esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey on Videos Data Augmentation for Deep Learning Models
## 3 Review on Video Data Augmentation
### Feature Space  
DL models often extract a one-dimensional, feature vector from the input images. Sometimes, it is more convenient to perform the data augmentation on the feature space instead than on the image space (lack of availability of the original videos due to privacy constraints, ad hoc organization of the feature space, etc.). In their works, [PERSON] et al. [31; 53] proposed a data augmentation strategy for a content-based video recommendation challenge. The authors did not have access to the RGB video frames and applied the data augmentation directly on the feature vector extracted from an InceptionV3 deep network. They propose a data augmentation technique similar to the one used by [PERSON] et al. [26] for video action recognition. Their frame-level data augmentation sub-samples each feature sequence skipping frames with a stride \(s\). Repeating the process starting from a different frame of the original feature sequence, they are able to generate \(s\) distinct new sequences. The authors compared the performance metric scores (recall/hit scores) of the network trained with and without data augmentation on the Hulu Content-based Video Relevance Prediction Challenge 2018. In the most recent work, the network trained with data augmentation achieved an improvement of the performances both for TV-Shows (2.708 \(\rightarrow\) 3.092) and Movies datasets (2.030 \(\rightarrow\) 2.289).
## 3 Machine Learning Models
### Support Vector Machines (SVM)
#### 3.1.4 Weather  
In a different field of research, the work of [PERSON] et al. [24] investigated and compared the prediction abilities of ARIMA, Wavelet Neural Network (WNN), and SVM models for drought time series in the Sanjiang Plain in China. The models of this research were based on the prediction of SPEI (standard precipitation evapotranspiration index) values collected during the period 1979-2016 from seven meteorological stations in the study area. For each of the sites, the parameters of the seasonal ARIMA model were optimized using Akaike's Information Criterion (AIC). The comparison between the raw data values and the predictions resulted in the following \(R^{2}\) and NSE (Nash-Sutcliffe coefficient of performance) values: 0.837 and 0.831 for the WNN network, 0.833 and 0.827 for the SVM network, while the corresponding values of the classical ARIMA algorithms were above 0.9. In addition, the ARIMA models had smaller MSE values and better overall performance than the other two models, while the analysis of variance showed that the ARIMA models had a clear advantage over the other two models in predicting the drought in the Sanjiang Plain in China based on various performance indicators (R2, MSE, NSE, and Kolmogorov-Smirnov distance). According to the results of the study, WNN and SVM machine learning models do not necessarily perform better than traditional ARIMA models in drought prediction. Different methods can be used to analyze data from different regions, and the characteristics of the data should be carefully analyzed in order to select the appropriate prediction model.
## 4 Results
### Reactive Control Tasks (Compensatory Corrections)  
* Correction with a Step Forward: The precision and recall values were close to 0.81, indicating task consistency and ability to handle both positive and negative cases well. This was reflected in a balanced precision of 0.8097.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Task** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Specificity** & **Balanced Accuracy** \\ \hline Sitting to standing & 0.6503 & 0.6009 & 0.9610 & 0.7496 & 0.2672 & 0.6317 \\ Stand on pittoe & 0.7548 & 1.0000 & 0.5290 & 0.6893 & 1.0000 & 0.7630 \\ Left single leg support & 0.7491 & 0.9880 & 0.6812 & 0.8066 & 0.9744 & 0.8278 \\ Right single leg support & 0.7333 & 0.8024 & 0.6931 & 0.7438 & 0.7841 & 0.7386 \\ Correction with a step forward & 0.8102 & 0.8079 & 0.8117 & 0.8340 & 0.8077 & 0.8097 \\ Correction with a step back & 0.8362 & 0.9655 & 0.8248 & 0.8917 & 0.9371 & 0.8828 \\ Correction with a left lateral step & 0.7229 & 0.8361 & 0.7795 & 0.8068 & 0.5597 & 0.6696 \\ Correction with a right lateral step & 0.7243 & 0.8640 & 0.7275 & 0.7902 & 0.7166 & 0.7220 \\ Standing, eyes open, firm surface & 0.7635 & 0.7335 & 1.0000 & 0.8463 & 0.3220 & 0.6610 \\ Standing, eyes open, foam surface & 0.7958 & 0.7248 & 0.9948 & 0.8386 & 0.5648 & 0.7816 \\ Bent over, eyes closed & 0.7727 & 0.9986 & 0.6207 & 0.7656 & 0.9987 & 0.8097 \\ Gait speed change & 0.8330 & 0.9413 & 0.8059 & 0.8683 & 0.8916 & 0.8487 \\ Walk with horizontal head turns & 0.7231 & 0.8163 & 0.7538 & 0.7838 & 0.6621 & 0.7079 \\ Walking with a pivot turn & 0.8713 & 0.9038 & 0.8493 & 0.8757 & 0.8966 & 0.8729 \\ Walking over obstacles & 0.8943 & 0.8728 & 0.9922 & 0.9287 & 0.6725 & 0.8324 \\ Timed Up and Go (TUG) test & 0.7841 & 0.7135 & 0.9670 & 0.8211 & 0.5918 & 0.7794 \\ Dual task over 3 m & 0.6744 & 0.7417 & 0.7963 & 0.7680 & 0.4190 & 0.6077 \\ All combined & 0.8855 & 0.9014 & 0.8793 & 0.8902 & 0.8924 & 0.8859 \\ \hline \end{tabular} Note: The values in this table are expressed in decimal form, with numbers like 0.87 representing 87%. These values reflect performance metrics such as accuracy, precision, recall, and specificity.  
\end{table}
Table 2: Classification performance metrics between fallers and non-fallers for each task in the Mini-BESTest.  
* Correction with a Step Back: This stood out for its high F1 score of 0.8917 and good specificity (0.9371), indicating a strong overall performance. It is one of the best tasks for predicting falls, with a solid balance between sensitivity and specificity.
* Corrections with Left and Right Lateral Steps: This demonstrated lower balanced accuracies (0.6696 and 0.7220), which indicated that the model had difficulty correctly differentiating between positive and negative cases under these lateral corrections.
## 5 Simulation and Experimental Verification
### Experimental Efficiency Results Comparison  
The efficiency of the LCL-DAB converter is mainly affected by the phase shift ratios, and the feedforward compensation type has no significant effect on it. Therefore, the experimental efficiency results are obtained with two values of \(d\), corresponding to the above experiments. All experiments are implemented for a fixed 50 V output voltage and 50 \(\Omega\) resistance of the load. As shown in Figure 17, the efficiency with the two outer phase shift ratios decreases with the increase in the input voltage, and the efficiency for \(d=0.22\) is higher than that for \(d=0.38\). Therefore, Figure 17 indicates the proposed VDCC method can improve the overall efficiency of the LCL-DAB converter in a wide range.
# Using Data Mining in Educational Administration: A Case Study on Improving School Attendance
## 6 Conclusions  
The \(\tt mt\) model, described in Equation (3) and detailed in [16], was adapted to improve school attendance at WPS. The algorithm detailed in Section 3.2.4, which included the \(\tt mt\) model, was used to identify the school session which was most impactful to be overall below the required average attendance. In line with this, the previous three years' attendance data from WPS was analysed and it was found that the Monday AM session was consistently the most impactful session to the overall below the required average attendance. Two initiatives were carried out at WPS that were based on approaches in previous studies and the collective wisdom of WPS leadership and staff [1; 20]. Initiative II provided more frequent and meaningful rewards for full attendance while I2 focussed on improving Monday attendance through the use of themes that were known to be exciting for the pupils.  
Both I1 and I2 resulted in a significant improvement of attendance at WPS, with attendance in 2018/19 being at its highest over the past four academic years. Overall average attendance for the 2018/19 academic year was at the required target of 96%, whilst the combined Spring and Summer term attendance was higher at 96.2%. Monday attendance during the Summer term also improved significantly from an average and range perspective. The average Summer term Monday attendance in 2018/19 was significantly higher than the three previous years at 96.5%, while its range was significantly lower 2.8%, implying that attendance on Mondays was consistently better throughout the term.  
