esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind Turbine Anomaly Detection Using Mahalanobis Distance and SCADA Alarm Data
## 4 Conclusions  
Based on the results of this study, the following conclusions can be generalized:  
1. The Mahalanobis distance approach can detect faulty behaviors of wind turbines at a high accuracy rate of 97%.
2. The input and output parameters of the predictive models where the Mahalanobis distance approach is calculated are essential factors in determining the component affected by faulty behavior.
3. In a data-driven monitoring system, the Mahalanobis distance approach can be used to further sustain and enhance the features of such a system.  
In order to further improve this study, future researchers can address the following aspects of the study:  
1. Integrate the proposed approach into the CMS of the wind turbine, and investigate its efficiency in the system.
2. Improve the reliability of the Mahalanobis distance approach by creating variations in the duration averaging of the approach, and choose the optimum in the process.
3. Integrate the Mahalanobis distance approach and the condition monitoring system into an automatic system environment to facilitate the recording of failure reports and further enhance productivity.
4. Develop a predictive model for the MD method that can detect anomalies at early stages.  
**Author Contributions:** Conceptualization, J.-H.L. and N.T.C.J.; data curation, J.-H.L.; formal analysis, J.-H.L., R.-M.L. and N.T.C.J.; investigation, J.-H.L. and C.-C.-W.; methodology, J.-H.L. and N.T.C.J.; project administration, J.-H.L.; validation, N.T.C.J. and C.-C.-W.; writing--original draft, N.T.C.J.; writing--review and editing, R.-M.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Ministry of Economic Affairs grant number M353 CH1330.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing is not applicable.  
**Acknowledgments:** The support from the Industrial Technology Research Institute and Taipower Company were highly appreciated.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Works
### Single-User Load Management Systems  
Different studies have addressed load scheduling for a single user with only two objectives. A dwarf mongoose optimization (DMO) and hybrid honey badger optimization (HBA) are used in [6] for applying the DR technique. The outcomes of the conducted simulations showed that the presented approach has managed to arrange the appliances according to the best applicable way. In [7], a peak shaving control strategy was proposed to optimally schedule the peak demand. A genetic algorithm is utilized to determine the required optimal inputs regarding the rule-based control method that in turn reduces the peak grid power. The outcomes show a reduction of 42.46%, 49.82%, 40.44%, and 33.1% in peak demand and 19.02%, 20.9%, 4.72%, and 0% in energy consumption, for case studies1, 2, 3, and 4, respectively. A load shifting-based earth worm optimization algorithm (EWOA) and virulence optimization algorithm (VOA) were used in [8] to optimally change the time slots of shiftable devices. A ToU is considered as a dynamic pricing scheme. The VOA has managed to achieve 59% reduction in PAR of power consumption and is more reliable than other contestants. A residential load scheduling model in [9] is proposed that is based on user preference (UP) and cost efficiency (CE) and is targeted for demand response in a smart grid. The UP has been derived according to the load scheduling framework, whereas the CE is being modeled according to UP. The simulation outcomes have demonstrated a positive correlation between UP and CE. The authors of [10] presented a DR scheduling technique, which covers four residential building loads' types. These four types are interruptible and deferrable loads, non-interruptible and deferrable loads, non-interruptible and nondeferrable loads, and air conditioning loads. A multi-objective optimization model that depends on nondominated sorting genetic model II is adapted to reduce the energy cost and inconvenience index. By adapting this approach, 25% of nocturnal peak load saving has been realized, while 10% of the daily power cost is minimized.  
Other studies consider a single user with more than two objectives. The authors of [11] proposed a DR in a smart grid using the lens-oppositional duck pack algorithm (LODPA). The LODPA is influenced by both conventional DPA and lens-oppositional-based learning (LOBL). This in turn led to a minimization in both the electricity price and peak-to-average ratio (PAR) with maximum user comfort (UC). The analysis has demonstrated that the LODPA scheme shows better performance as compared to other approaches regarding different aspects. The evaluations showed that the LODPA model has accomplished a lower PAR of 2.10.
