esa-sceva/satcom-chunk-collection huggingface dataset:
# Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology
## 1 Introduction
### Purpose  
The purpose of this study is to introduce a method of quantitative spatiotemporal characterization and modeling of cloud cover and vegetation phenology for monsoonal landscapes. In so doing, we demonstrate an example application of the method and illustrate its potential to:  
* determine the number and location of distinct spatiotemporal patterns of monsoon-phenological activity,
* identify relationships between vegetation abundance/phenology and duration of cloud cover, and
* quantify spatially explicit historical trends (if any) in the atmosphere-biosphere system.  
Our example application generates results that are consistent with field observations and local knowledge. Novel features of the study area are also identified, which, to our knowledge, have not been previously documented. While the effect of cloud cover has long been recognized to often (but not always) reduce the NDVI of a pixel (e.g., [24]), to our knowledge the NDVI differential between clouds and the land surface has not yet been leveraged for spatiotemporal phenological characterization using EOF analysis. Importantly, the method developed in this study does not rely on any location-specific parameters, and so is expected to be sufficiently general for application in any landscape in which seasonal precipitation patterns strongly influence wild or managed vegetation.
###### Abstract  
This study aims to develop PM\({}_{2.5}\) prediction models using air pollutant data (PM\({}_{10}\), NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\), CO, and PM\({}_{2.5}\)) and meteorological data (temperature, humidity, wind speed, atmospheric pressure, precipitation, and snowfall) measured in South Korea from 2015 to 2019. Two prediction models were developed using an artificial neural network (ANN): a nationwide (NW) model and administrative districts (AD) model. To develop the prediction models, the independent variables daily averages and variances of air pollutant data and meteorological data (independent variables) were used as independent variables, and daily average PM\({}_{2.5}\) concentration set as a dependent variable. First, the correlations between independent and dependent variables were analyzed. Second, prediction models were developed using an ANN to predict next-day PM\({}_{2.5}\) daily average concentration, both NW and in 16 AD. The ANN models were optimized using a factorial design to determine the hidden layer layout and threshold, and a seasonal (month) factor was also considered. In the optimal prediction model, the absolute error in 1 \(\sigma\) was 91% (in-sample 91%, out-of-sample 91%) for the NW model, and the absolute error in 1 \(\sigma\) was 86% (in-sample 88%, out-of-sample 84%) for AD model. The accuracy of these prediction models increases further when they are developed using the next-day weather data, assuming that the weather prediction is accurate.  
ANN; PM\({}_{2.5}\); prediction model; air pollutant data; meteorological data +
Footnote †: journal: Medical Imaging
# Study on the Seismic Effect of the Pebble Soil Site in the Zhongwei Basin
###### Abstract  
Based on a large amount of drilling and geophysical exploration work in the Zhongwei Basin, and combined with the collected borehole data of a seismic safety assessment, this paper summarizes and builds 16 typical pebble soil layer calculation models. The effects of the thickness of the pebble layer, the thickness of the overlying silty clay, the top interface of the pebble layer on the peak acceleration and the response spectrum of the site seismic response were analyzed using the equivalent linearization method of the one-dimensional soil layer seismic response. The analysis results showed that the variation in pebble layer thickness had no obvious effect on the peak acceleration of the ground surface under different inputs; the influence of the pebble layer thickness on the ground acceleration response spectrum was primarily concentrated in the middle/high-frequency band of 0.2-0.6 s. Within this range, the acceleration response spectrum of the site with a 30 m pebble layer thickness was small, and the response spectrum curve showed a \"trough\" shape with a certain \"weak isolation\" effect. Under the same pebble layer thickness, the upper ground surface peak acceleration with a silty clay layer thickness increased with the increase in the central basin pebble soil field, where a short cycle of the seismic wave amplification effect was more obvious. The response spectrum peak period points were within 0.1-0.2 s and were influenced by the action of rare earthquakes. Moreover, the response spectrum curve showed a more obvious phenomenon of \"twin peaks\", and the second peak point appeared in the period of 0.5-0.7 s. With the increase in the input intensity, the PGA amplification ratio of the pebble-top interface was significantly smaller than that of the site surface; under different intensities of input, the acceleration response spectrum of the pebble-top interface showed a \"trough\" phenomenon that was lower than the bedrock input at approximately 0.1 s. Under the action of rare ground motion, the acceleration response spectrum curve of the pebble-top interface showed a \"double peak\" phenomenon, and within 0.24-0.4 s, the spectrum value was lower than the bedrock input, showing an obvious shock absorption and isolation effect. Under the action of an earthquake, the energy of the pebble-top interface was concentrated in the low-frequency range of 1.1-2.2 Hz, and the amplification effect was obvious. In the range of 8-10 Hz, the amplitude was lower than the bedrock input, and the seismic isolation effect was obvious.  
