esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
If there is contamination from the OLTC gases, the rest of the evaluation will be performed by an expert who determines whether there is also an active fault; otherwise, proceed to Step 3, where the type of fault is identified by traditional DGA interpretation methods. Although the C\({}_{2}\)H\({}_{2}\)/H\({}_{2}\) ratio [6; 7] can be used to consider transformer oil contamination from the OLTC gases, it is necessary to take into account that high H\({}_{2}\) concentrations affect this ratio. A high H\({}_{2}\) concentration could be due to the fact that H\({}_{2}\) is produced in the transformer oil in almost all incipient faults. Applying this ratio with a high C\({}_{2}\)H\({}_{2}\) concentration would not show the contamination of the transformer oil because of the high H\({}_{2}\) concentration. This situation occurred in [12]: the application of the C\({}_{2}\)H\({}_{2}\)/H\({}_{2}\) ratio did not show the communication between oil compartments; it was necessary to inspect the conservator, and two holes were found on the barrier between the main conservator and the OLTC conservator. Machine learning (ML) techniques are widely used to assess the condition of a transformer [16; 17; 18; 19; 20; 21; 22] and identify potential faults in a transformer [23; 24; 25; 26; 27; 28] using DGA, oil quality analysis (OQA), the furfurraldehyde (FFA) content of the oil, polarisation and depolarisation current (PDC) measurements and dielectric frequency domain spectroscopy (FDS). Through the application of ML algorithms, models are generated that create predictions of the transformer health status and automatically diagnose potential faults in the transformer insulation from the data provided. According to [20; 23; 25], a decision tree (DT) classifier is easy to interpret because the output is a set of conditional if-then tests of the input data. In addition, it is easy to train, and it works better than other ML techniques. The accuracy of the results obtained in these studies was better than that of those obtained with the DT algorithm. This paper presents a method for determining the contamination of the transformer oil from the OLTC gases in a group of power transformers of a distribution system operator (DSO) based on the DGA results for the last two years, the application of the guides [6; 7] and the knowledge of experts. The method proposed in this paper improves the detection of transformer oil contamination from the OLTC gases that is not determined by traditional DGA interpretation methods. Based on this transformer oil contamination classification methodology, a DT algorithm is applied to develop a prediction model that automatically recognises transformer oil contamination from the OLTC gases in DGA samples.
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 2 System Overview
### Channel Model
To define a single model, the following probabilities of occurrence are defined:  
\[P_{LOS}(\theta) = \frac{1}{1+\alpha e^{-\beta(\theta-\alpha)}}, \tag{5}\] \[P_{NLOS}(\theta) = 1-P_{LOS}, \tag{6}\]  
where \(\alpha\) and \(\beta\) are parameters that depend on the scenario, in this case the urban one [4]. As it can be observed in Equation (5), the LOS probability depends on the elevation angle \(\theta\). Thus, Equation (4) has to be applied to obtain \(P_{LOS}(x-x_{i},y-y_{i},h_{i})\) and \(P_{NLOS}(x-x_{i},y-y_{i},h_{i})\) as a function of the coordinates of the positions of the user and the UAV. The parameters \(\xi_{LOS},\xi_{NLOS},\alpha,\beta\) and, in general, Equations (5) and (6) are scenario dependent (that is, they will be different in urban, suburban, rural, etc. deployments). Accordingly, it is expected the network operator controlling the positions of the UAVs will have some a-priori knowledge (obtained through drive testing) of those parameters and models.
# Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle
## 4 Simulation Study and Results
### Collision Avoidance in a Real Environment
#### 4.2.3 Simulation Verification

Table 4: The information of dynamic obstacles. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Environment**} & \multirow{2}{*}{**Index**} & **Speed of** & **Thruster Speed** & **Propulsion Angle** & **Computation** & **Distance** \\  & & **USV (m/s)** & **(r/min)** & **(\({}^{\circ}\))** & **Time (s)** & **(m)** \\ \hline \multirow{3}{*}{static} & maximum & 11.1 & 4000 & 20.0 & 0.21 & 200.0 \\  & minimum & 0.0 & 0 & \(-4.0\) & 0.09 & 75.1 \\  & average & 7.9 & 3153 & 1.7 & 0.13 & 116.3 \\ \hline \multirow{3}{*}{dynamic} & maximum & 11.1 & 4000 & 20.0 & 0.21 & 172.4 \\  & minimum & 0.0 & 0 & \(-10.0\) & 0.09 & 16.7 \\ \cline{1-1}  & average & 7.7 & 3117 & 2.1 & 0.14 & 97.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the evaluation index comprised of two tests in a real environment. Figure 14: _Cont._  
Figure 13: Demonstration diagram of the USV in a dynamic real environment. (**a**) Time 1 s; (**b**) Time 38 s; (**c**) Time 46 s; (**d**) Time 56 s; (**e**) Time 83 s; (**f**) Time 92 s; (**g**) Time 126 s; (**h**) Time 149 s; (**i**) Time 159 s; (**j**) Time 192 s; (**k**) Time 237 s.
## 2 Data Preprocessing
### Phase Space Reconstruction  
The mode obtained by VMD of the original wind power sequence is still a relatively complex signal sequence, which can be regarded as a chaotic time series. The chaotic time series is reconstructed into a high-dimensional phase space using PSR. The obtained high-dimensional phase space matrix not only retains the main features of the mode sequence, but also provides the implicit information of the mode. In this study, the PSR based on the coordinate delay method is used to reconstruct the phase space of the modes.  
For a mode sequence \(x_{1},x_{2},\cdots,x_{N}\), delay time \(\tau\) and embedding dimension \(m\) are used in the coordinate delay reconstruction method to form an \(m\)-dimensional phase space:  
\[X_{i}=\left[x_{i},x_{i+\tau},\cdots,x_{i+(m-1)\tau}\right] \tag{3}\]  
where \(i=1,2,\cdots,L\), \(L=N-(m-1)\tau\).  
The phase space trajectory matrix \(\mathsf{X}\) of the reconstructed mode is shown in Equation (4):  
\[X=\left[\begin{array}{c}x_{1}\\ x_{2}\\ \vdots\\ x_{L}\end{array}\right]=\left[\begin{array}{cccc}x_{1}&x_{1+\tau}&\cdots&x_{ 1+(m-1)\tau}\\ x_{2}&x_{2+\tau}&\cdots&x_{2+(m-1)\tau}\\ \vdots&\vdots&&\vdots\\ x_{L}&x_{L+\tau}&\cdots&x_{L+(m-1)\tau}\end{array}\right] \tag{4}\]  
where the row vector \(X_{i}\) constitutes the phase point of the multi-dimensional phase space \(L\) phase points that jointly constitute the phase space trajectory reconstructed by the modes.  
During the PSR of the modes, delay time \(\tau\) and embedding dimension \(m\) have to be chosen carefully. If the delay time is too small, the coordinate correlation is too strong to ensure that each phase point in the phase space can provide new information; however, if it is too large, it is difficult to ensure the continuity of the trajectory. Similarly, although a higher-dimensional phase space provides more information, it can also increase the computational time and obscure the structural relationship. Therefore, we use the recently developed C-C method to calculate the delay time \(\tau\) and the embedding dimension \(m\) for the PSR of the modes. For details on the C-C method, please refer to [26].
