esa-sceva/satcom-chunk-collection huggingface dataset:
# Interested Keyframe Extraction of Commodity Video Based on Adaptive Clustering Annotation
## 3 Keyframe Extraction by Adaptive Clustering Annotation Facing
### Personalized Keyframe Adaptive Clustering Based on Frame-to-Frame Difference
#### 3.2.1 Differential Frames Extraction  
We define the set of all video frames as \(\Psi=\{X_{1},X_{2},\ldots,X_{n}\}\), and after grayscaling the \(\Psi\), it is expressed as \(\Psi^{\prime}=\{X^{\prime}_{1},X^{\prime}_{2},\ldots,X^{\prime}_{n}\}\). Then, the frame-to-frame difference between frame \(i\) and frame \(i-1\), denoted as \(D(i)\), is shown in Equation (3).  
\[D(i)=\frac{\sum_{m=1}^{w}\sum_{j=1}^{h}\|X_{i}^{\prime}(j,m)-X_{i-1}^{\prime}(j,m)\|} {w\times h},i\in\{2,3,\ldots,n\} \tag{3}\]  
Frame-to-frame difference is often used to detect object movement. The frame having larger difference value than the threshold is selected as a keyframe. In our work, we adopt a differential frame selection strategy based on maximum value. For example, \(D(i)\geq\frac{D(i-1)+D(i+1)}{2}\) reveals that the frame \(X_{i}\) has great difference from other frames in \(\{X_{i-2},X_{i-1},X_{i},X_{i+1}\}\), so we consider that \(X_{i}\) is a differential frame. The set of differential frames is denoted as \(\Psi_{d}=\{X_{1},X_{2},\ldots,X_{L}\}\). In order to filter redundant information ulteriorly, we set the threshold \(\alpha\), and when \(L\leq\alpha\), the algorithm outputs the result; otherwise, the algorithm repeats the above process of differential frame extraction on \(\Psi_{d}\).
# Crude Oil Price Forecasting Model Based on Neural Networks and Error Correction
## 2 Methodology
### Forecasting Methods
#### 2.3.3 Echo State Network  
The ESN has been broadly utilized in nonlinear prediction [35]. An ESN consists of an input layer, dynamic reservoir, and output layer. The dynamic reservoir is an important module, which has many hidden layer neurons connected randomly and sparsely. Thus, an ESN has the ability of short-term memory. It has the following characteristics [36]: (a) the main construction is a random and invariable reservoir; (b) connections between neurons are generated randomly; and (c) the ESN can realize algorithm training through simple linear regression.
# Link Prediction Based on Feature Mapping and Bi-Directional Convolution
## 4 Experiment and Analysis  
### Dataset  
We evaluated our proposed method using two benchmark datasets, WN18 RR and FB15k-237. WN18 RR (WordNet-18 Reversed Relations) is a commonly used knowledge graph link prediction dataset for evaluating and comparing the performance of link prediction models. This dataset is an extension of the WordNet-18 dataset, an English vocabulary network representing relationships between words, with 40,943 entities and 11 relationships. The FB15k-237 dataset was pruned with respect to the original FB15k dataset by removing some of the frequently occurring and relatively easy to predict relationships. This was done to increase the difficulty of the dataset, making it more accurate to assess the performance of the model in dealing with complex and rare relationships, with a total of 14,541 entities and 237 relationships in the dataset. Other specific data are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Entities** & **Relations** & **Training** & **Validation** & **Test** & **Total** \\ \hline WN18 RR & 40,943 & 11 & 86,835 & 3034 & 3134 & 93,003 \\ FB15k-237 & 14,541 & 237 & 272,115 & 17,535 & 20,466 & 310,116 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the dataset.
# Late Shelf Life Saturation of Golden Delicious Apple Parameters: TSS, Weight, and Colorimetry
## 2 Materials and Methods  
A fresh \"Golden Delicious\" variety of apple fruit was collected directly from a farm near Kecskemet, Hungary, in 2021. The fruits were all picked at a maturity stage corresponding to the commercial harvest date.  
