esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Deployment Architecture  
In this paper, we have attempted to discover the most robust supervised classifier for building an accurate and high-performance network intrusion-detection system for real-time IoT networks. We have trained and evaluated the performance of seven state-of-the-art supervised machine-learning algorithms. We have explored a new and realistic BoT-IoT dataset in which different types of attack data have been injected. We extracted the most relevant features from the dataset using the Pearson Correlation Coefficient technique. To deeply examine their behavior, all models were trained three times using a different number of feature sets. The performance of algorithms was further analyzed and compared in terms of Precision, Recall, and F1 Score. The accuracy of Random Forest was the highest, at 99.2%, and the second-best performing algorithm was Naive Bayes, giving an accuracy of 98.8%. Thus, from the experimental analysis, it is evident that the Random Forest algorithm is the most effective and most robust algorithm out of the seven machine-learning algorithms for intrusion detection in IoT networks. Our proposed INIDS can be deployed in IoT networks in the following ways:Lightweight design: Our model's lightweight nature (after optimization) makes it feasible for integration into resource-constrained IoT devices.  
Edge or fog layer: Our proposed IDS can be deployed at the edge or fog layer of IoT networks to monitor and analyze traffic locally. This minimizes latency and ensures the faster detection of threats in real time.  
Cloud integration: The system can communicate with the cloud for centralized monitoring and management if necessary, enabling scalability for larger networks. However, we aim to integrate such a model with added performance into SDN-based networks.  
[PERSON] drafted the manuscript and did the experiments and [PERSON] proposed the main idea of the research and assisted in the experiments. [PERSON]-S.K. supervised the overall work and helped during the entire research and drafting of the paper. [PERSON] reviewed the paper and provided valuable guidance in paper writing and improving its quality. [PERSON]-Y.K. writing the original draft. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Culture, Sports, and Tourism R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture, Sports, and Tourism in 2024 (Project Name: Global Talent Training Program for Copyright Management Technology in Game Contents, Project Number: RS-2024-00396709, Contribution Rate: 100%).  
Not Applicable.  
The data that support the findings of this study are openly available at the following URL [[https://research.unsw.edu.au/projects/bot-iot-dataset](https://research.unsw.edu.au/projects/bot-iot-dataset)]([https://research.unsw.edu.au/projects/bot-iot-dataset](https://research.unsw.edu.au/projects/bot-iot-dataset)) (accessed on 16 August 2024).  
The authors declare no conflicts of interest.
# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 1 Introduction
This article reviews different methodologies used to retrieve water vapor information from GNSS processing in Section 2. The vast applications of these processing techniques are discussed in the following sections, namely the inter-comparisons with other techniques (Section 3), the spatio-temporal analysis of water vapor distribution (Section 4), and the impact of water vapor variability on meteorology and climate (Section 5). Finally, conclusions are drawn in Section 6. Figure 1: Current network of 506 stations belonging to IGS. Color and shape are related to the last data available date. Source: [[https://www.igs.org/maps/#station-map](https://www.igs.org/maps/#station-map)]([https://www.igs.org/maps/#station-map](https://www.igs.org/maps/#station-map)) (accessed on 19 May 2021).
# RMFNet: Redetection Multimodal Fusion Network for RGBT Tracking
## 5 Performance Evaluation
### Datasets and Metrics  
**GTOT Dataset:** The GTOT dataset consists of 50 pairs of visible and thermal infrared video sequences, including common targets, such as people, vehicles, ships, and aeroplanes, as well as various indoor, outdoor, daytime, and nighttime environments. The location of the current target is annotated in each frame by a human, and based on factors, such as the shooting environment, time, and target motion state, they are labelled as seven different attributes. The performance of the RGBT tracker in dealing with different challenging attributes is evaluated based on these seven attribute labels. We use two commonly used tracking evaluation metrics for experimental analysis: precision rate (PR) and success rate (SR). The PR and SR indicators represent the proportion of the number of frames whose centre position error and intersection ratio between the bounding box output by the tracker and the ground truth are less than the preset threshold in the current frame in the total number of video frames. Since targets in the GTOT dataset are usually small, 5 pixels is the threshold we set.  