###### Abstract  
High-altitude long-duration (HALE) flight capability is one of the ultimate goals pursued by human aviation technology, and the high-altitude solar-powered aircraft (SPA) is the most promising technical approach to achieve this target as well as wide application prospects. Due to the particularity of the energy system, the flight strategy optimization through the storage of gravity potential energy and other methods is a significant way to enhance the flight and application abilities for the SPA. In this study, a flight strategy optimization model has been proposed for the aim of HALE flight capability, which is based on the gravity energy reserving and mission altitude in practical engineering applications. This integrated model contains the five flight path phase model, the three-dimensional kinematic model, aerodynamic model, solar irradiation model and energy store and loss model. To solve the optimization problem of three-dimensional flight strategy, the Gauss pseudo-spectral Method (GPM) was employed to establish and calculate the optimal target as its advantages in treating process constraints and terminal constraints for the multiphase optimization problem. At last, the flight trajectory optimization with minimal battery mass for Zephyr 7 was studied by the GPOPS with some function files in MATLAB. The results indicate that the Zephyr 7 can reduce the battery mass from 16 kg to 12.61 kg for the day and night cycle flight and missions, which equals to increasing the battery specific energy by 23.1%. Meanwhile, the optimization results also show that the attitude angel may contribute to increasing the energy gained by photovoltaic cells. In addition, this optimized flight strategy brings the possibility of monthly or annual continuous flight for SPA as the simulation date is set to the autumnal day.  
high-altitude long-endurance (HALE); solar-powered aircraft (SPA); three-dimensional; flight strategy optimization; gravity energy reserving; mission altitude; Gauss pseudo-spectra method (GPM)
## 1 Introduction
A physically-based Plant phenology Index (PPI) was recently developed [36] to improve the satellite-based observations of vegetation phenology for the boreal regions. The index is derived from radiative transfer equations and is calculated from red and near-infrared (NIR) reflectance. The fundamental property of the PPI is its linearity to green leaf area index (LAI) and, unlike NDVI and EVI, PPI is reported to be well suited for phenology retrieval in boreal forests, due to its ability to minimize the snow influence on vegetation signal and increase the sensitivity to minor seasonal variation in dense canopy greenness of the boreal forests [36]. To date, PPI has been applied in a singlestudy that investigated plant phenology trends over Northern Europe for the period of 2000-2014 [48], showing that trends derived from PPI were significantly correlated to phenology ground observations and flux tower estimated gross primary productivity (GPP). Start of growing season (SOS) of the deciduous broadleaf and deciduous needleleaf forests corresponds to budburst and leaf emergence, whereas SOS of evergreen needleleaf forests is defined as the point in time when photosynthetic activity starts to increase, since evergreen forests retain green foliage all year round [15]. Research objectives of this study focus on comparing trend estimates of boreal zone SOS as derived from PPI in relation to the two widely applied indices: NDVI and EVI. Satellite-based SOS was evaluated against SOS derived from in situ measurements of GPP to test the performance of the VIs. The analysis focuses on the start of photosynthetic activity in spring SOS since onset of growth is known to be more distinct as compared to autumn phenology, which is often difficult to monitor and reveals scattered results [44; 49; 50].
## 3 Lattice Construction Algorithm
### Special-Case Workarounds and Continuity Fallback Mechanism  
The automated construction of smooth strut transitions using the general node construction step requires some basic properties of the lattice frame:  
1. [label=1.]
2. The neighbouring nodes of each node should not be coplanar or almost coplanar, i.e., more than three neighbouring nodes are required.  
Figure 6: Convex hull representation of a node. A triangular face of the convex hull and the corresponding \(G^{1}\)-continuous 6-patches that is constructed by the lattice algorithm are marked in orange.  
2. The edges converging to a node should be distributed equally in space around the node such that in every half-space exists at least one edge.
3. Sharp angles between edges should be avoided.  
If the first or the second basic property are not met, the convex hull representation is inappropriate to identify neighbouring struts that can be reasonably connected with smooth strut transitions, as shown in Figure 5f. In these cases, it is likely that the constructed strut transitions lead to self-intersections of the resulting surface. Similarly, if the wireframe has sharp angles, the resulting smooth strut transitions and 6-patches are more likely to intersect each other due to their high curvature. Therefore, nodes that fail to comply with these basic properties are automatically identified by the algorithm and a special-case workaround or a fallback mechanism are applied. The latter are explained in more detail in the following sections.
## 1 Introduction  
In recent years, with the increasing complexity of medical images, it is difficult for radiologists and physicians to give an accurate diagnosis all the time. However, with the integration of various advanced computer technologies, such as artificial intelligence [1, 2], Internet of Things [3], and cloud computing [4], medical image classification [5, 6] is increasingly favored by researchers. Among them, the convolutional neural network, one of the core algorithms in image recognition, has an efficient and stable performance when performing medical image classification tasks [7, 8].  
