esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Feature Fusion  
Prior studies [12; 13; 14; 15; 16] showed that the high-resolution shallow features in DCNN provide more information about location and detail but offer less semantic understanding. On the contrary, deeper features carry richer semantic information while being less perceptive to finer details. Feature fusion techniques have been widely adopted in computer vision to enhance model performance, particularly through a combination of deep and shallow features to form pyramidal features. In object detection, networks such as YOLOv3 [17] or RetinaNet [18] employ feature pyramid networks (FPN) [19] for feature fusion. Two common fusion strategies have been adopted for semantic segmentation tasks, namely, high-resolution net (HRNet) [20] and FPN. They typically involve upsampling to align feature sizes, leading to additional parameters and increased computational complexity. However, challenges arise when applying feature fusion to image classification in a similar way to those in object detection and semantic segmentation. Specifically, the method first obtains deep feature maps with high-level semantic information but less detail by layer-by-layer convolution. This is followed by upsampling to match the sizes of deep and shallow feature maps. These feature maps are then fused with corresponding shallow feature maps that contain low-level semantic information and richer details. Finally, the fused features are flattened into a one-dimensional vector and fed into the fully connected layer for prediction. This approach significantly increases the number of parameters of the fully connected layer, which can account for 80% of the total network parameters. While the increased parameter cost is worthwhile in object detection and semantic segmentation tasks where precise location information is vital in final prediction, image classification primarily focuses on category information. In classification tasks, semantic features play a key role, with high-level semantic features being particularly important. On the other hand, low-level semantic features are relatively less effective and are used only as supplementary information. Therefore, DCNNs typically utilize features extracted from the last convolutional layer which contains high-level semantic features for prediction in classification. But only a small portion of shallow features containing low-level semantic and location information contributes to the final results. It is not cost-effective to introduce a large number of parameters to leverage the information from these shallow features that have far less impact on the final results. Hence, the key challenge in feature fusion for image classification networks lies in the effective utilization of shallow feature information while minimizing the number of parameters introduced by feature fusion.
# A Survey on Swarm Robotics for Area Coverage Problem
## 3 Algorithms and Methods for Swarm Robotics
### Metaheuristic Algorithms-Swarm Intelligence  
In the field of swarm robotics, metaheuristic algorithms, especially those based on swarm intelligence, are crucial for covering large areas, and they have recently become powerful instruments for solving complicated issues. In order to create effective and adaptable tactics for robotic swarms, these algorithms take cues from the cooperative behaviour seen in natural systems, like ant colonies, beehives, and bird flocks. By encouraging decentralised decision making, the metaheuristic method allows swarm robots to jointly traverse large areas.
