esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Numerical Results
In particular, the \(\alpha\) parameter influences both the processing capacity available on satellites:  
\[\Gamma_{Sat}=v_{Sat}/n_{cyc}=\alpha\cdot\bar{f_{0}}/\tau \tag{37}\]  
and the unit-processing energy cost on satellites:  
\[\gamma_{p}^{i}=k\cdot n_{cyc}\cdot v_{t}^{2}=k\cdot n_{cyc}^{3}\cdot(\bar{f_{0 }}/\tau)^{2}\cdot\alpha^{2} \tag{38}\]  
where \(k\) represents the processor effective capacitance coefficient, and it will be set equal to \(10^{-27}\)[18]. Please notice that, given this energy consumption model, by increasing \(\alpha\) we linearly increase the number of services which can be processed in a time cycle, but at the same time we quadratically increase the energy consumption to process each service. While we assume that GSs have unlimited available energy, satellites nodes have a limited amount of energy available on board to be consumed for OEC-related operations (i.e., the processing and transmission of data). In particular, we assume that satellites have an amount of energy:  
\[\varepsilon_{Sat}=\
abla\cdot\bar{P}\cdot\tau \tag{39}\]  
available during each time cycle, whose value is dependent on the \(\
abla\) parameter indicating the percentage of the maximum power \(\bar{P}\) available on board a Sentinel-2 satellite [1]. This assumption is justified by the fact that the power available on board of satellites is always higher than the amount needed by the satellite (both for bus devices and payload instruments); then, an amount of this extra power (which is always generated, regardless of its actual consumption) can be used for OEC operations. For example, in the case of Sentinel-2 satellites, at the end of its life, each satellite has a power available equal to \(\bar{P}=1700\) W, but only \(1250\) W is actually consumed: thus, up to around \(25\%\) of the available power can be still used to support OEC. Moving to transmission links, following [40], we will consider a transmission power \(P_{t}=10\) W, antenna gain \(G=27\) dBi, carrier frequency \(\
u=26\) GHz, system noise temperature \(T_{s}=290\) K, and minimum required Eb/N0 ratio \(Eb/N0|_{min}=6\) dB. We also consider a data rate of \(R_{ISL}=500\) Mbps for ISLs and \(R_{dl}=520\) Mbps in case of downlink to the GS, as in the case of Sentinel-2 satellites [1]. Finally, the memory on each satellite is limited to \(M_{i}=100\) Gb, \(\forall i\in[0,\ldots,N_{S}-2]\) to support the worst case load of data in the service generation scenario described further on, while we will consider GSs to have an unlimited storage capacity, since we assume data centers with enough capacity to handle all the data produced by the constellation. The energy cost to store data in memory will be considered equal to zero.
# Development of an Electric Pulse Device for Coal Grinding
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Diameter of the**} & \multirow{2}{*}{**The Negative Electrode in the Electric Pulse**} & \multirow{2}{*}{**The Negative Electrode in the Electric Pulse**} \\  & & & **Grinding Device Is a Solid Flat Metal Plate** \\  & & & **with Openings of 2000 \(\upmu\)m** \\ \cline{2-5}  & **Average Mass of the** & **Output of the** & **Average Mass of the** & **Output of the** \\  & **Obtained Product** & **Finished Product** & **Obtained Product** & **Finished Product** \\  & **(m\({}_{1}\), g)** & **(K, \%)** & **(m\({}_{1}\), g)** & **(K, \%)** \\ \hline \hline
**d, \(\upmu\)m** & & **U = 32 kV, C = 1.2 μF, f = 2 discharge/s, \(\uptau\) = 10 min** & \\ \hline
100–200 & 30.4 \(\pm\) 0.8 & 7.6 & 82 \(\pm\) 2.1 & 20.5 \\ \hline
**d, \(\upmu\)m** & & **U = 32 kV, C = 1.2 μF, f = 3 discharge/s, \(\uptau\) = 10 min** & \\ \hline
100–200 & 41.6 \(\pm\) 1.1 & 10.4 & 106.8 \(\pm\) 3.7 & 26.7 \\ \hline
**d, \(\upmu\)m** & & **U = 32 kV, C = 1.2 μF, f = 4 discharge/s, \(\uptau\) = 10 min** & \\ \hline
100–200 & 47.2 \(\pm\) 1.3 & 11.8 & 125.2 \(\pm\) 4.2 & 31.3 \\ \hline
**d, \(\upmu\)m** & & **U = 32 kV, C = 1.2 μF, f = 5 discharge/s, \(\uptau\) = 10 min** & \\ \hline
100–200 & 38.4 \(\pm\) 0.9 & 9.6 & 128.4 \(\pm\) 3.9 & 32.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of coal grinding depend on the frequency of the electric pulse discharges (f). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Voltage of Pulse Discharge** & **U = 28 kV** & **U = 32 kV** \\ \hline
