esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Phone Multi-Axis Position  
The results of the UWB beacon measurement accuracy for two different distances (0.5 m and 2 m) across three different axes (X, Y, and Z) show notable variations in precision and accuracy. For both distances, the Z and X axes show relatively high consistency in measurements, indicated by their low standard deviations (0.01 m). The Y axis, however, shows higher variability (Figures 4 and 5), especially at the 2 m distance, with a standard deviation of 0.06 m and a wider range (1.28 to 2.35 m). At the 0.5 m distance, the mean measurements for the Z (0.40 m), Y (0.57 m), and X axes (0.58 m) indicate that the measurements are reasonably close to the expected value, though there is a slight overestimation in the Y and X axes. At the 2 m distance, the mean measurements for the Z (1.96 m) and X axes (1.88 m) are close to the actual distance, but the Y axis again shows a significant overestimation (2.12 m).  
Figure 3: UWB measurement graph for voltage 3.0.  
The results indicate that UWB beacon measurements are generally accurate and consistent along the Z and X axes, with minimal deviation from the actual distances. However, measurements along the Y axis exhibit greater variability, particularly at the 2 m distance, highlighting potential issues in the Y axis accuracy for longer distances.
## 5 Fiber Transmission Performance Results and Discussion
### High-Baud-Rate 64-Qam  
Compared with 16-QAM, the 64-QAM signal has 1.5\(\times\) more spectral efficiency, and thus can carry more information bits. Figure 7a,b illustrates the fiber transmission performance of the 45 GBd and 64 GBd 64-QAM signals over different link distances. Compared with the 16-QAM, the 64-QAM signal has a smaller Euclidean distance between neighboring constellation points. It is thus much more susceptible to noise sources, such as laser phase noise and Rx electrical noise. In addition, compared with 16-QAM, the nonlinearity introduced by the transfer function of the IQ modulator has more impact on the 64-QAM signals. In our experiment, the modulation nonlinearity is mitigated by adjusting the electrical driving signal. As shown in Figure 7b, with 6-dB more optical power of the Tx side optical power than \(P_{Tx\_Ref}\), the 64 GBd 64-QAM signal can transmit up to 40 km in the unamplified link, which gives greater than 600 Gb/s net data rate.  
Figure 5: Bit error ratio (BER) as a function of transmission distance in unamplified point-to-point link for (**a**) 56 GBd 16-QAM signals; (**b**) 64 GBd 16-QAM signals.  
Figure 6: BER as a function of transmission distance in unamplified point-to-point link for 16-QAM signals with (**a**) 6-dB more transmitter output power than the reference point; (**b**) transmitter output power at the reference point.
# Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
## 2 Related Work on Authorship Attribution
### Classic Machine Learning Models
[20] employed several classifier combinations, including Naive Bayes (NB), Decision Trees (DT), Support Vector Machines (SVMs), and k-Nearest Neighbor (k-NN), for gender identification problems. SVMs and heuristics-based classifiers, coupled with stylometric features, yielded the best results. Similarly, [PERSON] et al. [11] applied NB, SVM, and DT (J48) for author profiling, specifically for gender and age prediction in SMS messages; the J48 Decision Tree exhibited higher accuracy in gender prediction. A distinct study [9] explored SVM and k-NN algorithms for author attribution, experimenting with varying instances and k values; in their research, SVM outperformed other methods, and for k values, 1-NN and 5-NN produced superior results. [PERSON] et al. [21] compared their results with the literature's best outcomes, conducting experiments with diverse feature combinations and data-preprocessing methods. In their tweet data analysis, they applied the chunking technique, achieving a high accuracy across varying chunk sizes. A popular approach is to combine several ML models and train an ensemble of classifiers. [PERSON] et al. [22] proposed employing ensemble learning and traditional ML techniques for authorship attribution. Their method extracted valuable author characteristics using a count vectorizer and bi-gram term frequency-inverse document frequency (TF-IDF). The experiment involved a comprehensive news dataset divided into three subsets. This twofold study focused on selecting 10 authors in the first fold and 20 authors in the second fold for a comparative baseline. Results indicated that the proposed ensemble learning outperformed previous state-of-the-art studies, achieving accuracy gains of 3.14% and 2.44% for the first scope, while the second scope exhibited 5.25% and 7.17% improvement.
# Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features
## 2 Setting of the Computer Experiments
### ML Models  
Five ML models were considered in the computer experiment, namely, logistic regression (LR), ordinal logistic regression (OLR), two artificial neural networks (ANN), and support vector machine (SVM). These models allow to calculate the probabilities of being in a damage category, making them suitable for structural damage assessment to provide information about the underlying uncertainty in SHM. Additionally, such models provide the decision maker with the opportunity to adopt probabilistic decision-making frameworks [42; 43]. Details of each of these models are described below.
# Design of a Hand-Launched Solar-Powered Unmanned Aerial Vehicle (UAV) System for Plateau
## 2 Conceptual Design Method
### Power of Level Flight  
The lift and drag of the aircraft can be obtained from Formula (1), where \(C_{L}\) and represent \(C_{D}\) the lift and drag coefficients, respectively. \(\rho\) is air density and \(S\) is wing area. According to these two basic formulas, the mechanical power consumed during cruise can be obtained, as shown in Formula (2).  
\[\left\{\begin{array}{l}W=L=\frac{\rho}{2}V^{2}SC_{L}\\ T=D=\frac{\rho}{2}V^{2}SC_{D}\end{array}\right. \tag{1}\]  
\[P_{lev}=TV=\frac{mg}{(L/D)}\,V=\frac{C_{D}}{C_{D}^{3/2}}\sqrt{\frac{2 ARg^{3}}{ \rho}}\frac{m^{3/2}}{b} \tag{2}\]The total electrical power of the aircraft during cruise \(P_{elec\_tot}\) included mechanical power and electrical power. The efficiency of the motors \(\eta_{mat}\), electronic controllers \(\eta_{ctrl}\), and propellers \(\eta_{plt}\), as well as the power consumption of avionics \(P_{av}\) and payload instrument \(P_{pld}\) must be considered. Since the battery voltage was higher than the voltage of avionics and payload instrument, the efficiency of step-down \(\eta_{bec}\), also called battery elimination circuit (BEC), should also be considered. In order to improve the reliability and maintainability, MP UAV's propulsion system was propeller-motor direct drive. The efficiency of gearbox can be ignored. The total electrical power during cruise is shown in Formula (3).  
\[P_{elec\_tot}=\frac{1}{\eta_{ctrl}\eta_{mat}\eta_{plt}}P_{lev}+\frac{1}{\eta_{ loc}}\big{(}P_{av}+P_{pld}\big{)} \tag{3}\]  
The total energy consumed during the all-day flight is shown in the Formula (4). Unlike daytime flight, all energy consumed during the night flight was from the battery, so the charging efficiency \(\eta_{chrg}\) and \(\eta_{ddrg}\) discharging efficiency of battery also need to be considered.  
\[E_{elec\_tot}=P_{ele\_tot}\bigg{(}T_{day}+\frac{T_{night}}{\eta_{chrg}\eta_{ dchrg}k_{temp}}\bigg{)} \tag{4}\]  
where \(T_{day}\) and \(T_{night}\) present day time and night time respectively, and \(k_{temp}\) presents the temperature influence factor. At dusk and dawn, when the solar energy collected by solar cells is less than the required power, the energy comes from both the solar cells and battery, and the conversion from one source to another is gradual. The process of energy source conversion is seen as instantaneous to simplify the calculation.
# Design and Implementation of a Six-Degrees-of-Freedom Underwater Remotely Operated Vehicle
## 4 Electrical System Design
### Control Board  
The control board was connected to the PCB board, as shown in Figure 9. The control shield was mounted to Arduino. The shield was used to deliver the signals from the Arduino Mega to the components through terminals to fasten the wires gently and avoid disconnection. The shield contains 20 terminals (7 for the signal, 7 for the ground, and 6 for the small power). A Tether cable was mounted between the vehicle and the tether management system (TMS) for transmitting electrical power, optical signals and mechanical loads using a thin, flexible, and durable cable [31].
# MDPN: Multilevel Distribution Propagation Network for Few-Shot Image Classification
## 4 Experiment  
### Dataset  
We assess MDPN using the common benchmarks for few-shot learning, CUB-200-2011 [35]. A total of 200 bird species are represented by 11,788 photos in CUB-200-201, which is broken down into 100 base classes, 50 validation classes, and 50 novel classes.
