esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Rapid Recognition of Moving Small Targets by Robotic Arms Based on Attention Mechanisms
## 1 Introduction
Experimental results showed that after the introduction of the attention mechanism, YOLOv5s achieved an accuracy of 85.6%, a recall rate of 85.2%, and an average accuracy of 90.2% for specific ship items, effectively accomplishing its task of detecting specific small objects in ship cabins. The study illustrates how incorporating an attention mechanism can notably enhance the efficacy of detecting small targets. However, the reliance on the YOLOv5 framework suggests potential areas for further refinement in detection outcomes. Concentrating on deep learning-based methods for object detection and semantic segmentation in images, this research particularly addresses the classification of swiftly moving small objects on conveyor belts. It introduces an innovative detection and segmentation algorithm that leverages an attention mechanism, employing a deep learning-based modified basic network model. This methodology is effective for both identifying and segmenting stationary targets and tracking dynamic ones. Finally, the developed models were tested on common datasets for these detection tasks, and the improved algorithms achieved good experimental results. The main innovative points of this paper are as follows:  
(1) Given the challenges associated with accurately detecting small moving objects on high-speed conveyor belts in industrial settings--a task that conventional detection algorithms often struggle with--this study introduces an enhanced object detection algorithm aimed at reducing instances of missed detections and false positives. (2) The algorithm presented herein is founded on the YOLOv8 architecture and incorporates an attention mechanism as a plugin. This addition is designed to more effectively capture essential feature information, thereby ensuring that the refined algorithm meets the real-time demands of detection tasks. (3) With regards to the dynamic target detection problem, this paper employs a modified loss function to optimize the issue of poor detection results for dynamic small targets in the original algorithm.
# A Dense Small Object Detection Algorithm Based on a Global Normalization Attention Mechanism
## 3 Experiments
### Comparison of Detection Results of Different Object Detection Algorithms on VisDrone2019
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Backbone**} & \multicolumn{8}{c}{**Object Category**} & \multirow{2}{*}{**mAP\({}_{\text{BAS}}\)**} \\ \cline{2-2} \cline{5-11}  & & **Pedestrian** & & **People** & & **Bicycle** & **Car** & **Van** & **Tuck** & **Tii** & **Awn-tri** & **Bus** & **Motor** \\ \hline Faster R-CNN [21] & **ResNet-50** & 21.4 & 15.6 & 6.7 & 51.7 & 29.5 & 19.0 & 13.1 & 7.7 & 31.4 & 20.7 & 21.7 \\ Faster R-CNN [21] & ResNet-101 & 20.9 & 14.8 & 7.3 & 51.0 & 29.7 & 19.5 & 14.0 & 8.8 & 30.5 & 21.2 & 21.8 \\ VOLOv4 [22] & CSPMarknet & 24.8 & 12.6 & 8.6 & 64.3 & 22.4 & 22.7 & 11.4 & 7.6 & 44.3 & 21.7 & 30.7 \\ CenterNet [23] & Hourglass-104 & 33.3 & 15.2 & 12.1 & 55.2 & 40.5 & 34.1 & 29.2 & 21.6 & 42.2 & 27.5 & 31.1 \\ HR-Cacade++ [23] & HRNet-W40 & 32.6 & 17.3 & 11.1 & 54.7 & 42.4 & 35.3 & 32.7 & 24.1 & 46.5 & 28.2 & 32.5 \\ CDNet [23] & ResNeXt-101 & 35.6 & 19.2 & 13.8 & 55.8 & 42.1 & 38.2 & 33.0 & **25.4** & 49.5 & 29.3 & 34.2 \\ VOLOv5 & CSPMarknet & **44.4** & 36.7 & 18.5 & 74.2 & 37.7 & 37.4 & 25.3 & 12.7 & 48.6 & 43.3 & 37.9 \\
**GNYL (Ours)** & **CSPMarknet** & **57.9** & **46.3** & **22.0** & **86.2** & **53.1** & **42.3** & **37.6** & **21.0** & **64.5** & **57.4** & **48.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative experiments on different object detection algorithms. Figure 7: The confusion matrix of GNYL on VisDrone2019. altitude field of view, complex backgrounds, and object occlusion, as shown in Figure 8. At the same time, the detection performance of the baseline network and GNYL are compared under the limit of selecting a rich number of small objects, as shown in Figure 9.  
