esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Modern means of communication, economic crises, and political decisions play imperative roles in reshaping political and administrative systems throughout the world. Twitter, a micro-blogging website, has gained paramount importance in terms of public opinion-sharing. Manual intelligence of law enforcement agencies (i.e., in changing situations) cannot cope in real time. Thus, to address this problem, we built an alert system for government authorities in the province of Punjab, Pakistan. The alert system gathers real-time data from Twitter in English and Roman Urdu about forthcoming gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.). To determine public sentiment regarding upcoming anti-government gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.), the alert system determines the polarity of tweets. Using keywords, the system provides information for future gatherings by extracting the entities like date, time, and location from Twitter data obtained in real time. Our system was trained and tested with different machine learning (ML) algorithms, such as random forest (RF), decision tree (DT), support vector machine (SVM), multinomial naive Bayes (MNB), and Gaussian naive Bayes (GNB), along with two vectorization techniques, i.e., term frequency-inverse document frequency (TFIDF) and count vectorization. Moreover, this paper compares the accuracy results of sentiment analysis (SA) of Twitter data by applying supervised machine learning (ML) algorithms. In our research experiment, we used two data sets, i.e., a small data set of 1000 tweets and a large data set of 4000 tweets. Results showed that RF along with count vectorization performed best for the small data set with an accuracy of 82%; with the large data set, MNB along with count vectorization outperformed all other classifiers with an accuracy of 75%. Additionally, language models, e.g., bigram and trigram, were used to generate the word clouds of positive and negative words to visualize the most frequently used words.  
Twitter; alert system; sentiment analysis; machine learning algorithms; vectorization techniques; manual annotation; natural language processing (NLP); Roman Urdu; police intelligence
# Augmented MIMO: Body-Mounted Antennas for Tiny Wearable Devices
## 6 Discussion
### Cuff Antennas Influencing the Characteristics of Original Chip Antenna  
Comparing Figures 2b and 5b, we can see the influence of the proximity coupling of the cuff antenna array on the watch antenna characteristics. The resonant frequency is slightly lowered, and the minimum value of \(|S_{11}|\) is reduced. It may also have influenced the antenna radiation patterns as reported in the literature [2], although we did not evaluate this.  
Intuitively, the characteristic changing effects will be more significant with stronger proximity coupling. To increase the overall antenna gain \(G_{\text{augmented}}\) in (3), the proximity coupling loss \(L_{\text{prox}}\) should be reduced, which requires stronger coupling. However, stronger coupling changes the \(|S_{11}|\) frequency characteristics; therefore, the return loss may increase. In our design, \(|S_{11}|\) increased when the spacing was less than 1 mm, as shown in Figure 6, although \(|S_{31}|\) did not decrease. The increase in \(|S_{11}|\) can generally lead to an increase in \(L_{\text{prox}}\).  
This trade-off should be addressed in future work.
## 4 Results and Discussion
### Impact Assessment
#### 4.2.3 Impact Assessment Analysis  
According to the coefficient of factor sensitivity of various wind parameters and Table 4, the scale value between each layer was drawn, as shown in Table 5. The first five columns of Table 5 consist of an antisymmetric matrix, and the scale value ranged from 1/6 to 6. It could be found that the initial conditions have a huge influence on the distribution of wind parameters.  
Using the arithmetic average method to calculate the weights, the data in Table 5 meet the conditions of consistency and also verify the reliability of the quantification model. The final factor weights are shown in Table 6. And the shade of color indicates the size of the weight. The darker the color, the greater the weight.  
