esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 LSTM Networks  
The LSTM network, which was proposed by [PERSON] and [PERSON] (1997), is a special variant of a recurrent neural network (RNN), in which the connections between nodes from a directed graph are depicted along a temporal sequence. Any RNN and its variants have a trained form consisting of repetitive units, as shown in Figure 7. In traditional RNNs, the repetitive unit alone has a simple structure, such as a tanh layer. When dealing with long-term dependency problems, a traditional RNN involves multiple matrix multiplications, which can lead to exploding and/or vanishing gradient problems. To solve such problems, many RNN variants have been developed, such as the echo state network, the gated recurrent unit, and LSTM. Among these variants, LSTM is the most popular as it has the proven capability of solving exploding and vanishing gradient problems.  
Figure 5: Evolution of settlement markers of a departure trip.  
Figure 6: Evolution of settlement markers of a return trip.  
Figure 7: A recurrent neural network and its unfolding form.  
As an RNN variant, LSTM is connected by repetitive units. However, unlike a traditional RNN, the repetitive units of LSTM are composed of a cell state, an input gate, an output gate, and a forget gate (Figure 8). The cell remembers values over arbitrary time intervals, while the three gates regulate the flow of information into and out of the cell. In the typical LSTM unit depicted in Figure 8, the blue circle represents the network's input, the yellow boxes represent the learned neural network layers, and the orange shapes represent element-wise operations, such as addition.  
The key to LSTM is the cell state (C\({}_{t}\)); when it runs straight down the entire chain, only minor interactions occur. Information can easily and simply flow along the chain unaffected and maintain its integrity. The merging arrows represent vector concatenation, while the forking arrows represent vector duplication. The gates in LSTM models are designed to remove or add information to the cell state. They are composed of a sigmoid layer and multiplication operations. The sigmoid layer output is a value between zero and one, which indicates the weight of information flow. An LSTM model achieves control and protection of the cell state through these three gates. The forget gate decides what information will be disposed of from the cell state. This transfer can be defined as:  
\[\mathrm{f_{t}}=\sigma\left(
# Deep Transformer Language Models for Arabic Text Summarization: A Comparison Study
## 6 Discussion  
For the ANA dataset, shown in Table 2, PEGASUS-XSum and PEGASUS-Large, which are the two used versions of PEGASUS, report the best results on ROUGE-1 and ROUGE-2 with a good margin but were slightly beaten by AraBART on ROUGE-L and ROUGE-LSUM. Even though AraBART has a quarter size of parameters compared to the other models, it is still reporting the best or comparable results on all metrics on the ANA dataset because it is solely pre-trained and fine-tuned for Arabic ATS. mBART seems to be struggling irrespective of the used metric, which is also the case with the other two datasets, as we see later.  
Table 3 presents the obtained results with AHS dataset. It shows that for this comparison, PEGASUS models report the top two results, but PEGASUS-XSum demonstrates superior performance. In contrast to the ANA results, AraBART appears to be struggling with the AHS dataset managing only to score half of what was achieved by PEGASUS-XSum. Results of a similar nature were also obtained in Table 4 with the WikiHow dataset. In particular, the PEGASUS family tends to outperform other models. PEGASUS-LARGE reports the best performance scoring 95% in most metrics. Both mT5 and AraBART perform relatively well on some metrics but are not being able to achieve good results on ROUGE-2. It is also worth noting that the struggle is continuing with mBART.  
The TLMs-based ATS, PEGASUS surpasses the baseline model with a big margin regardless of the used datasets or the evaluation metric. These particular results justify the rapidly growing use of TLMs for ATS systems.  
