esa-sceva/satcom-chunk-collection huggingface dataset:
# The Analysis of Pneumatic Wheel Rim Deformation While Hitting an Obstacle
## 4 Modeling Results
### Hitting an Asymmetrical Obstacle  
Another analysis concerns the impact of the wheel on an asymmetrical obstacle while maintaining the same parameters, i.e., impact speed \(v\) = 50 km/h, impact time \(t\) = 0.0102 s. The map of stresses for this simulation is shown in Figure 8a.  
It can be seen that there are single points on the deformed surface of the rim flange with the same maximum stress value of 400 MPa. On most surfaces, the stresses of 260 MPa were observed as shown in Figure 5. However, the area of their occurrence is different. In the event of a collision with a symmetrical obstacle, the stress map of this value covers the area in the radial direction from the points on the outer diameter through the surface with bolt holes to the curve surrounding the central hole. After hitting an asymmetrical obstacle, the stresses equal to 260 MPa appear on the protective hump (HUMP) of the rim surface. The development of stresses on the flange on an arc equal to 2/5 of the rim circumference is also noticed. The deformation in the rim (Figure 8b) corresponds to the shape of the obstacle. Contrary to Figure 5b, the rim surface is clearly divided into two areas, with different deformation values.  
The maximum value of the displacement of individual points on the boundaries of the flanges is 47.4 mm from the initial position. By subtracting this value from the displacement of the global coordinate system (17.6 mm), we get the real value of the rim deformation, which is 29.8 mm.  
Figure 9 shows a comparative graph of stress increase as a function of time. The maximum stresses visibly fall into two stages (Figure 7). The first stage is the compression time of the tire. The stresses that arise in the rim then result from the interaction of the tire and compressed air. At the moment of direct impact of the obstacle on the rim, the stress increases rapidly to the maximum values and is maintained until the end of the simulation.  
The values of the stresses in the rim when hitting an asymmetric obstacle increase slower than in the case of a test with a symmetrical obstacle. This may be due to the smaller increments of pressure change within the tire due to the different obstacle surface acting on the wheel. When the obstacle is in direct contact with the rim of the wheel, the stress increases rapidly. It can be concluded that, apart from the extent of deformation, the change in the shape of the obstacle has no effect on the differences in the values of stresses in the rim.  
Figure 8: (**a**) Distribution of stresses and (**b**) displacements; after the wheel hits an asymmetrical obstacle—isolated rim.
## 4 Implementation
### RAM Memory Allocation  
Managing the allocation of RAM memory is one of the most critical aspects to consider when embedding software applications in WSN nodes. The management of memory allocation has to cope with the limited size of RAM memory and the lack of hardware memory protection that characterize constrained nodes. In this perspective, managing the RAM memory dynamically could increase the  
Figure 4: Wiring of the TinyCoAP interface for the CoAP message layer. PoolC is used to provide the memory needed by the components.  
probability of having failure nodes or could exhaust the available memory. In fact, the lack of hardware memory protection does not prevent the risks of having a collision between the heap and stack or a memory leak [41]. Furthermore, the size of the allocated RAM memory would be difficult to control with this allocation system.  
TinyCoAP avoids these risks by allocating RAM memory statically. The size of the allocated memory is known at compile time and the possibility of memory exhaustion is therefore avoided. Furthermore, static allocation would eliminate the risks of failures due to collision of the heap and the stack. Therefore it would enhance the network reliability. A further optimization is obtained allowing to TinyCoAP to create CoAP responses without allocating new memory. In TinyCoAP the responses are created using the memory already allocated for storing the relative CoAP requests. Besides the reduction of the RAM memory footprint this allows enabling a lighter packet processing with less impact on the CPU. As a consequence, the reduction of the CPU use would lower the energy consumption. As reported in [43], the CPU consumes 4.6 mA when active and 2.4 mA when idle while the radio uses 3.9 mA when receiving. Therefore, the TinyCoAP management of buffers would save CPU cycles and enhance the battery life of nodes.  
