esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Method for Wheat Spike Phenotyping Based on Instance Segmentation and Classification
## 3 Results and Discussion
### Counting Results of Wheat Grains  
After classifying the wheat grain quantities of the spikelets by the SVM model, this study added the number of grains in the corresponding spikelets together to automatically count the number of grains in the frontal spikes. Figure 13 shows the regression results of the true and predicted numbers of grains in frontal spikes. The regression dataset was the test dataset used to model the categories of spikelet grain quantities, and consisted of 843 spikelets and 90 spikes in three varieties and three growth periods. Each frontal spike contained 7-12 spikelets for different varieties and growth periods. Each spikelet contained 0-3 grains, and each frontal spike contained 14-26 wheat grains. As shown in Figure 13, this study correctly predicted the grain number of about two-thirds of the spikes, and the number of grains in the remaining spikes was incorrectly predicted with the maximum prediction error of four grains. The R\({}^{2}\) and MSE of the regression were 0.75 and 1.99, respectively, which proved that there was a good prediction of the number of grains in frontal spikes. Furthermore, the MAE was as low as 1.04, while the MAPE was 5%, and the two indicators indicated that the true and predicted numbers had a small degree of difference. Compared to previous research, [PERSON] et al. [20] realized an R\({}^{2}\) of 0.93, MSE of 3.13, and MAE of 2.30. Despite the relatively low R\({}^{2}\) acquired, our study showed superiority in the indicators of precision and accuracy, which are more crucial for measurement. Therefore, this study achieved a satisfactory effect in counting the number of grains in frontal spikes.
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 2 Research Methodology
### Research Questions  
The objective of this review is to examine how EMF exposure constraints can be effectively integrated into radio resource management strategies for 5G networks and beyond. As technologies such as massive MIMO, beamforming, RISs, and the mmWave spectrum become widespread, the need for EMF-aware resource allocation will have growing significantly. This survey provides a comprehensive analysis of existing methods, identifies research gaps, and highlights future opportunities to guide further development in this field. Specifically, the review addresses the following key research questions:  
* What frameworks and methodologies are available for evaluating EMF exposure in 5G networks and how effective are they?
* Which gaps in the current literature hinder the development of efficient EMF-aware radio resource management strategies for 5G and beyond?
* What are the unique challenges and potential research directions for incorporating EMF-aware radio resource management into RIS-assisted 5G networks?  
By addressing these questions, this review aims to offer insights that advance the design of safe and efficient mobile networks.
# The Impact of an Office Fire Combined with the Stack Effect in a Multi-Story Building
## 1 Introduction
On the other hand, we observe a significant gap in the current research addressing the dynamic interdependence between fire development and the stack effect in multi-story residential buildings. It is necessary to conduct full-scale numerical simulations to evaluate the fire behavior of tall buildings. These studies are essential to better understand the influence of ventilation conditions on fire growth and the intensification of the stack effect. To highlight the effects of fire on the indoor environment of a high-rise building, we performed a comparative analysis of two scenarios: S1 (normal scenario) and S2 (fire scenario). With the current research, we aim to address several questions related to changes in indoor environmental conditions:  
* What is the influence of a fire outbreak on the ground floor on the airflow and smoke movement within a 10-story building using a numerical model? * How do indoor environmental parameters, including temperature, visibility, airflow velocity, and concentrations of CO and CO\({}_{2}\) change because of a fire on the ground floor? * How effective is natural ventilation in controlling the spread of smoke and toxic gasses in a 10-story building? The following will describe the structure of the numerical model used for simulating the aforementioned phenomena: the geometry of the model, the simulation domain and discretization grid, the boundary conditions, the numerical setup, the fire source characteristics and also the results of simulation, discussion of the results, and the conclusions of the work.
## 3 Result and Discussion
### Device Implementation  
The device implementation can be looked at in Figure 7. The sensor node prototype was built with a compact and small board to be suitable for indoor CO\({}_{2}\) measurement. The size of the device is 25 \(\times\) 60 mm. The main board is composed of a processor, Wi-Fi module, and power circuit located on the bottom of the sensor node, as depicted in Figure 7a. The CO\({}_{2}\) sensor from Sensorion plug-in on the top of the main board, as shown in Figure 7b, includes a parallel of 2 units 5.5 V 5.0 F supercapacitor (Figure 7c). This configuration was considered for further development to integrate the developed sensor node with a micro-energy harvesting module coupled on the bottom of the main board.  
