esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Plant Density Prediction and Validation
Then, program codes were implemented to assess the plant density of the entire GSI with multiple components (specifically, bioretention at the back side of the CBEIS building). As shown in Figure 4, the results indicated that the plant density was 75.30% on 27 May 2021, and 74.51% on 28 June 2021. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Components** & **Canopee (\%)** & **Programming Code (\%)** & **Threshold** & **Category** \\ \hline Tree1 & 96.51 & 95.50 & 40 & \\ Tree2 & 69.97 & 65.90 & 70 & Tree \\ Tree3 & 43.09 & 42.19 & 85 & \\ Grass1 & 94.69 & 93.03 & 50 & \\ Grass2 & 100.00 & 100.00 & 40 & Grass \\ Grass3 & 95.84 & 94.20 & 30 & \\ Soil1 & 0.02 & 0.03 & 105 & \\ Soil2 & 2.51 & 2.65 & 113 & Soil \\ Soil3 & 4.05 & 4.49 & 131 & \\ Unhealthy\_Tree1 & 0.14 & 0.13 & 231 & \\ Unhealthy\_Tree2 & 5.47 & 4.96 & 180 & Unhealthy tree \\ Unhealthy\_Tree3 & 0.79 & 0.80 & 131 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of error estimations for GSI components. The number of each component (e.g., Tree1 vs. Tree2 vs. Tree3) of GSIs represents the different images collected at different regions. Threshold was used to identify pixels between plants and non-plants. Figure 4: Plant density prediction of bioretention at MSU Campus. Normally, there might be slight increase in vegetation growth and plant cover from May to June. However, this study found a slight decrease in plant density, which was attributed to the expansion of a stagnant water area (highlighted in red arrow) and inadequate maintenance of GSIs. In the meantime, expansion of the stagnant area was also observed by visual inspection. The minor differences in plant density may also be attributed to variations in growth stages in the previous study. In the previous study, the impact of growth stage on mean plant counting was investigated and the results indicated the significance of shape and size analysis of corn plant canopies/density on population estimation [20]. In addition, high-resolution imagery has been employed to estimate wheat plant density at early stages in external studies. Thus, growth stage and time point should be considered during the plant density identification. Previous studies have primarily focused on agriculture plants, making this study the first implementation on plants within GSIs. Therefore, this study establishes a framework for estimating plant density within GSIs. These preliminary findings also provide an insight into the performing of future studies to collect additional UAV-images monthly and annually to monitor the real-time changes in plant growth and to identify the necessary proper maintenance plans for the GSIs.
# A Case Study on Integrating an Eco-Design Tool into the Construction Decision-Making Process
## 2 Methodology
### UAveiroGreenBuilding Eco-Design Tool
#### 2.1.2 Materials Input Flow  
Information on input materials in construction must be provided to compare competitive solutions by product category. The data entered into the eco-design tool comprise several parameters extracted from the database structure by selecting the EPDs of the materials under analysis. The tool generates an Excel sheet compiling the information on environmental impacts, resource consumption, and potential output material flows. The collected data on environmental impacts and resource consumption are then normalized by dividing individual values by the maximum value observed among equivalent products in each impact category. This normalization step serves the purposes of standardizing all impact categories on a dimensionless scale ranging from 0 to 1, without being influenced by the nominal values and units of individual impact categories as present in the EPDs.  
The normalized data are processed by the eco-design tool to generate radar charts for easy visualization of the environmental performance of different products and to calculate the overall impact area to provide a numerical indicator for comparison. In the current version of this tool, the impact areas and resource consumption areas were determined as proposed by [36], according to Equation (1):  
\[\text{Total Area}=0.5*\sum_{i=1}^{n}r_{i}*r_{i+1}*sin\left(\frac{360}{n}*\frac{ \pi}{180}\right) \tag{1}\]  
where \(n\) stands for the number parameters, and \(r_{i}\) and \(r_{i+1}\) stand as the dimensionless values of adjacent environmental/resource consumption indicators.  
