esa-sceva/satcom-chunk-collection huggingface dataset:
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 5 Settings for Performance Assessment
### Performance Metrics
#### 5.1.1 PDR  
The impact of network density on the PDR performance of the protocols COOP, NCA(MPR), CACA, DyTE, RGoV, and CAEL is depicted in Figure 8. Due to the nature of wireless communication where no centralized administration is present, the higher the number of nodes, then the higher the number of route requests which is generated to reach the destination, causing network congestion. It can be observed in Figure 8 that when the number of nodes is low in density, the PDR achieved by every protocol is generally higher, and when the number of nodes starts getting higher, the PDR is drastically decreased. On the other hand, the proposed protocol CAEL delivers a 3.5% and 17% improved packet delivery ratio than RGoV and DyTE, respectively, and its performance becomes improved and stable, especially in dense networks. Because other protocols such as DyTE suppress the communication zone (only) and protocols such as COOP, NCA(MPR), CACA, and RGoV work on the principle of clusters, CAEL chooses the most dependable routes to minimize connection failures. While DyTE and RGoV both choose the shortest route between sources and destinations without regard for node speed or direction, the proposed protocol (CAEL) chooses the most reliable and efficient routing path by taking the node's speed and direction into account and eventually increasing the data packet delivery. In addition, CAEL also considers the important factor of link expiration time while finding effective routes.
# Trajectory Tracking Algorithm Study of Coal Mine Water Detector Drilling Bar Installation
## 3 Establishment of the Dynamic Model of the Supplied Material Device  
Use the Lagrange equation to construct the dynamic analysis model by describing the joint control's input force matrix and the relationship between the position, speed, and acceleration of the supplied material device. The specific process is as follows:  
Based on the definition of the Lagrange function \(\mathrm{L}\), first establish the Lagrange function, defining the difference between the system's kinetic energy \(\mathrm{K}\) and potential energy \(\mathrm{P}\) as follows [16]:  
\[\mathrm{L}=\mathrm{K}-\mathrm{P} \tag{7}\]  
Here, the coordinate representation method of \(\mathrm{K}\) and \(\mathrm{P}\) is not restricted. Defining the dynamic equation of the system, namely, the Lagrange equation, is achieved using the following formula [16]:  
\[\mathrm{F_{i}}=\frac{\mathrm{d}}{\mathrm{d_{i}}}\frac{\partial\mathrm{L}}{ \partial\dot{\mathrm{q_{i}}}}-\frac{\partial\mathrm{L}}{\partial\dot{\mathrm{ q_{i}}}},i=1,2,\ldots\mathrm{n} \tag{8}\]  
In the formula, \(\mathrm{q_{i}}\) represents the coordinates of the system's kinetic energy and potential energy, \(\dot{\mathrm{q_{i}}}\) is the corresponding speed, \(\mathrm{n}\) is the number of connecting rods, and \(\mathrm{F_{i}}\) is the force matrix of the \(i\)th joint on the supplied material device. Finally, the formulation, taking into account external interference and unmodeled dynamics, is modeled as follows [17]:  
\[\mathrm{M(q)\ddot{q}+C(q,\dot{q})\dot{q}+G(q)+d(t)=u(t)} \tag{9}\]  
In the formula, \(q=\left[\mathrm{q_{I}},\mathrm{q_{2}},\ldots,\mathrm{q_{n}}\right]^{T}\) represents the joint position vector of the supplied material device; \(\dot{q}\in\mathrm{R^{n}}\) and \(\ddot{q}\in\mathrm{R^{n}}\) represent the angular velocity vector and angular acceleration vector, respectively; \(\mathrm{M(q)\in\mathrm{R^{n\times n}}}\) represents the symmetrical inertia matrix of 2 \(\times\) 2; \(C(q,\dot{q})\in\mathrm{R^{n\times n}}\) represents the Cosmic force and centrifugal matrix of 2 \(\times\) 2; \(G(q)\in\mathrm{R^{n}}\) represents the gravitational vector of 2 \(\times\) 1; \(d(q)\in\mathrm{R^{n}}\) represents the external interference and unmodeled dynamics of the supplied material device of 2 \(\times\) 1; and \(u\in\mathrm{R^{n}}\) represents the joint-controlled force matrix vector of 2 \(\times\) 1.  
The expected angle \(\mathrm{q_{d}}\) and its derivative are bounded as a specific study premise, which is carried out by optimizing the control algorithm, achieving the positioning accuracy control of the supplied material device, while effectively shielding it from external interference.
