esa-sceva/satcom-chunk-collection huggingface dataset:
# A Stronger Adaptive Local Dimming Method with Details Preservation
## 4 Experiment
### Experiment of Simulated Images  
#### 4.4.1 Subjective Experiment  
The simulated comparisons of the proposed local dimming method with LUT method, CDF method, and the method based on Otsu [10] are shown in Figure 11.  
For Figure 11a, the dark areas in red rectangles of CDF and LUT algorithms are brighter, leading to a lower CR than the other two methods. In contrast, the method in [10] missed details caused by reducing luminance. The proposed method is a balance between improving CR and preserving details.  
For Figure 11b, the clouds in the red circles of CDF and LUT methods are brighter than Figure 10b, which should be darker. For the clouds in the red rectangle by the method in [10], the image distortion is caused by overcompensation. For the image by the proposed method, the image contents in both the red circle and the red rectangle are well-compensated to improve CR and preserve details.  
For Figure 11c, CDF and LUT methods simply improve the overall image luminance but without improvement of CR and image quality. The method in [10] improved the luminance of bright areas and decreased that of dark areas, which shows a preferable image. The image of the proposed method shows a higher CR with a higher saturation. However, the mountain part is slightly unsatisfactory compared with the image obtained by the method in [10]. This may be because the low luminance is mapped to smaller by histogram equalization.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image** & **Backlight** & **CR** & **DR** \\ \hline \multirow{2}{*}{(a)} & \(BL_{0}\) & 11,209.24 & 1,630,700 \\  & \(BL_{3}\) & **11,284.18** & **1,631,900** \\ \hline \multirow{2}{*}{(b)} & \(BL_{0}\) & 8.97 & **2,059,400** \\  & \(BL_{4}\) & **9.14** & 1,974,200 \\ \hline \multirow{2}{*}{(c)} & \(BL_{0}\) & 3228.41 & 1,056,333 \\  & \(BL_{4}\) & **3272.41** & **1,156,091** \\ \hline \multirow{2}{*}{(d)} & \(BL_{0}\) & 1913.71 & **1,659,500** \\  & \(BL_{3}\) & **5464.29** & 1,656,385 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Objective evaluation comparisons of CR and DR. \(\mathbf{BL_{0}}\), the target backlight; \(\mathbf{BL_{i}}\), the optimal backlight; (a)–(d), images in Figure 10. The better performance is marked in bold.  
For Figure 11d, the images of CDF and LUT methods are bright to show rich details. In contrast, the image obtained by the method in [10] is distorted. Note that the red rectangle, seen as a high spatial-frequency part, retains more details by the proposed method compared with the image obtained by the method in [10].
## 3 TCM Protocol
### The Topology Maintenance Phase
#### 3.3.1 Movement of the Nodes within a Cluster  
After entering the topology maintenance phase, for the purpose of saving communication energy consumption, the sensor nodes only upload the data in their own TDMA time plots. In other time plots, they do not communicate with any neighbor nodes, and at this time, they will be in sleep mode and wait to be awakened in the next round. In the public time plot of the member nodes, the nodes do not send messages to cluster heads directly, but wait for a data transmission request \(Data\_req\) from a cluster head. The nodes will transmit the data to a cluster head only after they receive \(Data\_req\). If the movement of some sensor nodes has happened within the cluster, it can be described by the schematic of Figure 4. \(S_{j}\) moves from position \(D\) to \(C\) in cluster \(C_{j}\), and cluster head broadcasts the request \(Data\_req\) in its time plot. Because it moves within the cluster, the nodes can receive the request and upload the collected data. The schematic description of nodes' mobility in a sensor network is shown in Figure 4.
