esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Experiment Results and Analysis
#### 4.3.6 Expansion Experiments  
In text classification applications in real life, taking into account colloquial expressions, the corpus of teachers' teaching in the classroom meets our research needs. In the classroom, the colloquial expressions of teachers and students will intersect in the classroom corpus because their interaction will have an important influence on the teaching effect. On this basis, we established the teacher classroom corpus theme analysis dataset TATC. This dataset comes from actual engineering classroom corpus data and has typical classroom teaching characteristics, such as teachers asking questions and waiting for answers. The dataset covers four categories, such as scientific and technological literacy, and contains about 4000 manually annotated corpora certified by experts. This dataset is divided into training and test datasets, with a division ratio of 0.8. Figure 8 shows the relevant information of TATC.  
We utilized APTrans to conduct extended experiments on the TATC dataset. Table 4 shows the quantitative results. The results show that our method still has strong competitiveness compared with the preliminary training model based on the Transformer. The accuracy on the self-built dataset can reach 83.33%, exceeding the XLNet baseline model by 1.30%. Although our method is built for written text, the comparison results show that APTrans can indeed fully capture the semantic information in the text and fuse the position information to achieve strong results. This notion further means that our method can be applied in practice, proving the universality of our model.  
Figure 8: Basic information of the TATC dataset. The dataset contains four categories, and the evaluation sentence length for each category and the total average sentence length of the text are shown in the figure above.
# Laser Fabrication of Titanium Alloy-Based Photothermal Responsive Slippery Surface
## 3 Results and Discussion
### Photothermal Property  
To investigate the photothermal conversion effect, we placed the initial titanium alloy sample and the laser-processed one under a stable light source for 10 min and recorded  
Figure 4: (**a**–**c**) SEM images of initial titanium alloy substrate. (**d**–**f**) SEM images of laser-treated titanium alloy substrate.  
Figure 5: Surface reflectance spectra of initial titanium alloy substrate and laser-treated titanium alloy substrate.  
the surface temperature with a thermal imaging lens. To make a clear comparison, we fabricated a pattern on titanium alloy surface. The results are shown in Figure 6a. It is obvious from the figure that the temperature of the laser-treated region is much higher than that of the untreated region. Consequently, it can be concluded that the photothermal conversion efficiency of the laser-treated sample is much higher than that of the untreated sample. The improved photothermal effect with respect to the laser-treated sample can be attributed to the anti-reflection property. Figure 6b shows the temperature-rising curve of the sample. Under the same initial temperature, the untreated sample and the laser-treated sample are irradiated under the same light source for 540 s. The results show that the temperature of the sample increased with the irradiation time. Notably, the temperature of the untreated sample increased from 15.5 \({}^{\circ}\)C to 30.1 \({}^{\circ}\)C within 60 s. With the increase of irradiation time, the temperature rose slowly, and the final temperature tended to be stable around 40.5 \({}^{\circ}\)C. On the contrary, the temperature of the laser-treated sample increased from 15.5 \({}^{\circ}\)C to 40.5 \({}^{\circ}\)C within 60 s. The temperature rise was much higher than that of the untreated sample. After irradiation for 600 s, the temperature rose to \(\sim\)65 \({}^{\circ}\)C. The anti-reflection property of the laser-treated sample promotes the light absorption, and thus, the surface temperature rises under light irradiation is faster and higher. Due to the anti-reflection effect, the light-utilization rate with respect to the laser-treated titanium alloy is also improved. We also tested the photothermal properties of the paraffin coated structured titanium alloy surface. After coating paraffin, the photothermal property nearly changes because of the thin paraffin film.
