esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Resisted Methods upon Sprint Performance in Rugby Players: A Systematic Review
## 4 Discussion
### Acute Post-Activation Potentiation Effect (PAP)  
Within the differentiation of studies that investigated the acute effects produced by the application of an overload on sprint performance, there is the PAP effect. The find-ings show that a load at 75% of body mass or loads ranging from 30 to 35% of the reduc-tion invelocity produce a beneficial effect on post-activation sprint performance over dis-tances ranging from 0 to 20 m. All studies evaluated agree that an optimal recovery time is between 4 and 12 min after the weighted sprint, which is observed in the findings of [47].  
In this regard, [PERSON] et al. [31] analyzed the acute potentiating effects of heavy sled pulls on sprint performance with experienced rugby players. They used two sled load conditions (75 and 150% of body mass) to determine whether sled load influences the potentiation response and which sled load and rest interval may be optimal for obtaining a PAP effect (at 4, 8, and 12 min after endurance sprint). The main findings showed that the 75% load significantly improved 15 m sprint times after 12 min of recovery, which matches the influence of recovery time on post-activation potentiation in professional rugby players [54; 55]. Loads of 125 and 150% of body mass produce no beneficial effect on PAP and cause a loss of running mechanics [30; 31]. In addition, a 55% decrease in velocity has a negative effect on movement mechanics, leading to a negative effect on fatigue, and inhibits peak performance capacity [47].  
On the other hand, the performance of the athlete in sprint actions can be benefited after the execution of pyometric exercises, always with adequate recovery time and whether or not an additional load is used during the pyometric exercise. [PERSON] et al. [56], in their study on PAP using a polymetric exercise of jumping alternating legs using body mass (plyometric) and body mass plus 10% (weighted pyometric), found that using the athlete's body mass plus 10% led to improvements in sprint performance. In this sense, it must be taken into account that PAP is a highly individualized phenomenon, so it can be influenced by many factors such as the volume and type of load [57; 58], in addition to the characteristics of the athlete [17], among other aspects.
## 1 Introduction
[15] proposed a life prediction method based on the Copula function under multiple degradations to predict the remaining life of the satellite momentum wheel and figured out a method to select the Copula function by the AIC criterion. [PERSON] et al. [16] used the Wiener process to describe each degradation process separately, assuming that different parameters are correlated, used the Copula function to establish an accelerated degradation model, and applied the MCMC algorithm to estimate the unknown parameters in the model, then deduced the reliability function of the product under the normal stress. [PERSON] et al. [17] proposed a method of bearing the remaining life based on the bivariate Wiener process and analyzed the correlation between the two performance indices by the Copula function. [PERSON] et al. [18] proposed a multi-degradation modeling method based on a Wiener process and Copula function and took IGBT as an example to verify the model in engineering. However, there are only a few results of reliability correlation studies in the existing literature, and their research chiefly focuses on the field of reliability evaluation, assuming that the multi-parameter of the product obeys the same stochastic process, ignoring the inconsistent degradation laws of different performance parameters, which may lead to inaccurate assessments. Up to now, the life prediction of TWTA has mainly been carried out based on the cathode's physics of failure. However, the working state of TWTA is affected by temperature, radiation, and other factors in-orbit. Some component performances may degrade slowly, leading to multiple failure modes of the product, and different failure modes may be related. Therefore, it is difficult to predict the life of the TWTA by using a one-dimensional degradation model which only has a deterministic feature. In this paper, we will use bivariate hybrid stochastic processes and the Copula function to perform the reliability analysis of TWTA. The layout of the paper is organized as follows. Section 2 is devoted to describing the performance degradation of TWTA and identifies the characteristic parameter. In Section 3, a model of performance degradation based on one-dimensional stochastic processes with random effects is introduced. In Section 4, an introduction to the Copula function, comprehensive determination, and modeling with multi-degradation are discussed in detail. Section 5 introduces the parameter estimation of a two-step method. A practical TWTA degradation case is used to validate the method proposed in Section 6, and we present conclusions and future research in Section 7.
