esa-sceva/satcom-chunk-collection huggingface dataset:
# Fish Consumption Frequency in the Adult Population in Poland
## 3 Results  
### Characteristic of Subjects  
Of the 1489 subjects, 48.5% were men and 51.5% were women. The average age of subjects was 51.6 \(\pm\) 19.6 (18-96) and was similar in men and women (51.6 \(\pm\) 19.6 and 51.5 \(\pm\) 19.7, respectively, \(p\) = 0.8689). Most respondents had an upper secondary education (42.7%), followed by vocational education (31.8%). More women than men had a higher education level (16.3% vs 8.7%) and more men than women had a vocational education (37.8% vs 26.1%) (_p_ < 0.0001). Most subjects described their economic status as \"moderate\" (52.7%) or \"good\" (37.6%). More respondents lived in an urban setting than a rural setting (58% vs. 42%). In the case of health status, respondents most often chose the answer \"good\" (45.1%), followed by \"moderate\" (31.6%). Most respondents had moderate physical activity level (44.3%), but one-third of the participants were characterized by a high level of physical activity. The average BMI value of subjects was 26.0 \(\pm\) 4.0 (16.1-41.5). Only 41.6% had a normal weight. Overweight or obesity was observed in 57.1% persons. The mean BMI value was lower in women than men (25.2 \(\pm\) 4.3 vs. 26.7 \(\pm\) 3.5, \(p\) < 0.0001). More women than men had a normal weight (51.9% vs. 30.7%), whereas more men than women were overweight (53.3% vs. 31.4%, \(p\) < 0.0001) (Table 1).  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Total**} & \multicolumn{3}{c}{**Men**} & \multicolumn{3}{c}{**Women**} \\
**Parameter** & \multicolumn{3}{c}{**n = 1489**} & \multicolumn{3}{c}{**n = 722**} & \multicolumn{3}{c}{**n = 767**} & \multicolumn{1}{c}{**M vs. W**} \\ \cline{2-7}  & **n** & **\%** & **n** & **\%** & **n** & **\%** & **\(p\) * \\ \hline Educational level: & & & & & & & \\ Primary education/lower secondary education & 142 & 9.5 & 65 & 9.0 & 77 & 10.0 & \\ Vocational education & 473 & 31.8 & 273 & 37.8 & 200 & 26.1 & \\ Upper secondary education & 636 & 42.7 & 305 & 42.2 & 331 & 43.2 & \(<\)0.0001 \\ Post-secondary education & 50 & 3.4 & 16 & 2.2 & 34 & 4.4 & \\ Higher education & 188 & 12.6 & 63 & 8.7 & 125 & 16.3 & \\ \hline Economic status: & & & & & & & \\ Very bad & 8 & 0.5 & 4 & 0.6 & 4 & 0.5 & \\ Bad & 74 & 5.0 & 27 & 3.7 & 47 & 6.1 & \\ Moderate & 784 & 52.7 & 384 & 53.2 & 400 & 52.2 & 0.2711 \\ Good & 560 & 37.6 & 279 & 38.6 & 281 & 36.6 & \\ Very good & 63 & 4.2 & 28 & 3.9 & 35 & 4.6 & \\ \hline Place of residence: & & & & & & & \\ Rural & 624 & 42.0 & 304 & 42.3 & 320 & 41.7 & \\ Urban & 862 & 58.0 & 415 & 57.7 & 447 & 58.3 & \\ \hline Health status: & & & & & & & \\ Very bad & 6 & 0.4 & 3 & 0.4 & 3 & 0.4 & \\ Bad & 43 & 2.9 & 26 & 3.6 & 17 & 2.2 & \\ Moderate & 471 & 31.6 & 226 & 31.3 & 245 & 31.9 & 0.6300 \\ Good & 672 & 45.1 & 325 & 45.0 & 347 & 45.2 & \\ Very good & 297 & 19.9 & 142 & 19.7 & 155 & 20.2 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sociodemographic characteristic, health status, physical activity level, and BMI status of subjects.