The developed program code was embedded in the microcontroller based on Algorithm 1. The code program will configure the sensor node using the low-power mode technique to perform CO\({}_{2}\) measurement and send the acquired CO\({}_{2}\) data to the cloud of the IoT system over a Wi-Fi connection.  
```
Start program while sensor node power is ON. Initialization Wi-Fi/internet connection. If Wi-Fi is connected successfully: Start CO\({}_{2}\) data sensing, CO\({}_{2}\) data processing, CO\({}_{2}\) data transmission, Presenting CO\({}_{2}\) data on Adafruit dashboard, Logging CO\({}_{2}\) data, Sleep mode until interval time is achieved. Or Else:
```  
Retry Wi-Fi/internet connection. Return to repeat measuring data. Stop program while sensor node power is OFF. ```  
**Algorithm 1** The logical flow of CO\({}_{2}\) measurement.  
The developed sensor node characteristic is presented in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Sensor technology & Non-Dispersive InfraRed \\ Sensing range & 0–40,000 ppm \\ Sensor accuracy & \(\pm\)(30 ppm + 3\%) \\ Operating voltage & 3.3–5.5 V \\ Power storage & 5.5 V 10 F supercapacitor \\ \hline \hline \end{tabular}
\end{table}
Table 3: A basic characteristic of the wireless CO\({}_{2}\) sensor node.  
Figure 7: The CO\({}_{2}\) sensor node prototype: (**a**) side view; (**b**) top view; (**c**) supercapacitor.
# Environmental Impact of the Natural Gas Liquefaction Process: An Example from China
## 3 Results and Discussion  
According to Table 1, China's natural gas liquefaction volume may reach 68,150,000 m\({}^{3}\) per year, and the output of LNG was about 45,768.97 t. From the perspective of an environmental impact assessment, the annual environmental impact loads of Xinjiang, Inner Mongolia, Shaanxi, Shanxi, Ningxia, Henan, Sichuan, and Hebei in the liquefaction stage of natural gas were 746,115 p-a, 4,117,479 p-a, 3,873,521 p-a, 540,931 p-a, 1,439,688 p-a, 194,030 p-a, 191,898 p-a, and 226,758 p-a, respectively. The total environmental impact load of these eight provinces was found to be 11,330,420 p-a.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Type of EI** & **Henan** & **Sichuan** & **Inner Mongolia** & **Shaanxi** & **Xinjiang** & **Shanxi** & **Ningxia** & **Hebei** \\ \hline
**GWP** & 0.0552 & 0.0495 & 0.0870 & 0.0835 & 0.0697 & 0.0537 & 0.0853 & 0.0532 \\
**AP** & 0.0051 & 0.0011 & 0.0017 & 0.0010 & 0.0101 & 0.0009 & 0.0004 & 0.0081 \\
**EP** & 0.0058 & 0.00004 & 0.0019 & 0.0010 & 0.0105 & 0.0009 & 0.0003 & 0.0092 \\
**POCP** & 0.2856 & 0.1424 & 0.8152 & 0.8721 & 0.4916 & 0.2666 & 0.0001 & 0.1567 \\
**DU** & 0.00009 & 0.00021 & 0.00016 & 0.00018 & 0.00013 & 0.00006 & 0.00009 & 0.00012 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Weightage value of the liquefied natural gas production in the eight provinces of China (per unit/kg).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Henan** & **Sichuan** & **Inner Mongolia** & **Shaanxi** & **Xinjiang** & **Shanxi** & **Ningxia** & **Hebei** \\ \hline
**Total EI** & 0.3518 & 0.1933 & 0.9060 & 0.9577 & 0.5820 & 0.3221 & 0.9322 & 0.2273 \\
**Potential** & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Total environmental impact (EI) potential of the liquefied natural gas production in the eight provinces of China (p-a).  
The environmental impact potential of natural gas liquefaction at unit output and the environmental impact potential of natural gas liquefaction at annual output were analyzed separately and are presented in the following sections.
# Integrating Digital Twins of Engineering Labs into Multi-User Virtual Reality Environments
## 4 Results
### Validation
#### 4.1.4 Questions and Answers for Students Comparing Both Experiences  
The responses to the section \"Questions and Answers for Students Comparing Both Experiences\" are presented in Figure 12, where students' feedback and insights regarding their experiences are analyzed and compared.  
When comparing the two experiences, students in both groups consistently showed a strong preference for virtual reality across all evaluated categories. One of the most significant advantages observed was the improvement in communication among participants, with a clear disparity between the two methods: 76% of students in Group 1 and 93% in Group 2 reported better communication using VR, compared to only 24% and 7%, respectively, in the traditional setting.  
