esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analyses  
GNSS data and recorded video were uploaded and analyzed using Titan Sensors software (Titan Sync 3.0.0, 2019 and Titan Video 3.7.0, 2019). Accelerometry data were downloaded to OmniSenseTM Analysis (version 4.1.4; Zephyr Technology Corporation, Annapolis, MD, USA), then exported to Microsoft Excel 2019 (Microsoft Corporation, Redmond, WA, USA) for analysis. Data were log transformed using the natural logarithms (LN) of DIST, SUM, MAG, PL, IL, and GNSSD to reduce the nonuniformity of error [29]. Ten simple linear regression models were created to assess the ability of each independent variable (SUM, MAG, PL, IL, and GNSSD) to predict DIST completed during the small circle and large circle. Residual and Q-Q plots were used to ensure the assumptions of homoscedasticity and normality were not violated. All data were analyzed using the statistical software JASP (JASP, Version 0.12.2, Amsterdam, The Netherlands).
## 2 CFD Modelling
### Numerical Model Boundary Conditions  
In the cylindrical coordinate of reference, the well-benchmarked CFD algorithm for moving mesh zones is used to solve the conservation differential equations for continuity, momentum, and scalar transport [18; 19]. The numerical analysis's boundary conditions closely match those in the author's relevant experimental data [16]. This would result in a numerically accurate simulation. The no-slip assumption imposes a wall boundary limitation on the domain's boundary surfaces, like the diffuser and impeller blades, as well as the inner portion of the volute casing. It is anticipated that the flow is completely established at the fan outlet, and Neumann boundary conditions are applied along the path of flow [16]. As recommended by [PERSON] et al. [16], the intensity of turbulence is set at 5% in the turbulence model to simulate the turbulent flow behavior. The average hydraulic diameter required for the investigation is estimated in proportion to the inlet diameter of the fan [20]. A first set of 100 steady flow iterations is undertaken in the current study to establish dependent flow variables over the full domain for the time-periodic transient nature of the analysis that follows.  
In the flow domains of centrifugal fans, fluid is frequently quite turbulent, having significant flow eddies and recirculation. Therefore, it is necessary to conduct a whole-field CFD analysis to capture the static pressure at several important parts of the flow domain and represent transient flow instabilities as time periodic fluctuations. Transient analysis is carried out using a pressure-based solver in conjunction with a second-order implicit velocity formulation [17]. Velocity, as well as pressure values, are coupled through the Patankar SIMPLE algorithm found in the CFD package. The second-order accurate upwind technique controls the discretization process. The turbulence model used in the analysis is the k-\(\epsilon\) model available in ANSYS Fluent [16]. Mesh motion technology is used between the stationary diffuser and the rotating impeller, as well as between the rotating impeller and the stationary inlet region, with a time step of 0.1111 ms corresponding to the relative revolution of one degree. This condition will virtually satisfy the calculation scheme's stability condition at a fan's rated speed of 1500 rpm [17].  
All of the residuals are given a pre-determined limiting value of 0.0001 as the first prerequisite for convergence. The moving mesh analysis is conducted iteratively twenty times for each stage. The weighted average values of velocity and pressure recorded at significant sites in the flow zone are acquired by conforming to one degree of revolution of the impeller using the time-step progression setup for the study.
## 5 Experimental Results and Discussion
### Simulation  
In order to validate the effectiveness of the proposed method, dual-arm co-simulation is carried out in Matlab R2023b and compared with the conventional single-arm sanding motion. The simulation scenario is as shown in Figure 2:  
Since there are motion errors in the robotic arms themselves, Gaussian noise with a mean of 0 and a variance of 0.01 mm is added to each arm here. The motion between the synthesized motion in the two-armed scenario and the individual motion in the one-armed scenario was compared and the respective motion errors in both cases were obtained, as shown in Figure 3:  
Figure 3: Synthetic and individual motion errors.  
Figure 2: Simulation scenarios of two-arm synergistic motion (**left**) and one-arm motion (**right**).  
From the simulation results, it can be seen that using the method of decomposing the motion trajectories into two orthogonal directions and then assigning them to the two collaborating arms has similar results to directly inputting the machining trajectories to the individual arms. However, the two-arm collaboration provides greater flexibility and the method of decomposing the trajectories is simpler.
