esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Malfunction Probability Models for Communication Delays and Bit Errors
### Refuse-Operation Probability Model for Consistent Delays and Bit Errors  
The two reasons for the refuse-operation of the current differential protection, the consistent delay and bit error, are independent [14,26]. The progress of refuse-operation of differential protection is shown in Figure 10.  
Therefore, the total refuse-operation probability can be obtained:  
\[P_{r}=1-(1-P_{rd})(1-P_{rb}) \tag{20}\]  
Applying (16) and (19) into (20), the total refuse-operation probability of the protection caused by the consistent delays and bit errors can be represented as:  
\[P_{r}=1-\varphi(\frac{t_{\max}-t_{d}}{\sigma_{1}})\times\sum_{m=0}^{m_{\max}} \frac{(mw_{1})^{m_{\max}}e^{-m_{1}}}{(mw)!} \tag{21}\]  
Figure 11 gives the total refuse-operation probability of relay protection caused by consistent delays and bit errors.  
Figure 10: The progress of refuse-operation.  
Figure 9: Refuse-operation probability for bit error.
###### Abstract  
Text sentiment analysis is an important but challenging task. Remarkable success has been achieved along with the wide application of deep learning methods, but deep learning methods dealing with text sentiment classification tasks cannot fully exploit sentiment linguistic knowledge, which hinders the development of text sentiment analysis. In this paper, we propose a sentiment-feature-enhanced deep neural network (SDNN) to address the problem by integrating sentiment linguistic knowledge into a deep neural network via a sentiment attention mechanism. Specifically, first we introduce a novel sentiment attention mechanism to help select the crucial sentiment-word-relevant context words by leveraging the sentiment lexicon in an attention mechanism, which bridges the gap between traditional sentiment linguistic knowledge and current popular deep learning methods. Second, we develop an improved deep neural network to extract sequential correlation information and text local features by combining bidirectional gated recurrent units with a convolutional neural network, which further enhances the ability of comprehensive text representation learning. With this design, the SDNN model can generate a powerful semantic representation of text to improve the performance of text sentiment classification tasks. Extensive experiments were conducted to evaluate the effectiveness of the proposed SDNN model on two real-world datasets with a binary-sentiment-label and a multi-sentiment-label. The experimental results demonstrated that the SDNN achieved substantially better performance than the strong competitors for text sentiment classification tasks.  
deep learning; sentiment attention mechanism; bidirectional gated recurrent unit; convolutional neural network +
Footnote †: journal:
# Congestion Control Mechanism Based on Backpressure Feedback in Data Center Networks
## 2 Motivation and Related Work
### Traffic Management in Data Centers
Responding to congestion in time can prevent packets from being excessively queued on the switch, thus reducing the end-to-end latency. In addition, rate control is combined with flow control. The switch adjusts the transmission rate as soon as it receives a congestion signal from downstream. The rate control of the switch is added to the flow control mechanism, and the congestion problems can be solved more flexibly and quickly.
## 2 System Description  
The system under study is depicted in Figure 1. The microgrid consists of several diesel generators (\(N_{d}\)), where each unit \(i\) has a rated power \(P_{di}\); a PV power plant, where the rated power is \(P_{pv\text{-}nom}\); and a battery with rated power and capacity \(P_{bat\text{-}nom}\) and \(C_{bat}\), respectively. Finally, all these generation and storage units feed the total power demanded by the loads (\(P_{c}\)). The layout is based on a real stand alone system. It has the particularity that all generation and storage units (controllable units) are connected to the same bus. Thus, the load side can be treated as a single aggregated load. Each controllable unit has its local controller (LC) which is in charge of managing each resource separately:  
* LC for diesel generation power plant: The local controller is in charge of controlling the frequency of the grid. A proportional-integral (PI) controller, where the input is the frequency error (filtered by a low pass filter), computes the mechanical torque setpoint of each diesel generator. This local controller also receives the required number of connected diesel generators and accordingly sends orders of connection/disconnection to each diesel unit. Each diesel generator has its internal controller in charge of reaching the torque setpoint and to perform its connection and disconnection according to the LC requirements. A similar control architecture is found in [23]. The main difference is that in the present paper the PI is a central controller that coordinates all the diesel units, while in [23] a single unit is considered.
* LC for the PV power plant: This LC implements a power-frequency droop curve to provide support to the grid. Reducing the active power will always be possible, but increasing it (under frequency events) will depend on the available active power. The controller can perform power curtailments. A maximum PV power setpoint is received externally and a PI controller computes the active power setpoint of each PV inverter. This controller is defined in [24], but the ramp rate limitation is not taken into account.
