esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Machine Learning Methods  
The following four ensemble regression methods of machine learning were chosen for power consumption forecast: random forest [51], adaptive boosting (AdaBoost) [51], extreme gradient boosting (XGBoost) [52], and light gradient boosting (LightGBM) [53]. They are used to build ensembles from regression decision trees:  
\[y_{i}^{*}=T(Z_{i}), \tag{15}\]  
where \(T\) is a hierarchic system of rules, each of which compares the value of a certain feature with the threshold.  
Random forest builds model as an ensemble consisting of \(k\) decision trees:  
\[y_{i}^{*}=f(Z_{i})=\frac{1}{k}{\sum_{j=1}^{k}T_{j}(Z_{i})}, \tag{16}\]  
AdaBoost, XGBoost, and LightGBM are based on the concept of gradual ensemble improvement (boosting):  
\[y_{i}^{*}=f(Z_{i})={\sum_{j=1}^{k}w_{j}T_{j}(Z_{i})}, \tag{17}\]  
where \(w_{j}\) is weight coefficient of model \(j\).  
In order to compare the results of experiments, the following metrics were calculated: mean absolute error (_MAE_), mean absolute percentage error (_MAPE_), root mean squared error (_RMSE_), and coefficient of determination (\(R^{2}\)), defined by Formulas (18)-(21), respectively.  
\[MAPE=\frac{1}{N}{\sum_{i=1}^{N}\frac{\left|y_{i}-y_{i}^{*}\right|}{y_{i}}}, \tag{18}\]  
\[MAE=\frac{1}{N}{\sum_{i=1}^{N}}{\left|y_{i}-y_{i}^{*}\right|}, \tag{19}\]  
\[RMSE=\sqrt{\frac{1}{N}{\sum_{i=1}^{N}}{\left(y_{i}-y_{i}^{*}\right)^{2}}}, \tag{20}\]  
\[R^{2}=1-\frac{{\sum_{i=1}^{N}}{\left(y_{i}-y_{i}^{*}\right)^{2}}}{{\sum_{i=1} ^{N}}{\left(y_{i}-\overline{y}\right)^{2}}}, \tag{21}\]  
where \(y_{i}\) is actual \(i\)-th value of power consumption, \(y_{i}^{*}\) is forecast \(i\)-th value of power consumption, \(N\) is the number of values, and \(\overline{y}\) is the mean actual value.  
Additionally, to assess the lower bound of accuracy, results for a ridge regression model with Tikhonov regularization (henceforth--Ridge) were obtained. The linear regression model can be formulated as follows [41]:  
\[y_{i}^{*}=X\cdot W+d\, \tag{22}\]  
where \(X\) is the independent variables (features, described in Formula (14)), \(W\) is regression coefficients, and \(d\) is bias coefficient.  
Data preprocessing, model construction, and testing were performed using Python 3 with open-source libraries: Scikit-Learn (AdaBoost, random forest, and Ridge) [51], XGBoost [52], and LightGBM [53].
## 3 Result and Discussions  
### Wave Model  
The input for the wave model employs a nesting model or a combination of models, integrating a larger area (denoted in red) with a smaller area (denoted in yellow) characterized by a higher grid resolution [43], as depicted in Figure 5. Simultaneously, Figure 6 presents bathymetric data obtained from surveys and global bathymetric data, which constitute essential inputs for the wave model.  
Figure 5: Model area.  
Figure 6: Bathymetric survey data (**left**) and global navigation bathymetric map (**right**).  
Graphical representations in Figure 7 show the data results for wave height (Hs), wave period (Ts), and wind speed spanning in Figure 8, where all the data were from 2012 to 2022. Subsequently, based on the depicted graph, a wave rose was generated using the ADCP survey method. The model's validity was assessed through a comparison with the wave rose derived from the ECMWF wave model data, as illustrated in Figure 9. The findings reveal a similarity between the wave rose data from the model and the survey data at the same location and during the same month. Hence, it can be inferred that ECMWF data are well suited for incorporation as input for the wave model, which will be coupled with the sediment model to capture the abrasion processes occurring in the study area.
# A Novel Context-Aware Reliable Routing Protocol and SVM Implementation in Vehicular Area Networks
## 3 Approaches behind the Proposed Protocol
### Svm  
SVM is a vector-oriented method that can perform pattern recognition and regression based on the principle of statistical study and the structural risk minimization principle. SVM provides several training examples, each designated as one of the various categories; an SVM training algorithm constructs a model that forecasts the category of the new examples. It separates two groups by a wide margin to keep them as far apart as possible, as shown in Figure 3. It is performed by transforming small input space into significant inputs, which turns non-distinguishable classes into discrete classes [40; 41].  
