esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
As mentioned above, although any relationship between a wide range of spatiotemporal scales residing in the SST variability and its related forces has been revealed in a specific time and space domain by many previous studies, there was nearly no approach based on defining spatiotemporal scales from oscillatory modes intrinsic in a singular SST time series; if any [32], the nonstationary and nonlinear features of the SST variability were not nearly considered. Sometimes, a purely data-driven approach can give insight into understanding the causal relationship between local SST anomaly variability and any exogenous forcing. In this study, we examine the spatiotemporal characteristics of SST variability in terms of intrinsic modes residing on individual SST anomaly (SSTA) time series, which can give rise to instantaneous frequency and amplitude values at every moment. To this end, we use empirical mode decomposition (EMD) [33] in order to extract intrinsic mode functions (IMFs) from a singular SSTA signal. First, based on IMFs, we examine the temporal evolution of the (amplitude) energy density of an IMF characterized by a specific varying frequency and investigate the spatial pattern of energy distribution for a specific time duration. Second, cross-correlation-based spatial patterns along with weak spatial causality are examined using complex network approaches. Basically, since a temporal scale tends to be proportional to its spatial scale, a network is constructed based on the ensemble of the same mode IMFs for revealing their spatial extent based on a specific timescale. In addition, any lag time feature between a pair of oscillatory time series with almost equal frequency, as the same mode IMFs usually do, is incorporated into the determination of the threshold value, from which a link between a pair of the same mode IMFs is determined. Thus, in terms of network metrics such as connectivity and community [34], the structure of spatial correlation and causality is examined. This article is organized as follows. In the materials and methods section, a brief description of data and a detailed description of methodology are presented. In the analysis result section, the aforementioned results are described in detail. Lastly, in the discussion section, we evaluate our results in comparison with previous studies and present the direction of further research, especially on causality.
# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 4 Experiments and Results Analysis
### Experimental Procedure
#### 4.1.3 Fog Synthesis  
Using the trained Fog Generator, we synthesized foggy images from the clear images in both the training and evaluation subsets of O-HAZY. The fog intensity was set to 0.2, reflecting a moderate fog level suitable for surveillance applications. Additionally, we generated foggy images using three baseline methods: Simple Fog Generation (Simple), Depth-Aware Fog Generation (DA), and Dark Channel Prior-based Fog Generation (DC).  
Figure 3 illustrates examples of synthetic fog generated by each method, showcasing their visual differences. For instance, the Depth-Aware Fog method highlights a depth-dependent fog distribution, while our GridNet-based approach demonstrates nuanced fog patterns.
# Determining Optimal Power Flow Solutions Using New Adaptive Gaussian TLBO Method
## 2 OPF Problem Formulation  
According to [14; 15; 16], the non-linear OPF optimization aims at objectives tradeoff in the power network subject to various inequality and equality constraints expressed as follows.  
\[\text{Min }J(x,\ u) \tag{1}\]  
\[\text{Subject to : }g(x,\ u)=0 \tag{2}\]  
\[h(x,\ u)\ \leq\ 0 \tag{3}\]  
It is assumed that \(u\) and \(x\) are the system's decision and state variables, respectively. \(J(x,u)\) denotes the objective function of the problem, \(g(x,u)\), the equality constraints of the load flow equation, and \(h(x,u)\), the inequality constraints of the transmission network's physical limits on the decision and state variables.  
The variables controlling the OPF problems include the active power outputs from the generation systems, the voltage magnitudes from the generator buses, changes in the transformer tap points, and the reactive power compensation. Additionally, voltage magnitudes in load buses, maximum transmission capacities of branches, active power outputs of reference buses, and reactive generation of generators are state variables in this context.