# Use of Evolutionary Computation to Improve Rock Slope Back Analysis
## 7 Case Study 2
### Comparisons of Failure Mechanisms  
Figures 11 and 12 shows comparisons of the original failure mode with those obtained from back calculation for \(T/H=\) infinity and \(T/H=2.5\), respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{_T/H = Infinity_} & \multicolumn{4}{c}{_T/H = 2.5_} \\ \cline{2-9}  & \(\sigma_{ci}\) (MPa) & _m\({}_{i}\)_ & _GSI_ & \(k_{h}\) & \(\sigma_{ci}\) (MPa) & _m\({}_{i}\)_ & _GSI_ & \(k_{h}\) \\ \hline Material 2 & 89.54 & 8.45 & 39.94 & 0.013 & 77.64 & 7.66 & 36.11 & 0.02 \\ Material 3 & 35.43 & 8.97 & 39.94 & 0.013 & 43.21 & 11.97 & 36.11 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 21: Back-calculated \(k_{h}\), \(\sigma_{ci}\), GSI and \(m_{i}\) from _PSO_.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{_T/H = Infinity_} & \multicolumn{4}{c}{_T/H = 2.5_} \\ \cline{2-9}  & \(\sigma_{ci}\) (MPa) & _m\({}_{i}\)_ & _GSI_ & \(k_{h}\) & \(\sigma_{ci}\) (MPa) & _m\({}_{i}\)_ & _GSI_ & \(k_{h}\) \\ \hline Material 2 & 89.54 & 8.45 & 39.94 & 0.013 & 77.64 & 7.66 & 36.11 & 0.02 \\ Material 3 & 35.43 & 8.97 & 39.94 & 0.013 & 43.21 & 11.97 & 36.11 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Back-calculated \(k_{h}\), \(\sigma_{ci}\) and _GSI_ from _GA_.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{_T/H = Infinity_} & \multicolumn{4}{c}{_T/H = 2.5_} \\ \cline{2-9}  & \(\sigma_{ci}\) (MPa) & _m\({}_{i}\)_ & _GSI_ & \(k_{h}\) & \(\sigma_{ci}\) (MPa) & _m\({}_{i}\)_ & _GSI_ & \(k_{h}\) \\ \hline Material 2 & 89.54 & 8.45 & 39.94 & 0.013 & 77.64 & 7.66 & 36.11 & 0.02 \\ Material 3 & 35.43 & 8.97 & 39.94 & 0.013 & 43.21 & 11.97 & 36.11 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 19: Back-calculated \(k_{h}\), \(\sigma_{ci}\) and _GSI_ from _PSO_.  
Figure 11: Comparison of original mode to failure modes of back-calculated parameters with \(T/H=\) infinity.  
Figure 12: Comparison of original mode to failure mode of back-calculated parameters with \(T/H=2.5\).  
The light green colors shown within the slope cross-sections in Figures 11 and 12 show the nature of the failure mechanisms. The results show satisfactory similarities in the failure mechanisms for parameters obtained from back calculation and the original slope failure mode. This indicates that with reasonable ranges for back calculation the failure mode is not significantly affected by the values of the parameters obtained from back calculation. In addition, \(T/H=2.5\) give a better representation of the failure mode when compared to the original failure mode as indicated by the dark line drawn on the original failure mode shown in Figure 12 when compared to that shown in Figure 11.
## 3 Methodology and Data Description
### Markov Processes for Uncertainty Modeling  
Markov processes are used to model the uncertainties and stochastic events that affect port operations and hinterland transport. This includes random delays due to equipment failures, congestion, and weather conditions. The Markov model is built on a set of possible states that represent different phases of the cargo movement, such as \"cargo at port\", \"cargo in transit\", and \"cargo delayed\".  
Formulation: The states and transition probabilities between them are defined using historical data on delays and breakdowns. The transition matrix governs the likelihood of moving from one state to another, allowing for the prediction of bottlenecks and delays.  
Steps: Historical data on cargo movement and delays are collected to estimate the transition probabilities. These probabilities are then used to simulate future scenarios and identify potential risks in the logistics chain.