Overall, according to the results detailed above, we notice that because of its nature and its dedication to the same type of tasks put in question, for abstractive text summarization, the PEGASUS models with the two used versions (PEGASUS-Large and PEGASUS-XSum) manage to obtain the best results. In the case of the BART multilingual version, mBART, the results are yet to be compared with superior models. However, the Arabic version, AraBART, shows many improvements on all datasets, especially with ANA. The highest reported results of the compared models were obtained with WikiHow datasets with the PEGASUS family. Furthermore, that might be explained by the nature of the models, as well as the length of the summary as an input at the time of training and its nature (e.g., title, highlight).
## 5 Omics Research
### Transcriptomics Research (RNA-Seq)  
The RNA-Seq data can be involved in many aspects, including sequence information, gene expression profiles, and gene function prediction [132]. Transcriptome analysis revealed that epigenetic mechanism, phytohormone signaling, and callose-related cellular communication may be involved in bud dormancy [133], and the gravitropism response and polar auxin transport were associated with the formation of zigzag-shaped shoots in tea plants [134]. Moreover, transcriptome analysis of self- and cross-pollinated pistils of \"Fudingdabai\" and \"Yulv\" cultivars revealed that three genes (UGT74B1, MCU2, and RLK) were involved in regulating the SI process of tea plants [135]. A transcriptome analysis of \"Zijuan\" tea plant reported the genes associated with several primary metabolic pathways, including flavonoid, theanine, and caffeine biosynthesis [136]. The molecular mechanism of theacrine metabolism of bitter tea (Kucha, _C. sinensis_) was revealed by transcriptome analysis [137]. RNA-Seq technology was not only used to explore the regulatory mechanism of growth and development, but also to reveal the response mechanism of tea plants to environmental stresses, including cold acclimation [138], drought stress [139], shade [140], thermal stress [141], and simulated acid rain [142]. In addition, transcriptome analysis was also used to reveal the molecular mechanism of the different nitrogen form-induced oxidative stress [143], nitrogen uptake [144], selenium accumulation [145], fluorine absorption and transportation [146], and the molecular mechanism of aluminum tolerance and accumulation in tea plants [147]. Aside from this, transcriptome analysis was also used to analyze the defense mechanism of plants against various biotic stresses, including the identification of candidate genes involved in blister blight defense [148], genes associated with resistance to Collettotrichum camelliae [149], and the defense mechanism of tea plants induced by Helopeltis theivora [150]. Although transcriptomics was widely used to study the plant growth and development and stress response, the identification of molecular mechanisms of tea extracts in human health using transcriptome analysis was very rare.
# On-Board Decentralized Observation Planning for LEO Satellite Constellations
## 2 Problem Description
### Mathematical Formulation  
In MSOOP, the constellation handles demands from ground users or generated spontaneously, and these demands can be considered as the set of observation tasks. Generally speaking, the set of observation tasks and the set of observation availability masks are taken as the input, and the planning scheme is taken as the output. According to the on-board situation, each satellite completes the task allocation in a fully cooperative mode, which maximizes the profits of the whole constellation to complete the observation tasks under the condition of meeting various constraints. Therefore, MSOOP is essentially a variant of MATAP, so we will modify and supplement the task allocation model in the original work of CBBA to formulate the mathematical model of MSOOP. * Satellite-related variables: Let \(\mathcal{I}=\{1,\ 2,\ \ldots,N\}\) be index set of \(N\) satellites. The \(i\)th satellite \(sat_{i}\) is associated with the maximum available storage \(st_{i}^{\text{sat}}\) and