esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background: Although a cross-legged sitting (CLS) posture has been commonly practiced as a daily activity, particularly in Arabic, Middle Eastern, and Asian societies, there is no medical study focusing on the effects of cross-legged sitting on body balance and muscular strength. Therefore, this study aimed to investigate the effect of CLS on lower extremity muscular strength, muscular electrical activity, and body balance. Methods: Thirty healthy volunteers participated in this research study by performing CLS for a 20 min duration. The balance tests included a static test, i.e., a single-leg-standing posture with eyes closed, to assess if the centre of the pelvis and centre of the shoulders (CoS) moved, and a dynamic test, i.e., four-square-returning, to assess if the moving speed changed. Regarding the muscular assessment, the electrical activity was assessed depending on the maximal value of activation and rooted mean of squared values, while the muscular strength was assessed according to the maximum force by the lower limbs using a force sensor. The balance and muscular results were statistically compared before and after CLS. Results: The duration of the static balance after CLS decreased by an average of 2.5 s, or approximately 15.64%, compared to before CLS (\(p\) < 0.05 *). Further, the Centre of Pelvis moved greater distances in the medial-lateral direction after CLS compared to before, but CoS was not significantly changed in the static balance test. However, in the dynamic balance test, the duration significantly decreased by 0.2 s, or approximately 8.5%, after CLS compared to before, meaning that dynamic balance ability improved. Considering the muscle results, only the lateral gastrocnemius muscle was noticeably electrically activated after CLS, while the hip extensor and knee flexor muscles became significantly stronger after CLS compared to before, roughly by about 14%, and the ankle plantar flexor maximum force increased noticeably, by about 4%, after CLS. Conclusions: CLS had a positive impact on the dynamic balance; the strength of the hip extensor, knee flexor, and ankle plantar flexion; and all lower limb muscles, in terms of electrical stimulation, except for the lateral gastrocnemius post-CLS compared to pre-CLS. Therefore, CLS can be safely included in one's daily routine and in any rehabilitation programme, except for patients who are suffering from static balance disturbance. Although this posture is commonly used in many societies, because this is the first study focused on the impact of CLS on body balance and muscular status, the results would supply knowledge and new understanding, as well as provide clear insight for sitting posture research.  
**Keywords:** cross-legged sitting; static balance; dynamic balance; centre of the pelvis; centre of the shoulders
## 2 Foundations of Peer-to-Peer
### Introduction  
P2P networks are used for file sharing, voice chat and many more services nowadays. While we focus on Distributed Hash Tables (DHT) in this paper, several other architectures were also proposed. We basically have to distinguish between _structured_ and _unstructured_ networks [1]. _Unstructured P2P_ networks can be further subdivided into _centralized_, _pure_, and _hybrid P2P_ networks.  
_Centralized P2P_ networks require a central entity for the lookup of the provided services (e.g., locating stored objects in a file sharing scenario). Requests for content and notifications of new content have to be sent to this entity which stores the IP addresses of nodes offering services, thus providing lookup information. This way, every request can be served after a single hop in the P2P overlay but at the cost of a very high load at the central entity and a single point of failure. Napster [1] is a well known example of a _centralized P2P_ network.  
While _centralized P2P_ systems still contain elements of traditional Client/Sever architectures, all nodes in _pure P2P_ networks are treated equally. Flooding-based lookup algorithms are required as the source node is unaware of the logical location of services available in the P2P overlay (_i.e._, the requesting peer has no knowledge on which peer offers the requested service). No single point of failure exists and no complex lookup algorithm is required. Yet, a considerable lookup overhead is introduced since the lookup requests are flooded through the network. Freenet [2] and Gnutella 0.4 [3] are examples for _pure P2P_ networks.  
The third kind of unstructured architectures are _hybrid P2P networks_. In hybrid networks, we distinguish between leafnodes and superpeers. Every superpeer is responsible for a set of leafnodes and maintains information about services provided by these nodes. Lookup requests are usually not flooded in the whole network but sent to the superpeers. This results in a reduced overhead at the cost of a more complex lookup algorithm compared to _pure P2P_ networks. Gnutella 0.6 [3] and KaZaA [4] are examples for _hybrid P2P_ networks.  
