esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique
## 3 Proposed Wind Power Curve Model
### Results of Wind Turbine Power Curve Modeling
In terms of the MAE, RMSE, and NMAPE, the proposed hybrid model ranks the highest among all models. However, in terms of \(R^{2}\), BPNN ranks first and the proposed hybrid model ranks second. Moreover, in terms of MAE and NMAPE, all nonparametric models rank higher than all parametric models. In terms of RMSE, except the ANFIS, all nonparametric models perform better than parametric models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Error Indicator**} \\ \cline{3-6}  & & **MAE** & **RMSE** & **NMAPE** & \(R^{2}\) \\ \hline \multirow{9}{*}{C} & 3-PLF & 21.7192 & 34.9560 & 1.4212 & 0.9891 \\  & 4-PLF & 21.7790 & 34.9837 & 1.4251 & 0.9891 \\  & 5-PLF & 19.9222 & 33.2145 & 1.3036 & 0.9902 \\  & 6-PLF & 20.6772 & 34.6191 & 1.3530 & 0.9894 \\  & MHTan & 20.5743 & 34.2099 & 1.3463 & 0.9896 \\  & BPNN & 19.3444 & 32.8101 & 1.2658 & **0.9904** \\  & ANFIS & 19.3400 & 33.7645 & 1.2655 & 0.9899 \\  & ELM & 16.0682 & 26.9427 & 2.1093 & 0.9425 \\  & WELM & 15.8361 & 26.6344 & 2.0788 & 0.9438 \\  & ORELM & 15.9102 & 25.9158 & 2.0885 & 0.9468 \\  & Hybrid & **9.0638** & **16.4979** & **1.1898** & 0.9784 \\ \hline \multirow{9}{*}{D} & 3-PLF & 15.0039 & 19.0627 & 1.8723 & 0.9811 \\  & 4-PLF & 15.0733 & 19.1098 & 1.8810 & 0.9810 \\  & 5-PLF & 10.1637 & 16.9569 & 1.2683 & 0.9850 \\  & 6-PLF & 10.1192 & 16.5730 & 1.2628 & 0.9857 \\  & MHTan & 11.1313 & 17.3347 & 1.3891 & 0.9844 \\  & BPNN & 10.0238 & 16.3810 & 1.2509 & 0.9860 \\  & ANFIS & 11.4776 & 20.5935 & 1.4323 & 0.9779 \\  & ELM & 9.8328 & 16.6137 & 1.2270 & 0.9856 \\  & WELM & 9.8043 & 16.4880 & 1.2235 & 0.9858 \\  & ORELM & 9.7503 & 16.5073 & 1.2167 & 0.9858 \\  & Hybrid & **9.4122** & **16.1494** & **1.1745** & **0.9864** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of all power curve models on test sets of Dataset C and Dataset D.  
The average ranks of all models based on four error indicators as well as the results in Tables 8 and 9 are shown in Figure 5. According to Figure 5, the proposed hybrid model performs the best among all models selected in this paper. Generally, the performance of nonparametric models is better than that of parametric models. However, sometimes 5-PLF is superior to ANFIS in wind turbine power output estimation. For different wind turbines, the estimated wind turbine power curves generated by the proposed hybrid model are presented in Figure 6. It can be seen from Figure 6 that the estimated power curves can fit the data of different wind turbines well. Based on the above analysis, it can be concluded that the proposed hybrid model is an effective technique for wind turbine power curve modeling, and can also be chosen and perform well when there are some outliers in the collected wind data. Figure 4: Averages ranks of different models on all datasets based on four error indicators. Figure 5: Averages ranks of different models based on all datasets and all error indicators.