Using the analytic hierarchy process to study the influence of initial conditions on wind parameters, we can see in Table 6 that for the wind parameters at the mid-span of the bridge in the U-shaped valley, the oncoming wind direction had the greatest influence on the wind attack angle and wind azimuth angle, and the weights were 0.608 and 0.500, respectively; the influence of wind direction and wind profile on wind speed was equivalent, reaching 0.387; and the influence of surface roughness and inlet wind speed on wind parameters was the smallest, not exceeding 0.21. The order of influence is as follows:  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{5}{c}{**Weight**} \\ \cline{2-6}  & **Surface Roughness** & **Inlet Wind Speed** & **Wind Speed Profile** & **Oncoming Wind Direction** \\ \hline Wind speed & 0.132 & 0.094 & 0.387 & 0.387 \\ Wind attack angle & 0.203 & 0.107 & 0.083 & 0.608 \\ Wind azimuth angle & 0.1 & 0.1 & 0.3 & 0.5 \\ \hline \end{tabular}
\end{table}
Table 6: Weight matrix of factors.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{5}{c}{**(a) Judgment matrix of the initial conditions effect on the wind speed**} \\ \hline
**Wind speed** & **Surface roughness** & **Inlet wind speed** & **Wind speed** & **Oncoming wind direction** & \(\psi_{L\_min}\) & \(\psi_{i\_max}\) & \(\Delta\psi_{i}\) & \(\lambda_{i}\) \\ \hline Surface roughness & 1 & 2 & 2/5 & 2/5 & 1.00 & 1.05 & 0.05 & 1.52 \\ Inlet wind speed & 1/2 & 1 & 1/5 & 1/5 & 0.97 & 1.00 & 0.03 & 1.00 \\ Wind speed profile & 5/2 & 5 & 1 & 1 & 0.46 & 1.01 & 0.55 & 17.74 \\ Oncoming wind direction & 5/2 & 5 & 1 & 1 & 0.37 & 1.32 & 0.95 & 30.61 \\ \hline \end{tabular}
\end{table}
Table 5: Judgment matrix of the plan level.  
1. For wind speed: oncoming wind direction = wind speed profile > surface roughness > inlet wind speed.
2. For wind attack angle: oncoming wind direction > surface roughness > inlet wind speed > wind speed profile.
3. For wind azimuth angle: oncoming wind direction > wind speed profile > surface roughness = inlet wind speed.
## 3 Experiment Protocol  
### Experiment Apparatus  
The sEMG signals during experiments were collected, amplified and transmitted by a ten-channel digital sEMG system (FlexComp Infiniti System, Thought Technology Ltd., Montreal, QC, Canada). The sensors (MyoScan sensor), which connected with electrodes, could record the sEMG signals up to 1600 micro-volts (\(\upmu\)V) and an active range from 20 to 500 Hz. An electrogionometer was used for measuring the elbow rotate joint angles. It has a voltage range of \(\pm\)2 V, which correspond to a complete 360\({}^{\circ}\) range of motion. All data were collected at 1024 Hz. The raw sEMG signals and the elbow angle measurements were transferred to a personal computer through a serial interface (USB) by setting the system to offline mode. EEG data were collected using the ActiveTwo64 channel EEG system from BioSemi, the Netherlands. According to the position defined by the international 10-20 standard electrode placement method, the data of C3 and P3 channels were collected, and the sampling frequency was also 1024 Hz.  
When the signal intensity of the target muscle is relatively low, crosstalk is more likely to occur between the signals of other muscles. This also directly affects the contraction process of the muscles.  
Therefore, in many studies, loads with a constant weight were used to enhance the output power of the target muscle in order to improve the interaction efficiency. For example, [PERSON] et al. [24] compared the effects of several loads on elbow joint angle recognition. For upper limb joints, if the load is held by hand, it is difficult to ensure constant force during a motion. Therefore, the experimental equipment was set to have a constant resistance, allowing the subject to perform isotonic motion throughout the experiment. The self-made wearable device is shown in Figure 2 to ensure that the elbow joint maintains constant resistance during the experiment. On the day before the experiment, the mean value of the maximum torque of the elbow joint of each subject was tested. We set the constant resistance to 20% of the maximum torque, which is the resistance value that is easy to maintain normal operation and not easy to fatigue. For identification of the elbow angle, the four muscles of arm were used as target muscles, including brachoradialis (BR), anconeus (AN), biceps brachii (BB) and triceps brachii (TB). By adjusting these muscle forces, the elbow angle could be arbitrarily controlled [25]. The sEMG signal of the target muscle was used to predict the angle of the elbow joint from 0\({}^{\circ}\) to 90\({}^{\circ}\).
