esa-sceva/satcom-chunk-collection huggingface dataset:
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project  
AP Access Point  
CI Cell Identity  
CINR Carrier-to-Interference Noise Ratio  
EARFCN E-UTRA Absolute Radio Frequency Channel Number  
eNodeB evolved Node B  
GNSS Global Navigation Satellite System  
kNN k Nearest Neighbors  
LPWAN Low-Power Wide Area Network  
LTE Long-Term Evolution  
MA Moving Average  
MM Moving Median  
Mobile Network Code  
NB-IoT Narrowband Internet of Things  
NPCI Narrowband Physical Cell Identifier  
NPRS Narrowband Position Reference Signal  
NSSS Narrowband Secondary Synchronization Signal  
OTDOA Observed Time Difference Of Arrival  
PCI Physical Cell Identifier  
PRS Position Reference Signal
# Maintenance Prediction through Sensing Using Hidden Markov Models--A Case Study
## 2 Theoretically Background
### Clustering K-Means  
Clustering is a fundamental unsupervised data mining technique that is used in a wide variety of areas for data analysis [37], where cluster analysis is done by multivariate statistical methods and the objective is to identify clusters of objects within the data, maximizing the homogeneity within each cluster and the heterogeneity between the different clusters [18; 37; 38; 39; 40; 41].  
The k-means algorithm was first proposed by [PERSON] in 1957 as a technique for pulse code modulation, with a more efficient version being proposed and published in Fortran by [PERSON] and [PERSON] in 1975/79 [42; 43].  
It is a numerical, unsupervised, iterative, and evolutionary algorithm, which aims to find the positions of the clusters that minimize the distance from the data points to the clusters, it then partitions the \(n\) observations into \(k\) clusters in which the observation belongs to the cluster with the most average next [42; 44].  
Each cluster in the partition is defined by its member objects and its centroid, with the centroid being the point at which the sum of the distances of all objects in that cluster is minimized [43]. Then, K-means finds a centroid to correlate the data to a specific group, and the process of estimating the data belonging to a given cluster is carried out iteratively until the convergence condition is satisfied [39].  
To define the cluster number \(k\), the Silhouette method can be used, because, according to [PERSON] [45], the diagnostic used to assess the quality of a partition must be consistent with the clustering method adopted to produce that partition, being Silhouette parsing a method that fits well with the K-means Clustering algorithm.  
A detailed explanation of this topic can be found in [37; 45; 46]. [PERSON] and [PERSON] [47] summarize by saying that the Silhouette Index (SI) is calculated for each resource and measures it through the ratio between intra-cluster and inter-cluster dissimilarity \(a(k)\) and \(b(k)\), respectively, as shown in Equation (3), generating a value between \(-1\) and \(1\).  
\[S(k)=\frac{b(k)-a(k)}{max\{a(k),b(k)\}} \tag{3}\]
# A Novel Reconstruction Method for Irregularly Sampled Observation Sequences for Digital Twin
## 4 Simulation Experiments and Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Unit** & **Physical Meaning** & **Value** \\ \hline \multirow{5}{*}{Input and Output Parameters} & \(W_{cf}\) & \(kg/s\) & Coal flow rate & [54; 80] \\  & \(V_{sa}\) & m\({}^{3}\)/s & Secondary air flow rate & [400, 600] \\  & \(V_{gs}\) & m\({}^{3}\)/s & Gas flow rate & [400, 650] \\  & \(\rho_{b}\) & kg/m\({}^{3}\) & Gas density & [1.3, 1.4] \\  & \(T_{gs}\) & \({}^{\circ}\)C & Gas temperature in furnace & [1000, 1100] \\  & \(P_{b}\) & kpa & Furnace pressure & [101, 102] \\  & \(O_{cp}\) & \% & Gas oxygen content & [2.6, 3.8] \\ \hline \multirow{5}{*}{Constant Parameters} & \(T_{a}\) & / & Inertial time constant & 0.9 \\  & \(C_{gs}\) & kJ/(kg\(\cdot\,^{\circ}\)C) & Specific heat capacity of gas & 2.03 \\  & \(H_{a}\) & kJ/kg & Air enthalpy & 934.1 \\ \cline{1-1}  & \(V_{b}\) & m\({}^{3}\) & Furnace volume & 16,855 \\ \cline{1-1}  & \(V_{0}\) & m\({}^{3}\)/kg & Theoretical air consumption & 5.834 \\ \cline{1-1}  & \(T_{sv}\) & \({}^{\circ}\)C & Temperature of water wall & 326.66 \\ \cline{1-1}  & \(p_{a}\) & kg/m\({}^{3}\) & Air density & 1.273 \\ \cline{1-1}  & \(R_{gs}\) & / & Universal gas constant & \(6.52\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Physical meaning of furnace parameters.
