esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Dynamic Model of FFPMSM
### Faulty Model under Open Circuit of Phase \"a\"  
For the sake of simplicity, the fault model of FPPMSM in this paper only covers the OCF in phase \"_a\"_. When the OCF occurs, the new Clarke transformation and the new Park transformation (A2) can be formulated based on preserving the back-EMF and the fundamental magnetic motive force, to maintain them the same as in a healthy model [12].  
By including an open-circuit permanent magnet flux linkage vector in the magnetic co-energy derivative relative to the electrical angle and applying the new transformations (A2), the torque can be obtained as follows [10]:  
\[T_{ef}=T_{ef1}+T_{ef3} \tag{6}\]  
\[T_{ef1}=\frac{5}{2}n_{p}\phi_{f1}i_{qp}+\frac{5}{2}n_{p}i_{dp}i_{qp}\Big{(}L_ {dp}-L_{qp}\Big{)} \tag{7}\]  
\[T_{ef3}=\frac{15}{4}n_{p}\phi_{f3}\big{[}-i_{qp}(\cos(2\theta)-\cos(4\theta)) \tag{8}\]  
where \(T_{ef}\) is the total torque in the presence of an OCF. \(T_{ef1}\) represents the torque influenced by the fundamental harmonic, corresponding to healthy conditions. \(T_{ef3}\) represents the torque influenced by the third-order harmonic, which produces torque ripples. Furthermore, the FPPMSM dynamic model under the open circuit to phase \"_a\"_ can be derived as follows [10]:  
\[\begin{cases}i_{dp}=c_{1}i_{dp}+c_{2}\Omega i_{qp}+b_{1}v_{dp}\\ i_{qp}=c_{3}i_{qp}-c_{4}\Omega i_{dp}-c_{5}\Omega+b_{2}v_{qp}\\ i_{\beta s}=c_{11}i_{\beta s}-c_{12}\Omega\cos(3\theta)+b_{\beta s}v_{\beta s} \\ \dot{\Omega}=a_{1}T_{ef}-a_{1}T_{L}-a_{2}\Omega\end{cases} \tag{9}\]  
where \(c_{11}=-\frac{R_{s}}{L_{qs}}\,c_{12}=\frac{3\phi_{f3}}{L_{bs}}n_{p}\,b_{\beta s}= \frac{1}{L_{bs}}\). \(L_{ls}\) and \(\theta\) are the leakage inductance and the electrical angle, respectively. \(i_{\beta s}\) and \(v_{\beta s}\) are the current and voltage in the \(\beta_{s}\)-axis, respectively. It should be noted that when OCFs occur, the FPPMSM loses a degree of freedom of the \(\alpha_{s}\)-axis, which was removed in (A2).
The main idea of the proposed method can be concluded as follows: first, plenty of insulator aerial images are collected and labelled ground-truth to construct an unprecedented dataset. Subsequently, a new deep convolutional network is trained and adopted to obtain the accurate insulator position. Finally, the obtained insulator location is set as a RoI (Region of Interest), and then a novel method is proposed to detect insulator faults in the RoI. The main contributions of this paper are summarized as follows. To compensate the shortcoming of the lack of the dataset. We construct a large UAV-based insulator dataset with plenty of images filmed in various aerial scenes, and we label the ground-truth for each image. This dataset is suitable for training and testing deep convolutional networks. It also can be applied for validating the performance of the traditional insulator detection methods. We propose an effective network to obtain accurate insulator position in the aerial image with complex background interference. Experimental results show that the performance of our proposed network is superior to the YOLOv2 and is close to the YOLOv3 network, in which YOLOv2 and YOLOv3 are considered to be the state-of-the-art object detectors. Most importantly, our proposednetwork's memory usage is 14.5% less than YOLOv3 and 21.5% less than YOLOv2, which means our proposed network is more conducive to the deployment of embedded devices. We develop a new idea to create insulator fault images to compensate for the lack of insulator faults dataset. After that, we propose a novel method for insulator faults detection in the RoI that are obtained by our proposed network. Experimental results show that our proposed network is more effective and efficient than two state-of-the-art insulator fault detection methods. Most importantly, compared with the previous works, our proposed method can accurately detect not only insulator one fault but also insulator multi-fault. The remainder of this paper is organized as follows. Existing methods for insulator detection and insulator fault detection are reviewed in Section 1. A detailed description of our proposed method is presented in Section 2. Experimental results and discussion are discussed in Section 3. Finally, conclusion and future work are shown in Section 4. The experimental results are exhibited in pictures, and please zoom in for a better view.
