esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Challenges and Directions for Future Research
### Evasion Tactics  
Attackers continuously evolve their tactics to evade detection mechanisms. Future research should investigate advanced evasion techniques, such as obfuscation, polymorphism, and steganography. Robust countermeasures need to be developed to effectively detect and mitigate these tactics.
## 3 Experiment and Results
### Building Extraction Results and Analysis
#### 3.3.2. GF-3  
For the extraction of buildings in GF-3 data, the buildings include relatively high residential buildings, individually distributed villas, houses under construction, and large single-unit buildings. Non-buildings include vegetation, water, roads, bare soil, etc. The building extraction effect is shown in Figure 5.  
From Table 1, the extraction accuracy rate and false alarm rate of the building using the three methods of the CNN are better than the methods of using the SVM method and the threshold to obtain buildings. Among the three methods using CNN, our proposed feature set and method for building extraction can better extract buildings, including the control of the boundary of the building and the suppression of false alarms. In terms of details, the method proposed by us can have a better suppression effect on non-buildings, such as streetlamps on the side of the road than other CNN methods. The accuracy comparison is shown in Table 2.  
From Table 2, we can see that the accuracy of the building extraction is higher in the three methods using CNN. The proposed method with the selected feature set can have higher building extraction accuracy and a lower false alarm rate than the comparison  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Method** & **AR (\%)** & **FAR (\%)** & **F1-Score (\%)** \\ \hline Eigenvalue & Threshold & 67.02 & 41.35 & 62.55 \\ PauliRGB + G\({}^{0}\) & SVM & 78.95 & 41.63 & 67.11 \\
6D-Vector [38] & CNN & 94.69 & 24.91 & 83.75 \\ Polarimetric Features [39] & CNN & 95.33 & 24.09 & 84.51 \\ PauliRGB + G\({}^{0}\) & CNN & **95.56** & 15.45 & 89.71 \\ PauliRGB + G\({}^{0}\) & CNN + Superpixel & 94.97 & **12.2** & **91.24** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different methods of building extraction effects under GF-3 data.  
Figure 5: GF-3 image building extraction results. (**a**) [PERSON]’s threshold extraction method; (**b**) PauliRGB and G\({}^{0}\) statistical texture parameters using SVM results; (**c**) RVCNN; (**d**) FFDCNN; (**e**) PauliRGB and G\({}^{0}\) Statistical texture parameters using CNN; (**f**) as the result of introducing superpixel constraints in (**e**).  
methods in building extraction. In the case of high accuracy in building extraction, the false alarm rate is reduced by about 11%, and the F1-score is increased by more than 6.7% by the proposed method.
###### Abstract  
Based on the theory of acoustic-solid coupling, the phase velocity-thickness product of a double-layer liquid-filled pipeline is analyzed, and the dispersion relationship between angular frequency and wavenumber-thickness product is analyzed, providing a theoretical basis for ultrasonic guided wave detection. The wave number analytical expression of the double-layer liquid-filled pipeline constructed, and the dispersion relationship of the double-layer liquid-filled pipeline under different frequency-thickness products and wavenumber-thickness products is calculated through parameter scanning. The dispersion curves of the double-layer liquid-filled pipeline are numerically analyzed in the domains of pressure acoustics, solid mechanics, and acoustic-solid coupling. The numerically simulated dispersion curves show high consistency with the analytically calculated dispersion curves. The analysis of the phase velocity frequency-thickness product indicates that the axial mode dispersion curves of the pipe wall decrease with the increase in frequency-thickness product in the coupling domain, and then tend to be flat and intersect with the radial mode dispersion curves in the coupling domain; these intersection points cannot be used for ultrasonic guided wave detection. The T(0,1) mode dispersion curve in the coupling domain of the pressure acoustics domain remains smooth from low frequency to high frequency. It is found that the dispersion curves of the phase velocity frequency-thickness product, angular frequency wavenumber-thickness product, and the acoustic pressure distribution map of the double-layer liquid-filled pipeline based on acoustic-solid coupling can provide theoretical support for ultrasonic guided wave detection of pipelines.  