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 5 Simulation and Results
### Simulation Setup
#### 5.1.3 Processing Delay Distribution  
The Pareto distribution was used for the processing delay to generate a downlink packet response. Scale parameter X m was 300 ms for the highest layer protocol over RRC and 80 ms for the other layers. We set tail index of the Pareto distribution between 2 and 4.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 3 MAC Layer and Beamforming for mmWave
### Taxonomy of mmWave MAC Protocols  
Traditionally, two approaches have been used for mmWave spectrum sharing and accessing [35]: centralized and distributed. Our survey classifies MAC protocols based on these two categories, as shown in Figure 1. A centralized approach is a refined and conservative approach in which a central unit called an access point (AP) or a PNC organizes the communication between the networks. Moreover, in the IEEE802.15.3c standard, a mmWave-WPAN can simply be called a piconet [36]. A PNC is used to centrally perform the channel assessment, power scheduling, and time slot distribution for the links. In contrast, in a distributed approach, the whole networks are coordinated with each other, and there is no mother-control AP or PNC. Figure 3 illustrates a PNC model with five connected devices.
# RM-Line: A Ray-Model-Based Straight-Line Extraction Method for the Grid Map of Mobile Robot
## 2 Materials and Methods  
### Edge Point Extraction Method Based on Connected Domain  
The line information in the grid map of the mobile robot is hidden in the contour of the obstacles, and the pixels inside the contour increase the search range of the line extraction algorithm and may cause interference. Therefore, this paper proposes a method based on connected domains to extract edge points from the grid map, thereby narrowing the search range of the line extraction algorithm.  
Figure 1 shows the extraction process of the edge points.  
(1) Figure 1b shows a grid map of the mobile robot used as input, where the yellow dot is the robot, the turquoise points are the BLANK grids, the cyan points are the OBSTACLE grids, and the steel-blue points are the UNKNOWN grids.  
(2) Create an auxiliary map, denoted as _AuxMap_, whose initial value is 0. Create a cache array, denoted as _OpenArray_; let \(Array[0]=p_{rob}\), where \(p_{rob}\) is the position of the robot.  
(3) Figure 1a shows the grid \(p_{i}(x_{i},y_{i})\) in the OpenArray; search for its neighbor point \(p_{ik}\), where \(p_{ik}\in\{p_{i0}(x_{i}+1,y_{i})\), \(p_{i1}(x_{i}-1,y_{i})\), \(p_{i2}(x_{i},y_{i}+1)\), \(p_{i3}(x_{i},y_{i}-1)\}\). If \(p_{ik}\) is a BLANK grid, and \(AuxMap(p_{ik})==0\), then add \(p_{ik}\) to OpenArray and let \(AuxMap(p_{ik})=1\). If \(p_{ik}\) is an OBSTACLE grid, let \(AuxMap(p_{ik})=2\), but do not add it to _OpenArray_.  
(4) Repeat (3) until all points in the _OpenArray_ are processed.  
Figure 1: The extraction process of the edge points: (**a**) four neighbor grids of the BLANK grid \(p_{i}\); (**b**) grid map of the mobile robot; (**c**) the connected domain; (**d**) the edge map.  
(5) Grids with \(AuxMap(p)=1\) form a connected domain, as shown in the blue part in Figure 1c. Grids with \(AuxMap(p)=2\) are the edge points, as shown in the cyan part in Figure 1d.  
It can be seen from Figure 1 that the edge point extraction process filters out the points inside the contour or outside the map, to reduce the search range of the line extraction algorithm.
# Particle-Velocity-Based Mixed-Source Sound Field Translation for Binaural Reproduction
## 4 Sound Field Translation and Synthesis for Binaural Reproduction  
As shown in the flowchart of the velocity-based mixed-source sound field translation for binaural reproduction in Figure 1, in this section, we translate the sound field in the spherical harmonic domain, and then synthesize the left and right ear signals at the translated position.  