* LC for the battery: This controller receives externally an active power setpoint and applies a power-frequency droop curve to provide grid support. The output is the droop modified setpoint. The inner control loops will be in charge of reaching this value of active power. The dynamic model is simplified as in [25], but the local frequency droop has been included.
# An Efficient Opportunistic Routing Based on Prediction for Nautical Wireless Ad Hoc Networks
## 4 Performance Evaluation  
The simulations are executed for the source node, and the destination node can be randomly designated. The nodes participating in the message forwarding do not have malicious functions. In the experiments, the proposed scheme introduces the movement of nodes and a propagation model into marine networking communication for transmitting data packets. The network simulator ns-3 was used to perform the experimental analysis, combining the proposed routing scheme and the characteristics of nautical wireless ad hoc networks. The deployment region was 1000 \(\times\)1000 km, and the initial communication range of the nodes was 100 km. The packet size was 2048 bytes, and the message rate was 150 kbps. The traffic source type was constant-bit-rate traffic, and the packets were generated at a fixed interval rate with a simulation time of 100 s.  
To validate the effectiveness of our proposed scheme (WRSP), we conducted comparative experiments with ad hoc on-demand distance vector (AODV) [16] and PASS (only broadcast packets for OppNets) [2] methods. The evaluation and experimental analysis are described as follows.
# Vegetation Dynamics and Recovery Potential in Arid and Semi-Arid Northwest China
## 4 Materials and Methods
### Data Collection and Processing
#### 4.2.2 Meteorological Data  
This study employs observational data encompassing monthly gridded temperature and precipitation, with a spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) ([[http://data.cma.cn/site/index.html](http://data.cma.cn/site/index.html)]([http://data.cma.cn/site/index.html](http://data.cma.cn/site/index.html)), accessed on 31 January 2020), sourced from 2472 meteorological stations in China spanning the period 1961-2019. Within this investigation, meteorological parameters, including precipitation, maximum temperature, minimum temperature, relative humidity, wind speed, and sunshine duration, were procured from the daily dataset of fundamental meteorological elements (V3.0) derived from China's national surface weather stations, as well as the monthly data originating from surface climate of China ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 12 September 2022). Meteorological stations exhibiting missing data, accounting for more than 5% of the time series, were subjected to exclusion, and the gaps in meteorological data were rectified through linear interpolation.
## 5 Conclusions  
In this study, we inspected the spatio-temporal variations of WUE from 2002 to 2021 and quantitatively analyzed factors influencing the spatial variation of WUE in QM and HC using the Geodetector method. Spatially, the results showed WUE gradually increases from northwest to southeast. The WUE in HC is higher than that in QM and shows opposite longitude zonality characteristics. WUE showed an upward trend with longitude increasing in QM, while a downward trend with longitude increasing in the oases of HC. Temporally, a decreasing trend in WUE occurred during 2002-2021 almost certainly due to the drop from May to July, which may be related to the change in temperature and phenology. The significant decreasing areas are primarily concentrated in the eastern and central HC and surroundings of Qinghai Lake. Temperature is the dominant factor controlling the spatial variation of WUE, and the interaction of the two factors among the selected ones is greater than the impact of either factor alone. Precipitation, LAI, and specific humidity also have strong explanatory power for the spatial variation of WUE, but there are obvious spatial differences. The impact of air temperature and precipitation on the spatial variation of WUE may be achieved by changing LAI. Precipitation effects more intensely the spatial variation of WUE in arid lands compared to relatively humid areas. Our study highlights that the Geodetector is a valid method to quantify the impacts of complex multi-factors on the spatial variation of WUE. These findings improve our understanding of the underlying mechanisms of WUE variations and the response of vegetation to climate change.  
Conceptualization, G.W. and X.L.; Methodology, G.W.; Software, G.W. and K.Z.; Validation, X.L.; Formal analysis, G.W. and K.Z.; Investigation, X.L.; Resources, X.S.; Data curation, X.S.; Writing--original draft, G.W.; Writing--review & editing, X.L., K.Z., Y.L. and X.S.; Supervision, Y.L.; Project administration, X.L.; Funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Sciences Foundation of China (42261026, 41971094, 42161025, and 42101096), Higher Education Innovation Foundation of Education Department of Gansu Province (2022A-041) and Gansu Province Science and Technology Plan Project (20 JR10 RA443).  
Data Availability Statement: Data are available from the corresponding author upon reasonable request.  
We acknowledge the National Tibetan Plateau Data Center and NASA's MODIS Science Team, who provided Terra MODIS data.  
The authors declare no conflict of interest.