pebble soil site; seismic effect; peak ground acceleration; acceleration response spectrum; excellent frequency; equivalent linearization method +
Footnote †: journal: Applied Sciences
## 3 Control Method and Control Characteristics of the Switching Process of the DFVSPS Unit
### Pump Mode
#### 3.2.2 On-Grid Operation Stage  
During on-grid operation stage, the unit also has the stages of load ramping, load rejection and stable operation, which is consistent with the generating mode. The on-grid operation stage mainly controls the input power of the grid to the unit; therefore, the power priority control method is also adopted as shown in Figure 10. Compared with the generating mode, the main difference is that, in the pump mode, the load ramping stage and load rejection stage occur in the small guide vane opening part of the unit, and the stable operation stage is in the large guide vane opening part, so the model of the pump turbine will be different. The control method of each stage is consistent with the generating mode. The stepwise increase or decrease of the power is adopted in the load ramping stage and load rejection stage, and the input power of the pump turbine can be adjusted in the stable operation stage. The simulation model was established in MATLAB/Simulink and the simulation waveform is shown in Figure 14.  
Figure 14 shows the simulation waveforms of the unit in the on-grid operation stage under pump mode. The red line in Figure 14a-c is the reference value of unit active power and the black line is the unit active power simulation waveform obtained by the electromagnetic transient simulation model. Figure 14a shows the output power uploaded from the 30 MW to 240 MW. Figure 14b shows the waveform of active in first stage of the stable operation stage, the output power of unit increased 30 MW from 240 MW to 270 MW, followed by subtracting the 30 MW power; Figure 14c shows the active power waveform in load rejection stage, the unit output power is reduced from 240 MW to 30 MW; Figure 14d shows the speed in the stable operation stage corresponding to the active power adjustment in Figure 14b, and Figure 14e shows the guide vane opening in this process; Figure 14f  
Figure 13: Speed waveform during start-up stage.  
shows actual dynamic responses for a DFVSPS unit in stable operation. The same as the generating mode, it can be found that the simulation waveforms in the stable operation stage are similar to the actual responses in stable operation by comparing Figure 14b,d-f. In addition, the output power of the unit can be quickly tracked, and the unit speed requires a long adjustment time due to the existence of the significant inertia.
## 3 Deep Neural Network for DHSPI Wrapped Phase Denoising  
### Denoising Method  
Convolutional neural networks (CNN) have been successful in processing various visual tasks and have demonstrated effective performance in handling Gaussian denoising [35]. In this work, the proposed network for DHSPI wrapped phase denoising is based on improved denoising convolutional neural networks (DnCNNs) [35]. The deeper neural network architecture for DHSPI wrapped phase denoising is illustrated in Figure 4.  
In the proposed method, the noisy sine and cosine fringe patterns are denoising by CNN for solving the \(2\pi\) discontinuities in the noisy wrapped phase. As can be seen from Figure 4, the noisy sine \(S_{N}(u,v)\) and cosine \(C_{N}(u,v)\) fringe patterns are input data. The input data are first computed by 35 convolutional layers, and the rectified linear unit (ReLU) [36] for non-linear mapping and batch normalization (BN) layers [37] for easier training are used after each convolutional layer. Each convolutional layer (from the 1 st to 35 th layer) has 32 filters of size \(3\times 3\times 32\). Supposing that the input data of each layer (from 1 st to 35 th) is \(\beta_{i}\), the learned features \(Z_{i}(\beta_{i})\) on each layer are expressed mathematically as [38]  
\[Zi(\beta_{i})=ReLU[BN(\beta_{i}*W_{i})],\hskip 36.135 pti=1,\quad 2\quad\dots \quad 35 \tag{10}\]  
where \(W_{i}\) represents the weights, and the * represents convolution operation.  