# Gaussian Distribution Model for Detecting Dangerous Operating Conditions in Industrial Fish Farming
## 2 Materials and Methods
### Gaussian Distribution Model Conceptualization
#### 2.2.3 Model Probability Distribution Identification  
The set of raw data formed by the fish hatchery water quality parameters presents a random probabilistic pattern in the cases investigated in this study. In view of this situation, PCA was applied to reduce the dimensionality of this data, increasing their interpretability, but at the same time minimizing the loss of information. After further analysis, it was found to have a well-defined Gaussian distribution with a probability density function given by  
\begin{table}
\begin{tabular}{l l l} \hline
**Fish Development Phase:** & **Pre-Fattening** & **Weaning** \\ \hline Observation year: & 2019 & 2018 \\ \hline Size (n\({}^{\circ}\) observations \(\times\) n\({}^{\circ}\) parameters): & 296 \(\times\) 10 & 365 \(\times\) 8 \\ \hline Fish hatchery water quality parameters: & temperature (\({}^{\circ}\)C) & temperature (\({}^{\circ}\)C) \\ salinity (mg/L) & salinity (mg/L) & salinity (mg/L) \\  & pH (0–14) & pH (0–14) \\  & ammoniacal nitrogen (mg/L) & ammoniacal nitrogen (mg/L) \\  & nitrogen dioxide (mg/L) & nitrogen dioxide (mg/L) \\  & water renewal rate 1 (m\({}^{3}\)/kg) & transmittance at 400 \(\mu\)m (\%) \\  & redox (mV) & transmittance at 500 \(\mu\)m (\%) \\  & bromine (mg/L) & fish mortality 2 (dead fish sum) \\  & amount of food provided (kg) & \\  & fish mortality 2 (dead fish sum) & \\ \hline \end{tabular} Legend: \({}^{1}\) It depends on the amount of food supplied in kg. \({}^{2}\) It is not necessarily a parameter for assessing water quality, but it does provide an analysis of production and it was used in this research.  
\end{table}
Table 1: Description of the data set collected regarding the two main Senegelese sole development phases.  
\[f(x,\mu,\sigma)=\frac{1}{\sigma\sqrt{2\pi}}exp\left[-\frac{(x-\mu)^{2}}{2\sigma^{2} }\right]. \tag{2}\]  
In this equation, \(x\), \(\mu\), and \(\sigma\) are the principal components values, the mean, and the standard deviation, respectively.
# Estimation of River Velocity and Discharge Based on Video Images and Deep Learning
## 2 Theory and Methods
### Improvement of ShuffleNetV2
#### 2.4.2 Bottleneck Attention Module (BAM)  
In order to enable the neural network to learn the feature information in complex scenes and improve the accuracy of texture angle recognition, we integrate a BAM attention module after the channel concatenation in the downsampling unit, followed by a channel shuffling operation. BAM is a lightweight Bottleneck Attention Module, as shown in Figure 6, which consists of a Channel Attention Module (CAM) and a Spatial Attention Module (SAM).  
Figure 5: Overall structure of the improved ShuffleNetV2_2.0.  
Global average pooling is performed on the input feature map \(F\) to aggregate and enhance feature information in the CAM, and more channel attention is obtained through the shared Multilayer Perceptron, and then the global maximum pooling result of each channel is transmitted through the two fully-connected layers to generate channel attention features \(M_{C}(F)\). The calculation process is shown in Equation (2).  
\[M_{C}(F)=BN(MLP(AvgPool(F))) \tag{2}\]  
The SAM adopts a bottleneck structure similar to ResNet to emphasize or suppress features at different spatial locations. Firstly, the feature is projected onto the reduced dimension \(R^{c/r\times H\times W}\) by \(1\times 1\) convolution, then two 3 \(\times\) 3 dilation convolutions are used to effectively utilize the context information, and finally further dimensionality reduction by a \(1\times 1\) convolution obtains spatial attention features \(M_{S}(F)\) with dimension \(R^{1\times H\times W}\). The calculation process is shown in Equation (3).  
\[M_{S}(F)=BN\Big{(}f_{3}^{1\times 1}\Big{(}f_{2}^{3\times 3}\Big{(}f_{1}^{3 \times 3}\Big{(}f_{0}^{1\times 1}(F)\Big{)}\Big{)}\Big{)}\Big{)} \tag{3}\]  
\(M_{C}(F)\) and \(M_{S}(F)\) are added together and activated by the _Sigmoid_ activation function to generate 3D attention features \(M(F)\), as shown in Equation (4). \(M(F)\) is multiplied by the input feature map \(F\) and then added to the input feature map \(F\) to obtain the final feature map \(F^{\prime}\), as shown in Equation (5).  
