esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Authentication and Credential Establishment in Narrowband IoT and 5G
## 3 Background
### Lo-CoAP-Eap
The original articles that present CoAP-EAP and LO-CoAP-EAP evaluate the protocol performance in comparison with PANA using the COOJA simulator. In this case, as we are introducing this protocol in a new technology (NB-IoT), we deliver experimental results using real devices. The experimental evaluation can be found in Section 6. Please note that NB-IoT is a Layer 2 MAC technology, as opposed to CoAP, which typically leverages UDP. As a consequence, no modifications of the NB-IoT serving network were necessary to implement our proposal. The security considerations regarding the proposed EAP lower layer in this work are inherited from the original work [10]. In this sense, we do not provide a security analysis on the authentication methods, since they have already been provided by each standard EAP method, which is in charge of providing security to the authentication exchange. The EAP lower layer  
Figure 2: Protocol flow of LO-CoAP-EAP with a generic EAP method (EAP-X) _Source:_[24]. needs only to be compliant with the requisites of the EAP standard [26], and, as explained in [10; 30], the proposed work is indeed compliant with said requisites. Furthermore, the establishment of a security association is left to the EAP lower layer, and, in the case of LO-CoAP-EAP, we use an authentication tag implemented in a CoAP option named AUTH, which contains an HMAC of the exchanged messages between the controller and the smart object once the EAP authentication is successful, using the key material derived from the EAP Key Management Framework [31].
# Constrained Connectivity in Bounded X-Width Multi-Interface Networks
## 1 Introduction
In other words, instead of finding the solution that minimizes the overall cost due to the activation of the interfaces along the whole network, or the one that minimizes the maximum cost spent by a single node, the aim becomes to minimize the overall cost subject to the constraint \(p\). This problem has been proved to be NP-hard also for simple instances such as \(p=2\) and unitary costs of the interfaces. For this reason, algorithms design activities have focused their attention to particular topologies where the problem can be solved in an optimal way. We follow  
Figure 1: Example of multi-interface network composed of heterogeneous devices connected by means of different interfaces. this trend by solving the problem in polynomial time for graphs (modeling the underlying network) of bounded pathwidth and for graphs of bounded carvingwidth, which represent two main graph classes frequently studied for hard problems such as \(p\)-Coverage. Actually, we first show that the problem is polynomially solvable for graphs of bounded treewidth. Even though both graphs of bounded pathwidth and graphs of bounded carvingwidth represent two (incomparable) subclasses of graphs of bounded treewidth, the requirement in exploring such classes comes by the fact that specific algorithms allow for better performance.
## 3 Proposed Yielding Protocol
### Overview of the Yielding Process  
The authors previously proposed a yielding protocol in [8] (hereafter referred to as the \"former method\") and verified its operation using an experimental system, which consisted of Raspberry Pi computers mounted on radio control cars. However, the former method had problems with the message transmission efficiency and vehicle identification accuracy. The present paper presents an improved method. This section details the improved method. The sequence of the yielding process is the same as the former method and is shown in Figure 1. Vehicle A sends a request. Vehicle B receives the request, makes a decision, and agrees to yield. Vehicle A proceeds to the intersection and sends a thanks message. The yielding protocol begins when a low-priority vehicle comes near the intersection, or when a priority vehicle wishing to turn right into a low-priority road is at the head of the line of vehicles and is running at a reduced speed or at a halt. Self-driven vehicles operate in accordance with this yielding process. Drivers in human-driven vehicles make the final judgment about driving and input their intentions by using a user interface (UI).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Comparison Item** & **[3]** & **[4]** & **[5,7]** & **Proposed Method** \\ \hline Intersection shape & Crossroads with multiple lanes & T-shaped & & Crossroads \\ \hline Traffic light & & & None & \\ \hline Roadside unit & & Present & & None \\ \hline Prioritization of roads at the intersection & No & Yes & No & Yes \\ \hline Pedestrians & & Not considered & & Considered \\ \hline Differences in judgments between self-driven and human-driven vehicles & Not considered & Only the difference in response time is considered & Not considered & All factors shown in Table 1 are considered \\ \hline  & The order in which vehicles enter each lane at intersection is adjusted based on the intersection & The order in which two competing vehicles enter the times of competing vehicles are adjusted based on the intersection & Whether a vehicle yields the way to the intersection is adjusted based on the intersection & whether vehicle is adjusted based on adjusted based on the intersection & adjusted based on the intersection \\ \multirow{3}{*}{Control of entry into the intersection passing reservation notices that vehicles send to the roadside unit sent to the roadside unit unit unit unit unit unit unit} & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of methods of controlling the traffic of both self-driven and human-driven vehicles.
