esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Carrier-Sense Threshold Selection for Improving Spatial Reuse in Dense Wireless LANs
## 5 Conclusions  
In this paper, we have proposed a medium access control protocol for wireless LANs where carrier-sense thresholds are dynamically selected to improve spatial reuse. When a node transmits a packet, it includes a CST value required to protect the transmission in the preamble. A neighboring node considers the advertised CST and its own CST, and transmits concurrently if it finds out that the on-going transmission and its transmission will both be successful. Mobile nodes near the APs can transmit more aggressively because their transmissions can still be received by the receiver in the face of high interference. At the same time, transmissions from edge nodes are protected by the advertised CST. Even if a node can successfully transmit its packet, it defers transmission if it receives a preamble from another node and finds out that its transmission will destroy the on-going transmission. When calculating CST, we have proposed two schemes; the model-based scheme and the measurement-based scheme. In the model-based scheme, each node calculates its own CST assuming the worst-case scenario in which an interfering node exists at the farthest position from the node. Although the scheme does not require any additional control messages, its performance may degrade if the gap between the model and the real environment is large. On the other hand, the measurement-based scheme uses neighbor information exchanged between the nodes to calculate the CST. The simulation results show that the proposed protocol achieves high throughput while protecting the channel share of the edge nodes. Currently, the CST calculation is based on a single interfering node, and multiple interfering nodes are accounted for using the margin parameter. The margin parameter is also used to address channel variations and possible existence of external interference. However, the fixed margin value could fail to cover these factors and may lead to either too conservative transmissions degrading spatial reuse, or too aggressive transmissions creating hidden terminals and degrading fairness of the system. As discussed in the paper, it could be better to have nodes independently select the margin based on their conditions such as density of neighbors and variation of measured RSSIs. Calculating proper margin based on the environment is a challenging issue and is a subject for future work. More accurate estimation of aggregate interference is another topic for future work, as it will lead to a better calculation of CSTs.  
Conceptualization, J.S., methodology, J.S. and J.L., validation, J.S. and J.L., writing--original draft preparation, J.S.
## 5 Case Study
### Protection Strategy Analysis
#### 5.5.2 Protection Strategy: Improving Redundancy  
Our analysis of the optimized protection strategy based on redundancy was conducted assuming that the same amount of budget is used for each protection strategy as installing the Level I protection method. The result suggests that a mixed strategy of providing 64% redundancy to component B and 36% redundancy to component A would be optimal, resulting in a total loss of 8,884,618 N.I.S. after a given terrorist attack using 20 kg of TNT. These improvements to the redundancy of these two key substation components would reduce the total loss from such an attack by 38%. However, it should be remembered that the loss reduction associated with a robustness-improvement strategy at the same level of investment could be as high as 56%. The ROI for redundancy improvement is presented below in Figure 5, alongside robustness-improvement methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Intensity of Terrorist**} & \multicolumn{8}{c}{**Protection Allocation to Each Component**} \\ \cline{2-10}  & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** \\ \hline
5 & 0.15 & 0.85 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
7.5 & 0.15 & 0.85 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
10 & 0.15 & 0.85 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
12.5 & 0.15 & 0.85 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
15 & 0.15 & 0.85 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
20 & 0.15 & 0.86 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Robustness Improvement Strategy for Varied Attacking Scenarios (Protection Level V).  
Figure 4: Effectiveness of Protection at Varied Attack Levels.  
It is clear that, compared to improving robustness, improving redundancy has a much lower effectiveness in improving infrastructural resilience to terrorist attacks. This may result from the fact that redundancy improvements do not reduce (and may slightly increase) losses from damaged facility equipment and injured staff during terrorist attacks, which together represent a large proportion of total losses caused (79% in this case study). Therefore, full redundancy of the entire substation was not considered in this case, but may be considered in CIs with significantly higher importance and indirect loss.
# Weighted Optimal Quadrature Formulas in Sobolev Space and Their Applications
## 4 Method for Constructing Weighted Optimal Quadrature Formulas Incorporating Derivatives  
Let \(x_{\beta}=[\beta],h=\frac{1}{N},N=\overline{1,N},N\geq m,C_{\beta}^{(\
u)}= C^{(\
u)}[\beta],[\beta]=h\beta\).  