Furthermore, virtual reality was widely recognized as providing a more suitable and effective learning environment. In both groups, students acknowledged that VR not only enhanced their ability to engage with the material but also fostered a more interactive and immersive experience. This, in turn, contributed to a deeper understanding of the laboratory process and reinforced its value in engineering education.  
Lastly, VR was perceived as a superior tool for mechanical engineering training, with a significant majority favoring it over the conventional approach. These findings underscore the transformative potential of virtual reality in technical education, demonstrating its capacity to improve knowledge retention, collaboration, and overall learning effectiveness.  
Figure 11: Questions and answers for Group 2.
## 3 Conversion Method for CCA-Secure KEM
### Conversion Method  
The conversion method is for a special case of KEM which is compatible with the NIZK schemes. We suppose that the KEM.Encap algorithm takes the public key pk and randomness \(x\) as inputs and returns a key \(\mathsf{k}\) and a ciphertext \(\mathsf{ct}\) (e.g., \((\mathsf{k},\mathsf{ct})\leftarrow\text{KEM}.\text{Encap}(\text{pk};x)\)). Then, we say that a KEM is NIZK-_compatible_ if a tuple \((\text{pk},\mathsf{ct},\mathsf{k})\) can be parsed as the statement for proving a relation for discrete logarithms using the witness \(x\). For example, the ElGamal KEM and the linear KEM [21] are NIZK-compatible [12].  
Let KEM\(=(\text{KEM}.\text{Setup},\text{KEM}.\text{Encap},\text{KEM}.\text{Decap})\) be an IND-CPA-secure KEM and NIZK-compatible. Using the KEM and NIZK scheme, the generic construction of IND-CCA-secure KEM\({}^{\prime}=(\text{KEM}.\text{Setup}^{\prime},\text{KEM}.\text{Encap}^{\prime},\text{KEM}. \text{Decap}^{\prime})\) by [PERSON] et al. [12] is described as follows.  
\[\begin{array}{l}\textbf{KEM}.\textbf{Gen}^{\prime}(\lambda).\end{array}\]  
Given security parameter \(\lambda\), the setup algorithm proceeds as follows.  
1. Generate \(\mathsf{sk}\) and \(\text{pk}\) by running \(\text{KEM}.\text{Setup}(\lambda)\).
2. Choose \(H_{1}:\{0,1\}^{*}\rightarrow\{0,1\}^{k}\) and \(H_{2}:\{0,1\}^{*}\rightarrow\{0,1\}^{\ell}\) for \(k,l\in\mathbb{Z}^{+}\).
3. Output \(\text{SK}=\mathsf{sk}\) and \(\text{PK}=(H_{1},H_{2},\text{pk})\).  
\[\begin{array}{l}\textbf{KEM}.\textbf{Encap}^{\prime}(\text{PK}).\end{array}\]  
Given a public key \(\text{PK}=(H_{1},H_{2},\text{pk})\), the Encap algorithm proceeds as follows.  
1. Choose the random coins \(x\) and compute \((\mathsf{ct},\mathsf{k})\leftarrow\text{KEM}.\text{Encap}(\text{pk};x)\).  
\begin{tabular}{l l}
2. & Set \(\mathsf{stmt}=(\mathsf{pk},\mathsf{ct},k)\) and \(\mathsf{wt}=x\). \\
3. & Compute \(\pi\leftarrow\mathcal{P}^{H_{1}}(\mathsf{stmt},\mathsf{wt})\). \\  & (a) & Choose the random value \(r\) and compute \(\mathsf{com}\). \\  & (b) & Compute \(c=H_{1}(\mathsf{stmt},\mathsf{com})\) and the relevant \(s\). \\  & (c) & Output the proof \(\pi=(s,c)\). \\
4. & Set \(\mathsf{K}\gets H_{2}(\mathsf{stmt},\mathsf{com},\pi)\). \\
5. & Output \(\mathsf{K}\) and \(\mathsf{CT}=(\mathsf{ct},\pi)\). \\
**KEM.Decap\({}^{\prime}(\mathsf{PK},\mathsf{SK},\mathsf{CT})\)**: Given a public key \(\mathsf{PK}=(H_{1},H_{2},\mathsf{pk})\), a ciphertext \(\mathsf{CT}=(\mathsf{ct},\pi=(s,c))\), and a private \(\mathsf{SK}=\mathsf{sk}\), the Decay algorithm proceeds as follows. \\
1. & Compute \(\mathsf{k}\leftarrow\mathsf{KEM.Decap(sk,ct)}\). \\
2. & Set \(\mathsf{stmt}=(\mathsf{pk},\mathsf{ct},k)\). \\
3. & If \(1\leftarrow\mathcal{V}^{H_{1}}(\mathsf{stmt},\pi)\), go on. Otherwise, abort. \\  & (a) & Deterministically compute \(\mathsf{com}\). \\  & (b) & If \(c=H_{1}(\mathsf{stmt},\mathsf{com})\), output \(1\). Otherwise, output \(0\). \\
4. & Set \(\mathsf{K}\gets H_{2}(\mathsf{stmt},\mathsf{com},\pi)\) and output \(\mathsf{K}\). \\ \end{tabular}
# Numerical Studies on Rockbursts in Tunnels with High In Situ Stresses and Geothermally Rich Areas
## 3 Model of Tunnel Excavation
### Setting of the Model Calculation Parameters  
Section 2 demonstrates that cooling may change the stress state of rock and cause rock damage. Next, we numerically simulate the temperature and stress evolution in the surrounding rock caused by cooling in geothermally rich tunnel excavation and then discuss the effect of cooling on rockbursts.  
