esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis
#### 2.3.2 Quantification of AET, GPP, and WUE Responses to SPEI and Drought Intensification  
Pearson's correlation coefficient was used in order to assess the response of the sAET/sGPP/sWUE and the lagging effect of the monthly SPEI at 6 timescales for the studied time series. The correlations were applied at both the regional and pixel scales (i.e., more than 4700 pixels) and for each land cover type and climate pattern. A significance test of the correlation coefficient was applied using the \(F\)-test at \(p<0.05\) (i.e., 95% confidence interval), which indicates the relationship between the sAET/sGPP/sWUE and the lag effect of the monthly SPEI time series is statistically significant.  
Numerous methods have been applied for drought identification and characteristics [88; 89], including empirical orthogonal functions, run theory, and the threshold level method [90]. In this study, we determined the drought year(s) based on a threshold value of SPEI \(<-1\) (i.e., all negative values less than \(-1\) were considered). The effect is most noticeable as it leads to the identification of extreme and severe droughts. In this study, drought intensification within a year was considered DS, and it was analyzed for specific drought years (Figure 2). Therefore, the DS of a drought year Di is the absolute sum of SPEI values less than \(-1\) during the 12 months. We considered the first 5 worst drought years (D1, D2, D3, D4, and D5) and ranked them by intensity from the highest to lowest severity based on the DS:  
\[DS=\left|\sum_{i=1}^{DD}SPE_{i}\right| \tag{6}\]To calculate DS for a specific year, we first identified all the negative SPEI values (SPEI \(<-1\)) for that year. Next, we summed up the absolute values of these negative SPEI values for the 12-month period (DD, drought duration), representing the total drought intensity for that year. The result is the DS value for that specific year, and we ranked the years from highest to lowest DS values to obtain D1 to D5 categories, with D1 being the most severe drought event. It is important to note that DS is calculated independently for each year, regardless of whether it is part of a longer-term drought event. This approach allows us to assess the individual impacts of drought intensification on ecosystem carbon-water fluxes for each year without assuming any specific temporal pattern of drought occurrence.
# A Probabilistic VDTN Routing Scheme Based on Hybrid Swarm-Based Approach
## 5 Proposed VDTN Solution
### Synthesis  
The presented flowchart in Figure 3 covers the entire forwarding cycle of bundle from the source to destination vehicle.
## 2 Literature Review
### Long Short-Term Memory (LSTM)  
Recurrent neural networks (RNNs) with LSTM are renowned for their capability to learn and handle long-term data dependencies [24]. They are especially suitable for processing sequential data, as they are explicitly designed to address the challenges posed by long-term dependencies. The network incorporates specialized memory cells that enable it to retain or discard information based on the encountered needs, thus allowing for the storage of data over extended periods of time.  
The architecture of an LSTM network is centered around the concept of a memory block, each of which is composed of an input gate, an output gate, and a forget gate, which are represented as \(i\), \(o\), and \(f\), respectively, as visually depicted in Figure 2[25]. These gates play critical roles in regulating the behavior of the network. The input gate controls the activation of the memory cell, while the output gate governs the activation of the remaining components of the network [26; 27]. Within this framework, two vital states are maintained: the cell state, denoted by \(C\), and the hidden state \(h\). These states are central to the network's functioning and carry crucial information. Additionally, the architecture incorporates an activation function \(\sigma\) and bias vectors \(b_{f}\), \(b_{i}\), \(b_{C}\), and \(b_{o}\), which are integrated into the network. To clarify further, the functions \(f_{t}\), \(i_{t}\), and \(C_{t}\) are described as follows:  
\[f_{t}=\sigma(W_{f}=[h_{t-1},x_{t}]+b_{f}) \tag{4}\]  
\[i_{t}=\sigma(W_{i}=[h_{t-1},x_{t}]+b_{i}) \tag{5}\]  
\[\bar{C_{t}}=tanh(W_{C}=[h_{t-1},x_{t}]+b_{C}) \tag{6}\]  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\bar{C_{t}} \tag{7}\]  
Figure 1: Multilayer perceptron architecture [22].  
