esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
The conducted experiments of a conventional landing gear UAV take-off from a sloped surface show that the UAV cannot safely move vertically upwards when taking-off from a sloped surface. In the case of UAV take-off from a flat surface, the thrust vector generated by the propellers of the UAV points downwards and therefore lifts the UAV upwards. From the conventional UAV take-off experiments from sloped surface shown in Figures 8 and 9, it is observed that the UAV posture remains tilted even after take-off. Therefore, the direction of the thrust vector is not pointing downwards, resulting in a push in non-perpendicular direction to the horizon, making the UAV move diagonally. Therefore, the UAV requires a wider open space to level itself after take-off in a tilted attitude or the UAV may crash into structures on its flightpath. The videos of all the experiments can be found in the Supplementary Material.  
The designed manipulator system uses a custom parallel link mechanism and therefore requires only one actuator to change the height of the manipulator while keeping the tip perpendicular to the horizon. From conducted experiments we show that it is possible to stand and stabilize the airframe, using three manipulators to maintain the airframe parallel to the horizon against the change in the surface angle. The IMU sensor data from the UAV's flight controller was used to obtain the UAV's roll and pitch angle. Depending on the roll and pitch angle change, the three manipulators height is controlled accordingly to maintain the airframe parallel to the horizon. Proper PD parameter tuning is required to quickly respond with the manipulators to the smallest change in the airframe angle, to keep the UAV stable. There is, however, a limit to the maximum angle change that can be compensated by the manipulator system and this limit depends on the physical design of the manipulator. The maximum angle change after which the UAV encounters a roll over event is calculated using Equations (8)-(11), which are all dependent on manipulator placement \(l_{a}\). The maximum roll and pitch angles of a surface till which the manipulator system can maintain the airframe horizontal is 35.29[\({}^{\circ}\)] and 39.29[\({}^{\circ}\)] respectively (a 70.78% or 81.82% slope), obtained using (15). In addition, a sudden large change in the airframe angle can disturb the UAV's stability and can lead to rollover before the manipulators starts stabilizing the angle.  
Although we show that is possible to realize the airframe stabilization with a minimum of three manipulators, more than three can provide better stability, at the cost of shorter flight time. The tips of the manipulator are made rounded with silicon outer layer, which  
Figure 11: Roll and pitch angles of the UAV and platform attitude during the experiment to maintain the airframe horizontal against surface slope change.  
provide some amount of friction in slopes but when landing on sloped surfaces with a lot less friction than the surfaces used in the experiments of this work, a material with higher fiction is necessary at the tips of the manipulators.
# Numerical Study of Flow Separation Control over a Hump Airfoil Using a Novel Sweeping Jet Actuator
###### Abstract  
Sweeping jet (SWJ) actuators have become a hot research topic in flow control due to their larger sweep range and higher control efficiency. However, the linear relationship between frequency and velocity ratio (VR = U\({}_{\text{jet}}\)/U\({}_{\infty}\)) in the SWJ actuator makes it challenging to determine the dominant factor affecting the control effect. Decoupling the frequency and VR and determining the control mechanism of the SWJ actuator is, therefore, a difficult task. In this study, a novel type of SWJ actuator was designed using periodic synthetic jets instead of feedback channels. This achieved the implementation of different frequencies under the same VR, effectively decoupling frequency and VR. The SWJ actuator was then applied in flow separation control of a Hump airfoil, with F\({}^{*}\) = \(f\times c\)/U\({}_{\infty}\) = 0.375, F\({}^{*}\) = 1, and F\({}^{*}\) = 10 being the three forcing frequencies studied. Numerical results demonstrated that all three forcing frequencies displayed a control effect on flow separation. At VR = 1.8, the control effectiveness is optimal for F\({}^{*}\) = 1, and as VR continues to increase, F\({}^{*}\) = 10 becomes the optimal control frequency.  
