esa-sceva/satcom-chunk-collection huggingface dataset:
# A Numerical Analysis of the Nonlinear Dynamics of Shimmed Conical Gas Foil Bearings
## 2 Theory
### Foil Structure  
In this work, the SEFM is applied for CGFB. The SEFM uses a linear stiffness and assumes viscous damping realised by a loss factor. The used model assumes a constant deformation of the foil structure in the axial direction. The interaction between bumps and the influence of the top foil is neglected. The SEFM has been used in many papers and has shown benefits regarding computing time. The used assumptions have been investigated in various papers and have been found to be mostly acceptable. Although the feasibility of the SEFM for CGFBs is questionable due to the possibility of a non-uniform stiffness in the axial direction, it is used to provide a runtime efficient solution.  
The SEFM can describe the velocity of the foil deformation as shown in Equation (4) where the non-dimensional foil deformation is given by \(\widetilde{w}=w/c_{0}\).  
\[\frac{\partial\widetilde{w}}{\partial\tau}=\frac{2}{\eta}\left(\frac{ \widetilde{p}_{\mathrm{g}}}{\widetilde{k}_{\mathrm{b}}}-\widetilde{w}\right) \tag{4}\]  
Equation (4) uses the non-dimensional mean pressure along the bearing length \(\widetilde{p}_{\mathrm{g}}\) and the non-dimensional foil stiffness \(\widetilde{k}_{\mathrm{b}}=(k_{\mathrm{b}}c_{0})/p_{\mathrm{a}}\) with the foil stiffness per unit area \(k_{\mathrm{b}}\). The unit of \(k_{\mathrm{b}}\) is \(\mathrm{N}/\mathrm{m}^{3}\). Together with an area \(A\) this yields a stiffness \(k_{\mathrm{b}}A\) with the expected unit of a mechanical stiffness \(\mathrm{N}/\mathrm{m}\). The loss factor \(\eta\) is used to introduce viscous damping into the foil structure. Due to the necessary conversion of hysteretic damping to viscous damping, the assumption of a reference vibration frequency is required. Typically, the rotational speed \(\Omega\) is used for this which yields the equivalent viscous damping coefficient per unit area \(c_{\rm D}=k_{\rm b}\eta/\Omega\). This equivalent viscous damping coefficient yields a viscous damping force \(F_{\rm D}\) of the form \(F_{\rm D}=c_{\rm D}A\cdot\partial w/\partial\tau\). Correlations with experiments have shown that the effect of this assumption for nonlinear analysis of sub-synchronous vibration is not significant in most cases [30; 31].
# YOLOv8 with Post-Processing for Small Object Detection Enhancement
## 1 Introduction
This study introduces a confidence-based re-detection (CR) process to mitigate this. The CR process involves cropping bounding boxes with a confidence score between 10% and 49% from the original image, focusing on small objects, and performing re-detection using the YOLOv8n model. Bounding boxes with a confidence score above 50% after re-detection are integrated with the initial detection results and output as the final result. Additionally, the YOLOv8n model uses a modified parameter, integrating a CARAFE module into the neck rather than using the basic model [22]. The CARAFE module offers a significant advantage by replacing the upsampling module typically used in the neck and adapting the upsampling kernels according to the content of the input feature map. This enables more effective aggregation of contextual information. As a result, feature reconstruction is more precise, minimizing information loss during upsampling and significantly improving small object detection performance. Notably, the CARAFE module does not rely solely on local pixel-based information. Instead, it utilized a broader contextual area to provide richer feature representations, enhancing model performance for small object detection and complex background environments. The YOLOv8n-CARAFE architecture used in this study aims to achieve higher accuracy and stability than the standard YOLOv8n model. Incorporating this architecture minimizes the loss of detailed information during the re-detection of small object candidate areas while maintaining computational efficiency.
## 4 Proposed Metaheuristic Schemes
### Particle Swarm Optimization (PSO)  
Swarm intelligence is an innovative distributed intelligence paradigm for solving optimization problems. The paradigm was inspired by biological phenomena, such as flocking, swarming, and herding in vertebrates. Particle swarm optimization (PSO) incorporates swarming behaviors observed in schools of fish, flocks of birds, swarms of bees, or even human social behavior [43; 44]. PSO is a population-based optimization technique that can be easily applied to solve various optimization problems. The major strength of a PSO algorithm is its fast convergence, which has advantages over other global optimization algorithms, such as GAs and simulated annealing.  
