esa-sceva/satcom-chunk-collection huggingface dataset:
# Accumulative Roll Bonding--A Review
## 3 Parameters
### Rolling Parameters
#### 3.3.2 Rolling Temperature  
ARB was firstly performed at elevated temperatures [10], however, it is currently being carried out at room temperatures as well. Bonding quality is usually affected directly by temperature, i.e., increasing temperature improves bond strength through increasing diffusion layer thickness across the interfaces as shown in Figure 5a for ARBed Al/Ti samples. The rolling temperature significantly affects bonding strength and overall strength. Although the aim is to increase overall strength using temperature, it can also reduce the overall strength by activating factors such as recovery and recrystallization. Despite the fact that recovery is unavoidable at low temperatures, the rolling must be performed below the recrystallization temperature (i.e., below the half of melting point) avoiding the elimination of beneficial effects of accumulated plastic deformations. It should be kept in mind that if the other involving parameters, such as material and reduction percentage, cause a high quality bonding between the sheets without increasing the temperature, the final strength will be greater due to the reduction of the aforementioned deteriorating factors.  
Figure 4: Optical microscope (OM) images of the Ti **(a-c)** and Cu **(d–f)** peeled surfaces, roll-bonded at the different thickness reductions of 44% **(a,d)**, 5% **(b,e)** and 62% **(c,f)**[44].  
Figure 5: _Cont._  
[PERSON] et al. [42] studied the peeling behavior of ARBed Al sheets where an increase in ARB temperature leads to an increase in bond strength in term of toughness (Figure 5b). They emphasized that a continuous elevation in bond strength does not guarantee a continuous improvement in overall strength; i.e., performing ARB at higher temperatures (especially above recrystallization temperatures) may lead to a reduction in final strength. [PERSON] et al. [48] reported the effect of rolling temperature on final strength of ARBed AA8011 where they showed that after the strains of -4.8 at room temperature, the ultimate tensile strength (UTS) of ARBed samples started to decrease due to dynamic recovery. For HT-ARB specimens preheated at 200 \({}^{\circ}\)C before each cycle, dynamic recrystallization and static and dynamic recovery may cause a reduced UTS at lower strains and also lower strength compared to those samples rolled at ambient temperature. [PERSON] et al. [46] also observed a reduction in the final strength of ARBed AZ31 alloy with increased rolling temperature (Figure 5c). Also, [PERSON] and [PERSON] [47] studied both aspects of the rolling temperature effects on mechanical strength wherein the bending behavior of ARBed Al/AZ31 sheets rolled at different temperatures was examined. As shown in Figure 5d increasing the rolling temperature from 250 to 300 \({}^{\circ}\)C enhanced the bending strength; however, further increase to 350 and 400 \({}^{\circ}\)C resulted in a reduction due to the recrystallization and microstructural refinement.
# RUL Prediction of Rolling Bearings Based on a DCAE and CNN
## 4 Prediction of the RUL Based on 1D-CNN
#### Generalization Performance Test  
A good intelligent diagnosis model must have a good generalization ability. For the data other than the learning set with the same law, the trained network can also give a suitable output. Two conditions were constructed for the purpose of assessing the generalization ability of the proposed life prediction scheme: (1) selecting a small sample of data to test whether the model could learn valuable features from a relatively small amount of data and (2) variable working conditions to asses the performance of the model in the new data without changing the model's structural parameters.  
Four bearings (Bearing2_1, Bearing2_2, Bearing2_3 and Bearing2_4) in the PHM2012 dataset under working condition 2 were selected for training the model in accordance with the scheme proposed in this paper. Figure 16 depicts the final prediction effect of the four bearings. The original vibration data, HI curve and life prediction effect diagram are all included in each figure. As can be observed, the model could likewise accurately predict the remaining life of the bearing and deduce the bearing degradation trend under variable working conditions. This shows that the model had good generalization performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Evaluation Index** & **Bearing1\_3** & **Bearing1\_5** & **Bearing1\_6** \\ \hline \multirow{3}{*}{HI–Method} & MAE & 0.037 & 0.103 & 0.063 \\  & RMSE & 0.054 & 0.068 & 0.083 \\  & Score & 0.686 & 0.586 & 0.487 \\ \hline \multirow{3}{*}{Method 2} & MAE & 0.083 & 0.184 & 0.153 \\  & RMSE & 0.073 & 0.099 & 0.121 \\  & Score & 0.374 & 0.311 & 0.366 \\ \hline \multirow{3}{*}{Method 3} & MAE & 0.091 & 0.213 & 0.237 \\  & RMSE & 0.087 & 0.121 & 0.131 \\ \cline{1-1}  & Score & 0.469 & 0.441 & 0.377 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of bearing RUL prediction models.  
