esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Quality Assessment of Water Level through Calibration and Validation
### Regional Calibration of SARAL/AltiKa Sea Surface Height in Corsica, at Harvest and at Bass Strait
This information shows that the variability of the bias estimates over the considered crossover points is stable from one site to the other, in the order of 1 cm. However, the mean bias estimates and the averaged standard deviation of the bias estimates strongly vary from one site to the other, with mean variability of about 60 mm in Harvest and Bass Strait, twice larger than in Corsica, which is directly linked to local conditions (open ocean with rough seas in Harvest and Bass Strait, sheltered harbours with low ocean variabilities in Corsica). Even after correcting the 30 mm offset in the Ajaccio tide gauge measurements (see Section 2.1.2) the 47 mm difference observed in the bias estimates between Ajaccio and Senetosa, is for the moment unexplained and need further investigation. However, the averaged SSH bias from Ajaccio and Senetosa is 61 mm and is close to the values found from the absolute calibration study (see Section 2.1.2). Further investigation is also needed to understand the \(\sim\)40 mm difference observed between the bias estimates in Corsica and the estimates in Harvest and Bass Strait. It is more than probable that high precision regional tidal models and dynamical atmospheric correction solutions would improve the computation of accurate altimeter and in situ sea surface heights in these regions. Figure 9: Generic diagram of the regional in situ calibration method. The points A and B represent the crossover points between, respectively, the satellite altimeter tracks 1 and 2, and the tracks 2 and 3. The tracks can belong to different altimeter missions. The in situ high resolution mean surface is used to link the tide gauge (TG) measurements to the altimeter data, and the comparison is done at the point C, located on this surface (adapted from [20]).
## 3 Materials and Methods
### Computational Methodology
#### 3.3.4 Rotor Geometry and Mesh  
The CAD geometry of the APC8x45 MR rotor was provided by APC and imported into Pointwise for surface mesh generation. The hub was modeled separately from the blades to avoid complex mesh generation at their intersection. The gap between the hub and the rotors was not expected to have a significant impact on the calculations due to the relatively low induced velocity inboard. However, the hub was included to mitigate the effects of artificially generated hub vortex caused by the separation. A picture of the computational model, including separate images for the rotor mesh and hub mesh, can be seen in Figure 3.  
The rotor mesh was generated with 202 points along the span and 250 points around the airfoil. The root cutout was located at \(0.1R\), and the tip was cut off at \(0.99375R\). However, even though the tip was slightly trimmed, the rotor radius was still assumed to be 4 in  
Figure 3: Computational model of the counter-clockwise rotor showing: top view of the rotor model demonstrating the separation from the hub and blade root, top view of a single blade mesh, and oblique view of the hub mesh.  
for the purpose of computing the performance coefficients and tip-tip distances. The wall spacing was computed to ensure \(y^{+}<1\) and set to \(10^{-4}c\), where \(c\) is the mean chord. The strand length was set to \(0.4c\) and contains 51 points. The wake spacing of the near-body volume mesh was set to \(0.025c\). A fixed refinement region of equal spacing was placed in the off-body volume mesh for proper interpolation of the solution between mStrand and SAMCart. These grid settings were determined to be sufficient for small scale rotors by [PERSON] et al. [12].  
The motors, load cells, test stand, and spinners were not included in the computational model but are not expected to significantly impact the aerodynamics. However, they may impact the acoustic measurements. In addition, the ground and surrounding walls were also not included, even though the recirculation effects may increase the noise as shown by [PERSON] et al. [4] and [PERSON] et al. [21]. The limitation of this modeling approach is further discussed in the results.
