esa-sceva/satcom-chunk-collection huggingface dataset:
# Muscle Activity and Biomechanics of Sprinting: A Meta-Analysis Review
## 4 Discussion
### Muscle Activation and Sprint Speed  
The findings of this study align with previous research emphasizing the central role of hamstrings and gluteus maximus in sprinting biomechanics [7, 36, 37]. The biceps femoris long head demonstrated peak activation (~110% MVC) during late swing phase, supporting the well-established function of the hamstrings in eccentric deceleration of the leg prior to foot contact [17].  
Interestingly, our results showed that semitendinos activation was greater than biceps femoris during the acceleration phase, highlighting the differential role of hamstring muscles at various sprinting stages. This suggests that semitendinosus may contribute more significantly to knee flexion and initial propulsion, whereas the biceps femoris plays a greater role in eccentric load absorption during maximal velocity sprinting. Understanding these phase-dependent activation patterns is crucial for refining sprint-specific training programs and injury prevention strategies [3].  
This finding extends previous studies that primarily focused on biceps femoris dominance in hamstring mechanics [38], suggesting that targeted training for semitendinosus may enhance early-phase sprint propulsion. Additionally, this differentiation in hamstring recruitment supports the need for a varied approach in strength training, incorporating both eccentric and concentric loading at different joint angles to better simulate sprinting demands [10].  
The gluteus maximus plays a crucial role in hip extension, with activation exceeding 90% of MVC at top speeds, reinforcing previous reports [39]. Given its function in horizontal force production, these results emphasize the importance of glute-dominant exercises in sprint training programs (resisted sprint drills, hip thrust variations). Furthermore, the interplay between gluteus maximus and hamstring activation highlights the necessity of coordinated strength and neuromuscular training. Sprint drills that optimize hip extension mechanics, such as resisted sprints or bounding exercises, could further enhance sprint efficiency and force application during ground contact [10; 29].  
These findings also have implications for injury prevention, particularly in hamstring strain mitigation. Given the high eccentric demands placed on the biceps femoris during the late swing phase, training interventions should incorporate eccentric loading, such as Nordic hamstring exercises, to build resilience against high-speed running injuries.
# Mobile Network Operators' Assessment Based on Drive-Test Campaign in Urban Area for iPerf Scenario
## Appendix B  
The PCC between a pair of two variables \((X,Y)\) is defined as follows [7]:  
\[PCC=\rho(X,Y)=\frac{\mathrm{E}\{(X-\mu_{X})(Y-\mu_{Y})\}}{\sigma_{X}\sigma_{Y}}, \tag{1}\]  
where \(X=\{x_{i}\}\) and \(Y=\{y_{i}\}\) represent sets of two analyzed parameters; \(\mathrm{E}\{\cdot\}\) is the expectation operator, \(\mathrm{E}\{(X-\mu_{X})(Y-\mu_{Y})\}\) is the covariance of \((X,Y)\); and \(\mu_{X}\), \(\mu_{Y}\), \(\sigma_{X}\), and \(\sigma_{Y}\) represent the mean values and standard deviations of \(X\) and \(Y\), respectively, i.e.,  
\[\mu_{X}=\mathrm{E}\{X\},\quad\sigma_{X}=\sqrt{\mathrm{E}\{\left(X-\mu_{X} \right)^{2}\}}, \tag{2}\]  
\[\mu_{Y}=\mathrm{E}\{Y\},\quad\sigma_{Y}=\sqrt{\mathrm{E}\{\left(Y-\mu_{Y} \right)^{2}\}}.\]  
In the further analysis, we adopted the interpretation of the PCC according to Table 2[54].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Band** & \multicolumn{4}{c}{**Frequency (MHz)**} \\ \hline  & **Orange** & **Play** & **Plus** & **T-Mobile** \\ \hline
2.1 GHz UL FDD * & 1920.1–1934.9 & 1965.1–1979.9 & 2500–2520 & 1935.1–1949.9 \\
2.1 GHz DL FDD & 2110.1–2124.9 & 2155.1–2169.9 & 2620–2640 & 2125.1–2139.9 \\
2.6 GHz UL FDD & 2520–2535 & 2550–2570 & 2500–2520 & 2535–2550 \\
2.6 GHz DL FDD & 2640–2655 & 2670–2690 & 2620–2640 & 2655–2670 \\
2.6 GHz UL TDD * & – & – & 2570–2620 & – \\
2.6 GHz DL TDD & – & – & 2570–2620 & – \\ \hline \hline \end{tabular}
\end{table}
Table 1: Radio frequency resource allocation for four Polish MNOs in the 2.1 and 2.6 GHz bands.
