esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Solution and Experimental Validation of the Electromagnetic Field in an IPT System
## 6 Experimental Verification
The largest relative error, which is below 6%, happens under the experimental condition that \(h_{1}\) = 80 mm, and the maximal relative error is below 2% at the other two positions. The reason why the analytical solution is always larger than the experiment data is that during the numerical integration, the coil resistance is not considered. It can be found that near the primary side, the analytical solution is just a little higher than the experiment data, the reason for this is that near the primary side the electric field intensity is determined dominatingly by the current in primary side. It is reasonable to believe that the experiment has validated the analytical solution. \begin{table}
\begin{tabular}{c c} \hline
**Parameter and Symbol** & **Value** \\ \hline Radius of primary coil, \(a_{p}\) & 100 mm \\ Radius of secondary coil, \(a_{S}\) & 100 mm \\ Wire radius, \(wire\) & 0.6 mm \\ Number of turns of primary coil, \(N_{p}\) & 5 \\ Number of turns of secondary coil, \(N_{S}\) & 5 \\ Distance between both coils, \(h_{1}\) + \(h_{2}\) & 100 mm \\ Load impedance, \(R_{L}\) & 20 \(\Omega\) \\ Compensation capacitor in primary side, \(C_{p}\) & 1000 pF \\ Compensation capacitor in secondary side, \(C_{S}\) & 1000 pF \\ Inductance of primary coil, \(L_{p}\) & 105.8 \(\mu\)H \\ Inductance of secondary coil, \(L_{S}\) & 105.8 \(\mu\)H \\ Resonant frequency, \(f_{0}\) & 486.7 kHz \\ Radius of measurement coil, \(a\) & 100 mm \\ \hline \end{tabular}
\end{table} 
Table 1: Experimental parameters. Figure 14: Electric field intensity at different \(h_{1}\) values. Figure 13: Waveforms of primary voltage \(u_{p}\), primary current \(i_{p}\), secondary current \(i_{s}\) and induced voltage \(u_{m}\) of the measurement coil.
# Efficient Machine Learning-Based Prediction of Solar Irradiance Using Multi-Site Data
## 1 Introduction
The results showed that the GPR model achieved high accuracy, with an MAPE of 1.97%, RMSE of 0.16, and EF of 0.99 for daily predictions, and that monthly models trained individually provided better performance. Furthermore, the model demonstrated strong generalizability and stability under different training set sizes using 5-fold cross-validation. Machine learning methodologies such as Hidden Markov Models (HMMs) and Support Vector Machine (SVM) regression have also shown promise in short-term solar irradiance forecasting [14]. The results show that these machine learning-based forecasting algorithms can accurately predict solar irradiance for future 5-30 min intervals under different weather conditions. The experimental evaluations, performed using the Australian Bureau of Meteorology dataset and analyzed via the MATLAB interface Weather Forecasting Platform, demonstrated the effectiveness and importance of these methodologies in enhancing forecasting precision. Deep learning techniques have also emerged as powerful tools for solar irradiance prediction. In [15], deep recurrent neural networks (DRNNs) outperformed traditional methods like support vector regression (SVR) and feedforward neural networks (FNNs) in terms of accuracy. Applied to real-world data from a Canadian solar farm, DRNNs showcased their potential for big-data applications in renewable energy forecasting. Other notable works are summarized in Table 2.
# Ultrasound Elastography in the Evaluations of Tendon-Related Disorders--A Systematic Review
## 1 Introduction  
Tendon-related disorders are common conditions in both sports medicine and orthopedic clinical practice; they are among the most important causes of suspension for sports activity and limitation in working ability [1]. Tendons can be affected by various pathological conditions; some of them could be associated with a systematic disease, and others result from local inflammation [2]. Tendinitis, tendinopathy, tenosynovitis, and peritendinitis are common terms, but as [PERSON] and colleagues observed, all these terms were misused and confusing [3]. Ultrasonography, power doppler ultrasound, and magnetic resonance imaging (MRI) are the most common methodologies used as diagnostic imaging modalities for assessing and monitoring tendon pathologies [4].  