## 5 Numerical Simulations and Analyses
For example, when \(d=23\) m, the lower bound is 2.9805 bit/s when the receiver plane is not inclined, while the capacity bound reaches 3.2240 bit/s after tilting with the optimal angle. This indicates that the tilted receiver can obviously eliminate the influence of the pointing error, and then optimize the channel performance of UWOC system. Figure 5 shows the distribution of the optimal tilting angle when the receiver is located at different positions on the X-Y plane, where it is not difficult to find that the optimal tilting angles are the same on the circumference of any circle on the plane. Here is the possible reason: since the distances \(d\) between the light source and the receiver on the same \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Parameter** & **Symbol** & **Value** \\ \hline Transmitting power & P/P\({}_{\text{s}}\) & 0.3 w & Planck’s constant & h & 6.6 \(\times\) 10\({}^{-34}\) J s \\ Ionization ratio & \(\xi\) & 0.06 & Speed of light in sea water & c & 2.26 \(\times\) 10\({}^{8}\) m/s \\ Boltzmann constant & K & 1.38 \(\times\) 10\({}^{-23}\) J/K & Filter bandwidth & B & 100 MHz \\ Load resistance & R\({}_{\text{L}}\) & 100 \(\Omega\) & Temperature in Kelvin & T & 290 K \\ The noise factor of the system & F & 1 & Average gain of APD & G & 10 \\ Quantum efficiency & \(\eta\) & 0.82 & Electron charge & e & 1.6 \(\times\) 10\({}^{-19}\) C \\ Fitting coefficient & C\({}_{1}\) & 0.9790 \(\times\) 10\({}^{-4}\) & Fitting coefficient & C\({}_{2}\) & \(-\)0.1499 \\ Fitting coefficient & C\({}_{3}\) & 4.4162 \(\times\) 10\({}^{-4}\) & Fitting coefficient & C\({}_{4}\) & \(-\)0.2876 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main simulation parameters. Figure 3: The capacity lower bounds versus tilting angle \(\theta\) for UWOC. circumference are the same, according to (46), (6), and (48), they have exactly the same optimization results for the tilting angle. As can be seen from Figure 5, when the receiver is set directly under the light source, we have the optimal tilting angle of \(0^{\circ}\); while when the receiver is moving to the boundary, that is, the distance \(d\) is going up, the optimal tilting angle will also increase and finally reach a maximum of \(33.2^{\circ}\). These results just validate the conclusion of Figure 3.
# Influence of Rake Angles of Multi-Position Tool on Cutting Characteristics
## 1 Introduction  
By changing different rake angles of tools, [PERSON] [1] verified that when the rake angle of tools is increased, the maximum equivalent stress, the unit cutting force, and the cuttings temperature will decrease. [PERSON] [2] once discussed the milling force and the post-processing surface roughness of the medium carbon steel and the fast cutting steel during turning and milling, and the experiment results showed that, under the same conditions, the smaller the cutting force endured by the tool during the cutting, the better the machinability was for the workpiece material. Moreover, a larger helical angle of the cutting tools means a bigger cutting rake angle, and thus the surface abrasion between the cuttings and the tool will be reduced and the shearing strain will also be reduced. However, the effective shear angle will be increased, which reduces the cutting force gradually [3, 4] so as to improve the processing efficiency. Many domestic scholars have carried out research in the field of measuring cutting force.  
This research will apply the same holder to adjust the rake angle of the tool, and the cutting force change will then be observed. Furthermore, the ploughing force and the size effect during machining will be discussed, and the shearing effect of the face will be analyzed.  
[PERSON] and [PERSON] [5] have reported the empirical equation related to tangential force:  
\[f_{t}=c_{t}(t_{c})\times t_{x}\times\sin\theta=k_{t}\times t_{x}\times\sin\theta \tag{1}\]In the mode of radial force established by [PERSON] and [PERSON] (2016), \(k_{r}\) is the fixed ratio of the radial force and tangential force, and their relationship is shown as below:  
\[f_{r}=k_{r}\times f_{t} \tag{2}\]  
where in \(t_{c}\) is the average cutting thickness, \(k_{t}\) is the tangential cutting pressure constant. If the above equation does not consider the size effect, the \(k_{t}\) is the definite value during cutting. However, if it does consider the size effect, \(k_{t}\) will change with the cutting angle and therefore, the material characteristics, cutting parameters, and other factors must be considered when establishing the cutting mode. However, most scholars regard \(k_{t}\) as a definite value during their research of the cutting force mode.
## 4 Experiments
### Baselines  
We compare SPMSpell to the following baseline methods. All these methods use character speech information in some way and represent the current state of the art on the SIGHAN baseline.  
* **GAD**[12]: This method learns the global relationship between potentially correct input characters and candidates for potentially incorrect characters.
* **DCN**[10]: Through a unique dynamic connection network, \(K^{n}\) paths (\(K\) denotes the number of candidate words, n denotes the length of the sentence) are generated in the output stage of the model, and then an optimal path is selected by scoring through the dynamic connection network; an attentional mechanism is introduced to model the dependency relationship between neighboring characters.
* **REALISE**[8]: The method predicts the output by encoding phonological and graphemic information based on semantic information and finally introduces a gating mechanism to selectively fuse semantic, phonological, and graphemic information.
* **uChecker**[38]: A masked pre-trained language model is proposed as an unsupervised Chinese spell checker. The method uses the pre-trained model to learn contextual information and uses a masked language modeling task to predict misspelled words for spell-checking.
