esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Investigation, conservation, and exploitation of seas require platforms capable of accomplishing a wide variety of missions in harsh environments with restricted human intervention for long periods of time. Autonomous Underwater Vehicles (AUVs) are excellent tools for carrying out these missions due to their versatility and ability to access remote sites. However, despite the improvement of their capabilities, their development is not devoid of challenges. Endurance, among others, such as underwater communications or autonomy, is still a pending subject. Current battery-based solutions do not offer sufficient endurance and innovative power plants with higher energy content are needed. This work studies the advantages, in terms of endurance, of using a power plant based on Direct Methanol Fuel Cells (DMFCs) to power AUVs. In order to accomplish this, a multi-objective optimization tool that makes use of a genetic algorithm was developed. This tool allows quick preliminary design of AUVs with a DMFC-based power plant, complying with a user-defined payload, operation profile, and restrictions. Six designs based on a real AUV model were studied, and endurance values up to 2 times longer than the corresponding reference AUV were obtained. These results support the benefits of using DMFCs to power AUVs to increase their endurance.  
AUV; methanol; DMFC; endurance; CO\({}_{2}\) capture; genetic algorithm +
Footnote †: journal: Applied Sciences
# Feasibility Study for Using Piezoelectric-Based Weigh-In-Motion (WIM) System on Public Roadway
## 2 Methods  
### Coupling Modes  
In different applications, piezoelectric materials convert mechanical energy into electric energy with different coupling modes. 33-mode and 31-mode are used in most of the piezoelectric applications. The concepts of 33-mode (Figure 1a) and 31-mode (Figure 1b) introduced by [PERSON], [PERSON] and [PERSON], [PERSON] (2018) are illustrated in Figure 1. Under 31-mode, the electrical output is maximized when the external forces are vertical to piezoelectric material's poling direction. Oppositely, under 33-mode, the electrical output from the piezoelectric material is maximized when it is stressed along its poling direction. Many researchers have done study and test about piezoelectric material's coupling modes [25; 26; 27; 28]. They have concluded that in a traffic monitoring application, which employs piezoelectric material working under 31-mode, the sensitivity to the external force depends on the vehicle speed. It means that, if the vehicle traveling faster or slower than a certain range of speed (depending on the design of the device), the sensitivity of the device will be much lower. Since the electrical output depends mostly on the applied external force, the sensitivity to applied loading of the device designed to work under 33-mode is uniform regardless of the vehicle speed. For these reasons, the P-WIM is designed to work under 33-mode.
# Decorrelation-Based Deep Learning for Bias Mitigation
## 4 Results
### Age Biased German Dataset
#### 4.2.3 Comparative Evaluations  
We compare the performance of our proposed DcANN with other existing pre-processing methods as shown in Table 2 on a holdout testing dataset. We report all fairness metrics and balanced accuracies for all the methods. The existing methods such  
Figure 6: Fairness scores and accuracies for different values of \(\lambda\): (**a**) SPD, EOD and AOD scores Vs \(\lambda\) (**b**) DI scores and Accuracies Vs \(\lambda\).  
as the baseline model, reweighing, optimized pre-processing, and adversarial debiasing are implemented using the AIF360 [33] open source Python toolkit. The baseline model is simple the ANN model where unprocessed data are used and bias mitigation method is not applied. Baseline DcANN (B-DcANN) has the same architecture as DcANN but with \(\lambda=0\) which means decorrelation is not present for bias mitigation. The main difference between the baseline model and B-DcANN is that B-DcANN uses regularization parameters such as dropout and weight decay. We notice that regularization helps the model in achieving higher accuracy as can be seen in Table 2.  
As we can see, our proposed method DcANN significantly reduces SPD, EOD, and AOD as compared to other methods without compromising the accuracy. The DI is higher for our DcANN method compared to other methods except for adversarial debiasing. However, as we can see from other fairness metrics and especially accuracy, adversarial debiasing does not provide a significant reduction in bias without significantly comprising accuracy when compared to our DcANN method. In general, we can say that our model performs best on all fairness metrics.
