esa-sceva/satcom-chunk-collection huggingface dataset:
# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 3 Experiments and Results
### Ablation Experiment
#### 3.4.2 Exploring the Effect of the Number of Modules on the Network  
In the first round of ablation experiments, the effectiveness of both the UMR module and the MRF module on the network was explored individually. To investigate the impact of module quantity on the network model, we selected the network model, which only retained the UMR1 module and MRF1 module, as the baseline network for the ablation study, as shown in dashed box 1 in Figure 7.  
The experimental design was as follows:  
Plan One: On the basis of the baseline model, the UMR2 module and the MRF2 module were added, corresponding to dashed box 2 in the figure.  
Plan Two: Building on Plan One, the UMR3 module and MRF3 module were added, corresponding to dashed box 3.  
Choosing dashed box 4 refers to selecting the full HRU-Net model.  
The quantitative evaluation of the DeepGlobe Road Dataset is presented in Table 5.  
The experimental results show an improvement in model performance, including the accuracy, recall, and IoU, as the number of UMR and MPF modules increased. This indicates that the augmentation of module quantity aided the model in learning more complex features and enhanced the network's representational capacity. Moreover, the UMR and MPF modules facilitated the capture of multi-scale features, enabling the model to understand data at different scales, which was crucial for the task at hand.  
The test results on the DeepGlobe Road Extraction Dataset are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **Precision (\%)** & **Recall (\%)** & **IoU (\%)** \\ \hline Baseline Model & 83.91 & 84.76 & 75.83 \\ Plan 1 & 84.45 & 85.03 & 76.32 \\ Plan 2 & 85.76 & 85.12 & 76.71 \\ \hline HRU-Net (ours) & 86.06 & 85.2 & 77.23 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy representation of different schemes on the DeepGlobe Road Dataset. The closer the index value is to 1, the better the effect. IoU: Intersection over union.  
Figure 7: Schematic diagram of the scheme exploring the effect of the number of modules on the network. The dashed box only limits the number of UMR modules and MRF modules inside the box and does not limit other operations outside the box.
## 4 Discussion  
### The Scheme of PESI-MS Based Machine Leaning  
FNAB samples has long been used for the malignancy determination of thyroid nodules, followed by cytopathology and mutation gene detection. In this study, the sample pretreatment followed the instructions of clinical samples for PESI-MS analysis [13] but was slightly modified. The pretreatment time is around 7 min (2 min extraction, vortex, and 5 min centrifugation) for one sample, which is very fast and efficient compared with the pretreatment and analysis of traditional cytopathology or the BRAF gene method (at least  
Figure 5: False predictions of the MLP classifier trained with ’sgd’ (**a**), ’adam’ (**c**), and ’lbfgs’ (**e**) as solver and corresponding ROC curves (**b**), (**d**) and (**f**).  
half to one hour). The obtained extractions of FNAB samples could be directly measured without further pretreatment. For the PESI-MS analysis, 9 uL of the sample was directly put on the sample plate, and several dozens of repetitions and hundreds of mass peaks were obtained within 1 min. The repetitions guaranteed the accuracy of the malignancy prediction. The hundreds of mass peaks include substances abundant in either malignant or benign nodules, which could be used as features for machine learning, as was done in previous reports [14; 15].  
Machine learning is widely used for the malignancy determination of thyroid nodules; however, most of the studies deal with images of cytopathology [16; 17]. Conversely, [PERSON] developed a machine learning method with FNAB samples to discriminate between malignant and benign thyroid nodules. After pretreatment, 43 cytological smears were directly analyzed with MALDI imaging with an accuracy of 81.8% after feature selection [18]. With a LC tandem Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometer (LC-QTRAP), [PERSON] analyzed peptides from prototypic proteins in thyroid tissue cores, and in terms of machine learning, the classification accuracy is over 90% for the classification of malignant and benign nodules [19]. Although the accuracy is acceptable, the sample analysis of MALDI imaging and LC-QTRAP is complicated, time-consuming, and costly. In this study, PESI-MS was firstly used to analyze thyroid FNAB samples with a dataset containing 267 patients. PESI-MS is less complicated and more cost-effective than MALDI imaging and LC-QTRAP. In addition, the number of patients in the dataset, to the best of our knowledge, is also the largest mass spectra dataset of thyroid FNAB samples. In general, PESI-MS based machine leaning is much easier, faster, and more cost-effective than traditional and reported methods to discriminate between malignant and benign thyroid nodules.
