esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study
### The Main Prediction Results
#### 5.3.2 The Prediction Results of Each Wind Farm  
The M2 GSNet is not only convenient for predicting the regional wind power, but it also can output the detail power of each wind farm by one training session. To verify the effectiveness of M2 GSNet on the single wind farm power prediction, the RMSEs of every wind farm on the 16 time steps are calculated. The RMSE data of 20 wind farms in 1 h are used for the boxplot analysis. We displayed the results of MLP, LSTM, ELM, LSTNet and M2 GSNet in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**1 h**} & \multicolumn{2}{c}{**2 h**} & \multicolumn{2}{c}{**3 h**} & \multicolumn{2}{c}{**4 h**} \\ \cline{2-9}  & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline MLP & 4.66 & 3.29 & 8.03 & 5.92 & 10.62 & 8.09 & 12.90 & 10.07 \\ \hline LSTM & 5.02 & 3.61 & 7.93 & 5.94 & 10.49 & 8.09 & 12.60 & 9.94 \\ \hline ELM & 4.52 & 3.29 & 7.74 & 5.79 & 10.33 & 7.97 & 12.51 & 9.88 \\ \hline LSTNet & 5.35 & 3.76 & 8.22 & 6.02 & 10.50 & 7.77 & 12.48 & 9.48 \\ \hline LSTNet\_NWP & 4.85 & 3.51 & 7.92 & 5.95 & 10.40 & 7.60 & 12.43 & 9.42 \\ \hline M2 GSNet & 4.40 & 3.22 & 7.11 & 5.47 & 9.08 & 7.15 & 10.68 & 8.43 \\ \hline w/o CW & 4.69 & 3.52 & 7.43 & 5.80 & 9.32 & 7.32 & 10.74 & 8.51 \\ \hline w/o AD & 4.84 & 3.66 & 7.68 & 6.05 & 9.53 & 7.57 & 10.95 & 8.81 \\ \hline w/o W & 4.77 & 3.60 & 7.59 & 5.94 & 9.49 & 7.50 & 10.90 & 8.69 \\ \hline w/o MT1 & 4.73 & 3.49 & 7.46 & 5.78 & 9.50 & 7.49 & 11.33 & 8.94 \\ \hline w/o MT2 & 5.01 & 3.72 & 7.42 & 5.72 & 9.36 & 7.43 & 11.14 & 8.89 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prediction results of different methods (%).  
From Figure 7, we can notice that the prediction errors of single wind farms are much higher than some other wind farms. However, due to the \"smooth effect\" of the wind farm cluster, the prediction error of the cluster is much smaller than the individual wind farms. This result is very meaningful for the power grid dispatching center. In addition, according to the mean value, max value and minimum value, the performance of the M2 GSNet is much better than the other methods, especially in the 3 rd hour and 4 th hour in the statistical sense. However, due to the NWP feature fusion, the prediction error of M2 GSNet in the 1 st hour is a little higher than the other methods that do not consider the NWP. It also enlightens us to design a mechanism to dynamically select the models. For example, for the ultra-short-term prediction within 1 h, we can choose a model with lower RMSE.
## 5 Experimental Results
### RQ1: How Effective Is LinearTGAN in Enhancing Linear Acoustic Image Quality?
#### 5.1.2 Qualitative Results  
Figure 4 presents qualitative comparisons of SR results for linear acoustic imaging, highlighting differences in reconstruction quality across models. Bicubic interpolation fails to reconstruct intricate structures, leading to excessively smoothed textures and significant blurring artifacts. While CARN achieves sharper reconstructions than Bicubic interpolation, it still struggles with artifact suppression and texture consistency. The GAN-based models (BSRGAN and Real-ESRGAN) enhance perceptual quality by generating sharper textures. However, they frequently over-enhance details, introducing noticeable distortions in complex patterns. TransENet, the Transformer-based model, effectively restores global structures but exhibits difficulty in recovering high-frequency textures, particularly in wafer regions with intricate structural variations.  