Our approach to constructing optimal quadrature formulas with derivatives proceeds as follows. Initially, for \(m=1\), that is, within the space \(L_{2}^{(1)}(0,1)\), by minimizing the squared norm of the error functional (31) concerning the coefficients \(C_{\beta}^{(0)}\) (for \(\beta=\overline{0,N}\)) under the constraints given by (28), we derive the following system for determining \(C_{\beta}^{(0)}\):  
\[\sum_{\gamma=0}^{N}\mathop{C}_{\gamma}^{(0)}\frac{(h\beta-h\gamma)sign(h\beta -h\gamma)}{2}+\lambda_{0}\]\[=\int_{0}^{1}p(x)\frac{(x-h\beta)sign(x-h\beta)}{2}dx,\ \beta=\overline{0,N}, \tag{32}\]  
\[\sum_{\gamma=0}^{N}\overset{\circ}{C}_{\gamma}^{(0)}=\int_{0}^{1}p(x)dx. \tag{33}\]  
Here (33) is obtained from (28) when \(m=1\). Systems (32) and (33) are solved in [8], i.e., here we find the optimal coefficients \(\overset{\circ}{C}_{\gamma}^{(0)}\) in the space \(L_{2}^{(1)}(0,1)\). The application of this quadrature formula to the linear integral equations given in [48].  
Next, let us consider the case m = 2. For this purpose, by substituting the found optimal coefficients into (31), then minimizing the square of the norm on the coefficients \(\overset{\circ}{C}_{\gamma}^{(1)}\) in the space \(L_{2}^{(2)}(0,1)\), we obtain the optimal coefficients \(\overset{\circ}{C}_{\gamma}^{(1)}\) (\(\beta=\overline{0,N}\)).  
Continuing this method, we successively find the optimal coefficients \(\overset{\circ}{C}_{\gamma}^{(0)}\), \(\overset{\circ}{C}_{\gamma}^{(1)}\), \(\cdots\), \(\overset{\circ}{C}_{\gamma}^{(k-1)}\). Substituting these coefficients into (31) and minimizing the square of the norm on the coefficients of \(\overset{\circ}{C}_{\gamma}^{(k-1)}\) in the space \(L_{2}^{(k)}(0,1)\), we obtain a system for finding the optimal coefficients of \(\overset{\circ}{C}_{\gamma}^{(k-1)}\) (\(\beta=\overline{0,N}\)):  
\[\sum_{\gamma=0}^{N}\overset{\circ}{C}_{\gamma}^{(k)}\frac{(h\beta-h\gamma) sign(h\beta-h\gamma)}{2}+(-1)^{k}k!\lambda_{k}=(-1)^{k}F_{k\beta}, \tag{34}\]  
\[\sum_{\gamma=0}^{N}\overset{\circ}{C}_{\beta}^{(k)}=\frac{g_{k}}{k!}-\sum_{i =0}^{k-1}\sum_{\gamma=0}^{N}\overset{\circ}{C}_{\gamma}^{(i)}\frac{(h\gamma) ^{k-i}}{(k-i)!},\beta=\overline{0,N}, \tag{35}\]  
Here  
\[F_{k\beta}=f_{k\beta}-\sum_{i=0}^{k-1}\sum_{\gamma=0}^{N}(-1)^{i}\overset{ \circ}{C}_{\gamma}^{(i)}\frac{(h\beta-h\gamma)^{k-i+1}sign(h\beta-h\gamma)}{2 (k-i+1)!}, \tag{36}\]  
\[f_{k\beta}=(-1)^{k}\int_{0}^{1}p(x)\frac{(x-h\beta)^{k+1}sign(x-h\beta)}{2(k+1 )!}dx,\]  
\[g_{k}=\int_{0}^{1}p(x)x^{k}dx,\ k=\overline{0,m-1}.\]
# Coffee-Leaf Diseases and Pests Detection Based on YOLO Models
## 4 Experimental Results and Discussion  
### Initial Considerations  
Initially, to assess the improvements achieved with the improved BRACOL dataset, tests were carried out with the YOLOv8s model and the quantitative and qualitative results were compared with those using the original BRACOL dataset.  
To evaluate the performance of the models, exhaustive experiments were carried out with the OLOv8, YOLOv9, YOLOv10, and YOLOv11 architectures. Versions v8, v10, and v11 were tested in four variations: nano (n), small (s), medium (m), and large (l). YOLO v9 was trained with specific variations: tiny (t), small (s), medium (m), and custom (c).  