The static allocation of memory done by TinyCoAP is compliant with the RAM memory management defined in NesC. In fact, NesC does not support dynamic memory allocation. This characteristic allows preventing memory fragmentation and run-time allocation failures [39]. However, a situation may arise in which applications might need dynamic allocation. To overcome this problem, TinyOS provides a component called PoolC that simulates the dynamic memory allocation. Should PoolC be used, the maximum pool memory size would be allocated statically at compile time. During the execution time, the applications will take the amount of RAM memory they need from that available in the pool. An eventual memory leak would cause the pool to empty, but the heap and stack would not collide. As mentioned above, TinyCoAP uses PoolC to allocate the buffers needed to store the CoAP packets and the linked lists. Differently from TinyCoAP, CoapBlip adopts a dynamic memory allocation management. It uses the malloc memory management library to allocate memory for buffers and linked lists.
## 3 Experiments
### Data Augmentation  
Data augmentation is a technique used in deep learning to artificially increase the size of a dataset by creating new samples from existing ones. The aim of data augmentation is to improve the generalization of the model by increasing the diversity and variability of the training data. By creating new examples, the model is exposed to a wider range of inputs, which can help it learn more robust and generalizable features [54; 55; 56].  
There are several types of data augmentation techniques that can be used in deep learning, including rotation, scaling, flipping, cropping, and noise addition. These techniques are designed to simulate the variability and noise that is typically present in real-world data, and to help the model learn to be invariant to such variations [55; 57; 58].  
In our work on breast tumor segmentation, we utilized several data augmentation techniques to improve the performance of our model. Specifically, we used random rotations, horizontal and vertical flips, and elastic deformations to augment the training data. These techniques were applied randomly to each image during training, effectively creating new examples that were slightly different from the original ones.  
The benefits of using data augmentation are numerous. Firstly, it can help prevent overfitting by increasing the size and diversity of the training data. Overfitting occurs when a model becomes too specialized to the training data and fails to generalize well to new examples. By augmenting the training data, we can make the model more robust to variations and less likely to overfit [55; 59].  
Secondly, data augmentation can improve the performance of the model on small datasets. In medical imaging applications, collecting large datasets can be challenging due to ethical and logistical constraints. Data augmentation allows us to create more training examples from a limited dataset, which can help improve the accuracy of the model [56; 59].  
Lastly, data augmentation can help the model learn to be more invariant to common variations and noise in the input data. By exposing the model to a wide range of variations, it learns to focus on the most important features that are invariant to such variations [56; 59].
## 5 Energy Requirement Estimation
### High Pressure Processing  
For HPP, the compression work (W) during pressurisation must be calculated in addition to the heat required to warm the fruit juices or purece to the processing temperature. Assuming that the temperature increase per 100 MPa for fruit juice and purece is the same as pure water which is \(\sim\)3 \({}^{\circ}\)C [64], the temperature increase due to pressurisation is 18 \({}^{\circ}\)C for a high pressure process at 600 MPa. Meanwhile, for compression work, as the temperature used for the high pressure process increases with a constant pressure of 600 MPa, the compression energy decreases. Table 6 show the energy estimation ranging between 139 to 269 kJ/kg. Heat and compression work equally contribute to the overall energy estimation for HPP-thermal processing. The latter explains the fact that HPP-thermal treatment require higher energy consumption compared to heat alone treatment at the same temperature, unless HPP alone is used for the treatment.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Fruit/Cultivar** & **Mass (kg)** & \(\Delta\)T (\({}^{\circ}\)C) & \begin{tabular}{c} **T Final** \\ (\({}^{\circ}\)C) \\ \end{tabular} & \begin{tabular}{c} **Cp (k/J/kg.K)** \\ \end{tabular} & \begin{tabular}{c} **Q = mc\({}_{\text{p}}\Delta\)T** \\ **(k)J** \\ \end{tabular} & \