acoustic-solid coupling; double-layer liquid-filled pipes; dispersion characteristics; parameter analysis +
Footnote †: journal: Applied Sciences
## 4 Results  
### Calibration and Validation of V\({}_{m25}\)  
To assess the performance of the Two-stage linear model (Equation (15)), the simulated V\({}_{\rm m25}\) was compared with the V\({}_{\rm m25}\) inversely solved by coupling the BESS and LRC using EC data (Figure 4, Table 2). Notably, the simulated V\({}_{\rm m25}\) aligns closely with that from LRC for both the calibration and validation samples at each site (Figure 4, Table 2). The performance of the Two-stage linear model was exceptional for the US-Ne2 and US-Ne3 sites, yielding R\({}^{2}\) values of 0.88 and 0.87, RMSE values of 6.82 \(\upmu\)mol m\({}^{-2}\)s\({}^{-1}\) for both, whereas the performance was not as good for Daman (R\({}^{2}\) = 0.71; RMSE = 6.44 \(\upmu\)mol m\({}^{-2}\)s\({}^{-1}\)), due to the pronounced fluctuation in V\({}_{\rm m25}\) during the early stages of the growth period at Daman (Figure 5y, z).  
Figure 4: Comparing the V\({}_{\rm m25}\) simulated by the Two-stage linear model (Simulated_V\({}_{\rm m25}\)) and the V\({}_{\rm m25}\) inversely solved by coupling the BESS and LRC using EC data (Inverted_V\({}_{\rm m25}\)) at (**a**) US-Ne1, (**b**) US-Ne2, (**c**) US-Ne3, (**d**) Daman, (**e**) Fenzidi. Black dots represent values for the calibration data, red dots for the validation data.
# Comparative Study for Multi-Speaker Mongolian TTS with a New Corpus
## 3 MnTTS2 Dataset
### MnTTS2
#### 3.2.2 Text Preprocessing  
Compared to mainstream languages, such as Mandarin and English, traditional Mongolian exhibits agglutinative characteristics. This makes Mongolian letters express different styles in different contexts and brings a serious harmonic phenomenon [16]. In order to solve this problem, we transformed the texts into a Latin alphabet, instead of a traditional Mongolian representation, for TTS training. The entire pipeline of converting Mongolian texts into Latin sequences was divided into three steps: encoding correction, Latin conversion, and text regularization. A detailed description can be found in our previous work MnTTS [16].
# Envisat RA-2 Individual Echoes: A Unique Dataset for a Better Understanding of Inland Water
## 2 Theoretical Model of Specular Returns from Inland Waters  
In this section, we describe the synthesis of radar altimetry echoes from water bodies, with reference to small relatively flat (with reference to wavelength domain and incidence angle) inland water surfaces. Quantitative assessments of the radar backscatter for low incidence angles in Ku band have been presented also very recently [9], but their scope is generally limited to the estimation of radar cross-section. Thus, it is now well established that nadir RCS is not very sensitive to surface roughness in the low to moderate wind conditions, but phase and coherence have not been investigated yet. As stated in Section 4 of this paper, the vast majority of the water bodies (usually <300 m in extent) for which we collected IE data, exhibited quasi-specular reflection mechanisms.  
The formalism introduced in this section is subsequently used in Section 3 to define algorithms for inland water ranging and water level measurement.
