esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Vibrational Analysis
Assuming the entire mass (except the \({}^{14}\)CH\({}_{2}\) moiety) of the 2-aminopropenoic acid moiety to be concentrated at the center of the atom C\({}_{17}\) and labeling it as C\({}^{\prime}\)\({}_{17}\), the C\({}_{14}\)-C\({}^{\prime}\)\({}_{17}\) bond has three normal modes: \(\
u\)(C\({}_{14}\)-C\({}^{\prime}\)\({}_{17}\)), \(\alpha\)(C\({}_{3}\)-C\({}_{14}\)-C\({}^{\prime}\)\({}_{17}\)) and \(\tau\)(C\({}_{3}\)-C\({}_{14}\)). Similarly, assuming the mass of the \({}^{22}\)NH\({}_{2}\) and \({}^{19}\)COOH groups to be concentrated at the centers of the atoms N\({}_{22}\) and C\({}_{19}\) and denoting these mass points as N\({}^{\prime}\)\({}_{22}\) and C\({}^{\prime}\)\({}_{19}\), one has the C\({}^{\prime}\)\({}_{17}\)N'\({}^{\prime}\)\({}^{\prime}\)\({}_{22}\)C\({}^{\prime}\)\({}_{19}\) group. This group has two stretching modes: \(\
u\)(C\({}^{\prime}\)\({}_{17}\)-N'C\({}^{\prime}\)\({}_{17}\)) was found to be coupled with the two stretching modes \(\
u\)(C\({}_{17}\)-N\({}^{\prime}\)\({}_{22}\)) and \(\
u\)(C\({}_{17}\)-C\({}^{\prime}\)\({}_{19}\)), whereas the modes \(\
u\)(C\({}_{17}\)-N\({}^{\prime}\)\({}_{22}\)) and \(\
u\)(C\({}_{17}\)-C\({}^{\prime}\)\({}_{19}\)) couple with each other. The computed frequencies for the modes \(\
u\)(C\({}_{14}\)-C\({}^{\prime}\)\({}_{17}\)), \(\
u\)(C\({}_{17}\)-N\({}^{\prime}\)\({}_{22}\)) and \(\
u\)(C\({}_{17}\)-C\({}^{\prime}\)\({}_{19}\)) were found to be 1064, 995 and 779 cm\({}^{-1}\) and correlated to the observed frequencies 1063(IR)/1060(R), 982(IR, R) and 777(IR, R) cm\({}^{-1}\), respectively. The modes \(\alpha\)(C\({}_{3}\)-C\({}_{14}\)-C\({}_{17}\)) and \(\tau\)(C\({}_{3}\)-C\({}_{14}\)) were computed to be 170 and 67 cm\({}^{-1}\) with the observed Raman bands 166 and 64 cm\({}^{-1}\). The remaining four modes of the C\({}^{\prime}\)\({}_{17}\)N\({}^{\prime}\)\({}_{22}\)C\({}^{\prime}\)\({}_{19}\) group result due to coupling between the corresponding angular deformations involving the atoms C\({}_{17}\), N\({}^{\prime}\)\({}_{22}\) and C\({}^{\prime}\)\({}_{19}\). These modes can be approximately described as the scissoring (\(\beta\)s), rocking (\(\rho\)), wagging (\(\omega\)) and twisting (\(\tau\)) modes of the C\({}_{17}\)N\({}^{\prime}\)\({}_{22}\)C\({}^{\prime}\)\({}_{19}\) group. These four modes were found to be strongly coupled with the other angle deformation modes. The computed frequencies for these modes were found to be 568, 420, 210 and 92 cm\({}^{-1}\), respectively. All these modes were observed in the Raman spectrum with weak intensities (Table 5).
## 2 Literature Review
### Boundary Construction and Linear Interpolation
[72] highlighted that invalid data points (e.g., zero-coordinate points) may distort boundary shapes, leading to erroneous visual envelopes. Against this backdrop, this study proposes a linear interpolation method based on extremum points to address the accuracy and stability issues in boundary fitting. By employing two complementary functions (LimitX and LimitY) to calculate precise positions on the boundary, this approach enhances boundary construction reliability. Furthermore, this method automatically filters out invalid data points caused by instrument errors or blinking events, ensuring the robustness of analytical results. These improvements not only simplify the analysis of large-scale fixation data but also provide more precise technical support for subsequent quantitative spatial analyses.
