esa-sceva/satcom-chunk-collection huggingface dataset:
# DGSS-YOLOv8s: A Real-Time Model for Small and Complex Object Detection in Autonomous Vehicles
## 3 Methodology
### Inner-Shape IoU Loss Function  
While YOLOv8 employs Complete IoU (CIoU) [51] for bounding box regression, CIoU can overlook the influence of the bounding box's own shape and scale. To address this, and to better handle varied overlap scenarios, especially for irregular and occluded targets, our proposed Inner-Shape IoU loss function integrates concepts from Shape-IoU [20] and Inner-IoU [21]. Shape-IoU introduces shape and scale factors for a more comprehensive regression, while Inner-IoU adaptively adjusts auxiliary bounding box scales based on IoU levels and focuses on internal overlap for improved convergence and generalization [20; 21].  
Our Inner-Shape IoU loss is defined as  
\[L_{\text{Inner-ShapeIoU}}=L_{\text{ShapeIoU}}+\text{IoU}-\text{IoU}_{\text{ inner}} \tag{2}\]  
Here, \(L_{\text{ShapeIoU}}\) represents the loss component derived from Shape-IoU principles, which penalizes discrepancies in distance, shape, and scale between predicted and ground-truth boxes (as detailed in [20], incorporating considerations for its distance loss \(D_{\text{shape}}\) and shape loss \(\Omega_{\text{shape}}\), where the parameter \(\theta\) is empirically set to 4). IoU\({}_{\text{inner}}\) is derived from  
Figure 5: Structural diagram of Detect_SA.  
Inner-IoU concepts, focusing on the Intersection over Union of adaptively scaled auxiliary bounding boxes to refine regression for varying degrees of overlap (details in [21]).  
The rationale for selecting this specific combination, Inner-Shape IoU, over other advanced IoU losses (like SIoU [59] or EIoU [60]) stemmed from an empirical investigation into addressing the key localization challenges in this work: irregular shapes and occlusion. By combining Shape-IoU's sensitivity to bounding box geometry [20] with Inner-IoU's adaptive mechanism [21], we aimed for a more comprehensive gradient signal for the specific complexities targeted by DGSS-YOLOv8s. Our experiments indicate that this combined approach improves convergence speed and enhances detection performance for complex-shaped objects compared to baseline losses.
## 5 Results
The erroneous value was still included in the evolutionary algorithm's calculations when one of the positioning systems failed. Due to the influence of SLAM, the trajectory of the coordinates was biased. Figure 6.0 The result from each positioning method (longitude). Figure 6.1 The result from each positioning method (latitude). The correct path in this situation was the most similar to that of the RTK. Thus, the result of using the RTK here is the benchmark. The coordinate prediction results (yellow line) were similar to the RTK results, proving that the prediction mechanism was efficient. The fusion results and the diagnosis mechanism (light blue line) were combined with reference to the results of RTK, SLAM, and data fusion. Equation (7) describes the positioning system results. This result is close to the coordinates of the correct route. The positioning error was calculated from Equation (4). The RTK position coordinates were compared with those obtained using SLAM, the original fusion algorithm, prediction, and fusion with a diagnostic mechanic. The results are shown in Figure 6.2 Table 1 shows a comparison between the values of each method. In order to compare trends and reduce errors caused by noise, 100 records were averaged from the peak in the comparison of the results. The average distance by which SLAM deviated in this test was 91.8 m.  