## 2 Methods
### Multiscale Gaussian Matched Filters  
Gaussian matched filters were proposed for vessel enhancement in medical images of the retina [6]. The original GMF method follows the assumption that a vessel cross-section can be approximated by a single-scaled Gaussian curve. Moreover, this method considers that blood vessels can be studied as individual segments that share a similar orientation and diameter, and consequently can be detected with matching templates. In the detection process of the GMF, the vessel structures are enhanced by the convolution of the original image with a gray-scale template defined under a Gaussian distribution. After filtering, the vessel detection response shows an increased contrast between vessel-like structures and the image background. The main idea behind the GMF is supported by the single-scale Gaussian template, which is defined as follows:\[GMF(x,y)=-\exp\left(-\frac{x^{2}}{2\sigma^{2}}\right)\ \|y\|<L/2\, \tag{1}\]  
where \(x\in[-T/2,T/2]\). The parameter \(T\) delimits the Gaussian curve trails and produce a filter of finite width. The value of \(L\) corresponds to the length of a vessel segment which shares the same cross-section profile and orientation. The average vessel diameter to be detected is specified by \(\sigma\) as the standard deviation of the Gaussian distribution. In addition, because vessel structures can be found in multiple orientations, the filter template is transformed in order to detect segments at different directions with the rotation matrix as follows:  
\[R(\theta)=\left[\begin{array}{cc}cos\ \theta&-sin\ \theta\\ sin\ \theta&cos\ \theta\end{array}\right]. \tag{2}\]  
Commonly, \(K\) directions are considered by defining \(\theta\) to be evenly spaced in the range \([-\pi/2,\pi/2]\).  
Observing the similarity to blood vessels in the retina, the GMF method has been applied to the detection of coronary arteries in X-ray angiograms [11]. However, due to the original GMF works with a single scale of \(\sigma\) for the whole image, only vessels with the same diameter are correctly enhanced. This fact represents a disadvantage because coronary arteries show a natural thickness variation over their span, and ramifications of vessels with minor diameters are commonly present along the coronary artery tree. To overcome this drawback, a multiscale approach was proposed by [PERSON] et al. [17] to increase the vessel detection capability of the GMF method.  
In order to illustrate the detection process carried out by the GMF, in Figure 1, Gaussian templates with different parameter values are presented. The variation between single-scale and multiscale filtering is the number of templates defined at different scales of \(\sigma\) that produce the directional filter bank, which is shown in Figure 1d,g. Each template is intended to enhance vessels of different diameters, providing a multiscale response for the same angiogram, which can be appreciated in Figure 1f,i. The multiscale analysis of the GMF presents a trade-off between the gain in vessel detection efficiency and the increment of computational cost because the high number of convolution operations.
# The Potential of Visible and Far-Red to Near-Infrared Light in Glaucoma Neuroprotection
## 6 Current and New Approaches in Glaucoma Treatment Modalities  
The most common treatment for glaucoma is prescription eye drops. In addition to the well-known hypotensive drugs, modern devices for instilling local and systemic drugs are also used to reduce intraocular pressure [57]. However, poor compliance and fluctuating adherence of patients to the therapy are the main disadvantages of eye drop treatment.  
Concerning laser therapy, diode laser trabeculoplasty (DLT) uses lower energy spots than SLT (selective laser trabeculoplasty) and ALT (argon laser trabeculoplasty) [58] for the same performance, and micropulse diode laser trabecculoplasty (MDLT) [59] emits microspots to limit laser heat-induced damage to adjacent structures.  
Regarding surgery, even if the traditional trabeculectomy technique remains the gold standard of treatment, the latest research has been focused on improving its risk/benefit ratio for minimally invasive glaucoma surgery (MIGS) [60]. This method could be used in eyes with mild or medium-mild grade glaucoma, or in patients who do not tolerate drops or do not respond to laser treatment, as it is a safe and efficient technique to lower IOP compared to standard surgery; however, its costs are elevated.  
In light of the growing costs of these drugs/devices and their impact on the economy of various countries, PBM therapy may offer a novel, safe, and effective therapy choice for improving the quality of life of patients, including the elderly and the poor; it may also have a major impact on medical practice and public finances, and cost a fraction of many current approaches [61]. However, the drawbacks of this technology are due to the lack of consistency in the choice of parameters such as the wavelength, the amount of energy consumed, the surface of contact, and the duration of treatment. Most researchers believe that, in relation to many diseases, the efficiency of PBM is positive [62]. Finally, the search for qualitative and quantitative standards should be undertaken in the clinical setting to achieve original protocols to treat diseases with photobiomodulation.