The 36 th layer is the convolutional layer, which outputs the denoised sine \(S(u,v)\) and cosine \(C(u,v)\) fringe patterns. Then, an enhanced wrapped phase map \(\varphi(u,v)\) can be provided by arctangent function  
\[\varphi(u,v)=\text{atan2}[S(u,v),C(u,v)]. \tag{11}\]  
Network training is performed by finding the minimum loss function between the output data \(O(k,r)\) and the ground truth \(G(k,r)\). The adaptive moment estimation (Adam) optimizer [39] is chosen as the optimization method. A mini-batch size is expressed in \(b\), and the used loss function \(L\) is as follows  
\[L=\frac{\sum\limits_{i=1}^{b}\left\|O(k,r)-G(k,r)\right\|^{2}}{b}. \tag{12}\]  
In this work, the learning rate is set to \(1\times 10^{-4}\). The weights are automatically updated after each iteration, and the training is completed after 100 epochs. Pytorch framework is used to perform network training, and the training process is processed on Ubuntu 14.04 with an Intel(r) CoreTM i7-7820X [EMAIL_ADDRESS] GHz \(\times\) 13 using the NVIDIA GTX 1080.  
Figure 4: The architecture of the proposed network.
## 3 The Proposed Resource Consolidation Algorithm
```
1:\(\mathit{edgeServers}\)[i], \(\forall i\in\{1\ldots\ max_{\mathit{edge}}\}\);
2:\(\mathit{Services}\)[j], \(\forall j\in\{1\ldots\ max_{\mathit{service}}\}\);
3:\(\mathit{Requests}\)[k], \(\forall k\in\{1\ldots\ max_{\mathit{request}}\}\);
4:\(\mathit{Count}\): Allocation of requests to edge servers or the central cloud server
5:Initialization:\(\mathit{isAllocated}\leftarrow\) false, \(\mathit{allocateEdge}\leftarrow\) false;
6:for all\(\mathit{edgeServers}\)[i]do
7:\(\mathit{edgeServers}\)[i] \(\leftarrow\) null;
8:endfor
9:call\(\mathit{checkRequests}\)();
10:call\(\mathit{allocateRequests}\)();
11:function\(\mathit{checkRequests}\)()
12:for all\(\mathit{requests}\)[k]do
13:\(\mathit{requests}\)[k],\(\mathit{services}\)[j],\(\mathit{freq}\) \(=\) 1;
14: put\(\mathit{requests}\)[k] to HMM process;
15:endfor
16:endfunction
17:functionallocateRequests()
18:for all\(\mathit{requests}\)[k]do
19:if\(\mathit{requests}\)[k],\(\mathit{services}\)[j],\(\mathit{freq}\) \(==\) 1 then
20:allocate\(\mathit{requests}\)[k] to the central cloud server;
21:else
22:for all\(\mathit{edgeServers}\)[i]do
23:if\(\mathit{requests}\)[k],\(\mathit{services}\)[j] \(\in\)\(\mathit{edgeServers}\)[i]then
24:allocate\(\mathit{requests}\)[k] to \(\mathit{edgeServers}\)[i];
25:\(\mathit{edgeServers}\)[i],\(\mathit{count_{\mathit{request}}}\) \(+\) = 1;
26:\(\mathit{isAllocated}\leftarrow\) true
27:endif
28:endfor
29:if\(\mathit{isAllocated}\) == false then
30:\(\mathit{allocateEdge}\leftarrow\mathit{checkHMM}\)(\(\mathit{requests}\)[k]);
31:if\(\mathit{allocateEdge}==\) true then
32:allocate\(\mathit{requests}\)[k] to \(\mathit{edgeServers}\)[i] whose \(count_{\mathit{request}}\) is lowest;
33:\(\mathit{edgeServers}\)[i],\(\mathit{count_{\mathit{request}}}\) \(+\) = 1;
34:else
35:allocate\(\mathit{requests}\)[k] to the central cloud server;
36:endif
37:endif
38:endif
39:endfor
40:endfunction
```  
**Algorithm 1** **The proposed resource consolidation algorithm**
## 3 Applying the Methodology to the Engineering Case
### Engineering Background
#### 3.1.4 Geological Conditions  
The twin tunnels and excavation were constructed in typical soft soil strata. The groundwater level was at a depth of 0.5-1.0 m below the ground surface. The burial distribution of the various soil layers at the construction site is presented in Figure 5, with detailed characteristics and properties provided in Table 2. Based on Figure 5 and Table 2, it can be observed