# A Multi-Scale Contextual Information Enhancement Network for Crack Segmentation
## 4 Experiments
### Comparison with Other Methods
#### 4.3.1 Results on DeepCrack-DB  
Figure 7 shows the segmentation results of the six methods on some images in the DeepCrack-DB dataset, and Table 1 shows a quantitative comparison of the IoU, precision, recall, and F1 score metrics. As can be seen in Figure 7, all methods, except our MCIE-Net, exhibit varying degrees of false detection when there are interference factors in the image  
Figure 6: Crack images of the two datasets. (**a**) DeepCrack-DB, (**b**) CFD, (**c**) CCSD.  
background region. As shown in Table 1, U-shaped networks such as U-Net and MCIE-Net achieve better results in crack segmentation, because their skip connection structures play an important role in capturing the fine structural features of the cracks, which avoids the problems of detail loss and segmentation discontinuities. MCIE-Net further enhances the contextual semantic information extraction and improves its adaptability to complex background conditions; thus, it achieves the best results in all four metrics, which are 91.28%, 95.67%, 94.59%, and 94.82%, respectively.
## 3 Methodology
### Video Encoding/Decoding  
In the Video Encoding block, the original YUV file is encoded to the H.264 standard with a frame rate of 50 FPS and a maximum duration of 10 s using FFmpeg (FFmpeg is a command line tool used to convert multimedia files between formats [29]). YUV is a raw format commonly used in video compression studies [30]. The videos used in the simulations are Cross, Crowd, Duck, Tree, and Park, which were obtained from Xiph.org ([[https://media.xiph.org/video/derf/](https://media.xiph.org/video/derf/)]([https://media.xiph.org/video/derf/](https://media.xiph.org/video/derf/)) (accessed on 4 April 2024)). The resolution, frame rate, number of frames, length of GOP, B-frames per GOP, and Quantization Parameter (QP) are considered as video codification parameters, as presented in Table 1. The Video Decoding block transforms the received H.264 video into YUV format, allowing for further processing and analysis.  
Figure 1: Simulation of video transmission.  
Figure 2: Noise variation in four selected frames.
# Few-Shot Continuous Authentication for Mobile-Based Biometrics
## 3 Method
### Random Pattern Mixing
#### 3.2.4 Smote  
The SMOTE [33] is a popular augmentation method for imbalanced data. It randomly selects a sample \(X\) from a minority class and its \(k\)-nearest sample \(X_{NN}\). Then, a synthetic sample is produced by interpolating these samples:  
\[\tilde{X}=X+\lambda|X-X_{NN}| \tag{4}\]  
where \(\lambda\) is a random value in [0, 1].  
These conventional augmentation methods generate synthetic samples of a minority class (seen impostors in our case) within the feature space of seen classes. However, unseen impostors have unbounded and arbitrary behavior patterns. Therefore, augmenting impostor data in class-constrained feature space may not sufficiently increase the generalizability against unseen impostors.  
**Random Pattern Mixing**  
To improve the robustness to unseen impostors, we propose random pattern mixing. We hypothesize that the feature space of unseen impostors is larger than that of seen impostors. This hypothesis is reasonable in our task because seen impostors are only partial profiles of a few impostors, while unseen impostor profiles are unbounded. To simulate unseen impostor features, we enlarge the feature space of impostors by mixing seen impostor and uniform random patterns. Since random patterns are class-unconstrained, our method can produce synthetic instances not present in the feature space of seen impostors.  
Given a matrix \(R=\{U_{1},U_{2},\dots,U_{d}\}^{\top}\in\mathbb{R}^{d\times T}\), in which each value in \(U_{i}\in\mathbb{R}^{T}\) follows the uniform distribution:  
\[U_{i}=\{\epsilon_{1},\epsilon_{2},\dots,\epsilon_{T}\},\forall\epsilon_{j} \sim\mathcal{U}(0,1) \tag{5}\]  
where the uniform distribution is bounded by \([0,1]\), supposing every sample is min-max normalized.  