the maximum available energy \(ene_{i}^{\text{sat}}\). * Task-related variables: Let \(\mathcal{J}=\{1,\ 2,\ \ldots,M\}\) be an index set of \(M\) tasks. The \(j\)th task \(task_{j}\) is associated with the estimated consumed storage \(st_{j}^{\text{task}}\), the estimated consumed energy \(ene_{j}^{\text{task}}\), the estimated duration \(dur_{j}^{\text{task}}\), the execution start time \(st_{j}^{\text{task}}\), the execution end time \(et_{j}^{\text{task}}\), the attitude maneuver time \(ant_{jj^{\prime}}^{\text{task}}\) between \(et_{j}^{\text{task}}\) and \(st_{j^{\prime}}^{\text{task}}\), the original profit \(op_{j}^{\text{task}}\) determined by the priority of the task, and the final profit \(fp_{j}^{\text{task}}\) obtained by the observation of the task. \(\lambda\) is the time-discounted parameter of \(fp_{j}^{\text{task}}\), and \(fp_{j}^{\text{task}}\) can be calculated as: \[fp_{j}^{\text{task}}=op_{j}^{\text{task}}e^{-\lambda st_{j}^{\text{task}}}.\] (1)  
Figure 1: Walker-\(\delta\) (30/3/1, 600 km, 60\({}^{\circ}\)) constellation configuration: (**a**) 3D constellation configuration at time \(t=0\); (**b**) 2D satellite ground tracks at time \(t=0\). Mask-related variables: Let \(\mathcal{K}_{ij}=\left\{1,\,2,\,\ldots,O_{ij}\right\}\) be index set of \(O_{ij}\)\(sat_{i}\)-to-\(task_{j}\) observation availability masks. The \(k\)th mask \(oam_{ijk}\) is associated with the start time \(st_{ijk}^{o\text{am}}\) and the end time \(et_{ijk}^{o\text{am}}\). * Decision variable: To finally find the \"task-mask\" mappings in MSOOP, \(x_{ijk}\) is introduced as a decision variable. \(x_{ijk}=1\) if \(task_{j}\) is assigned to \(oam_{ijk}\), and \(x_{ijk}=0\) otherwise.
## 4 System Architecture
### Packet Structure  
The format of the suggested scheme, as seen in Figure 4, is discussed in this section. Every packet can comprise numerous data sub-packets (DSs) to provide frame rate variation compatibility. Each sub-packet in the same packet contains the same payload with the  
Figure 3: Deep learning for the starting point of OFDM frame.  
same sequence number (SN). The SN represents the serial number of packets. We may actually split two scenarios based on the transmitter's packet rate and the camera's frame rate. In the case of undersampling, the frame rate is lower than the transmitter's packet rate (LED). In the case of oversampling, the frame rate is many times higher than the transmitter's packet rate. The transmitter's packet rate is defined as the number of packets that carry distinct payloads across the transmission media in a certain time period (for example, 20 packets/s). Many data packet frames are included in our proposed data frame structure. Each data sub-packet (DS) contains payload data and an SN, and each packet is made up of numerous data sub-packets (DS). The SN denotes a data packet's sequence information, which aids a receiver in determining the arrival state of a new payload in the oversampling case and detecting lost payloads in the undersampling case. We can modify SN length depending on the OCC system parameters and optical channel. Then, the number of missing packets discovered grows as the SN length increases. Figure 5 illustrates how to apply SN to two common scenarios: oversampling and undersampling.
## 2 Materials and Methods
### Mathematics Foundations
#### 2.1.2 XGBoost  
Gradient boosting sequentially builds an ensemble by training new models to rectify the errors of their predecessors. XGBoost is a highly efficient implementation of this technique [17].  
Sequential model building: XGBoost constructs an ensemble of decision trees iteratively. Let \(Fm(x)\) denote the predictive function of the ensemble at stage \(m\). Thus:  
\[F0(x)=0\ (initial\ prediction, often\ the\ average\ of\ the\ target\ variable)\]  
\[Fm(x)=Fm-1(x)+hm(x)\]  
where \(hm(x)\) is the \(m\)-th tree added to the ensemble.  
Error correction: To correct the errors of the preceding ensemble, \(Fm-1(x)\), each new tree, \(hm(x)\), is trained to minimize the loss function relative to the residual errors. The ultimate goal is to align the ensemble's prediction, \(Fm(x)\), with the actual target values, \(y\).  