The overall size of the numerical model is 200 m \(\times\) 200 m \(\times\) 200 m, and the radius of the tunnel is 5 m, located at the center. The mechanical boundary conditions of the model consist of limiting the z-direction displacement of the back surface, the x-direction displacement of the bottom surface, and the x-direction and y-direction displacements of the center point of the bottom surface. In situ stress is applied on the other four outer surfaces without a displacement boundary (see Figure 5a). The initial temperature of the whole model is set to 80 \({}^{\circ}\)C. The adiabatic boundary is used for the other 6 outer surfaces of the model. A convective heat transfer temperature boundary is adopted for the working face and tunnel surface, and the ambient temperature is set to 20 \({}^{\circ}\)C (see Figure 5b). The overall model is shown in Figure 5a,b.  
According to the existing geological survey data [20] of the Sichuan-Tibet railway, the material properties of the rock are set as follows: an elastic modulus of 50 GPa, a Poisson's ratio of 0.24, a density of 2500 kg/m\({}^{3}\), and a thermal expansion coefficient of rock of 8 \(\times\) 10\({}^{-6}\)\({}^{\circ}\)C\({}^{-1}\). The thermal conductivity of the rock is 1.9 W/(m\(\cdot\)K), and the specific heat is 750 J/(kg\(\cdot\)K). The temperature at the initial moment of the model is 80 \({}^{\circ}\)C, the convective heat transfer coefficient at the working face and tunnel surface is set to 15 W\(\cdot\)m\({}^{-2}\cdot\)K\({}^{-1}\), and the ambient temperature is 20 \({}^{\circ}\)C. \(\sigma_{ver}\) is set to 25 MPa, \(\sigma_{hor}\) is set to 20 MPa, and \(\sigma_{axi}\) is set to 30 MPa.  
At the same time, another large tunnel model (R = 10 m) is established and compared with the above tunnel model (R = 5 m) to discuss the influence of size on the tunnel rockburst tendency (see Figure 6). All parameters of the model (R = 10 m) are consistent with the original model (R = 5 m).  
Figure 5: Schematic diagram of the model and boundary conditions. (**a**) The setting of mechanical boundary conditions. (**b**) The setting of temperature boundary conditions. (**c**) The length and distribution of four different paths. (**d**) The names of the different locations of the tunnels.  
Of course, the results of numerical calculation may be different from those of the actual project, but the overall change trend is consistent with the reality. In this paper, the stress evolution process of the surrounding rock is briefly analyzed by a numerical method, and the influence of model accuracy is not further studied.
# Piezoelectric Transformers: An Historical Review
## 3 Other Applications of Piezoelectric Transformers
### Piezoelectric Transformers for Isolation Feedback and Gate Driver Applications  
There have been several publications recently on the use of piezoelectric transformers to isolate feedback circuits [126; 127; 128], as well as for driving the gate of Mosfet transistors while providing isolation. This topic was initially considered in the 1980s [46; 47] and there is now renewed interest with innovative technologies. Piezoelectric transformers provide an excellent isolation means, simultaneously providing power and signal transfer to drive the gate of transistors. Figure 24 exemplifies the use of PTs for control of the gate of a Mosfet. In this case, the control gate signal is modulated to the resonance frequency required for driving the PT. In the output of the PT, a demodulator circuit, which may consist of a rectifier circuit, converts the signal back into the input control signal.