## 2 Materials and Methods
### Selection and Scaling of Ground Motion Records
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Record** & \multicolumn{1}{c}{**Earthquake Name**} & \multicolumn{1}{c}{**Year**} & \multicolumn{1}{c}{**Station Name**} & \multicolumn{1}{c}{**Magnitude**} & \multicolumn{1}{c}{**Rjb (km)**} & \multicolumn{1}{c}{**Vs30 (m/s)**} \\
**Number** & & & & & & & \\ \hline
293 & Irpinia Italy01 & 1980 & Torre Del Greco & 6.90 & 59.63 & 593.35 \\
572 & Taiwan SMART1(45) & 1986 & SMART1 E02 & 7.30 & 51.35 & 671.52 \\
774 & Loma Prieta & 1989 & Hayward City Hall North & 6.93 & 54.97 & 735.44 \\
781 & Loma Prieta & 1989 & Lower Crystal Springs & & & \\  & & Dam dwnst & 6.93 & 48.24 & 586.08 \\
891 & Landers & 1992 & Silent Valley Poppet Flat & 7.28 & 50.85 & 659.09 \\
897 & Landers & 1992 & Twentynine Palms & 7.28 & 41.43 & 635.01 \\
946 & Northridge01 & 1994 & Antelope Buttes & 6.69 & 46.65 & 572.57 \\
1061 & Northridge01 & 1994 & Rancho Palos Verdes Hawth & 6.69 & 48.02 & 580.03 \\
1109 & Kobe, Japan & 1995 & MZH & 6.90 & 69.04 & 609.00 \\
1281 & ChiChi, Taiwan & 1999 & HWA032 & 7.62 & 42.87 & 573.04 \\
1284 & ChiChi, Taiwan & 1999 & HWA035 & 7.62 & 44.02 & 677.49 \\
1293 & ChiChi, Taiwan & 1999 & HWA046 & 7.62 & 47.79 & 617.52 \\
1302 & ChiChi, Taiwan & 1999 & HWA057 & 7.62 & 46.48 & 671.52 \\
1474 & ChiChi, Taiwan & 1999 & TCU025 & 7.62 & 52.18 & 665.2 \\
1786 & Hector Mine & 1999 & Heart Bar State Park & 7.13 & 61.21 & 624.94 \\
1795 & Hector Mine & 1999 & Joshua Tree N.M. Keys View & 7.13 & 50.42 & 686.12 \\
1836 & Hector Mine & 1999 & Twentynine Palms & 7.13 & 42.06 & 635.01 \\
3955 & Tottori, Japan & 2000 & SMNH11 & 6.61 & 40.07 & 670.73 \\
4054 & Bam, Iran & 2003 & [PERSON] & 6.60 & 46.20 & 574.88 \\
4893 & Chuetsuoki, Japan & 2007 & Toyotsu Nakano & 6.80 & 61.16 & 561.59 \\
5218 & Chuetsuoki, Japan & 2007 & NGNH07 & 6.80 & 62.96 & 562.18 \\
6891 & Darfield, New Zealand & 2010 & CSHS & 7.00 & 43.60 & 638.39 \\
6963 & Darfield, New Zealand & 2010 & RPZ & 7.00 & 57.37 & 638.39 \\  & & **Means** & & **7.09** & **50.80** & **627.78** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Selected ground motion records for 4611 station. Figure 13: Scaled spectra and Kahramanmaras record of 4615 station.