## 2 Basic Measurement Techniques for Localization in WSNs
### RSS Profiling Measurement  
RSS based measurement estimates the distance between sensor nodes as was discussed in the previous section. The localization algorithms then use this distance to calculate the position of the sensor nodes. However, the implementation of this kind of algorithm faces two major challenges: first, the wireless environments, especially the indoor wireless environments and the outdoor wireless environments with irregular objects inside the measurement area, make the distance estimation from RSS very difficult. In addition, second, the determination of model parameter is also a very difficult task. To overcome such difficulties, RSS profiling measurement techniques [55; 56; 57; 58] that estimate sensor location from the map of RSS measurements are used to improve the accuracy.  
The RSS profiling measurement works by first constructing a form of map of signal strength of anchor nodes at different locations of the measurement area. The map is obtained either offline via a priori measurements or online by deploying some sniffing devices [56] at some known locations. This kind of technique is mainly used for WLAN, but they would appear to be attractive for WSNs too [50].  
In RSS profiling based localization systems, in addition to anchor nodes and unknown sensor nodes, a large number of sample points, e.g., sniffing devices [56] or reference points are distributed throughout the coverage area. At each sample point, the RSS signal strength is obtained from different anchor nodes, where \(n_{th}\) entry corresponds to the \(n_{th}\) anchor nodes. Obviously, different entries have different signal strength and many of them have zero values or near to zero values due to the large distance from the anchor nodes. The collection of all these points constitute the RSS map of the interested region and is a unique signature corresponding to the anchor locations and the wireless environment. The model is stored in a central location. The non anchor node estimates its position by referring to the RSS map. It calculates the signal strength of its current location and then match the position from the corresponding map whose signal strength is a closest match.
## 2 Methodology of the Analysis Method
### Fitting Procedure  
The 2 DOF model represented by Equation (6) can now be fitted into the BTT sensors data and the vibration parameters of the blades can be identified by this procedure. Figure 2 shows in gray a typical response of the blade in the case of one isolated resonance. The blue and red curves are the displacement curves detected by the two sensors. The data of the two sensors curves can be fitted with the Equation (6). Starting from an estimated value of EO, the unknown parameters are determined by fitting analysis. The fitting unknowns include the natural frequencies \(\left(\omega_{n_{1}},\omega_{n_{2}}\right)\), the damping ratios \(\left(\zeta_{1},\zeta_{2}\right)\), the phase shifts \(\left(\psi_{1},\psi_{2}\right)\) and the two parameters \(\left(\tilde{\zeta}_{1},\tilde{\zeta}_{2}\right)\). The fitting analysis is carried out based on the nonlinear least square curve fitting technique. The value of residual from the least squares method indicates the goodness of fitting. By sweeping EOs and repeating the fitting process, the best curve fit and parameter estimation can be achieved. Defining constraints for unknown parameters reduce the computational cost and results in a faster and more accurate estimation of the parameters.
# Effect of NS-DBD Actuator Parameters on the Aerodynamic Performance of a Flap Lifting Device
## 4 Simulation Results and Analysis
### Discharge Energy
#### 4.3.2 Comparative Analysis of the Flow Field Structure  
A single discharge pulse after the actuator is turned on is selected as a reference to illustrate the different effects of different discharge energies on the flow separation zone. Figure 19 shows the change in velocity distribution. When the discharge energy is improved by one order of magnitude, the pulse intensity increases significantly, and the disturbance velocity and vortex strength are obviously higher than those of the low discharge energy. The disturbance of the low-speed zone and vortex structure on the upper surface of the flap is also significantly stronger than that in the case of low discharge energy, which can integrate the separation zone faster to make the flow reattach, thus having a more obvious effect on drag reduction. From the change of vorticity distribution (Figure 20), with the increase of discharge energy, the vortex falls off and slides downstream faster, and the destruction to the vortex structure near the actuator is stronger. Although it is better in enhancing and accelerating the separated flow and vortex shedding, it has some adverse effects on some unrelated vortex structures. From the pressure distribution in Figure 21, it is also obvious that there is a huge difference in different discharge energies. The higher energy pulse propagates faster, covers a larger range, and has a greater impact on the pressure distribution. However, overly strong energy can easily cause the actuator to breakdown and lose efficacy.  
Figure 19: The velocity distribution under single pulse discharges with different energy densities.  
Figure 18: Lift-drag ratio curve of variable energy.
