esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results  
This part firstly reports on the comparative analysis of six classical ML algorithms and five architectures based on the CNN model to select the best algorithm, and then the findings regarding the performance improvement of the best fitting model (InceptionV3) using a variety of activation functions and DL optimization algorithms.  
A detailed description of the parameters used has been given in the prior section. The ML and DL models were implemented in Python, through the scikit-learn library for the conventional algorithms and Keras as well as TensorFlow for the DL model. The program was run on the Google Colaboratory notebook, which provides freely available GPU, TPU, and CPU resources. The classical algorithms were trained on the GPU, while the DL model was trained on the TPU. The dataset was split into training and testing sets in an 80-20 ratio (We utilized 80% of the data for training and 20% for testing). For performance metrics, we employed f1-score, accuracy, recall, precision, the ROC curve, and the confusion matrix. Thus, the results are shown below in Figure 15. From the experimental findings, we found that the NB classifier achieved an accuracy of 60,09%, much lower than other classical ML algorithms. Unlike the RF model, with an accuracy of 97.54%, it was able to far outperform all other ML algorithms. Whereas CART and KNN produced results that were roughly comparable, ranging between 94.45% (for the CART) and 91.67% (for the KNN). On the other hand, for models based on deep transfer learning. The InceptionV3 model is the most efficient, with a precision of 98.01%, so it was able to outperform CNN from scratch and other pretrained models. This is due to its ability to handle both missing and unbalanced data without forgetting that its execution time is quite fast compared to other DL models.  
Figure 14: Curve represents the loss and accuracy value for the VGG19 model.  
Figure 15: Chart of machine learning and deep transfer learning models.
## 3 Materials and Methods
### Deep Learning Model Design
#### 3.3.1 Model Selection and Training Strategy  
The selection of an appropriate deep learning model is crucial for achieving high accuracy in pharmaceutical image classification. In this study, we conducted a comparative evaluation of multiple convolutional neural network (CNN) architectures, including DenseNet, EfficientNet, ResNet, MobileNet, SqueezeNet, and VGGNet, to determine the optimal model for real-time medication identification.  
Each model was evaluated based on classification accuracy, inference speed, and computational efficiency. ResNet101 emerged as the best-performing model, achieving an accuracy of 98.51%, outperforming the other architectures in both precision and recall metrics. Its residual learning framework mitigates gradient vanishing, enabling deeper networks to learn effectively while retaining computational feasibility.  
The training process was optimized to enhance model generalization and real-time deployment capabilities. Transfer learning was employed, leveraging pretrained ResNet101 weights from ImageNet to initialize the model for pharmaceutical classification. Fine-tuning was then applied to adapt the model to tablet-specific morphological details, including shape, color, and imprint text.  
To prevent overfitting and improve robustness, batch normalization and dropout regularization techniques were incorporated. The hyperparameters were systematically optimized to maximize performance, as outlined below:  
* Learning rate: Initially set to 0.001, with a step decay strategy reducing it by a factor of 0.1 every 10 epochs.
* Batch size: 32 images per batch to balance computational efficiency and training stability.
* Optimization algorithm: Adam optimizer, selected for its adaptive learning rate and improved convergence speed.
* Loss function: Categorical cross-entropy, ensuring accurate multi-class classification.
* Number of epochs: 50, with early stopping applied when the validation loss plateaued to prevent unnecessary computation.
# Prediction of Theoretical Derailments Caused by Cross-Winds with Frequency
## 4 Evaluation of Derailment Safety under Cross-Wind
### Derailment Safety According to Track Gauges  
Track gauges are generally classified as narrow gauges, standard gauges, and wide gauges. Various track gauges are used around the world.  
High-speed trains operating above 300 [km/h] generally use the standard gauge of 1435 [nm]. However, in order to compare the effects of the track gauges, simulations and theoretical formulas were calculated according to the track gauges. The risks of derailment were assessed considering the different gauges under the driving speed and the cross-wind condition in Table 15.  
Figure 14: The motor bogie of the KTX.  
Figure 15: The comparison of the ratios of wheel unloading between the models with dampers and without dampers (simulation results).  
