esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The result of multiple simulations is an optimised line heating source with a heated region of 4 mm wide as can be seen in Figure 5. Recently, our research group bought a line heater source (MES) in order to perform dynamic line thermography. We have chosen to match the dimensions of the MES-heater to the dimensions in our simulation model. This makes it easy to compare the optimised heat source to the commercially available source regarding the width of the heated region. The overall dimensions of the MES-heater are 239 \(\times\) 55 \(\times\) 125 mm (L \(\times\) W \(\times\) H). The width of the heated region of the MES-heater is inspected using a cooled IR camera, FLIR X6540 sc. According to the datasheet of the line heating source, the distance between the bottom of the housing and the specimen should be 1.5 cm. The gap is too narrow to measure the width of the heated region on the top of the object to be inspected. As an alternative, the heated region is being inspected using the IR camera in transmission: a piece of baking paper was inserted at a distance of 1.5 cm of the line heater and the camera captured the heated region. The measurement set-up can be found in Figure 6. The heated region of the MES line heater has a width of 1.13 cm according to the measurements (Figure 7). The optimised reflector on the other hand should deliver a heated width of 4 mm. One should notice that the comparison between the optimised reflector and the MES-heater only served the purpose to validate the results of the optimisation. The optimisation process, however, is generally suitable to optimise line heaters. Line heaters usually use an elliptical reflector [9; 15]. Consequently, the improvements with respect to the MES-heater should be comparable to other line heaters as well.  
Figure 5: The optimised reflector shape as a result of the ray-tracing simulation can be found on the left, the rays are in the middle, and the intensity of each ray and the width of the heated region are in the right image. This reflector shape resulted in the smallest heated region and has the least adverse effects if the halogen tube lamp is placed at a non-optimal height. Other reflector shapes that were investigated are shown in Figure 4.  
Figure 6: In the left picture, the MES-heater is visible in the measurement setup. The setup consists of the line heating source, a baking paper clamped between two wooden frames and the FLIR X6540 sc infrared camera. The baking paper is used to investigate the heated area of the sample in transmission mode. The baking paper was placed at a distance of 1.5 cm from the bottom of the line heater as recommended in the datasheet.  
Figure 7: Representation of the heated region with a temperature above 250 \({}^{\circ}\)C. A width of 1.13 cm is calculated using a Matlab®-script.
# TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance
## 2 Materials and Methods
### Support Vector Classifiers (SVCs)  
Support vector classifiers (SVCs) are classification tools based on support vector machines (SVMs). The objective of an SVC is to identify the optimal hyperplane that effectively separates different classes of data, while maximizing the margin, which is defined as the distance between the hyperplane and the closest data points from each class. As showed in Figure 2, in 2D, the hyperplane is a line who divides the plane into two halves(red and green); in 3D, it is a plane who splits 3D space into two regions. However, not all data points impact the placement of this boundary. Only the points nearest to the hyperplane, known as support vectors, influence its position and orientation. These critical points define the margin, effectively'supporting' the optimal boundary. Maximizing the margin based on these support vectors enhances the classifier's ability to generalize effectively to new data.  
To determine the optimal hyperplane using an SVC, the problem is formulated as a convex optimization problem [19]. Constraints are applied to ensure that data points are at least one unit distance from the hyperplane, creating a wide-margin separation. In real-world applications, however, data are often not perfectly separable. The SVC addresses this through a soft margin approach, introducing a tolerance parameter C that allows some points to be misclassified. This enables the model to maximize the margin while tolerating a certain level of error. The value of C controls tolerance in relation to misclassification: higher C reduces tolerance of error, seeking a stricter separation, while a lower C allows more flexibility.  
The optimization objective of an SVC can be expressed as follows [20]:  
\[\min_{\mathbf{w},b}\frac{1}{2}\|\mathbf{w}\|^{2}+C\sum_{i=1}^{n}\xi_{i} \tag{2}\]  
where \(\mathbf{w}\) is the weight vector of the hyperplane, \(\|\mathbf{w}\|^{2}\) represents the squared norm, which is minimized to maximize the margin between classes, and \(b\) is the bias term, which adjusts the position of the hyperplane. \(\xi_{i}\) are the slack variables that allow certain data points to violate the margin constraints, and \(C\) balances the trade-off between maximizing the margin and tolerating misclassifications, as reflected in the optimization objective.  