## 2 Related Work
In [28], the authors integrated spatially coupled low-density parity-check codes in turbo equalization. Better said, the authors proved the performances of the spatially coupled low-density parity-check codes over the additive white Gaussian noise and inter-symbol interference channels in obtaining the target BER. In [29], the authors depicted the configurations of the LDPC codes through one-dimensional (OD) and two-dimensional (TD) ISI channels, which are usually adopted to describe magnetic recording systems. In this sense, LDPC codes have proved their high quality for employment in magnetic recording systems. They also briefly presented the forwards in studying LDPC versions, for example, protograph codes, repeat-accumulate (RA) codes, or spatially-coupled (SC) codes, through the mentioned ISI channels. Based on this work, the authors have created some operative combined detection and decoding procedures for LDPC codes over OD-ISI channels. A scalable FPGA-founded architecture for a Bahl-Cock-Jelinek-Raviv (BCJR) equalizer to cancel the ISI was created in [30]. The authors adopted a turbo BCJR equalizer in connection to a binary forward-error-correction decoder to interchange soft information as extrinsic Log-likelihood ratios in each turbo iteration. The scope of [31] was to prove the performance of interference linear equalizer based on the minimum mean-square error in tandem with Bose-Chaudhuri-Hocquenghem (BCH) codes. The authors compared, in terms of BER, the performances of linear turbo equalization adopting both convolutional and BCH codes.
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 1 Introduction  
### Background  
As low-cost Internet of Things (IoT) devices become increasingly pervasive, their coordination into sensor networks has enabled new applications in industrial and home settings. To this end, Wi-Fi is an essential IoT enabler, facilitating communication between these coordinated devices to allow for data sharing and user interfacing. However, more recently, a new paradigm in wireless sensing has demonstrated that the Wi-Fi signals themselves can be used to sense the physical environment [1, 2]. It has been shown that artificial intelligence (AI) and machine learning (ML) can be applied to analyse Wi-Fi Channel State Information (CSI) in order to monitor environmental occupancy, movement and the presence of intruders [1]. These applications of Wi-Fi sensing have been extended towards more private human features such as hand movements [2] or keyboard inputs [3, 4]. This is creating new security, privacy and trust issues in IoT networks as our cyber information is at risk. To restore the privacy of IoT users, there is growing attention towards the development of defence strategies to prevent unwanted Wi-Fi sensing [5, 6, 7, 8]. In this endeavour, the use of adversarial interference has been beneficial for reducing sensing accuracy and restoringuser privacy as shown in Figure 1. Here, interference signals are injected (green) in order to impede the sensing signals (red). Unfortunately, the existing literature in this domain uses very high interference rates, which is a significant burden to the user in terms of bandwidth loss. Thus, there is a need for further work on interference-based defence techniques in order to improve their spectral efficiency.
## 2 Partial Discharge Detection Using the AE Method and Signal Processing Analysis  
### Piezoelectric Sensors  
The piezoelectric effect occurs in materials that produce an output voltage when subjected to mechanical stress. The reverse effect also occurs, so that a mechanical deformation arises by applying an electrical voltage between two sides of the piezoelectric material [14]. The piezoelectric transducers shown in Figure 1 are AE sensors available at low cost, ranging from a few cents to a few dollars, depending on the size and the manufacturer. Piezoelectric diaphragms are sound components with a simple structure consisting of a piezoelectric ceramic disk adhered to a brass plate, as shown in Figure 1. The ceramic is coated with a metal film, which serves as an electrode [14]. The LDT1-028K is a piezoelectric sensor that detects physical phenomena such as vibration or impact (Figure 1). The piezo film element is laminated to a sheet of polyester (Mylar) and produces a useable electrical signal output when forces are applied to the sensing area. This study compared two types of low-cost piezoelectric transducers, the microfiber composite (MFC) LDT1-028K type and the lead zirconate titanate (PZT). These sensors should be installed on the surface using an acoustic couplant. An acoustic couplant is a material used at the structure-to-sensor interface to improve the transmission of acoustic energy across the interface during AE monitoring.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 6 System Model  
### Simulation Software  
The network simulator 3 (ns-3) is the simulation tool used in this work [39]. It is open-source and, therefore, publicly available for development, education, and research activities. It is also modular, comprised of several models built primarily in C++ with some APIs available in Python. Its development is oriented by technical specifications from standard organizations, such as 3 GPP and IEEE. Besides, it is well documented, with an active and collaborative community. For these reasons, it has been widely adopted for research and is the primary simulation tool for this paper.