flow separation control; sweeping jet; synthetic jet; frequency +
Footnote †: journal: Applied Sciences  
13 May 2024  
Revised: 26 July 2024  
Accepted: 27 August 2024  
Published: 5 September 2024
# Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks +
## 2 Link and End-to-End Quality Prediction in Heterogeneous Networks
The evaluation of the proposed algorithm shows that it can achieve a high accuracy while generating a low computational load. The analysis performed by [PERSON] et al. [23] includes a comparison with a baseline (ETX last value). The best overall performance is achieved by both the hybrid online algorithm and the SVM batch algorithm, which outperforms the baseline performance. Regarding the computational cost of the prediction, the baseline has the smallest CPU consumption. When dealing with batch algorithms, we can configure how often the training is performed (this is not feasible with online algorithms). The cost of the prediction is computed as the cost of each training multiplied by the time frequency of its training. Therefore, we tune the training frequency to keep low the overall prediction cost. Finally, [PERSON] et al. [19] focused on End-to-End quality prediction by means of time-series analysis. This prediction technique is applied in the routing layer of large scale, distributed, and decentralized networks. It is demonstrated that it is possible to accurately predict End-to-End Quality with a small average Mean Absolute Error. Particularly, the path properties and path ETX behavior are analyzed to identify the best prediction algorithm. Moreover, the EtEQ prediction accuracy is analyzed some steps ahead in the future and also its dependency of the time of the day. In this work, we extend our previous work [19] but including several new analysis which would allow a better understanding of End-to-End quality prediction in Wireless Mesh Community Networks. Particularly, we provide: (1) a detailed analysis of path properties behavior showing that Path Quality prediction is possible and meaningful; (2) an analysis of the computational cost of the prediction in terms of CPU utilization, energy consumption and temperature to confirm that the prediction can be executed directly in the routers; and (3) a detailed analysis of an use case that shows the behavior of two orthogonal routing strategies, in terms of Packet Delivery Ratio, ETX of links, and End-to-End Delay.
# Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation
## 5 Conclusions  
We presented a solution for the slow encoding when using affine motion compensation by changing the motion estimation algorithm. We proposed three improvements: a fast affine transform model estimation, a skip affine prediction and a fast parameter estimation algorithm. The proposed method is able to predict the correct affine model with good accuracy, and also skip evaluating affine prediction in some cases, saving significant encoding time. When compared to the state of the art, the reduction in bitrate according to the BD-R metric is below 1% on average, with a reduction of the encoding overhead in half compared to Reference [10], and slightly faster than the gradient approach from Reference [9] with less complexity when it comes to hardware implementations. In future work, we plan to investigate ways to make the implementation of the transform faster to reduce further the overhead of affine motion compensation. We also plan to use the optical flow information for block splitting decisions and stop the costly evaluation of smaller blocks when they would bring no benefit.  
Methodology, A.C.; Software, A.C.; Validation, Y.S. and T.M.; Writing--Original Draft Preparation, A.C.; Writing--Review & Editing, Y.S. and T.M.; Supervision, S.O.; Project Administration, S.O.; Funding Acquisition, S.O. All authors have read and agreed to the published version of the manuscript.  
This study was partially supported by [PERSON] KAKENHI Grant Number 18K19772 and Yotta Informatics Project by MEXT, Japan  
We would like to thank [PERSON] and Yoshida for sharing the code of their method [10], and also their implementation of the other methods they used in their paper.  
The authors declare no conflict of interest.
## 3 Results and Analysis  
The harvesting efficiency is the most important index for a harvester. This work focuses on maximizing the harvesting efficiency with a wide range of incidence angles and a fixed operation frequency. The harvesting efficiency of the metasurface was calculated as \(\eta\) = \(P_{\mathrm{LOAD}}\)/\(P_{\mathrm{INC}}\), where \(P_{\mathrm{LOAD}}\) is the total time-average power dissipated on the loads and \(P_{\mathrm{INC}}\) is the total time-average power incident on the metasurface. High Frequency Structure Simulator (HFSS) software was used to analyze the performance of the harvester. The periodic boundary condition was applied to the unit cell to numerically realize an infinite array. The unit cell was excited by the Floquet port with two modes of transverse electric (TE) and transverse magnetic (TM) polarizations at the top boundary. These two modes correspond to a plane wave, with the electric component perpendicular to the xz- and yz-planes, respectively.
# Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management
## 1 Introduction  
Urban climate change is an important research issue that must be investigated using multidisciplinary approaches that include both engineering and socio-environmental sciences [1]. An effective approach to strengthen disaster reduction strategies is to build a decision support system (DSS) for each region. A DSS is based on the correlation between the infrastructure, industries, and related communities in a region. Information on the technical, social, and economic properties of a region is collected to establish an effective natural disaster prevention strategy. By analyzing the collected data, we can easily identify the vulnerabilities. Disaster-preparedness strategies for all citizens will make it easier to prepare and respond to real disasters.