\begin{tabular}{c} **Specific** \\ **Energy** \\ **(k/J/kg)** \\ \end{tabular} &
\begin{tabular}{c} **Data** \\ **Collected** \\ **from** \\ \end{tabular} \\ \hline Kalipatti sapota & & 55 & 75 & 3.94 [66] & 0.87 & 217 & [36] \\ pulp & & 65 & 85 & 1.02 & 256 & [37] \\ \hline Peach & & & & & & \\ (_Prunus persica cv._ & & & & & & & \\ Julibieu) & & 50 & 70 & 1.92 [67] & 39.8 & 96 & [37] \\ purece & & 70 & 90 & 55.7 & 134 & \\ \hline Blueberry & & 40 & 60 & 3.64 [67] & 7.28 & 146 & [38] \\ (_Vacciniumcorp-Bosum_) & & 60 & 80 & 10.9 & 218 & [38] \\ juice & & 45 & 65 & 1.94 [67] & 8.64 & 87 & [39] \\ \cline{2-6} Bayberry (_Myrica_) & & 55 & 75 & 10.6 & 107 & [39] \\ juice & & & & & & & \\ \hline Sugarcane & & & & & & & \\ (_Saccharum officiarum_) & 0.001 & 40 & 60 & 3.64 [68] & 0.21 & 146 & [35] \\ juice & & & & & & & \\ \hline Pear & & & & & & & \\ (_Pyrus communis_ cv. & & & & & & \\ Taylor’s Gold) & 0.020 & 60 & 80 & 2.06 [67] & 2.47 & 124 & [28] \\ purece & & & & & & & \\ \hline Apple (_Malus domestic_ & & & & & & \\ cv. Royal Gala) & 0.02 & 55 & 75 & 1.88 [69] & 2.07 & 103 & [28] \\ purece & & & & & & & \\ \hline Strawberries & & & & & & & \\ (_Fragaria ananassa_ cv. & & & & & & \\ Camarosa) & & & & & & & \\ purece & & & & & & & \\ \hline \end{tabular} T = temperature; \(\Delta\)T = Final T \(-\) Initial T (20 \({}^{\circ}\)C).  
\end{table}
Table 5: Energy estimation for at least 80% PPO inactivation by thermal treatment.
## 5 Results and Discussion of Findings
### Computational Time Analysis  
Computational resources, especially computational time, often play a pivotal role in the choice of an algorithm in time constrained applications. However, in cases where computational time is not a constraint, the selection of an algorithm is often based purely on performance. This subsection compares the computational time obtained for the binary optimizers considered in this study. Table 7 outlines the performance of all the algorithms with respect to each of the applied benchmark datasets.  
The computational time of BSNDO was abnormally distributed. However, we found that BWOA showed reduced computational time for the BreastEW, CongressEW, Exactly, Iris, Exactly2, Ionosphere, Sonar, SpectEW, Tic-tac-toe, Vote, Wine, Zoo and KrVskpEW datasets. BSPO performed best on the Lymphography dataset, while BDMO reported a reasonable computational time with the HeartEW and M-of-n datasets. The proposed method, BEOSA, demonstrated minimal computational time with the Leukemia and Colon datasets.  
Figure 10 shows a graphical illustration of the distribution of the computational time for each dataset with respect to all of the tested binary optimization methods. The figure shows that with most binary optimizer algorithms, BSFO often demanded the most computation time, followed by BDMO and then BIEOSA. BSNDO and BGWO were shown to require less computation runtime. The implication of this is that the proposed BEOSA algorithm achieved outstanding performance with an average computational time compared with those of the other binary optimizers.
# Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks
## 2 System Model and Problem Formulation
### Underwater Channel Model  
In the underwater environment, the signal transmission is usually achieved by acoustic transmission, which has characteristics of low speed and has long delay. Due to the reflection, refraction, and current, the pathloss in underwater transmission is large. The pathloss mode can be given as follows:  
\[A(d_{s},f)=d_{s}^{\tau}\phi(f)^{d_{s}}, \tag{15}\]  
where \(d_{s}\) is the distance between the sensor node and sink node, \(f\) is the carrier frequency, and \(\tau\) is a constant which is usually set as 1.5.  