# Effects of Roughage on the Lipid and Volatile-Organic-Compound Profiles of Donkey Milk
###### Abstract  
The lipid molecules and volatile organic compounds (VOCs) in milk are heavily influenced by diet. However, little is known about how roughage affects the lipid and VOC contents of donkey milk. Accordingly, in the present study, donkeys were fed corn straw (G1 group), wheat hulls (G2 group), or wheat straw (G3 group), and the lipid and VOC profiles of their milk were determined using LC-MS and GC-MS. Of the 1842 lipids identified in donkey milk, 153 were found to be differential, including glycerolipids, glycerophospholipids, and sphingolipids. The G1 group showed a greater variety and content of triacylglycerglycerol species than the G2 and G3 groups. Of 45 VOCs, 31 were identified as differential, including nitrogen compounds, esters, and alcohols. These VOCs were significantly increased in the G2 and G3 groups, with the greatest difference being between the G1 and G2 groups. Thus, our study demonstrates that dietary roughage changes the lipid and VOC profiles of donkey milk.  
donkey milk; roughage; lipids; volatile organic compounds; LC-MS; GC-MS Article
# How _Pseudomonas nitroreducens_ Passivates Cadmium to Inhib Plant Uptake
## 3 Results and Discussions
### Kinetics of the Biosorption Process  
It is well known that the adsorption rate is closely related to the initial metal ion concentration [35]. Under low-concentration conditions, the adsorption sites rapidly absorb metal ions and quickly reach equilibrium. However, at higher concentrations, the first stage has the strongest adsorption due to the active site on the surface of the adsorbent [36], and the next stage is equilibrium.  
The biosorption kinetics is one of the most important features to describe the biosorption rates [37] and provide the mechanism for the sorption reaction [34]. The experimental data in this study were described by the most popular kinetic models: the [PERSON]'s pseudo-first-order [38] as shown in Equation (3) and the pseudo-second-order [39] as shown in Equation (4).  
\[ln(q_{e}-q_{t})=lnq_{e}-\frac{k_{1}}{2.303}t \tag{3}\]  
\[\frac{t}{q_{t}}=\frac{1}{k_{2}q_{e}^{2}}+\frac{t}{q_{e}} \tag{4}\]  
\(q_{t}\) and \(q_{e}\) (mg/g) are the biosorption capacities at time and equilibrium, respectively.  
\(t\) is the reaction time (min).  
\(k_{1}\) and \(k_{2}\) are the adsorption rate constant of the first- and second-order equation, respectively.  
The present experimental biosorption did not follow the pseudo-first-order kinetic, with an R\({}^{2}\) for 20, 50 and 100 mg/L of 0.9987, 0.6094 and \(-\)0.7128, respectively, so the pseudo-first-order dynamics model diagram is not shown here. However, the pseudo-second-order kinetics provided a good correlation for the biosorption of Cd at all initial concentrations. As shown in Figure 3, \(q_{e}\) values were 15.95, 39.95 and 71.48 mg/g with R\({}^{2}\) values of 0.9999, 0.9995 and 0.9964 for 20, 50 and 100 mg/L, respectively. The \(k_{2}\) indicated that the biosorption rate of 20 mg/L (\(k_{2}\) = 0.1053) was faster than that of 50 mg/L (\(k_{2}\) = 0.0056) and 100 mg/L (\(k_{2}\) = 0.0012). This result suggests that the biological adsorption process was controlled by chemisorption [40].