Figure 16a shows the change of the ratios of wheel unloading when a train runs on a straight track according to the gauges. The results of our formula, [PERSON]'s formula, and MBD simulations showed that the larger the gauges, the less the risks.  
The deviations between our formula and the MBD simulation were only about 0.04 on the straight track. It is shown that the theoretical equations of this paper were more accurate to predict derailment risks than [PERSON]'s formula when considering variables such as rail irregularity which are not considered in existing theory.  
Figure 16b shows the change of the ratios of wheel unloading when a train runs on the curved track according to the gauges. The results show the decreasing tendency like a form of a quadratic curve. Although the overall trend of the results using [PERSON]'s formula and the theory of this paper was similar, the wheel unloading ratios using [PERSON]'s formula were relatively large. In addition, the results of simulations and the theoretical ones of this research showed very similar values, but our theoretical solutions could give more accurate results than those of [PERSON]'s formula.
## 2 Related Work  
With the advent of WSI over the last decade, a huge amount of tissue slides are routinely scanned in clinical practice, thereby increasing data availability. Consequently, and along with its important role in oncological clinical routine, more research opportunities are raised in computer-assisted imaging analysis, with this new source of \"big data\" [14]. In fact, due to the high-resolution and complex nature of this imaging technique, advances in image analysis are required, providing an opportunity to apply/develop more advanced image processing techniques, as well as machine and deep learning algorithms. [15].  
The analysis of digital images of digital breast pathology can be applied to tackle several clinical and pathology tasks such as, for example, mitosis detection [16; 17], tissue type classification/ segmentation [18; 19; 20], cancer grading [21] or histological classification [22; 23]. These approaches commonly use H&E stained slides and, in recent years, have been focused on applying deep learning techniques to improve the performance of the models and also to take advantage of the increasing availability of medical data.  
Besides H&E staining slides, some authors, such as [PERSON] et al. [24], [PERSON] et al. [25], [PERSON] et al. [26] have addressed diverse tasks using IHC slides. On the specific task of automatic breast cancer HER2 classification, the focus of this work, the literature is limited to the work by [PERSON] et al. [27], [PERSON] [28], and other studies related with the 2016 HER2 Scoring Contest [10]. The common disadvantage of these prior works is requiring IHC staining images to perform HER2 classification, since this modality requires additional cost and time. In contrast, our approach aims at using the H&E staining slides as the only source of information to obtain the IHC status for HER2 overexpression.  
In order to complete the proposed task, we followed the idea of using a data source as initialization for the framework, transferring some domain knowledge to the final training. This is a recent trend that has been applied to medical imaging processing for different purposes, such as cardiac structures segmentation [29], Alzheimer disease classification [30], radiological breast lesions classification [31] and even digital pathology classification/segmentation [32].  
Despite the growing popularity of digital pathology and the increasing number of publications in this area, to the extent of our knowledge, the task of predicting HER2 status on H&E stained slides has not yet been addressed in the literature.  
Figure 2: Image examples from used datasets: HER2 SC [10] IHC stained slides (**a**), HER2 SC [10] H&E stained slides (**b**), BRCA [11; 12] H&E stained slides (**c**). The tile extraction was solely done on tissue, here denoted by the delineated regions.
# Temporal Convolutional Network-Based Axle Load Estimation from pavement Vibration Data
## 4 Results and Discussion
### Network Architectures  
As previously mentioned, larger kernel sizes and more layers in the network capture low-frequency trends, while the network captures high frequencies. Therefore, we aim to compare the reconstruction performance of TCN networks with different kernel sizes  
Figure 8: Site 2 reconstruction results. (**a**) Passenger car; (**b**) Lorry; (**c**) Truck-I; (**d**) Truck-II.  
Figure 9: Performance of TCN model for load estimation.  
(\(K_{T}\)) and numbers of TCN blocks (\(N_{B}\)). As the network architecture's impact on the load reconstruction performance of various structures and load types is similar and the loads at Site 2 are more complex and closer to a real-world situation, this study only focuses on Site 2. We constructed and trained these networks with different hyperparameters, and the comparison results are presented in Figure 10.  
