esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Link Level Parameters and Channel Coefficient Generation
### Small Scale Parameters
#### 3.3.3 Arrival and Departure Angles for Both Azimuth and Elevation
First, generate \(\tilde{\theta}_{n,ZOD}\) with Zenith Spread of Departure (ZSD) instead of ZSA, then replace (49) with:  
\[\theta_{n,ZOD}= \begin{cases}X_{n}\tilde{\theta}_{n,ZOD}+Y_{n}+\theta_{ LOS,ZOD}+\mu_{offset,ZOD}&\text{for }NLOS\\ X_{n}\tilde{\theta}_{n,ZOD}+Y_{n}-X_{1}\tilde{\theta}_{1,ZOD}-Y_{1}+\theta_{ LOS,ZOD}&\text{for }LOS\end{cases}  \tag{51}\]  
where \(X_{n}\) is a discrete random variable uniformly distributed in the set \(\{-1,1\}\), \(Y_{n}\sim\mathcal{N}(0,(ZSD/7)^{2})\), and \(\mu_{offset,ZOD}\) is a modified factor related to the BS-UT distance and antenna height in case of \(NLOS\) conditions and is given in Tables 7.5-7 and 7.5-8 of [21], respectively. Finally, (50) is replaced by:  
\[\theta_{n,m,ZOD}=\theta_{n,ZOD}+(3/8)(10^{\mu_{lg2 SD}})\alpha_{m} \tag{52}\]where \(\mu_{lgZSD}\) is the mean of the ZSD log-normal distribution. Coupling of Rays within a Cluster  
Once we have all the per-ray angles, we need to random couple them within a cluster in the following way:  
* Couple randomly \(AOD\) angles \(\phi_{n,m,AOD}\) to \(AOA\)\(\phi_{n,m,AOA}\) within a cluster \(n\);
* Couple randomly \(ZOD\) angles \(\theta_{n,m,ZOD}\) to \(ZOA\)\(\theta_{n,m,ZOA}\) angles within a cluster \(n\);
* Couple randomly \(AOD\) angles \(\phi_{n,m,AOD}\) with \(ZOD\) angles \(\theta_{n,m,ZOD}\) within a cluster \(n\). where \(n=1,\ldots,N_{cl}\) and \(m=1,\ldots,M_{ray}\). Figure 4 shows cluster angles \(\theta_{n,ZOD}\), \(\phi_{n,AOD}\) at the BS and \(\theta_{n,ZOA}\), \(\phi_{n,AOA}\) at the UT for a particular channel realization, in the case of _LOS_ link.
# Models to Identify Small Brain White Matter Hyperintensity Lesions
## 1 Introduction
These lesions' shape, size, and intensity variability further complicate detection, increasing the risk of misdiagnosis or delayed intervention [25]. This variability is compounded by the limited availability of labeled medical imaging data and the lack of robust segmentation performance on small lesions, making automated segmentation and classification a complex task and struggling with classification accuracy for different lesion types. This project aims to address these challenges by developing deep learning models for detecting, segmenting, and classifying small WMH lesions related to ischemic and demyelination diseases in FLAIR MRI. This task is crucial due to the similarity between these two diseases; a misdiagnosis by an untrained or inexperienced physician could lead to incorrect treatment. Therefore, this project seeks to provide the scientific and clinical community with a tool that assists in diagnosing these diseases, serving as a second opinion and as a training resource for identifying brain lesions. Specifically, the project employs machine learning and deep learning techniques to understand lesion features and facilitate detection and classification. Given the amount of data available for algorithm development, several transfer learning approaches, classical data augmentation methods, and synthetic data augmentation methods were utilized. The methodology for detection and classification primarily involved the following models: (i) U-Net [26] and Segmenting Anything Model (SAM) [27] to explore the task of segmentation and (ii) YOLOv8 (You Look Only Once, version 8) [28] and Detectron2 (Mask R-CNN-based) [29] for detection and classification. The proposed models were trained and tested using the 2D slices of each series of FLAIR MRI on a private dataset (80 volumes--200 images) and the MICCAI public dataset (110 volumes--2650 images). Due to the limitations of using a big dataset, were explored transfer learning and fine-tuning techniques to leverage pre-trained models from COCO (YOLO and Detectron2) and 1 billion masks and 11M images for SAM, adapting them for small medical datasets. This paper is structured as follows. The next section contains a summary of related work concerning the small lesions WMHs, in this case, the ischemia and demyelination. The methodology section overviews the models and techniques applied to segment, detect, and differentiate the lesions. The results section shows the evaluation, comparisons, and metrics values of each model applied. Finally, a discussion and conclusions section of this work is presented.
