esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Machine Learning-Based Prediction of Solar Irradiance Using Multi-Site Data
## 3 Results and Discussion
### UNLV Station Use Case  
As previously shown, the UNLV station achieved the lowest RMSE and the second highest \(R^{2}\) with the MLP model for both 30 min- and 1 h-ahead forecasting. Additionally, it was demonstrated that the model's performance remains stable for predicting GHI across different shift values. Upon examining feature importance across various forecasting horizons in Figure 15, it becomes clear that the same key features consistently contribute to model performance. These features include the azimuth angle, the first and second lagged versions of GHI, airmass, zenith angle, and DNI values. Given their consistent significance, other features can be considered negligible, allowing for optimal predictions using only these six features.  
Figure 15: _Cont._One of the key factors contributing to the performance observed at the UNLV station is the inherent variation in the GHI, as illustrated in Figure 16. The data reveals a distinct pattern in the GHI readings over the selected years, which appears to be influenced by seasonal effects. Specifically, GHI values tend to rise from March through July and decline during the rest of the year, with relatively stable behavior from December to February. This seasonal trend represents a hidden pattern that can be effectively captured and leveraged by machine learning models, particularly by MLP. MLPs are particularly powerful in detecting such hidden patterns due to their ability to learn complex, non-linear relationships within data. By adjusting their weights through training, MLPs can discern subtle trends and variations in the GHI data that might not be immediately apparent. GHI values generally fall within a range of 10 to 350 on average. While this provides a certain boundary for the MLP's predictions, it also underscores the model's capacity to generalize within these limits, ensuring reliable performance across different periods and conditions. The ability of MLPs to detect and utilize these underlying patterns is a significant advantage in making accurate predictions based on GHI data.  
Figure 16: GHI variations at the UNLV station: (**a**) hourly and (**b**) daily.  
Figure 15: Sensitivity of regressor performance to time shift and features for UNLV station: (**a**) 30-min, (**b**) 1-h, and (**c**) 2-h shifts.
# Research on Microstructure and Mechanical Properties of Laser Welding of BR1500 HS Joints
## 1 Introduction
The effects of process parameters and quenching process on mechanical properties and microstructure of the joints were investigated to provide valuable guidance for BR1500 HS steel welding. The effects of process parameters of laser power and welding speed on the tensile strength, fracture morphology, microhardness, and weld formation are analyzed, and the influence of the quenching process on the grain diameter, grain orientation, and phase of the joint is revealed.
# VSFCM: A Novel Viewpoint-Driven Subspace Fuzzy C-Means Algorithm
## 4 Experimental Results
### Evaluation Indicator
According to the above definition, given two membership matrices B1 and B2, it is obvious that \(e\),\(f\),\(g\), and \(h\) can be rewritten as below when r and q are two soft partitions:  
\[e=|W\cap Y|=\sum\limits_{j_{2}=2}^{n}\sum\limits_{j_{1}=1}^{j_{2}-1}W(j_{1},j _{2})\otimes Y(j_{1},j_{2}), \tag{43}\]  
\[f=|W\cap Z|=\sum\limits_{j_{2}=2}^{n}\sum\limits_{j_{1}=1}^{j_{2}-1}W(j_{1},j _{2})\otimes Z(j_{1},j_{2}), \tag{44}\]  