**RGBT234 Dataset:** The RGBT234 Dataset is a massive RGBT tracking dataset that extends the RGBT210 dataset, containing 234 pairs of time and space-aligned visible and thermal infrared video sequences with approximately 210,000 frames, with the longest video sequence consisting of 8000 frames. Furthermore, the dataset encompasses a variety of challenging attributes, including, but not limited to, camera motion, deformations, motion blur, changes in illumination, and occlusions. These challenges are labelled separately in order to enable a more comprehensive evaluation of the algorithms. The algorithms are fairly evaluated using the maximum precision rate (MPR) and success rate (MSR) metrics. We set the threshold for MPR to 20 pixels. MPR and MSR use the maximum value, while PR and SR use the average value. MPR and MSR focus more on the accuracy and stability of the algorithm than PR and SR.
# On the Performance of Underlay Device-to-Device Communications
## 3 System Model
### Channel Modeling  
Let us denote \(h_{i,j}\) the channel coefficient between the transmitter \(j\in\{dt,c\}\) and the receiver \(i\in\{dr,b\}\); where \(dt,dr,b,\) and \(c\) denote the D2D transmitter, the D2D receiver, the base station, and the cellular user, respectively. Here, we assume that \(h_{i,j}\) follows complex Gaussian random variable with zero mean and \(\Omega_{i,j}\) variance. The absolute of \(h_{i,j}\), i.e., \(\left|h_{i,j}\right|\) is then followed by the Rayleigh distribution. In the present paper, Rayleigh fading is  
Figure 1: System model of D2D communication underlaying cellular network.  
adopted as it is considered the worst fading. As a result, the system performance under the impact of Rayleigh fading is the lower bound compared with other distributions such as Rician, Nakagami-\(m\), and Weibull distributions, etc. \(\Omega_{i,j}\) is also the large-scale path-loss from transmitter \(j\) to receiver \(i\). To be more precise, \(\Omega_{i,j}\) is formulated as [46; 47]  
\[\Omega_{i,j}=K_{0}d_{i,j}^{\beta}, \tag{1}\]  
where \(K_{0}=(4\pi/v)^{2}\) and \(\beta\) are the path-loss constant and the path-loss exponent; \(v=c_{0}/f_{c}\) is the wavelength; \(c_{0}\) is the speed of light measured in m/s; and \(f_{c}\) is the carrier frequency. We notice that the considered unbounded path-loss modeling can be straightforwardly extended to the bounded path-loss model to overcome the singularity issue [48].
## 2 Results and Discussion
### The Formation of Maleimide and Succinimide Products: Mechanism of Polymerization-Rupture
#### 2.1.4 NBO Analysis

Table 3: Degrees of rate-control (\(X_{RC}\)) and degrees of thermodynamic rate-control (\(X_{TRC}\)), for the elementary steps in the hypothetical DKP mechanism during the pyrolysis of asparagine processes. At first glance, the shape of the electronic energy plot corresponding to the mechanism of formation of 2,5-diketopiperazine is different from the plot corresponding to the mechanism by polymerization-rupture. This fact is in accordance with the already-mentioned differences from the mechanistic point of view. The electronic activation energy needed for asparagine to obtain the 2,5-diketopiperazine product (\(\Delta E^{\ddagger}\) = 175 kJ/mol) is practically the same as the barrier that corresponds to the polymerization-rupture mechanism (\(\Delta E^{\ddagger}\) = 174 kJ/mol), which is proposed for the production of maleimide and succinimide (the experimentally identified products of pyrolysis of asparagine). Then, the differences between the free energy commented on before are mainly associated with changes in the activation entropy of these two processes (\(\Delta\)\(\left[\Gamma\Delta S^{\ddagger}\right]\) = 32 kJ/mol), which points out that the process involved in the polymerization-rupture mechanism implies a significantly greater loss in rotational and vibrational degrees of freedom than DKP formation. Despite the similarity between the energetic barriers, when values of work are analyzed, it is interesting to note that the mechanism by polymerization-rupture achieves the present cyclization step with very similar contributions of geometrical and electronic rearrangements (ratio w\({}_{1}\)/w\({}_{2}\): 1.2), while the DKP mechanism has a higher contribution of geometrical rearrangements (ratio w\({}_{1}\)/w\({}_{2}\): 8.7). That reasoning supports the explanation of why asparagine prefers to form the five-membered ring instead of the six-membered one. Finally, the overall reaction free energies for both reactions are negative: \(-\)52 kJ/mol for the DKP formation and \(-\)87 kJ/mol for the polymerization-rupture reaction, which suggests that the final five-membered products (maleimide and succinimide) are more stable than the corresponding DKP product.