Our proposed LinearTGAN model produces visually superior reconstructions, resembling the ground-truth images. LinearTGAN effectively recovers fine textures and high-frequency details while maintaining structural coherence (see Supplementary Figure S3 for a detailed comparison of the final scanned image in polar coordinates). By integrating adversarial learning with a Transformer-guided enhancement strategy, LinearTGAN  
Figure 3: PSNR and FID results with the total number of parameters of different methods for image SR.  
can suppress artifacts, mitigate distortions, and preserve intricate spatial details with high fidelity. Its ability to accurately reconstruct fine features further demonstrates its robustness in high-fidelity SR reconstruction for acoustic imaging. In comparison to CNN and Transformer-based approaches, LinearTGAN effectively mitigates noise, restores fine textures, and preserves intricate spatial details, demonstrating its superior performance in high-fidelity SR reconstruction for acoustic imaging. These findings further validate the quantitative advances of LinearTGAN, emphasizing its superior performance in both pixel-level accuracy and perceptual quality.
## 5 Results
### Correlation Analysis and the Shape-Product Approach
The maximum and minimum flow depths are well negatively correlated to _WDPP_ calculated at LPs 1 and 5 (\(r=-0.751\) and \(-0.749\), respectively; Figure 6b). Figure 6: Correlation matrices show the correlation coefficients (\(r\)) between lake geometrical characteristics and the hydrodynamic modeling results. (**a**) Matrix for all lakes (69 models). (**b**) Matrix for all lakes with a maximum depth greater than 20 m (52 scenarios). Figure 7: Hyperbolic relations between _WDPP_ and (**a**) lake area and (**b**) water volume (X-axis in logarithmic scale, base 10). Circle size and color relate to lake depth. Related equations, coefficient of determination (\(R^{2}\)), and correlation coefficient (\(r\)) are also shown. The \(r\)-values labeled in red, which are not shown in Figure 6, are correlation coefficients specifically for medium-size lakes with a maximum depth of 20 m. The \(r\)-values labeled in black, shown in Figure 6b, are correlation coefficients for theoretical lakes with a maximum depth greater than 20 m.  
The Supplementary Materials include an Excel spreadsheet that summarizes the workflow processes, including the entire calculation procedure and includes informative charts to display the results. Figure 8: **(a)** Plot of _WDPP_ against _SlnpP_ (x-axis in logarithmic scale, base 2), and related equations, coefficient of determination (\(R^{2}\)), and correlation coefficient (\(r\)). Data size and color relate to maximum lake depth. The blue circle refers to the 2007 Chehalis Lake landslide-generated wave case, used as a test of the proposed approach (Section 7). The \(r\)-values with red font, which are not shown in Figure 6, correspond only to the red dots. **(b)** Linear regression plot obtained for the relation between _SlnpP_ and _WDPP_ (the resulting equation is the same as the one obtained with the trendline in Figure 8a). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Flow Depth at the Shoreline (m)** & \(r\) & **Equation** & \(R^{2}\) \\ \hline Maximum & \(-0.751\) & (6) y = \(29.376\)x\({}^{-0.696}\) & \(0.8745\) \\ \hline Mean & \(-0.849\) & (7) y = \(17.878\)x\({}^{-0.779}\) & \(0.9071\) \\ \hline Minimum & \(-0.749\) & (8) y = \(8.3583\)x\({}^{-0.583}\) & \(0.6578\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Relationships between _WDPP_ and flow depths at the shoreline, including correlation coefficients (\(r\)), equations, and coefficients of determination (\(R^{2}\)). In the equations, y is flow depth and x is _WDPP_ obtained from the equations in Table 2.
