esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Reconstruction of Indoor Space  
Current methods for the reconstruction of indoor spaces are mainly based on the extraction of surfaces [9; 15; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31] and lines [32; 33; 34; 35; 36; 37; 38; 39; 40].  
(1) Surface-Based Reconstruction  
The accuracy of reconstruction models mostly depends on the extraction of surfaces. [PERSON] and [PERSON] [9] proposed an innovative approach to segment walls using the Conditional Random Field and concluded that the generated wall clusters were better than traditional region growing. Other researchers have also extracted unconnected planes from 3D point clouds [23; 24], but only enabled visualization and excluded the spatial topological relationship. [PERSON] et al. [25] proposed an effective approach to extract Regular Arrangements of Planes (RAP) from unstructured point clouds in rebuilding man-made scenes. However, the method requires long computing time for the reconstruction of large scenes. [PERSON] et al. [26] proposed a novel 3D roof reconstruction technique that constructs an adjacency matrix to define the topological relationships among the detected roof planes, in addition, used the generated building models to detect 3D change in buildings. [PERSON] and [PERSON] [27] employed constructive solid geometry (CSG) operations to generate volumetric wall model, which focused on the large-scale reconstruction without semantic information. To overcome this deficiency, [PERSON] et al. [15] extracted the piece planar surfaces by the RANSAC approach [28], constructed partitions based on the wall surfaces, utilized the global optimization to reconstruct wall elements, and then finally built the volumetric model of single room by extruding the walls. However, the thickness of model walls was assigned a fixed threshold that led to significant errors. [PERSON] et al. [29] extracted the permanent components used in constructing adjacent relations and partitions of 3D polyhedral cells. The final general three-dimensional interior model was reconstructed using multi-label to optimize cell selection; however, this method was only applied to small-scale scenes. [PERSON] et al. [22] extracted wall candidates and formulated the optimization method to arrange volumetric wall entities to build the structural model. Reconstructing the model presents latent difficulties due to occlusion and clutter point clouds in the indoor environment. While this approach can reshape the model manually, its main limitation includes slanted walls, ceilings and floors, and detailed pillar reconstruction.
###### Abstract  
In practical engineering, the vibration-based fault diagnosis with few failure samples is gaining more and more attention from researchers, since it is generally hard to collect sufficient failure records of centrifugal pumps. In such circumstances, effective feature extraction becomes quite vital, since there may not be enough failure data to train an end-to-end classifier, like the deep neural network (DNN). Among the feature extraction, the entropy combined with signal decomposition algorithms is a powerful choice for fault diagnosis of rotating machinery, where the latter decomposes the non-stationary signal into multiple sequences and the former further measures their nonlinear characteristics. However, the existing entropy generally aims at processing the 1D sequence, which means that it cannot simultaneously extract the fault-related information from both the time and frequency domains. Once the sequence is not strictly stationary (hard to achieve in practices), the useful information will be inevitably lost due to the ignored domain, thus limiting its performance. To solve the above issue, a novel entropy method called time-frequency entropy (TfEn) is proposed to jointly measure the complexity and dynamic changes, by taking into account nonlinear behaviors of sequences from both dimensions of time and frequency, which can still fully extract the intrinsic fault features even if the sequence is not strictly stationary. Successively, in order to eliminate the redundant components and further improve the diagnostic accuracy, recursive feature elimination (RFE) is applied to select the optimal features, which has better interpretability and performance, with the help of the supervised embedding mechanism. To sum up, we propose a novel two-stage method to construct the fault representation for centrifugal pumps, which develops from the TfEn-based feature extraction and RFE-based feature selection. The experimental results using the real vibration data of centrifugal pumps show that, with extremely few failure samples, the proposed method respectively improves the average classification accuracy by 12.95% and 33.27%, compared with the mainstream entropy-based methods and the DNN-based ones, which reveals the advantage of our methodology.  
fault diagnosis; time-frequency entropy-based feature extraction; recursive feature elimination-based feature selection; centrifugal pumps +
Footnote †: journal: Applied Physics Letters
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### HetNet Deployment with Uniformly Distributed Small Cells
#### 3.2.1 Transmit and Receive Power  
The expressions for the transmit and receive power of the macro and macro cells were taken from Equations (2) and (3).