# Improving Geotechnical Properties of Expansive Subgrade Using Sugar Cane Molasses and Cement
## 4 Results and Discussion
### Comparison with Alternative Stabilizers  
To better understand the effectiveness of molasses and cement for soil stabilization, it is essential to compare them with microbial-based stabilizers, such as MICP (Table 7). While molasses and cement effectively improve compressive strength and durability, they can have limitations, such as increased plasticity and environmental impacts, due to cement's high carbon footprint. On the other hand, MICP uses bacterial activity to precipitate calcium carbonate, providing a self-healing mechanism and improved environmental sustainability [PERSON] et al. (2014). Studies have shown that MICP can enhance the strength of soils and repair cracks over time, making it a promising alternative. However, MICP requires specific conditions for microbial growth and can be slower in providing immediate improvements, which may limit its applicability for large-scale soil stabilization projects.
# A Reliable Low-Latency Multipath Routing Algorithm for Urban Rail Transit Ad Hoc Networks
## 3 The Improved Multipath Routing Algorithm
### The Network Process  
The network process can be divided into six phases described in Table 2. The detailed processes are shown in Figure 5.  
Figure 4: Inter-cell routing repair.  
Figure 3: Intra-cell routing repair.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Stage** & **Function** \\ \hline Stage 1: Static configuration & Initialize the network nodes and configure multiple independent disjoint static paths with the position information of the nodes. \\ \hline Stage 2: Routing discovery & The mobile node generates RREQ and floods RRE; when it finds the route information of the destination node, it sends an RREP message to the source node, and then the source node records the available path information after receiving the RREP messages and goes to the next stage. \\ \hline Stage 3: Routing Selection & The number of parallel paths is calculated according to the latency requirements of the service data to be transmitted, and then, based on the available path information, it calculates each link cost and selects the path with the smallest cost as the primary path; the detailed process is shown in Figure 5a. \\ \hline Stage 4: Data Transmission & The packets are transmitted along the primary path to the destination node, and during transmission, the intermediate nodes predict whether the next hop can be successfully received based on the transmit power and path loss model. If the failure message is received, it enters the route maintenance stage. \\ \hline Stage 5: Routing Maintenance & The node generates an RERR message to send to the maintenance node after the maintenance node receives it, which will confirm the location information of the next hop based on the failure location, and then adopts the corresponding maintenance plan according to the location information; the detailed process is shown in Figure 5b. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network process.  
Figure 5: RLLMR algorithm flowchart (**a**) Routing Selection process. (**b**) Routing repair process.
## 4 Discussion and Conclusions
This phenomenon is consistent with the description of mechanical oscillator mechanism, which has been proposed to account for the generation of gravity waves excited by convective systems [36; 37]. Meanwhile, strong generation sources of IGWs also exist in the upper troposphere and midtroposphere. Note that the two maximum centers of pressure vertical flux (Figure 8a) are right at the bottom of the two diabatic heating subranges (Figure 2b); thus, it is reasonable to relate the generation of IGWs in the upper troposphere and midtroposphere with the forcing of heating [38]. Other interesting phenomena about IGWs are also worth noting: the specific height where the waves are generated near the tropopause is different for waves with different scales; for waves with wavelengths (wavenumbers) between 90 and 200 km (\(2\pi\)/9 \(\times\) 10\({}^{-4}\) and \(\pi\times\) 10\({}^{-5}\) rad m\({}^{-1}\)), the tropopause and upper troposphere are even not effective emission layers; and the upward-propagating waves in the lower troposphere are only remarkable for wavelengths (wavenumbers) slightly larger (smaller) than 100 km (\(2\pi\times 10^{-5}\) rad m\({}^{-1}\)). The selectivity of the generated IGWs on height and horizontal scale deserves further confirmation and study. In addition, why does sporadic negative buoyancy forcing in the