Ultrasound (US) elastography was initially developed in the 1990s for a precise evaluation of tissue stiffness, with the aim of replicating/substituting the subjective clinician's evaluation performed through manual palpation [5]. Two types of US elastography areavailable. The first is strain elastography (SE), which requires an external rhythmical pressure applied by the examiner on the target tissue, allowing the software to measure the tissue stiffness. The strain ratio obtained is the ratio between the examined tissue and the reference tissue [6]. Strain elastography provides a qualitative evaluation thanks to a color diagram [7]. The second is shear wave elastography, which is based on the use of acoustic radiation impulses to displace the tissue at several points of interest. This technique returns quantitative information about tissue stiffness (in kPa) based on the shear wave propagation velocity, quantified in meters/second (m/s) [6]. US elastography ensures the possibility of implementing several applications with respect to the body tissues, being commonly used to evaluate fascial structures, nerves, soft tissue masses, muscles, and tendons [8]. Multiple studies have been published on the use of US elastography on human tendons [9; 10], most of them being experimental trials with small sample sizes [11]. To our knowledge, no systematic reviews focused on ultrasound elastography addressing clinical efficiency [12] in tendon-related disorders have been published, so far. Therefore, the aim of this SR was to evaluate the clinical efficiency of US elastography (strain elastography and/or shear wave elastography) in the aforementioned application field.
# Recognition of Hand Gesture Sequences by Accelerometers and Gyroscopes
## 3 The Proposed Gesture Recognition Algorithms and Systems
### Gesture Data Collection for Training  
Because this study aims to recognize a sequence of \(K\) gestures, a basic approach for the collection of training sequences for the proposed algorithms is to require each sensory data sequence for training should contain \(K\) different gestures. A drawback of this approach is that the proposed algorithms should be re-trained when the applications for other lengths of gestures \(K\) are desired. In addition, the collection of the training sets should also be carried out again when different number of gesture classes \(Q\) are adopted for the new applications. The reusability of the neural network models and/or the training data are low. Furthermore, large efforts are required for accurate labeling of \(K\) gestures for each sensory data sequence.  
To improve the reusability of neural network models and training data for gesture sequences with different lengths of gestures \(K\) and/or different number of gesture classes \(Q\), each sensory data sequence for training contains only a single gesture. Since there are \(Q\) gesture classes, the training set can be separated into \(Q\) subsets. The sensory data sequences belonging to the same subset represent the same gesture. All the training sequences in the \(Q\) subsets should be involved in the training of the proposed algorithms. The resulting neural network models can be applied to the recognition of gesture sequences with different \(K\) values, where \(K\) is known a priori. The same neural network models can then be re-used even though \(K\) varies. When the incorporation of new gesture classes or removal of existing gesture classes are desired for the new applications, the training subsets common to the new and original applications can be re-used. Consequently, the reusability for the neural network models and training sets can be improved. Moreover, because each training sensory sequence represents only a single hand gesture, the efforts for labeling can be minimized.
# Principles and Characteristics of Different EDM Processes in Machining Tool and Die Steels
## EDM Process Details
### Various EDM Process Variants
#### 2.4.5 Dry EDM  
Dry EDM uses high-pressured gas as the dielectric instead of liquid which is used in other EDM processes [16; 102; 101; 100]. Herein, the tool electrode is in the form of a thin-walled tube through which high-pressure gas or air is supplied. The pressurized gases flow through the gap and carry the debris away from the machining area. Pressure gases also reduce the machining area temperature and reduce the harmful environmental effects. This method has several other advantages; for example, the use of gas decreases the cost of managing debris, and it enhances the machining performance and work environment with regard to worker health. The dry EDM process positively influences the MRR and reduces the electrode wear ratio [101; 103; 99].