Figure 16: _Cont._
## 3 Modeling and Control Strategy Design
### ECMS Problem Formulation
#### 3.3.2 NVH Constraint  
In SHEVs, the engine is decoupled from the drivetrain; ideally, it can operate at any power and speed, and there is no direct relation between the engine's operating condition and the vehicle's driving condition. Based on the optimal control strategy, which only considers the fuel economy, it is quite possible that the engine will run at high loads when the vehicle speed is low or the required power is low. This brings in an NVH issue and results in relatively bad drivability.  
In this study, an NVH constraint is set to limit the engine-generator set's output power at different vehicle speeds and pedal positions. The maximum allowed power, \(p_{NVH}\), under different driving conditions was calibrated during real-world driving based on the driver's intentions. The values of \(p_{NVH}\) are shown in Figure 6. When the vehicle speed is low, \(p_{NVH}\) is also relatively low; however, as the driver presses the accelerator pedal harder, higher power is subjectively expected; thus, the constraint is looser. As the vehicle's speed increases, wind and road noise increasingly suppress the engine noise; therefore, a larger \(p_{NVH}\) is allowed at a higher vehicle speed.
## 3 Results and Discussion
### Transient Stability Assessment  
The security condition of the OCs is labeled with a heuristic transient stability criterion based on the angle coherency. When the difference between rotor angles of any two generators is greater than a heuristic threshold during the post-disturbance period, the sample is labeled \"unstable\". Otherwise, the sample is labeled \"stable\". The threshold is usually set to \(\pi\)[13] or \(2\pi\)[18]. The latter is used in this paper.  
Three-phase to ground fault on \(AC29\) connecting Bus 19 and Bus 30 is set as one of the pre-defined faults and is taken as an example to be tested. Sufficient OCs are collected considering the variation of the power system. According to the feature selection stated in the previous section, 120-dimension features are collected to the knowledge base. The perceptron structure of the MLP model is set as [500-100-50-10] empirically. The total number of data points is 4415, 2640 of which are stable, and the rest are unstable. Eighty percent of the data are selected randomly for model training, and the rest are used for testing. The adaptive strategy is adopted in the learning process. The initial learning rate is 0.001. Each time two consecutive epochs fail to decrease training loss by at least 0.0001, the current learning rate is divided by 5. In the experiment, the training time of the model is 9.312 s, while the prediction time is within several milliseconds. The accuracy rate is 98.1%, the false-positive rate is 3.1%, and the false-negative rate is 1.13%. The model has a fast prediction speed, high accuracy rate, low false-negative rate, and low false-positive rate. Thus, the model is suitable for real-time TSA.
# TSKT-ORAM: A Two-Server \(k\)-ary Tree Oblivious RAM without Homomorphic Encryption +
## 5 Final Construction: TSKT-ORAM
### Storage Organization  
The data storage of each server is organized as a \(k\)-ary tree, with height \(H_{k}=\lceil\frac{\log N+1}{\log k}\rceil\), where \(k\) is a power of two. As shown in Figure 3, each node on the \(k\)-ary tree, called a k-node, can be mapped to a binary subtree with \(k-1\) nodes called b-nodes; also, each k-node contains the following data structures:  
* Data array (DA): a data container that stores \(3c(k-1)\) data blocks, where \(c\) is a system parameter. As \(c\) gets larger, the failure probability of the scheme gets smaller, and meanwhile, more storage space gets consumed; hence, an appropriate value should be picked for \(c\). As demonstrated by the security analysis presented later, when \(c=4\), the failure probability can be upper-bounded by \(2^{-\lambda}\). Therefore, we use four as the default value of \(c\).
