esa-sceva/satcom-chunk-collection huggingface dataset:
# Engineering Hydroponic Systems for Sustainable Wastewater Treatment and Plant Growth
## 3 Results
### Dissolved Oxygen  
Initially, all hydroponic systems showed low DO levels (GP: 1.28 mg L\({}^{-1}\), NFT: 1.87 mg L\({}^{-1}\), MT: 2.01 mg L\({}^{-1}\), EF: 1.5 mg L\({}^{-1}\), FF: 1.80 mg L\({}^{-1}\), and AP: 2.27 mg L\({}^{-1}\)), some below the recommended minimum (DO = 5 mg L\({}^{-1}\)) [25]. To account for temperature variations, the theoretical saturation point corresponding to the recorded temperature was utilized due to the temperature-dependent nature of DO concentration. Figure 5a shows the percentage of the DO compared to the theoretical saturation point for all experiments, and Figure 5b shows the average DO for each hydroponic. In the beginning of the experiment, the theoretical saturation averaged between 10% and 20%. After the initial 24 h, the saturation levels increased in all systems as follows: GP (61.39%), NFT (59.44%), MT (60.43%), EF (40.37%), FF (48.70%), and AP (96.1%,), except for the control group (C) which remained at 11.88%, 1.61 mg L\({}^{-1}\). It was found that the rice runs showed noticeably lower average saturated oxygen.  
Figure 5: (a) Average DO throughout the experiment for all the systems averaged to the experiment run. (b) Average DO throughout the experiment for all the systems. Geyser pump (GP), nutrient film technique (NFT), misting (MT), ebb and flow (EF), full flow (FF), aeroponics (AP), control (C).  
All the hydroponic systems started at a low level, some below the recommended minimum level of the DO, but quickly increased. In contrast, the C tended to have a level below the recommended level and at some points dropped close to the level of inhibition. The relationship between DO levels at the start, after 1 day, and at the end of the experiment was examined using a \(t\)-test, with significance set at \(p\leq 0.05\) (Table 2).  
Table 2 highlights that all hydroponic systems experienced a significant (\(p\leq 0.05\)) increase in DO, with the GP system exhibiting the highest increase, followed by AP. FF and EF, characterized by their slow flow, showed a comparatively lower DO level and displayed the lowest growth. Surprisingly, the MT system demonstrated the greatest growth, despite not having the highest DO level, while the AP system, with the second-highest DO level, showed the next highest growth.
## 5 Conclusions  
The analysis revealed that the majority of in-game actions among U14 players occurred at low to moderate intensities. Specifically, 94.25% of acceleration actions were below 1.546 m/s\({}^{2}\), and 96.64% of deceleration actions were above \(-\)1.223 m/s\({}^{2}\), indicating a predominance of submaximal efforts. Similarly, 96.13% of movement velocities were below 14.64 km/h, although players demonstrated the capacity to reach sprint speeds exceeding 20 km/h. Player load data showed that 77.59% of effort occurred within moderate to high intensity zones, with 19% in the highest zone (>1.95 AU/min), suggesting a substantial capacity for high-intensity effort in this age group.  
Impact analysis indicated that 84.62% of contact events occurred within moderate-to-high-frequency zones (133.45-206.59 contacts/min), while only 3.36% exceeded this range. High-impact actions (>8G) were relatively infrequent, with only 8.37% occurring in the highest intensity zone (>4.42 contacts/min), reflecting the developmental nature of physical contact at this age. These findings underscore the importance of age- and context-specific monitoring to support appropriate training load management and long-term athlete development.  
The five intensity zones established for each variable are as follows:  
* For acceleration (n = 74 million actions), five zones were defined: <0.37, 0.37-0.81, 0.81-1.54, 1.54-3.49, and >3.49 m/s\({}^{2}\).
* For deceleration (n = 53 million actions), the zones were: \(<\)\(-\)0.26, \(-\)0.27 to \(-\)0.63, \(-\)0.63 to \(-\)1.22, \(-\)1.22 to \(-\)2.545, and >\(-\)2.54 m/s\({}^{2}\).