Golden Delicious apples were placed in six boxes, and each one separately contained 24 apple samples. All apples have an identity code (Example: B1A1, B for box A for apple). The apples were sorted manually for approximately similar sizes and a lack of any external damage. The average weight of the total fruit sample was 150 \(\pm\) 12 g, and the average diameter size of the apple samples were d = 70 \(\pm\) 2 mm; three measurements were applied for each sample for geometry and weight measurement. The apples were stored at ambient conditions (24 \(\pm\) 1 \({}^{\circ}\)C) under relative humidity (RH 60%) for six weeks. The samples in the first three groups were examined with non-destructive methods: weight measurement (g) and color parameters (L*, a*, b* in CIELab color space [5; 26; 32]) acquisition. The fourth, fifth, and sixth batches were prepared to measure the total soluble solids (TSS), which required a destructive approach.
# Displacement Monitoring of Subway Tracks and Tunnels According to Adjacent Construction
## 1 Introduction
A substantial amount of long-term measurement data was quantitatively evaluated using the Gaussian probability density function. The vertical and horizontal behaviors of subway tunnels are monitored using a tunnel convergence meter sensor. The vertical behavior of subway tracks is monitored using a tracked settlement sensor. In this study, data were collected from sites where adjacent excavation work was performed for 18 months. Subsequently, the safety of subway tunnels and track structures was evaluated using the collected long-term measurement data. In addition, the analysis results of the track bed settlement sensor and the tunnel convergence meter sensor were compared. Since the track bed settlement measurement results are like the tunnel invert displacement, the differences with the tunnel lining were analyzed. In this study, tunnel analysis results were used as input data for numerical analysis. A technique for analyzing the continuous behavior of tunnels was presented using numerical analysis. It is expected that existing cross-sectional measurement results can be efficiently analyzed through this technique.
## 3 Results
### Normalized Maximum Ground Surface Settlement and Maximum Lateral Displacement Relationship  
There was a close relationship between the ground settlement behind the wall and the lateral displacement of the retaining structure, which varied with the excavation depth. We adopted a normalization treatment for the maximum settlement and maximum lateral displacement, as shown in Figure 20. The vertical axis represents the nondimensionalized maximum ground settlement (\(s_{max}/Z\)), and the horizontal axis represents the dimensionless maximum lateral displacement (\(\delta_{max}/Z\)). As illustrated in the figure, the majority of the data points fell within the range (\(s_{max}/\delta_{max}=0.51\)-1), which was consistent with the work of [PERSON] [5] and [PERSON] [6], who concluded that the maximum lateral displacement was 50-75% of the maximum ground surface settlement. This test obtained the average value (\(s_{max}/\delta_{max}=0.54\)), and the ratio increased gradually with the excavation depth. This indicated that the maximum settlement behind the wall was about half of the maximum lateral displacement. The settlement deformation rate gradually increased with the depth of the excavation, but it was always lower than the deformation rate of lateral displacement.
# Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming
###### Abstract  
Traffic systems, where human and autonomous drivers interact, are a very relevant instance of complex systems and produce behaviors that can be regarded as trajectories over time. Their monitoring can be achieved by means of carefully stated properties describing the expected behavior. Such properties can be expressed using Signal Temporal Logic (STL), a specification language for expressing temporal properties in a formal and human-readable way. However, manually authoring these properties is a hard task, since it requires mastering the language and knowing the system to be monitored. Moreover, in practical cases, the expected behavior is not known, but it has instead to be inferred from a set of trajectories obtained by observing the system. Often, those trajectories come devoid of human-assigned labels that can be used as an indication of compliance with expected behavior. As an alternative to manual authoring, automatic mining of STL specifications from unlabeled trajectories would enable the monitoring of autonomous agents without sacrificing human-readability. In this work, we propose a grammar-based evolutionary computation approach for mining the structure and the parameters of an STL specification from a set of unlabeled trajectories. We experimentally assess our approach on a real-world road traffic dataset consisting of thousands of vehicle trajectories. We show that our approach is effective at mining STL specifications that model the system at hand and are interpretable for humans. To the best of our knowledge, this is the first such study on a set of unlabeled real-world road traffic data. Being able to mine interpretable specifications from this kind of data may improve traffic safety, because mined specifications may be helpful for monitoring traffic and planning safety promotion strategies.  
Keywords:context-free grammar genetic programming: grammatical evolution; traffic monitoring: formal methods +
Footnote †: journal: Applied
# Mechanism and Application of Soilbags Filled with Excavated Soil in Soft Soil Subgrade Treatment
## 5 Conclusions  
This study presents the results of laboratory tests, on-site application, and numerical simulations to investigate the reinforcement mechanism and effects of soilbags filled with excavated residual soil for soft soil subgrade treatment. The main conclusions can be summarized as follows:  
1. It was found from the unconfined compression test that soilbags, created by encasing residual clayey soil within polypropylene bags, exhibited high compressive strength under external loads, a consequence of the tensile forces provided by the polypropylene material, which guarantee the mechanical properties of soilbags for subgrade enhancement.
