esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D Mesh Model Classification with a Capsule Network
## 4 Experimental Evaluation
### Accuracy Test  
In order to compare the superiority of the classification performance of the proposed method, this paper compares the traditional manual feature-based classification method SPH [28] and MeshNet [10], MeshCNN [9] by directly applying deep learning to the new method of three-dimensional grid classification. Table 1 shows for the classification results and average accuracy of the different categories of the data set.  
Through experiments on different methods, it can be seen from Table 1 that the classification performance of the MeshCaps algorithm proposed in this paper was higher than other comparison methods. On the SHREC15 data set, the average accuracy rate reached 93.8%, which is comparable to the best results of the comparison method. In comparison, the average accuracy of the proposed method improved by 2.1% on the SHREC15 dataset. The experimental results show that the proposed 3D grid classification method based on the capsule network can obtain better results in the classification of 3D grid data.  
In order to further prove the effectiveness of the proposed method and compare the convergence performance of different methods, it can be intuitively observed from the curve in Figure 6 that the MeshCaps method had good convergence. In the 15 th round of iteration, the accuracy rate reached 86.63% and the convergence was reached earlier. The inflection point and the final convergence point prove that the dynamic routing in the capsule network performed unsupervised clustering of vector features to make the entire network converge quickly.  
Because there are too many categories in the SHREC15 dataset, only the confusion matrix of 13 categories is displayed. As can be seen from Figure 6, MeshCaps was able to recognize most models. However, there were also several types of models with low recognition rates, such as glasses. We guess that, due to the use of curved surface units, the tails of glasses were more similar to pliers, which caused the model to have a higher misrecognition rate when recognizing these two types of models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Network** & **Alien** & **Ants** & **Cat** & **Dog1** & **Hand** & **Man** & **Shark** & **Santa** & **Pliers** & **Glasses** & **Dog2** & **Camel** & **Snake** & **Avg** \\ \hline SPH [28] & 87.4\% & 86.2\% & 90.4\% & 89.3\% & 88.6\% & 89.1\% & 90.2\% & 89.4\% & 87.1\% & 89.9\% & 86.7\% & 88.1\% & 87.9\% & 88.2\% \\ MeshNet [10] & 89.5\% & 89.6\% & 89.6\% & 91.4\% & 90.5\% & 90.8\% & 90.1\% & 89.8\% & 88.0\% & **91.4\%** & 90.5\% & 90.3\% & 89.9\% & 90.4\% \\ MeshCNN [9] & 91.2\% & **91.4\%** & **92.1\%** & 90.2\% & 90.5\% & 91.6\% & 92.7\% & 90.5\% & 91.8\% & 90.4\% & 92.3\% & **93.7\%** & 90.3\% & 91.7\% \\ Ours-MeshCaps & **92.7\%** & 91.2\% & 91.9\% & **92.4\%** & **94.2\%** & **92.8\%** & **93.0\%** & **92.9\%** & **94.1\%** & 90.2\% & **95.3\%** & 92.3\% & **95.0\%** & **93.8\%** \\ \hline \hline \end{tabular}
\end{table}
Table 1: SHREC15.  
Figure 6: Convergence graph of each method.
## 4 Experimental Setup and Results
Figure 11a shows FOE symbol-block length N1 versus the BER curve of the proposed JCSCR under the 10 Gbps QPSK CFSO communication experimental system. Experiments showed that the BER performance of the algorithm decreased with decreases in symbol block N1 and it had a low BER at N1 = 512 for the medium-turbulence channel. Based on the experimental results, 512 symbols were selected as the block length of the proposed scheme for the FOE. On this basis, Figure 11b shows the curves of the proposed scheme for phase-offset-estimation symbol-block length N2 versus the BER for the case of N1 = 512. As shown in the figure, the system had a lower BER and better noise immunity than the other values when N2 was 32. Therefore, N2 = 32 was used as the phase offset estimation block length in this experiment. To visually compare the performance of the proposed JCSCR with the traditional 4 th-PD-VV carrier scheme, when N1 = 512 and N2 = 32, we compared the transmitting power versus the BER for strong-, medium-, and weak-turbulence channels, respectively. Figure 12 shows that the proposed algorithm exhibited a better performance than the 4 th-PD-VV scheme in BER limit (3.8 \(\times\) 10\({}^{-3}\)), improving receiver sensitivity by 3.3 dB, 2.1 dB, and 1.1 dB in the strong-, medium-, and weak-turbulence states, respectively. In addition, there was a difference of nearly 3 dB between the experimental and simulated results, owing to the lack of polarization multiplexing in this experiment. It can thus be seen that the conclusions drawn from the experiments and simulations are in agreement. Figure 10: Laboratory system scenario diagram: (**a**) transmitter, receiver, and (**b**) atmospheric-turbulence-simulation optical-experiment platform.