# A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges
## 4 Fault Diagnostic Algorithms for the Li-Ion Battery System
### Internal Battery Fault Diagnosis
#### 4.1.1 Model-Based Methods
In [53], a model-based switching model method was proposed to improve the accuracy of the internal resistance estimates during an internal short circuit fault, and RLS was also used for parameter estimation and fault detection. [PERSON] et al. [54] proposed a fault detection method for the micro-short circuit, using the cell difference model and extended Kalman filters to estimate the cell SOC differences. The extra depleting current is identified with RLS to diagnose the short circuit resistance. All the above methods for internal short circuit detection use RLS for parameter estimation for low computational cost, but they all require information from other cells in the pack, which can be influenced by online cell balancing leading to unreliable fault detection. Figure 4: A simplified schematic of state estimation fault diagnosis. A string of studies on thermal fault detection using the battery thermal model and the ECM was introduced by the same group of authors in [55; 56; 57]. In [55], the Li-ion battery was modeled via ECM and a two-state thermal model. A diagnostic scheme based on the Luenberger observer was proposed to detect and isolate three thermal faults, which were internal thermal resistance fault, convective cooling resistance fault, and thermal runaway, by generating and filtering two unique residuals from the models. This work is further developed in [56], where nonlinear observers using Lyapunov analysis was implemented, and an adaptive threshold generator was developed to deal with the presence of modeling uncertainties. Most recently, the authors proposed a partial differential equation model-based scheme with two state observers to improve the accuracy and reliability of thermal fault detection [57]. The method proposed in these works becomes progressively more precise and reliable, but the computational effort also increases as the method develops.
## 3 Data and Methods  
### Input Data  
The climate data of calibration and validation sites for GPP and SOC come from the FLUXNET. Available online: [[https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)]([https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)) (accessed on 21 April 2022) that provide daily climate data including temperature, atmospheric pressure, precipitation, wind speed, etc. For the sites that climate data cannot be directly obtained, we downloaded the daily data of the nearest location from Global Historical Climatology Network daily (GHCNd). Available online: [[https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily)]([https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily)) (accessed on 21 May 2022). In this way, we have all sites formed daily weather-driven data from 1901 to 2017.  
We initialized soil carbon and soil C:N ratios in our model using the global soil dataset, IGBP-DIS (2000). The soil textures including clay, sand, slit fraction and soil pH data are provided by Global Digital Elevation Model (DSMW) obtained from FAO/UNESCO Soil Map of the World. Available online: [[http://www.fao.org/geonetmork/srv/en/metadata.show?id514116](http://www.fao.org/geonetmork/srv/en/metadata.show?id514116)]([http://www.fao.org/geonetmork/srv/en/metadata.show?id514116](http://www.fao.org/geonetmork/srv/en/metadata.show?id514116)) (accessed on 21 April 2022). And DSMW is the basis of soil classification map.  
Topographic input data were extracted from the global digital elevation model GTOPO30, with an approximate spatial resolution of 1 km.  
Data on atmospheric \(\rm CO_{2}\) concentrations for the 1958-2017 period were taken from the Mauna Loa Observatory in Hawaii [72]. Data before 1958 were taken mainly from the IS92a dataset of annual global \(\rm CO_{2}\) concentrations, obtained by spline fitting ice core data to the Mauna Loa data [73].
## Materials and Methods  
### Determination of Minimum Inhibitory Concentration (MIC) and Minimum Fungicidal Concentration (MFC)  
The study included the evaluation of the activity of essential oils obtained from Pollena Aroma Company ((Nowy Dwor Mazowiecki, Poland). Antifungal susceptibility testing of _C. albicans_ and _glabrata_ clinical isolates was performed according to the NCCLS reference microdilution method. Serial two-fold dilutions of the tested substances were prepared in RPMI 1640 medium (Sigma Aldrich, St. Louis, MO, USA) buffered to pH 7.0 with MOPS buffer (3-\(N\)-morpholinopropanesulfonic acid, EMD Chemicals, Gibbstown, NJ, USA) in 96-well microtiter plates in a final volume of 100 \(\mu\)L. Cinnamon oil was diluted 10-fold in DMSO (Sigma Aldrich). The final concentrations of the oils were in the range 0.005-2.5% (\(v/v\)) and 0.002-0.125% (\(v/v\)) for cinnamon oil. The final concentration of the solvent did not exceed 2.5% for DMSO, and did not influence the growth of yeast. Suspensions of the microorganisms were prepared by taking one loop of pure culture into sterile water and adjusting the optical density to 0.1 at 660 nm wavelength before further 50-fold dilution in an RPMI 1640 medium resulting in 2 \(\times\) 10\({}^{4}\) CFU/mL. One hundred microliters of such suspension were inoculated to each well of the microtiter plate, leaving a drug-free column as sterility controls. Plates were incubated for 24 h at 37 \({}^{\circ}\)C. MIC values were read visually as the first concentration where no growth was observed.  