## 4 Simulation Results and Discussion
### Test Using Live GPS Signals  
The proposed algorithm is then tested using live GPS signals. Due to a lack of available GPS-simulating pseudolites, a live GPS L1 signal is used instead of GPS-simulating pseudolites. Live GPS signals were collected using a GP 2015 RF board and an NI DAQ card [12]. The raw data was stored as a binary data file on a PC. The stored data is then processed by a Software Defined Radio (SDR) developed by the Seoul National University GNSS Laboratory [13]. The SDR output pseudorange includes the distance from the GPS satellites to the GPS antenna, clock errors, tropospheric and ionospheric delays, etc. The signal delay from the pseudolite to the user was manually generated and added. This simulated pseudorange was then used to test the proposed algorithm. Figure 17 illustrates the simulation procedure.  
Four GPS satellites and four corresponding pseudolites are used in this test. The GPS and pseudolite constellation is shown in Figure 18. To test for different geometries, user positions are set to both (3,3,0) and (20,20,0) meters. The PDOP for pseudolites with respect to User #1 and User #2 are 2.2371 and 5.7674, respectively. The PDOP for the GPS satellites is 15.0590.  
Test results for each case are illustrated in Figure 19. For both cases, the GPS receiver output is biased. The amount of bias is greater when the geometry is poor. However, the final estimated results place the users in the correct positions. The horizontal and vertical drums errors are 1.3709 m and 1.7564 m for User #1. For User #2, horizontal and vertical drums errors are calculated to be 2.7491 m and 2.4726 m.  
Figure 17: Simulation procedure.
# Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach
## 2 System Model  
Let us consider an uncoded massive MIMO uplink scenario, where the BS is equipped with \(N\) number of antennas, communicating with \(K\) single-antenna users over a Rayleigh fading channel [46; 47]. Rayleigh fading is commonly used to model wireless communication environments due to its practical relevance in reflecting multipath propagation effects. The received signal at the BS can be mathematically expressed as  
\[\mathbf{r}=\mathbf{G}\mathbf{x}+\mathbf{w} \tag{1}\]  
where \(\mathbf{r}\in\mathbb{C}^{N}\) is the received signal vector. The channel matrix between users and the BS is represented as \(\mathbf{G}\in\mathbb{C}^{N\times K}\). \(\mathbf{x}\in\mathbb{C}^{K}\) is the vector of the transmitted signal from the user end, and \(\mathbf{w}\in\mathbb{C}^{N}\) is the additive white Gaussian noise (AWGN) with zero mean and variance \(\sigma^{2}\). The described system model in the form of a matrix can be written as  
\[\begin{bmatrix}r_{1}\\ r_{2}\\ \vdots\\ r_{M}\end{bmatrix}=\begin{bmatrix}g_{11}&g_{12}&\cdots&g_{1N}\\ g_{21}&g_{22}&\cdots&g_{2N}\\ \vdots&\vdots&\ddots&\vdots\\ g_{M1}&g_{M2}&\cdots&g_{MN}\end{bmatrix}\begin{bmatrix}x_{1}\\ x_{2}\\ \vdots\\ x_{N}\end{bmatrix}+\begin{bmatrix}w_{1}\\ w_{2}\\ \vdots\\ w_{M}\end{bmatrix} \tag{2}\]  
The objective is to recover the transmitted signal vector \(\mathbf{x}\) from the received vector \(\mathbf{r}\). To accomplish this, we employ the linear MMSE criterion, which minimizes the mean square error between the estimated signal vector \(\hat{\mathbf{x}}\) and the actual transmitted signal vector \(\mathbf{x}\). The estimate of \(\mathbf{x}\) using linear MMSE is given by  
\[\hat{\mathbf{x}}=(\mathbf{G}^{H}\mathbf{G}+\beta\mathbf{I})^{-1}\mathbf{G}^{H }\mathbf{r} \tag{3}\]  
Here, \(\mathbf{G}^{H}\) denotes the Hermitian transpose of the channel matrix \(\mathbf{G}\), \(\mathbf{I}\) is the identity matrix, and \(\beta\) is a regularization parameter. For simplicity, we will refer to the linear MMSE estimator simply as MMSE in subsequent sections to maintain consistency.  