_Distributed Hashtables_ (DHT) are _structured P2P_ networks in which each node is assigned an ID (e.g., by hashing its IP address). By this ID, nodes are identified and can be looked-up within the P2P network. During lookup, cooperation of other nodes in the network is required, as in the routing tables that are used to route lookup requests only the addresses of a fraction of the available nodes in the network is stored. While the particular lookup algorithm differs depending on the specific architecture, most DHTs scale logarithmically to the network size regarding the routing table size and the number of hops required to forward a request from source to destination.
# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
## 2 Methodology
### Model Performance Evaluation  
The goodness-of-fit approaches applied to evaluate various simulation performances are percent bias (PBIAS), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and coefficient of determination (R\({}^{2}\)) ([PERSON] 2013, [PERSON] 2020). PBIAS measures the mean tendency of the simulated data to be larger or lower than the observed data. The perfect value of PBIAS is 0, while values that lie within \(\pm 15\%\) are considered acceptable. Positive and negative values identify the model overestimation and underestimation, respectively. RMSE is a normal method to compute the error of a model in predicting quantitative data. The NSE determines how well the observed plot fits the computed plot and R\({}^{2}\) describes the degree of collinearity amid observed and computed data. Both NSE and R\({}^{2}\) values lie between 0 and 1, with higher values showing less error, and values greater than 0.5 are considered acceptable. All these parameters were calculated by the following equations:  
\[PBIAS=100\times\left(\frac{\sum_{i=1}^{n}(S_{i}-O_{i})}{\sum_{i=1}^{n}O_{i}}\right) \tag{11}\]  
\[RMSE=\sqrt{\sum_{i=1}^{n}\frac{\left(S_{i}-O_{i}\right)^{2}}{n}} \tag{12}\]  
\[NSE=1-\frac{\sum_{i=1}^{n}(O_{i}-S_{i})^{2}}{\sum_{i=1}^{n}\left(O_{i}-O_{avg} \right)^{2}} \tag{13}\]  
\[R^{2}=\left(\frac{\sum_{i=1}^{n}(O_{i}-O_{avg})\left(S_{i}-S_{avg}\right)}{ \sqrt{\sum_{i=1}^{n}\left(O_{i}-O_{avg}\right)^{2}}\sqrt{\sum_{i=1}^{n}\left( S_{i}-S_{avg}\right)^{2}}}\right)^{2} \tag{14}\]  
where \(S\) is the simulated value and \(O\) is the observed value, \(n\) is the number of observed data entries, and \(avg\) is the average of the total values.
## 2 Research Data and Methods
### Description of Used Data and Variables
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Variable** & **Mean** & **Median** & **Min.** & **Max.** & **SD** & **SK** & **KU** \\ \hline \multirow{8}{*}{Training} & W-D & 12.57 & 8.00 & 0.00 & 30.00 & 11.19 & \(-\)1.13 & 0.49 \\  & CSAFR & 0.25 & 0.13 & 0.11 & 0.51 & 0.18 & \(-\)1.47 & 0.73 \\  & DMR & 3.26 & 3.37 & 2.34 & 4.63 & 0.71 & \(-\)0.94 & 0.39 \\  & \(\sigma_{3}\) & 69.35 & 69.00 & 0.00 & 138.00 & 49.60 & \(-\)1.34 & \(-\)0.02 \\  & \(\sigma_{d}\) & 173.50 & 208.00 & 69.00 & 277.00 & 78.73 & \(-\)1.40 & \(-\)0.02 \\  & M\({}_{\rm r}\) & 3690.88 & 3422.00 & 585.00 & 9803.00 & 1862.06 & 1.42 & 1.12 \\ \hline \multirow{8}{*}{Testing} & W-D & 13.32 & 16.00 & 0.00 & 30.00 & 11.10 & \(-\)1.19 & 0.35 \\  & CSAFR & 0.26 & 0.13 & 0.11 & 0.51 & 0.19 & \(-\)1.67 & 0.58 \\ \cline{1-1}  & DMR & 3.28 & 3.37 & 2.34 & 4.63 & 0.73 & \(-\)1.07 & 0.34 \\ \cline{1-1}  & \(\sigma_{3}\) & 71.94 & 69.00 & 0.00 & 138.00 & 47.16 & \(-\)1.22 & \(-\)0.04 \\ \cline{1-1}  & \(\sigma_{d}\) & 167.89 & 138.00 & 69.00 & 277.00 & 75.06 & \(-\)1.26 & 0.11 \\ \cline{1-1}  & M\({}_{\rm r}\) & 3668.12 & 3443.00 & 773.00 & 9644.00 & 1861.16 & 1.65 & 1.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical evaluation of training and testing datasets.