midtroposphere only appear at wavelengths (wavenumbers) around 100 and 400 km (\(2\pi\times 10^{-5}\) and \(\pi\)/2 \(\times\) 10\({}^{-5}\) rad m\({}^{-1}\))? What makes the effect of nonlinear interactions so complicated in the troposphere for TC in the real atmosphere? And what determines the downward HKE vertical flux associated with the drag effect of precipitation particles at wavelengths (wavenumbers) around 100 km (\(2\pi\times 10^{-5}\) rad m\({}^{-1}\)) and below 60 km (above \(\pi\)/3 \(\times\) 10\({}^{-4}\) rad m\({}^{-1}\))? These problems referring to selectivity on scale are also under explored. For our simulation, the swelling of the HKE spectrum in the troposphere just extends to wavelengths (wavenumbers) around 100 km (\(2\pi\times 10^{-5}\) rad m\({}^{-1}\)); meanwhile, the vibrant spectral HKE budgets are restricted to wavelengths larger than 100 km. It is thus reasonable to connect the intensification of TC, the activation of energy activity at smaller scales, and the downscale extension of the arc-like spectral shape. The details and mechanisms are still unclear, pending further investigation. Conceptualization, Y.Z. and Y.W. ; formal analysis, H.Z. ; methodology, L.Z. and J.P.; software, S.L. and A.L. ; writing--original draft, H.Z. ; and writing--review and editing, Y.Z. and Y.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 41975066; the Beijing Open Research Fund for Nanjing Joint Center of Atmospheric Research, grant number NJCAR2018 ZD03; the National Natural Science Foundation for Young Scientists of China, grant number 41705037 and 41805092. The authors declare no conflict of interest.
## 1 Introduction  
In recent years, the rapid development of information industries, such as autonomous driving, 5G, and so on, has led to an urgent demand for data transmission, especially for optical interconnection. Intensity modulation/direct detection (IM/DD) is widely used for optical interconnection at conventional 1310 nm and 15,500 nm wavebands, and has recently been extended to the 2 micron waveband [1; 2]. Advanced modulations have been proposed for versatile IM/DD systems [3; 4; 5], in which nonlinear distortion becomes a very fatal issue due to the much tighter signal-to-noise ratio (SNR) budget, as well as the higher peak-to-average-power-ratio (PAPR). Normally, DD system can only be applied to IM signal for the detection of amplitude information rather than phase information [6]. In 2016, [PERSON] et al. proposed a Kramers-Kronig (KK) self-coherent receiver [7]. On the basis of satisfying the minimum phase condition of signal [8], the phase of the optical field can be restored by its intensity through a KK relationship by using a single photodetector (PD) [9; 10; 11; 12]. This scheme has attracted widespread attention since it was proposed. Many studies have been carried out which prove the excellent performance of the KK receiver [13; 14; 15].  
However, the KK receiver requires that the signal should meet the minimum phase condition, leading to a single sideband modulation (SSB) with a considerably high carrier-to-signal power ratio (CSPR), which could be over 10 dB. Serious nonlinear distortion can be expected for such a SSB signal due to the high CSPR, high PAPR, and tight SNR budget. The high CSPR is needed to maintain a small bit error rate (BER) [16; 17; 18]. However, on the other hand, nonlinear distortion by stimulated Brillouin scattering (SBS) will be easily excited for km-level distance optical fiber transmission. Particularly, with a guard band between the carrier and signal, the carrier itself can be considered as a standalone narrow linewidth continuous wave, which makes the SBS threshold even smaller, leading to worse SBS nonlinear distortion. Therefore, the investigation of SBS based nonlinear distortion to KK receiver-based optical fiber transmission is urgently needed.  
In this work, we carried out a proof-of-concept study with solid experiments to unfold the nonlinear distortion limitation by SBS for KK signals, which is a unique phenomenon for KK receiver due to the high CSPR property of SSB signal. A significant reduction in the transmission performance due to SBS is observed for 10 Gbaud and 28 Gbaud SSB QAM16 signal transmissions over 80 km SSMF based on a KK receiver. The method of sideband filtering is proposed to reduce nonlinear distortion by SBS. Those results are of increasing value to researchers in this field, along with the increased use of a KK receiver in optical communication applications, which offer a higher data rate and long transmission distance.