Figure 8: GNYL detection effect in different scenarios. Figure 9: Comparison of detection effects.
## 2 Materials and Methods
### Temperature-Related Risk RiskT  
There are few indexes or metrics that integrate the weather parameters with ease to understand the proper sensation of people outdoors. Most of them require rather extensive on-site surveys to feed the model with several measured quantities, or to feed the virtual model for lengthy computer simulations. Fortunately, indexes such as the Universal Thermal Climate Index (\(UTCI\)) [29] can be used in a simplified manner, which requires rather commonly surveyed parameters to estimate the pedestrian's sensation of his/her surroundings; that is:  
* Dry bulb air temperature (\(t_{db-air}\))
* Mean Radiant Temperature (\(MRT\))
* Relative Humidity (\(RH\))
* Air/wind velocity (\(V_{a}\))  
However, for a preliminary and large-/meso-scale outdoor human-centered analysis, there is limited information on where the pedestrian is or will be located and how he/she is or will be exposed to solar and emitted surface-heat radiation. Thus, computing spatial-location variables, such as \(MRT\) and spot-specific \(V_{a}\), goes beyond the scope of this study. Moreover, established simplifications for \(MRT\) estimations (as the ones listed in ASHRAE 55-2017 [30] for an indoor assessment) cannot be applied given the exposure to direct solar radiation, high registered \(V_{a}\), high variance of the type of pedestrian activity, and the clothing worn.  
Therefore, for surveyed data obtained from open-source data such as national, regional, or local weather station network repositories, another approach is needed to anticipate and identify potential moments during the year in which pedestrians can be more susceptible to risk. A susceptible temperature-related risk is then interpreted as the conjoint frequency of undesirable conditions of \(t_{db-air}\), solar radiation (\(I_{tot}\)), \(RH\), and \(V_{a}\).  
To do so, a simple Boolean and weighting analysis was performed following fuzzy logic; that is, for every hour of the year, the condition of each parameter was verified; if so, this was allocated a certain risk weight according to the parameter's influence on thermal stress, and all the weights were summed for every single Hour Of the Year (\(HOY\)) (see Equation (1)).  
\[RiskT_{HOY}=\left(T_{bool}*T_{weight}+I_{bool}*I_{weight}+RH_{bool}*RH_{weight }+V_{bool}*V_{weight}\right)_{HOY} \tag{1}\]
# Fault Diagnosis Across Aircraft Systems Using Image Recognition and Transfer Learning
###### Abstract  
With advances in machine learning, the fault diagnosis of aircraft systems is becoming more efficient and accurate, which makes condition-based maintenance possible. However, current fault diagnosis algorithms require abundant and balanced data to be trained, which is difficult and expensive to obtain for aircraft systems. One solution is to transfer the diagnostic knowledge from one system to another. To achieve this goal, transfer learning was explored, and two approaches were attempted. The first approach uses relational similarity between the source and target domain features to enable the transfer between two different systems. The results show it only works when transferring from the fuel system to ECS but not to APU. The second approach uses image recognition as the intermediate domain linking the distant source and target domains. Using a deep network pre-trained with fuel system images or the ImageNet dataset finetuned with a small amount of target system data, an improvement in accuracy is found for both target systems, with an average of 6.90% in the ECS scenario and 5.04% in the APU scenario. This study outlines a pioneering approach that transfers knowledge between completely different systems, which is a rare transfer learning application in fault diagnosis.  
fault diagnosis; machine learning; maintenance; transfer learning; environmental control system; fuel system; auxiliary power unit +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00  
accurate AI algorithm for each system, one way that could make CBM more efficient is to transfer the diagnostic capacity from one system to another, such as between an aircraft's fuel system and its environmental control system (ECS), thereby reducing the cost of developing CBM technology for aircraft.
## 4 Identification of Faulty Events
### Three Phase Fault Involving Ground  
A fault involving all phases and grounds (ABCG fault) was simulated as an incident at the sixth cycle. The currents recorded on node 650 were processed using the algorithm and results for a period of 0.2 s (12 cycles) are depicted in Figure 7. It can be seen from Figure 7a that a magnitude of currents corresponding to all phases increases after the ABCG fault. Figure 7b depicts the WD-index corresponding to all the phases with a high magnitude during the incidence of a fault. Figure 7c illustrates the ALN-index which has a high magnitude corresponding to all the phases at the time of fault incidence. Figure 7d illustrates the fault index in which the FI has a high magnitude compared to the threshold corresponding to all the phases at the time of a fault incidence. Hence, the algorithm proposed in this paper effectively detects the ABCG fault.  