## 3 Methodology
Very effective wireless communication technology ('communication') makes data exchange feasible even in areas without standard smartphone coverage, a common situation in everyday farming, and allows for centimeter-level localization (RTK) via differential GNSS positioning. Today, thanks to significant technological innovations, there is the possibility to shift from, or more interestingly, to pair the cloud-centric approach--where data are sent to remote computers for analysis--with the edge paradigm, where data are processed at the point of collection, i.e., by the rover. This is the main focus of works in the 'IoT' category. Such implementations are favored by the recent emergence of edge computing devices (CPUs, GPUs, and hardware accelerators for deep learning models) that offer a suitable performance and limited power consumption ('HW' group). Unsurprisingly, the effective implementation of the edge paradigm requires dedicated software design. This, along with the model environments required for the advanced simulation of robots' responses to real in-field situations, is the focus of the 'UGV SW' group. The final group, 'DT', stands for 'digital twin'. Similar to other application fields, agriculture can greatly benefit from this concept, which involves generating 3D models of cultivated plants to provide quantitative measures for use in decision support systems and tools for harvest evaluation. This methodology is in its early stages; there is limited work available, which is mainly focused on vineyards and orchards. As anticipated, our target was to create a two-level bibliographic dataset. We used bibliometric filters for the automatic preprocessing of the large (level 0) dataset, both from existing libraries and those developed in-house from scratch. This approach saved a significant amount of time. However, we believe that such computer procedures often fail to deliver the high-quality results provided by human expert labor. Therefore, a further iteration involving manual annotation and selection was performed. While time-consuming, this process provided us with a refined, high-quality, and well-balanced dataset of more than 150 titles, which is the focus of this work.
# Classifying X-Ray Tube Malfunctions: AI-Powered CT Predictive Maintenance System
## 5 Classifiers  
### Classifier Selection  
As we are dealing with a rather small dataset, we opted for both models that need hand-crafted features as inputs and models that can extract features from the data themselves. Depending on the complexity of the data and the fact that we do not utilise transfer learning, the latter models' performance could be problematic, as they usually require more data to learn the underlying patterns. As we are dealing with time series data, many different classifiers can be used.  
For the classification of hand-crafted features, we have selected the following classifiers:  
* Logistic Regression (LR);
* Decision Trees (DT);
* Random Forest (RF);
* Gradient Boosting decision trees (GBDT);
* Support Vector Machines (SVM).  
When selecting the classifiers, we went for both performance and diversity. GBDT, RF, SVM, and DT all rank among the top-5 classification algorithms [32]. Together with LR, they also create a diverse group with different principles being applied.  
For the models that can learn underlying patterns themselves, we have selected the following:* Long short-term memory (LSTM);
* Convolutional Neural Networks (CNN).
## 4 Experimental Validation  
The objective is the identification of the PWM-angular velocity relationship that characterizes the motion of a small UGV equipped with tracks (Figure 3). The robotic platform is moved by two geared DC motors, one for the left and one for the right side, the behavior of which is assumed identical in this discussion. Each DC motor has an encoder integrated with the shaft. A pulse width modulation (PWM) technique is used for driving them: the signal is generated by a microcontroller, amplified by the H-bridge and used to power the motors and consequently move the tracks. The use of encoders allows the collection of the angular velocity, while the PWM duty cycle is obtained from the recording of the commands sent to the robot from a remote controller. The complexity of the system suggests the use of a black box approach instead of a classic lumped-parameter approach. In particular, a nonlinear model is selected to capture all nonlinearities and guarantee model fidelity. The model of the robot is completed by the introduction of a kinematic model (see [25]), which establishes the relationship between the angular speed of the tracks and the linear and angular velocities of the platform, thus giving the position and the orientation of the vehicle.
