esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Relationship Between SLCMJ Height and Kinematic Variables  
The novel finding of the present study was the significant negative relationship between femur inclination in the frontal plane and SLCMJ height (r = \(-\)0.30). This result suggests that a smaller deviation of the knee from the frontal plane (i.e., reduced knee valgus), which reflects a better alignment of the lower-limb joints in the lowest position of the center of mass, was associated with a higher SLCMJ height in the present study. Biomechanically, the optimal alignment of the lower-limb joints facilitates more efficient force transmission from the ground through the kinetic chain, minimizing energy losses and enhancing vertical propulsion [14; 19; 32]. A reduced femur inclination may also indicate better neuromuscular control of the hip and knee, particularly from the hip abductors and external rotators, which are essential for maintaining proper alignment during dynamic movements [20]. The research on this topic is scarce, as there are only a few studies examining the relationships between hip strength, frontal plane knee alignment, and jumping  
Figure 5: A graphical summary of the study results. MVIC—maximal voluntary isometric strength and RTD—rate of torque development during explosive isometric contractions.  
performance. In agreement with our study, [PERSON] et al. (2020) [20] found that a higher gluteus medius strength predicted lower knee abduction moments during drop vertical jumps in female athletes. In contrast, [PERSON] et al. (2023) [19] demonstrated conflicting findings. Healthy male physical education students performed single-leg jumps on a force plate, and knee and hip kinetics and kinematics were measured. They found no significant correlation between jump kinetics (specifically, propulsive impulse) and knee valgus during the SLCMJs. These mixed findings suggest that the relationship between hip strength, knee alignment, and jump performance is complex and may be influenced by factors such as sex, neuromuscular coordination, and movement strategy. Additionally, methodological differences, such as variations in participant training status, task constraints, or biomechanical analysis techniques, may have contributed to the differing outcomes. These discrepancies also highlight the need for further research to investigate whether targeted training interventions aimed at improving frontal plane alignment and hip strength translate to enhanced jump performance.
# Robust Transceiver Design for IRS-Assisted Cascaded MIMO Communication Systems
## 3 Achievable Rate (Full-Duplex Relay)  
### Overview  
Suppose that the relay employs a DF strategy. In the full-duplex scenario, both source-relay and relay-destination links are active at the same time. As a result, the signals from the relay transmitter interfere with the receiving signal at the relay receiver. We assume that an estimate of the SI channel \(\mathbf{H}_{r}\) is available at the relay denoted by \(\mathbf{\hat{H}}_{r}\). Hence, the ISI represented by \(\mathbf{\hat{H}}_{r}\) is given as  
\[\mathbf{H}_{r}=\mathbf{H}_{r}-\mathbf{\hat{H}}_{r}. \tag{5}\]  
In the rest of the paper, we try to find an approach to deal with this ISI.