## 4 Results
### Milankovitch Cycle Analysis  
In the spectral analysis plot (Figure 3), four primary frequency points (A, B, C, and D) were identified with confidence levels exceeding 90%. Their relative amplitudes decrease in sequence, with frequencies of 0.028, 0.102, 0.272, and 0.559 m\({}^{-1}\), corresponding to cycle thicknesses (1/frequency) of 35.71, 9.98, 3.68, and 1.79 m, respectively. The cycle thickness ratio is 1:2.05:5.48:19.96, which closely approximates 1:2:5.20, indicating a strong correlation among the long eccentricity, short eccentricity, obliquity, and precession cycles. Previous studies [29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44] have shown that, if the precession/obliquity/short eccentricity/long eccentricity cycle thickness ratio approaches 1:2:5:20, the presence of Milankovitch cycles can be preliminarily inferred. Additionally, wavelet transform analysis was performed on this interval to verify the findings. The wavelet transform results (Figure 3b) indicate that the spectral energy at the 35.7 m cycle thickness, corresponding to long eccentricity, is the strongest and exhibits good continuity, followed by short eccentricity. This indirectly confirms that high-amplitude, non-random frequencies align with Earth's orbital cycles. Furthermore, as shown in Table 1, the theoretical periods of 18.4 ka, 37.5 ka, 100 ka, and 405 ka were identified, with a ratio of 1:2.04:5.43:20.01, which closely matches the observed cycle thickness ratio of 1:2.05:48:19.96, with an error of less than 1%. This further confirms that the stratigraphic deposition was controlled by astronomical cycles, with the corresponding orbital periods of long eccentricity, short eccentricity, obliquity, and precession being 405 ka, 100 ka, 37.5 ka, and 18.4 ka, respectively.  
Figure 2: Composite columnar diagram of periodic changes in paleoclimate environment (cored section of Well N10).
## 3 Vehicle-Embedded Speech Recognition Front-End for Multi-UAV Control
### Noise Reduction for UAV Environments
#### Conventional Noise Reduction Approaches
\tag{1}\]  
The adaptive comb filtering-based approach eliminates noise components by enhancing speech harmonics using the fundamental frequency [25]. The filter is based on the observation that speech waveforms are periodic, corresponding closely with the fundamental frequency. While deemphasizing the harmonic valleys of noise-contaminated speech, the filter removes noise components based on the assumption that noise components exist between the harmonics in the speech spectrum. As a result, this filter enhances the spectral magnitude in frequency bins corresponding to the harmonic frequencies. The adaptive comb filter is expressed as follows:  
\[h(n)=\sum_{k=-L}^{L}\alpha_{k}\times\delta(n-N_{k}) \tag{2}\]  
where \(\delta(n)\) is a unit sample function and the length of the filter is \(2L+1\). \(N_{k}\) corresponds to the fundamental frequency, and \(\alpha_{k}\) denotes a filter coefficient that satisfies \(\sum_{k=-L}^{L}\alpha_{k}=1\). Note that the filter coefficient is predetermined by a window function, e.g., the Hamming function. The filtering approaches are highly dependent on prior knowledge. The spectral subtraction requires correct detection of non-speech regions to estimate noise components in the regions, while adaptive comb filtering depends on the fundamental frequency of the speech signals. Thus, these approaches may be incapable of reducing non-stationary noise or severe noise signals in which detection of non-speech regions or estimation of the fundamental frequency is not available. The MMSE targets speech components, whereas other approaches concentrate on estimating noise components in non-speech regions [26; 27]. This technique estimates noise components in the spectrum and restores clean speech with signal processing level. Thus, it efficiently reduces non-stationary noises.
