esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To address the challenges in RUL prediction with limited historical data, the Bayesian filter is leveraged, combining the benefits of physics-based models and data-driven approaches by the integration of domain knowledge about bearing fault and the uncertainty estimation. In this study, the RUL prediction approach for rolling-element bearings is presented based on a combination of the envelope spectral indicator (ESI) and the extended Kalman filter. First, the ESI is proposed as the bearing degradation indicator such that it is solely related to the bearing faults. The ESI is derived from the spectral amplitude of the bearing fault characteristic frequencies (FCFs) in the averaged envelope spectrum (AES), which is computed via multiple envelope signal segments for the compression of spurious frequency peaks. By isolating the bearing defect components present in the vibration data, the ESI is more aligned with the actual degradation state of the bearing. Also, the ESI allows for the exclusion of unwanted factors such as rotor dynamics and faulty components linked to shafts and gears, which fosters a more realistic prediction of the bearing RUL. Secondly, the incipient fault in the bearing is detected based on the envelope analysis to facilitate the determination of the first detection time (FDT) of a fault, marking the start of the bearing degradation stage. Once the fault is detected, the EKF is leveraged to predict the bearing end-of-life time as early as possible given only a few ESI observations. By converting the RUL prediction to a state estimation problem, the simplified exponential state-space model is introduced to represent the acceleratingly incremental trajectory of the bearing degradation state, which is observed through the ESI. The EKF-based RUL prediction technique is evaluated on the public experimental bearing vibration data in [7]. The applications to the test bearings under various working conditions further verify the effectiveness of the proposed method in the early detection of bearing fault and the timely prediction of RUL. The main contributions of this study are summarized as follows:  
* It determines the first detection time of fault for life prediction based on envelope spectrum inspection for the XJTU-SY dataset. * The bearing degradation indicator, ESI, is utilized for more realistic RUL prediction. * Bearing RUL is predicted at the early fault stage based on EKF state estimation. The rest of the paper is organized as follows: Section 2 discusses the settings of the run-to-failure experiments on the publicly available bearing vibration data. Section 3 presents the overall methodology for bearing RUL prediction based on the combination of the envelope spectral indicator and the EKF, where detailed explanations on the development of the ESI and EKF are elaborated. The applications of the proposed methods and observations of the results are discussed in Section 4. Finally, the discussion and the concluding remarks are provided in Sections 5 and 6.
# Simulation-Based Evaluation of V2X System with Variable Computational Infrastructure
## 4 Results  
We will now analyze the results obtained through the simulation. For each scenario described in Section 3.3, ten generations are considered for two average speed variants: 30 km/h and 50 km/h. Additionally, two path loss models, as described in Section 3.1, are evaluated. Results are shown in Figures 6-8.  
The simulation results demonstrate that, across all three scenarios, direct connections between each RSU and the EC result in significantly lower losses compared to the cluster-based approach (Figure 9). The graphs illustrate the percentage of unprocessed messages generated by the system, including those lost during transmission. Two connection configurations were analyzed: RSU-EC and RSU-RSU-EC (cluster). In the case of direct RSU-EC communication, losses remain minimal due to the use of high-speed fiber-optic connections, which ensure high transmission rates with negligible attenuation. However, as previously mentioned, the maintenance and operation of such a system are costly. Therefore, further comparison should be limited to utilizing clustering.  
1. Scenario 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**VANET 2.0.0**} & \multicolumn{2}{c}{**Other Simulation System**} \\ \cline{3-4}  & & **Basic** & **Express** \\ \hline Memory utilization increase (peak) & +2.5\% & +3.4\% & +4\% \\ \hline Memory utilization increase (mean) & +2.5\% & +3.4\% & +4\% \\ \hline CPU utilization (peak) & 38\% & 60\% & 76\% \\ \hline CPU utilization (mean) & 14\% & 28\% & 35\% \\ \hline Simulation time & 300 s & 300 s & 300 s \\ \hline Simulation duration & 300 s & 1680 s & 40 s \\ \hline Output file size & 89.7 KB & 1.12 MB & 1.12 MB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation system comparison.  
Figure 6: Average percentage of losses at 10 iterations (no mRSU): (**a**) average speed 30 km/h; (**b**) average speed 50 km/h.