Figure 6: Identification of phase-A and phase-B to a ground fault incident on node 671 of the test system (**a**) Current waveform (**b**) WD-index (**c**) ALN-index (**d**) Fault index.
## 3 Results
### Dexterous Grasping
#### 3.2.2 Gaussian Process  
The Gaussian Process (GP) model demonstrated reliable predictions and offered the crucial benefit of uncertainty quantification, which is essential for handling fragile and deformable objects. The model achieved an average Mean Squared Error (MSE) of 0.00833, slightly higher than that of LSTM, yet it provided more conservative and stable predictions. For delicate items such as Strawberries (1.6 N for a two-finger grasp) and Lemon (1.7 N for a two-finger grasp), the GP model effectively minimized the risk of damage by applying lower forces. Additionally, it achieved average forces of 1.9 N, 2.2 N, 1.7 N, and 1.7 N for three-finger configurations for the Ball, Banana, Orange, and Tomato, respectively, as shown in Figure 12.  
Figure 11: Force profile for three-finger grasp configuration for the LSTM model: (**a**) Ball, (**b**) Banana, (**c**) Orange, (**d**) Tomato.  
Figure 12: Force profile for three-finger grasp configuration for the GP model: (**a**) Ball, (**b**) Banana, (**c**) Orange, (**d**) Tomato.  
The model maintained an overall efficiency of 91.3%, comparable to that of the LSTM model. This conservative methodology minimizes damage risks, making it particularly valuable in safety-critical applications. For heavier objects like boxes, the GP exhibited reasonable force estimations but occasionally under-predicted optimal grasp forces, reflecting its cautious nature. Overall, GP's probabilistic approach proved advantageous for safety-critical scenarios, although it faced challenges in adapting to varying grasp configurations.
## 2 Low-Cost Monitoring Stations and Data Collection
The formula that yields the final corrected values by combining the two correction factors and the sensor measurements is shown in Equation (4):  
\[GAS_{corrected_{Measurement}}=\left(\frac{(GAS_{Measurement}+A)}{B}\right) \tag{4}\]  
where \(GAS_{corrected_{Measurement}}\) is the calibrated gas sensor value, \(Gas_{Measurement}\) is the corrected measurement concentration value of the gas pollutant by Equation (3), where \(A\) represents the level factor, which increases or decreases the measurement values to be corrected, and B represents the scaling factor, from which the corrected values of the measurement are derived. The last step of the procedure is the conversion of ppb to \(\mu\)g/m\({}^{3}\). The initial calibration period was carried out in February 2021, at the field, by co-installing low-cost sensors next to a reference instrument. This process resulted in the determination of the A and B coefficients for each low-cost gas sensor. According to the manufacturer, to avoid incorrect gas measurements due to the cross-sensitivity of the Alphasense OX-B431 sensor, Equation (5) must be applied to the measurements in order to obtain the \(O_{3}\) and \(NO_{2}\) concentrations, since the \(O_{3}\) electrochemical sensors are also triggered by \(NO_{2}\),  
\[O_{3}ppb=O_{3}All-NO_{2}ppb \tag{5}\]  
where \(O_{3}ppb\) is the ozone concentration measurement, \(NO_{2}ppb\) is the \(NO_{2}\) concentration value measured by the nitrogen dioxide sensor, and \(O_{3}All\) is the concentration value measured by the ozone sensor.
## 5 Discussions  
### Evaluation of Existing Solutions for IoE Security  
As the IoE continues to expand, security and privacy threats are becoming increasingly complex. Emerging technologies such as Blockchain, Edge AI [16; 25], Quantum-Resilient Cryptography [18; 35; 54], and FL [23; 144; 170] have been proposed to address these challenges. However, these solutions are still under development and face various limitations [10; 14; 17; 59]. For example:  
* Blockchain offers a decentralized security model but must be adapted to handle IoE's high-volume data traffic due to scalability constraints and high transaction costs [20; 73; 152; 178; 179].
