esa-sceva/satcom-chunk-collection huggingface dataset:
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 4 Discussion  
### Visualization  
As shown in Figure 10, we compared the experimental results of different attention mechanisms on the UAVid dataset to further clarify their specific roles. We found that LNA had the worst results and FLA obtained the best overall performance, proving that our method can achieve equivalent or better results than the Softmax attention mechanism. In addition, we visualized the attention effect of the segmentation head and found that the attention was equally distributed in the important regions of the image, which ensures that the model can accurately segment different classes. The visualization results further showed that the segmentation effects of our method outperformed traditional LNA, and it can achieve equivalent results to the Softmax attention mechanism while only introducing a small computational complexity.
# Impact of Plasma Combustion Technology on Micro Gas Turbines Using Biodiesel Fuels
## 2 Materials and Methods
### Fuel Characterization  
We adopted biodiesel fuel extracted from animal fat organic waste (B20, B50, B75 and B100) blended mixtures with conventional diesel, as well as kerosene (control) aviation fuel for the operation of the fabricated MGT engine. The six proposed six liquid fuels require stringent preconditioning before adoption in GTEs due to their more comprehensive range of hydrocarbons and particulate contents. The physical and chemical properties of each proposed fuel for the operation of the MGT engine were characterized to investigate the engine's combustions performance [42]. The physical and chemical properties of the fuel must be determined for significant adoption GTE operations [43]. The characterization of these is based on selected factors (specific gravity, density, kinematic viscosity, total acid number, water content, total sulfur, flash point, lubricity, cloud points, and pour points). These were validated in compliance with the American Society for Testing and Materials (ASTM) D6751 standards [44]. The test was conducted at the Petroleum Research Center, Kuwait Institute for Scientific Research. The properties of viscosity, density, and surface tension play a central role in spray combustion. Fuel viscosity affects the power required to pump the fuel through the fuel system, as well as atomization and droplet evaporation. The higher the viscosity of the fuel, the lower the quality of atomization, leading to soot formation and resulting in carbon deposits within the combustion system. Carbon deposits can damage hardware due to high thermal radiation and clogging. Most liquid fuel systems also require separate air atomizing systems during the ignition process, depending on the type of atomizer used in the fuel injection system. The six characterized fuel samples for fabricated MGT testbed engine deployment are displayed in Figure 1.
## 5 Results and Discussions  
### Impact on Average Latency  
Figure 6 shows the performance of the proposed approach and other state-of-the-art approaches in a linear wireless network. The size of the content catalogue is 100 and the zipf distribution parameter of \(\alpha=1.3\). The cache size of each ICN node is assumed to be \(\mathcal{B}=25\) and The buffer size of \(\mathcal{B}=25\), enables each  
As the distance between a node and the server increases, the latency increases while the request satisfaction decreases and vice versa. Similar is the case of hit ratio and throughput. The proposed caching scheme \(SOCC\) performed better than the others by a minimum of 1.3 hops. The NO-CACHE scheme performed worst compared to other approaches and is considered our reference point. The Cache Everything (CEE) and Random Caching \(p\) = 0.9 got the same results but failed to perform better than our proposed approach.  
In the mobile scenario, we assume that the server is equipped with a catalogue of 1000 contents and a Zipf popularity parameter of \(\alpha=0.8\). The cache size of all the nodes is kept the same, varying from 10 MB to 500 MB. In Figure 7, as the cache size increases, the possibility of satisfying users' requests increases, thus, decreasing the average latency for all the approaches. The greater the caching capacity, the greater the chance to cache the content; hence, the latency that depends on the number of hops will be smaller. For smaller cache sizes, such as 50 MB, the proposed approach achieved a minimum average latency of 3.75 hops compared to CEE, Random caching \(p\) = 0.9 and 0.5, which achieved average latency of 4.00, 4.5 and 5.4 hops, respectively. As the cache size increases to 100 MB, the average latency for the proposed scheme decreases to 3.29 hops, while for CE, Random caching \(p\) = 0.9 and 0.5 decreases to 3.4, 4.00 and 5.1, respectively. Figure 7 shows that at a cache size of 500 MB, our proposed approach outperformed other state-of-the-art approaches by achieving a minimum average latency of 2.0 hops.