# Online Monitoring of Power Converter Degradation Using Deep Neural Network
## 3 Experimental Results and Discussion  
In order to evaluate how the proposed greyscale-based approach and the spectrogram-based approach can determine which power switch is experiencing abnormal snubber resistance in the power converter for BLDC motor drive, experiments are conducted on dataset A and dataset B1. For dataset A with greyscale images, the images are first resized to 224 \(\times\) 224 pixels and converted to RGB images to fit the input layer of the transferred ResNet50. Then, the dataset is split into four folds for cross-validation; each time, three folds will be used as the training set, and the remaining fold will be used as the test set. Finally, the Adam optimizer trains the transferred deep CNN for ten epochs, with a mini-batch size of 10. The obtained average cross-validation accuracy by using the proposed method is 0.9533. For dataset B1, with the same experimental setup, the average classification accuracy is 0.9772. The confusion matrices on datasets A and B1 are shown in Figure 7a,b, respectively. The number 7 corresponds to normal condition, and the number from 1 to 6 correspond to abnormal conditions in \(S_{AH}\), \(S_{AL}\), \(S_{BH}\), \(S_{BL}\), \(S_{CH}\), and \(S_{CL}\), respectively. Comparing the two approaches featuring greyscale image and spectrogram image, the latter has a mild improvement (3%) in the classification accuracy, but STFT is needed, which increases the burden of calculation, while the former one is simpler. Thus, it is proposed to use the greyscale approach for faster detection and use the spectrogram approach if higher classification accuracy is desired.  
Then, to gain insight into the range of abnormal snubber resistance, the proposed approach can best distinguish the abnormal power switch from the normal one; experiments are carried out on datasets B2 and B3. With the same setup as before, the proposed approach acquires an average accuracy of 0.5169 on dataset B2 and merely 0.0843 on dataset B3. Since it is known that dataset B2 has a range of abnormal snubber resistance closer to the normal condition than B1, and the snubber voltage waveforms are also closer to those under normal conditions, the classification is, therefore, more difficult, and the accuracy is almost half of that on dataset B1. As for dataset B3, since the range is even closer to normal conditions, the proposed approach can no longer classify the seven classes with acceptable accuracy. The confusion matrices on dataset B2 and dataset B3 are shown in Figure 7c,d, respectively. Using the proposed approach carefully is suggested, since it only takes effect when the degradation reaches a certain level.  
Figure 6: Overview of the monitoring system.
# A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification
## Channel Mixing Attention  
### Spatial Mixing Attention  
SMA is similar to CMA. The difference between the two is that CMA groups feature maps in channel latitude, mainly focusing on the attention of each set of channels. In contrast, SMA groups feature maps in spatial dimensions. The input matrix \(\mathbf{X_{S}}\in R^{HW/N\times C}\). In SMA, attention is used to weight and select features in the spatial dimension. However, in CMA, the attention mechanism is used to weight and choose features in the channel dimension.  
The SMA was also divided into two branches, one of which went through \(G_{n}\), shuffling, and multiplied with \(\mathbf{x_{s}^{i}}\) to extract channel features. The other one went through the adaptive average pool,shuffing and multiplying by feature map \(\mathbf{x_{s}^{i}}\) to extract spatial features. Finally, the output dimension after concat is \(HW/M\times C\).  
Figure 3: **An overview of spatial mixing attention (SMA) and channel mixing attention (CMA).** The process for CMA and SMA is similar. The CMA and SMA mainly contain three components: (1) The input feature map is divided into two sub-feature maps. (2) One branch is multiplied by GroupNorm and shuffing to extract the features of the space. After adaptive average pooling and shuffing, the other is multiplied by subfeature maps to extract channel features. (3) Finally, concat obtains the fusion feature.  
By dividing the molecular space, the model can divide the feature map into different subregions and calculate the attention weight of each subregion. This approach focuses more accurately on the importance of different spatial locations, allowing the model to capture local information about the vehicle better. This approach considers the relationship between different locations and channels while retaining the importance of spatial location. It helps the model understand the interaction of different channels at various locations, thus improving the consistency and accuracy of feature representation. It has great potential when dealing with complex UAV perspectives.