###### Abstract  
This study introduces a reduced-order leg dynamic model to simplify the controller design and enhance robustness. The proposed multi-loop control scheme tackles tracking control issues in legged robots, including joint angle and contact-force regulation, disturbance suppression, measurement delay, and motor saturation avoidance. Firstly, model predictive control (MPC) and sliding mode control (SMC) schemes are developed using a simplified model, and their stability is analyzed using the Lyapunov method. Numerical simulations under two disturbances validate the superior tracking performance of the SMC scheme. Secondly, an Nth-order linear active disturbance rejection control (LADRC) is designed based on a simplified model and optimization problems. The second-order LADRC-SMC scheme reduces the contact-force control error in the SMC scheme by ten times. Finally, a fourth-order LADRC-SMC with a Smith Predictor (LADRC-SMC-SP) scheme is formulated, employing each loop controller independently. This scheme simplifies the design and enhances performance. Compared to numerical simulations of the above and existing schemes, the LADRC-SMC-SP scheme eliminates delay oscillations, shortens convergence time, and demonstrates fast force-position tracking responses, minimal overshoot, and strong disturbance rejection. The peak contact-force error in the LADRC-SMC-SP scheme was ten times smaller than that in the LADRC-SMC scheme. The integral square error (ISE) values for the tracking errors of joint angles \(\theta_{1}\) and \(\theta_{2}\), and contact force \(f\), are \(1.6636\times 10^{-28}\) rad\({}^{2}\) - s, \(1.7983\times 10^{-28}\) rad\({}^{2}\) - s, and \(1.8062\times 10^{-30}\) N\({}^{2}\) - s, respectively. These significant improvements in control performance address the challenges in single-leg dynamic systems, effectively handling disturbances, delays, and motor saturation.  
Keywords:linear active disturbance rejection control; foot force control; joint-angle control; legged robot; sliding mode control; model predictive control; Smith predictor +
Footnote †: journal:
# A Dual Stage Low Power Converter Driving for Piezoelectric Actuator Applied in Micro Mobile Robot
## 3 Design and Control of the Drive Circuit
### DC/AC High-Voltage Drive Stage
All the unused energy is transferred from the upper side actuator to the lower side actuator, which enhances the overall efficiency of the system. Mode (d) starts as soon as the transistor Q\({}_{\rm L}\) is turned on. The unused energy in the electrostatic actuator at the lower side begins to be transferred to the upper side. The inductor current \(i_{L}\) rises reversely, thereby causing the discharge of the low side actuator \(C_{2}\). The underdamped drive signal \(v_{c2}\) descends with attenuated oscillation as follows:  
\[v_{c2}(t)=V_{\rm C}-V_{\rm C}\cdot e^{-\beta t}\left(\cos\omega_{d}t+\frac{ \beta}{\omega_{d}}\cdot\sin\omega_{d}t\right), \tag{10}\]  
Mode (d) ends as soon as \(v_{c2}\) drops to zero in the first oscillation cycle. At the end of mode (d), Q\({}_{\rm L}\) is turned off, and the inductor current \(i_{L}\) increases through the freewheel diode D\({}_{\rm H}\). The circuit operates alternatively in modes (e) and (b). The initial voltage condition in mode (e) is zero. Similar to mode (b), the underdamped drive signal \(v_{c2}\) in mode (e) can be expressed as  
\[v_{c2}(t)=-v_{c2}(t_{d})\cdot e^{-\beta(t-t_{d})}\cdot\left[\cos\omega_{d}(t-t_ {d})+\frac{\beta}{\omega_{d}}\cdot\sin\omega_{d}(t-t_{d})\right], \tag{11}\]  
where (\(v_{c2}\) (\(t_{d}\)) = 0) represents the initial voltage condition at the end of mode (d) at \(t_{d}\). The high side actuator \(C_{1}\) is being charged by the stored energy of inductor \(L\). After the oscillation ends, the drive signal \(v_{c2}\) becomes zero as soon as the inductor current turns zero until the next operation cycle begins. In mode (f), the drive signal voltage \(v_{c1}\) is charged up to \(V_{C}\) until the unused energy is fully transferred from the lower actuator to the upper one. The inductor current \(i_{L}\), the drain-to-source voltage \(v_{ds}\), and the drive voltage \(v_{c2}\) waveforms during each switching cycle are illustrated in Figure 7, with an operation sequence of a-b-e-b-c-d-e-b-e-f. Modes (a) and (d) are the modes at the beginning of two half-operation cycles. Modes (b) and (e) are the oscillation modes, which occur alternatively in both half-operation cycles. Modes (c) and (f) are the stable modes at the end of two half-operation cycles. Energy conversion is replaced by energy transfer in a working cycle. With the exception of the loss of the equivalent resistance of inductor \(i_{L}\), the half-bridge stage is a theoretically lossless structure that ensures high energy efficiency of the entire system.