# A Review of Indoor Air Quality in Social Housing Across Low- and Middle-Income Countries
## 3 Results of the Literature Survey
### Comparative Studies (CS)  
Comparative studies focus on the complex relationship between housing typologies, infrastructure, and IAQ. [PERSON] et al. [55] found that relocating families from slums to public housing significantly reduced indoor PM2.5 concentrations from 77.8 \(\upmu\)g/m\({}^{3}\) to 55.7 \(\upmu\)g/m\({}^{3}\), but both levels still exceeded the WHO's recommended daily average of 15 \(\upmu\)g/m\({}^{3}\). The use of solid fuels for heating water, which is common in slums but eliminated in public housing, was attributed to the improvement in indoor pollutant levels. However, high levels of PM2.5 in both slums and public housing in Mumbai, ranging from 150 to 300 \(\upmu\)g/m\({}^{3}\), with daily spikes exceeding 1000 \(\upmu\)g/m\({}^{3}\) during cooking times was revealed, despite the predominant use of clean-burning cooking fuels [56]. The study noted that 80% of residents in public housing tended to keep openings closed during cooking to maintain privacy, worsening the indoor pollution levels. In both types of settlements, outdoor PM2.5 levels often exceeded indoor levels, with over 50% of measurements showing indoor-to-outdoor (I/O) ratios below 1.0. Therefore, designers and planners should focus on building envelope and ventilation systems that limit the infiltration of outdoor generated pollutants because increasing air exchange without considering the external pollution could worsen IAQ. Furthermore, a study in Surabaya, Indonesia, compared modern high-rise affordable apartments and traditional kampong houses and found higher levels of formaldehyde (average: 0.05 ppm) and TVOC (average: 964 \(\upmu\)g/m\({}^{3}\)) levels in the apartments due to newer building materials [57]. These results align with findings in Section 3.2, where newly constructed housing exposed even more severe IAQ risks [40]. Both studies suggest stricter building material regulations are required to mitigate greater risks to health in new construction housing. Next, comparative research in Malaysia examined two high-rise social housing typologies with differing ventilation strategies [58]. The atrium-based design typology recorded lower PM2.5 and CO levels (35 \(\upmu\)g/m\({}^{3}\) and 0.9 ppm, respectively) compared to the light-well design typology (40 \(\upmu\)g/m\({}^{3}\) and 1.2 ppm), compact design hindered airflow and pollutant dispersion. Both typologies struggled to manage outdoor pollutants, emphasizing the necessity of hybrid ventilation strategies that combine passive and mechanical systems to ensure effective filtration. In Kenya, a comparative study showed similar mean CO concentrations across social and more affluent housing. However, individuals in social housing were at greater health risks due to the higher proportion in social housing reaching CO levels that would trigger an alarm [59].
# A Hybrid Adaptive Particle Swarm Optimization Algorithm for Enhanced Performance
## 5 Engineering Application  
Under the background of the rapid development of Industry 4.0 and intelligent manufacturing, the particle swarm optimization algorithm provides innovative solutions for energy saving, emission reduction, and resource optimization in the field of industrial manufacturing by virtue of its efficient characteristics. The algorithm has shown significant application value in many industrial fields. In the design of pressure vessels in the chemical and petroleum industries, the wall thickness to diameter to height ratio and other parameters are optimized to ensure safety while reducing material costs. In the field of industrial spring manufacturing, the fatigue life and lightweight level of products are simultaneously improved by multi-objective optimization of parameters such as steel wire diameter and effective turn number. In the production of automobile reducers, the key parameters such as gear modulus and tooth width can be optimized to reduce weight and improve transmission efficiency. In addition, the algorithm can be applied to other areas, such as multimodal inseparable problems, black-box optimization with moderate variable coupling, and continuous/semi-continuous search spaces. These typical applications fully prove that particle swarm optimization algorithm has a certain contribution to promote the development of industry and intelligent manufacturing.
## 2 Experimental Testing
### Results of the Test Stage
#### 3.2.1 Results of Stage One  
During Stage one, the pore pressure was always increased by 0.2 MPa at a rate of 0.01 kPa/s. However, different increments were tried for the confining pressure. Meanwhile, different increasing rates were used for the confining pressure to make the confining pressure and pore pressure reach their peak values simultaneously. The variations of the pressures and strains during Stage one are recorded in Figure 6 (negative values represent expansion, whereas positive values represent compression).  
Figure 5: The pressure curve during the saturation testing: (**a**) the pressure curve during the saturation; (**b**) testing the saturation for the first time; (**c**) testing the saturation for the second time.  
It is well known that effective stress acts on the skeleton directly. When the effective stress changes, the skeleton will undergo corresponding deformation, which is manifested as when the effective stress decreases, the skeleton of the claystone will expand outwards, and the internal pores will be filled with more water; when the effective stress increases, the framework of the claystone will be compressed, and the pore water will be squeezed out. An obvious law summarized by the test data is illustrated in Figure 6:  
1. The samples undergo a significant expansion when the increment of pore pressure \(\Delta\sigma_{p}=0.2\), 0.2, 0.2, 0.2 MPa is greater than the increment of confining pressure \(\Delta\sigma_{c}=0\), 0.1, 0.14, 0.16, 0.18 MPa; This means that the effective stress is reduced under this confining and pore pressure condition.