# Empirical Remarks on the Translational Equivariance of Convolutional Layers
## 2 Equivariance versus Invariance  
Formally, a function \(f\) is equivariant with respect to a transform \(T\) if \(f(T(x))=T(f(x))\). This means that applying the transformation \(T\) to \(x\) is equivalent to apply it to the result \(f(x)\). Meanwhile, a function \(f\) is invariant with respect to a transform \(T\) if \(f(T(x))=f(x)\). In other words, the result by the function \(f\) does not change when you apply the transformation \(T\) to the input \(x\). Figure 1 modified from [6] illustrates the translational equivariance. A transform \(T\) translates the star object of the input image \(I(x)\) into right side resulting in the transformed image \(T[I]\). The function \(f\) is a feature representation of the input. The result \(T[f(I)]\) by applying the transform \(T\) to the feature representation \(f(I)\) is the same to the feature representation of the transformed image \(f(T[I])\).  
The CNNs achieve the property of translational equivariance by the concept of weight sharing, i.e., convolutional layers [10]. This equivariant property is effective for multiple occurrences of objects in an  
Figure 1: Illustration of a translational equivariance.  
image. Meanwhile, invariance is more important than equivariance in object recognition. The CNNs have some level of invariance by pooling layers, especially max-pooling layers, but it is not sufficient to obtain strong invariance. That is generally achieved by a proper data augmentation [2].
## 3 Theoretical Background
### Magnetically Induced Birefringence in Liquids  
Optical birefringence appears when the optical property (refractive index) of the system is different along different spatial directions. Unlike in solids, where this property is often inherent and given by stable atomic structure, in liquid systems induction of preferred orientation of molecules is required. Such preferential orientation is usually achieved by application of external physical fields (electric, magnetic, shear, etc.) [23; 24]. In addition, the molecules need to be optically anisotropic, meaning that their high-frequency electronic properties depend on the molecule coordinate system (optical polarizability tensor is not symmetric). In the case of magnetically induced birefringence, the preferential orientation of molecules is triggered by the imposition of an external magnetic field. In this case, the energy of the magnetic dipole in the external magnetic field will be minimized when the direction of the dipole and the field coincide. This results in a torque acting on a dipole trying to orient it along the external field direction. Perfect orientation is always impeded by thermal motions and the degree of equilibrium orientational order is given by the ratio of magnetic to thermal energy of the system. If such an orientational process is applied to all dipolar and optically anisotropic molecules of the liquid system, it will translate to its macroscopic optical anisotropy, and thus make it birefringent.
## 3 Design of the Control System  
This chapter delves into the control issues of EMFF port-Hamiltonian systems under non-zero disturbances. Utilizing damping injection techniques and Nonlinear Disturbance Observer (NDOB) technology, a composite disturbance rejection passivity-based controller is designed as \(\mathbf{u}_{DRPBC}=\mathbf{u}_{FB}+\mathbf{u}_{DO}\), where \(\mathbf{u}_{FB}\) is a baseline feedback controller in the absence of disturbances and \(\mathbf{u}_{DO}\) is a disturbance compensation controller with \(\mathbf{d}(t)\
eq 0\).
# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 4 Diagnostics
### Hybrid Methods
in [15] by devising a hybrid fault detection methodology for sensor health monitoring of the integratednavigation system of UAVs. Their proposed methodology uses a KF to estimate the residual sequences of sensor data. Then, a fuzzy logic system is employed to detect and determine the fault type where the membership functions are updated using a neural network. [PERSON] and [PERSON] proposed a structured hierarchical framework for FDI within SFF [79]. In this framework, different diagnostic techniques are incorporated in a hierarchical structure, facilitating the integration of model- and knowledge-based diagnostic techniques. As part of our investigation, they used fuzzy reasoning to isolate faults in a leader-follower formation control system, focusing on ACS faults. The method is also considerably more efficient than traditional diagnostic approaches, which rely on individual satellite telemetry data trend analysis.
## 2 Related Work
### Scene Flow Estimation  
[PERSON] et al. proposed a method for estimating dense rigid scene flows in 3D Lidar scans [17]. The method formulates scene flow estimation as an energy minimization problem, and by assuming local geometric constants and incorporating the regularization of smooth motion fields, analyzing the dynamics at the point level helps to infer fine-grained details of the motion. With the research on deep learning in scene flow estimation methods, most of the studies tend to use raw point cloud data directly for scene flow estimation. In 2019, FlowNet3D [18] was proposed to learn the scene flow of 3D point clouds in an end-to-end manner, and the model proposes a flow embedding layer, which makes it possible to accurately obtain the scene flow by learning deep layered features of the point cloud geometry and the flow embedding that represents its motion. HPLFlowNet [19] operates directly on large-scale 3D point clouds based on bilateral convolutional layers to recover structural information from unstructured point clouds and to fuse information from two consecutive point clouds. Compared to estimating the scene flow directly through the point approach, [PERSON] et al. proposed the FLOT model based on the graph matching work using the optimal transmission theory [20], and considered the point cloud matching error and used the feature similarity to establish the transmission-cost relationship. To address the challenges of high computational costs and latency associated with complex matching mechanisms and feature decoding, FH-Net [21] employs a robust local geometric prior to directly derive keypoint flows via a lightweight cross layer. Additionally, it optionally backpropagates the computed sparse flows through the inverse cross layer to obtain hierarchical flows at varying resolutions.  
Despite the large number of studies obtained for both motion segmentation and scene flow estimation, there are still problems such as high resource consumption and slow speed, so further in-depth research is needed to acquire the motion state of moving objects in the scene.
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 3 Method
### Semi-Automatic Green Tide Extraction Method
#### 3.2.3 Threshold Selection  
After removing the influence of terrestrial vegetation, selecting a suitable threshold range to binarize the RS images to separate seawater from the green tide is necessary. The _NDVI_ values of RS images were counted using a frequency distribution histogram [27; 28], and the statistical principle was to divide the interval of _NDVI_ values into \(k\) disjoint intervals (\([x_{1},\,x_{2})\), \([x_{2},\,x_{3})\),..., \([x_{k-1},\,x_{k}]\)), divide the sample observations into each interval one by one, and calculate the frequency \(ni\) of the samples falling in each interval: the frequency \(f=ni/n\), where \(n\) is the total number of samples, and the frequency/interval length is given as \([f_{1}\,(k)=f(k)/(x_{k},\,-x_{k-1})]\). In this study, we chose \(k=100\) to obtain the frequency histogram of _NDVI_ values (Figure 3). Then, using the statistical results \([x_{1},f_{i}(1)]\), \([x_{2},f_{i}(2)]\),..., \([x_{k},f_{i}(k)]\) from Equation (2), the parameters \(a_{1}\), \(a_{2}\), \(b_{1}\), \(b_{2}\), \(c_{1}\), and \(c_{2}\) of the second-order Gaussian fit function were calculated to obtain the final second-order Gaussian fit curve, as shown in Figure 3.  