## 2 Materials and Methods
### Data Processing Methods  
#### 2.3.1 Convolutional Neural Network (CNN)  
A convolutional neural network (CNN) is a deep learning model designed for image recognition and classification. Its core structure comprises a convolutional layer, a pooling layer, an activation layer, and a fully connected layer [17]. A CNN automatically learns complex feature representations by extracting features from the input data layer by layer while simultaneously reducing the number of model parameters and enhancing computational efficiency [18]. The convolutional layer, as the cornerstone of a CNN, extracts local features by sliding a convolution kernel across the input feature map to preserve critical spatial information [19]. The convolution operation is calculated as follows:  
\[y_{j}^{p}=f\left(\sum_{i=1}^{N_{i}^{p-1}}x_{j}^{p-1}\otimes W_{i,j}+b_{j}^{p} \right),j=1,2,\ldots M \tag{1}\]  
where \(y_{j}^{p}\) represents the output of the \(p\)-th convolutional layer for the \(j\)-th feature map, \(x_{j}^{p-1}\) denotes the input feature map from the previous layer, \(W_{i,j}\) is the convolution kernel (weight matrix), \(b_{j}\) is the bias term, \(\otimes\) indicates the convolution operation, \(f\) is the activation function (e.g., ReLU), \(N_{i}^{p-1}\) is the number of input feature maps, and \(M\) is the number of output feature maps.  
Following the convolutional layer, the pooling layer applies maximum or average pooling to the feature map, reducing its dimensionality to decrease computational complexity while retaining key features [15]. This operation is expressed as follows:  
\[y_{ij}=\max_{m,n}\,x(i+m)(j+n) \tag{2}\]  
where \(y_{ij}\) is the output of the pooling operation at position \((i,j)\), \(x(i+m)(j+n)\) represents the input feature values within the pooling window, and \(m\) and \(n\) denote the offsets within the pooling region.  
This multilevel feature extraction can preserve the spatial information of the input image and also effectively reduce the model parameters to improve the computational efficiency. In addition, the activation function (e.g., ReLU) enhances the expressive ability of the model through nonlinear transformation, which makes the CNN perform well in high-dimensional image data processing.
## 3 Results and Discussion  
### Selection of Bacteria in Microaerobiosis  
The eighteen obtained bacterial strains were evaluated for 48 h under microaerobiosis in 96-well microplates. This experiment aimed to evaluate the elite strain. It was observed that 17 strains did not produce a significant amount of lactic acid, nor did they significantly consume glycerol. It is known that the pyruvate pathway is favored under anaerobic conditions. As lactic acid is the final product of interest, fermentations were carried out with a sealed 96-well plate. The main challenge is to predominantly produce lactic acid, inhibiting the other products derived from the pyruvate metabolic pathway, such as 2,3-butanediol, 1,3-propanediol, and ethanol [9].  
The 2 GPP strain produced lactic acid and 1,3-propanediol, and consumed a significant amount of glycerol. After 48 h, 92.5% of the initial glycerol was consumed, and the bacteria metabolized 3.8 g\(\cdot\)L\({}^{-1}\) lactic acid and 5 g\(\cdot\)L\({}^{-1}\) 1,3-propanediol. The Y\({}_{\text{LA}/\text{Gly}}\) was 0.10 and Y\({}_{\text{1,3-PDO/Gly}}\) 0.14. Therefore, 2 GPP was selected for identification.  
[PERSON] et al. [43] investigated the efficiency of batch cultivation by _Lactobacillus brevis_ with MRS medium, supplemented with 3% (w\(\cdot\)v\({}^{-1}\)) glucose and 20 g\(\cdot\)L\({}^{-1}\) pure glycerol as a carbon source. Anaerobic batch fermentation was carried out at 37 \({}^{\circ}\)C and 200 rpm for 72 h. This experiment produced approximately 14.8 g\(\cdot\)L\({}^{-1}\) of 1,3-propanediol, 14 g\(\cdot\)L\({}^{-1}\) of lactic acid, and 13 g\(\cdot\)L\({}^{-1}\) of acetic acid, with a yield of 0.65 g13-PDO\(\cdot\)8 Glycerol\({}^{-1}\), 0.61 gLA\(\cdot\)8 Glycerol\({}^{-1}\), and a 0.56 gAceticAid\(\cdot\)8 Glycerol\({}^{-1}\) conversion rate.
# Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT
## 5 Security Analysis
### Descriptive Security Analysis
#### 5.2.5 Impersonation Attacks Resistance  
Firstly, we analyze the \(S\) impersonation attack. If \(\mathcal{A}\) tries to impersonate \(S\) to generate the message \(\{A_{s},EID_{s},TS_{s},V_{s}\}\) to make \(SP\) believe that the message is legitimate and generated by \(S\), \(\mathcal{A}\) cannot generate valid information and impersonate S in polynomial time without knowing parameters such as \(k_{s}\) and \(x_{s}\).