The peak and the axle impulse represent high-frequency and low-frequency features, respectively. Figure 10 illustrates that when utilizing a large value for \(N_{B}\), the influence of \(K_{T}\) on recognition performance is diminished. Specifically, the value of \(K_{T}\)-21 results in a smaller PE compared to the values of \(K_{T}\)-31 and \(K_{T}\)-41 under \(N_{B}\)-12 while the opposite trend is observed for MIE. \(N_{B}\), on the other hand, plays a vital role in reconstructing the impact load-time history, as the network's fitting ability increases with its depth. However, deeper layers require longer training times. Hence, we select the network with \(K_{T}\) = 31 and \(N_{B}\) = 12 as the optimal architecture, taking both factors into account. Finally, the mean estimation error, computed by averaging the values of PE, MIE, and RMSE, is found to be 10.89%.
## 3 Results and Discussion
### Linear Infrastructure Monitoring Applied Results  
The photographic imagery acquired by the drone sensor is transmitted by the 4G network thorough the VPN and received in the EXO Data server endpoint. There, the imagery is stored in a docker, namely the photo log repository.  
The retrieval of information related to the road infrastructure and its affectation zone is not required in real-time. Moreover, mapping processing to obtain Digital Surface Models (DSM) or orthomosaic products is heavy time consuming and needs all the individual images to mosaic them. Also, it needs geometric [44], radiometric [45] and thematic [46] quality control. Commonly a Structure from Motion (SfM) processing of the images (alignment, dense point cloud generation, mosaicking of images, orthomosaic generation) is used with photogrammetric software, and Remote Sensing and Geographic Information System (RS&GIS) software to extract new information and to combine the drone acquired data with existing cartographic databases.  
In our implementation, we used Mapir Survey 3 RGB sensor and Flir Vue Pro R 640 thermal sensor to acquire imagery over linear infrastructures. We used Metashape Photoscan photogrammetric software to perform the SfM processing, and for terrain modelling and land use land cover classification we used QGIS software.  
The availability of linear infrastructure networks shapefiles in most of the official mapping agencies avoids the manual digitalization of a given road. Nevertheless, the operator must define the buffer distance from the road axis to delimit the affectation area to be monitored. Once the imagery is processed, georeferenced and clipped with the area of interest, the road manager can exploit and analyze the information (Figure 9).  
Figure 8: Traffic flow Computer Vision algorithms process the video received in EXO Data server. This allows near real-time information for traffic management, such as in-out roundabout matrix (segmented by object class) and speed estimation.  
The main applications of these mapping products, are:  
1. [label=()]
2. Map update of constructed elements surrounding the linear infrastructure.
3. Detection of horizontal signing and concrete degradation.
4. Monitoring of conservation/maintenance works.
5. Locate wildlife paths or other thermal indicators of fauna activity.
## 2 Research Data and Methods
### Theoretical Backgrounds and Model Development  
#### 2.2.1 Pso  
Particle swarm optimization (PSO) is defined as a stochastic optimization technique introduced and developed by [PERSON] and [PERSON] [26]. The general description and application of this method are presented in [27; 28; 29]. A summary of this method can be presented in six steps as follows [28; 30]:  
Step 1: a population of random potential solution is designed as a searching space. Suppose \(D\) and \(N\) are the dimensions of the searching space and the number of particles, respectively. Each potential solution is assigned a random \"position\" (\(x_{i}^{k}\)) and \"velocity\" (\(v_{i}^{k}\)) of the \(i^{\text{th}}\) particle at iteration \(k\). These particles are then \"flown\" through the search space of potential solutions as follows:  
\[v_{i}^{k}(t+1)=wv_{i}^{k}(t)+c_{1}.rand()\big{(}p_{i}^{k}(t)-x_{i}^{k}(t)\big{)} +c_{2}.rand()\big{(}g_{i}^{k}(t)-x_{i}^{k}(t)\big{)} \tag{1}\]  
\[x_{i}^{k}(t+1)=x_{i}^{k}(t)+v_{i}^{k}(t+1)\ 1\leq i\leq N,\ 1\leq k\leq D \tag{2}\]  
where \(w\) represents the iteration weight; \(c_{1}\) and \(c_{2}\) stand for the different acceleration coefficients. \"_rand_ (\(0\))\" denotes a constant value in the interval \([0,1]\) and is set randomly. \(p_{i}^{k}\) and \(g_{i}^{k}\) are the best position of the \(i^{\text{th}}\) particle in the search stage, and the global best position found in the population, respectively.  
