esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Unlike more complex mathematical software (e.g., MATLAB or Python), it does not require extensive programming expertise, making it accessible to a broader range of users, including those in educational settings (i.e., in university degrees such Sports Science, Kinesiology, or Physiotherapy). The flexibility of the spreadsheet allows researchers to tailor the analysis to specific research questions or educational objectives, enhancing its utility in both research and teaching environments. This is particularly valuable in settings where the budget for specialized software is limited, yet there is a need for reliable and valid biomechanical analysis tools. Furthermore, the spreadsheet's ability to analyze each leg separately during bipodal jumps offers insights into inter-limb coordination and asymmetries. This feature is crucial for identifying compensatory strategies or imbalances that could predispose athletes to injury [35; 36]. While the current study focused on jump height and propulsive impulse, future research could expand the validation of this tool to include other kinetic variables, such as rate of force development, explore its reliability across different populations and conditions, or compare the accuracy with that of systems such as 2D and 3D photogrammetry or inertial sensors. The spreadsheet could also provide several physical outcomes useful for training or rehabilitation purposes such as (i) muscle power, (ii) rate of force development, (iii) impact peaks upon landing, (iv) outcomes related to the degree of muscle recovery, or (v) the improvements in muscle strength or jumping ability following an intervention program. It can also be utilized to assess specific research issues such as (i) the effect of different pylemetric jump programs on asymmetries [37]; (ii) the inter-limb coordination during jumping, which can also be used as a measure of neuromuscular fatigue e.g., generated by competition [38]; (iii) the effects of shoe soles designed to minimize energy dissipation and improve the jumping performance [14]; or the relation between inter-limb asymmetries and jumping performance [39]. Additionally, inter-rater and intra-rater reliability studies could further establish the robustness of this tool, ensuring its broader applicability in diverse research contexts. Moreover, it is noteworthy that, in the past, the high cost of force plates often restricted their accessibility, compelling modest laboratories to rely on photogrammetric systems or contact mats for jump evaluations. However, with the recent availability of force plates priced under USD 1,000, the utilization of conventional spreadsheets becomes increasingly relevant.
## 3 Results Analysis
### AvB Estimation Results
#### 3.3.3 Experimental Results in 10 Gbps Network  
The results of the third test scenario are presented in Figure 18. In this case, the 10 Gbps network with the presence of 1 Gbps cross traffic was analyzed only by _Kite2_ tool since no other tools are able to produce that probe packet rate.  
Hereby, another crucial limitation was revealed during the test using _Kite2_ with the system timers. Using the system timing operations, _Kite2_ was not able to exceed a packet sending data rate of 3.6 Gbps. However, with more efficient _HighPerTimer_ (as shown in previous experiments), up to 8.9 Gbps could be estimated for the given link. On higher data rates (more than 1 Gbps), overhead of _System Call_ becomes much more notable. Moreover, through the higher deviation, we can see the impact of an inaccurate _uSleep()_ call. In both cases (Figure 18a,b), _Kite2_ tended to underestimate the AvB, which is caused by its algorithm logic--to avoid overestimation and consequently overloads and packet loses. However, in the case of _HighPerTimer_ estimation, mean and median are close to one another, which is an indicator of normal distribution. It is worth noting that estimation outliers without _HighPerTimer_ are much more frequent and greater. The variance of 135.1 Mbps with standard timer in a contradiction to 20.82 Mbps of _HighPerTimer_ points to the inconsistency of AvB measurements in high-speed networks using the first approach.  
Figure 17: Yaz AvB estimation of 1 Gbps Network with 500 Mbps cross traffic: (**a**) standard system operations; and (**b**) with HighPerTimer.  
Previous results show that the most precise timing operations have been achieved in Qemu environment. In Figure 19, the results of AvB estimations on a VM with Qemu virtualization are presented. They show significantly higher underestimation outliers in a contradiction to the estimations on a host, but the median values for the case with HighPerTimer timer are close to one another--8871 Mbps and 8922 Mbps, respectively. For the case of standard timing operations, the send data rate of a tool could not exceed 1.9 Gbps, which led to 82% of estimation error. The MAD value for the case with HighPerTimer increased to 197.16 Mbps in a Qemu environment and is almost 10 times higher than in the case when the experiments were performed on a host. AvB estimation results of both experiments--on a host and in a virtual environment--confirm that virtualization has a significant negative impact on the network application performance. The experiments with AvB tools with standard timing operations have shown their absolute inconsistency for the 10 Gbps networks. Hence, we can conclude that a low overhead of time operations and therefore timestamp on incoming and outgoing packets are crucial points in available bandwidth estimations algorithms in a virtual environment and increases its significance with increasing data rates.  