## 2 Image Sensor: System Description  
In this section we will provide details about the design of our proposed image sensor, showing the components of the sensor node architecture and describing its operating software. As explained before each image sensor will be installed inside a trap.  
In Figure 2, we show a trap example specially designed for RPW pest monitoring and a snapshot of trap contents where several RPW individuals were captured. The proposed image sensor will be located inside the trap, fixed at the top and facing the onboard camera towards the bottom of the trap where the RPW captured individuals will remain. It is important to provide enough illumination inside  
Figure 1: An example of trap deployment. Each circle represents a trap equipped with wireless image sensor. The triangle in the middle represents the location of the control station.  
the trap, so a translucent plastic material is recommended for the trap. Also, by simply providing some holes at the top should be enough to guarantee proper lighting conditions. The size of the image sensor (batteries included) is 60 mm \(\times\) 35 mm \(\times\) 20 mm, being small enough to be placed in any trap design.  
Traps equipped with image sensors are distributed across the monitoring area in such a way that each sensor may contact with at least another one (connected network) and so forming a Wireless Sensor Network (WSN). In this way, if a particular image sensor is in the radio coverage of the central station, its captured images may be directly delivered to the control station (single hop communication), otherwise the captured images should be delivery through intermediate image sensors to reach central station. The WSN topology, the way each sensor node is connected with other network nodes, typically follows a mesh pattern in order to get regular distributed measurements over the whole monitoring area. This would provide an interesting property for our pest monitoring application: high error resilience since, in mesh topologies, each node has more than one available neighbor in its radio coverage area (_i.e._, neighborhood degree greater than 1), so in case one sensor fails (hardware error or battery depletion), the network remains connected, since its neighbor nodes may reach the central station bordering failed node.  
In this work we will focus in the image sensor design and operation, so in order to simplify our study we will suppose all image sensor nodes are in the radio coverage of central station (single hop communication). Despite of this, more complex topologies, such as tree-clustered organization, have been successfully deployed in other projects, and would be also applicable to this environment.
## 3 Verification of the STAR-Derived Excess Phase and Bending Angle Profiles
### STAR Bending Angle Comparison with UCAR and ERA5
The mean fractional bending angle difference between heights 10-35 km is about \(-\)0.34 \(\pm\) 2.96% for GPS L2C signals, \(-\)0.28 \(\pm\) 3.81% for GPS L2P, and \(-\)0.09 \(\pm\) 3.20% for GLONASS. Generally, the STAR-ERA5 fractional bending angle difference and standard deviation (Figure 6b) are more significant than STAR-UCAR comparisons. At 5 km impact height, the mean fractional bending angle difference between STAR and ERA5 is 2%. The mean difference turns negative with decreasing altitude and fewer retrieved bending angle data points. For 8 km and below, a positive difference increases from 8 km to around 5 km. While this positive fractional bending angle difference may be partially from the ROPP processing due to different configurations/parameterizations, the difference might be because the GNSS bit time series is not used for NDM removal. The internal bit removal process is suitable for large SNR values above 10 km with the phase model. Once it goes to the intensive cycle slip losing stage due to atmospheric multi-path effects and low SNR values, the ROPP's internal NDM removal methods do not effectively remove the NDMs. This will slightly change the excess phase values toward the surface. In our processing, we plan to use the GNSS bit time series from other operational centers, such as UCAR or the EUMETSAT. The bending angle and refractivity mean difference and standard deviation compared with UCAR and ERA5 between 10 km and 30 km are also listed in Table 2.
## Data  
### Overview  
For the retrieval of the daily ET\({}_{0}\) over the 500 m grid in South Korea, we used the following input variables: normalized difference vegetation index (NDVI), leaf area index (LAI), and fraction of photosynthetically active radiation (FPAR) from MODIS; standardized precipitation index for three months (SPI3) from GPM; air temperature, land surface temperature, soil temperature, relative humidity, and wind speed from UM LDAPS. The ML models were built using these input data and the in situ ET\({}_{0}\) data taken from KMA (Figure 1). The database was constructed for the period between March and November of 2013-2019, excluding the winter season when evaporation from soil and transpiration from vegetation are insignificant. For the data processing, we employed the MODIS Reprojection Tool (MRT) and the R programming with Geospatial Data Abstraction Library (GDAL). Additionally, R programming with the h2o library was conducted for the optimization of the ML model and the creation of the ET\({}_{0}\) data over the 500 m grid.