\[o_{t}=\sigma(W_{o}=[h_{t-1},x_{t}]+b_{o}) \tag{8}\] \[h_{t}=o_{t}*tanhC_{t} \tag{9}\]
# Potato Leaf Disease Segmentation Method Based on Improved UNet
## 3 Results
### Experimental Results and Analysis
#### 3.4.2 Comparison of Experiments with Different Models  
To test the improved RS-UNet network alongside UNet, SegNet, FCN, and CENet on augmented datasets, the relationship between loss and iteration count during convergence was examined, as depicted in Figure 10. The five network models exhibited a rapid decrease in loss within the initial 50 iterations and stabilized around 80 iterations. Figure 10 also illustrates that the improved UNet network outperformed the other four models, yielding the best results after 300 iterations. This improvement could be attributed to the adoption of ResNet50 as the encoder in the enhanced UNet network, which led to enhanced training speed and segmentation performance.
## 3 RS Applications in Ocean
### Ocean Surface Current (OSC)
#### 3.2.1 Optical  
Optical satellites provide high/medium spatial resolution and multitemporal and multispectral images for oceanographic studies over large areas [112; 127]. These images are typically captured in the visible (Red/Green/Blue) (0.4-0.7 \(\upmu\)m) and Near-, Mid-, and Far-Infrared (0.7-3.5 \(\upmu\)m) spectral ranges, and can be directly/indirectly used to investigate the spatiotemporal characteristics of OSC [112; 127]. For example, OC products of optical satellites can be indirectly applied to estimate the OSC [112; 116]. For instance, [PERSON] et al. [128] employed two matching algorithms, including area-based cross-correlation and feature-based optical flow approaches, to estimate OSC from OC image pairs. The results demonstrated a high similarity between the derived OSC and the Ocean Surface Current Analysis Real-time (OSCAR) products. It was also observed that the robust optical flow method outperformed the Maximum Cross-Correlation (MCC) algorithm in terms of accuracy. Furthermore, optical RS images can be directly used to retrieve surface wave directional properties and OSC characteristics. For instance, [PERSON] et al. [127] developed a new technique based on cross-spectral phase estimation to directly retrieve OSC and the characteristics of propagating ocean surface waves from Sentinel-2 images. The results showed that the retrieved OSC was well-matched with medium-resolution OSC models as well as the derived velocities from altimeter observations in deep ocean regions. The results over shallow water areas demonstrated that the retrieved wave propagation characteristics corresponded well with the sea-depth variations. These results confirmed the high potential of optical images in direct estimation of OSC velocity.  
Although the optical satellite images are rich in content and are easy to use for OSC mapping, they are sensitive to cloud cover and are not suitable for nighttime. Moreover, the long revisit time and inappropriate latency of some optical datasets have made some of them unsuitable for real-time OSC measurements.
# M2 Former: Multiscale Patch Selection for Fine-Grained Visual Recognition
## 2 Related Work
### Multiscale Processing  
Multiscale features are important for most vision tasks, e.g., object detection [34; 35; 38; 39; 40], semantic segmentation [41; 42; 43], edge detection [64; 65], and image classification [66; 33; 67], because visual patterns occur at multiscales in natural scenes. CNNs naturally learn coarse-to-fine multiscale features through a stack of convolutional operators [68; 69], so most research has been proposed to enhance the multiscale capabilities of CNNs [33; 34; 35; 36; 37]. FPN [35] introduces a feature pyramid to extract features with different scales from a singleimage and fuses them in a top-down way to generate a semantically strong multiscale feature representation. SPPNet [33] proposes spatial pyramid pooling to improve backbone networks to model multiscale features of arbitrary size. Faster R-CNN [38] proposes region proposal networks that generate object bounding boxes of different scales. FCN [36] proposes a fully convolutional architecture that generates dense prediction maps for semantic segmentation from hierarchical representations of CNNs.  