Figure 18: Kite2 AvB estimation of 10 Gbps Network on a host with 1 Gbps cross traffic: **(a)** standard system operations; and **(b)** with HighPerTimer.
## 3 Results
### Chemical Composition of the Essential Oil
\begin{table}
\begin{tabular}{c c c c} \hline
**Compound** & **LRI \({}^{*}\)** & **Lit. RI** & **\%** \\ \hline n-nonane & 900 & 900 & 0.5 \\ \(\alpha\)-pinene & 939 & 939 & 0.3 \\ myrcene & 991 & 991 & 0.6 \\ p-cymene & 1027 & 1027 & 0.3 \\ limonene & 1031 & 1031 & 3.6 \\ \(\gamma\)-terpinene & 1062 & 1062 & 0.1 \\ n-undecane & 1100 & 1099 & 0.2 \\ estragole & 1197 & 1195 & 21.2 \\ N-decanal & 1205 & 1204 & 0.2 \\ trans-carveol & 1219 & 1217 & 0.1 \\ carvone & 1244 & 1242 & 0.2 \\ cis-chrysanthenyl acetate & 1263 & 1262 & 1.0 \\ (E)-anethole & 1290 & 1289 & 59.4 \\ carvacrol & 1300 & 1298 & 0.2 \\ undecanal & 1305 & 1310 & 0.4 \\ dodecanal & 1409 & 1409 & 4.4 \\ \(\beta\)-caryophyllene & 1418 & 1418 & 0.4 \\ \(\alpha\)-humuliene & 1456 & 1456 & 0.3 \\ (E)-\(\beta\)-farnesene & 1460 & 1458 & 0.1 \\ germacrene D & 1480 & 1480 & 0.4 \\ \(\beta\)-selinee & 1485 & 1485 & 0.3 \\ valencene & 1493 & 1490 & 0.2 \\ tridecanal & 1509 & 1511 & 0.1 \\ myristicin & 1520 & 1520 & 0.8 \\ carophyllene oxide & 1581 & 1581 & 0.4 \\ tetradecanal & 1612 & 1611 & 0.7 \\ epi-\(\alpha\)-bisabolol & 1686 & 1685 & 0.1 \\ hexadecanal & 1824 & 1825 & 0.1 \\
**Grouped compounds [\%]** & & & \\ Monoterpene hydrocarbons & & & 4.9 \\ Oxygenated monoterpenes & & & 82.1 \\ Sesquiterpene hydrocarbons & & & 1.7 \\ Oxygenated sesquiterpenes & & & 1.3 \\ Non-reprene derivatives & & & 6.6 \\ Total identified & & & 96.6 \\ \hline \end{tabular} \({}^{*}\)LRI = Linear retention indices (HP-5 column).  
\end{table} 
Table 1: Chemical composition of the essential oil from the aerial parts of _Daucus reboudii_.
## 5 Rolling Bearing Reliability Model
### Logistic Regression Model  
Logistic regression is a commonly used method for evaluating the reliability of mechanical equipment. It is employed to predict the probability of an event occurring based on a set of feature parameters. The core of the model consists of linear regression and the Sigmoid function.  
The process of a bearing transitioning from normal state to failure is usually divided into a healthy stage and failure stage. Logistic regression can well describe the state transition by mapping the input features to failure probability through the Sigmoid function. Logistic regression assumes that the input characteristics have a linear relationship with the logarithmic probability, while some physical quantities in the process of bearing degradation often have an approximate linear relationship with the failure risk. Its high efficiency, interpretability, and low data requirements make it an ideal choice for engineering practice.  
Through the Sigmoid function, logistic regression maps output values to the range of 0 to 1, representing the probability of the event occurring. The Sigmoid function is a monotonically increasing function, and its characteristics are illustrated in Figure 3.  