## 5 Results
### Model Precision
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Low Frequency Example**} & \multicolumn{2}{c}{**High Frequency Example**} \\  & \multicolumn{2}{c}{**6 \(\frac{\text{rad}}{\text{s}}\)**} & \multicolumn{2}{c}{**6000 \(\frac{\text{rad}}{\text{s}}\)**} \\
**Algorithm** & **Mean Err. Sq.** & **Variation** & **Mean Err. Sq.** & **Variation** \\ \hline \multicolumn{5}{c}{Stator \(d\)-axis - \(\varphi_{sd}^{n}\)} \\ SIM-DSCR & 34.2e-7 & \(-37.1\%\) & 120.0e-5 & \(+113.6\%\) \\ FAST 1 int. & 54.3e-7 & 0\% & 57.2e-5 & 0\% \\ FAST 2 int. & 20.2e-7 & \(-62.8\%\) & 26.3e-5 & \(-53.9\%\) \\ FAST 3 int. & 12.5e-7 & \(-76.9\%\) & 18.7e-5 & \(-67.3\%\) \\ FAST 5 int. & 7.8e-7 & \(-85.7\%\) & 13.5e-5 & \(-76.4\%\) \\ FAST 10 int. & 4.9e-7 & \(-90.8\%\) & 10.2e-5 & \(-82.2\%\) \\ FAST 15 int. & 4.2e-7 & \(-92.3\%\) & 9.2e-5 & \(-84.0\%\) \\ \hline \multicolumn{5}{c}{Stator \(q\)-axis - \(\varphi_{sd}^{n}\)} \\ SIM-DSCR & 33.9e-7 & \(-37.0\%\) & 150.0e-5 & \(+113.3\%\) \\ FAST 1 int. & 53.9e-7 & 0\% & 72.3e-5 & 0\% \\ FAST 2 int. & 20.8e-7 & \(-61.4\%\) & 33.6e-5 & \(-53.5\%\) \\ FAST 3 int. & 13.4e-7 & \(-75.1\%\) & 24.0e-5 & \(-66.8\%\) \\ FAST 5 int. & 8.8e-7 & \(-83.7\%\) & 17.5e-5 & \(-75.8\%\) \\ FAST 10 int. & 6.1e-7 & \(-88.7\%\) & 13.4e-5 & \(-81.5\%\) \\ FAST 15 int. & 5.3e-7 & \(-90.1\%\) & 12.1e-5 & \(-83.3\%\) \\ \hline \multicolumn{5}{c}{Rotor \(d\)-axis - \(\varphi_{rd}^{n}\)} \\ SIM-DSCR & 14.9e-7 & \(-87.7\%\) & 48.5e-5 & \(-19.9\%\) \\ FAST 1 int. & 121.8e-7 & 0\% & 60.6e-5 & 0\% \\ FAST 2 int. & 29.2e-7 & \(-76.0\%\) & 18.9e-5 & \(-69.3\%\) \\ FAST 3 int. & 12.6e-7 & \(-89.7\%\) & 10.0e-5 & \(-83.6\%\) \\ FAST 5 int. & 4.4e-7 & \(-96.4\%\) & 5.0e-5 & \(-91.8\%\) \\ FAST 10 int. & 1.3e-7 & \(-98.9\%\) & 2.4e-5 & \(-96.0\%\) \\ FAST 15 int. & 0.8e-7 & \(-99.3\%\) & 1.8e-5 & \(-97.1\%\) \\ \hline \multicolumn{5}{c}{Rotor \(q\)-axis - \(\varphi_{rq}^{n}\)} \\ SIM-DSCR & 1.9e-7 & \(-95.7\%\) & 42.3e-5 & \(-22.8\%\) \\ FAST 1 int. & 44.6e-7 & 0\% & 54.8e-5 & 0\% \\ FAST 2 int. & 12.7e-7 & \(-71.4\%\) & 16.8e-5 & \(-69.3\%\) \\ FAST 3 int. & 6.7e-7 & \(-84.9\%\) & 9.0e-5 & \(-83.6\%\) \\ FAST 5 int. & 3.6e-7 & \(-92.0\%\) & 4.5e-5 & \(-91.8\%\) \\ FAST 10 int. & 2.1e-7 & \(-95.1\%\) & 2.1e-5 & \(-96.1\%\) \\ FAST 15 int. & 1.9e-7 & \(-95.7\%\) & 1.6e-5 & \(-97.2\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean error squared results of low and high frequency simulations. Base error is the one of the 1 sub-interval model. execution speed. At high speed, however, the FAST-DSCR solver performs much better both in precision and in calculation speed. Considering both factors, it is verified that the use of the FAST-DSCR solver with a minimum of 5 sub-intervals and a maximum of 10 yields the best compromise between execution speed and precision for most applications.