# Synthesis, Characterization and Biological Investigation of New N-Modified Spinorphin Analogs
## 2 Results and Discussion
### Biological Analysis  
#### 2.3.1 Anticonvulsant Activity  
Six Hz Test for Psychomotor Seizures  
Dose-dependent study of the activity in the 6-Hz test of new hybrid spinorphin analogs modified at the N-terminus with rhodamine B demonstrated that close to the heptapeptide Rh-S they have a protective effect against the 6-Hz-induced psychomotor seizures (Table 5). Although, Rh-S exhibited significant protection at the three doses used (5, 10, and 20 \(\upmu\)g) (\(p\leq 0.05\) compared to control), both Rh-S5 and Rh-S6 had a significant anticonvulsant effect at the highest dose of 20 \(\upmu\)g (\(p\leq 0.05\) compared to control).
# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 4 Navigation Integration Enhanced by the ANN
### More Recent Development of NNs for Navigation System Designs
#### 4.3.1 Nonlinear Autoregressive Exogenous Neural Network  
Figure 14 shows a typical nonlinear autoregressive (NAR) NN, which is a feed-forward NN with three layers, namely the input, hidden, and output layers. A GPS/INS-fusion-based integrated system based on an autoregressive NN was proposed in which the time series forecasting field has made extensive use of a nonlinear autoregressive exogenous (NARX) neural network [104]. The system made use of the ANARX (additive nonlinear autoregressive exogenous) model, which is nonlinear autoregressive intelligent fusion technology. The findings demonstrated that in situations in which GPS measurement results in a time delay, the suggested system could still achieve the best positioning effect by utilizing the information from sensors already in place. Therefore, a method known as \"NARX-aided UKF\" has been studied to estimate the location during GPS outages, where the readings for UKF during GNSS signal losses are predicted using the NARX-based system. Additionally, an algorithm for INS error compensation that incorporates UKF and BPNN was created. Therefore, when GPS is available, UKF implements KF with excellent performance and guarantees high accuracy.  
In Figure 15, an NARX-NN model is presented, which can predict the velocity during DVL malfunction is built. A new SINS/DVL-integrated navigation process based on NNs is studied in light of the possibility that the DVL may malfunction during the missions. For underwater applications, velocity prediction can also be seen as a time varying forecasting issue. The NARX neural network is used because it can provide accurate predictions. The NN is updated by using the derived SINS and DVL parameters, body frame velocity, and its advancement while the DVL is available. When DVL is not accessible, the well-trained neural network's velocity forecast is used to support SINS and keep the accuracy of integrated navigation high [102]. When specific circumstances arise, such as passing over marine life or the DVL going outside its maximum measuring range, the DVL will not keep the bottom lock and will not send velocity updates. Using partial basic data from the DVL and extra data, an extended loosely coupled (ELC) technique can provide virtual vehicle velocity to get around this issue. A hybrid strategy to predict the DVL data while it is malfunctioning. The predictor's inputs are the past and present velocities acquired by SINS. In the absence of DVL, the research suggests an NN-based method to forecast body frame velocity. The literature on SINS/GNSS-integrated navigation has related approaches for related circumstances [90; 101].  
Figure 14: A typical NAR neural network.
## 2 Characteristic Analysis of the Iridium System
### The Satellite Level
#### 2.1.3 Elevation Angle/Azimuth Angle/Satellite-to-Receiver Distance  
In LEO SOP positioning, epochs with distinct azimuth and elevation angles correspond to different velocity vectors. As discussed in Section 2.1.2, incorporating epochs with more diverse velocity vectors can improve PDOP and consequently enhances positioning accuracy. Therefore, both azimuth and elevation angles should be treated as critical epoch features for screening purposes.  
