esa-sceva/satcom-chunk-collection huggingface dataset:
# An Effective Wide-Bandwidth Channel Access in Next-Generation WLAN-Based V2X Communications
## 3 Proposed Scheme and Its Variants
### Alternative Channel Access Schemes for 20-MHz Transmission
#### 3.2.2 Sensing of 20-MHz Channel for all Durations of Back-Off Procedure (All Back-Off EIFS)  
Another 20-MHz channel access variant scheme is the modification of the proposed channel access method described in Section 3.1.2 to allow for one-transceiver design. In order to guarantee fairness, 20-MHz channel sensing for the duration of the back-off procedure is performed, as in the proposed method. Since this scheme also requires one transceiver, ED sensing can only be performed on the secondary channel during the back-off procedure. In this scheme, the 20-MHz channel is determined to be idle when each 10-MHz channel is sensed to be idle as a result of PD and ED sensing in the primary channel and ED sensing in the secondary channel. When a station has data to transmit using a 20-MHz channel, AIFS sensing and a back-off procedure are performed in the 20-MHz band, which can ensure a channel idle state for each decrement of the back-off counter. Since only ED sensing can be applied to the secondary channel, any detected busy state invokes EIFS duration waiting, as shown in Figure 7.
# Preparation and Characterization of Gold Nanorods Coated with Gellan Gum and Lipoic Acid
## 2 Materials and Methods
### In Vitro Evaluation of the Hyperthermal Effect on HCT116 Cell Line  
Human colon cancer cells (HCT116) were seeded in 24-well plates at the density of \(8\times 10^{4}\) cells per well, in supplemented DMEM and maintained at 37 \({}^{\circ}\)C (5% CO\({}_{2}\)) for 24 h. Cells were then incubated with AuNRs/LA, AuNRs/LA,GG, and AuNRs/GG-LA dispersions in DMEM at the concentration of 5 \(\mu\)g\(\cdot\)mL\({}^{-1}\) and placed again at 37 \({}^{\circ}\)C for 24 h. Subsequently, cells were treated with a diode laser (\(\lambda\) = 810 nm) setting the power of \(10\times 10^{-3}\) W/mm\({}^{3}\) for 20, 40, 60, 80, or 100 s. On completion, the supernatant was discarded and substituted with 450 \(\mu\)L of DMEM and 50 \(\mu\)L of MTS solution. Cells were thus incubated for 2 h at 37 \({}^{\circ}\)C and the absorbance at 492 nm was read in each well using a microplate reader (Eppendorf PlateReader AF2200). The same procedure was applied to untreated cells, with or without laser treatment. Cell viability was calculated as a percentage with respect to the untreated cells used as control.  
The cytotoxic efficacy of the tested systems was also qualitatively assessed by performing the LIVE/DEAD assay (Thermo Fischer, Eugene, OR, USA) to discriminate living cells from seriously damaged or dead ones. In particular, the experiment was carried out on cell samples (\(8\times 10^{4}\) cells per well) incubated with AuNRs/LA, AuNRs/LA,GG, and AuNRs/GG-LA and treated for 100 s with a diode laser (\(\lambda\) = 810 nm; power = \(10\times 10^{-3}\) W/mm\({}^{3}\)). LIVE/DEAD assay was carried out following the manufacturer's specifications. Images were acquired by a fluorescence microscope using an Axio Cam MRm (Zeiss, Oberkochen, Germany).
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### Rar & Racaa  
Reference [59] proposes two routing schemes, namely the reliability aware routing scheme or RAR for applications that require reliability and low end-to-end delays, and the reliability aware cooperative routing with adaptive amplification or RACAA, for applications that require reliability, but are delay tolerant. RAR involves the establishment of reliable end-to-end connected routes from source to destination based on neighborhood information (Figure 15), before the actual transmission of data packets. Multiple routes are considered, and the route with the highest probability of successful packet delivery is selected for transmission. However, the quality of the underwater channel is prone to fluctuation, and the performance of the pre-determined routes of RAR may fluctuate too. Therefore, RACAA is introduced to compliment RAR. In RACAA, a forwarder may increase its transmission power if the error in the data that it received for forwarding is higher than a certain threshold value. The rest of the operations of RAR and RACAA are the same. Neither of the proposed protocols require location information, which is a challenging task in the underwater environment, and involves further communication overhead. This makes the design of RAR and RACAA simpler, compared to other routing protocols that do require location information in order to route data packets. Moreover, adaptive amplification based on error probability (which is based on channel conditions) improves reliability and the end-to-end delivery ratio. Reliability is further improved by combining copies of the same packet (at the destination node) from the source and a relay node.
