esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 3 VBDA Architectures and Core Technologies
### VBDA Core Technologies  
Next-generation VBDA systems rely on integrated technological stacks to achieve real-time performance, intelligent interpretation, and secure, scalable data management. As video data continues to grow in volume, velocity, and variety, system architectures must evolve to handle end-to-end processing needs from ingestion and distributed computation to semantic understanding and visualization. This section provides a focused analysis of five critical components underpinning modern VBDA platforms: scalable data processing frameworks, deep learning-based video analytics, FL combined with edge AI, advanced storage and retrieval infrastructures, and visualization tools for system interpretability. These technologies serve as the core foundation for intelligent VBDA pipelines, enabling low-latency inference, model coordination across devices, and privacy-conscious data management across diverse domains.
# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 3 Material and Method
### Online (Learning-and-Test) Phase
#### 3.2.2 Linear Discriminant Analysis  
Linear Discriminant Analysis (LDA) [48] is a well-established dimensionality reduction technique primarily used for classification tasks. It aims to maximize the separation between different classes by projecting the data onto a lower-dimensional space where class distinctions are most prominent. This is achieved by computing the between-class variance, which captures the spread of different class means, and the within-class variance, which accounts for variations within each class. The optimal projection is found by solving the eigenvalue problem for the matrix that balances these variances, ensuring that data points from the same class are close together while those from different classes are well-separated. LDA is widely used in applications such as pattern recognition, face recognition, and medical diagnostics, though it has limitations when dealing with non-linearly separable classes or small sample sizes, requiring modifications such as kernel LDA or regularized LDA.
# Unveiling LoRa's Oceanic Reach: Assessing the Coverage of the Azores LoRaWAN Network from an Island
###### Abstract  
In maritime settings, effective communication between vessels and land infrastructure is crucial, but existing technologies often prove impractical for energy-sensitive IoT applications, like deploying sensors at sea. In this study, we explore the viability of a low-power, cost-effective wireless communication solution for maritime sensing data. Specifically, we conduct an experimental assessment of the Azorean Long Range Wide Area Network (LoRaWAN) coverage. Our tests involve positioning the gateway at the island's highest point and installing end nodes on medium-sized fishing vessels. Through measurements of received signal strength indicator (RSSI), signal-to-noise ratio (SNR), and lines of sight (LOS), we showcase the potential of LoRaWAN transmissions to achieve communication distances exceeding 130 km in a LOS-free scenario over the ocean. These findings highlight the promising capabilities of LoRaWAN for reliable and long-range maritime communication of sensing data.  
maritime communications; LoRa; LoRaWAN; low power wide area network (LP-WAN); Internet of Things (IoT) +
Footnote †: journal: Sensors
## 3 Methods
### Effect of Vegetation
#### 3.2.2 Modification of the Vegetation Backscattering Model  
One assumption of the WCM described in Section 3.2.1 is that volume scattering is the predominant mechanism responsible for the backscattering from vegetation [26]. However, in the actual environment, vegetation is usually unevenly distributed and does not satisfy this assumption. Because of the strong influence from the underlying ground surface in sparse vegetation cover areas, the backscattering contribution of bare soil surfaces must be considered a key component in the total backscattering. In a previous study, the total backscattering was divided into the contributions from the surface covered by vegetation and the fraction representing direct backscattering from bare soil surfaces [44, 45, 46]. In this study, the scattering mechanism for the vegetation cover component and bare soil component in a pixel were separated by vegetation coverage. First, the backscattering was calculated using a vegetation backscattering model under the assumption that a pixel was fully covered by vegetation. Then, the actual backscattering coefficient from the fraction of the surface covered by vegetation was calculated based on the vegetation coverage. Then, the actual backscattering coefficient from the fraction of the surface covered by vegetation was calculated based on the vegetation coverage. Similarly, the actual backscattering coefficient for the fraction representing direct backscattering from the bare soil surface was calculated using the fraction of the bare soil. Therefore, the total backscattering from a pixel can be expressed as:  
\[\sigma_{total}^{o}=f_{vgg}\sigma^{o}+(1-f_{vgg})\sigma_{soil}^{o} \tag{9}\]  
where \(f_{vgg}\) is the vegetation coverage. \(\sigma_{soil}^{o}\) can be calculated using the IEM.  