* Encrypted index table (EI): a table of \(3c(k-1)\) entries recording the information for each block stored in the DA. Specifically, each entry is a tuple of format \((ID,lID,bnID)\), which records the following information of each block:
* \(ID\): ID of the block;
* \(lID\): ID of the leaf k-node to which the block is mapped;
* \(bnID\): ID of the b-node (within \(u_{l,i}\)) to which the block logically belongs. In addition, the EI has a ts field, which stores a time stamp indicating when this k-node was accessed the last time.  
Figure 3: _Cont._  
For example, k-node \(u_{0,0}\) in Figure 3a is mapped to the binary subtree with \(v_{0,0}\) as the root, and \(v_{1,0}\) and \(v_{1,1}\) as leaves in Figure 3b. This way, the physical \(k\)-ary tree can be treated as a logical binary tree. Note that all the b-nodes within the same k-node share the storage space (i.e., DA).
# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 2 Materials and Methods
### Data Pre-Processing  
This study classifies the land cover types of the study area into 6 categories according to the Classification of Land Use Status Quo [31]: farmland (paddy field and dry land), woodland (natural woodland, artificial gardens and shrubland, etc.), grassland (natural or semi-natural herbaceous vegetation), bare land (natural bare land and uncultivated land after harvest), artificial surfaces (urban and rural residential sites, industrial mines and roads, etc.) and water bodies (rivers and lakes, etc.), as shown in Table 2.  
At the same time, FY-4B/AGRI Level1 data, FY-4B/AGRI operational precipitation product, topographic data, land cover type data and GPM IMERG product are matched in time and space to establish spatio-temporal matching dataset.  
Based on the space-time of the GPM IMERG product, the average value of FY-4B/AGRI Level1 data is matched to FY-4B/AGRI operational precipitation product in the corresponding period ((1) time matching). For example, the GPM IMERG product in 0900 UTC-0930 UTC corresponds to the average value of two satellite images in this period. Next, the nearest satellite pixel within 4 km is searched ((2) spatial matching of satellite data). Thirdly, data within 4 km are searched and the mean value is taken to represent the central point information ((3) spatial matching of topographic data). Lastly, the data within 4 km are searched and the mode is taken to represent the central point information ((4) spatial matching of land cover type data). Since the channels of visible and near-infrared wavelengths are not available at night, the dataset is divided according to the solar zenith angle (SZ) into the daytime dataset (SZ \(<\) 85\({}^{\circ}\)) and the night dataset (SZ \(\geq\) 85\({}^{\circ}\)) [32].
## 7 Conclusions
**Abbreviations**  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project  
5G  Fifth Generation  
5G-NR 5G-New Radio  
5G-PPP 5G Infrastructure Public Private Partnership  
6G  Sixth Generation  
B5G  Beyond Fifth Generation  
BS  Base Station  
AN  Access Network  
CAPEX  Capital Expenditure  
CPU  Central Processing Unit  
C-RAN  Centralized-Radio Access Network  
DL  Downlink  
E2E  End-to-end  
eMBB  Enhanced Mobile Broadband  
ETSI  European Telecommunications Standards Institute  
FPGA  Field Programmable Gate Array  
FW  Firewall  
gNB  gNodeB  
GPU  Graphics Processing Unit  
ICT  Information and Communications Technology  
IDPS  Intrusion Detection/Prevention System  
LCM  Lifecycle Management  
MANO  Management and Orchestration  
MEC  Multi-Access Edge Computing  
MILP  Mixed Integer Linear Program  
mMTC  Massive Machine Type Communication  
NAT  Network Address Translation  
NFV  Network Function Virtualization  
OPEX  Operational Expenditure  
OSI  Open System Interconnection  
QoS  Quality of Service  
RAN  Radio Access Network  
RB  Resource Block  
RF  Radio Frequency  
SINR  Signal to Interference plus Noise Ratio  
State-of-the-Art  SoA  
SC  Small Cell  
SDN  Software Defined Networking  
SDO  Standards Developing Organization  
SFC  Service Function Chain  
TM  Traffic Monitor  
uRLLC  Ultra Reliable Low Latency Communication  
UE  User Equipment
# Implementing AutoML in Educational Data Mining for Prediction Tasks
## 2 Related Work
### Related Work on Predicting Student Grade
In order to predict final grades in a distance learning course, the authors proposed a Multi-Scheme Semi-Supervised Regression Approach (MSSRA) employing RF and a set of three k-NN algorithms as the base regressors. A plethora of features related to students' characteristics, academic performance and interactions within the learning platform throughout the academic year formed the training set. The results indicated that the proposed algorithm outperformed typical classical regression methods. Finally, a recent study utilized eight familiar supervised learning algorithms - LR, Random Forests (RF), Sequential Minimal Optimization algorithm for regression problems (SMOreg), 5-NN, M5 Rules, M5, Gaussian processes (GP), Bagging - for predicting students' marks [35]. The training data contained selected demographic variables, students' first semester grades along with the number of examination attempts per course. The reported results seem rather satisfactory, ranging from 1.217 to 1.943. It was observed that RF, Bagging and SMOreg took precedence over the other methods.