Kernelized SVM is a standard method for addressing classification problems. The use of the SVM classifier in applications like clustering, multi-class grouping, and ranking, on the other hand, adds to the computational load. The SVM approach can also manage vehicle information and produce routing parameters to improve routing efficiency. By applying a large number of classified data features, including the distance among forwarding and next-hop nodes, moving direction of next-hop nodes, and acceleration of next-hop and forwarding nodes, the model is obtained by training such dataset in SVM. SVM works well for many practical problems, including linear and nonlinear problems. The approach works by separating the data into classes through a line or hyperplane. The hyperplane that maximizes the margin between the two classes is represented in Figure 4.  
Figure 3: Support vector Principle.  
Figure 2: K-means clustering approach.
# Cybernetic Model Design for the Qualification of Pharmaceutical Facilities
## 3 Discussion and Model Verification, Validation, and Application
### Documentation Review Process and Release
The newly designed cybernetic model for the qualification of pharmaceutical facilities is considered universal due to several key characteristics:  
* Applicability across different technologies--the model has been implemented in various types of production technologies within the pharmaceutical industry, including sterile liquid forms, solid forms, ointments, gels, microbiological laboratories, physical-chemical laboratories, and R&D facilities. This broad application demonstrates its versatility and ability to adapt to different manufacturing environments. * Flexibility and adaptability--the model has been designed to be flexible and adaptive, allowing it to be tailored to specific project requirements and varying operational contexts. Its adaptability enables it to address the unique challenges and complexities of different pharmaceutical facilities, making it suitable for a wide range of applications. * Comprehensive regulatory aspect--the model incorporates a comprehensive regulatory aspect, ensuring that it meets the requirements of regulatory authorities. By aligning with industry standards, guidelines, and legal regulations, the model can be universally applied in pharmaceutical plants across different regions and jurisdictions. Also, by applying the model, it is possible to qualify smart facilities and equipment that are in line with the Pharma 4.0 concept, all within the framework of regulations. * Standardization and process definition--through participation in numerous reconstructions and projects, the model has largely standardized and defined all processes during system commissioning and qualifications. This standardization enhances its universal applicability by providing a structured framework that can be implemented consistently across various projects and facilities. * Search for better solutions--the model constantly initiates the search for better solutions and process improvements, reflecting a commitment to ongoing enhancement and optimization. This proactive approach ensures that the model remains relevant and effective in different settings, contributing to its universal character. With this approach and solutions for process improvement, it is possible to move from Pharma 4.0 to Pharma 5.0.
## 5 Numerical Examples
### Parameter Settings for Different Approaches  
The following parameter settings were used for the experiments: the planning horizon (\(H\)) was set to one week, so that the ETAs of ships would be within the interval [0, 168] (hours); the quay length (\(L\)) was set to 1000 m; the length of a ship \(j\) (\(l_{j}\)) was a random value within the range [50, 200] (meters); the desired berthing position of a ship \(j\) (\(d_{j}\)) was a random value within the interval [0, \(L\)-\(l_{j}\)]; the cost rates \(c_{1}\) and \(c_{2}\) were set to USD 1000/h; and the \(\beta=2/(100*60)\) (per hour), which means the deviation of 100 m from the desired berthing position of a ship would increase handling time by 2 min.  
The parameter values for the three different methods used in the first stage of the two-stage procedure are listed in Table 1. The parameter \(F\) indicates the total number of frogs in the swarm; the parameter \(m\) indicates the total number of memeplexes; the parameter \(l\_iter\) specifies the number of local search for each frog; the parameter _iterations_ indicates the total number of iterations; the parameter \(q\) indicates the total number of frogs in each memeplex; the parameter \(\delta\) indicates the rate controlling the use of TM; the parameter \(\theta\) indicates the mutation rate used; the symbol \"-:\" indicates not used.