In PSO, a swarm of particles explores the solution space of an optimization problem to identify an optimal or quasioptimal solution. Each particle represents a candidate solution and is identified with specific position in the \(D\)-dimensional search space. The position and velocity of \(i\)th particle is represented as \(X_{i}=(x_{i1},\ x_{i2},\ldots,\ x_{iD})\) and \(V_{i}=(v_{i1},\ v_{i2},\ldots,\ v_{iD})\), respectively. The fitness function is evaluated for each particle in the swarm, and the result is compared with the best previous fitness obtained by that particle and to the best fitness ever achieved by any particles of the swarm. Then, each particle updates its velocity and position in accordance with the following equations:  
\[V_{i}^{t+1}=\omega*V_{i}^{t}+c_{1}*r_{1}*\left(P_{i}^{t}-X_{i}^{t}\right)+c_{2 }*r_{2}*\left(G^{t}-X_{i}^{t}\right) \tag{6}\]  
\[X_{i}^{t+1}=X_{i}^{t}+V_{i}^{t+1} \tag{7}\]  
where \(1\leq i\leq P\); \(P\) is the size of the swarm; \(V_{i}^{t}\) and \(X_{i}^{t}\) are the velocity and position of the \(i\)th particle at iteration \(t\), respectively; \(P_{i}^{t}\) is the current best solution for the \(i\)th particle at iteration \(t\); \(G^{t}\) is the global best solution at iteration \(t\); \(\omega\) is the inertia weight; and and \(c_{2}\) are acceleration coefficients. Here, \(r_{1}\) and \(r_{2}\) denote uniform random numbers between \((0,1)\).
## 3 A Few Notes on the Mathematical Model
### Trajectory Optimization and Optimal Control Laws  
The heliocentric trajectory of the CubeSat propelled by BIT-3 was obtained by solving an optimization problem that allows us to study the rapid transfer between the heliocentric (Earth-like) parking orbit and an assigned three-dimensional Keplerian target orbit. The problem studied here is the so-called \"orbit-to-orbit\" transfer, where the actual ephemerides of the two celestial bodies involved in the transfer are not taken into account, in order to obtain optimal global results in terms of the selected (scalar) performance index. In particular, the optimization process minimizes the flight time required to complete the transfer by also finding the optimal angular position of the spacecraft along both the parking and the target orbits. In this sense, the procedure used in this paper parallels the approach employed in a recent study by the author ([PERSON], 2016) when the CubeSat is equipped with a hybrid propulsion system given by an electric thruster and a photonic solar sail.  
The CubeSat's trajectory optimization was performed using an indirect approach [55, 56], while the optimal control law, in terms of temporal variation of both the direction of the thrust unit vector \(\boldsymbol{\hat{T}}\) and the operating level \(\mathrm{Id}\), was obtained by resorting to the PMP [49]. In particular, the optimal control law allows us to maximize the local value of the Hamiltonian function [57] which depends, in addition to the seven state variables (scalars), also on the seven Lagrange multipliers of the problem.
## 3 Results and Discussion
### Corrosion Resistance Measurements  
For the evaluation of the corrosion resistance of the hybrid specimens, the ASTM B895 standard test was employed. Hybrid samples from each condition were evaluated i.e., as-built, heat treated, and HIP. All the samples were polished with 120 grit paper, followed by degreasing and cleaning with acetone and deionised water, the samples were dried in air prior to being tested. Immediately, before testing they were weighed and assigned a testing tray. The samples were kept in the solution for up to 90 h. As presented in Figure 5a,b, the conventionally heat treated sample demonstrated some corrosion within 36 h in the solution and this was attributed to the lack of Mo, which is reported to have a substantial role in the corrosion resistivity of stainless steel [66]. Conversely, the HIP ones showed corrosion resistance, even after 36 h and this is attributed to the retention of Mo concentration and the lack of pores in the structure that enhance the corrosion resistivity of the material, Figure 5c. The as-built sample did not show any signs of corrosion even after 90 h in the solution, and this is consistent with other studies [56]. All specimens were weighed again after they were removed from their respective testing trays, although, the weight change was negligible \(\sim\)0.1 g.
## 6 Enhanced Oil Recovery  
In the Vaca Muerta shale formation, the prospects for enhanced oil recovery (EOR) are notably promising. This is attributed to the abundant in situ hydrocarbon reserves, subpar yields from initial extraction methods, and a well-established infrastructure and data landscape. A thorough assessment has been conducted to gauge the efficacy of multiple EOR technologies in elevating both initial and ultimate oil recovery rates for horizontal wells in this specific shale play.  