## 2 Materials and Methods
### MG Modeling  
Since using DGs can have different effects on power system characteristics, application and modelling of DGs can cause various issues. One possible solution to maximize the benefits of using DGs is to have an effective method to optimally model the generation of relevant power and loads in the network as a subsystem in a form of MG. Additionally, by integrating DGs into a MG and using RESs in large quantities, issues related to the optimization of cost and environmental pollutants reduction can be investigated in the targeted system. Such strategies can make appropriate decisions to a better energy management and enhance the performance of operation.  
In addition, using DGs in forms of RESs such as fossil fuel engines, gas and micro turbines (MTs), photovoltaics (PVs) and fuel cells (FCs), and wind turbine (WT) in MG needs higher and complicated technologies, which can significantly lower emissions and air pollution. For instance, FCs which generate power from oxygen and hydrogen are emitting only vapor. However, at the same time of modification of natural gas, various forms of \(NO_{x}\) and \(CO_{2}\) air pollutants are emitted. Another example could be the use of MTs with lower emissions; however, currently they are more expensive.  
In this research work, a typical low-voltage (LV) smart-MG is considered which is shown in Figure 1 consisting of different DGs including MTs, FCs, PVs, WT, and storage system devices (batteries).  
Figure 1: A typical LV-MG model.  
In this regard, it is presumed that all DG resources generate active power at the power factor one, without any requesting or generating reactive power. In addition, there is a link for power exchange from the presented MG and the grid utilized for power trading in various hours of a day according to decisions of MG control center (MGCC).
# A Multi-Plant Height Detection Method Based on Ruler-Free Monocular Computer Vision
## 6 Conclusions  
In summary, the monocular multi-plant height measurement method has a good measurement effect, and it has stronger practical significance for dealing with the situation of multi-plant height measurements, which can meet the requirements of current agriculture for plant height identification to a certain extent. The method can obtain more accurate plant height measurement results. Excluding abnormal data in dark/light environments, the absolute error of AE was not greater than \(\pm 10\) mm, and the proportional error APE was not greater than \(\pm 4\%\). This study conducted experiments within a measurement distance range of 200 cm-300 cm, and no significant effect was found between the result and distance. The current solution in this study needs to improve the measurement effect in dark/light conditions and needs to be further improved in the grayscaling and binarization scheme. There is still room to improve the accuracy of the method in different light conditions and different reference distance values, which still needs to be further explored.  
Conceptualization, Y.L. and H.T.; methodology, Z.X. and M.S.; software, Z.X. and M.S.; validation, Z.X. and M.S.; formal analysis, Y.L., H.T., Z.X. and M.S.; investigation, Z.X.; writing--original draft preparation, M.S. and Z.X.; writing--review and editing, Y.L.; supervision, Y.L. and H.T.; project administration, Y.L. and H.T. funding acquisition, Y.L and H.T. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Light Conditions** & **Distance d/cm** & **Plant 2** & **Plant 3** & **Plant 4** & **SSR** \\ \hline \multirow{3}{*}{Normal light} & 200 & \(-1.757\) & \(-7.232\) & \(-4.861\) & \(79.010\) \\  & 250 & 3.030 & \(-2.778\) & \(-0.846\) & \(17.610\) \\  & 300 & 7.016 & \(-6.043\) & \(-5.506\) & \(116.062\) \\ \hline \multirow{3}{*}{Dark/light} & 200 & \(-0.512\) & 3.345 & 2.996 & \(20.427\) \\  & 250 & \(-1.126\) & 0.900 & 2.154 & \(6.716\) \\  & 300 & \(-0.485\) & \(-1.903\) & 1.038 & \(4.935\) \\ \hline \multirow{3}{*}{Filling light} & 200 & 9.158 & \(-0.154\) & 2.175 & \(88.617\) \\  & 250 & 7.534 & \(-3.892\) & \(-1.777\) & \(75.062\) \\ \cline{1-1}  & 300 & 4.897 & \(-6.950\) & \(-2.750\) & \(79.843\) \\ \hline \end{tabular}
\end{table}
Table 7: Error analysis of the results under different light and distance conditions.  