* Edge AI enhances IoE device security through AI-powered threat detection but remains vulnerable to adversarial AI attacks and model poisoning threats [25; 27; 61; 64].
* Quantum-Resilient Cryptography provides protection against quantum computing threats, yet the high computational overhead of PQC algorithms makes them difficult to implement on resource-constrained IoE devices [52; 53; 54; 70; 181; 54].
* FL allows decentralized AI training while preserving privacy, but poses security risks in model updates and incurs communication overhead [57; 144; 169; 170].  
A systematic analysis of these technologies' advantages and limitations is essential when evaluating current solutions. Table 19 provides a summary of this evaluation.
# Swarm Intelligence Algorithms for Feature Selection: A Review
## 6 Applications of Feature Selection with Swarm Intelligence
### Datasets  
The used datasets can be divided into three groups (Figure 7), regarding the number of features: Small datasets (up to 150 features), large datasets (more than 2000 features), and mixed datasets between the other two. The first group is composed of many datasets, such as Iris (4 features), Diabetes (8 features), Heart-Statlog (13 features), Wine (13 features), Australian Credit (14 features), Labor (16 features), Hepatitis (19 features), Auto (25 features), Ionosphere (34 features), Spambase (57 features), Sonar (60 features), Splice (62 features), 9_Tumors (90 features), and Mushrooms (112 features).  
Researchers who tested the proposed algorithms only on small datasets are, for example, [38; 44; 63].  
Large datasets consist mainly of medical data (DNA microarray datasets) such as Colon Tumor (2000 features), Leukemia (5,147 features), Brain_Tumor1 (5920 features), Nervous-System and DLBCL-Harvard (both 7129 features), Brain_Tumor2 (10,367), 11_Tumors (12,533 features), LungCancer-Harvard1 (12,600 features), LungCancer-Harvard2 (12,534 features), 14_Tumors (15,009 features), Ovarian cancer (15,154 features), and Breast cancer (24,481 features).  
Some researchers who used only such data are [45; 46; 50; 64; 65; 66].  
The remaining group is composed of mixed datasets. In [42], researchers used eleven large datasets and one small. Datasets from 73 up to 5000 features are used in [34]. Some other researchers in this group are, for example, [28; 54; 55; 67; 68].  
The majority of datasets is available from four sources, i.e., UCI Machine Learning Repository [69], Gems System [70], LIBSVM Data [71], and Kent Ridge Bio-medical Dataset [72].  
In Table 2, the research articles are collected and ordered based on used datasets' dimensions and taxonomy. We left out _Group Hunting_, because we were unable to find any work which included the HS algorithm for FS.
# Research on Hard, Transparent and Hydrophobic Coating on PMMA Sheet
## 3 Results and Discussions
### Microorganism Attachment Rate of Coatings  
The microorganism attachment rates of the PMMA substrate and the S05 coating were measured by counting the mean attachment area ratio of 20 random fields magnified by 40 times. As shown in Figure 9, the attachment area (indexed by pink color) of phaoodactylum tricornutum on the PMMA substrate was apparently larger than that of the S05 coating. Attributed to a better hydrophobicity, the attachment of phaoodactylum tricornutum on the coating surface was weaker and easier to wash away by the water flow. The calculated microorganism attachment rate of the S05 coating was 0.17%, which was about half that of the PMMA substrate.  
Figure 8: Visible light transmission spectra of S05 coatings with different scouring time.  
Figure 7: Water contact angles of S05 coatings with different scouring time (**a**) 0 h, (**b**) 20 h, and (**c**) 40 h.  
Figure 9: FM images of samples after microorganism immersion test for (**a**) PMMA sheet and (**b**) S05 coating.