# Electrocatalytic Nanomaterials Improve Microbial Extracellular Electron Transfer: A Review
## 2 EET Mechanisms
### Outward EET from EMAs to Anodes  
Exoelectrogenic EAMs perform outward EET for electricity production in microbial anodes. Dozens of exoelectrogens have been identified so far [1], and the EET pathways of two typical Gram-negative strains, _Shewanella oneidensis_ MR-1 and _Geobacter sulfurreducens_ PAC, have been well elucidated. For _S. oneidensis_ MR-1, the Mtr pathway that is composed of multiple Cyts (CymA, Fcc\({}_{3}\), MrA, MtrC, OmCA, and STC) and a porin-like protein MtrB is responsible for efficient direct EET [2; 26]. CymA, located at the cytoplasmic membrane, acquires electrons from the quinone pool and transfers them to the periplasmic Cyts of Fcc\({}_{3}\) and STC and then to the ternary complexes of MtrA, MtrB, and MtrC for the transmembrane transport of electrons [27; 28]. MtrC and OmCA, located at the cellular outer membrane, interact with each other and deliver electrons to microbial anodes [29]. A similar direct EET pathway, the porin-cytochrome (Pcc) pathway, is present in _G. sulfurreducens_ PAC, in which the inner-membrane quinol oxidases ImcH and CbcL link the electron flow from the intracellular electron transport chains to periplasmic Cyts (including PpcA, PpcB, PpcC, PpC, PpC, and PpC), followed by being transferred to the porin-cytochrome complexes [30]. The complexes consist of periplasmic Cyts OmaB/C, porin-like outer-membrane proteins OmbB/C, and outer-membrane Cyts OmcB/C [30; 31]. Moreover, both _S. oneidensis_ MR-1 [32] and _G. sulfurreducens_ PAC [33; 34; 35] can produce bacterial nanowires with electric conductivity to execute direct EET over relatively long distances. In addition, _S. oneidensis_ MR-1 can secrete flavins or use natural redox-active molecules (such as humic substrates) as electron shuttles for the indirect EET [36; 37]. In recent years, some Gram-positive bacteria have been found to perform EET [1], although the thick and dense peptidoglycan layer in their cell wall is intuitively hostile to EET. However, the EET mechanism of Gram-positive bacteria is currently less well understood, with only a few studies. The multiheme Cyts that localized at the cell wall or cell surface were suggested to take part in the EET of _Thermincola potens_ through genomic analysis [38]. In contrast, _Listeria monocytogenes_ has been identified to use a distinctive flavin-based EET mechanism to deliver electrons to iron or an electrode [39]. This evidence substantially demonstrates the diversity of EET pathways.
# UAV Based Relay for Wireless Sensor Networks in 5G Systems
###### Abstract  
Relay is one of the most significant issues in smart industrial wireless sensor networks (WSN) due to the low transmitting power of sensors. By relay, the signals of sensors can be concentrated at the relay and further transmitted to the base station for decreasing energy consumption in the system. In the past decades, the relay in WSN is generally one super sensor with large transmitting power. However, the placement of the super sensor is static, which leads to the instability of performance in WSN under the time-varying wireless environment. Fortunately, unmanned aerial vehicles (UAV) can provide an effective leverage to improve the environment-adaptation in WSN compared to the static relay in WSN. In this paper, we employ UAV as the relay in WSN, which can move in three-dimensional space to possess a better position to minimize the system power consumption. We use a simple case study to demonstrate the effectiveness of UAV in WSN. Extended simulations are also given to verify the preferable performance of the UAV based relay in WSN.  
wireless sensor networks; unmanned aerial vehicle; relay; power consumption +
Footnote †: journal: Sensors
## 3 Results
### Study of Scatter Correction on True CXRs  
Figure 10 shows the estimations of the scatter contribution and the scatter-corrected images of three real CXRs after applying the single-energy algorithm, along with the corresponding image used as input of the network, i.e., the original CXR (with scatter) with pixel values transform as explained in Section 2.5. In all cases, the NN model is able to make a proper estimation of the scattered-rays image and thus provide a scatter-corrected image comparable to what was expected according to the ground-truth simulation and the corresponding estimations (see Figures 1 and 7), both qualitative and quantitatively.  
Figure 9: (**a**) Graphic representation of the percentage contrast improvement factor for ground-truth image, single-energy model estimation, 1-output dual-energy model, and 2-output dual-energy model estimation. (**b**) Relative difference in the contrast value between ground truth and deep-learning-based estimations. In both graphics, the red solid line represents the average value, while the blue dash-dotted line represents the median value.  
Table 2 shows the ratio between a region of the lung with and without rib in the CXR before and after the single-energy scatter correction algorithm is applied to the 10 test images. The ratio increases in every sample when the NN model of scatter correction is implemented, with an average rise of 15.0% for the selected cohort, with a maximum increase of 40.2%.
## 4 Conclusions  
Oxygen and argon have different impacts on the CoCr fiber laser cutting process. Oxygen helps molten melt burning, and an amount of oxide particles were observed on the surface of the oxygen cuts. Dross formation could be easily eliminated by the acid pickling process, and therefore neat cutting kerfs and non-dross formation could be obtained. Through the acid pickling process, the cut compositions were similar to the raw material. Argon is a type of inert gas, and, when protected with argon, the cutting process does not change the composition of the cuts. The cutting surface was compact and the acid pickling process had no effect on the cutting surface topography or dress formation. Dross formation and un-neat kerfs are challenges in argon cuts for manufacturing CoCr stents. Compared with inert gases, oxygen is preferential as the assisting gas in the fiber laser cutting of CoCr stents.  