Then, we mix the seen impostor and random patterns. By letting a seen impostor sample be \(X\), it follows:  
\[\tilde{X}=M\odot X+(1-M)\odot R \tag{6}\]where \(\odot\) is the element-wise product, and \(M\in\mathbb{R}^{d\times T}\) is a mask of zeros and ones, in which the ratio of the number zero is adjustable. As the mask contains fewer zeros, the feature space of the generated patterns becomes as narrow as seen impostors. When the values in the mask are all ones, our method is equivalent to up-sampling.  
In a nutshell, the proposed random pattern mixing can efficiently enlarge the feature space of impostors, diversifying impostor features in training data.
# Surgical Instrument Recognition Based on Improved YOLOv5
## 2 Materials and Methods  
### Images Dataset and Augmentation  
In this study, we utilized a blue non-woven fabric commonly found in surgical instrument inventories as the background for our dataset. We carefully selected eight representative surgical instruments for inclusion. A total of 740 original images were collected to construct our comprehensive surgical instrument dataset. Among these, 540 images depicted non-stacked surgical instruments, while the remaining 200 images showcased stacked surgical instruments across the eight instrument types. Each image was manually labeled by our team. Figure 1 provides visual examples of these labeled images.  
The dataset consists of eight representative surgical instruments: hemostat, speculum, napkintong, scissors, tweezers, colposcope, attractor, and stripper. These instruments were selected to encompass distinct inter-class differences and minimal intra-class differences. Figure 2 provides examples of some of the eight surgical instruments.  
Due to the limited number of images available in the dataset of surgical instruments, the training process of deep learning is highly at risk of overfitting. This requires a substantial amount of data and diverse regularization methods. Data augmentation is considered as one of these methods. We applied techniques of image augmentation to increase the dataset and thereby reduce the threat of overfitting in the neural network. A random-mixed image augmentation method was applied, integrating seven essential image augmentation techniques: panning, cropping, brightness adjustment, noise introduction, angle rotation, horizontal flipping, and the cutout technique. Cutout, a straightforward yet potent regularization method for convolutional neural networks, was employed during the training process. Cutout is achieved by selecting a random location within the input image and setting the pixel values within a square region centered at that location to zero (or another constant value). In the random-mixed image augmentation process, these seven augmentation methods are randomly selected and incrementally applied six times, resulting in six augmented images. Figure 3 showcases an example of the results produced by the random-mixed image augmentation method.  
After applying image augmentation, our dataset expanded to over 20,000 samples across the eight instrument types. We randomly generated training and testing sets from these samples, allocating 90% for training and 10% for testing. Additionally, a separate validation set was collected. Table 1 showcases the number of data samples for each instrument.  
Figure 1: Surgical instruments image examples in the dataset: (**a**) is the example of non-stacked; and (**b**) is the example of stacked surgical instruments.  
Figure 2: Examples of the eight surgical instruments.
# Dynamic Stiffness Effect of Mechanical Components on Gear Mesh Misalignment
## 5 Numerical Study
### Numerical Results and Discussion
(**a**) \(f_{G}=0\) Hz (static stiffness) and (**b**) \(f_{\beta}=500\) Hz (dynamic stiffness). The differences in \(F_{\beta X}\) and \(K_{H\beta}\) caused by the dynamic stiffness effect may lead to changes in the peak-to-peak gear transmission error (PPTE), which is a main source of whine noise. Figure 11 compares the PPTE of the three different gear mesh frequencies when the applied torque is 140 Nm. Although the differences are not significant, the effect of the dynamic stiffness becomes much more important when we consider a high-speed gear train, such as what is used in electric vehicles. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Cases** \\ \hline Applied torque (Nm) & 70, 105, 140 \\ Gear mesh frequency (Hz) & 0 (static stiffness), 100, 200, 300, 400, 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Gear mesh misalignments, \(F_{\beta X}\), of the components in relation to applied torque when using static stiffness (\(f_{\beta}\)= 0 Hz). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Normal Force** & **Units** & \
