esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### EHTC Simplified Calculation Model  
#### 2.3.1 Calculation Principle  
The external building surface in the pluvial region is often wet due to the influence of rainfall, and the liquid water on the surface must transfer heat by evaporation. On wet surfaces, the water mass flow of evaporation can be calculated by the following formula [39]:  
\[m_{water}=h_{d}(C_{\rm w}-C_{\rm a})=\frac{h_{d}}{R_{water}}\bigg{(}\frac{P_{ \rm w}}{T_{\rm w}}-\frac{P_{\rm a}}{T_{\rm a}}\bigg{)} \tag{13}\]  
Putting the Lewis relation \(h_{c}/h_{d}=\rho c_{\rm p}Le^{2/3}\) into Equation (13), Equation (13) can be converted to Equation (14):  
\[m_{water}=\frac{h_{c}}{R_{water}\rho c_{\rm p}Le^{\frac{2}{3}}}\bigg{(}\frac{P_ {\rm w}}{T_{\rm w}}-\frac{P_{\rm a}}{T_{\rm a}}\bigg{)} \tag{14}\]  
where \(Le\) is the Lewis number, which can be determined as 0.857 when water vapor diffuses through the air [30].  
For the partial pressure of water vapor, it can be obtained by the following relationship:  
\[p=\varphi p_{s} \tag{15}\]  
where \(p_{\rm s}\) is the saturated vapor pressure (Pa), which can be calculated in the following form [40]:  
\[p_{s}=3385.5\exp\left[-8.093+0.936(t+42.61)^{1/2}\right] \tag{16}\]  
By equalizing the evaporative heat transfer to the Newton cooling formula, the following relation can be found:  
\[m\times r=h_{c}(T_{\rm w}-T_{\rm a}) \tag{17}\]  
where \(r\) is latent heat of water (KJ/kg), which can be calculated by the formula below:  
\[r=2497.848-2.324t_{a} \tag{18}\]  
If \(\beta\) is defined as \(h_{e}/h_{c}\), then \(\beta\) can be expressed as Equation (19):  
\[\beta=\frac{r}{R_{water}\rho c_{\rm p}Le^{\frac{2}{3}}(T_{\rm w}-T_{\rm a})} \bigg{(}\frac{P_{\rm w}}{T_{\rm w}}-\frac{P_{\rm a}}{T_{\rm a}}\bigg{)} \tag{19}\]  
Therefore, \(\beta\) can be calculated by merely measuring \(T_{\rm w}\), \(T_{\rm a}\), \(\varphi_{w}\), and \(\varphi_{a}\), and then the evaporative heat transfer coefficient can be acquired by combining with CHTC.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(t_{\rm m}/^{\circ}\)C & 40 & 37.5 & 35 & 32.5 & 30 & 27.5 \\ \hline \(T_{\rm m}/\)K & 313 & 310.5 & 308 & 305.5 & 303 & 300.5 \\ \hline \(\theta/\)K\({}^{3}\) & 1.23 & 1.20 & 1.17 & 1.14 & 1.11 & 1.09 \\ \hline RHTC/[W/m\({}^{2}\cdot\)K] & 5.91–6.61 & 5.77–6.45 & 5.63–6.30 & 5.50–6.14 & 5.36–5.99 & 5.23–5.85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Radiation heat transfer coefficients (RHTCs) under different average temperatures.
# A Compound Approach for Monitoring the Variation in Wind Turbine Power Performance with SCADA Data
## 5 Conclusions  
In this study, a data-driven modeling method is proposed for wind turbine power performance trends monitoring using SCADA data. The method integrates a KPCA approach for characteristic dimensionality reduction and an AdaBoost enhanced regressor for power prediction, which allows for the incorporation of a sufficient number of inputs, including both basic and derived variables, into the model and somehow enhances the prediction performance. The experimental results indicate that the proposed method outperforms traditional approaches like LinearRegression on the test datasets, as it generates more  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Turbine Label** & **Prior \(K_{T}\)** & **Posterior \(K_{T}\)** & \(K_{T}\)**Increase** & **Uncertainty Error** \\ \hline A28 & 0.26\% & 5.23\% & 4.97\% & 0.17\% \\ A30 & \(-\)0.15\% & 4.45\% & 4.60\% & \(-\)0.2\% \\ A32 & 0.08\% & 5.46\% & 5.28\% & 0.48\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary table of evaluation results.  