Step 2: Evaluate the fitness of each particle in the swarm. Step 3: For every iteration, compare each particle's fitness with its previous best obtained fitness (\(p_{i}^{k}\)). If the current value is better than \(p_{i}^{k}\), then set \(p_{i}^{k}\) to be equal to the current value and the \(p_{i}^{k}\) location to be equal to the current location in the \(d\)-dimensional space. Step 4: Compare the \(p_{i}^{k}\) of particles with each other and update the swarm global best location with the greatest fitness (\(g_{i}^{k}\)). Step 5: The velocity of each particle is changed (accelerated) towards its \(p_{i}^{k}\) and \(g_{i}^{k}\). This acceleration is weighted by a random term. A new position in the solution space is calculated for each particle by adding the new velocity value to each component of the particle's position vector. Step 6: Repeat steps (2)-(5) until convergence is reached based on the desired criteria. The rudimentary structure of the PSO algorithm is shown in Algorithm 1.  
```
1. Initialize \(P\) number of particles with some random position;
2. Evaluate the fitness function of particles;
3. \(gbest=\) global best solution;
4. For l = 1 to maximum number of iterations do
5. For j = 1 to \(P\)do
6. Update the velocity and position for the \(j^{\text{th}}\) particle using Equations (1) and (2), respectively;
7. Evaluate the fitness function of \(j^{\text{th}}\) particle;
8. Update the personal best (\(pbest\)) of \(j^{\text{th}}\) particle;
9. Update the \(gbest\);
10. Keep \(gbest\) as the best problem solution;
11. End for
12. End for
```  
**Algorithm 1**The PSO algorithm for optimization problem of \(d\)-dimensional decision variables.
# Modifications of Wound Dressings with Bioactive Agents to Achieve Improved Pro-Healing Properties
## 3 Pro-Healing Wound Dressings
### The Effect of Vitamins on Skin Regeneration
The research showed the positive effect of the tested vitamin on the wound closure rate. Produced alginate hydrogels loaded with vitamin D3 enhanced cell proliferation compared to the control sample and vitamin-free biomaterial. The same research team also investigated the impact of different \(\alpha\)-tocopherol (vitamin E) concentrations on wound healing in a rat model. The dose of 400 units turned out to be the most effective dose of vitamin E, whichsignificantly increased the rate of wound closure. Chitosan/alginate hydrogel loaded with the same dose of \(\alpha\)-tocopherol was characterized by the best re-epithelialization among all tested samples [80]. In another study, vitamin E was used to improve antioxidant and antibacterial activities of the resultant biomaterial. Obtained data confirmed that biomaterials loaded with vitamin E significantly decreased intracellular ROS level after cell treatment with 100 \(\upmu\)M tertiary-butyl hydroperoxide (t-BuOOH). Nevertheless, the addition of vitamin E did not contribute to the improvement of the antibacterial properties of the tested biomaterials [85]. In turn, [PERSON] et al. [86] incorporated vitamins A and E into gelatin nanofiber using an electrospinning technique to gain better wound healing performance. A potent antibacterial property (_E. coli_, _S. aureus_) was observed in the case of the biomaterial containing only vitamin E. Cell growth investigation revealed the greatest amount of cells for biomaterial loaded with both vitamins. A positive effect of vitamin E on the wound healing process was also observed by [PERSON] et al. [87], who proved enhanced antioxidant activity of silk fibroin-PVA-_Aloe vera_/vitamin E nanofibrous dressing. It was also shown that a combination of vitamin E and _Aloe vera_ gel had a positive impact on cell-matrix interaction and cellular viability. The studies presenting a positive impact of biomaterials loaded with vitamins on wound healing process are summarized in Table 3.  