On the other hand, ViTs suffer from being unsuitable to handle multiscale features due to fixed-scale image patches. After the initial patch embedding layer, ViTs maintain image patches of the same size, and these single-scale feature maps are not suitable for many vision tasks, especially those requiring pixel-level dense prediction. To alleviate this issue, model architectures that adapt multiscale feature hierarchies to ViTs have recently been proposed. CvT [44] adapts convolutional operations to patch embedding layers and attention projection layers to enable feature hierarchies with improved efficiency. MViT [30] introduces attention pooling that controls feature resolution by adjusting the pooling stride of queries to implement multiscale feature hierarchies. PVT [31] proposes a progressive shrinking strategy that conducts patch embedding with different patch sizes at the beginning of each stage. SwinT [32] generates multiresolution feature maps by merging adjacent local patches using a patch merging layer. We also focus on feature hierarchies of fine-grained objects using MS-ViTs. However, we propose additional methods to further boost the multiscale capability, i.e., MSPS, CTT, and MSCA.
# Motor Vector Control Based on Speed-Torque-Current Map
###### Abstract  
In order to effectively extend the mileage of pure electric vehicles, the influence of the electromechanical energy conversion principle and the dynamic control of a permanent magnet synchronous motor (PMSM) on the performance of pure electric vehicles are studied with a dual-motor drive system as a carrier in this paper. A vector control strategy based on the speed-torque-current map of a motor is proposed, considering the bus voltage fluctuation influenced by battery charge and discharge. By constructing a complete vehicle model including the dynamic control model of the motor, the power distribution control strategy of a dual-motor coupling mode considering the dynamic characteristics of the motor is developed and simulated on the MATLAB platform. The results show that the dynamic model of the motor is closer to the actual running conditions. The proposed control strategy effectively reduces the demand power, decreases the energy consumption of the electric drive system, and extends the mileage of the vehicle.  
control strategy; dynamic control; pure electric vehicle; speed-torque-current map +
Footnote †: journal: Applied Sciences
## 1 Introduction
As a hyper-parameter, the tensor ranks play a crucial role in determining the architecture and total size of the deconstructed CNN model by quantifying the linear correlations in each dimension. Determining the most suitable rank is very complex and faces significant difficulties. Various automated rank-selection approaches have been developed to address this problem in model compression, ranging from reinforcement learning [14] to genetic algorithms [15]. In comparison to traditional manual selection methods, automatic rank-selection methods reduce human efforts for testing. However, almost all automated techniques have the following issue: as the compression ratio grows, the time required also increases because of the non-linear development in the complexity of finding a more optimal combination. To address the above issue, a novel approach is proposed to realize an efficient automatic rank-selection method based on sensitivity. Each convolutional layer's tensor rank strongly correlates with its redundant information and compression rate. Consequently, to effectively determine the optimal ranks for tensor decomposition, a simple principle is utilized to estimate the rank: the more significant the redundancy of a layer, the lower the rank that layer requires. Therefore, we define the sensitivity of a layer as the measure of its redundancy. For layers with greater sensitivity, any perturbation will have a significant effect on the network output, indicating that it is more difficult to compress and contains less redundancy. In this work, we leverage sensitivity to deal with two main issues with CP decomposition: rank selection and stability maintenance. Our main contributions are as follows:  
1. The sensitivity of each convolutional layer is computed by Hessian trace to determine the amount of redundancy of each layer, and then select the optimal ranks for CP decomposition by considering the redundancy we obtained. 2. In order to overcome the CP decomposition instability problem, an efficient optimization method is developed to quantify the sensitivity of CP decomposition and add sensitivity-constrained optimization during the process. 3. Another potential approach is proposed to address the problem of instability in CP decomposition by using iterative fine-tuning techniques. The layer that exhibits more sensitivity should be prioritized for compression to minimize the accuracy degradation of the compressed model. The rest of the paper is organized as follows: In Section 2, we cover related efforts in CNN compression. A detailed description of our approach is provided in Section 3. Our experimental results and discussion are described in Section 4. Finally, we present our conclusions in Section 5.