## 3 Proposed Methodology
### Fast Local Laplacian Filter
The use of the Laplacian pyramid and modified Laplacian with resizing ensures that the enhancement process is applied on multiple scales, preserving fine details and textures while improving contrast. The final result, '\(E\)', represents the enhanced image that retains local characteristics while having better contrast. Top of Form. The Fast Local Laplacian Filter technique offers a sophisticated solution for contrast enhancement in images. By exploiting the Laplacian pyramid and the bilateral filter, it achieves localized enhancement while preserving essential details and textures. This adaptability makes the FLLF technique suitable for a wide range of images with varying contrast levels and textures, contributing to improved visual quality while retaining the natural appearance of the image. The FLLF algorithm (Algorithm 1) is presented below;  
```
Input: Image \(A^{ip}\)Mathematical Symbols: GaussianBlur \(\textit{GaussianBlur}\left(\textit{image, Kernel\_size}\right)\)Resizing: Resized \(\textit{G}[i+1]=\textit{Resize}\left(\textit{G}[i+1],\ \textit{Dimensions}_{of}(\textit{G}[i])\right)\)Laplacian: \(L_{i}=\textit{G}[i]-\textit{Resized\_G}[i+1]\)BilateralFilter: \(\textit{BilateralFilter}\left(\textit{image, \ d_{\textit{base}, \ d_{range}}}\right)\)Resizing: Resized\(\_\textit{ML}[i]=\textit{resize}\left(\textit{ML}[i],\ \textit{dimensions}_{of}(\textit{G}[i])\right)\)EnhancedImage: \(E=\textit{G}(0)+\textit{Resized\_ML}[0]+\sum i=1\textit{num\_level}-1\textit{ Resized\_ML}[i]\)Parameters: \(d_{\textit{base},d_{range}}\,\ num_{\textit{levels}}\)Step 1. Generate Gaussian Pyramid: Create an empty Gaussian pyramid list \(G\)  Append \(I\) to \(G\) (base level) // Now this is a for-loop For Loop i in range \((1,\ \{num_{\textit{levels}}\})\) : Apply Gaussian blur to \(G[i-1]\) with kernel size proportional to \(i\)  Append the blurred image to \(G\) End For Loop Step 2. Generate Laplacian Pyramid: Create an empty Laplacian pyramid list \(L\) // Now this is a for-loop
```  
**Algorithm 1** Fast Local Laplacian Filter (FLLF)
```
ForLoop\(i\) in range \((0,\ \{num_{levels}\}-1)\) :  Resize \(G[i+1]\) to the dimensions of \(G[i]\)  // Compute Laplacian pyramid \(L_{i}=G[i]-Resized\ G[i+1]\) using Equation (1)  Append \(L_{i}\) to \(L\) EndForLoop Step 3. Enhance using Modified Laplacian:  Create an empty modified Laplacian pyramid list \(ML\)  // Now this is a for-loop ForLoop each level \(L_{i}\) in \(L\) :  Apply Bilateral filter to \(Li\) with spatial distance \(d_{base}\) and range similarity \(d_{range}\)  Append the filtered result to \(ML\) EndForLoop Step 4. Reconstruct Enhanced Image:  Create an empty list \(E\)  // Now this is a for-loop ForLoop\(i\) in range \((\{num_{levels}\})\):  // Compute modified Laplacian  Resize \(ML\)\([i]\) to the dimensions of \(G[i]\) using Equation (2)  Append the resized \(ML\)\([i]\) to \(E\) using Equation (3)  Sum all elements of \(E\) and \(G\)\([0]\) element-wise  The reconstructed enhanced image \(E\) is obtained EndForLoop Output: Enhanced Image E
```  
**Algorithm 1**\(Cont\).
## 3 Application of Automatic Optimization System
### Build MCM Optimization Equation  
Before the automatic optimization framework for the MCM thermal layout, it is necessary to first construct the optimization equations for the MCM model, which involves determining the variable space, constraints, and objective function of the MCM model.  