Figure 8: Trends in power performance changes of the modified turbines.  
accurate prediction results with smaller prediction errors. Additionally, a day-oriented deviation indicator is constructed to quantify the performance fluctuations. The results of two industrial cases indicate that the proposed method exhibits outstanding performance in early warnings of blade damage and the quantitative assessment of power performance. In the first case, the proposed method can provide early warning alerts for blade damage faults several weeks in advance. In the second case, it demonstrates higher evaluation accuracy compared to power curve modeling approaches, with an assessment uncertainty within \(\pm\)0.5%. The proposed method establishes advantages in reducing the uncertainty of power generation performance evaluation through sufficient feature construction, decisive data dimensionality reduction, and the reinforcement of learning algorithm, thereby breaking through the dependence on power curves. In future studies, the methodology will be optimized based on more application scenarios to enable broader industrial applications.  
Conceptualization, X.W. and D.L.; methodology, X.W. and L.Z.; software, X.W.; validation, X.W. and C.L.; formal analysis, X.W; investigation, X.W; resources, X.W. and L.Z.; data curation, X.W. and C.L.; writing--original draft preparation, X.W.; writing--review and editing, D.L. and L.Z.; visualization, X.W.; supervision, D.L.; project administration, D.L.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data can be obtained by contacting the authors.  
Author [PERSON] is employed by the company Windey Energy Technology Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Examples of Scheduling Optimization of Apron Support Vehicles  
In this paper, certain apron support is taken as the scheduling optimization object, and a certain type of aircraft at the airport is used as an example for analysis. The average support time of each support vehicle is shown in Table 3. The basic parameters of the multi-layer coding genetic algorithm are set as follows: the population number is 40; the maximum number of iterations is 50; the crossover probability is 0.8; the mutation probability is 0.6, and the specific code is implemented in Matlab. They are simulated in the environment of Windows 10 operating system with Intel(r) Core(tm) i7-11800H CPU 2.30 GHz. The apron support operation in the direct aircraft preparation stage must be completed within 120 min. To ensure the smooth completion of other support operations and shorten the support time to the maximum extent, it is stipulated that the completion time of the three tasks of refueling, oxygenation, and charging shall be controlled within 120 min. Power vans, oxygenation refilling trucks, and refueling trucks are the optimization objects for the operation scheduling of apron support vehicles. The operating positions of the three types of support vehicles at the airport are shown in Figure 3. There is no conflict of operation positions among the support vehicles, and it is ignored. The travel time of the vehicle is guaranteed, thereby satisfying all the previous assumptions. Figure 3: Operation location of each support vehicles. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Vehicle** & **Tractor** & **Refueling Truck** & \begin{tabular}{c} **Oxygenation** \\ **Refilling Truck** \\ \end{tabular} & \
\begin{tabular}{c} **Power Van** \\ **Air-Launched Weapon** \\ \end{tabular} &
\begin{tabular}{c} **Loader of** \\ **Air-Launched Weapon** \\ \end{tabular} \\ \hline Time/min & 10 & 30 & 15 & 10 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average operating time of each support vehicles. It can be seen from Figure 4 that with the increase of the number of iterations, the average _fitness_ value of the population tends to be stable. After 50 iterations, the _fitness_ value of the population converges, and the corresponding shortest times are 90 min, 60 min, 90 min, and 90 min, respectively, indicating that the multi-layer coding genetic algorithm is feasible to solve the optimization problem of vehicle operation scheduling for vehicles with 3 to 6 vehicles on the apron. Figure 4: Change of population _fitness_ value. (**a**) 3 aircraft, (**b**) 4 aircraft, (**c**) 5 aircraft, (**d**) 6 aircraft.
## 4 Experimental Results and Analysis
### Metrics and Parameter Setting
#### 4.2.2 Parameter Settings  
The experiment was conducted in a Windows 10/64 bit/i7 environment using Python 3.7 with PyTorch 1.13.1, NumPy 1.12.5, Pandas 1.2.4, Jieba 0.42.1, and Math libraries. The model parameter settings are shown in Table 3. The optimal values of the _TF-IDF_ threshold and GCN hidden layer size were verified by experiment 4.5, i.e., 0.03 and 128, respectively. In many previous experiments, it was found that the model classification task works best when the number of GCN layers is two. Therefore, we set the number of GCN layers to two, and the dropout ratio, learning rate, and weight decay rate adopt commonly used values: 0.2, 0.05 and 5 \(\times\) 10\({}^{-4}\), respectively. The number of epochs was 1000. The same settings were used for both datasets (Xuzhou and Zhuji).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Enterprise Name** & **Region** & **Major Categories** & **Minor Categories** \\ \hline Huanjiang Water Co., Ltd., Hechi, China & Zhuji & D Electricity, heat, gas, and water production and supply industry & 46 Water production and supply industry \\ \hline Zhejiang Zhengyu Municipal Garden Co., Ltd., Zhengyu, China & Zhuji & E Construction industry & 48 Civil engineering construction industry \\ \hline Zhuji Master Trade Co., Ltd., Zhuji, China & Zhuji & F Wholesale and retail trade & 51 Wholesale trade \\ \hline Zhejiang Jialke Real Estate Co., Ltd., & Zhuji & M Scientific research and technical services & 73 Research and experimental development \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline _TF-IDF_ threshold & 0.03 \\ Hidden layer of GCN & 128 \\ GCN layers & 2 \\ Word embedding dimension & 384 \\ Dropout ratio & 0.2 \\ Learning rate & 0.05 \\ Weight decay rate of Adam optimizer & \(5\times 10^{-4}\) \\ Epoch & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the model.