For some users with limited computing resources (such as township clinics and community hospitals), it is difficult to train a model of a convolutional neural network with a large number of local medical records. Therefore, users prefer to upload medical data to the servers on the cloud platform [9]. Users can then obtain the classification results of medical images. However, it may raise some critical privacy issues that hinder the deployment of the envisioned service. The users' input contains sensitive personal health information which cannot be disclosed to the cloud in plaintext. At the same time, the model is considered intellectual property and embedded with trace data of (sensitive) training data. Therefore, how to protect the privacy of the input and the model becomes an issue.  
The existing privacy-preserving schemes for the convolutional neural network are mainly based on homomorphic encryption [10] or differential privacy [11]. From a practical point of view, homomorphic encryption schemes [12] are limited to evaluating low-order polynomials on encrypted data. Otherwise, the computation of the encryption process is too large. The method of differential privacy is to add random noises to the data, which may lead to a decline in the availability of the data.
# Optimization of AZ91D Process and Corrosion Resistance Using Wire Arc Additive Manufacturing
## 2 Materials and Methods  
### WAAM (Wire and Arc Additive Manufacturing)  
SLS and EBM techniques rely either on a high energy laser or an electron beam for the creation of 3D structures. Both cases required large amounts of energy for the creation of these heat sources. The creation of a localized atmosphere is also required for powder bed and powder feed systems. In laser sintering techniques the temperature of the chamber must be increased to almost the melting temperature of the material which is being printed. To achieve these temperatures, a large consumption of energy is required. In the case of EBM, the necessity of a high vacuum also affects the required energy input [14]. Wire and Arc Additive Manufacturing (WAAM) combines an electric arc (heat source) and a  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Density** & **Elastic Modulus (GPa)** & **Compressive Strength (MPa)** & **Fracture Toughness (MPa/m\({}^{2}\))** \\ \hline Natural Bone & 1.8–2.1 & 3–20 & 130–180 & 3–6 \\ Ti alloy & 4.4–4.5 & 110–170 & 758–1117 & 55–115 \\ Co-Cr Alloy & 8–3–9.2 & 230 & 450–1000 & N/A \\ Stainless Steel & 7.9–8.1 & 189–205 & 170–310 & 50–200 \\ Magnesium & 1.74–2.0 & 41–45 & 64–100 & 15–40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of steels and bone. Reproduced from [6]. Copyright 2006, Elsevier.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Element** & **Wt \%** \\ \hline Al & 8.5–9.5 \\ Zn & 0.45–0.9 \\ Si & 0.05 max \\ Mn & 0.17 max \\ Cu & 0.015 max \\ Fe & 0.004 max \\ Ni & 0.001 max \\ Other & 0.01 max \\ Mg & Balance \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of magnesium alloy AZ91D. Reproduced from [14]. Copyright 1998, Elsevier.  
metal wire as feedstock to create 3D models in a bottom-up approach (layer upon layer) [15]. Due to its simplified design and the feedstock, WAAM presents many advantages over powder bed and powder feed systems. WAAM requires less energy consumption when compared to powder bed methods because there is no need for a high-powered energy source to heat the material to melting temperature. In contrast to EBM techniques that use accelerating voltages from 30 to 60 kV [15], Plasma Arc Welding (PAW) systems can be used on 110 V configurations. Furthermore, powdered metal is made up of individual morphed spheres in different sizes. This dispersion of metal restricts the heat transfer and complicates the fusion across particle boundaries. Due to the lack of continuity in the structure, more heat is required to commence phase transformation [16; 17; 18]. Thus, less energy is necessary for melting wire metals, making it more energetically efficient [19].