## 4 Proposed Protocol Procedure
### Authentication and Key-Exchange Phase
#### 4.2.2 Step 3
The process of authentication and key-agreement in the proposed protocol is illustrated in Figure 3.  
**Summary of the Authentication and Key-Exchange Phase:**  
(1) Node:  
Start  
Select random number \(\mathrm{a_{i}}\) and \(\mathrm{b_{i}}\)  
Compute \(\mathrm{S_{1}}=(\mathrm{PV}_{\mathrm{NODe}}\ ^{*}\mathrm{a_{i}})+\mathrm{b_{i}}\)  
Compute \(\mathrm{K}=\mathrm{b_{i}}\).P  
Compute \(\mathrm{N_{i}}=\mathrm{b_{i}}\).PU\({}_{\mathrm{SERVER}}\)  
Compute \(\mathrm{A}=\mathrm{H}(\mathrm{S_{1}}\ |\ \mathrm{N_{i}}\ |\ \mathrm{AC_{i}})\)  
Compute \(\mathrm{S_{2}}=\mathrm{H}(\mathrm{S_{1}}\ |\ \mathrm{N_{i}}\mathrm{ }|\ \mathrm{I}\ |\ \mathrm{A}\mathrm{C_{i}})\)  
\(\mathrm{S_{3}}=\mathrm{H}(\mathrm{A}\ |\ \mathrm{S_{2}}\ |\ \mathrm{I}\ \mathrm{AC_{i}})\)  
Send Message include \(\mathrm{S_{3}}\), \(\mathrm{S_{1}}\) Encrypted and \(\mathrm{K}\) to server  
End  
(2) Server:  
Start  
Check \(\mathrm{\Delta T}\)  
If Message is Fresh Continue the process  
Else  
Disconnect the communication  
Compute \(\mathrm{A^{\prime}}=\mathrm{H}(\mathrm{S_{1}}\ |\ \mathrm{I}\ |\ \mathrm{N^{ \prime}}\ |\ \mathrm{AC_{i}})\)  
Compute \(\mathrm{S^{\prime}}_{2}=\mathrm{H}(\mathrm{S_{1}}\ |\ \mathrm{I}\ |\ \mathrm{N^{ \prime}}\ \mathrm{(}\mathrm{)}\ |\ \mathrm{I}\ |\ \mathrm{K}\ |\ \mathrm{A^{\prime}})\)  
Compute \(\mathrm{S^{\prime}}_{3}=\mathrm{H}(\mathrm{A^{\prime}}\ |\ \mathrm{I}\ |\ \mathrm{S^{ \prime}}_{2}\ |\ \mathrm{AC_{i}})\)  
if \(\mathrm{S_{3}}=\mathrm{S_{3}}\) Continue the process  
else  
Disconnect the communication  
Select Random Number \(\mathrm{c_{i}}\) and \(\mathrm{d_{i}}\)  
Compute \(\mathrm{SA}=(\mathrm{PV}_{\mathrm{SERVER}}\mathrm{c_{i}})+\mathrm{d_{i}}\)  
Compute \(\mathrm{SK}=\mathrm{H}(\mathrm{S_{1}}\ |\ \mathrm{A^{\prime}}\ |\ \mathrm{I}\ |\ \mathrm{S^{ \prime}}_{2}\ |\ \mathrm{S^{\prime}}_{  
Check \(\Delta\)T  
If Message is Fresh Continue the process  
Else  
Disconnect the communication  
Compute SK\({}^{{}^{\prime}}\) = H(S\({}_{1}\mid\mid\) A \(\mid\)\(\mid\)S\({}_{2}\mid\)\(\mid\)S\({}_{3}\mid\)\(\mid\)SA \(\mid\)\(\mid\) AC\({}_{\text{i}}\))  
Compute S\({}^{{}^{\prime}}\)4 = H(SK\({}^{{}^{\prime}}\mid\)SA \(\mid\)\(\mid\)N\({}_{\text{(y)}}\))  
if S\({}_{4^{\prime}}\) = S\({}_{4}\) Continue the process  
else  
Disconnect the communication  
Select SK\({}^{{}^{\prime}}\) as Session key  
End
# Analysis and Evaluation of Performance Gains and Tradeoffs for Massive MIMO Systems
## 3 Massive MIMO Performance Gains
### Spatial Multiplexing Gain  
One of the main features of massive MIMO system is the ability to transmit multiple independent data streams in parallel at the same time and bandwidth, which offers a linear increasing in the data rate through the spatial multiplexing. Under suitable channel condition such as rich scattering, the receiver can decompose data streams. The need for multimedia services is rapidly increased, thus the capacity of wireless communication has to be increased in order to guarantee the quality of service requirements for mobile application. Therefore, wireless cellular networks are continuously developing to reach the user's demand for high data rate. Massive MIMO system is the key technology for high data throughput due to improve the spectral efficiency, which is necessary for increasing the channel capacity. Here, we consider the practical case where massive MIMO channel suffers from the effect of inter-cell interference in multi-cell massive MIMO system due to pilot contamination.
