esa-sceva/satcom-chunk-collection huggingface dataset:
## LSTMCaps Autoencoder Network
### Experimental Results
#### 2.6.3 Drone Dataset Outlier Resilient Anomaly Detection
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **Score** & **MAE Threshold 1** & **MAE Threshold 2** & **MAE Threshold 3** & **Precision** & **Recall** & \(F_{1}\) \\ \hline \multirow{3}{*}{Design A} & Average over 5 runs & 6.34 & 6.65 & 10.91 & 0.37 & 0.59 & 0.45 \\  & Best over 5 runs & 6.20 & 7.56 & 10.87 & 0.37 & 0.67 & 0.48 \\  & Standard Deviation & 0.20 & 0.63 & 0.56 & 0.02 & 0.05 & 0.03 \\ \hline \multirow{3}{*}{Design B} & Average over 5 runs & 4.09 & 4.11 & 9.61 & 0.46 & 0.47 & 0.46 \\  & Best over 5 runs & 3.92 & 3.97 & 9.77 & 0.49 & 0.52 & 0.50 \\  & Standard Deviation & 0.17 & 0.10 & 0.61 & 0.05 & 0.04 & 0.04 \\ \hline \multirow{3}{*}{Design C} & Average over 5 runs & 7.73 & 6.10 & 9.25 & 0.54 & 0.50 & 0.52 \\  & Best over 5 runs & 7.10 & 5.57 & 9.41 & 0.49 & 0.51 & 0.50 \\  & Standard Deviation & 0.47 & 0.36 & 0.39 & 0.03 & 0.01 & 0.01 \\ \hline \multirow{3}{*}{
\begin{tabular}{l} **Design D** (proposed) \\ \end{tabular} } & Average over 5 runs & **4.44** & **4.15** & **9.58** & **0.52** & **0.53** & **0.53** \\  & Best over 5 runs & **4.19** & **4.08** & **10.36** & **0.60** & **0.65** & **0.62** \\ \cline{1-1}  & Standard Deviation & **0.24** & **0.17** & **0.52** & **0.05** & **0.07** & **0.06** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Best and average test results out of five runs for anomaly detection using optimised hyperparameters in Table 3 for each NN Design. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Source** & **Degrees of Freedom (DP)** & **Sum of Squares (SS)** & **Mean Square (MS)** & **F-Ratio** & \(p\)**-Value** \\ \hline Between Groups & 3 & 0.0219 & 0.0073 & 5.526 & 0.0085 \\ \hline Within Groups & 16 & 0.0211 & 0.0013 & \\ \hline Total: & 19 & 0.043 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: ANOVA source table for \(F_{1}\) scores of the drone experiment with outliers in the training data. than the most commonly used MSE, but considers them to some extent. In the case of extreme outliers in the training data or outliers that correspond to faulty operation, this data cannot be used, and is easily distinguished from this kind of data in either the data collection phase or the data analysis phase that precedes model fitting, and thus it is easy to avoid using such data to train a NN model.
## 5 Intrusion Detection Scheme Based on Relaxed Flow Conservation Constraints
### Two-Hop Energy-Efficient and Secure Reporting Scheme  
In order to report information to a two-hop neighbor (i.e., grandparent in the tree), the trivial solution is that each node \(\omega\in Child_{u}\) acts as a witness and sends a packet containing \(f(\omega,u)\) to the monitoring node \(m\) through an intermediate node. However, a malicious intermediate node can simply drop this packet and prevent node \(m\) from receiving it. The other solution is to use an adaptive power transmission that can reach the two-hop neighbors by using higher power transmission level. However, this solution exhausts the node's battery faster than the normal power and makes the packet contend with more transmitting nodes.  
To address the above drawbacks, we propose an energy-efficient and secure method, allowing for nodes that can monitor the flows entering and leaving \(u\) to report their observations to the monitoring node \(m\).