Assume at time \(t\), the \(i\)-th dimensional feature parameter set is \(X_{l}(t)=(x_{1}(t),x_{2}(t),\ldots,x_{l}(t))\), and \(y(t)=1\) indicates that the bearing can operate normally at time \(t\). The reliability can be expressed as the logistic function of \(X_{l}\):  
\[R(t\mid X_{l})=P(y_{l}=1\mid X_{l})=\frac{\exp(\epsilon_{0}+\epsilon_{1}x_{1} (t)+\epsilon_{2}x_{2}(t)+\ldots+\epsilon_{l}x_{l}(t))}{1+\exp(\epsilon_{0}+ \epsilon_{1}x_{1}(t)+\epsilon_{2}x_{2}(t)+\ldots+\epsilon_{l}x_{l}(t))} \tag{23}\]  
where \(\epsilon_{0},\epsilon_{1},\ldots,\epsilon_{i}\) are the regression coefficients of the feature vector set. The regression coefficients of the logistic regression model are usually estimated using the maximum likelihood estimation method. First, the above equation is transformed:  
\[\text{In}\frac{P(y_{l}=1|X_{l})}{1-P(y_{l}=1|X_{l})}=\epsilon_{0}+\epsilon_{1 }x_{1}(t)+\epsilon_{2}x_{2}(t)+\ldots+\epsilon_{l}(t)x_{l}(t) \tag{24}\]  
Figure 3: Sigmoid function.  
Let \(D=\{\epsilon_{0},\epsilon_{1},\ldots,\epsilon_{l}\}\) be the set of regression coefficients. Substituting these into the previous equation can obtain  
\[\log_{10}L(D)=\sum_{l}[y_{l}DX(t)-\ln(1+\exp(DX(t)))] \tag{25}\]  
By applying the gradient descent method to solve the above equation, the regression coefficients corresponding to the feature vector set can be obtained. These regression coefficients can then be substituted into the reliability calculation formula to construct a logistic regression reliability evaluation model, enabling the quantitative analysis of bearing reliability.
# A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks
## 5 Summary and Conclusions
### Summary  
This study analyzed different optimization techniques: AOA, MFO, SCA, GWO, WOA, HHO, and the proposed HHAOA. The tests were conducted on various benchmark functions and optimal router placement and connectivity for WMN benchmark functions. Additionally, the optimized FOPPI controller for a real-time pressure process plant was examined. The HHAOA technique outperformed all other algorithms in every comparison analysis, including the rigorous Friedman ranking test. This statistical evidence unequivocally proves the superiority of the HHAOA method over existing techniques. Moreover, it is crucial to highlight the notable contributions that further emphasize the effectiveness of the proposed technique.  
1. Utilizing a range of multi-hopping methodologies, the proposed HHAOA can effectively detect the global minima with fewer attempts throughout most benchmark functions, resulting in a more efficient and accurate optimization process. These can be evidenced by its consistently higher mean, best, and standard deviation scores in benchmark functions testing.
2. Through various multi-hopping techniques, the proposed HHAOA is able to identify the global minima in significantly fewer attempts in most of the benchmark functions.
3. In the WMN, the HHAOA demonstrated a highly competent desired objective searching technique, producing the most optimal path for routing network traffic. The algorithm significantly reduces congestion by minimizing the data transmitted across the network, resulting in improved network performance.
4. HHAOA carefully selects the appropriate access points to connect to the network, ensuring that clients are always connected to nearby routers. This makes it an essential optimization technique for successfully deploying wireless mesh networks for the best performance.
5. Notable improvements in producing the robust and smoother control signal of the pressure process were achieved by optimizing the FOPPI controller parameters using HHAOA. These include smaller peak overshoot, dynamic set-point tracking, and practical disturbance rejection ability.