# Building Wireless Control Applications with XBee and LabVIEW
## 4 Experimental Tests
### Interferences with Wi-Fi  
The influence of Wi-Fi interferences over ZigBee is well-known, since both technologies work at the radio band of 2.4 GHz. Several works, such as [29; 30], provide a detailed analysis about this topic. In particular, in [30] it has been shown that ZigBee might be severely interfered by Wi-Fi, and they proposed a theoretical model that explained the phenomenon. In [30], a safe distance of 8 m between both technologies, has been recommended, to reduce interferences.  
In the present work, we were devoted to simply confirming the influence of interferences in communication. For this reason, the tests were carried out in a clearly unfavorable scenario, to experimentally measure the ill effects of the interferences between both technologies. The proposed test consisted on setting the two antennas (ZigBee and Wi-Fi) close to each other, at a distance of 30 cm, and analyzing the experimental results. In addition, large files were downloaded on the computer executing the application, while the XBee application was working. The results were analyzed to check whether the dropout increased due to the interference.  
A high-resolution video was downloaded by means of the 802.11n interface, while at the same time, the controller was acquiring data from the sensor by means of XBee. The authors experimentally measured the download Wi-Fi speed at the computer which, on average, was of 4.9 Mbps (see Figure 6). During this test, the message dropouts were checked and saved in one file for off-line analysis.  
The authors analyzed the behavior of the XBee communication at the Wi-Fi speed peaks and observed that at the peaks, up to the 60% of the messages were not received by the XBee on the computer (note that this test was clearly very unfavorable, since the authors maximized all the drawbacks, short distance, high traffic, etc.). This value went far beyond the dropout data obtained in Test 2. Obviously, this significant dropout was caused by interferences, since both XBee and Wi-Fi work at 2.4 GHz.  
Figure 6: Wi-Fi download speed during the test.  
Interferences caused by Wi-Fi at 2.4 GHz could be avoided by either using the XBee modules in a Wi-Fi free zone or using the XBee modules that operate in a different frequency (some XBee devices work at 868 MHz or 900 MHz).
## 4 Fault Localization Method Using Memory Updates
### Data from Test Specification and Memory Updates  
In the previous section, we prepare memory-updated information (MUI) by accumulating the frequency of the specific address and by extracting the symbol names from the executable file. Additionally, we have to obtain a test specification such as show the number of inputs, the test condition, the expected value and the input interval from the test script.  
Depending on the test suite, the details of the script may vary. However, the test condition and the expected values are essential. The test condition is input after initialization to confirm the normal operation of the function. The interval means time duration between inputs--between initialization and input or between inputs. The following is summarized.  
* Number of Input(N\({}_{\text{in}}\))--The number of inputs including initialization in the test script
* Input interval(T\({}_{\text{in}}\))--Time interval between inputs
* Test condition--Input values including initialization of input signal
* Expected value--Expected value including initial value of output signal  
Figure 6 shows an example of MUI. Figure 6a,b are the memory snapshots in color. Figure 6a shows a set of MU\({}_{\text{A, n}}\) of n-th frame in red, which means \"_updated_\". The addresses of the white area that look like the background means there are no changes in the values at that point of n-th frame. In (b), it is possible to identify the updated frequency of each address in the range of the full-frame. The updated frequency is visualized using different color palettes (white, yellow, green, blue, red). As frequency increases, the color changes from white to red and turns red if an address is updated on all frames. In (c), you can see the additional details of the MUI. The symbol \"_request_\" in the first line is assigned to address 0x2037 and the total number of updates is 10 because the value is continuously changed from #488 to #497 in every frame.