# Effects of the 12 May 2021 Geomagnetic Storm on Georeferencing Precision
## 2 Materials and Methods
### Apparent Position Variation Using Precise Point Positioning  
The RINEX files of 868 GNSS stations were processed in the postprocessing kinematic precise point positioning with ambiguity resolution (PPP-AR) mode in the Precise Point Positioning of Canadian Spatial Reference System (CSRS-PPP) online service ([[https://webapp.geod.nrcan.gc.ca/geod/tools-utils/PPP.php](https://webapp.geod.nrcan.gc.ca/geod/tools-utils/PPP.php)]([https://webapp.geod.nrcan.gc.ca/geod/tools-utils/PPP.php](https://webapp.geod.nrcan.gc.ca/geod/tools-utils/PPP.php)), accessed on 3 December 2021) [53]. The CSRS-PPP provides centimeter-level estimations with converged float solutions [54; 55; 56; 74]. The CSRS-PPP returns to the user a processing report via email.  
Usually, the report provides a different reference value for different days. We process the data to have an equal reference for all the used data to facilitate the evaluation of the position-variation time series. At each of the 868 stations, we also apply the common noise filter to correct the time series of the North, East, and Up components (Equation (5)). Then, we estimate the 3D resultant.  
\[CAPdoy_{t}=APdoy_{t}-RP_{t} \tag{5}\]  
where \(t\) is the epoch, \(CAPdoy_{t}\) is the corrected apparent position, \(APdoy_{t}\) is the apparent uncorrected position, and \(RP_{t}\) is the reference position. We use \(\overline{AP}\) from reference DoYs 129, 130, and 131 to calculate \(RP_{t}\).  
At each of the 868 stations, the maximum error was calculated for eight days and with time windows of 15 min. Subsequently, the percentage of stations in East, North, Up, and 3D position errors intervals was calculated.
# A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning
## 2 State-of-the-Art Techniques
### Work Related to the VLC Channel
#### 2.1.1 Work Related to Underground Channels  
Earlier efforts modelled the VLC channel in underground mining environments using the same Lambertian channel model as an indoor VLC channel. This meant that dust scattering, reflections on uneven walls, light obstruction, also known as shadowing, and the relative tilt and rotation of LEDs and PDs were disregarded. Furthermore, light has a dual nature and, depending on the quantum interpretation of the observer, can be modelled as either a particle or a wave [35]. The majority of current research on VLC communication in underground channels focuses on on-off keying (OOK) modulation, which is similar to amplitude modulation. In terms of phase, frequency modulation includes both coherent and noncoherent signals. Coherent FSK signals are those whose phase remains constant over time. Inconsistency also exists when the phase changes or varies over time. The continuous use of pneumatic hammers to excavate rocks in underground tunnels generates a substantial quantity of airborne dust that is detrimental to any channel. Prior generation processes have always sought to include [36] VLC or hybrid communication. To model the scattering effect and incorporate it into the overall model of the underground mining visible light communication channel (UM-VLC), a robust mathematical infrastructure is required for UM-VLC [37; 38]. Consequently, the effect of dust particles in the air is disregarded during testing in nonscattering indoor environments, such as offices, homes and hospitals; consequently, such models cannot function in underground tunnels.