The economic profile of competitive solutions was also examined by the eco-design tool by considering both acquisition and maintenance costs. Maintenance costs have been estimated over a pre-defined lifespan and by considering the periodicity and unit cost of maintenance actions according to Equation (2):  
\[\text{Life cycle maintenance cost}(\mathbf{\hat{\epsilon}})=\frac{\text{Cost per action}(\mathbf{\hat{\epsilon}})*\text{Estimated life (years)}}{\text{Periodicity (years)}} \tag{2}\]
## 4 Results
### General Results  
The user's subjective workload assessment was performed using two tests: NASA-TLX and SUS. The results are presented in Table 2.  
For cases when solving the exercises without the feedback mechanisms, the results of the SUS test were found to be below the acceptable level of 70% (i.e., 65%). According to the authors, [PERSON] et al. [50], anything below 70% has usability issues and creates a cause for concern. The use of the developed method with and without the pre-exploration phase increases the level of system usability. Additionally, the use of the proposed feedback mechanism mode also slightly increases this level.  
The NASA-TLX rating scale is a multidimensional assessment tool that enables the participants to rate cognitive loads across six subscales: mental resource demand, physical resource demand, temporal demand, effort, performance, and task frustration. In this study, the participants who were blind solved exercises without any imposed time limit and, thus, time pressures were not evaluated. The results demonstrated that the proposed method significantly reduced the exercise load. The type of mode, with or without feedback mechanisms, also affected the results. When comparing the results of individual categories, the demand for physical load decreases the most. Students rated their performance as improving, directly resulting from the proposed method. In both the effort and frustration categories, there was a similar reduction in task load.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Name** & \
\begin{tabular}{c} **Solving the Exercises without** \\ **Feedback Mechanisms** \\ \end{tabular} &
\begin{tabular}{c} **Solving the Exercises with** \\ **Feedback Mechanisms** \\ \end{tabular} \\ \hline SUS & 65 & 78 \\ (total score) & 73 & 61 \\ NASA-TLX & 74 & 62 \\ (total score) & 93 & 76 \\ Mental Demand * & - & - \\ Performance & 50 & 40 \\ Frustration & 74 & 63 \\ Effort & 75 & 62 \\ \hline \hline \end{tabular}  
* During the research we assumed that a blind person was solving exercises without the imposed solving time, and therefore, the time pressure was not assessed in the test.  
\end{table}
Table 2: Results of the SUS and NASA-TLX tests.
## 4 Experimental Results and Discussions
### Optimal Neighborhood Scale Results and Analysis  
The optimal neighborhood scale was determined by maximizing the F1-score across various values of k. Table 3 presents the recall, precision, and F1-score for various k-values across both datasets. For Dataset 1, the highest F1-score of 91.18% was achieved with a k-value of 5. For dataset 2, the highest F1-score of 97.84% was obtained with a k-value of 10. Therefore, the optimal neighborhood scales for the two datasets are 5 and 10, respectively. The variation in optimal scales between the two datasets may be attributed to the larger minimum point spacing in Dataset 2, which requires a larger scale to capture a sufficient number of neighborhood points.
## 2 Underwater Sensor Networks
### Underwater Platforms  
The hardware includes underwater sensor nodes, surface sinks and mobile Autonomous Underwater Vehicles (AUVs), as shown in Figure 4[7]. Each sensor node is equipped with a processor, memory, storage and an acoustic modem. Nodes also include temperature sensor, pressure sensor, a gyroscope and a camera to monitor their environments [8]. The sensor nodes communicate with each other using a Time Division Multiple Access (TDMA) protocol to avoid signal collisions. The signal propagation speed of an underwater acoustic channel is about 1.5 \(\times\)10\({}^{3}\) m/s, which is much lower than the Radio  
Figure 3: Cross layer protocol stack.  
Figure 2: The attenuation of the acoustic signal.  
Frequency (RF) propagation speed (3.0 \(\times\)10\({}^{8}\) m/s) [9]. The data rate is less than 8,000 bits/s because the available bandwidth of underwater acoustic channels is limited and depends on both the transmission range and the frequency [10]. These limitations are caused by the absorption of acoustic signals that operate below 30 kHz. The features of underwater acoustic channels are a long propagation delay, limited available bandwidth and high error probability [11].  