Analysis of the 2018/19 data using the \(\tt mt\) model revealed that Monday AM was no longer the most impactful session to be overall below the required attendance, instead, it is now Friday AM. The underlying dynamics as to why this is the case may also include a shift away from school refusal and more towards school withdrawal (parental condoned absence) which is underpinned by a variety of reasons including cheaper holidays [20; 23]. Addressing this is considered to be part of future work and is detailed in Section 6.2.
## 2 Materials and Methods
### Model Training, Hyperparameter Optimization, and Evaluation  
To assess the effect of hyperparameter optimization on model performance, we trained, optimized, and evaluated five distinct ML algorithms for predicting breast cancer recurrence within 5 years, namely Logistic Regression (LR), Decision Tree (DT), Gradient Boosting (GB), eXtreme Gradient Boosting (XGB), and Deep Neural Network (DNN). The implementation of the ML algorithms and hyperparameter tuning has been carried out in Python, using the Scikit-Learn [30], XGBoost [31], and Tensorflow [32] libraries. To analyze the effectiveness of hyperparameter optimization on model prediction, we compared the performance of each model before (i.e., using default hyperparameters of the respective ML libraries) versus after optimization. To build and evaluate the models, we adopted a combination of hold-out and cross-validation strategies, thereby ensuring a robust evaluation by assessing model performance on unseen data [33]. The dataset was randomly divided into two mutually exclusive sets with 90% of the samples allocated for training and fine-tuning and 10% for testing. First, we trained all the algorithms using the default value for hyperparameters specified in the corresponding software package based on experience and general recommendations. In the case of the DNN, some extra configuration was required. A network with a single hidden layer with as many neurons as input features was chosen as the basis. In addition, as recommended for classification problems [34], binary cross-entropy was set as the loss function. Subsequently, hyperparameter optimization was performed using the grid-search method, implemented through three rounds of stratified 6-fold cross-validation on the training set. Each pass of cross-validation utilized 75% of the whole dataset for training and 15% for validation. This strategy allowed us to systematically explore multiple combinations of hyperparameters and select the most effective set for each algorithm. Table 1 describes the parameters that were optimized in each of the algorithms, along with the search space used. A critical problem in training ML algorithms on healthcare data is the inherent imbalance of the datasets. The class imbalance distribution biases classifiers towards the majority class, which results in an unsatisfactory prediction performance for the minority class [35]. In our dataset, we found that the number of patients who eventually had cancer recurrence in five years was 13% of the total sample. To mitigate the impact of imbalanced data, we applied the Synthetic Minority Over-sampling Technique (SMOTE) [36] in each cross-validation pass. This technique oversampled the minority class, ensuring equal representation and reducing bias in the model towards the majority class. A k-value of 5 was employed to generate synthetic samples, contributing to a more representative training set. Once the best hyperparameters for each algorithm were identified, we refitted the models using the entire training partition. Subsequently, we evaluated the performance of
# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
## 3 Proposed Method
### Network Architecture
This allows the model to effectively capture and represent picture characteristics in both channel and spatial dimensions. Figure 4 displays the structure of the Reconstruction module. The reconstruction module consists of three convolutional layers, each with filter sizes of \(7\times 7,5\times 5\), and \(3\times 3\), respectively.The stride is set to 1 with padding, and the number of feature maps obtained in each convolutional layer is set to 256, 128, and the number of bands of input HSI. Figure 3: Network structure of Attention mechanism module. It is worth noting that in order to speed up the convergence of the model training and make it more stable, the activation function of IFNPMH is LeakyReLU except for the fixed collocation of the attention mechanism module and the last layer activation function which is tanh. With the exception of the last layer, IFNPMH utilizes batch normalization. This technique effectively addresses the issue of sensitivity to data initialization and prevents the problem of gradient vanishing.