Figure 11: The microstructure of cutting kerfs with metallographic preparation.  
Three types of topographies were obtained in the oxygen cuts: molten globule topography, striation topography, and molten pool topography. Cuts with molten globule topography were difficult to separate from the tubes. In this case, in the manufacturing process of stents, fragments attach to the stent struts and lead to waste products. Molten pool topography is also not ideal, as pools on the cutting surface lower the surface quality. Striation topography is therefore the best choice.  
The repetition rate and pulse width had similar impacts on cutting quality, including the surface topography and cutting kerf. The power had less of an impact on cutting quality than repetition rate and pulse width because of the cutting mechanism of pulsed lasers. Cutting speed had a more complex impact on cutting quality because it not only influenced the value of \(E_{\text{pcd}}\), but also the cooling effect, ejecting effect, and combustion-supporting effects.  
In conclusion, a guideline regarding profiling CoCr stents with fiber lasers can be given: first, use oxygen as the assisting gas and confirm the gas pressure; the gas should eject the molten melt smoothly, and cannot bend the stents as stents are always flexible. Second, melt the right mass of metal with a high frequency, low pulse width, suitable power, and speed. Third, adjust the pulse or frequency to obtain striation topography.  
This work was financially supported by Open Research Fund of Jiangsu Provincial Key Laboratory for Interventional Medical Devices (jr1505), International Science and Technology Cooperation Project of Huian City (HAC201615), Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (13 KJB460002) and Prospective Joint Research Project of Jiangsu Province (BY2015051-04).  
[PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data and wrote the paper.  
The authors declare no conflict of interest and the founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 2 Related Works
### Simulation Modeling Contributions  
* An RPL comparison to another routing protocol called LRP is provided in [22]. The authors compare RPL and LRP in small-scale deployments. They measure the impact of assymetric links on the chosen protocols and demonstrate the features of LRP to deal with such settings. They also demonstrate the behavior of both protocols when the active links vary over time.
* An RPL comparison to another routing protocol called LOADng is provided in [23]. The authors compare the performance of the two protocols under heterogenous traffic patterns in realistic non-sparse non-uniform network deployments. They use the Cooja simulator and the the ContikiRPL implementation and an implementation of their LOADng.
* A latency-aware proxy selection method comparison to RPL is provided in our past paper [24]. In this paper, we exploit distributed data management to overcome the following issue: Given a set of data, the set of consumer nodes and the maximum access latency that consumers can tolerate, we consider a method for identifying and selecting a limited set of proxies in the network where data needed by the consumer nodes can be cached. We evaluate its performance using in large-scale deployments.  
To the best of our knowledge, the majority of such simulation modeling contributions employ high-level simulation tools (such as Matlab) and are therefore not able to uncover in-depth the impact that each method has on the underlying networking settings. This observation leads us to the conclusion that there is a research gap in the literature which refers to accurate, fine-grained simulation modeling of WirelessHART and proxy employing methods.