# Using Educational Chatbots with Metacognitive Feedback to Improve Science Learning
## 4 Results
### Correlation Analysis (RQ4)  
Regarding the metacognitive feedback group, correlation analyses were conducted to explore factors influencing learning performance. As for the transfer test, the result showed a significant positive correlation between transfer test scores and perceived competence  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Group** & **N** & **Prelearning M** & **Prelearning SD** & **Post-Learning M** & **Post-Learning SD** & **Adjusted M** \\ \hline Metacognitive feedback & 31 & 6.10 & 1.54 & 9.34 & 2.79 & 9.38 \\ No feedback & 31 & 6.29 & 1.70 & 6.52 & 3.33 & 6.47 \\ \hline
**Source of Variation** & **SS** & **df** & **MS** & **F** & \(\mathbf{p}\) & **Partial \(\mathbf{\eta}^{2}\)** \\ \hline Covariates & 30.60 & 1 & 30.60 & 3.37 & 0.071 & 0.05 \\ Intergroup & 130.6 & 1 & 130.59 & 14.39 & \textless{}0.001 & 0.20 \\ Intragroup & 535.3 & 59 & & & & \\ Corrected Total & 689.4 & 61 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: ANCOVA of post-learning knowledge transfer.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Dimensions** & **Group** & **M** & **SD** & **t** & **F** & \(\mathbf{p}\) \\ \hline \multirow{2}{*}{Interest} & Metacognitive & 5.52 & 1.14 & \multirow{2}{*}{3.59} & \multirow{2}{*}{3.72} & \multirow{2}{*}{0.001} \\  & No feedback & 4.32 & 1.46 & & \\ \hline \multirow{2}{*}{Competence} & Metacognitive & 4.87 & 1.26 & \multirow{2}{*}{2.69} & \multirow{2}{*}{1.91} & \multirow{2}{*}{0.009} \\  & No feedback & 3.94 & 1.47 & & \\ \hline \multirow{2}{*}{Value} & Metacognitive & 6.12 & 0.79 & \multirow{2}{*}{3.03} & \multirow{2}{*}{2.70} & \multirow{2}{*}{0.004} \\  & No feedback & 5.43 & 0.99 & & \\ \hline \multirow{2}{*}{Pressure} & Metacognitive & 3.06 & 1.23 & \multirow{2}{*}{\(-1.29\)} & \multirow{2}{*}{0.85} & \multirow{2}{*}{0.203} \\  & No feedback & 3.48 & 1.33 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparing intrinsic motivation between two groups.  
(r = 0.39, \(p\) = 0.032). However, transfer test scores were not significantly correlated with post-learning metacognitive awareness (r = 0.26, \(p\) = 0.164), interest-enjoyment (r = \(-\)0.02, \(p\) = 0.908), and perceived value (r = 0.28, \(p\) = 0.124). This indicates that students in the metacognitive feedback group who perceived higher competence would be more capable of transferring what they learned to a new problem.  
However, as for the knowledge retention test, the result showed that post-learning knowledge retention scores were not significantly correlated with post-learning metacognitive awareness (r = 0.12, \(p\) = 0.534), interest-enjoyment (r = \(-\)0.20, \(p\) = 0.279), perceived competence (r = 0.24, \(p\) = 0.199), or perceived value (r = 0.23, \(p\) = 0.139). Therefore, these results partially support Hypothesis 4.
## 1 Introduction
Furthermore, considering the importance of real-time performance in industrial applications, we employ the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique. This technique not only helps reduce computational complexity but also, in some cases, significantly improves anomaly detection performance. In summary, the main contributions of this paper are as follows:  
* Proposed the Time-Block attention module, aimed at efficiently integrating global information and long-range dependencies, thus providing a deep characterization of the dynamic patterns in time-series data. * Proposed the Variable-Block attention module, aimed at capturing complex interactions between different variables, making it one of the few Transformer models that explicitly explore and utilize cross-variable dependencies for anomaly detection. * Proposed the STformer framework for unsupervised network attack detection and evaluated its overall performance under DoS attacks and FDIA to validate its effectiveness and feasibility.
## 4 Experimental Analysis  
Given that the characteristics of network traffic are highly abstract, evaluating the accuracy of their reconstruction via the result of the loss function is challenging. Therefore, we integrate the evaluation process with downstream tasks such as network traffic classification and intrusion detection. By doing so, we can illustrate the impact of reconstruction results on the subsequent analysis results.  
Typically, a deep learning model is necessary for the network traffic analysis task. As Convolutional Neural Networks (CNNs) [43] are widely used to learn spatial data characteristics, and Long-Short Term Memory (LSTM) [44] is effective for learning temporal data characteristics, we select either CNN or LSTM as the classifier of downstream analysis tasks. This enables us to compare the efficacy of TCReC across different application scenarios.  
In this section, we evaluate the reconstruction ability of TCReC through four groups of experiments. The reconstruction ability mainly includes the following aspects:  
1. Reconstruction ability of various models (Section 4.4) using CNN as a classifier to analyze the accuracy of reconstructed attack traffic data (CIC-IDS-2017 dataset).
2. Reconstruction ability of TCReC adapting different classifiers (Section 4.5) using CNN and LSTM as classifiers to analyze temporal and spatial features TCReC reconstructed (CIC-IDS-2017 dataset).  
Figure 6: (**a**) t-SNE plot of original traffic, (**b**) t-SNE plot of 70% packet loss traffic, (**c**) t-SNE plot of TCReC reconstructed traffic.  
3. Reconstruction ability of TCReC under different packet loss rates (Section 4.6) using CNN and LSTM as classifiers to analyze the effect of packet loss rates (CIC-IDS-2017 dataset).
