esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study aimed to investigate the effects of combined training with maximal aerobic speed and small-sided games on football players' aerobic capacity development. Methods: The football players were divided into three groups as a combined training group (n = 20) (Mean \(\pm\) SD age 23.40 \(\pm\) 2.92 yrs, BMI was 23.67 \(\pm\) 1.59 kg/m\({}^{2}\), mass: 74.80 \(\pm\) 5.46 kg, height: 177.73 \(\pm\) 4.31 cm), maximal aerobic speed group (n = 20) (Mean \(\pm\) SD age 23.93 \(\pm\) 2.46 yrs, BMI was 23.32 \(\pm\) 1.42 kg/m\({}^{2}\), mass: 72.66 \(\pm\) 5.38 kg, height: 176.46 \(\pm\) 4.99 cm) and regular training group (n = 20) (Mean \(\pm\) SD age 24.80 \(\pm\) 5.84 yrs, BMI was 22.87 \(\pm\) 1.67 kg/m\({}^{2}\), mass: 73.06 \(\pm\) 6.74 kg, height: 178.33 \(\pm\) 7.98 cm). In addition to regular football training, maximal aerobic speed training with small-sided games was applied to the combined training group (CT) and only maximal aerobic speed training was applied to the maximal aerobic speed group (MAS) twice a week for 12 weeks. The normal training group (NT) continued their routine football training programme. All participants applied the Yo-Yo IR1 test in the pre-test and post-test of the study. As a result of normality tests, it was determined that the data showed normal distribution, and the ANOVA test and [PERSON]'s multiple comparison test were used in the intergroup evaluation. Results: When the Yo-Yo IR1 Test pre-test and post-test results were analysed, maximal aerobic speed training with small-sided games (CT) and maximal aerobic speed (MAS) groups were significantly higher compared to the normal training (NT) group regarding training load, running distance, and VO2 max value among the football players (\(p\) = 0.001). There was no difference in the normal training group (\(p\) > 0.05). As a result, it was determined that combined training with maximal aerobic speed, small-sided games, and only maximal aerobic speed effectively improved football players' aerobic capacity and general performance.  
small-sided games; maximal aerobic speed; aerobic capacity; amateur; football
## 2 Feature Extraction of Cutting Sound Signal
### Composite Multi-Scale Permutation Entropy  
Composite multi-scale permutation entropy is a nonlinear dynamic method that can reflect the randomness and complexity of time series. It can effectively deal with information loss caused by multi-scale permutation entropy with the increase in scale factor. The calculation steps of the CMPE are as follows.  
1. According to the formula (9), the original time series \(\{y(i),i=1,2,\ldots,N\}\) is coarse-grained to obtain the coarse-grained sequence \(y_{k}^{(\tau)}=\left\{y_{k,1}^{(\tau)},y_{k,2}^{(\tau)},\cdots y_{k,p}^{(\tau )}\right\}\). \[y_{k,j}^{(\tau)}=\frac{1}{\tau}\sum\limits_{i=(j-1)\tau+k}^{j\tau+k-1}y_{i}, \ 1\leq j\leq N/\tau,\ 1\leq k\leq\tau\] (9)  
where \(N\) is the length of time-series data, \(\tau\) is the scale factor. When \(\tau\) = 1, the coarse-grained time series is expressed as the original time series. Meanwhile, when \(\tau\) > 1, the original time series is divided to produce the \(\tau\) coarse-grained sequence with a length of \(P\) = \([N/\tau]\). \([\cdot]\) is expressed as an integer, \(y_{k}^{(\tau)}\) represents the \(k\)-th coarse-grained sequence under the \(\tau\) scale factor, and \(j\) represents the \(j\)-th point of the coarse-grained sequence.
2. Calculate the permutation entropy of \(\tau\) coarse-grained sequences according to the scale factor \(\tau\).
3. Calculate the mean value of the permutation entropy of \(\tau\) coarse-grained sequences, and obtain the CMPE as: \[\mathit{CMPE}=(y,\tau,m,\lambda)=\frac{1}{\tau}\sum\limits_{k=1}^{\tau}{PE(y_ {k}^{\tau},m,\lambda)}\] (10) where \(\lambda\) is the time delay.  