# Flexible-Link Multibody System Eigenvalue Analysis Parameterized with Respect to Rigid-Body Motion
## 3 Polynomial Representation of the Eigenpairs of Flexible-Link Multibody Systems  
### Taylor Series for Multivariate Function  
Let \(\mathbf{E}\) be a matrix depending nonlinearly on a set of \(p\) parameters collected in the vector \(\mathbf{x}=\left\{x_{1}\ \ \ \ldots\ \ x_{p}\right\}^{\mathsf{T}}\in\mathbb{R}^{p}\), if the parameter dependency is analytically known, it is possible to approximate the multivariate function \(\mathbf{E}(\mathbf{x})\) with a polynomial by means of a Taylor series truncated at order \(t\):  
\[\mathbf{E}(\mathbf{x})\approx\sum_{\sigma_{1}=0}^{t}\cdots\sum_{\sigma_{p}=0 }^{t}\frac{1}{\sigma_{1}!\ldots\sigma_{p}!}\frac{\partial^{\sigma_{1}+\cdots+ \sigma_{p}}\mathbf{E}(\mathbf{x}_{0})}{\partial x_{1}^{\sigma_{1}}\ldots \partial x_{p}^{\sigma_{p}}}\cdot\delta x_{1}^{\sigma_{1}}\ldots\delta x_{p}^{ \sigma_{p}}=\sum_{|\sigma|=0}^{t}\underbrace{\frac{\partial^{\sigma}\mathbf{E} (\mathbf{x}_{0})}{\sigma!}}_{:=\mathbf{E}^{(\sigma)}}\delta x^{\sigma} \tag{19}\]  
where \(\mathbf{x}_{0}=\left\{x_{1,0}\ \ \ldots\ \ x_{p,0}\right\}^{\mathsf{T}}\in \mathbb{R}^{p}\) is the expansion point, and \(\delta x_{i}=x_{i}-x_{i,0}\) with \(i=1,\ldots,p\). The last term on the right-hand side of Equation (19) has been obtained by using a multi-index formulation. In particular, \(\sigma\) is the multi-index, i.e., a tuple of non-negative integers in a number equal to the one of system parameters: \(\sigma=(\sigma_{1},\sigma_{2}\ldots,\sigma_{p})\) with \(\sigma_{i}\in\mathbb{N}^{+},i=1,\ldots,p\). In Equation (19) and in the remaining of the paper, the following definitions for a multi-index are adopted [36]:  
1. norm: \(|\sigma|=\sigma_{1}+\sigma_{2}+\cdots+\sigma_{p}\)
2. factorial: \(\sigma!=\sigma_{1}\sigma_{2}!\ldots\sigma_{p}!\)
3. power: \(\mathbf{x}^{\sigma}=x_{1}^{\sigma_{1}}x_{2}^{\sigma_{2}}\ldots x_{p}^{\sigma_ {p}}\)
4. partial derivative: \(\partial^{\sigma}\mathbf{E}=\frac{\partial^{\sigma}\mathbf{E}}{\partial x_{1 }^{\sigma_{1}}\partial x_{2}^{\sigma_{2}}\ldots\partial x_{p}^{\sigma_{p}}}\)
5. number of ordered combinations of \(p\) positive integers whose sum is equal to \(|\sigma|\): \(n_{p,\sigma}=\frac{(p+|\sigma|-1)!}{(|\sigma|!)!(p-1)!}\)
6. identity: two multi-indices \(\sigma=(\sigma_{1},\sigma_{2}\ldots,\sigma_{p})\) ans \(\varsigma=(\varsigma_{1},\varsigma_{2}\ldots,\varsigma_{p})\) are identical, i.e., \(\sigma=\varsigma\), if and only if \(\sigma_{1}=\varsigma_{1}\), \(\sigma_{2}=\varsigma_{2}\), \(\ldots\), \(\sigma_{p}=\varsigma_{p}\).
## 3 Results
### Inspection Scale Investigations (Global Techniques Versus Local Techniques)  
It is mentioned in Section 1 that the employed techniques in the damage identification process are classified into global techniques or systems and local techniques or systems. In global systems, a given load is applied. The associated response with the given load is assessed accordingly. Acceleration and velocity are the commonly used parameters in global systems. In contrast, the measurement of a response to a given load, which can only be assessed in a particular component/portion, is deployed in local systems. Typically, measured parameters in local techniques are strain, crack, and tension forces.  
As can be seen in Table 6, the global systems are frequently utilized (31 research studies). On the other hand, the local methods are deployed for simple structures only. Although the local methods identify the location of the faulty component, a tremendous amount of time and cost are required. Therefore, global techniques are generally preferred in complicated structures. Global techniques provide useful data by utilizing vibration characteristics.  
In addition to the use of global and local techniques in isolation, a combination of global and local techniques is also used. The global approach is first used for damage detection. Subsequently, the local approach is used for damage assessment and localization. For example, [PERSON] et al. [68] presented a solution with a combination of local and global methods. It consists of two layers of sensors, including RFID and PZT sensors. The RFID coupled with the PZT sensor is used to detect the peak strain from the RF signal provided by unmanned aerial vehicle (UAV). If the values of peak strain are not within the limits, the activation of corresponding nodes takes place, and the damage is detected accordingly.
# Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs
## Study Area and Data
### Simulated Coarse Resolution Data from Landsat Images  
Due to the variations in solar-viewing geometries and the characteristics of land surface directional reflectance, MODIS data are less consistent over time than are Landsat visible and NIR reflectance data [40]. There have been efforts to improve the consistency between Landsat and MODIS data; for example, by using surface reflectance instead of top of atmosphere reflectance and by using nadir BRDF-adjusted reflectance rather than directional reflectance. However, inconsistency between Landsat and MODIS still exists and can greatly affect the predictive accuracy of data fusion systems [15]. To minimize the effects caused by the data inconsistency between different datasets, in some parts of the analyses simulated coarse resolution data were generated using finer resolution Landsat images to assess the predictability of pairs at different dates and to evaluate pair selection strategies (used in Sections 4.3-4.5).  
Landsat images at 30 m spatial resolution were aggregated to 480 m spatial resolution (about the resolution of the MODIS NBAR product) by averaging Landsat pixels in a 16 by 16 pixel window. Generally, a remotely sensed image will be affected by the point spread functions (PSF) of the sensor, which can reduce the spatial variability of the image. According to the study of [PERSON] et al. [41], the mean absolute differences (MAD) in reflectance between the images convolved from Landsat images by the ideal and actual MODIS PSF were small (less than 0.5% on the average). Therefore, it is feasible to ignore the actual point spread function (PSF) effect of MODIS here.  
All cloud-free Landsat 8 OLI and Landsat 7 ETM+ images in Figure 2 were used in the simulation. The simulated coarse resolution images were used in the data fusion process to replace MODIS data. The Landsat-MODIS fusion results were compared to the original Landsat data. In this experiment, using simulated coarse-scale inputs, the results from the fusion will exclude effects caused by inconsistencies between the two data sources and, thus, let us focus on the impact of data fusion and data pair selections.  
Figure 2: Cloud-free Landsat 7 (green dots) and 8 (red dots) images from 2015 were used in the study. Landsat 8 images from p28r31 and p29r31 were used as pair images while Landsat 7 images were used to validate data fusion results in study site 1. Both Landsat 7 and 8 images for p25r36 can be used as pair images since Landsat 7 ETM+ images in study site 2 are not affected by the SLC-off problem. All validations only used Landsat images that were not used as input pair images.
## 6 Conclusions and Future Perspective  
Several researchers have suggested different processes and technologies for the remediation of textile wastewater [40; 41; 42; 43]; most of them have employed microbial agents, including bacterial and fungal species, from a variety of sources [44]. Agricultural residual materials have complex structures with high strength, environmentally friendly nature, low cost, and global availability. A few important agro-waste products, which are suitable for reducing chemical contaminants in water, have been reviewed for their applicability for remediation of chemical contaminants in water systems [45]. A review of the above research suggests that cheaply available agricultural residues are available globally as low-cost materials, which could be used for two purposes: Firstly, their economic and safe recycling to comply with a re-use strategy for environmental protection, and secondly, for the reduction of colour in textile wastewater. The renewable sources of agricultural residues such as wheat-straw, corncob and barley-husk are cheaper materials for the cultivation of microorganisms such as mycelial-fungi for the biosynthesis of enzymes active in the breakdown of dye-molecules. The crude enzyme preparations of microbial cultivations in solid-state fermentation were found to be rich in three types of enzyme activities, which could be used without any expensive purification process, as biodegradable chemicals (instead of toxic chemicals) for colour-stripping from unevenly dyed fabrics in the textile industry.  
[PERSON] has reviewed the literature, collected relevant references from published articles and prepared first draft. author [PERSON] has added more information, references, pictures of lab-scale experiments and submitted final version of this review article. All authors have read and agreed to the published version of the manuscript.  
This work involved no experimental labwork and therefore, received no external funding  
There is no research grant used in compiling of this review article.  
None, as this is a review of literature, and contains no data, all information presented in articles has been supported with relevant references.
# Calibration of Magnetometers with GNSS Receivers and Magnetometer-Aided GNSS Ambiguity Fixing
## 3 Calibration of Magnetic Flux Sensors
### Attitude Determination with Three GNSS Receivers  
In this section, we derive the attitude information needed for calibration of the magnetometer from three GNSS receivers.  
