esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Future Directions
### Machine Learning for Reliability Improvement of Electromagnetic Devices  
High reliability, especially lifetime reliability, is crucial to all electromagnetic devices. Besides the monitoring of devices' operational status, some important work can be done in the stage of design optimization. Many techniques/methods have to be integrated, such as robust topology design optimization, robust tolerance design optimization [156], and multidisciplinary design optimization. Besides the performance modeling, aspects of the manufacturing and the process itself should be considered within the design optimization, like the integrated product and process development of electric drives using a knowledge-based system [157].  
Another important technology is the digital twin. Digital twin is an emerging and fast-growing technology which connects the physical and virtual world. It has attracted much attention worldwide recently [158; 159; 160]. The future of product and service design will be hugely impacted by digital twin technology. With the help of digital twin, the reliability of the product can be controlled with more freedom. Regarding the design optimization of electromagnetic devices, it may have benefits in three main aspects, product development (design process), manufacturing/production, and operation and management. In the design process, digital twin can be used to test the virtual design scheme given by optimization. Thus, possible design defects can be avoided/corrected in the early design stage of electromagnetic devices. Regarding the production process, digital twin can be applied to determine the best manufacturing process (including product chain and quality control). This will increase the robustness and production efficiency and decrease the production cost of the electromagnetic devices. Regarding the operation and management, digital twin can be employed to find out the best control strategy and parameters for electromagnetic devices, like offshore wind generators, to increase their lifetime reliability.
# Microbial Application to Improve Olive Mill Wastewater Phenolic Extracts
## 2 Results  
### Extraction of Phenols from OMW  
#### 2.1.1 OMW Analyses  
Raw OMW was centrifuged and filtered to remove solid soil residues, olive oil and fragments of olive fruit and stone. The obtained sample was analyzed for the values of pH, dry matter, and total phenols, while hydroxytyrosol (HT), tyrosol (TY), and oleuropein (OLE) concentrations were detected by high performance liquid chromatography (HPLC) (Table 1). These results confirmed the acidic trait of the sample, which showed 6.24 g/100 mL of dry matter value, although subjected to centrifugation, and a high content of total phenolic compounds, for which it is considered a polluting effluent. As expected, among the three quantified peaks, HT showed the highest concentration.  
Moreover, HT was the most abundant phenol in OMW, as shown in Figure 1. OLE was quantified as gallic acid too, but this was done by calculating the response factor of the instrument in respect to the Internal Standard (I.S.) under the same HPLC operating conditions. HT, TY, and OLE were analyzed as principal markers of OLE enzymatic degradation. They were identified by comparing their retention times and absorption spectra with those of pure commercial standards.
# On-the-Fly Olive Tree Counting Using a UAS and Cloud Services
## 3 Materials and Methods
### Data Processing  
Figure 3 illustrates the data flow of the tree counting application. For each image, the segmentation module detects the olive trees in the image (L1). Then, at the registration and merge modules, the image pixel coordinates are projected to a global reference system (L2), and multiple observations of the same tree are merged (L3). Finally, the geolocation module computes the geographic coordinates of each new tree and sends them to the ground station, so that the results can be visualized in real time.  
Figure 2: Original UAV used to capture the images and tested embedded board. (**a**) DJI Phantom4. (**b**) RaspberryPi.  
Two different techniques have been tested to detect the olive trees: color-based segmentation and stereo vision-based segmentation.
## 4 Case Verification and Analysis
### The Evaluation Index  
The indexes of mean average precision (\(mAP\)), precision (\(P_{r}\)), recall (\(R_{e}\)) and \(F1\) score, and frames per second (\(FPS\)) are adopted to evaluated the performance of the ECA-YOLOX-Tiny model. \(P_{r}\) represents the proportion of correct predictions among all targets predicted by the model, \(R_{e}\) represents the proportion of correct predictions among all real targets, \(F1\) score is the harmonic average of \(P_{r}\) and \(R_{e}\). The formulas of \(P_{r},R_{e}\) and \(F1\) score are shown in (1), (2) and (3), respectively. In (1) and (2), \(T_{P}\) is the number of samples whose ground truth and prediction value are positive, \(F_{P}\) is the number of samples whose  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hardware Configuration** & **Version or Value** & **Software Development Environment** & **Version** \\ \hline Operating system & Windows 10–64 bit & PyCharm Community Edition & 2020.35 \\ Graphics card & NVIDIA GeForce GTX 1050 & Anaconda3 & 2020.11 \\ Processor & Intel(R) Core (TM) 15–830H CPU \(\equiv\) 2.30 GHz & Python & Python 3.6 \\ Operating memory & 8 GB & Keras & Keras 2.2.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The hardware configuration and software development environment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline Input shape & 640 \(\times\) 640 & Momentum & 0.937 \\ Optimizer & Adam & Freeze batch size & 32 \\ Freeze epochs & 0–50 & Unfreeze batch size & 16 \\ Unfreeze epochs & 50–100 & Maximum value of learning rate & \(1\times 10^{-3}\) \\ Confidence & 0.5 & Minimum value of learning rate & \(1\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters settings for network training.  