# A Novel Mobile Communications Authentication Scheme with Roaming Service and User Anonymity
## 5 Security Analysis
### Solve Corresponding Problem of Session Key in Roaming Phase  
In [PERSON]'s scheme, when the FA receives the roaming message, it calculates the session key, decrypts the cipher text m\({}_{i}\), and performs a comparison with mac. However, there are in fact hundreds of thousands of people in a wireless network environment, and [PERSON]'s scheme did not clearly discuss how to calculate the session key in the roaming phase. Each person's session key is not the same, and it did not clarify how to calculate one hundred thousand different session keys or save them in the table. It can be saved as a form, but this may pose some risks, which need to be discussed further. In the proposed scheme, the FA can decrypt the cipher text m\({}_{i}\) by using the session key calculated by the MU in the authentication phase. On the other hand, we encrypt the session key by using EC calculated by the MU in the authentication phase. In this manner, our scheme resists data disclosure to attackers, who intercept the session key during the transfer processes. Even if the message is intercepted, it can only reveal the cipher text. Therefore, the proposed scheme solves the corresponding problem of the session key.
## 3 Study Framework
### SUMO Calibration  
A one-hour video was recorded for a TLTW road section of 375 m from the Mansoura-Damietta Road in Egypt between 12:00 and 1:00 p.m. on 27 June 2021. The total road width is 8.50 m, with 1.50 m and 1.00 m widths for the right and the left shoulders, respectively. Table 2 shows a summary of the traffic characteristics of the road section during the one-hour period.  
In this analysis, for the SUMO simulation, the Wiedemann-99 car-following and the LC2013 lane-change models were adapted. The tested values for both the car-following and the lane-change parameters are shown in Table 3. A Python script was developed with several loops for each parameter range. A single simulation run for each combination of parameters was executed. Then, the combination that has the lowest difference between the simulated travel speed and the observed travel speed in both directions, and at the same time has the lowest difference between the simulated number of overtakes and the observed number of overtakes, was chosen. Then, 10 simulation runs for the selected combination were executed to make sure that the calibrated parameters can successfully represent the range of observed speed and observed number of overtakes. The calibrated car-following and lane-change parameters are shown in Table 3.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **1 st Direction** & **2 nd Direction** \\ \hline Traffic volume (vehicle/h) & 189 & 212 \\ Average travel speed \(\pm\) standard deviation & 64.39 \(\pm\) 11.06 km/h & 66.42 \(\pm\) 13.94 km/h \\  & 17.88 \(\pm\) 03.07 m/s & 18.45 \(\pm\) 03.87 m/s \\ Number of successful overtakes & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the observed traffic characteristics.  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Parameter** & **Description** & **Default Values** & **Tested Values** & **Calibrated Values** \\ \hline Car-Following Parameters (Wiedemann-99) & & & \\ \hline CC0 & Standstill gap (m) & 2.50 & 1.50 * & 1.50 \\ \hline CC1 & Headway time: Time gap that the following driver keeps for a safety-in-moving state (s) & 1.30 & 0.50–1.50 (step = 0.1) & 1.00 \\ \hline CC2 &
\begin{tabular}{l} ‘Following’ variation: Range of \\ gap between the two vehicles \\ in the “following” regime (m) \\ \end{tabular} & 8.00 & 4.0–10.0 (step = 1.0) & 4.50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation Parameters.  