CMPE adopts a composite multi-scale method, which reduces the dependence on the length of the original time series in the calculation process. It can reflect the abundant characteristic information contained in the cutting sound signal at multiple scales.
## 4 Numerical Results and Discussion
### Small Air Bubble Scenario
Figure 3: Average BER performance when considering an empirical UOWC channel in the presence of small air bubble-induced fading under different levels of antacid concentration at a link distance of 3 m and a wavelength of 520 nm.
# An Insightful Overview of the Wiener Filter for System Identification
## 1 Introduction
In addition, several regularization methods were developed for denoising applications and image restoration, e.g., [11; 12; 13] and the references therein. Nevertheless, there is still room for performance improvement, especially when using the Wiener filter in challenging conditions, as explained above. The goal of this paper is to provide new insights into the Wiener filter for system identification problems. The main focus concerns the important practical aspects related to the regularization of this optimal filter in difficult conditions, e.g., working in noisy environments and/or handling a limited set of available data/information. Following this introduction, Sections 2 and 3 briefly present the system model behind the conventional Wiener filter and some useful background, respectively. Next, Section 4 details different regularization methods of the Wiener filter, also showing how the sparse character of the system can be exploited in this context. Furthermore, a solution obtained using the Kronecker product decomposition of the impulse response is developed in Section 5, which is suitable for low-rank systems identification. In Section 6, the quadratic eigenvalue problem is used for the regularization of the Wiener filter, which can be successfully applied when some a priori knowledge on the impulse response is available. All these techniques and theoretical findings are supported by the simulation results provided in Section 7. Finally, the main conclusions are summarized in Section 8.
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 4 Security Technical Framework
### Cryptography Support Layer  
Encryption, authentication, digital signature, key agreement and other primitive cryptography methods provide attribute-based fine-granularity encryption, hierarchical key-based hybrid encryption, hybrid encryption methods supporting key tracking, lightweight encryption, ciphertext fragmentation storage, dynamic adjustment of ciphertext deduplication, data trusted deletion, smart contracts based on multi-policies, trusted regulatory chains based on data operation, bidirectional anonymous authentication, routing table security query, key management and distribution, etc. These provide basic cryptography technical support for the whole life cycle security protection of data assets in an uncontrolled environment.  
Figure 5: Technical framework for secure sharing and circulation of data assets.
# Analysis of Wind Speed Characteristics Along a High-Speed Railway
## 6 Conclusions  
This study aims to ensure the safe operation of high-speed trains in windy areas and to enhance their ability to predict wind speed. The wind speed characteristics of WSs along an HSR are analyzed, and the predictability of wind speed at various time scales is quantified. The value of wind speed prediction is evaluated based on its predictability. The following conclusions are drawn from the analysis presented in this paper:  
(1) Compared to the nearby WS wind speed, the RS wind speed exhibits greater variability. As the time scale increases, the correlation between the two wind speeds fluctuates but generally rises. The Weibull and Rayleigh distributions are effective for modeling RS wind speeds, while the Weibull and generalized extreme value distributions are more suitable for modeling WS wind speeds based on their respective distribution characteristics.  
(2) NCP and [PERSON]'s tests can be used to measure how predictable wind speed is. The sampling time has an impact on the wind speed attribute of predictability. The predictability and the timeframe of historically expected wind speeds both decline as sampling time increases.  
(3) RS wind speed was predicted using several models. The results indicate that incorporating WS wind speed as auxiliary data improves prediction accuracy, although the improvement is limited. After conducting a predictability analysis, it was found that retaining historical wind speed data with time series dependence as training data enhances model input, thereby greatly improving prediction accuracy. Additionally, the proposed predictability rate helps determine the optimal input duration, further enhancing the model's predictive capability.  
The research presented in this paper is a sensible step toward achieving technological breakthroughs for safety in train transportation that will be highly valuable and important to society.  