## 3 The Proposed Algorithm
### The Ant Colony Optimization Method
\(\tau_{ij}(t)\) is updated at the end of each iteration, according to Equation (15):  
\[\tau_{ij}(t+1)=(1-\rho(t))\tau_{ij}(t)+\sum_{k=1}^{m}\Delta\tau_{ij}^{k}, \tag{15}\]  
where \(\rho(t)\) is the pheromone volatilization factor and \(\Delta\tau_{ij}^{k}\) is the pheromone left by ant \(k\) on path \(ij\). \(\rho\) and \(\Delta\tau_{ij}^{k}\) are improved by Equations (16) and (17):  
\[\rho(t+1)=\rho(t)(1-e^{-t}), \tag{16}\]  
\[\Delta\tau_{ij}^{k}=\frac{Q}{L_{best}}(1-q_{j}(t)), \tag{17}\]  
in which \(Q\) is a constant and \(L_{best}\) represents the length of the best route. According to Equation (16), the pheromone volatilization factor gradually increases with the number of iterations, which can increase the search depth and avoid falling into local optimum. Compared with the traditional method Equation (17) adds the node's load factor and can avoid selecting congested nodes during routing.
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 4 Conclusions
From our perspective, advanced models can provide us with great utilities, like XGBoost, autoencoders, or radial basis function networks (RBFNs); however, we decided to use Artificial Neural Networks (ANNs) and Random Forests (RFs) as the pathway to provide computational efficiency and adequate performance from prior work. Moving forward, we should incorporate efforts into improving the precision and transparency of forecasting models by enhancing data preprocessing; exploring hybrid artificial intelligence (AI) applications with corresponding methods or use-cases; employing explainable AI models; and, lastly, studying AI-enabled optimizations for dynamic electricity pricing systems with demand-side management models. Being able to evaluate the feasibility of the modeling across a variety of scenarios will provide an anchor of understanding and credence for energy performance in nZEBs. Conceptualization, K.M.S., A.O.E., F.J.R.-M. and J.M.R.-H.; methodology, K.M.S., A.O.E., F.J.R.-M. and J.M.R.-H.; software, K.M.S., A.O.E., F.J.R.-M. and J.M.R.-H.; validation, K.M.S. ; formal analysis, K.M.S., A.O.E., F.J.R.-M. and [PERSON]-H.; investigation, K.M.S., A.O.E., F.J.R.-M. and [PERSON]-H.; resources, K.M.S., A.O.E., F.J.R.-M., and [PERSON]-H.; data curation K.M.S., A.O.E., F.J.R.-M. and J.M.R.-H.; writing--original draft preparation, K.M.S. ; writing--review and editing, K.M.S., A.O.E., F.J.R.-M. and [PERSON]-H.; visualization, K.M.S., A.O.E., F.J.R.-M. and J.M.R.-H.; supervision, [PERSON] and [PERSON]; project administration, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. **Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request. **Acknowledgments:** We would like to acknowledge the support received from \"LIFE23-CET-Re-Energize\" European Project by University of Malaga, Spain; \"EUSUVa4.0\" Project by University of Valladolid (Spain); \"Lime4 Health\" National Project by Technical University of Madrid (UPM) (Spain); RED-\"TRAPECIO\" IberAmerican Project by CYTED (Ibero-American Program of Science and Technology for Development); and ITAP Research Institute at University of Valladolid. We would like to acknowledge the use of MATLAB (Version R2018a, MathWorks, [[https://www.mathworks.com](https://www.mathworks.com)]([https://www.mathworks.com](https://www.mathworks.com))) for data analysis and visualization in this study. Additionally, the images included in this document were created by the authors and are original works. **Conflicts of Interest:** The authors declare no conflicts of interest.
# Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings
## 2 Integrated Bearing Dynamic Performance Analysis
### Impact of Operating Conditions on Vibration of Integrated Bearings
#### 2.3.2 Impact of Radial Load on the Vibration of Integrated Bearings  
Under the operating conditions of a shaft speed of 50,000 r/min and an axial load of 0 N, the relationship between the radial load in the Y-direction and bearing vibration is illustrated in Figure 14. As shown, with the gradual increase in the working load of the integrated bearing, both the vibration velocity and vibration acceleration level tend to increase progressively. Selecting an appropriate radial load can effectively reduce bearing vibration.  