The original algorithm was able to correct the error value correctly. However, the magnitude of the correction was limited. The maximum error was 18.3 m after correction. The original algorithm corrected the error at a rate of 80%. The prediction coordinate was from Equation (2). The prediction of the coordinates was based on the previously valid coordinates, with reference to the speed and heading of the vehicle. The error between the RTK coordinates and the predicted value was about 0.64 m. As can be seen from Figure 13, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Error Condition** & **Positioning Error (m)** & **Correction Rate (\%)** \\ \hline Limited two-meter error & 89.80 & 97.82 \\ RTK—original fusion algorithm & 18.30 & 80.06 \\ RTK—prediction & 0.64 & 99.30 \\ RTK—fusion with diagnostic & 0.25 & 99.73 \\ RTK—fusion with diagnostic & 2.25 & 89.89 \\ (Considering the limited two-meter error) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The comparison between each method based on the max positioning error of 91.8m. Figure 13: Comparison of the results based on the RTK results. this result is very similar to the coordinate value of RTK. It can be used as reference data for the fault detection mechanism. Equation (6) presents the fusion algorithm with the diagnostic mechanism. The coordinate result of this algorithm differed from the RTK coordinate value by 0.25 m. The results showed that the error was further corrected. Finally, the positioning errors of the algorithms were considered. A 2 m lateral error was set in the experiment by considering the lane width. Comparing the values, the difference between the RTK and the diagnostic mechanism was 2.25 m. This method corrected 89.8% of the errors. This result proves that our proposed method is useful.
# Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis
## 3 Methodology
### Ensemble  
Ensemble strategies have been widely used in the literature both in power output prediction, as well as fault diagnosis [27; 28; 29].  
The output of the previous algorithms is merged into a composed indicator, with the objective to produce better predictions by overcoming the limitations of the two methods. To build a valuable ensemble, it is important to choose base indicators that are complementary and not redundant, thus a key step is to verify that the correlation of the indicators is limited. The correlation of the indicators is addressed in Section 4.3. The normality indicator provides an individual analysis of the turbines, by comparing a historical model with current conditions, whereas the anomaly indicator is based on a comparison within the different turbines of the wind farm.  
The ensemble strategy that is used in this research does not require training of an additional meta-learner, instead the normality indicator (1) and anomaly indicator (3) are combined through a rolling windowed sum. The pseudocode to compute the ensemble indicator is given in Algorithm 1. In particular, the first step is to rank the turbines, assigning the corresponding percentile in which they fall when compared to the indicator distribution of the whole wind farm. Possible ties between turbines are managed using a ranking scheme that assigns to the tying turbines the lowest rank of the group. Being the single model indicators calculated on a weekly basis, each turbine will be assigned a percentile rank per each week. The next step consists in the application of a rolling window sum. More specifically, a window of \(p=4\) weeks size is chosen. Applying a rolling window sum allows to highlight long-lasting changes, limiting the influence of isolated peaks. Finally, a decision threshold is applied to decide whether an alarm must be or not triggered.  
```
Result:\(i_{e}\): indicator\({}_{\texttt{ensemble}}\):  Given \(i_{a}\), \(i_{n}\): indicator\({}_{\texttt{anomaly}}\), indicator\({}_{\texttt{normality}}\) respectively;  Set DT: decision threshold (e.g. 0.85);  Set \(p\): rolling window size (e.g. 4);  STEP 1: Rank turbines w.r.t. wind farm; for\(w\) in [\(w\), w+1,..., \(w\)+\(p\)-1] do for\(wt\) in turbines do \(x_{[anomaly|wt,w}\) = percentile of \(i_{a}\) for turbine \(wt\) w.r.t. distribution of the entire windfarm during week \(w\); \(x_{[ normality|wt,w}\) = percentile of \(i_{n}\) for turbine \(wt\) w.r.t. distribution of the entire windfarm during week \(w\);  end for  STEP 2: Calculate the ensemble ranking during the period of observation for each turbine; for\(wt\) in turbines do \(x_{[ensemble],wt}\) = \(\sum_{w=1}^{w+p-1}x_{[anomaly],wt,w}+x_{[ normality],wt,w}\);  end for \(x_{[max-ensemble]}\) = 2\(p\); STEP 3: Calculate the ensemble indicator of a turbine; \(i_{e,wt}\) = \(\frac{x_{[anomaly|start,}}{x_{[max-ensemble]}}\); STEP 4: Apply decision threshold; if\(i_{e,wt}\) > \(DT\)then  Turbine requires maintenance; else  No maintenance required;  end
```  
**Algorithm 1**Computation of the ensemble indicator
## 2 Materials and Methods
### Simulated Environment in Python  
The simulated environment is pivotal for evaluating solar-powered UAVs' operational efficacy under varied conditions. This Python-based simulation incorporated azimuthal solar angles to mirror the sun's positional changes over time, which directly impacted the efficiency of the solar panels and, consequently, the energy availability for the UAVs. This simulation also introduced complex navigational scenarios with randomly distributed obstacles and cloud cover, which not only challenged flight path optimization but also realistically affected solar energy capture. Such a comprehensive setup is crucial for assessing the UAVs' adaptability to environmental dynamics and optimizing their flight paths for improved energy efficiency and reliability [21; 43; 48].  