## 4 Experiments
### Second-Stage: Relational Knowledge Distillation-Based Pig Face Recognition  
#### 4.2.1 Implementation Details  
To validate the efficacy of our methodology, we constructed a dedicated pig face dataset tailored for the pig face recognition task. Utilizing the pre-trained YOLO-v8 model from the previous phase, we processed an extensive collection of images from 56 unlabeled pigs. Through the application of the Multi-Scale Structural Similarity (MS-SSIM) technique, we filtered the dataset to retain images with low similarity, thus ensuring a diverse range of facial features for robust recognition. From this refined dataset, we randomly selected 400 distinct images per pig class, resulting in a comprehensive dataset that spanned 50 categories with a total of 20,000 images. This dataset was thoughtfully partitioned into training, validation, and testing sets with a ratio of 5:2.5:2.5. Such a distribution was designed to optimize model performance by providing a balanced and varied set of examples for training while allowing for thorough evaluation during validation and testing.  
For the implementation of our method, we selected the Vision Transformer (ViT) as the teacher model and ShuffleNet-V2 as the student model, leveraging Relational Knowledge Distillation for efficient knowledge transfer. The ViT was pre-trained on the ImageNet dataset, providing a solid foundation for feature extraction capabilities. In the training phase for both models, we utilized a Stochastic Gradient Descent (SGD) optimizer with an initial learning rate of 0.1. The optimizer's parameters were fine-tuned with the stochastic  
Figure 9: Predicted unlabeled images of 56 pigs using YOLO-v8.  
gradient descent (SGD) optimizer and a learning rate of 0.5, ensuring optimal convergence rates and regularization. A tailored learning rate schedule was employed to adapt the learning rate during training dynamically. We used the learning policy for the first 20 epochs to maintain a steady learning rate, promoting stable initial learning progress. After the 20 th epoch, another scheduler with a polynomial decay was applied, starting from the 50 th epoch, to gradually reduce the learning rate, allowing for fine-tuning of the models' weights as training progressed. The entire training was set to run for 100 epochs.  
For the evaluation of model performance, we used a comprehensive suite of metrics that included Accuracy-top1, Precision, Recall, and F1 Score, alongside computational metrics such as FLOPs (Floating Point Operations per second) and the number of parameters. These metrics provided a holistic assessment of the models' abilities to recognize pig faces accurately, considering both the proportion of correct identifications and the balance between precision and recall. These detailed configurations of the training and evaluation process were meticulously planned to ensure the robustness and generalizability of the pig face recognition models, aligning with the goal of deploying these models in environments with computational constraints.
# Channel Measurement and Modeling for 5G Urban Microcellular Scenarios
## 4 Evaluations
### Path Loss
This, of course, is related to the scenario, but also because the LS fit has been limited to the distance range \(5\text{--}80\,\text{m}\) (propagation around one building corner only) to avoid outage at both \(10\) and \(60\,\text{GHz}\) to maintain full comparability. For the measured RA scenario, the PL exponent is higher for \(60\,\text{GHz}\). Figure 4: Measured and simulated path loss (PL) versus distance with linear regression line; (**a**) OS-CC scenario; (**b**) SC-CC scenario. Figure 4 shows the simulated PL in addition to the measured results. For OS-CC, the simulation shows a good agreement with the measurement: the regression lines are very close to each other and the standard deviation is predicted very well (see Table 2). Discrepancies arise for the SC-CC scenario at larger distances: the simulations yield a lower PL exponent and a smaller \(\sigma\). Making use of recorded videos during the measurements, investigations revealed that the LOS was temporarily obstructed by persons with increasing occurrence for increasing distance. Since pedestrians have not been included in the simulations, these effects cannot be reproduced. For the OS-CC scenario, obstruction was unlikely due to the TX positions on the lawn and the much lower density of pedestrians.
###### Abstract  
It is well-known that the reduction of noise levels is not strictly linked to the reduction of noise annoyance. Even earthmoving machine manufacturers are facing the problem of customer complaints concerning the noise quality of their machines with increasing frequency. Unfortunately, all the studies geared to the understanding of the relationship between multidimensional characteristics of noise signals and the auditory perception of annoyance require repeated sessions of jury listening tests, which are time-consuming. In this respect, an annoyance prediction model was developed for compact loaders to assess the annoyance sensation perceived by operators at their workplaces without repeating the full sound quality assessment but using objective parameters only. This paper aims at verifying the feasibility of the developed annoyance prediction model when applied to other kinds of earthmoving machines. For this purpose, an experimental investigation was performed on five earthmoving machines, different in type, dimension, and engine mechanical power, and the annoyance predicted by the numerical model was compared to the annoyance given by subjective listening tests. The results were evaluated by means of the squared value of the correlation coefficient, \(R^{2}\), and they confirm the possible applicability of the model to other kinds of machines.  
annoyance; noise; numerical model; earthmoving machines; sound quality +
Footnote †: journal: Applied Sciences
## 4 Experiments  
### Datasets  
To test our material-related observations, we utilized two datasets, one for classification and another for object detection.  
