esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Analysis of PWV Values  
Precipitable water volume (PWV) has indicative significance for small-scale catastrophic weather forecasting and is one of the critical parameters in response to low-level atmospheric characteristics. This article explores the relationship between PWV changes and volcanic eruptions by studying the global PWV distribution on 15 January 2022. Specifically, the global PWV anomaly distribution on 15 January 2022 was obtained by using the 15 January 2020 and 2021 data as the background value and finding the difference between the date of 15 January 2022 and the background value (Figure 2). At 0:00 (Figure 2a), the PWV anomaly was mainly distributed in the Tonga region, northern Africa, the Indian Ocean, and southern South America. From 2:00 to 16:00 (Figure 2b-i), the PWV remained unusually significant in Tonga, but the Northern Hemisphere anomaly began to disappear at 1400 (Figure 2h). From 18:00 to 22:00 (Figure 2j-l), the negative PWV variation gradually spread from the Tonga region to the whole South Pacific, while the North Pacific PWV was positive. It can be seen from Figure 2 that the PWV changes were mainly concentrated in the Tonga region, and the degree of PWV anomalies in the Tonga region was the largest before and after the HTHH volcanic eruption. Thus, there is a specific correlation between PWV anomalies and volcanic eruptions.  
In addition, in order to more accurately reflect the characteristics of PWV anomalies during volcanic eruptions, in this paper, data from 11 stations in the Tonga region were selected for analysis (Figure 1). Using the data of January 2020 and 2021 as the background values and finding the difference between the data of January 2022 and the background values, we obtained the water vapor anomaly map for the period from 1 January 2022 to 24 January 2022 for the 11 stations (Figure 3).  
As can be seen from Figure 3, the PWV around Tonga was normal from 1 January to 9 January (Figure 3a-i), and the PWV on the south side of Tonga was positively weird on 10 January and remained so until 12 January (Figure 3j-l), when the PWV returned to normal. A negative water vapor anomaly was observed in the lower right of Tonga on 13 January (Figure 3m), and a positive water vapor anomaly north of Tonga and a negative water vapor anomaly east of Tonga occurred on the 14 th (Figure 3n). The anomaly reached its highest value on the 15 th (Figure 3o). The positive water vapor anomaly was the highest on the 16 th (Figure 3p), and after the 16 th, the water vapor did not increase, but the anomaly decreased (Figure 3q-x). From Figure 4, it can be seen that the PWV anomaly in Tonga was an overall increase in January, and the PWV anomaly was an overall decrease in February. Therefore, volcanic activity has a strong impact on the changes in PWV in the lower atmosphere of the surrounding area.  
Figure 3: _Cont_.  
Figure 2: Global PWV anomaly distribution from 0:00 to 22:00 (UTC) on 15 January 2022. Blue is the PWV anomaly reduction area. Red is the PWV anomaly increase area. Red box indicates Tonga region.
# A Computational Framework for 2D Crack Growth Based on the Adaptive Finite Element Method
## 2 Procedure of the Developed Program
### Crack-Kinking Criteria  
In this study, the crack growth angle was calculated using the maximum circumferential stress theory [40]. According to this theory, when mixed-mode loading is applied to isotropic materials, the crack develops in a path that is normal to the direction of the maximum tangential tensile stress. According to this theory, the tangential stresses in polar coordinates are given by the following expressions [40; 41]:  
\[\begin{array}{l}\sigma_{r}\ =\ \frac{1}{\sqrt{2\pi r}}\cos(\theta/2)\left(K_{I}[1 +\sin^{2}(\theta/2)]+\frac{3}{2}K_{II}\sin\theta-2K_{II}\tan(\theta/2)\right)\\ \sigma_{\theta}\ =\frac{1}{\sqrt{2\pi r}}\cos(\theta/2)\left[K_{I}\cos^{2}(\theta/2)- \frac{3}{2}K_{II}\sin\theta\right]\\ \tau_{\theta}\ =\frac{1}{\sqrt{2\pi r}}\frac{\cos(\theta/2)}{2}[K_{I}\sin\theta+K_{II}(3 \cos\theta-1)]\end{array} \tag{1}\]  
where \(\sigma_{r}\) represents the normal stress in the radial direction, \(\sigma_{\theta}\) represents the normal stress in tangential direction, and \(\tau_{\theta\theta}\) represents the shear stress.  
