esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Intelligence, Sensors and Vital Health Signs: A Review
## 4 Internet of Things (IoT) for Healthcare Delivery
### Epidemic diseases treatment and location-aware solutions  
The recent COVID-19 pandemic brought new approaches for non-contact measurement of temperature, fever, etc. This resulted in low-cost smart sensors that are environmentally friendly. For example, the AutoTriage system, presented in [61], can run real-time deep learning algorithms at the edge level that identify the forehead and lip regions and permit the temperature estimate of the frontal region of the face with an infrared camera, while cyanosis is detected from lips region in the visible spectrum. In another similar work, [62]proposed a new framework for identifying symptoms of COVID-19 based on smartphone sensors. The framework can be used by both experts (radiologists) and non-experts on smartphone devices for malware detection purposes.  
In general, IoT-based health monitoring is gradually being widely accepted by medical practitioners in the areas of real-time medication and rehabilitation. In related work, the relationship between edge and cloud computing was presented in [63]. In the reported work, a COPD patient used a portable oxygen concentrator (POC) with an IoT device to transmit information aimed at adjusting and tailoring the oxygen concentration to the patient's real-time situation. This safeguards a therapy tuned to patients' activity by constantly collecting and processing health-related information.
## 5 Results and Discussions
### Discussions  
The measured ground settlement difference for vacuum preloading is smaller than that of surcharge preloading. This may be because the vacuum preloading applies a relatively more uniform pressure on the ground than surcharge preloading, which initializes the trapezoid pressure with small value at the sides and a large one in the center. This is verified by [PERSON] et al. [19]. Therefore, the vacuum preloading benefits to reduce differential ground settlement [20; 36; 37].  
As can also be seen from Figure 3c, the stratified settlements at the depth of 21 m are less than 100 mm, approximately 7% of the ground settlements, in the vacuum and surcharge preloading. Therefore, the depth of 21 m can be regarded as the influence depth of vacuum preloading and surcharge preloading. Figure 6 shows that the \(U_{st}\) decreases as depth increases, while there are increases at the depth of 21 m in the two preloading regions. This may be because the permeability of soil (k) and the void ratio (e) of this site decreases with increasing depth, while sharply increasing at a depth of approximately 21 m close to the stratigraphic boundary between clay and silt clay (seen Table 1) [34; 36; 38]. The sharp increasing of strata permeability coefficient will induce the increase in strata \(U_{st}\) and has little effect on the ground \(U_{st}\).  
Figure 8: Historical curve of horizontal settlement.  
The consolidation degrees denoted as \(\overline{U}_{t},\ U_{st},\ U_{wt}\), calculated by the consolidation model method [39], \(S_{t}\) and \(u_{t}\) for vacuum and surcharging preloading, respectively, are plotted in Figure 9. Figure 9 shows that in vacuum and surcharging preloading, the \(\overline{U}_{t},\ U_{st}\) are close to each other within 35 days after preloading, while they deviate significantly after the 35 th day. On the 83 rd day, the sequence of consolidation degree from the maximum to the minimum is \(\overline{U}_{t},\ U_{st},\ U_{wt}\). The \(U_{wt}\) is less than \(U_{st}\) within 50 days and is relatively close to \(U_{st}\) after the 50 th day.  
Figure 9: The consolidation degrees denoted as \(\overline{U}_{t},\ U_{st},\ U_{wt}\) and \(\frac{S_{t}}{S_{1\omega}}\): (**a**) vacuum preloading; (**b**) surcharge preloading.  
Based on the definition of \(\overline{U}_{t}\) and \(U_{st}\), the ratio of \(S_{st}\) to \(S_{1,\infty}\) is calculated by:  
\[\frac{S_{st}}{S_{1,\infty}}=U_{st}-\overline{U}_{t}\frac{S_{p}}{S_{1,\infty}}- \frac{S_{i}}{S_{1,\infty}} \tag{15}\]  
The \(S_{st}\) develops with the increasing of the difference between \(U_{st}\) and \(\overline{U}_{t}\frac{S_{p}}{S_{1,\infty}}\). On the 83 rd day, the \(S_{st}\) accounts for 9% of \(S_{1,\infty}\) in vacuum preloading and 10% in surcharge preloading.  
In the drainage consolidation improvement of soft soil, vacuum preloading is more beneficial to reduce the uneven settlement, but in order to reduce the ground settlement of ground during its service, surcharge preloading should be given priority compared with vacuum preloading.