## 2 Materials and Methods
### Statistical Analysis  
The analysis was performed by first grouping all PDC cases together (Table 1) and then dividing them into groups based on the location of impaction (right, left, or bilateral) (Table 2).  
\begin{table}
\begin{tabular}{l l c c} \hline  & & **Prevalence** & **Percent** \\ \hline \multirow{3}{*}{Gender} & Female & 50 & 73.5 \\  & Male & 18 & 26.5 \\  & Total & 68 & 100.0 \\ \hline \multirow{3}{*}{Ectopic} & Left unilateral & 24 & 35.3 \\  & Right unilateral & 26 & 38.2 \\  & Bilateral & 18 & 26.5 \\  & Total & 68 & 100.0 \\ \hline \multirow{3}{*}{Family history of canine impaction} & Yes & 16 & 23.5 \\  & No & 52 & 76.5 \\  & Total & 68 & 100.0 \\ \hline \multirow{3}{*}{Presence of agenesis} & Yes & 9 & 13.2 \\  & No & 59 & 86.8 \\  & Total & 68 & 100.0 \\ \hline \multirow{3}{*}{Family history of agenesis} & Yes & 1 & 1.5 \\  & No & 67 & 98.5 \\  & Total & 68 & 100.0 \\ \hline \multirow{3}{*}{Conoid or microdontic lateral incisors} & Yes & 23 & 33.8 \\  & No & 45 & 66.2 \\  & Total & 68 & 100.0 \\ \hline \multirow{3}{*}{Other inclusions} & Lateral incisor & 1 & 1.5 \\  & Lower canine & 1 & 1.5 \\  & Premolars & 1 & 1.5 \\  & Molars & 1 & 1.5 \\  & Third molars & 7 & 10.2 \\  & No & 57 & 83.8 \\  & Total & 68 & 100.0 \\ \hline \multirow{3}{*}{Supernumerary teeth} & Yes & 1 & 1.5 \\  & No & 67 & 98.5 \\  & Total & 68 & 100.0 \\ \hline \multirow{3}{*}{Microdontic dentition} & Yes & 1 & 1.5 \\  & No & 67 & 98.5 \\  & Total & 68 & 100.0 \\ \hline \multirow{3}{*}{Crossbite} & Yes & 21 & 30.8 \\  & No & 47 & 69.1 \\  & Total & 68 & 100.0 \\ \hline \multirow{3}{*}{Upper dental midline deviation} & Yes & 27 & 39.7 \\  & No & 41 & 60.3 \\  & Total & 68 & 100.0 \\ \hline \multirow{3}{*}{Growth pattern} & Hypodivergent & 25 & 36.8 \\  & Normodivergent & 30 & 44.1 \\  & Hyperdivergent & 13 & 19.1 \\  & Total & 68 & 100.0 \\ \hline \multirow{3}{*}{Malocclusion} & Class I & 18 & 26.47 \\  & Class II & 25 & 36.76 \\  & Class III & 25 & 36.76 \\  & Total & 68 & 100.0 \\ \hline \end{tabular}
\end{table}
Table 1: Prevalence and descriptive data of the sample (N = 68).  
Data were analyzed using SPSS 15 software. Descriptive statistics included frequency tables for qualitative variables. [PERSON]'s exact test, which is equivalent to the chi-square test, was used to examine relationships between qualitative variables. The chi-square test was also used to test hypotheses regarding population proportions. A significance level of 0.05 was used throughout  
Table 1 and Figure 1 show the prevalence of the studied variables. Among the participants, 73.5% were females (50), and 26.5% were males (18).