The classification constraints for each data point are given as follows:  
\[\mathcal{Y}_{i}(\mathbf{w}\cdot\mathbf{x}_{i}+b)\geq 1-\xi_{i},\ \forall i=1,2,\ldots,n \tag{3}\]  
where \(\mathcal{Y}_{i}\) represents the label of the i-th data point (+1 or -1), \(\mathbf{x}_{i}\) is the feature vector of the i-th data point, and the notation \(\forall i=1,2,\ldots,n\), indicates that the constraint applies to all n data points.  
This constraint ensures that correctly classified points are on the correct side of the hyperplane and have a margin of at least 1. For points within the margin or misclassified, \(\xi_{i}\geq 0\) accounts for the violation.
## 2 Materials and Methods
### Analyses  
After generating ERPs for all four stimulus categories for each participant (see Figure 2), the software EEGDisplay (version 6.4.9) was further used to export neurophysiological data for statistical analyses. For this purpose, all ERP data were again down-sampled to reduce data volume. The result was 16 ms long time-windows (averages across four 4 ms potential values). Those time-windows were then subject to statistical analysis. At this point, it is important to mention that six specific electrode locations were selected for further analyses (selection based on the prior literature). Those are the left and right frontal electrode positions (F7 and F8), the left and right temporal electrode positions (T7 and T8), and the left and right parietal electrode positions (P7 and P8). First, repeated measures analysis of variance (ANOVA) was applied for all time-windows between 99 ms and 803 ms after stimulus onset. The following within-subject factors were defined: condition (4 levels: 'words', 'number words', 'digits', 'non-words') and electrode (6 levels: F7, F8, T7, T8, P7, P8) (Greenhouse-Geisser correction was applied). Afterwards, for each time-window showing a significant interaction of both factors, \(t\)-tests were calculated to compare each possible pair of stimulus condition separately for each electrode position. Benjamini-Hochberg correction was applied to correct for multiple comparisons [38]. This was performed to provide detailed statistical results to be used for comparisons between single ERPs of all stimulus conditions. Finally, topographical maps including data from all electrodes were created for representative time points for each of the 3 time-windows showing significant results (see Figures 3-5).
# Multi-User AF Relay Networks with Power Allocation and Transfer: A Joint Approach
## 3 Proposed Power Allocation and Transfer Algorithm  
By fixing the values of \(\Omega_{k,m}\) and \(\Omega_{r,m}\) and for given \(\rho_{j,i}\) and \(\beta_{j,i}\), **(P.4)** is convex, and thus, it can be solved with convex optimization tools [26]. Using a dual decomposition technique, we propose an iterative algorithm. The dual problem can be defined as follows [26]:  
\[\min_{\lambda,\mu,\eta,\
u,\vartheta,\kappa}\geq 0\,\max_{\tilde{ \mathbf{\Omega}}_{k},\tilde{\mathbf{W}}_{r},\delta}\mathcal{L}(\tilde{\mathbf{ P}}_{\mathbf{s}},\tilde{\mathbf{W}}_{\mathbf{r}},\delta,\lambda,\mu,\eta, \