\[M(F)=\sigma(M_{C}(F)+M_{S}(F)) \tag{4}\]  
\[F^{\prime}=F+F\otimes M(F) \tag{5}\]
## 4 Discussion
### The Effectiveness of the Multi-Head Loss Block  
In all kinds of segmentation tasks, the problem that small objects are easy to ignore has puzzled scholars. To solve this problem, multi-scale feature maps should be an effective and classical method. However, only loss is calculated for the final segmentation results during back propagation. Each encoder that extracts multi-scale feature maps cannot be accurately optimized separately in the training process, and the pre-training on the classification dataset aggravates this problem. Aiming at the above problems, this paper designs a Multi-head Loss Block to assist training. It involves the feature maps of each scale directly in the calculation of the loss, which affects the subsequent back-propagation process, so as to accurately optimize each encoder so that multi-scale feature maps can be extracted. Multi-head Loss Block enables the encoder weight of the backbone, pre-trained on classification datasets, to migrate to segmentation tasks better and faster. As shown in Table 4, experiments show that GLFFNet with a Multi-head Loss Block has a faster and better convergence process.
## 5 Discussion
### Influence of Sandbody Genetic Types on Reservoir Development  
In fluvial deltaic depositional systems, sandbody genetic types exert a critical influence on reservoir characteristics. The delta plain and delta front subfacies represent two typical depositional environments, each with unique sandbody formations that directly impact reservoir quality.  
Within the delta plain subfacies, the predominant sandbodies include deeply incised abrupt-transition, deeply incised gradual-transition, and growth gradual-transition types. These sandbodies are formed by the deposition of coarse-grained materials transported by rivers, exhibiting good sorting and substantial thickness. Due to their excellent reservoir properties, these sandbodies represent the primary high-quality reservoirs in the delta plain subfacies of the study area (Figure 9).  
Figure 8: Lithofacies assemblage types of different genetic sandbodies.  
Conversely, in the delta front subfacies, progradational mouth bar sandbodies and progradational superimposed sandbodies are the key contributors to reservoir development. These sandbodies form through sediment accumulation at river distributary mouths, displaying relatively favorable reservoir properties and serving as primary reservoirs within these subfacies. By contrast, suspension accretion sandbodies, which develop within sheet sand microfacies farther away from the shoreline, are characterized by fine-grained and poor sorting. As a result, they exhibit the poorest reservoir properties (Figure 9) and the lowest thickness.  
The distribution of various sandbody genetic types within the fluvial deltaic system is closely related to their depositional environment. Their control over reservoir physical properties is determined by factors such as sorting, the presence of muddy interbeds, and the scale and lateral extent of the sandbody, which collectively dictate reservoir quality. By analyzing reservoir properties based on sandbody genetic types, targeted research can enhance the classification and understanding of sandbody types in the Shaximiao Formation in the Shunan area. This approach not only provides valuable insights into geological theory but also significantly aids exploration and reservoir development strategies.
# MM-ConvBERT-LMS: Detecting Malicious Web Pages via Multi-Modal Learning and Pre-Trained Model
## 2 Related Work
### Motivation  
While there are many methods for detecting malicious web pages, a significant number of them are trained from scratch on labeled data, without utilizing any prior knowledge. These approaches can lead to a more challenging training process and results that are highly dependent on the size and quality of the dataset. Although some studies have attempted to use word2 vec and fastText to extract semantic features, these static word vector methods struggle with issues, such as polysemy, making them less suitable for malicious web analysis tasks. Furthermore, most mainstream methods focus on feature engineering for single-modal features and do not consider mining for cross-modal features, which can result in additional processing costs, information loss, and insufficient learned representations. Therefore, to solve these problems, we propose a malicious web page detection method based on ConvBERT and multi-modal learning. Our approach improves upon existing work, as shown in Table 1. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **Models** & \begin{tabular}{c} **Prior** \\ **Knowledge** \\ \end{tabular} & **Features** & \begin{tabular}{c} **Feature** \\ **Engineering** \\ \end{tabular} & \
\begin{tabular}{c} **Multimodal** \\ **Matching** \\ \end{tabular} &
\begin{tabular}{c} **Analysis** \\ **Analysis** \\ \end{tabular} \\ \hline [PERSON] [6] & AdaBoost & ✗ & URL & ✓ & ✗ & **Methods based on a single feature and statistical machine learning** \\ \hline [PERSON] [11] & [PERSON] et al. & ✗ & JS & ✓ & ✗ & 1. They perform well on small-scale datasets and are able to respond quickly to meet real-time requirements. \\ \hline [PERSON] [19] & Decision Tree & ✗ & HTML & ✓ & ✗ & 3. Due to their relatively small number of model parameters, these models can quickly reach convergence on large-scale datasets, thereby entering a plain stage in a shorter time frame. \\ \hline [PERSON] [7] & Dynamic Conv. & ✗ & URL & ✗ & ✗ & **Deep learning models based on a single feature and supervised learning** \\ \hline [PERSON] [9] & Composite NN & ✗ & URL & ✓ & ✗ & 1. It is proved that the representation ability of the deep learning models is stronger than the statistical machine learning models in malicious web page detection tasks. \\ \hline [PERSON] [14] & DBN & ✗ & JS & ✓ & ✗ & 2. Complex feature engineering is often not required. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the proposed method and the existing mainstream methods. ✓ represents that the technology is used, and ✗ represents that the technology is not involved.