# Attention-Based Two-Branch Hybrid Fusion Network for Medical Image Segmentation
## 2 Related Work
### CNN and Transformer
Consequently, efforts have been made to integrate the strengths of both CNN and Transformer. For instance, TransuNet [23] proposed a mixed CNN and Transformer encoder along with a multi-stage upsampling decoder for better global context modeling in medical image segmentation. In contrast, SwinUnet [26] is a mirror-symmetric, U-Net inspired transformer-based model for medical image segmentation, featuring a Swin Transformer block and patch expanding layer in its design. Additionally, Medical Transformer [27] introduces a transformer architecture equipped with gated axial attention blocks to efficiently train on medical images. Meanwhile, TransFuse [28] employs an innovative dual-branch approach combining ViT with ResNet to enhance global context modeling without losing low-level local detail, demonstrating significant advantages in pathological image segmentation research [29; 30].
## 3 Results
### Impact on GPP of Mangrove Forest  
The damage brought by SuperTY Sarika to the mangrove GPP in Danzhou Bay lasted for 2 years, and it had recovered to the pre-disturbance state. In the two years, if the mangrove had not been affected by SuperTY Sarika, the GPP of 1683.65 \(\pm\) 362.85 and 1812.04 \(\pm\) 387.78 gC/m\({}^{2}\)/yr could have been generated. On average, the annual loss of GPP could reach 143.29 \(\pm\) 84.89 gC/m\({}^{2}\)/yr, and the most severe area could exceed 600 gC/m\({}^{2}\)/yr which was more than half of the multi-year average (loss rate of GPP over 65% and a total of 1294.02 gC/m\({}^{2}\) in two years). Meanwhile, there were also some mangroves not affected by SuperTY Sarika (the average annual loss rate of mangrove forests in 1.92% of the area is less than 1%). The spatial distribution of the annual GPP loss is shown in Figure 3, and the averaged annual loss rate was 8.39 \(\pm\) 6.01%/yr. We used the buffer analysis to establish the correlation relationship between the distance to the coastline and the distance to the typhoon eye and GPP loss (Figure 5 subplot). Each point represents the average loss within the 1 km buffer zone, and the five points in the figure are 0-1, 1-2, 2-3, 3-4, and 4-5 km. The loss of mangrove GPP decreased with the increase in the distance to the track of SuperTY Sarika, and the linear average was 21.68 gC/m\({}^{2}\)/yr/km (R\({}^{2}\) = 0.69). Simultaneously, the losses increased as the distance to the coastline increased (from land to sea) with the linear average loss rate of 115.42 gC/m\({}^{2}\)/yr/km (R\({}^{2}\) = 0.78).
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 2 Review Methodology
### Eligibility Criteria and Selection Refining  
We established a set of criteria for selecting the literature: (1) written in English; (2) published in 2010 or later; (3) related to navigation or addressing navigation issues; (4) using voxel models or voxel-based approaches. Based on these criteria, we initially selected 48 papers from the 157 retrieved documents, and through citation analysis we ultimately included 87 papers for the final review. The selection of 2010 as the starting point was made to focus on more contemporary research, reflecting the rapid progress in voxel-based techniques, computational methods, and their integration with emerging technologies like autonomous systems, which have gained considerable attention in recent years.
## 3 Theoretical Models and Verifications
### Structural Model  
The structural performance can be represented by the cross-sectional composite properties of each blade section. In order to evaluate the cross-sectional composite properties of the blade section, classical lamination theory [22] is applied during the optimization procedure. For the laminated plates, the CLT model is an extension of classical plate theory [23] which has improvements in reasonable validation and computational efficiency. Thus, the CLT model can confirm the overall effective performance of composite laminated structure of wind turbine blade. [PERSON] et al. [18] developed a FORTRAN code named PreComp (pre-processor for computing composite blade properties) to calculate the blade structural properties based on CLT. PreComp requires input files of the blade geometric shape, interior structural layout and materials properties. In addition, PreComp allows various internal geometry configurations and a general layup of composite laminates. The CLT model can transfer a complex three-dimensional problem into calculating two-dimensional blade section properties. In this paper, PreComp is applied to evaluate the structural properties of wind turbine blade sections. The structural parameters like blade geometry and layup schedule are set the same as the actual blade. After that, the stiffness and the mass per unit length distributions can be calculated. Figure 3 shows that the calculated stiffness values are in good agreement with the manufacturer's data based on simulations by WINDnovation Engineering Solutions GmbH.  
Figure 2: Comparison between the power coefficient and power curves obtained by blade element momentum (BEM) code and wind turbine manufacturer’s data.