u,\vartheta,\kappa)\,, \tag{28}\]where \(\mathcal{L}(\tilde{\mathbf{P}}_{\mathbf{s}},\tilde{\mathbf{W}}_{\mathbf{r}},\delta, \boldsymbol{\lambda},\boldsymbol{\mu},\boldsymbol{\eta},\boldsymbol{\
u}, \boldsymbol{\vartheta},\boldsymbol{\kappa})\) is the Lagrangian function of **(P.4)** which is written as  
\[\mathcal{L}(\tilde{\mathbf{P}}_{\mathbf{s}},\tilde{\mathbf{W}}_{ \mathbf{r}},\delta,\boldsymbol{\lambda},\boldsymbol{\mu},\boldsymbol{\eta}, \boldsymbol{\
u},\boldsymbol{\vartheta},\boldsymbol{\kappa})\] \[\qquad=\frac{1}{2}\sum_{j=1}^{N}\sum_{i=1}^{N_{T}}\left[\frac{ \rho_{j,i}}{\log 2}\bigg{\{}\tilde{P}_{j,i}+\tilde{W}_{r,i}+2\log\left(g_{j,i}h_{j,i}\right)\right.\] \[\qquad\left.-\log\left(e^{\tilde{W}_{r,i}}\sum_{k=1,k\
eq j}^{N}e ^{\tilde{\mathbf{P}}_{k,i}}|g_{j,i}h_{k,i}|^{2}+e^{\tilde{W}_{r,i}}|g_{j,i}|^{ 2}\sigma_{r}^{2}+\sigma_{d_{j}}^{2}\left(\sum_{k=1}^{N}e^{\tilde{\mathbf{P}}_{ k,i}}|h_{k,i}|^{2}+\sigma_{r}^{2}\right)\right)\bigg{\}}+\beta_{j,i}\right]\] \[\qquad=\frac{1}{2}\sum_{j=1}^{N}\sum_{i=1}^{N_{T}}\left[\frac{ \rho_{j,i}}{\log 2}\bigg{\{}\tilde{P}_{j,i}+\tilde{W}_{r,i}+2\log\left(g_{j,i}h_{j,i} \right)\right.\] \[\qquad\left.-\log\left(e^{\tilde{W}_{r,i}}\sum_{k=1,k\
eq j}^{N}e ^{\tilde{\mathbf{P}}_{k,i}}|g_{j,i}h_{k,i}|^{2}+e^{\tilde{W}_{r,i}}|g_{j,i}|^{ 2}\sigma_{r}^{2}+\sigma_{d_{j}}^{2}\left(\sum_{k=1}^{N}e^{\tilde{\mathbf{P}}_ {k,i}}|h_{k,i}|^{2}+\sigma_{r}^{2}\right)\right)\bigg{\}}+\beta_{j,i}\right]\] \[\qquad=\frac{1}{2}\sum_{j=1}^{N}\sum_{i=1}^{N_{T}}\left[\frac{ \rho_{j,i}}{\log 2}\bigg{\{}\tilde{P}_{j,i}+\tilde{W}_{r,i}+2\log\left(g_{j,i}h_{j,i} \right)\right.\] \[\qquad\left.-\sum_{k=1}^{N}\sum_{m=1}^{N_{T}-1}\left.\eta_{k,m} \left(\sum_{i=1}^{m+1}\left(E_{k,j}-\delta_{k,i}\right)-\Omega_{k,m}-B_{k,max }^{\mathrm{s}}\right)\right.\right.\] \[\qquad\left.-\sum_{m=1}^{N_{T}-1}\
u_{m}\left(\sum_{i=1}^{m+1} \left[E_{r,i}+\sum_{k=1}^{N}\beta_{k}\tilde{\delta}_{k,i}\right]-\Omega_{r,m}- B_{r,max}\right)\right.\] \[\qquad\left.-\sum_{k=1}^{N}\sum_{m=1}^{N_{T}-1}\vartheta_{k,m} \left(\sum_{i=1}^{m}\left(e^{\tilde{\mathbf{P}}_{k,i}}+P_{k,i}^{\mathrm{c}} \right)-\Omega_{k,m}\right)-\sum_{m=1}^{N_{T}-1}\kappa_{m}\left(\sum_{i=1}^{m} \left(e^{\tilde{W}_{r,i}}+P_{r,i}^{\mathrm{c}}\right)-\Omega_{r,m}\right)\,.\]  
The dual problem (28) can be resolved iteratively as described in following subsections.
# Enterprise Security for the Internet of Things (IoT): Lightweight Bootstrapping with EAP-NOOB
## 2 Background
### Proposed Solutions in Research Literature  
We examined existing research literature to find other bootstrapping protocols that use EAP as a building block. Slim EAPOL (SEAPOL) [20] is a lightweight variation of the standard EAP over Local Area Network (EAPOL) [21]. According to the authors, SEAPOL provides the same functionalities as EAPOL, but with significantly less overhead. Instead of the six bytes required by EAPOL, it only uses three bits. They argue that SEAPOL, therefore, is a more suitable option when bootstrapping resource-constrained devices.  