# The Use of the Internet of Things in the Distribution Logistics of Consumables
## 3 Results
### Consumable Dispensing System before the Introduction of IoT
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Price per** & **1–12/2022** & **Total Value** & **1–6/2023** & **Average/Month** & **Monthly** \\  & **Item (EUR)** & & **2022 (EUR)** & & & **Consumption in** \\ \hline Cutting gloves A4587 size: 6 & 4.19 & 2526 & 10,584 & 1312 & 213 & 893 \\ \hline Cutting gloves A4587 size: 8 & 4.19 & 10,874 & 45,562 & 5314 & 899 & 3768 \\ \hline Cutting gloves A4587 size: 10 & 4.19 & 12,311 & 51,583 & 6580 & 1050 & 4397 \\ \hline ESD gloves C 1283 size: 6 & 2.32 & 5820 & 13,502 & 2714 & 474 & 1100 \\ ESD gloves C 1283 size: 8 & 2.32 & 8412 & 19,516 & 5478 & 772 & 1790 \\ \hline ESD gloves C 1283 size: 10 & 2.32 & 315 & 731 & 156 & 26 & 61 \\ \hline Safety Glasses R41 & 3.17 & 3814 & 12,090 & 1796 & 312 & 988 \\ \hline Respiratory 9322+ & 2.98 & 12,965 & 38,636 & 7200 & 1120 & 3338 \\ \hline Transparent tape 50 mm & 7.32 & 2478 & 18,139 & 1547 & 224 & 1637 \\ \hline High visibility tape 25 mm (Yellow) & 9.45 & 1518 & 14,345 & 941 & 137 & 1291 \\ \hline Marker pen (black) & 2.13 & 312 & 665 & 205 & 29 & 61 \\ \hline Marker pen (red) & 2.13 & 38 & 81 & 24 & 3 & 7 \\ \hline Marker pen (white) & 2.13 & 15 & 32 & 9 & 1 & 3 \\ \hline Total & & & 225,466 & & & 19,335 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Items consumed at SkyBlue. The results of the previous research were discussed with the management of SkyBlue and, based on the discussion, the basic requirements that must be taken into account when designing the solution were defined:  
* Products must be available to employees at the workplace, so that there are no transfers of employees. * Products must be available to employees 24/7. * The disbursement system must be simple for employees. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Maximum** & **Minimum** & **Average** & **Median** \\  & **(Minutes)** & **(Minutes)** & **(Minutes)** & **(Minutes)** \\ \hline Request for the provision of consumables & 7 & 2.12 & 3.54 & 2.68 \\ \hline Approval of the application by the superior & 2 & 1.04 & 1.54 & 1.12 \\ \hline Uploading a request to ERP & 3 & 0.95 & 1.42 & 1.29 \\ \hline The way to the central warehouse & 14 & 2.54 & 5.47 & 3.45 \\ \hline Picking up goods & 4 & 0.45 & 1.12 & 0.95 \\ \hline The way to the workplace & 12 & 2.47 & 5.89 & 3.84 \\ \hline Total & 42 & 9.57 & 18.98 & 13.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Time to dispense consumables without IoT. Figure 5: Consumable dispensing process (internal resource). It is necessary to minimize the need for other employees (storekeepers, buyers) in the entire process of issuing. * Records of issued items must be personalized (preferably up to the employee). * Expenditure must be subject to claims so that costs are not exceeded.
## 3 Results and Discussion  
### Vapour Pressures and Vaporisation/Sublimation Enthalpies of Alkylbenzenes  
The experimental vapour pressures \(p_{i}\) measured in this work (see Table S1) were fitted with the following equation [15; 16]:  
\[R\times ln(p_{i}/p_{ref})=a+\frac{b}{T}+\Delta^{\mathrm{g}}_{\mathrm{crJ}}C^{ \mathrm{o}}_{\mathrm{p,m}}\times ln\left(\frac{T}{T_{0}}\right) \tag{3}\]  
where \(R\) = 8.31446 J\(\cdot\)K\({}^{-1}\cdot\)mol\({}^{-1}\) is the molar gas constant, the reference pressure \(p_{ref}=1\) Pa, \(a\) and \(b\) are adjustable parameters, the arbitrary temperature \(T_{0}\) applied in Equation (3) was chosen to be \(T_{0}\) = 298.15 K and \(\Delta^{\mathrm{g}}_{\mathrm{crJ}}C^{\mathrm{o}}_{\mathrm{p,m}}\) is the difference in the molar heat capacities of the gas and the crystal (or liquid) phases, respectively (see Table S2). The vapour pressures measured at different temperatures, \(T\), in this work were used to derive the enthalpies of sublimation/vaporisation using the following equation:  
\[\Delta^{\mathrm{g}}_{\mathrm{crJ}}H^{\mathrm{o}}_{\mathrm{m}}(T)=-b+\Delta^{ \mathrm{g}}_{\mathrm{crJ}}C^{\mathrm{o}}_{\mathrm{p,m}}\times T \tag{4}\]  
Sublimation entropies at temperatures \(T\) were also derived from the temperature dependences of the vapour pressures using Equation (5):  
\[\Delta^{\mathrm{g}}_{\mathrm{crJ}}S^{\mathrm{o}}_{\mathrm{m}}(T)=\Delta^{ \mathrm{g}}_{\mathrm{crJ}}H^{\mathrm{o}}_{\mathrm{m}}/T+R\times ln(p_{i}/p^{ \mathrm{o}}) \tag{5}\]  
with \(p^{\mathrm{o}}\) = 0.1 MPa. The original absolute vapour pressures available in the literature were also treated using Equations (3)-(5) to evaluate sublimation/vaporisation enthalpies at 298.15 K (see Tables 1-4). Uncertainties in the literature results have been re-assessed. They include uncertainties from the fitting equation and uncertainties from temperature adjustment to \(T\) = 298.15 K.  