## 4 Automated Online Traffic Collection, Sample Construction, and Correlation
### Cross-Platform Traffic Collection and Sample Construction
#### 4.2.3 Android  
The current methods to realize traffic collection under the Android mobile devices need to obtain root permission. However, root access is easy to cause irreversible damage to the system and limits the large-scale construction of the experimental bed. Therefore, we propose a method to collect the traffic by using the _VPN Service_ interface of Android without obtaining root permission and realizing application filtering. The Android-optimized A3C architecture is shown in Figure 6.  
The _VPN Service_ interface is realized by establishing a VPN communication link to a configured VPN server. However, since A3C-Android is only used for sample collection and construction, the VPN server can be deployed near the end and support multiple A3C-Android instances. It is worth noting that because the VPN Service adopts the TUN mode, the collected labeled samples do not contain Ethernet frame headers.  
Since the VPN service interface has been occupied, if an Android device needs to collect application samples in a specific VPN, it needs to use the VPN bridge to collect application samples (the actual VPN client is deployed on the A3C-Android VPN Server). The script is one way to achieve full automation on Android. However, due to the limitations of Android, we propose a method to monitor the changes of UI controls in the foreground of Android devices by using the _Accessibility Service_ interface.
## 3 The Flat System
### Pose Estimation  
To express the relationship between the position and the orientation of the robot with respect to the target object (the pose), we will use the principle of 2D visual servoing. Two-dimensional visual servoing utilizes the relationship between the camera velocity \((V)\) and the time variation in the visual information (\(\dot{S}\)). This relationship is expressed by Equation (7)  
\[\dot{S}=L_{s}V, \tag{7}\]  
where \(L_{s}\) is the interaction matrix, also noted by the image Jacobian which links the camera's instantaneous velocity \(V\) to the time variation in the visual information \(S\). If we note:  
\[e(t)=S(t)-S^{*}(t),\] (  
In this paper, we consider \(S=(x,y)\) to be the coordinates of the point in the image plane coordinate system. We test if \(S=(x,y)\) is a flat output of the considered system. According to Equation (7), we have:  
\[V=L_{s}^{+}\dot{S}, \tag{12}\]  
where \(V=\left(\dot{x}_{R},\dot{y}_{R},0,0,\dot{\theta}\right)^{T}\) is the velocity of the camera, and \(L_{s}^{+}\) is the pseudo-inverse of the interaction matrix. \(V=\left(\dot{x}_{R},\dot{y}_{R},0,0,0,\dot{\theta}\right)^{T}\) can be expressed using \(S\) and its derivative. Thus, we can write:  
\[V=h\Big{(}S,\dot{S}\Big{)}, \tag{13}\]  
where \(h\) is a nonlinear function \(\mathbb{R}^{2}\times\mathbb{R}^{2}\rightarrow\mathbb{R}^{6}\) and can be expressed analytically. \(\left(\dot{x}_{R},\dot{y}_{R},\dot{\theta}\right)\) can be also expressed according to \(S\) and its derivative. Hence, we can write:  
\[\theta=arctan\Big{(}\frac{\dot{y}_{R}}{\dot{x}_{R}}\Big{)}=f_{1}\Big{(}S,\dot {S}\Big{)}, \tag{14}\]  
\[u_{1}=\sqrt{\dot{x}_{R}^{2}+\dot{y}_{R}^{2}}=f_{2}\Big{(}S,\dot{S}\Big{)}, \tag{15}\]  
\[u_{2}=\frac{\ddot{y}_{R}\dot{x}_{R}-\dot{y}_{R}\ddot{x}_{R}}{\dot{x}_{R}^{2}+ \dot{y}_{R}^{2}}=f_{3}\Big{(}S,\dot{S}\Big{)}, \tag{16}\]  
where \(f_{1},f_{2},f_{3}\) are three nonlinear functions \(\mathbb{R}^{2}\times\mathbb{R}^{2}\rightarrow\mathbb{R}^{6}\), which can be deduced from (13). Finally, \(S=(x,y)\) are flat outputs of the considered system. We conclude that the location and the orientation of the robot can be deduced using the coordinates of a single point in the image [10].
