esa-sceva/satcom-chunk-collection huggingface dataset:
# Field Trials of SC-FDMA, FBMC and LP-FBMC in Indoor Sub-3.5 GHz Bands
## 3 Test-Bed and Field Trial Environment  
### Test-Bed Configuration  
In order to implement a test-bed in a real wireless channel, we used an SDR device (USRP-2901) and LabVIEW Comms 3.0 as a software platform. Table 1 shows the RF specifications of the SDR device, and Figure 4 shows the overall configuration of the test-bed. A Vert900 antenna was used for performance tests in the sub-1 GHz band, and an LP0965 antenna was used in the band higher than 1 GHz.  
The overall operation processes of the test-bed are as follows. On a host PC, the transmit signal is modulated using LabVIEW Comms 3.0 and it is sent to the SDR device. In the SDR device, the modulated signal is converted into a continuous analog signal and it passes through I-Q mixing and up-conversion. Subsequently, an RF signal is generated and it is propagated to the chosen frequency band through the RF antennas, Vert-900 or LP0965. The SDR device of the receiver performs the reverse process of the transmitter. The received signal is transmitted to the host PC, and then the host PC performs synchronization, demodulation, and bit detection.
# Early Detection of Residual/Recurrent Lung Malignancies on Post-Radiation FDG PET/CT
## 2 Materials and Methods
### Fusion Model  
Radiomics models excel in extracting quantitative features related to tissue heterogeneity and texture from medical images, offering detailed insights into subtle and often imperceptible patterns. Conversely, CNN models are highly proficient in learning complex spatial hierarchies and patterns directly from raw imaging data through advanced deep learning techniques. Fusing the predictive probabilities from radiomics models and CNN models leverages the complementary strengths of both methodologies to achieve more robust results.  
The fusion process was realized by an evidential reasoning approach [13], which integrates evidence from various sources (the radiomics and the CNN models) under uncertainty consideration. Figure 4 illustrates the workflow of the fusion model.  
Figure 3: An example of the augmented sample through 3D image rotation method.  
In the ER fusion stage, firstly we converted the probabilities from each model into basic probabilities assignments (BPAs) based on the Dempster-Shafer theory [19]. BPAs reflect the degree of belief in each possible outcome given the evidence from each model. Then based on Dempster's combination rule accounting for both the agreement and conflict between the two models, a combined BPA that represents a consensus of the evidence via mathematically aggregating the BPAs from each model is derived (see Equations (3)-(6)). For hypothesis \(H_{i}\) (_e.g., benign or malignant_):  
\[BPA_{fusion}(H_{i})\ =\ \frac{BPA_{CNN,\ weighted}(H_{i})\cdot BPA_{ radioics,weighted}(H_{i})}{1-\sum H_{k}\
eq H_{i}\Big{(}BPA_{CNN,\ weighted}(H_{k})\cdot BPA_{ radioics,weighted}(H_{k})\Big{)}} \tag{3}\]  
where  
\[BPA_{CNN,weighted}(H_{i})\ =\ w_{CNN}\cdot BPA_{CNN}(H_{i}) \tag{4}\]  
\[BPA_{radioics,weighted}(H_{i})\ =\ w_{radioics}\cdot BPA_{CNN}(H_{i}) \tag{5}\]  
\[w_{CNN}+w_{radioics}\ =\ 1 \tag{6}\]  
Here, \(w_{CNN}\) and \(w_{radioics}\) are the weighting factors to adjust the contribution of each model based on their relative importance or reliability. In this study, the weighting factors were derived to achieve the best validation performance.  
The combined BPA is the final prediction probability that we use for decision-making.
# Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning
## 5 Experimental Analysis
### Comparison with Existing Models  
The proposed scheme is compared with some existing models by using the experiment parameters above in Figure 9 to evaluate its performance. Among them, the prediction effect of LSTM is the worst because the single LSTM model cannot effectively extract local features, and the increase in the number of neurons leads to an increase in calculation quantity and the risk of overfitting.  