**Condenser capacity** & **Energy consumption of the installation at a pulse discharge frequency of** **f = 4 discharges/s** \\ \hline C = 0.4 μF & 0.37 kWh & 0.44 kWh \\ \hline C = 0.8 μF & 0.75 kWh & 0.86 kWh \\ \hline C = 1.2 μF & 1.14 kWh & 1.31 kWh \\ \hline
**Condenser capacity** & **Energy consumption of the installation at a pulse discharge frequency of** **f = 5 discharges/s** \\ \hline C = 0.4 μF & 0.44 kWh & 0.51 kWh \\ \hline C = 0.8 μF & 0.87 kWh & 1.33 kWh \\ \hline C = 1.2 μF & 1.31 kWh & 1.52 kWh \\ \hline C = 1.6 μF & 1.75 kWh & 2.14 kWh \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of studies determining the energy consumption of the electric pulse installation. Studies were conducted to determine the energy efficiency of the electric pulse method compared with traditional mechanical methods. A laboratory cone mill with a motor power of 1.5 kW was used as the traditional mechanical mill. The grinding time of the raw materials was 5 min. The diameter of the finished product was d < 200 mm. The experimental data indicated that the mass of product obtained through mechanical grinding could be achieved using the electric pulse method at a discharge frequency of f = 4 discharge/sec (Table 6). The laboratory electric pulse installation demonstrated a productivity of approximately 2-3 kg/h for the finished product, with a particle diameter of 200 \(\mu\)m.
# A Cyclic Graywater Treatment Model for Sustainable Wastewater Management Applied in a Small Scale
## 1 Introduction
Other research has indicated that quinoa, like many plants, has the capacity to accumulate substantial amounts of heavy metals, particularly Ni, Cr, and Cd, in its leaves [26]. Notably, quinoa has been classified as a hyperaccumulator for six trace metals, with a bioconcentration factor exceeding 1 [27]. When compared to other plants such as _Brassica napus_ and _Eichhornia crassipes_, quinoa's adaptability to a wide range of contaminants, combined with its superior salinity tolerance, positions it as a more versatile candidate for phytoremediation [28]. While aquatic species like _Eichhornia crassipes_ and _Centrella asiatica_ exhibit notable removal efficiencies for specific pollutants, such as _E. crassipes_, removing up to 98% of phosphates in aquaculture wastewater [29]. Quinoa's ability to stabilize and accumulate heavy metals makes it a more comprehensive solution. Additionally, plants like _Lemma minor_ and _Salvinia minima_ have demonstrated high removal efficiencies for total suspended solids and ammonia nitrogen in wastewater [30], but quinoa's enhanced ability to sequester heavy metals positions it as a superior option for treating contaminated soils. Considering these factors, particularly its tolerance to salinity and its phytoremediation capabilities, quinoa was selected as the ideal plant for our nature-based cyclic graywater treatment system, offering both environmental sustainability and enhanced treatment performance. However, there are practically no reported studies with valid scientific experiments examining the effects of cyclic treatment on quinoa, soil, and drainage water characteristics. Thus, the aim of the present study is to evaluate the efficiency of cyclic graywater treatment in improving water quality at the farm level. Specifically, the study will assess the impact of irrigation with raw versus treated graywater from each treatment level on soil characteristics (pH, ECe, BOD5, COD, and contaminants) under various conditions (with and without plants). Additionally, it will investigate the changes in drainage water characteristics (volume, pH, EC, soluble salts, BOD5, COD, and microbiological parameters). Finally, the study will analyze quinoa growth parameters for each treatment level. It is hypothesized that cyclic graywater treatment will improve soil and drainage water quality, while influencing quinoa growth positively under varying conditions of irrigation.
# HYDRUS-1D Simulation of Soil Water Dynamics for Sweet Corn under Tropical Rainfed Condition
## 2 Material and Methods
### HYDRUS-ID Model
#### 2.2.2 HYDRUS-1D Input Parameters  
Estimation of Soil Hydraulic Parameters  
The soil hydraulic parameters such as \(\theta_{s}\), \(\theta_{r}\), \(m\), \(a\), and \(n\) (Table 3) were estimated using the ROSSETA computer program for the HYDRUS-1D input. The value of the pore connectivity parameter (\(l\)) was assumed to be (0.5) [13].  
