esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Progress and Hydrodynamic Design of Integrated Motor Pump-Jet Propulsion
## 2 Electric Motor and Control Technology
It should be pointed out that the unsteady pulse power and jitter generated by the rotation of the integrated motor propeller blades in the water flow can be directly transmitted to the motor rotor through the integrated rotating structure, which causes the fluctuation of the motor torque. In addition, the integrated motor's low speed, frequent starting, high acceleration and deceleration dynamic performance, and high system operation noise requirements make its position sensorless control more difficult, and it is also one of the key technologies that restrict the development of high-performance shaftless propellers. Hence, for the high-performance control requirements of integrated motors, new integrated position-sensing technologies can also be explored in the overall plan. \begin{table}
\begin{tabular}{p{12.3 pt} p{12.3 pt} p{12.3 pt} p{12.3 pt} p{12.3 pt}} \hline \hline
**Electric Motor Type** & **Year** & **Reference** & **Geometry of the Electric Motor** & **Performance** & **Problem** \\ \hline \multirow{6}{*}{**Iduction motor (IM)**} & \multirow{6}{*}{1989} & \multirow{6}{*}{[14]} & \multirow{6}{*}{[15]} & \multirow{6}{*}{[16]} & A 16-pole 3-phase motor with 48 stator slots and 72 rotor slots, 394 mm in diameter, and with a 1 mm air gap was used in the system. & The prototype test detected the problem of bearing friction loss and the eddy-current loss of the excessive stator sheath. \\ \cline  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Electric Motor Type** & **Year** & **Reference** & **Geometry of the Electric Motor** & **Performance** & **Problem** \\ \hline \hline \multirow{6}{*}{ \begin{tabular}{} \end{tabular}  } & \multirow{6}{*}{\ \begin{tabular}{} \end{tabular}  } & \multirow{6}{*}{ \begin{tabular}{} \end{tabular}  } & In this design, the stator and rotor structure were all installed inside the duct, which eliminated the existence of liquid flow air gaps and did not affect the flow area. This reduced the influence of the stator and rotor structure on the propulsion performance of the propeller blades. \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & Low eddy-current losses and cost were achieved with a PM motor that used composite materials for the propeller, housings, structural blading, motor canning, and fairings. \\ \cline{1-1}  & & & The PMs were rectangular and were installed on the rotor yoke’s surface, which was made of solid soft iron. The stator lamination had a thickness of 0.5 mm. The magnets used were neodym magnets (NdFeB) with 1.2 T remains. Harmonics and winding overhang were decreased with a two-layer fractional winding. \\ \cline{1-1}  & & & Because a large magnetic gap was required and slot leakages were absent, the slot
# Study on Reinforcement Mechanism and Reinforcement Effect of Saturated Soil with a Weak Layer by DC
## 1 Introduction
### Main Research Contents  
This paper presents an improved fluid-solid coupling method with a Drucker-Prager-Cap model for saturated soil to investigate the improvement mechanism of DC for layered grounds with a weak interlayer. The numerical model based on finite element method (FEM) is introduced briefly at first and then validated by comparing with published test data. Using the FEM model, influences of the thickness and location of the soft interlayer under DC and tamping energy on the development of excess pore water pressure and improvement effects are studied. Finally, some engineering solutions are suggested to effectively improve the soil foundation with an embedded weak layer.
# Outage Performance of Wireless Powered Decode-and-Forward Relaying Networks in Rician Fading
## 5 Conclusions  
This paper investigated the outage performance of SWIPT-enabled relay networks with the DF relaying protocol, where the effect of the energy triggering threshold at the relay on the system performance was considered. The closed-form expressions of the  
Figure 11: Outage probability versus the time assignment factor under non-linear EH model.  
Figure 12: Throughput versus the time assignment factor under non-linear EH model.  
system outage probability and throughput were derived in Rician channel fading. The Monte Carlo Simulation method was used to verify the accuracy of the derived closed-form expressions. The effects of some system parameters were discussed via simulations. It has been observed that the system outage probability increase with the increase of the minimum transmission rate required by the system and decreases with the increase of the energy conversion efficiency. Besides, with increased source node transmit power or the energy conversion efficiency, the system throughput increase and vice versa. In addition, the outage performance of the system with an equal two-hop distance is better than with an unequal two-hop distance.  