###### Abstract  
Relative wavelet energy entropy (RWEE) is proposed to detect and quantify damage to hysteretic dampers used for the passive seismic control of building structures. Hysteretic dampers have the role of dissipating most of the energy input of an earthquake. Minor or moderate earthquakes do not exhaust the energy dissipation capacity of the dampers, yet they damage them. For this reason, continuous or periodic damper-health evaluation is required to decide if they need to be replaced. Such evaluation calls for the application of efficient structural health monitoring techniques (SHM). This paper focuses on the well-known vibration technique, which is applied to a particular type of hysteretic damper called Web Plastifying Damper (WPD), patented by the University of Granada. Vibration signals, properly recorded by piezoelectric sensors attached around the damaged area of the dampers, are decomposed by means of wavelet packet analysis. Then, the relative wavelet energy entropy of these decompositions is used to calculate the proposed index. Validation of RWEE for this particular application involved dampers installed in two different specimens of reinforced concrete structures subjected to earthquake sequences of increasing intensity. When compared with a well-established mechanical energy-based damage index, results demonstrate that RWEE is a successful and low-cost technique for reliable in-situ monitoring of dampers.  
structural health monitoring; vibrations; wavelet; entropy; hysteretic dampers; seismic engineering +
Footnote †: journal: Applied Physics Letters
# Heatwave Damage Prediction Using Random Forest Model in Korea
## 2 Methodology
### Random Forest Regression
It was implemented using an R package [44] to confirm whether certain variables can be used as predictive model inputs. Typical parameters of the random forest algorithm are ntree and max depth. To select optimal hyperparameters, the minimum loss function value (MAE) was found by increasing the number of decision trees (n-tree) and their maximum depth (max depth). After separating the dataset comprising the selected variables into training and test datasets, we evaluated the model trained using the training dataset by comparing it with other traditional regression models. Finally, the mean decrease in impurity (i.e., Gini importance) was used to extract the variable importance values, i.e., to determine the predicted contribution of each variable's model.
# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
## 2 Method
### K-Nearest Neighbors (K-NN)  
Classification and regression problems can be tackled with KNN, a non-parametric instance-based learning technique ([PERSON] et al., 2017), by finding the k-number of points closest to a given data point and using the mean of their values as the projected value for that point.  
The KNN algorithm starts by calculating the distance between the new data point and the training points. The distance can be determined using a variety of distance metrics, such as the Euclidean distance, the Manhattan distance, or the Minkowski distance. After calculating the distances, the KNN algorithm selects the k-nearest neighbors from the training set that are most similar to the new data point. In regression, the algorithm calculates the average of the k-nearest training points to estimate the new data point's value. The mathematical expression that may be used to describe KNN is as follows: First, determine how far away the new data point is from each position in the training group. Second, Select the k-nearest neighbors that best describe the new data point based on the computed distances. Finally, predict the value of the new data point by taking the average of the values of the k closest training points ([PERSON] et al., 2017).  
The KNN algorithm is a flexible, simple, and easy-to-implement algorithm that can be applied to various data types, including those with incomplete values and categorical factors. However, it can be computationally expensive due to the need to calculate the distance between each new data point and each training point. It can also be susceptible to unimportant or noisy features. To prevent overfitting and underfitting, selecting the k value carefully is vital. In this research, the KNN algorithm was used with a k value of 13, which was found to be the optimal number through experimental results for predicting gold concentration.
## 4 Results and Discussions
### Ensemble Learning  
Ensemble learning is an important technique for model improvement and is also a widely used technique in data science competitions. Ensemble learning can be further divided into three categories, boosting, bagging, and stacking. In the comparison methods, the gradient boosting regressor and XGBoost models belong to the boosting category, random forest is a bagging method, and the work conducted by [PERSON] et al. used a stacking approach to combine several base models. This work conducted a simple experiment to explore whether the proposed method benefited from ensemble learning. For the ensemble of fusion networks, we used the training set to train ten fusion networks. For each test sample, the ten models had to make their predictions, and the final prediction outcome was simply the average of the ten predictions. It is apparent that this approach is very simple, but it is also one of the most commonly used techniques to combine multiple base models. We used the same architecture with random initialization weights to train the ten fusion networks, which means that each fusion network was slightly different from the others.  
Figure 7 shows the experimental results measured by MSE and MAE, each of which is the average of ten experiments. It can be seen from the results that the ensemble of the fusion network still worked well but the performance difference between these two approaches was very minor. Thus, the proposed method did not benefit from a simple ensemble approach. We conclude that the main reason for the result is that the difference between the fusion networks in the ensemble was very minor. Note that ensemble learning tended to yield better results than the underlying base models when there was significant diversity among the base models. However, the diversity of the fusion networks in the ensemble model was expected to be minor, which is why a simple ensemble approach did not result in a significant performance improvement.