By solving \(d\sigma_{\theta}/d\theta=0\) for \(\theta\), the result is expressed as:  
\[K_{I}\sin\theta+K_{II}(3\cos\theta-1)=0 \tag{2}\]  
Figure 1: Program flowchart.  
from which the kinking angle can be obtained as:  
\[\theta=\cos^{-1}\left(\frac{3K_{II}^{2}+K_{I}\sqrt{K_{I}^{2}+8K_{II}^{2}}}{K_{I}^{ 2}+9K_{II}^{2}}\right) \tag{3}\]  
To achieve the maximum stress associated with incremental crack expansion, the sign of \(\theta\) must be opposed the sign of \(K_{II}\)[42].
###### Abstract
[4; 12] used a similar drift term on the space of symmetric positive definite (covariance) matrices, exploiting the exponential map, which in the space of covariance matrices is a global diffeomorphism. The present paper extends these ideas to general Lie groups and homogeneous spaces. As an application, we consider discrete-time observations in Lie groups and homogeneous spaces regarded as incomplete observations of sample paths of Brownian motions arising from left- (or right-) invariant Riemannian metrics. The bridge simulation schemes allow interpolating between the discrete-time observations. Furthermore, we observe how varying the metric on Lie groups affects pushforwards of the Brownian motion to homogeneous spaces being quotients of the group. These distributions encode the covariance of the data resulting from the metric structure of the Lie group. We define this family of distributions and derive estimation schemes for recovering the metric structure of the group both with Lie group samples and with homogeneous space samples. One particular example is the two-sphere, \(\mathbb{S}^{2}\cong\mathrm{SO}(3)/\mathrm{SO}(2)\). Changing the metric structure on \(\mathrm{SO}(3)\) results in anisotropic distributions on \(\mathbb{S}^{2}\), arising as the pushforward measure from \(\mathrm{SO}(3)\). Figure 1 illustrates the isotropic and anisotropic distributions on \(\mathbb{S}^{2}\) induced by a bi-invariant and left-invariant (not bi-invariant) metric on \(\mathrm{SO}(3)\), respectively. The resulting distributions are analogous to the von Mises-Fisher and Fisher-Bingham-Kent distributions [13; 14]. However, the approach is independent of the chosen embedding and uses only the geometric relation between the group and quotient space. For the simulation on homogeneous spaces, we use bridges to submanifolds as developed by [PERSON] [15] to condition on the fibers in the Lie group \(G\) over the target point \(v\in M=G/H\) for some closed subgroup \(H\subset G\). The resulting guiding term guides in the direction closest to the fiber. Statistics on Lie groups and homogeneous spaces find applications in many diverse fields, including bioinformatics, medical imaging, shape analysis, computer vision, and information geometry; see, e.g., [16; 17; 18; 19; 20]. Statistics in Euclidean spaces often rely on the distributional properties of the normal distribution. Here, we use Brownian motions and the heat equation to generalize the normal distribution to Lie groups and homogeneous spaces as introduced by [PERSON] [21]. The solution to the heat equation is the transition density of a Brownian motion. Through Monte Carlo simulations of bridges, we can estimate the transition density and maximize the likelihood with respect to the Riemannian metric.