## 3 Scientometric Analysis: Results and Discussion
### Science Mapping
#### 3.2.1 Mapping of Publication Sources  
In our preliminary analysis, we included a total of 236 sources in the study. Figure 3 shows the relationship between the number of sources and documents published. The results showed a low concentration of documents per source, as well as a measure of the interest in the field; moreover, 185 sources (78.4%) published only one document in the field, while the highest number of documents per source was 46, followed by 12. To identify sources with more importance in the field, journals with a minimum of five documents and five citations of a source were identified, resulting in 12 sources, which are summarized in Table 1 in terms of publication count, total citations, and average normalized citations. The journal _Science of the Total Environment_ clearly leads the ranking, with 46 documents and 1548 citations; on the other hand, the _Chemical Engineering Journal_ leads the ranking in terms of average normalized citations, with only five documents published but 375 citations (the second position in this ranking) and 5.2879 average normalized citations. Only two of the journals included in the top five in terms of the number of documents are also included in the same ranking of the sources with a higher number of citations and average normal citations, namely _Science of the Total Environment_ and the _Marine Pollution Bulletin_. The highest value of the average normal citations in the case of the _Chemical Engineering Journal_, _Journal of Hazardous Materials_, _Science of the Total Environment_, _Journal of Environmental Chemical Engineering_, and _Marine Pollution Bulletin_ shows that, despite having a smaller number of documents, these journals received more citations than the others. Figure 3: Relationship between number of sources and documents published. Table 2 summarizes the categories of the sources leading the rankings, showing that 8 of the 12 journals are included in the _Environmental Sciences_ category, with 3 in _Engineering_, and 1 in environmental. In addition, _Marine Pollution Bulletin_ is included in the category marine and freshwater biology, while _Sustainability_ is in the category green and sustainable science and technology. These results emphasize the negative environmental effects of waste from PPE, especially marine pollution [4; 17], and the need to provide solutions to them.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Ratios (Cooking Periods/Reference Periods \$)**} & \multicolumn{3}{c}{**Differences (Cooking Periods – Reference Periods \$)**} & \multirow{2}{*}{\(p\)**-Value \#**} \\ \cline{2-3} \cline{5-10}  & & & & & & & & & \\ \cline{2-3} \cline{5-10}  & **Median** & **S.D.** & **Min.** & **Max.** & **Median** & **S.D.** & **Min.** & **Max.** & \\ \hline CO (ppm) & 0.93 & 0.22 & 0.46 & 1.51 & \(-\)0.25 & 0.84 & \(-\)3.53 & 0.61 & \(<\)0.01 \({}^{\ddagger}\) \\ CO\({}_{2}\) (ppm) & 1.06 & 0.14 & 0.85 & 1.58 & 26.17 & 90.21 & \(-\)111.67 & 342.5 & \(<\)0.01 \({}^{\ddagger}\) \\ NO\({}_{2}\) (ppb) & 1.11 & 0.98 & 0.51 & 5.43 & 5.40 & 29.71 & \(-\)71.17 & 101.75 & \(<\)0.01 \({}^{\ddagger}\) \\ O\({}_{3}\) (ppb) & 1.08 & 0.69 & 0.46 & 4.36 & 0.27 & 8.89 & \(-\)35.14 & 17.08 & 0.94 \\ PM\({}_{2.5}\) (ug/m\({}^{3}\)) & 1.09 & 0.30 & 0.60 & 2.56 & 5 & 14 & \(-\)45 & 56 & 0.04 \({}^{\ddagger}\) \\ \hline \hline \multicolumn{10}{l}{\({}^{\#}\) Paired Student’s \(t\)-test, \({}^{\dagger}\)\(p<\) 0.05, \({}^{\ddagger}\)\(p<\) 0.01. \({}^{\lx@sectionsign}\) Reference periods were the one-hour period before cooking periods.} \\ \end{tabular}
\end{table} 
Table 4: The ratios of air pollutants during cooking periods to reference periods and the differences in air pollutants between during cooking periods and reference periods. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Ratios (Window Opening Periods/Reference Periods \$)**} & \multicolumn{3}{c}{**Differences (Window Opening Periods \$)**} & \multirow{2}{*}{\(p\)**-Value \#**} \\ \cline{2-3} \cline{5-10}  & **Median** & **S.D.** & **Min.** & **Max.** & **Median** & **S.D.** & **Min.** & **Max.** & \\ \hline CO (ppm) & 0.98 & 1.34 & 0.57 & 4.44 & 0.00 & 1.31 & \(-\)2.42 & 3.67 & 0.53 \\ CO\({}_{2}\) (ppm) & 1.05 & 0.18 & 0.73 & 1.43 & 29 & 128 & \(-\)141 & 296 & 0.21 \\ NO\({}_{2}\) (ppb) & 1.56 & 1.30 & 0.94 & 5.23 & 18.71 & 16.05 & \(-\)9.40 & 53.25 & \(<\)0.01 \({}^{\ddagger}\) \\ SO\({}_{2}\) (ppm) & 0.00 & 0.92 & 0.00 & 3.27 & 0.00 & 0.02 & 0.00 & 0.08 & 0.21 \\ O\({}_{3}\) (ppb) & 1.18 & 0.59 & 0.56 & 2.19 & 0.81 & 4.44 & \(-\)11.91 & 10.05 & 0.52 \\ PM\({}_{2.5}\) (ug/m\({}^{3}\)) & 1.13 & 0.31 & 0.69 & 1.85 & 7 & 16.20 & \(-\)6 & 44 & 0.04 \({}^{\ddagger}\) \\ \hline \hline \multicolumn{10}{l}{\({}^{\#}\) Paired Student’s \(t\)-test, \({}^{\dagger}\)\(p<\) 0.05, \({}^{\ddagger}\)\(p<\) 0.01. \({}^{\lx@sectionsign}\) Reference periods were two one-hour periods before and after window opening periods.} \\ \end{tabular}
\end{table} 
Table 3: The ratios of air pollutants during window opening periods to reference periods and the differences in air pollutants between window opening periods and reference periods.