The environment factors were as follows:  
1. **Grid size**: Defines the dimensions of the 3D environment.
2. **Number of obstacles**: Specifies the quantity of physical obstacles within the environment.
3. **Obstacle size**: The fixed side length of each cubic obstacle.
4. **Number of clouds**: Indicates the amount of cloud formations affecting sunlight.
5. **Cloud size**: The fixed side length of each cubic cloud.
6. **Sunlight intensity**: Varies based on time of day and cloud coverage, impacting solar energy calculations.
7. **Sun direction**: Determined by azimuth and altitude angles, influencing the solar panel efficiency.
8. **Temperature**: Changes with altitude, affecting atmospheric conditions.
9. **Pressure**: Atmospheric pressure, which decreases with altitude.
10. **Sunlight reduction factor**: Fraction of sunlight blocked by cloud coverage.
11. **Time of day**: Impacts the position of the sun and overall sunlight intensity.
## 3 Approach to Background Classification  
First, to detect a moving object in the image sequences, a Harris detector, which is mostly used in image registration [28, 29], is applied to obtain the interesting points in the image. Next, the correspondences of the feature points in two consecutive images are calculated [30]. In well-known methods, the corresponding feature points can just be used to calculate the transformation matrix of the two consecutive images [31]. However, as mentioned earlier, the feature points of moving objects can be included in the corresponding feature points, and these moving object feature points can adversely affect the accuracy of the transformation matrix calculation. It is difficult to determine whether an individual feature point belongs to a moving object or not.  
To determine the criteria for the classification of feature points, a previous work [27] used the distance from the epipolar line to the feature point. Epipolar geometry is commonly used in stereo vision systems to obtain the correspondence of the two images from each camera. However, in the proposed method, epipolar geometry is used in the consecutive images of the moving mono camera. A normalized eight-point algorithm [31] is frequently used to calculate the fundamental matrix with the corresponding feature points of consecutive images. However, the epipolar line might be inaccurate because the correspondences used in the algorithm contain the feature points of the moving object. Therefore, we used both an image sensor and IMU sensor to obtain a more accurate fundamental matrix and epipolar line.  
Using IMU data and an image, the initial fundamental matrix of two consecutive images can be calculated. With this fundamental matrix, the epipolar line can be drawn to classify the feature points. In the consecutive images, the corresponding feature points of the background feature must lie on the epipolar line [32]. Therefore, the feature point that is far from the epipolar line can be classified as the foreground point (i.e., the moving object).  
Once the feature points are classified for the background and foreground points, the fundamental matrix is calculated from a normalized eight-point method using the background feature points. The epipolar line is then newly drawn without the outliers in the initial classification. This classification process can be applied iteratively until the fundamental matrix converges on a reliable result. In addition, the RANSAC technique [33] is applied to remove the outliers, if any, in the background feature points classification.  
The advantage of the proposed classification method is that it can detect moving objects accurately, even in an environment where moving objects are dominating the image, which is different from  
Figure 1: Overview of the proposed moving object detection method.  
previous methods. Moreover, any IMU sensor errors are substantially mitigated by the image sensor since the proposed method facilitates the complementary use of the two different sensors. The overview of the proposed background classification method is shown in Figure 2.
