esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### CO\({}_{2}\) Emission Calculation  
To produce 440 kW of power for one hour, the PEM fuel cell system requires 22 kg of hydrogen. While fuel cells do not directly produce CO\({}_{2}\) emissions, the method of hydrogen production does. If hydrogen is generated through the steam methane-reforming process, 8 kg of CO\({}_{2}\) is emitted per 1 kg of hydrogen produced [38]. Thus, the amount of CO\({}_{2}\) emitted from producing 22 kg of hydrogen is 176 kg of CO\({}_{2}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Material Properties**} & **Grade 5 (T1-6 Al-4V)** & \multirow{2}{*}{**304 Stainless Steel**} & **6061-T6** \\  & **Titanium** & & **Aluminium** \\ \hline
**Density** & 4620 Kg m\({}^{-3}\) & 7750 Kg m\({}^{-3}\) & 2770 Kg m\({}^{-3}\) \\ \hline
**Young’s modulus** & 9.6 \(\times\) 10\({}^{10}\) Pa & 1.93 \(\times\) 10\({}^{11}\) Pa & 7.1 \(\times\) 10\({}^{10}\) Pa \\ \hline
**Poisson’s ratio** & 0.36 & 0.31 & 0.33 \\ \hline
**Tensile yield strength** & 930 MPa & 207 MPa & 280 MPa \\ \hline
**Compressive yield strength** & 930 MPa & 207 MPa & 280 MPa \\ \hline
**Tensile ultimate strength** & 1070 MPa & 586 MPa & 310 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties for compressed hydrogen vessel design.  
Figure 1: ANSYS design of compressed hydrogen vessel: (**a**) 2D axisymmetric of compressed hydrogen vessel and (**b**) 3D view of compressed hydrogen vessel.  
The amount of jet fuel required for a conventional gas turbine APU to produce 440 kW of power for one hour of operation and for a system with 30% efficiency can be calculated by using Equation (1) above, setting the heating value of jet fuel at 43,147 kJ/kg.  
\[Jet\ fuel\ required\ (\mathrm{Kg})=\frac{(440)\times(1)\times(3600)}{(0.30)\times(431 47)}=122.37\ \mathrm{Kg}\]  
It has been reported that a conventional gas turbine APU for Boing 777 produces 3.16 kg of CO\({}_{2}\) of per 1 kg of jet fuel combusted [39]. So, the emission of CO\({}_{2}\) for 1 h of operation is (122.37 \(\times\) 3.16 = 386.68 kg) of CO\({}_{2}\). This approach might be applicable to other aircraft models such as the Airbus A350 or Boeing 787; however, further investigations are required in future work.
# Accelerated Inference of Face Detection under Edge-Cloud Collaboration
## 3 Proposed Method
### Deconvolution Pruning  
In the CenterNet structure, the deconvolution layer obtains high-resolution feature layers by upsampling the feature data extracted by the backbone network. In addition, this layer can be similar to upsampling methods (bilinear interpolation [11; 27], etc.), but upsampling methods cannot be learned and the model cannot be trained end-to-end.  
Generally, for a convolutional neural network with \(L\) convolutional layers, input tensor is \(X^{l}\in\mathbb{R}^{c^{l}\cdot h^{l}_{in}\cdot w^{l}_{in}}\), output tensor is \(Y^{l}\in\mathbb{R}^{c^{l}\cdot h^{l}_{out}\cdot w^{l}_{out}}\), and weight tensor is \(W^{l}\in\mathbb{R}^{n^{l}\cdot c^{l}\cdot k^{l}\cdot k^{l}}\), where \(n^{l}\) and \(c^{l}\) represent the number of input channels and output channels, \(k^{l}\cdot k^{l}\) is filter size, and \(l\) is the layer index. The deconvolution operation can be expressed as Equation (1).  
\[X=\left(W^{l}\right)^{\mathrm{T}}\otimes\mathrm{Y}^{l} \tag{1}\]  
Here, \(\otimes\) is represented as a convolutional layer operation. In this way, it can be found that the deconvolution operation is the inverse process of the convolution operation, but the deconvolution can only restore the size of the tensor, not the value in the tensor. Thus, there is redundancy in the deconvolution and the pruning operation can be performed. In addition, the bias operation is omitted in the formula. \(\tilde{W}^{l}\in\mathbb{R}^{\tilde{n}^{l}\cdot c^{l}\cdot k^{l}}\) indicates the weight after pruning. Therefore, the pruning process can be expressed as a function \(\tilde{W}^{l}=F\left(W^{l},\textit{percent}\right)\). The relationship between the input channels is shown in Equation (2) and the \(p\) is expressed as the pruning rate. It is worth noting that before the pruning operation, this algorithm sorts all the weights, and the sorting method is also L\({}_{1}\) regularization.  