* For speed (n = 5 million actions), the intervals were: <5.42, 5.42-10.19, 10.20-14.63, 14.64-18.59, and >18.59 km/h.
* For player load (n = 763 AU/min), the zones were: <1.07, 1.07-1.36, 1.37-1.63, 1.64-1.95, and >1.95 AU/min.
* For total impacts (n = 717 game quarters), the intervals were: <133.45, 133.45-158.75, 158.76-181.45, 181.46-206.59, and >206.59 contacts/min.
* For high impacts (n = 717 game quarters), the zones were: <1.13, 1.14-2.11, 2.12-3.13, 3.14-4.42, and >4.42 contacts/min.  
Finally, the comparative analysis reinforces the need for contextual specificity in sports science research. Performance characteristics in basketball vary significantly with age, competitive level, gender, and game structure. Therefore, generalizing findings across populations may lead to inaccurate interpretations. This study contributes to the growing body of evidence supporting individualized and developmentally appropriate approaches to load monitoring in youth sports.
## 4 Analysis of the Rock Mass Discontinuities
As can be seen in Figure 7, seven dominant orientation groups (i.e., J1-J7, dip direction \(\angle\) dip angle) on the studied surface can be categorized, namely J1 (95.19\({}^{\circ}\)\(\angle\) 76.54\({}^{\circ}\)), J2 (213.11\({}^{\circ}\)\(\angle\) 66.35\({}^{\circ}\)), J3 (154.29\({}^{\circ}\)\(\angle\) 71.01\({}^{\circ}\)), J4 (251.57\({}^{\circ}\)\(\angle\) 79.26\({}^{\circ}\)), J5 (51.71\({}^{\circ}\)\(\angle\) 59.91\({}^{\circ}\)), J6 (310.91\({}^{\circ}\)\(\angle\) 50.59\({}^{\circ}\)), and J7 (354.09\({}^{\circ}\)\(\angle\) 69.53\({}^{\circ}\)). The detailed statistical results of each dominant group are shown in Table 2.  
The common method is to project the orientation of rock mass discontinuities onto the Schmidt equal-area projection to obtain the distribution of discontinuity poles. The dominant groups of the orientation of discontinuity can be calculated by converting the normal vector into a stereographic projection, and the pole density within each projection area can be analyzed; then the local maxima of the pole density can be computed. For convenience, a contour map can be constructed based on the pole density in each area, as illustrated in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dominant** & **Dip Direction/\({}^{\circ}\)** & **Dip Angle/\({}^{\circ}\)** & **Point Density** & \begin{tabular}{c} **Point Cloud** \\ **Radio \%** \\ \end{tabular}  \\ \hline J1 & 95.1948 & 76.5416 & 1.7201 & 29.54 \\ J2 & 213.1130 & 66.3470 & 0.7480 & 14.04 \\ J3 & 154.2879 & 71.0093 & 0.5924 & 15.49 \\  
On the studied surface, as can be seen from the calculated results shown in Figures 7 and 8, the dominant groupings J1-J4 are widely distributed and account for a significant proportion of the point cloud (-75.47% of the total point cloud, Table 1), and they have relatively high dip angles, ranging from 75\({}^{\circ}\) to 80\({}^{\circ}\). Conversely, the dominant groupings J5-J7 are less distributed, comprising 24.52% of the total point cloud, with relatively lower dip angles, ranging from 50\({}^{\circ}\) to 70\({}^{\circ}\).