In massive MIMO systems, directly computing the inverse of the matrix \((\mathbf{G}^{H}\mathbf{G}+\beta\mathbf{I})\), as shown in Equation (3), is computationally prohibitive due to the high dimensionality of the matrices involved. MMSE can obtain near-optimal performance. However, when we employ a large number of antennas on a large scale, then the matrix inversion of \((\mathbf{G}^{H}\mathbf{G}+\beta\mathbf{I})\) becomes very high. To mitigate this computational complexity in detection, iterative methods are often employed.
## 3 CNN Model Construction
### Convolutional Block Attention Feature Refinement Module (CBAM-F)
#### 3.2.2 Spatial Attention  
The working principle of this method is to generate spatial attention maps by using the spatial relationships between features (Figure 7). The average and maximum pooling layers were applied to the channel dimension to generate feature maps, denoted as \(F^{s}_{avg}\in R^{H\times W\times 1}\) and \(F^{s}_{\max}\in R^{H\times W\times 1}\), respectively. The two feature maps were then concatenated along the channel dimension and processed using a standard convolution operation with an output channel of 1, followed by a sigmoid activation function. This generated a spatial attention map known as \(M_{s}\in R^{H\times W\times 1}\).  
The mathematical equation is as (5) shown.  
\[\begin{array}{ll}M_{s}(F)&=\sigma(f^{3\times 3}([Avgpool(F^{s});Maxpool(F^{s})]))\\ &=\sigma(f^{3\times 3}([F^{s}_{avg};F^{s}_{\max}]))\end{array} \tag{5}\]  
Equation (5), where: \(\sigma\) is the sigmoid activation function, \(f^{3\times 3}\) is a two-dimensional convolutional layer with a convolutional kernel size of \(3\times 3\), and \(F^{s}\) is the model input.
## 2 Tools and Methods
### Model Assessment Metrics
#### 2.2.1 Akaike Information Criterion (AIC)  
Model selection criteria serve as a valuable resource for identifying a model with appropriate structure and complexity from a range of potential candidates. The criterion can assess whether the selected model provides an optimal balance between goodness of fit and complexity [29]. This is the role of the information criterion. It is a general information-theoretic measure of model quality based on selection [30; 31; 32]. It is used when we have several models that we can use to predict the data [33; 34]. Essentially, the selection criterion will discard potential models that are either too simple for the data or too complicated [29].  
\[AIC=nln\left(\frac{SSE}{n}\right)+2k+\frac{2k(k+1)}{n-k-1}+n\ln(2\pi)+n \tag{12}\]  
where \(n\) is number of values, \(k\) is the total number of parameters estimated in the model, and \(SSE\) is the Sum of Squared Errors.  
In model design, it is possible to increase the fit of the model by adding parameters, which may result in an increase in model complexity. In this sense, AIC, AICc (corrected AIC used for small sample sizes) [30] and BIC are the most commonly used selection criteria. The goal is to achieve the simplest model with the fewest variables. This minimises the risk of excessive model complexity and prevents overfitting [28].