Our proposed model has higher recognition accuracy than the other existing models under different SNR conditions. Compared with CNN and LSTM, the CNN-LSTM model with pruning improves the recognition accuracy by more than 10% under 0 dB and higher SNR. These improvements are mainly because the radio signal is a time-related sequence, so the LSTM layer number and unit size to the appropriate state can be adjusted to achieve better accuracy. Moreover, pruning technology is used to reduce the amount of computation, so that the model can be trained faster without reducing its accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Batch Size** & **Loss** & **Time Per Epoch (s)** \\ \hline
64 & 1.153881827990214 & 26 \\
128 & 1.1463038523991902 & 17 \\
256 & 1.119464596112569 & 13 \\
512 & 1.1418431202570598 & 11 \\
1024 & 1.1403136253356934 & 9 \\
2048 & 1.1303218603134155 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The impact of the size of the batch size on classification accuracy.  
Figure 8: The impact of the batch size on classification accuracy.
# Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning
## 3 Interference Monitoring Research
### Interference Monitoring Technology Based on Artificial Feature Extraction
#### 3.3.4 Model Training  
The data is simulated by MATLAB, and the monitoring bandwidth is 2 MHz. The sampling data is stored in the format of binary bin file as the input of the model. The training set and test set data are divided by 10:1. There are 27,000 sample data in the training set and 2700 sample data in the test set.  
Both the training set and the test set contain six kinds of interference patterns. Each group of data has 9 combination modes according to three channel types and three SNR. The training set consists of 500 samples \(\times\) 9 species, a total of 4500 samples. Similarly, a set of data is tested by 50 samples \(\times\) 9 species, a total of 450 samples.  
The interference monitoring model uses self-made training set and test set to train the model parameters (Figure 8). Image size is 512 \(\times\) 512 px, because the model needs 5 down samples, the length and width of the picture should be an integral multiple of 32. The number of iterations is set to 20, and 16 pictures are processed in batches each time. The learning rate is set to 0.01 and the weight attenuation rate is 0.0005.  
Figure 8: Variation diagram of model loss function during training.  
Figure 7: Time frequency diagram of single frequency interference with different SNR in Gaussian channel. (**a**) SNR = 5 dB, (**b**) SNR = 8 dB, (**c**) SNR = 10 dB.
## 6 Conclusions  
In this study, we develop a threshold method for the large-scale SAR-based water mapping based on massive experiments with different land-cover type samples across China. The threshold-based water mapping method, which is more universal than previous studies on individual water bodies mapping, is proven to be robust and applicable across different seasons in a year. The Sentinel-1 images from 2016 to 2018 are then employed to construct the HSWDC using the cloud-based GEE platform and other auxiliary data. Compared with the existing surface water datasets, the HSWDC has the advantages of unprecedented spatial resolution (10 m) and period observation at the month scale. In addition, the HSWDC can effectively provide surface water distribution even in the freezing period. Due to the characteristics of SAR, which are not affected by clouds, the HSWDC can be an ideal alternative data source for future wetland mapping in cloudy regions.  
Figure 11: (**a**) Result obtained by the Refined Lee filter before water body extraction, (**b**) result of original image, (**c**) result obtained by morphological operators (first erosion, then dilation) after water body extraction.  
**Funding:** This research has been supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19030203), the National Key Research and Development Program of China (2017 YFA0603004), and the National Natural Science Foundation of China (41971390).  
**Acknowledgments:** We acknowledge Google Earth Engine for providing computing platform. The radar images used in this study are free Copernicus Sentinel data, which also are provided in GEE, and other data used in this study are freely available. We thank the authors and institutions who provided us with these data. We also thank those anonymous reviewers who provide valuable suggestions for the revision of this paper.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that would have influenced the outcomes reported in this paper.  
**Data Availability:** HSWDC dataset can be obtained by contacting the corresponding author.
# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 4 Detailed Overview of the Brain Stroke Detection and Prediction System
* Integration with EHR: Constant data on patients risk levels and work on entering data for medical services experts are made conceivable via consistent cooperation with HER [24]. The brain stroke detection and prediction system is an effective and dependable technique for predicting stroke risk, allowing clinicians to make educated, proactive decisions that can save lives and improve patient outcomes.