\[\tilde{n}^{l}=n^{l}\cdot p \tag{2}\]  
In the deconvolution operation, the size relationship between the input and output is as shown in Equation (3). \(s^{l}\) is the step of the deconvolution layer, \(p^{l}\) is the pace of the layer. If you change the number of input channels, the natural number of output channels will also change.  
\[c^{l}=s^{l}\left(n^{l}-1\right)-2p^{l}+k^{l} \tag{3}\]  
Figure 4: L\({}_{1}\) weight pruning flowchart.
Therefore, a meta-learning approach is employed to enable the dynamic SFC placement model to quickly adapt to new IoT-MEC networks by learning improved initial parameters. The primary objective of this paper is to solve the multiobjective optimization problem [18; 19], which is the aggregate of the routing cost and end-to-end delay, with appropriate weights associated with an IoT-SR. Moreover, in order to speed up the training of neural networks and deal with uncertain link status information, we utilize fuzzy logic to evaluate whether a link is good or not based on available bandwidth, transmission delay, and packet loss rate. To streamline the complexity of the dynamic SFC placement problem, we bifurcated it into two subproblems: (1) the VNF placement problem, i.e., deciding which cloudlets are the required VNF instances to be placed, and (2) the routing path search problem, i.e., selecting the appropriate path for each chosen adjacent cloudlet which includes the source node, the set of ordered VNF instances, and the destination node. The two subproblems are addressed using the Double Deep Q-Network (DDQN), a typical value-based DRL approach. Specifically, an initial parameter training model based on meta learning is proposed to quickly adapt to a dynamic IoT-MEC environment. It is worth noting that after obtaining a initial parameter, an agent can quickly converge in a few steps, and the DRL-based method can quickly provide a high-reward SFC placement solution for each arrived IoT-SR. To the best of our knowledge, our work is the first to utilize the meta reinforcement learning approach for traffic routing implementation jointly considering the dynamic placement of SFC in IoT-MEC networks.
# Horizontal Wind Effect on the Aerodynamic Performance of Coaxial Tri-Rotor MAV
## 5 Conclusions  
In this paper, low-speed wind tunnel tests and numerical simulations are performed to obtain the aerodynamic performance of the coaxial Tri-rotor MAV with the horizontal wind ranged from 0 to 5 m/s. Conclusions are as follows:  
(1) For a lower rotor speed ranging from 1500 to 1800 r/min, the power required is constant, while the thrust increased up to 9%, which indicated that the coaxial Tri-rotor system with lower speed has larger power loading and better aerodynamic performance. In fact, part of the rotor interference is offset by the horizontal inflow.  
(2) The velocity and streamline distribution proved that the required power increment is the result of the downwash deformation with the horizontal wind effect. At the same time, the greater the deformation of downwash comes along with a larger the horizontal wind, which will decrease the whole flight efficiency. Combined with the pressure distribution, it also can be seen that the aerodynamic performance is related to the instantaneous thrust variation.  
(3) Compared with the case of no wind, the horizontal wind can promote the aerodynamic coupling between the rotors and improve the aerodynamic performance of the coaxial Tri-rotor system at a lower speed. Conversely, the interaction between rotor tip vortices is stronger with a higher rotor speed, thus the interaction between rotors is transferred by the horizontal wind, resulting in reduced thrust. In this case, the stronger coupling interference directly affects the rear rotor with the action of the horizontal wind, which may lead to the rotor vibration with extra power consumption.  
(4) For the rotor speed ranging from 1900 to 2300 r/min, part of the horizontal flow is interacted with the downwash flow, resulting in stronger interference to form an unstable flight. Hence, further study will focus on the compensation of the control strategy, considering a lager wind speed.  
[PERSON] carried out experiments; [PERSON] wrote the manuscript with assistant of [PERSON]; [PERSON] is the funding raise. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 51505087 and 11502052), Fuzhou University Jinjiang Science and Education Park (No. 2019-JFDKY-59) and Fujian Provincial Industrial Robot Basic Components Technology Research and Development Center (2014H21010011).  
The authors thank the Key Laboratory of Fluid Power and Intelligent Electro-Hydraulic Control (Fuzhou University), Fujian Province University for applying the experimental field.  
The authors declare no conflict of interest.