## 3 Results and Discussion
### Wall Thickness Analysis to Determine \(d_{\textit{mean}}\), \(d_{\textit{eq}}\) and msth
This kind of analysis of measuring \(d_{mean}\) and \(d_{eq}\) separately at different sections and along the entire length as a single geometry helps in understanding the consistency of the manufacturing process to fabricate channels with bends with different inclination angles. In addition, it can be seen that the measured _mtsth_ is between 23 \(\upmu\)m and 24 \(\upmu\)m in both specimens irrespective of the analysed section. This further illustrates that _mtsth_ is independent of the channel diameter and is more dependent on the material used for fabrication and the L-PBF process parameters. Therefore, _mtsth_ can be regarded as a surface irregularity parameter that is dependent on the manufacturing process and the material used for fabrication rather than the geometrical dimensions. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Part**} & \multirow{2}{*}{**ROI**} & \multirow{2}{*}{\(d_{mean}\)/**mm**} & \(d_{eq}\)/**mm** & \(d_{eq}\)/**mm** & \(d_{mean}\)/**mm** & \(d_{mean}\)/**mm** & _mtsth_/**mm** & _mtsth_/**mm** \\  & & & **(Cubic Spline)** & **(Gaussian Fit)** & **(Cubic Spline)** & **(Gaussian Fit)** & **(Cubic Spline)** & **(Gaussian Fit)** \\ \hline N1 & F & 1.5 & 1.27 & 1.27 & 1.31 & 1.33 & 0.024 & 0.024 \\ N1 & S & 1.5 & 1.29 & 1.29 & 1.32 & 1.33 & 0.024 & 0.023 \\ N1 & I & 1.5 & 1.29 & 1.29 & 1.32 & 1.32 & 0.024 & 0.024 \\ N2 & F & 1 & 0.79 & 0.78 & 0.85 & 0.84 & 0.023 & 0.023 \\ N2 & S & 1 & 0.79 & 0.79 & 0.85 & 0.85 & 0.023 & 0.022 \\ N2 & I & 1 & 0.79 & 0.78 & 0.85 & 0.85 & 0.023 & 0.023 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(d_{eq}\), \(d_{mean}\) and _mtsth_ evaluation for specimens N\({}_{1}\) and N\({}_{2}\). Figure 9: Wall thickness histogram with fitted Gaussian for geometrical mean diameter (\(d_{mean}\)) for specimen N\({}_{1}\).
###### Abstract  
Mapping of agricultural crop types and practices is important for setting up agricultural production plans and environmental conservation measures. Sugarcane is a major tropical and subtropical crop; in general, it is grown in small fields with large spatio-temporal variations due to various crop management practices, and satellite observations of sugarcane cultivation areas are often obscured by clouds. Surface information with high spatio-temporal resolution obtained through the use of emerging satellite constellation technology can be used to track crop growth patterns with high resolution. In this study, we used Planet Dove imagery to reveal crop growth patterns and to map crop types and practices on subtropical Kumejima Island, Japan (lat. 26\({}^{\circ}\)21\({}^{\prime}\)01.1\({}^{\prime\prime}\) N, long. 126\({}^{\circ}\)46\({}^{\prime}\)16.0\({}^{\prime\prime}\) E). We eliminated misregistration between the red-green-blue (RGB) and near-infrared band imagery, and generated a time series of seven vegetation indices to track crop growth patterns. Using the Random Forest algorithm, we classified eight crop types and practices in the sugarcane. All the vegetation indices tested showed high classification accuracy, and the normalized difference vegetation index (NDVI) had an overall accuracy of 0.93 and Kappa of 0.92 range of accuracy for different crop types and practices in the study area. The results for the user's and producer's accuracy of each class were good. Analysis of the importance of variables indicated that five image sets are most important for achieving high classification accuracy: Two image sets of the spring and summer sugarcane plantings in each year of a two-year observation period, and one just before harvesting in the second year. We conclude that high-temporal-resolution time series images obtained by a satellite constellation are very effective in small-scale agricultural mapping with large spatio-temporal variations.  
planet dove; random forest; sugarcane; time series; vegetation indices +
Footnote †: journal: Remote Sensing, 2020, 12, 2419; doi:10.3390/rs12152419
## 3 Results and Discussion  
### SEM and EDX  
SEM images show silver and gold nanoparticles covering the silicon rods with an average length of 0.5 \(\upmu\)m, see Figure 2a,b. The Si nanorods are multi-oriented in different spatial directions, distributed randomly in the space. This can increase the enhancement of the surface plasmon excitation towards the incident and scattered wave vectors' mismatch. Energy-dispersive X-ray analyzer (EDX) scanning images were recorded on the surface of Au/Ag-covered silicon nanorods, see Figure 3a. The percentage of the surface components was determined, including the essential ones, such as silicon (13% and 18%), silver (39.7% and 48%), and gold (2.5% and 3.5%). The high concentration of indium could be derived from such contaminants existing in the deposition chambers.  