Chemical additives designed for enhanced recovery can be integrated into the initial hydraulic fracturing stage. This early intervention contrasts with the more conventional EOR techniques, which are generally deployed later in the well's operational lifecycle. The advantage of this early stage chemical introduction is twofold: it not only augments immediate production, but also shortens the time needed to replenish fluids in a depleted reservoir, thereby improving the overall economic viability of the well. While the existing literature does not provide explicit details on the chemical compositions used, reservoir simulation models do emphasize the potential gains achievable through such treatments.
## 4 Case Analysis
[10], for the seismic gaps in the central and southern sections of the North-South seismic belt, the relationship between the quiet period, the semi-major-axis length, and the predicted magnitude is given as follows: \(M\) = 0.0965 lg\(T\) (month) + 4.8300 \(\pm\) 1.6342 and \(M\) = 0.8832 lgL (km) + 4.3670 \(\pm\) 1.8046, where \(M\) denotes the predicted magnitude, \(T\) is the quiet period of the seismic gap, and \(L\) denotes the semi-major-axis length of the seismic gap. By substituting the average quiet period (19 months, Table 1) and the average semi-major-axis length (987 km, Table 1) of the six seismic gaps examined in this paper into the above equation, the predicted magnitudes are \(M6.2\) and \(M7.0\). The results are basically consistent with the magnitude \(M\geq 6.7\) in the present study. Overall, the \(M\geq 4.0\) seismic gap images in the study area can be utilized as predictive indexes for \(M\geq 6.7\) strong earthquake occurrence. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Earthquake ID**} & \(T0\) = 400 Days & \(T0\) = 500 Days & \(T0\) = 600 Days & \(T0\) = 700 Days \\  & \(T\) (Day/Month) & \(T\) (Day/Month) & \(T\) (Day/Month) & \(T\) (Day/Month) \\ \hline No. 1 & 419/14 & 319/11 & 219/7 & 119/4 \\ \hline No. 2 & 573/19 & 473/16 & 373/12 & 273/9 \\ \hline No. 3 & 377/13 & 277/9 & 177/6 & 77/3 \\ \hline No. 4 & 605/20 & 505/17 & 405/14 & 305/10 \\ \hline No. 6 & 423/14 & 323/11 & 223/7 & 123/4 \\ \hline Average & 479/16 & 379/13 & 279/9 & 179/6 \\ \hline \end{tabular} \(T0\): start of forecasting; \(T\): predicted occurrence time.  
\end{table} 
Table 2: Dynamic prediction results of the occurrence times of earthquakes corresponding to the five seismic gaps.
# Comparison of Field and Laboratory Tests for Soil Suitability Assessment in Raw Earth Construction
## 2 Experimental Program  
### Study Area and Soil Sampling  
The Nouvelle-Aquitaine region has rather sandy subsoils, but more clayey layers are also present. The Bureau of Research of Geology and Mines (BRGM) geological maps provided initial soil characterization data. These maps classify soils according to standardized geological criteria [39]. However, these maps can vary significantly sometimes within 100 m, as well as in depth, and the soil remains quite inhomogeneous. Furthermore, these maps do not indicate whether the soil is suitable for earth construction. These resources were previously exploited for fired earth construction [40].  
For sourcing, sand or gravel quarries are interesting because it is often necessary to remove a more clayey layer of earth to reach the desired layer. This earth is more interesting as it constitutes a by-product of the quarry. These extractions are then called scalping or discovery earth. Thus, the general analysis of the region's resources has led to the choice of sites where the soils were collected:  
-   Quarries (soils no. 1, 2, 3, 4, 5, 9, 10, 15, 17, 24, 25, 29, 30, 32, 35, 36, and 37) constitute valuable deposits because of their generally large quantity and homogeneity,  
-   Ongoing construction sites soil excavations (soils no. 6, 7, 8, 11, 12, 13, 20, 31, and 34) allow to extend the reflection on earth construction in urban areas while reducing the distance traveled by the earth,  
-   Storage sites as well as existing earth heritage sites (soils no. 18, 19, 22) allow to inscribe earth construction in a dynamic circular economy.  
A total of 39 soils from Nouvelle-Aquitaine region were collected, as shown in Figure 1.  
Figure 1: Study area and soil sampling.
# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms
### Multilateration Algorithm  
The multilateration method is an extension of the triangulation method with more than three reference points in estimating a target location [156; 163]. Radio frequency multilateration method estimates the location of the target using the strength of signals received from many non-collocated and non-collinear transmitters [164]. In the multilateration method, the localization accuracy highly depends on the distance measurement between a target device and an access point. The multilateration-based localization methods are used in the domain of an information-oriented construction site for simply realizing ad hoc wireless locating networks [165]. A true-range multilateration method is also utilized for a bidirectional target tracking and navigation system [142]. Although time-based multilateration localization techniques are chosen for the positioning of wideband signals, these techniques are not so insignificant with narrowband signals such as GSM. The time-based process challenges are due to the needs of synchronization precision and timestamp determination, both in the nanoseconds range [62]. Furthermore, the radio frequency fingerprinting method gives better results than the radio frequency multilateration method, even though the radio frequency multilateration method gives less of an error rate and enhanced solution for small spaces [164].  
Multilateration is the most common method for deriving a position. From the estimated distances and known positions of the anchors, the following system of equations can be derived [131; 162]:  
\[\begin{array}{c}(x_{1}-x)^{2}+(y_{1}-y)^{2}=d_{1}^{2},\\ \vdots\\ (x_{n}-x)^{2}+(y_{n}-y)^{2}=d_{n}^{2}.\end{array} \tag{1}\]  
where the unknown position is denoted by \((x,y)\). The system can be linearized by subtracting the last equation from the first \(n-1\) equations:  
\[\begin{array}{c}x_{1}^{2}-x_{n}^{2}-2(x_{1}-x_{n})x+y_{1}^{2}-y_{n}^{2}\\ -2(y_{1}-y_{n})y=d_{1}^{2}-d_{n}^{2},\\ \vdots\\ x_{n-1}^{2}-x_{n}^{2}-2(x_{n-1}-x_{n})x+y_{n}^{2}-1\\ -y_{n}^{2}-2(y_{n-1}-y_{n})y=d_{n-1}^{2}-d_{n}^{2}.\end{array} \tag{2}\]  
Reordering the terms gives a proper system of linear equations in the form \(Ax=b\), where:  
\[A=\left[\begin{array}{cc}2(x_{1}-x_{n})&2(y_{1}-y_{n})\\ \vdots&\vdots\\ 2(x_{n-1}-x_{n})&2(y_{n-1}-y_{n})\end{array}\right],b=\left[\begin{array}{c }x_{1}^{2}-x_{n}^{2}+y_{1}^{2}-y_{n}^{2}+d_{n}^{2}-d_{1}^{2}\\ \vdots\\ x_{n-1}^{2}-x_{n}^{2}+y_{n-1}^{2}-y_{n}^{2}+d_{n}^{2}-d_{n-1}^{2}\end{array} \right]. \tag{3}\]  
The system is solved using a standard least-squares approach:  
\[\hat{x}=\left(A^{\top}A\right)^{-1}A^{\top}b. \tag{4}\]The symbol \(\hat{x}\) expresses the estimated location.
# Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
## 3 Method
### Linguistic Features
#### 3.2.2 Feature Selection  
The choice of features is key in authorship attribution, as it helps improve the efficiency of classification models by focusing on the most relevant and discriminative features. Since most features are non-normally distributed, we employ the Kruskal-Wallis mean rank [54], a non-parametric statistical method commonly used for feature selection in various ML tasks. This approach evaluates the relevance of different features by comparing their mean ranks across multiple groups or classes. In the context of authorship attribution, Kruskal-Wallis mean rank analysis helps identify the most discriminative features that contribute to distinguishing between authors' writing styles. By considering the mean ranks of features across authors on the train documents, this method enables the selection of those features that exhibit the greatest effect sizes, thus characterizing authorial styles.  
Therefore, we used the Kruskal-Wallis method to select the top features, and we conducted experiments with varying numbers of features for each text set, namely FT and PP. The transformer model achieved optimal performance with the top 100 features, whereas for the ML-based approach, we employed different numbers of features ranging between 50 and 500 as illustrated in Table 3.