## 3 Results and Discussion  
ICESat-2 elevation datasets are used as a reference for measuring vertical error for the DEMs (TanDEM-X 90 and CartoDEM V3 R1), as an alternative approach to the usual method of conducting DGPS (Differential Global Positioning System)/DGNSS (Differential Global Navigation Satellite System) surveys. The DGPS/DGNSS surveys can provide more accurate results, but are time consuming, costly and challenging to conduct in inaccessible or busy areas. The strong beam data were collected among the 6 spiled beams (3 stong and 3 week) for the analysis in order to ensure the higher accuracy of the ICSat-2 elevation data. TanDEM-X 90 and CartoDEM V3 R1 elevation data were extracted using ICESat-2 reference elevation datasets and analyzed for statistical comparison to determine the vertical accuracy of both of the openly accessible DEMs. According to the findings, as shown in Figure 4, it is observed that the performance of TanDEM-X 90 (RMSE = 2.19 m) is better than CartoDEM V3R1 (RMSE = 3.82 m) with similar results for the ME and MAE. The accuracy of TanDEM-X 90 m is comparatively higher, indicating that TanDEM-X90 is more strongly correlated with ICESat-2 space-borne LiDAR datasets in plain terrain.  
Figure 4: Plot of ME, MAE, and RMSE for TanDEM-X 90 and CartoDEM V3 R1 with ICESat-2 elevation data as a reference.  
Figure 3: (**a**) ICESat-2 (ALT08) points superimposed on TanDEM-X 90; (**b**) ICESat-2 (ALT08) points superimposed on CartoDEMV3 R1.  
The comparative study found the better performance of CartoDEM V3 R1 using smartphone GPS datasets for flat urban terrain regions [11]. ICESat-2 data were found to be the optimal choice for DEM accuracy assessment, due to the higher accuracy and wider coverage range over terrain than accuracy assessment using an A-GPS smartphone.
# Relationship between Fault Level and System Strength in Future Renewable-Rich Power Grids
## 3 Fault Level Estimation Using Measurement-Based Thevenin Impedance  
The fault level is a quantity defined to represent the MVA associated with a three-phase symmetrical bolted short circuit current flowing at a faulty node. Once the short circuit current is known, then the fault level can be calculated according to Equation (1):  
\[Fault\ Level\ (MVA)\ =\ \sqrt{3}\ V_{N}\ I_{F} \tag{1}\]  
where \(V_{N}\) is the pre-fault line-line voltage of the point \(N\) in (kV) and \(I_{F}\) is the value of the three-phase symmetrical bolted short circuit current in (kA).  
Note that this calculation process requires conducting a short circuit analysis to determine the value of the short circuit current at the node of interest. Alternatively, the fault level can be also captured by employing the Thevenin equivalent of the network seen from the node of interest as shown in Figure 2. Hence, the fault level can be expressed in Equation (2):  
\[Fault\ Level\ (MVA)\ =\ \frac{V_{N}^{2}}{|Z_{th}|} \tag{2}\]  
As mentioned earlier, some methods in the literature employ measurements for online estimation of the Thevenin impedance. This is undertaken mainly by measuring the voltage and current variations in response to active and reactive power (P and Q) variations due to a perturbation [8]. Depending on the source of the perturbation, the Thevenin impedance estimation methods can be categorized into two main groups: _passive_ and _active_. The latter method can be also divided into two sub-categories: transient and steady-state, depending on the type of the event that caused the perturbation. Regardless of those categories, Thevenin impedance requires at least two consecutive measurements to be captured [25; 26].  
In this paper, to capture the Thevenin impedance that represents the system in steady-state conditions, two methods have been adopted to create the voltage/current variations for estimating the steady-state impedance of the system [8; 27; 28]. The estimated Thevenin impedance is then employed for fault level calculations These include: (1) Thevenin impedance measurement using step load change, and (2) Thevenin impedance measurement using switching events (e.g., capacitor bank switching). Note that these two measurement-based methods are supposed to be providing similar results when applied to the load buses. However, the capacitor bank switching method can be applied to any bus in the system, more specifically unloaded buses. While these might be converging into the same results in the conventional systems based on SGs only, they might lead to  
Figure 2: Thevenin equivalent of the system.  
different results with the presence of a high share of RESs. Hence, the assessment of these two methods is one of the expected findings of this paper.
## 4 Characterization of the Discharge of Insulation Defects in Switchgear
### Characterization of Characteristic Gases Generated by Metal Protrusion Discharge
#### 4.1.2 Relationship Between Characteristic Gases and Discharge Volume in Metal  
The changes in CO, NO2, and O3 characteristic gases generated during partial discharge of metal protrusion defects were tested using the constructed switchgear insulation defect partial discharge characteristic gas testing platform. The characteristic curves of the relationship between the production of characteristic gases CO, NO2, and O3 during different discharge times and the partial discharge amount are shown in Figure 10.  