## 5 Discussion: Future Work
### Implementation of Human Factors and Decision Support Systems  
Although human and organizational factors are critical to the safety of fishing vessels, they are often overlooked or only partially integrated into existing safety analysis models. As mentioned earlier, human factors can vary significantly depending on the country, region, and individual, making it quite challenging to effectively account for such diversity. Consequently, efforts to analyze human errors along with technical and environmental factors are increasing, utilizing methods such as CREAM, Fuzzy Logic, SRK, and HFACS ([PERSON] et al., 2024 [46]; [PERSON] et al., 2024 [42]; [PERSON] et al., 2024 [50]; [PERSON] et al., 2024 [59]).  
Beyond these, advanced models, like STAMP (Systems-Theoretic Accident Model and Processes) and FRAM (Functional Resonance Analysis Method), are crucial for in-depth analysis and assessment of accident factors ([PERSON] et al., 2022 [65]; [PERSON] et al., 2020 [66]).  
Moreover, due to the diverse cultural and operational characteristics inherent in the fishing industry, models that account for such variability are essential. For this purpose, investigations tailored to specific cultural contexts are needed. While surveys and interviews are commonly used, they can be influenced by interviewer bias. Additionally, conducting such research requires a considerable amount of time and expense. Thus, developing more objective and efficient tools, such as standardized checklists, should be considered to ensure comprehensive and unbiased assessments.
# Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
## 3 Geospatial Data Processing
### GPS Time Series Tendency and Seasonality Analysis
#### 3.3.3 Seasonality Components  
From the IMFs, a frequency analysis of the seasonal components was performed through the Fourier transform. Moreover, in order to assess the link between the altimetric GPS signal and the water level of the lakes, we calculated the correlation coefficients between the IMFs obtained and the gauge data of lakes Argentino, Viedma, and O'Higgins and the La Leona River.
## 3 Results and Discussion
### Statistical Analysis
#### 3.2.2 Multi-Response Optimization Using Desirability Function
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Simulation No**} & \multirow{2}{*}{\
\begin{tabular}{c} **Adjusting** \\ **Nozzle Position** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Seat to** \\ **Valve Distance, mm** \\ \end{tabular} } & \multicolumn{2}{c}{**Greater Is Better**} & \multicolumn{2}{c}{**Smaller Is Better**} \\ \cline{3-6}  & & & **Composite** & \multirow{2}{*}{**Rank**} & \multicolumn{2}{c}{**Composite**} & \multirow{2}{*}{**Rank**} \\  & & & **Desirability** & & **Desirability** & \\ \hline
1 & S0 & 1 & 0.364 & 8 & 0.728 & 1 \\ \hline
2 & S0 & 2 & 0.720 & 1 & 0.462 & 7 \\ \hline
3 & S0 & 3 & 0.103 & 11 & 0.357 & 9 \\ \hline
4 & S0 & 4 & 0.550 & 5 & 0.703 & 2 \\ \hline
5 & S1 & 1 & 0.228 & 9 & 0.598 & 4 \\ \hline
6 & S1 & 2 & 0.620 & 3 & 0.443 & 8 \\ \hline
7 & S1 & 3 & 0.000 & 12 & 0.312 & 10 \\ \hline
8 & S1 & 4 & 0.455 & 6 & 0.639 & 3 \\ \hline
9 & S-1 & 1 & 0.436 & 7 & 0.577 & 5 \\ \hline
10 & S-1 & 2 & 0.701 & 2 & 0.190 & 12 \\ \hline
11 & S-1 & 3 & 0.158 & 10 & 0.245 & 11 \\ \hline
12 & S-1 & 4 & 0.582 & 4 & 0.572 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Composite Desirability and Ranks in case of water environment. Figure 33: Multi-response optimization plots in case of air environment: (**a**) ”greater is better” for TKE, (**b**) ”smaller is better” for TKE. for the water environment: S0/H2 for \"greater is better\" option of turbulent kinetic energy and S0/H1 for \"smaller is better\" option of turbulent kinetic energy.