The vaporisation/sublimation enthalpies, which are derived from the experimental vapour-pressure-temperature dependencies according to the Clausius-Clapeyron relation, are referenced to the average temperature, \(T_{\mathrm{av}}\), of the examined interval. For engineering calculations, these enthalpies should be adjusted to the reference temperature \(T\) = 298.15 K. Adjustment details are given in ESI. The enthalpies of vaporisation evaluated in this work are given in Table 1 for the iso-propyl-substituted benzenes and in Table 2 for the tert-butyl-substituted benzenes. The enthalpies of sublimation of tert-butyl-benzenes are given in Table 3.
# Cleaning of Phototrophic Biofilms in a Show Cave: The Case of Tesoro Cave, Spain
## 4 Conclusions  
The phototrophic biofilms covering the speleothems, walls and ground in Tesoro Cave, Rincon de la Victoria, Spain, were successfully cleaned using a protocol involving the mechanical removal of dense biomass, treatment with hydrogen peroxide and, when needed, it was followed by sodium hypochlorite. To guarantee long-term efficiency, the old lighting system should be replaced with a new design with monochrome or LED lamps to be located in the driest area of the cave, or alternatively, implementing visits with electric lanterns, which is possible due to the easy walkable cave trail.  
Conceptualization, C.S.-J.; investigation, V.J., M.H.-M., M.A.R.-C. and C.S.-J.; cleaning and restoration, F.R., C.A. and J.A.; writing--original draft preparation, C.S.-J.; writing--review and editing, C.S.-J. All authors have read and agreed to the published version of the manuscript.  
The research and cave cleaning were funded by Rincon de la Victoria City Hall.  
Not applicable.  
Figure 11: (**A**) Biofilms in Sala del Lago (T1) before cleaning. (**B**) After cleaning. (**C**) Biofilms in Sala del Aguila (T3) before cleaning. (**D**) After cleaning. Cleaning consisted of mechanical removal of biofilms and treatment of the residues with hydrogen peroxide.
# Estimating Destination of Bus Trips Considering Trip Type Characteristics
## 5 Results
### Classification Result of the Trip Types  
We classified the bus trips made in April 2020 (210,920,089 trips/month) in the Seoul metropolitan area into four types (continuous, non-continuous and recurrent, non-continuous and non-recurrent, and route pattern). According to the analysis results, approximately 67.68% of the bus trips are classified as continuous travels. Table 4 shows the result of the analysis.  
Non-continuous travels are classified into recurrent and non-recurrent travels. We utilized the pattern data (weekdays/weekends) built based on the one-year boarding/alighting OD data for each card ID. As a result of classifying the trip type, 13,377,146 (6.34%) trips were counted as non-continuous recurrent travels. When no candidate stops of residence and workplace were selected, the non-continuous travels were classified as non-recurrent travels (19.80%).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Division** & **Number of Data** & **Ratio** \\ \hline Continuous travel & 142,743,224 & 67.68\% \\ \hline \multirow{2}{*}{Non-continuous} & recurrent travel & 13,377,146 & 6.34\% \\ \cline{2-3}  & non-recurrent travel & 41,760,849 & 19.80\% \\ \hline Route pattern & 13,038,870 & 6.18\% \\ \hline Total & 210,920,089 & 100.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Result of travel type classification (metropolitan area, as of April 2020).  
Regarding route patterns, the one-year OD patterns for each route ID were constructed for all trips that could not be estimated as continuous/non-continuous trips, and the alighting stop percentages were calculated based on the boarding stop to assign the alighting stop based on the percentage. Here, we considered the moving direction on the route and assigned the alighting stop based on the probability of the corresponding stop after the route stop. Accordingly, 13,038,870 (6.18%) trips were counted as route patterns.
# Consensus Tracking Control of Multiple Unmanned Aerial Vehicles Subject to Distinct Unknown Delays
## 3 Control Strategy  
We consider a leader-follower strategy for the formation control of the MAS. The system has the scheme shown in Figure 1, in which it is considered that the quadrotors are underactuated systems. For all agents, the coordinates \(z_{i}(t)\), \(\phi_{i}(t)\), \(\theta_{i}(t)\), and \(\psi_{i}(t)\) can be controlled directly and individually, without sharing information between the vehicles. At the same time, the translational movement is conducted in a coordinated way.  