Figure 10: \(Cont.\)  
Figure 9: The O3 gas volume fraction of gap discharge.  
Based on Figure 10, it is evident that CO and O\({}_{3}\) gas volume fractions exhibit a linear increase trend with the increase in partial discharge, whereas NO\({}_{2}\) initially increases linearly with the increase in partial discharge but later tends to saturate.  
1.3 Regression Equation of Characteristic Gas Volume Fraction and Discharge Volume in Metal Prottrusion Discharge  
To study the effect of the partial discharge of switchgear air gap defects on the characteristic gas, this paper introduces a regression equation to fit the partial discharge and gas volume fraction into a one-variable quadratic equation, which is shown in Equation (5).  
\[\varphi=ax^{2}+bx+c \tag{5}\]  
where \(\varphi\) is the O\({}_{3}\) gas volume fraction; \(x\) is the partial discharge; \(a\), \(b\), and \(c\) are the coefficients of the quadratic term, the coefficients of the primary term, and the constant term of the regression equation, respectively. The 95% confidence interval for the overall mean is calculated as shown in Equation (5).  
The regression equation for the volume fractions of CO, NO\({}_{2}\), and O\({}_{3}\) was found as shown in the following equation:  
\[y=0.273766x_{\text{CO}}+2.84508 \tag{6}\]  
\[y=8.73874e^{-5}x_{\text{NO}_{2}}^{2}+0.03904x_{\text{NO}_{2}}+1.40024 \tag{7}\]  
\[y=1.42644e^{-4}x_{\text{O}_{3}}^{2}+0.11887x_{\text{O}_{3}}-18.92568 \tag{8}\]  
where \(x_{\text{CO}}\), \(x_{\text{NO}_{2}}\), \(x_{\text{O}_{3}}\) represents the volume fractions of CO, NO\({}_{2}\), and O\({}_{3}\) when the magnitude of partial discharge is \(y\).  
The characteristic gases generated under different partial discharge amounts within a certain period of time (here 24 h) were fitted, and Figure 11 was obtained. Figure 11 shows the regression equation, 95% confidence interval, and 95% prediction space for partial discharge volume.
## 2 Data
### Radiosonde Data  
Globally there are 18 types of radiosondes where the uncertainty and accuracy vary with different sensor types ([32], Figure 1). Studies have shown that the quality of radiosonde temperature and moisture sensors change with environmental conditions [33, 34, 35]. The RAOB temperature measurements are profoundly affected by the solar radiation effect on sensors [32].  
For the past two decades, Vaisala RS92 has provided the backbone temperature and moisture measurements for NWP and satellite validation. A new type of Vaisala radiosonde, RS41, equipped with advanced temperature and humidity sensors, is replacing all RS92. The temperature measurement precision (defined as the uncertainty of accuracy) of RS41 in the lower stratosphere (above 100 hPa) is within 0.15 K, where that for RS92 is within 0.2 K. Below 100 hPa, the precision of RS41 temperature is within 0.3 K, where that of RS92 is within 0.5 K (see Table 2 in [24]). Several inter-comparison campaigns have been undertaken in various atmospheric conditions [35, 36]. The mean differences between RS41 and RS92 humidity are within 2% at high latitude and within 1-3% in tropical nights [36]. However, the RS92 has an obvious 0.1 K temperature bias above 100 hPa and humidity biases below 100 hPa at some atmospheric conditions [36]. We downloaded the radiosonde data from the NCAR data archive ([[http://rda.ucar.edu/datasets/ds351.0](http://rda.ucar.edu/datasets/ds351.0)]([http://rda.ucar.edu/datasets/ds351.0](http://rda.ucar.edu/datasets/ds351.0))). Figure 3 depicts the geophysical locations for RS41 and RS92 data from June 2019 to October 2019. Most of the radiosonde data are collected twice per day.
# Design and Analysis of Independently Adjustable Large In-Pipe Robot for Long-Distance Pipeline
## 4 Simulation and Experimental test  
### Static Simulation of Internal Force Model  
From the previously obtained Equations (1) and (2), we can analyze the force acting on the crawler to determine its size and whether it exceeds the pressure center. The previously defined parameter values are a = 0.052 m, b = 0.3085 m, c = 0.226 m, d = 0.1021, e 2 [0.200,0.290], f = 0.5504 m, g = 0.16 m, G\({}^{0}\) = 112 N, j = 0.521 0.5 m. Based on the Taylor expansion, the \(F_{N}\) and s were obtained by piecewise linear interpolation method. The result is shown in Figure 12 below.  
