esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Computational Principle of the Particle Flow Model
### The Equation for Pressure Gradient  
In porous media, the interaction forces between the fluid and solid can be represented by the pressure gradient equation, as shown in Equation (15) [38]. In this study, [PERSON]'s law is adopted to describe this interaction.  
\[\frac{d_{p}}{d_{x}}=-\frac{\rho_{f}g}{k}\mu_{x0}=-\frac{\mu_{f}\rho_{f}}{k}\mu _{x0} \tag{15}\]In the equation, \(\mu_{x0}\) represents the average flow velocity, and \(k\) denotes the intrinsic permeability coefficient.  
In the context of low Reynolds number flow in porous media, [PERSON]'s law is formulated as follows [38]:  
\[\overset{\rightarrow}{v}=\frac{K}{\eta\varepsilon}\overset{\rightarrow}{ \
abla}p \tag{16}\]  
In the given equation, \(\overset{\rightarrow}{v}\) denotes the flow velocity of the fluid, \(K\) represents the permeability matrix, \(\eta\) stands for fluid viscosity, \(\varepsilon\) represents the porosity matrix, and represents the fluid pressure. It is commonly acknowledged that the fluid is considered incompressible, thus:  
\[\overset{\rightarrow}{\
abla}\cdot v=0 \tag{17}\]  
When imposing pressure gradients on the fluid grid, it determines the average flow velocity of the fluid within that grid using the aforementioned equation. The velocity is considered piecewise linear within each element. Therefore, the grid must be adequately refined, considering that the number of grid cells and its complexity will influence both the computation speed and the accuracy of the calculation. Consequently, the selection of an appropriate grid is of paramount importance. Additionally, ensuring a sufficient number of particles within a fluid unit is crucial to prevent particle jumping and maintain smoothness.  
In order to account for the influence of particle motion on fluid flow, the permeability coefficient is computed based on the porosity within the PFC3D model. The permeability estimation is carried out using the Kozeny-Carman equation [38]:  
\[K(\varepsilon)=\left\{\begin{array}{r@{\quad}l}\frac{1}{180}\frac{\varepsilon^ {3}}{(1-\varepsilon)^{2}}(2r_{e})\quad&\varepsilon\leq 0.7\\ K(0.7)\quad&\varepsilon>0.7\end{array}\right. \tag{18}\]  
In the given equation, \(r_{e}\) denotes the particle radius, and \(\varepsilon\) represents the default minimum porosity in the system, assumed to be 0.3. To compute the permeability coefficient, the upper limit of porosity is set at 0.7. Consequently, when the porosity exceeds 0.7, the permeability coefficient is considered to be the value corresponding to a porosity of 0.7. This approach ensures the consistency of the permeability coefficient even when the porosity exceeds the specified upper limit.
# Throwaway Shadows Using Parallel Encoders Generative Adversarial Network
## 5 Comparison and Discussion
### Quantitative Evaluation  
In this section, we describe a quantitative comparison of the proposed method with previous state-of-the-art methods such as EdgeConnect [10], Partial convolution [14], Gated convolution [15] and Ghost-free Shadow removal [6] in terms of Root Mean Square Error (RMSE), Naturalness Image Quality Evaluator (NIQE) [50] and Blind Referenceless Image Spatial Quality Evaluator (BRISQUE) [51]. NIQE and BRISQUE measure the naturalness of an image without any reference. Smaller NIQE and BRISQUE scores are better. To measure NIQE and BRISQUE, we used only generated images without providing corresponding ground truths. We have evaluated RMSE on the test images from our synthetic database, which has corresponding ground truths. Table 1 provides a quantitative comparison with previous methods such as EdgeConnect [10], Partial convolution [14], Gated convolution [15] and Ghost-free Shadow removal [6]. The table shows that for the shadow removal problem, the results of our shadow removal method are better than or comparable to those of the state-of-the-art methods.  
Figure 3: Additional qualitative results of our model for complex and large size shadow samples in our synthetic database.
## 2 Materials and Methods
### Kinematic GNSS Measurements  
Kinematic GNSS measurements took place in 2015, 2017 (east coast only), 2020 (about 500 km), and 2021. The antenna was mounted on the pulka of an expedition member, and there were second-by-second recordings of the individual GNSS points. The 2 systems, GPS and GLONASS, were used (see Figure 4).  