## 7 Bird Nest Deployment
### Tenet Application for Bird Nest Monitoring  
The goal of our application is to repeatedly collect from every sensor node a Cyclops image along with MDA300 sensor readings as frequently as possible. To achieve this, the following two Tenet tasks were used;  
TASK1:  ImageGetPackBits(0, 40, 100, 1, 16, 200, 200, 5, x) -> SendRcrt();  
TASK2:  Wait(1000 millisc)  -> MDA300(ch0, ADC1, a, 0) -> MDA300(ch0, ADC2, b, 0)  -> MDA300(ch0, ADC3, c, 0) -> MDA300(ch0, ADC0, d, 153)  -> MDA300(ch1, ADC0, e, 153) -> Send(E2_ACK);  
The first task takes a 200 \(\times\) 200 resolution grey-scale image, compresses the image using modified PackBits algorithm with a threshold of five, fragments it into 40 data bytes per packet and sends the packets back using the RCRTprotocol [18]. The second task reads the five ADC channels on the MDA300 board and sends the data back using the packet reliable transport protocol. Then, a simple server-side script executes the first task, waits for all responses to arrive, executes the second task and repeats this process indefinitely.  
In this way, we are practically running the imager as soon as the image transfers for the previous image transfer task have completed for the whole network. Due to the time-varying link and network conditions, individual image transfer completion time will differ between nodes. Therefore, the server side application checks whether all nodes have competed by checking whether there are any partially decoded images and waits until all started image transfers have completed. Once complete, the application re-issues the next task. It does not wait for an image transfer that has not started at all (e.g., down node). Any partially-completed transfer will timeout after some time (e.g., 2 min without any packet reception).  
For compression, we have used the modified PackBits algorithm, as described in Section 5. We chose a threshold of '5' as a reasonable balance; we achieved a 16.7% reduction in image size, while experiencing little visible degradation in image quality.  
The RCRT protocol [18] performed congestion control and rate adaptation, as well as loss recovery during the image transfers. Reliable delivery was critical, since the PackBits algorithm requires 100% packet reception for successful decompression. Congestion control was required, since a large number of nodes were sending images simultaneously, and rate adaptation allowed the system to dynamically adapt to the network condition and deliver images as fast as possible. In our deployment, each node delivered an image approximately every 8\(\sim\)15 min depending on the network and environmental conditions. During the three-month period, RCRT successfully delivered 99% of the images (achieved 100% packet delivery per image for 99% of the images) while transporting approximately 83 million packets in total.  
Finally, note that there was a similar deployment at James Reserve before [3] that did not use Tenet. Our deployment not only replaced the previous system, but also improved upon that in several aspects: multihop communication, reliable data delivery, easier-to-use end-to-end system, _etc._
## 3 Methodology Research
### Logistic Regression (LR)  
The logistic regression model is a multivariate statistical model used to describe the regression relationship between a dependent variable and independent variables. This model is well-suited for fitting binary classification problems with independent variables and can reveal multivariate regression relationships between a dependent variable and multiple unrelated independent variables. It can handle both continuous and categorical variables, and the independent variable data do not need to follow a normal distribution pattern [39]. The expression for the logistic regression function is as follows:  
\[\mathrm{P}=\frac{1}{1+\mathrm{e}^{-\mathrm{y}}}\]  
\[\mathrm{y}=\mathrm{w}_{0}+\mathrm{w}_{1}\mathrm{x}_{1}+\cdots+\mathrm{w}_{n} \mathrm{x}_{n}.\]  
In the equation, \(\mathrm{P}\) represents the probability of landslide occurrence; \(\mathrm{e}\) is the natural constant; \(\mathrm{w}_{0}\) is a constant representing the logarithm of the odds ratio of landslide occurrence to non-occurrence, given all influencing factors; \(\mathrm{w}_{n}(\mathrm{n}=1,2,3,\ldots\mathrm{n})\) are the logistic regression coefficients; \(\mathrm{x}_{n}(\mathrm{n}=1,2,3,\ldots\mathrm{n})\) represent the independent features of the sample.  