\[g=|X\cap Y|=\sum\limits_{j_{2}=2}^{n}\sum\limits_{j_{1}=1}^{j_{2}-1}X(j_{1},j _{2})\otimes Y(j_{1},j_{2}), \tag{45}\]  
\[h=|X\cap Z|=\sum\limits_{j_{2}=2}^{n}\sum\limits_{j_{1}=1}^{j_{2}-1}X(j_{1},j _{2})\otimes Z(j_{1},j_{2}). \tag{46}\]  
Among them,  
\[W\!\!=\{W(j_{1},j_{2})\Big{|}W(j_{1},j_{2})=\oplus_{i=1}^{k}\big{(}r_{j_{1}} \otimes r_{j_{2}}\big{)},\,j_{2}=2,\cdots,N,\,j_{1}=1,\cdots,j_{2}-1\} \tag{47}\]  
is the set of data pairs belonging to a cluster in B1. Similarly, \(Y\) is the set of data pairs belonging to the same cluster in B2. And  
\[X\!\!=\{X(j_{1},j_{2})\Big{|}X(j_{1},j_{2})=\oplus_{i_{1},j_{2}\in[1,k]|i_{1} \
eq i_{2}}\big{(}r_{i_{1}j_{1}}\otimes r_{j_{2}}\big{)},j_{2}=2\cdots N,j_{1}= 1\cdots j_{2}-1\} \tag{48}\]  
is a set of data pairs that do not belong to the same cluster in B1. Similarly, \(Z\) is a collection of data pairs that do not belong to the same cluster in B2. \(\otimes,\oplus\) are t-norm and s-norm,and min, and max are often used for actual processing, respectively. EARI is specifically obtained by the following formula:  
\[\text{EARI}^{(+)}=\frac{e-\frac{(e+f)(e+g)}{e+f+g+h}}{\frac{(e+f)+(e+g)}{2}- \frac{(e+f)(e+g)}{e+f+g+h}}. \tag{49}\]  
(5) Xie-Beni indicator  
The Xie-Beni (XB) indicator [40] is a highly recognized indicator of the effectiveness of fuzzy clustering, whose formula is as below:  
\[\text{XB}=\frac{\sum\limits_{i=1}^{C}\sum\limits_{k=1}^{n}u_{ik}^{m}d(x_{k},v_ {i})}{n\times\min_{i\
eq j}d(v_{i},v_{j})}. \tag{50}\]
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 5 Dimensionality Reduction Techniques
### Self-Organizing Map (SOM)  
The self-organizing map (SOM) or the Kohonen map corresponds to a neural network that helps in dimensionality-reduction-based feature selection. The map here signifies the low-dimensional depiction of the features of the specified signal. SOM is based on unsupervised ML with node arrangement as a two-dimensional grid. Each node in the SOM is connected with a weighted vector to make computing easier [78]. SOM implements the notion of a race network, which aims to determine the utmost alike detachment between the input vector and the neuron with weight vector \(\mathrm{w}_{i}\). The architecture of SOM comprising both the input vector '\(\mathrm{x}\)' and output vector '\(\mathrm{y}\)' is shown in Figure 7.  
The size of the weight vector '\(w_{i}\)' in SOM is controlled using the learning rate functions like the linear, the inverse of time, and the power series as given in Equations (30)-(32) [79].  
\[\alpha(t)=\alpha(0)\cdot\frac{1}{t} \tag{30}\]  
Figure 7: The architecture of SOM.  
\[\alpha(t,T)=\alpha(0)\cdot\left(1-\frac{t}{T}\right) \tag{31}\]  
\[\alpha(t,T)=\alpha(0)\cdot e^{\frac{t}{T}} \tag{32}\]  
where \(T\) represents the number of iterations and \(t\) is the order number of a certain it.  
It is distinct from the other ANNs in the case of implementing the neighborhood function. A neighborhood function is a function that computes the rate of neighborhood change around the winner neuron in a neural network. Usually, the bubble and Gaussian functions (Equations (33) and (34)) are used as neighborhood functions in SOM.  
\[h_{ij}^{l}=\left\{\begin{array}{ll}\alpha(t),\ (i,j)\in N_{l}\\ 0,\ (i,j)\
otin N_{l}\end{array}\right. \tag{33}\]  
\[h_{ij}^{c}=\alpha(t)\cdot e^{\frac{(-\frac{-1|R_{c}-R_{ij}|^{2}}{2(t_{ij}^{c}( \Omega))^{2}})}{2(t_{ij}^{c}(\Omega))^{2}}} \tag{34}\]  
where \(N_{c}\) = the index set of neighbor nodes close to the node with indices c, \(R_{c}\), and \(R_{ij}\) = indices of \(w_{c}\) and \(w_{ij}\), respectively, and \(\eta^{c}{}_{ij}\) = the neighborhood rank between nodes \(w_{c}\) and \(w_{\bar{y}}\).
