esa-sceva/satcom-chunk-collection huggingface dataset:
# Coal Mine Water Inflow Prediction Model Based on Multi-Factor Pearson Correlation Analysis
## 4 Case Study
### Comparative Experiment  
To validate the performance of the present prediction model, it is compared to other prediction models, including XGBoost, Random Forest (RF), and KNN, where the model parameters and dataset are the same. As listed in Table 2, compared to other prediction models, MAE is reduced by 4.174, MAPE is reduced by 0.51, and RMSE is reduced by 4.159, while R\({}^{2}\) is increased by 0.012; this means the present prediction model can predict the mag  
Figure 8: Uncertainty of predicted coal mine water inflow.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Risk Level** & **High** & **Relatively High** & **Medium** & **Relatively Low** & **Low** \\ \hline Water Inflow (m\({}^{3}\)/h) & \(\geq\)3000 & [1650, 3000) & [840, 1650) & [300, 840) & \(<\)300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Risk level of coal mine water inflow.  
nitude and development trends of coal mine water inflow in the next 8 h more accurately. Under the same model parameters and dataset, the CNN-BiLSTM-Attention model can explore deep information from data related to water inflow; combining data correlation analysis, the present prediction model can predict the magnitude and development trends of coal mine water inflow in the next 8 h more accurately.
# The Production of Curli Amyloid Fibers Is Deeply Integrated into the Biology of _Escherichia coli_
###### Abstract  
Curli amyloid fibers are the major protein component of the extracellular matrix produced by Enterobacteriaceae during biofilm formation. Curli are required for proper biofilm development and environmental persistence by _Escherichia coli_. Here, we present a complete and vetted genetic analysis of functional amyloid fiber biogenesis. The Keio collection of single gene deletions was screened on Congo red indicator plates to identify _E. coli_ mutants that had defective amyloid production. We discovered that more than three hundred gene products modulated curli production. These genes were involved in fundamental cellular processes such as regulation, environmental sensing, respiration, metabolism, cell envelope biogenesis, transport, and protein turnover. The alternative sigma factors, \(\sigma^{5}\) and \(\sigma^{\mathrm{E}}\), had opposing roles in curli production. Mutations that induced the \(\sigma^{\mathrm{E}}\) or Cpx stress response systems had reduced curli production, while mutant strains with increased \(\sigma^{5}\) levels had increased curli production. Mutations in metabolic pathways, including gluconeogenesis and the biosynthesis of lipopolysaccharide-acrylamide (LPS), produced less curli. Regulation of the master biofilm regulator, CsgD, was diverse, and the screen revealed several proteins and small RNAs (sRNA) that regulate _csgD_ messenger RNA (mRNA) levels. Using previously published studies, we found minimal overlap between the genes affecting curli biogenesis and genes known to impact swimming or swarming motility, underlying the distinction between motile and sessile lifestyles. Collectively, the diversity and number of elements required suggest curli production is part of a highly regulated and complex developmental pathway in _E. coli_.  
amyloid; curli; Keio collection; csgA; csgD; LPS; RpoS; RpoE; _Escherichia coli_; nhaA; rafF
###### Abstract  
Radar altimetry in the context of sea ice has mostly been exploited to retrieve basin-scale information about sea ice thickness. In this paper, we investigate the sensitivity of altimetric waveforms to small-scale changes (a few hundred meters to about 10 km) of the sea ice surface. Near-coincidental synthetic aperture radar (SAR) imagery and CryoSat-2 altimetric data in the Beaufort Sea are used to identify and study the spatial evolution of altimeter waveforms over these features. Open water and thin ice features are easily identified because of their high peak power waveforms. Thicker ice features such as ridges and multiyear ice floes of a few hundred meters cause a response in the waveform. However, these changes are not reflected in freeboard estimates. Retrieval of robust freeboard estimates requires homogeneous floes in the order of 10 km along-track and a few kilometers to both sides across-track. We conclude that the combination of SAR imagery and altimeter data could improve the local sea ice picture by extending spatially scarce freeboard estimates to regions of similar SAR signature.  