## 2 Methodology
### Bulk Wave and Guided Wave Inspection Methods  
Ultrasound-based non-destructive inspection approaches can be briefly divided into two categories according to the wave types: bulk wave inspection and guided wave inspection. The main difference between the two methods is the wave coverage area. Generally, a traditional ultrasonic bulk wave evaluation with normal-beam excitation can only be over a limited volume based on the specification of the ultrasonic probe (Figure 3a). The operator shall move the ultrasonic device in the longitudinal direction to detect the damage (e.g., cracks). However, this approach only applies to ordinary objects with relatively smaller volumes. For railway damage inspection, which requires long monitoring distance, this approach would be time-consuming. Therefore, UGWs monitoring has overcome this drawback. The guided waves are formed through reflections and refractions inside the rail segment, forming stabilizing wave packets propagating along the rail longitude (Figure 3b). The guided wave inspection requires the actuator and the receiver to be located at the beginning and the end of the testing area, respectively. The inspection is completed by analyzing the waveforms. This approach has shown a higher sensitivity level to guided wave inspection than other normally used NDT methods, according to the literature [1,43].  
The propagation of UGWs in rails depends on the frequency and the wavelength of the ultrasound. Taking the rail web as an example, when an actuator is placed on the rail web, it is expected that the wavelength of the guided waves would generally be greater than the structural thickness. [PERSON] [43] made a comparison of the currently used ultrasonic bulk wave technique and the UGW inspection procedure for plate and pipe inspection, as shown in Table 1.  
Figure 3: Comparison of: (**a**) bulk wave inspection; versus (**b**) guided wave inspection.  
Figure 2: Schematic of ultrasound high-speed FBG demodulation.  
Compared to the bulk waves, the guided waves travel as a packet with ultrasonic waves of different velocities. However, the UGW has a general solution to describe the propagation velocity of a wave group where each wave has a similar frequency. The equation given below is utilized to define the group velocity of a UGW:  
\[c_{g}=c_{p}+k\frac{dc_{p}}{dk} \tag{3}\]  
where \(c_{g}\) and \(c_{p}\) are the group and phase velocities and the parameter \(k\) is the wave number. Thus, it is discovered that the individual waves travel at various speeds of \(c_{p}\), while the superimposed packet propagates at the speed of \(c_{g}\). Considering the dispersion of waves, \(c_{p}\) varies with different wave frequency \(f\). Then the \(c_{g}-c_{p}\) relation can be obtained as a reference for conducting guided wave inspections to estimate the propagation in different materials.
# Terpenes as Potential Anti-Alzheimer's Disease Agents
## 2 Activities of Terpenes on the Mechanisms Associated with AD
### Inhibition of Acetylcholinesterase (AChE)
#### 2.4.5 Meroterpenes  
The inhibition of AChE by several meroterpenes, extracted from the fungus _Aspergillus versicolor_ (Vuill.) Tirab. obtained from the mud of the South China Sea, was evaluated. Asperversins A (**214**), B (**215**), C (**216**), D (**217**), E (**218**), and F (**219**), and the related compound asperdemin (**220**), are inactive (IC\({}_{50}\)\(>\) 40 uM). Asperversin G (**221**) exhibits AChE inhibition with an IC\({}_{50}\) value of 13.6 uM. Molecular docking studies were conducted for asperversin G (**221**) to obtain an insight into the binding pattern of the binding of the compound with AChE. The basic skeleton of compound **221** provides a better binding than the other asperversins, forming several hydrogen bonding interactions with the amino acid residues Glu 291 and Tyr 341. These results suggest that the \(\alpha\), \(\beta\)-unsaturated ketone group is important for the inhibition of AChE [136].
