(E_{in}\) (J) transmitted by the motor can be expressed as follows. \[E_{in}=\int^{t}Sf\cdot e_{1}dt=\int^{t}Sf\cdot\frac{q_{2}}{C_{2}}dt \tag{24}\]  
Similarly, the useful work of a system is the energy transmitted to the corrective device and helicopter. The effective energy consumption \(E_{out}\) (J) of the transmission system can be expressed as follows. \[E_{out}=\int^{t}e_{17}\cdot f_{17}dt=\int^{t}Se\cdot\frac{p_{17}}{I_{17}}dt \tag{25}\]
# Experimental Study on Particle Size Distribution Characteristics of Aerosol for Fire Detection
## 4 Discussion  
The comparison from Figure 3 to Figure 6 shows that there is a huge difference in the distribution range of fire smoke and non-fire aerosols. Most of the fire smoke particles are concentrated at the submicron level, with the particle sizes ranging from tens to hundreds of nanometers, and most smoke particles have no particle or only a small number of particles above 1 \(\upmu\)m. In contrast, the particle size of most non-fire aerosol samples is in the micron scale and concentrated in the range of 1-10 \(\upmu\)m. Comparing the particle size distribution of fire smokes and non-fire aerosols, it is obvious that these two kinds of aerosols have different particle size distribution ranges. Specifically, the particle size of non-fire aerosols is generally much larger than that of fire smokes. According to the Mie scattering theory, non-fire aerosols with larger particle size tend to have a stronger light scattering ability than small fire smokes, which would trigger false alarms of photoelectric fire detectors more easily.  
The experimental results indicate that typical fire smokes and interference aerosols can be differentiated around 1 \(\upmu\)m. Although some of the dust particles contain particles smaller than 1 \(\upmu\)m, the proportion of sub-micron particles in these aerosols is very low. As discussed above, fire smoke and non-fire aerosols have different particle size distribution characteristics, which are closely related to light scattering characteristics.