# Outpatient Total Knee Arthroplasty: A Meta-Analysis
## 4 Discussion
Fast-track regimes, multimodal pain control, and early mobilization have markedly reduced the length of hospitalization [42]. Peri-articular injections of local anesthetic and adductor canal block provide better pain control and faster functional recovery [43; 44]. Several surgical approaches have been compared in terms of hospitalization; the minibusstatus approach has demonstrated a faster recovery and better pain control when compared to the medial parapatellar approach [45; 46]. Cardiovascular disorders, obesity, chronic renal or hepatic insufficiency, diabetes mellitus, physical and cognitive impairment, age greater than 75, and social support at home are considered pivotal for outpatient TKA [31; 47; 48; 49]. Patients who already received a successful contralateral TKA were more likely suitable for an outpatient procedure, suggesting the importance of patient expectations [50]. However, there are no clear recommendations and eligibility criteria, and further investigations are necessary to define the best candidates. The present study has several limitations. The inpatient group included different hospitalization protocols and timing of discharge. Moreover, the reason of the prolonged hospitalization in some studies was poorly described. Different heath care systems may also influence the results. The limited number of included studies represented an important limitation of the present study. To improve data pooling, both clinical investigations and database analyses were included for analysis, improving the risk of selection bias. Interestingly, database studies have shown conflicting results with an overall greater rate of complication in the outpatient group [17; 18; 26; 28]. On the other hand, the included clinical studies have shown similar risk of adverse events and readmissions [1; 19; 20; 21; 27; 29]. The strength of the databases analyses is the large scale of the population investigated; however, results may be highly biased, and the quality of the recommendation is limited. Subject selection was often biased, heterogeneous, and poorly described. Some included studies did not report separately the surgical outcomes, with the diagnoses not described. Furthermore, the description of the surgical technique and postoperative rehabilitation were not adequately reported by some studies. Since outpatient TKA remains relatively uncommon with limited data available for inclusion, there is considerable discrepancy in the sample size included for analysis between the two groups; this may jeopardize the frequency of complications in the outpatient TKA group. Given these limitations, results from the present study must be interpreted with caution. Further high quality clinical trials are required to improve evidence. A widely standardized protocol for patient eligibility and education for outpatient TKA is strongly recommended.
# Autonomous Mutual Authentication Protocol in the Edge Networks
## 2 Literature Survey
#### Analysis of the Survey Limitations  
1. The need for additional infrastructure for the authentication protocol: Several recent works that are developing authentication protocols include blockchain-basedoperations by having dependency on the service server, ticket-granting server (TGT), and authentication server. Therefore, these authentication protocols are not suitable for the autonomous devices as they require higher dependency on the multiple systems for the purpose of authentication.
2. High-calculation requirements for the IoT resource-constrained devices: The traditional cryptographic algorithms and protocols are not suitable for the autonomous devices as most of the IoT resource-constrained devices possess limited memory and processing power. Therefore, design of a new authentication process is required to avoid high calculations on the IoT devices and to perform efficiently for multiple authentications.
3. Design issues limiting the protocol performance: The inclusion of popular technologies and references, i.e., blockchain, Kerberos, elliptical curve cryptography (ECC), in the protocol design without a specific objective is one of the large mistakes in many works. Such design issues lead to low performance and bottlenecks within the system, which are not suitable for the autonomous devices.
# Advanced Scale-Propeller Design Using a MATLAB Optimization Code
## 2 Materials and Methods
### Propeller Theory--Geometry
#### 2.1.2 Propeller Geometry  
Aside from the diameter, the pitch of the propeller is the most important geometric parameter when designing a propeller. The pitch of a propeller is defined as the distance that, if the propeller were moving through a soft solid, the propeller would move forward in one complete rotation. Of course, in conventional propellers the pitch will decrease from the hub to the tip, and so it has become convention to specify the pitch of a fixed pitch propeller at 75% of the distance from the hub to the blade tip. Propellers with high pitch values can produce higher thrust levels at higher speeds than low pitch propellers, whilst low pitch propellers can produce higher thrust levels at low speeds. Therefore, the main driver in selecting the pitch for a propeller is the velocity of the propeller. From the pitch, the theoretical angle of twist can be calculated for the length of the blade using Equation (2).  
\[Angle\,of\,Twist=tan^{-1}\Big{(}\frac{p}{2\pi r}\Big{)} \tag{2}\]  
Another important geometric aspect of a propeller is the taper profile. Whilst rotor blades found on helicopters tend to have no taper, and wings on fixed wing aircraft tend to have linear tapers, the taper profile of propellers is typically quite complicated, involving complex curves. The taper profile is defined as the variation of chord length along the blade. Typically, the chord length is small at the hub, it increases, resulting in a maximum chord length between 25% and 75% along the blade radius, and then it decreases towards the blade tip.