Equation (9) may be combined with Equation (6) and rewritten as:  
\[\sigma_{total}^{o}=f_{vgg}(\sigma_{vgg}^{o}+\tau^{2}\sigma_{soil}^{o})+(1-f_{ vgg})\sigma_{soil}^{o} \tag{10}\]  
In this study, the vegetation coverage was delineated using a Dimidiate Pixel Model (DPM) [47] with optical remote sensing data. Thus, vegetation coverage (\(f_{vgg}\)) can be expressed as:  
\[f_{vgg}=\frac{NDVI-NDVI_{\min}}{NDVI_{\max}-NDVI_{\min}} \tag{11}\]  
where \(NDVI\) is the normalized difference vegetation index calculated from the HJ-1 CCD data. \(NDVI_{\max}\) and \(NDVI_{\min}\) are the normalized difference vegetation indexes of an area covered by full green vegetation and bare soil, respectively. In the DPM, the NDVI of a pixel is the sum of the complete vegetation NDVI (\(NDVI_{\max}\)) and the non-vegetation NDVI (\(NDVI_{\min}\)).
# Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images
## 5 Conclusions  
In this paper, the effectiveness of the proposed multiple preprocessing HLSM was verified in both posterior and wide-angle fundus images. Furthermore, the segmentation results were evaluated objectively with IoU and subjectively with FSE. The proposed approach achieved the following results: average IoU of 0.9275 and average FSE of 4.6426 in the DRISHTI-GS posterior fundus image dataset, and average IoU of 0.8179 and average FSE of 3.5946 in the TMUEH wide-angle fundus image dataset. The proposed multiple preprocessing HLSM solves the effect of PPA regions and bright noise in OD segmentation. This is the first time to segment OD from wide-angle fundus images. The FSE is proposed to partially evaluate OD segmentation results and prove that OD segmentation results are clinically meaningful.  
In HLSM, the optimization of parameters is time-consuming, and even though the quality of each image is different, their parameters are same. Therefore, in the future, an automatic parameter optimization method could be proposed, and each image could automatically generate the most suitable parameters according to their quality.  
Figure 26: Comparison of some representative cases (green contour: GT, blue contour: segmentation results).  
Figure 25: Unsolved problems in OD segmentation (green contour: GT, blue contour: segmentation results).  
**Author Contributions:** Conceptualization, X.X., W.D. and Y.P.; methodology, X.X. and Y.F.; software, X.X.; validation, X.X., L.W. and W.D.; formal analysis, X.X.; investigation, X.X.; resources, L.W.; data curation, X.X. and L.W.; writing--original draft preparation, X.X.; writing--review and editing, W.D.; visualization, X.X.; supervision, W.D.; project administration, X.X.; funding acquisition, W.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The DRISHTI-GS database in this study is openly available at [[https://cvit.iiit.ac.in/projects/mip/drishti-gs/mip-dataset2/enter.php](https://cvit.iiit.ac.in/projects/mip/drishti-gs/mip-dataset2/enter.php)]([https://cvit.iiit.ac.in/projects/mip/drishti-gs/mip-dataset2/enter.php](https://cvit.iiit.ac.in/projects/mip/drishti-gs/mip-dataset2/enter.php)) accessed on 12 October 2020.  
**Acknowledgments:** [PERSON] and [PERSON] gratefully acknowledge the support of the Kyoto Institute of Technology University.  