As shown in Table 4, the relative difference between the simulated and the observed traffic volume and traffic speed are within the 10% error, and the observed number of over-takes is within the range of the simulated ones, which suggests that the calibrated parameters would be good representatives of the TLTW road from which the data was collected.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **1 st Direction** & **2 nd Direction** \\ \hline Traffic volume (vehicle/h) & 189.10 \(\pm\) 0.57 (0.00\%) & 212.20 \(\pm\) 0.63 (0.00\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of the simulated traffic characteristics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
###### Abstract
studied the regularities of the dynamic impact induced by the movement of thick and hard strata, proposing that mining in critical working faces leads to the rupture of thick and hard strata, thereby inducing strong mine tremors or rockburst [18]. Regarding the mechanism of rockburst in the isolated coal pillar area, [PERSON] et al. studied the mechanism of rockburst in the isolated coal pillar area and developed a rational design method for the width of isolated coal pillars to control rockburst [19]. [PERSON] et al. investigated the mechanism of rockburst in irregular the isolated island coal pillar area, revealing that as the working face advances, the stress distribution inside the coal pillar shows an asymmetric'saddle-shaped' pattern, and the stress peak continually increases, leading to coal pillar instability [20]. [PERSON] et al. concluded that the transfer load of the overlying strata in the goat increases the static load stress in the goat side of the fully mechanized top coal caving pillar, which is superimposed with a hard roof breaking dynamic load to induce rockburst [21]. [PERSON] et al. studied the mechanism of rockburst during the mining process of the working face with a large coal pillar on one side of the fold structure area [22]. [PERSON] et al. researched the mechanism of rockburst in the roadway under the disturbance of remaining coal pillars in large mining height mining faces, determining the reasonable layout positions for the roadway [23]. [PERSON] et al. optimized the pillar size of isolated island panel gob-side entry driving in deep inclined coal seams [24]. [PERSON] et al. studied the coal pillar width and surrounding rock control of gob-side entry in extra-thick coal seams [25]. [PERSON] et al. studied the reasonable width of coal pillars in high-strength mining roadways [26]. The mining depth of a mine in Binchang is nearly 1000 m, and the sandstone aquifer of the Luohe formation is overlying it. The inclined mining scale of the second panel of the mine is 900 m, which leads to the rockburst caused by the fracture and instability of the upper super-thick main key strata, accompanied by water inflow. To prevent rockburst and control water inflow, the third panel adopts the small panel and large coal pillar layout pattern. However, at present, there is limited research on the layout pattern of the panels beneath the thick water-bearing key strata. Therefore, through the methods of theoretical analysis, field measurement, and numerical simulation, from the stability of the isolated coal pillar and the main key strata above the goat, this paper studies the overlying rock breaking rule and the stress evolution characteristics of the isolated coal pillar under the different layout pattern of a small panel and a large coal pillar, and optimizes the width of the isolated coal pillar and the mining scale of the panel. It provides a reference for the prevention and control of rockburst in mines with similar conditions.
## 5 Results and Discussion
### Analysis of FPI Index Changes Between Different Years  
In 2002, the average annual FPI was 0.621, one of the highest values observed across all years. This high value can be attributed to a series of significant precipitation events that increase the flood potential. In the following years from 2003 to 2005, the FPI index shows a clear downward trend, particularly in 2004, when the FPI drops to 0.296. This indicates a low flood potential during that year, possibly due to reduced precipitation and unfavorable soil conditions for flood formation.  
In 2006, the FPI exhibited moderate increases, peaking above 0.7 during specific months, indicating significant flood potential that aligned well with recorded flood events. Precipitation during this period was moderate but concentrated, contributing to localized  
Figure 8: The monthly FPI index, precipitation, and true flood events.  
soil saturation and increased runoff, which likely amplified flood risks. In contrast, 2008 demonstrated one of the highest FPI peaks around 0.8, corresponding to substantial precipitation during the summer months. The combination of high precipitation intensity and antecedent soil saturation effectively drove the elevated flood potential, with the occurrence of flood events strongly correlating with the FPI predictions.  
Starting in 2012, the FPI index exhibited an upward trend, peaking in 2016 at 0.658, one of the highest values since 2002. This suggests that more frequent or intense precipitation events occur during these years, resulting in increased soil moisture and higher flood potential. The high FPI in 2016 indicates a particularly elevated flood risk. Moreover, between 2015 and 2017, flood events occurred frequently, and the FPI index reached its peak five times, which better predicts the occurrence of flood events. In 2021, the FPI index remains relatively stable, with an annual average of 0.310, reflecting a low flood potential. Actual data show that flood events occur less frequently during this period. In 2022, the FPI exhibited moderate peaks during key months. However, the number of flood events recorded was notably lower. This divergence suggests that other hydrological factors, such as lower soil moisture or reduced runoff generation, may have mitigated flood risks despite moderate precipitation.  