2. Conversely, when \(\Delta\sigma_{p}=0.2\) MPa is less than \(\Delta\sigma_{c}=0.22\) MPa, these samples are compressed; This means that the effective stress is increased under this confining and pore pressure condition.
3. The deformation of the samples are hardly observed when \(\Delta\sigma_{p}=0.2\) MPa and \(\Delta\sigma_{c}=0.2\) MPa are equal; This means that the effective stress is constant under this confining and pore pressure condition.
## 1 Introduction
However, data in LSDP tasks contains a lot of interactions and state changes, with strong continuity and correlation over time. Sparse update methods may not effectively capture the complex dependencies in session data, as sparse updates may miss the subtle differences and continuity between actions in the session. Prompt tuning typically works by adding special prompt tokens to the input, guiding the model to produce the desired output. However, it is difficult for the model to generalize to various scenarios and contexts since the prompt is manually defined and not trainable. Specifically, prefix-tuning is a lightweight fine-tuning technique. In each transformer layer, the prefix vectors are connected with the internal key-value pairs of the transformer structure to form a new input. Compared to adapter modules, it does not require adding small fully connected networks after every transformer layer, reducing the number of additional parameters. Compared to prompt-tuning, which adds prompt tokens only at the input layer, prefix-tuning adds trainable prefix tokens at every layer to influence the model's internal representations more deeply. In prefix-tuning, modules adapt to downstream tasks by freezing all PLM parameters and optimizing only continuous task-specific prefix vectors. Prefix-tuning may effectively solve the problem of fragmented learning behavior and unclear implicit associations. To solve the problem of low accuracy in the LSDP task with limited data, this paper proposes the FAP-Prefix model based on PLM and prefix-tuning methods. FAP-Prefix uses a multi-layer transformer structure. It models the relationships between individual behaviors and the associations among consecutive actions based on a large-scale PLM. This paper makes the following contributions:  
* Reducing the number of adjustable parameters in fine-tuning by freezing all parameters of the PLM and optimizing the prefix parameters only, saving computational resources and time. * Outperforming the existing SOTA learning session dropout model, maintaining excellent accuracy even in data-limited scenarios. * Modeling both the relationships between individual behaviors and the associations between consecutive actions.
## 2 Significance and Methods  
For the herringbone gear train working under the conditions of high speed and heavy load, a small increase in efficiency will reduce power loss. The curve of power loss varying with the efficiency of the star gear train is shown in Figure 1. From the figure, the conclusion can be obtained that, when the transmission efficiency increases by only 0.1%, the power loss can be reduced obviously with the increase in efficiency. In the five-way split star gear train, the input power is 20,000 KW and the efficiency of the star gear train is 98.1372% without load-sharing measures. When the efficiency increases by 0.1%, the power loss of the star gear train can be reduced by 5.37% and the value of the power loss reduction can reach 20 KW.  
At present, the improvement in transmission efficiency is mainly studied from the perspective of the meshing pair through various optimal designs of the tooth profile. However, the efficiency of the star gear train is also affected by the power split of the gear train. In this study, the main focus was on the influence of the load-sharing performance on the efficiency improvement from the perspective of the gear train. Therefore, the combination of theoretical analysis and case verification was adopted to carry out relevant research. Firstly, from the perspective of gear train unit, the influence model of power splitting on gear train efficiency was established. Then, taking the meshing group as the research object, the meshing force relationship between the meshing pairs of the meshing group was studied, and the influence of errors on the meshing force was analyzed. Finally, the unit efficiency under different load distribution coefficients was verified in the Romax environment, and the load-sharing performance and gearbox vibration were further analyzed.