Based on [15; 16; 17; 35], Table 2 lists the details of the models used in the analysis.  
Although the YOLO family models have optimized architectures for real-time detection, it is important to note that the number of parameters directly influences computational complexity and the feasibility of using them in devices with limited resources. Larger models, such as the large variants (YOLOv8l, YOLOv9c, YOLOv10l, and YOLOv11l), have more than 24 million parameters, requiring more processing power and memory, which can make them difficult to implement in embedded environments, such as drones or remote field stations. On the other hand, more compact variants, such as the nano and tiny versions, have lighter architectures, with less than 4 million parameters, favoring real-time applications that require low power consumption and fast response.  
The results of the detection task are analyzed quantitatively and qualitatively, considering quantitative metrics such as mAP, F1-Score, and recall, and a qualitative analysis of four selected samples. These metrics allowed for a detailed assessment of the accuracy and efficiency of the models in diseases and pests detection on coffee leaves, enabling a comparison between different versions and variations.
## 11 Exploring Hybrid Approaches: Integrating Diverse Techniques
### Exploring Hybrid Approaches for Internal Artifact Removal: Case Studies
Each electrode is treated as a separate subsystem to be filtered, assumed uncorrelated and uncoupled. The results show a correlation between the raw and processed signal of more than 95-99.9% in the regions not affected by ocular artifacts and a correlation of 40-70% in the regions dominated by ocular artifacts. The results were compared with Independent Component Analysis (ICA) and artifact space reconstruction methods, showing how some local quantities are better handled by the real-time adaptive framework. The integration of Convolutional Neural Networks (CNN) further improves the discrimination and classification ability of EEG signals. The decoding performance, compared with multi-day experimental data from two subjects, for a total of 19 sessions, with and without raw data filtering, highlights a significant increase, justifying the effectiveness of the method for real-time closed loop BMI applications.
## 4 Decision Support System
### Optimum State and Optimal Risk Point  
Flood management must have the ultimate purpose of reducing risk. In such situations, risk cannot be entirely nullified. Flood measures reduce flood losses, but, at the same time, these measures incur high capital and maintenance costs for operational maintenance. This means that the risk cannot be reduced beyond a certain threshold in a floodplain. At this threshold, the combined annual expected losses due to flood damage and the cost of measures are reduced to a minimum.  
Measures taken in a floodplain generally have high BC ratios in the initial stages. For measures taken after that, the BC ratio decreases and may reach 1 or even lower for subsequent measures. The stage in which marginal measurement costs and marginal benefits are equal reflects the \"optimum state\" (see Figure 3) [62; 63].  
Figure 3: Graphs representing the increasing costs of flood measures and reduction in flood damages, along with the variation in total costs within the floodplain, which is minimum at the optimum state (OS).  
Various flood management schemes may show different optimum states. Under all favorable combinations of flood measures, the optimum state with the lowest risk (expected costs) is known as the \"optimal risk point\" (ORP). As to how different strategies (consisting of appropriate measures) are compared, the ORP concept is explained in Figure 4. It also compares the feasibility of the land-use practices proposed and supports the idea that only when ORP is achieved in a floodplain can maximum land-use benefits be obtained.