**Conflicts of Interest:** the authors declare no conflict of interest.
# Secure Aviation Control through a Streamlined ADS-B Perception System
## 3 Related Work
### Research Gap  
As the studies suggested, ADS-B is currently facing problems related to its security, and much work is still needed to improve on that aspect before fully deploying it in the aviation industry. These vulnerabilities would affect the security and safety of the different aircraft types.  
We thoroughly discussed currently proposed solutions and categorized them based on the technique used and the limitations they face, focusing on showing how researchers tried implementing those techniques to minimize or eliminate the risks caused by these vulnerabilities. Some studies suggested securing ADS-B physically, while others proposed a framework such as the TESLA framework and considered it a satisfactory method to secure ADS-B communications without enough proof of robustness and reliability.  
While most proposed solutions focused on solving the issues using hardware or cryptography, time, and location information, these techniques proved costly and time-consuming to produce and implement in the different types of aircraft or sensors within an ADS-B network.  
However, as we also mentioned, some studies utilized machine learning techniques, which still show limitations, most importantly detection rate, and require a change in the ADS-B protocol to render their solution effective.  
The biggest gap observed is that there was a need to aid in developing a detection model that is more dependable, powerful, long-lasting, and time-efficient. Thus, we decided to address the issues we observed within previous studies and tried to develop a solution that is efficient in terms of time and cost, is robust, and that does not require any change to be carried out on the ADS-B protocol. We also decided that we would mainly focus on the detection of fake and forged messages that are used to carry out the following attacks: path modification, ghost aircraft injection, and velocity drift attack to boost the security of this protocol, which would eventually increase the reliability and safety of it and allow to deploy and utilize it fully within the aviation industry.
## 3 Proposed Methodology
### Datasets  
The Brain Tumor MRI Dataset (Kaggle Dataset) [25] consists of 7023 MRI image slices, meticulously annotated and classified into four distinct categories. Specifically, it includes 1321 slices corresponding to glioma, 1339 slices representing meningioma, 1595 slices depicting no tumor cases, and 1457 slices of pituitary tumors. This dataset's \"no tumor\" class images are sourced from the Br35H dataset. Sample images of the Kaggle dataset are shown in Figure 2. The Brain Tumor Dataset (FigShare Dataset) [26] comprises  
Figure 1: Overall architecture of the proposed model.  
3064 MRI image slices, each meticulously annotated and categorized into three distinct brain tumor types. Specifically, it contains 708 slices corresponding to meningioma, 1426 slices representing glioma, and 930 slices depicting pituitary tumors. Sample images of the FigShare dataset are shown in Figure 3, whereas Table 1 shows the summary of images in each class of selected datasets.  
The Kaggle dataset includes MRI scans of glioma, meningioma, pituitary tumors, and healthy cases. The FigShare dataset has a variety of MRI scans with the same tumor diagnoses, assuring consistency. These datasets were chosen for their availability, visual diversity, and broad use in past studies, making them reliable standards for deep learning model evaluation. SMOTE was used to improve underrepresented classes and solve data imbalance. Simple augmentations like random rotations and horizontal flipping improved generalization. Our GATransformer model was compatible with datasets pre-processed to normalize image sizes. Both datasets have been extensively used in peer-reviewed medical AI studies, boosting their legitimacy and importance. Public availability facilitates reproducibility and comparability with existing methods, making them ideal for building strong deep learning models in brain tumor classification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Glioma** & **Meningioma** & **Pituitary** & **No\_Tumor** & **Total** \\ \hline Kaggle [25] & 1321 & 1339 & 1457 & 1595 & 5712 \\ \hline FigShare [26] & 1426 & 708 & 930 & - & 3064 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of selected brain tumor datasets.  
Figure 3: Sample images of the FigShare dataset.  
Figure 2: Sample images of the Kaggle dataset.