\begin{tabular}{c} **Static Stiffness** \\ \end{tabular} &
\begin{tabular}{c} **Dynamic Stiffness** \\ \end{tabular} \\ \cline{3-4}  & & **0 Hz** & **300 Hz** & **500 Hz** \\ \hline Maximum & N/mm & 340 & 330 & 320 \\ Average & N/mm & 283 & 280 & 281 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Maximum and average normal forces in contact patterns when torque is 140 Nm. Figure 10: Face load factor, \(K_{H\beta}\), in relation to torque and gear mesh frequency. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Applied Torque** & **Components** & **Input Gear (\(\mu\)m)** & **Output Gear (\(\mu\)m)** \\ \hline \multirow{3}{*}{70 Nm} & Shaft & \(-6.696\times 10^{-2}\) & \(-4.311\times 10^{-2}\) \\  & Bearing & \(1.180\times 10^{1}\) & \(-4.481\times 10^{-1}\) \\  & Housing & \(2.191\times 10^{0}\) & \(-1.493\times 10^{0}\) \\ \hline \multirow{3}{*}{105 Nm} & Shaft & \(-6.674\times 10^{-2}\) & \(-3.801\times 10^{-2}\) \\  & Bearing & \(1.023\times 10^{1}\) & \(-3.517\times 10^{-1}\) \\  & Housing & \(2.455\times 10^{0}\) & \(-1.548\times 10^{0}\) \\ \hline \multirow{3}{*}{140 Nm} & Shaft & \(-6.781\times 10^{-2}\) & \(-3.062\times 10^{-2}\) \\  & Bearing & \(8.181\times 10^{0}\) & \(-2.592\times 10^{-1}\) \\ \cline{1-1}  & Housing & \(2.830\times 10^{0}\) & \(-1.621\times 10^{0}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Numerical cases.
# Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area
## 3 Experiment
### UNet  
The UNet [39] semantic segmentation model is a deep learning architecture designed for pixel-wise classification tasks, particularly in image segmentation. In this sample, weutilized the UNet model to perform semantic segmentation on UAV RGB images. The model employs a symmetrical encoder-decoder structure, with skip connections between corresponding encoder and decoder layers to preserve spatial information. This aids in capturing both local and global features, making it particularly effective for such tasks as object detection and boundary delineation.  
Our UNet semantic segmentation model (Figure 6) is designed to process 128 \(\times\) 128-pixel RGB images with a structured architecture aimed at capturing intricate spatial details. The model architecture is structured into two main sections: a contraction path (c1 to c5) and an expansive path (u6 to u9).  
In the contraction path, successive convolutional blocks progressively increase the number of filters to extract and encode features from the input image. Max-pooling layers (p1 to p4) are strategically placed to down sample the spatial dimensions, thereby reducing computational load while preserving essential features. At the bottleneck layer (c5), the model captures high-level context and semantic information, which is crucial for accurate segmentation.  
Conversely, the expansive path of the UNet model focuses on upsampling the feature maps using transposed convolutional layers. These layers help recover spatial resolution lost during the contraction path and enable the model to reconstruct detailed information. Each block in the expansive path (c6 to c9) sequentially reduces the number of filters, allowing the model to refine segmentation boundaries and enhance localization accuracy.  
The final layer of the UNet model (outputs) employs a 1 \(\times\) 1 convolutional layer with a sigmoid activation function, facilitating pixel-wise predictions for each class in the segmentation task. This setup enables the model to output probability maps where each pixel indicates the likelihood of belonging to a specific land cover class.  
During training, the model is compiled using the Adam optimizer, which efficiently adjusts learning rates for each parameter during training. The choice of binary cross-entropy loss function aligns with the pixel-wise nature of the segmentation task, thereby optimizing the model to produce accurate binary predictions for each pixel. The accuracy metric provides insights into the model's performance by measuring the proportion of correctly classified pixels compared to the total number of pixels.