# Multi-Head Self-Attention-Based Fully Convolutional Network for RUL Prediction of Turbofan Engines
## 3 The Proposed Neural Network
### Task Statement  
The RUL prediction of equipment is essentially a classical regression task based on a large amount of raw monitoring data containing both historical and real-time data. Therefore, the proposed neural network serves as a deep supervised learning-based regression approach to achieve RUL prediction.  
Firstly, the raw monitoring data \(X=\left\{X^{1},\ldots,X^{i},\ldots,X^{L_{s}}\right\}\) are transformed and divided to a training dataset and a testing dataset, as shown in Equations (7) and (8), where \(L_{s}\) represents sequence length of the data.  
\[A=\left\{\left(A_{1},y_{1}^{tra}\right),\ldots,\left(A_{i},y_{i}^{tra}\right), \ldots,\left(A_{N_{tra}},y_{N_{tra}}^{tra}\right)\right\}, \tag{7}\]  
\[T=\left\{\left(T_{1},y_{1}^{tes}\right),\ldots,\left(T_{i},y_{i}^{tes}\right), \ldots,\left(T_{N_{tra}},y_{N_{Ins}}^{tes}\right)\right\}, \tag{8}\]  
where \(A_{i},y_{i}^{tra}\), \(T_{i},y_{i}^{tes}\), \(N_{tra}\), and \(N_{tes}\) represent the \(i\)th training sample, the \(i\)th RUL training label, the \(i\)th testing sample, the \(i\)th RUL testing label, the number of training samples, and the number of testing samples, respectively. After that, a neural network is established and trained by continuously narrowing the gap between the label \(y_{i}^{tra}\) of training sample \(A_{i}\) and the corresponding predicted RUL \(y_{i}^{pre,tra}\). Finally, the trained neural network predicts the RUL \(y_{i}^{pre,tes}\) of testing sample \(T_{i}\), and the difference between the label \(y_{i}^{tes}\) and the predicted value \(y_{i}^{pre,tes}\) is also calculated to evaluate the performance of the neural network.
# Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
## 2 Materials and Methods
### Statistical Analysis  
Least squares linear regression analyses were performed to calculate the correlation between HSI- and PAM-derived \(\phi_{PSII}\) measurements, which evaluated the goodness of different methodsfor maximal fluorescence estimation. To avoid potential effects due to outliers, these analyses were conducted using robust regression option (\"bisquare\") in MATLAB (The MathWorks, Inc., Natick, MA, USA). To test the effectiveness of standardized ETR values, ANOVA tests were performed on the five growth parameters and standardized ETR values estimated using four approaches (PAM and three HSI-based methods). After testing the effects due to genotype, treatment, and the interaction between genotype and treatment, ANOVA tests were further performed on the traits between treatments for each genotype. ANOVA tests were performed at the significance level of 0.05 in R [57]. In addition to ANOVA tests, Pearson correlation analysis was conducted between growth traits, sETR, and sPRI, evaluating the potential of using the sETR for growth prediction. Pearson correlation analyses were also conducted in R.
## 5 Modeling of Power Supply
### Example Calculations and Results  
The final results based on the PROMETHEE method show that the 150 kW charging station has the highest net flow, making it the most preferred configuration for cost, environmental impact, and user convenience.  
Normalized criteria weights and performance scores are presented in Table 4:  
The PROMETHEE analysis compares pairs of charging station alternatives, considering their differences across criteria such as cost, CO\({}_{2}\) savings, efficiency, and user convenience, as shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Variants** & **Cost** & **CO\({}_{2}\) Savings** & **Efficiency** & **Convenience** \\ \hline
22 kW & 0.214 & 0.16 & 0.5 & 0.77 \\
50 kW & 0.43 & 0.4 & 0.33 & 0.88 \\
150 kW & 1 & 1 & 0.44 & 1 \\ \hline \end{tabular}
\end{table}
Table 4: Evaluation of variants based on cost, CO\({}_{2}\) savings, efficiency, and convenience.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Variant** & **Cost (EUR/year)** & \begin{tabular}{c} **CO\({}_{2}\) Savings** \\ **(kg/year)** \\ \end{tabular} & \
\begin{tabular}{c} **Efficiency** \\ **(kWh/m2)** \\ \end{tabular} &
\begin{tabular}{c} **Convenience** \\ **(Score 1–10)** \\ \end{tabular} \\ \hline
22 kW & 15,000 & 50,000 & 200 & 7 \\
50 kW & 30,000 & 120,000 & 180 & 8 \\
150 kW & 70,000 & 300,000 & 170 & 9 \\ \hline \end{tabular}
\end{table}
Table 3: Evaluation matrix.  