###### Abstract  
The current work examines the effect of acetone on the electrochemical characteristics of a polycrystalline nickel electrode in a 0.1 M NaOH electrolyte, with respect to the kinetics of alkaline water electrolysis (HER: hydrogen evolution reaction, and OER: oxygen evolution reaction). Cyclic voltammetry (CV) and impedance spectroscopy (EIS) electrochemical techniques were employed to examine these processes for acetone concentrations stretching from 1.0 \(\times\) 10\({}^{-7}\) to 1.0 \(\times\) 10\({}^{-3}\) M. The introduction of small amounts of (CH3)2C=O clearly facilitated the catalytic efficiency of both examined electrochemical gas evolution processes. The latter is believed to result from a significant reduction in the surface tension parameter, due to the mutual interactions of acetone and water molecules, thus facilitating the detachment of gas bubbles from the nickel electrode surface. These findings suggest considerable opportunities for the introduction of tiny amounts of organic additives into alkaline electrolytes to improve the industrial alkaline water electrolysis process that is performed on technologically valuable electrode materials.  
alkaline water electrolysis; AWE; EIS; HER; OER; poly Ni electrode +
Footnote †: journal: Applied Sciences
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 4 Limiting Factors of Qualification
### The Worst Case TEST Conditions
#### 4.1.1 Example of the Worst Case Test Conditions in TID Tests  
As it was presented in the previous section, during system-level TID tests of the PoL it was possible to track the most sensitive component in the system and the most critical parameter, the MOSFET switching time. Analysis performed in [22] has shown that for the high enough degradation, the MOSFET doesn't open fully (ON resistance is high), therefore it doesn't provide enough current to the PoL output. However, this gives some clue that the system worst case might be dependent on the PoL output current configuration, i.e., it might be expected that for the higher output current (lower load resistance) the PoL would fail at a lower TID level because the MOSFET would not be able to give this higher current. On the other hand, it is possible that for the lower output current configuration, the TID failure threshold would be even higher than it was observed during the X-rays experiment.
## 4 Discussion
### Studies' Risks of Bias
#### 4.5.1 Considerable Issues with Selection Bias and Performance Bias  
Out of the five types of bias we focused on, the analyzed studies overall seem to raise the most concerns or confusion about selection bias. Several of the studies [37; 40; 41; 44; 47] did not describe their recruiting process at all, apart from possibly stating the target demographic. Several others [42; 43; 46] clearly displayed a lack of complete randomness with regards to either the recruiting process or the splitting of participants into groups but still had some random elements. The study by [PERSON] et al. [36] was the most concerning since the control group had six more people than the experimental group but the latter had more men, and no explanation was provided. The studies were also overall somewhat susceptible to performance bias. Some of them [41; 42; 43; 46] had multiple sessions with considerable breaks in between, meaning participants might have tried to prepare themselves more during these breaks. However, they most likely did not know what exactly to prepare for.