sea ice; SAR; altimetry +
Footnote †: journal: Accepted: 1 November 2019; Published: 6 November 2019
## 4 Conclusions  
In non-forested sites in water-limited areas, the MODIS GPP product is well correlated with the GPP EC tower estimations when averaged at an annual level, although it tends to overestimate. However, the existence of occasional and large precipitation events during the growing season led to infrequent underestimations. Since there is a correlation between precipitation and GPPec in these sites, the use of precipitation data at a finer spatial resolution than DAO data can improve the accuracy in the estimations of annual GPP. However, the development of a general model based on precipitation would require a larger sample of water limited sites.  
The seasonal dynamics and the beginning and end of the growing season were not well captured by GPPm in the sites where (i) the productivity was low throughout the year or (ii) the changes in the flux trend were abrupt, usually due to the restrictions in water availability. In the grasslands and shrublands located in water limited sites, water stress is the primary limiting factor that controls photosynthesis and therefore GPP; hence, soil water stress should be added to the MOD17 algorithm calculations for these dry areas. Using the existing weekly GPPm data, the GPP estimations can be improved in non-forested sites in water-limited areas by including in the model meteorological data at a finer spatial scale as well as soil water content and temperature measurements.
# Design and Stability Analysis of Six-Degree-of-Freedom Hydro-Pneumatic Spring Wheel-Leg
## 5 Conclusions  
Given the continuous development of new drive modes, a dual in-wheel motor wheel-leg drive mode is proposed. A six-arm suspension device based on hydro-pneumatic springs was designed for this drive mode. First, a static analysis was conducted, revealing that during wheelset steering (horizontal rotation), the suspension structure had strong self-stabilizing properties within a certain range, owing to the nonlinear stiffness characteristics of the hydro-pneumatic springs. However, the rotational lever arm of the hydro-pneumatic springs that promote rotation increased and the rotational lever arm of those that hinder rotation decreased. The load force of the hydro-pneumatic springs that impede rotation increased exponentially, which ultimately ensured stability. Finally, with the auxiliary action of tension springs, the rotational torque of the hydro-pneumatic springs and the tension resistance torque of the tension springs counterbalanced each other. The resultant moment of the wheelset unit is basically maintained at 0 N\(\cdot\)m. Considering the inconsistency issues in spring manufacturing, an adjustment mechanism was employed to calibrate the springs. The calibration kept the stiffness error within \(\pm\)5%, yielding a maximum torque error of approximately 50 N\(\cdot\)m. By relying on the dynamic adjustment of the wheelset differential, ideal stability and steering conditions can be achieved despite the spring inconsistency. The dual in-wheel motor wheel-leg drive unit designed in this article can be applied in the next generation of wheeled combat vehicles in the future. It will bring a brand new interior space layout scheme for vehicles.  
Conceptualization, Z.W. and B.J.; methodology, Z.W. and B.J.; software, C.S.; validation, C.S., Z.X. and Y.J.; formal analysis, B.J.; investigation, Z.W.; resources, H.Z.; data curation, C.S. and Z.X.; writing--original draft preparation, Z.W.; writing--review and editing, Z.W.; visualization, B.J.; supervision, Z.W.; project administration, H.Z.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Variation curves of torques with respect to the rotation angle under spring inconsistency.  
