esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Algorithm Descriptions
### Feature Fusion and Recognition
#### 5.2.2 SVM (Support Vector Machine)  
SVM is a classification method based on statistical learning theory [32]. It adopts principles of structural risk minimization and solves problems like limited samples, nonlinearity and high-dimensional pattern recognition to a large extent. Its main thought can be expounded as that no matter whether samples are linear and detachable or not, input sample vector can be mapped to a high-dimensional space. Then, optimal linear classification surface can be solved in the new space. A high-dimensional space adopts kernel function, as shown in following formula.  
\[f(x)=\text{sgn}(\sum_{i=1}^{l}a_{i}y_{i}K(x,x_{i})+b) \tag{3}\]  
where \(K(x,\,x)\)is a kernel function and \(a_{i}\) is Lagrange multiplier. In this paper, \(k\)-fold cross validation is used to optimize classification results of SVM. Its basic idea is that original training data is divided into training data and test data, where \((k-1)n/k\) elements are selected from \(n\) sample as the training data and the left \(n/k\) acts as the test data to train SVM classifier, which is repeated for \(M\) times. Then, we can obtain the recognition rate for an average of \(M\) times, and get the final average recognition rate.
## 2 Results
### Analysis of Persister Cells  
#### 2.4.1 Induction of Persister Cells by the Stringent Response  
The persistence of _P. aeruginosa_ can be induced by amino acid starvation and subsequent induction of stringent response [34; 35]. For this purpose, the amino acid analog serine hydroxamate (SHX) was added to the M9 medium, and growth was monitored (Figure 5). As expected, in response to SHX addition, a growth arrest of _P. aeruginosa_ was observed.  
To further confirm the induction of the stringent response from the addition of SHX, we measured the transcript levels of the _relA_ and _spoT_ genes, which encode the key proteins of the stringent response regulating the (p)ppGpp alarmone levels in response to starvation [35]. In addition, the transcript level of the _lon_ (encoding lon protease) gene was tested since the corresponding protein is involved in arresting growth in favor of bacterial survival under starvation conditions [35]. The relative expression of the target genes was normalized to _rpoS_ as a reference gene. The stability of _rpoS_ under the experimental conditions was assessed based on Cohen's d [36; 37], showing a small effect size of 0.2, as illustrated in Figure S3. The transcript levels of _relA_, _spot_ and _lon_ increased approximately 2.53, 2.77 and 2.23-fold, respectively, upon SHX addition (Figure 6). This result corroborates that the experimental condition induced persister cells.  
Figure 2: Influence of illuminated chlorophyll (CHL) and polyethylenimine (PEI) on _P. aeruginosa_ planktonic cells after exposure to the red light for 1, 3, 5 and 24 h. The dose-response matrices show the eradication percentage (4 log\({}_{10}\) reduction) of the double serial dilution of CHL and PEI individually and in combination. ZIP (zero interaction potency) synergy score matrices depict the effect of PEI. The matrices were plotted using RStudio SynergyFinder R package. The results are an average of three biological replicates (exposure time 1–5 h) and six biological replicates (exposure time 24 h).  
Figure 4: Antibioticfilm activity of CHL and PEI on established colony biofilm. Error bars represent standard deviation of the mean (\(n=3\)). The photo represents the established _P. aeruginosa_ colony biofilm on a polycarbonate membrane on BHI agar (1: growth control, 2: CHL/ PEI 2000/100 \(\upmu\)mL\({}^{-1}\), 3: CHL/ PEI 1000/100 \(\upmu\)mL\({}^{-1}\), 4: sterility control, 5: CHL/PEI 500/100 \(\upmu\)mL\({}^{-1}\), 6: CHL/PEI 250/100 \(\upmu\)g mL\({}^{-1}\)). The untreated sample was normalized at 100%. * *-t-test: \(p\)-value < 0.05.  
Figure 3: Influence of illuminated and unilluminated chlorophyllin (CHL) and polyethyleneinimine (PEI) on _P. aeruginosa_ biofilm. The dose–response matrices show the detachment percentage of the double serial dilution of CHL and PEI individually and in combination. ZIP (zero interaction potency) synergy score matrices depict the synergistic and antagonistic effect between CHL and PEI. The matrices were plotted using RStudio SynergyFinder R package. The results are an average of eight biological replicates.