Figure 4: Flow chart presenting model simulation process and calculation of water balance components.  
Estimation of Potential ET  
HYDRUS-1D requires potential evaporation and potential transpiration fluxes as input values to simulate soil water effect on transpiration. The Penman-Monteith equation [25] was used to calculate the reference evapotranspiration (\(ET_{0}\)) using available soil, crop, and climate data. The crop potential evapotranspiration (\(ET_{c}\)) was calculated from the reference evapotranspiration (\(ET_{0}\)) and the crop coefficient (\(K_{c}\)) as given in Equation (7) under normal conditions [25]. The Kc values for four stages (early, development, mid, and late seasons) of sweet corn were 0.4, 0.8, 1.15, and 1, respectively [26]. Potential evapotranspiration (\(ET_{P}\)) was divided into potential evaporation (\(E_{P}\)) and Potential transpiration (\(T_{P}\)) using the AquaCrop model.  
\[ET_{0}\ =\ \frac{0.408\Delta(R_{n}\ -\ G)\ +\ \gamma\ \frac{900}{T+273}u_{2}(e_{s}\ -\ e_{a})}{ \Delta\ +\ \gamma(1\ +\ 0.34u_{2})}, \tag{6}\]  
where, \(ET_{0}\) = reference crop evapotranspiration (mmday\({}^{-1}\)), Rn = net radiation at the crop surface (MJm\({}^{-2}\)day), G = soil heat flux density (MJm\({}^{-2}\)day), T = air temperature at 2 m height (\({}^{\circ}\)C), u\({}^{2}\) = wind speed at 2 m height (ms\({}^{-1}\)), \(e_{s}\ -\ e_{a}\) = saturation vapour pressure deficit (kPa), \(\Delta\) = slope vapor pressure curve (kPa/\({}^{\circ}\)C), \(\gamma\) = psychometric constant (kPa/\({}^{\circ}\)C), and 900 = conversion factor  
\[ET_{c}\ =\ ET_{0}\ \times\ K_{c}. \tag{7}\]  
The root water uptake was determined from [27].  
\[S\ =\ \alpha(h)S_{max}, \tag{8}\]  
where \(S\) is the rate of root water uptake, \(S_{max}\) is the maximum rate of root water uptake, and \(\alpha(h)\) is the coefficient of root water uptake. The values of coefficients h\({}_{0}\), h\({}_{\mathrm{opt}}\), h\({}_{\mathrm{2H}}\), h\({}_{\mathrm{2L}}\), and h\({}_{3}\) were taken as \(-\)10, \(-\)25, \(-\)500, \(-\)1000, and \(-\)16,000 respectively [13]. It is assumed that the water uptake is zero near saturation and for pressure heads less than the wilting point (h\({}_{3}\)). The root water uptake is assumed optimal between pressure head h\({}_{\mathrm{opt}}\) and h\({}_{2}\), whereas it shows a linear increase or decrease for pressure heads between h\({}_{2}\) and h\({}_{3}\).
# Reutilization of Recycled CDW Sand in Mortars, Paving Blocks, and Structural Concrete
## 1 Introduction
One RA was predominantly composed of concrete (98.8%), while the other contained 77.7% concrete and 21.8% natural stones. Their findings showed that replacing NAs with 25% or 50% RA had a negligible effect on compressive strength, whereas higher replacement levels caused a reduction in compressive strength. Additional tests, including assessments of shrinkage, skid resistance, and plant trials, yielded positive results. In later research, [PERSON] et al. [18] identified particle packing as a key factor influencing the performance of concrete paving blocks. The appropriate packing optimization led to a significant increase in the compressive strength of concrete paving blocks made entirely with RAs, rising from approximately 30 MPa to 79 MPa. This strength was close to the maximum strength achieved in samples made with only NA, which was 84.8 MPa. According to the report on a Sustainable Future for the European Cement and Concrete Industry [35], mortars and plasters comprise 24% of cementitious downstream products in Europe, while non-reinforced cement-based products represent 19%. Additionally, cur rently there are no standards that address the incorporation of CDW into non-structural elements. Therefore, this study aims to characterize one particular CDW RA and to evaluate its performance when used to partially replace natural calcareous aggregates in the manufacture of paving blocks, mortars, and structural concrete. The findings of this study aim to encourage the reuse of this particular CDW as raw material for high-value applications, thereby fostering a construction industry aligned with circular economy principles.