4. Reconstruction ability of TCReC in common network traffic analysis scenarios (Section 4.3) using CNN to analyze Internet application traffic data (ISCX-VPN-2016 dataset).
## 3 Discrete Element Analysis  
This study analysed the macroscopic mechanical properties, such as the uniaxial compressive strength and elastic modulus, of similar materials through UCTs. A mesoscopic interpretation of these tests was obtained using Particle Flow Code 2D (PFC2D) software  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test No.** & **H** & **Material Ratio** & \begin{tabular}{c} **Density** \\ **(g/cm\({}^{3}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Compressive** \\ **Strength (MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Elastic Modulus** \\ **(MPa)** \\ \end{tabular} \\ \hline
1 & 0.1 & F:Q:G:W = 50:450:50:50 & 2.188 & 1.208 & 335 \\ \hline
2 & 0.2 & F:Q:G:W = 100:40:50:50 & 2.235 & 1.257 & 354 \\ \hline
3 & 0.3 & F:Q:G:W = 150:350:50:50 & 2.245 & 1.292 & 372 \\ \hline
4 & 0.4 & F:Q:G:W = 200:300:50:50 & 2.244 & 1.377 & 388 \\ \hline
5 & 0.5 & F:Q:G:W = 250:250:50:50 & 2.277 & 1.429 & 405 \\ \hline \hline \end{tabular} Note: fly ash—F; quartz sand—Q; gypsum—G; water—W.  
\end{table}
Table 1: Results of each uniaxial compression test with varying RFCAs.  
Figure 2: Damage diagram for uniaxial compression test.  
(v.6.0) for discrete element analysis. Seven groups, labelled A through G, were modelled with different RFCAs. The RFCAs for groups A through E corresponded to those utilised in the laboratory tests (Table 1) and served as the control group. RFCAs for groups F and G were additional numerical simulation cases, extending beyond those acquired from the laboratory tests.  
Given that water in the materials evaporates during the sample preparation process, the mass of gypsum is relatively small; thus, the volume of gypsum was omitted in the discrete element analysis. Only the formation of fine and coarse aggregates was taken into consideration. The fly ash particles, with a smaller diameter (particle size of 0.04-0.05 mm), represented the fine aggregate, while the quartz particles, with a larger diameter (particle size of 0.6-0.9 mm), represented the coarse aggregate. A parallel bonding model was employed to simulate the adhesion caused by the mixture of gypsum and water.  
Since the ratio of the combined mass of gypsum and water to the total mass was constant across all groups in the laboratory tests, it was assumed that each group experienced the same cementing effect due to the hydration reaction triggered by gypsum and water. Thus, a fixed set of mesoscopic parameters was selected to calibrate the parameters of each model group, with the focus solely on the effect of RFCA.
## 1 Introduction  
Proteins are an integral component for most of the reactions taking part in the cell. An important aspect in protein research is their three-dimensional structure, which is required to understand their function in detail. The most common methods to determine these structures are X-ray crystallography and NMR spectroscopy. To date almost 47,000 protein structures have been deposited in the PDB [1]. However, cellular functions are rarely carried out by single proteins but rather by complexes of several interacting proteins and only a very small part of the deposited structures correspond to protein-protein complexes. It is currently estimated that each protein has on average nine interaction partners [2]. High-throughput methods for detecting protein interactions, like yeast-2-hybrid assays or tandem-affinity-purification mass spectrometry, produce large expected protein-protein interaction maps.  
These experimental approaches are supplemented by bioinformatic methods such as phylogenetic profiling, investigations of gene neighborhoods, and gene fusion analysis.  
Unfortunately it is not possible to determine the structures for all of them by experimental methods since there are often limitations concerning large or transient complexes. In addition the experimental structure determination of complexes is in most cases a very time-consuming and challenging process. For that reason computational approaches such as docking algorithms that predict the structure of protein-protein complexes are needed.  
During the last few years considerable effort has been put in the development and application of docking algorithms. For a recent overview on protein-protein docking readers should refer to the review by [PERSON] [3]. The success of docking algorithms has consistently improved over the last years as measured by the CAPRI (Critical Assessment of PRedicted Interactions) blind docking experiment [4]. Therefore, in many cases reliable results can be obtained.