2. The pore water pressure generated in the soft soil subgrade was found to decrease significantly due to the efficient drainage and consolidation properties of soilbag reinforcement, owing to the superior drainage characteristics at the contact interfaces and the interstices among the soilbags. Such characteristics enable the direct use of encasing excavated clayey soil in soilbags for constructing subgrades.
3. Data from the field monitoring and simulations reveal that the soft soil subgrades treated with residual clayey soil-filled soilbags performed adequately. The settlement distribution at the same depths within the subgrade was remarkably uniform. The maximum settlement occurred at the subgrade surface, which was notably slight, measuring only 11.96 mm.  
In conclusion, filling soilbags is an effective way to comprehensively utilize excavated soil resources for soft soil subgrade treatment.  
Conceptualization, S.X. and J.L.; methodology, S.X., J.L. and K.F.; software, J.L.; validation, J.L. and K.F.; formal analysis, K.F.; investigation, J.L.; resources, J.L.; data curation, K.F.; writing--original draft preparation, S.X.; writing--review and editing, J.L. and K.F.; visualization, J.L.; supervision, K.F.; project administration, J.L.; funding acquisition, S.X., J.L. and K.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (Grant No. 2022 YFE0105000), the Natural Science Research Project of Jiangsu Province Colleges and Universities (Grant No. 23 KJB560022), the Excellent Postdoctoral Program of Jiangsu Province (Grant No. 2022 ZB441, Grant No. 2023 ZB828), the Youth Foundation Project of Nanjing Hydraulic Research Institute (Grant NO. Y323004), and the Youth Talent Promotion Project of China Society for Hydropower Engineering (Grant No. Hydropower Secret Words [2023] No. 15).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study may be available upon reasonable request from the first or corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 14: Settlement contours of soilbag-treated subgrade 18 months post-construction.
# Assessment of Bioactive Surfactant Levels in Selected Cereal Products
## 2 Materials and Methods
### Sample Preparation  
In the solid sampling analysis, a modified QuEChERS method with dispersive Solid Phase Extraction (d-SPE) was used according to a previously described method [22]. Micro-high-performance liquid chromatography-tandem mass spectrometry (micro-HPLC-MS/MS) was applied for the simultaneous determination of 10 PFASs. This procedure, which was the same for samples and for sample blanks, consisted of the following steps: (1) All the samples, except flour, were ground; (2) An amount of 5g (for bran) or 10 g (forthe other products) of sample was accurately weighed into a 50 mL polypropylene (PP) centrifuge tube; (3) The samples were fortified with 10 \(\mu\)L of IS\({}_{1}\) and IS\({}_{2}\) solutions, both with a concentration of 2.5 \(\mu\)g/mL; (4) An amount of 10 mL of deionized water and then 10 mL of extraction solvent (MeCN with 150 \(\mu\)L of FA) was added; (5) The tubes were placed in an ultrasonic bath for 2.5 min and then mixed vigorously for 1 min (using a vortex mixer); (6) An amount of 4 g MgSO\({}_{4}\) and 1 g NaCl were added to each tube, and the mixture was immediately vortex-mixed for 1 min; (7) The mixture was centrifuged for 15 min at 8700 RCF and 4 \({}^{\circ}\)C; (8) An amount of 6 mL of MeCN extract was transferred to a 15 mL PP centrifuge tube containing 0.15 g ENV SPE Bulk Sorbent and 0.90 g MgSO\({}_{4}\); (9) The mixture was vortexed immediately for 30 sec; (10) The tubes were centrifuged (5 min, 5000 RCF and 4 \({}^{\circ}\)C) for phase separation; (11) An amount of 4 mL of supernatant was put into round-bottom tubes just after passing through a paper filter with 12-15 \(\mu\)m pore size (MUNKTELL-FILTRAK, Germany); (12) The tubes containing extract were put into vacuum concentration, where they were evaporated to dryness at a temperature of 40 \({}^{\circ}\)C; (13) The residue was reconstituted with 1 mL of MeOH and diluted fivefold with deionized water with 1% (\(v/v\)) of FA addition; (14) Finally, each sample was injected into the micro-HPLC-MS/MS. All samples were prepared in triplicate.  