The positive and negative preference flows are then computed to determine the relative ranking of the alternatives. For example, for the 22 kW station,  
\[\begin{split}\phi^{+}(22)&=(22,50)+(22,150);\\ \phi^{-}(22)&=(50,22)+(150,22);\end{split} \tag{13}\]  
Similarly, the net flows for the 50 kW and 150 kW configurations are calculated using  
\[\begin{split}\phi(22)&=\phi^{+}(22)-\phi^{-}(22);\\ \phi(50)&=\phi^{+}(50)-\phi^{-}(50);\\ \phi(150)&=\phi^{+}(150)-\phi^{-}(150);\end{split} \tag{14}\]  
The flow evaluation is presented as a final proposition for the system.  
The results from Table 6 clearly indicate that the 150 kW charging station configuration is superior in terms of both environmental impact and user convenience while also providing competitive cost efficiency compared to the smaller alternatives. This makes it the most appropriate option for large-scale deployments in areas with high energy demand and a focus on sustainability. These findings support the broader adoption of larger-capacity EV charging stations, particularly when integrating renewable energy sources like solar power.
## 3 Proposed Configuration and Beamforming Method Eliminating CSI in the Downlink
### Residual Interference Elimination Method at the UTs  
Although the separation of multiple users can be achieved by the configuration as described in Section 3.1, the interference signals are still received by the sidelobes of the beams facing the other UTs. In order to realize perfect interference rejection at the UTs, CMA is applied at the UT with two antennas as the simplest configuration and elimination of the CSI estimation. In this paper, LS-CMA is also used at the UTs. The interference signals are suppressed by the CMA and finally, only the desired signals are received at the UTs.
# Performance Model for Video Service in 5G Networks
## 3 Approaches
### Interference Coordination Approach
#### 3.1.3 Interference Coordination  
Interference coordination is performed at the intra- and inter-cluster levels, which are summarized in Table 1.
# Feasibility Analysis on the Use of Ultrasonic Communications for Body Sensor Networks
## 3 Method
### BFSK Modulation  
Two discrete frequencies are required for BFSK. According to our survey, almost all commercial ultrasonic transducers have a narrow bandwidth; empirical tests had to be carried out to obtain the same amplitude at different frequencies. We used 500 kHz central frequency ultrasonic transducers (Olympus Immersion Transducer V318) in our experiment. The transducer response to frequency is shown in Figure 8. The test was performed five times and the average voltage was recorded. The amplitude test results are shown in Table 1. The input amplitude is 5 V. The 470 and 490 kHz frequencies have similar amplitudes, as indicated by the bold characters. Finally, 470 and 490 kHz were selected to represent 0 and 1, respectively.  
Figure 6: (**a**) Baseband digital signal at 500 bps; (**b**) Transmitted BPSK signal; (**c**) Received BPSK signal.  
Figure 7: (**a**) Transition of transmittedbinary PSK (BPSK) signal; (**b**) Transition of received BPSK signal.  
BFSK modulation was used for the experiment in water. The baseband digital signal, transmitted BFSK signal, and received BFSK signal are compared in Figure 9. The received BFSK signal is not as severely delayed as the OOK and BPSK signals. The data rate and shape were also not affected much. The frequency shift was examined with an oscilloscope. The shift time depended on the data rate, as in the other two modulation schemes.