During post-processing, the TEQC software quality check was performed to verify the quality of the obtained data and to adjust the approximate position in the header of the observation files *.yyO of the RINEX data [26]. Because Greenland is a remote location and the technology has evolved, no extension systems or ground stations were used as reference stations for the kinematic measurements. Precise Point Positioning (PPP) in the International Terrestrial Reference Frame (ITRF2014) is used to achieve the precision of the data. Natural Resources Canada (NRCan) is used as the provider. Uploading is done through a web interface, and corrected positions are sent by mail. Using accurate ephemerides and clock corrections, the position can be determined to the nearest centimeter [28]. It takes 13 days to calculate the final corrections, so this period should be weighted between data collection and precision. Care should also be taken to use the correct evaluation method (static/kinematic) with the associated data, to always have enough satellites available, and to minimize the individual error sigma.  
In the next step, the plate kinematics are considered, using 01.06.2015 (00:00:00) as the reference date, and the corresponding displacements and rotations of the North American plate are included in the plate motion model according to ITRF2014. This allows us to compensate for the effect of the Glacial Isostatic Adjustment (GIA) in Greenland [29].  
Figure 4: Kinematic GNSS measurement during GKE 2020 (**a**) on the pulka. (**b**) Trimble R12.  
Figure 3: Static GNSS measurement during GKE 2015.  
After the data have been specified and reduced, they are further processed with Matlab. An overview of the program flow can be seen in Appendix A. First, the entries for the sled sinking depth and the antenna height are taken from the field book records. This is followed by a temporal sort and subsequent low-pass filtering of the data using Gaussian filtering to minimize the influence of noise. After a parameter study, a filter order of m = 50 is used as a target for the local topography of the Greenland ice sheet [30; 31; 32]. Due to the second-by-second measurement points and the largely tall jump-free relief, more distant points can also be considered for smoothing.  
Two different principles are used to compare the GNSS kinematic data. First, crossing points are investigated, which requires a linear interpolation of two data points at the same position coordinates. Second, the principle of minimum distances between each data point of the 2015 expedition and subsequent expeditions is considered [33; 34; 35; 36].
# Modeling Actual Evapotranspirationiration with MSI-Sentinel Images and Machine Learning Algorithms
## 2 Materials and Methods
### Response Variable
The METRIC model provides two methodologies to quantify G, one developed by [PERSON] [4] and another by [PERSON] [25] (the details can be seen in [PERSON] [7]). For this work, the methodology of [PERSON] [25] was chosen, as it was developed in irrigated areas. Thus, G was quantified by Equation (6a,b). \[\frac{\rm G}{\rm R_{n}}{\rm=0.05+0.18e^{-0.521\leavevmode\
obreak\ LAI}\leavevmode\
obreak\ for\leavevmode\
obreak\ LAI\leavevmode\
obreak\ \geq\leavevmode\
obreak\ 0.5} \tag{6a}\] \[\frac{\rm G}{\rm R_{n}}{\rm=\frac{1.80(T_{s}-273.15)}{R_{n}+0.084}\leavevmode \
obreak\ for\leavevmode\
obreak\ LAI\leavevmode\
obreak\ <\leavevmode\
obreak\ 0.5} \tag{6b}\]  
where LAI is the leaf area index (m\({}^{2}\) m\({}^{-2}\)) estimated by the methodology applied by [PERSON] [2]. Finally, H represents the rate of heat loss to air by convection and conduction influenced by a temperature difference. H was calculated by Equation (7). \[{\rm H=\rho_{air}C_{p}\frac{dT}{r_{ah}}} \tag{7}\]  
where \(\rho_{air}\) is the air density (kg m\({}^{-3}\)); C\({}_{p}\) is the specific heat of air at constant pressure (J kg\({}^{-1}\) K\({}^{-1}\)); dT is the temperature difference between two heights (z1 and z2) in an area close to the surface; r_ah is the aerodynamic drag (s m\({}^{-1}\)) between these two heights. All calculations to reach ET\({}_{\rm r}F\) described in this topic were performed using the Water package developed by [PERSON] [26] for the R language environment [27].
# Efficient Capture of Sr\({}^{2+}\) Ions by a Layered Potassium Neodymium Phosphate
## 3 Results and Discussion
### Removal of Sr\({}^{2+}\) from Aqueous Solutions  
#### 3.2.1 Adsorption Isotherms  
Firstly, we studied the adsorption isotherm to obtain the maximum adsorption capacity of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) for Sr\({}^{2+}\) ions at room temperature. Langmuir (Equation (S1)), Freundlich (Equation (S2)) and Langmuir-Freundlich (Equation (S3)) models [52; 53] were used to fit the experimental results of adsorption isotherms (Table S1).  