The MCM physical model mainly consists of 14 heat sources and a PCB board divided into specified dimensions. The problem can be abstracted as distributing 14 variable points in 36 grids in a reasonable manner to simultaneously minimize the local maximum temperature of the PCB board and maximize the temperature uniformity of the entire PCB board. Therefore, the MCM optimization equations are formulated as shown in Equations (1)-(4).  
Variance:  
\[x_{i}i=1,2,3,\ldots,14 \tag{1}\]  
Fitness:  
\[minF_{1}(x_{i})=maxT_{n} \tag{2}\]  
\[minF_{2}(x_{i})=maxT_{n}-minT_{n}n=1,2,3,\ldots,36 \tag{3}\]  
S.T.  
\[1<x_{i}<36,x\subset Zi\
eq j,x_{i}\
eq x_{j},(i,j)=1,2,3,\ldots,14 \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Module** & **Material** & **Thickness [\(\mu\)m]** & \(\lambda\)[\(\frac{W}{(m\cdot K)^{-1}}\)] & c [\(\frac{1}{kg}\cdot^{\circ}\)C] & \(\rho\)[\(\frac{g}{cm^{3}}\)] \\ \hline \multirow{3}{*}{Substrate} & Al\({}_{2}\)O\({}_{3}\) & 2000 & 18 & 840 & 3.5 \\  & Sintered W & 20 & 173 & 130 & 19.25 \\  & Gold-boardd & 0.5 & 317 & 130 & 19.3 \\ \multirow{2}{*}{Chip 1 \(\sim\) 14} & Silicon & 300 & 139 & 703 & 2.32 \\  & Sn-Ag-Cu & 50 & 78 & 76 & 7.4 \\ \multirow{2}{*}{Bonded material} & Air & - & 0.027 & 1006 & 1.205 \\ \hline \hline \end{tabular}
\end{table}
Table 2: MCM thermal parameters.  
Figure 5: Diagram illustrating the structure of a single chip.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Chip** & **Power [W]** & **Chip** & **Power [W]** \\ \hline A1, A2 & 0.1 & E1, E2 & 0.5 \\ B1, B2 & 0.2 & F1, F2 & 0.6 \\ C1, C2 & 0.3 & G1, G2 & 0.7 \\ D1, D2 & 0.4 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Equivalent power consumption of 14 chips.  
In this context, \(x\) is the variable of the MCM model, \(T_{n}\) is the temperature value of the number n grid. The variance refers to the variables of the MCM model, with a total of 14 variables, and each variable has the number of \(1\sim 36\) sample space. The fitness function aims to minimize the maximum temperature of the grid while maximizing the temperature uniformity of the grid. S.T. represents the constraints, indicating that any two variables cannot have the same value, further specifying that chips cannot be placed in units 1 and 36. This constraint is applied to simulate real-world engineering limitations related to installation or processing factors.  
This part utilizes specific constraints to describe the limitations of chip placement within the MCM which are also constraints added by the optimization module in the automatic system.
# On Sharing Spatial Data with Uncertainty Integration Amongst Multiple Robots Having Different Maps
## 4 Obstacle Removal and Update in T-Node Map
### Integrating Uncertainty in Confidence Decay Mechanism
If the blocked flag is set to 1 and the current navigational path is affected, the timestamp and other meta-data for the blocked edge are checked. Based on the obstacle's confidence value, path re-planning or continuation on the same path can be decided according to the priority of the task at hand. A major benefit of tightly coupling the obstacle's uncertainty with confidence decay mechanism is minimizing the false map updates corresponding to the dynamic obstacles in vicinity. Generally, the uncertainty of dynamic obstacles is larger than that of static obstacles estimated by the underlying SLAM module. In the absence of uncertainty integration, confidence all the obstacles irrespective of their positional uncertainty decays at the same rate. Therefore, if there is a false map update corresponding to a dynamic obstacle (like moving people), it decays at the same rate like other fixed obstacles. This increases the chances of false map updates due to dynamic obstacles. However, with the uncertainty integration, the confidence of obstacles with larger positional uncertainty decay faster than those with less uncertainty. In effect, this allows minimizing false map updates, as they decay out quickly. This also prevents false notifications to other robots.