## 1 Introduction
However, AOP needed high irradiation times (120-300 min) to achieve mineralization, resulting in high operating costs and reagent usage. On the other hand, constructed wetlands (CWs) are known for their efficiency and versatility in wastewater treatment by the interaction of physical, chemical and biological processes, such as oxidation, reduction, precipitation, sedimentation and adsorption [17]. Recently, these biological treatments have been used for industrial effluents, mine and agricultural drainages as well as the removal of other persistent organic pollutants [18]. Likewise, natural wetlands around cropland drainages have demonstrated to perform a feasible treatment [19]. Because CropWW is characterized by its low biodegradability, conventional CWs are technically limited. Nonetheless, this technology has evolved for treating emerging contaminants by using emerging vegetation and/or local bacterial communities improving their efficiency as bioaugmented CWs [20]. However, this type of CW has not been scaled up mainly because they need high HTR, which implies large land areas. The technologies available for the treatment of wastewater containing pesticides, for instance AOP and biological systems, offer advantages and some degree of efficiency. Hence, the proper coupling of both technologies would suggest an increase in the removal efficiency and the possible reduction of toxic byproduct formation, as well as reduced area requirements for CWs. Ref. [6] studied the coupling of CW to photo-Fenton, ferrioxalate and photo-Fenton/TiO\({}_{2}\) for the removal of clopyralid contained in synthetic wastewater. Total organic carbon (TOC) removal reached 93, 92 and 87% respectively, almost twice as high as the treatment before the coupling with the biological process. Similarly, [21] coupled a photocatalytic system TiO\({}_{2}\)-visible light with CW to treat decabromodiphenyl ether. Removal achieved in the combined system (93.6 \(\pm\) 2.2%) was significantly higher than in the photocatalytic system alone (56.3 \(\pm\) 2.8%). The aforementioned, suggest the technical feasibility of treating CropWW using AOP coupled to CWs, since biological systems are characterized by operational robustness to low pH values and byproducts likely to be present in the effluent of AOP. In this context, it is possible to achieve high removal rates, or even mineralization, of organic matter producing an effluent with acceptable characteristics for reuse in the same agricultural areas. In this study the main objective was to assess technical feasibility of a UV/H\({}_{2}\)O\({}_{2}\)-CW coupled system to treat CropWW with high malathion and nutrient loads, which is discharged into agricultural drainages in tropical regions [13]. Before coupling, the AOP was optimized to achieve the highest removal rates as well as a lower energy consumption.
# Validation of Drifting Buoy Data for Ocean Wave Observation
###### Abstract  
Diffiting buoys collect wave data in the open ocean far from land and in areas with strong currents. However, the validation of the drifting buoy wave data is limited. Here, we compared the drifting buoy wave data, ERA5 wave data, and moored GPS buoy wave data. Data from 2009 to 2018 near the coast of Japan were used. The agreement of the drifting buoy-observed wave parameters with the moored GPS buoy-observed wave parameters is better than that of ERA5 wave parameters, which is statistically significant. In particular, the accuracy of the ERA5 wave heights tends to be lower where the ocean currents are fast. On the other hand, the agreement between the drifting buoy-observed wave heights and the moored GPS buoy-observed wave heights was good even in the areas with strong currents. It is confirmed that the drifting buoy wave data can be used as reference data for wave modeling study.  
wave height; wave period; drifting buoy; ERA5; GPS buoy +
Footnote †: journal: Marine Science and Engineering  
In addition to wave hindcast data from ocean wave models, reanalysis data are also used for the wave research in the open sea. The reanalysis wave data is produced by assimilating the altimeter data to the wave hindcast data. ERA5 reanalysis data is often used for research. The JMA (Japan Meteorological Agency) produces wave assimilation dataset with a spatial resolution of 0.05\({}^{\circ}\). However, it incorporates moored buoy data and drifting buoy data and is not an independent dataset.  
The accuracy of the ERA5 wave data was verified by Reference [10], which shows the good accuracy. If it is verified that the drifting buoy wave data is more accurate than the ERA5 wave data, it can be shown that the drifting buoy wave data is useful for verification of the wave prediction data.  
The objective of this study is to validate the accuracy of the drifting buoy wave data with moored GPS buoy data. We verify whether the drifting buoy data can be used as reference data by comparing the drifting buoy wave data and the ERA5 wave data with the moored buoy wave data.  
Section 2 describes the data and methods. The system used to observe the drifting buoy wave data, ERA wave data, and GPS wave data is described in this section. The results are presented in Section 3. Section 4 discusses the wave data comparison. Section 5 provides the conclusion.