Langmuir and Freundlich adsorption models are two-parameter models commonly used to describe the adsorption equilibrium behavior, while the Langmuir-Freundlich adsorption model is a three-parameter model [54]. The Langmuir model assumes that the surface of the adsorbent is uniform, the energy of each adsorption center is the same, and each site receives at most one molecule or ion adsorption. The maximum adsorption capacity (\(q_{\rm m}\)) (Equation (S1)) corresponds to the saturation number of ions adsorbed on the surface of the adsorbent in a single layer. The Freundlich and Langmuir-Freundlich isotherms assume that the adsorbent surface is heterogeneous [55; 56].  
Figure 1: **(a)** Coordination of Nd\({}^{3+}\) ion by [PO\({}_{4}\)] tetrahedra in K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\); **(b)** an anionic layer of [Nd(PO\({}_{4}\))\({}_{2}\)]\({}_{3}\)\({}^{3n-}\) in K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\); **(c)** the schematic diagram of ion exchange between Sr\({}^{2+}\) and K\({}^{+}\) ions in layered K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\). (The purple tetrahedra are PO\({}_{4}\) and the lavelorber polyhedra are NdO\({}_{7}\)).  
The fitting curves of all models are shown in Figure 2a, and the correlation coefficients are listed in Table 1. The results show that the Langmuir model had the highest coefficient of determination (\(R^{2}\) = 0.9938) compared to the Langmuir-Freundlich model (\(R^{2}\) = 0.9929) and the Freundlich model (\(R^{2}\) = 0.8912). Thus, the adsorption behavior of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) for Sr\({}^{2+}\) can be better fitted with the Langmuir model. The maximum adsorption capacity (\(q_{\mathrm{m}}\)Sr) (Equation (S1)) fitted by the Langmuir model was 42.60 mg/g, which was much larger than that of the commercial AMP-PAN (15 mg/g) [57] and close to the experimental result (40.7 mg/g, Table S1, calculated by Equation (S4)). However, it was quite different from the theoretical adsorption capacity (240.52 mg/g, calculated by Equation (S5)) (assuming that K\({}^{+}\) is completely exchanged by Sr\({}^{2+}\)). This indicates that the K\({}^{+}\) ions between the layers were partially exchanged by Sr\({}^{2+}\) ions.
## 6 Category II: Consensus Mechanism
### Other Consensus Mechanisms
#### 6.3.1 PBFT  
It is necessary to know the Byzantine Generals problem before we can understand this consensus algorithm [124]. In short, it is an abstract model of how a computer system handles faults. When certain parts of the system fail, they may send conflicting messages to different parts of the system. Generally speaking, this kind of fault that falsifies the response are called Byzantine fault, while those that do not falsify the response are called non-byzantine fault. The problem of dealing with Byzantine fault is abstracted as a Byzantine general problem, in which a group of general lead the Byzantine army capped outside an enemy city, and each general would observe the enemy and communicates with each other only by a messenger. They need to work out a common plan of actions. However, there may be traitors among the generals, who are trying to confuse the loyal generals. Hence, we need an algorithm to make sure that, no matter what the traitors do, the loyal generals will have a unified plan of action.  
The practical Byzantine fault-tolerant algorithm is a replication algorithm that can tolerate Byzantine failures. It is practical and works in asynchronous environments [20; 125]. PBFT is suitable for small networks, consortium blockchain or private blockchain.Because the consensus process requires three rounds of voting and each of which is broadcast, each node needs to know the identity of every other node in the network. Because it does not have the same mining process as POW, it saves a lot of energy. The right consensus can be reached when the number of malicious nodes is less than one-third of the total number of nodes. Hyperledger Fabric uses PBFT to reach consensus [126].  
In the BeeKeeper system [24], the consensus mechanism of PBFT is also adopted. To ensure that the blockchain does not fork, the system assumes that at least two-thirds of the nodes are honest.
# Disentangling Long Trajectory Contributions in Two-Colour High Harmonic Generation
## 2 Two-Colour HHG  
Here, we build on concepts first introduced in Ref. [18], and related to Refs. [28; 29]. [PERSON] et al. [18] employ a two-colour HHG method to extract the instant of time when electrons exit from their bound state and enter the continuum. Figure 2 illustrates the underlying concept.  