## 2 Materials and Methods  
A schematic of our experimental setup is shown in Figure 1a. An acoustic levitator comprising two parallel transducer arrays in a vertical orientation was utilized to suspend a liquid droplet measuring 2 to 3 mm in diameter [27]. This was achieved by generating a 40 kHz sinusoidal electrical signal using a PC scope, which was then amplified and directed to the transducers, thereby generating a stable standing acoustic field. Within the acoustic levitator, the solution droplet was inserted into the node of the sound wave, an area characterized by lower air density. For the levitation experiment, urea solutes with a purity of 99.8% (provided by [PERSON]) were dissolved in deionized water with a resistivity of 18.2 M\(\Omega\cdot\)cm. The samples underwent filtration through a syringe filter with 220 nm pore size (BIOFIL) to ensure the removal of any unwanted impurities. The meticulously prepared solution was then introduced to the injection needle positioned close to the designated sound node. Through precise adjustments of the flow rate facilitated by a motorized syringe pump, the solution was shaped into a droplet at the tip of the needle. The high-precision motorized syringe pump (KDS 100), capable of delivering a flow rate with a precision of 0.1 \(\upmu\)L/h, and the optical magnifying lens setup allowed us to levitate droplets of the same diameter with a precision of 20 \(\upmu\)m so that our result was self-consistent. Once the droplet achieved stable suspension between the acoustic nodes, the needle was gently detached from the sample. Subsequently, time-varying sizes of the evaporating droplet were monitored and recorded using a high-resolution CCD camera (Lumenera Lt365) equipped with a microscopic zoom lens, allowing 5 times magnification at a frame rate of 100 Hz. Concurrently, a thermographic camera (FLIR) was used to measure the evolution of the surface temperature of the solution droplet during evaporation as well as crystallization. To ensure accurate temperature measurement, the temperature of the suspended water droplets was compared using both contact-based thermal coupling and IR thermography. It should be noted that infrared thermography may introduce a potential source of error due to the altered emissivity of the highly supersaturated aqueous solution.
## 5 Results
### Reference Trajectory Generation  
The reference trajectory was generated using a professional-grade NovAtel ProPak6(tm) GNSS receiver. The position estimates of the receiver were enhanced with RTK corrections computed using raw observation data from GNSS ground reference stations in Finland (belonging to the Finnish Permanent GNSS Network, FinnRef) and Estonia (belonging to the Estonian permanent GNSS network, ESTREF). The GNSS+RTK combination was performed in post mission utilizing a software tool Inertial Explorer(IE) from NovAtel Inc., Calgary, AB, Canada. Four stations (with station identification MET3, SUR4, MUS2, and KUSA) cover the entire sea trajectory between Helsinki and Tallinn.
# Influence of Drivetrain Hybridization on Transmission Lifetime
## 2 Materials and Methods
### Lifetime Calculation
\(\mathrm{n_{i}}\) denotes the number of bearing revolutions occurring for an individual load class in the cycle. \[\mathrm{D}=\sum_{i=1}^{k}\frac{\mathrm{n_{i}}}{\mathrm{L_{i}}} \tag{3}\]  
Based on the cumulative damage D and the cycle duration \(\mathrm{T_{cycle}}\), the component lifetime \(\mathrm{B_{10}}\) of a bearing is calculated (Equation (4)). \[\mathrm{B_{10}}=\frac{\mathrm{D}}{\mathrm{T_{cycle}}} \tag{4}\]  
The failure distribution of a single bearing \(\mathrm{F_{j}(t)}\) over time is described using the Weibull distribution (Equation (5)). \(\mathrm{t_{0}}\) is the failure-free time and can take values between \(0.1\cdot\mathrm{B_{10}}\) and \(0.3\cdot\mathrm{B_{10}}\) for bearings [32]. In this paper \(\mathrm{t_{0}}=0.2\cdot\mathrm{B_{10}}\) is used. \(\mathrm{b}\) is a shape parameter and equals 1.1 for ball bearings and 1.35 for roller bearings [32]. The characteristic lifetime \(\mathrm{T_{j}}\) of a single \(\mathrm{j}\) is calculated using Equation (6). \[\mathrm{F_{j}(t)}=1-\mathrm{e}^{-(\frac{t+\mathrm{t_{0}}}{\mathrm{T_{j}}-t_{0}} )^{\mathrm{b}}} \tag{5}\]\[\mathrm{T_{j}}=\frac{\mathrm{B_{10}-t_{0}}}{\sqrt[b]{-\log(0.9)}}+t_{0} \tag{6}\]  
The system failure distribution or the failure distribution of all bearings as a group \(\mathrm{F_{B}(t)}\) over time is calculated by a superposition of the failure probabilities of the individual bearings. Here, it is assumed that the group of bearings fails as soon as one bearing fails. The underlying Boolean relationship is given in Equation (7). \[\mathrm{F_{B}(t)}=1-\prod_{j}\left(1-\mathrm{F_{j}(t)}\right) \tag{7}\]  
The system lifetime or the lifetime of all bearings as a group with a failure probability of 10% is the time t at which \(\mathrm{F_{B}}\) reaches the value 0.1. For further information on the approach described, we refer the reader to references [28; 29].