## 4 Experimental Simulation Analysis
### The Influences of Semi-Spread Factor on System Performance  
In order to explore the influences of the semi-spread spectrum factor on the system performance, the control variable method is adopted. The initial values of chaotic mapping used in the simulation are 0.6, \(P=2\), and the semi-spread spectrum factors are 100, 200, 300, and 400 groups of data, respectively. Figure 8. Is the influences diagram of the semi-spread spectrum factor on the SR-ODBR-DCSK system. Figure 8a-d, respectively, use sine mapping, tent mapping, circle mapping, and improved logistic mapping. It can be seen from the figure that for the four typical discrete chaotic maps, when the selected half spread spectrum factor is small, the error rate is small. With the increase in the semi-spreadspectrum factor, the BER performance of the system increases. When the SNR of Tent mapping is small, the error performance of the system increases with the increase in the semi-spread spectrum factor, and there is a special case when the SNR is greater than a certain value, as shown in Figure 8b. When the SNR is greater than 15 dB, there is a case where the semi-spread spectrum factor is small, and the error performance of the system is poor.
# Experimental Study of Scour Hole Depth around Bridge Pile Using Efficient Cross-Section
## 2 Research Significance  
Local scour (Appendix C) around bridge piers is an important research topic in coastal and ocean engineering. Although considerable amounts of experimental investigations and empirical models are available examining the bridge pier scour, studies investigating scour around piles of various cross-sections are still lacking to a large extent. The present study aims to provide new insight into the scouring around a group of piles of different shapes, i.e circular, rectangular and lenticular. Because no empirical predictive models have been proposed for assessing the scour depth around piles. However, the comparison of pier empirical results versus the experimental results of piles proved the inadequacy of the pier models. The current study also suggests that lenticular cross-section piles are the most efficient pile shape as compared to circular and rectangular shapes.
# Multi-UAV Cooperative Anti-Submarine Search Based on a Rule-Driven MAC Scheme
## 4 Simulation Experiment and Analysis
### Scenario 3: Comparison of the Number of Targets Searched with or without Association Rules  
Tables 3 and 4 show the target-discovery benefits of UAVs when using the RI-MAC algorithm proposed in this paper. In order to compare the target-benefit gap between the two search scenarios more intuitively, 20 targets are placed under the search task area. Two search methods are used to search, with the number of iterations set to 200, using the Monte Carlo method for 150 experiments. The target-discovery revenue statistics are shown in Figure 9. It can be seen from the figure that with an increase in the number of iterations, the number of search targets with and without association rules does not differ significantly. However as the number of targets searched increases, the group using
# Evaluation of Bol Corrosion Degree Based on Non-Destructive Testing and Neural Network
## 3 Propagation Mechanism and Signal Processing of Guided Waves in Anchor Bolts
### Propagation of Ultrasonic Guided Waves in Anchor Bolts
The dispersion curve of a 20 mm diameter anchor bolt was plotted using MATLAB R2021b software, and these dispersion curves are shown in Figure 6. From the dispersion curves, it can be observed that within the frequency range of 0-1000 kHz, the \(L(0,1)\) mode exhibits a frequency band with the fastest group velocity and minimal variation, spanning from 20 to 40 kHz, as depicted in Figure 5. This characteristic enables the \(L(0,1)\) mode to arrive at the receiving sensor first among all echo signals, without waveform distortion, making it easily identifiable and distinguishable in the time domain. Additionally, this frequency band falls below the cut-off frequency of other modal guided waves, thus ensuring that the \(L(0,1)\) mode does not generate higher-order modal guided waves during detection. A five-cycle Hanning window-modulated pulse with a center frequency of 30 kHz was chosen as the driving signal.