## 5 Conclusions  
In this study, a rock image classification model based on EfficientNet and a triplet attention mechanism is proposed to achieve accurate end-to-end rock image classification. To begin, we expanded the rock image dataset through various data augmentation methods to prevent overfitting of model training and improve model performance. In building the model, we utilized EfficientNet as the benchmark network, which boasts an efficient network structure thanks to NAS technology and a compound model scaling method. On this basis, the triplet attention mechanism was introduced to improve the original EfficientNet and enhance the model's ability to extract spatial features of rock images. The experimental results demonstrate that the classification model in this study outperforms other mainstream models on both the training set and the test set, the accuracy reached 92.6% and 93.2% respectively. In training the model, we employed the transfer learning method during the training process to accelerate model convergence and significantly enhance the model's training performance. The training accuracy of the model with transfer learning increased by 48.4% compared with that without transfer learning.  
Through further research, it has been revealed that the number of samples and rock types in the rock dataset have a significant impact on the number of rock types that can be recognized by the model and the final classification accuracy. With this in mind, our future research endeavors will concentrate on expanding the variety of rock types and the quantity of rock images, while ensuring that the classification accuracy is further enhanced with the addition of more rock types. Furthermore, given the difficulty of obtaining timely feedback on rock-type recognition results through networks in field exploration, we intend to deploy our model on mobile devices in addition to improving it. This will allow geological surveyors to conveniently identify rocks using the classification model under offline conditions.  
Conceptualization, Z.H. and L.S.; methodology, Z.H.; software, Z.H.; validation, Z.H., J.W. and Y.C.; formal analysis, Z.H.; investigation, Z.H.; resources, Z.H.; data curation, Z.H.; writing--original draft preparation, Z.H.; writing--review and editing, Z.H.; visualization, Z.H.; supervision, L.S.; project administration, Z.H.; funding acquisition, L.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61903315, and the Natural Science Foundation of the Department of Science and Technology of Fujian Province, grant number 2022J1011255.  
**Institutional Review Board Statement:** Not applicable.
## 4 Discussion
It can allow several applications, such as autonomous transportation, disaster recovery, and IoT-enabled urban and rural automation.
# Future Renewable Energy Communities Based Flexible Power Systems
## Abbreviations  
A  
Contained area (m2)  
a  
Anemometer mast height (m)  
AC  
Alternating current  
BSS  
Battery Storage System  
BTB  
Back-to-back  
CSC  
Current Source Converter  
CSI  
Current Source Inverter  
CSR  
Current Source Rectifier  
CSMMC  
Current-Source Modular Multilevel Converter  
DC  
Direct Current  
DG  
Distributed Generation  
DER  
Distributed Energy Resources  
EMI  
Electromagnetic Interference  
EMS  
Energy Management System  
ES  
Energy Source  
Energy Storage System  
EV  
Electric Vehicle  
FC  
Flying Capacitor  
G\({}_{\text{b}}\)  
Solar beam radiance (W/m2)  
GTO  
Gate Turn-Off thyristor  
H  
Horizontal  
h  
height (m)  
IGBT  
Insulated-Gate Bipolar Transistor  
k  
Adjustment factor  
LPG  
Liquid Petroleum Gas  
M CSC  
Multilevel Current Source Converter  
MMC  
Modular Multilevel Converter  
MPPT  
Maximum Peak Power Tracking  
MTBF  
Middle-time between failures  
NPC  
Neutral Point-Clamped  
PCS  
Power Conversion System  
PEI  
Power Electronic Interface  
PEM  
Proton Exchange Membrane  
PV  
Photovoltaic  
PWM  
Pulse Width Modulation  
R  
Tidal range (m)  
SM  
Sub-Module  
STATCOM  
Static Synchronous Compensator  
STC  
Standard Test Conditions (1000 W/m2, 25 \({}^{\circ}\)C, air mass 1.5 and no wind)  
T  
Wave period (week)  
THD  
Total Harmonic Distortion  
V  
Vertical  
v  
Wind speed (m/s)  
v\({}_{\text{z}}\)  
Wind speed at z meters from ground  
v\({}_{\text{h}}\)  
Wind speed at h meters from ground  
VPP  
Virtual Power Plant  
VSC  
Voltage Source Converter  
WECS  
Wind Energy Conversion System  
WP
Wind Power  
z  
Nacelle height (m)  
\(\eta\)  
Efficiency (%)  
\(\theta_{\text{z}}\)  
Zenith angle (between the sun's rays and the earth vertical) (dg)