## 3 Methods
### S2 MP for SSM Estimation
#### 3.1.1 SSM Estimation at Plot Scale  
The proposed approach relied on identifying variations in the level of surface soil moisture at the plot scale. For each SAR date, the SSM\({}_{\text{S1}}\) values were estimated at each plot, denoted by SSM\({}_{\text{P}}\) (Figure 4). When the SSM\({}_{\text{P}}\) level increased between two successive synthetic SAR acquisitions, the \(\sigma_{\text{P}}^{0}\) value also tended to increase, leading to an increasing SSM\({}_{\text{P}}\). Consequently, if an irrigation event occurred between two SAR acquisitions, the SAR SSM\({}_{\text{P}}\) value would show an increase [16].  
Additionally, the stability or slight decrease of SSM\({}_{\text{P}}\) values between two dates could also be linked to irrigation and not only when increasing values of SSM\({}_{\text{P}}\) are observed (as mentioned in the previous paragraph), particularly when SSM\({}_{\text{P}}\) values are already high. In fact, without any interventions like irrigation, rainfall, soil activities, or vegetation growth, the SAR signal tends to decrease between two SAR acquisitions (within a maximum of 6 days), particularly during the spring and summer seasons. This decrease is influenced by various water cycle factors, such as infiltration, evaporation, and evapotranspiration, which contribute to a decline in SSM values. Thus, the stabilization or slight decrease in SSM\({}_{\text{P}}\) between the SAR acquisition date t(i) (SSM\({}_{\text{P}_{(i)}}\)) and the subsequent acquisition date t(l) (SSM\({}_{\text{P}_{(i)}}\)) could have been due to an additional water supply if no rainfall had occurred between t(i) and t(l).  
Since the method revolved around detecting changes in SSM\({}_{\text{P}}\) values, we proposed calculating the rate of change between the SSM\({}_{\text{P}}\) value at each SAR  
values decreased or remained stable between two SAR acquisitions, it likely indicated the absence of precipitation or low precipitation amounts, resulting in negative or null values of \(\Psi\)SSM\({}_{\text{C}_{(1)}}\). By contrast, as discussed in the previous sections, increasing values of SSM at the plot scale (positive \(\Psi\)SSM\({}_{\text{P}_{(1)}}\)) could be attributed to irrigation, rainfall (leading to positive \(\Psi\)SSM\({}_{\text{C}_{(1)}}\)), or a combination of both. Therefore, a positive difference between \(\Psi\)SSM\({}_{\text{P}_{(1)}}\) and \(\Psi\)SSM\({}_{\text{C}_{(1)}}\) (\(\Psi\)SSM\({}_{\text{P}_{(1)}}-\Psi\)SSM\({}_{\text{G}_{(1)}}>0\)) was assumed to signal the occurrence of plot-scale irrigations.
## 3 Dataset and Methods
### Methods
#### 3.2.3 SQp and SQs Attributes  
The outcomes of pre-stack simultaneous inversion, Vp/Vs, and density 3D cubes are implemented to generate 3D SQp, and SQs attributes through math trace and equation. Equation (3) was used to generate 3D SQp attributes, and Equation (4) was utilized to obtain 3D of SQs attribute.  
Figure 8: Regression coefficients, as determined from elastic rock property cross plot. The outputs of simultaneous inversion of P-impedance, S-impedance, density, and Vp/Vs ratio were further implemented to generate 3D cubes of SQp and SQs attributes. Additionally, these outputs were also used for further ANN modelling.  
Figure 7: (**a**) The extracted statistical seismic wavelet and (**b**) The extracted wavelet from wells.  
These attributes are proven to be effective in litho-fluid delineation [26]. The changes of these attributes' values will help to identify hydrocarbon-bearing intervals and their continuity in sections. Low values of SQp attribute indicate the presence of sand lithology and high values of SQs attribute indicate the sand porosity filled with hydrocarbons [27]. The sections and averaged data horizons of SQp and SQs attributes were analyzed to identify hydrocarbon-bearing zones and their continuity.
# Characterization of Narrowband Noise and Channel Capacity for Powerline Communication in France
## 2 Experimental Measurement Setup and Methods
### Experimental Procedure
#### 2.2.3 Measurement Scenarios  
The noise temporal and frequency characteristics can be extracted from specific scenarios. The first scenario consists in measuring the noise PSD at the same time of the day in all sites. The aim is to get a better understanding of the noise profile variations at a specific time (i.e., 10 a.m.) depending on the various locations. At that time, the number of loads connected to the network is low.  