## 4 Solving Method
### The Augmented \(\epsilon\)-Constraint Method
Set \(k\gets K\) where \(k\) is an iteration counter initializing to the value of \(K\). In each iteration of the algorithm, the value of \(k\) will be updated based on the results of the optimization in that iteration. We solve the following single-objective optimization model:  
\[\max\Bigg{\{}f_{2}(g)+\epsilon\frac{f_{1}(g)-f_{1}^{\min}}{f_{1}^{\max}-f_{1}^{ \min}}\Bigg{\}} \tag{3}\]  
subject to  
\[f_{1}(g)\geq f_{1}^{\min}+k\epsilon,\ g\in G, \tag{4}\]  
Then, we let \(\hat{g}(k)\) be an optimal solution obtained by (2) and \(\Omega\leftarrow\Omega\cup\big{\{}\hat{g}(k)\big{\}}\) in each iteration. Let \(\hat{k}\) be the largest integer satisfying \(f_{1}^{\min}+\hat{k}\epsilon<f_{1}\big{(}\hat{g}(k)\big{)}\). If \(\hat{k}<0\), return \(\Omega\) and stop. Conversely, if \(\hat{k}\geq 0\), we update the value of \(k\) by \(k\gets\hat{k}\). Then, the procedure of solving (2) is repeated. This iterative process continues until \(\hat{k}<0\), gradually refining and expanding the set of optimal solutions stored in \(\Omega\). Through this iterative mechanism, the algorithm systematically explores the solution space of the bi-objective optimization problem, aiming to converge at the Pareto-optimal set. Compared to classical scalarization techniques, this method provides a balance between exploration and efficiency by embedding trade-off control directly into the objective function and requiring only one model to be solved per iteration, making it especially suitable for large-scale or computation-intensive bi-objective optimization problems. The pseudocode for the augmented \(\epsilon\)-constraint method is shown in Algorithm 1. ```
1:Calculate \(f_{1}^{\max}=\underset{g\in G}{\max}f_{1}(g)\)
2:Calculate \(f_{2}^{\max}=\underset{g\in G}{\max}f_{2}(g)\)
3:Calculate \(f_{1}^{\min}=\max f_{1}(g)\) subject to \(f_{2}(g)\geq f_{2}^{\min},g\in G\)
4:\(\epsilon\leftarrow\) a small positive number such that \(f_{1}^{\max}-f_{1}^{\min}\) is an integer multiple of \(\epsilon\)
5:\(k\leftarrow\big{(}f_{1}^{\max}-f_{1}^{\min}\big{)}/\epsilon\)
6:\(\hat{g}(k)\leftarrow\) null
7:\(\Omega\leftarrow\emptyset\)
8:\(\hat{k}\gets 0\)
9:while\(\hat{k}\geq 0\)do
10: Solve the model (14), and obtain an optimal solution \(\hat{g}(k)\). 11:\(\Omega\leftarrow\Omega\cup\big{\{}\hat{g}(k)\big{\}}\)
12: Calculate the largest integer \(\hat{k}\) satisfying \(f_{1}^{\min}+\hat{k}\epsilon<f_{1}\big{(}\hat{g}(k)\big{)}\). 13:\(k\leftarrow\hat{k}\)
14:endwhile
15: Return \(\Omega\). ```  
**Algorithm 1** Augmented \(\epsilon\)-constraint method
## 7 Conclusions  
This study demonstrates the potential of combining multiple data sources to predict network traffic. It highlights the effectiveness of two deep learning models, multi-source CNN-RNN (MSCR) and transformer-enhanced CNN-RNN (TE-CNN-RNN), in achieving this task. The MSCR model integrates various data types, such as weather, social trends, and network traffic patterns. It significantly improves the accuracy and adaptability as it scales from a limited 3 \(\times\) 3 grid to an extensive 27 \(\times\) 27 grid. The TE-CNN-RNN model performs exceptionally well in precision and shows enhanced feature extraction capabilities. Both models outperform established benchmarks such as TSMixer, SAMformer, and classic ARIMA, setting a new standard for network traffic forecasting. The study highlights the importance of tailoring deep learning techniques to handle complex network traffic data effectively. Future research efforts will enhance these models to process a broader range of data inputs, identify subtle distinctions in low-flow areas, and incorporate real-time and multi-source data from various network scenarios. These advancements will substantially improve network management technologies and pave the way for more advanced network management systems.  