# An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers
## 4 Experiments and Results
### Dynamic Experiment
The success fixing rates of the three methods are shown in Table 8. The success rate of the LAMBDA method is still low. Although the CLAMBDA method has a higher success rate, wrong fixing still exists. The MCLAMBDA method shows relatively better results in both quantity and quality. In contrast, the CLAMBDA-search method is less sensitive to the satellite number, satellite geometry and environmental multipath. The CLAMBDA-search method has obvious advantages, in terms of the Euler angle success rate and Euler angle correctness rate. Figure 7: Dynamic experiment. **Top left panel:** heavily degraded (Taiyuan roundabout). **Top right panel:** lightly degraded (Peacefully Plaza). **Bottom left panel:** open-sky (Development Avenue turntable). **Bottom right panel:** the antennae configuration. Recorded with Ublox MST, patch antenna on 19 March 2020.
## 3 Signal Processing
### Electric Circuit  
The sensor signal generation is based on the piezo effect, leading to an increasing voltage on the sensor output. However, the sensor has no external power supply, resulting in voltage changing without generating new current. Consequentially, the sensor signal has too low an impedance to be measured with common ADCs. Therefore, the low impedance signal must be transformed into a high impedance signal to ensure the correct ADC conversion; additionally, the value range of the ADC must be secured. For this reason, the first process step is an impedance converter, which infuses additional energy to the signal without changing the voltage characteristic (cp. Figure 8). The amplifier used should not influence the sensor signal frequency, and it should work with the  
Figure 7: Novel system architecture for real-time processing on the basis of Zynq SoC (XC7Z010) with complete process flow.  
onboard voltage as a power supply. In the following steps, the measuring range of the XADC, from 0 V to 1 V, is ensured. Therefore, the voltage has to be shifted to the positive voltage range by applying an offset of 0.5 V, leading to an inversion of the signal determined by the characteristics of the used operational amplifier (LM324N). For this reason, the subsequent step inverts the signal again. In the last step, the limitation of the measurement range must be secured. This is realized by two limiters cutting the signal at less than 0 V and higher than 1 V. As a result, the signal is converted from a low impedance signal with an arbitrary range to a signal with high impedance shifted by 0.5 V, limited to the ADC measurement range.
# Characterization of Nutrient Disorders of _Camabis sativa_
## 2 Materials and Methods
#### 3.1.3 Calcium  
Symptoms of Ca deficiency developed five weeks after treatment. The plants first displayed slight stunting when compared to the control. The growing tips and newly expanding leaves showed signs of stunting and irregular growth habits (Figure 2). As the newly expanding leaves developed, the basal portion of the leaflets remained narrower and displayed a lighter green coloration when compared to the leaflet tip (Figure 2). As symptoms progressed, the yellowing at the leaflet basal portion became more intense, and the leaflets began to show symptoms of interveinal chlorosis. The new leaves that expanded showed severe stunting and marginal necrosis, resulting in leaves with irregular geometries and orientations (Figure 2). In advanced symptoms, the yellowing leaves and growing tips became necrotic. The death of the growing tip caused a proliferation of axillary shoot development to occur, resulting in a more branched architecture (Figure 2).  
Foliar Ca concentrations were 90% lower in the Ca-deficient plants than in the controls. Ca-deficient plants contained 0.39% Ca, while the control plants contained 3.73% Ca (Table 2). Foliar Ca concentrations in control plants were within the published survey range of 1.47-4.42% ([PERSON] et al., [9]).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Treatment** & **–N\({}^{\ddagger}\)** & **–P** & **–K** & **–Ca** & **–Mg** & **–S** \\ \hline \multicolumn{8}{c}{Tissue nutrient concentration (\% dry weight)} \\ \hline Element & N & P & K & Ca & Mg & S \\ \hline Complete & 4.28 & 0.43 & 2.85 & 3.73 & 0.61 & 0.41 \\ Disorder & 1.62 & 0.09 & 0.41 & 0.39 & 0.12 & 0.11 \\  & *** \(\ddagger\) & *** & *** & *** & *** & *** \\ \hline Survey & & & & & & \\ Range \({}^{\ddagger}\) & 3.30–4.76 & 0.24–0.49 & 1.83–2.35 & 1.47–4.42 & 0.4–0.81 & 0.17–0.26 \\ \hline \hline \end{tabular}  
* Nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg) and sulfur (S), \(\ddagger\), **, or *** indicate statistically significant differences between sample means (n=4) based on \(F\)-tests at \(P<0.05\), \(P<0.01\), or \(P<0.001\), respectively. NS (not significant) indicates the \(F\)-test difference between sample means was \(P>0.05\). \({}^{\ddagger}\) Reference survey values from [PERSON] et al. (2014).  