## 1 Introduction
The structure of the paper is as follows. In the next \"Background\" subsection, the classification and brief description of the existing tracking algorithms, such as center of gravity (COG) and iterative closest point (ICP) approaches, are presented briefly. Then, Section 2 presents the hardware components of the demonstration system and the procedure for sensor georeferencing. This section also introduces the proposed algorithms to estimate the parameters of the underlying motion model from point clouds of low point density, including the details of the algorithms and the implementation challenges. In the Section 3, two tests are presented using the demonstration system installed at an airfield. During the two tests, data from taxiing and landing aircraft were captured. Section 4 presents the results of the two tests followed by Section 5 that provides the performance analysis, the comparison of the algorithms as well as the qualitative and quantitative error assessments. The papers ends with a conclusion and appendices. The terminology and most common definitions used throughout the paper can be found in Appendix A.
## 4 Investigating CRPS-Sum Properties
### CRPS-Sum Sensitivity Study  
In this study, we inspected the sensitiveness of CRPS-Sum concerning the changes in the covariance matrix. This study extends the sensitivity study that was previously conducted by [29, 32] for various scoring rules, including CRPS and ES. For easier interpretation of the scoring-rule response to changes in a model or data, we defined relative changes in the scoring rule \(\Delta_{\text{rel}}\).  
We ran our experiment \(N\) times, where \(\text{CS}_{i}\) denotes the obtained CRPS-Sum from the \(i\)-th experiment. We define  
\[\overline{\text{CS}}=\frac{1}{N}\sum_{i=1}^{N}\text{CS}_{i}\,, \tag{11}\]  
as the mean value of CRPS-Sum for the N experiments. Furthermore, let \(\text{CS}^{*}\) signify the CRPS-Sum for a model that is identical to the true data distribution. Now, the relative changes [29] in CRPS-Sum is defined as  
\[\Delta_{\text{rel}}(\text{CS})=\frac{\overline{\text{CS}}-\overline{\text{ CS}}^{*}}{\overline{\text{CS}}^{*}}\,. \tag{12}\]  
This metric frames the relative changes in the CRPS-Sum of a forecasting modeling across our experiments as the differences between the predicted and actual density of the stochastic process. The main idea is to determine the sensitivity of the scores with respect to some biased non-optimal forecast in a relative manner.  
In this study, we have a true data distribution that follows a bivariate normal distribution with \(\mu=\begin{pmatrix}0\\ 0\end{pmatrix}\) and \(\Sigma=\begin{pmatrix}1&\rho\\ \rho&1\end{pmatrix}\) where \(\rho\in[-1,-0.8,\ldots,0.8,1]\). Furthermore, we specified a forecasting model \(f\) that follows the same distribution; however, this time the off-diagonal element of the covariance matrix is \(\varrho\in[-1,-0.9,-0.8,\ldots,0.8,0.9,1]\). In our study, we sampled \(n=2^{14}\) windows of size \(w=2^{9}\), as suggested in [32].  
Figure 2 illustrates the relative change in CRPS-Sum and ES with respect to changes in correlation \(\rho\) of the data-generating process as a function of the correlation coefficient \(\varrho\) of the family of models we studied. We can observe that ES behavior is unbiased with regard to \(\rho\) and its figure is symmetric. This is the expected behavior from a scoring rule in this scenario. In contrast, the response of CRPS-Sum to change in \(\rho\) is not symmetric. It is more sensitive to the changes when the covariance \(\rho\) of the data is negative, and it is almost indifferent to the changes when the covariance \(\rho\) of the data is positive.  
Hence, the sensitivity of CRPS-Sum to changes in covariance is dependent on the dependency structure of true data. In real-world scenarios, where we do not have access to the covariance matrix of the data-generative process, we cannot reliably interpret CRPS-Sum and compare various models based on CRPS-Sum.