## 3 Optimized Joint Signal Processing Schemes
### Sparsity Reconstruction-Based Improved Parameter Estimation
#### 3.1.1 Conventional Processing
Therefore, according to the structural characteristics of the MIMO-OFDM-IM system, we use the diagonal matrix group in (9) to extract data from different channels through staggered allocation. The received signal of the \(p\)th pulse on the \(g\)th channel after separation can be represented as \[\mathbf{y}_{p,g}=N_{r}\sum_{q=1}^{Q}\beta_{q}e^{2\pi\tau_{d}^{d}pT_{r}}e^{-j2\pi(g-1) d_{t}\sin\theta_{t}/\lambda}\mathbf{F}_{N_{c}}^{-1}\mathbf{\psi}_{g}\big{(}\mathbf{s}_{p} \odot\mathbf{\tau}_{q}\big{)}+\mathbf{\tilde{\omega}}_{p,g} \tag{12}\]  
Next, we perform \(N_{c}\)-points DFT on the signal in (12) to obtain the following equation  
\[\mathbf{\hat{y}}_{p,g}=N_{r}\sum_{q=1}^{Q}\beta_{q}e^{2\pi\tau_{d}^{d}pT_{r}}e^{-j2 \pi(g-1)d_{t}\sin\theta_{t}/\lambda}\mathbf{\psi}_{g}\mathbf{s}_{p}\odot\mathbf{\tau}_{q}+ \mathbf{\hat{\omega}}_{p,g} \tag{13}\]  
where \(\mathbf{\hat{\omega}}_{p,g}=\mathbf{\tilde{\omega}}_{p,g}\mathbf{F}_{N_{c}}\).
# Instance Segmentation and Number Counting of Grape Berry Images Based on Deep Learning
## 1 Introduction  
The grape planting area is relatively large across the globe, and traditional grape planting, management, picking, grading techniques, and other work require many humans, material, and other resources [1]. Grape is a kind of spike-shaped fruit with a complex shape. The size of grape berries is an important fruit quality of grapes, and the yield and quality are determined by the size of grape berries. Grape berry size is an important factor affecting grape grading, quality grading and price evaluation. Considering the entire bunch of grapes as the research object does not meet production demand; therefore, it is necessary to consider the grape berry as the research object to meet the actual production needs. However, due to the complexity of grape morphology and the mutual occlusion of the berries, the large difference in the size and color of the berries makes the segmentation of grape berries a challenging problem.  
In this paper, we propose an improved Mask R-CNN-based grape berry segmentation method to address the problem of missed detection caused by berry occlusion in grape images, and propose to improve the Mask R-CNN model by using linear weighting to solve this problem, while achieving grape berry counting by filtering out instances with low segmentation accuracy and the number of instances that are far from other nearest berries.
# Enhanced Multiple Speakers' Separation and Identification for VOIP Applications Using Deep Learning
## 4 Experiments
### Evaluation Metrics  
#### 4.3.1 For Separation  
The scale invariant signal-to-noise ratio improvement (_SI-SNRi_) score [25] is applied on the test set to evaluate the employed model. This score is calculated as follows:  
\[SI\_SNRi(s,\hat{s},x)=\frac{1}{C}\ \sum_{i=1}^{c}SI\_SNR(s\_i,\ \hat{s}_{i})-SI\_ SNR(s_{i},\ x) \tag{4}\]  
We defined \(s_{i},\ \hat{s}_{i},x\) as the ground truth (clean) signal, the estimated audio signal, and the mixed audio signal, respectively. \(C\) is the number of speakers. The SI-SNR (\(s_{i},\hat{s}_{i}\)) is defined as the estimated scale invariant signal-to-noise ratio while the SI-SNR (\(s_{i},x\)) belongs to the clean signal.  
Figure 5: State transition diagram [28].