**Funding:** This work was supported by the Tianjin Municipal Science and Technology Plan Project of China (Grant No. 23 YDTJPJC00370, 22 ZYCGSN00080).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results
### Frictional Stress  
The maximum frictional stress was highest under the first metatarsal head without CMI and with CMI fabricated from various materials and their combinations (Figure 5). All  
Figure 4: The contact pressure distribution with soft material to stiffer material and its combinations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**CMIs**} & \multicolumn{3}{c}{**Peak Contact Pressure (kPa)**} & \multicolumn{3}{c}{**Ratio**} \\ \cline{2-7}  & **FF** & **MF** & **HF** & **MF/FF** & **HF/FF** & **MF/HF** \\ \hline w/o & 275.0 & - & 279.0 & - & 0.98 & - \\ CMI-A & 41.3 & 39.3 & 45.2 & 0.95 & 0.91 & 0.87 \\ CMI-B & 40.2 & 39.5 & 45.6 & 0.98 & 0.88 & 0.87 \\ CMI-C & 39.7 & 39.4 & 45.9 & 0.99 & 0.86 & 0.86 \\ CMI-D & 41.3 & 39.2 & 45.5 & 0.95 & 0.90 & 0.86 \\ CMI-E & 40.1 & 39.4 & 45.6 & 0.98 & 0.87 & 0.86 \\ \hline \hline \end{tabular} w/o = without CMI; FF = forefoot; MF = midfoot; HF = hindfoot.  
\end{table}
Table 3: Peak contact pressure without and with CMI fabricated from various materials and their combinations.  
CMIs showed reduction of maximum frictional stress compared to without CMI. Among the CMIs the maximum reduction was observed with CMI-A fabricated from EVA. As CMI-A being a softer material than CMI-C (TPU), the frictional stress was lowered by 3.36% under the first metatarsal head. However, the combination of EVA and TPU (CMI-D) resulted in an increase of the maximum frictional stress by 4.11% compared to CMI-A during balanced standing.
## 4 Discussion
### NZ Influence on Soil N and C Pools  
The introduction of natural zeolitites (5 NZ and 15 NZ, respectively) affected the soil C and N pools. The decrease in the total reserve of Fix N-NH4\({}^{+}\) in 15 NZ in Nov-2012 was probably due to a dilution effect operated by the high amount of NZ (devoid of N) introduced. Instead, the decrease of Fix N-NH4\({}^{+}\) during the growing season suggest an exploitation by plants, confirming the active role of this pool in crop nutrition [55; 56]. Nevertheless, no significant differences in the amount of Fix N-NH4\({}^{+}\) pool were found between the various treatments, suggesting that the N subjected to CEC processes by zeolitites belongs to the exchangeable pool. The generally lower N-NO3\({}^{-}\) content in the NZ treatments may be attributed both to the aforementioned controlled retention of NH4\({}^{+}\) by the zeolitite, which can reduce the availability of N to nitrifying bacteria and to the lower amount of N fertilizer (from 30 to 50%) with respect to CNTR (where N-NO3- pool increased significantly). The introduction of NZ also affected microbial biomass N and C immobilization. In Nov-2012, lower Cmic was observed in all ZT, especially in 5 NZ, suggesting that NZ introduction cause an initial disequilibrium in microbial biomass. During the sorghum-growing season (Jun-2013), microbial biomass was probably influenced by the competition with the plants, resulting in a consistent decrease in Cmic and Nmic in all the treatments. Apparently, 15 NZ treatment had a stronger influence on microbial biomass N, resulting in a lower N immobilization that was already visible in May-2013 (in this case even Cmic pool was affected) and lasted until Sep-2013, suggesting more zeolitite was in the soil the less N was easily available to soil microorganisms. The C/N ratio of microbial biomass may give information regarding bacterial or fungal prevalence in the microbial community; usually bacteria have on average a lower C/N than fungi because of a lower N requirement by the latter [57; 58]. Agricultural management (tillage or no-tillage), nutrient availability, soil pH, temperature and moisture may influence fungal/bacterial ratio [15; 59; 60]. In this respect, Cmic/Nmic ratio suggested that the introduction of natural zeolitites caused a shift in microbial population through a greater fungal prevalence [15; 59; 60] especially in 15 NZ. A greater fungal/bacteria biomass ratio can be often interpreted as marker of a more sustainable agricultural system, where the nutrients required for the plant growth are supplied by OM decomposition and N mineralization with probably lesser N loss in the environment [15; 59; 60]. It is thus likely that the presence of zeolites caused a lower N accessibility into the N soil pools, making it less available to soil microorganisms and favoring the development of fungal biomass. These implications on microbial biomass affect the soil C cycle, where a lower HI and a greater FA fraction content can contribute to lower C immobilization.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 4 Performance Metric and Simulation Setup
### Results
#### 4.4.1 Comparative Analysis 1  
This case considers a static scenario with propagation model (TK) and simulation time 20 s, node speed 20 m/s, mobility model Random-Way Point as constant factors. The transmit power and transmission range of each node are also fixed. The routing protocols are compared based on the performance metrics varying the number of nodes. Figure 5a depicts OLSR outperforms AODV and DSDV in terms of end-to-end delay. For packet delivery ratio and throughput as shown in Figure 5b,c OLSR shows better performance with the increase in the number of nodes. Thus, overall, the performance of OLSR is better than AODV and DSDV in terms of throughput, end-to-end delay, and packet delivery ratio.