# Machine Learning Models for the Classification of Histopathological Images of Colorectal Cancer
## 2 Materials and Methods
### Feature Extraction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Formula** & **Description** \\ \hline Mean & \(\overline{x}=\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{M}ip_{ij}\) & This gives the contribution of an individual pixel’s intensity to the whole image. \\ \hline Variance & \(Var(X)=E\Big{[}\big{(}X-\overline{x}\big{)}^{2}\Big{]}\) & Variance is used to find the difference between a pixel and a neighboring pixel. \\ \hline Entropy & \(\sum\limits_{i=0}^{N}\sum\limits_{j=0}^{M}-P_{ij}\) & Entropy is a statistical measure of randomness that can be used to characterize the texture of an image. \\ \hline Contrast & \(\sum\limits_{i=0}^{N}\sum\limits_{j=0}^{M}(i-j)^{2}\) * \(P_{ij}\) & Contrast measures the difference in brightness or color across the image. \\ \hline Homogeneity & \(\frac{\sum_{i=0}^{N}\sum_{j=0}^{M}p_{ij}}{1+(i-j)^{2}}\) & This expresses how similar certain elements (pixels) of the image are. \\ \hline ASM & \(\sum\limits_{i=0}^{N}\sum\limits_{j=0}^{M}p_{ij}^{2}\) & ASM is a measure of the textural uniformity of an image. \\ \hline Dissimilarity & \(\Big{(}\sum\limits_{i=1}^{n}(x_{i}-y_{i})^{p}\Big{)}\dfrac{1}{p}\) & This is a numerical measure of how different two data objects are. When \(p=2\), it corresponds to the Euclidean distance. \\ \hline Correlation & \(\frac{\sum_{i=0}^{N}\sum_{j=0}^{M}(i,j)P_{ij}^{2}-\mu_{i}P_{ij}}{\sigma_{i}^{2} \sigma_{j}^{2}}\) & Correlation is the process of moving a filter mask, often referred to as a kernel, over the image and calculating the sum of the elements at each location. \\ \hline Gabor & Energy & The Gabor filter, a linear filter utilized for texture analysis, essentially examines the presence of particular frequency content within an image along specific directions within a localized region surrounding the point or area under scrutiny. \\ \hline \multirow{3}{*}{HOG} & The Histogram of Oriented Gradients (HoG) technique counts the occurrences of gradient orientation in a localized portion of an image. \\ \cline{2-2}  & Red & The RGB color model operates as an additive system where combinations of red, green, and blue primary colors of light are blended together to create a wide spectrum of colors. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the variables in the dataset.
## 4 Time-Varying Convolution
This becomes then a function of two variables, frequency \(k\) and time \(n\), and can be evaluated by taking its DFT at every \(N\) samples:  
\[W(n,k)=\sum_{m=0}^{2N-1}w_{n}(m)e^{-j\omega k}, \tag{24}\]  
where \(w_{n}(m)\) is the result of applying a rectangular window of length \(N\) to \(w(n)\), localised at time index \(n=lN\), with \(l\) a non-negative integer, and \(\omega=2\pi m/N\). Equally, we can take the input signal DFT,  
\[X(n,k)=\sum_{m=0}^{2N-1}x_{n}(m)e^{-j\omega k}, \tag{25}\]  
with \(x_{n}(m)\) similarly defined. The time-varying spectrum of the output of this filter is defined by  
\[Y(n,k)=W(n,k)X(n,k). \tag{26}\]  
The resulting spectrum is therefore a sample-by-sample multiplication of the short-time input spectra. The convolution waveform can be obtained by applying an inverse DFT of size \(2N\). Therefore, as in the time invariant case, the filter can be implemented either in the time domain with a tapped delay line or in the frequency domain using the fast Fourier transform (FFT). The time-varying convolution effect is a type of cross-synthesis of two input signals. It tends to emphasise their common components and suppress the ones that are absent in one of them. The size of the filter will have an important role in the extent of this cross-synthesis effect and the amount of time smearing that results. As with this class of spectral processes, there is a trade-off between precise localisation in time and frequency. With shorter filter sizes, the filtering effect is not as distinct, but there is a better time definition in the output. With longer lengths, we observe more of the typical cross-synthesis aspects, but the filter will react more slowly to changes in the input signals.
# Optimal Meshing Degree Performance Analysis in a mmWave FWA 5G Network Deployment
## 2 Literature Review
### WMN Node Placement and Routing  
WMN node placement is known to be an NP-hard problem, and many of the main issues are still relevant and being actively researched [29]. In this section, we will summarise previous research on WMN optimisation in order to describe the work that is relevant to our study on meshing before examining the current approaches to routing and again highlighting related work.
## 2 Materials and Methods
### BESS GPP Product  
The Breathing Earth System Simulator (BESS) algorithm used seven MODIS-driven atmosphere and land data, four different satellite products, four reanalysis data, and three ancillary input data sets to produce global-scale GPP products. As an ecological process-based model, the modules including atmosphere and canopy radiative transfer algorithm, canopy photosynthesis algorithm, transpiration algorithm, and energy balance algorithm were coupled together [47]. The coupled carbon-water module composed of [PERSON]'s algorithm for biochemical photosynthetic pathways such as C\({}_{3}\) and C\({}_{4}\) plants, a two-leaf canopy radiative transfer algorithm, and the quadratic Penman-Monteith model is integrated to quantify terrestrial GPP for sunlit and shade canopy by iterative steps. [PERSON] [48] improved the model algorithm and evaluated BESS GPP against FLUXNET 2015 dataset with MPI-BGC product across multiple spatio-temporal scales. The performance has also been evaluated by previous research [21; 49]. Now, the global BESS GPP dataset between 2000 and 2015 with 1 km spatial resolution for 8-day periods can be accessed at the Ecological Sensing Artificial Intelligence (AI) Laboratory at the Soul National University (the website: [[http://environment.snu.ac.kr/](http://environment.snu.ac.kr/)]([http://environment.snu.ac.kr/](http://environment.snu.ac.kr/)), accessed on 22 October 2021).