Trust Extension Protocol for Authentication in Networks Oriented to Management (TEPANOM) is an authentication, bootstrapping, and configuration protocol for IoT devices, introduced by [PERSON] [22]. [PERSON] et al. [23] implement TEPANOM over SEAPOL as a lightweight alternative for authenticating and bootstrapping resource-constrained devices. They claim that the combination is one of the most lightweight options when compared to other authentication and bootstrapping methods.  
The protocol for Carrying Authentication for Network Access (PANA) [24] is a network layer protocol that transports EAP messages over User Datagram Protocol (UDP) [25] packets. Rather than defining a new authentication method, it offers a lightweight alternative for transporting EAP messages over an unreliable network. [PERSON] [26] suggests using PANA for the secure bootstrapping of resource-constrained devices. [PERSON] et al. [27] propose a similar solution that extends PANA for secure bootstrapping of smart objects.
# A Novel Hybrid Elementary Cellular Automata and Its Application in a Stream Cipher
## 2 Preliminaries  
### Elementary Cellular Automaton  
The cellular automaton (CA) proposed by [PERSON] and [PERSON] in 1948 [30; 37] was originally employed to depict the self-reproduction of a bio-system. A CA consists of four parts: cell, cell space, cell neighbor, and cell rule. The mathematical expression of it is given as follows:  
\[CA=(A^{N},\Sigma,f,E), \tag{1}\]  
where \(A^{N}\) is cell space, and \(N\) denotes the dimension of the CA. \(\Sigma\) indicates the set of status values. \(f\) is the transition function under the cell rule. \(E\) represents the boundary condition of CA. Moreover, [PERSON] [40; 41] simplified the CA and proposed the elementary cellular automaton (ECA), which only possesses two status values and a neighbor radius of one. In the ECA, the next status value of a cell is determined by the current status value of itself and its two neighbors. The transition function of an ECA is defined as follows:  
\[S_{i}^{t+1}=f_{r}(S_{i-1}^{t},S_{i}^{t},S_{i+1}^{t}), \tag{2}\]  
where \(i\) (\(i\) = 1, 2, 3, \(\ldots\), \(l\)) and \(t\) (\(t\) = 1, 2, 3, \(\ldots\), \(n\)) denote space and time dimension, respectively. \(l\) is the total number of cells in the ECA. \(n\) indicates the total number of iterations. Meanwhile, the boundary of the ECA is periodic. That means the cell 1 st is the neighbor of cell \(l\)th. \(S_{i}^{t}\) represents the status value of the cell \(i\)th at \(t\) times, which belongs to the set {0,1}. \(R\) is the rule of the transition function \(f\), which is a mapping from {000, 001, 010, \(\ldots\), 111} to {0, 1}. For instance, the transition of function \(f\) under rule _No._ 183 can be depicted by a truth table such as Table 1.  
There are 256 rules classified into null rules, fixed-point rules, periodic rules, locally chaotic rules, and global chaotic rules [40; 42]. Moreover, the global chaotic rules with a long period are listed in Table 2.  
In Table 2, those inside the parenthesis are rules equivalent to the representative rule. For example, we assign the number of cells \(l\) = 200 and the iterations \(n\) = 200. The iterative results of ECAs with rules _No._ 126 and _No._ 129 are shown in Figure 1.  
The \(X\)-axis and \(Y\)-axis represent the indexes of cells and counts of iterations in Figure 1, respectively. The status values between Figure 1a,b are mutually inverse. Additionally, the sequence generated by any cell is a long cycle [38].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(S_{i-1}^{t},S_{i}^{t},S_{i+1}^{t}\) & 111 & 110 & 101 & 100 & 011 & 010 & 001 & 000 \\ \hline \(S_{i}^{t+1}\) & 1 & 0 & 1 & 1 & 0 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The transition of function \(f\) under rule _No._ 183.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Rule Number** \\ \hline Global & 18(183), 22(151), 30(86, 135, 149), 45(75, 89, 101), 60(102, 153, 195), 90(165), 105, 106(120, 169, 225), 129(126), 137(110, 124, 193), 146(182), 150, 161(122) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The global chaotic rules [42].
# Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation
## 2 Related Work
### Maize Leaf Severity Analysis
#### 2.2.3 Machine Learning and Deep Learning  
Diseases mainly alter the color, shape and intensity of leafs [48]. It is, therefore, reasonable to extract meaningful information from such deformations and use it as features to represent the respective diseases. The most common features that authors use from the color moments are the mean, standard deviation and skewness, as defined in Equations (1)-(3) [48]:  
\[\mu_{i}=\frac{1}{N}\sum_{j=1}^{N}f_{ij} \tag{1}\]  
\[\sigma_{i}=\left[\frac{1}{N}\sum_{j=1}^{N}(f_{ij}-\mu_{i})^{2}\right]^{\frac{1} {2}} \tag{2}\]  
\[\gamma_{i}=\left[\frac{1}{N}\sum_{j=1}^{N}(f_{ij}-\mu_{i})^{3}\right]^{\frac{1 }{3}} \tag{3}\]  
where \(\mu_{i}\), \(\sigma_{i}\) and \(\gamma_{i}\) (\(i=1,2,3\ldots\)) represent the mean, standard deviation and skewness of each image, respectively [48].  
Several authors have also reported success with features extracted from the histograms of diseased leaf images. Without a doubt, the histogram of an image provides important statistics that are relevant in image processing tasks [49]. They are simple to calculate, making them a tool of choice in near real-time digital image processing applications [49]. For example, we can use Equations (4) and (5) to extract some statistical variables that can help us segment the digital image. Developing from the previous intuition of Section 2.2.3, if we have a grayscale image \(I\) with a total of \(N\) pixels and \(K\) possible intensity values \(0\leq g<K\), we can compute the mean and variance from the histogram of the image as follows [49]:  
\[\mu_{I}=\frac{1}{N}\cdot\sum_{u,v}I(u,v)=\frac{1}{N}\cdot\sum_{g=0}^{K-1}g \cdot h(g), \tag{4}\]  
\[\sigma_{I}^{2}=\frac{1}{N}\cdot\sum_{u,v}[I(u,v)-\mu_{I}]^{2}=\frac{1}{N}\cdot \sum_{g=0}^{K-1}(g-\mu_{I})^{2}\cdot h(g). \tag{5}\]  
where \(\mu_{I}\) and \(\sigma_{I}^{2}\) represent the mean and the variance of the image, respectively.  
In general, extracting leaf features such as color, shape or intensity is the basis of any leaf disease quantification problem. In fact, a majority of digital image processing techniques and third-party software packages for severity analysis rely on using one or more such features.
# Communication Manager for Hyper-Connected RPAS Environments
## 4 Validation: From the Lab to the Air
### Laboratory Ground Truth
#### 4.1.1 Choosing the Minimum RTT Path
Finally, connectivity is impossible when the figure is shaded with a red background. Figure 5 also displays the OpenFlow messages exchanged (in packets per second) between the RPS, the RPA2, and the SDN controller (axis on the right). As can be noticed, a periodic OpenFlow message is sent every 5 s (2 Kb/s). In the same way, when a rule is installed in a switch, there is an increase in OpenFlow packets. Let us compare the performance of this system, which includes the CIM combined with the SDN approach, and uses all the available interfaces to communicate with the RPA and RPS domains, with a typical system that only uses a single channel (either Wi-Fi or cellular). With the presented solution, the average availability time is around 94% for this test (combining Wi-Fi and 4G) with an average RTT of around 65 ms. However, when a single network interface is used, one or more metrics are usually sacrificed depending on the choice. In our example scenario, there are 12 s of communication losses using the CIM, compared to 118 s for Wi-Fi-based (41% availability). Alternatively, when 4G is chosen as the unique interface to increase availability, our trial's average RTT is increased to 100 ms. These numbers are too dependent on the selected scenario (e.g., number of RPAs, flight distance, etc. ), but in any case, the results and main conclusions can be naturally extrapolated to more complex use cases.