# Mass Spectrometry: A Rosetta Stone to Learn How Fungi Interact and Talk
## 3 Common Mass Spectrometry-Based Strategies Used in Mycology Studies
### Bioptyping: Microbial Classification and Identification  
Rapid and reliable biological identification, especially for pathogens, is crucial in clinical diagnosis for prescribing the most efficient treatment. In fact, biottyping is a type of chemotaxonomy. Chemotaxonomy is a biological classification based upon the analytical measurement of chemical constituents, either as unique biomarkers or as contributors to a fingerprint profile (e.g., distinctive ratios of chemical components). Hence, biottyping is a potential alternative to genetic-based taxonomy, when there are no genome information nor genetic sequencing facilities available [64; 151; 152]. Mass spectrometry has been routinely used in chemotaxonomic studies by utilizing MS data from known microbes as reference data (e.g., using internal standards or reliable databases) [78; 153; 154]. In particular, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has emerged as a tool for the accurate, cost-effective, sensitive and robust biottyping of microorganisms in daily routine clinical microbiology [73; 74; 75; 76; 77; 78; 79; 80; 85; 86; 87; 88; 89; 90; 91; 92]. The analysis of protein profiles via MALDI-TOF MS can help to differentiate and/or identify unknown microorganisms at the genus and even species level by matching microbial mass spectra against spectral libraries collected from known organisms. Taxonomical identification based on protein profiles has been reliably achieved at the species level in some fungi, such as _Aspergillus_, _Candida_, _Cryptococcus_, _Galactomyces_, _Micosporum_, _Penicillium_, _Rhodorula_, _Saccharomyces_ and _Trichosporum_, among others [73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 155]. Biotyping has also been found useful in describing new fungal species [156]. However, whether MALDI-TOF MS data are appropriate for identification and discrimination below the species level (e.g., strains) is still controversial [157]. Nevertheless, new approaches and rapidly increasing data bases are in constant development to further increase the resolution of biottyping. The Biotyper (Bruker, Bremen, Germany) and SARAMIS (AnagnosTec, Potsdam-Golm, Germany) databases, which are associated to their respective Bruker Daltonics (Bruker, Bremen, Germany) and Shimadzu (Shimadzu-Biotech Corp., Kyoto, Japan) MALDI-TOF MS systems, have been widely applied in fungal identification [75; 76; 84; 88; 157; 158]. Samples of undigested cell extracts are most commonly used for MALDI-TOF MS biottyping [152; 153; 156; 159].
## 5 Simulation Results and Analysis
### Motor Rotary Velocity Regulation Performance Analysis
#### 5.1.3. Based on PID-Bang-Bang-Fuzzy Compound Intelligent Algorithm  
When the PID-bang-bang-fuzzy compound intelligent algorithm was applied to the motor rotary velocity control system, the law of the motor rotary velocity changed with the different target rotary velocities shown in Figure 17. As shown in Figure 17, given a certain target rotary velocity, the control system based on the PID-bang-bang-fuzzy compound intelligent algorithm could ensure that the actual rotary velocity of the motor followed the target rotary velocity. In general, the rise time, peak time, and regulation time of the rotary velocity regulation process were short; the motor rotary velocity fluctuation was small; the dynamic response effect was significantly improved. The specific law was that, with an increase in the target rotary velocity, the rise time, peak time, and regulation time of the motor rotary velocity regulation gradually increased and showed an approximately linear growth trend. However, the maximum overshoot gradually decreased, and its decreasing trend presented a nonlinear law. When the target rotary velocity was lower than 2000 rpm, the decreasing trend was large; whereas, when the target rotary velocity was higher than 2000 rpm, the change tended to be gentle.  
Figure 16. Rotary velocity regulation effect of PID–bang-bang compound algorithm under different target rotary velocities.  
Figure 17. Rotary velocity regulation effect of PID–bang-bang–fuzzy compound intelligent algorithm under different target rotary velocities.  
Figure 18 shows that, when the motor rotary velocity control system applied the PID-bang-bang-fuzzy compound intelligent algorithm, and the target rotary velocity was 3000 rpm, the law of the motor rotary velocity changed with the different algorithm thresholds. Evidently, different algorithm thresholds had different effects on the rotary velocity regulation effect. The specific law was that, with an increase in the algorithm threshold, the rise time of the motor rotary velocity regulation remained unchanged. However, the peak time, regulation time, and maximum overshoot gradually decreased, and the decreasing trend was nonlinear; when the algorithm threshold was lower than 300 rpm, the decreasing amplitude was large, and when the algorithm threshold was higher than 300 rpm, the change tended to be gentle.
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 4 Fault-Types Classification (FC)
### FC Based on Support Vector Machine (SVM)  
[PERSON] and [PERSON] invented the support vector machine (SVM) in 1995 [87]. A theoretical foundation can be found in [88]. SVM structure is shown in Figure 8[89]. SVM classifiers find optimal hyperplane which maximizes the margin between two entities. SVM avoids over-fitting and does not fall in local optima due to its risk-minimizing ability, which made SVM an attractive tool for fault classification in transmission lines. SVM is employed on series compensated TLs for fault classification in [90; 91], where three SVMs were employed for three phases and separate SVM for ground. In [17; 92; 93; 94], features extracted by the DWT are directed as input to SVMs. SVM classifiers in [95; 96] used features extracted from S-transform. [PERSON] et al. selected a quarter sphere support vector machine (QSSVM) to identify and classify the faults [97]. QSSVM gives satisfactory fault detection and classification results through temporal-attribute QSSVM and attributes QSSVM, respectively.