## 3 Results
### Anderson-Darling Normality Test Results  
In the present work, the residuals are the difference between the experimental \(\Delta p\) and the predicted \(\Delta p\) by the established model (Equation (2)). The AD test was conducted by the software Minitab 17.0, and the probability distribution plot of the residuals is shown in the following Figure 3. As can be seen from the figure, the obtained AD value (0.484) is less than a critical value of 0.752, and the associated \(p\)-value (0.594) of the AD test is significant at a 5% level (\(p\)-value \(>\) 0.05) [25]. The results indicate that the residuals follow a normal distribution, and the deviation between the experimental \(\Delta p\) and predicted \(\Delta p\)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & \
\begin{tabular}{c} **Sum of** \\ **Squares** \\ \end{tabular} & **df** &
\begin{tabular}{c} **Mean** \\ **Square** \\ \end{tabular} & _F-Value_ & _p-Value_ \\ \hline Model & \(5.480\times 10^{5}\) & 9 & 60,885.15 & 148.56 & \textless{}0.0001 ** \\ \(Mc\) & 32,848.69 & 1 & 32,848.69 & 80.15 & \textless{}0.0001 ** \\ \(v_{air}\) & \(2.393\times 10^{5}\) & 1 & 2.393 \(\times\)\(10^{5}\) & 583.84 & \textless{}0.0001 ** \\ \(L\) & \(1.681\times 10^{5}\) & 1 & \(1.681\times 10^{5}\) & 410.13 & \textless{}0.0001 ** \\ \(Mc\cdot\ v_{air}\) & 8379.57 & 1 & 8379.57 & 20.45 & 0.0027 ** \\ \(Mc\cdot\ L\) & 9255.40 & 1 & 9255.40 & 22.58 & 0.0021 ** \\ \(v_{air}\cdot L\) & 83,096.71 & 1 & 83,096.71 & 202.76 & \textless{}0.0001 ** \\ \(Mc^{2}\) & 256.14 & 1 & 256.14 & 0.62 & 0.4551 \\ \(v_{air}\)\({}^{2}\) & 6858.82 & 1 & 6858.62 & 16.74 & 0.0046 ** \\ \(L^{2}\) & 85.53 & 1 & 85.53 & 0.21 & 0.6616 \\ Residual & 2868.84 & 7 & & 409.83 & \\ Lack of Fit & 2138.59 & 3 & 712.86 & 3.90 & 0.1106 (NS) \\ Pure Error & 730.26 & 4 & & 182.56 & \\ Std. Dev & & & 20.24 & & \\ Mean & & & 184.31 & & \\ R\({}^{2}\) & & & 0.9948 & & \\ Adj R\({}^{2}\) & & & 0.9881 & & \\ Pred R\({}^{2}\) & & & 0.9358 & & \\ Adep Pre & & 43.223 & & \\ C.V.% & & 10.98 & & \\ PRESS & & & 35,358.41 & & \\ \hline \hline \end{tabular} Note: The symbol “**” indicates the term is extremely significant.  
\end{table}
Table 5: ANOVA evaluation of linear, quadratic, and interaction terms for response and coefficient of \(\Delta p\) model.  
are random [26]. Accordingly, it can be concluded that the null hypothesis can be rejected and the model predictions are correlated with the experimental data over the factor-space under evaluation in the present work.
# Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods
## 6 Conclusions  
In our paper, we proposed a method for predicting the real driving-cycle-based SOC for EV batteries using deep-learning-based algorithms. For the RDC data, the authors used the data collected through the OBD II standard while driving from home to university in an actual vehicle. The proposed method first classifies detailed routes based on the section with traffic lights using GPS information, and finds the most similar patterns through DTW among the driving cycles of each section. Next, we predicted these sections by learning speed using TA-LSTM based on temporal attention, and predicted SOC based on this predicted speed. From the experimental results, it was confirmed that the case of using TA-LSTM could most accurately predict the speed, and that this could be very close to the actual values when used as a basis to predict the SOC. Therefore, if the proposed method is applied to the Real Driving Cycle, in which the same section is repeatedly driven, an accurate SOC prediction can be expected.  
All the algorithms we propose can be implemented as an in-vehicle embedded system. It has been confirmed that DL models can be pre-trained and executed in an MCU. FMU simulation for the battery SOC can also be executed in an embedded system, such as an MCU or a sensor node [4; 40]. If a high-capacity memory is required due to an increase in the demands of storing a massive amount of data or improving the calculation speed, a prediction can be performed in conjunction with a cloud data center or an edge server through the vehicular network, such as the Vehicle to Infrastructure (V2I) network.  
The idea of the paper is to propose an algorithm that accurately predicts the SOC value of the next time step. However, predicting the SOC after multiple time steps may also be useful to the driver. Therefore, in a future work, the algorithm could be adapted so that it may predict, for example, the SOC after the completion of the entire driving cycle.  
Methodology, S.H., H.H. and D.K.; Project administration, S.H.; Software, S.H., H.H., D.K. and S.C.; Supervision, I.J.; Writing-original draft, S.H. and H.H. All authors have read and agreed to the published version of the manuscript.  
Figure 16: Temperature effect on the battery SOC.  
Figure 17: Battery aging effect on the battery SOC.  
**Funding:** This work was supported partly by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. 2020-0-00107, Development of the technology to automate the recommendations for big data analytic models that define data characteristics and problems), and partly by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019 RII1A1A01058964).  
**Conflicts of Interest:** The authors declare no conflict of interest.