# Reversing File Access Control Using Disk Forensics on Low-Level Flash Memory
## 2 General Background
### Flash Translation Layer (FTL)  
To remain compatible with traditional block file systems, a flash storage device (e.g., an SSD) usually exposes a block access interface. This is achieved by introducing a new flash translation layer (FTL) (Figure 2), a piece of special firmware which stays between the file system and the NAND flash, transparently managing the special characteristics of NAND flash [18, 19, 20].  
**Out-of-place updates**. The FTL implements an out-of-place update strategy [7, 21, 22]. Since the erasure is so expensive, it is unfeasible to overwrite a page by actually writing the location the page is stored at again. As such, the FTL uses a system of logical and physical page addresses, linked by a page mapping table (PMT). The logical address of a page is where it appears to be to the OS and filesystem, and does not change as the page is overwritten. The physical address, on the other hand, is the actual location of the data on the drive. If this page is searched by the FTL, it will pull the data located exactly at that location regardless of any translation. The PMT handles translation between the two addresses, stylized as a table which maps the logical address of a page to its physical counterpart. In out-of-place updates, a write to a logical address instead causes a new physical page to be found which is currently empty. The new page is then written and the PMT changes the logical address of the target page accordingly. But, the data previously stored at the logical address is temporarily retained on the original physical page (Note that our work mainly utilizes this phenomenon to restore the ACL entries compromised by the adversaries at the OS level). In other words, given the logical address on the block device, by searching the PMT table, we can identify the physical flash page where the corresponding data is stored. An example of the PMT table is shown in Figure 3.  
Figure 2: The layers used in operation of a filesystem and FTL.  
**Wear leveling**. Flash blocks have a limited number of erasures that they can perform before losing reliability. As the out-of-place update scheme is also implemented in flash systems, the best way to determine where data should be placed on the drive must be determined. This is done by a system called wear leveling, which periodically checks how many erasures have been performed on blocks across the drive and moves pages to other locations on the drive accordingly. This increases the lifespan of the drive as each block will take approximately equal erasure counts.  
**Garbage collection**. A side effect of the out-of-place update and wear leveling systems is the fact that blocks of data will eventually fill up with out-of-date, or \"dirty\", pages. These blocks must be erased in order to use the full drive. The FTL periodically checks for blocks which have a certain number of dirty pages (or greater) and moves any valid data to a new block before erasing the old one. The PMT is then adjusted. This allows for filled blocks to be emptied and reused.
# Creep-Fatigue Behavior and Life Prediction of Medium-Si-Mo Ductile Iron
## 2 Experimental Materials and Methods  
The chemical composition of the medium-silicon molybdenum ductile iron investigated in this study was determined using a spark optical emission spectrometer (LMX08), as detailed in Table 1.  
The experiments were conducted following Chinese standard GB/T 15248-2008 [21], \"The Test Method for Axial Constant-Amplitude Low-Cycle Fatigue of Metallic Materials,\" using an MTS fatigue testing machine [22]. In the tests, the strain amplitudes ranged from 0.2% to 0.8% and were applied at room temperature (23 \({}^{\circ}\)C), 400 \({}^{\circ}\)C, 600 \({}^{\circ}\)C, and 700 \({}^{\circ}\)C. Strain control was applied using a triangular waveform, as shown in Figure 1a, with a strain ratio of \(-1\). The dimensions and physical appearance of the test specimens are illustrated in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Element** & **C** & **Si** & **Mn** & **Mo** & **P** & **S** & **Mg** & **Re** \\ \hline Quantity & 3.18 & 4.43 & 0.35 & 0.63 & 0.06 & 0.012 & 0.038 & 0.031 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The chemical composition of the ductile iron (wt. %).  