# Novel Approach to Manufacture an AUV Propeller by Additive Manufacturing and Error Analysis
###### Abstract  
Autonomous underwater vehicle (AUV) is an unmanned tether-free vehicle which is powered by battery or fuel cell. The weight of the AUV is a major issue to be decided when considering its performance. To manufacture a propeller that is lighter in weight and able to carry the pressure applied to the blades is an involving process. The present study investigates the performance of the propeller of an AUV, manufactured by additive manufacturing, using ABS plastic material. The propeller blade designed in SolidWorks was transferred to the CUBPRO (DUO), followed by setting the parameters for a 3D printing machine. A comparative study was carried out for ABS (Acrylonitrile Butadiene Styrene) material between the required dimensions and a 3D printed model dimension propeller blade. An error analysis was carried out and we observed that ABS material is the most suitable for an AUV propeller. A stress-strain analysis for the propeller was carried out using the Finite Element Method.  
AUV propeller; additive manufacturing; FEM +
Footnote †: journal: Applied Sciences
# Neurofeedback Effects on EEG Connectivity among Children with Reading Disorders: I. Coherence
## 2 Materials and Methods
### EEG Recording and Analysis  
The electroencephalogram was recorded in a resting condition with eyes closed from the 19 leads of the international 10-20 system (Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6, Fz, Cz, and Pz), referenced to the linked earlobes (A1A2). For this purpose, a Medicid(tm) IV instrument (Neuronic Mexicana, SA, Mexico City, Mexico) with a v2.0 Track Walker(tm) recording system was used. The amplifier's bandwidth was set between 0.5 and 50 Hz. All electrode impedances were equal to or less than 5 k\(\Omega\), and the signal was amplified with a gain of 20,000. EEG data were sampled every 5 ms and edited offline. Overall, 24 artifact-free segments of 2.56 s were selected. EEG analysis was performed offline. The fast Fourier transform was applied to the data, and the cross-spectral matrices were obtained every 0.39 Hz to calculate the AP and coherence values of each of the bands (delta: 0.5-3.5 Hz, theta: 3.6-7.5 Hz, alpha: 7.6-12.5 Hz, and beta: 12.6-19 Hz).
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 1 Introduction
Arterial tonometry is a method of gradually applying external pressure to achieve equal internal and external pressure in the blood vessels and then using a pressure sensor placed above the artery to measure external pressure values to calculate arterial blood pressure [37]. Currently, blood pressure monitoring devices based on this method, such as the US TL-300 and Japan CBM-7000 noninvasive blood pressure measurement systems, have good blood pressure measurement accuracy [38; 39]. Existing studies have achieved good results. However, the current fabrication processes for related flexible sensors are relatively complex and still in the developmental stage. Therefore, in this study, a flexible packaging sensor combining parylene and PDMS was fabricated to enhance the comfort of the wearer and prevent sweat erosion, while achieving the stability and reliability of traditional rigid pressure sensors. The arterial tonometry method, which relies solely on the peak and trough values of the pulse waveform for blood pressure calculation, fails to comprehensively consider other pulse wave parameters. This limitation leaves room for improvement in the accuracy of blood pressure measurement using this method. Therefore, we compared and analyzed various machine learning algorithms and determined that the random forest model was found to be the optimal regressor. A blood pressure monitoring system based on an improved arterial tonometry method was designed and implemented. The proposed blood pressure monitoring system, based on flexible packaging sensors, provides a theoretical foundation and has application value for achieving wearable blood pressure measurement and precise diagnosis and treatment of conditions such as hypertension.