ground truth is negative and prediction value is positive, \(F_{N}\) is the number of samples whose ground truth is positive and prediction value is negative.  
\[P_{r}=\frac{T_{\text{P}}}{T_{P}+F_{P}} \tag{1}\]  
\[R_{e}=\frac{T_{P}}{T_{P}+F_{N}} \tag{2}\]  
\[F1=\frac{2P_{r}\times R_{e}}{P_{r}+R_{e}} \tag{3}\]  
The average precision (\(AP\)) is the contained area under the \(PR\) curve. The \(mAP\) is the average value of all categories \(AP\) for multi-target detection. The formula of \(mAP\) is shown in (4). In (4), \(C\) represents the number of insulator categories, \(N\) represents the number of \(IoU\) thresholds (\(T\)), and \(P(x)\) and \(R(x)\) represent the precision and recall, respectively.  
\[mAP=\frac{1}{C}\sum_{T=i}^{N}P(x)\times R(x) \tag{4}\]  
In order to compare the detection speed of different models and reflect the real-time detection ability of models, \(FPS\) is introduced as an evaluation index. \(FPS\) value is greatly influenced by the performance of the computer device, so \(FPS\) is only used for comparative analysis between different models on the same computer condition.
# Time Series Anomaly Detection Using Signal Processing and Deep Learning
## 3 Methodology
### Model Training
#### 3.2.1 MLP  
The Multi-Layer Perceptron (MLP) Autoencoder [24] is a specific architecture of Autoencoders that utilizes fully connected (dense) network layers in both the encoder and decoder networks. The architecture can be described as follows:  
* Encoder: Input data \(x\), where \(x\) is a vector stitched together over the time points and features, pass through multiple fully connected layers with traditional neurons. The output of the latent representation \(z\) is given as follows: \[z=\sigma(b+Wh_{z-1}),\] where \(b\) and \(W\) are the bias and weight parameters, \(h_{z-1}\) is the output of the previous layer, and \(\sigma(\cdot)\) is the activation function.
* Decoder: The decoder mirrors the encoder with fully connected layers, reconstructing the input from \(z\): \[\hat{x}=\sigma(b+Wh_{l-1}),\] where \(h_{l-1}\) is the output of the previous layer before the output and \(l\) denotes the total number of layers in the network.  
Figure 2: General Autoencoder architecture.  
In an MLP architecture, data flow through the dense layers without loops or recurrences. The encoder and decoder are normally constructed symmetrical.
# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
## 3 Proposed Method
### Color Palette for Effective Object Detection in IR Cameras  
This study proposes a technique to enhance object detection performance by applying color palettes to maximize the advantages of IR cameras. By converting the single-channel infrared signals of IR cameras into various colors, the technique provides clearer representations of objects' structures and features, enabling high detection performance even in environments where EO cameras are limited.  
The signals detected by IR cameras are based on energy emitted or reflected by objects, but the original single-channel images often make it difficult to distinguish between objects and the background in complex environments. Applying color palettes transforms these signals into diverse colors, maximizing contrast and clearly highlighting structural features.  
This study compares and analyzes the performance of six color palettes--Black Hot, White Hot, Ironbow (JET), Rainbow, Sepia, and Arctic [18; 19; 20]--commonly used in thermal imaging cameras, when applied to IR cameras. The results demonstrate that this approach mitigates the limitations of IR imagery and enables stable and reliable object detection across diverse environments.  
Figure 1 shows the color transformation process using an IR camera. Rainbow expresses high infrared emissions in red and low emissions in blue, allowing intuitive differentiation of temperature variations. Ironbow uses bright yellow or white for high infrared emissions and dark blue for low emissions, making small differences easy to distinguish due to the sharp contrast. White Hot represents high infrared emissions in white or bright tones and low emissions in dark tones, providing strong contrast and effectiveness across various environments. Black Hot, on the other hand, displays high emissions in dark tones and low emissions in bright tones, making it easier to identify objects in nighttime or complex backgrounds. Sepia represents the distribution of infrared emissions in pinkish tones, reducing visual fatigue and making it suitable for long-term observation. Finally, Arctic uses blue and white, providing visual contrast that is particularly effective for detection in low-temperature environments.