###### Abstract  
Few data exist concerning the right ventricular (RV) physiological adaptation in American-style football (ASF) athletes. We aimed to analyze the RV global and regional responses among ASF-trained athletes. Fifty-nine (20 linemen and 39 non-linemen) ASF athletes were studied before and after inter-seasonal training. During this period, which lasted 7 months, all athletes were exposed to combined dynamic and static exercises. Cardiac longitudinal changes were examined using three-dimensional transthoracic echocardiography. A computational method based on geodesic distances was applied to volumetrically parcellate the RV into apical, outlet, and inlet regions. RV global and regional end-diastolic volumes increased significantly and similarly in linemen and non-linemen after training, with predominant changes in the apex and outlet regions. RV global and regional ejection fractions were preserved. Morphological changes were uniformly distributed among the four cardiac chambers, and it was independent of the field position. Assessment of RV end-diastolic global, inlet and apical volumes showed low intra-observer (3.3%, 4.1%, and 5.3%, respectively) and inter-observer (7%, 12.2%, and 9%, respectively) variability, whereas the outlet regional volumetric assessment was less reproducible. To conclude, ASF inter-seasonal training was associated with a proportionate biventricular enlargement, regardless of the field position. Regional RV analysis allowed us to quantify the amount of exercise-induced remodeling that was larger in the apical and outlet regions.  
keywords: 3D echocardiography; chronic exercise; right ventricular volume; regional remodeling; football +
Footnote †: journal: Journal of Acoustics and Speech
# How to Correctly Detect Face-Masks for COVID-19 from Visual Information?
## 5 Results and Discussion
### Classification Results
DenseNet-121 performs slightly worse, but is considerably faster and has a smaller memory footprint. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model Size [MB]**} & \multicolumn{3}{c}{**Time**} & \multirow{2}{*}{**Accuracy (\(\mu\pm\sigma\)) [\%]**} \\ \cline{3-3} \cline{5-5}  & & **Overall [s]** & & \\ \hline ResNet-152 & 223 & 79 & 6.23 & \(98.93\pm 0.10\) \\ DenseNet-161 & 103 & 76 & 5.99 & \(98.75\pm 0.12\) \\ DenseNet-201 & 71 & 60 & 4.73 & \(98.62\pm 0.14\) \\ ResNet-101 & 163 & 64 & 5.04 & \(98.55\pm 0.13\) \\ DenseNet-121 & 28 & 51 & 4.02 & \(98.50\pm 0.12\) \\ VGG-19 & 513 & 62 & 4.89 & \(98.49\pm 0.12\) \\ DenseNet-169 & 49 & 54 & 4.26 & \(98.48\pm 0.13\) \\ ResNet-50 & 90 & 51 & 4.02 & \(98.42\pm 0.14\) \\ ResNet-34 & 82 & 41 & 3.23 & \(98.28\pm 0.13\) \\ ResNet-18 & 43 & 38 & 2.99 & \(98.24\pm 0.12\) \\ MobileNet & 8.8 & 38 & 2.99 & \(98.19\pm 0.14\) \\ SqueezeNet v1.1 & 2.8 & 35 & 2.76 & \(98.09\pm 0.15\) \\ SqueezeNet v1.0 & 2.9 & 39 & 3.07 & \(98.06\pm 0.15\) \\ GoogLeNet & 22 & 38 & 2.99 & \(98.01\pm 0.16\) \\ AlexNet & 218 & 33 & 2.60 & \(97.81\pm 0.16\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The average classification accuracy of each model on the FMLD test set together with the prediction speed and the size of the model on disk, arranged in descending order. The processing time is computed over all 12,688 face images and on a per—image basis on a NVIDIA Titan Xp GPU. The prediction accuracy is reported for a bootstrapping protocol with 100 sampled test sets of 5000 images. At the other end of the spectrum are the SqueezeNet models, which show an even weaker recognition performance than DenseNet-121, but are, therefore, among the fastest and smallest of all tested models. If we disregard AlexNet, which is somewhat older than the rest of the tested models, the results point to a trend, in that the smaller models (represented by small radii in Figure 9) are typically slightly worse in terms of performance compared to the larger models, but have an edge with respect to processing speed.