* **MDCSpell**[14]: The method designs a multitasking framework with BERT as a corrector that captures visual and phonetic features of characters and integrates the hidden state of the detector to minimize the impact of errors.
* **ECOPE**[13]: This thesis proposes a method for Chinese spell correction using error-driven comparison probability optimization to improve the accuracy of future spell-checking by learning the comparison between past spelling errors and correct spellings.
* **UMRSpell**[39]: It aims to unify the detection and correction parts of the pre-trained model to achieve Chinese missing, redundancy, and spelling correction. The method utilizes the contextual information of the pre-trained model and the Transformer structure to achieve spelling detection and correction by means of joint learning.
## 3 Method Description
### Numerical Implementation Details  
The objective function and the stability constraint in Equation (12) are hard to approach with gradient-based methods. It is well known that genetic algorithm-based search (GA) can be more accurate to solve optimization problems of moderate complexity, as non-convex ones [33]. A GA implementation was developed, tailored to find a solution for Equation (12).  
Given the particular solution \(\mathbf{k_{0}}\) and the null space basis \(\mathbf{V}\), the solution for the optimization problem in Equation (12) is achieved by following some simple steps. First, randomly define a set of \(\mathbf{k_{r}}\) vectors. Then, evaluate each one individual in this set for fitness and constraints. The procedure is summarized in the flowchart displayed in Figure 1. In the step devoted to update the population, functions to execute crossover and mutation can be chosen and adjusted following the theory of Genetic Algorithms [34]. In the step of individuals combination, first, it is ensured that the best rated individual in the actual generation is saved for the next one. Furthermore, it is selected some possible solutions to gives rise to a new population. Those are called parents, and they are chosen in a draw where the best-rated individuals have a greater chance of being selected. Once the parents are chosen, the combination is done, and this could be achieved using any kind of crossover methods available in the GA theory. Finally, a percentage of individuals is slightly randomly modified in the mutation process. In the test cases of the following sections, GA was programmed for (i) a maximum number of generations of one-hundred and (ii) a fitness function tolerance of \(10^{-12}\).
## 3 Results and Discussion
### Properties of MH/PCS/DVB
The former atomic content of the C element in MH (18.20%) was due to the contamination of C during the XPS test, while the latter in MH/PCS/DVB (39.99%) was due to PCS/DVB, which indicates that the MH was coated by PCS/DVB. The TG and DTG curves of MH/PCS/DVB and their specific parameters are shown in Figure 7 and Table 4, respectively. It can be seen that the thermal degradation curves of MH/PCS and MH/PCS/DVB shifted to a higher temperature in comparison with MH. The initial thermal degradation temperature (T\({}_{5}\)) of MH/PCS and MH/PCS/DVB increased by 20 \({}^{\circ}\)C from 369 \({}^{\circ}\)C to 389 \({}^{\circ}\)C, indicating a significant improvement in the thermal stability of MH after modification. The T\({}_{5}\) of MH/PCS/DVB was the same as that of MH/PCS, while the T\({}_{10}\) and T\({}_{\max}\) were higher than those of MH/PCS. The gap increased gradually with the increase in temperature (Table 4). The Rmax of MH/PCS/DVB was \(-0.73\)\({}^{\circ}\)C/min weaker than that of MH/PCS (\(-0.91\)\({}^{\circ}\)C/min). In addition, the residue at 600 \({}^{\circ}\)C of MH/PCS/DVB was 72.4% higher than that of MH/PCS by 0.5 points, which indicates that the thermostability of modified MH can be improved by surface modification with  
Figure 5: Digital images of the water contact angles of MH (**a**) and MH/PCS/DVB (**b**). Figure 6: The total XPS spectra (**a**) of MH and MH/PCS/DVB. High-resolution XPS spectra of Mg2p (**b**) and Si2p (**c**). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Element** & **MH (Atomic \%)** & **MH/PCS/DVB (Atomic \%)** \\ \hline Mg & 22.78 & 7.76 \\ O & 59.03 & 40.98 \\ C & 18.20 & 39.99 \\ Si & & 11.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The contents of elements in MH and MH/PCS/DVB. PCS and PCS/DVB and that the mass loss of MH is further inhibited by PCS/DVB. Such a discernible difference in MH modified by PCS and PCS/DVB is due to the cross-linking degree of PCS. The latter is much higher than the former, as evidenced in Figure 2. The results indicate that the cross-linking treatment of PCS with DVB is beneficial for improving the thermal degradation behavior of MH.