## 4 Results and Discussion
### Results
#### 4.1.3 Stacking Classifier  
In this model, the accuracy result for the CIC-MalMem-2022 dataset was 99.98%. According to the confusion matrix in Figure 18, the model correctly predicted 5808 malicious and 5803 benign instances. However, one malicious instance and one benign instance were incorrectly predicted. In the CIC-IDS-2018 dataset, the model achieved a 99.61% accuracy, and per the confusion matrix in Figure 19, 2083 malicious instances were correctly predicted, whereas 4427 benign instances were correctly predicted. However, the model predicted 4 malicious instances as benign and 23 benign instances as malicious. For the CIC-IDS-2017 dataset, the accuracy rate was 99.86%, and based on the confusion matrix in Figure 20, the model predicted only two malicious samples as benign. Figure 21 indicates the learning curve for the CIC-MalMem-2022 dataset. According to Figure 21, the training score increases as the number of training examples increases. As the model receives more examples of the data, it becomes more knowledgeable about the data. As a result, the cross-validation score also increases, indicating that the model does not overfit the training data. In this case, a good sign is that the model learns the data effectively and can also generalize to new data. The Stacking learning curve in Figure 22 for the CIC-IDS-2018 dataset shows that the training and cross-validation scores increase as the number of training examples increases. By seeing more examples, the Stacking model can better understand the data; the cross-validation score eventually plateaus around 95.51%, whereas the training score continues to increase, reaching 100%. The CIC-IDS-2017 learning curve in Figure 23 shows that the training score increases with the increase in the number of training examples and cross-validation. A training score of 99.66% is achieved and continues to increase, reaching 99.85%; a cross-validation score of 99.25% is achieved and continues to increase, reaching get 99.81%. This model's passthrough hyperparameter can be set to \"True\" or \"False\". In the CIC-MalMem-2022 dataset, the best parameter is \"True\", with a mean test score of 99.94% and a mean test time of 0.640 s. The best parameter for the CIC-IDS-2018 dataset is \"False\", with a mean test score of 99.38% and a mean test time of 0.45 s. The best parameter value for the CIC-IDS-2017 dataset is \"True\", with a mean test score of 99.30% and a mean test time of 0.414534 s.  
Figure 18: Confusion matrix for Stacking classifier on CIC-MalMem-2022 dataset.  
Figure 19: Confusion matrix for Stacking classifier on CIC-IDS-2018 dataset.  
Figure 20: Confusion matrix for Stacking classifier on CIC-IDS-2017 dataset.  
Figure 19: Confusion matrix for Stacking classifier on CIC-IDS-2018 dataset.
## 5 Simulation Case Analysis
### Validation of Asymmetrical Case Identification Results  
In the actual operating conditions of energy storage systems, it is essential to consider not only symmetrical faults but also asymmetrical faults. In this section, we describe identification experiments conducted under FP, MP, and SP levels in both charging and discharging modes, where voltage sags to 0%Un and 50%Un and swells to 130%Un are applied. For LVRT validation, an asymmetrical FP condition with a voltage sag to 20%Un was tested under both charging and discharging modes. To verify identification accuracy under diverse scenarios, HVRT validation was performed using asymmetrical FP conditions with a voltage swell to 125%Un under charging mode and 120%Un under discharging mode. The parameter identification results for asymmetrical scenarios are listed in Tables 8-12 and compared with the measured data in Figures 10-13.  