## 2 Discovery Algorithm
In the second phase, the number of potential transmitters is streamlined based on parameters linked to luminosity and the size of illuminated regions. To accomplish this, the images associated with the first two seconds of video are analyzed, as they are considered sufficient to detect at least one bit set to 1 if a continuous frame transmission is performed, as shown in Figure 2. In this phase, the average luminosity of one of the images is obtained, and the luminosity and region size thresholds are determined based on that value. Subsequently, the luminosity threshold is applied to the image, resulting in a matrix of ones and zeros. Next, all pixels that have passed the threshold and are contiguous are grouped together, and the number of pixels in each group is counted. If the number of pixels is less than the calculated threshold, the position of the central pixel is stored in a list of potential candidates. The different processes within the Pool generate different lists of solutions based on the list of images they receive to which they apply the aforementioned tasks. Before these lists are passed to the next phase, duplicate elements are removed, and they are merged. This list contains the pixels that are potential transmitter candidates. In the third phase, the determination is made regarding which pixels are definitively linked to optical signals from transmitters based on the resemblance of their luminosity variation with the defined frame structure. To accomplish this, the corresponding frame is acquired for every pixel added to the potential transmitter list. To achieve this, the luminosity value of each pixel in the grayscale-converted images associated with 4 s of video is considered. Three filters are applied to each frame of each pixel, and if they pass these filters, the pixels are considered transmitters. The first filter is the standard deviation of luminosity, the second is the number of rising edges, and the third is the duration of the second period of the frame. To pass the last two filters, the frame is converted to 1 s and 0 s by applying the luminosity threshold. Thus, to obtain the number of rising edges, the frame is subtracted from itself shifted one position, and the number of \(-1\) s is counted. On the other hand, to determine the duration of the second period, the absolute difference between the position of the first rising edge and the first falling edge is calculated. The resulting list from this phase contains only the pixels associated with transmitters. Figure 3: Algorithm phases. Finally, in the fourth phase, the list of pixels classified as transmitters in the previous phase is traversed to check if there are pixels that are very close to each other, because it is assumed that these pixels likely belong to the same transmitter. The limit used is half of the value calculated using Equation (4). In this assessment, the distance between pixels is considered as the sum of the distance along the x-axis and the y-axis.
## 3 Mutual Coupling Suppression using Frequency-Selective Surface
### FSS Wall and Metallic Strip  
The proposed isolation can be further improved by adding a 3 mm metallic strip beneath the FSS wall. With this enhancement, the total size of the FSS becomes 16.72 mm \(\times\) 19.08 mm. Figure 10 shows that isolation in Design 1 is improved from \(-\)20 dB to \(-\)33 dB in certain regions of the desired bands. On the other hand, Design 2 exhibits consistent isolation improvement in the entire desired band (below \(-\)25 dB), as indicated in Figure 11.
# ZSM Framework for Autonomous Security Service Level Agreement Life-Cycle Management in B5G Networks
## 4 ETSI PoC  
To demonstrate the potential of ETSI's ZSM standard, ETSI selected several Proof-of-Concepts (PoCs) to present to the community, including PoC 6: 'Security SLA Assurance in 5G Network Slices' [11]. This PoC envisions the provisioning of 5G network slices in a multi-domain scenario through security-aware closed loops, aligned with the ZSM proposal. This process is initiated by SSLA reception and leverages contributions from this paper and the authors' prior work [10]. The PoC focuses on the reception of two SSLAs. The first defines the security objectives for the dynamic deployment of a 5G network, while the second SSLA pertains to IoT. This paper focuses on the first SSLA, emphasizing the ZSM framework's ability to dynamically manage the deployment and security of a 5G network with end-to-end traffic and resource protection, as depicted in Figure 7.  
In the following sections, we describe the security assets incorporated into the ZSM framework to meet the SSLA's security requirements. Additionally, we explain how the ZSM framework operates both proactively and reactively to deploy and maintain the SSLA between two SMDs, even when security requirements are compromised.  