LR requires an ample amount of sample data and entails high computational complexity. When using this model for susceptibility assessment, it is crucial to address multicollinearity issues among the variables, reducing the degree of interrelation among them. Failure to do so may lead to excessively sensitive predictions and significant biases in estimation [26]. The logistic regression model boasts high accuracy, a low memory footprint, and a fast training speed. It can establish regression relationships between landslides and various evaluation factors, identify optimal fitting functions, and, consequently, determine landslide occurrence probabilities [33]. Through feature weights, it elucidates the impact of each feature on the final outcome, providing strong interpretability. [PERSON] et al. [62] utilized three machine learning algorithms--logistic regression (LR), random forest (RF), and the decision regression tree (DRT)--to develop and evaluate landslide susceptibility maps for the Chatrogram District in Bangladesh. The ROC values for the three models were 0.943, 0.917, and 0.947. The results indicate that the logistic regression model achieved the highest prediction accuracy. In eastern Tennessee, USA, researchers [63] conducted statistical tests to identify the significant factors driving landslides, using logistic regression to model these factors, and successfully created a regional landslide susceptibility map with high predictive accuracy (AUC score of 0.94), offering insights for infrastructure protection and development.
# Self-Organized Fuzzy Neural Network Nonlinear System Modeling Method Based on Clustering Algorithm
## 3 Implementation of Modeling Methods  
The modeling process of SOFNN includes structure initialization, structure identification and parameter estimation. A suitable number of FNN neurons can improve the training speed and generalization ability of the network [8]. Therefore, SOFNN-CA is initialized by k-means clustering offline learning, and the \(q\) centers of the MF layer are obtained after determining the number of clusters from experience. A self-organization mechanism is then adopted to add or merge neurons online to obtain a network structure with better generalization performance. The parameter estimation of SOFNN is optimized online using an improved LM algorithm, which ensures the convergence of the system model.
# Real-time Conflict Resolution Algorithm for Multi-UAV Based on Model Predict Control
## 2 Problem Definition  
There are \(N_{v}\) UAVs in the airspace at the same height; each UAV can be denoted as \(U_{i}\) (i = 1,..., \(N_{v}\)); the flight state variable of each UAV in discrete time sequence \(k=1,2\cdots t_{k}\) is denoted as \(\mathrm{x}_{i}(k)\), where \(\mathrm{x}_{i}(0)\) and \(\mathrm{x}_{i}(t_{k})\) represent the initial and final state, respectively. The fundamental purpose of the conflict resolution problem is to design an efficient adjustment strategy to guide each UAV to its target without any collision with other UAVs, and to ensure the global cost minimum [15; 16]. To solve this problem, assuming that UAVs within a certain range can receive each other's predicted trajectories between UAVs, the solution scheme can be described as Figure 1[17]:  
In the scheme, the whole system is divided into two main modules: the conflict management module and the control & coordination module. The conflict management module consists of 3 sub-modules: conflict detection, conflict group (construction) and resolution rule. Conflict detection sub-module receives and analyses predicted trajectories of adjacent UAVs within a certain range, and determines whether there are conflicts, then delivers the conflict information. Conflict group (construction) sub-module sorts the conflicts and establishes conflict groups based on the conflict information. Resolution rules sub-module generates resolution plans for each conflict group based on several collision free rules and constraints. The control & coordination module resolves the current conflicts depending on the resolution plan, local UAV trajectory and conflict trajectories, then deliver corresponding control strategies to UAVs with conflicts and generate new trajectories without collisions in the current time period. The entire coordination process is run once in a one-step cycle, and is continuously cycled to form a rolling real-time online optimization process, which is similar to the conception of model predictive control. Thus, this paper introduces a real-time coordination algorithm based on MPC, and can implement online rolling resolution to flight conflicts for UAVs.  
Figure 1: Scheme of solution for conflict resolution problem.