Figure 10: Visible- (**a**) and SWIR-range (**b**) imaging examples of real and artificial fruits/vegetables data.  
Figure 9: Region-based intensity analysis for mannequin and human subjects: (**a**–**c**) show the intensity analysis for different regions of the mannequin (head, left hand, and body), while (**d**–**f**) present the corresponding analysis for a human subject.  
* _Fruit and Vegetable Dataset (Classification)_: This dataset consists of paired visible-spectrum RGB and SWIR images of fruits and vegetables, captured using a Crevis HG-A130 SW camera, as we detailed in Section 3.2. The dataset encompasses a variety of real and artificial fruits and vegetables, including bell peppers, bananas, carrots, and oranges. Three real and three artificial samples were acquired for each fruit type, resulting in a total of 24 objects. For each object, RGB-SWIR paired images were captured, with data from two objects used for training and the remaining object's image used for inference. Figure 11 showcases examples from this dataset (the RGB-SWIR Fruit and Vegetable Dataset is publicly available for research purposes at the following link: [[https://huggingface.co/datasets/STL-Yonsei/SWIR-Fruit_and_Vegetable_Dataset](https://huggingface.co/datasets/STL-Yonsei/SWIR-Fruit_and_Vegetable_Dataset)]([https://huggingface.co/datasets/STL-Yonsei/SWIR-Fruit_and_Vegetable_Dataset](https://huggingface.co/datasets/STL-Yonsei/SWIR-Fruit_and_Vegetable_Dataset)) (accessed on 25 November 2024)).
* _Human vs. Others Dataset (Object Detection)_: This dataset comprises paired data across different wavelengths in both RGB and SWIR ranges, captured using an ANT SWIR multispectral snapshot camera. The objects included in this dataset are a person, a fabric mannequin, a plastic mannequin, and a banner with a human image on it. Data were captured for each object individually and in scenarios where all objects were present within a single image. As depicted in Figure 12, the individually captured data were used for training, while images with all objects were used as inference data.
## 5 Evaluation
### Security Evaluation
#### 5.3.2 Unauthorized Access  
The unauthorized access attack consists of accessing the content of the encrypted data without a valid key. We perform an evaluation against this attack, which involves two steps: first, a secure encryption of data with a valid secret key and then an attempt to decrypt these data by using a different, unauthorized secret key. This approach simulates a real-world attack scenario where an attacker tries to gain access to encrypted information. The system successfully identified the unauthorized attempt as it continuously failed to decrypt the data by using the unauthorized key. This resulted in either an empty output or an error. These results show the proposed solution's robust security and efficiency in preventing unauthorized access and the reliability of system encryption and key management schemes in protecting sensitive data.
# Optimization of Effective Throughput in NOMA-Based Cognitive UAV Short-Packet Communication
## 3 Solutions of the Formulated Problems
### Information Bits Optimization with Fixed \(p_{1}\),\(p_{2}\), and \(N_{s}\)  
When the power allocation and sensing time are given, we only need to optimize the information bits \(I\). In the following theorem, we prove that \(\Phi_{2}(I)\) is a concave function with respect to \(I\).  