###### Abstract  
Climate, human disturbances, and management practices jointly control the spatial and temporal patterns of land surface phenology. However, most studies solely focus on analyzing the climatic controls on the inter-annual variability and trends in vegetation phenology. Investigating the main and interacting effects of management practices and climate might be crucial in determining vegetation phenology and productivity. This study examined the impacts of climate and management practices on vegetation phenology and productivity in adjacent native and introduced prairie pastures, which have detailed long-term management records, by combining climate, management, and satellite remote sensing data (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat). Modeled gross primary production (GPP) using vegetation photosynthesis model (VPM) was also included to investigate the dynamics of productivity. When comparing the impacts of the same management practices on different pastures, we used paired comparison, namely, comparing the native and introduced prairie side by side in the same year. The interactions of management practices and climate were investigated through comparing years with similar management but different climate (e.g., years with rainfall or not following baling events) in the same pasture. Results showed that air temperature (Ta) was an important factor in determining the start of the season (SOS) and the length of the season (LOS). Total rainfall (RF) during the annual growing season (AGS, derived from vegetation indices (VIs)) had the largest explanatory power (R\({}^{2}\) = 0.53) in explaining the variations in the seasonal sums of VIs. The variations in GPP were better explained by RF (R\({}^{2}\) = 0.43) than Ta (R\({}^{2}\) = 0.14). Using the thermal growing season (March-October) or AGS climate factors did not show large differences in determining the relationships between phenology, GPP, and climate factors. Drought shortened the LOS and decreased GPP. In terms of management practices, grazing generally reduced the VIs and burning induced early greening-up and enhanced vegetation growth. Drought plus other management practices (e.g., grazing or baling) greatly affected vegetation phenology and suppressed GPP. The negative impacts (i.e., removal of biomass) of grazing on vegetation was compensated by enhanced vegetation growth after good RF. This study demonstrated that the interactions of climate and management practices could be positive (burning plus baling in a good RF year) or negative (grazing/baling plus drought), and can significantly affect vegetation phenology and production.  
MODIS; Landsat; interacting effects; gross primary production (GPP); drought; grazing; baling; burning +
Footnote †: journal: remote sensing  
00233152529. [[https://doi.org/10.3390/rs15102529](https://doi.org/10.3390/rs15102529)]([https://doi.org/10.3390/rs15102529](https://doi.org/10.3390/rs15102529))
## 5 Silicon Accumulation Improves Plant Disease Resistance
### Formation of Biochemical Barriers as Influenced by Si
The third is the molecular mechanisms, Si and Si transporters induce gene expression associated with plant defense mechanisms, interact with disease-resistant signal molecules,such as salicylic acid, jasonnic acid and ethylene for signal transduction [101]. Studies have found that Si applications can induce the transcription of PR-1 and peroxidase genes closely related to disease resistance, thereby enhancing the resistance of tomato to early blight [65]. Furthermore, Si can resist _Ralstonia solanacearum_ by inducing the expression of the tomato transcription factor _WRKY1_ and the disease-resistance response protein ferritin and trehalose phosphatase genes [102]. In addition, _Arabidopsis_ overexpressing the wheat Si-influx transporter gene _TaLsi1_ showed enhanced resistance to powdery mildew.
# Reliable User Datagram Protocol as a Solution to Latencies in Network Games
## 4 Performance Evaluation  
A test bed experiment has been conducted for the RUDP model by using the OPNET Modeler (14.5 PL8, OPNET Technologies/Riverbed, Bethesda, MD, USA, 2010) in Transport Layer and also the Microsoft Visual Studio (2013, Microsoft, Redmond, WA, USA, 2013) as a compiler. Interworking with Microsoft Visual Studio 2013 and OPNET Modeler 14.5 PL8, all the functions necessary for the node models for the simulations were coded and debugged with the C++ language.
## 5 HAWT Blade Design
### Blade Shape Summary  
An efficient rotor blade consists of several aerofoil profiles blended at an angle of twist terminating at a circular flange (Figure 8) [4; 34]. It may also include tip geometries for reducing losses. To facilitate production, several simplifications maybe made:  
* Reducing the angle of twist.
* Linearization of the chord width.
* Reducing the number of differing aerofoil profiles.  