# A Molecularly Imprinted Polymer for Selective Extraction of Phenolic Acids from Human Urine
## 2 Materials and Methods
### Synthesis of Molecularly Imprinted Polymers  
MIP was synthesized using 152 mg 4-HPA as template, 4-Vpy (431 \(\upmu\)L) as functional monomer, EGDMA (3.77 mL) as crosslinker, AIBN (50 mg) as polymerization reaction initiator, and ACN (10 mL) as porogen. Porogen volume influences polymer morpholgy, and according to some authors, dilution conditions lead the size of the gel particles in a crosslinking copolymerization [23]. The porogen volume has been determined by preliminary study (70.4 vol % relative to the total volume). The polymerization was carried out in a 20 mL glass vial, and a molar ratio of 1:4:20 (template/4-Vpy/EGDMA) was maintained in excess of AIBN. The pre-polymerization mixture was purged with nitrogen during 15 min and then was sonicated during 15 min in an ultrasonic bath (Selecta Ultrasones, Barcelona, Spain). The solution was cooled on an ice bath and purged with nitrogen for 15 min, in order to remove oxygen and establish an inert atmosphere. Afterwards, the glass vial was sealed and placed in a thermostatted bath. The polymerization process was thermally initiated and maintained at 60 \({}^{\circ}\)C for a period of 24 h. The monolithic polymer obtained was crushed and sieved to give particles of size between 100 and 50 \(\upmu\)m, suitable for SPE applications. An ETHOS SEL microwave oven (Milestone, Sorisole, Italy) was used to remove the template from the imprinted polymer. Standard microwave-assisted extraction (MAE) conditions were as follows: MIP and 30 mL of washing solution (methanolcontaining 10% \(v/v\) acetic acid) were placed in the extraction vessels and exposed to MAE over a 5 min heating ramp period up to 100 \({}^{\circ}\)C. This temperature was then maintained for 20 min. After extraction, the vessel content was transferred to a vacuum filtration system for removal of the solvent. This process was performed three times in order to ensure the total extraction of the template and washing solutions were kept for posterior extraction efficiency studies. Finally, the polymer particles were dried at 60 \({}^{\circ}\)C in a hot air oven and stored at room temperature prior to use. Non-imprinted polymers (NIP) were prepared in the same manner as the MIP polymer but in the absence of template (4-HPA).
## 3 Blockchain
### Technologies
#### 3.2.1 Cryptography  
Each user has two keys: a public and a private one. Asymmetric encryption is used for communications, where the public and private keys are utilized for encrypting and decrypting transactions, respectively [45, 46]. The private key is also used to sign transactions before sending, and the receivers can verify this signature by using the sender's public key. This verification is made by every node that receives the transaction, which then disseminates it further. When the nodes that take part in the transaction verify and accept it, it is validated and placed on a timestamped block with the previous block's required hash. This block is then broadcasted back to the network, verified by the nodes, and then added to the Blockchain.
## 2 NB-PLC and RF Characterization
### NB-PLC Characterization
#### 2.1.1 NB-PLC Attenuation
The input impedance \(Z(f)\) is given in Equation (5) as follows:  
\[Z(f)=\frac{R}{1+j.Q.\left(\frac{f}{f_{0}}-\frac{f_{0}}{f}\right)} \tag{5}\]  
where \(R\) is the resistance, \(Q\) is the factor quality that determines selectivity, and \(f_{0}\) is the resonance frequency. Examples of the behaviors of used loads are depicted in Figure 3.  