## 4 Results
### Cross-Dataset MultiClass Classification
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **Class** & **Precision** & **Recall** & **F1 Score** & **Accuracy** & \
\begin{tabular}{l} **Training** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{l} **Prediction** \\ **Time (s)** \\ \end{tabular} \\ \hline \multirow{6}{*}{Decision Tree} & 0 & 0.00 & 0.00 & 0.00 & & & \\  & 1 & 0.39 & 1.00 & 0.56 & & & \\  & 2 & 0.90 & 0.87 & 0.89 & & & \\  & 3 & 0.00 & 0.00 & 0.00 & & & \\  & 4 & 0.00 & 0.00 & 0.00 & 0.578093 & 350.40 & 112.76 \\  & 5 & 0.70 & 0.92 & 0.80 & & & \\  & 6 & 0.00 & 0.00 & 0.00 & & & \\  & 7 & 0.00 & 0.00 & 0.00 & & & \\  & 8 & 0.57 & 0.68 & 0.62 & & & \\ \hline \multirow{6}{*}{Naive Bayes} & 0 & 0.34 & 0.96 & 0.50 & & & \\  & 1 & 0.85 & 0.54 & 0.66 & & & \\  & 2 & 0.87 & 1.00 & 0.93 & & & \\  & 3 & 0.73 & 0.52 & 0.61 & & & \\  & 4 & 0.51 & 0.89 & 0.65 & 0.721787 & 257.32 & 85.67 \\  & 5 & 1.00 & 0.85 & 0.92 & & & \\  & 6 & 1.00 & 1.00 & 1.00 & & & \\  & 7 & 0.57 & 0.27 & 0.37 & & & \\  & 8 & 0.94 & 0.47 & 0.63 & & & \\ \hline \multirow{6}{*}{Logistic Regression} & 0 & 0.58 & 0.57 & 0.58 & & & \\  & 1 & 0.91 & 0.91 & 0.92 & & & \\  & 2 & 1.00 & 1.00 & 1.00 & & & \\  & 3 & 0.72 & 0.45 & 0.56 & & & \\  & 4 & 0.86 & 0.96 & 0.91 & 0.873166 & 569.23 & 296.55 \\  & 5 & 1.00 & 1.00 & 1.00 & & & \\  & 6 & 1.00 & 1.00 & 1.00 & & & \\  & 7 & 0.62 & 0.64 & 0.63 & & & \\  & 8 & 0.76 & 0.73 & 0.75 & & & \\ \hline \multirow{6}{*}{KNN} & 0 & 0.98 & 1.00 & 0.99 & & & \\  & 1 & 0.99 & 0.99 & & & & \\  & 2 & 1.00 & 1.00 & 1.00 & & & \\  & 3 & 0.99 & 1.00 & 1.00 & & & \\  & 4 & 0.99 & 1.00 & 1.00 & 0.986780 & 2256.54 & 1542.26 \\  & 5 & 1.00 & 1.00 & 1.00 & & & \\  & 6 & 1.00 & 1.00 & 1.00 & & & \\  & 7 & 0.91 & 0.96 & 0.94 & & & \\  & 8 & 0.98 & 0.94 & 0.96 & & & \\ \hline \multirow{6}{*}{Gradient Boost} & 0 & 1.00 & 1.00 & 1.00 & & & \\  & 1 & 1.00 & 1.00 & 1.00 & & & \\  & 2 & 1.00 & 1.00 & 1.00 & & & \\  & 3 & 1.00 & 1.00 & 1.00 & & & \\  & 4 & 1.00 & 1.00 & 1.00 & 0.999840 & 1864.25 & 5421.87 \\  & 5 & 1.00 & 1.00 & 1.00 & & & \\  & 6 & 1.00 & 1.00 & 1.00 & & & \\  & 7 & 1.00 & 1.00 & 1.00 & & & \\  & 8 & 1.00 & 1.00 & 1.00 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of multiclass classification of best 20 features.
# Fast Path Generation Algorithm for Mobile Robot Navigation Using Hybrid Map
## 2 Related Works
### Mapping
Furthermore, the robot may struggle with precise localization and obstacle avoidance when relying solely on topological maps, as they do not provide detailed spatial resolution. Constructing an accurate topological map also requires robust feature detection algorithms, which may not perform well in unstructured or highly dynamic environments. Nevertheless, these maps remain a valuable tool for high-level navigation, particularly when combined with other methods to address their limitations.