An autonomous underwater vehicle (AUV) is a vehicle which travels underwater without requiring commands from an operator. AUVs include sensors to prevent collisions, propulsion devices to move them forward, and batteries to supply power. They collect data from head nodes as they travel among head nodes. A surface sink node is attached onto a floating buoy with satellite or RF capabilities to transmit data to an onshore control center. It aggregates data sent from head nodes by using the acoustic signal. To reduce propagation delay, the surface sink connects to the acoustic modem by wire near the bottom of the ocean. The onshore control center manages and monitors the entire network.
# L2-CalSat: A Calibration Satellite for Ultra-Sensitive CMB Polarization Space Missions
## 6 Conclusions  
A first design has been carried out for L2-CalSat, a calibration satellite for CMB space missions at the second Lagrange point of the Sun-Earth system. This design is in compliance with the requirements and objectives of a small-aperture CMB polarization mission, but can also be adapted to others, both in the microwave or in a different frequency range (optical, IR, etc.). The calibration satellite design has been developed at the CDF of the IDR/UPM in Madrid (Spain), following the CubeSat conceptual frame of implementation. The satellite and calibration sources have been described and a simple but efficient and secure calibration strategy has been proposed. It has been shown that L2-CalSat would reduce the polar angle error to the level of 1 arc-minute thanks to calibration sources providing SNR values between 36 and 47 dB. Also, it would allow the characterization of both, the intensity and polarization optical main beam and far-side lobes placed at 20\({}^{\circ}\) or more from the center, thanks to NF levels lower than \(-\)72 dB provided in all the frequency range of interest. This level of accuracy can not be achieved by using celestial calibration sources as Tau-A or Jupiter due to the limited knowledge of the polarised emission and the reduced access time for intensity and polarization beam calibration, which affects mainly at lower frequencies. As a consequence, the use of a calibration system as the one proposed here would allow the exploitation of the high sensitivity provided by current and future CMB instrumentation and the achievement of ambitious scientific results.  
[PERSON]: Setting of technical requirements; First design of calibration source (payload); thermal, power budget and calibration performance analysis; Writing of the paper. [PERSON], [PERSON] and [PERSON]: Setting of scientific and technical requirements; Analysis of calibration impact; Advice on microwave polarization experiments; Writing of the paper. [PERSON], [PERSON], [PERSON] and A.S.: CubeSat first design; Thermal modeling and simulation; Formation flying, access time and viewed detectors modeling and simulation; Writing of the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Agencia Estatal de Investigacion (AEI, MICIU) by means of the projects with references ESP2017-92135-EXP, ESP2017-83921-C2-1-R, AYA2017-90675-REDC, and PID2019-110610 RB-C21/AE1/10.13039/501100011033 co-funded with EU FEDER funds. We also acknowledge the funding from Unidad de Excelencia Maria de Maeztu (MDM-2017-0765). We make use of the HEALPix[58] and CAMB[59] codes.  
The authors declare no conflict of interest.
# Exploring Multi-Stage GAN with Self-Attention for Speech Enhancement
## 5 Results and Discussion
### Ablation Study  
To understand the effect of the self-attention blocks in the ISEGAN-Self-Attention and the DSEGAN-Self-Attention networks, we ablate the generators in each network to observe the contribution of the self-attention in multi-generator speech enhancement GAN in generating enhanced speech signals. In this study, our focus was to find out which generator with the self-attention blocks in the multi-generator of the networks has the most contribution to the networks' performance. For instance, if the self-attention blocks are only placed in the first generator, how does it contribute to enhancing the speech signals? Here, we only considered the setup for \(N=2\) for both the ISEGAN-Self-Attention and the DSEGAN-Self-Attention. This choice was made on the basis that the setup for \(N=2\) produced the best results, in both quality and intelligibility of the generated signals, among all the experiments we performed. With \(N=2\), we had 4 different cases to investigate (i.e., 2 cases for each network) by removing all the self-attention blocks from one of the two generators in the network to observe the contributions of the remaining self-attention blocks. For example, in the setup for case 1 in the ISEGAN-Self-Attention network, we removed all the self-attention blocks in the second generator and trained the network with the same training settings mentioned in Section 4.  