# Digitalizing Industrialized Construction Projects: Status Quo and Future Development
## 4 Qualitative Analysis
### Construction Phase  
#### 4.3.1 Information Communication and Exchange  
Information communication and exchange harnesses digital technologies to proficiently manage information generated throughout the construction process, so as to improve the visibility, transparency, and collaborative efficiency of the entire construction phase. Central to this is standardization. For instance, [PERSON] et al. [59] introduced a naming system tailored for standardizing the nomenclature of prefabrication components. This standardization lays a foundational framework for efficient coordination in the digital era. [PERSON] et al. [60] developed a construction management information model to ensure interoperability in graphical databases.  
A primary challenge in information transmission revolves around inefficient transfer and potential data loss. [PERSON] et al. [61] documented wood panelized construction processes and the associated information requirements for digital design tools and automated machinery. Their proposed generic simulation-based approach can measure the impacts of information transfer and data loss during construction processes. To mitigate data loss due to poor data interoperability, [PERSON] et al. [62] developed a BIM management system for different stakeholders that can be accessed by designers, contractors, and occupants. Such a system provides a standard framework for information exchange, as well as the integration of relevant data such as building energy performance, building acoustics, and occupant behavior. [PERSON] et al. [63] designed an IoT-enabled BIM platform that linked component information throughout the stages of production, transportation, and on-site assembly. [PERSON] et al. [64] developed a blockchain-based digital twin platform that realized information exchange across the business, technical, and management domains.  
Furthermore, studies have also endeavored to fortify data accuracy and security. [PERSON] et al. [65] combined BIM and reverse engineering to accurately communicate building geometry information and rapidly reconstruct 3D digital models. [PERSON] et al. [66] proposed a two-tier adaptive blockchain model tailored for supervising off-site modular housing production. This model struck a balance between storage costs and privacy protection. These collective efforts contribute to the advancement of information communication and exchange practices in industrialized construction.
## 2 The Proposed VA-RAIM
### Vision Model with Calibration
#### 2.2.2 Calibration Method
It is easy to obtain that \(\mathbf{\hat{\textit{{k}}}}_{V(k)}\) follows a normal distribution with zero mean and covariance matrix  
\[\mathbf{\hat{\textit{{k}}}}_{V(k)}=\mathbf{\Sigma}_{V(k)}+\mathbf{A}_{(k)}^{-1 }\mathbf{B}_{(k)}\mathbf{B}_{(0)}^{-1}\mathbf{\Sigma}_{\mathbf{C}_{(0)}} \mathbf{B}_{(0)}^{-1}\mathbf{B}_{(k)}\mathbf{B}_{(k)}(\mathbf{A}_{(k)}^{-1})^{ \textit{T}}+\mathbf{A}_{(k)}^{-1}\mathbf{B}_{(k)}\mathbf{B}_{(0)}  
image processing, _i.e._, \(\Delta\mathbf{c_{(k)}}\), will still exist. According to Equation (17), a fault in \(\Delta\mathbf{c_{(k)}}\) or \(\Delta\mathbf{c_{(0)}}\) will ripple through all the elements of \(\hat{\mathbf{z}}_{{}_{V(k)}}\). To mitigate the impacts of \(\Delta\mathbf{c_{(k)}}\) and \(\Delta\mathbf{c_{(0)}}\), a modified vision equation with single fault is obtained as follows.  