For the translational subsystem, it is considered that the \(x_{i}(t)\) coordinate can be controlled indirectly by the \(\theta_{i}\) coordinate, and the \(y_{i}(t)\) coordinate is controlled indirectly using the coordinate \(\phi_{i}(t)\). In this work, we address the problem of a possible actuator time-varying delay affecting the motion in the \(x-y\) plane. Therefore, the coordinates \(\theta_{i}\) and \(\phi_{i}\) are controlled by schemes based on state estimation to isolate the time-varying delay. Specifically, a coordinated control of the translational movement based on a leader-follower scheme is considered. Thus, a consensus tracking control based on an unknown input observation is proposed, for the follower agents to maintain formation flight, with the leader agent's estimated translational position as the reference. In contrast, a disturbance observer-based control is formulated for the leader agent, to follow a desired trajectory despite the presence of time-varying delays.
## 2 Materials and Methods  
### Ellipse-Based Spatial Interpolation and Image Reconstruction  
Assuming that stress wave propagation in wood follows a straight path, a certain propagation ray can represent the velocity and path of a stress wave between a pair of sensors. As shown in Figure 1a, after the acquisition of stress wave velocity signals, the velocity matrix which records the propagation velocity between any pair of sensors can be visualized as a graph of propagation rays. The red ray indicates that the wave velocity is slow, which means that the stress wave passes through the defect area. In contrast, the green ray indicates that the wave velocity is fast, which means that the stress wave passes through a healthy area. Figure 1b shows the gridding of the imaging area, which is commonly applied in existing image reconstruction algorithms for stress wave tomography. The quality of the reconstructed image depends on the correct estimation of grid values. Based on the visualization of propagation rays and the grid graph, an ellipse-based spatial interpolation (EBSI for short) method for image reconstruction has been developed in recent years [25; 31; 32]. As shown in Figure 1c, each ray affects the surrounding area (called affected zone) and the shape of the affected zone is elliptical. Then, the value of the grid cells in the affected zone is equal to the value of the corresponding ray. If a certain grid cell is in several affected zones simultaneously, the value of this grid cell will be determined by the value of those affected zones using different strategies.
## 2 System Architecture
### Information Management System
#### 2.2.2 Modality and Channel Module  
The Modality and Channel module defines the suitable mode and adequate style of information presentation to the driver in order to minimize distraction effects. For example, in visually challenging situations due to high traffic density and/or poor weather conditions, visual presentation of information should be avoided. Furthermore, under illuminated conditions, visual messages are hard to perceive, and, therefore, auditory warnings should be preferred. Equivalently, in noisy environments, visual presentation should be preferred, as audio information could be missed by the driver. Similarly to Filter and Queue, this module uses a truth table to select the most suited modality in which the application requests should be provided to the driver on the basis of current DVE state [37]. A channel selector is also involved to choose the output channel, solving conflicting channel requests through preemption of high-priority requests over low-priority requests.
## 4 Experimental Analysis
### Experimental Analyses
#### 4.3.3 Visualisation Analysis  
Figure 13 shows the classification confusion matrices of MARN under the four datasets. The horizontal and vertical coordinates of the confusion matrix represent the true and predicted labels, respectively, and diagonal numbers represent the prediction accuracies of two labels. Taking the randomly divided validation set as example, it can be seen that label2 has the lowest accuracy in \(\mathrm{D_{A}}\) and \(\mathrm{D_{C}}\), label4 has the lowest accuracy in \(\mathrm{D_{B}}\) and \(\mathrm{D_{D}}\), and the other labels are able to achieve full classification. The reason for the individual misclassification of labels is that some sample images are more similar to other categories, but overall MARN has excellent fault classification capabilities.  
The method of t-SNE dimensionality reduction (Figure 14) can be used to visualise the classification effect of the MARN.  
In the left half of each subgraph is the original feature distribution of each dataset (training set), and the right half is the feature classification result of the fully connected layer after training by the MARN (different colours represent different fault categories). From the figure, it can be seen that the dataset is haphazardly distributed in the feature space before training, and after training, the fault features of each category become uniformly clustered. There is no significant overlap between different categories, so the MARN has a good classification effect on balanced/unbalanced fault data.  
Figure 12: Training curves for different attention mechanisms: (**a**) Validation accuracy; (**b**) training loss.