## 5 Discussion
### Comparative Analysis on the Average Velocity of Ground Displacement  
To thoroughly analyze the capacity for ground displacement monitoring of the three methods, statistical analyses on monitoring results of the PS-InSAR, SBAS-InSAR, and the proposed method in A, B, C, and D zones were carried out as shown in Figure 9 and Table 3. In the first place, as shown in Figure 9, the range of average velocity of ground displacement obtained by the three methods is variable. According to the previous analysis, the quantity of CPs obtained by different methods is different, indicating that the quantity of CPs affects the range of values for ground displacement monitoring [38]. Furthermore, according to the \"Ground Subsidence Preventive Plan of Shandong Province 2012-2020\" and the \"Ground Subsidence Interferometric Radar Data Processing Technical Regulations\", the displacement value lower than \(-20\) mm/year was determined as the displacement state (shown as red wireframes vertical line in Figure 9) and the increase in the quantity of CPs with the average velocity of ground displacement less than 20 mm from the proposed method compared to conventional method was estimated, as shown in Table 3. The results show that the proposed method has improved the quantity of CPs in areas with large displacement (\(<\)\(-20\) mm/year), indicating that the proposed method provide a more comprehensive displacement and also in areas with large displacement, which is very evident in A and D zones relative to PS-InSAR technique (the increase in the total CPs relative to PS-InSAR is 5.1 and 5.3 as shown in Table 2, while the increase in the CPs \(<-20\) mm/year is 15.1 and 17.47 as shown in Table 3). Consequently, the proposed method is able to acquire more CPs in areas with large displacements, providing more informative information about the ground displacement.  
Figure 9: Average velocity of ground displacement distribution histogram in A, B, C, and D zones: (**a–c**) are the results in A zone obtained by PS-InSAR, SBAS-InSAR, and the proposed method, respectively; (**d–f**) are the results in B zone obtained by PS-InSAR, SBAS-InSAR, and the proposed method, respectively; (**g–i**) are the results distributions in C zone obtained by PS-InSAR, SBAS-InSAR, and the proposed method, respectively; (**j–l**) are the results in D zone obtained by PS-InSAR, SBAS-InSAR, and the proposed method, respectively. The red line is the boundary line for the average velocity of ground displacement of \(-20\)mm/year.
###### Abstract  
The Gram positive opportunistic human pathogen _Staphylococcus aureus_ induces a variety of diseases including pneumonia. _S. aureus_ is the second most isolated pathogen in cystic fibrosis patients and accounts for a large proportion of nosocomial pneumonia. Inside the lung, the human airway epithelium is the first line in defence with regard to microbial recognition and clearance as well as regulation of the immune response. The metabolic host response is, however, yet unknown. To address the question of whether the infection alters the metabolome and metabolic activity of airway epithelial cells, we used a metabolomics approach. The nutrition uptake by the human airway epithelial cell line A549 was monitored over time by proton magnetic resonance spectroscopy (\({}^{1}\)H-NMR) and the intracellular metabolic fingerprints were investigated by gas chromatography and high performance liquid chromatography (GC-MS) and (HPLC-MS). To test the metabolic activity of the host cells, glutamine analogues and labelled precursors were applied after the infection. We found that A549 cells restrict uptake of essential nutrients from the medium after _S. aureus_ infection. Moreover, the infection led to a shutdown of the purine and pyrimidine synthesis in the A549 host cell, whereas other metabolic routes such as the hexosamine biosynthesis pathway remained active. In summary, our data show that the infection with _S. aureus_ negatively affects growth, alters the metabolic composition and specifically impacts the de novo nucleotide biosynthesis in this human airway epithelial cell model.  