All manufacturing simplifications are detrimental to rotor efficiency and should be well justified. The introduction of new moulding techniques and materials has allowed the manufacture of increasingly complex blade shapes. However, the economics of production coupled with difficulty of design analysis still dictate final geometry. Leading wind turbine suppliers now include most optimisation features such as angle of twist, variable chord length and multiple aerofoil geometries.  
Figure 8: A typical modern HAWT blade with multiple aerofoil profiles, twist and linear chord length increase.
## 2 NB-IoT System Detailed Design and Implementation
### NPDSCH Receiver Chain Implementation
#### 2.2.9. De-Scrambler  
The de-scrambler block has the same working operation of the scrambler block discussed earlier in the NPUSCH transmitter chain. The resource de-mapper provides the frame and subframe numbers and when the resource de-mapper completes its function and raises its done signal, the De-scrambling starts to work. After initialization, the de-scrambler sends a ready signal to the channel equalizer block. Additionally, the output of de-mapper, which is 24 serial bits is the gold sequence's code word length, and the rate of the de-scrambler is equal to the de-modulation rate. The de-scrambler measures the length of the time slot in the resource de-mapper. If it equals 10 symbols, it stops and stores the remaining code for the next slot, as the NRS signals are not descrambled [9].
# A Survey on Different Plant Diseases Detection Using Machine Learning Techniques
## 2 Basic Steps in Identification of Diseases from Leaf Images
### Classification  
Classification is the numerical analysis of various image features, and it organizes the leaf image data into some of the disease categories. Classification is categorized as supervised and unsupervised classification. Some of the commonly used classification
## 2 Simulation Methods
### Phenomenological Turbulence Models and Particle Interaction  
The expression of the mixing term in Equation (4) is presented below:  
\[\frac{\partial\mathrm{F}_{\mathbf{\phi}}}{\partial\mathrm{t}}\bigg{|}_{\mathrm{ mix}}=\frac{\mathrm{C}_{\mathbf{\phi}}\beta}{\tau}\bigg{[}\int_{\Delta\psi}\mathrm{F}_{ \mathbf{\phi}}(\psi-\Delta\psi,\mathrm{t})\mathrm{F}_{\mathbf{\phi}}(\psi+\Delta\psi, \mathrm{t})\mathrm{d}(\Delta\psi)-\mathrm{F}_{\mathbf{\phi}}(\psi,\mathrm{t}) \bigg{]} \tag{5}\]  
\(\mathrm{C}_{\mathbf{\phi}}\) and \(\beta\) are two model parameters that in the present study have been set to 2.0 and 1.0, respectively. The mixing time history, \(\tau\) in Equation (5), is the main input parameter of the SRM. The mixing time is needed to model the turbulent mixing occurring in the combustion chamber due to various phenomena such as: fuel injection, swirl motion, tumble motion, etc. The shape and magnitude of the mixing time profile controls how intense the SRM particle mixing process is. Since \(\tau\) influences mixture inhomogeneities in both composition and enthalpy spaces, a strong influence on the auto-ignition process, the local rates of heat release and pollutant formation are seen when the mixing time is changed.  
A simple approach to construct the mixing time history is by using a set of empirical constants as done by [PERSON] et al. [43; 44] for Diesel combustion studies. A more comprehensive approach is to calculate \(\tau\) during run-time by employing a k-\(\varepsilon\) or K-k based turbulence model. Depending on whether Diesel or gasoline combustion are considered, different approaches have been implemented in the present work. For Diesel combustion, the approach initially proposed by [PERSON] [45] was adopted and validated by [PERSON] et al. [46] across a large set of operating points. For gasoline engine conditions, the K-k model proposed by [PERSON] et al. [47] was implemented and validated in the SRM framework for different gasoline fuel surrogate mixtures in [48; 49]. Examples of the mixing time histories that are computed with the mentioned turbulence models are shown in Figure 2 as function of crank angle degrees (CAD) assuming 0 as firing top dead center (TDCf).  
Figure 2: Exemplary mixing time histories computed using turbulence models for (**a**) Diesel engine conditions [47] and (**b**) gasoline engine conditions [49].  
Once the mixing time history has been computed, an additional sub-model is employed to decide which and how many particles are selected in each mixing step. In this work the Curl model [50] has been used to describe the particle interaction for both the Diesel and the gasoline engine simulations.