In this paper, we assume that there is one fault in \(\Delta\mathbf{c_{(k)}}\) and the fault index is invariant in a short period of time, _i.e._, the fault index in \(\Delta\mathbf{c_{(k)}}\) and \(\Delta\mathbf{c_{(0)}}\) are the same. Multiplying Equation (17) by \(\mathbf{A_{(k)}}\) yields a modified vision equation as:  
\[\hat{\mathbf{z}}_{{}_{V(k)}}=\mathbf{H}_{{}_{(k)}}\mathbf{x}_{{}_{(k)}}+\hat{\mathbf{e}}_{{}_{ V(k)}}+\hat{h}_{{}_{(k)}} \tag{18}\]  
where \(\hat{\mathbf{z}}_{{}_{V(k)}}=\mathbf{A_{(k)}}\hat{\mathbf{z}}_{{}_{V(k)}}\), \(\mathbf{H}_{{}_{(k)}}=\mathbf{A_{(k)}}\mathbf{H}_{{}_{(k)}}\), and \(\hat{\mathbf{e}}_{{}_{V(k)}}=\mathbf{\delta}\mathbf{c_{(k)}}-\mathbf{B_{(k)}}\mathbf{B_{(k)}}^{-1} \mathbf{\delta}\mathbf{c_{(0)}}+\mathbf{B_{(k)}}\mathbf{B_{(k)}}^{-1}\mathbf{A_{(0)}}\mathbf{H}_{{}_{(0 )}}\mathbf{\chi}_{{}_{(0)}}\),which follows a normal distribution with zero mean and covariance matrix  
\[\hat{\mathbf{\Sigma}}_{{}_{V(k)}}=\mathbf{A_{(k)}}\mathbf{\Sigma}_{{}_{V(k)}}\mathbf{A}_{(k) }^{T}+\mathbf{B_{(k)}}\mathbf{B}_{(0)}^{-1}\mathbf{\Sigma}_{{}_{(0)}}\mathbf{B}_{(k)}+\mathbf{B_ {(k)}}\mathbf{B}_{(0)}^{-1}\mathbf{A_{(0)}}\mathbf{H}_{{}_{(0)}}\mathbf{A_{(0)}}\mathbf{H}_{{}_{(0 )}}\mathbf{\chi}_{{}_{(0)}}\mathbf{H}_{{}_{(0)}}^{T}\mathbf{A}_{(0)}^{T}\mathbf{B}_{(0)}^{-1} \mathbf{B}_{(k)}\]  
In Equation (18), \(\hat{h}_{{}_{(k)}}=\Delta\mathbf{c_{(k)}}-\mathbf{B_{(k)}}\mathbf{B}_{(0)}^{-1}\Delta\mathbf{c _{(0)}}\). According to Equation (2), \(\mathbf{B_{(k)}}\) and \(\mathbf{B}_{(0)}^{-1}\) are diagonal matrices. Thus, the fault index in \(\Delta\mathbf{c_{(k)}}\) and \(\mathbf{B_{(k)}}\mathbf{B_{(0)}}^{-1}\Delta\mathbf{c_{(0)}}\)  are the same in a short period of time. If there is one fault in \(\Delta\mathbf{c_{(k)}}\), only one fault will exist in the modified observation \(\hat{\mathbf{z}}_{{}_{V(k)}}\). In the following section, the modified vision equation is integrated with the GPS equation for integrity monitoring.
## 4 Numerical Setup
### Multiple Reference Frames (MRF) Model  
The lubricating oil interacts with the high-speed gears, bearings, and surrounding flow field. Both dynamic and static areas exist inside the casing. To accurately simulate the movement characteristics of the gears, bearings, and the surrounding flow field, the MRF model [24] was used. The basic principle of the MRF model is for coordinate transformation. The MRF model converts the flow equation of motion into a rotating coordinate system, and provides a numerical solution in a relatively stationary rotating coordinate system.  
The model of the intermediate gearbox is simplified as follows: (1) the assumed clearance between the rollers and raceway is 0.5 mm; (2) the distance between the driving gear and the driven gear is enlarged until each gear can be surrounded by the reference frame; (3) the cage is ignored and only the revolution of the bearing is considered. The rotating coordinate system is used to simulate the movement of the gears and bearings. As shown in Figure 5a, the fluid around the gears is relatively static within the reference frames 1 and 2. The rotational speed of the rotating coordinate system is equal to the rotational speed of the gear (driving gear is 5000 r/min and driven gear is 3804 r/min). In addition, because the bearing inner ring rotates at the same speed as the gear, the bearing inner ring is included in the reference frames 1 and 2. As shown in Figure 5b, the fluid around the small rollers and big rollers is relatively static within the reference frames 3 and 4. It should be noted that the rotation of the roller about its own axis was ignored. The rotational speed of the roller was regarded as the motion boundary of the reference frames 3 and 4, and is given by [25]:  
\[n_{\text{w}}=\frac{1}{2}n\bigg{(}1-\frac{D_{\text{w}}\cos\alpha}{d}\bigg{)} \tag{8}\]  
where \(n_{\text{w}}\) is the rotational speed of the roller, \(n\) is the rotational speed of the inner ring, \(D_{\text{w}}\) is the mean diameter of the roller, \(\alpha\) is the bearing contact angle, and \(d\) is the pitch diameter of the bearing.  