host cell metabolism; infection; metabolomics; _Staphylococcus aureus_
# A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting
## 3 Background Knowledge
### Bidirection LSTM  
Bidirectional LSTM (BiLSTM) is a variant of LSTM which not only uses the past information, but also captures the future information. BiLSTM consists of two LSTM layers with opposite propagation directions. In the first LSTM layer, series information is delivered in chronological order, and in the second layer, series information is delivered in reverse chronological order. In each LSTM layer of BiLSTM, the computation process is consistent with common LSTM layer from Equations (1)-(6). The only difference is that the second LSTM layer of BiLSTM is reversed. As shown in Figure 2, the two LSTM layers simultaneously propagate in the opposite directions. BiLSTM combines the forward \(\overrightarrow{h_{t}}\) and backward hidden state \(\overleftarrow{h_{t}}\) sequences by a function \(f\), which can be a summation, a concatenating, or an average function [24]. BiLSTM obtains a new sequence, and every element of it contains the previous information and future information. The process and result can be represented as:  
\[h_{t}=f(\overrightarrow{h_{t}},\overleftarrow{h_{t}}) \tag{7}\]  
\[H=[h_{1},h_{2},\ldots,h_{N}] \tag{8}\]  
where \(\overrightarrow{h_{t}}\) and \(\overleftarrow{h_{t}}\), respectively, represent the forward hidden state and backward hidden state in time step \(t\). \(H\) represents the final series that consists of the combination of forward hidden state and backward hidden state in different time steps. \(N\) represents the length of series. In this way, BiLSTM generates a series that contains previous information and future information in each element. It easily processes a series and can capture bidirectional features of the whole series rather than directly processing a raw series. BiLSTM performs well in many time series forecasting tasks compared with LSTM models [17].  
Although LSTM and BiLSTM perform well in time series forecasting, discretizing observation and emission intervals prevent LSTM and BiLSTM from acquiring deeper internal relation of time series and learn rich dynamic of series. As a continuous neural network, NODE can help to solve the above problem of LSTM while building an infinitely  
Figure 1: The structure of LSTM.  
deep network. Both LSTM and BiLSTM structures are used in this paper to test the performance of NODE in electricity load forecasting.
## 3 Q2: What Are the Challenges of the Current UIoT System  
This section describes the UIoT system's challenges, including channel characteristics, technical challenges, security challenges and privacy issues.
# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
## 3 Methodology
### Module 1: Distribution-Based Drift Detection
We then compute a \(replacement\_ratio\in[r_{\text{min}},r_{\text{max}}]\) using linear interpolation:  
\[replacement\_ratio=r_{\text{min}}+(r_{\text{max}}-r_{\text{min}})\cdot\text{ drift\_score}\]  
In the implementation, these values are set as \(r_{\text{min}}=0.1\) and \(r_{\text{max}}=0.6\). This ensures that the model is updated incrementally. The lower bound \(r_{\text{min}}=0.1\) is chosen to maintain a minimal level of incremental adaptation. Even in the absence of statistically significant drift, small updates allow the model to gradually incorporate new patterns from evolving data, supporting smooth incremental learning. The upper bound \(r_{\text{max}}=0.6\) prevents excessive forgetting and model instability. Replacing more than 60% of base detectors at once could lead to overreaction to temporary fluctuations or noise in the data stream. By capping the update intensity, the model is able to retain sufficient historical knowledge and maintain robustness across consecutive windows. The full procedure is summarized in Algorithm 1. ```
1:Input: Base window \(T_{b}\), current window \(T_{j}\), minimum and maximum replacement ratios: \(r_{\text{min}},r_{\text{max}}\)
2:Output:\(replacement\_ratio\)
3:Compute \(p_{\text{KS}}\leftarrow\) KS test between \(T_{b}\) and \(T_{j}\)
4:Compute \(p_{\text{MWU}}\leftarrow\) MWU test between \(T_{b}\) and \(T_{j}\)
5:Compute drift score: \[drift\_score\leftarrow\frac{(1-p_{\text{KS}})+(1-p_{\text{MWU}})}{2}\]
6:Compute replacement ratio: \[replacement\_ratio\gets r_{\text{min}}+(r_{\text{max}}-r_{\text{min}}) \cdot drift\_score\]
7:returnreplacement ratio
```  
**Algorithm 1** Distribution-Based Drift Detection (Module 1)
###### Abstract  
Complex underground conditions make it challenging to conduct extensive coring, and it is difficult for laboratories to carry out a large number of rock mechanics experiments due to the limited number of cores. Rock-like specimens are commonly used in the laboratory to replace coal-rock specimens. In this paper, rock-like specimens with different proportions are produced to study the mechanical properties, failure characteristics, and acoustic emission laws of rock-like specimens under uniaxial compression. The results show that when the rock-like specimen does not contain gypsum, the stress of the specimen decreases rapidly after reaching the peak stress, which is similar to the mechanical properties of hard brittle rock. When the rock-like specimen contains gypsum, the stress of the specimen decreases slowly after reaching the compressive limit, and the failure of the specimen is gentle, which is similar to the mechanical properties of soft rock. When the specimen lacks gypsum, the strength of the rock-like specimen is larger, and the strength of the specimen is positively correlated with the proportion of cement. When the specimen contains gypsum, the strength of the rock-like specimen decreases sharply, and the strength of the rock-like specimen is negatively correlated with the proportion of gypsum. The maximum acoustic emission ringing count increases with higher cement content but decreases with increased gypsum content. The cumulative count change of acoustic emissions approximately underwent four stages, aligning well with the four stages of uniaxial compression failure observed in typical rock. The research results have important reference value for the selection of rock-like materials to replace the original rock materials for laboratory research.  