## 3 Results and Discussion
### Downstream Processing
Good results were obtained also in the case of HMF and furfural removal, which showed a decrease from 9.7 mg/L to 0.5 mg/L (MN 502) and <0.004 mg/L (charcoal) in the case of HMF. Furfural decreased from 139 mg/L to 2.3 mg/L after treatment with MN502 and <0.01 mg/L after charcoal treatment. In contrast, phenol concentrations did not decrease significantly. Monosaccharide concentrations were also reduced, with xylose being lowered from 9.4 mg/L to 1.3 mg/L after treatment with the resin and 0.09 mg/L after charcoal treatment. XOS registered a slight decrease, from 1.33 mg/L in the initial hydrolysate to 1.31 mg/L in the hydrolysate treated with the resin and to 1.26 mg/L in the hydrolysate treated with charcoal. [PERSON] et al. used activate carbon treatment at different dosages (0.2%, 0.4%, 0.5%, 0.8%, 1% and 1.2%) to purify wood chip hydrolyses and observed that at higher dosage (1.2%), a loss of 20% xylosugars was noticed (1.5% xylose, 21% XOS loss), even if lignin and furfural were removed at a high percentage 70% [64]. It was reported that the adsorption behavior of the materials was affected by the molecular structure and weight [65]. An- and cation exchange chromatography was performed to further reduce the concentration of salts and minerals. It was previously shown that this method was successful in removing salt ions and other undesirable compounds and obtaining purified fractions of specific compounds such as lactic acid [66; 67; 68; 69; 70]. The anion exchange resin was more efficient in reducing the concentrations of sulphate, phosphates and nitrate, showing statistically significant differences (1.36 \(\pm\) 0.01 mg/L PO\({}_{4}\)\({}^{3}\)--P vs. the initial content of 58.58 \(\pm\) 0.50 mg/L PO\({}_{4}\)\({}^{3}\)--P; 24.3 \(\pm\) 0.04 mg/L SO\({}_{4}\)\({}^{2}\)-- vs. the initial content of 80.82 \(\pm\) 0.50 mg/L SO\({}_{4}\)\({}^{2}\)--). [PERSON] et al. showed that a weak anion exchange resin (Amberlyst A2) was successful in removing carbohydrate fractions, chlorides and other anions such as sulfates and phosphates from ferulic acid contained in wheat bran, due to the resin strong affinity for OH- [71]. Cation concentrations were lowered after the cation exchange, showing statistically significant differences; however, sodium concentrations increased significantly (507.22 \(\pm\) 38 mg/L). Levels of HMF, furfural and phenols were also reduced during the downstream process (Table 6). Monosaccharide (xylose and arabinose) removal was the most efficient after the activated charcoal adsorption (92%). It was noticed that XOS concentrations registered a slight decrease during the down-streaming process; however, no statistically significant differences were noted. Finally, it should be noted that the optimization of the downstream processing was not aim of the present work but is certainly needed to create feasible processes.
###### Abstract
Using representative backscattering values from the driest and wettest conditions at a grid point, the relative soil moisture is retrieved here by scaling the individual backscattering value between these minimum and maximum values. Most recently, [PERSON] et al. [29] published a volumetric soil moisture data set based on Sentinel-1 time series using a change detection method, with a spatial resolution of 1 km and a temporal resolution of 6 days. However, current methods have limited applicability in agriculture due to their coarse spatial or temporal resolution [30]. When using physical backscatter models to decompose the SAR signal, the lack of publicly available full-polarimetric SAR observations often limits their use; in addition, they require a lot of surface information. For empirical models, their site dependence is an obstacle to their representation in other areas due to the large variability within the agricultural landscape [31]. In this regard, short-term change detection approaches are promising because they are independent of surface parameters that do not change significantly relative to soil moisture (e.g., soil roughness, soil texture, vegetation water content) and can be applied on the freely available dual-polarized C-band Sentinel-1 time series. Nevertheless, especially for agricultural areas with distinct bare soil and vegetation conditions, the influence of changing vegetation still limits their application. In this regard, new methods for addressing the effect of changing vegetational conditions need to be developed. We developed a new method to address this issue by minimizing the time period between consecutive SAR observations. This is achieved by extending the alpha approximation approach by [PERSON] et al., 2011 to multi-orbit acquisition time series. Moreover, we developed a method to detrend the vegetational effect on the related backscatter ratios. The method is established within the cloud-processing platform Google Earth Engine (GEE),using temporal and spatial high-resolution Sentinel-1 time series. Its integration into cloud computing environment allows a fast and applicable way for on-demand computation of soil moisture for individual time periods and areas of interest on a global scale.
# Hard-Decision Coded Modulation for High-Throughput Short-Reach Optical Interconnect
## 4 Numerical Analysis for an Optical Transmission System
### Multi-Span Fiber System Setup  
In the considered multi-span transmission link scenario, each SSMF span of length 80 km was followed by an EDFA compensating exactly for the link loss (16 dB) and with a noise figure of 5 dB. The receiver DSP algorithms utilised are identical to the ones described in single-span transmission scenario. \(I_{\rm HD}\) of four different modulation formats are calculated to predict the shaping gain and finding the optimal launch power. In addition, pre-FEC BER and post-FEC BER are measured to verify the performance of the CM scheme.
# Time Series Classification with Shapelet and Canonical Features
## 5 Experimental Analysis
### Parameter Settings
To find the optimal setting of _shift_, different values from \(l\):5% to \(l\):50% are tested with Small60 datasets. As shown in Figure 6, as the _shift_ value increases, the training time becomes significantly longer. This is the result of expanding the searching space of Shapelet matching. The classification accuracy increases from the beginning, reaches a stable state when the value of shift reaches 20% of the time series length, and even encounters a small drop at the end. This shows that although time warping exists commonly in time series datasets, the vast majority of shifts occur only within small areas, and it may be counterproductive to expand the matching space of the Shapelet. Again, to reach a compromise between efficiency and accuracy, we choose \(l/10\) as the optimal value of _shift_. To sum up, subsequent experiments take the default parameter settings given in Table 1.  