Figure 1: Schematic representation of the different processes of fabrication. 1—cleaning process, 2—Electroless etching, and 3—Sputtering of gold and silver nanoparticles.  
Figure 3: EDX spectra on different positions of the sample. (**a**) SEM image shows the two locations where the EDX spectra were measured, scale bar 2.5 μm and magnification 5000\(\times\). (**b,c**) EDX spectra of the two positions indicating the presence of silver, gold, and silicon.  
Figure 2: SEM images of silicon nanorods covered with silver and gold nanoparticles. (**a**) With a low magnification \(\times\)22,000, 1 μm scale bar, and (**b**) with a high magnification \(\times\)100,000, 100 nm scale bar.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 7 BackCom: Range Extension and Reliability
### Coverage and Range Improvement
#### 7.1.2 Increasing Range  
Various techniques have been used to increase the communication range in BackCom. [PERSON] et al. maximized the range of the BiS radio channel by proposing the coherent receiver [66]. Similarly, long-range (LoRa)-dependent BackCom, to extend communication ranges and to include a wide area network, was achieved by chip spread spectrum (CSS) design [129]. The CSS design can successfully backscatter at a distance of 475 m. Figure 35 delineates the operating distance of the LoRa BackCom compared to the traditional RFID and WiFi backscatter systems. [PERSON] et al. proposed PLORA, an extended range data network for Amb-BackCom LoRa transmissions [162], PLoRA uses LoRa transmissions as excitation signals to convey the data. In order to improve the range, RFID technology combined with the bar-code technique on the same label was proposed [163]. A range extension of 12.5 m was achieved for a 12 cm\({}^{2}\) sized tag. Quantum tunneling-based BackCom considers a highly sensitive 5.8 GHz tag that can backscatter in free space up to 1.2 km and consumes only 20.4 uW [164; 165; 166]. The quantum tunneling tag (QTT) consists of a battery-assisted quantum tunneling reflection (QTR) connected to a tag antenna, and a waveform generator [167]. The tag antenna and waveform generator are responsible for the modulation, amplification, and backscattering of input carrier waves.  
Figure 35: Increased range in the LoRa BackCom.  
A power amplifier with an energy storage device for passive RFID tags was proposed to extend the communication range of backscattered devices by amplification [136]. [PERSON] et al. demonstrated a backscatter-based WPCN that could attain a high range compared to its active counterpart [168]. Ultra-wide-band technology was also considered to increase the range and bandwidth of a BackCom system [169]. [PERSON] et al. suggested a stochastic geometry approach in passive IoT networks to enhance the field of powered communications [170]. Their study showed that the communication range of BackCom can be equal to a traditional wireless communication range if power beacons are densely deployed.
# Coordinated Control of Virtual Power Plants to Improve Power System Short-Term Dynamics
## 1 Introduction
### Literature Review
#### 1.2.2 Control Structures  
The common control architecture of VPP are two kinds: centralized and decentralized. The centralized control structure is widely used for microgrids as well as VPPs [12; 13; 14; 15]. However, with the increasing number of loads and renewable devices, the communication and coordination between the control center and DGs, ESS and controllable loads has become a challenge. The communication system, in fact requires a higher transceiver power [16] and higher efficiency of the communication protocol [17] as the amount of transmitted data increases. If the bandwidth of the communication network is not adequate, the latency increases, which can negatively impact on the stability of the overall system [18]. To avoid the limitation of the communication network, a recently proposed solution consists of optimizing the decentralized control strategy through algorithms based on graph theory [19; 20; 21; 22; 23; 24]. Moreover, the cybersecurity of the communication network, e.g., replay attack, false data injection and denial of service might also affect the performance of VPPs [25; 26; 27]. The impact of the communication networks should be taken into account in relevant studies of VPPs, where this paper will duly discuss and illustrate.