Conceptualization, X.C. and X.Y.; methodology, X.C.; software, Y.Z.; validation, X.Y. and Y.Z.; formal analysis, Y.Z.; investigation, X.C.; resources, X.Y.; data curation, X.C.; writing--original draft preparation, X.C.; writing--review and editing, X.C. and X.Y.; visualization, Y.Z.; supervision, X.Y.; project administration, X.X.; funding acquisition, X.Y. and X.X. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Natural Science Foundation of China under Grant no. 42205150 and no. 42275156; the China Postdoctoral Science Foundation under Grant 2024M761470.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest. The funders had no role in the design of this study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results and Discussions
### Active Flow Control
#### 3.2.3 Influence of Control Frequency
The drag coefficient signal in Figures 24a and 25a shows that the high control frequency leads to a significant fluctuation in the drag coefficient; however, the time-averaged drag coefficient still decreases as the control frequency increases. When the synthetic jet is applied, analysis of PSD at \(\rm f_{c}=34\) Hz and \(\rm f_{c}=68\) Hz (Figure 24b) reveals that the dominant frequency coincides with the control frequency; however, there is also the existence of a secondary peak frequency. In contrast, at high frequency (\(\rm f_{c}=272\) Hz), only a distinct peak at the control frequency was observed. In the case of the unsteady jet, there is no secondary frequency peak at all control frequencies, as shown in Figure 25b. Since the unsteady jet oscillates only for a half-cycle, and thus the dominant frequency defined in Figure 25b is twice the control frequency. Figure 24: Spectral analysis of drag coefficient with a synthetic jet at different control frequencies. (a) \(\rm C_{D}\) signal; (b) PSD of \(\rm C_{D}\). Figure 25: _Cont._  
Finally, the development of small-scale vortices behind the vehicle at different control frequencies is analyzed based on the visualization using the Q-criterion at a level of 300,000 s\({}^{-2}\). Figure 26 shows that the synthetic jet with a high control frequency tends to push the vortices in the shear layer away from the rear edges of the vehicle when compared to the control frequency f\({}_{\rm c}\) = 34 Hz in Figure 16b. In particular, at the highest control frequency f\({}_{\rm c}\) = 272 Hz, a significant reduction in both the size and number of vortices in the shear layer occurred (Figure 26b), leading to their merge to form larger-scale vortices being limited, and thus the size of rear-wake section is reduced. Similar phenomena are also pointed out when using an unsteady jet with high control frequencies (Figure 27). Figure 26: The normalized streamwise contour at the iso-surface Q = 300,000 s\({}^{-2}\) with the synthetic jet at different control frequencies. (**a**) fc = 68 Hz; (**b**) fc = 272 Hz. Figure 25: Spectral analysis of drag coefficient with an unsteady jet at different control frequencies. (**a**) C\({}_{\rm D}\) signal; (**b**) PSD of C\({}_{\rm D}\).
###### Abstract  
Objective: To evaluate the differences in frictional resistance between in-house 3D-printed resin brackets (IH3 DBs) and two types of commercially available brackets in combination with three different archwires. Methods: Friction tests were performed using a dynamometer and a millimetre sled to simulate first premolar post-extraction space closure. Three different brackets, namely PRIMO metallic brackets, Crystal composite brackets and in-house 3D-printed brackets, were tested in combination with three different archwires (0.016-inch NiTi, 0.019 \(\times\) 0.025-inch NiTi and 0.019 \(\times\) 0.025-inch SS). Statistical analysis was performed to verify the differences in friction among the bracket and archive combinations. For all the tests, the significance level was set at \(p<\) 0.05. Results: There were significant differences among the three brackets tested with both the 0.016-inch NiTi and 0.019 \(\times\) 0.025-inch SS archwires (\(p=\) 0.026 and \(p=\) 0.017, respectively). Only tests with the 0.019 \(\times\) 0.025-inch NiTi archive yielded no statistically significant differences between the groups. The composite bracket generated clinically and statistically more friction than both the IH3 DB and metallic bracket, with no differences between the latter two. Conclusions: The IH3 DBs demonstrated comparable frictional resistance properties to the metal brackets and better than the composite brackets with all the archwires tested.  