# Environmental Remediation of Metribuzin Herbicide by Mesoporous Carbon--Rich from Wheat Straw
## 2 Materials and Methods
### Adsorption Experiments  
Adsorption experiments were performed in batch systems. In each experiment, 0.2 g biochar and 20 mL metribuzin aqueous solution with the desired initial concentration (5, 50 and 100 mg L\({}^{-1}\)) were added to a set of 100 mL Erlenmeyer flasks. The experiments were performed in triplicate and the averages values were calculated. The mixture was shocked on an orbital shaker, at 350 rpm, at room temperature. The influence of the contact time was explored at various concentrations (1-100 mgL\({}^{-1}\)). After shaking, the liquid sampleswere collected. using PVDF filters at various preset times (10, 20, 60, 80, 240 and 24 h). The removal efficiency and the adsorbed amount q (mg g\({}^{-1}\)) were evaluated by the following:  
\[\mathrm{q}=\frac{(\mathrm{C}_{0}-\mathrm{C})}{\mathrm{W}}\mathrm{V} \tag{1}\]  
where C (mg L\({}^{-1}\)) and C\({}_{0}\) (mg L\({}^{-1}\)) are the residual and initial concentrations of metribuzin. The agri-waste/biochar mass and the volume solution are W (g) and V (L), respectively.  
To investigate the adsorption isotherms, the equilibrium experimental data were fitted by Langmuir and Freundlich models as follows:  
[PERSON] isotherm:  
\[\mathrm{q}_{\mathrm{e}}=\mathrm{q}_{\mathrm{max}}\frac{\mathrm{K}_{\mathrm{L }}\mathrm{C}_{\mathrm{e}}}{1+\mathrm{K}_{\mathrm{L}}\mathrm{C}_{\mathrm{e}}} \tag{2}\]  
Freundlich isotherm:  
\[\mathrm{q}_{\mathrm{e}}=\mathrm{K}_{\mathrm{F}}\mathrm{C}_{\mathrm{e}}\mathrm{ }^{1/\mathrm{n}} \tag{3}\]  
where q\({}_{\mathrm{max}}\) is the maximum adsorption capacity (mg g\({}^{-1}\)); C\({}_{\mathrm{e}}\) is the concentration of metribuzin at equilibrium (mg L\({}^{-1}\)); K\({}_{\mathrm{L}}\) (mg g\({}^{-1}\)), K\({}_{\mathrm{F}}\) (mg g\({}^{-1}\)) and n are the Langmuir and Freundlich constants.  
The kinetic experimental results were obtained in the time range of 0-24 h, and were modeled using pseudo-first order [52] and pseudo-second order [53] models, as follows:  
Pseudo-first order:  
\[\mathrm{q}_{\mathrm{t}}=\mathrm{q}_{\mathrm{e}}(1\mathrm{-e}^{-\mathrm{k}_{1} }\mathrm{-}) \tag{4}\]  
Pseudo-second order:  
\[\mathrm{q}_{\mathrm{t}}=\frac{\mathrm{q}_{\mathrm{e}}^{2}\mathrm{K}_{2}}{1+ \mathrm{K}_{2\mathrm{q}\mathrm{e}}} \tag{5}\]  
where q\({}_{\mathrm{e}}\) (mg g\({}^{-1}\)) and q\({}_{\mathrm{t}}\) (mg g\({}^{-1}\)) are the metribuzin adsorbed at equilibrium and time t, respectively; k\({}_{1}\) and k\({}_{2}\) (mg\({}^{-1}\).min\({}^{-1}\)) are the rate parameters of both models.
# Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis
## 3 Dataset Description
### Training Set Compression in Sequences Domain  
The amount of (structured) data contained in the training set boosts the complexity of the whole training procedure. In order to improve processing speed and overall accuracy, we aim to reduce this complexity by performing a compression stage on the training set.  
The compression strategy consists of the execution of the Basic Sequential Algorithmic Scheme (BSA) [27]. BSAs is a clustering algorithm that aims to isolate per-label cluster representative patterns [28] to build a new compressed training set.  