that the soil layers within the excavation depth of the foundation pit consist of 2a muddy clay, 2c mud, and 2d muddy clay, characterized by their gray color, fluid-plastic state, thick stratification, and high compressibility. These layers contain minor organic streaks and sility clumps, exhibiting non-uniform soil properties with an average moisture content of 48%. These layers belong to the Holocene (Q4) deposits, formed during the Quaternary period (approximately 10,000 years ago to the present). The strata where the tunnel is located comprise 3a clayey silty sand and 3b silty clay interbedded with silty sand. These soils are gray, loose to slightly dense, saturated, and thickly stratified and exhibit medium to low compressibility, with non-uniform properties and an average moisture content of 27%. These layers belong to the Late Pleistocene (Q3) deposits, formed during the Quaternary period (approximately 126,000 to 10,000 years ago). Overall, the site's soil layers are characterized by high moisture content, high porosity, and low density, representing a typical soft clay formation. The bottom of the isolation piles primarily lies within layers of silty clay and sand, which are more favorable for construction due to their lower water content, reduced compressibility, and higher strength, offering substantial end-bearing capacity.
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 2 Related Works
### Algorithms for Transmission Scheduling of Physiological Signals
#### 3.4.6 Method 6: WFQ with Timestamp and Priority (WFQ_Timestamp_Priority)  
The timestamp is provided so that the first arriving signal, even if it is low priority, can be delivered first. However, considering that high-priority signals still have the requirement for timeliness, the priority ranking is still necessary. Therefore, after getting the timestamp of the current first packet in each queue, the first arriving queue will be served first, and the remaining queues will be sent in order of priority. While clearing the queue can reduce the number of polling, the waiting time may become longer for higher priority packets. And so, the transmission bandwidth is assigned with the Equation (2), the queue is polled to the next queue after transmission, and the first packet in the queue is dropped first according to the FIFO principle. As shown in Figure 16, comparing the timestamp of the current first packet in each queue (e.g., the timestamp of ECG = 1,662,219,425.096878 (s), EEG = 1,662,219,425.099889 (s), and EMG = 1,662,219,425.072183 (s), respectively).  
Figure 14: The procedure of the WFQ with timestamp and empty buffer.  
The first arriving queue EMG will be served first, and then considering the priority of the remaining queues, the higher priority queue ECG will be delivered according to the allocated bandwidth before the EEG is forwarded.
# MPH-M, AODV-M and DSR-M Performance Evaluation under Jamming Attacks
## 4 Proposed Mitigation Mechanism for Jamming Attacks
### Mitigation of Jamming Attacks Criteria  
Detection of nodes under jamming: Each node is running a local-based decision mechanism to detect if jamming is affecting its communication parameters. Control packets are sent to notify its neighbors when jamming is detected in order to eliminate it from their routing table as a route.  
Routes reconfiguration: Once a node under jamming attack has been identified, it will be removed from the neighbor tables of the nodes that received the control packet. The self-configuring mechanism of the routing protocol will adjust the routes for excluding the jammed nodes.