In [41], three CSI-free schemes were proposed, i.e., one antenna (OA) scheme, all antennas at once (AA) scheme, and switching antennas (SA) scheme. In OA, only one of PB's antennas transmits with full power. In AA, all antennas of PB transmit at the same time by sharing the available transmit power. In SA, PB transmits with full power by selecting one antenna at each time slot, and all antennas are used during a block. Actually, the scheme used in our work was very similar to the AA scheme, where we focused on evaluate the outage performance of SWIPT-enabled relay networks in Rice fading channel. In the future work, we may consider OA and SA in the SWIPT-enabled relay networks.  
Conceptualization, Z.Z., P.D. and K.X.; methodology, Z.Z., X.T. and Y.L.; software, X.T. and Y.L.; validation, X.T., Y.L. and K.X.; formal analysis, Z.Z., X.T. and Y.L.; investigation, X.T. and Y.L.; resources, Z.Z. and P.D.; data curation, P.D. and Y.L.; writing--original draft preparation, X.T. and Y.L.; writing--review and editing, X.T. and K.X.; visualization, X.T.; supervision, Z.Z. and K.X.; project administration, P.D. and K.X.; funding acquisition, Z.Z., P.D. and K.X. All authors have read and agreed to the published version of the manuscript.  
This work was in part supported in part by the Fundamental Research Funds for the Central Universities under Grant No. 2022 JBGP003 and also the National Natural Science Foundation of China (NSFC) under Grant No. 62071033.  
The authors declare no conflict of interest.
## 2 LiCSAR and LiCSBAS  
### LiCSAR: Automatic Sentinel-1 InSAR Processor  
In the LiCSAR processing chain, interferograms are automatically produced on a predefined LiCSAR frame basis (generally consisting of 13 bursts on each of the three subswaths for IWS mode corresponding to an area of 250 km \(\times\) 250 km). Newly acquired data are coregistered to a single primary image with the support of an auxiliary secondary image (nearer in time to the latest image to preserve coherence) that has been already coregistered (if available) using the enhanced spectral diversity method [32; 33]. Each acquisition is then used to produce interferograms, by default, with three preceding and three subsequent acquisitions, although this number might be increased in the future. The interferograms are multilooked, with a factor of 20 \(\times\) 4 in range \(\times\) azimuth (46 \(\times\) 56 m spacing), respectively, and spatially filtered to reduce noise using a GAMMA adaptive power spectrum filter with an alpha value of 1.0 [34]. LiCSAR unwraps the phase in two dimensions using a statistical cost approach with the SNAPHU software [35; 36]. During the phase unwrapping, decorrelated areas in the interferograms are masked where the phase noise coherence estimate of the filtered interferogram is \(<\) 0.5. Wrapped and unwrapped interferograms and coherence images are geocoded with a pixel spacing of 0.001 degree (\(\sim\)100 m) and converted to the GeoTIFF format. The GeoTIFF files and quick-look images are published on the COMET-LiCS Sentinel-1 InSAR portal web and available for free download [30]. Other metadata (e.g., line-of-sight (LOS) vectors and perpendicular baselines) are also available on the COMET-LiCS web portal.  
Currently (as of November 2019), up-to-date LiCSAR products are available primarily over the Alpine Himalayan Belt, Japan, the East African Rift, and for global volcanoes, although coverage continues to be expanded. For more details about LiCSAR, please refer to [PERSON] et al. [37].
## 7 Conclusions  
We designed a novel maximum likelihood strategy for evaluating the channel impulse response as well as the users, relay, and destination IQM for the upstream multiple-user AF dual-hop SC-FDMA systems. We created two unknown components called the equivalent channel impulse response by merging the effects of IQM at the users, relay, and destination terminals with the channel impulse response across the nodes. Several strategies for monitoring channel impulse responses across nodes and calculating the IQM at each terminal  
Figure 10: The effect of the Doppler shift on the system BER at SNR = 8 dB. Blue color represents the performance of the algorithm reported in [50], green color represents the performance of the algorithm reported in [51], and brown color describes the performance of the proposed algorithm.  