## 4 Experiment and Evaluations
### Experimental Environment and Parameters  
The experimental platform operates on a Linux system, utilizing an NVIDIA GeForce RTX 3090 GPU with 24 GB of memory. The CUDA version used is 11.6, and the PyTorch framework is version 1.12.0. The network model is trained within the PyTorch deep learning framework, with a batch size of 16, 400 epochs, an initial learning rate of 0.0001, momentum set to 0.9, and the AdamW optimizer for gradient descent. The initial learning rate of 0.0001 was selected based on both preliminary experiments and best practices reported in similar studies. Furthermore, an excessively high learning rate may cause the optimizer to overshoot the minima of the loss function, while an excessively low rate could prolong the training process unnecessarily. Our choice of 0.0001 represents a balanced approach, ensuring both training stability and efficiency. The selection of 400 epochs is the result of comprehensive consideration of multiple factors such as model convergence, architecture complexity, dataset size, and computing resources. The model parameters are not fixed during the experimental process but are dynamically adjusted through iterative experimentation and performance evaluation. This adjustment involves optimizing key hyperparameters, such as learning rate, weight initialization, regularization parameters, and optimizer settings. The objective is to iteratively refine the model based on its performance on the training and validation datasets, ensuring that the final trained model achieves optimal performance and generalization capability.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variety** & **Training Set** & **Validation Set** & **Total** \\ \hline Red leaf spot & 1135 & 126 & 1261 \\ Tea white spot & 1076 & 119 & 1195 \\ Tea coal disease & 916 & 101 & 1017 \\ Tea brown blight & 997 & 110 & 1107 \\ Total & 4124 & 456 & 4580 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample classification data of different tea diseases.  
Figure 7: Augmentation processing of tea brown blight image.  
This paper evaluates the algorithm's detection efficacy proposed in this study by comparing the differences between the original RT-DETR model and the improved model in disease detection, while exploring the feasibility of each improvement method. In terms of quantitative evaluation, the main indicators selected are as follows: precision, recall, and mean average precision (mAP), including mAP50 and mAP50:95, etc. The relevant equations are as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{7}\]  
\[Recall=\frac{TP}{TP+FN} \tag{8}\]  
\[\text{AP}=\int_{0}^{1}\text{Precision}(r)dr \tag{9}\]  
\[\text{mAP}=\frac{1}{C}\sum_{i=\,1}^{C}\text{AP}_{i} \tag{10}\]  
In this context, TP (True Positive) refers to the count of accurately identified positive samples in the detection outcomes. FP (False Positive) indicates the count of incorrectly identified positive samples. FN (False Negative) denotes the count of positive samples from a specific category that were missed. C represents the total number of categories.
# Minimal Collagen-Binding Epitope of Glycoprotein VI in Human and Mouse Platelets
## 1 Introduction
### CRP-Binding Sites of GPVI Receptor  
Besides collagen, synthetic collagen-related peptides (CRP) can also bind GPVI, mimicking collagen-induced platelet activation [16]. CRP is composed of 10 repeated GPO triplets from collagen. CRP peptides are cross-linked, thereby becoming a potent and selective agonist of the GPVI receptor to induce ITAM-signaling events. Snake venom toxins, such as convulkin and alborhagin, also bind and activate GPVI [17; 18; 19], but their interaction is different from collagen and does not require GPVI dimerization, suggesting an alternative mode to cluster and activate GPVI on the platelet surface. Convulkin can induce GPVI dimers (\(\alpha\)4 and \(\beta\)4 rings) in solution, thereby clustering several copies of GPVI, indicating that these interactions are maintained by multiple binding sites [20]. Amino acid residues of GPVI have been identified as being involved in the recognition and binding of collagen [21; 22; 23]. However, as described by [PERSON] et al, the collagen-binding site may be located at a distant surface from the potential CRP-binding site [21; 22]. They proposed that the collagen-binding surface thus appears to be widespread on GPVI, and covers a larger binding surface compared to the CRP-binding site. In other studies, several amino acids have been implicated in both collagen- and CRP-binding, Ref. [23] suggesting an overlapping binding surface, whereas some amino acids are involved in binding of collagen, but not CRP.