# Evaluation of the Large-Length Indoor Standard Installation Based on Single Point Scanning
## 2 Measurement Platform
There was no relative motion between the target mirror and the platform during the aiming of the laser tracker, that is, the optical target mirror remains in the local coordinate system \(\mathrm{o_{1}x_{1}y_{1}z_{1}}\) of the platform;  
Figure 1: (**a**) The CAD structure diagram of the LLISI: 1—Linear guide, 2—Bearing Platform, 3—Target mirror, 4—The Indoor Large-length Standard, 5—Laser interferometer, 6—Laser tracker; (**b**) The physical structure of the LLISI: 1—Linear guide, 2—Bearing Platform, 3—Target mirror, 4—Laser interferometer. 2. The laser tracker was installed at a proper position while ensuring that the optical target mirror was within the measurement range of the laser tracker and the field range of the lens of the laser tracker. The position of the laser tracker remained unchanged during measurement, that is, the measurement coordinate system \(\rm{o_{g}x_{g}y_{g}z_{g}}\) of the laser tracker does not change relative to the earth coordinate system during measurement;
3. The laser tracker was adjusted to aim at the optical target mirror on the multi-functional bearing platform, and the measurement parameters of the laser tracker were set. The automatic tracking mode was enabled. The multi-functional bearing platform was controlled to move in the axial direction x1 of its coordinate system \(\rm{o_{1}x_{1}y_{1}z_{1}}\), and data were collected;
4. The data measured by the laser tracker were the spatial coordinates of the optical target mirror while moving with the multi-functional bearing platform. Since the relative position between the optical target mirror and the multi-functional bearing platform remained unchanged, the motion trajectory of the optical target mirror was that of the multi-functional bearing platform. Through data processing such as fitting and difference, the speed, displacement, and positioning accuracy of the multi-functional bearing platform was obtained.
## 2 Problem Formulation
### Outlier Detection Problem (ODP) and Algorithm (ODA)  
Consequently, the definition of the outlier index has been made as follows:  
**Definition 1**.: _For a set of data \((y)_{k}=(x)_{k}+(o)_{k}+(b)_{k}+(w)_{k}\) for \(k=1,...,n\), where \((y)_{k}\) is measured. The number of data points \(n\) is given. The outlier detection problem (ODP) is to find \(L=(l_{1},...l_{n})\) for which \((o)_{k}\
eq 0\), that is:_  
\[L=\left\{\begin{array}{cc}(l)_{k}=1&(o)_{k}\
eq 0,\\ (l)_{k}=0&\text{elsewhere.}\end{array}\right. \tag{2}\]  
_The index set \(L\) itself does not specify what will happen in case of an outlier being actually detected. It is supposed to be a mere specification of the detection problem. \(\qed\)_  
This contribution is not aimed towards proving some of the intrinsic characteristics of a data cleaning filter for solving ODP. It has already been accomplished in [5]. In any case, more definitions are required for assessing the performance of the filter, which is used with the noise detection. Carrying out of the outlier detection algorithm (ODA) will label the data points within the time series \((y)_{k}\) that are thought to be outliers. This may be represented by the following definition of a mapping \(F\). The map \(F\) becomes a value equal to one, if the considered data point satisfies the criteria, as it was implemented in the Hampel filter in [5].  
**Definition 2**.: _For a set of data \((y)_{k}=(x)_{k}+(o)_{k}+(b)_{k}+(w)_{k}\) for \(k=1,...,n\) where \((y)_{k}\) is measured. The  
This inquiry is principally directed towards the peak noise level evaluation. This is due to the fact that the estimator of the peak noise level reconstructs the noise peaks of the signal and then calculates the variance. In this case, talking about a peak noise level means that the result will be occasionally close to the signal standard deviation, and occasionally closer to the present noise level peak. The aim was to get a robust estimator for a great variety of S/N ratios in the presence of outliers, and then a very accurate estimator for a small range of S/N ratio without outliers. The technique that is considered is based on the wavelets. The local variance of the Lipschitz constant of the signal over a sliding time horizon is evaluated by the implemented algorithm. In case of the local Lipschitz being constant lying outside the computed limit, the outlier is identified. A flow pipe for the Lipschitz constant is graphically produced and if the local Lipschitz constant is situated outside the flow pipe, the data point is marked, and, after that, it is substituted.
## 4 Status Monitoring of Propeller Motor
### Model Comparison  
The experimental data are the SCADA data of a wind farm in China from January 2012 to December 2014. In each round of the learning and verification process, the above-mentioned data preprocessing method was used to build the training and test sets. Among them, the model was trained based on 8000 historical records, and the next 400 records were used as test data. Then, the training and test windows were continuously pushed forward according to the time sequence to implement the learning and verification of the model. In order to ensure that different training sets have some difference, the two consecutive training windows were made to have at least 2000 different samples.  