Figure 14: Trend of vibration with variation in the radial load. (**a**) Trends in vibration velocity variation. (**b**) Trends in vibration acceleration variation.  
Figure 13: Trend of vibration with variation in the axial preload. (**a**) Trends in vibration velocity variation. (**b**) Trends in vibration acceleration variation.
## 3 Materials
### Feature Extraction
#### 4.1.2 Audio Feature Extraction  
We used the Wav2 Vec 2 multilayer convolutional feature extractor to obtain latent speech representations from the raw input audio. Wav2 Vec 2 is a transformer-based model that uses a self-supervised approach to learn representations from raw audio data. This model first encodes the speech audio via a multilayer convolutional neural network, obtaining latent speech representation that are later fed to a transformer network in order to build contextualized representations.  
For our task, we used the Wav2 Vec 2 feature extractor, which consists of seven consecutive one-dimensional convolutions with 512 channels and respective kernel sizes of (10, 3, 3, 3, 3, 2, 2) and stride of (5, 2, 2, 2, 2, 2, 2). For a more detailed technical description, we refer the interested reader to the original paper [44]. We split the raw audio signal in frames of one second, and each of these frames was given as input to the feature extractor. We then obtained the embeddings from the extractor and saved them on files for later use. Each one-second frame became an embedding of size (49, 512).
## 5 Conclusions  
Hybrid precoding-companding techniques are attractive ones. They provide high PAPR reduction gain with small computational complexity. Previous precoding-companding techniques are designed to increase PAPR reduction gain. Here, we propose a precoding-companding technique to reduce the BER and OOB better than previous ones, as this is the goal of PAPR reduction. Results showed that SCR-Log achieves OOB radiation reduction and BER performance better than the previous precoding-companding techniques. So, SCR-Log can be considered as the best precoding-companding technique. Moreover, SCR-Log achieves PAPR reduction performance better than PTS, with 99% CCRR to PTS. In the end, SCR-Log is an attractive precoding-companding technique to be used in OFDM systems. In the future, SCR-Log can be integrated with power allocation strategies to attain high-level PAPR optimization.  
Conceptualization, M.M. ([PERSON]), M.B.E., and S.B.; Methodology, M.M. ([PERSON]), and M.B.E.; Formal analysis, M.M. ([PERSON]), and M.B.E.; Software, M.M. ([PERSON]); Results interpretation, M.M. ([PERSON]), M.B.E., and G.S.G.; Visualization, M.M. ([PERSON]), S.B., and G.S.G.; Writing--original draft preparation, M.M. ([PERSON]), M.B.E., and S.B.; Writing--review and editing, M.M. ([PERSON]), G.S.G. and M.M. ([PERSON]); Supervision, M.B.E., G.S.G. and M.M. ([PERSON]); Project administration, G.S.G. and M.M. ([PERSON]); Funding acquisition, M.M. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by Taif University Researchers Supporting Project number (TURSP-2020/10), Taif University, Taif, Saudi Arabia.  
We are grateful for the support from Taif University Researchers Supporting Project number (TURSP-2020/10), Taif University, Taif, Saudi Arabia.  
The authors declare no conflict of interest.
## 6 Conclusions
Group delay measurements confirm in-band ripple performance, where the EZ design effectively minimizes peak-to-peak variation. Testing verified the absence of spurious resonances within \(\pm\)650 MHz of the center frequency (worst-case scenario). The achieved \(Q_{L}\) values remained below simulated predictions due to several factors: the reduced aspect ratio (AR = 0.48), adhesive-related inconsistencies (including excess adhesive and conductive material degradation--particularly in the PCB ground plane bonded to the metallic carrier), and conductive losses in tuning screws. These losses primarily affect input-output and cross-coupling paths, along with \(TE_{01\delta}\) resonator interactions during frequency tuning. These findings highlight the need for more comprehensive laboratory characterization to improve manufacturing repeatability and reduce performance variations. This initial characterization phase establishes the foundation for DRMF technology, contributing several key advancements, including novel input-output coupling techniques, enhanced understanding of TE\({}_{01\delta}\) mode behavior, and controlled generation of cross-coupling for precise transmission zero and equalization zero placement. This research represents a significant advancement in microwave filter technology, effectively bridging the performance gap between microstrip and cavity-based implementations.