\end{table}
Table 2: Foliar nutrient concentrations of _Cannabis sativa_ ’T1’ grown with a deficient macronutrient compared to plants grown with a complete fertilizer regime.  
Plants grown in Ca-deficient conditions did not result in statistically significant differences in biomass when compared to the control (Table 1) when sampled at the onset of visual symptoms.
## 4 Solution Procedure
### Upper Bounds
#### 4.1.2 LR2  
Another possible combination of constraints to be relaxed is the set of constraints (3) and (6). Let \(\zeta_{jlt}\geq 0\ \forall j,l,t\) and \(\chi_{it}\geq 0\ \forall\ i,t\) be the multipliers associated with constraint set (3) and (6), respectively. Then the Lagrangian relaxation problem, LR2 is defined as:  
\[\begin{array}{ll}Z_{LR2}=&\text{Max}\ \sum\limits_{i\in I}\sum\limits_{j \in I}\sum\limits_{k\in I}\sum\limits_{k\in T}\ \alpha_{j}g_{ij}a_{jlt}x_{ijlt}-\sum\limits_{i\in I}\sum\limits_{l\in I}\sum \limits_{k_{i}\in K_{i}}\sum\limits_{l\in T}h_{ilk,l}y_{ilk,l}\ +\sum\limits_{j\in I}\sum\limits_{l\in L}\sum\limits_{l\in T}\zeta_{jlt}\ \left(1-\sum\limits_{i\in I}x_{ijlt}\right)\\ &\ +\sum\limits_{i\in I}\sum\limits_{l\in T}\chi_{it}(\beta_{i}v_{i}-\sum \limits_{j\in I}\sum\limits_{l\in L}a_{jlt}\ x_{ijlt})\\ \end{array} \tag{10}\]  
Subject to (2), (4), (5), (7), and (8).  
The objective function of the problem LR2, \(Z_{LR2}\), provides an upper bound on the objective function of the MMCMCLP for \(\zeta_{jlt}\geq 0\ \forall j,l,t,\text{ and }\chi_{it}\geq 0\ \forall i,t\).
# Research on Monitoring and Early Warning of the Mine Backfill System Based on Blockchain Technology
## 4 System Experiment and Analysis Evaluation
### Storage Performance Analysis
#### 4.1.2 Experimental Analysis  
The combined data management scheme of IPFS and blockchain storage is proposed in this paper. The blockchain layer is constructed by the Hyperledger Fabric project. The data storage performance test takes the administrator node as an example and deploys a leader node, an orderer node, and two general peer nodes. A leader node is responsible for data storage and communication with other organizational nodes. An orderer node completes the consensus of transaction sequencing. Additionally, it deploys relevant chain codes in the organization.  
The comparison between the system model designed In this paper and the original blockchain model to store the same amount of data is shown in Figure 6. The blocks generated by the system are reduced by more than 72% compared with the original ordinary block, greatly reducing the capacity of the blockchain.
# On a Novel Approach to Correcting Temperature Dependencies in Magnetic Observatory Data
## 1 Introduction
According to INTERMAGNET, quasi-definitive data are processed geomagnetic data with no difference of more than 5 nT from definitive data of the given observatory [5]. In contrast to definitive data, which become available to the scientific community more than one year after the measurement, quasi-definitive data are produced within a one month delay [20]. Such data are extremely important for updating magnetic field models on a timely basis and calibration of satellite observations [21]. This paper proposes a new method of temperature correction tested in the preparation of quasi-definitive data of the INTERMAGNET Saint Petersburg Observatory (IAGA code SPG) [22; 23; 24]. In addition to temperature correction, the full data processing cycle also involves the removal of jumps and spikes from the magnetometer recordings [25]. However, the corresponding algorithms are out of the scope of the present paper.
# Design and Development of an SVM-Powered Underwater Acoustic Modem
## 2 Materials and Methods
### Methodology and Data Analysis
This ensured that the SVM was trained on accurately categorized data, which was crucial for the robust performance of the demodulation process. The labeling also allowed for visualizing and verifying the distribution of classes and how the features corresponding to each class manifested within the data, confirming the balanced representation of each class across the dataset. These features were derived from the CWT of the acoustic signals, encapsulating the characteristic time-frequency behavior of the transitional \"C\" symbol, which was central to this communication system. The dataset was compounded of features extracted from the CWT resulting in an array of 11 \(\times\) 5001 dimensions. Note that the CWT reduced the bins of interest to 11, as described in subsequent sections, in contrast to the 26 bins shown in Figure 3. To ensure the robustness of the SVM classifier, its performance was evaluated across different training and validation splits using K-fold cross-validation. This approach ensured that the model performance was not contingent on a specific data split, but was generally reliable.