**Funding:** This research was funded by the Fund for Guiding the Transformation of Scientific and Technological Achievements of Shanxi Province, grant number 2021104021301061, by the Fund for Fundamental Research Program of Shanxi Province, grant number 202203021221106, and by the Research Fund of Shanxi Key Laboratory of High-end Equipment Reliability Technology, grant number 446110103.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# MT-FBERT: Malicious Traffic Detection Based on Efficient Federated Learning of BERT
## 1 Introduction
In this paper, we focus on the pre-training-based method for malicious traffic detection in an FL framework by addressing the following questions: (1) Can we pre-train the BERT and fine-tune it for malicious network traffic detection across multiple organizations using an FL framework? (2) Can we achieve an excellent malicious traffic detection performance while using cost-effective model training in resource-constrained clients in an FL framework? In order to address these questions, we propose a malicious traffic detection model based on an efficient federated learning framework of BERT, called MT-FBERT. MT-FBERT firstly pretrains BERT using self-supervised learning on large-scale, unlabeled network traffic data to learn generic traffic representations. Specifically, it employs two pre-training tasks separately, and the overall pre-training task loss. Then, MT-FBERT finetunes the pre-trained model using FL by creating small local models for each client. In order to consume fewer computational resources and transmit fewer weights, only a subset of crucial neurons in the global model is selected and updated. The main contributions of this paper are as follows:* We propose a novel pre-training model for malicious network traffic detection, called MT-FBERT. It leverages BERT to take advantage of the unlabeled traffic data to learn generic traffic patterns without relying on expert experience. And then the fine-tuning for malicious traffic detection in the FL framework can assure both the data privacy and the detection accuracy for multiple organizations. * We introduce an efficient malicious traffic fine-tuning mechanism with FL for MT-FBERT on labeled malicious traffic data. It selects and updates the important neurons of the global model in each client to save computational resources and transmission bandwidth. * We conduct experiments on several public datasets, which demonstrate the excellent malicious traffic detection performance of MT-FBERT by comparing it to the state-of-the-art baselines with multiple evaluation metrics. Under the conditions of limited labeled samples, distribution shifts, or constrained computational resources, MT-FBERT consistently performs with stability and efficiency.
# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 2 Models and Data
### Deep Learning LSTM Model  
#### 2.2.1 Methodology Description  
It is known that the observations of ionospheric critical frequency foF2 are time sequences. The long-short-term memory network LSTM model is based on the recurrent neural network (RNN) model which can infer the relationships between the time-series data. The RNN model is the addition of the recurrent connection to a typical neuron network structure that uses the result at the previous epoch as an input at the current epoch.  
Figure 2: A block diagram of the BPNN and GABP models.  
By improving the internal structure of RNN neurons, the LSTM model has the ability of long-term memory for important historical information, avoiding the effect of gradient vanishing or gradient exploding that might occur in the deep learning RNN model. Many researchers have extensively explored the prediction of the ionospheric TEC (total electron content) variability for short terms by using LSTM methods [43; 44], and also some studies on the forecasting of ionospheric foF2 parameters [45].  
The LSTM model is the addition of several gates and memory cells to the existing neuron structure. The LSTM network utilizes three special \"gate\" structures: input gate, forget gate, and output gate. Through their cooperation, information is selectively memorized and status is fed back to the neural network at each moment. Past data can be stored in the memory cell and awakened in due course, or forgotten if deemed unnecessary. In this way, it can remember any long-term behaviors of data. Information forgetting and retention are determined by the combination of \"forget gate\" and \"input gate\", making LSTM preserve long-term memory effectively. Through the memory of historical data and used as input for the next moment, the LSTM model can make good use of the information from the historical data to predict the data in the future.  
The deep learning LSTM model contains the following layers: input layers, LSTM layers, hidden layers, and output layers, as depicted in Figure 3. At every time step, the learning rule is applied by forwarding the previous trained information to each layer and allowing it to perform sequence forecasting. \(H_{t}\) is the output of the current LSTM network, which is based on the cell state \(C_{t}\). It is noteworthy that \(H_{t}\) maintains information from the previous step's hidden state and thus, this algorithm can make use of all previous foF2 values of time series.