Theorem 5: _Given the power allocation \(p_{1}\),\(p_{2}\), and the sensing time \(N_{s}\), \(\Phi_{2}(I)\) is a concave function with respect to \(I\)._Proof.: Given the power allocation \(p_{1}\),\(p_{2}\) and the sensing time \(N_{s}\), \(\tilde{\epsilon}_{2}\) is determined by \(I\). Therefore, we employ \(\tilde{\epsilon}_{2}(I)\) instead of \(\tilde{\epsilon}_{2}\). The first- and second-order derivatives of \(\Phi_{2}(I)\) are  
\[\Phi_{2}^{\prime}(I)=P(H_{0})\frac{1}{N}\Big{[}1-P_{f}(N_{s})\Big{]}\big{[}1- \tilde{\epsilon}_{2}(I)-\tilde{\epsilon}_{2}^{\prime}(I)I\big{]}, \tag{29}\]  
\[\Phi_{2}^{\prime\prime}(I)=-P(H_{0})\frac{1}{N}\Big{[}1-P_{f}(N_{s})\Big{]} \big{[}1\tilde{\epsilon}_{2}^{\prime\prime}(I)+2\tilde{\epsilon}_{2}^{\prime} (I)\big{]}. \tag{30}\]  
Then, to simplify the notation, we denote \(f(I)=f(\gamma_{2},N_{s},R_{2})\), the first- and second-order derivatives of \(\tilde{\epsilon}_{2}(I)\) are  
\[\tilde{\epsilon}_{2}^{\prime}(I)=-\frac{1}{\sqrt{2\pi}}e^{-\frac{\tilde{ \epsilon}_{2}^{\prime}(I)}{2}}f^{\prime}(I), \tag{31}\]  
\[\tilde{\epsilon}_{2}^{\prime\prime}(I)=\frac{1}{\sqrt{2\pi}}e^{-\frac{\tilde{ \epsilon}_{2}^{\prime}(I)}{2}}\Big{[}f(I)(f^{\prime}(I))^{2}-f^{\prime\prime} (I)\Big{]}, \tag{32}\]  
where the first- and second-order derivatives of \(f(I)\) are given by  
\[f^{\prime}(I)=-\ln 2[V_{2}(N-N_{s})]^{-\frac{1}{2}}, \tag{33}\]  
\[f^{\prime\prime}(I)=0. \tag{34}\]  
Based on (33) and (34), we can obtain that \(\tilde{\epsilon}_{2}^{\prime}(I)>0\) and \(\tilde{\epsilon}_{2}^{\prime\prime}(I)>0\). Thus, \(\Phi_{2}^{\prime\prime}(I)<0\), which leads to the fact that \(\Phi_{2}(I)\) is a concave function with respect to \(I\). Theorem 5 is proved.  
Based on Theorem 5, we can obtain the optimal information bits \(I^{*}\) via the bisection method by solving the following equation:  
\[\Phi_{2}^{\prime}(I)=0. \tag{35}\]
# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
## 5 The Management Layer
In addition, Machine Learning techniques will be used in the smart monitoring platform in order to estimate the extracted parameters of the crops' growth rate and also help to identify objects or animals trough the collected images by using Object-Based Image Analysis (OBIA) [42]. Moreover, new machine learning models--algorithms focused on data classification [43; 44]--can be used to minimize the size of redundant data and fasten the analysis. Furthermore, all these services are hosted in the cloud to be able to access them remotely from any geographical location. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Service Type** & **Tools** & **Description** \\ \hline Information management & Database & The central server database for storing and maintaining the sensor collected data, management commands and application user information. \\ \cline{2-3}  & Management logic & The process of managing the systems units, organizing and displaying the evaluated data into a user-friendly way. \\ \hline Big Data analytics & Apache Hadoop Framework & Complex process of examining large and varied data sets with an intention to uncover meaningful and useful information that can help in deriving conclusion and take decisions. \\ \cline{2-3}  & Big Data hardware platforms & The use of different hardware platforms for Big Data analytics according to the available hardware, scale-ability and performance characteristics of each platform. \\ \hline Data and Image processing & Photogrammetry techniques & Extracting three-dimensional digital surface or terrain models of the field and orthophotographs. \\ \cline{2-3}  & Machine learning classification algorithms & Classification of data to decrease the size of redundant information and identify objects or animals. \\ \hline Data mining & Apache Mahout Framework & Systematic and sequential process of identifying hidden patterns and information in a large dataset. \\ \cline{2-3}  & Object-Based Image Analysis & Identify objects or animals through the collected images \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Smart farming monitoring system services.