# Application of Quantum Dots for Photocatalytic Hydrogen Evolution Reaction
## 2 Quantum Dots-Based Photocatalyst for Hydrogen Evolution
### Carbon Quantum Dots Based Hybrid Photocatalyst
These advantages demonstrate that the modification of GQDs demonstrates promise in relation to the improvement of photocatalytic efficiency in conventional photocatalysts. [PERSON] et al. [111] synthesized CdS/GQDs nanohybrids with \"dot-on-particle\" heterodimer structures, and the as-synthesized hybrids exhibited a higher H\({}_{2}\) evolution rate of 95.4 umol-h\({}^{-1}\), which value is about 4 times greater than that obtained by the CdS photocatalyst. As for the photocatalytic mechanism underpinning the improved photocatalytic performance, the results reveal that GQDs act as occatalysts for CdS/GQDs nanohybrids, mainly acting as an electron reservoir instead of a photosensitizer. This offers a novel understanding of the essential roles of GQDs when used in hybrid photocatalysts for photocatalytic applications (Figure 9). Figure 8: The photocatalytic mechanism of TiO\({}_{2}\)-001 and CQDs/TiO\({}_{2}\)-001. Reprinted with permission from Ref. [22]. Figure 9: Photocatalytic mechanism of the CdS/GQDs nanohybrids. Reprinted with permission from Ref. [111]. [PERSON] et al. [62] studied the effects of GQDs in GQDs-modified hexagonal tubular carbon nitride. During photocatalysis, GQDs play dual roles as a photosensitizer and an electron trap, which can extend the range of visible light absorption and inhibit the recombination of photoinduced charge carriers. Therefore, the composite exhibits a significantly higher photocatalytic efficiency, with a H2 generation rate of 112.1 umol/h; this value is about 9 times greater than that of pure carbon nitride. The abovementioned photocatalytic applications of CQDs all involve combinations with other semiconductors; in other words, CQDs work as cocatalysts in the photocatalytic H2 generation reaction, while single CQDs have been reported to directly act as a photocatalyst. For instance, [PERSON] et al. [112] reported that unmodified pure CQDs (3-4 nm) in the absence of a cocatalyst are also active in the photocatalytic hydrogen evolution reaction. The hydrogen evolution rate of CQDs can even reach 3615.3 umol g-1.h-1, which is nearly 36 times that of the commercial P25 photocatalyst under identical conditions.
## 3 Preliminaries
### System Model  
In this study, the vehicular communication network comprises the following entities, as shown in Figure 2.  
1. The RSU: RSUs are stationary devices located along roads that facilitate wireless communication between themselves and surrounding vehicles within a particular range. Each RSU acts as a relay between vehicles, extending the communication range and improving the network's reliability. It is equipped with wireless communication capabilities and can support various applications, such as traffic management, safety warnings, and entertainment services. It also has a reliable communication link with the RIS's intelligent controller, so configurations of reflecting units can be optimized. Through this mechanism, the RSU effectively manages the RIS to enhance the transmission of signals towards a designated direction while simultaneously reducing the strength of signals toward potential unauthorized interceptors, commonly referred to as \"Eve.\"
2. The vehicle's onboard units (OBUs): OBU is a communication device installed within each vehicle in the network. It can communicate with other OBUs and RSUs within range, facilitating the exchange of traffic-related messages in 100-300 ms intervals based on the dedicated short-range communication protocol [26]. In this way, OBUs play a crucial role in the functioning of the vehicular network.  
Figure 1: The PHY-layer secret key extraction scheme in a noiseless channel.  
3. The RIS: RISs are intelligent surfaces that can dynamically change their electromagnetic behaviors to improve the performance of wireless networks. RISs can be used to manipulate the propagation of radio signals, allowing for better signal quality, increased network coverage, and improved energy efficiency. The intelligent controller is an integral component of each RIS. It manages and configures the multiple meta-surface reflecting units (RUs) of order \(N\) elements that make up the RIS. It plays a crucial role in optimizing the performance of the RIS in the network.
4. The adversary Eve: \"Eve\" is an active attacker who overloads the network with excessive traffic, causing it to become unavailable to legitimate users. In this attack, the adversary overwhelms the target's resources and prevents it from functioning properly, therefore denying service to its intended users. By constructing and launching a flooding DoS attack, the attacker aims to disrupt the system's normal functioning and cause inconvenience or harm to its users.
## 5 Results
### Assessment of the Surface Distribution of RFD  
RFD was measured at 156 points distributed along 15 profiles (Figure 4). The principal studies were conducted within and in the immediate vicinity of the known boundaries of the Volchya kimberlite pipe. Furthermore, two profiles were investigated outside the kimberlite pipe, one to the northeast and one to the southwest, at a distance of 0.5 km from the pipe. The distance between the measurement points was 30 m. Meteorological parameters, namely atmospheric air temperature and relative humidity, were recorded during the installation and removal of the accumulation chambers.  