Previous studies have shown that delamination usually occurs at hole entry, due to the so called \"peel-up\" phenomenon, and hole exit, due to the so called \"push-out\" phenomenon, along the drilled hole periphery [4; 23]. The influence of the thrust force for push-out delamination is very high compared to the effect of torque [7]. The optimization of drilling conditions includes the optimal selection of cutting speed and feed rate as well as the thrust force value obtained. Feed rate affects the onset of delamination more severely than spindle speed: in [24], it is shown that minimum peel-up and push-out delamination is verified at minimum feed rate value. However, using too low a feed rate, an increase of contact time between tool and workpiece is generated causing possible thermal damage to the composite material [25]. A higher automation of drilling processes for assembly applications would certainly enhance the productivity of assembly procedures but it needs an effective and dependable drilling process control to ensure hole quality and to fully exploit tool life. A solution can be represented by the development of robust and reliable on-line real-time process control techniques. In this paper, smart multi-sensor process monitoring based on thrust force and torque signal detection and analysis was applied during drilling of CFRP/CFRP laminate stacks for the assembly of aircraft fuselage panels with the scope to evaluate the tool wear state. Different signal-processing methods were utilised to extract diverse types of features from the detected sensor signals in the time domain, in the frequency domain, and using fractal analysis. A machine-learning approach based on artificial neural network (ANN) data processing was implemented using selected signal features to make smart decisions on the timely execution of tool change, which is highly functional for CFRP drilling process automation, on the basis of tool wear level estimation and tool wear curve reconstruction.
# Anomaly Detection for Service-Oriented Business Processes Using Conformance Analysis
###### Abstract  
A significant challenge for organisations is the timely identification of the abnormalities or deviations in their process executions. Abnormalities are generally due to missing vital aspects of a process or possession of unwanted behaviour in the process execution. Conformance analysis techniques examine the synchronisation between the recorded logs and the learned process models, but the exploitation of event logs for abnormality detection is a relatively under-explored area in process mining. In this paper, we proposed a novel technique for the identification of abnormalities in business process execution through the extension of available conformance analysis techniques. Non-traditional conformance analysis techniques are used to find correlations and discrepancies between simulated and observed behaviour in process logs. Initially, the raw event log is filtered into two variants, successful and failed, based upon the outcome of the instances. Successfully executed instances refer to an ideal conduct of process and are utilised to discover an optimal process model. Later, the process model is used as a behavioural benchmark to classify the abnormality in the failed instances. Abnormal behaviour is compiled grounded on three dimensions of conformance, control flow-based alignment, trace-level alignment and event-level alignment. For early predictions, we introduced the notion of conformance lifeline presenting the impact of varying fitness scores during process execution. We applied the proposed methodology to a real-world event log and presented several process-specific improvement measures in the discussion section.  
Keywords:process mining; conformance analysis; knowledge discovery; process analytics; business processes +
Footnote †: journal: _Algorithms_
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 3 Materials and Methods
### Operational Objectives  
#### 3.2.1 Durability Objectives  
The PULSAR UAV is capable of taking off from and landing on various types of terrain, including gravel, dirt, grass, and snow, thanks to its landing gear. It is anticipated to withstand winds of up to 6 Beaufort scale (6 bft) speed and maintain its position. The drone's flight duration depends on the selected configuration and payload; however, it typically offers an average flight time of 24-28 min.
# ScienceIoT: Evolution of the Wireless Infrastructure of KREONET
## 3 ScienceLoRa: IoT Infrastructure for Science Research and Education  
ScienceLoRa is one of the revolutionary projects of KREONET for expanding the supporting research areas and penetrating network coverage. In 2018, KREONET constructed ScienceLoRa, which is a wireless IoT infrastructure based on LoRa/LoRaWAN technology for scientific research. Along with the characteristics of LPWAN, ScienceLoRa is especially suitable for research areas involving low-rate data collection from many sensors over a wide area, such as in agriculture, environmentally, and oceanography. Moreover, the research experiments conducted in hostile or mobile environments, such as underground research facilities and experiments on autonomous vehicles, can be supported by ScienceLoRa. ScienceLoRa is an access network that provides a wireless connection to researchers for collecting a variety of data. The data collected by ScienceLoRa are transferred through KREONET and shared with researchers worldwide.