Furthermore, variations in satellite-to-receiver distances lead to differing signal free-space path loss (FSPL), which can be expressed as:  
\[L_{fs}=\left(\frac{4\pi df_{c}}{c}\right)^{2} \tag{8}\]  
\[L_{fs}(\text{dB})=20\log_{10}(d)+20\log_{10}(f_{c})+92.45 \tag{9}\]where \(d\) denotes the satellite-to-receiver distance and \(f_{c}\) denotes the carrier frequency. A longer satellite-to-receiver distance results in greater FSPL, leading to degraded received signal quality. Therefore, it is essential to incorporate the satellite-to-receiver distance at the epoch reception time as a critical epoch characteristic.
# A New In Vitro Model to Evaluate Anti-Adhesive Effect against Fungal Nail Infections
## 2 Materials and Methods
### In Vitro Evaluation of Reducing Fungal Adhesion to the Nail Plate  
#### 2.4.1 Sample Preparation  
For this experiment, a commercially available antifungal product (AP) and a commercially available water-repellent base coat (BC) were used.  
The AP was a 5% solution of amorolfine, an antifungal belonging to the class of morphologies. It is a well-known drug used for the treatment and prevention of dermatomyosis and onychomycosis [29].  
The BC was a very common product used in nail care practice. It creates a barrier, helping shield nails from the damaging effects of nail polish. Furthermore, it prevents staining and peeling.  
For this test, a BC was chosen as a negative control--in comparison with an AP as a positive control--to understand if the water-repellent physical barrier created by the products contributed to avoiding fungal adhesion on the nail. In this work, the contact angle measurement was used to test the water-repellent capability of the chosen base coat. Briefly, the contact angle is a measure of the wettability of a solid by a liquid and, in this case, the membranes treated with a base coat and water [30]. Generally speaking, high angle values were indicative of a poor interaction between the surface and the liquid, and a low value indicated high affinity between them. Three membranes with the same morphological properties were analyzed using a DMe-211 Plus contact angle meter (KYOWA Interface Science Co., Ltd., Nobutome, Saitam, Japan) before and after water-repellent treatment application.
## 4 Network Implementation and Learning Algorithm  
This section investigates the simulation environment, techniques, and implementation tools used to build and analyze the FLoRa network for oil pipeline leak detection and localization within the OMNeT++ framework. We will explore how OMNeT++ can be integrated with several network libraries and how Python libraries such as Keras and TensorFlow can be utilized to implement and analyze deep learning algorithms in real-time network scenarios.
## 3 Application and Experiments  
### Application Description  
The flight data and BIT records we focused on were from the TYW-001, a fixed-wing UAV of BHUAS, that had multiple test flights between 2018 and 2019. The endurance of TYW-001 is over 40 h with a ceiling of 8000 m, take-off weight of 1500 kg and external payload of 370 kg. All flight tests have been successfully completed so far. Apart from the fire control system and redundant backups, the flight data of the TYW-001 has a total of 457 variables, including sensor monitoring and operation input. A total of 219 single faults or over-limit states could be detected in BIT, of this amount: 58 navigation and flight control system alarms, 112 electromechanical actuator alarms, 34 aeroengine alarms, 7 equipment condition monitoring system alarms, 7 from transponder alarms and 1 stall warning.  
Through the analysis of the BIT records in the early stage, it could be found that there are often multiple concurrent alarms of single faults (or over-limit state) showing during the actual flight test. These faults, mostly, would be directly diagnosed by BIT, although considerable false alarms and omissions would still exist due to external interference, etc. However, BIT cannot diagnose the unknown complicated states found during the flight test, especially the states of concurrent multiple single faults. To deal with the problem, the previous approach was that the ground maintenance team checked all the single faults on record after the test flight, then carried out fault analysis to determine the fault modes for manual labeling of flight data. The original fault diagnosis models based on GBDT and FCNN were trained with these labeled flight data after that, which have been successfully used in the officially verified aircraft. It is tough to label the flight data manually due to the difficulty of locating the precise time stamp of the fault symptoms and huge labeling workload for the UAV in the flight test stage. The demand exists for using more accurately labeled data to improve the fault diagnosis model.  
Our experiments selected three flight data sets and BIT record sets, No.18100223 (59,946 instances), No.19012332 (75,724 instances) and No.19012433 (149,447 instances), from the test flight phase of TYW-001 for analysis.