## 4 Conclusions  
In summary, by utilizing a subwavelength cross-meandering resonator (0.49\(\lambda_{0}\times\) 0.49\(\lambda_{0}\)), power frequency electromagnetic field shielding and wireless communication  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** &  
signal transmission were successfully demonstrated. The resonator had a quadruple rotational symmetry, and its element size (\(-\lambda_{0}/11\)) is much smaller than that of previous works [9; 11; 13; 19; 26; 27; 28]. Due to the significant near-field enhancements of \(\mid\!E\!\mid^{2}\) at \(f\) = 900 MHz, which is about 180 times that at \(f\) = 1 GHz, a resonant mode with a power ratio of 12 dB was supported by the cross-meandering resonator. Based on the self-made shielding box experimental setup, the power frequency electromagnetic field shielding and wireless sensing communication performance of the resonator sample were tested. The measured shielding effectiveness could exceed 33 dB, and the probability of a wireless communication signal being received could reach up to 100%. Therefore, the proposed cross-meandering resonator simultaneously realized the goals of power frequency electromagnetic shielding and transmission of wireless sensing communication signals. By integrating the dual-functional resonator and MEMS devices, we expect to solve many of the physical problems faced by wireless sensors in strong electromagnetic environments and greatly promote the development of MEMS wireless sensing technology adapted to strong electromagnetic field environments.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/s24175615/s1](https://www.mdpi.com/article/10.3390/s24175615/s1)]([https://www.mdpi.com/article/10.3390/s24175615/s1](https://www.mdpi.com/article/10.3390/s24175615/s1)), Figure S1: Composition method of the cross-meandering resonator; Figure S2: Schematic diagram of the simulation calculation and power ratios at different vertical distances; Figure S3: Calculated transmission spectrum of the resonator array by using the equivalent circuit model; Table S1: Comparison of the proposed resonator with other works.  
Conceptualization, F.G. and W.L.; methodology, F.G.; software, F.G., X.S., X.Y. and S.L.; validation, F.G., X.S. and X.Y.; writing--original draft preparation, F.G., X.Y. and X.S.; writing--review and editing, F.G., X.S. and Y.Z.; project administration, X.S., X.Y. and S.L.; funding acquisition, W.L., F.G. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (92163134, 62075232, and 62305366).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors thank Zhongguang Huayan Electronic Technology Co., Ltd. for the air pressure sensor technical support.  
The authors declare no conflicts of interest.
# Using LSTM to Identify Help Needs in Primary School Scratch Students
## 2 Materials and Methods
### User Experience with Scratch
Before the students participated, both parents or legal guardians had enough time to consider participation;
* Then, the students whose parents had authorized their participation started by filling out a questionnaire. * After that, they started the experience by carrying out the assessments. During them, they could not request any kind of help to evaluate the real prior knowledge of Scratch, but they could exit from the assessment at any time. Once the students had completed or exited from all the assessments, the knowledge level was set;
* Finally, the students could start performing the Scratch exercises in their preferred order. In those exercises they could ask for help and also switch to another exercise at any time. From the first interaction with Scratch up to the end of the exercises, all the interactions were recorded in a database with the aim of analyzing them later. All this information was made anonymous by previously giving a random number to each participant. The stored information of this experiment is hereunder listed:  
* Student data:
* Student number: the student number was randomly assigned to the student;
* Gender: provided by the student in the opening questionnaire and stored in numerical format (1 for boy and 2 for girl);
* Mother tongue: provided by the student in the opening questionnaire. It is a boolean field that indicates if the student's mother tongue is Spanish or not. Given that the exercises and the user experiences were carried out in Spanish, we think that the variable \"language\" may affect whether a student understands the statements of the exercises and the teacher's explanations. Therefore, this variable could influence the number of help requests made by the students;
* Age: provided by the student in the opening questionnaire;
* Competence: set by the aforementioned starting assessments. * Exercise data:
* Name: exercise name;
* Description: exercise description;
* Skills: each exercise may help to improve students' CT skills to different degrees. These skills and their degrees (which are numerical values from 0 to 10) were set by experts;
* Evaluation: indicates if the exercise is an evaluation level exercise or not. * Solution distance data: family distance, element distance, position distance, input distance, total distance (explained in Section 2.3). * Interaction data:
* Date time: date and time of the interaction;
* Request help: indicates if the student requested help or not;
* Seconds help open: when a student requested help, this parameter indicates how many seconds the help popup was open before the student closed it;
* Last login: date and time of the student's last login. * Workspace data:
* Elements: block elements that the student currently has in their workspace. This information is then used to calculate the distance between the student workspace and the solution.