## 4 Dynamic Analysis of the PIM System
### Motion Response of the PIM
Subsequently, the tension gradually stabilizes and reaches 1.355 \(\times\) 10\({}^{8}\) N at 280 s. In contrast, the simulation results of the catenary method indicate that the tension slightly increases in the first few seconds, followed by a sharp drop and gradual growth, peaking at 1.537 \(\times\) 10\({}^{8}\) N at 120 s, and then slowly decreasing to a level comparable to that of the ANCF. Through comparative analysis, it is clear that the two modeling methods exhibit certain differences. The simulation results of the ANCF highlight the phenomenon of the \"resonance zone\", a topic widely mentioned in the literature. In this study, as the fluctuation of the \"resonance zone\" did not significantly impact the stability of the peak, this phenomenon was not explored further. Additionally, unlike the ANCF, a unique peak phenomenon was noted in the results of the catenary, due to the limitations of ADAMS in simplifying hydrodynamics into concentrated forces. The model used in this paper aligns with existing research results [15; 18]. Currently, commercial software has yet to achieve the perfect integration of kinematics and fluid dynamics. Therefore, compared to the catenary, the ANCF's suspended deployment simulation exhibits its superiority, thereby confirming the advanced modeling capabilities of this model in addressing kinematics and fluid dynamics coupling issues. From Figure 12, it is observed that, under the condition of lateral water flow, both models achieve a maximum horizontal velocity of 21 m/s and a consistent maximum vertical velocity of 32 m/s. The horizontal and vertical velocities of the ANCF are influenced to varying degrees within the \"resonance zone\", exhibiting small fluctuations over a narrow range and maintaining a high degree of consistency during the initial seconds as well as post 75 s.  
In conclusion, the modeling approach presented in this paper is feasible and can be used to simulate the suspended deployment process of large floating structures.
## 4 Discussion
### Construction/Manufacturing Time  
The main drivers of industrialized construction include savings in labor, overall cost reduction, and decreased construction time. It is widely agreed that industrialized construction reduces construction time compared to traditional methods, primarily due to the reduction in time spent resolving on-site conflicts [97]. This is crucial because delays in construction schedules lead to various adverse effects such as cost overruns, disputes, arbitration, and project underperformance. However, the duration of industrialized projects may exceed traditional ones due to technological advancements and industry knowledge gaps [98]. Prefabricated construction projects are noted to involve multiple workspaces, more complex construction techniques, and more stakeholders, increasing the likelihood of schedule delays compared to traditional on-site construction [94]. Lightweight materials and structures are highlighted in prefabricated construction over dense systems for their superior performance in prefabrication contexts [68]. Therefore, the development of new industrialized construction systems should focus on these types of structures.  
Few scientific studies provide specific indicators of personnel or machinery performance associated with the manufacturing or assembly time of construction elements. This is significant because most studies only offer favorable statements regarding the productivity of industrialized elements compared to traditional systems. However, [PERSON] and [PERSON], 2023 [28], found contradictory results based on productivity indicators for on-site concrete and steel box prototypes, with performances of 0.24 and 0.38 [m\({}^{2}\)/hour-person], respectively. The productivity of the concrete box prototype was about one-third lower than that of the steel prototype. Overall labor productivity when comparing on-site module production was higher in both scenarios. Prefabricated concrete and steel box prototypes had performances of 0.12 and 0.1 [m\({}^{2}\)/hour-person], respectively, indicating a 50% lower productivity for the concrete prototype and 75% lower for the steel prototype. This increase in construction time using prefabricated modules was attributed to a lack of digital tool management and specific construction techniques among personnel. Additionally, construction time sensitivity is acknowledged to depend heavily on on-site conditions and preliminary preparation [99].
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 2 Framework for HWLNet
### Architecture  
There are two basic ways to incorporate LiFi into a WiFi network: autonomous and centralized. In the autonomous method, the WiFi network is extended with LiFi APs to a degree to which a user can freely choose any type of AP from the two technologies. Even though this approach is less complicated in terms of network management, it offers low performance in terms of user handover and connectivity. A centralized approach offers a more controllable network via a central control unit. This enables us to optimize routing tables and resource allocation via a software defined networking controller (SDN). One type of this network uses an SDN-enabled switch to connect LiFi and WiFi APs with the use of SDN agents, which in turn send information to the SDN controller, which uses given information to make decisions on packet routing and user handover, as shown in Figure 2.  
To the best of our knowledge, a fully operational SDN for real-world applications has not yet been developed. There are many models and research projects such as 5G-CLARITY that simulate the use of SDN controller, but the development of HWLNet is still in its experimental phase [6].