# Intra-System Reliability Assessment of 2-Dimensional Shear Wave Elastography
## 2 Materials and Methods
To remain consistent with published guidelines, a single circular ROI was utilised for sampling [33]. The mean SWV (metres/sec) was recorded using a ROI sampling size of 10 mm and repeated using a ROI size of 5 mm. Each operator utilised the on-system SWE measurement software and placed the ROI within each target. Using the wavefront visual propagation map, each operator then placed the sampling ROI in an area of the highest reliability. The intervals between the contours on the visual propagation map are wider in regions where the shear waves travel faster and closer together when travelling slower (Figure 1). Areas of parallel contour lines represent areas in which shear waves propagate smoothly and indicate that the reliability of data acquisition in these areas is highest [8]. Statistical analysis was performed using IBM SPSS Statistics for Windows Version 26.0 (IBM Corp. Armonk, NY, USA, 2019). The independent samples \(t\)-test was used to compare means of SWV from the same target at the two different depths (3 cm and 6 cm) and the two sampling methods (ROI size; 5 mm and 10 mm) [34]. Homogeneity of variances was tested using [PERSON]'s variance test [35]. Inter-operator agreement was calculated using an analysis of the Intraclass Correlation Coefficient (ICC) [35]. The ICC utilised a two-way random effects model and selection of absolute agreement between the operators [36]. The ICC values were classified according to the common criteria [37]: excellent (ICC > 0.75), fair to good (ICC 0.40-0.75) and poor (ICC < 0.40). The repeatability coefficient (RC) and coefficient of variation (CV) were used to assess, test and retest reliability [16]. The RC and CV were calculated independently for each operator to ensure the conditions between repeated measurements were the same [16]. The RC is the value below which the absolute differences between two measurements would lie with 0.95 probability [38] and can be calculated by the formula:  
\[\text{RC}\ =\ \sqrt{2}\times 1.96\ \times\ \text{SD}_{\text{(within-subject)}}\]  
The CV is calculated as the ratio of the standard deviation (SD) to the mean, with a smaller percentage indicating less variability. The significance level was set at \(p\leq 0.05\).
## 3 Analysis and Results
### Visualization Analysis of Countries
As shown in Figure 3. China and the United States are the leaders in construction robotics research, with both countries producing the most research output and leading the field in technology application. Additionally, South Korea, Germany, and Japan have strong advantages in the development and application of construction robotics technology. The cooperation between China and the United States is particularly close, especially in technology innovation and applied research, with frequent collaborative exchanges between scholars and institutions from both countries. At the same time, European countries such as Germany and Switzerland have played significant roles in integrating construction robotics with smart buildings and BIM technologies. South Korea and Japan have rich research outcomes in high-performance construction robots and construction automation.
## 3 Emissions Testing Results  
The results are shown in Table 2. These figures are consistent with data registered by [PERSON] (2012) [52]. To estimate the use of the degeneration unit, the average electricity production for 15 years of life expectancy was assumed from the Ecoinvent database.  
CO emissions were in lower concentration in assays performed with diesel fuel. This fact can be related to the physical properties of the fuel that show their effect during its use in an internal combustion engine [53]. In general terms, diesel fuel has been reported to contain a larger amount  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Fuel Type**} & **CO** & **CO\({}_{2}\)** & **Hydrocarbons** \\ \cline{2-4}  & **kg/MWh** & **kg/MWh** & **kg/MWh** \\ \hline \hline   Diesel & 10.977 & 322.076 & 28.8 \\ RPO & 23.63 & 483.85 & 86.30 \\ WCO & 13.48 & 499.14 & 37.40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Emissions from the generation of 1 MWh using diesel, Waste Cooking Oil (WCO), and Refined Palm Oil (RPO).  
Figure 3: (**A**) WCO received in containers, (**B**) WCO filtration, (**C**) Emission testing.  
of energy per mass or volume unit than vegetable oils [54]. In addition, WCO registered a lower CO emission factor than RPO. In such a context, this issue can be associated with the chemical modification that cooking oil may suffer during its use. These changes can affect its properties, such as viscosity or calorific value, which are also relevant for determining its performance in an internal combustion engine [54].  
The CO emission figures registered during the experimental test are concordant with the CO\({}_{2}\) figures as well. This issue is highlighted by the fact that the diesel appears the most efficient choice in terms of energy and that WCO showed better performance when compared to RCO. In this context, it is affirmed that WCO is a better alternative as a fuel despite being considered a waste material.  