For the construction of calibration curves, wheat flour was used as a blank sample extract. A series of standard solutions at concentrations ranging from 0.01 to 20 ng/mL were prepared to create a matrix-matched calibration curve in accordance with EU recommendations (2010/161/EU) [43]. To each standard solution, 10 \(\mu\)L of L-PFOA and L-PFOS (concentration of both: 2.5 ug/mL) was added. Blank samples were prepared in parallel in MeCN.  
Due to the lack of suitable certified reference materials for laboratories analyzing PFAS in cereal products and related foods, the recovery ratio of the analyzed compounds was investigated. Recovery studies involved spiking the samples of wheat flour with the standard solution of PFAS to a fortification level of 5 ng/g.
## 3 Results and Discussion
### Composition and Morphology  
Figure 2 presents the SEM images of the surface of the photocatalysts coated on glass sheets before the photocatalytic tests. The rough surface morphology of immobilised photocatalysts and appearance of assembled agglomerates of as-prepared photocatalysts of pure TiO\({}_{2}\) and rGO/TiO\({}_{2}\) nanocomposite are observed as shown in Figure 2A,B, respectively.  
The optoelectronic properties were measured by DRS in the UV-Vis, as shown in Figure 3. The band gap energy (\(E_{g}\)) is calculated using Kubelka-Munk transformation: \((F(R)\times hv)^{\frac{1}{n}}=C\left({{I}u-{E}_{g}}\right)\), where \(n\) is the constant for the type of optical transition and its value is 2 for permitted indirect transition [35]. The \(E_{g}\) values are determined from plotted graphs. \(E_{g}\) of the pure TiO\({}_{2}\) was determined to be 3.18 eV (Figure 3A), whereas the band gap for the rGO/TiO\({}_{2}\) nanocomposite was 2.87 eV (Figure 3B). The observed reduction in the band gap energy is a result of the inclusion of rGO material into the nanocomposite. These results indicate that the optical characteristics of TiO\({}_{2}\) are strongly influenced by the presence of rGO nanosheets and an increased amount of rGO narrows the band gap in nanocomposite materials. According to the literature, this result can  
Figure 1: Picture of immobilised (**A**) rGO/TiO\({}_{2}\) nanocomposite and (**B**) pure TiO\({}_{2}\).  
be assigned to the formation of Ti-O-C covalent bonds in the nanocomposites during hydrothermal synthesis [35; 36].
## 4 Experiment Results
### Breakdown Strength Analysis
#### 4.2.1 Normal Distribution  
Figure 7 presents the normal distribution graph of the AC-BDS values of epoxy/Al\({}_{2}\)O\({}_{3}\) micro-composites for the two voltage application methods. Additionally, Tables 2 and 3 summarize the results of the normality test performed on the AC-BDS values for each experimental method. While Table 2 shows the result for the RT experiment, Table 3 shows the result for the ST experiment. In each table, the statistics from Shapiro-Wilk [19], Anderson-Darling [20], Ryan-Joiner [21], and Kolmogorov-Smirnov [22] tests are shown along with the \(p\)-values for each method. In addition, compliance with the normal distri  
Figure 6: Relative permittivity and dissipation factor of epoxy/Al\({}_{2}\)O\({}_{3}\) micro-composites.  
bution is also indicated. At all test statistic levels ([PERSON]: W, Anderson-Darling: A-D, Ryan-Joiner: R-J, Kolmogorov-Smirnov: K-S), the \(p\)-values were all higher than the significance level of 0.05, indicating that the experimental data follow the normal distribution law. Figure 8 shows the histogram of the average distribution of the BDS obtained from both breakdown tests (i.e., RT and ST), and the corresponding skewness and kurtosis values are provided in Table 4. According to [PERSON]. [PERSON] et al. (2018), this representation allows for the detection of potential anomalies in the distribution of the BDV across various voltage ranges. In this context, potential anomalies refer to outliers (or abnormal patterns) that may occur in the distribution of RT-BDS or ST-BDS. However, when considering the skewness and kurtosis values, it was observed that the epoxy/Al\({}_{2}\)O\({}_{3}\) micro-composites slightly deviates from a perfect normal distribution, where skewness and kurtosis should be 0 and 3, respectively. The experimental results show that the skewness values for RT-BDS and ST-BDS are close to 0, with values of 0.75 and \(-\)0.19, respectively. On the other hand, the skewness values deviate somewhat from the normal distribution, with RT-BDS at +0.80 and ST-BDS at \(-\)0.79. Additionally, since the peak kurtosis value is less than 1.4, this distribution can be considered slightly platykurtic, as noted in ([PERSON] et al., 2019; [PERSON] et al., 2019). There are various software options available for obtaining skewness and kurtosis. For practical reasons, most statistical software packages, such as R ([PERSON] et al., 2019) or SPSS (Statistical Package for the Social Sciences) ([PERSON] et al., 2019), are commonly used. In this study, skewness and kurtosis were calculated using the Python 3.9 software package ([PERSON] et al., 2019) and its statistical libraries.