## 3 Passive Thermal Control Optimization Design  
The findings of the sensitivity analysis of the thermal parameters have facilitated the identification of the pivotal parameters that exert a substantial influence on the temperature distribution of the optical system, in addition to their optimal value ranges. It is important to note that some of these critical parameter values are closely associated with the structural and material composition of the camera. Consequently, through the optimization of the camera's structural design and the selection of suitable materials, it is possible to ensure that these critical parameter values remain within the optimal range, thereby facilitating the fulfillment of the temperature requirements of the aerial camera.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Parameter** & **Description** & **Initial Value** & **Optimum Range** \\ \hline Thermal conductivity & \(k_{1}\) & Optical components & 1.0 & Unadjustable \\ (W\(\cdot\cdot\mathrm{m}^{-1}\cdot\mathrm{c}^{-1}\)) & \(k_{2}\) & Frame & 160 & 80–180 \\ Convective heat transfer & \(k_{5}\) & Between lens barrel and & 0.2 & 0.1–0.5 \\ coefficient between two & & inner frame & \\ concentric cylinders & \(k_{6}\) & Between inner frame and & 0.3 & 0.1–0.8 \\ (W\(\cdot\mathrm{c}^{-1}\)) & & outer frame & & \\ Convective heat transfer & \(k_{7}\) & Between optical window & 7.8 & 2–15 \\ coefficient in planar & & and ens & & \\ cavity (W\(\cdot\mathrm{m}^{-2}\cdot\mathrm{c}^{-1}\)) & \(k_{8}\) & Between lens barrel and & 6.7 & 2–10 \\ External convective heat & & near cover & & \\  transfer coefficient & \(k_{10}\) & Between camera surface & & \\ (W\(\cdot\mathrm{m}^{-2}\cdot\mathrm{c}^{-1}\)) & & and environment & & \\ (W\(\cdot\mathrm{m}^{-2}\cdot\mathrm{c}^{-1}\)) & & Frame surface & 0.1 & 0.03–0.2 \\ Emissivity & \(k_{14}\) & Optical window surface & 0.4 & 0.2–0.9 \\ Thermal resistance of & \(k_{15}\) & Frame insulation structure & 50 & 15–50 \\ insulation structure & & Insulation structure of the & & \\ (\(\mathrm{c}^{\prime}\mathrm{C}\cdot\mathrm{W}^{-1}\)) & \(k_{16}\) & optical window & 5 & 3–5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key thermal parameters of the aerial camera.
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 7 Results and Discussion
### Throughput Performance  
#### 7.3.1 Total Throughput  
Figure 9 shows the total throughput for the downlink and uplink of both algorithms. The result shows that the proposed algorithm achieves higher throughput by approximately 45% in both downlink and uplink, which is a significant improvement over the 3 GPP scheme. This result is expected as the proposed algorithm was designed to provide users with links having the best possible CQI to improve their data rates rather than to simply maintain their connectivity.
# A Collision Risk Assessment Method for Aircraft on the Apron Based on Petri Nets
## 4 Simulation Experiments
### Simulation Result Data Analysis
#### 4.2.1 Analysis of Aircraft Operation Simulation Results  
After obtaining the operational data of the aircraft, the upper and lower bounds of its taxing position are calculated based on the time of operation \([L_{x}(t),\;U_{x}(t)]\). Subsequently, the probability of the aircraft reaching different positions at different times is computed using the Brownian motion Formula (14). The arrival probabilities at different positions \(h(x)\) can be color-coded and mapped onto their respective trajectory diagrams.  
As shown in Figure 5, the brighter the color, the higher the arrival probability it indicates. Regardless of the time, the arrival probability at the ideal position point for the aircraft at the current time is always the highest. Additionally, the farther the position is from the ideal point, the lower the arrival probability, which aligns with the actual movement patterns of the aircraft. Additionally, at the beginning and the endpoint of the taxiing process, the range of positions with arrival probabilities is smaller than that at the midpoint of the taxiing route. Based on this, it can be inferred that as the aircraft approaches the middle of the taxiing route, the range of possible positions with arrival probabilities increases. This indicates that the aircraft has a greater number of positions it can potentially occupy, leading to a larger deviation from the ideal position and a wider area of potential impact.  
Figure 4: Airport apron operation Petri net example (south direction).  
Figure 5: Probability diagram of arrival of aircraft at different time points.