In Table 3, we present the results of the study and compared them with ISEGAN and DSEGAN results. We observed that, in both ISEGAN-Self-Attention and DSEGAN-Self-Attention, having the self-attention blocks in only the first generator (i.e., \(G_{1}\)) produces better-enhanced speech signals than when we place the self-attention blocks only in the second generator (i.e., \(G_{2}\)). This result indicates that the second generator \(G_{2}\) is performing refining on the predecessor generator \(G_{1}\); therefore, the first generator \(G_{1}\) is responsible for capturing the core features and generating almost-enhanced samples which the second generator \(G_{2}\) just needs to refine. Also, we observed that placing the self-attention blocks in only one generator of the ISEGAN-Self-Attention and the DSEGAN-Self-Attention networks did not show much improvement over ISEGAN and DSEGAN and, in most cases, trailed behind ISEGAN and DSEGAN, respectively. To achieve the best performance, the self-attention blocks might be integrated with more than one generator of the ISEGAN-Self-Attention and the DSEGAN-Self-Attention networks.  
Figure 7: A comparison of the input speech signal, clean speech, and the spectrogram of the enhanced speech. The speech features in the spectrograms are marked with red-, black-, and yellow-coloured rectangles for the corresponding wave signal to demonstrate the preservation of the temporal dependence in the enhanced speech signal.
## 5 Conclusions  
The fault diagnosis method which uses LMD and the ratio correction method is proposed for the short-time signals of rolling bearing. The fault signal of rolling bearing at the inner raceway and the outer raceway were analyzed by the proposed method. The results show that this method can gain a high frequency resolution, and extract the fault frequency accurately under the condition of the short-time signals. Therefore, this method has a certain value of engineering applications. The proposed method of this paper can reduce the size of data samples while ensuring accuracy. The deep learning models can directly work on the raw data without any data preprocessing. Thus, it is worthwhile to make further study of combining spectrum correction with the deep learning models for processing the short-time signals. In earthquake engineering, many methods are utilized to solve problems related to earthquakes. For example, probabilistic risk-based performance evaluation of seismically was used to analyze base-isolated steel structures under the influence of far-field earthquakes [38]. Besides, signal processing is one of the most typical applications in earthquake engineering. Seismic signals have the characteristics of non-stationary and \"short-time\" random impulses. Thus, it is worthwhile to make further study for handling the short-time signals of earthquake data by the proposed method of this paper.  
Figure 15: The sample sets \(S_{out}\): (**a**) The sampling number is 12,000; (**b**) the sampling number is 2048.  
**Author Contributions:** Conceptualization, Y.D. and C.W.; methodology, Y.D.; software, Y.D. and C.W.; validation, Y.D. and C.W.; formal analysis, Y.D., C.W. and P.L.; investigation, Y.C.; resources, Y.C.; data curation, Y.D., C.W. and P.L.; writing--original draft preparation, Y.D.; writing--review and editing, Y.D., Y.C. and C.W.; visualization, C.W.; supervision, C.W.; project administration, C.W.; funding acquisition, C.W.  
**Funding:** This research was funded by the National Key R & D Plan Program of China (2018 YFB0106100), the Sichuan Science and Technology support Program (2019 YFG0352, 2019 YFG0098, 2017 GZ0395 and 2017 GZ0394).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 7 Prospects for Future UAV Research
### Advancing Real-Time Data Processing and Transmission  
For applications like emergency response and traffic monitoring that demand quick analysis and decision-making, developing techniques for real-time data processing and transmission is essential. Future research should focus on optimizing data compression, transmission protocols, and edge computing techniques to enable swift and efficient data handling directly on UAVs.