## 3 Methodology
### NN Sensitivity Tests
#### 3.3.2 Equalization of Water and Non-Water Pixel Number  
For this test, instead of using an equal number of water and non-water pixels in the training dataset, 10% of each Sentinel-1 image is selected randomly to train the neural network, meaning that the number of non-water pixels is several times higher (10-15 times depending on each image in the training dataset) than the number of water pixels (as seen in Figure 7). The intersection between histograms of the NN outputs for water pixels (blue) and non-water pixels (red) moves to 0.5 (see the histogram in Figure 8), meaning that the value 0.5 should be selected to separate water from non-water  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Output Threshold: 0.80**} \\ \hline  & **Non-Water(0)** & **Water(1)** & **Overall** & **Spatial** \\  & **(Predicted)** & **(Predicted)** & **Accuracy** & **Correlation** \\ \hline
**Non-water(0)** (Actual) & 99.3\% & 0.7\% & 98\% & 91\% \\ \hline
**Water(1)** (Actual) & 8\% & 92\% & & \\ \hline  & **Output Threshold: 0.85** & & & \\ \hline  & **Non-Water(0)** & **Water(1)** & **Overall** & **Spatial** \\  & **(Predicted)** & **(Predicted)** & **Accuracy** & **Correlation** \\ \hline
**Non-water(0)** (Actual) & 99.5\% & 0.5\% & 99\% & 92\% \\ \hline
**Water(1)** (Actual) & 9\% & 91\% & & \\ \hline  & **Output Threshold: 0.90** & & & \\ \hline  & **Non-Water(0)** & **Water(1)** & **Overall** & **Spatial** \\  & **(Predicted)** & **(Predicted)** & **Accuracy** & **Correlation** \\ \hline
**Non-water(0)** (Actual) & 99.6\% & 0.4\% & 99\% & 91\% \\ \hline
**Water(1)** (Actual) & 11\% & 89\% & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix of the NN classification for different thresholds. The NN uses the five initial inputs and the training dataset is equalized.  
Figure 7: Histograms of the NN outputs, for water (blue) and non-water (dashed red) pixels separately, according to the corresponding Landsat-8 surface water maps. The NN uses the five initial inputs and the training dataset is equalized. The y axis range is selected to illustrate the peak of the water histogram.  
clusters. As shown in Table 4, the resulting NN is very efficient at detecting non-water pixels with a true negative detection of 99.7%, but it misses 14% of the actual water pixels (86% of true positive detection only, compared to 91% with the equalized training dataset--Table 3). The true positive detection of water pixels decreases because in the training database, the non-water pixels are more numerous and as such have more weight in the retrieval than the water pixels. As a consequence, the NN is more effective at detecting non-water pixels, and less effective at detecting water pixels. It is concluded that the use of an equalized training data set is very important in this classification framework.
## 2 Modeling Method
### Dataset
For the collected data, the following processing is done: (1) The sampling interval for 60 min was selected considering the periods of observations include 15, 30, and 60 min; (2) the monthly median values of \(f_{0}\)F\({}_{2}\) were calculated at each station where these values were not given in source dataset such as Okinawa, and Magadan station; (3) the stations with less than half solar activity period, such as Jeju station that has only data for the years of 2013-2017, are used for verification. Therefore, 17 stations excepting Jeju are used in temporal and spatial reconstruction. The ionosonde data for Manzhouli, Jeju and Haikou stations in the years of 2013 and 2017 were used for verification because they cover three ionospheric regions (high, middle and low latitudes) and covered two solar activity years (high and low solar activity years). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Station** & **Station** & **Geog.** & **Geog.** & **Geomag.** & **Geomag.** & **Geomag.** & **Modify** & **Data** \\
**Label** & **Name** & **Lon. (\({}^{\circ}\)E)** & **Lat. (\({}^{\circ}\)N)** & **Lon. (\({}^{\circ}\)E)** & **Lat. (\({}^{\circ}\)N)** & **Lon. (\({}^{\circ}\)N)** & **Lat. (\({}^{\circ}\)N)** & **Lon. (\({}^{\circ}\)N)** & **Volume** (Entries)** \\ \hline
1 & Akita & 39.70 & 140.10 & 205.33 & 29.01 & 53.67 & 46.88 & 6683 \\
2 & Beijing & 40.00 & 116.30 & 184.60 & 28.04 & 57.28 & 48.80 & 8772 \\
3 & Chongqing & 29.50 & 106.40 & 175.80 & 17.54 & 42.56 & 38.52 & 9551 \\
4 & Guangzhou & 23.10 & 113.40 & 182.25 & 11.11 & 31.44 & 29.77 & 9239 \\
5 & Haikou & 20.00 & 110.30 & 179.34 & 8.00 & 25.07 & 24.30 & 8213 \\
6 & Irkutsk & 52.50 & 104.00 & 174.40 & 40.57 & 71.24 & 57.89 & 12,720 \\