Figure 4: Average accuracy and variance of RSFCF over Small60 datasets under different ensemble sizes.
## 5 Aapical Plug in Teeth with Periapical Lesions
### Aypical Barrier  
Calcium hydroxide has for many years been the material of choice for tooth apexification in teeth with an open apex or pathological resorption. The purpose of the application was to cause the formation of a calcium barrier over a period of 5-20 months. This long period of treatment requires a high level of patient compliance. For these reasons, the  
Figure 3: (**A**) Preoperative X-ray on tooth #46, which is planned for retreatment; (**B**) Control X-ray after the separating instrument (lentulo) has been totally removed; (**C**) Postoperative X-ray—mesial canals are filled with a bioceramic sealer (TotalFill) and gutta-percha, and the apical 5 mm of the distal canal is obturated with MTA due to the width of the foramen (ISO #60). An apical barrier made from collagen is applied before the MTA plug; (**D**) Control X-ray after 1 month; (**E**) at the 6-month follow-up; (**F**) at the 1-year follow-up; (**G**) at the 4-year follow-up (with permission of [PERSON]).  
method of treatment for these clinical cases has been changed. The contemporary materials of choice are MTA and bioceramic root repair materials (putty material), which have the characteristics of a fast-setting material. Thus, clinicians have the ability to complete the treatment/retreatment with an apical plug or perforation seal in one visit.  
In the past few years, new CSCs have been released with similar characteristics to MTA. Five mineral oxide CSC (5 MO) is derived from Portland cement and was designed to treat multiple dental problems and endodontic complications. It is regarded as an efficient pulp closure material, apicoectomies filling material, and perforation sealer [69; 70]. It is very important to use an apical barrier (collagen piece or calcium phosphate bioceramic), which has the potential to stop bleeding and accelerate repairing processes in periapical/lateral bone lesions [71; 72] (Figure 4).
## 3 Results and Discussion  
### Workability and Air Content of Fresh Mortars  
Fresh mortar properties are the ones which determine the application range of the material. During the course of the present study, workability as well as air content of the fresh nano-reinforced mortars was maintained, in similar values throughout mixtures of variable nanotube concentration, by addition of differential amounts of Viscocrete 600 superplasticizer as needed. As can be observed in Figure 4, the workability of the mortars reinforced with different concentrations of carbon nanotubes does not vary significantly from the starting, plain-mortar value. It must be recalled that the sole dispersion assistive agent used in the mixture, which affects workability, was Viscocrete Ultra 300, a native concrete additive which has proven highly effective for CNT dispersion, hence rendering surfactant usage and chemical tube functionalization, unnecessary [8; 55]. The invariance of workability with respect to tube loading is a favorable finding, it ensures that the latter can act as the sole parameter responsible for any documented variation in properties.  
Following completion of the flow table test, proper volumes of fresh mortars were used for air content testing. Results from each mixture are plotted as a function of the tube content in Figure 4. Here again, all specimens are observed to share similar air contents, a finding which confirms the workability results as well as the role of Viscocrete 600 superplastisizer in the mixing procedure.  
Figure 4: Change of flowability and air content across nano-modified mortars with different concentrations of carbon nanotubes.
## 6 Conclusions  
With the growing electrification requirements for more-electric aircraft (MEA), the enabling technology and systems must be on par with high reliability standards and maintained to the highest operational readiness levels. The existing health management regime that leverages several condition-based maintenance methods lacks the efficient and highly representative assessment techniques that could pinpoint the occurrence of faults in mission-critical systems and help realign the maintenance approach to foster system reliability. The qualitative validation approach presented in this paper plugs this gap and provides an efficient way of enhancing the existing CBM capabilities. Oriented towards electro-mechanical actuators (EMA) of an electric braking system (EBS), the newly developed qualitative validation is a rubric-scoring-based method that takes into consideration the sensor-based data on the change in behaviour of operational parameters under different aircraft operating conditions, differentiates, and classifies them according to the weighting score from 1 (min. change) to 4 (max. change). This helps with rapid identification of fault occurrence, building more robust remedial measures, and augmenting CBM capability for EMAs.  