Regarding the HC content, a trend like that for CO was found. Despite diesel fuel having no oxygen content in its composition, other properties such as its viscosity (determining fuel injection and air-fuel blending inside the engine) [55] and calorific value caused the combustion efficiency to reach results better than those of the assayed oxygenated fuels. In addition, WCO appeared to be a better fuel when compared to RPO. This can be associated with the partial hydrogenation that oils suffer during the cooking process due to their contact with water at high temperatures [56]. Saturation implies augmented hydrogen content in the oil composition; hence, its calorific value and viscosity increase at a similar rate [57]. The electricity generation test results show fuel consumption levels of 0.216 L/kWh for WCO, 0.328 L/kWh for RPO, and 0.162 L/kWh for diesel.
## 2 Families of Fractional-Order Polynomials  
The FO calculus represents the real or even complex order generalization of the IO calculus. It can be formally introduced by the unified continuous integro-differential operator (differintegral), which is defined as follows [40; 43; 44; 45]:  
\[{}_{a}D_{t}^{\alpha}=\left\{\begin{array}{cc}\frac{d^{\alpha}}{dt^{\alpha}}& \text{Re}\,\alpha>0\\ 1&\text{Re}\,\alpha=0\\ \int\limits_{a}^{t}\left(d\tau\right)^{-\alpha}&\text{Re}\,\alpha<0\end{array}\right. \tag{1}\]  
where \(\alpha\) is the order of the differintegration that is restricted to a real number in this paper, \(t\) is the independent variable and \(a\) is a lower bound of it. If \(\alpha\geq 0\) and \(a=0\), the notation \(a\) can be omitted. Similarly, the independent variable \(t\) can also be omitted if there is no other variable. The differintegral itself can be defined in several ways. The three most widely used definitions bear the names of Riemann-Liouville (RL), Grunwald-Letnikov (GL), and Caputo. The RL and GL definitions are more suitable to problems under zero initial conditions, and they are equivalent for engineering applications. On the other hand, the Caputo's definition is particularly useful for problems under nonzero initial conditions because the nonzero initial values of the function \(f(t)\) are not handled satisfactorily in RL and GL definitions [45].  
The RL definition is given by [43; 44; 45]:  
\[{}_{a}D_{t}^{\alpha}f(t)=\frac{1}{\Gamma(n-\alpha)}\frac{d^{n}}{dt^{n}}\int\limits _{a}^{t}\frac{f(\tau)}{\left(t-\tau\right)^{\alpha-n+1}}d\tau \tag{2}\]  
where \(\alpha\) is supposed to lie within the interval \(n-1<\alpha\leq n\) and where \(\Gamma(.)\) is the Gamma function.  
The Laplace transform of the RL derivative is given as [38; 43; 44]:  
\[L\Big{[}_{0}D_{t}^{\alpha}f(t)\Big{]}=\int\limits_{0}^{\infty}e^{-st}{}_{0}D_ {t}^{\alpha}f(t)dt=s^{\alpha}F(s)-\sum\limits_{m=0}^{n-1}{s^{m}}_{0}D_{t}^{ \alpha-m-1}f(t)\Big{|}_{t=0} \tag{3}\]  
for \(n-1<\alpha\leq n\), where \(s\) is the complex frequency variable.  
Under zero initial conditions, the Laplace transform of all three mentioned fractional derivatives is reduced to [44]:  
\[L\Big{[}_{0}D_{t}^{\alpha}f(t)\Big{]}=s^{\alpha}F(s) \tag{4}\]  
The FO polynomial with parametric uncertainty has the form [9]:  
\[p(s,q)=\rho_{n}(q)s^{\alpha_{n}}+\rho_{n-1}(q)s^{\alpha_{n-1}}+\cdots+\rho_{1} (q)s^{\alpha_{1}}+\rho_{0}(q)s^{\alpha_{0}} \tag{5}\]  
where \(q\in\mathbb{R}^{k}\) is the vector of real uncertain parameters (or just uncertainty in short), \(\alpha^{n}>\alpha^{n-1}>\cdots>\alpha^{1}>\alpha^{0}\) are real numbers and \(\rho_{i}(q)\) for \(i=0,1,\ldots,n\) are coefficient functions.  