# Imbalanced Diagnosis Scheme for Incipient Rotor Faults in Inverter-Fed Induction Motors
## 1 Introduction
Moreover, in engineering scenarios, the reduced machine information about abnormal conditions limits intelligent fault diagnosis performances since diagnosis tools with small and imbalanced data reduce the accuracy of fault identification [12; 14]. This paper aims to improve the diagnosis of faults in inverter-fed induction motors, mainly focusing on rotor bar failure. It proposes using the SMOTE oversampling technique to address imbalances in datasets to enhance diagnostic performance and interpretability. The proposed method is intended to work with a small and unbalanced dataset. Additionally, the fault features will be derived from the stator current in an inverter-fed motor, which is a source of high harmonic and polluted content in this signal (as mentioned before). Hence, this study aims to create an effective diagnostic tool for the circumstances previously described. This work begins by reviewing various techniques used to generate synthetic data to balance the initial dataset, focusing on the computed feature space. The implemented techniques will be Safe-Level SMOTE, Relocating Safe-Level SMOTE, and Majority weighted minority oversampling technique (MWMOTE), which have been proposed for balancing datasets. Firstly, the Relieff algorithm performs the feature selection. Then, the developed diagnosis methodology uses a resampling technique for balancing data. When the training set is balanced, a supervised classifier is used for the fault diagnosis of rotor asymmetries with different degrees of bar breakage. The fault signatures proposed in the bibliography are introduced in the next section. These signatures are computed from signals collected during experimentation from an induction motor fed from inverters and the line.
## 2 Wastewater Treatment by Microalgae
### Industrial Wastewater
#### 2.3.3 Microalgae Treatment of Industrial Wastewater  
Despite the great variety of industrial effluent, (Tables 11 and 12 and Figures 6 and 7), microalgae have mostly been utilized to remediate wastewaters from the food industry, because they contain a lot of organic materials and are nontoxic for microalgae cultivation. For instance, in pilot scale conditions (open troughs with a semi-cylindrical barrel form, 29-42 \({}^{\circ}\)C, 28.944-196.015 W m\({}^{-2}\) light intensity), _Ascochloris_ sp. ADW007 demonstrated its capacity to reduce the pollutants found in raw industrial dairy wastewater (pH: 3.69, COD: 7110 mg L\({}^{-1}\), TN: 137 mg L\({}^{-1}\), TP: 105.1 mg L\({}^{-1}\)) as well as its ability to produce lipids for the generation of biodiesel [113]. In comparison to laboratory scale conditions, the strain produced the most biomass (0.207 g L\({}^{-1}\) d\({}^{-1}\)) and lipids (34.98%) while also having the greatest wastewater treatment efficiency with hollow fiber filtration and activated carbon treatment as post-harvesting techniques (COD: 95.1%, TN: 79.7%, TP: 98.1%) [113]. Lactic acid wastewater was also used to cultivate _Senedensmus dimorplus_ for 10 days, allowing total nitrogen and total phosphate elimination rates of 96.31% and 90.78%, respectively, as well as a high lipid production of 1.54 g L\({}^{-1}\) compared to a BG11 culture medium (1.04 g L\({}^{-1}\) of lipids) [124]. Authors found that this treated wastewater might be used four times to produce lactic acid, potentially offsetting the expenses of lactic acid wastewater disposal [124]. Recently, a piece of research examined the potential of microalgae in producing electricity and industrial dairy wastewater treatment [115]. They isolated several marine microalgae and evaluated their electrogenic and redox peak activities. Of 18 microalgae isolates, _Coelastrella_ sp. showed a better electrogicity and redox peak and capability to remove nutrients from dairy wastewater, with the best removal efficiencies of NH\({}_{4}\)-N (90.38%), NO\({}_{3}\)-N (90.24%), P (66.75%), COD (69.44%) and TOC (83.51%) in a lab-scale raceway pond [115]. In addition, soy processing wastewaters treatment was also investigated using microalgae treatment [89; 91; 96]. Three different dilutions were used to cultivate two _Chlorella_ species L166 and L38 in soybean effluent (0, 10 and 100 times) [94]. The results showed that _Chlorella_ sp. L38, grown in a 10-times-diluted soybean effluent, had the highest removal efficiencies for TN (84.7%), TP (97.3%), and COD (70.5%), but that _Chlorella_ L166 strain produced the highest levels of lipids by about 7.22 mg L\({}^{-1}\) d\({}^{-1}\) and polysaccharides by about 2.86 mg L\({}^{-1}\) d\({}^{-1}\) under the same conditions of culture [94].
# Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization
###### Abstract  
Accurate short-term power load forecasting (STPLF) is critical for balancing electricity supply-demand and ensuring grid reliability. To address the challenges of fluctuating power loads and inaccurate predictions by conventional methods, this paper presents a novel hybrid framework combining Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), and the Improved Sparrow Search Algorithm (ISSA). First, the power load series is decomposed into intrinsic mode functions (IMFs) via VMD, where the optimal decomposition order K is determined using permutation entropy (PE). Next, the decomposed IMFs and meteorological covariates are reconstructed into feature vectors, which are then input into the LSTM network for component-wise forecasting, and, finally, the prediction results of each component are reconstructed to obtain the final power load prediction result. The Improved Sparrow Search Algorithm (ISSA), which integrates piecewise chaotic mapping into population initialization to augment the global exploration capability, is employed to fine-tune LSTM hyperparameters, thereby enhancing the prediction precision. Finally, two case studies are conducted using Australian regional load data and Detu'an City historical load records. The experimental results indicate that the proposed model achieves reductions of 73.03% and 82.97% compared with the VMD-LSTM baseline, validating its superior predictive accuracy and cross-domain generalization capability.  
short-term power load forecasting; variational mode decomposition; improved Sparrow search algorithm; long short-term memory network Article
# Space Robot Sensor Noise Amelioration Using Trajectory Shaping
## 2 Materials and Methods
### Second-Order Structural Filters  
After the addition of the PID controller, it was determined that additional filtering was needed to compensate for the system's flexible modes. Additional filtering was added in the form of second-order structural filters. Classical second-order structural filters were designed to compensate for the flexible modes, following the convention defined in Equation (17), where \(\omega_{z}\) and \(\omega_{p}\) are the frequencies of the zeros and poles respectively and \(\zeta_{z}\) and \(\zeta_{p}\) are the damping ratios of the zeros and poles. A tutorial elaboration of classical filter design is available in reference [23].  
\[\frac{Output(s)}{Input(s)}=\frac{\frac{s^{2}}{\omega_{z}^{2}}+\frac{2\zeta_{z }^{2}s}{\omega_{z}^{2}s}+1}{\frac{s^{2}}{\omega_{p}^{2}}+\frac{2\zeta_{p}}{ \omega_{p}^{2}}s+1} \tag{17}\]  
Equation (17) was used to generate bandpass and notch filters to compensate for the valley and peaks for each of the flexible mode cantilever responses.
## 5 Experiments and Discussions
### Discussions  
According to the experimental results, it can be seen that the trained HDL model can accurately distinguish the state of DTW. However, limited by the amount of training datasets, the accuracy of the model still needs to be improved. At the same time, due to the limited wear tool samples used in the collected datasets, the model's identification ability of slight diamond-tool wear is poor. Due to the lack of samples with smaller wear amounts and the limitation of the generalization ability of the HDL model, the trained model cannot judge the specific state of the diamond tool whose wear amount is tiny. The minimum tool wear amount that can be identified by the model can only reach the front-tool-face wear amount of about 1.5 \(\upmu\)m. The classification accuracy will decrease greatly when the HDL model deals with a diamond tool with a wear less than 1.5 \(\upmu\)m on the front tool face. It is still necessary to further expand the amount of the dataset and the number of diamond-tool samples to train a model that can distinguish the diamond-tool states with smaller wear. On the other hand, future research could be conducted on introducing deep reinforcement learning techniques [28; 29] to the DTW and its following-up decisions.  
Due to different wear mechanisms when cutting different materials, the cutting force signals are greatly different. Thus, it is difficult for same HDL model to classify tool wear condition accurately with different materials. To make the model adapt for different materials, a meta-learning module, which can fine-tune the parameters of the HDL model with different cutting conditions, can be added in this HDL model.
## 2 Materials and Methods
### Contact Angle Measurement  
The measurements of contact wetting angles were taken using the standard method of a lying drop using a LK-1 goniometer manufactured by RPC OperScience Ltd. (Krasnogorsk, Russia). Data acquisition and subsequent digital processing of droplet images for the direct calculation of angles using the Young-Laplace equation were carried out using DropShape software v.1.0. The error of measurements was \(\pm 2^{\circ}\). The temperature at which the experiments were conducted was equal to the ambient temperature and was \(21\pm 2\ ^{\circ}\)C.