## 2 Materials and Methods  
### Design of the Prototype System  
Numerical analysis of the electric field optimization for the developed system was performed. The distance and shape of the electrode were designed and simulated according to the experimental setup. The electrode size was 3 mm \(\times\) 10 mm in height and width, and the spacing of each electrode was designed to be 6 mm. The end of the electrode was of a round shape, and the material was defined as stainless steel. Electrically conductive material is essential for providing an effective microcurrent, with most metals showing conductivity in a range of 10\({}^{6}\) S/m. We have chosen stainless steel with 1.45 \(\times\) 10\({}^{6}\) S/m. The alternating current (AC) conduction module was used to analyze the electric field. Two voltage conditions were applied, 0.7 and 5.0 V, respectively, at 10 MHz, based on previous work [8, 9]. The media condition was assumed to be water, corresponding to the experiments.  
The electronic part includes an electric circuit that generates a specific frequency of AC signal with direct current (DC) offset. A 10 MHz crystal oscillator was applied for precision AC supply, and the DC was realized using resistors and capacitors (Figure 1). Since this circuit consists of passive components, the total electric power consumption was minimized in the 1 mW range, enabling battery-operated electronics. The electric circuit was integrated into a 1.44 cm\({}^{2}\) footprint.  
To investigate the spatial distribution of the electric field in the testing package, numerical analysis (Ansys Inc., Canonsburg, PA, USA) that focused on the relative magnitude of the field generated between the electrodes was performed as the electric potential was changed from 0.7 to 5.0 V.  
Figure 1: Circuit diagram of the electronics.
## 4 Numerical Simulation Analysis
### Analysis of Full-Face Excavation Results  
Based on the numerical simulation results, as shown in Figures 4-6, after a full-face excavation of 20 m, the maximum displacement of the tunnel surrounding rock occurs at the bottom and top, with a maximum displacement of approximately 0.031 m. The deformation displacement of the left and right shoulders of the tunnel surrounding rock is about 0.016 m.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rock Layer Name** & **Bulk Modulus K** & **Shear Modulus G** & **Tensile Strength \(\upsigma\)** & **Cohesion C** & **Internal Friction** & **Density \(\upsigma\)** \\
**Change** & **(Gpa)** & **(Gpa)** & **(Mpa)** & **(Mpa)** & **(\(\upsigma\))** & **(\(\upsigma\))** & **(kg\(\cdot\)m\({}^{-3}\))** \\ \hline Metamorphic & 8.91 & 7.01 & 5.46 & 6.20 & 30 & 2500 \\ Rock & 0.36 & 0.16 & 0.03 & 0.6 & 26 & 2255 \\ Tunnel & 0.36 & 0.16 & 0.03 & 0.6 & 26 & 2255 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical properties of rock layers.  
Figure 3: Numerical model diagram.  
As shown in Figures 7-9, after a tunnel excavation of 20 m, stress concentration occurs on both sides of the tunnel surrounding rock. The maximum compressive stress is approximately 6.44 \(\times\) 10\({}^{6}\) Pa, with lower compressive stress values distributed at the top and bottom of the tunnel surrounding rock. The minimum compressive stress is located at the bottom of the tunnel, with a value of about 5.23 \(\times\) 10\({}^{5}\) Pa. After a tunnel excavation of 40 m, stress concentration appears on both sides of the tunnel surrounding rock. The maximum compressive stress is approximately 6.56 \(\times\) 10\({}^{6}\) Pa, with lower compressive stress values at the top and bottom of the tunnel surrounding rock. The minimum compressive stress is located at the bottom of the tunnel, with a value of about 5.55 \(\times\) 10\({}^{5}\) Pa. After a tunnel excavation of 60 m, stress concentration occurs on both sides of the tunnel surrounding rock. The maximum compressive stress is approximately 6.83 \(\times\) 10\({}^{6}\) Pa, with lower compressive stress values at the top and bottom of the tunnel surrounding rock. The maximum compressive stress is located at the bottom of the tunnel, with a value of about 5.86 \(\times\) 10\({}^{5}\) Pa.  
Figure 4: Full-face excavation tunnel surrounding rock displacement diagram at a 20 m excavation.  
Figure 5: Full-face excavation tunnel surrounding rock displacement diagram at a 40 m excavation.  