Solving (26) or (27) gives us the reproduced virtual-reality sound field, expressed as  
\[\widetilde{P}(\mathbf{x},k)=\sum_{\ell=1}^{2L}\omega_{\ell}\psi(k,R_{\ell}\mathbf{ \hat{y}}_{\ell};\mathbf{o})R_{\ell}e^{-ikR_{\ell}}\frac{e^{ik||R_{\ell}\mathbf{\hat{y} }_{\ell}-\mathbf{x}||}}{4\pi||R_{\ell}\mathbf{\hat{y}}_{\ell}-\mathbf{x}||}. \tag{28}\]  
With respect to the translated position \(\mathbf{d}\), the virtual sources are located at \(\mathbf{z}_{\ell}=R_{\ell}\mathbf{\hat{y}}_{\ell}-\mathbf{d}^{\prime}\); therefore, the translated sound field can be written as  
\[\widetilde{P}(\mathbf{x},k;\mathbf{d})=\sum_{\ell=1}^{2L}\omega_{\ell}\psi(k,R_{\ell} \mathbf{\hat{y}}_{\ell};\mathbf{o})R_{\ell}e^{-ikR_{\ell}}\frac{e^{ik||\mathbf{z}_{\ell}- \mathbf{x}||}}{4\pi||\mathbf{z}_{\ell}-\mathbf{x}||}. \tag{29}\]  
We decompose (29) in the spherical harmonic domain about the position \(\mathbf{d}\), and the pressure coefficients of the translated sound field due to the virtual sources at \(\mathbf{z}_{\ell}\) can expressed as  
\[\gamma_{vu}(k;\mathbf{d})=\sum_{\ell=1}^{2L}\omega_{\ell}\psi(k,R_{\ell}\mathbf{\hat{ y}}_{\ell};\mathbf{o})ikR_{\ell}e^{-ikR_{\ell}}h_{v}(k|\mathbf{z}_{\ell}|)Y_{vu}^{*}( \mathbf{z}_{\ell}), \tag{30}\]  
where \(v\) and \(u\) index the translated harmonics, centered at \(\mathbf{d}\). The left and right ear signals can be synthesized, in the spherical harmonic domain, directly from the translated pressure coefficients \(\gamma_{vu}(k;\mathbf{d})\) and the coefficients of the listener's HRTFs, \(H_{\text{L,R}}^{vu}(k)\), with [60; 61]  
\[\widetilde{P}_{\text{L,R}}(k;\mathbf{d})=\sum_{v=0}^{\infty}\sum_{u=-v}^{v}\gamma _{vu}(k;\mathbf{d})H_{\text{L,R}}^{vu}(k). \tag{31}\]  
Note that \(H_{\text{L,R}}^{vu}(k)\) is obtained from the spherical harmonic decomposition of the HRTF \(H_{\text{L,R}}(k,\mathbf{y};\mathbf{d})\).  
Figure 1: The flowchart of the velocity-based mixed-source sound field translation for binaural reproduction.  
Alternatively, the left and right ear signals can be obtained by applying the mixed-source driving functions to the HRTFs based on the listener's translated position, given as  
\[\widetilde{P}_{\text{L,R}}(k;\mathbf{d})=\sum_{\ell=1}^{2L}\omega_{\ell}\psi(k,R_{ \ell}\hat{\mathbf{y}}_{\ell;}\mathbf{o})H_{\text{L,R}}(k,\mathbf{y}_{\ell;}\mathbf{d}). \tag{32}\]  
We also direct readers to [62; 63] for other options of rendering the ear signals at the translated positions.