uniaxial compression; rock-like; mechanical properties; failure characteristics; acoustic emission
# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
## 4 Results
### Comparision of Two Studied Provinces  
The modeling accuracy of Jilin Province is higher than that of Liaoning Province. The R\({}^{2}\) of the four yield estimation models in Jilin Province ranged between 0.7680 and 0.8201, the CCC value ranged between 0.9093 and 0.9761, and the MARE value ranged between 0.0925 and 0.0961. The R\({}^{2}\) of the four yield estimation models in Liaoning Province ranged between 0.7075 and 0.7466, the CCC value ranged between 0.8435 and 0.8918 and the MARE values ranged between 0.1128 and 0.1326. This is likely because the stability of the technical yield in Jilin Province is conducive to ensuring the modeling accuracy. The contribution of the technical yield to the total yield is usually greater than that of the climatic yield. A good prediction of the technical yield can ensure the accuracy of yield estimations. In our study, the stability of the technical yield in Jilin Province is higher than that in Liaoning Province (Figure 12). If we only use the technical yield as the input variable to build the yield estimation model, the R\({}^{2}\), CCC, and MARE are 0.5679, 0.7319, and 0.1257 in Jilin Province and 0.3570, 0.4990, and 0.1996 in Liaoning Province, respectively.  
Figure 11: The cumulative number of times that variables were selected as one of the input variables for building models 1–4.  
The contribution of agrometeorological indexes and remote sensing parameters to improving yield estimation accuracy in Liaoning Province is greater than that in Jilin Province (Figure 10). In Jilin Province, after adding agrometeorological indexes and remote sensing parameters, the accuracy of modeling is not significantly improved; R\({}^{2}\) increases between 0.0944 and 0.2522, CCC increases between 0.0964 and 0.2442, and MARE decreases between 0.0103 and 0.0314. In Liaoning Province, after adding agrometeorological indexes and remote sensing parameters, the accuracy of modeling is significantly improved; R\({}^{2}\) increases between 0.1948 and 0.3784, CCC increases between 0.1378 and 0.3794, and MARE decreases between 0.0279 and 0.0749. The above results show that for areas with a low technical yield stability (i.e., large fluctuations in the climate yield), adding agricultural climate factors has great benefits in improving the modeling.
## 2 Materials and Methods
### Test and Parameter Identification  
A PEBC05-500 tester connected with both the LSB and LIB for the experiments is depicted in Supplementary Figure S5. The experiment started at a full-charge state and was  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **LSB Pack** & **NCM LIB Battery Pack** \\ \hline Cell Nominal Capacity & 3.3 Ah & 50 Ah \\ Cell Weight & 74.956 g & 961.77 g \\ Cell Size & 90-60-6.36 (mm) & 28.7-148.6-103.7 (mm) \\ Pack Nominal Energy & 60 kWh & 57.6 kWh \\ QTY of Serial cells & 196 Serials & 96 Serials \\ QTY of Parallel cells & 44 Parallel & 3 Parallel \\ Pack Nominal Voltage & 400 v & 384 v \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the technical parameters of the LSB and LIB NCM battery packs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Basic Parameter** & **Unit** & **Value** \\ \hline Curb Weight & kg & 1610 \\ Maximum Mass & kg & 1610 + 375 \\ Maximum windward area & m\({}^{2}\) & 2.26 \\ Rolling resistance coefficient & / & 0.0132 \\ Air resistance coefficient & / & 0.331 \\ Wheel radius & m & 0.308 \\ Gravitational acceleration & m/s\({}^{2}\) & 9.8 \\ Maximum torque of e-motor & N-m & 245 \\ Maximum motor speed & r/min & 8950 \\ Final ratio & / & 7.816 \\ Transmission mechanical efficiency & / & 0.96 \\ Moment of inertia of the wheel & kg-m\({}^{2}\) & 3.45 (experience value) \\ Motor inertia & kg-m\({}^{2}\) & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vehicle’s basic parameters.  