The strong fundamental driving field acts along the longitudinal coordinate \(x\), as described in Equation (2). It dominates the ionisation rate and the major component of the photoelectron trajectories. A weak co-propagating second harmonic field with relative intensity \(I_{2\omega}/I_{\omega}=\xi\) acts along the orthogonal direction \(y\)  
\[F_{y}(t,\varphi)=f(t)F_{0}\sqrt{\xi}\cos(2\omega t+\varphi)\hat{y}, \tag{4}\]  
slightly displacing the trajectories, depending on the two-colour delay \(\varphi\).  
In Figure 2, examples of short trajectories are drawn, always with an initial transverse momentum \(v_{i,y}=0\). For each set of initial conditions \((t_{i},v_{i,y})\), a specific two-colour delay \(\varphi(t_{i},v_{i,y})\) can be found for which the transverse displacement cancels out. The trajectory then returns to the parent ion at return time \(t_{r}\) and can contribute to the HHG spectrum. The transverse velocity \(v_{i,y}=0\) is the most probable initial condition both in the adiabatic case [30; 31] and in the non-adiabatic (almost) linear case [32; 33]. Therefore, the HHG signal of harmonic order \(n\) is maximised exactly for  
\[\varphi_{\text{max}}(n):=\varphi(t_{i},v_{i,y}=0). \tag{5}\]  
This displacement gate method as developed by the [PERSON] group gives access to the initial and return times for different harmonic orders in the HHG spectrum [18; 28; 34]. To perform the analysis and extract the times, [PERSON] et al. [18] neglected the effect of the ion Coulomb potential on the trajectories  
Figure 2: Short trajectories in two-colour HHG displacement gate. Some trajectories are displaced and miss the parent ion on the return, and others can come back to where they originated. The colour code corresponds to the kinetic energy carried by the electrons at the return, blue = high energy, red = low energy.  
of the photoelectrons, according to SFA, allowing them to analytically describe the photoelectron trajectories. Then, combining the requirement that a trajectory must return to the origin after time \(t_{r}\) with the extracted \(\varphi_{\text{max}}\) from the measurement and setting \(v_{i,y}=0\), one can solve for the initial and return times \(t_{i}\) and \(t_{r}\)[18].
### Fouling Detection  
Current detection methods require halts in production to carry out monitoring such as pigging. New tools in pigging have been used, such as ultrasonic inspection equipment, for not only cleaning but also for detecting failures and corrosion, or to verify the cleaning efficiency. In recent years, the usage of Ultrasonic Guided Waves has been recognized as a potential solution for the detection of fouling accumulation through structural health monitoring but is still in its initial stages of readiness for industrial practice [11].
## 1 Introduction
### Method Overview  
In this article, at first a review of recent IPv6-related research papers and online resources is conducted. The Internet Engineering Task Force (IETF) regularly publishes Requests for Comments (RFCs) that cover most (if not all) IPv6 standards, related protocols, specifications, security issues, best practices as well as other information about IPv6, even including methods that are still in an experimental stage. RFCs related to IPv6 were filtered and categorized into five main categories, each with multiple subcategories. The complete list can be found in Appendix A. Based on these RFCs, the scores _completeness_ and _depth_ were derived. Content completeness is a score indicating the breadth of RFC coverage within the guides. Content depth is based on completeness and indicates to what extent the content of relevant RFCs is covered. A detailed explanation of these scores can be found in Section 3. Since not all topics are equally relevant for a secure deployment guide, they need to be weighted and ranked. For this purpose an expert survey was carried out. This procedure can be reused and generalized to a universal use case of a group of people introducing IPv6 into an organization.  
Weights for categories were calculated using the method of Analytic Hierarchy Process (AHP) discussed in Section 3.3. Subcategories were rated for importance using a scale, and results were aggregated and ranked. The weights were applied to the completeness values of the guides. Finally, the list of relevant RFCs, completeness and depth, the weights, and importance ratings were used to evaluate the guides. Individual evaluations can be found in Sections 4 and 5; a guideline comparison in Section 6. Based on these results, recommendations for usage of the guidelines and possible improvements are discussed in Section 7. Limitations and future work is presented in Section 8.