Best-Estimate for System Codes (BeSYC): A New Software to Perform Best-Estimate Plus Uncertainty Analyses with Thermal-Hydraulic and Safety System Codes for Both Fusion and Fission Scenarios  
[PERSON]  
1 Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany; [EMAIL_ADDRESS] (B.-.G.);[EMAIL_ADDRESS] (X.Z.J.); [EMAIL_ADDRESS] (M.I.-B.); [PERSON](R.S.)  
[PERSON]  
2 Dipartimento di Ingegneria Civile e Industriale, University of Pisa, Largo Lucio Lazzarino, 56122 Pisa, Italy; [EMAIL_ADDRESS] (M.A.); sandro.paciding.unipi.it (S.P.)  
R. G.  
opment of a software to apply the Best-Estimate Model Calibration and Prediction through Experimental Data Assimilation methodology [4; 5] to the RELAP5-3D and MELCOR 1.8.6 codes for code's validation purposes (to ease the reading of the paper, hereinafter, the methodology is called \"BEPU methodology\"). This software is called Best-estimate for SYstem Codes (BeSYC; version 1; KIT: Karlsruhe, Germany), and it was developed as a MATLAB App. The aim of BeSYC is not only limited to the application of the BEPU methodology, but it also covers the writing and the execution of the code runs required to apply the BEPU methodology itself. The present paper describes the structure, the mathematical framework, and the validation of this software. This paper is published in conjunction with two other papers showing the application of BeSYC with both RELAP5-3D and MELCOR 1.8.6 codes against the data gathered from an experimental campaign on a helium-cooled first wall mock-up (FWMU) under Loss Of Flow Accident (LOFA) conditions [8; 9]. Even if not yet used for any real-case scenario, the BeSYC software supports also MELCOR 2.2.
# Ankle Joint Dynamic Stiffness in Long-Distance Runners: Effect of Foot Strike and Shoes Features
## 2 Materials and Methods
### Experimental Protocol  
Tests were performed on an instrumented treadmill (Advanced Mechanical Technology Inc., Watertown, MA, USA) that collects ground reaction forces [31] at a sampling rate of 1000 Hz. Three-dimensional kinematics data of the lower extremities was recorded at a sampling rate of 250 Hz from a 14-camera VICON B-10 system (Oxford Metrics Ltd., Oxford, UK). Kinematic and ground reaction force data were synchronised using a VICON MX-Net control box and collected through Nexus 2.6 software (Vicon Motion Systems Ltd., Oxford, UK). A biomechanical model was reconstructed from 45 retroreflective markers, placed on proper landmarks of body segments (Appendix A).  
After completing a standardized and progressive 7 min warm-up, participants repeated three times a 5 min running test, with different shoes for each trial; the three shoe models were characterized by their different minimalist indexes (MI). The minimalist index is a classification that takes into account structure, flexibility, pronation support and other footwear features, and ranges from 0% (maximum assistance) to 100% (least interaction with the foot) [32]. The shoes adopted in our experiments were classified at low-MI (Mizuno(r) Wave Rider 21, MI = 18%), medium-MI (Mizuno(r) Wave Sonic, MI = 56%) and high-MI (Vibram(r) Five fingers, MI = 96%). The order of presentation was pseudorandom, which means that combinations were balanced within each group and equal between groups. Testing speed was fixed for all participants at 11 km/h.
# SCoPE: Service Composition and Personalization Environment
## 5 ECE Environment  
The ECE environment supports three main process related to care services: Preparation; Execution; and Monitoring.  
The _ECE Preparation phase_ corresponds to the creation of ECE and definition of its rules and functionalities within a collaborative environment. It involves representing the main body of information and knowledge, identifying the target audience, the involved stakeholders, namely partners in the various groups (support entities, regulation entities, private companies, governmental institutions, freelancing professionals, caregivers, etc.) which are members of ECE, ICT and human resources, business and management rules; and characterizing the available services. Based on the templates it creates the care needs list, goals lists, links between care need and goals (taxonomy of care needs), identifies the services providers and services profile and the profile of customers.  
The _ECE Execution phase_ relates to the process of composition and personalization (SCoPE) of services, including the ranking of the pairs offer (services) and demand (customer care needs). This process is the focus of this paper and is more detailed in Section 6.  