Gradient descent: gradient boosting trains each new tree, \(hm(x)\), to approximate the negative gradient of the loss function, which represents the direction of the steepest descent:  
\[hm(x)\approx-\eta\,\times\,\
abla L(y,Fm-1(x))\]  
where:  
\(\eta\) (eta): the learning rate controls the magnitude of each tree's influence.  
\(\
abla L(y,\,Fm-1(x))\) is the rate of change of the loss function \(L\) relative to the previous ensemble's prediction, \(Fm-1(x)\).  
Regularization: XGBoost incorporates regularization terms into its objective function to mitigate overfitting.  
Regularization term: the regularization term \(\Omega(hm)\) imposes a penalty on the complexity of the tree \(hm\), and this penalty can involve both \(L1\) and \(L2\) regularization.  
\[\Omega(hm)=\lambda/2\ \mid\mid w\mid\mid 2+\gamma T\]  
where:  
_Pruning:_ pruning techniques help prevent overfitting by simplifying the decision tree and removing parts that may be overly specific to the training data.  
_Cost-complexity pruning formula:_  
\[C(T)=Err(T)+\alpha\mid T\mid\]  
where:  
\(C(T)\) measures the cost complexity of subtree \(T\).  
\(Err(T)\) is the error rate.  
\(\alpha\) is the complexity parameter.  
\(\mid T\mid\) is the number of leaves in subtree \(T\).
# Large-Area Coverage Path Planning Method Based on Vehicle-UAV Collaboration
## 2 Related Works
[32] proposed a hierarchical UAV/ UGV platform. These studies provide valuable strategies for solving the vehicle-mounted UAV path planning problem, but mainly focus on how to efficiently visit specific points rather than covering the whole area. The vehicle-UAV cooperation path planning method proposed in this paper is dedicated to solving this problem.
## 3 Finite Element Analysis of the Dryer Screw
### Theoretical Background
#### 3.2.2 Turbulence Model  
The SST \(k\)-\(\omega\) turbulence model is a two-equation eddy-viscosity model that is used for many aerodynamic applications. It is a hybrid model combining the Wilcox \(k\)-\(\omega\) and the \(k\)-\(\varepsilon\) models. A blending function, \(F_{1}\), activates the Wilcox model near the wall and the \(k\)-\(\varepsilon\) model in the free stream. This ensures that the appropriate model is utilized throughout the flow field: The \(k\)-\(\omega\) model is well suited for simulating flow in the viscous sub-layer. The \(k\)-\(\varepsilon\) model is ideal for predicting flow behavior in regions away from the wall. The turbulence kinetic energy (\(k\)) and its dissipation rate (\(\varepsilon\)) are shown in Equations (3) and (4), where \(\mu\) and \(P\) are the viscosity and pressure term containing the turbulence production  
Figure 7: Generation of mesh.  