## 3 Experiment Results and Analysis of Dynamic Characteristics
### Influence of Loading Frequency on Reinforced Grawelly Soil
#### 3.2.2 Effect of Loading Frequency on Resilient Modulus  
The \(E_{d}\)-\(N\) curve of the samples loaded under different frequencies is shown in Figure 12. Similar to the results shown in Figure 8, the resilient moduli of the samples vary slightly during the first 100 loading cycles. Between 100 and 1000 loading cycles, the resilient moduli of the samples increase rapidly, then slow down after that. After 1000 loading cycles, the resilient moduli increase nearly logarithmically with the loading cycles. Figure 8a shows that the lower the loading frequency the higher the resilient modulus of a sample. This is opposite to that observed by [PERSON] et al. (2017) on cyclic behavior of coarse-grained subgrade. This may be due to the material difference (particle size distribution) and the inclusion of reinforcement. This suggests that train speed and interval between trains may affect the resilient modulus of the subgrade of tracks. By normalizing the resilient modulus of each sample with its maximum value, we can see that after about 4000 loading cycles, the resilient moduli of the samples increase with loading cycles almost at the same rate as shown in Figure 8b. This suggests that loading frequency may not affect the resilient increment rate in long term.  
Figure 11: Variation of cumulative axial strain with number of loading cycles under different loading frequencies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency (Hz)**} & \multicolumn{4}{c}{**Fitting Parameters**} & \multirow{2}{*}{**R2**} \\ \cline{2-2} \cline{4-5}  & **a** & **b** & **c** & **R2** \\ \hline
0.5 & \(-6.326\) & 2.764 & 8.887 & 0.994 \\
1.0 & \(-6.747\) & 2.469 & 11.46 & 0.999 \\
3.0 & \(-6.397\) & 2.091 & 8.972 & 0.983 \\
5.0 & \(-6.887\) & 1.933 & 10.470 & 0.997 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cumulative axial strain fitting parameters at loading different frequencies.
# Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN
## 4 Simulation and Numerical Analysis
### Numerical Analysis  
Figure 2 depicted the total residual energy of the proposed scheme as well as existing schemes with the comparison. The remaining energy is the most crucial parameter to estimate the performance of the wireless rechargeable sensor network. The remaining energy is computed after the completion of one energy cycle of the system. The evaluation shows that the total remaining energy of the system with the help of firework algorithm with the adaptive transfer function (FWA-ATF) significantly improved as compared to existing techniques.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Number of sensor nodes, N & 50 \\ Length & 1000 m \\ Breadth & 1000 m \\ E\({}_{max}\)—Maximum energy of a node & 10800 J \\ E\({}_{min}\)—Minimum energy of a node & 540 J \\ Base station location & [500, 500] m \\ WCV location & [0, 0] \\ Speed of WCV & 5 m/s \\ U & 5 W \\ Path loss & Log-Normal Shadowing \\ Antenna & Omni Directional \\ Algorithm & FWA-ATF \\ Simulation time & 1 h \\ Data communication radius & 100 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters [23; 24; 25].  
Figure 2: Remaining energy of nodes.  
Energy consumption refers to the total energy conserved by the network reception, transmission, and data processing. In the proposed model, a comparison is performed among the different techniques such as single hop, GR protocol [10], grid clustering [11], and firework algorithm with the adaptive transfer function (FWA-ATF) based on energy consumption. Energy consumption helps to predict the lifetime of the network. Figure 3 shows the comparison of power consumption of proposed and existing algorithms regarding energy consumption. It is evident that swarm intelligence based clustering algorithm achieves the best results compared with existing algorithms.  
Vacation time refers to the idle time of wireless charging vehicle after completion of one energy cycle. WCV will return to the service station to recharge itself. Figure 4 shows maximum vacation is significantly improved with the help the firework algorithm with the adaptive transfer function (FWA-ATF), achieving an almost 80% higher vacation time in comparison to GR protocol and grid clustering algorithms.  
Figure 4: Vacation Time.  
Figure 3: Energy Consumption.