# Cooperative Admission Control with Network Coding in 5G Underlying D2D-Satellite Communication
## Problem Formulation
### Multiple Access Network Coding (Lossy Second Stage)
Now, the selection of the packets from the source depends upon the average number of successful receivers in the set of \(X_{c}\) and \(X_{s}\) and is equal to the \(\sum_{n\in R}\left(1-\vartheta_{n}\right).\) At the same time, the average maximum successful receiver from \(X_{d}\) within vector \(W_{l}\) is equal to \(arg\ max_{pcX_{d}}\sum_{m(W_{l}[n]\
eq 0)}(1-\vartheta_{n})\). Since, \(\gamma_{r}\) is a subset of \(\gamma_{n}\), so \(\sum_{n\in R}(1-\vartheta_{n})\) is the average successful receiver in \(X_{c}\parallel X_{s}\) higher than \(arg\ max_{nc\gamma_{r}}\sum_{n(W_{l}[n]\
eq 0)}(1-\vartheta_{n})\). Between \(X_{c}\) and \(X_{s}\), \(X_{c}\) is sent first on the priority and after that \(X_{s}\) packets are sent from the satellite link. Now the order of transmitting the is, \(X_{c}\) at first, \(X_{s}\) as second and \(X_{d}\) as the third in the sequence. For \(X_{d}\), the device with the maximum number of the average receivers is selected as a transmitter. Now consider the packet p from \(X_{s}\) within in vector \(W_{l}\). Each device \(n\in R\) transmit some part of the data available in its attained set \(A\). Suppose \(W_{l}[x]\) is set of packets vector that is required by the device x. The average number of successful receivers for transmission from x is equal to \(\sum_{m(W_{l}[n]\
eq W_{l}[p])}\left(1-\tau_{x,y}\right)\). In [7] of Algorithm 4, it determines the maximum number of the average receivers for the \(X_{d}\). The upper bound of the packet completion time is given as:  
\[T\leq\left[\max\left(T_{tx,c},\frac{(T_{tx,c}+T_{s,s})(T_{tx,c}+T_{tx,d}+T_{tx, s})}{T_{s,d}+T_{s,s}+T_{tx,d}+T_{tx,s}}\right)\right]. \tag{25}\]  
The proof is provide in Appendix D.
# Traffic-Based Heuristics for Regenerator Placement in Translucent Dynamic Optical Networks
## 3 Definitions
### Premises and Notation  
Suppose a network with \(N\) nodes and MOR given by \(T_{R}\). The route between nodes \(i\) and \(j\) is defined as \(\pi_{ij}=[n_{1},n_{2},\ldots,n_{H_{ij}}]\), where \(H_{ij}\) is the number of nodes in the route. Let us assume that \(\pi_{ij}\) has a total physical length \(T_{ij}\) and a total optical length \(V_{ij}=T_{ij}+(H_{ij}-2)\cdot\epsilon\). The optical length accounts for the physical length added by an extra \(\epsilon\) length per intermediate node, representing the degradation suffered by the signal passing through these nodes [2]. Using \(\pi_{ij}\), \(V_{ij}\) and \(T_{R}\), it is possible to determine the minimum number of regenerators, \(K\), that keeps the optical length of all \(K+1\) resultant TSs in \(\pi_{ij}\) shorter than \(T_{R}\).  
For a network under ad-hoc traffic, lightpaths are set up and torn down dynamically. In these networks, the traffic is usually assumed memoryless and evaluated in an erlangunit, defined as the ratio between the average connection holding time and the average time between requests. The ad-hoc traffic matrix is represented in this paper as \(\Omega=\{\omega_{ij}\}\), such that \(\omega_{ij}\) represents the traffic between nodes \(i\) and \(j\) (in erlang unit), with \(\omega_{ij}=0\) if \(i=j\) and the total network load is \(L=\sum_{i=1}^{N}\sum_{j=1}^{N}\omega_{ij}\). The traffic matrix that considers only those paths \(\pi_{ij}\) that require mandatory regeneration (i.e \(V_{ij}>T_{R}\)) is \(\Gamma=\{\gamma_{ij}\}\), in which \(\gamma_{ij}=\omega_{ij}\) if \(V_{ij}>T_{R}\) and \(\gamma_{ij}=0\) otherwise.
# Navigation Scenario Permutation Model for Training of
## 2 Materials and Methods
### Navigation Element Distribution Analysis
#### 2.2.2 Navigation Element Extraction  
The features used from the data included \"course over the ground (COG)\" and the \"date and time.\" Since the GPS position data points are connected continuously without decided waypoints, the experiment had to use certain windows and thresholds to extract the element values. The time-series data were used as a window, and the standard deviation of the COG in the window was used as a threshold. In this study, a 10-minute window and 5-degree threshold for the standard deviation of COG were used for element extraction. In other words, when the ship had over five degrees of COG standard deviation in each 10-minute period of navigation, the \"course altering angle at the waypoint\" and the \"distance between the waypoints\" were extracted. The process is illustrated in Figure 3 to support the explanation of element extraction.  
The selected element's values are the actual ship's actions, including the decisions of the ship's operators for all moments. Note that the size of the window and the threshold values can be optimized through repeated experiments; however, in this study, only the suggested window and threshold value were used in the element extraction process.