As explained in Figure 12a, during length e increase, equivalent force \(F_{N}\) rises rapidly and drops rapidly form a peak. Then increases slowly and almost linearly for the rest of the time, However, the arm of equivalent force position s suddenly decreases and then rises slowly. When the robot is in contraction state, as shown in Figure 12b, which the central body at the front of the crawler, the reaction force must be large enough due to the shorter arm. When the body and the crawler are closed in the axial direction, equivalent force \(F_{N}\) and the arm of equivalent force s changed linearly.  
Figure 11: In-pipe robot passes directly through obstacles.  
Figure 9: In-pipe robot positive-attitude control.  
In the actual situation, the position of the force could be different due to the position of the central body barycenter and crawler states. Figure 12c,d depict the variations of s and \(F_{N}\) as the parameter j changes. The distance from \(O_{1}\) to \(G^{0}\) (j) was set as 0.521 0.25, 0.521 0.40, 0.521 0.50 and 0.521 0.75 m. Compared to Figure 12a,b, the general trend has not changed and shows the same pattern. \(F_{N}\) and s change with e and form linear zone. However, they fluctuate and shorter where the electric putter length e is shorter.  
In addition, other meaningful parameters in the equation, such as the force of the putter \(F_{3}\), the forces of the trusses on the central body \(F_{1}F_{2}\) can obtain continuous effective solutions, which provide a reference for the robot design of similar institutions.  
According to the ground pressure theory [23; 24; 25] the grounding pressure should be in the core area, so the range of s should be limited 0.024-0.329 m. To achieve the robot running stability in long-distance pipes, the length of the electric putter should be limited to the range allowed by the ground pressure center.
## 4 Discussion of Inversion Results and Accuracy Evaluation
### The Influence of a Priori Constraints on Accuracy  
A priori information of the inversion model as the inversion constraint is bound to affect the accuracy. In the previous study, a priori temperature used is directly from the MSISE-00 atmospheric model database, which is far from the real temperature; so, even if the optimal estimation algorithm with Tikhonov regularization is adopted, the accuracy can only reach about 3.4 K. In this section, the influence of a priori constraints on accuracy is studied by further improving the accuracy of a priori. Currently, for the existing MLT temperature detection instruments in the world, the uncertainty of the measured temperatures at night is about 5 K [12]. Therefore, as shown in Figure 19 below, the a priori vectors are, respectively, taken from the MSISE-00 and controlled within \(\pm\)5 K from the truth perturbance. The optimal estimation method for the above two groups of a priori vectors is carried out. In Figure 19, the blue and red profiles represent the retrieved results  
Figure 17: Retrieval of atmospheric temperature in MLT region combined with regularization constraint and without regularization constraint.  
Figure 18: The bias between retrieved temperature and actual temperature combined with regularization constraint and without regularization constraint.  
obtained by using the a priori vectors from the MSISE-00 database, and the a priori vectors with higher accuracy (the accuracy is better than \(\pm\)5 K).  
Figure 20 evaluates the impact of the accuracy of a priori temperature information on the accuracy from the point of view of the bias between the retrieved temperature and actual temperature. The accuracy of atmospheric temperature can be significantly improved by improving the accuracy of a priori temperature information. When the accuracy of a priori temperature is controlled within \(\pm\)5 K, the mean absolute bias in the vertical field of view is reduced by about 2 K compared with the a priori directly obtained from the MSISE-00 atmospheric model database. At this time, the average accuracy of retrieved temperature can reach 1.35 K. The average accuracy of retrieved temperature is as high as 0.58 K between 95 and 110 km. Compared with the accuracy of temperature estimation of OSIRIS at about \(\pm\)2 K at 90 km and \(\pm\)6 K at 105 km, the accuracy of retrieved temperature is improved through this a priori. Meanwhile, according to the above characteristics, it is speculated that if a priori temperature accuracy is further improved, the inversion accuracy will be further improved.  
Figure 19: Retrieval of atmospheric temperature in MLT region based on different a priori constraints.  
Figure 20: The bias between retrieved temperature and actual temperature based on different a priori constraints.