## 3 Methods
### SE-DenseNet
#### 3.3.2 SE Module  
The Squeeze-and-Excitation (SE) module is a computing unit, which learns the importance of each channel of the input feature map, multiplies the obtained weight with the corresponding channel, and outputs the calibrated feature map by the weight of each channel [30]. It strengthens the useful features and weakens the useless features, so as to improve the discrimination ability of the neural network. As shown in Figure 4, first, the  
Figure 3: DenseNet model diagram.  
feature graph \(X_{L}\) of the \(L\)-th layer is transformed into a tensor \(U_{1}\) by convolution operation, that is:  
\[U_{1}=W^{L}\bigotimes f\Big{(}BN\Big{(}W^{L-1}\bigotimes f(BN([X_{0}X_{1}\cdots X _{L-1}]))\Big{)}\Big{)} \tag{1}\]  
In Equation (1), \(BN\) represents batch normalization processing; \(f(\cdot)\) is the ReLU function; \(W^{L-1}\) and \(W^{L}\) are the convolution kernel of size 1 by 1, 3 by 3, respectively.  
Then, the Squeeze operation is carried out, which is the global pooling layer. The shape of the feature graph \([M,H,C]\) is compressed into \([1,1,C]\), and the numerical distribution of c channels in the feature graph of this layer is obtained. The mathematical description is shown in Equation (2):  
\[z_{c}=F_{sq}(u_{c})=\frac{1}{M\times H}\sum_{i=1}^{M}\sum_{j=1}^{H}u_{c}(i,j) \tag{2}\]  
where, \(u_{c}\) represents the feature diagram of the \(c\) channel after the convolution operation. \(z_{c}\) is the feature diagram of the \(c\) channel after Squeeze operation. \(M\), \(H\), \(C\) represent the three-dimensional information of the eigenmatrix \(U_{1}\).  
Then the Excitation operation is carried out; its mathematical principle is shown in Equation (3):  
\[s_{c}=F_{ex}(z,W)=\sigma(g(z,W))=\sigma(W_{2}f(W_{1}z_{c})) \tag{3}\]  
where, \(W_{1}\in R^{(C/\beta)\times C}\); \(W_{2}\in R^{C\times(C/\beta)}\); \(\sigma\) is the Sigmoid function, \(\beta\) is the dimensional transformation rate.  
Finally, the \(c\) elements of the obtained scale matrix \(s_{c}\) and the c channels of the feature graph \(U_{1}\) are corresponding and multiplied one by one to get the output \(Y=[y_{1}y_{2}\cdots y_{c}]\). The mathematical principle is showed in Formula (4):  
\[Y=F_{scale}(u_{c},s_{c})=s_{c}\cdot u_{c} \tag{4}\]  
In Formula (4), \(s_{c}\) represents the vector obtained after Squeeze and Excitation, and its dimension is \(c\); \(u_{c}\) represents the feature map after the convolution operation, and the number of channels is \(c\).  
The SE module, first Squeeze and then Excitation on the input, maps the feature graph into a global real number in the channel unit, and finally multiplies the real number corresponding to the input to complete the correlation learning of each channel of the feature graph.
# Research on a Rail Defect Location Method Based on a Single Mode Extraction Algorithm
## 2 An Accurate Modal Identification Method
### Modal Identification
#### 2.3.2 Simulation Analysis  
Due to the excessive modes of high frequency guided waves, the results of modal identification are complex and difficult to study. Therefore, in the process of research, we first judged the correctness of the algorithm by studying 200 Hz low-frequency guided waves, and then increased the frequency to ultrasound band to study the modal identification results of high-frequency guided waves.  
Firstly, the rail model with a railroad defect was established, and the excitation response of the rail was simulated using ANSYS. The rail was then excited by signals at a low center frequency of 200 Hz and a high center frequency of 60 kHz. The rail and defect models are shown in Figure 5.  
1. Low center frequency of 200 Hz.  
The length of the rail model was 200 m, and the defect was located at 120 m and had a length of 70 mm, width of 50 mm, and depth of 20 mm. The excitation signal was a sinusoidal wave modulated using Hanning window, with a center frequency of 200 Hz and five cycles. The excitation position was the center point of the side of the railhead at 90 m. A total of 32 sections were sampled from 100 m to 101.55 m. Seven nodes were selected for each section, as shown in Figure 2.  
At 200 Hz, there were only four modes in the rail: the horizontal bending mode, the vertical bending mode, the torsional mode, and the extensional mode. The collected signals were substituted into the modal identification in Equation (8), which is extended into the form of Equation (9).  
Figure 5: Rail model with head defect.