## 1 Introduction
At this scale, numerous studies have explored the response of SIF to photosynthesis in agricultural systems, such as maize (_Zea mays_ L.), rice (_Oryza sativa_ L.), and broadleaf tree species, such as beech (_Fagus grandifolia Ehr_) and red oak (_Quercus rubra_) [24; 25; 26]. In ENFs, the relationship between SIF and GPP varies depending on the forest type. A linear relationship has been observed in lodgepole pine (_P. contorta_ Douglas ex Loudon) and Engelmann spruce (_Pica engelmannii_ Parry ex [PERSON]) in Colorado [21], whereas a nonlinear relationship has been reported in Korean pine (_Pinus koraiensis_ Siebold et. [PERSON]) ecosystems [27]. These contrasting patterns may be driven by differences in canopy structure, leaf area index, and environmental conditions, suggesting that the SIF-GPP relationship in ENFs is modulated by a combination of biophysical and ecological factors [28, 29]. The Mongolian Scots pine (_Pinus sylvestris_ var. _monoglica_), a key afforestation species in China's \"The Great Green Wall\" Shelterbelt Program, plays an important ecological role in combating desertification. However, as a representative semi-arid evergreen needleleaf ecosystem, its carbon exchange dynamics and seasonal variations in photosynthetic activity remain unclear, and it is still uncertain whether SIF can accurately reflect the temporal dynamics of its GPP. In this study, we conducted tower-based synchronous SIF and EC observations in a Mongolian Scots pine plantation in northern China and estimated GPP using EC measurements. Our objectives were to (1) evaluate the carbon sequestration capacity in this ENF ecosystem; (2) investigate the relationship between SIF and GPP, with an emphasis on its seasonal dynamics; and (3) examine the environmental drivers of SIF and GPP during seasonal transitions.
## 3 Method
### Segmentation Network  
The U-Net technique has become increasingly popular in deep learning due to its accurate results, rapid processing speed, and minimal need for advanced and costly hardware [33; 34]. The U-Net architecture is a convolutional neural network initially designed to segment semantic information in biological images. The network's architectural design is strikingly similar to the contour of the English letter U, thereby earning it the famous moniker U-Net. The U-Net architecture has gained prominence in image segmentation due to its capacity to extract local and global properties by employing methods that can handle diverse scales. The U-Net design consists of three stages: encoder (contraction), bridge (bottleneck), and decoder (expansion). The encoder module leverages the attributes of the input image to reconstruct the segmented output. The decoder component arranges each pixel to display the segmented composition.  
Augmenting the depth of a multi-layer neural network can amplify its efficacy. However, this could hinder the training process, decreasing the gradient [35]. Residual networks are formed by combining cumulative residual units [36] that aid the network's training. Using jumpers within a residual unit allows the transmission of information beyond the unit's boundaries. The primary function of the remaining blocks is to ease the transmission of information between layers, allowing for the creation of a more complex neural network. Utilizing channels decreases the computational expense and increases the dependence on those channels.  
Our proposed model is an enhanced version that integrates U-Net and Residual networks, resulting in a higher learning capability. Figure 8 provides a concise summary of the intended architectural concept. Each convolutional block in the proposed framework is constructed from group normalization, leaky ReLU activation, and convolutional layers. Table 3 shows the parameters and internal structure of the blocks.
## 4 Characteristics of Different Time Distribution Systems
### Precision Time Protocol
#### 4.2.4 Reliability and Security  
PTP networks are vulnerable to various cyber-attacks, including delay attacks, packet modification attacks, transparent clock attacks, and time reference attacks. These attacks can significantly degrade the protocol's accuracy, rendering it unsuitable for some or all clocks in the network, potentially leading to severe consequences [85].  
The delay attack is the most critical vulnerability in PTP networks. PTP assumes symmetry in the master-to-slave and slave-to-master path delays when calculating the propagation delay. A delay attack exploits this assumption by introducing asymmetrical delays, which are hard to distinguish from regular network congestion or delays. This makes the attack difficult to detect and effective in disrupting the synchronization of PTP slave clocks [18].