## 3 Results  
### Characteristics and Variation of LUCC in Inner Mongolia  
As the LUCC matrix (Table 4) shows, grassland is the dominant land-use type (46.88% of the total area in 2020) in Inner Mongolia, followed by bare land, cultivated land, and forest (24.99%, 13.52%, and 11.81% in 2020, respectively). These four land-use types covered 97.20% of the total area of Inner Mongolia in 2020. During the study period, the LUCC in 11.80% of the total area (136,322.38 km\({}^{2}\)) changed. From 2000 to 2020, the area of cultivated land increased the most (9,461.19 km\({}^{2}\)), followed by artificial surfaces (7,208.50 km\({}^{2}\)), grassland (4,728.69 km\({}^{2}\)), shrubland (3,965.25 km\({}^{2}\)), and forest (755.94 km\({}^{2}\)), while the areas of water (205.50 km\({}^{2}\)), wetland (1,959.31 km\({}^{2}\)), and bare land (23,954.75 km\({}^{2}\)) reduced. \begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{\theta_{TS}}\) & \(\mathbf{Z}\) & **NPP Change Trend** \\ \hline \(>0\) & \(>1.96\) & Significantly improved \\ \(>0\) & \(\leq 1.96\) & Insignificantly improved \\ \(<0\) & \(>1.96\) & Significantly degraded \\ \(<0\) & \(\leq 1.96\) & Insignificantly degraded \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Rules for the trend classification of NPP change. However, there were certain differences in the LUCC between the first 11 years and the second 11 years. From 2000 to 2010, the area of cultivated land and water decreased (by 5,235.00 and 1013.94 km\({}^{2}\), respectively), while they showed a pattern of growth during 2010-2020 (by 14,696.19 and 808.44 km\({}^{2}\), respectively). The area of grassland and shrubland increased from 2000 to 2010 (by 14,287.31 and 4069.25 km\({}^{2}\), respectively), while the area of grassland decreased during 2010-2020 (by 9558.63 and 104.00 km\({}^{2}\), respectively). The change trends of forest, wetland, artificial area, and bare land remained the same during the two periods. In general, the land-use change in Inner Mongolia during the study period mainly focused on the conversion between grassland and cultivated land, forest, and bare land. During 2000-2010, about 9813.00 km\({}^{2}\) of cultivated land changed into grassland, with 5173.50 km\({}^{2}\) of grassland changed into cultivated land. During 2010-2020, the shifts between cultivated land and grassland were more intense, and far more grassland was changed into cultivated land (26,495.25 km\({}^{2}\)) than changed from cultivated land (10,865.88 km\({}^{2}\)). Furthermore, the shift between forest and grassland remained in relative balance during the study period, ranging from 6405.00 to 8964.25 km\({}^{2}\). In addition, the trend of change from bare land into grassland was significant in these two periods. \begin{table}
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 3 Carbon Satellite Remote Sensing Inversion Techniques
### Advances in Remote Sensing Algorithms for Satellite Atmospheric CO\({}_{2}\)
#### 3.2.1 Physical Inversion Algorithms
[68] from the University of Bremen developed the BESD algorithm, which integrates optimal estimation with WFM-DOAS to retrieve CO\({}_{2}\) from SCIAMACHY and GOSAT L1B open data.
## 1 Introduction
In simple cases, the equation can be analytically integrated with respect to space and time to yield accurate solution (see Figure 1). However, because of a complicated phenomenon, such as solidification, it is impossible to carry out a corresponding analytical solution directly. In this case, it is necessary to perform numerical calculations. In particular, we study the temporal and spatial evolution of the temperature field in the casting. In such case, the precision of computations is affected by two main factors: domain decomposition--so-called semi-discretization (i.e., size and type of a finite element) and time discretization (i.e., size of a time step). In this work, we focus on the time discretization--so-called time decomposition to different sub-domains. We explore improvement in computational efficiency of time steppers by studying a combination of different time integration schemes used in various sub-domains. We develop an approach, that is a generalization of two other techniques, namely sub-cycling methods and partitioning integration methods [8; 11; 13] (see Figure 2). Such combination allows to better estimate the size of time step and time integration scheme for the problem of interest.