f _applied_  
_sciences_  
**Citation:** [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Comparison of the Sliding Resistance of Metallic, Composite and In-House 3D-printed Brackets: An In Vitro Study. _Appl. Sci._**2024**, _14_, 6303. [[https://doi.org/10.3390/app14146303](https://doi.org/10.3390/app14146303)]([https://doi.org/10.3390/app14146303](https://doi.org/10.3390/app14146303))  
Academic Editors [PERSON] and [PERSON]  
Received: 14 June 2024  
Received: 3 July 2024  
Accepted: 16 July 2024  
Published: 19 July 2024
## 3 Materials and Methods
### Methodology
#### 3.4.2 Predictive Model: AdaBoost  
AdaBoost is a boosting ensemble learning model. The boosting ensemble learning primarily considers similar weak learners. It also uses the decision tree as the weak learners and learns them sequentially. Each decision tree learns from the previous model's mistakes by increasing the weights of the misclassified data points. Finally, AdaBoost makes a weighted sum by combining the outputs from the weak learners. The weighted sum is considered as the final output [36; 37].
# Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains
## 6 Blind Tests
### Results with 2-Axle Half-Car  
UCD also used a 2-axle 2-degree-of-freedom half-car to represent the bogie of the NTNU 4-axle railway carriage model. The objective was to determine if the method is effective when the numerical model (of UCD) is simpler than the true behaviour (simulated by NTNU). The researchers attempt to create a situation where, as in real life, the model used in the damage calculation is less sophisticated than the model (or the reality) used to generate the input data to the algorithm. The 2-axle model has the capacity to capture the bouncing and rocking motions of the bogie but not the carriage rocking motion that transfers load between bogies.  
For a 2-axle half-car, given the vertical accelerations and angular velocities, APs under each axle can be calculated using the Inverse Newmark-\(\beta\) integration scheme. Here, all the vehicle properties relate to the bogie and are global properties. The property values, before the application of random noise, are shown in Table 6. Using the calculated APs under the two axles, the MR-ILs of each run are determined using the method described in Section 5. To calculate the MR-ILs, the 1 st and 2 nd axle weights, \(W_{A}\) and \(W_{B}\), are determined from carriage mass, \(m_{v}\), bogie mass, \(m_{b}\), and wheelset mass, \(m_{w}\). However, in this case, the individual carriage mass, \(m_{v}\), for each run is not given. The mean value of \(m_{v}\) is used to calculate axle weights.  
A box plot showing the mid-span MR-ILs for Profile 1 is plotted in Figure 13. For this calculation, there is considerably more variability in the damage indicators for individual runs, relative to the differences due to damage. However, the calculated damage levels still compare well with the corresponding true levels, as shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Property** & **Symbol** & **Unit** & \multicolumn{2}{c}{**Corresponding Property in**} & \multirow{2}{*}{**Value**} \\ \hline Sprung mass & \(m_{s}\) & kg & \(m_{b}\) & 2615 \\ Sprung mass moment of inertia & \(I_{s}\) & kg m\({}^{2}\) & \(I_{b}\) & 1476 \\ Spring stiffness & \(K_{1}\), \(K_{2}\) & N/m & \(K_{p}\) & 2.4 \(\times\) 10\({}^{6}\) \\ Damping & \(C_{1}\), \(C_{2}\) & N s/m & \(C_{p}\) & 7 \(\times\) 10\({}^{3}\) \\ Distance of axle to body centre of gravity & \(D_{1}\), \(D_{2}\) & m & \(L_{b}\) & 1.28 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Half car vehicle properties in the blind test.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Bridge State No.** & & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline \multirow{3}{*}{4-axle railway carriage model} & Profile 1 & \(D1\) & 3\% & 12\% & 9\% & 6\% & 4\% & 7\% & 0\% \\  & \(D2\) & 4\% & 14\% & 11\% & 7\% & 5\% & 8\% & 0\% \\  & Profile 2 & \(D1\) & 5\% & 11\% & 9\% & 7\% & 4\% & 7\% & 0\% \\  & \(D2\) & 5\% & 12\% & 10\% & 7\% & 5
## 2 State-of-the-Art of Systems for Indoor Mobile Mapping (iMMS)
Experiences such as Microsoft's indoor localization competition [61] or IPIN (International Conference on Indoor Navigation and Positioning) [62; 63] have made significant and valuable contributions to the development of SLAM algorithms [64; 65; 66; 67; 68; 69]. To use such systems for the surveying of building geometries and for the geolocation of objects and their attributes, which is the focus of this contribution, it is essential to concurrently capture images of the navigated environments along with geometries. Initially, systems capable of acquiring geometries and simultaneously capturing high-quality images were limited to equipment mounted on mobile trolleys, such as Viametris(r) iMS3D (Louverne, France) [69; 70], NAVIS(r) M3 (Munich, Germany) [71; 