## 4 AC/DC Converter
### Operation Principles of AC/DC Converters Architectures
#### 4.1.1 Three Phase Diode Bridge  
The three-phase diode bridge rectifier is taken as reference for the other topologies/architectures analyzed in this work. Figure 21 shows the architecture of three-phase diode bridge rectifier. The average DC output is improved using a capacitor between the diodes and resistive load, C\({}_{\text{out}}\). At the same time, this capacitor not only reduces the ripple of the signal but also acts as a storage device.  
The waveforms present in a three-phase diode rectifier architecture are shown in Figure 22.  
Each of the three phases have the same behavior, with conduction angles for each diode of 120\({}^{\circ}\), as the diode current waveforms of Figure 22a show. Focusing on the diode pair D1 and D2 of phase 1 (see the details of Figure 22a), D1 starts its conduction angle when its anode voltage, Vin1, is higher than its cathode voltage, Vin2 + Vin3. When D1 is in conduction, as D2 is off for its cathode voltage is higher than its anode one, the return path for the current could only be done through D4 for the first 60\({}^{\circ}\) of conduction and later through D6 for the remaining 60\({}^{\circ}\). The ripple of the diode current and the output voltage waveforms are primarily functions of the load, capacitance value and the windings inductance. As the inductance is a fixed value, only the output capacitance value and the load change. Figure 22b shows the waveforms of the three-phase diode bridge when the output capacitance is high; the diodes conduction angles are reduced because the output capacitance has changed and provides most of the energy that the load demands, reducing the output voltage ripple, but increasing the peak current accordingly.
# An Ensemble Approach for Speaker Identification from Audio Files in Noisy Environments
## 3 Proposed Framework
### Preprocessing  
The purpose of preprocessing is to represent the raw audio signal in such a representation that its essence comes to the surface. In this phase, we perform a set of activities, namely, detection and extraction of voice activity. The preprocessing activities performed for the proposed system are discussed below.  
Voice Activity Detection (VAD): The idea behind VAD is to take into account only actual speech frames and discard noisy frames. For this purpose, two indices are used which are (i) the spectrum flatness index and (ii) the energy ratio index. The former is a measure of noise in the signal spectrum, whereas the latter separates noise from speech frames taking into account the energy distribution along the signal spectrum.  
Feature Extraction: We extract two types of features from audio files, which are MFCCs and spectrograms.
## 2 Results
### Identification of Terpene Synthuses from Stevia  
Terpenoids were the major VOCs in all four tissues studied (Figure 1 and Table 1). From Stevia RNA-seq data [13], we were able to find five full-length open reading frames (ORFs) of mono- or sesqui-TPSs, which were later designated as _SrTPS1-5_. Phylogenetic analysis showed that five SrTPSs were grouped into TPS-a and TPS-b subfamilies (Supplementary Figure S2). TPS-a and TPS-b subfamilies are known to include TPSs for the biosynthesis of sesquiterpenoids and monoterpenoids, respectively [7]. Deduced amino acid sequences of _SrTPS1-5_ contained typical motifs of TPS enzymes. DDXXD and NSE/DTE that are required for binding of substrates and cofactors were conserved in all SrTPSs (Supplementary Figure S3). Another conserved motif, R(R)X8W, which plays a role in the complexation of the pyrophosphate group after ionization of the substrate, was found in all SrTPSs. ChloroP analysis showed that among SrTPSs, SrTPS1 contained 30 amino acid plastitial transit peptide (Tp) sequence at N-terminus.  
The relative expression levels of five _SrTPSs_ in four Stevia tissues were examined by qRT-PCR (Figure 2). _SrTPS2_ did not show much variation in expression levels among the four tissues. _SrTPS1_ and _SrTPS3_ were abundant in stems, whereas _SrTPS4_ and _SrTPS5_ showed the highest expression in roots with little or no expression in other tissues (Figure 2).  
To observe the subcellular localization of SrTPSs, we transiently expressed the full-length ORF of each _SrTPS_ fused with the _YFP_ gene in _Nicotiana benthamiana_ leaves using _Agrobacteriumum_-mediated infiltration. Figure 3 shows that YFP-fused SrTPS2-5 were localized in the cytosol, where they might be associated with the production of sesquiterpenoids. On the other hand, SrTPS1 was localized in chloroplasts, indicating that it might be a mono-TPS.  
Figure 2: qRT-PCR analysis of _SrTPS_ genes in different tissues. L, leaf; F, flower; S, stem, and R, root. The housekeeping gene _actin_ was used as control. Data are the mean \(\pm\) standard deviation of three readings.