## 4 Preparation of EEG Signals for Double Filtering and Classification: Preclassification Part
### EEG Signal Filtering Method for Nervous Tics Detection  
The next stage of preclassification was filtering the signal again. The high-pass filtering method was used again. The sampling frequency was \(f_{\mathrm{S}}=128\) Hz and the bandwidth was \(f_{\mathrm{B}}=60\) Hz. Figure 10 shows the final form of the refiltered signal. We can notice clearly distinguished increases in amplitude at the time of simulating nervous tics, which allowed for their effective classification.  
Equation (4) presents the method of data collection using the \(\mathbf{H}\) variable, in which the signals from 32 channels measured during the test were collected. Hence, the variable \(\mathbf{H}\) was a 32-row matrix with \(V\) columns (number of samples).  
\[\mathbf{H}_{(\mathbf{1}\dots\mathbf{V})}=\begin{bmatrix}\mathbf{Channel\ 1}\\ \vdots\\ \mathbf{Channel\ 32}\end{bmatrix}=\begin{bmatrix}\mathbf{Cz}(1\dots V)\\ \mathbf{Fz}(1\dots V)\\ \vdots\\ \mathbf{F8}(1\dots V)\\ \mathbf{F9}(2\dots V)\end{bmatrix}. \tag{4}\]  
Figure 10: The observation of first-filtered and second-filtered EEG signals.  
Figure 9: An example of the power spectrum estimation result for participants 7 and 8—left.  
Figures 11-14 effectively present the signal waveforms for each of the nervous tics. Graphics were collected from dedicated EmotivPRO software for the Epoc Flex Gel headset.  
By analyzing Figures 11 and 12, which illustrate the signals during which tics of the arm and shoulder muscles occurred, one can come to interesting conclusions. As you can see, this kind of neural tic caused an increase in amplitude on almost all electrodes. It was therefore distinctive, but did not affect the aspect from which part the artifact comes from. A similar waveform of the signal occurred for both the right and left parts.  
Figure 13 shows an example of a signal waveform with an artifact caused by a contraction nervous tic in the neck. You can immediately see its similarity with the results of the arm tic (Figures 11 and 12). This may indicate some difficulty in distinguishing them by the classification system. However, it could be concluded that the neck tic artifact was somewhat \"smoother\" and had a similar amplitude value on all channels.  
A completely different situation occurred for the eye blink tic signal (Figure 14). When this artifact was induced, the greatest electrical activity could be observed in the channels around the eyes.  
The next section effectively discusses the issues related to the classification part, i.e., the expert classification system based on artificial intelligence in the form of neural networks.  
Figure 11: An example of a nervous tic involving the lifting of the right shoulder (shoulder muscle tightening).  
Figure 12: An example of a nervous tic involving the lifting of the left shoulder (shoulder muscle tightening).
# A Novel Feeder Link Handover Strategy for Backhaul in LEO Satellite Networks
## 4 Problem Analysis and Strategy Design
### Handover Workflow  
Figure 4 illustrates the flow chart of the feeder link handover procedure. At first, the GSs monitor the periodic broadcast information sent by visible satellites and measure the received signal strength (RSS). Then, the GSs calculate the visible relationships and send the required handover information to the NCC, including RSS, visible relationships, and connection relationships. The NCC calculates the remaining service time and available satellite capacity based on Equation (18) for all satellites. Based on these values, the NCC figures out the handover utility values based on Equation (21) and builds a graph to solve the maximum flow problem with Algorithm 1. Then, the NCC sends the handover instructions to the corresponding satellites and GSs. The visible satellites release the old links and build new links to the GSs. At this step, a round of feeder link handover decisions is finished.