due to the stresses in the flow, respectively. The constants \(\beta,\ \ \sigma_{k},\ \sigma_{\omega}\) are computed by a blend from the corresponding constants via the following formula [18,19].  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial\big{(}\rho ku_{j}\big{)}}{ \partial x_{j}}=P-\beta^{*}\rho\omega k+\frac{\partial}{\partial x_{j}}\Bigg{[} (\mu+\sigma_{k}\mu_{t})\frac{\partial k}{\partial x_{j}}\Bigg{]} \tag{3}\]  
\[\frac{\partial(\rho\omega)}{\partial t}+\frac{\partial\big{(}\rho\omega u_{j} \big{)}}{\partial x_{j}}=\frac{\gamma}{v_{t}}P-\beta\rho\omega^{2}+\frac{ \partial}{\partial x_{j}}\Bigg{[}(\mu+\sigma_{\omega}\mu_{t})\frac{\partial \omega}{\partial x_{j}}\Bigg{]}+2(1-F_{1})\frac{\rho\sigma_{\omega 2}}{\omega}\frac{ \partial k}{\partial x_{j}}\frac{\partial\omega}{\partial x_{j}} \tag{4}\]  
\[P=\tau_{ij}\frac{\partial u_{i}}{\partial x_{j}}\]  
\[\tau_{ij}=\mu_{t}\bigg{(}2S_{ij}-\frac{2}{3}\frac{\partial u_{k}}{\partial x_ {k}}\delta_{ij}\bigg{)}-\frac{2}{3}\rho k\delta_{ij}\]  
\[S_{ij}=\frac{1}{2}\bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{ \partial u_{j}}{\partial x_{i}}\bigg{)}\]  
\[\mu_{t}=\frac{\rho\alpha_{1}k}{max(\alpha_{1},\ \omega,\ \Omega\Gamma_{2})}\]  
\[F_{1}=tanh\bigg{(}arg_{1}^{4}\bigg{)}\]  
\[arg_{1}=min\Bigg{[}max\Bigg{(}\frac{\sqrt{k}}{\beta^{*}\omega d},\ \frac{500\upsilon}{d^{2}\omega}\Bigg{)},\ \frac{4\rho\sigma_{\omega 2}k}{ CD_{k\omega}d^{2}}\Bigg{]}\]  
\[CD_{k\omega}=max\Bigg{(}2\rho\sigma_{\omega 2}\frac{\partial k}{\omega}\frac{ \partial\omega}{\partial x_{j}},\ 10^{-20}\Bigg{)}\]  
\[F_{2}=tanh\bigg{(}arg_{2}^{2}\bigg{)}\]  
\[arg_{2}=max\Bigg{(}2\frac{\sqrt{k}}{\beta^{*}\omega d},\ \frac{500\upsilon}{d^{2} \omega}\Bigg{)}\]  
\[\varnothing=F_{1}\varnothing_{1}+(1-F_{1})\varnothing_{2}\]  
Constants for \(k\)-\(\omega\) closure  
\[\sigma_{k1}=0.85,\ \ \sigma_{\omega 1}=0.65,\ \ \beta_{1}=0.075\]  
Constants for \(k\)-\(\varepsilon\) closure  
\[\sigma_{k2}=1.00,\ \ \sigma_{\omega 2}=0.856,\ \ \beta_{2}=0.0828\]  
Constants for SST closure  
\[\beta^{*}=0.09,\ \ \alpha_{1}=0.31\]
# A Multi-Channel Data Simulator Based on the Time Unification System
## 3 The Design of the Time Unification  
The clock of each device in the target system is generated by the internal oscillator, but the oscillator is highly sensitive to the environmental conditions, and time synchronization is required between different devices [15]. Time synchronization in the target system is usually achieved via the Global Positioning System (GPS) satellite timing method. A time reference generator with a GPS module can provide a 1 Hz PPS signal indicating the second signal [6]. The simulator needs to synchronize the data transmission times of multiple different protocol ports under the control of the PPS signal sent by the time reference generator in order to achieve the simulation of the real working environment of the target system. This section introduces the relevant design methods for the simulator with time synchronization.
## 1 Introduction
Recently, a conceptual solution of using SWE for integrating VGI with the Sensor Web has been suggested for creating a (more general) Observation Web [12], but again semantic aspects have not been considered explicitly. In this paper, we propose a novel approach to integrate conventional sensor information and VGI in a forest fire case. We describe a traditional forest fire information system based on satellite data and an unconventional use of VGI as \"citizen\" observation system. We show the co-occurrence of forest fire events as the result of the integrated approach. Contrary to common logic-based approaches, we base our developments on another formalization paradigm from software engineering: _algebraic specifications_[13]. The advantages of this approach are at least threefold. First of all, the fact that sensing is a function from the world to data is directly reflected in the formalization. Second, any form of information integration is computation, which is also a function. The power of functions provides an inherent solution to describe unambiguously the processing steps from natural phenomena to value-added information. Third, algebraic specifications--and thereby the ontologies--can be directly tested using functional programming languages, such as Haskell [14]. The work presented in this article is based on a previous publication [10], which we substantially extend with detailed descriptions of the use case, the ontological commitments, and ways of generalizing the proposed solution. Compared to the initial conference paper, we present a completely reworked code and the final alignment with DOLCE Ultra Lite and the SSN Ontology. The remainder of this paper is organized as follows. We provide the overall background and briefly illustrate the proposed solution to sensor integration in the next section. Then we argue for the use of algebraic specifications for ontology engineering and present related work in Section 3. The formalization of our approach to sensor integration is provided in Section 4. This first application of this approach is based on several assumptions and simplifications, which are discussed in Section 5. We conclude the paper with a discussion, a summary of our main findings and an outline of future work. Throughout the paper, the VGI use cases of forest fire detection as examples.