The term \(\phi(f)\) is called as absorption coefficient and given as follows:  
\[\phi(f)=10^{\frac{\lambda(f)}{10}}. \tag{16}\]  
The \(\lambda(f)\) can be estimated by Thorps formula as  
\[\lambda(f)=\frac{0.11f^{2}}{1+f^{2}}+\frac{44f^{2}}{4400+f^{2}}+\frac{2.75}{10 ^{4}}f^{2}+0.003. \tag{17}\]  
Ideal power control is assumed in this paper. Then, suppose that the power required by the sink node is \(P_{0}\), and the sensor nodes need to take the power as least \(P_{0}/A(d,f)\) to transmit the signal. Based on the analysis above, the energy cost model can be provided as follows:  
\[E_{i}=\left\{\begin{array}{ll}E_{c}+E_{u}A^{-1}(d_{s},f),&\text{if the node is selected,}\\ E_{c},&\text{otherwise.}\end{array}\right.\forall i=1,2,...,N, \tag{18}\]  
where \(E_{c}\) denotes the circuit energy consumption and \(E_{u}\) is the energy cost that can meet the requirement of minimum power without path loss. Compared with \(E_{u}A^{-1}(d_{s},f)\), the circuit energy consumption can be ignored because it is the same for each sensor node. The main focus of this paper is that the node selection contributes to the influence of network lifetime due to different energy consumption of each node.
## 6 Experimental Results  
The characteristics of the mmW propagation were observed with a two-port vector network analyzer measuring the scattering parameter S\({}_{21}\). The data of these results were stored for later processing, in order to find the received signal respective magnitude and phase shift. The attenuation coefficient and the group delay are extracted and compared to the results obtained from the model. The study was focused on frequencies in the vicinity of 28 GHz allocated for the 5G cellular network.
## 3 The Proposed Satellite Images DSM Generation Method  
In this paper, we propose a DSM generation method from satellite images based on the DPBA optimization algorithm, which is not only suitable for satellite image pairs but also for multi-date satellite images. The block diagram of the proposed method is shown in Figure 1.  
The input of the proposed method consists of satellite images and corresponding RPC files. In Figure 1, different types of images have different processing methods. For the satellite image pair, the proposed method first directly matches the features extracted from the image pair, then refines the RPC parameters with the DPBA algorithm, and finally implements the stereo reconstruction with the improved RPC files and the feature matching results to obtain the 3D point cloud and DSM. For multi-date satellite images, the image pair selection is implemented before feature matching, and the 3D point cloud is aligned and fused after stereo reconstruction to generate the final 3D point cloud and DSM.
## 3 Methodology
### Speed Control-Based Intersection Coordination Model (SICM)  
#### 3.3.1 Calculation of Spatio-Temporal Trajectory  
Before establishing the model, we need to study the computing method of the spatio-temporal trajectory. For the sake of clarity, some parameters and variables need to be classified (Table 2), which can be obtained via V2I techniques. Figure 9: Flowchart of conflict detection and resolution. After that, the occupation time for the four conflict points of each vehicle can be calculated as follows (the procedures are shown in Figure 9). 1. The vehicle will accelerate uniformly from the initial speed \(v_{1}^{init}\) to the design speed \(v^{def}\) with acceleration \(a_{\max}\) in the approach lane, then we have \[id=1,2,\cdots,num\] (3) \[t_{id}^{act}=\left|v_{id}^{init}-v^{def}\right|\div a_{\max}\] (4) \[s_{id}^{act}=\left(\left(v^{def}\right)^{2}-\left(v_{id}^{init}\right)^{2} \right)\div 2a_{\max}\] (5) where \(t_{id}^{act}\) represents the time needed by the \(id\)th vehicle to accelerate uniformly and \(s_{id}^{act}\) represents the distance traveled during acceleration. 2. The vehicle will move at a constant speed through the remaining part of the approach road and intersection until it enters the departure road, then we have \[t_{id}^{unif}=\left(len^{entna}-s_{id}^{act}+len^{inter}\right)\div v^{def}\] (6) \[TB_{2\times v_{i}^{ul}-1}^{id}=\left(len^{entna}-s_{act}+dist_{id,i}^{cp} \right)\div v^{def},\;i=1,2,3,4\] (7) \[TB_{2\times v_{i}^{ul}}^{id}=\left(len^{entna}-s_{id}^{act}+dist_{id,i}^{cp} +L_{id}\right)\div v^{def}\] (8) where \(t_{id}^{unif}\) represents the time of constant motion, \(i\) represents the index of four conflict points, \(cp_{i}^{id}\) represents the serial number of the \(i\)th conflict point through which the \(id\)th vehicle will pass, and \(dist_{id,i}^{cp}\) represents the distance between the \(i\)th conflict point and the stop line at the intersection. Additionally, \(TB_{2\times cp_{i}^{ul}-1}^{id}\) and \(TB_{2\times cp_{i}^{ul}}^{id}\) represent the start and end time of occupation at the \(i\)th conflict point, respectively.