## 1 Introduction
They found that by absorbing electrical energy from the battery, the ETC can lead to significant Brake-Specific Fuel Consumption (BSFC) reductions of up to 4% at the highest engine speeds and loads for the SI engine and up to 6% for the CI engine at 4000 rpm half load. Furthermore, calculations have shown that in the case of the CI engine, the maximum electric power that can be recovered by the ETC is around 4 kW at 4000 rpm full load, while in the case of the SI engine, the maximum power is 1.5 kW. [PERSON] et al. [16] presented a comprehensive study on engine performance improvement attributable to the application of different electrically-assisted turbocharger topologies, including a single-stage turbocharger, an electrically-assisted turbocharger, a turbocharger with an additional electrically-driven compressor and an electrically-split turbocharger (supercharger and turbo-generator). The results revealed that all of the electrically-assisted turbocharger topologies improve the transient response of the engine and, thus, the drivability of the vehicle. However, no electrically-assisted turbocharger topology could clearly be favoured in general. In this paper, a detailed investigation of the potential of e-turbocharging to control load while providing energy recovery for increasing the overall system efficiency and if possible replace the wastegate boost control is provided. The current approach of e-turbocharging requires larger turbine systems that do not build up backup pressure and provide electrical assistance at low engine speeds. However, with this configuration, the energy regeneration occurs only at high speed and load areas of the engine, as shown in Figure 1a. The proposed study focusses on shifting the energy regeneration towards the low-speed area, which represents more realistic driving conditions, as shown in Figure 1b. Energy assistance is provided to the engine when the maximum power characteristics of the engine need to be met. The study is performed on a 2.0 L turbocharged SI engine under steady-state and transient driving conditions. The research work focuses on the availability of energy and the effects of component sizing, the transient behaviour for eliminating turbo lag and the overall system energy balance during various driving conditions. After a comprehensive introduction in this section, Section 2 outlines the engine model used for the study and any relatively small modifications occurring for the purpose of electrifying the turbocharger. A detailed model validation against experimental data is also presented in Model Validation. Section 3 describes the methodology followed for all of the simulation studies conducted under steady-state and transient conditions. The simulation results for all of the studies performed are analysed and discussed in Section 4. Finally, Section 5 summarizes the main findings of this work and proposes future work in the area of the electrification of turbocharging systems.
## 2 The Csr Network
### Are the Regulatory Csr/Rsm sRNAs Redundant?  
The question of Csr/Rsm sRNAs redundancy was approached in different species. In general, it appears that these sRNAs are redundant. For examples, in _P. fluorescens_, the single _rsmY_ or _rsmZ_ mutants are not affected for extracellular enzymes production while the double mutant is unable to produce these virulence factors [69]. In _S. typhimurium_, mutants of both _csrB_ and _csrC_ genes are required to obtain phenotypes similar to that of a CsrA overexpression strain [70]. Furthermore, in _E. coli_, expression of one of the Csr sRNA is increased in mutants deleted of the other one, suggesting a compensatory mechanism [38]. Although these regulatory sRNAs are functionally and structurally related and regulated by the BarA/UvrY TCS or its homologues, they present specific features. One important functional difference resides in their capacity to bind a variable number of CsrA molecules. As a result, CsrC shows lower affinity for CsrA than CsrB [38]. In addition, molecular mechanisms regulating CsrB and CsrC stability in _E. coli_ are different, although both require RNAseE. CsrB degradation requires CsrD and PNPase (polynucleotide phosphorylase, an exonuclease that is also part of the degradosome (for review, see [71]) while that of CsrC is independent of these two enzymes [49]. Additional differences between CsrB and CsrC expression and/or stability are observed in other species. Although expression of CsrB and CsrC in _Y. pseudotuberculosisis_ is regulated by UvrY, catabolite repression contributes to differential expression of the sRNAs with CsrB being up-regulated in a _crp_ (cAMP receptor protein) mutant and CsrC down-regulated [72]. This observation provides an additional example of connection between carbon metabolism and the Csr network. In _P. aeruginosa_, global regulators belonging to the H-NS family (MvaT and MvaU) regulate expression of the _rsmZ_ sRNA but not that of _rsmY_ (Figure 1) [73]. In addition, RetS and the HptB protein involved in phosphotransfer cascade protein, control _rsmY_ expression, whereas _rsmZ_ expression is exclusively controlled by RetS [74]. Regarding sRNAs stability, data indicate that differential regulation may also occur. In _P. aeruginosa_, Hfq binds and stabilizes RsmY [75]. In _S. typhimurium_, CsrA positively regulates CsrC half-life, suggesting that CsrA binding might lead to CsrC stabilization [70].  