## 3 Results and Discussion
### Minor Volatile Compounds
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **CAS** & **WY** & **WY + Act** & **AGGLO** & \multicolumn{3}{c}{**AGGLO + Act**} & **HG** & **Y** & **A** & **Y** \(\times\) & **A** & **OPT** & **OS** \\ \hline Esters (15) & & & & & & & & & & & & & \\ \hline Ethyl butyrate & 105-54-4 & \(61\pm 2\)\({}^{\rm bc}\) & 57.4 \(\pm\) 0.3\({}^{\rm b}\) & \(30\pm 5\)\({}^{\rm a}\) & \(64\pm 5\)\({}^{\rm c}\) & 3 & *** & *** & 20\({}^{\rm I}\) & 1 \\ Butyl acetate & 123-86-4 & \(53.7\pm 0.6\)\({}^{\rm c}\) & \(40\pm 3\)\({}^{\rm b}\) & 20.6 \(\pm\) 0.8\({}^{\rm a}\) & 42.2 \(\pm\) 0.8\({}^{\rm b}\) & 3 & *** & *** & 66\({}^{\rm VII}\) & 1,6 \\ Ethyl & 7452-79-1 & \(6.6\pm 0.4\)\({}^{\rm b}\) & \(6.4\pm 0.9\)\({}^{\rm b}\) & \(3\pm 1\)\({}^{\rm a}\) & \(15\pm 1\)\({}^{\rm c}\) & 3 & *** & *** & 18\({}^{\rm VIII}\) & 1,2 \\ Ethyl & 108-64-5 & \(17\pm 1\)\({}^{\rm c}\) & 13.4 \(\pm 0.5\)\({}^{\rm b}\) & \(6\pm 1\)\({}^{\rm a}\) & \(13\pm 1\)\({}^{\rm b}\) & 3 & *** & *** & 3\({}^{\rm II}\) & 1,2 \\
3-methylbutyrate & & & & & & & & & & & & \\ Isoamyl acetate & 123-92-2 & \(165\pm 2\)\({}^{\rm c}\) & 201 \(\pm\) 3\({}^{\rm d}\) & 70 \(\pm 12\)\({}^{\rm a}\) & \(138\pm 12\)\({}^{\rm b}\) & 4 & *** & *** & 30\({}^{\rm I}\) & 1 \\ Ethyl hexanoate & 123-66-0 & \(22\pm 0.2\)\({}^{\rm a}\) & 21 \(\pm\) 0.2\({}^{\rm a}\) & 21 \(\pm\) 2\({}^{\rm a}\) & \(32\pm 2\)\({}^{\rm b}\) & 2 & *** & *** & 14\({}^{\rm II}\) & 1,2 \\ Heylyl acetate & 142-92-7 & \(298\pm 12\)\({}^{\rm b}\) & 382 \(\pm\) 4\({}^{\rm c}\) & 211 \(\pm\) 10\({}^{\rm a}\) & 300 \(\pm 10\)\({}^{\rm b}\) & 3 & *** & *** & ns & 670\({}^{\rm IX}\) & 1,2 \\ Ethyl heptanoate & 106-30-9 & \(2.2\pm 0.2\)\({}^{\rm b}\) & 2.01 \(\pm\) 0.04\({}^{\rm ab}\) & 19.9 \(\pm\) 0.2\({}^{\rm a}\) & 2.1 \(\pm\) 0.2\({}^{\rm ab}\) & 2 & ns & ns & * & 2.2\({}^{\rm VIII}\) & 1,2 \\ Ethyl octanoate & 106-32-1 & \(18.5\pm 0.3\)\({}^{\rm a}\) & 20 \(\pm\) 0.4\({}^{\rm ab}\) & 21 \(\pm\) 1\({}^{\rm b}\) & 21 \(\pm\) 1\({}^{\rm b}\) & 2 & **ns** & ns & 5\({}^{\rm I}\) & 1,8 \\
2-Phenylethyl acetate & 103-45-7 & \(1094\pm 48\)\({}^{\rm b}\) & 1094 \(\pm\) 40\({}^{\rm b}\) & 902 \(\pm\) 154\({}^{\rm a}\) & 1877 \(\pm\) 154\({}^{\rm c}\) & 3 & *** & *** & *** & 250\({}^{\rm I}\) & 7,9 \\ Ethyl decanoate & 110-38-3 & \(48\pm 2\)\({}^{\rm a}\) & 50 \(\pm\) 2\({}^{\rm a}\) & 136 \(\pm\) 8\({}^{\rm c}\) & 99 \(\pm\) 8\({}^{\rm b}\) & 3 & *** & *** & *** & 200\({}^{\rm I}\) & 1,8 \\ Phenethyl hexanoate & 101-60-0 & \(0.5\pm 0.07\)\({}^{\rm a}\) & 0.54 \(\pm\) 0.05\({}^{\rm a}\) & 5.7 \(\pm\) 0.2\({}^{\rm c}\) & 1.8 \(\pm\) 0.2\({}^{\rm b}\) & 3 & *** & *** & *** & 250\({}^{\rm I}\) & 2,8,9 \\ Ethyl tetradacanoate & 124-06-1 & \(66\pm 3\)\({}^{\rm c}\) & 57 \(\pm\) 3\({}^{\rm b}\) & 48 \(\pm\) 6\({}^{\rm a}\) & 108 \(\pm\) 6\({}^{\rm d}\) & 4 & *** & *** & *** & 4000\({}^{\rm I}\) & 8 \\ Phenethyl benzoate & 94-47-3 & \(13\pm 1\)\({}^{\rm c}\) & 9.6 \(\pm\) 0.3\({}^{\rm b}\) & 3.7 \(\pm\) 0.3\({}^{\rm a}\) & 8.6 \(\pm\) 0.3\({}^{\rm b}\) & 3 & *** & *** & *** & n.f.
## 2 Materials and Methods
### CMJ and ABKJ Testing  
The players completed two jumps to their preferred depth with 20 s of rest between jumps [25; 26]. Verbal instructions were provided, directing them to \"jump as fast and as high as possible.\" For the CMJ, the players kept their hands on their hips, while, for the ABKJ, they were allowed free arm movement. The players were also instructed to perform the countermovement (downward phase) and propulsion (upward phase) \"as quickly as possible while achieving maximum jump height.\" These instructions were standardized due to their known influence on the force-time characteristics of the CMJ [27; 28]. To record ABKJ and CMJ metrics, a wireless dual force plate system was used, operating at a sample rate of 1000 Hz (ForceDecks FD4000 Dual Force Platforms, Vald Performance, Brisbane, QLD, Australia). The force-time metrics analyzed in this study were chosen based on prior research in basketball [29; 30; 31]. The variables examined during the concentric phase of the CMJ and ABKJ included concentric maximum RFD (CMRFD), calculated as the maximum force observed over the force-time curve during the jump [32; 33]; concentric peak force (CPF); and concentric peak velocity (CPV). Jump height was also calculated using the \"Impulse-Momentum equation,\" which determines jump height based on specific impulse metrics before takeoff. Contraction time began when the athlete's system mass was reduced by 20 N (movement onset) and ended at takeoff. Takeoff was defined as the moment when vertical force dropped below 20 N. Detailed descriptions of CMJ force-time metrics are available in the VALD user manual ([[https://valdperformance.com/forcedecks/](https://valdperformance.com/forcedecks/)]([https://valdperformance.com/forcedecks/](https://valdperformance.com/forcedecks/)); accessed on 1 June 2024) and prior research [29; 30; 31].