It is assumed that all of the three GNSS receivers and the magnetometer are mounted on the same platform. The first GNSS receiver serves as reference receiver for attitude determination. The reference receiver and the other two receivers span two independent _baselines_ pointing from the reference receiver to the other receivers. GNSS enables the estimation of these baselines in a local North-East-Down coordinate frame. The relation of the baseline in the North-East-Down and local body-fixed coordinate frames provides the attitude information.  
GNSS receivers provide two types of range measurements with the following characteristics:  
* Carrier phase measurement \(\lambda\varphi_{r}^{k}\), \(\oplus\) carrier phase can be tracked with millimeter accuracy, \(\ominus\) carrier phase is period with \(\lambda=19\) cm and requires ambiguity resolution,
* Pseudorange measurement \(\rho_{r}^{k}\), \(\oplus\) pseudorange is an unambiguous range measurement, \(\ominus\) pseudorange measurement is more sensitive to multipath, \(\ominus\) pseudorange measurement can only be tracked with meter-level accuracy.
# A Review of Innovative Electromagnetic Technologies for a Totally Artificial Heart
## 4 Linear Electromagnetic Actuators
### PM-LOAs
In the Vernier-type configuration, the insertion of PMs in the stator teeth slots enhances the thrust density at the expense of increased supply frequency, taking advantage of the multiplication effect of the reluctance force. In [67], the peak force using the double E-shaped stator of Figure 7c is 70 N; however, it has a significant ripple (\(\approx\)30 N). The single E-shaped stator proposed in [68] enables a robust structure and a high force density by combining two different PM magnetization directions of PMs and ferromagnetic poles. The quasi-Halbach magnetization significantly enhances the thrust performance (\(\sigma_{f}\cong 0.75\) N/cm\({}^{3}\)) with respect to a pure radial one, halving at the same time the peak value of the cogging force. A transverse flux configuration (Figure 7d), originally proposed for a compressor application, consists of an inner stator yoke, a concentrated armature winding enclosed between the ferromagnetic poles of two outer stators, and a six-PM mover sliding between the inner and outer stators [69]. The prevailing radial orientation of the flux lines enables the adoption of axially laminated stator yokes to reduce the eddy current losses. A single-phase sinusoidal current supply combined with the connection of the mover ends to a flexible spring gives rise to a reciprocating motion. A 100 N thrust is achieved for a 10 mm stroke length. However, the design must consider the radial forces that act on the PMs for the reliability of the system. Figure 7: Examples of PM-LOA configurations; (**a**) PM stator with moving coils [30]; (**b**) double U-shaped stators with PM, windings, and VR mover [66]; (**c**) cross section of a double E-shaped stator tubular Vernier PM-LOA [67]; and (**d**) 3D schematic portion of the transverse-flux MM-LOA [69]. Republished with permission of Elsevier and IEEE. Despite a possible lower thrust density and an uneven thrust profile, MM-LOA are deemed to be the most convenient for the TAH application because of the easier manufacturing process and their higher suitability to comply with strict size constraints. Therefore, the main proposed configurations and related performances are discussed in the following subsections.
## 2 Materials and Methods
### Pore structure and Petrophysical Properties Analysis
#### 2.3.2 Porosity, Permeability and Capillary Pressure  
To quantitatively characterize the effect of micro-fractures on fluid flow, we have selected porosity, permeability, and capillary pressure as key parameters. Specifically, the porosity is calculated as the percentage of pore pixels in relation to the total number of pixels.  
Through the application of mass conservation in each pore throat of the pore network model, we can analyze the single-phase fluid flow in each direction of the network and calculate the permeability using Darcy's law. The boundary condition is a constant pressure boundary condition; only the value of the pressure drop needs to be considered, and the general value can be taken as a reference.  
\[K_{i}=\frac{\mu q_{i}L}{A(P_{\text{in}}-P_{\text{out}})}, \tag{3}\]  
where \(K_{i}\) is the absolute permeability in \(i\) direction, \(\upmu\)m\({}^{2}\), \(\mu\) is the viscosity of the fluid, mPa-s; \(q_{i}\) is the total flow at the pressure difference across the model when the model is fully saturated, cm\({}^{3}\)/s; \(L\) is the length, cm; \(A\) is the cross-sectional area, cm\({}^{2}\).  
For mercury intrusion, the mercury is injected at a constant rate from the inlet, the whole pressure will be computed. The capillary pressure curves can be obtained in each direction.