Figure 9: The effect of the number of operating users on the system BER at SNR = 8 dB. Blue color represents the performance of the algorithm reported in [50], green color represents the performance of the algorithm reported in [51], and brown color describes the performance of the proposed algorithm.  
were removed by the proposed approach. We employed a space-alternating generalized expectation maximization strategy for a straightforward execution of the provided maximum likelihood method. We benefited from the channel decoder's calculations of the a posteriori expectations of the data symbols, which were then used as training symbols. We showed how the decoding method was carried out using the comparable channel response. The simulation results showed that, when the recommended detector was used in cooperation with the provided estimate approach, the BER achievement was very close to the BER of the ideal case when all of the variables were known beforehand.  
Conceptualization, M.M. and H.M.; methodology, M.E. and H.M.; software, M.M. and H.M.; validation, M.E. and H.M.; formal analysis, M.M. and H.M.; investigation, M.M. and H.M.; resources, M.E. and H.M.; data creation, M.M. and H.M.; writing--original draft preparation, M.M. and H.M.; writing--review and editing, M.E. and H.M.; visualization, M.M. and H.M.; supervision, M.M. and H.M.; project administration, M.M. and H.M.; funding acquisition, M.E. and H.M. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) of this publication.  
The authors declare no conflict of interest.
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 6 Results and Discussions
### Performance Comparison between Federated Learning and Machine Learning
#### 6.1.2 Trained Model Performance  
The autoencoder was trained individually using typical machine learning as well as using federated learning principles. In the case of machine learning, the model was trained for 100 epochs. In federated learning, the model was trained for three communication rounds and each communication round consisted of 100 epochs. The model was also trained with federated learning with 20 PCA components. The trained model performance was evaluated by testing the model on the testing dataset. The performance of models trained with different methods is tabulated in Table 4, where the numbers of true positives (TPs), true negatives (TNs), false positives (FPs), and false negatives (FNs) are listed. For clarity, the implications of these terms in an ECG anomaly detection scenario are stated below:It is intuitive that, for ECG anomaly detection, it is desirable that the TN and TP are high and the FP is low. The FN is not of much importance in this scenario as a false alarm is better than a missed anomaly; however, it should also be kept low.  
From Table 4, it can be observed that, for the representative application, the model trained with federated learning outperforms the model trained by machine learning. When the PCA algorithm is used along with federated learning, the performance of the model is very similar to that of the model trained without the PCA algorithm, with a slight increase in false alarms.
# The Perspective of Large-Scale Production of Algae Biodiesel
## 3 Production of Algal Biodiesel  
Algae use light as an energy source to generate biomass from water and CO2 through photosynthesis. Algae also require nitrogen and phosphorus as significant nutrients [8]. Various factors influence the optimal growth of algae and their lipid accumulation. Among others, this includes the availability of micro and macronutrients [20], light intensity [21], CO2, water temperature [22], and pH [23]. Optimum conditions are not equal for all algae and mainly refer to the temperature and intensity of light.  
Therefore, the global feedstock potentials from algae cultivation are limited in areas with enough radiation, water, and nutrients. Most species of algae favor the temperature from 20 to 30 \({}^{\circ}\)C [9]. Theoretically, microalgae can transform 12.8-14.4% of average solar irradiation into biomass [24] with a yield of 77 g/m\({}^{2}\)/day, which gives approximately 280,000 kg/ha/year of biomass [25]. However, the actual biomass yield achieved so far from algae is much lower [6].  
Asia (2.5 EJ/year) and Africa (0.75 EJ/year) have the highest potential for algal biomass cultivation until 2035. North and South America (0.75 EJ/year), Oceania (0.2 EJ/year), and, finally, Europe (0.15 EJ/year) follow them. The assessment of the potential of the algal raw material is very unreliable because the algae cultivation systems are still in the development phase [14].  
Numerous algae farming systems are being successfully developed today, from outdoor ponds and indoor photobioreactors, heterotrophic productions to the systems that combine these systems. Each system aims at the faster growth of algae biomass for the production of biofuels or other industrial products [8]. Algae can grow on incapable land for growing food crops, and since they can use saltwater, there is much less need for fresh water, whose quantities are limited. Production of hundreds of thousands of barrels of diesel per day requires less than 1% of the land used for soybean and corn cultivation. Microalgae can yield more oil per hectare of the occupied area compared to traditional biodiesel crops (Table 1). For example, the yield of algae with 30% oil in dry biomass is up to 58,700 L/ha/year, which is much higher compared to oilseed rape or soybeans [26; 27].