The results of the RFD distribution studies demonstrated a considerable range in values, from 1 mBq/m\({}^{2}\)/s to 56 mBq/m\({}^{2}\)/s, with an average value of 7.5 mBq/m\({}^{2}\)/s (Figure 7). The meteorological parameters were registered throughout the entirety of the field measurements, and the recorded values demonstrated a variation in atmospheric air temperature from 13.4 \({}^{\circ}\)C to 26.2 \({}^{\circ}\)C, with a corresponding variation in relative humidity from 39.1% to 62.1%. It is notable that atmospheric precipitation was absent throughout the research period, which allowed for unobstructed sorption of \({}^{222}\)Rn on the carbon sorbent.  
The highest values of RFD are observed in the area situated to the south of the junction that separates the south Volchya and north Volchya pipes. The primary contour of \({}^{222}\)Rn distribution aligns with the surface boundaries of the kimberlite pipe, exhibiting a range of RFD from 8 to 36 mBq/m\({}^{2}\)/s. It is established that fractured zones are typically situated in the near-pipe space of kimberlite bodies, which provides the necessary conditions for radon transport in the host rock massif [58]. However, two radon anomalies with RFD values ranging up to 16 mBq/m\({}^{2}\)/s are observed in the northwest and southeast of the Wolf south pipe, which may indicate the true boundaries of the pipe. An assessment of RFD on two separate profiles outside the Volchya pipe from the northeast and southwest revealed the presence of weak radon anomalies in the central parts of the profiles. The positioning of the anomalies may suggest the existence of a controlling fault aligned with the northeast-to-southwest line.
## 3 Experiments and Results  
Three different types of experiments were performed to verify the feasibility of the proposed system in various applications. First, a morphology test was conducted to measure the shape of a specimen with a curved surface. Second, rigid body displacement measurement was performed, whereby in-plane and out-of-plane displacement was conducted using a piezoelectric drive micro displacement platform and a flat plate. Finally, a tensile experiment was conducted to verify the accuracy of strain measurement. The standard 3D-DIC algorithm can be utilized directly to perform the evaluation, and the Istra4D provided by Dantec Dynamics was used for the evaluation because of its good performance in multi-camera calibration and DIC calculations.  
Figure 4 shows the experimental system presented in this study. In this system, the optical path of the red light is shorter than those of the blue and green lights; thus, the three channels may not be simultaneously focused. The aperture of the lens should be adjusted to small to make all channel images as clear as possible. The focal length of the lens used in the experiments was 35 mm. Adjusting the location and angle of the mirror can change the relative angle between the images of different channels. The cross dichroic prism was fixed at 8 mm in front of the camera lens. Careful adjustment was required to ensure the high quality of images of the three channels and similarity of brightness and size of the specimen being measured in the three images.  
Figure 3: Scheme of the improved multi-perspective 3D-DIC system.  
Figure 5 shows that an 8 \(\times\) 8 chessboard was used to calibrate the system. The size of the small square of the calibration board was 11 mm \(\times\) 11 mm. The three circles on the calibration board were used to mark the direction. Eight 24-bit images of the calibration board in different positions and directions were captured, and each 24-bit color image was converted into three 8-bit grayscale images by the R, B, and G channels. The R, B, and G channels data of the 24-bit color image were converted into three grayscale images, respectively; every image can be seen as captured by a virtual camera. The intrinsic and extrinsic parameters of the three virtual cameras can be calculated by [PERSON]'s calibration algorithm. The reprojection errors are shown in Figure 6.  
Figure 4: Experimental setup of the improved multi-perspective 3D-DIC system.  
Figure 5: Calibration images (including the original and channel images).
## 3 Methodology
### Evaluation and Statistical Analysis of Performance  
The tested methods were evaluated with the commonly used accuracy, precision, recall, and F-score metrics. With the null hypothesis that the medians of the two variables differ, we used the Wilcoxon rank-sum test with the null hypothesis indicator \(H\) and the significance level \(p\)-value to determine whether the performance differences between sentence transformers (used as a baseline) and the suggested method were statistically significant. We have used the Friedman test and the post hoc Nemenyi test to examine the effectiveness of various machine learning techniques. The Friedman test is a strong non-parametric statistical ranking test that does not require the assumption of normality. It has been used in various studies in the past to evaluate the effectiveness of machine learning techniques. All pairwise algorithm comparisons were performed using the non-parametric Nemenyi test, with a 0.05 significance level. The critical distance (CD) diagram [64] is used to represent the outcomes (mean rankings of compared methods).