# The Impact of Using Mobile Phones on Gait Characteristics: A Narrative Review
## 4 The Differences between Impacts of Mobile Phone Usage Tasks  
Some studies investigated the differences between the influences of various mobile phone use tasks on gait [24; 26; 31]. The observed changes predominantly occurred when walking while texting or reading, as opposed to when walking while talking on a mobile phone [24; 26; 31]. The reason for this might be that completing the walking task predominantly relied on the visual-spatial resources of the working memory. Texting or reading required the participants to focus on the screen while being aware of their surroundings, such as their walking direction. This was not the case with walking while talking on a mobile phone. Although walking and talking occurred concurrently, the head position was typically upright with the eyes free to take in environmental information, which allowed more resources to be devoted to gait control. Another viewpoint is that the dual task of walking while talking engaged only low cognitive level elements (walking and talking) whereas walking while texting required the execution of a high cognitive level task (texting) and a low cognitive level (walking) element [39]. Although texting is common motor task, it is still not a well-practiced skill compared to walking or talking. Furthermore, the impactof texting on gait is greater than that of reading [35]. In texting tasks, motor demands were required for communication interchange, including both reading and texting, which could explain why texting had the greater effects on walking gait compared to reading. In addition, a further study found that searching online or taking a selfie have a larger impact on gait than reading or dialing [23]. Speculatively, searching likely employs motor and mental demands that are comparable to reading or texting, whereas taking a selfie seems to be more challenging because of additional motor tasks, such as moving the arms.
## 4 Discussion
### Implications for Cropland Protection  
Urbanization can be categorized into reversible and irreversible processes [52]. The irreversible transformation of cropland into construction land results in the destruction of the topsoil structure and fundamental changes in the soil's physical and chemical properties, leading to permanent GPP loss [53]. During urbanization, the government can leverage high-precision remote sensing technology to improve the monitoring of cropland loss hotspots, establishing a dynamic, comprehensive, and full-range cropland protection monitoring system. This allows for timely tracking of cropland occupation, effectively guiding urban expansion patterns, and achieving coordinated development of cropland protection and the economy. Furthermore, the issue of cropland abandonment in Guangdong Province is becoming progressively more severe, and the GPP loss caused by abandonment cannot be ignored. The government should closely address the issue of cropland abandonment. Under the guidance of the \"three-in-one\" new cropland protection pattern, comprehensive strategies for preventing and managing abandoned land should be implemented based on the driving factors and agricultural development trends [54]. This involves institutional reform, innovation in mechanisms, and supporting policies. Specifically, to address the primary cause of abandonment-loss of the rural labor force [55]--a well-established land transfer market needs to be developed. For abandoned land with good production conditions, priority should be given to its use through rotation and reclamation. In recent years, despite the government taking various policy measures to ensure food security, such as land development, land consolidation, and land reclamation projects to supplement cropland area, it is also important to focus on supplementing cropland based on the quantity, quality, and structure of the cropland to ensure balanced food productivity [56; 57]. Our research findings indicate significant regional differences in the quality of supplemented cropland. Areas with better-supplemented cropland quality are mainly distributed in the WG, whereas the EG has poorer-quality supplemented cropland.
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
## 5 Conclusions  
In this paper, the analysis of SINR and the average transmitted power requirement of GEO SAR system in the presence of RFI impact are discussed. Two RFI cases, the ground RFI and the bistatic scattering RFIs from the LEO SAR and GNSS systems, are considered.  
In the ground RFI case, the average transmitted power requirement depends on the RFI intensity and system frequency. A lower RFI intensity and a higher frequency can help to reduce the required average transmitted power, which makes the system more practical to be implemented. Moreover, for the point-like RFI sources, the relative distance between the beam centre of GEO SAR and the position of the point-like RFI also affects the required average transmitted power of GEO SAR. The average transmitted power requirement will decrease when the position of the point-like RFI is far away from the position of the GEO SAR beam centre.  
With respect to the bistatic scattering RFI, in LEO SAR RFI case, the non-specular scattering case has a high occurrence probability of about 40%, while the occurrence of the specular scattering case is very low, less than 0.1%. Under the non-specular scattering case, no special requirements for average transmitted power are needed. However, when the specular scattering case happens, we can obtain the achievable average transmitted power to reach the required SINR only for C and X band systems when the LEO SAR beam centre is thousands of kilometres away from GEO SAR beam centre. For the point-like target, Doppler filtering will be an effective method to resist the LEO SAR RFI when there is a large Doppler rate difference between GEO SAR and LEO SAR.  
In the GNSS RFI case, it has very high probabilities (even 100%) of the non-specular and the specular scattering cases due to its high orbit and the wide beam width. The required average transmitted power is still very small in the non-specular scattering case, while an impossible average transmitted power requirement is needed in the specular scattering case.  