###### Abstract
Oppositely to TD, Q-learning estimates the state-action values \(Q(S_{t},A_{t})\), which represent how good it is to perform action \(A_{t}\) in the state \(S_{t}\). Particularly, these approximations are meant to converge to \(q_{*}(S_{t},A_{t})\). Equation (1) describes Q-learning's update rule. Algorithm 1 describes the solution of the Q-learning scheme. All state-action value pairs are initialized with a random value in a range matching the reward signal. Then, the learning procedure is divided into episodes, which are sub-sequences of agent-environment interactions. In this paper, an episode will be conceived as a simulation run. Next, an initial state \(S\) and action \(A\) are selected for each episode by using an action selection method like \(\epsilon\)-greedy. An \(\epsilon\)-greedy selection chooses the best action \(1-\epsilon\) percent of the time and a random one otherwise. Having taken action \(A\), a reward \(R\) and new state \(S^{\prime}\) are observed. Subsequently, the state-action value \(Q(S,A)\) is updated with Equation (1) and \(S\) is replaced with \(S^{\prime}\). If \(S\) is a terminal state, then the episode ends, and a new one is built. ```
Input:\(\alpha\in(0,1]\), the learning rate \(\gamma\in(0,1]\), the discount rate \(\epsilon\in(0,1]\), an small value for performing an \(\epsilon\)-greedy action selection Output:\(Q\), a set of state-action value pairs  Initialize \(Q(s,a)\) for all states and corresponding actions; foreachepisodedo  Initialize state \(S\); foreachstep of the episodedo  Choose \(A\) from \(S\) derived from \(Q\) (e.g., \(\epsilon-greedy\));  Take action \(A\), observe \(R,S^{\prime}\); \(Q(S,A)\gets Q(S,A)+\alpha[R+\gamma\max_{a}Q(S^{\prime},a)-Q(S,A)]\); \(S\gets S^{\prime}\); if\(S\) is a terminal statethen  break; return\(Q\);
```  
**Algorithm 1**Q-learning for estimating an optimal policy \(\pi_{*}\)[16]  
\[Q(S_{t},A_{t})\gets Q(S_{t},A_{t})+\alpha\Big{[}R_{t+1}+\gamma\max_{a}Q(S_{ t+1},a)-Q(S_{t},A_{t})\Big{]} \tag{1}\]  
The potential of combining Reinforcement Learning with ABMS for modeling human behavior during pandemics, specifically focusing on COVID-19, is evident. Consequently, it is essential to gain insights into the dynamics of COVID-19 and its interplay with individual behavior within semi-enclosed communities like university campuses.
# Comparison of Modern Deep Learning Models for Speaker Verification
###### Abstract  
This research presents an extensive comparative analysis of a selection of popular deep speaker embedding models, namely WavLM, TitaNet, ECAPA, and PyAnnote, applied in speaker verification tasks. The study employs a specially curated dataset, specifically designed to mirror the real-world operating conditions of voice models as accurately as possible. This dataset includes short, non-English statements gathered from interviews on a popular online video platform. The dataset features a wide range of speakers, with 33 males and 17 females, making a total of 50 unique voices. These speakers vary in age from 20 to 70 years old. This variety helps in thoroughly testing speaker verification models. This dataset is especially useful for research on speaker verification with short recordings. It consists of 10 clips for each person, each clip being no longer than 10 \(s\), adding up to 500 recordings in total. The total length of all recordings is about 1 h and 30 min, which averages to roughly 100 s for each speaker. This dataset is a valuable tool for research in speaker verification, particularly for studies involving short audio clips. The performance of these models is evaluated using common biometric metrics such as false acceptance rate (FAR), false rejection rate (FRR), equal error rate (EER) and detection cost function (DCF). The results reveal that the TitaNet and ECAPA models stand out by presenting the lowest EER (1.91% and 1.71%, respectively) and thus exhibiting higher discriminative features, ensuring, on the one hand, a reduction in intra-class distance (the same speaker), and, on the other hand, maximizing the distance between different speaker embeddings. This analysis also highlights the ECAPA model's advantageous balance of performance and efficiency, achieving an inference time of 69.43 milliseconds, slightly longer than the PyAnnote models. This study not only compares the performance of models but also provides a comparative analysis of respective model embeddings, offering insights into their strengths and weaknesses. The presented findings serve as a foundation for guiding future research in speaker verification, especially in the context of short audio samples or limited data. This may be particularly relevant for applications requiring quick and accurate speaker identification from short voice clips.  
speaker embedding models; speaker verification; non-English speech dataset evaluation 2024202024141329[[https://doi.org/10.3390/app14041329](https://doi.org/10.3390/app14041329)]([https://doi.org/10.3390/app14041329](https://doi.org/10.3390/app14041329))