# Quadratic Multilinear Discriminant Analysis for Tensorial Data Classification
## 1 Introduction
By allowing different sample covariance matrices for each class--i.e., different sets of coordinates for each class--we can similarly interpret QDA in this framework. If \(\mathcal{X}\) is a matrix (or tensor), applying a change of coordinates after vectorization might radically alter the structure of \(\mathcal{X}\). This can be especially nefarious when the data is sparse, e.g., it has low rank or few non-zero entries. To solve this issue, we can apply a different change of coordinates in each mode of \(\mathcal{X}\), i.e., multiply by an invertible matrix in each mode. This strategy would preserve the rank. To further preserve the structure of the data, one might want to consider coordinate changes that preserve volumes; therefore we will consider invertible matrices with the determinant 1. The restriction to matrices with determinant 1 does not have a large impact on the method, as it amounts to rescaling an invertible matrix to make its determinant equal to 1. The question now is how to appropriately choose such a change of coordinates. The Kempf-Ness theory gives us a choice of coordinates by solving a well-behaved optimization problem which reduces to Mahalanobis distance in the case of vectorial data. The proposed method was tested on synthetic data of several types, some of which have already appeared in the literature [3; 20]. The results demonstrate that there are classes of data on which the proposed method achieves significantly better results than other existing methods. The method was also employed to classify tensors extracted from ECG signals. The analysis of ECG recordings with tensor-based approaches has proven to be very fruitful in a variety of settings, such as the detection and localization of myocardial infarction, irregular heartbeat classification, ECG data compression, detection and quantification of T-wave alternans, and analysis of changes in heartbeat morphology (see [4] for an overview). In our experiments, we consider third-order tensors extracted from multi-lead ECG signals from the publicly available PhysioNet PTB dataset [21]. Thereare several ways of constructing tensors from ECG data; here we follow the approach of [22] by extracting features from taut-string approximations of the signals. In [22], feature vectors were extracted from ECG signals via taut string as well as other methods, and then classification was performed with standard machine learning techniques, among which linear support vector machine (SVM) performed best. We found that the same applies to our experiments, which is why in the comparisons we also include the results of an SVM classifier on the vectorized tensors. We believe that the method by which we construct third-order tensors via taut string from multi-lead ECG signals (which we have not found anywhere else) is also a potentially interesting new way of extracting tensors from signals.
# Development of an Alternative Alloying Concept for Additive Manufacturing Using PVD Coating
## 4 Discussion  
To qualify the alternative alloy concept, an existing filler metal produced by secondary metallurgy with the same chemical composition was produced with the reference wire by appropriate modification. When comparing the weld metal microstructures, no difference was found between the different alloying methods. The mechanical properties related to hardness and static strength showed marginal deviations. However, these can be explained by an inhomogeneous chemical composition and different welding parameters, and can therefore be neglected. It could be shown that alloying by means of PVD thin film coating on the surface of filler metals in the field of high-strength low-alloy steels is an alternative alloying concept. This can considerably reduce the effort of alloy development with regard to additive manufacturing with high-strength low-alloyed materials. During the production of specimens, only the influence of the welding process with regard to the change in the arc length has to be taken into consideration. However, this influence has no effect on the mechanical properties of the weld metal.  
The results show that the alternative alloy concept can be used in the scope of high-strength low-alloy steels for the alloying elements that have been investigated. This is especially valid for the thin film coating and the correspondingly low-alloy contents. The qualification is successful for this application case, as the different alloy concepts match in their effects on the microstructure morphology and mechanical properties. A clamped tensile specimen is shown in Figure 10.  
For further investigations, thin film coatings are to be produced in a continuous pass-through PVD system. This will lead to a more homogeneous coating and a more targeted adjustment of the chemical properties. Furthermore, influencing the surface tension of steel melts with regard to additive manufacturing is of major interest. By means of the thin film coating, a targeted adjustment of the melt pool of steel melts can be made. Considering additive manufacturing, an improvement in the shape retention can be achieved by the modification. In addition, it is possible to adapt the chemical composition of the filler materials locally and specifically to the loads on the component and the mechanical properties required. In this way, higher flexibility in the production of complex components can be achieved.  