# A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing
## 5 Evaluations
### Basic Setting  
Our prototype is built on a workstation Precision T7910 with Intel Xeon(R) E5-2620 CPU, NVIDIA RTX5000 GPU, 128 Gb memory, and a 2 Tb hard disk, which runs a Linux operating system using python. We simulate our edge computing architecture based on the campus of Beijing University of Technology with a range of \(500\times 500\) m\({}^{2}\) and set up 10 mobile edge servers in synthetic datasets, as shown in Figure 4. For each server, the setting of computing capacity randomly ranges from 20 to 25 GHz. The storage of each server ranges from 8 to 16 GB, and the bandwidth between each pair of edge servers is 0.2 GHz. We set the transmission power to be \(tr=0.5\) W and the noise power to be \(N=2\times 10^{-3}\)[24]. In order to analyze the total delay with different numbers of users, we construct the synthetic dataset into three groups of size 20, 30, and 40. The data size of uninterrupted requests sent by users in a continuous timescale randomizes in [0.1 GB, 0.5 GB]. The settings of hyperparameters are listed in Table 2. In addition to the proposed placement algorithm, two state-of-the-art algorithms are used: dynamic service placement with no migration (DSP-NM) and dynamic service placement with all migration (DSP-AM).  
* DSP-NM: Services are placed on the initialized edge server, and there is no migration in the timescale of multiple mobile users.
* DSP-AM: Services always migrate according to the users' dynamic trajectories in the timescale.
## 5 Tunnel Limited Discharge Values Based on Vegetation Protection
### Research on Grouting Parameters for Tunnels  
As the nation advances in ecological civilization and high-quality development, stringent ecological requirements have been set for constructing mountain tunnels in fragile ecological areas [34; 35]. These areas are environmentally sensitive, and activities such as excavation, support, water inflow, and drainage during construction can cause irreversible ecological damage. Therefore, implementing reasonable grouting parameters to maintain groundwater depth and protect vegetation growth is crucial. Grouting parameters of tunnels are different depending on the specific geological and hydrological conditions of each tunnel (shown in Table 4). For example, the Yuanliangshan Tunnel, which experienced high water pressure, used a grouting circle with a permeability coefficient of 1.0 \(\times\) 10\({}^{-6}\) cm/s and a thickness of 5 m in high-pressure parts [36]. The groundwater level was 270-280 m above the tunnel crown in the Liaoshan Tunnel, where both radial and circumferential thickness of grouting reinforcement were 1 m [37]. Faults, rock integrity, and weathering affect grouting thickness. The vertical range of grouting reinforcement was 2-8 m in Guangfu Tunnel where there were many faults [38]. A grouting thickness of 6 m was optimal in the Cenxi Tunnel with fully weathered granite [39]. According to the above, these grouting parameters were obtained based on ensuring structure safety and preventing water inflow. In order to guarantee the normal growth of typical herbaceous plants, grouting parameters were studied in this paper. The reasonable grouting parameters were a thickness of 2 m and a permeability coefficient of 4.9 \(\times\) 10\({}^{-3}\) m/d. Thus, these parameters were different from the references.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Research** & \multirow{2}{*}{**Species**} & \multirow{2}{*}{**Plant Type**} & \multirow{2}{*}{**Precipitation**} & **Groundwater** & \multirow{2}{*}{**Reference**} \\  & & & & & & **Depth/m** & \\ \hline
1 & Qinghai & Herbaceous & _Artemisia annua, Stipa_ & Low & 0.6–0.8 & [32; 33] \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{Inner Mongolia} & Herbaceous, & _Pemisetum_ & & & \\  & & Shrub & _centrasiticum_ & Low & 0.5–2.0 & [30] \\  & & & _Artemisia halodendron_ & & & \\ \hline
3 & Xinjiang & Arbor &
\begin{tabular}{c} _Haloxylon_ \\ _annodendron_ \\ \end{tabular} & Low & 4.6–11 & [31] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Groundwater depth requirements for normal plant growth in different regions.
# Evaluation of Emotions from Brain Signals on 3D VAD Space via Artificial Intelligence Techniques
## 2 Materials and Methods
### Classes  
In this study, classes are set by considering high and low values of valence, arousal, and dominance scores. Class placements on VAD space are shown in Figure 5, in which the emotions are placed in the eight emotion regions. The valence dimension is considered high and low (not as positive or negative), just like the other dimensions (valence and dominance) in this study, to simplify the understanding.  