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 3 Methodology
### Augmentation Methods in Lung Nodule Detection
#### 3.4.5 Injecting Noise into the Images  
Introducing noise to medical images is a double-edged sword; it simulates realistic scenarios where scans might be affected by various factors like sensor quality or environmental interference, potentially lowering image quality. Noise injection, such as adding Gaussianor salt-and-pepper noise, tests the model's resilience against real-world, imperfect data conditions. However, excessive noise can obscure critical diagnostic features, complicating the task of the model in identifying subtle cues crucial for accurate diagnoses. This method must be carefully calibrated, to ensure that it does not overwhelm the meaningful content of the images. Despite these risks, noise injection remains a valuable technique for enhancing the robustness of medical imaging models, preparing them for diverse and less-than-ideal conditions they might encounter in clinical use.
# Rotor Position Alignment of FSTPI Based PMSM Drive Using Low Frequency Signal Injection
## 3 Mathematical Analysis of the Proposed Zero Angle Initial Rotor Position Alignment Scheme
#### Analysis of Inverter Capacitor Effect  
Since the circuit given in Figure 4 has stator resistances, damping occurs in the system. Resonance will naturally be determined by the damping of the circuit irrespective of the applied low-frequency AC signal. If the circuit is underdamped, the system resonates otherwise the circuit operates as overdamped without resonance. The circuit in Figure 4 has a damping factor calculated as  
\[\zeta=\frac{3R_{s}}{2}\sqrt{\frac{C}{3L_{s}}} \tag{2}\]  
Since the damping factor \(\zeta=2.4042\) is greater than 1, the system behaves as overdamped without oscillation. The critically damped condition of the circuit occurs at  
\[C=\frac{12L_{s}}{9{R_{s}}^{2}} \tag{3}\]  
where \(R_{s}\) is stator resistance, \(L_{s}\) is stator inductance and \(C=C_{1}=C_{2}=380\)\(\mu\)F considering the electrical machine parameters are constant. The Semikron SEMITEACH\({}^{\text{TM}}\) inverter that has been used in this  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Voltage Vector \(V(S_{1}\ S_{3})\)** & **Voltage Space Vector (\(\overline{V_{s}}\))** \\ \hline \(V_{1}(0\ 0)\) & \(V_{\text{DC}}/3+(2/3)kV_{LF}sin(\omega_{L}t\)) \\ \(V_{2}(1\ 0)\) & \((2/3)kV_{LF}sin(\omega_{LF}t)+j(V_{\text{DC}}/\sqrt{3})\) \\ \(V_{3}(1\ 1)\) & \((2/3)kV_{LF}sin(\omega_{LF}t)-j(V_{\text{DC}}/\sqrt{3})\) \\ \(V_{4}(0\ 1)\) & \(-V_{\text{DC}}/3+(2/3)kV_{LF}sin(\omega_{LF}t)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Non-zero voltage vectors of FSTP inverter with DC-link fluctuations under LF signal injection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**\(V(S_{1}\ S_{3})\)** & \(|\overline{V_{s}^{\star}}(\boldsymbol{\oplus}\ \boldsymbol{\theta_{LF}}=\mathbf{0}^{\circ}, \mathbf{180}^{\circ})|\) & \(|\overline{V_{s}^{\star}}(\boldsymbol{\oplus}\ \boldsymbol{\theta_{LF}}=\mathbf{90}^{\circ}, \mathbf{270}^{\circ}|\) \\ \hline \(V_{1}(0\ 0)\) & \(V_{\text{DC}}/3\) & \((V_{\text{DC}}\pm 2 kV_{LF})/3\) \\ \(V_{2}(1\ 0)\) & \(V_{\text{DC}}/\sqrt{3}\) & \((\sqrt{3V_{\text{DC}}^{2}+4k^{2}V_{LF}}^{2})/3\) \\ \(V_{3}(1\ 1)\) & \(V_{\text{DC}}/\sqrt{3}\) & \((\sqrt{3V_{\text{DC}}^{2}+4k^{2}V_{LF}}^{2})/3\) \\ \(V_{4}(0\ 1)\) & \(V_{\text{DC}}/3\) & \((V_{\text{DC}}\pm 2 kV_{LF})/3\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Non-zero voltage vectors of FSTP inverter with DC-link fluctuations under LF signal injection at \(0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), and \(270^{\circ}\).  
Figure 3: Block diagram of the proposed LFS injection based zero angle initial rotor position alignment for FSTP inverter-fed PMSM drives.  
system has two 2200 \(\mu\)F capacitors. Since the calculated damping factor in Equation (2) is greater than 1 with the already built-in capacitors in the inverter, the system acts as overdamped without oscillation.