## 4 Experiment and Analysis
### Experiment Result  
#### 4.3.1 The Result of the First Experiment  
The first experiment was conducted in the laboratory. There were five AP signals covered in the laboratory. The results of AO-PSO-PF, federated particle filter (FPF) [19], PDR, and WiFi of the indoor positioning in the first experiment are shown in Figures 12-17, which show the positioning paths, positioning errors and positioning cumulative distribution function (CDF) values, respectively. In this experiment, the WiFi results are optimized using the PSO model. The AO-PSO-PF algorithm demonstrates significant improvements across all motion modes compared to the PDR, WiFi, and FPF algorithms. It achieves the lowest values in average error (AE), root-mean-square error (RMSE), maximum error (ME), circular error probable (CEP) of 75%, and CEP of 95%, showcasing its superior positioning accuracy. Specifically, it reduces the AE by 86.9% compared to PDR, 15.2% compared to WiFi, and 20.2% compared to FPF. The RMSE is reduced by 86.9% compared to PDR, 11.1% compared to WiFi, and 15.3% compared to FPF. The ME is reduced by 85.5% compared to PDR, 11.1%  
Figure 11: Illustrations on the effectiveness of AO, red circle denotes the low weight particle before AO. Figure 12: Positioning trajectory for PDR in the 1 st experiment: (**a**) calling gesture; (**b**) dangling gesture; (**c**) handheld gesture; (**d**) pocketed gesture. compared to WiFi, and 19.5% compared to FPF. Additionally, the CEP of 75% is reduced by 86.3% compared to PDR, 9.3% compared to WiFi, and 7.4% compared to FPF, while the CEP of 95% is reduced by 85.3% compared to PDR, 13.2% compared to WiFi, and 20.3% compared to FPF. The detailed data for Experiment 1 are presented in Table 3.  
Figure 14: Positioning trajectory for FPF in the 1 st experiment: (**a**) calling gesture; (**b**) dangling gesture; (**c**) handheld gesture; (**d**) pocketed gesture. Figure 13: Positioning trajectory for WiFi in the 1 st experiment: (**a**) calling gesture; (**b**) dangling gesture; (**c**) handheld gesture; (**d**) pocketed gesture. Figure 16: Positioning errors for PDR, WiFi, FFF, and AO-PSO-PF under different smartphone usage gestures in the 1 st experiment: (**a**) positioning errors in the calling gesture; (**b**) positioning errors in the dangling gesture; (**c**) positioning errors in the handheld gesture; (**d**) positioning errors in the pocketed gesture. Figure 15: Positioning trajectory for AO-PSO-PF in the 1 st experiment: (**a**) calling gesture; (**b**) dangling gesture; (**c**) handheld gesture; (**d**) pocketed gesture. Figure 17: CDF errors for PDR, WiFi, FPF, and AO-PSO-PF under different smartphone usage gestures in the 1 st experiment: (**a**) CDF errors in the calling gesture; (**b**) CDF errors in the dangling gesture; (**c**) CDF errors in the handheld gesture; (**d**) CDF errors in the pocketed gesture.