# Near Infrared Illumination Optimization for Vein Detection: Hardware and Software Approaches
###### Abstract  
Venepuncture is one of the most crucial processes in many medical procedures. However, finding a real-time and vibrant visualization of the vein structures faces many difficulties. Several devices were introduced to solve this problem, yet, these devices shared common drawbacks, primarily when visualizing deep veins or veins in a thicker tissue of the human body. This study proposes a novel method for visualizing vein structures using a near-infrared (NIR) imaging technique enhanced with Hessian ridge detection. Several factors, including the wavelength of NIR light, square LED and ring LED arrangement and the effect of the diffuser and number of LEDs, were evaluated in the study. This study improves the overall quality of the acquired vein images and highlights the vein-morphological structure through image processing techniques. The study's main aim is to achieve the highest number of visible veins. Based on the optical window, the maximum absorption range in the NIR spectrum was found from 700 to 950 nm. The NIR light absorption of human deoxygenated blood in the vein was highest at 850 nm peak of wavelength. The image processing further enhances the vein image by highlighting the extracted vein. The study also suggests that the square LED arrangements of NIR illumination are much more robust than the ring LED arrangement in ensuring excellent light penetration. The light diffuser further adds promising effects to the NIR illumination process. In terms of the square LED arrangement, increasing the square LED for enlarging the illumination area did not show any degradation effects in the visualization process. Overall, this paper presents an integrated hardware and software solution for the NIR image acquisition of a vein visualization system to cope with the image visualization of the vein for a thicker part of the human tissue, particularly on the arm and palm area.  
infrared imaging near infrared; vein detection; NIR projection; infrared LEDs wavelength; LEDs arrangement; vein counting
# Numerical Simulation of Hot Jet Detonation with Different Ignition Positions
## 1 Introduction
So, alternatives to increase the length of hot jet tubes needed to be explored. Previous studies had shown the ignition position inside the detonation chamber was an important factor in flame propagation and formation of DDT [28; 29; 30]. [PERSON] and [PERSON] [31] numerically investigated the effects of different ignition position on DDT. They indicated that wall ignition had advantages over closed-end ignition in the initiation time and distance of DDT. [PERSON] et al. [32] studied the effects of different wall ignition positions on flame propagation and DDT by hydrogen/air experiments. The results showed that the expansion of burnt fuel against the closed section of the tube behind flame front increased flame speed and turbulence when the ignition position was a certain distance from the closed section. From these studies of the ignition position, it was clearly seen that ignition position inside the detonation chamber had a great influence on the detonation initiation time and distance, and wall ignition was more conducive to flame acceleration. Using this principle, optimization of the ignition position inside the jet tube may also obtain a fast jet velocity to reduce the detonation distance without changing the length of the jet tube. However, the influence of the different ignition positions inside the jet tube on hot jet detonation initiation had not been fully studied and needed more detailed analysis and discussion. Motivated by the above considerations, the present study performs the 2D numerical simulations to investigate the effect of different ignition positions inside the hot jet tube on DDT. Firstly, the mechanism of hot jet detonation initiation and flow characteristics are analyzed in detail through the study of the vortex-flame interaction, temperature, pressure, and velocity. Secondly, the jet parameters and flame acceleration performance of top and wall ignition are compared to investigate the reasons why wall ignition is more favorable for DDT. Finally, the wall ignition position is further optimized, it expects to lay the foundation for the design and application of the efficient and compact hot jet detonation initiation device.
## 3 Results and Discussion
### Kinetic Parameters of Sorption
#### 3.2.3. Weber and Morris Intraparticle Diffusion Model  
Figure 5 shows the applicability of intraparticle diffusion model in description of DY50 adsorption on A21, A23 and A24. The kinetic parameters calculated from the Weber-Morris equation are given in Table 2. The graphs illustrate multi-linearity, the adsorption data can be fitted with two or three straight lines. The first part of the graph can be attributed to the diffusion effect by the boundary layer while the second linear part is associated with intraparticle diffusion (diffusion into the polymer network) [65]. The slope of the second linear part is characterized by the speed parameter corresponding to the intraparticle diffusion. The values of intraparticle diffusion rate constants \(k_{i}\) increase in the range 0.441-1.732 g/mg-min\({}^{0.5}\), 0.923-5.612 g/mg-min\({}^{0.5}\) and 0.560-3.184 g/mg-min\({}^{0.5}\) with the increasing initial concentration of DY50 from 100 to 500 mg/L for A24, A21 and A23, respectively. This may be associated with a driving force that increases with the increasing dye concentration in the solution and contributes to an increase in the dye diffusion rate. The values of determination coefficients \(R^{2}\) are in the range of 0.809-0.946, 0.874-0.987 and 0.529-0.998 for A24, A21 and A23, respectively.  
Figure 5: Intraparticle diffusion model applied for adsorption of DY50 on (**a**) A21, (**b**) A23 and (**c**) A24.