# A Geographical Heuristic Routing Protocol for VANETs
## 4 Geographical Heuristic Routing Protocol  
In this section, we present our proposal of the geographical routing protocol named the Geographical Heuristic Routing (GHR) protocol. GHR follows the DTN approach of carrying the packet when there is not a suitable next forwarding node. Moreover, our routing protocol can use any routing criteria to select the next forwarding node to make forwarding decisions at each hop. The GHR protocol combines Tabu search and the Simulated Annealing (SA) adaptation summarized in the previous Section 3 to avoid loops and select the next node with a certain degree of randomness, respectively. GHR does not need to add any information to the hello messages. Algorithm 2 shows the procedure of our proposed GHR. First, it needs five input parameters:  
* The packet \(p\) to extract its Tabu list \(\tau\). * The set of possible destination nodes, \(Dst\), according to the anycast address of the destination. * The Boolean variable \(Tabu\) that indicates if Tabu routing has to be used. This means that a node cannot forward packets to the nodes in the Tabu list \(\tau\). * The Boolean variable \(First\) to select the first neighbor that meets the routing conditions. * The forwarding factor \(\alpha\) affects the probability of selecting a random legal neighbor, which is closer to the destination than the current node \(v\) to forward the packet. The factor \(\alpha\) plays the role of \(\gamma\) in Equation (1) for the forwarding Simulated Annealing (SA) explained in previous Section 3.2.2. If \(\alpha=0\), then the next forwarding node will be selected randomly among the legal neighbors (i.e., random forwarding). On the contrary, if this factor \(\alpha\rightarrow+\infty\), then the best neighbor closer to the destination than the current node will always be selected (i.e., best forwarding). For \(0<\alpha<+\infty\), we obtain probabilities \(p\) between zero and one, which provides randomness to the forwarding decision. * The recovery factor \(\beta\) tunes the probability of avoiding the \"carry and forwarding\" approach in favor of forwarding a packet to a legal neighbor that is farther from the destination than the current node. The factor \(\beta\) plays the role of \(\gamma\) in the Equation (1). When \(\beta=0\), the routing protocol will always select a legal neighbor, if there is any, as the next forwarding node. On the other hand, when \(\beta\rightarrow\infty\), carry and forwarding is always applied. Similar to the forwarding factor, if \(0<\beta<+\infty\), then the GHR protocol uses the recovery simulated annealing, which forwards the packet to a farther node to the destination with a probability \(0<p<1\), computed according to Equation (1). The first operations performed by the GHR are the following:  
1. Extraction of the Tabu list \(\tau\) from the packet \(P\) and
2. Set the initial value of the decision variables \(v_{\text{nh}},\ v_{f}\) and \(v_{r}\) to the current node ID (Lines 1 and 2 of Algorithm 2).
## 5 Statistical Analysis  
The Kolmogorov-Smirnov test was used to verify the normality of the distribution. Paired \(t\)-tests were applied to assess within-group differences and pre- to post-intervention changes. A two-way repeated measures ANOVA was used to examine the main effect of time and the effect of the interaction between groups and time on the outcome. When significant main effects or interactions were identified, post hoc pairwise comparisons were conducted. To adjust for multiple comparisons, Bonferroni correction was applied, adjusting the significance threshold (\(\alpha\) = 0.05) by dividing it by the number of comparisons made. The partial eta squared was considered as the effect size and interpreted as follows: 0.01 (small effect), 0.06 (medium effect), and 0.14 (large effect). Statistical analyses were conducted using SPSS (version 27.0, IBM, Armonk, NY, USA), with a significance threshold set at \(p\) < 0.05. The analyses were carried out by someone uninformed of the group assignment and not involved in the experimental intervention.
# Ultrafast and Wideband Microwave Photonic Frequency-Hopping Systems: A Review
## 2 Microwave Photonic Frequency-Hopping Systems
### MWP RF Switch for Frequency-Hopping Signal Generation
#### 2.4.2 Nonlinear Polarization Rotation Enabled Lyot Loop Filter as an RF Signal Switching
It has been proven that a polarization rotation angle of up to 180\({}^{\circ}\)[49] and a nonlinear polarization rotation response speed at GHz can be achieved [50]. By turning the two reconfigurable passbands in the dual-passband MWP filter on and off via nonlinear polarization rotation in the SOA, either \(f_{1}\) or \(f_{2}\) of the input RF signal can be switched out, resulting in a frequency-hopping signal. As a result, ultrafast switching of RF signals via an optically controlled comb filter can be successfully achieved. Figure 9c shows the measurement of nonlinear polarization rotation response speed, where a 1 Gbps \"10101100\" hopping code sequence is used to modulate the pump laser via an electro-optic intensity modulator. The rise/fall time of the nonlinear polarization rotated output is 200 ps. Figure 9d shows the study of RF signal switching speed and the purity of removing the unwanted frequency. A 6.8 GHz RF signal is used to modulate the optical carrier, and the MWP filter is configured to turn on and off the passband at 6.8 GHz, resulting in an on/off switching of the RF signal with a 200 ps switching speed.
# A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion
## 3 Materials and Methods
### Structure of System
#### 3.2.4 Normalization Layer  
The normalization step improves the network's efficiency in DL models. In addition to improving the model's performance, normalizing the data dimensions allows the input data to be plotted and scaled to a certain range. Batch normalization emphasizes the standardizing of the inputs to any particular layer. Standardizing the inputs means the network should have approximately unit variance and zero means to any layer in the network. Batch normalization transforms the input by dividing the subtraction of the input mean in the current mini-batch with the standard deviation in the current mini-batch. Equations (3) and (4) illustrate the normalization process.  