# Exploring the Direct and Indirect Use of ICT Measurements in DODME (Dynamic OD Matrix Estimation) +
## 6 Consideration on the Quality of the DODME Results
This proposed weighting average is defined as follows:  
\[MSSIM(X,X^{H})=\frac{\sum_{i=1}^{N_{S}}W(a_{i},b_{i})SSIM(a_{i},b_{i})}{\sum_{i=1 }^{N_{S}}W(a_{i},b_{i})} \tag{17}\]  
where \(a_{i}\) and \(b_{i}\) are the \(i\)th windows of \(\mathbf{X}\) and \(\mathbf{X}^{H}\), respectively, while the weight \(W(a_{i},b_{i})\) is given by:  
\[W(a_{i},b_{i})=\log\left[\left(1+\frac{\sigma_{a_{i}}^{2}}{C_{2}}\right)\left( 1+\frac{\sigma_{b_{i}}^{2}}{C_{2}}\right)\right] \tag{18}\]  
In terms of the variance of the selected windows in the origin-to-destination (OD) matrices, these weighting factors correspond to the variance of the total generated trips from an origin (or total attracted trips to a destination) to all destinations (from all origins); thus, the weighting is increased as the origin is distributed to all destinations (or the destination attracts trips from origins) becomes more non-uniform. Moreover, these weights also take into account the magnitude of their contribution (implicitly in the variance value) so that the contribution of each origin or destination to the overall demand pattern is well balanced. Under the assumption of the reliability of the historical OD matrix \(\mathbf{X}^{H}\), at least concerning the structural information on the mobility patterns that it contains, in this study, we proposed to use the relative difference in the MSSIM calculation in Equation (17) between the estimated OD matrix \(\mathbf{X}\) at iteration \(k\) and the historical OD matrix \(\mathbf{X}^{H}\) as stopping criterion. The stopping criterion is:  
\[\frac{MSSIM\left(X^{(k)},X^{H}\right)-MSSIM\left(X^{(k-1)},X^{H}\right)}{MSSIM \left(X^{(k-1)},X^{H}\right)}<\epsilon \tag{19}\]  
When no historical OD matrix is available, or the historical OD matrix is unreliable, a way of avoiding the fitting process distorting the structure could be to apply the alternative criterion:  
\[\frac{MSSIM\left(X^{(k)},X^{(k-1)}\right)-MSSIM\left(X^{(k-1)},X^{(k-2)} \right)}{MSSIM\left(X^{(k-1)},X^{(k-2)}\right)}<\epsilon \tag{20}\]  
This is based on the relative structural change of the estimated matrix after each step of the iterative process.
# Development and Finite Element Analysis of a Novel Bent Bone Plate
## 2 Materials and Methods
### Geometrical Model of a Bone Plate  
Prior to performing any FEA analysis, a bone plate is designed using CAD software like SolidWorks. By utilizing the average size of the human femoral bone, a bone plate is designed with an overall length of 260 mm, width of 30 mm, and thickness of 3 mm, as can be seen in Figure 1. Here, during the design process, the newly developed flat plate (Figure 2) is bent in the middle with the bending angle varying from 10\({}^{\circ}\) to 50\({}^{\circ}\) (Figure 3), in order to find out which one is the best. These plates will be compared to the conventional bone plate with a size of 180 mm in length and the same width and thickness, as can be seen in Figure 1. After the design is complete, it is then exported into a format that could be read by the FEA software, such as COMSOL Multiphysics. The simulation is performed  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Mechanical Properties** & **Value** \\ \hline
1. & Density & 4430 [kg/m\({}^{3}\)] \\ \hline
2. & Young’s Modulus & 113.8 [GPa] \\ \hline
3. & Poisson’s Ratio & 0.342 \\ \hline
4. & Thermal Expansion Coefficient & 8.6 [\(\mu\)m/m\({}^{\circ}\)C] \\ \hline
5. & Yield Strength & 880 \(\times\) 10\({}^{3}\) [MPa] \\ \hline
6. & Tensile Strength & 950 \(\times\) 10\({}^{3}\) [MPa] \\ \hline \end{tabular}
\end{table}
Table 1: Titanium alloy 6 Al-4V mechanical properties [18; 19].  
using COMSOL Multiphysics 5.2, version 5.2.0.166 by COMSOL AB, Stockholm, Sweden. The simulation is done on a PC using Microsoft Windows 10 Pro 64-bit with Processor Intel(r) Xeon(r) CPU E3-1230 v3 at 3.30 GHz (8 CPUs) by Intel Corporation(r), California, USA; 32,768 MB RAM, and 455 GB storage.  
During the FEA simulation, the back of the plate is constrained, as it will be sticking to the bone (Figure 4). The boundary load is applied on every wall of the screw hole (Figure 5) to observe the deformation when the plate is fixated using screws onto the bone. Although, generally, not all screw holes will be used, the load is still applied onto all screw holes as the deformation of all screw holes should be analyzed. Various types of tests are conducted using COMSOL Multiphysics, in order to determine the best bone plate, under the same condition. The results observed are displacement, von Mises stress, and first principal strain.