In the future, some RFI mitigation methods based on post signal processing are needed to address the RFI impact, especially for the ground RFI with a very high intensity and the specular scattering cases for the bistatic scattering RFI.  
**Acknowledgments: This work is supported by National Natural Science Foundation of China (Grant no. 61471038, 61501032, 61427802), Beijing Natural Science Foundation (Grant no. 4162052) and Chang Jiang Scholars Program (T2012122) and 111 project of China under Grant B14010.**  
**Author Contributions: [PERSON] and [PERSON] conceived and designed the methods and wrote the paper; [PERSON] performed the simulation and analysed the data. [PERSON] commented about the paper and helped editing. [PERSON] gave many suggestions. Conflicts of Interest: The authors declare no conflict of interest.**
# Metaheuristics and Support Vector Data Description for Fault Detection in Industrial Processes
## 2 Theoretical Background
### Particle Swarm Optimization  
Particle Swarm Optimization (PSO) is a population search algorithm based on the simulation of the social behavior of birds, bees, or a school of fishes [49]. In this stochastic search technique, a multidimensional vector represents a particle in a multidimensional search space. These particles move toward the next position depending on the velocity vector associated with them. The velocity is updated based on the current velocity and the best position it has explored so far. This algorithm used the global best solution concept to obtain the optimal solution. At each iteration, the global best solution is recorded and updated [50].  
Let \(\mathbf{y}_{i}=(y_{1i},y_{i2},...,y_{id})^{t}\) be the \(i\)-th particle of the swarm in a \(d\)-dimensional search space with a corresponding velocity \(\mathbf{v}_{i}=(v_{i1},v_{i2},...,v_{id})^{t}\). Thus, the equations that conduct the particle's movements read  
\[\mathbf{v}_{i}(t+1) =w\mathbf{v}_{i}(t)+c_{1}\phi_{1}(\mathbf{p}_{ibest}-\mathbf{y}_ {i}(t))\] \[+c_{2}\phi_{2}(\mathbf{p}_{ghest}-\mathbf{y}_{i}(t))\] \[\mathbf{y}_{i}(t+1) =\mathbf{y}_{i}(t)+\mathbf{v}_{i}(t+1)\]  
where \(c_{1}\) and \(c_{2}\) are acceleration coefficients, and \(\phi_{1}\) and \(\phi_{2}\) are random variables with uniform distribution in \([0,1]\). \(\mathbf{p}_{ibest}\) and \(\mathbf{p}_{gbest}\) are the best local and global particle positions so far, respectively. \(w\) denotes the inertia weight which shows the effect of previous velocity vector on the new vector.
# Transaction Mechanism Based on Two-Dimensional Energy and Reliability Pricing for Energy Prosumers
## 3 ERT Implementation Issues
### Utility Side
#### 3.1.2 Reliability Band and Price Design  
In the proposed ERT, the reliability band and price are adjusted according to the utility's requirements. Basically, the reliability band is narrow and the price is set high where the utility requires high reliability. On the other hand, however, this leads to discrimination between prosumers with different characteristics.  
Another way to design the reliability band and price is to consider prosumer priority in terms of the utility. Figure 4 shows the contribution of wind generation at net demand from the Bonneville Power Administration (BPA) balancing authority in 2016 [21]. Relative to contribution, the utility decides the prosumer priority, and it is used as a reference to determine the reliability band range. In Figure 4, the average contribution of wind generation is around 22%, so the priority \(\omega_{t}\) is set as 3 for 25% or more, 2 for 25-20%, and 1 for less than 20% as an example. Using the priority, the utility designs the relative price function in (3). Narrower reliability bands will be allocated to the higher priorities to effectively manage uncertainty, as follows:  
\[f_{i}(\gamma_{t})=\begin{cases}1,&\gamma_{t}\leq\frac{1}{\omega_{t}},\\ -\gamma_{t}+1,&\text{otherwise.}\end{cases} \tag{7}\]