# Advancing Biological Research: New Automated Analysis of Immunofluorescence Signals
###### Abstract
For instance, in cases where multiple cells are captured in a single image, the cell area measurement may not perform optimally since the software may recognise neighbouring cells as a single entity. Similarly, in dealing with nuclear condensation measurement, the authors recommend using images where nuclei are well separated, as the software may interpret closely positioned nuclei as a single unit [10]. While different methodologies for image analysis have been developed recently, the translational application of automated software remains uncommon due to various obstacles. Firstly, many algorithms are tailored to specific systems and rely on predefined threshold values. This limitation is significant, as it fails to account for the extensive variability that signals can exhibit. Moreover, a significant constraint is the lack of accessibility to the underlying codes, which hinders transparent adjustments of the algorithms by users. Finally, some software is only available with a computer script, making it difficult to use for researchers who hardly handle programming codes. Indeed, having an intuitive and user-friendly graphical interface would allow greater use of automatic software in this field. Taken together, these findings stimulated us to develop an automatic system capable of automatically measuring the cellular IF signals, named Q-IF: Quantifier of ImmunoFluorescence. The whole process, from the IF signal quantification to the statistical analysis, is carried out automatically, evading operator biases and speeding up the analysis workflow. Furthermore, we develop an intuitive graphical interface available for both Windows and MacOS operating systems. We provide an accurate description of the software and, subsequently, we validate the results obtained from the automatic analysis in different biological scenarios: SH-SYSY neuroblastoma cell line, human fibroblasts and sagittal rat brain sections. Furthermore, the outcomes achieved through the Q-IF algorithm are compared with those obtained through [PERSON]'s thresholding, a widely employed technique in the analysis of data images [11]. The remainder of the paper is organised as follows. Section 2 provides a comprehensive overview of the experimental settings and procedures employed in our study to ensure the reproducibility and rigour of our findings. Section 3 exposes the results achieved with the developed tool, evaluated in different biological models, both in cell cultures and rat brain sections. Section 4 discusses the tasks facing researchers and the need for unbiased and efficient software to support their work, while also offering an overview of the limitations of the state of the art. Furthermore, it reflects on Q-IF tools considering the context of cell analysis. Section 5 concludes the paper, also posing future perspectives.
# A Sensor Data-Driven Decision Support System for Liquefied Petroleum Gas Suppliers
## 5 Consumption Forecasts
### Short-Term LPG Consumption Prediction
#### 5.1.2 Application of Short-Term Prediction Results
The second aspect corresponds to the situation when the delivery was sent too early and a tanker arrived at a customer for whom the gas level will be maintained above level \(L\) in the next few days, and therefore, the delivery would not be optimal in terms of the volume of gas to be poured into the tank. An ideal situation would be to deliver gas at the \(L\) level (or slightly lower) and filling the tank up to \(H\) (or slightly above \(H\)). The gas consumption forecast of each predictor can be enriched with the ranges calculated in relation to the errors of the given forecasting model and their distribution calculated on historical data (Figure 7). In order to formally define how the ranges influence the recommendations for logistic specialists, let us determine by \(f\) the predicted value of gas consumption in the given forecast horizon, and additionally, let us characterize the prediction error distribution on the last \(n\) examples by the following values: \(Q_{1}\) --first quartile, \(Q_{2}\)--median, \(Q_{3}\)--third quartile, \(IQR\)--interquartile range (\(Q_{1}\) to \(Q_{3}\)), \(V_{1}=Q_{1}-1.5*IQR\), and \(V_{3}=Q_{3}+1.5*IQR\), where typically, \(Q_{2}\) is a value around zero, whereas \(V_{1}\) and \(Q_{1}\) are values below zero. The location in a given forecast horizon is indicated as:  
* must go if the forecast for the tank's fuel level resulting from the range \(r_{Q}\) (where \(r_{Q}=f+Q_{1}\)) would cause the fuel level to drop below the value of \(L\),
* can go if the forecast for the tank's fuel level resulting from the range \(r_{Q}\) would not cause the fuel level to drop below \(L\), whereas the level resulting from the range \(r_{V}\) (where \(r_{V}=f+V_{1}\)) consumption forecast would already cause such a drop,
* do not go if the forecast for the tank's fuel level resulting from the range \(r_{V}\) would not cause the fuel level to drop below the value of \(L\). Additionally, in each group of tanks, they may be ordered according to the proximity of the predicted corrected fuel level to the group boundary \(\{r_{Q},r_{V}\}\). The values \(Q_{1}\) and \(V_{1}\) would typically illustrate the error distribution on a boxplot. When they are combined with the predicted LPG level \(f\), the boxplot characterized by the values \(r_{Q}\) and \(r_{V}\) is received, which is presented in Figure 7. Considering the seventh day recommendation in Figure 7, the must go recommendation would be issued for Case (a), and the can go recommendation would be issued for Case (b).