# A Pilot-Pattern Based Algorithm for MIMO-OFDM Channel Estimation
## 3 Channel Estimation Algorithm Based on Space-Time Block-Code
### LS Channel Estimation Algorithm Based on STBC
#### 3.2.2 Channel Estimation Algorithm for a Double-Symbol scheme  
To minimize the cost function of LS that is expressed as \(\mathbf{C}=\big{[}\mathbf{Y}-\hat{\mathbf{Y}}\big{]}^{\mathrm{H}}\big{[}\mathbf{Y}-\hat{\mathbf{Y}} \big{]}\), the constraints are listed as following equations [16]:\[\left\{\begin{array}{l}X_{1,l}^{\mathrm{H}}X_{1,l}\hat{H}_{1,l}+X_{1,l}^{H}X_{2,l}\hat{H}_{2,l}=X_{1,l}^{\mathrm{H}}Y_{l}\\ X_{2,l+1}^{\mathrm{H}}X_{1,l+1}\hat{H}_{1,l+1}+X_{2,l+1}^{\mathrm{H}}X_{2,l+1} \hat{H}_{2,l+1}=X_{2,l+1}^{\mathrm{H}}Y_{l+1}\end{array}\right.\]  
where subscript l denotes the \(l\)th symbol. If the conditions of \(\hat{H}_{1,l}=\hat{H}_{1,l+1}\), \(\hat{H}_{2,l}=\hat{H}_{2,l+1}\) are given to solve the channel response from the above equations, then it is necessary to configure symbol \(X\) for each pilot. The configuration follows \(\left[\begin{array}{cc}\mathbf{X}_{1,l}&\mathbf{X}_{2,l}\\ \mathbf{X}_{1,l+1}&\mathbf{X}_{2,l+1}\end{array}\right]\), in which \(\mathbf{X}_{2,l+1}=\mathbf{X}_{2,l}=-\mathbf{X}_{1,l+1}=\mathbf{X}_{1,l}\). When substituting them from the aforementioned equations, the channel response can be solved as:  
\[\left[\begin{array}{c}\hat{H}_{1,l+1}\\ \hat{H}_{2,l+1}\end{array}\right]=\left[\begin{array}{c}\mathbf{X}_{1,l}^{-1}( \mathbf{Y}_{l}-\mathbf{Y}_{l+1})\\ \mathbf{X}_{2,l}^{-1}(\mathbf{Y}_{l}-\mathbf{Y}_{l+1})\end{array}\right]\]  
The observation of the pilot block can identify that it is really a special case of the STBC algorithm once all of the pilot symbols are real values [19; 20].
## 6 Open Challenges, Future Prospects and Research Directions
### Open Challenges
#### 6.3.3 New Materials and Devices  
Advancements in materials science and photonic devices are crucial for the evolution of FSO systems. Researching new materials and designs for high-performance photodetectors that offer higher sensitivity, faster response times, and better performance in diverse environmental conditions is essential. Also, developing low-loss optical components, such as lenses, mirrors, and modulators, to improve the efficiency and extend the range of FSO systems is highly imperative, as is investigating the use of quantum dots and other nanomaterials to create novel light sources and detectors that can operate efficiently under  
new materials and devices, enhancing security, and innovating network architectures and protocols, researchers can pave the way for more reliable, efficient, and versatile PSO communication systems. These advancements will enable FSO to play a pivotal role in meeting the growing need for high-speed, high-capacity wireless communication in the future.
# Communication Network Standards for Smart Grid Infrastructures
## 2 Network Standards That Support Communication in Smart Grid Infrastructures
### Blackout Prevention Management  
Energy transmission networks use mechanisms that process large data sets which, with the help of appropriate algorithms, can make real-time decisions about managing the load flow in the grid. These decisions are then compared with the operational limit values of each individual network equipment to timely inform the operator for overloads, limit value violations or general malfunctions [15]. In addition, the analysis of the load flow provides the mathematical background required by optimization procedures so that appropriate corrective actions can be taken with high accuracy [16; 17].  
The need for distributed production disperses the energy production centers and, consequently, the exchange of information has to be performed over large geographical areas. Therefore, there is a need for the incorporation of specialized, bandwidth-demanding measurement systems, such as power quality control systems, remote monitoring technologies, and system integrity protection schemes (SIPS) [18], which minimize the possibility of an extended power outage in case of an emergency.  
A basic condition for meeting the above requirements is the deployment of a telecommunication protocol that can support broadband communications services. In addition, it is also necessary to have an extensive network of sensors, such as the phasor measurement units (PMU) [19; 20; 21; 22], which can be implemented in various topologies and use different data and protocol formats [23; 24]. However, IEC 61850 [14] is the only internationally recognized standard used to exchange high-, medium-, and low-voltage metering data with support for broadband TCP/IP connections. In addition, IEC 61850 complies with IEEE C37.118, which allows the integration of synchrophasors [20].
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 3 Trajectory Data Sparsity and Anomaly Detection
### Data Preprocessing  
Data preprocessing mainly contains two parts: data cleaning and trajectory extraction. First, by deleting invalid data, the erroneous, invalid, or redundant values of AIS trajectory points can be effectively eliminated to improve the reliability of trajectory data. Second, based on extracting different vessel trajectory data, non-contiguous vessel trajectories are removed to ensure data availability for subsequent studies.