Overall, the FPI index exhibits cyclical fluctuations, reflecting changes in annual precipitation and hydrological conditions, which directly influence flood potential. These findings emphasize the value of FPI as a robust tool for flood risk management and its potential application in informing early warning systems and flood prevention strategies.
## 9 Recent Advances in GPC
### 3D Printing Using Geopolymer  
3D printing is gaining wide applications in different fields like aerospace, automotive manufacturing, and in the construction industry due to its sustainable development. In the construction industry, 3D printing is considered environment friendly due to designing freedom, automation, less waste generation, reduced raw material consumption [223], and labor cost, while GPC reduces carbon footprints as discussed earlier. Combining both the sustainable solutions is an innovative idea for construction industry and can help in environmental management and technology development.  
Different studies were conducted by researchers to determine the fresh and hardened properties and mix design proportions of different types of geopolymers in 3D applications. The fresh properties of geopolymer containing FA, slag, and silica fume as a binder were investigated and the effect of activator percentage and water/binder ratios were assessed [224]. The study revealed that the better mechanical properties with compressive strength of 50 MPa at 21 days achieved at water/solid ratio of 0.33 along with 8% activator percentage. Likewise, the effect of addition of different superplasticizers on mechanical, rheological, and microstructural properties of GPC were assessed under ambient curing condition [225]. It was reported that with inclusion of slag in FA-based GPC improved the microstructure and early age strength without significant improvement in rheology. However, addition of silica fume improved both yield stress and viscosity of GPC that was due to its particle shape and surface area.  
Hence, changing activator or aluminosilicate type and content or effect of addition of superplasticizer, etc., can influence the GPC mixture designs due to presence of unique geopolymerization reactions involved. Further, the selection of appropriate 3D printing technique is crucial step in concrete printing technology. For different field or lab scale applications, different types of 3D printers are available with their advantages and limitation and selection of any printable technology depends on application and usage.  
Figure 38: Comparison of GPC and OPC concrete with respect to environmental impacts [220].
## 3 Results
### CBD and CBDA  
No differences in CBD concentrations between the light treatments were observed. The average CBD concentrations were 0.61%, 0.55% and 0.45% for Kanada, A4 and E19, respectively. In contrast, the CBDA concentrations displayed significant differences in the interactions between light spectra and flower positions, as well as between light spectra and strains and individual flower positions within the respective strains (Figure 3).  
CBDA concentrations varied according to flower position, especially under the LED treatments, with higher concentrations measured for the main (MTBs) and side top buds (STBs) and lower concentrations for the side buds (SBs). Such variation in CBDA concentration in the CHD plants was not observed (Figure 3a). The effects of the light spectra on CBDA concentrations were not consistent between the cultivars (Figure 3b); no difference in CBDA concentration was observed in the cultivar Kanada; however, a 38% increase was detected in cultivar A4 under the LED treatments compared to CHD, and, on the contrary, the CBDA concentration for E19 was 11-fold higher under CHD compared to the LED treatments.  
Across the three light spectra, each strain exhibited different quantities and distributions of CBDA concentrations at different flower positions (Figure 3c). Kanada had the highest CBDA concentration (6.39%), followed by A4 (3.52%) and E19 (0.78%). Furthermore, Kanada showed a significant gradient across flower positions, following the order MTBs > STBs > SBs. The lowest CBDA concentration for A4 was found in the SB flower position; however, MTBs and STBs showed similar concentrations. No significant differences were found for strain E19 with respect to flower positions.  
Figure 3: CBDA concentrations (% DW) for the three flower positions (MTB: main top bud, SBs: side buds, STBs: side top buds) under the three light spectra (SOL, AP67 and CHD) (**a**), for the three strains (A4, E19 and KAN) under the three light spectra (**b**) and for the three flower positions of the respective strains (**c**). Means covered by at least one identical letter did not differ significantly, as indicated by a Tukey test (\(\alpha\) = 0.05). Lowercase letters indicate comparisons between flower positions (**a**) or strains (**b**) within light spectra, or flower positions within strains (**c**); uppercase letters indicate comparisons between light spectra within flower positions (**a**) or strains (**b**), or strains within flower positions (**c**). Error bars indicate the standard errors of the means (\(n\) = 3).