## 4 Role of sRNA in Antibiotic Resistance
### Biofilm-Mediated Increased Drug Resistance in Bacterial Pathogens
#### 4.4.2 PhoS  
PhoS is a 66-nt scRNA that is encoded at the 3'UTR of _phoR_, although the expression of PhoS is driven by its own promoter (Figure 3c). PhoR is a sensor kinase that regulates bacterial responses to phosphate availability by toggling between kinase and phosphatase modes. Under phosphate-limiting conditions, PhoR functions as a kinase, phosphorylating its cognate response regulators, PhoB or PhoP, which then activate the expression of genes involved in the adaptation to phosphate scarcity. Conversely, under phosphate-replete conditions, PhoR assumes a phosphatase role, maintaining PhoB/PhoP in an inactive, dephosphorylated state (Figure 3c).  
Studies on _E. coli_ and _B. subtilis_ have identified two mechanisms regulating PhoR activity. In _E. coli_, the predominant pathway involves the PstSCAB/PhoU complex, which transduces phosphate transporter conformational changes to PhoR. However, in _B. subtilis_, an alternative mechanism links PhoR regulation to wall teichoic acid metabolism, where biosynthetic intermediates modulate PhoR autokinase activity. PhoS regulates biofilm formation in _Bacillus_ sp. and is induced under phosphate limitation [224]. The disruption of _phoS_ reduced EPS production, whereas the overexpression increased EPS levels, confirming its role in biofilm synthesis. PhoS enhances the translation of PhoP mRNA by preventing an inhibitory structure at its 5'UTR, forming a self-reinforcing autoregulatory loop that allows _Bacillus_ sp. to adapt to phosphate scarcity. Through PhoP, PhoS promotes biofilm formation by influencing the expression of operons involved in cell wall metabolism, such as _tauA_-_H_, which facilitates teichuronic acid synthesis. Additionally, PhoS potentially affects biofilm development through the PhoP-regulated _comQXPA_ operon, impacting quorum sensing and surfactin signaling. While the deletion of _phoS_ weakly reduces biofilm formation, its overexpression strongly enhances it, likely due to the regulatory loop amplifying PhoP levels. Transcriptomic analyses reveal that PhoS modulates key biofilm-related genes, though it does not directly target _eps_ or _tapA_-_sipW_-_tasA_ operons in _B. subtilis_ and _B. velezensis_ strain FZB42.
# Multi-Phase Adaptive Recoding: An Analogue of Partial Retransmission in Batched Network Coding +
## 3 Multi-Phase Adaptive Recoding
### Multi-Phase Systematic Adaptive Recoding Problem (SAP)
Combining with the expected rank, we have  
\[\mathbb{E}[\tilde{R}_{k+1}] =\sum_{r_{1},r_{2},\ldots,r_{k+1}}r_{k+1}\Pr(r_{1})\prod_{j=1}^{k }\text{Bin}(r_{j},p;r_{j}-r_{j+1})\]2 \[\mathbb{E}[\tilde{R}_{k+1}] =\sum_{r_{1},r_{2},\ldots,r_{k+1}}r_{k+1}\Pr(r_{1})\prod_{j=1}^{k }\text{Bin}(r_{j},p;r_{j}-r_{j+1})\]3 \[\mathbb{E}[\tilde{R}_{k+1}] =\sum_{r_{1},r_{2},\ldots,r_{k+1}}r_{k+1}\Pr(r_{1})\prod_{j=1}^{k }\text{Bin}(r_{j},p;r_{j}-r_{j+1})\]4  
The proof is then completed by combining the two cases above. This corollary shows that when there are enough resources, \(\infty\)-phase SAP is \"lossless\". This is not surprising as the \(\infty\)-phase SAP can be regarded as a complete hop-by-hop retransmission scheme. More specifically, if the packet loss rate \(p\) is the same at all the links and \(t_{\text{avg}}=M\) at all the non-destination nodes, then \((1-p)t_{\text{avg}}\) is the capacity of the network before normalization and can be achieved by SAP. Although we say \(\infty\) phases, the constraint in SAP states that every batch sends \(t_{\text{avg}}\) packets on average--that is, it is unlikely to have a very large number of systematic phases before a batch has no more innovative ranks. The following theorem provides an upper bound on this expected stopping time. **Theorem 4**. : _Let \(U\) be the random variable for the smallest number of systematic phases required such that a batch reaches \(0\) innovative rank. For \(k\geq 1\),_  
\[\mathbb{E}[\tilde{R}_{k+1}] =\sum_{r_{1},r_{2},\ldots,r_{k+1}}r_{k+1}\Pr(r_{1})\prod_{j=1}^{k }\text{Bin}(r_{j},p;r_{j}-r_{j+1})\]5  
_Also, \(\Pr(U=0)=\Pr(R_{1}=0)\). Further, \(\mathbb{E}[U]<\frac{\mathbb{E}[R_{1}]}{1-p}\)._  
Proof. : See Appendix B. We will see from the numerical evaluations in Section 4 that two-phase, or at most three-phase, is already good enough in practice.