The _ECE Monitoring phase_ supports the service evolution and monitoring of the ECE environment. Considering the dynamic environment and stages of life, the ECE broker analyses the situation (in collaboration with the relevant stakeholders) and adapts the services to fit each new context. In this way, a service solution evolves to cope with the new phase of the customer's life (for instance, handling new or obsolete care needs, new customer inputs, technological changes, new strategies of service providers, etc.). The detailed process of the self-adaptive system approach for service evolution into ECE can be seen in [16; 50; 51].  
To better understand the Execution process, preliminary data on the customer profile and an example of scenario are presented in the next sections.  
Figure 4: The main elements of ECE and their relationships.
## 3 Results
### Functional and Technological Indicators
#### 3.2.1 Water and Oil Absorption Capacities
Earlier studies attributed the increased oil absorption capacity in germinated grains [52; 78] to the partial disruption of the native protein structure during germination. This disruption facilitates the release of lipophilic proteins and hydrophobic amino acids associated with the hydrocarbon side chains of lipids, thereby increasing the oil absorption capacity of the grain. The research revealed that the oil absorption capacity of germinated tritcale grain varies dynamically in response to germination time, resulting from structural modifications of protein molecules. In particular, this parameter increased to 0.68% at 22 h of germination in GT22, which aligns with previously published data [52; 78] and results from studies on wheat and its germinated form, tritcale, and brown rice [80; 124]. During germination, partial denaturation of proteins occurs, leading to the release of lipophilic  
Figure 3: The effects of germination time on the water absorption capacity (WAC) and oil absorption capacity (OAC) of tritcale samples (WGT and GTs) are shown. The values are presented as mean \(\pm\) standard deviation (n = 3). A moderately strong positive correlation was found between germination time and WAC, with r = 0.6799 on the Chedok scale. A similar relationship was observed for OAC, with r = 0.6458, indicating the influence of germination on the grain’s ability to retain water and oil. fragments and hydrophobic amino acids that interact with lipids, thereby increasing the oil absorption capacity. The obtained data also demonstrated an increase in this parameter at 20 and 22 h of germination in GT20 (0.64%) and GT22 (0.69%), respectively, which can be linked to the activation of protein structural changes at these stages. A higher oil absorption capacity is desirable as it imparts better sensory properties, enhances taste, and improves mouthfeel, which are fat characteristics [125]. A decrease in oil absorption capacity was observed at the later stage of germination, for example, after 24 h in the GT24 sample (0.66% compared to the previous stage), which may indicate the completion of key protein structural transformations and stabilization of lipid components in the grain.
###### Abstract  
In this paper, a computational fluid dynamics simulation method is developed to study the influence of the rotor overlapping azimuth on the aerodynamic performance of compound coaxial helicopter. The simulation method is verified by comparing the numerical simulation results with the wind tunnel experiment data of the NASA coaxial rotor. Two overlapping azimuths of the upper and lower blades are considered, and the aerodynamic performance of the isolated rotor and the compound coaxial helicopter in hover and forward are analyzed respectively. State 1 means the upper and lower blades overlap at azimuth \(0/180^{\circ}\) or \(90/270^{\circ}\), state 2 means the upper and lower blades overlap at azimuth \(45/225^{\circ}\) or \(135/315^{\circ}\). It is found that the performance of isolated rotors is not affected by rotor overlapping azimuth in hover, but the total thrust fluctuation amplitude of isolated rotors in state 2 is 76.3% smaller than that in state 1 in forward. In the hovering flight of compound coaxial helicopter, compared with state 1, the fluctuation amplitude of the lift of the wing in state 2 is 42.7% smaller; the lift fluctuation amplitude of the flat tail in state 2 is 52.4% smaller. In the forward flight of compound coaxial helicopter, compared with state 1, the total thrust fluctuation amplitude in state 2 is 83.5% smaller; the fluctuation amplitude of the lift of the wing in state 2 is 61.2% smaller. It can be concluded that the compound coaxial helicopter working in state 2 has better aerodynamic performance than the compound coaxial helicopter working in state 1; changing the rotor overlapping azimuth of the upper and lower rotors has a high engineering application value, which can increase aerodynamic stability and reduce lift fluctuations.  
compound coaxial helicopter; rotor overlapping azimuth; polyhedron nested grid; aerodynamic performance