## 5 Evaluation and Results
### Limitations of the Approach  
One of the few limitations of our experimental set-up is that, to test the efficacy of SWIFTNET, the fire sources are generated using the heat gun. The reservation here would be that the heat gun fire source does not exhibit exactly the same intensity as in a real wildfire scenario, in which case the rate of change of the signal amplitude may be high within a short period of time. In addition, in the event of intense wildfire disasters, the air temperature might be very high (over \(100\ ^{o}C\)); as a consequence, a few of the nodes could be physically damaged due to the very high temperature and could result to packet loss. To avoid this, it is envisaged that a very good thermal protective cover could be used in a dense deployment, such that packet loss from a few nodes would not greatly impact the information gathering process. Moreover, we acknowledge that the experiments have been conducted on a small testbed of a few nodes. In the future, it is our intention to extend this work to cover several nodes, while using a different application setup, such as micro-changes in climate condition in a conservatory or museum.
# Network Visualization of Cooperative Games
## 6 Examples
### Myerson Games
#### 6.5.5 Myerson Chain Messages Game  
In this example section, we are using a chain of ten players as an example of a communication situation. In this case, the value of a coalition is based on the number of adjacent pairs within it, as each pair signifies a direct communication link. For instance, the coalition of players P2, P3, P4 has two adjacent pairs, (P2, P3) and (P3, P4), giving it a value of 2. More details about the relationships and player relevance of this game are given in Table 15.  
Analyzing this game in Table 16, it shows identical values across \(I\), \(I^{+}\), and \(I^{|\cdot|}\). As in the Myerson Chain Cost Game in Section 6.5.4, negative interactions are absent, meaning there is no interest in breaking collaboration. We also see that players in the middle of the chain tend to have a higher participation, as they possess more opportunities to form valuable coalitions with their adjacent neighbors. This is depicted in Figures 14 and 15. The game's variability remains reduced, as indicated by a Gini Index of 0.1904 and a packed range of data values. The distribution tilts towards a left skew (skewness = \(-0.5504\)), and  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(Sh\) & \(I\) & \(P1\) & \(P2\) & \(P3\) & \(P4\) & \(P5\) & \(P6\) & \(P7\) & \(P8\) & \(P9\) & \(P10\) \\ \hline \(-0.5\) & \(P1\) & \(0\) & \(1\) & \(0\) & \(0\) & \(0\) & \(0\) & \(0\) & \(0\) & \(0\) \\ \(0\) & \(P2\) & \(1\) & \(0\) & \(1\) & \(0\) & \(0\) & \(0\) & \(0\) & \(0\) & \(0\) \\ \(0\) & \(P3\) & \(0\) & \(1\) & \(0\) & \(1\) & \(0\) & \(0\) & \(0\) & \(0\) & \(0\) \\ \(0\) & \(P4\) & \(0\) & \(0\) & \(1\) & \(0\) & \(1\) & \(0\) & \(0\) & \(0\) & \(0\) \\ \(0\) & \(
# Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification
## 2 Related Work and Background
### Region Covariance Descriptor  
Region covariance descriptor has been applied in the computer vision and brain computer interface problem [30; 31; 32]. [PERSON] et al. introduced the descriptor to HSI processing [26; 27; 28]. Suppose that \(\mathcal{X}\in\mathbb{R}^{W\times H\times D}\) represents the original HSI cube, \(\mathcal{X}_{i}\in\mathbb{R}^{w\times h\times D}\) denotes the spatial region around the \(i\)th pixel, and \(s=w\times h\) is size of a spatially local window, then the region covariance descriptor is as follows:  
\[\mathbf{C}_{i}=\frac{1}{s-1}\sum_{t=1}^{s}\left(\mathbf{x}_{t}-\boldsymbol{ \mu}_{i}\right)(\mathbf{x}_{t}-\boldsymbol{\mu}_{i})^{T} \tag{5}\]This can be defined as a spectral-spatial feature of \(\mathcal{X}_{i}\), where \(\mathbf{\mu}_{i}=\frac{1}{s}\sum\limits_{t=1}^{s}\mathbf{x}_{t}\). Since the covariance feature is connected to a symmetric positive definite matrix and lies on a Riemannian manifold [32], the Log-Euclidean distance metric shown below can measure the similarity between \(\mathbf{C}_{i}\) and \(\mathbf{C}_{j}\):  
\[d_{LE}(\mathbf{C}_{i},\mathbf{C}_{j})=\ \|\ \log(\mathbf{C}_{i}{}^{-1}\mathbf{C}_{ j})\|_{F}=\left[\sum\limits_{k=1}^{n}\log^{2}\lambda_{k}\right]^{1/2} \tag{6}\]  
where \(\lambda_{k}\) is the \(k\)th eigenvalue of \(\mathbf{C}_{i}{}^{-1}\mathbf{C}_{j}\).  