In the prediction model, the pitch motor temperature was selected as the prediction variable, and the pitch motor torque, wind speed, hub speed (generator speed), generator power, ambient temperature and other variables were input to the model. In the training of the ANFIS model, we used 72 fuzzy rules and a hybrid learning algorithm [17]. NN has three hidden layers. The number of units in each hidden layer is 15, and the activation function of the hidden layer is the sigmoid function. In the NN, the \(L_{2}\) regularization term was also used to prevent overfitting. For the NN with dropout, the dropout rate is 0.5, and the learning rate of the model is 0.05. Similarly, the model has three hidden layers, and the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Record Time** & **Env. Tem.** & **Torque** & **Power** & **Hub Speed** & **Wind Speed** & **Pitch Motor Tem.** \\  & (\({}^{\circ}\)C) & (N-m) & (KW) & (r/min) & (m/s) & (\({}^{\circ}\)C) \\ \hline
1 January 2012 0:00 & \(-2.58\) & 4.95 & 0.05 & \(-3.77\) & 1.00 & 21.03 \\
1 January 2012 0:10 & \(-2.20\) & 4.95 & 0.05 & \(-3.7  
number of units in each hidden layer is 30. For the ESN model, the size of the reservoir is 2000. Each entry in \(\mathbf{W}^{in}\) and \(\mathbf{W}\) obeys the uniform distribution of [\(-1\), 1]. The spectral radius \(\rho(\mathbf{W})\) is 0.8, and the sparsity is the reciprocal of the size of the reservoir. When training the ESN model, the number of the forge points was set as 400. Because the model is more stable by adding white noise coming from the uniform distribution, with the noise level of 0.08 and the shrinkage ratio of \(\alpha\) = 0.2. We have tested the data from two turbines (No. 1 and No. 2). Table 2 summarizes the average prediction results of RMSE and MAPE of each model on 20 non-repeated test sets.  
It can be seen from Table 2 that the prediction performance of ESN is far better than ANFIS, NN, NN + dropout for two turbines. In ANFIS, the number of fuzzy rules is relatively large, which leads to the slow running speed of the model when the hybrid learning algorithm is used. If the backpropagation algorithm is used directly, its accuracy is lower. On the contrary, ESN only needs to calculate the connection weight between the reservoir and the output layer, which is more efficient. At the same time, the prediction results of NN and NN + dropout are similar.
# A Low-Overhead Countermeasure against Differential Power Analysis for AES Block Cipher
## 3 DPA Attack and Our Proposed Countermeasure
### Our Proposed Countermeasure  
To mitigate the DPA attacks, we have proposed our countermeasure using the masking technique. As mentioned earlier in Section 2, AES consists of four functions (i.e., ShiftRows, MixColumns, AddRoundkey and SubBytes). The ShiftRows, MixColumns, and AddRoundkey functions are linear, while a SubBytes function of AES is non-linear. The attack could be employed on both linear and non-linear functions of the AES. We propose a countermeasure for both linear and non-linear functions. Therefore, Boolean masking can be applied easily to the linear functions. The problem arises in the case of a non-linear function, where Boolean masking could not be applied. According to [23; 29], the SubBytes function would be easy to mask through multiplicative masking. Consequently, we employed Boolean masking over the linear functions of AES (ShiftRows, MixColumns, and AddRoundkey), while multiplicative masking is applied to the non-linear function (SubBytes).
# IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management
## 2 Related Works
Additionally, there is ongoing research in developing more advanced autonomous equipment and integrating multiple systems for a more comprehensive approach to precision agriculture. Overall, precision agriculture is a rapidly evolving field, with new technologies and techniques constantly emerging. The state-of-the-art is driven by a focus on improving efficiency, reducing waste, and increasing yields through the use of advanced technologies and data-driven decision making. Our proposed system advances the state of the art in precision agriculture by combining the benefits of both remote sensing and ground-based sensor systems. By using both satellite imagery and ground-based sensors, our system can provide a more comprehensive view of crop health and environmental factors, with greater detail and accuracy than either system alone. Additionally, our system utilizes machine learning algorithms to analyze the data and provide real-time recommendations for crop management, further improving efficiency and yields. Overall, our proposed system offers a more advanced and integrated approach to precision agriculture that can help farmers make more informed decisions and optimize crop production.