The specimens for the creep-fatigue interaction tests at high temperatures were the same as those used for the low-cycle fatigue tests. Axial tension-compression strain control was applied during loading with the waveform shown in Figure 1b. Due to the fact that creep effects in metallic materials are usually negligible at room temperature, we only conducted the interaction tests at 400 \({}^{\circ}\)C, 600 \({}^{\circ}\)C, and 700 \({}^{\circ}\)C.
# Optimization Design of Planar Circle Coil for Limited-Size Wireless Power Transfer System
## 5 Conclusions  
This article takes S-S compensation topology as an example and analyzes the relationship between power transfer efficiency (PTE), transferred power (TP), and system parameters. The normalized optimization objectives \(K\) and \(\eta\) and corresponding design variables are obtained. When the input voltage range is restricted, in order to ensure that \(K\) and \(\eta\) have ideal values at the same time, the TESOM algorithm is used to optimize both of them at the same time. Finally, the Pareto front visualization is shown, which allows designers to easily balance two design goals. COMSOL and MATLAB allow for the automated optimization design of coils in a complicated environment. The coils beyond the size of the underwater vehicle are deleted during the data transmission step of the joint computation to guarantee that the optimal values can be acquired in the restricted area of the underwater vehicle. Experiments show that, compared to traditional design and maximize of \(M/R\) to improve efficiency, multi-objective optimization of coil parameters can achieve higher voltage gain at the expense of efficiency, which can guide the design process when the range of input power supply is restricted, such as battery power supply.  
Although the compensation topology and coupler structure are chosen throughout the optimization process, these two factors have no bearing on the automated optimization process. The optimization technique in this study may be easily extended to the design of coil couplers with different topology, surroundings and size limitations by deriving alternative compensation topology equations and developing additional coupler models.  
Conceptualization, Y.M.; methodology, Y.M. and Z.M.; software, Y.M.; validation, Y.M.; formal analysis, Y.M. and Z.M. and K.Z.; investigation, Y.M.; resources, Y.M.; data curation, Y.M.; writing--original draft, Y.M.; writing--review and editing, Y.M., Z.M., and K.Z.; visualization, Y.M.; supervision, Z.M. and K.Z.; project administration, Z.M. and K.Z.; funding acquisition, Z.M. and K.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52171338.  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
Objective: The stability of the flight phase in ski jumping is crucial for athletes' performance and safety. This study aims to investigate the influence of unfavorable conditions on aerodynamic characteristics and flight stability through computational fluid dynamics (CFD) numerical simulations. Methods: The ski jumper and the skis are considered a multi-body system. A detailed three-dimensional (3D) model of this multi-body system under a commonly observed posture during flight is established. The Partially Averaged Navier-Stokes (PANS) turbulence model is employed, and CFD simulations are conducted to predict the aerodynamic characteristics of the multi-body system under lateral environmental wind and asymmetric postures during the flight phase. The conditions of asymmetric postures include yaw rotation and roll rotation. Results: (1) Lateral environmental wind generated a yaw force, yaw moment, and roll moment, which influenced the lift, drag, and pitch moment of the athlete. These forces and moments were relatively small at lower wind speeds (less than 3 m/s) and became more significant at higher wind speeds (greater than 4.5 m/s). (2) Under the influence of yaw rotation or roll rotation, the multi-body system exhibited a noticeable yaw force, yaw moment, and roll moment, all showing a monotonic increasing trend. Moreover, they had a significant impact on the lift, drag, and pitch moment of the multi-body system. Conclusion: (1) The influence of unfavorable conditions was complex, resulting in a significant yaw force, yaw moment, and roll moment on the multi-body system. The adverse effects of roll rotation were generally greater than those of yaw rotation. (2) The multi-body system exhibited self-stabilizing tendencies in yaw and roll. This phenomenon can provide a solution to maintain flight stability by employing appropriate yaw or (and) roll rotation angles, effectively compensating for or even eliminating the adverse effects of lateral environmental wind. (3) Understanding the mechanisms of how unfavorable conditions affect aerodynamic characteristics and stability during flight in ski jumping can provide valuable assistance for real-time prediction and decision making during competitions, as well as scientific guidance for training athletes' stable flight control and techniques for improving their sport performance.  
Computational fluid dynamics; aerodynamic characteristics; flight phase in ski-jumping; flight stability; sport performance