## 3 Results
### Result Summary with MLP-MNF and LSTM-MNF  
The algorithm used for this result was applied to the YD 2, YD 3, and YD 4 DMAs. Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and R-Squared were used as evaluation criteria. As shown in Table 3, the average absolute ratio errors for each DMA showed good predictive power as a result of predicting through YD 2 (6.231%), YD 3 (3.682%), and YD 4 (3.806%). The R-squared is the degree of contribution of the parameters with significant difference in each subgroup. R\({}^{2}\geq\) 0.7, 0.7 \(>\) R\({}^{2}\geq\) 0.5, 0.5 \(>\) R\({}^{2}\geq\) 0.3, and 0.3 \(>\) R\({}^{2}\) were considered high, moderate, low, and very low values, respectively [14]. The reason for the relatively low R-squared result of YD 3 might be that it was highly variable and noisy compared to YD 2 and YD 4.
## 2 Related Work
### System Architecture  
In existing works, there are three main system architectures: Centralized architecture, distributed architecture, and independent architecture. In centralized architecture [14; 15; 16; 28], a trusted third party (a server, called TTP) is introduced into the system to complete anonymity. However, once the TTPis untrusted or compromised, a user's information may completely leak, since the TTP has all her information. Besides, the anonymous server is a bottleneck of system performance.  
In the distributed architecture [29; 30; 31], the system completes anonymity without the TTP; this is called TTP-free. In [29; 30], \(k\) peer users communicate over a short distance and form a collaborative group to achieve anonymity. However, these users are required to exchange query information and to be honest and trustworthy. In addition, sometimes it may take a long waiting time for \(k-1\) users joining for completing anonymity. In [31], authors introduced an anonymous group re-clustering method to solve the problems of a long waiting time completing anonymity and anonymous failure caused by insufficient users and to improve the success rate of clustering.  
To overcome the drawbacks of the TTP and the TTP-free methods. The methods in [17; 18] presented the independent architecture. In [17], the authors put forward DLS and enhanced DLS to complete anonymity. This method adopted independent architecture and can effectively solve the shortcomings of the above two system architectures. In [18], the authors presented TTcloak that uses dummy techniques to achieve \(k\)-anonymity of location and \(l\)-diversity of query content.
## 5 Conclusions  
An analytical study based on linear theory has been presented that considers the development of instabilities that arise when a thin layer of a stably stratified fluid is heated from above for the Marangoni-Benard problem. Particular focus was directed towards the value of the critical Marangoni number and to the influence of the lower boundary condition.  
The stress-free model modifies the predicted critical Marangoni number to depend on the Galileo number (gravity effects), but reduces to \(M_{crit}=12\) under microgravity. It was not possible to find the cut-off wavenumber analytically as it requires solution of the dispersion relation to \(O(k^{2})\) in small wavenumber \(k\), but numerical solutions of the secular equation indicates, for all parameters tested, that \(k_{cutoff}<\sim\)0.05. This quantitative verification of the assumptions of KdV-KSV theory [8] under the stress-free boundary conditions leads one to conclude that any deficiencies of the theory must lie in the lower boundary condition. The analytic study of the long wave solutions to the secular equation for the no-slip model led to the surprising discovery that there are no infinitely long propagating modes. The three modes identified as \(k\to 0\) are a neutral pure temperature mode, a damped viscous (pure velocity) mode, and a damped thermal (pure temperature) mode. The temperature and viscous modes both decay with increasing \(k\), but the thermal mode increases in growth rate with small \(k\). The prediction of an intersection point is computed by [PERSON] continuation of the nonlinear solution of the secular equation. At the intersection of the two modes, the crossover point, there is co-existence of two propagating modes with the two stationary long modes. At higher wavenumbers, only the propagating modes exist. These modes are mapped by parameter space continuation in wavenumber, and a parametric study by continuation in the physical parameter space is made. Further developments in this research area could be facilitated by full numerical simulations.  
Figure 6: Scatter plot of \(c_{0}=\sqrt{(G+M)\Pr}\) versus \(s_{crit}/k_{crit}\) for the 8000 critical parameter sets mapped. This suggests that \(c_{0}\) is the correct order of magnitude and a fair approximation for the group velocity of the critical mode regardless of the parameter set in the range mapped. Dotted lines have slope \(\frac{2}{3}\) and \(\frac{1}{2}\).  
**Acknowledgments:** This work was supported by funding from the Engineering and Physical Sciences Research Council (EPSRC) grant EP/I019790/1 on Microbubble cloud generation from fluidic oscillation: underpinning fluid dynamics. Funding was provided from the University of Sheffield for covering the costs to publish in open access.  