## 1 Introduction
_plantarum_ strain D747; Mitsui Agriculture International S.A./N.V, Woluwe-Saint-Pierre, Belgium), NEXY\({}^{\circledR}\) (_Candida oleophila_ strain 0: Agrauaine S.A., Marcq en Baroeul, France), TUSAL\({}^{\circledR}\) (_Trichoderma supellum_ strain T25 and _Trichoderma atrovide_ strain T11, Timac Agro Espana S.A., Navarra, Spain), and POLYVERSUM\({}^{\circledR}\) (_Pythiaum oligandrum_ strain M1: Agriculture, S.A., Madrid, Spain) [14]. Despite the progress made in post-harvest biocontrol over the past 30 years, commercial applications of microbial antagonists remain limited. Economic and regulatory constraints are the main obstacles to the successful introduction of these products into the market [15; 16]. In addition to microbial antagonists, low-toxicity chemical alternatives such as sodium bicarbonate (SB) have been evaluated for controlling post-harvest [17]. SB exhibits adequate antifungal activity, low risk of phytotoxicity, and minimal environmental impact, and it is readily available and inexpensive, with a lack of restrictions for many applications [17]. Furthermore, SB has been approved as a food additive and is generally recognised as a safe (GRAS) substance by the United States Food and Drug Administration (FDA). As a result, it has become the most commonly used food preservative for controlling decay in citrus fruits [18; 19]. It has also been successfully used to reduce post-harvest decay in carrots, mangoes, avocados, bell peppers, melons, and sweet cherries [20; 21; 22; 23; 24]. The activity spectrum of these alternative control methods is not as broad as that of chemical fungicides, and many of them cannot achieve maximum effectiveness, even under optimal conditions. Therefore, a combination of treatments must be used to develop a multibarrier control strategy that is suitable for commercial applications. In this regard, microbial antagonists and NaHCO\({}_{3}\) have been combined to achieve effects similar to those of chemical fungicides in reducing the post-harvest decay of several fruits, such as sweet cherry, pears, mandarin, and grapes [25; 26; 27; 28]. The objective of this study was to investigate the biocontrol activities of different microbial antagonists and SB, both in situ and to control post-harvest diseases in sweet cherries (_Prunus avium_ L. cv. Sweethearts) and plums (_Prunus salicina_ [PERSON] cv. '[PERSON]').
# Dynamic Analysis and Trajectory Tracking Control for a Parallel Manipulator with Joint Friction
## 3 Inverse Kinematics
### Velocity Analysis  
The hypothesis is that \(\mathbf{\omega}_{j}\) and \(\dot{\mathbf{\omega}}_{j}\) are the angular velocity and angular acceleration of the moving platform \(m_{j}\), respectively. The translational velocity of slider \(S_{ji}\) is obtained by finding the first-order derivative of Equation (1) with respect to time.  
\[\dot{\mathbf{L}}_{j}=\mathbf{I}_{aj}^{-1}\cdot\mathbf{J}_{bj}\cdot\mathbf{\omega}_{j}=\mathbf{J}_{ j}\cdot\mathbf{\omega}_{j} \tag{3}\]  
where \(\dot{\mathbf{L}}_{j}=\left[\dot{L}_{j1},\,\dot{L}_{j2},\,\dot{L}_{j3}\right]^{T}\), and \(\dot{L}_{ji}\) is the translational speed of the slider \(S_{ji}\); \(\dot{L}_{ji}=\frac{\left(r_{ji}\times\epsilon_{ji}\right)^{T}}{\epsilon_{ji}^{ T}\cdot\mathbf{\omega}_{j}}\mathbf{J}_{aj}=diag\left(\mathbf{e}_{j1}^{T}\cdot\mathbf{e}_{1},\,\mathbf{e}_{j2}^{T} \cdot\mathbf{e}_{2},\,\mathbf{e}_{j3}^{T}\cdot\mathbf{\varepsilon}_{3}\right)\), \(\mathbf{J}_{bj}=\left[\mathbf{r}_{j1}\times\mathbf{e}_{j1},\,\mathbf{r}_{j2}\times\mathbf{e}_{j2}, \,\mathbf{r}_{j3}\times\mathbf{e}_{j3}\right]^{T}\), \(\mathbf{J}_{j}=\left[\frac{r_{ji}\times\epsilon_{ji}}{\epsilon_{ji}^{T}\cdot \epsilon_{ji}},\frac{r_{j2}\times\epsilon_{ji}}{\epsilon_{ji}^{T}\cdot \epsilon_{ji}},\frac{r_{j3}\times\epsilon_{ji}}{\epsilon_{ji}^{T}\cdot \epsilon_{ji}}\right]^{T}\).  
Figure 3: Vector diagram of a closed kinematic chain.  