[25] recognizing that factors such as Mohs hardness, color, scale, and especially light intensity impact recognition accuracy, they developed a luminance equalization algorithm. By combining histogram equalization with the Laplace algorithm to preprocess sample luminance and utilizing the YOLOv5 model for identification, their method achieved 95.6% accuracy in identifying 50 common minerals, demonstrating the effectiveness of this luminance equalization-based deep learning approach. [PERSON] et al. [26] demonstrated the utility of CNNs in classifying rock images, employing deep residual networks with transfer learning to achieve enhanced accuracy. This approach enables the adaptation of pre-trained models, such as VGG16 trained on large datasets like ImageNet, for specialized geological applications. Similarly, [PERSON] et al. [27] used a Res-VGG-UNet model to identify fractures and minerals in rock samples, showcasing the efficacy of deep learning in analyzing geological features. [PERSON] [28] applied a pre-trained VGG16 model with transfer learning and data augmentation to classify seven mineral types, achieving 83% accuracy while addressing challenges in differentiating visually similar minerals. Many of the approaches highlighted in previous studies, while highly effective in controlled environments, are not always suitable for real-time applications due to their computational demands and reliance on extensive datasets. Techniques such as CNNs or transformer-based models often require significant processing power, making them less practical for dynamic, high-frame-rate scenarios like video-based mineral identification. In this context, the HCC which unlike deep learning models does not require extensive computational resources, could be ideal for embedded systems or devices with limited processing power, presenting a promising solution for real-time applications by efficiently detecting regions of interest (ROIs) in video frames. These ROIs can then be passed to pre-trained CNNs for detailed classification, leveraging their powerful feature extraction and pattern recognition capabilities. This integration balances the speed and efficiency of HCC with the accuracy of CNNs, creating a scalable, robust framework suitable for real-world, dynamic environments where both speed and precision are critical. By addressing the limitations of purely CNN-based systems, the HCC-CNN synergy offers a practical pipeline for sophisticated, real-time mineral detection and classification. To showcase the practical utility of this methodology, it has been embedded in Mineral Quest, an interactive educational quiz game designed for real-time mineral recognition and classification. The game provides an engaging platform for users to learn while exploring advanced mineral identification technologies. Detailed descriptions of the game's features and methodology will follow.
# Optics Based Label-Free Techniques and Applications in Brain Monitoring
## 4 Current PAI Technology Used in Brain Imaging  
Noninvasive imaging is highly important for the detection and management of cerebral abnormalities and neuroscience research. Imaging and monitoring of cerebral blood oxygenation are necessary for diagnostics and management of patients with traumatic brain injury, stroke and other neurological conditions. Compared with other deep imaging techniques, for example functional connectivity DOT or fNIRS, PAI has a better spatial resolution and pure absorption sensing. In addition, PAI can do molecular imaging by using a wide variety of optical biomarkers. Moreover, PAI can simultaneously sense blood oxygenation, HbT and blood flow.  
There are several types of PAI techniques in development, roughly classifying as optical resolution PAM (OR-PAM, <1.5 mm imaging depth in soft tissues), acoustic-resolution PAM (AR-PAM, <5 mm depth) and photoacoustic computing tomography (PACT or PAT, <7 cm depth) with different imaging resolutions [13]. Although initial PAI was developed to image human breast cancer and tissue tumors from the 1990s, these techniques including AR-PAM, OR-PAM and PAT have successfully achieved great progresses in small animal study, such as in the fields of imaging brain (Figure 3), breast, ear, rear, abdomen or even all body, as well as internal organs such as arterial internal wall, esophagus lumen and colorectum (by so-called photoacoustic endoscopy, PAE). There is a huge amount of excellent publications in these important study fields.  
Figure 3: The basic setup of photoacoustic imaging (PAI) includes a pulse laser, ultrasound transducer and data acquisition system.