Performing a region covariance descriptor can yield an excellent spectral-spatial covariance feature for HSI classification [26; 27; 28], which has the advantage of robustness to noise and spectral variabilities over traditional spectral feature (the original spectral signature) [33; 34; 35].  
Following one of the most effective HSI classification routines which comprises three successive phases, i.e., feature extraction, dimensionality reduction, and classification, the combination of various features and various manifold dimensionality reduction ways can yield different HSI manifold learning schemes which can be testified with a subsequent HSI classifier.
# Embedded AI-Based Digi-Healthcare
###### Abstract
In this regard, AI & ML technology would be extremely useful for performing data analytics, classification, and predicting healthcare conditions based on this data. Based on the urgent needs of remote patient monitoring and its benefits, we have developed and implemented a smart remote monitoring system based on IoT. The system uses different wearable sensors to record and send data to the edge device, which uses edge computing to locally make decisions using ML techniques. The proposed system is a technology used to monitor a patient's vitals i.e., oxygen saturation, body temperature, and heart rate from a remote location. The IoT node will generate large amounts of data regularly and from a variety of patients, therefore, AI & ML technologies are used for performing data analytics, classification, and predicting healthcare conditions at the edge. ML techniques acquire statistics from the raw data and derive insights about the patient's health condition. The system offers a wearable device in the form of a smartwatch (which can be always worn by a patient). The device is small, easy to use, and flexible. For smart inference of the patient's health status on edge, the ML model is trained at the cloud level and this model then executes on the edge device. The edge device uses this prediction model to predict the patients' health status based upon the readings. The sensor data as well as the ML predictions are sent to the cloud over the internet for archival purposes. The system offers a web application that gives access to patients' vitals, personal and medical information to the doctor, along with the prediction from the ML model. The device comprises of both hardware and software components that are utilized to keep track of patients vital and health status. The key contribution of the paper are highlighted below:  
* Development of an Internet of Medical Things (IoMT) based wearable healthcare device for continuous health monitoring of patients. * Integration of edge computing for low latency real-time anomaly detection using local ML inference on health vitals, as well as, pushing critical notifications to doctors and caretakers in case of an emergency. * A web-based GUI for live data visualization along with ML classification results on the authenticated doctor and patient profiles.
# On the Use of Transformer-Based Models for Intent Detection Using Clustering Algorithms
## 3 Materials and Methods
### Dimension Reduction  
Dealing with high-dimensional data poses a significant challenge as the distance between points loses its meaning. In other words, as data dimensionality increases, the distance between any two points becomes roughly equidistant from all other points. Moreover, measuring such distances can be computationally expensive, further exacerbating the problem. Furthermore, high-dimensional data can be problematic for clustering algorithms; hence, there might be a need for the use of a dimension-reduction algorithm.  
Principal component analysis [37] is one of the most commonly used dimensionality reduction algorithms. Although it can show the data's global patterns, it can fail to identify local ones. More advanced algorithms, such as t-Distributed Stochastic Neighborhood Embedding (t-SNE) [38] and Uniform Manifold Approximation and Projection (UMAP) [39], address this problem by trying to more accurately represent the data. Therefore, both algorithms were tested.