## 3 Results and Discussion
### Electrical Conductivity of the Development CFRCM and SFRCM
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mixture**} & \multicolumn{2}{c}{**Fiber Volume Fractions (\%)**} & \multicolumn{2}{c}{**Average Electrical Conductivity, \(\sigma\) (S/cm)**} \\ \cline{2-6}  & **CF** & **SF** & **28 Days** & **56 Days** & **91 Days** \\ \hline Carbon10 & 0.10 & - & \(1.71\times 10^{-4}\) & \(1.37\times 10^{-4}\) & \(7.44\times 10^{-5}\) \\  & & & \((12.3\times 10^{-5})\) & \((11.8\times 10^{-5})\) & \((7.80\times 10^{-6})\) \\ Carbon20 & 0.20 & - & \(2.16\times 10^{-4}\) & \(1.62\times 10^{-4}\) & \(7.67\times 10^{-5}\) \\  & & & \((4.00\times 10^{-5})\) & \((3.64\times 10^{-5})\) & \((9.01\times 10^{-6})\) \\ Carbon30 & 0.30 & - & \(3.25\times 10^{-4}\) & \(2.63\times 10^{-4}\) & \(1.78\times 10^{-4}\) \\  & & & \((5.54\times 10^{-5})\) & \((5.50\times 10^{-5})\) & \((3.62\times 10^{-5})\) \\ Carbon40 & 0.40 & - & \(2.54\times 10^{-3}\) & \(2.67\times 10^{-3}\) & \(2.66\times 10^{-3}\) \\  & & & \((3.37\times 10^{-4})\) & \((2.97\times 10^{-4})\) & \((2.23\times 10^{-4})\) \\ Carbon50 & 0.50 & - & \(3.23\times 10^{-3}\) & \(3.32\times 10^{-3}\) & \(3.29\times 10^{-3}\) \\  & & & \((9.24\times 10^{-4})\) & \((9.61\times 10^{-4})\) & \((9.69\times 10^{-4})\) \\ Carbon60 & 0.60 & - & \(3.45\times 10^{-3}\) & \(3.51\times 10^{-3}\) & \(3.57\times 10^{-3}\) \\  & & & \((1.08\times 10^{-3})\) & \((1.08\times 10^{-3})\) & \((1.11\times 10^{-3})\) \\ Carbon75 & 0.75 & - & \(3.68\times 10^{-3}\) & \(3.65\times 10^{-3}\) & \(3.64\times 10^{-3}\) \\  & & & \((1.13\times 10^{-3})\) & \((1.15\times 10^{-3})\) & \((1.19\times 10^{-3})\) \\ Steel25 & - & 0.25 & \(2.65\times 10^{-4}\) & \(2.06\times 10^{-4}\) & \(1.19\times 10^{-4}\) \\  & & & \((3.76\times 10^{-5})\) & \((3.01\times 10^{-5})\) & \((2.05\times 10^{-5})\) \\ Steel50 & - & 0.50 & \(2.79\times 10^{-4}\) & \(2.31\times 10^{-4}\) & \(1.49\times 10^{-4}\) \\  & & & \((1.10\times 10^{-5})\) & \((1.19\times 10^{-5})\) & \((1.40\times 10^{-5})\) \\ Steel75 & - & 0.75 & \(2.87\times 10^{-4}\) & \(2.58\times 10^{-4}\) & \(1.57\times 10^{-4}\) \\  & & & \((3.18\times 10^{-5})\) & \((2.89\times 10^{-5})\) & \((2.46\times 10^{-5})\) \\ Steel100 & - & 1.00 & \(6.12\times 10^{-4}\) & \(4.57\times 10^{-4}\) & \(2.18\times 10^{-4}\) \\  & & & \((1.21\times 10^{-4})\) & \((8.89\times 10^{-5})\) & \((5.10\times 10^{-5})\) \\ Steel125 & - & 1.25 & \(7.94\times 10^{-4}\) & \(5.51\times 10^{-4}\) & \(3.57\times 10^{-4}\) \\  & & & \((3.80\times 10^{-4})\) & \((1.17\times 10^{-4})\) & \((1.41\times 10^{-4})\) \\  & & & \(1.05\times 10^{-4}\) & \(8.66\times 10^{-5}\) & \(6.18\times 10^{-5}\) \\ PM & - & - & \((5.01\times 10^{-6})\) & \((6.70\times 10^{-6})\) & \((1.18\times 10^{-5})\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The results of average electrical conductivity measurements for the studied specimens. Figure 12: Variation of electrical conductivity by fiber volume fractions in CFRCM, SFRCM, and PM specimens at 28 days, 56 days, and 91 days, respectively.
# A Study on a Radio Source Location Estimation System Using High Altitude Platform Stations (HAPS)
## 3 Numerical Analysis and Results
### Comparison of LoS Rate between the HAPS and UAVs
#### 3.1.2 Results of LoS Rate Measurement Simulation  
The results of the simulation conducted above are shown in Figure 12. The LoS ratio of the HAPS was about 100% when the center of the urban area (where the radio source was generated) was directly below the HAPS, and about 60% at a distance of 20 km (elevation angle 45\({}^{\circ}\)). On the other hand, when a UAV was used, the LoS ratio was about 30% even when the center of the urban area was directly below the UAV, indicating that the majority of the radio sources were out of sight. Therefore, a specific altitude for the HAPS is considered necessary for position estimation by angle estimation.  