Figure 7: ETSI PoC #6: SSLA assurance in B5G networks.
## 4 Implementation and Results
### Selection of Feature Extraction Methods and Classification Algorithms
#### 4.1.2 Classification Algorithms  
In response to question Q2, regarding which algorithms are used for emotion classification, Figure 8 shows the results in the considered papers of the SLR. SVM achieves better results in 32% of the articles; 23% of articles obtained better results with ANN, and 18% with the k-NN classifier, followed by the NB and RF classifiers, with 13.5% each.  
In the papers considered in our SLR, the percentage of the frequency and performance of the algorithms used in the research was calculated, based on which the SVM, k-NN, and ANN algorithms were selected to be implemented in this work because they have percentages equal to or greater than 18%. The parameters of the classification algorithms are discussed in Section 4.3.
# Laboratory Evaluation of Eco-Friendly Pervious Concrete Pavement Material Containing Silica Fume
## 3 Results and Discussion
### Permeability  
The permeability coefficient of silica fume-modified pervious concrete is shown in Figure 8. Figure 8 concludes that the permeability coefficient was not affected by the dosage of silica fumes. The permeability coefficients of all groups were about 3.8 mm/s when the porosities of all mixtures were about 15%, which meets the standard requirement of 0.5 mm/s for pervious concrete in the Chinese national standard [34]. Just as the report in the literature [32], the porosity is the most important and direct factor that determines the permeability coefficient of pervious concrete. In terms of mono-sized pervious concrete, when the porosity does not change, the permeability will not change. Silica fumes were added with equivalent volume replacement of cement in the experiment, and the porosity volume did not change with the silica fume incorporation levels, so the permeability coefficients of silica fume-modified concrete remained constant.  
Meanwhile, the permeability coefficients of all mixtures were predicted with the equation between effective porosity and permeability given by Reference [32], and the predicted permeability coefficients  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**Mix ID**} & \multicolumn{3}{c}{**Compressive Strength**} & \multicolumn{1}{l}{**And Its Loss Rate After Freeze–Thaw Cycles**} & \multicolumn{1}{l}{\multirow{2}{*}{**\begin{tabular}{} \end{tabular}**}} & \multicolumn{1}{l}{\multirow{2}{*}{**\begin{tabular}{} \end{tabular}**}} & \multicolumn{1}{l}{\multirow{2}{*}{**\begin{tabular}{} \end{tabular}**}} & \multicolumn{1}{l}{\multirow{2}{*}{**\begin{tabular}{} \end{tabular}**}} & \multicolumn{1}{l}{\multirow{2}{*}{**\
\begin{tabular}{} \end{tabular}**}} & \multicolumn{1}{l}{\multirow{2}{*}{**
\begin{tabular}{} \end{tabular}**}} \\  & **CS 1 (MPa)** & **L 2 (\%)** & **CS (MPa)** & **L (\%)** & **CS (MPa)** & **L (\%)** & **CS (MPa)** & **L (\%)** \\ \hline Control & 21.2 & 4.5 & 18.1 & 18.5 & 16.7 & 24.8 & 14.2 & 36.0 \\ SF-3 & 22.6 & 4.2 & 21.5 & 8.9 & 20.2 & 14.4 & 17.9 & 24.2 \\ SF-6 & 23.8 & 4.0 & 22.5 & 9.3 & 20.8 & 16.1 & 19.3 & 22.2 \\ SF-9 & 24.2 & 4.3 & 23.1 & 8.7 & 21.6 & 14.6 & 20.7 & 18.2 \\ SF-12 & 24.7 & 2.4 & 24.1 & 4.7 & 23.5 & 7.1 & 22.9 & 9.5 \\ \hline \hline \end{tabular}  
* Compressive strength; 2 Loss rate.  
\end{table}
Table 7: Freeze-thaw cycle results of silica fume-modified pervious concrete.  
Figure 7: Porosity of silica fume-modified pervious concrete.  
are also shown in Figure 8. The data shows that the measured and predicted permeability coefficients were basically consistent and the maximum error between them was 6.5%.