Figure 10: Clamped tensile specimen.
## 3 Results and Discussion
### Multivariate Analysis  
The results from Table 1 revealed that the total peak areas of the different meat alternatives were significantly influenced by both addition of Mb and grilling as well as their interaction. PCA is conducted to visualize the relationship between plant-based meat alternatives in terms of their volatile profile (Figure 1). The score plot (Figure 1A) shows that the first two principal components (PC) explained 79.8% of the total variability. All VOCs (n = 40) are shown in the plane of the first 2 PC (PC1 and PC2); the circles indicate if variables are reconstructed at the 50% (inner circle) and 100% (outer circle) of the total explained variance (Figure 1B).  
The first PC, accounting for 52% of variance, separates GMA + Mb samples from GMA, RMA and RMA + Mb. Additional contribution of PC2, explaining 28% of variance, drives scores upwards as a function of increasing Mb concentration and down as a result of grilling. In the resulting score plot, raw and roasted samples are clearly separated by a diagonal line. Grilling causes a strong increase in pyrazine content, which is characterised by a shift towards the lower left. Additionally, the samples migrate along the separation line towards the upper right corner as a function of Mb addition. Evidence for a significant interaction between Mb addition and grill treatment is apparent from the fact that the direction of RMA and RMA + Mb shifts to their corresponding GMA and GMA + Mb scores are not parallel, and from the greater distance between GMA and GMA + Mb clusters, compared to RMA and RMA + Mb. The addition of Mb is necessary for aroma precursor formation whereas grilling is mainly responsible for transforming these precursors into volatile components. While most volatile components clearly play a role, acids (1-3) appear to be less important. GMA + Mb were mainly described by saturated (11-13) and unsaturated aldehydes (15-21) and pyrazines (32-37). As explained above, RMA are characterized by mainly (branched) hydrocarbons (22-26), 1-hexanol (5) and decanal (14).  
Figure 1: PCA scores (**A**) and correlation loadings (**B**) plots of PC1 and PC2 for volatile compounds in raw and grilled plant-based burgers enriched with varying concentrations of myoglobin. Volatiles (loadings) are denoted by their numbers in Table 1 [Acids (1–3), Aldehydes (7–21), Hydrocarbons (22–27), Pyrazines (32–37)].
# Combined Navigation and Tracking with Applications to Low Earth Orbit Satellites +
## 4 Experimental Results  
To validate the platform, the Xona team defined an XL open-sky simulator scenario with a constant CN0 of 43 dB Hz on all satellites (Figure 2(b),c). The complete Xona ephemeris (orbital parameters) was simulated. For simplicity, both ionospheric and tropospheric errors were set to zero. GPS L1 C/A signals with the same CN0 were also simulated and synchronized with the Xona signal to serve as a reference.  
Single-point position (SPP) and PPP algorithms were applied to verify the consistency of the Xona observables, for both the pseudo-range and carrier phase. The positioning results with loop assistance switched off are illustrated in Figure 8. The SPP horizontal error was obtained by a least-square, pseudo-range-based algorithm, whereas the PPP was a classic, single-band algorithm with float solution.  
Figure 8: Xona-only positioning results: (**a**) SPP CEP (least squares); (**b**) PPP vs. SPP CDF.
## 3 Evaluation and Application of PWV Derived from CPRAP  
In this section, the PWV with the different spatio-temporal resolutions was first derived from the CPRAP and the accuracy of corresponding PWV values derived from GNSS,satellite remote sensing (FY-3A / MERSI and Sentinel-3A/OLCI) and reanalysis dataset (ERA5) was further evaluated. Finally, the CPRAP-derived PWV was applied in drought and rainfall monitoring in Yunnan and Zhejiang provinces, China, respectively. Given that the high-precision PWV can be retrieved from RS data [51], the corresponding PWV calculated by RS data was also obtained from the CPRAP and regarded as the reference to evaluate the performance of PWV derived from other techniques.