The family of FO polynomials is defined as [5]:  
\[P=\{p(s,q):q\in Q\} \tag{6}\]  
where \(Q\) represents the uncertainty bounding set.  
As can be seen, the families of FO polynomials are determined by the combination of the uncertain FO polynomial (5) (with emphasis on its uncertainty structure) and the uncertainty bounding set \(Q\).
## 6 Experimental Results
### Parameter Setting  
The parameter settings used by GA, PSO and CSA to optimize the gain of the PID controller for the developed experiments are displayed in Table 1. The parameters' values were selected based on the design of the experiments to conform each algorithm to the fundamental nature of the controlling problem. In fact, the values of the parameters were changed several times until a reasonable solution was found. However, only good settings are often obtained, not the _best_ settings.
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 4 Simulation Results and Discussions  
This section presents the results obtained from extensive simulations. These results have been compared with MEQSA-OLSRv2 and MP-OLSRv2 routing schemes. Furthermore, the effectiveness of the proposed MBMQA routing scheme has been assessed with a mobility awareness evaluation study in the D2D communication-based IoT 5G network. In addition, the RWP model was employed at the maximum achievable speed of devices, which varied between 10 m/s and 60 m/s.
## 3 Experimental Work
### Direction of Casting and Testing of Slab Specimens  
In underground RC box culverts, the concrete casting direction of the slab and wall members may be different due to the limited working space in ground excavations. In order to consider differences in casting positions, slab specimens were cast and tested in different directions to have a better representation of the field conditions in estimating crack widths. Slab specimens were cast and tested in three different ways: (1) cast parallel to the direction of loading (PL), (2) cast opposite to the direction of loading (OP), and (3) cast perpendicular to the direction of loading (PR).
# Multi-Source Satellite Imagery and Machine Learning
## 5 Experiments and Results
### Performance of Geological Types According to Combinations of Parameters  
Table 10, presented in Appendix A, provides the performances of the Gradient Boosting Classifier (GBC), Random Forest (RF), and XGBoost (XGB) algorithms in percentage, applied to different types of geological data (alluvium, basalt, etc.), using three combinations of parameters: DGP-DPCAP, DGP-DOLIP, and DPCAP-DOLIP. The DGP-DPCAP parameters stand out for their overall superior performance, with results such as 93% for alluvium and 70% for pyroclastics obtained by Random Forest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithms** & **Time (ms)** & **Accuracy (\%)** & **Precision (\%)** & **MSE** & **F-Score (\%)** & **Recall (\%)** & **MCC (\%)** \\ \hline Gradient Boosting & 14.08 & 50.51 & 49.79 & 4.88 & 49.79 & 50.51 & 39.14 \\ XGBoost & 7.91 & 51.53 & 51.04 & 4.59 & 51.04 & 51.53 & 40.35 \\ Random Forest & 3.16 & 51.53 & 50.29 & 4.75 & 50.29 & 51.53 & 40.28 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Evaluation of the performance of combining geographic parameter data with OLI spectrometric parameter data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithms** & **Time (ms)** & **Accuracy (\%)** & **Precision (\%)** & **MSE** & **F-Score (\%)** & **Recall (\%)** & **MCC (\%)** \\ \hline Gradient Boosting & 15.99 & 41.29 & 40.87 & 5.33 & 40.87 & 41.29 & 27.12 \\ XGBoost & 5.61 & 40.27 & 39.90 & 5.68 & 39.90 & 40.27 & 26.34 \\ Random Forest & 3.38 & 45.05 & 44.52 & 4.88 & 44.52 & 45.05 & 31.81 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Evaluation of the performance of combining OLI spectrometric parameter data with PCA-derived data.  