###### Abstract  
Even today, the automatic digitisation of scanned documents in general, but especially the automatic optical music recognition (OMR) of historical manuscripts, still remains an enormous challenge, since both handwritten musical symbols and text have to be identified. This paper focuses on the Medieval so-called square notation developed in the 11 th-12 th century, which is already composed of staff lines, staves, clefs, accidentals, and neumes that are roughly spoken connected single notes. The aim is to develop an algorithm that captures both the neumes, and in particular its melody, which can be used to reconstruct the original writing. Our pipeline is similar to the standard OMR approach and comprises a novel staff line and symbol detection algorithm based on deep Fully Convolutional Networks (FCN), which perform pixel-based predictions for either staff lines or symbols and their respective types. Then, the staff line detection combines the extracted lines to staves and yields an \(F_{1}\)-score of over 99% for both detecting lines and complete staves. For the music symbol detection, we choose a novel approach that skips the step to identify neumes and instead directly predicts note components (NCs) and their respective affiliation to a neume. Furthermore, the algorithm detects clefs and accidentals. Our algorithm predicts the symbol sequence of a staff with a diplomatic symbol accuracy rate (dSAR) of about 87%, which includes symbol type and location. If only the NCs without their respective connection to a neume, all clefs and accidentals are of interest, the algorithm reaches an harmonic symbol accuracy rate (hSAR) of approximately 90%. In general, the algorithm recognises a symbol in the manuscript with an \(F_{1}\)-score of over 96%.  
optical music recognition; historical document analysis; medieval manuscripts; neume notation; fully convolutional neural networks +
Footnote †: journal: Accepted: 22 June 2019; Published: 29 June 2019
## 1 Introduction
For example, [PERSON] _et al._[27] proposed an ANN-based precipitation retrieval algorithm, called precipitation estimation from remotely-sensed information using artificial neural networks (PERSIANN), to estimate rain rate with visible and infrared data; [PERSON], [PERSON], [PERSON] and [PERSON] [17] estimated the rain rate over land and oceans using ANN with the SI and PCT derived from microwave observations as the input and the radar-derived precipitation data as the output. Previous studies showed the remarkable capability of ANN to illustrate the complicated relationship between the satellite observations and precipitation and indicated that it is a promising approach to accurately delineate rain areas. In this study, a Bayes-based ANN, termed a probabilistic neural network (PNN), was introduced to delineate rain areas using the brightness temperature derived from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). Furthermore, the PNN result was used to improve the Scatter Index (SI) microwave-based precipitation retrieval algorithm, in order to investigate the influence of this new rain area delineation method on the high-resolution satellite precipitation retrieval algorithm.
## 3 Results and Discussion
### Impact of Ingredients and Processing Conditions on Phenolic Content and Antioxidant Activity  
3.3.1. Free Radical Scavenging Activity--DPPH Method  
The spectrophotometric method allowed to determine ability of prepared extracts to scavenge free radicals. The obtained results explicitly indicate on high antioxidant activity of the analyzed samples. The parameter increased with increasing additive content (87% of free radical scavenging for 10% of additive content; 94.22% for 30% of additive content after 30 min. of reaction). Important is fact that the high free radical scavenging was observed within 5 min from reaction initiation and plateau was reached immediately. The feature is significant for determination power of antioxidants. Extracts from functional food enriched with 10% of blueberry is active against free radicals nevertheless the activity is weaker in comparison to samples with higher content of the fruit (Figure 1). In all cases, the increase in antioxidant activity was significant in comparison to potato base for which free radical scavenging was equal 22.71% at the end of reaction.  
Equally important influence on biological activity of analyzed samples had production parameters used for preparation of functional food products. The parameters, namely moisture (sm) and screw rotation speed (rpm) influenced on products with both (10% and 30%) content of fruit but more significant was observed for samples with lower amount of blueberry (Figure 2). In this case, the difference in activity for the better and the weaker parameters was above 29%. Samples with higher levels of additive did not show such a large difference in activity which did not exceed 1%.  