# Conceptual Framework for Implementing Temporal Big Data Analytics in Companies
## 6 Verification of the Proposed Conceptual Framework
### Participant Perspectives
#### 6.2.1 Outcome 1: Validity of Bringing Temporality to the Foreground  
Question: _The main idea of the framework is to focus on temporal big data analytics. Is it justified to set time as the primary dimension of big data analysis? What are your thoughts on big data and time?_  
This question was about the general opinion of the presented conceptual framework and its most distinctive feature--temporality. The participants admitted that the time dimension is very important in business analytics, however, they have different experiences with this issue. The most outstanding notions in the discussion were as follows:  
_We use timestamps, timestamp data._  
_We use time series forecasting._  
_Data ordered in time as a must._  
_Causal relationships as a very interesting idea._  
_Time dimension, variability in time._  
_Temporality and time--a canon for business analysis._  
_Time representation as an interesting problem._  
_Usefulness of temporality in the context of business._  
_There is no atemporal business analytics in fact._  
As seen from the utterances being cited, the focus participants consider bringing the time dimension to the forefront as an advantage of the proposed framework. Some of them only deal with simple, calendar time (time series)--which more formally may be described as linear point time. Some of them are also aware of richer temporal analyses, such as causal relationships or sequences of events. Regardless of experience, everyone admitted that the inclusion of the time dimension as the primary dimension in big data analytics can bring great benefits, even if time will not be used in every business case.
## 1 Introduction  
Medicinal plants have contributed immensely to health care in Africa. These plants are easily accessible and cheap sources of therapeutic agents [1]. Essential oils from medicinal plants contain certain constituents that play a pharmacological role in the prevention and treatment of chronic diseases such as cancer, cardiovascular disease, diarrhoea diseases and infections [2].  
Infection is the invasion of the body tissues by disease-causing agents like bacteria, virus and fungi. The host tissue's reaction to these organisms as well as the toxins produced by the organisms is attributed to inflammation in patients with HIV or AIDS, cancer, solid organ transplantation, immune deficiencies, chronic inflammatory gastrointestinal, liver, respiratory, urinary tract and other infections [3]. The bacteria associated with common infections like wounds, gastrointestinal and urinary tract infections include _ Klebsiella pneumoniae_, _Streptococcus pyogenes_ and _Serratia mersecens_[4, 5]. Essential oils are known to sothe infections by acting as antimicrobial agents, or by reducing and neutralising reactive oxygen species (ROS) generated during the pathophysiology of disease infections [6].  
Medicinal plants play important roles as a source of antimicrobial and antioxidant. One of these plants is the genus _Hydanora africana_ (HA) Thumb (Hydronaceae (Piperales)) (Figure 1A,B). The plant is also known as Jackal food, Ubnklunga (Xhosa) or Umavumbuka (Zulu) [7]. It comprises 15 species in South Africa. This plant, similar to about ten species of parasitic flowering plant, lives a greater part of its life cycle underground and is unable to create chlorophyll or perform photosynthesis. In traditional medicine, the root of _Hydnora_ species, tuber, fruits, leaves and fruit pulp (such as potato) is used for the treatment of infectious diseases such as dysentery, diarrhoea, amenorrhoea, bladder and kidney complaints. Other ailments include swollen glands and inflamed throat [7]. Although progress has been made in validating the chemical components and biological activities of the root parts of the plant, the chemical composition, antioxidant and antimicrobial properties of HA essential oil remain obscure.  
Therefore, this study aimed at identifying the chemical constituents of _H. africana_ essential oil using gas chromatography interfaced with mass spectrometry (GC-MS). We also aimed to assess its antioxidant, antibacterial and antifungal potential, and to relate our findings to its possible use in folk medicine.
## 4 Case Study and Results
The base case does not contain VSCs and, therefore, the only control actions available to the DSO are the LTC voltage control, DG curtailment, and flexible demand shifting. In both, P2P and MVDC configurations, all available active network management schemes in the base case are used, in addition to the control actions of the VSCs. The D-SCOPF algorithm described in Section 2 has been implemented in AIMMS [33]. The optimisation problem is solved using CONOPT 3.14V on a PC of 3.5 GHz and 8 GB RAM. The performances of the three configurations are compared in terms of operating costs, demand shift, DG curtailment, line power loadings, and tap ratio optimal settings.