## 2 Materials and Methods
### Feature Extraction  
The raw sEMG signals were first transformed into low-dimensional feature vectors with increased information density, i.e., one feature point was calculated from a sliding window of 100 ms (with 200 sampling points) and the information of the whole window was compressed into one feature point. Generally, the features extracted from a time-frequency domain (TFD features) contain more information from both the temporal and frequency domains [25]. In this study, marginal discrete wavelet transform (mDWT) was used to extract the TFD features, for they performed better in the prior study [26]. The mDWT features were calculated as Equation (1), where S represents the deepest decomposition level (\(\mathrm{S}=\mathrm{log}2^{\mathrm{N}}\); N represents the wavelet order) and was set at 3 in this study; and \(\mathrm{d}_{\mathrm{x}}(\mathrm{s},\mathrm{u})=\mathrm{x}(\mathrm{t})\). In this work, a Daubechies 7 wavelet (N = 7) served as the mother wavelet. The sliding window was 200 ms in length with a 10 ms increment. The comparison of the raw signals and the corresponding mDWT features is presented in Figure 3.  
\[\mathrm{mDWT,\,m_{x}(s)=\sum_{u=0}^{\frac{\mathrm{N}}{2}-1}|d_{x}(s,u)|,s=1, \ldots,S} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multirow{2}{*}{**Handedness**} & **Amputated** & **Remaining** & **Year Since** & **Prosthesis** \\  & & **Hand(s)** & **Forearm (\%)** & **Amputation** & **Use** \\ \hline
1 & Right & Right & 50 & 13 & Myoelectric \\
2 & Right & Left & 70 & 6 & Cosmetic \\
3 & Right & Right & 30 & 5 & Myoelectric \\
4 & Right & Right and Left & 40 & 1 & No \\
5 & Left & Left & 90 & 1 & Kinematic \\
6 & Right & Left & 40 & 13 & Kinematic \\
7 & Right & Right & 0 & 7 & No \\
8 & Right & Right & 50 & 5 & Myoelectric \\
9 & Right & Right & 90 & 14 & Myoelectric \\
10 & Right & Right & 50 & 2 & Myoelectric \\
11 & Right & Right & 90 & 5 & Myoelectric \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computation information of 11 amputees in DB3.  
Figure 3: Twelvechannel raw sEMG signals from an amputee vs. the corresponding mDWT features.
# Advanced Micro/Nanocapsules for Self-Healing Coatings
## 4 Classification of Self-Healing Coatings Based on the Polymeric Matrix
### Thermoplastic Polymers
#### 4.2.1 Polyacrylate-Based Self-Healing Coatings  
Polyacrylate (PAL) and its derivative-based materials (polymers) have gained widespread recognition in the coating industries due to numerous attributes, including low cost, high transparency, excellent adhesion, processability, and durability, particularly under ambient conditions. One particularly intriguing aspect of PAL (polyacrylate) materials is the ability to tailor their physical properties through the judicious selection of monomers for the formation of the polymer network. Numerous studies have been conducted to enhance the recurrent self-healing characteristics, typically relying on reversible bonds like covalent bonds, hydrogen bonds, coordinate covalent bonds, ionic interactions, and host-guest interactions [52; 53]. The use of van der Waals interactions aims to eliminate both chemical and physical interactions, allowing for multiple recoveries under mechanical stress without the need for any external triggering agent. [PERSON] et al. [54] investigated the mechanical and self-healing characteristics of anisotropic composites by incorporating multiple reversible bonds, specifically strong coordination bonds and weak hydrogen bonds. Through FTIR and combined 2D FTIR analysis, it was determined that the rapid self-healing capability (within 30 s with a healing efficiency of approximately 95%) of the designed system was attributed to the weak hydrogen bonds. Meanwhile, the robust mechanical properties (12.6 MPa) were primarily a result of the strong coordinate interactions. Such systems can be utilized as a soft actuator that is further employed for the manufacturing of smart self-healing polymer materials for many applications, especially in the medical/biomedical sectors.
# LogEDL: Log Anomaly Detection via Evidential Deep Learning
## 4 Methodology
### Uncertainty in Log Sequence Detection  
In this paper, we apply EDL combined with the MLM method to construct anomaly log sequence detection.  
First, valuable semantic information is mainly contained in log sequence variations such as trends, periods, and peaks and valleys, which may correspond to the system state. For log sequences, there is a correlation between the logs at each location in the log sequence and the nearby logs. Anomalous log sequences are often the result of unexpected logs appearing at log locations where they should be.  
Second, recent models using the MLM approach in BERT cannot detect anomalies autonomously. However, researchers have been able to detect anomalies through some phenomena such as low prediction probability of anomalous logs and large loss values [20]. Because the logs on the mask can be in multiple situations, normal logs can be mistaken for anomalous logs by simple presentation methods.  
We propose a log anomaly detection system, LogEDL, that exploits the fact that EDL can be characterized by high uncertainty values for unseen patterns to overcome the limitations of existing methods. Uncertainty modeling is performed for each [mask] location, and the uncertainty of the [mask] location responds to the likelihood of an anomaly occurring there. In the training phase, by applying EDL to the MLM learning of normal logs, we are essentially trying to collect evidence of the classes reflected by the context at each mask. In the testing phase, the assumption of contextual uncertainty at each mask allows the model to \"know the unknown\". Therefore, uncertainty is utilized to detect anomalies in the logs.