Figure 2: Design of lab-cell-result-based battery pack configuration: (**a**) LSB pack configuration; (**b**) LIB NCM battery pack configuration.  
carried out by applying various discharge currents to measure the voltage until the terminal voltage dropped below the cutoff voltage. The NCM LIB and LSB voltage platforms were 2.5-4.5 V and 1.5-2.6 V, respectively. Accordingly, the parameters of the cells including OCV and r\({}_{0}\) at room temperature were recorded at different SOC levels. The test results are shown in Figure 3. It is noted that Figure 3d exhibits a sudden increase in resistance from 40 to 70% SOC for LSB. Theoretically, a battery internal resistance is the sum of the liquid phase (electrolyte) and solid phase (electrode) resistance. The sudden increase in resistance is very likely to result from the poor conductivity of the sharply increased various charge products including intermediates (Li\({}_{2}\)S\({}_{\kappa}\), x = 1-8) [27]. However, when SOC becomes deeper, the high order Li\({}_{2}\)S\({}_{\kappa}\) forms with better conductivity, decreasing the resistance. Similar resistance-vibration phenomena have been reported in previous reported works [18]. Furthermore, Figure 3b,d shows that the internal resistance of LSB was much higher than that of LIB, which apparently can be ascribed to the same reason as discussed above. Nevertheless, it is noteworthy that the testing was conducted on a 3 Ah LSB battery cell and 50 Ah LIB cell, so an accurate resistance difference between two kinds of batteries should be obtained after configuring the cells to battery packs with comparable energy.
# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
## 5 Results and Analysis
### Image Data
#### 5.2.2 Access  
After all the remote sensing image data are inserted into MongoDB and PostgreSQL, the access experiments can be conducted. The average access time with two databases can be computed through multiple experiments with Formula (1), which is chosen for comparison in this experiment. The experimental results are displayed in Figure 8.  
Figure 7: The comparison of the image data storage time with different databases.  
Figure 8 shows that, as the remote sensing image data amount grows, there is a growing trend in the time required to access data in both MongoDB and PostgreSQL, while the growth of the PostgreSQL database is relatively faster. In addition, when accessing the same image data file, PostgreSQL consumes more time than MongoDB. For instance, when accessing the data \"LT05_L1 GS_123046_\", the time that PostgreSQL takes is two point eight times longer than that of MongoDB. Thus, MongoDB performs better than PostgreSQL in accessing large remote sensing image data.  
Compared with the method proposed by [PERSON] and [PERSON] [16], which managed the LiDAR point cloud data in the MongoDB database with two shard key strategies including \"files_id\" and \"n\", we used the image data in the same file size as the former to conduct the comparison experiments. Here, we label the method proposed in this paper as \"Method 1\" and the other as \"Method 2\". The experimental results are presented in Figure 9.  
As can be observed from Figure 9, no matter which shard key is applied, Method 1 consumes less time in accessing data than the other method. For instance, compared with Method 2, the data access time with \"files_id\" decreases by 60.9%, 52.9%, 39.7%, 31%, 58.6%, and 69.4%, respectively,  
Figure 8: The comparison of the image data access time with different databases.  
Figure 9: The comparison of the image data access time with different shard keys between the two methods. (**a**) “files_id”; (**b**) “n”.  
under different data sizes, which indicates that the performance of Method 1 outweighs that of Method 2.