## 3 Data and Methods  
To construct the land cover maps, Sentinel-2 images were used, which were captured on 18 th September 2019 at 09:06 a.m. and on 27 th September 2021 at 09:06 a.m., with minimal cloud coverage (<0.1%) [40]. Both images were level 2 Sentinel-2 products, and thus they had already received atmospheric correction. Next, the spectral bands B03, B04, and B08 were extracted from the original images, with spatial resolution of 10 m, in order to produce the color infrared images needed for the land classification. Finally, to classify the color infrared image, we used a supervised machine learning (SML) model, using the application of the support vector machine (SVM) algorithm. SVM algorithms have been proven to be a reliable method of creating land cover maps from Sentinel-2 images [31; 32; 35].  
For the calculation of the topographic factors, the freely available digital elevation model (DEM) of the Copernicus Land Monitoring Service was used. The Copernicus DEM offers spatial resolution of 25 \(\times\) 25 m, with vertical accuracy of \(\pm\)7 m (RMSE). For the purpose of our study, the 1000 \(\times\) 1000 km tile with codename E50N20 was used, and the elevation of our study area was isolated from it [38]. The remaining topographic factors (slope, aspect, and TWI) were derived by analyzing the DEM. The roads and settlement locations were downloaded from open data sources [41; 42]. For the raster analysis and calculations mentioned above, the GIS software ArcGIS Pro 2.9.1. was used.  
In order to validate our results and examine the impact of the fire on land cover and subsequently the fire risk, detailed burn scar maps from the National Management Body of Dadia-Lefkimi-Soufli Forest National Park were used [37], which depict the extent of fires that occurred during October 2020 [43] and July 2021 [44]. VIIRS hotspot locations were also used to validate the spatial extend of the burned areas [45]. The workflow of our method is represented in Figure 2.  
Figure 1: (**a**) Location of the study area in Greece (**b**) and in Evros county. (**c**) Longitude and latitude of Natura 2000 area (GR1110005) that coincides with the National Park Forest of Dadia-Lefkimi-Soufli, including the protected zones A1 and A2.
## 2 Theoretical Background
### Complex-Valued Neural Networks  
Artificial neural networks (ANNs) have recently garnered significant attention across nearly all fields as a powerful data-driven tool. They can efficiently model complex, multivariable, nonlinear systems without any prior assumptions [18; 19]. Using input and output datasets, ANNs can identify the correlations between them and derive the corresponding output when new inputs are provided. Hidden layers are added between the input and output layers to effectively represent their nonlinear relationships. The nodes in each layer are interconnected through parameters such as weights and biases [18; 19].  
A neural network composed of real-valued elements in the input, hidden, and output layers is called a real-valued neural network (RVNN). While RVNNs have achieved great success in various fields, they are challenging to apply to polar coordinate systems for efficiently representing directions and are inconvenient for expressing phase differences [18; 19].  
Figure 1: Definition of the wave direction.  
Therefore, in this study, a complex-valued neural network (CVNN) is applied to address these shortcomings of RVNNs effectively [21].  
The architecture of CVNN is identical to that of a general RVNN, as shown in Figure 2. However, the parameters that compose each layer, such as weights, biases, and activation functions, are all complex numbers. The output of the n-th hidden layer can be computed as shown in Equation (5). Here, f(.) represents the activation function, which is also expressed as a complex function. The prediction \(\hat{y_{0}}\) can be computed as shown in Equation (6). In this study, the activation function used is the Complex ReLU (CReLU) [18; 20; 21]. The ReLU activation function applied to RVNNs is given by Equation (7). This function has gained significant popularity due to its computational efficiency and its ability to mitigate the vanishing gradient problem. For CVNN, the CReLU is defined by Equation (8), which involves separating the real and imaginary parts of the input, applying the ReLU function to each part separately, and then recombining them into a complex number [18; 21].  