## 2 Materials and Methods
### Training Process and Measures
\[d=\frac{\bar{X}_{1}-\bar{X}_{2}}{spooked},\quad spooked=\sqrt{\frac{(n_{1}-1) \sigma_{1}^{2}+(n_{2}-1)\sigma_{2}^{2}}{n_{1}+n_{2}-2}}, \tag{14}\]  
where \(\bar{X}_{1}\) and \(\bar{X}_{2}\) are the sample means of different models (Model A and B--pairwise comparison), and \(spooked\) is the pooled standard deviation calculated in Equation (14) using \(n_{1}\) and \(n_{2}\) number of samples and the \(\sigma_{1}\) and \(\sigma_{2}\) of the corresponding standard deviations of Models A and B, respectively. Large effects are indicated with \(d\) values greater than 0.8, noticeable improvements are above 0.5, and negligible differences are below 0.2. The Adam optimizer was used to train both architectures, with an initial learning rate of 0.0001 for all existing models. Adam adaptively adjusts learning rates for each parameter based on estimates of the first and second moments of the gradients, which accelerates convergence and improves stability, particularly for noisy data, such as air quality measurements. A learning rate of 0.0001 was consistently used in all AQI forecasting models due to its stability during training, especially in deep architectures and time series data. More aggressive rates, such as 0.001 or 0.01, often caused unstable training with oscillating losses, given the small dataset and complex patterns. In contrast, 0.0001 allowed for a gradual learning process, helping the networks, especially the GRU-based models and deeper slideNN variants, to converge on meaningful representations without overshooting the local minima. The slideNN model was trained for 400 epochs and used a batch size of 16, while the GRU model was trained for approximately 25-50 epochs using a batch size of 32. These values, along with the other relevant hyperparameters used in the experiments, are summarized in Table 2 for clarity. The available data were first divided into a training set and a separate testing set. During training, 20% of the training data were set aside for validation using the validation_split = 0.2 parameter in TensorFlow. This approach ensured that the validation set was drawn exclusively from the training data while the testing set remained isolated for final model evaluation. The appropriate neural network architecture was chosen based on the hyperparameters and the specific requirements selected by the end user, such as the desired forecast horizon (short-term vs. long-term) or computational resource limitations. At the same time, the deployment strategy was decided based on whether to use cloud infrastructure or edge computing. When the most suitable option was identified, the corresponding technical setup and installation were performed, involving either deployment on a local edge device or within a cloud environment.
# Electronic and Transport Properties of Epitaxial Graphene on SiC and 3C-SiC/Si: A Review
## 4 Electronic and Transport Properties: Effect of Interactions with the Surroundings
### Mobility and Sheet Carrier Concentration: Power-Law Relationship  
A power-law relationship of mobility and sheet carrier concentration was demonstrated by several research groups for both EG on SiC and EG on 3C-SiC [4; 21; 25; 57; 59; 123]. [PERSON] et al. [56] described the functional power-law relationship between increasing mobility and decreasing carrier density as an intrinsic property of EG. The general power-law behaviour of mobility and sheet carrier concentration indicate that the tunability of graphene transport properties is constrained.  
In Reference [21], the authors of this review demonstrate that the mobility versus sheet carrier concentration values for EG/SiC(0001) from Reference [56], and EG/3C-SiC can be fitted with good confidence using the same power law, which demonstrates a common conductivity of \(\sim\)3 \(\pm\) 1 (e\({}^{2}\)/h) close to the minimum quantum conductivity of graphene (Figure 13). Note that the grain sizes of EG/SiC(0001) are at least 100 nm and more [124; 125; 126] whereas the EG/3C-SiC is smaller, which indicates that grain sizes do not determine the transport properties of EG [21].  