Figure 1: DPPH scavenging activity of extracts obtained from functional food products enriched with 10% and 30% of blueberry.
## 2 Scientific Background. Related Works
UNIMINUTO university has implemented a low-cost and open-source remote laboratory based on tanks control to learn automatic control systems [19]. Recently, Turin Polytechnic University in Tashkent (TTPU, Tashkent, Uzbekistan) and the Politecnico di Torino (PoliTo, Italy) have developed an academic cooperation framework. The remote laboratory allow students to apply automatic control theory on a real magnetic levitation system [20]. On the other hand, the Complutense University of Madrid has implemented a remote laboratory using the Dobot Magician educational robot. This robot is used in the practices of electronic engineering in communications and allows students to acquire knowledge about the basic principles in robotics and to detect failures in practice [21]. Based on the literature review, the authors conclude that there have been few remote laboratories developed for engineering education, and, within these few works, only one offers a network of remote laboratories, consisting of four laboratories located in different universities in Jordan. The lack of common standards in the definition and implementation of remote laboratories as well as the lack of connectivity specifications are major drawbacks to adopting and creating wide-scale networks of educational remote laboratories based on renewable energies. In this paper, the authors propose a solution to build a complete course which integrates a standardized remote-laboratory-based network, supplementary learning material, and self-evaluation sheets, where each laboratory is located at a different place (each laboratory is located at each university). The student accesses through a common Moodle-based LMS, and after a single login step, he/she can select the module and the remote laboratory he/she wants to practice with. Table 1 compares the authors' proposal with related works and summarizes the main contributions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
The main novelties of the proposal are:  
1. A collaborative training platform based on standardized remote laboratories is developed. The training platform is made up of five EU universities. 2. The application: the remote laboratory is based on renewable energy with the aim of contributing to training skills required from students in the new energy model. 3. An LMS using a cooperative Moodle platform. All participating universities are able to create content and teaching material for the cooperative training platform. 4. A booking system: with this tool, the student selects the date and time to carry out the remote experience. 5. A standardized ICT solution that can unify the criteria for the five participant universities. All higher institutions have followed the same ICT standards. This provides replicability and scalability and makes it easy to add new devices at the laboratory and incorporate new universities in the collaborative network. 6. ICT hardware and software: Arduino + LabVIEW (UI) + Enlarge/MyFrontier + Moodle platform.
# Adoption of Multiphase and Variable Flux Motors in Automotive Applications
## 4 Results and Discussion
### Torque Ripple  
The torque ripple was calculated in the aforementioned working points, considering low speed, high speed, low torque, and high torque. The torque ripple waveforms are shown in Figure 11, while the numeric values are collected in Table 3. The percentage values refer to the peak-to-peak amplitude with respect to the average value.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **3-Phase** & **DTP** & **3-Phase VF** & **DTP VF** \\ \hline Maximum torque [Nm] & 353.6 (100\%) & 368.8 (+4.3\%) & 353.6 (+0\%) & 368.8 (+4.3\%) \\ Maximum speed power [kW] & 228.8 (100\%) & 226.1 (\(-\)1.2\%) & 245.1 (+7.1\%) & 245.5 (+7.3\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maximum torque and power at maximum speed in the analysed motors.  
Figure 10: Maximum torque– and power–speed envelopes.  
Figure 9: Efficiency maps of the 3-phase VF motor at different magnetisation states and overall efficiency map. (**a**) FE-MS. (**b**) FW-MS. (**c**) Chosen MS. (**d**) Combination.  
Regarding Table 3, the DTP motors had the smallest torque ripples in all the working points. The 6 th order was the first torque ripple harmonic in the 3-phase motors, and the 12 th in DTP ones. Eliminating the 6 th order harmonic was the main reason why DTP motors had lower torque ripples. With regard to the VF motors, they also showed a reduction in torque ripple in the FW-MS. The reason for this could be the smaller PM field and stator current.