\[H_{n}=f({W_{n0}}^{1}+\sum_{i=1}^{m}{W_{ni}}^{1}X_{i}) \tag{5}\]  
\[\hat{y_{0}}=f({W_{0}}^{2}+\sum_{i=1}^{n}{W_{i}}^{2}H_{i}) \tag{6}\]  
\[ReLU(x)=max\left(0,x\right) \tag{7}\]  
\[CReLU(z)=ReLU((Re(z))+iReLU(Im(z)) \tag{8}\]
# Stripe-Assisted Global Transformer and Spatial-Temporal Enhancement for Vehicle Re-Identification
## 3 Methodology
### SaGT
#### 3.1.2 Extracting Global and Stripe-Based Features  
From Figure 2, we employ a transformer layer to encode the basic features \(E_{b}\), and the resulting class embedding serves as the global feature \(f_{g}\) of the vehicle image, which is described in Equation (4) as follows:  
\[E_{g}^{\prime} =\text{MHSA}(\text{LN}(E_{b}))+E_{b}, \tag{4}\] \[E_{g} =\text{MLP}(\text{LN}(E_{g}^{\prime}))+E_{g}^{\prime},\] \[f_{g} =e_{g}^{0},\]  
where \(E_{g}=\left[e_{g}^{0},e_{g}^{1},\dots,e_{g}^{N}\right]\).  
Meanwhile, we also designed an SFM to construct stripe-based features, capturing local details from specific stripe regions. As depicted in Figure 2, the SFM utilizes a transformer layer to encode patch embeddings from the identical stripe region, along with an additional class embedding \(\tilde{x}_{cls}\) to represent the stripe-based feature. The class embedding is initialized by the basic class embedding \(e_{b}^{0}\), as expressed in Equation (7). As described in Section 3.1.1, the vehicle image is divided uniformly into \(N\) patches. Assume that \(K\) stripe-based features are constructed along the vertical direction, resulting in \(M=\frac{N}{K}\) patches in each horizontal stripe region. Note that proper \(K\) must ensure that \(M\) is an integer. The embeddings of the \(j\)th horizontal stripe \(X_{s}^{j}\) can be formed according to Equation (5) as follows:  
\[X_{s}^{j}=\left[e_{b}^{(j-1)\ast M+1},e_{b}^{(j-1)\ast M+2},\dots,e_{b}^{j\ast M }\right], \tag{5}\]  
where \(j\in\{1,\dots,K\}\). Then, the input \(\tilde{Z}_{0}^{j}\) for the \(j\)th stripe fed to the SFM can be described in Equation (6):  
\[\tilde{Z}_{0}^{i}=\left[\tilde{x}_{cls},x_{s}^{j,1},\dots,x_{s}^{j,M}\right], \tag{6}\]  
\[\tilde{x}_{cls}=e_{b}^{0}, \tag{7}\]  
where \(\tilde{x}_{cls}\in\mathbb{R}^{1\times D}\) and \(X_{s}^{j}=[x_{s}^{j,1},\dots,x_{s}^{j,M}]\). Then, the \(j\)th stripe-based feature \(f_{l}^{j}\) is produced by the SFM according to Equation (8).  
\[E_{l,j}^{\prime} =\text{MHSA}(\text{LN}(\tilde{Z}_{0}^{i}))+\tilde{Z}_{0}^{i}, \tag{8}\] \[E_{l,j} =\text{MLP}(\text{LN}(E_{l,j}^{\prime}))+E_{l,j}^{\prime},\] \[f_{l}^{j} =e_{l,j}^{0},\]  
where \(E_{l,j}=\left[e_{l,j}^{0},e_{l,j}^{1},\dots,e_{l,j}^{M}\right]\).  
Finally, we can obtain \(K\) horizontal stripe-based features \(F_{l}\) for one vehicle image, i.e., \(F_{l}=\left[f_{l}^{1},f_{l}^{2},\dots,f_{l}^{K}\right]\).