Many measurements are carried out at 2 p.m. to study the long-time variation of the noise PSD. The choice of this specific time is due to the increase of the number of loads connected to the electrical network. The measurements are carried out both in the transformer substation and the customer installation. The aim is to compare the noise PSD measurement at 2 p.m. with the measurement at 10 a.m. The results will be discussed in the next sections of the manuscript. All measurements are performed during the day, because it is impossible to measure the noise PSD at night.  
The last scenario focuses on the cyclo-stationary behavior of NB-PLC noise in time-frequency representation. The duration of one orthogonal frequency division multiplexing (OFDM) does not exceed one mains period. Regarding the G3-PLC protocol, the duration of one OFDM frame is between 20 and 190 ms. The duration of one PRIME OFDM frame varies between 145.65 and 581.63 ms. The short time variation in the meter side was presented in a previous work [35]. The results will be analyzed in the next sections of the manuscript.
## 3 Results
### Variability and Drivers  
EOFs are used to explore timescales and drivers of variability of sea heights in the Caribbean basin where Jamaica is located. As previously noted, EOF analysis was performed (i) on the raw data, (ii) with the linear trend removed (detrended), (iii) with the seasonal cycle removed (deseasoned), and (iv) the detrended, deseasoned and smoothed data. The variance explained by the first three principal components in each case (PC1, PC2, and PC3) are shown in Table 3. The spatial loadings and corresponding timeseries for the first principal components for each case (referred to respectively as PC1_raw, PC1_detrended, PC1_deseasoned, and PC1_des_det) are shown in Figure 3. The calculated correlations between the PC1 and PC2 timeseries and the 10 indices listed in Table 2 are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & \multicolumn{3}{c}{**Explained Variance (\%)**} \\ \cline{2-5}  & **Raw** & **Detrended** & **Deseasoned** & **Detrended and Deseasoned** \\ \hline PC1 & 36.84 & 29.88 & 26.68 & 54.28 \\ \hline PC2 & 7.17 & 8.09 & 6.67 & 8.40 \\ \hline PC3 & 5.65 & 6.06 & 6.36 & 6.47 \\ \hline \end{tabular}
\end{table}
Table 3: The explained variance of the first three principal components (PCs) from EOF analysis of altimetry data from 1993–2019 for the Caribbean basin. Columns two through four show, respectively, (i) the raw monthly data, (ii) the data with linear trend removed (detrended), (iii) the data with the seasonal cycle removed (deseasoned), and (iv) the detrended and deseasoned data.  
Figure 3: Spatial variability (**Left column**) and timeseries of principal components (**Right column**) of the leading EOF of sea levels in the Caribbean for raw (**a**,**b**), detrended (**c**,**d**), deseasoned (**e**,**f**), and detrended and deseasoned (**g**,**h**) altimetry data, spanning 1993–2019.
## 5 Discussion
### Engineering Significance  
During current tunnel construction in western China, the biggest obstacle is the large deformation problem of the surrounding rock, especially for tunnels with layered rocks, such as carbonaceous slate, which has a low metamorphic grade and developed foliation. Due to the limited understanding of the creep deformation characteristics of such rocks, the design institute still designs support according to the ordinary rocks for different surrounding rock grades. The construction unit also does not have a good solution strategy for the asymmetric deformation of the tunnel. The occurrence of the left and right sides of the tunnel changes greatly, and different deformation behaviors of carbonaceous slate have varying impacts on the support structure.  
[PERSON] et al. [26] found that when \(\beta\) = 75\({}^{\circ}\), the steel arch undergoes obvious shear deformation, while when \(\beta\) = 0\({}^{\circ}\), the rock mass protrudes outward. Therefore, the support can be carried out in a targeted manner according to the different occurrences. Through laboratory tests, the creep characteristics of carbonaceous slate were obtained, and a constitutive model that reasonably describes the creep behavior was derived. The creep parameters and constitutive model can be applied in discrete element [39] or finite difference [40] software. Creep simulation is carried out on the established model of the surrounding rock and support structure to study the failure process and long-term safety of the secondary tunnel lining in the carbonaceous slate stratum. Therefore, this research has certain value for the subsequent exploration of the long-term stability of carbonaceous slate tunnels and the guidance of tunnel support.