Figure 2: (a) Single branch; (b) elementary branch parameterization. Based on the VRA method, an analytic model for industrial area is developed using Equation (3) and implemented in the R2014a Matlab tool. This model is then compared to the circuit model in Pspice, a CAD tool, for a three-core cable consisting of phase, neutral, and ground branches and when the cable type is assumed to be the same for the three branches. The simulation results obtained from both models are illustrated in Figure 4. The curves for attenuation, impedance, and phase exhibit non-smoothed traces. These irregularities arise due to the presence of discontinuities at various frequencies, resulting in the reflection of propagating waves and power loss. In [32], a coupling interface was implemented to build a measurement platform, facilitating a comparison of simulated results. This interface serves two major roles: The first is instrument protection. The second is the injection or extraction of the test signal to or from the mains. Then, a two-step verification methodology was proposed using the bottom-up approach. Measurements were performed in a real environment where many computers were plugged, as well as spectrum analyzers. All the measurement setups are connected to the same line phase. The test circuits were constructed using both 1.5 mm\({}^{2}\) and 2.5 mm\({}^{2}\) core cables, with each section spanning a length of 1 m. For NB-PLC noise, the authors opted to use the cyclostationary-based filter model already published in [33]. Figure 4: Example of NB-PLC behavior. \[Z_{inB}=Z_{0}\frac{Z_{B}+Z_{0}\text{tanh}(\gamma l_{3})}{Z_{0}+Z_{0}\text{tanh }(\gamma l_{3})}\text{ and }Z_{inC}=Z_{01}\frac{Z_{C}+Z_{01}\text{tanh}(\gamma_{1}l_{2})}{Z_{01}+Z_{C} \text{tanh}(\gamma_{1}l_{2})} \tag{1}\]0 Behavior of loads. The impact of some events on the network is shown either by a translation of the channel response magnitude or by an attenuation at distinct frequencies. Figure 5 shows that simulated transfer function magnitude agrees with the measured one, with an error lower than 3 dB.
# Continuous Autonomous Ship Learning Framework for Human Policies on Simulation
## 4 Experiment
### Training: Learning Avoidance of Dynamic Obstacles (ILFAS vs. Random)  
Considering the random RL, four random inward neighboring ships were generated in international waters. The autonomous ship aimed to sail to a randomly generated destination to avoid the neighboring ships from using various routes. The autonomous ship started to converge at 25 million steps and was finally stabilized at 50 million steps. Learning was implemented until the autonomous ship arrived at a destination 100 consecutive times in 100 attempts. The average step length (\(H\)) per episode was 810 (no. of learning episodes = total number of learning steps/average number of scenario-ending steps). The training result is shown in Figure 7.  
Regarding the ILFAS RL, the case-based curriculum system operates neighboring ships for human-defined general inward ships. The case is the inward ship curriculum  
Figure 6: (**a**) Randomly selected destination set for the autonomous ship to learn departure (right bottom). Coordinates to a destination are given in the GPS. (**b**) Autonomous navigation after the completion of learning. A sea route is selected through a greedy method. The red rectangle in the experiment is the concerned zone, where human-defined navigation rules shall be applied because of the frequent harbor entry and departure. (**c**) Convergence of learning from approximately eight million steps, stabilizing after 16 million steps (approximately one learning scenario—no. of learning steps/800).  
Figure 7: (**a**) Randomly generated neighboring ships starting to enter into the harbors. (**b**) Autonomous ship having lots of acceleration and deceleration controls to avoid the risk areas. (**c**) Learning results expressed with rewards and episode length. Convergence of learning from approximately 25 million steps, stabilizing after 60 million steps (approximately one learning scenario = No. of learning steps/780).  
for several ships that are generally observed in harbors. Although the neighboring ships avoid collisions when collisions are estimated in the simulation environment using the ILFAS, the episode ends when collision occurs. The autonomous ship started to converge at three million steps and was stabilized from five million steps. The experiment aimed to successfully arrive at a destination 100 times in 100 attempts. The average episode length was approximately 700. The training result is shown in Figure 8.  
Learning results: Although random RL had various kinds of experiences through random inward neighboring ships, it required 10 times more learning time than the ILFAS until the autonomous ship arrived at a destination 100 times in 100 attempts. The average episode length (the ship control signal) was 100 steps more than that of ILFAS. _V(s)_ in Figure 9 demonstrates that \(Trajectory(R(\tau))\) is stabilized as the autonomous navigation continues. Although the random RL showed new learning owing to catastrophic forgetting, RL by the ILFAS indicated that additional learning was performed based on the previous learning experience.