Difference in regulation of sRNA levels may provide clues to understand the benefit of having two or more seemingly redundant sRNAs. This may allow more possibilities for integrating various signals, leading to efficient and precise regulatory responses via gene dosage effect [73].
# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
## 2 Materials and Methods
### The Proposed Segmentation Method
#### 2.2.3 Proposed Method and Introduction of the Overall Scheme  
For remote sensing image segmentation of mariculture cage, we propose a segmentation method based on the Swin Transformer and ensemble learning strategy. The proposed method description based on the ensemble learning strategy is shown in Algorithm 1, and the overall technical scheme based on this method is shown in Figure 5, which is different from the traditional machine learning segmentation method. Firstly, multiple remote sensing images of mariculture cage are collected by Google Maps and annotated, and then the datasets are divided into a training set and a test set, and the training set is cropped to \(256\times 256\) and \(512\times 512\) pixels and expanded to obtain two training sets with different resolutions [19,20], respectively. The Swin Transformer, Attention U-Net and Mask Rcnn networks are used to train the two training sets and generate six trained models. The test sets are then cropped to \(256\times 256\) and \(512\times 512\) size respectively according to the rules and input to the trained models of corresponding resolutions for segmentation. Then, the output images are stitched according to the rules [21]. Next, images of the same  
Figure 4: Diagram of filling and cropping with test sets overlap clipping.  
resolution from different networks are combined by an ensemble learning strategy, and then the final segmentation image is obtained by voting on the two segmentation images of different resolutions. Finally, the estimation of aquaculture density is performed based on the segmentation results. This work is presented in Section 2.3.  
```
Input: An image \([H\times W\times 3]\). Output: Segmentation image.
1 Preprocess: Data augmentation and crop the training set to \(256\times 256\) and \(512\times 512\) pixels respectively. Training
2 for each network \(n=1,2,3\)do
3 Control the proportion of positive and negative samples. Train with \(256\times 256\) and \(512\times 512\) pixels datasets respectively.
4 end for
5 Acquire six trained model. Test
6 Overlap crop the test set to \(256\times 256\) and \(512\times 512\) pixels respectively. for each kind of resolution \(n=1,2\)do
7 Segment images with the trained models of corresponding resolutions. Gain the segmentation image blocks.
8
9 end for
10 Use an ensemble learning strategy to combine image blocks of the same resolution from different networks.
11 Obtain the final segmentation image by voting on the two segmentation images of different pixels.
```  
**Algorithm 1**Description of the ensemble learning algorithm for segmentation  
Figure 5: The proposed overall scheme.
## 4 Conclusions  
In conclusion, we have proposed a patch array with tunable functions of beam steering, focusing and generation of OAM vortex beams at the working frequency of 300 GHz. The patch array consists of \(7\times 7\) elements and the beam scanning angle could reach \(110^{\circ}\) by designing a 3 rd order array. By actively adjusting the phase of the array elements, we have achieved beam focusing at different positions with a distance of five times the wavelength from the patch array. In addition, we also designed the patch array to generate vortex beams with multiple topological charges by high-order phase modes. These capabilities will make the patch array has a good prospect of applications.  
Conceptualization, Z.Y. and Q.Z.; methodology, K.G.; software, Q.Z. and K.G.; writing--original draft preparation, Z.Y and Q.Z.; writing--review and editing, Z.G., and K.G.; supervision, Z.G.; funding acquisition, Z.G.  
National Natural Science Foundation of China (61775050, 11804073), Natural Science Foundation of Anhui Province, China (1808085 QA21), Fundamental Research Funds for the Central Universities (PA2019 GDZC0098, JZ2018 HGBZ0309, JZ2018 HGTB0240).  
The authors declare no conflict of interest.