In addition, Reference [21] demonstrated that the charge transport in EG/3C-SiC is dominated by the EG-substrate interaction resulting in p-type graphene due to a charge transfer from the EG into the interface silicates. The substrate interaction is stronger in EG/3C-SiC (100) and smaller in the case of EG/3C-SiC(111) due to the presence of a buffer layer in between EG and the substrate, which screens the charge transfer up to an extent. Figure 13 also indicates the different power-law nature of C-face EG from that of the Si-face EG and EG on 3C-SiC, which is due to distinct levels of EG-substrate interaction, according to [PERSON] et al. [11]. This was confirmed by [PERSON] et al. [21].
# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 2 Related Basic Theory and Algorithm
### Merging Feature Subsets  
Merging feature subsets usually has two ways. One is the intersection approach and the other is the union approach.  
Let \(S\) be the set of samples and \(F=\{f_{1},f_{2},\ldots,f_{i}\}\) be the feature set after preprocessing, \(FS_{1}=\{f_{1},f_{2},\ldots,f_{m}\}\) is a subset of features selected with filter (\(M_{1}\)), where \(m\) denotes the number of features which are selected by \(M_{1}\) and \(m<i\). \(FS_{2}=\{f_{1},f_{2},\ldots,f_{n}\}\) is a subset of features selected with filter (\(M_{2}\)), where \(n\) denotes the number of features which are selected by \(M_{2}\) and \(n<i\).  
The union approach is to create a feature subset \(FS_{3}\), which has the number of features \(p\) (\(p\geq\{m,n\}\)), by merging all features in \(FS_{1}\) and in \(FS_{2}\):  
\[FS_{3}=FS_{1}\cup FS_{2} \tag{5}\]  
The intersection approach is to create a feature subset \(FS_{4}\), which has the number of features \(q(q\leq\{m,n\})\), including these features that are present in both feature subsets \(FS_{1}\) and \(FS_{2}\).  
\[FS_{4}=FS_{1}\cap FS_{2} \tag{6}\]  
Usually, the two approaches mentioned above are utilized to merge feature subsets selected by different filter-based feature selection methods. The union approach selects all features in both subsets. At the same time, it increases the number of features and does not achieve the goal of reducing data dimensions. On the contrast, the intersection approach selects only common features. It reduces the total number of features. However, it is possible to lose some features which are proficient [31].
# Anomaly Detection and Early Warning Model for Latency in Private 5G Networks
## 4 Analysis of Results
### Anomaly Detection Results  
For the input of the autoencoder-based anomaly detection model, all the data were randomly divided into training and test sets in a ratio of 2:1, and zeros were added after the existing 179-dimensional KPIs, thus forming a 12 \(\times\) 15 matrix, which was fed into the convolutional network and decoded again to 179 dimensions after being compressed and coded to 32 dimensions. Owing to the high dimensionality of the data, MSE was used for the loss function to avoid large local errors. The learning rate was reduced by half every 50 epochs during the training process.  
\begin{table}
\begin{tabular}{c c} \hline
**Class ID** & **Meanings** \\ \hline PRB.UsedDI & Total number of downlink PRBs used \\ PRB.AvailDI & Total number of downlink PRBs available \\ MAC.TxBytesDI & Number of bits sent at the downlink MAC layer \\ MAC.TransIniTBDI & Initialize TB count at downlink MAC layer \\ HARQ.ReTransTBDI & Retransmission TB number \\ CQI.NumInTable & CQI Report \\ PRB.NumRbOfTableMCSDI & Table of the number of PRBs used in each level of MCS \\ PHYCell.MeanTxPower & Transmit power of physical layer \\ CELL.PowerSaveStateTime & Host standby sleep time \\ \hline \end{tabular}
\end{table}
Table 1: Sample wireless data.  
Figures 6 and 7 show the distribution curves and the cumulative distribution function (CDF) of reconstruction error after the autoencoder for abnormal and normal samples, respectively, where red and blue lines represent the abnormal and normal samples, respectively.  
The results show that there is a significant difference between the error distribution of normal and abnormal samples, and the decoding error of abnormal samples is significantly higher than that of most the normal samples; however, there are also some normal samples with larger errors. This is due to the fact that, when the autoencoder is trained, all the data used in this study are expected to be normal samples, but in fact, only one criterion of latency is used as the screening basis in the data screening process and the actual screened normal samples are obtained, but there may be some abnormal samples that cannot be characterized by latency, and the performance in the training results is attributed to some \"fake samples\" having a large error.