This step is performed in the sequence domain since the dissimilarity measure in Section 3.1 and the DTW in Section 3.2 for this kind of structured data do not require specific critical parameter tuning, especially if compared to the GED in Section 3.3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attribute Name** & **Data Type** & **Description** \\ \hline frame.len & Numerical & Frame length \\ radiotap.dbm\_antsignal & Numerical & Received Signal Strength \\ wlan.fc.type & Nominal & Frame Type \\ wlan.fc.subtype & Nominal & Frame Subtype \\ wlan.fc.ds & Nominal & Distribution System status \\ wlan.fc.frag & Boolean & More Fragments \\ wlan.fc.pwrmgt & Boolean & Power management \\ wlan.fc.order & Boolean & Order flag \\ wlan.duration & Numerical & Duration \\ wlan.ra & Nominal & Receiver address \\ wlan.da & Nominal & Destination address \\ wlan.ta & Nominal & Transmitter address \\ wlan.sa & Nominal & Source address \\ wlan.bssid & Nominal & BSS ID \\ wlan.frag & Numerical & Fragment number \\ wlan.seq & Numerical & Sequence number \\ wlan.fcs\_good & Boolean & FCS correctness \\ wlan\_mgt.fixed.listen\_ival & Numerical & Listen Interval \\ wlan\_mgt.fixed.intestamp & Numerical & Timestamp \\ wlan\_mgt.fixed.beacon & Numerical & Beacon Interval \\ wlan\_mgt.fixed.reason\_code & Nominal & Reason code \\ wlan\_mgt.fixed.sequence & Numerical & Starting Sequence Number \\ wlan.wep.iv & Nominal & Initialization Vector \\ wlan.wep.icv & Nominal & Integrity Check Value \\ data.len & Numerical & Data Length \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of packets’ features.  
Figure 1: Single instances to graph preprocessing.  
The BSAS hyperparameters have been chosen as follows:  
* maximum cluster radius \(\theta=0.05\);
* maximum number of allowed clusters \(Q_{\text{max}}=\{800,10,000\}\).  
Two values of \(Q_{\text{max}}\) have been used in order to study two kinds of scenarios that is a strongly compressed training set and a larger one.
## 2 System Arrangement
### Formulation of System Arrangement
#### 2.4.3 Monte Carlo Solution  
The optimal arrangement problem can be directly solved by enumerating variables \(x_{i}\) into Boolean strings with brute force and the complexity is o(\(2^{N}\)). Hence, we have to weigh the computation time that a non-deterministic polynomial (NP)-complete problem will bring. To avoid unnecessary state enumeration, the Monte Carlo technique [25] is used to estimate the value of objective function after modeling the optimal arrangement problem as a probability calculation problem. Random variables \(x_{0},x_{1},\ldots,x_{N+1}\) are defined as 0-1 distribution in Equation (10):  
\[P(x_{i})=\left\{\begin{array}{ll}p_{e}&x_{i}=1\\ 1-p_{e}&x_{i}=0\end{array}\right.. \tag{10}\]  
Assuming that input variables \(x_{i}\) are independent, the combination variable \(x_{0}x_{1}\)\(\ldots\)\(x_{N+1}\) can be generated randomly according to the probability distribution. \(M\) inputs of combination variables are generated by a Monte Carlo experiment and \(M_{\mathrm{s}}\) of them are supposed to satisfy the constraints in Equations (4)-(9). The objective function in Equation (2) can be calculated by:  
\[f_{2}=\frac{M_{\mathrm{s}}}{M}. \tag{11}\]
## 3 Features Selection and Preprocessing  
The raw observations of BDS include \(C/N_{0}\), PR, EA, carrier phase, and Doppler shift [36]. These raw observation parameters can be used as the features for ML, and we selected three of these representative features as input data.