###### Abstract  
Rapid advancements in construction technologies have accelerated the development of complex and deep underground structures, raising concerns about the impact of groundwater on structures, particularly anti-floating measures. Traditional tensioned anchors, commonly used for preventing flotation, suffer from limitations like low pull-out bearing capacity, shallow critical anchoring depth, and localized stress concentration. To overcome these limitations, this paper introduces a tension-compression dispersed composite anchor, which combines casing, load-bearing plates, and tensioned anchors. Comparative tests were conducted between these composite anchors and traditional tensioned anchors to analyze their anchoring behavior. Our results show that tensioned anchors exhibit a stable axial force distribution as anchoring length increases. By identifying abrupt changes in the axial force curve, optimal anchoring lengths for load-dispersed anchors can be determined, thereby enhancing rock and soil strength utilization. The tension-compression-dispersed composite anchor outperforms tensioned anchors, with 1.44 times the ultimate bearing capacity for equivalent anchoring lengths and 1.1 times the capacity for an additional 1 m length. It also displays superior deformation adaptability and structural ductility under high-bearing loads compared to tensioned anchors with extended anchoring lengths. Effectively mobilizing the strength of the lower anchoring segment within the rock and soil results in a lower critical anchoring depth and a more uniform distribution of lateral friction resistance. In conclusion, the tension-compression-dispersed composite anchor offers significant advantages, making it a promising engineering solution for anti-floating anchor systems in complex underground environments.  
foundation pit engineering; anchoring performance; field test; ultimate bearing capacity; distribution law of axial force
# Review of Advanced Medical Telerobots
## 2 Medical Telerobotic Topologies
### Multilateral Teleoperation
#### 2.3.4 Trilateral Teleoperation
In [258], for instance, an expertise-based surgical training framework was introduced, in which novice trainees will be guided with haptic guidance, and when the acquiring higher expertise, the force feedback would gradually switch from the haptic guidance toward environmental force reflection. Fuzzy logic is used in this work to fuse several objective measures about the expertise of the novice surgeon. Dual-User Redundancy Control (DURC)  
The difference between this topology and DUST topology is that here the two operators control different degrees of freedom, joint, or motions of the follower robot, while in DUST topology the two leader motions are fused to control the Cartesian motion of the follower robot in the task space. This feature improves the operability of the follower robot for complex environments and enables multitasking due to the separate assignments of the degrees of freedom of the follower robot to the operators [279]. DURC topology is used for tasks such as robotic telerehabilitation [280] in which a 6-DoF arm motion of the patients is to be controlled using two 3-DoF leader systems controlled by two operators to perform complex rehabilitative tasks. It is crucial for the leader robots to operate in synchrony to coordinate arm movements safely (for which a local intelligence can be of high benefit). Other examples can be found in [281, 282, 310]. Section Vision  
Having established various topologies of teleoperation systems, their design, usages, challenges, the future direction of this field of research should be evaluated holistically. We have shown that several topologies of telerobotic architecture have allowed for a large spectrum of tasks and applications, which would not be possible in the absence of such technology. Among all the challenges facing teleoperation, uncertainty, and time delay are still the most prominent problems resulting in asynchrony, instability, and task failure. New advanced intelligent control architecture, predictive models, and the use of autonomous agents can help to mitigate the mentioned challenges.