# Review of Functionally Graded Thermal Sprayed Coatings
## 3 Principal Properties of Thermal Spray Coatings and Methods of Their Characterizations
### Mechanical Properties
#### 3.1.3 Elastic Properties  
The elastic properties of thermal sprayed coatings are strongly dependent on porosity and inter-lamellar contacts [216]. This property helps in appreciation of coatings quality. The methods of elastic modulus testing can be divided based on destructive and non-destructive testing [1; 209; 214]. The destructive methods include indentation technique, which uses the cross section of samples. This method was developed by [PERSON] et al. [217]. The method useful for free-standing coatings is tensile or compression test developed for bulk materials. Finally, a destructive method is cantilever beam test, which can be made during and after coating's spraying [218; 219; 220; 221; 222].  
The non-destructive methods include [1; 223; 224; 225] laser acoustic test and ultrasonic spectroscopy one. These methods are very sensitive on defects in the coatings.
# Bio-Based Polymers with Potential for Biodegradability
## 3 Poly(lactic acid) (PLA) and Related Polymers
### PLA-Degrading Microorganisms  
Many microorganisms have been identified that will biodegrade aliphatic polyesters, such as poly(\(\beta\)-hydroxyalkanoate), poly(\(\varepsilon\)-caprolactone), poly(hexamethylene carbonate), andpoly(tetramethylylene succinate) [160]. However, isolating microorganisms that biodegrade PLA has been more difficult. In 1997, [PERSON] et al reported the first PLA degrader, _Amyclatoppsis_ strain HT-32, which was isolated from soil [161]. According to an earlier review by [PERSON] and [PERSON], most PLA-degrading microorganisms are bacteria, belonging to related genera within the taxonomic family of _Pseudonocardiaceae_, including _Amyclatoppsis_, _Lentzea_, _Kidelosporangium_, _Streptouloteichus_, and _Sacharothrix_[162]. In 2001, _Tritirachium album_ was the first isolated fungal strain reported to degrade PLA [163].  
Ongoing research continues to isolate previously unknown PLA degraders belonging to other families and genera. A new strain of mesophilic bacterium, _Stentrophomonas maltophilia_ LB 2-3, was isolated from compost on a pear-tree farm in South Korea [145]. Previously unreported strains of _Rhizobium_ sp. and _Alpha proteobacterium_ found in agricultural soils from Vietnam were able to biodegrade PLA/ethylene vinyl acetate (EVA) [146]. Recently, a study reported the isolation of a PLA-degrading bacteria (_Thermopolyspora flexuosa_, or FTPLA) [125].
## 7 Discussion and Conclusions  
A compression-aware tunnel crack localization technique using the OMP recovery algorithm in the field of railway transportation is proposed. This CS-based crack detection method relies on the pressure difference at a point in the rock mass, considering both  
Figure 8: Cumulative distribution function of estimated error in two crack positions.  
Figure 9: Relationship between crack detection error and spatial sample size.  
the presence and absence of cracks. As the linear representation of crack positions is not feasible, CS for modeling analysis, utilizing the OMP algorithm for signal recovery, is employed, followed by experimental simulations to analyze the results. The specific conclusions are as follows:  
1. The existing frequency-domain transient wave analysis method is applicable to tunnel rock mass environments. Through data analysis and simulation, the existing frequency-domain transient wave model is applied to the rail transportation field to solve the crack detection technology of tunnel rock, which has scalability.
2. According to the proposed crack detection method, a more efficient scheme can be proposed for sensor deployment based on the results obtained from the simulations. Sensor placement in practical engineering can be guided by the expected error range, providing practical guidance for engineering applications.
3. The accuracy of CS-based crack detection primarily depends on the number of spatial samples. These simulation results demonstrate that increasing the number of spatial sample points can reduce the error.
4. The resolution of the proposed crack identification technique is related to the wavelength of the transient wave. If the distance between the two cracks is smaller than the wavelength of the transient wave, it becomes challenging to distinguish between the two crack points.
5. In the experimental simulations for localization detection, the employed physical formulas and corresponding relationships are currently based on theoretical derivations. To improve the accuracy of localization, further research should focus on enhancing the content related to these aspects.  
The CS-based framework utilizing the transient wave model shows potential for crack detection in tunnel rock masses. The findings of this study provide insights into the application of CS-based methods; different models should be used to study specific rock properties and future works will analyze the theoretical performance of CS-based tunnel crack detection.  
Data curation, J.C.; Writing--original draft, M.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Special Fund Project supported by Shanghai Municipal Commission of Economy and Information Technology under Grant 202201034 and in part by the Fundamental Research Funds for the Central Universities.  
Not applicable.  
The data presented in this study are available on request from the first author. The data are not publicly available due to experiment.  
The authors declare no conflict of interest.