## 4 Discussion
### Leachates and PHREEQ-C Simulations
#### 4.2.2 Hg in Eluates  
Mercury concentrations in the leachates, except for sample LM23 (7.7 \(\mathrm{\SIUnitSymbolMicro\text{g}}\cdot\mathrm{L}^{-1}\)), (Table 6) are lower than the maximum permissible concentration (1 \(\mathrm{\SIUnitSymbolMicro\text{g}}\cdot\mathrm{L}^{-1}\)) for drinkable water. However, considering the distribution of the data, the sample LM23 can be considered as an outlier. According to the Eh-pH diagram of Hg-Cl-S (Figure 7) and the PHREEQC simulations, the main Hg species present in all samples is Hg\({}^{0}\). This is apparently confirmed by [1], who stated that about 60% of total Hg at the Le Lame mining dump is Hg\({}^{0}\). Nevertheless, a decrease in pH allows Hg to enter the stability range of HgS. This can be attributed to the leachate acidity (ca. 4.3), which favors the solubilisation of Me-sulfides occurring in soils and consequent re-precipitation of metals, which decreases their content in solution. The low concentrations of Hg in the leachates can be related to either the high efficiency of the roasting process or because Hg is not retained by HFO (e.g., [69; 70]).
## 5 Discussion  
### Significance of This Study  
An adaptive thresholding method was proposed for extracting water coverage (AT-EWC) for flood monitoring with the assistance of cDSWE. The probability of persistent water and non-water were first aggregated from cDSWE at a 30-m resolution to a 20 km \(\times\) 20 km grid system for stratified sampling. Then, cDSWE was further used to assist in determining the shape of the image histogram and generating flood products. Our method responded well to the change of water surface before and after the flood. Specifically, there are three significant outcomes.  
First, a fully-automated thresholding method was developed for extracting flood ranges with higher temporal resolution. Because Sentinel-1 data has been provided since 2014, few studies have explored large-scale and multi-temporal water monitoring through optical remote sensing products (e.g., Landsat). Moreover, most of the existing water monitoring products comprise annual composite data. The inundation area during the flood time can be established by our method, which makes up for the low temporal resolution achieved by optical products. Moreover, our method is completely automated, and there is no human influence on the process.  
Second, good adaptability has been shown on two different types of sites. Our proposed thresholds that were calculated at the WeiFang site were used at the Ji'An site, where they also demonstrated good adaptability. From our experimental results in Tables 1 and 2, the overall accuracy of the results reached 96.5% and the kappa coefficient remained at around 0.8. This indicated that our proposed thresholds can be applied to other regions. From the comparison results of the three sets of experiments, the accuracy of 1 June and 25  
Figure 9: Comparison with Otsu method at the WeiFang site. (**a**) 20 km \(\times\) 20 km block (high) (**b**) 20 km \(\times\) 20 km block (medium) (**c**) 20 km \(\times\) 20 km block (low) (**d**) 4000 by 4000 pixels image.  
June was relatively lower compared to June 3. This indicates that our proposed thresholds are not only adaptable to all regions but also have better extraction accuracy during floods.  
Third, in terms of accuracy and efficiency, our proposed method demonstrated excellent performance. In the block with the low and medium probability of water, the Otsu method showed obvious shortcomings, and the overall accuracy was around 80%, which is much lower than its classification accuracy on the image. In contrast, our method performed well in the classification results in the above four types. The Otsu method used the image histogram for the calculation of maximum interclass variances. This worked well in the case of bimodal image histograms. However, in the unimodal case, the Otsu method showed poor performance where there were inland watersheds with few lakes. Thus, Otsu's shortcomings in small water bodies were improved by the AT-EWC method. Table 4 showed that our algorithm outperforms the Otsu method in terms of computation time. This indicates that our method is more efficient and meets the time-sensitive needs of flood monitoring well.