Among all the bioactive compounds that were presented in the work, vitamin-loaded dressings were characterized by the widest variety of improved properties and the lowest limitations. This indicates the possibility to significantly increase the biomedical potential of wound dressings by incorporation of vitamins into the structure of biomaterials during the production of wound care products.
# Observing Pictures and Videos of Creative Products: An Eye Tracking Study
## 4 Data Acquisition and Elaboration  
### Areas of Interest and Eye-Tracking Data  
As mentioned in the previous sections, the visual behavior of participants was acquired through an ET technology. However, in order to compare the two forms of representations in terms of quantitative data, it is necessary to resort to Areas of Interest (AOIs). The AOIs are portions of the pictures (frames when it comes to videos) on which specific ET data can be collected. In other words, the creation of an AOI enables the collection of specific ET data ascribable to that portion of the picture/frame. In particular, in this paper, the Total Visit Duration (TVD) on the AOIs has been taken as a reference of AOI's attraction and devoted attention. The TVD is the amount of time that one spends gazing a specific AOI--it considers both the time of fixations and saccades located in the same AOI. Since the AOIs are created to map the TVD on specific areas of pictures/frames, these areas include pictures/frames' characteristics useful for the scope of the study. Clearly, the AOIs are static across the product exposition when the products are shown as pictures. On the other hand, since the spatial position of the specific products' features could change along videos, dynamic AOIs that follow these features' changes were created. More in details, Figure 3 shows the static AOIs created on the products' pictures. AOIs with the same name corresponding to the same products' features were created on products' videos. Here, the dynamic changes of AOIs are based on interpolation of polygons across a number of frames in which the authors have adapted AOIs' shapes. A full explanation of the interpolation mechanism is available in the user's manual of the software Tobii Pro Studio used in the present experiment, accessible here (see \"Dynamic AOIs\", p. 79). Figure 4 shows, as an illustrative example, the different polygons depicting AOIs created on the OnPot's video in different timeframes indicated in the bottom right of the corresponding frames, which represent the boundary conditions for the creation of dynamic AOIs. Illustrative dynamic AOIs can be viewed in the video OnPot.avi, provided as supplementary material. Figure 3: Areas of Interest studied for each product. A list of all AOIs created, along with the reason for considering them, is illustrated in Table 2. Eventually, 23 different AOIs were created across the six creative products. Clearly, the size of each AOI and its time of exposition could have an influence on the TVD. Indeed, due to the dynamic nature of the AOIs created on the videos, it is possible that AOIs' size changes or they disappear for limited videos scenes. These sources of variability will be discussed in light of the results emerged.
# Optimizing Geospatial Data for ML/CV Applications:
## 3 Methods
### Retrieval and Processing of Orthoinagery from Geoportal
#### 3.4.3 Asynchronous Image Fetching  
Asynchronous tasks were created to fetch individual tiles using WMS 'GetMap' requests. The parameters included the request type, layers, WMS version, srs, bbox (the bounding box of the tile), width and height (dimensions corresponding to the 10-m tile size), and the response format. The appropriate WMS endpoint was selected based on the determined resolution, i.e., 'HighResolution' for 5 and 10 cm per pixel images or 'StandardResolution' for 25 cm per pixel images.  
Figure 9: An exemplary parcel of the P3152Z road divided into segments (Top: Segment 1; Bottom: Segment 2).  
The 'asyncio' and 'aiohttp' modules facilitated asynchronous tile downloads, with a semaphore limiting concurrent tasks to 32. The 'aiohttp' session managed the HTTP connections. Tasks were executed using 'asyncio.gather()'. The asynchronous approach significantly improved the image download speed compared to sequential requests.  
The fetched tiles were assembled into a single image using 'Pillow'. Then, a mask was created based on the segment geometry. This mask removed areas outside the parcel shape, retaining only the region of interest, an example is presented in Figure 11. The processed images were saved in PNG format with transparency. To ensure an organized dataset structure, each parcel had its own subdirectory containing the images of its segments and associated metadata.