The rotational angular velocity and joint lever center velocity of \(b_{ji}a_{ji}\) can be obtained as follows:  
\[\left\{\begin{array}{l}\mathbf{\omega}_{lji}=\frac{1}{T_{ji}}\left[-\mathbf{e}_{ji\times }\cdot\mathbf{r}_{ji\times}-\left(\mathbf{e}_{ji}\times\mathbf{e}_{i}\right)\frac{\left(\mathbf{ r}_{ji}\times\mathbf{e}_{ij}\right)^{T}}{\mathbf{e}_{ji}^{T}\cdot\mathbf{e}_{i}}\right] \cdot\mathbf{\omega}_{j}=\mathbf{J}_{j\omega i}\cdot\mathbf{\omega}_{j}\\ \mathbf{v}_{lji}=\left[\mathbf{e}_{i}\cdot\frac{\left(\mathbf{r}_{ji}\times\mathbf{e}_{ji} \right)^{T}}{\mathbf{e}_{ji}^{T}\cdot\mathbf{e}_{i}}-\frac{l_{ji}}{2}\mathbf{e}_{ji\times }\cdot\mathbf{J}_{j\omega i}\right]\cdot\mathbf{\omega}_{j}=\mathbf{J}_{j\omega i}\cdot\bm {\omega}_{j}\end{array}\right. \tag{4}\]  
where \(\mathbf{e}_{ji\times}\) and \(\mathbf{r}_{ji\times}\) are antisymmetric matrices corresponding to \(\mathbf{e}_{ji}\) and \(\mathbf{r}_{ji}\), respectively.
## 4 Experiments
### Time Efficiency Study  
The time efficiency comparison is shown in Table 4. As observed from the data, our method maintains a stable inference time across different input nodes, ranging from 0.0155 s to 0.0172 s. In contrast, the retrieve-from-database approach achieves a shorter computation time (0.0058 s to 0.0070 s), but its applicability may be limited by the database size. Manual design consumes significantly more time than automated methods and exhibits an increasing trend as the number of input nodes grows. Experts, who are familiar with the topological patterns of bubble diagrams, understand which nodes should be connected by door links or wall links. As a result, their primary time consumption comes from clicking on nodes to establish links, which takes at least two seconds per adjacent node pair. On the other hand, non-expert users are less aware of the necessary links between nodes, requiring additional thinking time, leading to longer completion times (16.1 s to 115.7 s). If they need to create bubble diagrams for new structures, even more time is required for design considerations. Overall, our method outperforms manual design in computational efficiency and does not rely on a database, making it more versatile and practical for various applications.  
We use [PERSON]'s response time model [44] to quantify user experience, with response time classifications shown in Table 5. Combining the data from Table 4 with response time thresholds, our method completes all test cases within 0.02 s, which is well below the 0.1 s perceptual delay threshold. As a result, users do not perceive any delay, ensuring a smooth  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Input Nodes**} & **Inference** & **Retrieve from** & **Manual** & **Manual** \\  & **Time** & **Database** & **Design** & **Design** \\  & & & **(Expert)** & **(Non-Expert)** \\ \hline
4 & 0.0155 s & 0.0058 s & 8.5 s & 16.1 s \\
5 & 0.0158 s & 0.0063 s & 15.6 s & 43.7 s \\
6 & 0.0160 s & 0.0060 s & 25.4 s & 61.3 s \\
7 & 0.0163 s & 0.0060 s & 34.7 s & 85.9 s \\
8 & 0.0172 s & 0.0070 s & 44.1 s & 115.7 s \\ \hline \hline \end{tabexperience. The retrieval-from-database approach also maintains response times below 0.1 s, but its effectiveness depends on the comprehensiveness of the pre-stored data. In contrast, manual design significantly exceeds the 10 s acceptable threshold, making users likely to experience frustration due to prolonged waiting times. Overall, compared to manual design, our method greatly enhances interactive experience and is better suited for real-time generation and interactive design systems.
## 4 Analysis of Influencing Factors
### Influence of the Horizontal Spacing Between the Foundation Pit and Tunnel
#### 4.2.1 Tunnel Horizontal Displacement  
Figure 11(a) illustrates the tunnel's maximum horizontal displacement resulting from the completion of the foundation pit excavation at various horizontal spacings. The tunnel's horizontal displacement decreases by 37.23%, 64.44%, 77.82%, 84.93% and 89.96%, respectively, as the tunnel horizontal spacing increases from 0.5 H to 3 H. The tunnel's horizontal displacement decreases exponentially as the horizontal spacing increases. When the horizontal spacing is \(\leq\)1.5 H, the rate of the decrease in the tunnel's horizontal displacement is faster, resulting in larger displacements. Beyond 1.5 H, the rate of decrease in displacement gradually diminishes, approaching zero; this suggests that the tunnel is significantly impacted by the excavation of the laterally adjacent foundation pit within 1.5 H, while the effect weakens beyond this threshold. Taking the tunnel deformation control standard in the existing specification as a reference, horizontal displacement remains below the warning value when the tunnel horizontal spacing surpasses 1.0 H.