The whole qualitative validation scheme is built around a highly representative digital model of EMA integrated with an anti-skid braking system (ABS) model to provide a holistic EBS environment, more-realistic simulation with carefully selected three fault modes (open circuit (OC), intermittent open circuit (IOC), and jamming), and rigorous assessment of their impact on EMA performance and reliability.  
As an early stage detection and analysis method, qualitative validation can be very useful to discard or include any relevant fault mode affecting the system. In addition, the developed qualitative validation method can be employed as an add-on to a digital twin (DT) environment for more rapid and realistic identification of faults in a given system. Furthering this research, we intend to expand the scope of fault modes (such as spalling, leadscrew structural problems, and other sensor issues) to build a large-scale EMA reliability database using qualitative validation for safe implementation in future more- and all-electric aircraft systems.  
Conceptualization, P.G., S.P. and A.W.; Investigation, P.G.; Methodology, P.G. and A.W.; Supervision, S.P., S.A. and A.W.; Writing--original draft, P.G.; Writing--review & editing, S.P. and S.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 3 Experiments and Results
### Results and Analysis
#### 3.3.2 Test on DeepGlobe Road Dataset  
In this study, we proposed the HRU-Net, an enhanced model building upon the established foundations of the U-Net and HRNet. In order to further verify the effectiveness of the HRU-Net model, we put these three methodologies to the test using the DeepGlobe Road Extraction Dataset. The quantitative comparisons of the three methodologies, as conducted on the DeepGlobe Road Extraction Dataset, are displayed in Table 3. As demonstrated by the results, the HRU-Net model outperformed the others across all three metrics. It achieved an accuracy increase of 4.55% over HRNet and 2.63% over U-Net. In terms of recall, the HRU-Net model surpassed HRNet by 2.11% and U-Net by 1.75%. Regarding the IoU metric, the model exhibited improvements of 4.00% and 1.87% compared to HRNet and U-Net, respectively. These results underscore the superiority of our approach on the DeepGlobe Road Extraction Dataset.  
Figure 5 presents selected examples from the DeepGlobe Road Extraction Dataset, demonstrating the efficacy of different models. From top to bottom, the images show the original input, followed by the results from the HRNet, U-Net, and our proposed HRU-Net, respectively. In the first image, a blurred road scenario is depicted. Here, the HRNet and HRU-Net models showcase commendable extraction capabilities, with the U-Net model being the only one to exhibit over-extraction. The second image presents a case of severe shadow occlusion on the road, where both the HRNet and U-Net struggled to maintain road connectivity and the U-Net additionally failed to extract some road sections. In the third image, we have an example of a well-defined road where, intriguingly, only the HRU-Net model successfully avoided road disconnection when addressing the minor road on the left. Lastly, the fourth image provides an example of a complex urban road system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **Precision (\%)** & **Recall (\%)** & **IoU (\%)** \\ \hline HRNet & 81.55 & 83.09 & 73.23 \\ U-Net & 83.43 & 84.45 & 75.36 \\ \hline HRU-Net (ours) & 86.06 & 85.2 & 77.23 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative evaluation of six methods conducted on the DeepGlobe Dataset. The closer the index value is to 1, the better the effect. IoU: Intersection over union.  
In terms of road extraction integrity and continuity, the HRU-Net model demonstrated superior performance over both the HRNet and U-Net models.
## 3 Numerical Results
### Power Spectral Density  
One of the main disadvantages of OFDM is its high out-of-band (OOB) radiation, which is mainly due to the rectangular pulse [47,48]. Due to high OOB, the symbols would interact with neighboring channels. As a result, OFDM is not considered suitable for 5G communication. The OOB radiation of the broadcast signal for the 5G base stations must be below the necessary limitations, i.e., \(-45\) dB, to satisfy the spectrum regulatory masks [49]. GFDM is known to have lower OOB radiation as compared to OFDM [40,50,51]. This is mainly due to the fact that a prototype filter that is circularly shifted in both the time and frequency domains shapes the data symbols [49]. Figure 5 shows the power spectral density (PSD) of proposed GFDM and compares it with that of conventional GFDM. It is evident from this figure that the PSD of the proposed GFDM is superior in terms of OOB radiation, as it is lower than that of the conventional GFDM.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **No.** & \(p\) & \(q\) & _ln\(\frac{p}{q}\)_ \\ \hline
14 & 5 & 16 & 0.3125 \\ \hline
15 & 6 &