7 & Jeju & 33.50 & 126.50 & 193.90 & 21.89 & 47.86 & 42.45 & 1771 \\
8 & Khabarovsk & 48.50 & 135.10 & 199.90 & 37.36 & 63.78 & 53.83 & 10,002 \\
9 & Magadan & 60.00 & 151.00 & 210.09 & 50.13 & 71.46 & 60.45 & 8219 \\
10 & Manzhouli & 49.60 & 117.50 & 185.32 & 37.66 & 67.45 & 55.64 & 8589 \\
11 & Okinawa & 26.30 & 127.80 & 195.54 & 14.77 & 36.84 & 34.18 & 10,167 \\
12 & Seoul & 37.20 & 126.60 & 193.74 & 25.59 & 52.73 & 45.88 & 3120 \\
13 & Taipei & 25.00 & 121.50 & 189.77 & 13.19 & 35.01 & 32.70 & 10,824 \\
14 & Tokyo & 35.70 & 139.50 & 205.31 & 24.99 & 48.91 & 43.45 & 15,046 \\
15 & Tunguska & 61.60 & 90.00 & 164.56 & 50.18 & 78.38 & 63.25 & 9480 \\
16 & Wakkanai & 45.40 & 141.70 & 205.89 & 34.82 & 59.61 & 51.15 & 15,271 \\
17 & Yakutsk & 62.00 & 129.60 & 193.60 & 50.46 & 75.88 & 62.64 & 9840 \\
18 & Yamagawa & 31.20 & 130.60 & 197.76 & 19.83 & 44.11 & 39.77 & 11,167 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Geographic and geomagnetic positions of the ionosonde stations used for model reconstruction and verification.
## 2 Background and Related Work  
### Underground Mining  
Underground mining consists of extracting and transporting material through tunnels and galleries that reach the surface. Mineral recovery must be based on safety and economy while providing adequate roof and floor support at the production fronts to preserve the surface from subsidence.  
The development of an underground mine can be divided into five phases: prospecting, exploration, development, exploitation, and reclamation [17]. The first two phases, exploration and exploitation, are involved in finding and determining the efficiency of mining and exploration. The third phase, development, enables the processing of the ore through the development of drift (tunnels) in the ore and surrounding rock mass. The fourth stage, exploitation, is the actual exploration of the ore. Finally, the fifth stage, the recovery, is intended to restore the affected environment to its original state as far as possible.  
Mining planning and scheduling aim to seek the rational use of the deposit, allowing the maximum recovery of reserves so that the project is technically, economically, and socially sustainable over time, and environmentally viable. Mine planning is usually divided into long-term, short-term, and production schedules. The short-term planning varies in time from about six months up to three years and plays a crucial role in ensuring that the day-to-day operations align with the long-term strategic goals of the mine. Long-term planning refers to the planning process, including operations and strategic decisions, through the years, and even decades. A long-term plan defines a master plan to seek the rational use of the deposit, while the short-term plan contains the activities to reach the objectives of long-term goals. In the Colombian context, a long-term plan must include information about:  
* The exploitation method.
* The design of miners' activities like use of explosives, loading and transport, support, ventilation, electrification, drainage system, dumps, and occupational health and safety systems.
* Design of the mine, ventilation, drainage blueprints, and blasting and drilling designs.
* Machinery and equipment.
* Required staff.
* Schedule.
* Financial evaluation of the project.  
Long-term planning takes advantage of an IIoT system because it helps to plan the occupational health and safety system by locating sensor nodes to monitor the environment to improve safety, establishing a network of communications integrating these nodes into emergency response strategies, and ensuring regulatory compliance. Moreover, integrating an IIoT system into the planning can be used to optimize ventilation systems and predict potential problems.
###### Abstract  
Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.  
activity recognition; smartphone; accelerometer sensor; smart health +
Footnote †: journal: Sensors
## 5 Discussion
### Limitations and Future Works  
Despite a high resolution in the simulation, CMSB-based methods provide a poor resolution performance in the deeper region in the point phantom experiment compared with DMAS. This is mainly because of the low SNR in the deeper region, which results in undistinguishable strong off-axis clutter beside the point target. In addition, CMSB (\(\delta=\delta_{ac}\)) obtains a worse resolution performance in the deeper region compared with CMSB (\(\delta=1.3\)) as illustrated in Table 3. The resolution of CMSB in the deeper region is insufficient, and this is left for future work.  