## 2 Design Procedure
### Proposed Dual-Band BPF  
In order to realize an independently controllable dual passband and to shift the operating frequencies of the filter to the lower bands without scaling the filter layout, which leads to an undesirable large circuit size, a pair of flag-shaped resonators are designed and connected to the aforementioned bended coupling configuration as shown in Figure 5a. Applying the flag-shaped resonators pushes down the lower and upper passbands to 4.42 GHz and 9.5 GHz, respectively, as shown in Figure 5b. Each flag-shaped resonator comprises a high impedance line, a high impedance open stub and a low impedance open-end stub. Each section can be modeled using a series inductor and one shunt capacitor as depicted in Figure 5c. In this model, \(\mathrm{C_{H1}}\) and \(\mathrm{L_{H1}}\) describe capacitance and inductance of the high impedance line. \(\mathrm{C_{L1}}\) and \(\mathrm{L_{L1}}\) are capacitance and inductance of the low impedance open-end stub. Additionally, capacitance and inductance of the open stub are presented by \(\mathrm{C_{O}}\) and \(\mathrm{L_{O}}\), respectively. In order to calculate the lumped element values of this LC model, the method described in [6; 56; 61] can be used, in which the initial values are calculated [60] and then optimized to match the frequency responses of the LC model and the electromagnetic (EM) simulation.  
Figure 4: Adjustability of the lower passband by varying A6: (**a**) \(\mathrm{|S21|}\), (**b**) \(\mathrm{|S11|}\).  
To lower the insertion loss of the second passband, a pair of stepped-impedance resonators, including a high impedance line and a low impedance open-end stub, are added to the BPF. The final geometrical configuration of the proposed BPF is shown in Figure 6a. The EM simulation results of this structure are presented in Figure 6b. The capacitances and inductances of the stepped-impedance lines are displayed in Figure 6c. In the LC model, the capacitance and inductance of the high impedance line are depicted by C\({}_{\rm H2}\) and L\({}_{\rm H2}\). C\({}_{\rm L2}\) and L\({}_{\rm L2}\) denote the capacitance and inductance of the low impedance open-end stub, respectively.  
Figure 5: Integration of flag-shaped resonators with the bended coupled system. (**a**) Layout, (**b**) EM simulation, (**c**) LC model of flag-shaped resonator. The dimensions of the introduced flag-shaped resonators are as follows: A\({}_{7}\) = 2.7, A\({}_{8}\) = 1.9, A\({}_{9}\) = 0.2, A\({}_{10}\) = 4.3, A\({}_{11}\) = 2.2, A\({}_{12}\) = 0.2, A\({}_{13}\) = 0.2, A\({}_{14}\) = 1.32, A\({}_{15}\) = 3.2 (all in mm).
## 4 Numerical Simulation Results and Discussion
### Numerical Simulation Results and Discussion
#### 4.2.2 Comparison of Blade Width  
Figure 19 shows the contact force, contact force range and standard deviation of four kinds of disc cutter with the same blade fillet (5 mm) and different blade widths (19, 22, 25 and 28 mm) at different penetration depths (5, 10, 15, 20 and 25 mm). It can be seen from Figure 19a,b that with the same blade fillet, the wider the blade width, the greater the contact force. As can be seen from Figure 19c,d, with the same penetration, the range and standard deviation of contact force both increase with increasing blade width.  
Figures 20 and 21 show the equivalent stress and stress contour of rock breaking using four kinds of disc cutter with different blade widths and a 5 mm blade fillet. It can be seen that the influence range of the disc cutter during rock breaking increases with increasing blade width. At the same time, the shapes of the rock-breaking influence range with the disc cutter under the four different blade widths are all trapezoid.  
Figure 19: The contact force of BF 5 mm disc cutter with different blade widths. (**a**) Normal force; (**b**) Rolling force; (**c**) Range and STD of normal force; (**d**) Range and STD of rolling force.  
Figure 21: Stress contour of rock breaking using disc cutter with 5 mm blade fillet. (**a**) Stress contour of rock breaking (Blade width 19 mm); (**b**) Stress contour of rock breaking (Blade width 22 mm); (**c**) Stress contour of rock breaking (Blade width 25 mm); (**d**) Stress contour of rock breaking (Blade width 28 mm).  
Figure 20: Equivalent stress isosurface of rock breaking using disc cutter with 5 mm blade fillet. (**a**) Isosurface of equivalent stress (Blade width 19 mm); (**b**) Isosurface of equivalent stress (Blade width 22 mm); (**c**) Isosurface of equivalent stress (Blade width 25 mm); (**d**) Isosurface of equivalent stress (Blade width 28 mm).