# Development of a Two-Dimensional Hybrid Sediment-Transport Model
## 3 Numerical Method
### Multipoint Momentum Interpolation Correction  
After discretization, the momentum Equations (2) and (3) can be linearized as follows:  
\[\frac{A^{Us}}{r_{u}}\mathbf{U}^{*}+\sum_{1}^{b}A_{b}^{U}\mathbf{U}_{b}^{*}=\mathbf{U}^{u}- g\Delta t(\
abla\eta)+\frac{(1-r_{u})}{r_{u}}A^{Us}\mathbf{U}^{m}+S^{U} \tag{27}\]  
where \(\mathbf{U}^{*}=(u^{*},v^{*})\) is the provisional velocity (m/s); \(\mathbf{U}^{m}\) is the velocity of the previous iteration (m/s) with the superscript \"\(m\)\" denoting the iteration step; \(r_{u}\) is the under-relaxation parameter for velocity; \(\
abla\eta=(\partial\eta/\partial x,\,\partial\eta/\partial y)\) is the pressure gradient; \(\mathbf{A}^{*U}=(A^{*u},A^{*v})\) is the nondimensional matrix coefficient; \(\mathbf{A}_{b}^{U}=(A^{u}_{b},A^{v}_{b})\) is the matrix coefficient for neighboring cells; and \(S^{U}\) is the source term.  
For the collocated mesh system, the momentum interpolation (MI) method proposed by [PERSON] and [PERSON] [50] is to interpolate the discretized momentum equation at the edge between two neighboring cells to evaluate the edge velocity and flux.  
\[\mathbf{U}_{0-m}^{*}\approx\mathbf{I}S_{0-m}-\frac{g\Delta t}{A_{0-m}^{U}}(\
abla\eta)_{ 0-m} \tag{28}\]where \(\mathbf{IS}=\mathbf{U}^{*}+\frac{\delta\Lambda t}{\mathbf{A}^{U}}(\
abla\eta)\) denotes the interpolation subject (m/s) at cell centers; \(\mathbf{IS}_{0-m}\) denotes the cross-edge interpolation subject with \(\mathbf{IS}_{0-m}=s_{0-m}\mathbf{IS}_{0}+(1-s_{0-m})\mathbf{IS}_{m}\) with \(s_{0-m}(\in[0,1])\) the linear interpolation coefficient; and the matrix coefficient at the edge is defined as \(A_{0-m}^{U}=s_{0-m}\cdot A_{0}^{U}+(1-s_{0-m})\cdot A_{m}^{U}\).  