The speckle regions at the left and right bottom of CMSB-based methods show visually black-spot artifacts as seen from Figures 4 and 6. One possible reason for this is that the speckle intensity in the shallow region is improved, and thus incoherent components-dominated speckle signals (bottom left and right) are suppressed. However, a smaller maximum subarray length \(L_{max}\) might solve this problem at the expense of contrast. In addition, it can be seen from Figures 4 and 6 that black-spot artifacts appear at the deeper region (bottom left and right) in LOC-weighted CMSB images. This is because LOC estimated from the channel signals of the homogeneous speckle region is sensitive to local SNR levels [31].  
In addition, the resolution of CMSB-based methods is worse than DAS when the number of transmit angles decreases to 23 in the simulation and 37 in the experiment as illustrated in Figure 11. This is likely because the channel SNR decreases notably when transmitting a small number of plane-waves, and thus the amplitude standard deviations for desired targets and incoherent noise are close. This limits the imaging performance of CMSB-based methods in higher-frame-rate CPWC imaging with a smaller number of transmit angles. One possible way to address this problem is to set the maximum subarray length \(L_{max}\) to the total number of transmit angles rather than half the total number of transmit angles in this study. This might improve the resolution performance of CMSB-based methods at the expense of the speckle quality. Furthermore, the signal sparse representation [51] and coded excitation technique [52] used to enhance SNR and the tensor completion (TC) to achieve the same image quality using 20% of a total angle of 75 [53] can be utilized to improve the imaging performance of CMSB-based methods when using a much lower number of transmit plane-waves.  
Figure 11: Image quality metrics of different methods as a function of the number of transmit angles in (**a**) simulation, and (**b**) experiment.
# Anaerobic Digestion of Duckweed Used to Remediate Water Contaminated with Zinc and Ammonium
###### Abstract  
This study presents an integrated approach for the remediation of zinc- and ammonium-contaminated water using duckweed, followed by the valorization of the harvested biomass through anaerobic digestion for biogas production. Duckweed was cultured with various initial concentrations of zinc (Zn, 0 mg/L, 2.5 mg/L, and 5 mg/L) and ammonium (NH4\({}^{+}\)-N, 0 mg/L, 20 mg/L, and 40 mg/L). Subsequently, duckweed was subjected to chemical pretreatment with sulfuric acid and the obtained residual solid and liquid fractions were evaluated as substrates for methane production. The liquid fraction consistently yielded higher methane production compared to the solid fraction. However, when duckweed was grown in zinc- and ammonium-rich conditions (2.5 or 5.0 mg/L Zn and 20 mg/L NH4\({}^{+}\)-N), methane production from the liquid hydrolysate was significantly reduced (120.90 \(\pm\) 12.03 mL/g COD and 129.82 \(\pm\) 10.65 mL/g COD, respectively) compared to the control duckweed (201.67 \(\pm\) 5.72 mL/g COD). The lowest methane yields were observed for duckweed grown solely in zinc (111.32 \(\pm\) 5.72 and 99.88 \(\pm\) 10.49 mL/g COD for 2.5 and 5.0 mg/L Zn, respectively), attributed to the inhibitory effect of high dissolved zinc concentrations in the liquid fraction. The applicability of this integrated system is particularly relevant for the agricultural and industrial sectors, where wastewater streams are often co-contaminated with nutrients and trace metals. By demonstrating that acid-pretreated, zinc-rich duckweed biomass can be used for biogas production--provided that process conditions are optimized to mitigate metal inhibition and acidification--this study provides actionable strategies for developing circular, sustainable wastewater treatment systems. The approach not only maximizes pollutant removal and resource recovery, but also addresses environmental safety concerns associated with residual metals in the digestate.  
phylogenetic; duckweed; zinc; ammonium nitrogen; anaerobic digestion; biogas; methane
# A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data
## 4 Experiment Preparation and Result Analysis  
### Datasets  
To verify the effectiveness and reliability of the proposed algorithm EKR for imbalanced classification, we use 16 imbalanced datasets from KEEL dataset repository, Table 1 introduces the selected datasets in detail, including the number of features (#Features), the number of data samples (#Samples), the imbalance ratio (IR), and the optimal \(k\)-value calculating by Average Silhouette Coefficient for each dataset. The imbalance ratio of these datasets are between 1.87 and 41.4 with the number of data samples ranging from 214 to 5472.