\[\hat{\mathbf{X}}^{\mathrm{k}}=\mathrm{x}^{\mathrm{k}}-\mathrm{E}\Big{(} \mathrm{x}^{\mathrm{k}}\Big{)}/\sqrt{\mathrm{var}(\big{(}\mathrm{x}^{\mathrm{ k}}\big{)}} \tag{3}\]  
Here, \(\hat{\mathbf{X}}^{\mathrm{k}}\) is the normalized value of the \(\mathrm{k}^{\mathrm{th}}\) hidden neuron and \(\mathrm{E}(\mathrm{x}^{\mathrm{k}})\) is the dimension's average. The variance of the \(\mathrm{k}^{\mathrm{th}}\) hidden unit is represented here by \(\sqrt{\mathrm{var}(\big{(}\mathrm{x}^{\mathrm{k}}\big{)}}\), and \(\beta\) and \(\gamma\) are the two learnable variables.  
\[y^{(k)}=\gamma^{k}\mathrm{x}^{k}+\beta^{k}, \tag{4}\]
## 4 Discussions
### Uncertainties Resulted from Model Parameters  
The model parameters can lead to uncertainties in the results of the RS-CFLUX model. The model calibration suggested that only a few parameters were well constrained. The number of parameters that can be well constrained depends on the amount and quality of measurement that used in calibration. It is also reported by previous study that there are only few parameters can be well constrained when only the carbon flux data was used in the reversion process [38; 39]. Based on the probability density function proposed by MCMC for each parameter, we used the Monte Carlo method to assess the uncertainties in the model outputs resulting from each parameter (see Figures 9 and 10). \(\mathrm{LUE_{max}}\) resulted in the greatest uncertainties in GPP, followed by \(\mathrm{LST_{opt}}\). \(\mathrm{LST_{min}}\) and \(\mathrm{LST_{max}}\) both resulted in low uncertainties in the GPP. Rref, E0 and a did not result in uncertainties in the GPP prediction. However, E0, Rref and \(\mathrm{LUE_{max}}\) led to the greatest uncertainties in the NEP, followed by \(\mathrm{LST_{opt}}\) and a. \(\mathrm{LST_{min}}\) and \(\mathrm{LST_{max}}\) led to low uncertainties in the NEP prediction. In addition to the parameters, forcing variables and model structure also resulted in uncertainties in the model outputs [40]. Observation instruments contain stochastic error. Furthermore, data processing also results in errors. The model is a simple function with a few environmental factors; it does not include all exchange processes between the ecosystem and atmosphere [41].  
Figure 9: Uncertainties in the GPP caused by parameters in the RS-CFLUX model. N (mu, delta) is a normal distribution with a mean mu and standard deviation delta.
## 5 Analysis of the Results
### Multipath Component
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{MN} & \multirow{2}{*}{**SCN**} & \multirow{2}{*}{**LOS/NLOS**} & \multirow{2}{*}{**PDF**} & \multicolumn{6}{c}{**868 MHz NB**} & \multicolumn{6}{c}{**6489 MHz UWB**} \\ \cline{5-10}  & & & **P\({}_{1}\)** & **P\({}_{2}\)** & \(\chi^{2}\)** & \(r\)** & **P\({}_{1}\)** & **P\({}_{2}\)** & \(\chi^{2}\)** & \(r\)** \\ \hline \multirow{10}{*}{HE\({}_{\text{R}}\)} & \multirow{5}{*}{LOS} & Rice & 0.036 & 0.831 & 13.72 & 0.90 & 0.934 & 0.298 & 5.78 & 0.86 \\  & & Nakagami-m & 1.164 & 1.384 & 12.45 & 0.88 & 3.262 & 1.050 & 5.23 & 0.89 \\  & & \multirow{5}{*}{LOS} & Weibull & 1.181 & 2.036 & 11.61 & 0.89 & 1.090 & 3.363 & 5.75 & 0.82 \\  & & Rayleigh & 0.832 & - & 13.72 & 0.90 & 0.725 & - & 5.74 & 0.64 \\  & & \multirow{5}{*}{NLOS} & \multirow{5}{*}{NLOS} & \multirow{5}{*}{NLOS} & \multirow{5}{*}{NLOS} & \multirow{5}{*}{NLOS} & \multirow{5}{*}{NLOS} & \multirow{5}{*}{NLOS} & \multirow{5}{*}{NLOS} & \multirow{5}{*}{NLOS} & \multirow{5}{*}{NLOS} & \multirow{5}{*}{NLOS} & \multirow{5}{*}{NLOS} & \multirow{5}{*}{NLOS} \\  & & & **Lognormal** & \(\mathbf{-0.081}\) & **0.484** & **10.66** & **0.97** & \(\mathbf{-0.056}\) & **0.275** & **11.90** & **0.95** \\ \cline{1-1} \cline{5-10}  & & \multirow{5}{*}{NLOS} & Rice & 0.814 & 0.523 & 7.70 & 0.91 & 0.923 & 0.400 & 10.11 & 0.94 \\  & & Nakagami-m & 1.552 & 1.209 & 7.87 & 0.94 & 2.001 & 1.173 & 9.14 & 0.98 \\  & & Weibull & 1.138 & 2.398 & 7.65 & 0.92 & 1.141 & 2.909 & 9.91 & 0.95 \\channel were obtained for the lognormal and Nakagami-\(m\) distributions. However, it was decided to choose the lognormal distribution, which yields slightly better GoF test results. It is worth noting that, for the analyzed measurement scenarios, the Rice and Rayleigh distributions should not be used to describe the small-scale fading amplitude distribution, which confirms the conclusions from the research carried out so far [38].