# A Portable Optical Sensor for Microplastic Detection: Development and Calibration
## 3 Results and Discussion
### Reproducibility and Statistical Analysis
We also evaluated temporal dispersion by plotting the time-resolved standard deviation for each configuration (Figure 19), which shows that incorporating optics systematically reduces variability and accelerates convergence toward a stable baseline. Figure 18: The figure shows the scatter plots of mean reflectance values for (**a**) the green laser without optics, (**b**) the green laser with optics, (**c**) the blue laser without optics, and (**d**) the blue laser with optics. Red trend lines with corresponding R\({}^{2}\) values indicate the variance explained by linear regression. Lower R\({}^{2}\) values indicate stable reflectance across observations, while higher values reflect greater variability, aiding in the assessment of measurement consistency for microplastic detection. Understanding error and variance in an FDA is key for validating the accuracy of reflectance measurements and optimizing device configurations used in optical experiments. By identifying configurations that minimize variance and error, researchers can improve the reproducibility of their experiments and increase the reliability of their conclusions. Implementing FDA techniques for reflectance curve analysis in this study allows for a  
Figure 19: The figure shows the reflectance variability (standard deviation) over time for four laser configurations: (**a**) green laser without optics shows high initial variance that stabilizes gradually; (**b**) green laser with optics exhibits lower overall variance, indicating enhanced precision from optical components; (**c**) blue laser without optics mirrors the trend in (**a**), displaying decreasing variance over time; and (**d**) blue laser with optics has the lowest initial variance, which further decreases, emphasizing optics’ role in improving measurement consistency. These trends underscore the precision and reliability of laser-based microplastic detection. robust evaluation of the effects of different laser configurations, ensuring that decisions based on these analyses are well-founded and statistically valid.
## 6 Analysis of Schemes  
### Proof of Correctness  
The proposed scheme incorporates message verification, a decryption process, and the use of a proxy key to ensure data confidentiality and integrity. The correctness of the proposed scheme is demonstrated as follows.
# Characterization of a Rectangular-Cut Kirigami Pattern for Soft Material Tuning
## 3 Results
### FEM Results  
FEM analysis demonstrates how deformation occurs when force is applied. Figure 6 shows the transitional state of the kirigami-cut test specimen for one configuration. The analysis indicates that all the cuts experience uniform deformation along the test specimen, except for those at the extreme points. Due to varying constraints, these cuts do not reach maximum deformation, in contrast to the central cuts. Deformations outside the plane of the specimen also occur as the force increases, which are estimated by the FEM simulation, as shown in Figure 6a and observed in experimentation. The FEM simulation also provides us with the results of the patterns' behavior concerning the strain-time simulation shown in Figure 6b and the stresses at the nodes distributed along the central points of the specimen. It is important to note that while the FEM study gives us results per node, the conducted experiment considers the entire specimen as a single mechanical entity.
# Risk-Sensitive Policy with Distributional Reinforcement Learning
## 3 Materials and Methods
### Methodology  
#### 3.2.1 Objective Criterion for Risk-Sensitive RL  
As previously explained, the objective in classical RL is to learn a decision-making policy \(\pi\in\Pi\) that maximises the expectation regarding the discounted sum of rewards. Formally, this objective criterion can be expressed as the following:  
\[\underset{\pi}{\text{maximise}}\ \mathbb{E}\Bigg{[}\sum_{t=0}^{\infty}\gamma^{t }r_{t}\Bigg{]}. \tag{10}\]  
Figure 1: Intuitive graphical comparison between classical RL and distributional RL, for a decision-making policy \(\pi\).  
In order to effectively take into consideration the risk and value its mitigation, this research work presents an update for the former objective. In fact, coming up with a generic definition for the risk is not trivial since the risk is generally dependent on the decision-making problem itself. In the present research work, it is assumed that the risk is assessed on the basis of the worst returns achievable by a policy \(\pi\). Therefore, a successful decision-making policy should ideally maximise the expected discounted sum of rewards while avoiding low values for the worst-case returns. The latter requirement is approximated with a new constraint attached to the former objective, defined in Equation (10): the probability of having the policy achieving a lower return than a certain minimum value should not exceed a given threshold. Mathematically, the alternative objective criterion proposed for risk-sensitive RL can be expressed as follows:  
\[\underset{\pi}{\text{maximise}} \mathbb{E}\left[\sum_{t=0}^{\infty}\gamma^{t}r_{t}\right],\] (11) such that \[p\left[\sum_{t=0}^{\infty}\gamma^{t}r_{t}\leq R_{\text{min}} \right]\leq\epsilon\,\]  
where:  
* \(p[\star]\) denotes the probability of the event \(\star\),
* \(R_{\text{min}}\) is the minimum acceptable return (from the perspective of risk mitigation),
* \(\epsilon\in[0,1]\) is the threshold probability that is not to be exceeded.