# Analysis and Evaluation of Clustering Techniques Applied to Wireless Acoustics Sensor Network Data
## 2 Materials and Methods
### Unsupervised Learning Algorithms  
There are a large number of algorithms in the literature dedicated to data clustering. In this research, several representative algorithms from three unsupervised learning approaches, in particular, hierarchical, partitional, and model-based techniques have been considered to evaluate which one performs better over acoustic data. As it is mentioned in Section 1, Hierarchical Agglomeration and K-means have been previously applied to acoustic data. In this paper, other clustering algorithms, together with the mentioned above, were trained to fit the data:  
1. HC: Hierarchical Agglomeration [28];
2. DIANA: a divisive hierarchical algorithm [29];
3. KM: K-means [30];
4. PAM: Partitioning Around Medoids [31];* CLARA: the sampling-based algorithm [29];
* SOM: Kohonen Self-Organizing Maps [32];
* SOTA: the Self-Organizing Tree Algorithm [33];
* GAUSS: Expectation Maximization (EM) algorithm over a finite mixture of Gaussian distributions [34].  
Hierarchical Agglomeration [28] and DIANA [29] methods, belonging to hierarchical clustering methods, create the clusters grouping the elements in hierarchical steps. K-means [30], PAM [31] and CLARA [29] methods, belonging to partitional clustering methods, are based on centroids and they iterative the algorithm until convergence. Moreover, SOM [32] technique applies an unsupervised neural network, and SOTA [33] is an evolution of the SOM algorithm which included a binary tree topology, both belonging to model-based methods, in this case in machine learning algorithms. Finally, GAUSS [34] technique is based on the maximization of the likelihood for a statistical distribution, belonging to model-based methods, in this case in statistical normal distributions.  
In reference [35], a revision of different approaches for grouping similar objects into different groups is presented with an analysis of the advantages and disadvantages of every algorithm family. The features for the clustering algorithms chosen for this work are summarized in Table 1.
# WAID: A Large-Scale Dataset for Wildlife Detection with Drones
## 2 Related Work
### Diverse Datasets for Wildlife Detection
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Animal10** &
\begin{tabular}{c} **Animal-** \\ **Pose** \\ \end{tabular} & **AnimalWeb** & **Animal Kingdom** & **WAID (Ours)** \\ \hline Year & 2019 & 2019 & 2020 & 2022 & 2023 \\ multicategory & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Wilderness & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Multiscale & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) \\ ValidationSet & \(\times\) & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) \\ AerialView & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of the animal image datasets currently available. Some images from the datasets are shown in Figure 2.  
the current aerial wildlife datasets, these limitations have restricted their applicability in diverse wildlife monitoring scenarios. Figure 2 shows partial images from WAID in comparison to these datasets. Our WAID dataset contains 14,375 drone aerial images from a variety of geographic locations and environmental conditions, covering six wildlife species (sheep, cattle, seals, camels, Tibetan wild asses, and zebras) and a variety of habitat types (deserts, grasslands, and sandy beaches, etc.). It is the largest and most species-rich publicly available wildlife aerial photography dataset, which addresses the limitations of aerial wildlife datasets that are small in size and not species-rich. In addition, the WAID dataset has accurate box-level labeling, with wildlife in the images accurately labeled, including species classification and location bounding boxes. Each animal target has been manually confirmed, with an average labeling time of over 3 min. High-quality image annotation helps researchers to detect and identify wildlife more accurately [49] and helps us to address the limitation  
Figure 2: Some images from our (**a**) WAID dataset and four other datasets for comparison: (**b**) ANIMAL, (**c**) APD, (**d**) AnimalWeb, and (**e**) Animal Kingdom. of poor annotation quality of existing aerial wildlife datasets. Meanwhile, we release freely the WAID dataset to address the limitation of the scarcity of existing datasets. The open-source dataset means that any researcher can access and use the data for free without worrying about copyright issues. WAID provides multiple types of image data, enabling researchers to develop more accurate, general, and robust algorithms, and serves as a public benchmark, which can greatly lower the research threshold of wildlife detection algorithms, enabling more researchers to join the cause of wildlife conservation [50]. Taken together, these motivate us to propose a large public dataset for the researchers of wildlife monitoring using drones.