## 1 Introduction
### Novelty and Contribution  
In this work, we build on the model proposed in [36], with a focus on the first phase, in which the devices communicate with the satellites. However, differently from [36], here we consider that the erasure probabilities at the different satellites are not necessarily the same. Then, we reformulate the throughput and the packet loss rate to take into account the different erasure probabilities at the satellites. Next, using the novel throughput formulation, we exploit non-uniform traffic load distribution as a function of the number and position (which implicates in the erasure probabilities) of the multiple satellites. We show that adequately allocating the traffic intensity according to the number and positions of the satellites brings considerable benefits, and for that sake the modeling proposed in this work is fundamental.  
The rest of this work is organized as follows. In Section 2 we present the system model. In Section 3 we derive expressions for the throughput and packet loss rate considering different erasure probabilities at the satellites. An algorithm for traffic load optimization is introduced in Section 4. Numerical results are discussed in Sections 5, and Section 6 concludes the paper.
# A Study of EEG Feature Complexity in Epileptic Seizure Prediction
## 2 Materials and Methods
### Extracted Features  
We queried relevant papers and reviews [7; 26; 27] which addressed epileptic seizure prediction, to collect a superset of 88 features, univariate, as well as bivariate, commonly used in epilepsy prediction. We focused on algorithm-based seizure prediction studies. Only studies which used features from EEG records were included in the study. Image-based representation studies, for instance, were excluded.  
We extracted a total of 21 univariate linear features, including statistical measures, such as variance, \(\sigma^{2}\), skewness, \(\chi\), and kurtosis, \(\kappa\), temporal features, such as Hjorth parameters, HM (mobility), and HC (complexity) [28], the de-correlation time, \(\tau_{0}\), and the prediction error of auto-regressive modeling, \(e_{err}\), as well as spectral attributes, for instance, the spectral band power of the delta, \(\delta_{r}\), theta, \(\theta_{r}\), alpha, \(\kappa_{r}\), beta, \(\beta_{r}\), and gamma, \(\gamma_{r}\), bands, the spectral edge frequency, \(f_{50}\), the wavelet energy, \(E_{w}\), entropy, \(S_{w}\), signal energy, \(E\), and accumulated energy, \(AE\). Eleven additional univariate non-linear features from the theory of dynamical systems [29; 30; 31] have been used, characterizing the behavior of complex dynamical system, such the brain by using observable data (EEG records) [32; 33]. Time-delay embedding reconstruction of the state space trajectory from the raw data was used to calculate the non-linear features [34]. The time delay, \(\tau\), and the embedding dimension, \(m\), were chosen according to previous studies [33; 35]. Within this framework, we determine the correlation dimension, \(D_{2}\)[36], and correlation density, \(D_{e}\)[37]. We used also the largest Lyapunov exponent, \(L_{max}\)[33], and the local flow, \(\Lambda\), to assess determinism, the algorithmic complexity, \(AC\), and the loss of recurrence, \(LR\), to evaluate non-stationarity and, finally, the marginal predictability, \(\delta_{m}\)[38]. Moreover, we used a surrogate-corrected version of the correlation dimension, \(D_{2}^{*}\), largest Lyapunov exponent, \(L_{max}^{*}\), local flow, \(\Lambda^{*}\), and algorithmic complexity, \(AC^{*}\). We investigated also 48 linear bivariate attributes, including 45 bivariate spectral power features, \(b_{k}\)[24], cross-correlation, \(C_{max}\), linear coherence, \(\Gamma\), and mutual information, \(MI\). As for bivariate non-linear measures, we used 6 different characteristics for phase synchronization: the mean phase coherence, \(R\), and the indexes based on conditional probability, \(\lambda_{cp}\), and Shanon entropy, \(\rho_{se}\), evaluated on both the Hilbert and wavelet transforms. Finally, we also retained two measures of non-linear interdependence, \(S\) and \(H\).