Figure 11: Analysis of LoS ratio when sensors are away from the evaluation center.  
Figure 12: Relationship between distance and LoS ratio.
# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 2 Literature Review
### Support Vector Machine  
Support Vector Machine (SVM) is a supervised learning model used for pattern recognition and classification or regression analysis. It works by finding an optimal hyperplane that separates the data classes by maximizing the distance between the margin and the classes, reducing classification errors [6]. SVM is an effective machine learning algorithm for sign language prediction, particularly in high-dimensional spaces and when the number of samples exceeds the number of dimensions [23]. While SVM is excellent at classifying, it requires a lot of computational power if the dataset size is too large. Therefore, it can also have computational issues if implemented in real-time applications [24]. According to [24], having a good training set with quality labels and balanced data is essential to creating a successful SVM model. Such as the KNN classifier, SVM is a classifier that employs the scikit-learn library in Python to build the model. Therefore the optimal hyperparameters for the model can be determined using GridSearchCV or RandomizedSearchCV.  
Ref. [25] used SVM to create a static PSL (Pakistan Sign Language) model. They used K-means clustering segmentation to separate the fingers and palm area (foreground) from the background. They then used multiple kernel learning to test which feature extraction method was the most effective. They tried EOH (Edge Orientation Histograms), LBP (Local Binary Patterns), and SURF, and the results showed that HOG combined with linear kernel function achieved the best accuracy of 89.52%. They used a dataset containing 6633 static images of 36 alphabet signs from 6 signers to build this model. In [26], they created an SVM model that performed pre-processing by converting the images to grayscale, normalizing the data, and applying gamma correction. For feature extraction, they used Multilevel-HOG. They performed this on an ISL complex background dataset containing 2600 images of 26 classes in grayscale and an ASL dataset containing 2929 images of 29 classes in grayscale. They achieved 92% accuracy on the ISL complex background dataset and 99.2% accuracy on the ASL dataset.
# Image Classification of Parcel Boxes under the Underground Logistics System Using CNN MobileNet
## 3 Methodology
### MobileNet Algorithm  
The traditional CNN is proven to have an excellent performance in the image recognition field, but it requires a lot of computation to learn and test the algorithm. Therefore, a high-performance GPU and large memory is essential. In order to solve this problem, MobileNet, which is one of the methods of changing the convolution filter, was proposed. MobileNet was developed by Google and has the advantage of a fast execution time because it is optimized for a small size, and is ideal for operating in mobile devices or devices with limited resources [16].  
MobileNet consists of a neural network that uses depthwise separable convolution from the existing convolution. Depthwise separable convolution is a concept for factoring an existing convolution and it consists of two steps: depthwise convolution, which filters the convolution, and pointwise convolution, which performs summation processing. Depthwise convolution performs a single-filter operation processing for each channel for all channels of input data, and then outputs the result. Pointwise convolution is composed of a \(1\times 1\) convolution, and it is summed and printed out through depthwise convolution [18]. The result of depthwise separable convolution, based on RGB images with a \(3\times 3\) filter and a movement interval of 1, can be defined as Equations (2) and (3) [17]:  
\[\hat{O}=\sum_{j=1}^{3}\sum_{i=1}^{3}K_{i,j,c}\cdot F_{x+i,y+j-1,c} \tag{2}\]  
Figure 3: Convolutional neural network architecture.  
\[O=\sum_{c=1}^{3}\hat{K}_{c,n}+\hat{O}_{x,y,c} \tag{3}\]  
where \(\hat{O}\) is the output of the depthwise convolution, \(K\) is the single filter of the depthwise convolution and \(F\) represents the input. \(O\) is the output of the pointwise convolution, \(\hat{K}\) represents the filter of the \(1\times 1\) convolution. In the case that the traditional \(3\times 3\) convolution is changed to a depthwise separable convolution, the amount of computation is reduced by about eight to nine times [18].  
Figure 4 shows the general architecture of MobileNet, which consists of a total of 28 layers. The first layer in the architecture consists of \(3\times 3\) convolutions and is used for lightweight filtering that applies a single convolutional filter per input channel. The second layer consists of a \(1\times 1\) convolution, also called a pointwise convolution. The rectified linear unit (ReLU), called the activation function, has a faster computation speed and higher accuracy than the existing sigmoid function. The ReLU function is used in each convolutional layer [19].