Figure 6: Full-face excavation tunnel surrounding rock displacement diagram at a 60 m excavation.
# Evaluation of Urban Transportation Resilience under Extreme Weather Events
## 4 Case Study in Fuzhou  
### Rainstorm Events  
Fuzhou is the capital of Fujian Province, China, with a resident population of over 7 million people in 2018 [45]. On the night of 2 May 2018, the city experienced a rainstorm. The China Meteorological Administration (CMA) issued a yellow rainstorm warning, which indicates that the precipitation in Fuzhou city reached more than 50 mm within 6 h of the warning [46]. The rainstorm poured down at 23:00 and did not end until 12:00 the next day, causing waterlogging on some roads in the central area of Fuzhou, slowing down the movement of vehicles, and causing great disturbances.  
About a month and half later, the city experienced another more severe rainstorm. It started at 14:00, 20 June 2018, and the CMA issued an orange rainstorm warning soon after, at 16:10. An hour later, the warning level was raised to the highest warning level: red. The rainstorm caused widespread disruptions in transportation and communication, destroyed crops, and also triggered geological disasters such as landslides and mudslides in Fuzhou. The rainstorm lasted for about 10 h, with the total precipitation exceeding 90 mm. Among them, the maximum hourly precipitation reached 46.8 mm. According to the records of the Fujian Meteorological Bureau, the hourly precipitation levels in Fuzhou during the yellow and red rainstorm warnings are shown in Figures 6 and 7, respectively.  
Figure 6: Hourly precipitation during the yellow rainstorm warning in Fuzhou.
# Operationalisation of Building Inspections and Repair: Systematisation-Based Approach
## 3 Results
### Most Common Diagnosis Methods to Prepare an Inspection
#### 3.1.2 Diagnosis Methods in Facade Elements/Claddings
Next, the cell area should be measured to determine the scale factor for the cell/tube combination. Then, the cell is attached to the surface and sealed to ensure uniform pressure. After filling the cell with water, the container is closed, and flow measurements are taken along the capillary tube (readings at 10, 30, and 60 min) after the first wetting of the surface. As for the Karsten tube test, it will require three (minimum) Karsten tubes (glass tubes graduated from 0 to 4 cm\({}^{3}\), shaped as pipes), silicone (or mastic, plasticine or putty) for fixing the tubes to the surface (included an application gun, if applicable), a water container, and nozzle. First, the surface is cleaned. Next, the edge of the tube is covered with silicone (or other adhesive material), and the tube is attached to the wall in the area to be tested, pressing it onto the wall. Silicone should dry for at least 20 min at room temperature. Then, the tubes are filled with water up to the maximum level (4 cm\({}^{3}\)). After 5 min, the first reading is taken on each of the three tubes, based on the lowering of the water level observed in the tubes. The reading should be repeated at 10, 15, 30, and 60 min, registering the water level drop. For each time interval, an average value is considered (using the three readings) and using these values, a graph may be drawn of the water level drop as a function of time. The slope of the line corresponds to the absorption coefficient of the cladding.
# Weak Multiple Fault Detection Based on Weighted
###### Abstract  
As one of the important parts of a mechanical transmission system, a rolling bearing often has multiple faults coexisting, and the mutual coupling between multiple faults poses a challenge for accurate diagnosis of rolling bearings. Aiming at the above problems, this paper proposes a weighted Morlet wavelet-overlapping group sparse (WOGS) algorithm for the multiple fault diagnosis of rolling bearings. On the basis of the overlapping feature of Morlet wavelet transform coefficients, a WOGS optimization model was initially constructed. Thereafter, the weight coefficients in the model were constructed by analyzing the impulse features of the signal. Thus, majorization-minimization was used to solve the optimization problem. A case study on weak multiple fault diagnosis of rolling bearings was performed to validate the effectiveness of the WOGS algorithm. Quantitative indexes are used to further discuss the extraction accuracies of different algorithms, and the results show that the proposed algorithm exhibits better performance than other algorithms.  
**Keywords:** overlapping group sparse; multiple fault diagnosis; morlet wavelet transform; rolling bearing; sparse optimization