**Author Contributions:** [PERSON] and [PERSON] contributed equally to the theory and computation.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 5 Experiments
### Ablative Analysis
#### 5.4.1 The Effectiveness of Proposed Blocks on Various Captioning Models  
We tried to explore the effectiveness of Candidate Sentences Selection, Semi-Fixed Word Embedding on various captioning models, i.e., UpDown [19] and XLAN [4], and insert each block into these approaches. The experiment was performed on the Nocapsdataset [45], where the images in \"In-domain\" only contain frequently seen objects, the image in \"Near-domain\" contain frequently seen, rarely seen and never-seen objects, the images in \"Out-Of-domain\" only contain rarely seen or never seen objects. To illustrate the impact of Semi-Fixed Word Embedding more clearly, we also compare it with the commonly used glove initialization.  
From the results shown in Table 2, we can see the efficacy of each block. Comparing the results of rows 1, 2, and 3 with other rows shows that CBS reduces the score of in-domain, but significantly improves the scores of out-of-domain, indicating that CBS is necessary for the novel object captioning task.  
The comparison of rows 4/5, 6/8, and 7/9 shows the effectiveness of Candidate Sentences Selection. The improvement of CIDEr and SPICE on in-domain/near-domain/out-of-domain is 4.93/4.49/5.28 and 0.64/0.34/0.69 for rows 4 and 5, 4.86/5.76/8.23 and 0.27/0.5/0.54 for rows 6 and 8, 3.23/5.04/7.12 and 0.71/0.44/0.51 for rows 7 and 9, respectively. It is clear that Candidate Sentences Selection can effectively solve the problem of semantic mismatch, and therefore, the accuracy of generated sentences is significantly improved.  
The effectiveness of Semi-Fixed Word Embedding is shown by the comparison of rows 6/7. The improvement of CIDEr and SPICE on in-domain/near-domain/out-of-domain is 0.79/1.2/0.81 and -0.01/0.25/0.34 for rows 6 and 7. The Semi-Fixed Word Embedding outperforms the glove in the near-domain and out-of-domain, showing that the Semi-Fixed Word Embedding assists the captioning model in understanding the meaning of the novel words. In particular, the SPICE metrics reflect the semantic matching of the described object and relationship. The most significant improvement on the out-of-domain shows that Semi-Fixed Word Embedding works better for novel words.  
Rows 8/9 show the result of using Semi-Fixed Word Embedding or glove on the basis of Candidate Sentences Selection, respectively. The metrics of SPICE are improved in all domains, which means that the semantic matching can be improved by Semi-Fixed Word Embedding.  
When replacing the captioning model with the XLAN [4], the same results can be observed, indicating the effectiveness and robustness of the proposed blocks.
## 5 Concluding Remarks  
In the present work the post-seismic relaxation is viewed as a superposition of several different relaxation times, each one activated in different mesoscopic domains of the macroscopic relaxed seismic volume. The macroscopic relaxation that is observed is a result of the superposition of different relaxation times on the microscopic-mesoscopic scale. This approach is a new one since most of the models used to describe post-seismic relaxation are based on the assumption of a single relaxation time estimated for each relaxation mechanism proposed. The slow thermodynamic recovery of the post-seismic relaxation could be explained by a physical model based on the Arrhenius equation and an appropriate distribution of relaxation times.  
In summary, we can state that, in the present work:  
1. A multiscale post-seismic relaxation mechanism based on the existence of a distribution in relaxation time is presented.  
* Assuming an Arrhenius dependence of the relaxation time with the uniform distributed activation energy in a mesoscopic scale, we conclude there is a generic logarithmic type relaxation on a macroscopic scale.
* The hierarchically constrained dynamics model could be used to understand the evolution of post seismic relaxation.
* The model was applied in the case of 2015 Lefkas (Greece) Mw6.5 earthquake, where cGNSS time series were recorded in a station located in the vicinity of the epicentral area. The application of the present approach to the Lefkas event fits the observed displacements, implying a distribution of relaxation times in the range \(\tau_{min}\approx 3.5\) days to \(\tau_{max}\approx 350\) days.  
These results offer a stimulating finding for further studies that will investigate postseismic deformation timeseries for other strong earthquakes, which may provide new insights into the underlying dynamics of earthquake relaxation processes, along with the possible parameters that influence this behaviour (i.e., fault type, local conditions, regional stress field, etc.).  