Figure 12: CH: comparison of simulated and measured results for asymmetrical HP voltage swell to 125%Un: (**a**) positive-sequence voltage; (**b**) active power; (**c**) reactive power; (**d**) active current; (**e**) reactive current.  
Figure 13: DH: comparison of simulated and measured results for asymmetrical HP voltage swell to 120%Un: (**a**) positive-sequence voltage; (**b**) active power; (**c**) reactive power; (**d**) active current; (**e**) reactive current.
# Frost Resistance Prediction of Concrete Based on Dynamic Multi-Stage Optimisation Algorithm
###### Abstract  
Concrete in cold areas is often subjected to a freeze-thaw cycle period, and a harsh environment will seriously damage the structure of concrete and shorten its life. The frost resistance of concrete is primarily evaluated by relative dynamic elastic modulus and mass loss rate. To predict the frost resistance of concrete more accurately, based on the four ensemble learning models of random forest (RF), adaptive boosting (AdaBoost), categorical boosting (CatBoost), and extreme gradient boosting (XGBoost), this paper optimises the ensemble learning models by using a dynamic multi-stage optimisation algorithm (DMSOA). These models are trained using 7090 datasets, which use nine features as input variables; relative dynamic elastic modulus (RDEM) and mass loss rate (MLR) as prediction indices; and six indices of the coefficient of determination (R\({}^{2}\)), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (CC), and standard deviation ratio (SDR) are selected to evaluate the models. The results show that the DMSOA-CatBoost model exhibits the best prediction performance. The R\({}^{2}\) of RDEM and MLR are 0.864 and 0.885, respectively, which are 6.40% and 11.15% higher than those of the original CatBoost model. Moreover, the model performs better in error control, with significantly lower MSE, RMSE, and MAE and stronger generalization ability. Additionally, compared with the two mainstream optimisation algorithms (SCA and AOA), DMSOA-CatBoost also has obvious advantages in prediction accuracy and stability. Related work in this paper has a certain significance for improving the durability and quality of concrete, which is conducive to predicting the performance of concrete in cold conditions faster and more accurately to optimise the concrete mix ratio whilst saving on engineering cost.  
concrete; relative dynamic elastic modulus; mass loss rate; ensemble learning; dynamic multi-stage optimisation algorithm 2025  
4
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
## 6 LoRa Performance: Using Error Correction and Filtering
### LoRa with Forward Error Correction and Filtering
#### 6.1.1 One to Many Mode  
Analysis of Figure 16 highlights the performance of different coding rates in OtM mode within an industrial channel equipped with four receivers. Channel coding gain fluctuation is influenced by the coding rate used, with the 4/8 rate standing out as clearly superior to the 4/5 rate in all the configurations considered. The LOS configuration is particularly revealing: even with a signal-to-noise ratio as low as \(-\)10 dB, packets are received without error with a spreading factor of 12. When this factor is 7, this result is reproduced with a signal-to-noise ratio of 1 dB. The contrast is striking between the gains associated with the two coding rates in question in this configuration: over 7 dB for the 4/8 rate versus less than 3 dB for the 4/5 rate. This is because the 4/5 coding rate only enables error detection, whereas 4/8 also enables error correction [36]. This observation highlights the importance of choosing the right coding rate, especially in industrial contexts where transmission reliability is paramount.  
Furthermore, the detailed analysis in Figure 17 highlights the performance of coding rates in the NLOS (non-line-Of-Sight) configuration. This configuration, combined with the inherent challenges of potential obstacles interfering with signal transmission, calls for judicious technical choices. The 4/8 code rate is clearly the optimal choice, offering a robust gain of over 7 dB for spreading factors 12 and 7. On the other hand, the 4/5 coding rate, although a high-performance choice, offers a significantly lower gain, not exceeding 2 dB for the same factors. These observations are reinforced by an examination of the signal-to-noise ratios. For rate 4/5, packets are correctly received with ratios of 15 dB and 28 dB for spreading factors of 7 and 12, respectively. However, rate 4/8 still stands out with ratios of 10 dB and 22 dB, guaranteeing flawless transmission for these same spreading factors. Thus, Figure 17 clearly illustrates the advantage of the 4/8 rate, particularly under NLOS conditions, offering essential insights for anyone seeking to optimize data transmission in complex environments.  
Figure 15: LoRa architecture with forward error correction.