The letters in Figure 5 indicate the combined coordinates of arousal, valence, and dominance, respectively: A (HaHvHd); B (LaHvHd); C (LaLvHd); D (HaLvHd); E (HaHvLd); F (LaHvLd); G (LaLvLd); and H (HaLvLd). Four comparisons are conducted between the 3-dimensional regions as A&G (HaHvHd-LaLvLd), B&H (LaHvHd-HaLvLd), D&F (HaLvHd-LaHvLd), and C&E (LaLvHd-HaHvLd). In total, 5, 4-6, and 3-7 class separations are used for the A&G, B&H, and D&F emotion comparisons. Furthermore, 5, 4.5-5.5, and 4-6 class separations are used for the C&E emotion comparisons. 0-midpoint on VAD space in Figure 5 indicates 5-midpoint for the 1-9 scale for 5 class separation. For instance, for a 4-6 separation, a high value refers to \(\geq\)6, and a low value refers to \(\leq\)4.
# A Bandwidth-Efficient Dissemination Scheme of Non-Safety Information in Urban VANETs +
## 3 Design of a Scheme for Urban Dissemination
### Broadcast Suppression Mechanism
#### 3.1.1 Map-Based Adaptation for Cities  
The first method for detecting junctions that we are considering is the use of a digital map. At the moment of receiving a new packet, a vehicle checks its coordinates to determine if it is at an intersection or not. Given the case, it will use the map again to find out the distance to the junction center, \(d_{center}\). It will use this distance to compute a different contention delay, as shown in Equation (2):  
\[t_{j}=T_{j}\times(d_{center}/r_{junction}) \tag{2}\]  
Here, \(T_{j}\) is the maximum waiting time for vehicles at junctions and \(r_{junction}\) is the radius of the given intersection. This radius can be obtained from the digital map, as well. In this case, we want to prioritize the vehicle that is closest to the intersection center, in order to reach as many other vehicles as possible.  
We mentioned above that a vehicle located at an intersection will ignore every duplicate, except if it comes from the same intersection. If it is using the map-based method, it will be easy to check the other's location and its own in the map and see if they belong to the same intersection.
# Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions
## 2 Materials and Methods
### Methods
#### 2.4.4 A Method Based on Classification and Adaptive Window  
Reconstruction takes the classification results as the reference, and defective LST images can be filled using high-quality images obtained at a different time. For each missing value, similar pixels are defined as the closest effective pixels in the same class. To search for similar pixels, an adaptive window is used for both the target LST and the fill LST. The number of point pairs obtained by searching is defined as K, and a threshold value is set for K. The window size is initially set to a particular value, and if the threshold K is not satisfied, it is increased by two pixels. Because a large window size will take a long time to compute, a maximum window size can be defined. If the maximum window size is reached and threshold K is still not met, this signifies that this reference LST is not acceptable for this position; as a result, the missing value will be filled during post-processing.  
In the reconstruction process, threshold K is a crucial parameter. The size of the searching window will likewise be smaller with a smaller threshold. Due to the high spatial correlation of LST, the linear connection will fit better if similar points are concentrated in a short window. However, if the threshold is not high enough, a small number of outliers can cause a lot of havoc with the linear relationship. As a result, the threshold should be suitably modified to ensure accuracy. Based on our experience, K should be set to between 20 and 30 in most circumstances to obtain good results.  
Some invalid values cannot be recovered, because a suitable fill LST cannot be found. The invalid pixels in this area can be reconstructed by establishing their linear relationship with AMSR2 or ERA5 data within 10 days. Specifically, the time coordinates of the target LST and the fill LST are determined, and these two coordinates are taken as the starting point and endpoint of the timeline, respectively. All daily AMSR2 and ERA5 data on this timeline are extracted. We take the LST value of AMSR2 or EAR5 at the same position as the invalid pixel value as the ordinate and the position on the time axis as the abscissa; then, we determine a linear equation that simulates the changing trend of LST during this period. As a result, the LST value of invalid pixels can be calculated on the basis of the linear equation and its position on the time axis.  
Ideally, most invalid pixels can be recovered using the above method. Except for some outliers, almost all missing values will be recovered. This is because when most similar pixels are far away from the target pixel, the linear relationship is unrealistic. To solve this problem, we establish a 3 \(\times\) 3 pixel mean filter centered on outliers to remove these abnormal values.