Additionally, minimal fungicidal concentrations (MFC) were investigated. A small aliquot of suspension (around 5 \(\mu\)L) from each well was transferred using a 48-well stamp to YPD (Yeast extract Peptone Dextrose) (A&A Biotechnology, Gdynia, Poland) agar plates and incubated for 24 h at 37 \({}^{\circ}\)C. Concentrations where no growth was observed were assigned as MFC.
## 1 Introduction
The ground contact model (GCM) used in the study enables effective contact detection and simple modeling of non-uniform soil conditions. In addition, by using the DEM-MBD coupling method, the influences of design parameters, including the pre-tensioning force of the electric excavator's track chassis, wheel spacing, sprocket speed, and track shoe height on the chassis, tracking performance was studied by [PERSON] et al. [10]. The corresponding track plate structure was optimized by using a genetic algorithm. Based on the ground mechanics theory of vehicles, [PERSON] et al. [11] built a pressure and shear characteristic model of specific soil, and studied the walking performance of tracked vehicles. According to the steering characteristics of tracked vehicles, [PERSON] et al. established the slope steering dynamics model, combined with the ground mechanics theory, analyzed the influence of slope Angle, ground properties, and steering radius on the slope steering performance of tracked vehicles, and the research results have certain significance for the design of tracked vehicles [12]. According to previous studies, the structure and dynamic characteristics of tracked vehicles equipped with vertical and tilted suspensions have been studied and used widely by experiments and simulations. More attention should be paid to investigating the dynamics characteristics of a tracked vehicle equipped with symmetrical suspensions, which could be beneficial to the development of the machinery adapted to complex terrains. Therefore, this paper aims to build an MBD-DEM coupling model to analyze the dynamic characteristics of a homemade tracked vehicle equipped with symmetrical suspensions to verify the effectiveness of the homemade tracked vehicle. An MBD model of a homemade tracked vehicle equipped with symmetrical suspensions will firstly be built and verified in detail. By coupling the DEM model, a corresponding MBD-DEM model will be constructed in the third section. Finally, the dynamic characteristics of a homemade tracked vehicle equipped with symmetrical suspensions will be investigated in detail by using the proposed MBD-DEM model.
# Earthquake Prediction for the Duzce Province in the Marmara Region Using Artificial Intelligence
## 3 Materials and Method
### Calculation of Earthquake's Magnitude  
To calculate an earthquake's magnitude, primary (P) and secondary (S) waves are evaluated using formulas and the magnitude of the earthquake is scientifically derived [22]. In P or compressional waves, the vibration of the rock occurs in the direction of propagation. The P waves are the fastest propagating waves in the ground and consequently the first to be detected via seismometers [23].  
The velocity of P waves in such a medium is obtained using Equation (1) [23].  
\[Vp=\sqrt{\frac{(\lambda+2\mu)}{\rho}} \tag{1}\]  
where \(\mu\) is the shear modulus (modulus of rigidity, sometimes denoted as G and also called the second Lame parameter), \(\rho\) is the density of the material through which the wave propagates, and \(\lambda\) is the first Lame parameter.  
In S or shear waves, the rock vibrates perpendicularly to the direction of wave propagation. In ground, the \(S\) waves usually propagate about 60% as fast as P waves, and the S wave always follows the P wave in Equation (2) [24].  
\[Vs=\sqrt{\frac{\mu}{\rho}} \tag{2}\]In the first phase, the time from the beginning of the P waves to the beginning of the S waves is calculated, the nomogram is marked, and the corresponding km value is read. It is the distance of the station from the epicenter.  
In the second step, the amplitude of the strongest S wave is measured and plotted on the nomogram. The first two signs are combined linearly. An earthquake's magnitude is where the line connecting these two signs crosses the scale.  
This method calculated the earthquakes of magnitudes 3 and above (M \(\geq\) 3) that occurred in the Duzce Province between 1990 and 2022. The 1-year dataset for 1990 is shown in (Table 2).  
The 33-year histogram distribution highlighting the number corresponding to the size of the earthquake dataset is shown in Figure 1.  
The earthquakes' time, location, and depth were added to the record. The Kandilli Observatory Earthquake Research Institute provided us with the seismic data.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Date** & **Month** & **Day** & **Local Time** & **Latitude** & **Longitude** & **Depth** & **Magnitude** \\ \hline
1990 & February & 09 & 18:20:00.00 & 41.0000 & 31.9000 & 10 & 3.7 \\
1990 & February & 14 & 12:17:01.40 & 40.7400 & 29.1000 & 7 & 3.0 \\
1990 & April & 11