## 6 Countermeasures in EC-IoT
### AI Methods
#### 6.2.2 Deep Learning  
DL methods play a critical role in enhancing the security of EC-IoT systems by providing sophisticated mechanisms to detect and mitigate various types of attacks. Convolutional neural networks (CNNs) are used to analyze network traffic patterns, identifying anomalies indicative of network-level attacks such as SYN flooding and de-synchronization [115]. Recurrent neural networks (RNNs), including long short-term memory networks (LSTMs), are effective for time-series analyses of network traffic, enabling the detection of temporal anomalies [42]. Autoencoders, another DL method, are utilized for detecting anomalies in application-layer data by reconstructing normal patterns and identifying deviations, making them effective against application-layer attacks [42]. Generative adversarial networks (GANs) are employed to simulate attack scenarios and improve the robustness of intrusion detection systems (IDS) via training on both normal and adversarial examples [115].  
For data-level attacks, RNNs and variational autoencoders (VAEs) are used to detect data anomalies over time and in high-dimensional data, respectively, helping to identify tampered or unauthorized data entries [42; 115]. Access control attacks are mitigated using LSTM networks and deep belief networks (DBNs), which model complex user behavior patterns and detect deviations indicative of unauthorized access [42; 115]. Protocol-based attacks are countered using autoencoders for detecting anomalies in protocol-specific data and GANs for generating synthetic attack data to improve IDS robustness [42; 115].  
To address side-channel attacks, CNNs analyze side-channel signals like power consumption and electromagnetic emissions to detect anomalies, while RNNs perform temporal analyses of these signals to identify patterns indicative of attacks [42; 115]. For supply chain attacks, autoencoders detect anomalies in supply chain data, identifying tampered or counterfeit components, and GANs simulate supply chain attack scenarios to enhance detection capabilities [42; 115]. Lastly, RNNs and deep neural networks (DNNs) are employed to analyze communication patterns and detect social engineering attacks such as phishing emails and other deceptive tactics [42; 115; 116; 117; 118; 119].
## 3 Methodology
### Fuzzy Image Classification
#### 3.2.2 Vegetation  
As mentioned earlier in this article, vegetation extraction is one of the most straightforward methods in image classification. In this research, 2 parameters are used for vegetation detection: NDVI and NIR ratio, which are explained in this section.  
**NDVI**: Normalized Difference Vegetation Index (NDVI) is a proper tool for vegetation extraction [30].
**NIR Ratio**: Because of high reflectivity of vegetation in the NIR region of the electromagnetic wave spectrum, the value of the NIR ratio, which is given by Equation (7), is higher in vegetated areas compared to other parts of the image [28].  
\[NIR\ Ratio=\frac{NIR}{NIR+R+G+B} \tag{7}\]  
The value of the NDVI and the NIR ratio in vegetated areas slightly differ from one image to another. Generally, the NDVI value for vegetation is around 0.2 and the NIR ratio is around 0.3, depending on the density of vegetation. However, if a crisp threshold is selected to classify the image into vegetated and non-vegetated areas, there might be some vegetation segments in the image with the NDVI and the NIR ratio values slightly less than thresholds which will be miss-classified as non-vegetation. In order to improve error of omission, the presented fuzzy rule-based system offers a wider margin to include uncertain segments and provides them a second chance to be examined for classification qualification.  
The used membership function is presented in Figure 5 and the associated formula is given in Equation (8).  
\[\mu_{N}(x)=\begin{cases}1,&\text{for }0\leq x-T_{2}\leq 1-T_{2}\\ 1-\frac{T_{2}-x}{T_{2}-T_{1}},&\text{for }0\leq T_{2}-x\leq T_{2}-T_{1}\\ 0,&\text{otherwise}\end{cases} \tag{8}\]  
The related fuzzy rule is:  
If **NIR ratio** is _high_ and **NDVI** is _high_ then segment is _Vegetation_.