## 3 Results  
### LHRH-MNP Conjugation  
Water-dispersible MNPs with approximately 180 free amine groups available on the surface were obtained, allowing for further modification and functionalization (Private Communication, Ocean Nanotech, 2016) [25]. LHRH peptides were conjugated to the surface of MNPs through glutaraldehyde crosslinking chemistry following protocols established in our previous studies (Figure 1A) [25]. The associated changes of nanoparticle surface chemistry before and after LHRH conjugation were characterized using FTIR. IR spectra of MNPs glutaraldehydechyde activated MNP and LHRH conjugated MNPs are shown in Figure 1B. Briefly, all spectra exhibited peaks at 590 cm\({}^{-1}\) that were characteristics of Fe-O in Fe\({}_{3}\)O\({}_{4}\). The IR spectra also showed the characteristics of the amine-terminal PEG with methylene signature peaks at 2916 cm\({}^{-1}\) and 2864 cm\({}^{-1}\) for asymmetric and symmetric stretching of C-H, respectively [31; 32]. Additional peaks at 1010 cm\({}^{-1}\) and 1107 cm\({}^{-1}\) were related to the C-O stretching in the PEG coating. In glutaraldehydechyde activated MNPs, carbonyl groups were introduced at 1724 cm\({}^{-1}\) (C=O stretching) as the amine groups on MNP surfaces reacted with glutaraldehydechyde. In addition, the peak at 1643 cm\({}^{-1}\) was attributed to C=N stretching of mine groups that were formed between amine of MNPs and glutaraldehydechyde [33]. When LHRH was introduced into glutaraldehydechyde active MNPs, the amine groups in LHRH peptides reacted with the remaining carbonyl groups in glutaraldehydechyde. As a result, the carbonyl groups that were present in glutaraldehydechyde activated MNPs disappeared in the spectra of the LHRH-MNPs, indicating successful conjugation.  
Figure 1: **(A)** Conjugation process of LHRH-MNPs. LHRH peptides were conjugated to the surfaces of MNPs via glutaraldehyde crosslinking chemistry to generate LHRH-MNPs. **(B)** Fourier-transform infrared spectroscopy (FTIR) spectra of MNPs, glutaraldehydechyde activated MNPs and LHRH-MNPs, showing successful conjugation. **(C)** Hydrodynamic diameter distribution of MNPs and LHRH-MNPs, as measured by dynamic light scanning (DLS). **(D)** Transmission electron microscopy (TEM) micrographs of MNPs and LHRH-MNPs. Scale bars are 100 \(\upmu\)m. [25].
## 4 Conclusions  
To optimize the hardening process of 42 CrMo steel, this study developed the RSM and WOA-BPNN models for the laser-induced hybrid phase transformation process. Conclusions drawn from the study are as follows:  
1. The WOA-BPNN model outperforms RSM in stability and generalization, with R\({}^{2}\) values of 0.995 and 0.996 for the depth and hardness of the hardened layers, respectively.
2. The WOA-BPNN model achieved higher accuracy, with RMSEs of 0.099 mm for depth and 1.734 HV\({}_{0,3}\) for hardness, and MAPEs of 0.697% and 0.787%, respectively.
3. The WOA-BPNN model demonstrated superior generalization and multi-objective prediction capability, making it more suitable for non-linear problems in the laser-induced hybrid hardening process.  
Figure 8: Optimization results of process parameters. (**a**) Distribution map of hardened layers; (**b**) hardness distribution map.  
Figure 7: Comparison of RSM and WOA-BPNN generalization capabilities. (**a**) Depth; (**b**) hardness.  
In summary, WOA-BPNN provides an effective method to enhance 42 CrMo steel's surface properties, offering valuable insights for optimizing parameters to extend component life and reduce production costs. Furthermore, the WOA-BPNN model exhibits high reliability, which can help researchers promptly obtain the optimal depth and hardness through limited processing parameters.  
Q.Z. and J.L.: conceptualization, methodology, data curation, investigation, software, visualization, validation, writing\(-\)original draft, writing\(-\)review and editing. J.Y.: conceptualization, project administration, funding acquisition, supervision, writing\(-\)review and editing. Z.C., G.W., J.Z., Z.Y., Y.W. and J.Z.: methodology, writing\(-\)review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Program of China under Grant 2023 YFB4603400, the \"Elite\" Program of Zhejiang Province under Grant 2023C01064/2024 SJCZX0040, and the Key Science and Technology Program of Long-You County (JHXM2023072).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing
## 3 Results
### Application in Automated Mining-Induced Microseismic Events Classification
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Wavelet** & \(w\) & **Wavelet** & \(w\) & **Wavelet** & \(w\) & **Wavelet** & \(w\) & **wavelet** & \(W\) \\ \hline haar & \(-4.78\) & db10 & \(-5.56\) & coif5 & \(-5.51\) & bior3.5 & \(-9.72\) & rbio2.8 & \(-2.25\) \\ db1 & \(-4.78\) & sym2 & \(-5.19\) & bior1.1 & \(-4.77\) & bior3.7 & \(-9.22\) & rbio3.1 & \(-0.03\) \\ db2 & \(-5.19\) & sym3 & \(-5.38\) & bior1.3 & \(-4.83\) & bior3.9 & \(-9.16\) & rbio3.3 & \(-0.86\) \\ db3 & \(-5.38\) & sym4 & \(-5.40\) & bior1.5 & \(-4.87\) & bior4.4 & \(-6.50\) & rbio3.5 & \(-0.98\) \\ db4 & \(-5.39\) & sym5 & \(-5.41\) & bior2.2 & \(-9.64\) & bior5.5 & \(-3.51\) & rbio3.7 & \(-1.04\) \\ db5 & \(-5.44\) & sym6 & \(-5.40\) & bior2.4 & \(-8.47\) & bior6.8 & \(-6.54\) & rbio3.9 & \(-1.09\) \\ db6 & \(-5.52\) & sym7 & \(-5.62\) & bior2.6 & \(-8.19\) & rbio1.5 & \(-5.65\) & rbio4.4 & \(-4.31\) \\ db7 & \(-5.46\) & sym8 & \(-5.46\) & bior2.8 & \(-8.20\) & rbio2.2 & \(-1.70\) & rbio5.5 & \(-6.66\) \\ db8 & \(-5.48\) & coif3 & \(-5.43\) & bior3.1 & \(-33.48\) & rbio2.4 & \(-2.06\) & rbio6.8 & \(-4.47\) \\ db9 & \(-5.61\) & coif4 & \(-5.48\) & bior3.3 & \(-11.46\) & rbio2.6 & \(-2.18\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The decomposition stability \(w\) of each wavelet concerning M\({}_{1}\). Figure 6: Automatic classification of microseismic signals using wavelet packet decomposition and deep learning. After performing a 3-level wavelet packet decomposition, the eight wavelet coefficients can be obtained from the mining-induced microseismic waveform. These wavelet coefficients are formed into a feature matrix adopted as input for training the convolutional neural network. average accuracy of 93.13%. However, the average classification accuracy of the other wavelets can only reach 90.89%, 89.81%, 91.53%, 90.86%, and 91.44%. The comparison reveals that feature extraction using wavelets selected by our method is beneficial for the subsequent analysis. Therefore, it can be considered that our method is effective.
## 2 Background
### Self-Starting Forecasting Process with EWMA Model  
To ensure the satisfactory performance of these EWMA models, sufficient historical time series observations should be prepared beforehand. However, in many real situations, only a small number of time series observations are available. One of possible solutions to this problem is a self-starting approach, which both forecasting and model updating are simultaneously done as a new time series observation is added to the training instances. With that approach, the base EWMA model is built without requiring large preliminary initial time series observations. In addition, in the self-starting approach, the models can effectively accommodate the time-varying patterns of time series in that the base EWMA models are continuously updated as time series observations are newly recorded. It should be noted that other forecasting methods, including ARIMA, GARCH, and VAR, cannot be used for the self-starting forecasting process. These methods estimate a number of parameters, and in order to estimate them, more time series observations than the number of parameters are required [35; 36]. Accordingly, other forecasting methods are not available for the self-starting forecasting process. Conversely, as described in Section 2.1, only a small number of time series observations (two) are required to estimate the initial factors of EWMA models; therefore, we only consider the EWMA models as a base model for the self-starting forecasting process.  
Because the self-starting forecasting process was proposed to address the situation where there was no or only very small initial training observations available, it starts using only the minimum observations required for the base model as initial training data. Thus, we also constructed the initial base EWMA model using only the first two observations. Then, with the base EWMA model, we forecasted \(s\)-step ahead future observations (i.e., forecasting window is denoted as \(s\)). The EWMA models were continuously updated when a new observation was added, and \(s\)-step ahead forecasting was also continuously conducted through the updated EWMA model. Figure 1 exemplifies the self-starting forecasting process, which was considered in this study, when the forecasting window, \(s\), is set as 4, and the total length of the time series observation is 120.  
As shown in Figure 1, the initial EWMA model \(f(y_{1},\,y_{2})\) is constructed by only two initial observations (\(y_{1}\) and \(y_{2}\)), and the \(f(y_{1},\,y_{2})\) generates the four-step ahead forecasting value, \(y_{6}^{\star}\). Then, when the observation is in the third time point, \(y_{3}\), is newly recorded, the initial forecasting model is updated to \(f\left(y_{1},\,y_{2},\,y_{3}\right)\). These forecasting and model updates are repeated until 116 th observation is recorded. This continuous model update process was repeated until (120 s) time series observations were accumulated.  
Figure 1: Graphical illustration of self-starting forecasting process when the forecasting window (s) and total time series length are 4 and 120, respectively.