## 2 Materials and Methods
### Initial Dataset  
At the stages of searching and accumulating data, we obtained primary data on the generation of the SPP, solar irradiance at its location, and consumption by feeders at the nearest substation, which consisted of several Microsoft Office Excel Spreadsheet (version 2021) files (XLSX) with a time series of various discreteness levels. At the same time, data on weather and climatic factors were taken from the weather archive of the open meteorological database RP5 and covered the period from 1 January 2019 to 23 February 2022 with a resolution of 180 min (three hours). Then, NASA open data of solar irradiance at the top of the atmosphere on a plane normal to the incident radiation were added to the dataset. Table 1 provides a list of the main data in the original raw and unsorted files. Table 2 presents the initial meteorological features (readings from weather station No. 36259). The dimensions of the original array are 9206 rows and 29 columns (Table 2  
omits some of them, which are obviously not information-bearing for the problem being solved). When combining and subsequently cleaning the accumulated raw data in relation to the object under consideration, problems specific to these data were identified:  
* Data on generation, solar irradiance, consumption, and weather features have different time ranges of coverage;
* Weather data have a lot of missing values;
* Some of the names of the attribute columns are absolutely non-informative in the context of the real electrical circuit of the station (for example, feeders are represented by telemetering points, not by the names or numbers of outgoing power lines);
* The time resolutions of the data obtained for generation, solar irradiance, consumption, and weather data are different and range from thirty minutes to three hours; \begin{table}
\begin{tabular}{c c c c} \hline \hline
**File Id** & **Period, dd.mm.yyyy** & **Time Resolution, min** & **Data Type** \\ \hline
1 & 28 October 2019–31 December 2020 & 60 & Solar irradiance \\
2 & 01 January 2019–31 December 2020 & 60 & SPP generation \\
3 & 28 October 2019–31 December 2020 & 30 & Power consumption \\
4 & 06 May 2021–30 September 2021 & 30 & Power consumption \\
5 & 01 January 2021–30 September 2021 & 60 & SPP generation \\
6 & 01 October 2022–23 February 2022 & 30 & Power consumption \\
7 & 01 October 2022–23 February 2022 & 60 & SPP generation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of basic data in the original uncleaned and unsorted files.
###### Abstract
This leads to an increased rate-of-change-of-frequency (ROCOF), and a low frequency nadir (minimum frequency point) in a very short time. The primary frequency control cannot respond within the small time frame (typically less than 10 s) to arrest the system frequency change. This period is highlighted as section AB in Figure 3. It is clear from the figure that in systems with lower inertia, the frequency nadir is considerably lower along with a high ROCOF. Such situations can lead to tripping of frequency relays (causing under-frequency load shedding (UFLS)) and, in the worst case, may lead to cascaded outages [12; 13]. The solution to such scenarios is to add virtual inertia in the system. The basic requirements of a virtual inertia system is that it has to operate in a very short time interval (typically less than 10 s) and in autonomous fashion. Deployed appropriately, virtual inertia systems would enhance system stability and enable greater penetration of RESs. This paper presents a literature review of the various topologies used for virtual inertia implementation. The major topologies and the consequent improvements in these topologies are reviewed through a literature search followed by a restady through simulations. The problem of large frequency variations due to high penetration of RESs are introduced first in Section 2. The \"first generation\" of virtual inertia systems are introduced next in Section 3. The topologies and control algorithms to effectively emulate inertia of synchronous generators (SGs) through power electronic based converters are discussed. After a literature review of the virtual inertia topologies, three main topologies are compared and evaluated in a common benchmark in Section 4. The \"second generation\" of virtual inertia systems is then reviewed in Section 5. The optimization of these systems in terms of dynamic performance and energy usage is discussed. Finally, a review of the challenges involved with integrating virtual inertia systems into the existing power system and some future research directions are discussed in Section 6. Section 7 discusses the conclusions of the paper.