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
## Abbreviations  
The following abbreviations are used in this manuscript:  
CCF Cross-Correlation Function  
CMLE Conditional Maximum Likelihood Estimator  
CRB Cramer Rao Bound  
CRE CLEAN RELAX Estimator  
CSM Conditional Signal Model
## 1 Introduction
### Research Gap and Motivation  
All the analyzed works from the literature [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18] used biomedical signals for medical applications with regard to different points of reference. Even many devices are built for data-gathering methodologies in existing models, and some drawbacks are also observed. However, in real-time implementation, most of the methods fail to analyze the effectiveness of biomedical signals by using the knowledge representation process. In the absence of fuzzy system models, the biomedical signals will have high interruption on the receiver side, and no other prevention techniques can be applied inside it. Moreover, one or a few methods have implemented the use of the Hadoop infrastructure for passing biomedical signals with high security and distance separation. In order to build the gap that is present in existing methods, the proposed method is implemented with a knowledge representation process.  
In the proposed method, the applications of biomedical signals are processed by using an AI algorithm which indicates that automatic operation is carried out. To be more specific, a fuzzy interpretation mechanism is applied at the correlation state, using an activation function, and the output is indicated by using 0 s and 1 s. Furthermore, the knowledge representation of biomedical signals is carried out by using a Hadoop infrastructure wherein the loss of functionality of the signals is minimized. Moreover, a four-layer data model with fuzzy is incorporated for collecting the data at several authenticated health centers. The abovementioned process is usually carried out by using a real-time setup, and a working model for five different scenarios is also observed.
Low-Cost Quantitation of Multiple Volatile Organic Compounds in Air Using Solid-Phase Microextraction  
[PERSON]  
[PERSON] and [PERSON]  
1 Center of Physical Chemical Methods of Research and Analysis, Al-Farabi Kazakh National University, Almaty 050012, Kazakhstan; [EMAIL_ADDRESS] (O.P.I. partially automate the sampling process; integrate the sampling and measurement stages; and reduce laboratory work and time costs. Additional problems may include carryover of analytes and clogging of the cryogenic focusing system [7], which considerably limit the application of standard methods. Therefore, low-cost, simple and solvent-free methods for quantification of VOCs in the air combining sampling and sample preparation in one step are needed. Solid-phase microextraction (SPME) that is based on extraction of VOCs by a micro coating, followed by desorption in a GC injection port, meets these requirements [8]. SPME is widely used for the determination of VOCs in ambient air (Table 1), indoor air and different emissions [9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. Methods based on SPME do not need a pump and a thermal desorption system. Desorption of analytes is fast resulting in narrow peaks of analytes without a cryogenic focusing. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sampling Principle** &
\begin{tabular}{c} **SPME Fiber,** \\ **Extraction Time** \\ \end{tabular} & **Instrument** & **Analytes** & **LOD (ug m\({}^{-3}\))** & **Ref.** \\ \hline \multirow{6}{*}{SPME from open air} & 75-\(\mu\)m Car/PDMS, & & & Car/PDMS & PDMS \\  & 100-\(\mu\)m PDMS, & GC-AED & Methanen Thiolol & 0.04–0.06 & 4 & [9] \\  & 20 min & & Dimethyl sulfide & 0.003–0.004 & 2 & \\  & & Isopropanol & 0.005–0.007 & 2 & \\  & & Isoquaten Thiol & 0.003–0.004 & 0.7 & \\ \hline \multirow{6}{*}{SPME from static or moving air} & & & & Methanol & & \\  & & & Acetone & & \\  & & Dichoformethane & & & \\  & & Methyl ethyl ketone & & & \\  & 75-\(\mu\)m Car/PDMS, & & Ethyl acetate & & \\  & 1 min & & Dichoformethane & & \\  & & & Methyl isothi ketone & & \\  & & & Toluene & & \\  & & & Butyl acetate & & \\  & & & Ethylbenzene & & \\  & & & \(p\)-Xylene & & \\ \hline \multirow{6}{*}{SPME from fan-blown air} & & & & \(\Delta^{3}\)-Carene & & \\  & & & \(\Delta\)-Prime & & \\ \cline{1-1}  & 65-\(\mu\)m PDMS/DVB, & GC-MS & Limonene & & \\ \cline{1-1}  & 2 h & & Promaldehyde & n/a & [19] \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1} \cline{2-6}  & & & & & \\ \cline{1-1} \cline{2-6} Air purging via bubbler impinger with KOH & 75-\(\mu\)m Car/PDMS, & & & & \\ \cline{1-1}  & & 5 min & GC-MS & HCN & 0.16 & [20] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Methods for quantification of organic compounds in ambient air by exposed solid-phase microextraction (SPME) fibers.