Conceptualization, F.V.; methodology, F.V.; software, F.V. and V.S.; validation, F.V.; data curation, V.S.; writing--original draft preparation, F.V.; writing--review and editing, F.V. and V.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Operational Program \"Competitiveness, Entrepreneurship and Innovation\" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).  
Data Availability Statement: Data are available in www.geodesy.gein.noa.gr (accessed on 30 December 2020).  
We acknowledge support of this study by the project \"HELPOS--Hellenic Plate Observing System\" (MIS 5002697), which is implemented under the action \"Reinforcement of the Research and Innovation Infrastructure\", funded by the Operational Program \"Competitiveness, Entrepreneurship and Innovation\" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).  
The authors declare no conflict of interest.
# Enhanced Tuna Detection and Automated Counting Method Utilizing Improved YOLOv7 and ByteTrack
## 3 Research Methods
### Tuna Target Tracking Counting Based on ByteTrack Algorithm
#### 3.2.2 Area-Specific Tracking Counting Method  
When using ByteTrack for tuna target tracking, the final count is affected by overestimation due to occlusion by crew movements and the back-and-forth jumping of tuna on the deck. Therefore, all tuna IDs generated by the ByteTrack algorithm are not directly used as the statistical value for the total count of tuna.  
To address the issue of duplicate counting mentioned above, a specific area tracking counting method has been devised. As shown in Figure 8, at the entry point where a tuna is captured and enters the fishing vessel, a rectangular box defined by the coordinates of its top-left corner \((x_{a},y_{a})\) and bottom-right corner \((x_{b},y_{b})\) is utilized to delineate the specific counting area. The dimensions of this rectangular box are 365 \(\times\) 430. The counting results are displayed in the top-left corner of the video.  
Figure 6: Flowchart of ByteTrack algorithm.  
The process of the specific area tracking and counting method is shown in Figure 9. The specific area tracking counting method begins by initializing the counter. Then, the tracking algorithm is executed to obtain the detection boxes of tuna. Subsequently, the count is updated based on the first appearance of the center coordinates of the tuna detection boxes within the specific area. The addition of this specific area effectively mitigates the impact of target occlusion and irregular movements, reducing the issue of repetitive counting.  
Given the minimal worker activity near the water gate, incorporating a specific area counting method in this location can effectively reduce the problem of target occlusion. Additionally, since tuna exhibit less erratic movement in the area near the water gate before entering the deck, incorporating a specific area counting method in this location can further reduce the issue of double counting.  
Figure 8: Specific tracking count areas for tuna. The box is the current target detection box, type abacore and number 1.  
Figure 7: Example of target tracking results. The green box is the current target detection box, type abacore and number 1.
# Hierarchical Self-Supervised Learning for Knowledge-Aware Recommendation
## 5 Experiments
### Benefits of HKRec (RQ3)  
Effective utilization of the KG offers a solution to mitigate the challenges of the cold-start and long-tail issues for recommender systems. In this subsection, we verify the performance of HKRec in addressing these challenges.  
Cold-start recommendation. We divide the users in the Alibaba-iFashion dataset into five groups based on the number of interactions. The lower the group number, the fewer interactions with the user. As can be seen, the experimental results regarding Recall@20 and NDCG@20 are illustrated in Figure 4. Notably, even under cold-start scenarios where user interaction data are limited, the HKRec model consistently demonstrates superior recommendation performance compared to all baseline models. These results emphasize the effectiveness of the hierarchical self-supervised learning strategy in providing enhanced solutions for addressing the cold-start challenge.  
Long-tail item recommendation. To evaluate the model's efficacy in mitigating the long-tail issue, we divided the datasets into five groups with different interaction density degrees of items. Each group maintains an equal item count while witnessing a progressive rise in interaction density. Figure 5 illustrates the empirical results for our HKRec and several state-of-the-art knowledge-aware recommendation baselines. By distilling the semantic relatedness under item contexts, HKRec can effectively handle the imbalances present in long-tail distributions. Overall, the experimental result empirically shows that HKRec not only captures popular items but also understands the fewer interaction items, leading to improved performance in addressing the long-tail issue.  
Figure 3: Hyperparameter sensitivity study of HKRec with respect to different connection masking rates, feature masking rates, and node dropout rates.
# Research Trends in Collaborative Drones
## 7 Networked Drones Using Cellular Technologies
### Problem Description  
Research issues that stem from BVLOS include:  
1. Low latency connections for drone command-and-control.
2. Interference mitigation due to altitudes of drones.
3. Cooperative drone group communications.