\[H(x)=a_{1}\cdot e^{\frac{-(x-b_{1})^{2}}{c_{1}^{2}}}+a_{2}\cdot e^{\frac{-(x-b _{2})^{2}}{c_{2}^{2}}} \tag{2}\]  
For vegetation, \(-1\leq\textit{NDVI}\leq 1\)[29]. Negative values indicate ground coverage by features such as clouds, water, and snow; 0 indicates the presence of features such as rocks and bare soil, whose _NIR_ and _Red_ are approximately equal; and positive values indicate green tide coverage and increase with increasing coverage [30]. When the green tide coverage of the water body is incomplete, an area with a small amount of green tide distribution may also show negative values [23]; therefore, we chose the first inflection point after the maximum distribution point of the _NDVI_ value as the lower limit of the threshold (i.e., the green dot in Figure 3) and 1 as the upper limit of the threshold (elimination of anomalous values) for the discrimination of green tide; i.e., the _NDVI_ value less than the  
Figure 2: Terrestrial masking process.  
lower limit of the threshold was judged as seawater (blue part in Figure 3), and a value greater than the lower limit of the threshold but less than 1 was indicative of the green tide (green part in Figure 3).
## 2 Materials and Methods  
In this retrospective study, we collected data of all patients who consecutively underwent HoLEP from December 2017 to October 2019 in our high-volume tertiary hospital. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the ethics committee (CEAS n. 3154/18). Written informed consent was obtained from all patients. All procedures were performed by a single HoLEP-naive urologist with high endoscopic experience, tutored by a skilled HoLEP surgeon for the first 18 cases. Inclusion criteria were patients with prostate volume of \(\geq\)30 mL, moderate/severe LUTS, pathologic uroflow parameters and/or significant post-void residual (PVR), and a negative urine sample culture. Exclusion criteria included the first 25 patients treated during the learning curve; patients with asymptomatic BPH, prostate volume of <30 mL, prostate cancer (PCa) on prostate biopsy, and ongoing urinary tract infection (UTI); and unfit for surgical treatment. All data were retrospectively analyzed.  
Preoperative baseline characteristics such as age, drugs, LUTS, and history of urinary retention were collected. A digital rectal examination, ultrasound of the urinary system, prostate transrectal ultrasound, prostate-specific antigen (PSA) dosage, uroflow test, prostate MRI, and/or biopsy, when indicated, were performed before surgery. The International Prostate Symptom Score (IPSS) was used to objectify LUTS. Uroflow findings, including the maximum flow rate (Qmax) and post-void residual urine, were considered pathologic when they were \(\leq\)15 mL/s and \(\geq\)50 mL, respectively.  
We evaluated the operative time (OT), delivered amount of energy (DaE), intraoperative bleeding, time to catheter removal, intra- and postoperative complications, and length of stay (LOS). Intraoperative complications were reported according to the Intraoperative Adverse Incident Classification by the European Association of Urology (EAUiaiC) ad hoc Complications Guidelines Panel [12]. Intraoperative bleeding was estimated by calculating the difference between preoperative and postoperative (at 24 h after surgery) hemoglobin (Hb) values. Postoperative complications were assessed according to the Clavien-Dindo classification.  
The postoperative outcomes were assessed as follows: evaluation of LUTS at 1, 3, 6, and 12 months, including urinary incontinence, as defined by the International Continence Society [13]; the IPSS; uroflow parameters at 1, 3, and 6 months from surgery; postoperative serum PSA at 3 months; and detection of incidental PCa (iPCa).  
A descriptive statistical analysis summarizes the data of our study. Continuous variables are presented as the median value and interquartile range (IQR) and qualitative variables as a frequency and percentage. We performed univariate and multivariate analyses (logistic regression) to identify potential predicted factors for UI at one postoperative month with preoperative variables. All reported \(p\)-values are two sided, and statistical significance was set at 0.05. Statistical analysis was performed using SPSS v.25 software (IBM Corp., Armunch, NY, USA).