72], and Trimble(r) (Westmister, CO, USA) TIMMS [73; 74]. The main advantage of such instruments was clearly to relieve the operator from carrying heavy instruments and to provide high resolution images. On the other hand, managing such a large number of high resolution images required a sufficiently powerful data processing unit and, consequently, an adequate battery pack. The combined effect of the two components resulted in significant weight of the PC and batteries, necessitating the use of trolleys (Figure 1) to move the instrument. This significantly restricted the use of such solutions in complex locations such as multi-level building sites and industrial facilities due to the difficulty of accessing and moving with a trolley in areas with stairs, steps, and uneven surfaces. Figure 1: The mobile mapping devices installed on the trolley. (**a**) Viametris® iMS3D [69]; (**b**) Trimble® TIMMS [74]. Without going into the specifics of obsolete platforms, modern developments have transformed these systems into solutions that are more manageable for operators. Examples of such instruments include the Gexcel Heron(R) Twin Color (Brescia, Italy) (Figure 2) [75; 76], Viametris(R) MS-96 [77], and NAVVIS(R) [78; 79] (Figure 3a) and Leica(R) BLKZGo(R) (Heerbrugg, Switzerland) [80; 81] (Figure 3b), which, despite employing different solutions, stand out by increased portability, with wearable solutions that can be carried on the operator's shoulders, facilitating access to multi-level buildings or construction sites. The latest wearable solutions are also enriched by synchronized panoramic cameras that support the generation of colorized three-dimensional models and particularly allow the production of virtual tours inside the mapped areas. This enables the easy identification  
Figure 3: (a) VLX2 by NAVVIS® [79]; (b) BLKZGO® by Leica® [81]. Figure 2: Heron® Twin Color by Gexcel (image from the author). of objects and elements within the scanned sites while enabling their localization, the measurement of interior geometries, and the acquisition of three-dimensional data if BIM model extraction is required. Portable systems can now be used in an affordable and logistically feasible way for the 3D mapping of complexes in the geospatial domain, extending beyond strictly topographical contexts [69].
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 5 Outlook on Key Technologies for Lunar Constellation Navigation and Orbit Determination
### Quantity Measurement Error Propagation Mechanism and Suppression  
Quantity measurements in the system are affected by environmental noise, measurement technical errors, sensor noise, etc., and errors are inevitable. In order to improve observation accuracy, error sources that affect navigation performance should be properly analyzed and processed.  
For example, for measurement errors, source model errors, spacecraft system errors, etc. in astronomical navigation, the system errors are compensated for via the extended state method and the epoch difference method. In addition, pulsar profile processing technology is also a key factor in improving accuracy, including period ambiguity problems, fast signal processing, the Doppler effect, phase delay, etc. An accurate model of pulsar observations can ensure the high positioning performance of the spacecraft. However, accurate observation models are usually nonlinear, and the filter method needs to be improved based on the characteristics of pulsar navigation to precisely estimate the position, velocity and other status information of the spacecraft. Velocity measurement navigation requires accurate modeling of the source-end errors of the astronomical spectrum, so the quality of measurement information should be significantly improved to ensure that the accuracy of the navigation system meets the demand.  
Spaceborne GNSS observations must go through three phases of processing. These three phases also bring three error sources, which will directly affect the accuracy of multi-constellation GNSS satellite orbit determination. The errors come from the navigation satellite, the GNSS signal receiver as the receiving source, and signal propagation. It is necessary to effectively correct the observation errors to improve the quality of measurement information to ensure the calculated orbit satisfies the accuracy requirements.  
Further research is needed on the mechanism and suppression methods of error propagation in the system, with a focus on studying the variation patterns of navigation measurement information error characteristics during the process of source end transferred to sensor processing, and constructing corresponding models. In terms of error suppression, the focus of research is on designing unique filter algorithms for different navigation methods and researching efficient and applicable filter algorithms.