[PERSON] and [PERSON] [51] extended the cross-edge MI to the surrounding edge centers by defining the multipoint interpolation subject as the averaged surrounding edge interpolation subject \(\mathbf{IS}_{0-m}^{MP}=\sum\limits_{1}^{4}w_{n}\mathbf{IS}_{i-j}\) with \(w\) the weighting coefficient. Specifically, at edge \"0-1\" in Figure 1, the multipoint interpolation subject is \(\mathbf{IS}_{0-1}^{MP}=w_{1}\mathbf{IS}_{0-1}+w_{2}\mathbf{IS}_{0-2}+w_{3}\mathbf{IS}_{1-4}+w_{ 4}\mathbf{IS}_{1-6}\) with the superscript \"MP\" representing \"multi-point\". Therefore, the momentum interpolation with multipoint interpolation correction reads:  
\[\mathbf{U}_{0-m}^{*}\approx\mathbf{IS}_{0-m}-\frac{g\Lambda t}{A_{0-m}^{U}}(\
abla\eta )_{0-m}+(\mathbf{IS}_{0-m}^{MP}-\mathbf{IS}_{0-m})\cdot r_{I} \tag{29}\]  
where \(r_{I}\) is a relaxation factor in the range of \([0,1]\) to control the correction of the last term on the RHS of Equation (29). With the multipoint-momentum interpolation corrections, nonphysical oscillations can be removed, especially those possible oscillations induced by the wetting-and-drying process, which is common due to the morphological changes in the unsteady sediment transport.
## 3 Methodology of the Proposed Concept
### Image Classifier
Finally, to introduce non-linearities into the algorithm, an activation function is used. In the absence of this, the model will learn only linear mappings. The ReLU function is the most often utilized activation function [50]. ReLU is frequently used instead of tanh and sigmoid because it significantly accelerates stochastic gradient descent convergence when compared to the other two functions. Furthermore, unlike tanh and sigmoid, which need substantial computation, ReLU is achieved by simply thresholding matrix values at zero (see Equation (14)). \[f\left(h_{\theta}(x)\right)=h(x)^{+}=max(0,\,h_{\theta}(x)) \tag{14}\]  
As shown in Figure 8, the first convolutional layer consists of 32 filters with a 3 \(\times\) 3 pixel dimension. After filtering, a ratio of 2 was used to enable the max-pooling process. The primary function of the convolutional layers is to detect the local connections of features from the preceding layer. The output result of the feature maps is then sent to the activation layer ReLU. The max-pooling procedure was used in vision systems for two reasons: (1) to exclude non-maximal values, which reduces the calculation time for layers; and (2) to perform down-sampling operations on 2x2 subregions to reduce the dimension of the intermediate feature vectors. Then, filters are stacked together, and fully connected layers can be used to compute the class scores. In the proposed system, the output from the fully connected layer becomes the input feature vectors for the SVM classifier. Table 5 presents the hyper-parameters which were manually assigned and implemented in our experiments. Figure 9 shows the legend of the confusion matrix for the classification. Figure 8: The proposed CNN architecture. Below are the following equations for the confusion matrix interpretation. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{15}\]  
\[Precision=\frac{TP}{TP+FP} \tag{16}\]  
\[Sensitivity=\frac{TP}{TP+FN} \tag{17}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{18}\]  
\[Negative\;Predictive\;Value=\frac{TN}{TN+FN} \tag{19}\]
## 3 Methodology
### Data Pre-Processing
#### 3.1.5 Data Normalization  
Data standardization is essential to ensure comparability between indicators with potentially diverse numerical ranges. This process normalizes the data by scaling them to a specific, narrower interval. In this study, a uniform mapping to the range \([-1,\,1]\) was employed. Standardization offers several benefits: it accelerates model convergence and improves accuracy while also mitigating the risk of gradient explosions. Equation (4) represents the normalization method, where \(x\) represents the value to be converted, \(x^{\prime}\) is the converted result, \(x_{max}\) is the maximum boundaries, and \(x_{min}\) is the minimum boundaries of the attribute values.  
\[x^{\prime}=2*\frac{x-x_{min}}{x_{max}-x_{min}}-1 \tag{4}\]