Figure 10: Accuracy map of Max/Avg/Min value. (**a**) INT(Max), (**b**) ST-ResNet(Max), (**c**) T-DenseNet(Max), (**d**) INT(Avg), (**e**) ST-ResNet(Avg), (**f**) T-DenseNet(Avg), (**g**) INT(Min), (**h**) ST-ResNet(Min), (**i**) T-DenseNet(Min).  
**Author Contributions:** Conceptualization, I.A., R.B. and C.-W.H.; methodology, I.A. and C.-W.H.; software, I.A. and C.-W.H.; validation, I.A., R.B. and C.-W.H.; formal analysis, I.A. and C.-W.H; investigation, I.A. and C.-W.H.; resources, I.A. and C.-W.H.; data curation, I.A. and C.-W.H.; writing--original draft preparation, I.A. and C.-W.H; writing--review and editing, I.A., R.B. and C.-W.H.; visualization, I.A.; supervision, C.-W.H.; project administration, I.A. and C.-W.H; funding acquisition, C.-W.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research is based on work supported by the National Science and Technology Council, Taiwan, under Grant number NSTC 112-2221-E-008-059-MY2.  
**Data Availability Statement:** The datasets used in this study are openly accessible under the Harvard Dataverse. They include telecommunications, weather, news, and social networks data for Milan and Trentino from November to December 2013, organized by TIM in 2015. For more details and access, please refer to the Harvard Dataverse at DOI: 10.7910/DVN/UTLAHU and the related publication in Scientific Data, 2015 (DOI: 10.1038/sdata.2015.55).  
**Acknowledgments:** While preparing this work, the author(s) used ChatGPT and Grammar to check grammar and enhance English language skills. After using these services, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the publication's content.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Determination of Optimum Frequency for Electromagnetic-Assisted Nanofluid Core Flooding
## 3 Results and Discussion
### EM Wave Propagation
#### 3.4.2 Transmitter Propagation Pattern  
Figure 9 shows the simulated radiating pattern of the antenna. It can be seen from Figure 9a,b that the antenna becomes directional and beamforming with increasing frequencies (beyond 700 MHz) as indicated by the gradual appearance of side lobes. As a result, more propagation distance was covered in one direction. However, the angular coverage decreases, and the radiation pattern is non-uniform, implying an unequal distribution of the radiated energy. Therefore, the antenna should be operated at a frequency less than 800 MHz to achieve a uniform radiating field pattern, which is one of the major goals of downhole antenna design [28]. The radiation pattern was found to be uniform at 600 MHz, as shown by the omnidirectional pattern in Figure 9b, indicating that a roughly equal amount of power was radiated from all sides of the antenna. Interestingly, this coincides with the resonant frequency of the medium as earlier determined. Figure 9c shows a three-dimensional image of the far-field propagation pattern obtained at 600 MHz, indicating the E-field distribution around the antenna within the sandstone medium saturated with oil, brine and nanofluid.  
It is important to establish that the far-field pattern, antenna gain, radiated power (Figure 10) and other parameters are evaluated and quantified within this region [32].  
Figure 11a shows the antenna propagation within the modelled reservoir geometry. The figure gives a visual representation of E-field distribution within the medium. The EM wave attenuation phenomenon is likewise visible from the figure, where the field strength is maximum at the antenna surface, and gradually diminishing at a distance. The penetration depth at 600 MHz was computed as 5.7 cm. An alternate setup for increasing the EM propagation distance by using double transmitter placement is shown in Figure 11b.  
Figure 9: A far-field distribution pattern in sandstone saturated with oil, brine and nanofluid showing (**a**) a polar plot of the side view from the XZ plane (conical cut at theta), (**b**) a polar plot of the top view from the XY plane (planar cut at phi) and (**c**) a 3D polar plot at 600 MHz