[PERSON] et al. [26] used the MRF model to simulate the motion characteristics of an impeller. [PERSON] and [PERSON] [27] used a multi-reference coordinate system to simulate the movement of gears with oil-injected lubrication and obtained the heat transfer law of an accessory gearbox. Numerous other studies have also used the MRF model [15; 17; 18]. However, these studies have primarily focused on oil-injected lubrication, and the MRF method has not yet been applied to splash lubrication, especially for complex models such as an intermediate gearbox. In this study, the VOF and MRF models were combined to study the lubrication and temperature characteristics of an intermediate gearbox with splash lubrication.  
Figure 5: Moving reference frames of the computational model: (**a**) moving reference frame for gears; (**b**) moving reference frame for bearings.
# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 5 Experiments
### Qualitative Analysis
#### 5.4.1 Baselines Comparing  
As shown in Figure 12, our model achieved accurate predictions in both scenarios compared to other baselines. The difficulty of the prediction increased from (a) to (b). In (a), the cargo ship had a straight route. Most of the models produced satisfactory predictions, especially VEPO and METO. However, the GRU deviated from the true trajectory. (b) shows a container ship turning, where our model performs the best. Additionally, the METO-S2S model accurately predicted the first four points, but it deviated from the actual trajectory in the last point. The GRU-GRU model performs better than the GRU model; however, it still struggles to achieve satisfactory prediction results. In practice, incorrect predictions can easily lead to accidents. It can be observed that the results predicted by VEPO-S2S were superior to others, which can avoid safety issues.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ablation** & **RMSE** & **MAE** & **ADE** & **FDE** \\ \hline VEPO-S2S & \(\mathbf{3.17\times 10^{-4}}\) & \(\mathbf{1.60\times 10^{-4}}\) & \(\mathbf{2.52\times 10^{-4}}\) & \(\mathbf{4.57\times 10^{-4}}\) \\ \(w\)/o _sa_ & \(3.53\times 10^{-4}\) & \(1.88\times 10^{-4}\) & \(2.96\times 10^{-4}\) & \(5.27\times 10^{-4}\) \\ \(w\)/o _k1_ & \(3.50\times 10^{-4}\) & \(1.82\times 10^{-4}\) & \(2.88\times 10^{-4}\) & \(5.07\times 10^{-4}\) \\ \(w\)/o _k2_ & \(3.50\times 10^{-4}\) & \(1.88\times 10^{-4}\) & \(2.96\times 10^{-4}\) & \(5.19\times 10^{-4}\) \\ \(w\)/o _k3_ & \(3.55\times 10^{-4}\) & \(1.95\times 10^{-4}\) & \(3.07\times 10^{-4}\) & \(5.38\times 10^{-4}\) \\ \(w\)/o p\_s_ & \(4.48\times 10^{-4}\) & \(1.99\times 10^{-4}\) & \(3.15\times 10^{-4}\) & \(5.84\times 10^{-4}\) \\ \(w\)/o f\_f_ & \(3.42\times 10^{-4}\) & \(1.81\times 10^{-4}\) & \(2.88\times 10^{-4}\) & \(5.13\times 10^{-4}\) \\ \(w\)/o muti\_d & \(3.32\times 10^{-4}\) & \(1.76\times 10^{-4}\) & \(2.77\times 10^{-4}\) & \(4.87\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Quantitative results on different ablation studies.
# Feature Detection of Focused Plenoptic Camera Based on Central Projection Stereo Focal Stack
## 2 Refocusing Model Conforms to Central Projection
### Problems of the Traditional Refocusing Algorithm  
For the traditional refocusing algorithm [2], given the virtual depth \(v_{F}\) of any refocusing point \(F\), the projection point \(p\) on the raw image can be calculated through the following equation.  
\[\begin{bmatrix}1&0&(p_{u}-l_{u})\\ 0&1&(p_{v}-l_{v})\end{bmatrix}\begin{bmatrix}F_{u}&F_{v}&v_{F}\end{bmatrix}^{T}= \begin{bmatrix}l_{u}\\ l_{v}\end{bmatrix}. \tag{1}\]  
In Equation (1), \((p_{u},p_{v})\) represents the pixel coordinates of \(p\) in the \(ouv\) coordinate system, \((l_{u},l_{v})\) illustrates the orthographic projection coordinates of micro-lens' center related to \(p\) in \(ouv\), and \((F_{u},F_{v})\) stands for the orthographic projection coordinates of \(F\) in \(ouv\). By weighting the pixel values of all projection points, the pixel value of \(F\) in the refocused image can be obtained. However, due to the use of orthographic projection coordinates and virtual depth, the traditional refocusing model has the following problems.