The DGP-DOLIP combination also offers good results, notably 90% for alluvium and 73% for trachytes, confirming its effectiveness for exploiting geographic and spectrometric data. In contrast, due to dimensionality reduction, the DGP-DPCAP combination produces slightly lower scores in several categories, such as 56% for granites and 51% for pyroclastics, which could result in a loss of information.  
Random Forest is the best-performing algorithm in most cases, particularly with the DGP-DPCAP and DGP-DOLIP combinations, which effectively exploit geological data's spatial and spectrometric relationships.  
Figure 12 illustrates the data from Table A2 in the form of a histogram.
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 3 Wireless Protocols Architecture
### Nb-IoT  
NB-IoT is an IoT communication protocol that was designed to reduce some of the capabilities of legacy LTE while simultaneously enhancing the ones necessary for IoT networks [44]. This makes NB-IoT an appropriate choice for IoT applications, as it is suitable for low-power IoT devices. As IoT end devices do not need constant back-end broadcasting, the broadcasting frequency is reduced, which decreases the power consumption [29; 44]. One has to keep in mind that this technology shares the same 3 GPP licensed frequency bands with the LTE bands [45]. As a result, only certain operation modes are possible in order to ensure interference-free operation. NB-IoT uses the Quadrature Phase Shift Keying (QPSK) modulation scheme to modulate the bits [46]. A QPSK-modulated transmit signal can be shown as follows:  
\[S_{QPSK}(t)=\frac{1}{\sqrt{2T}}a_{1}(t)cos\Big{(}2\pi f_{c}t+\frac{\pi}{4} \Big{)}+\frac{1}{\sqrt{2T}}a_{2}(t)sin\Big{(}2\pi f_{c}t+\frac{\pi}{4}\Big{)} \tag{5}\]  
A binary bit stream of \(a(t)\) with a period of \(T\) is demultiplexed into two different bitstreams \(a_{1}(t)\) and \(a_{2}(t)\), that represent \(+1\) and \(-1\) before multiplying by sine and cosine carriers to form a QPSK signal (Figure 3).
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 4 Supervised Learning for Estimating the Bit Error Rate (BER)
### Data Collection
#### 4.1.3 Examples of Urban Model Specification Using SUMO  
In this subsection, we present two illustrative examples of urban model specification using SUMO.  
Signal Degradation between Two Vehicles Due to Obstacle Presence  
The specific scenario can be described as follows: At time instant \(t_{0}\), two vehicles, \(V_{1}\) and \(V_{2}\), act as the transmitter and receiver, respectively, and are located on a road segment. At \(t_{0}+\Delta t\), the transmission between \(V_{1}\) and \(V_{2}\) is disrupted due to the presence of an obstacle. This scenario effectively demonstrates the impact of obstacles, such as walls or buildings, on signal degradation. To quantify the extent of signal degradation caused by an obstacle, our study focuses on the transmission between these two vehicles. By limiting the transmission to only two vehicles, we aim to gain a deeper understanding of the obstacle's impact on the signal degradation phenomenon. Figure 4 below visually depicts the aforementioned scenario using SUMO.  
Figure 3: Specification process of the urban scenario through SUMO.  
Signal Degradation Between Two Vehicles Caused by Bridge Presence  
In this scenario, we consider two vehicles located within transmission range, traversing a two-lane bridge. The communication signal between these vehicles experiences attenuation due to the presence of the bridge. This scenario emphasizes the phenomenon of signal attenuation in an enclosed space. Figure 5 below depicts the described scenario, illustrating the signal attenuation caused by the presence of the bridge.  
In Figures 4 and 5, the vehicles \(V_{1}\) and \(V_{2}\) are depicted in red and yellow, respectively. The pink illustrations represent the obstacles that obstruct the transmission signal between \(V_{1}\) and \(V_{2}\).  
Figure 4: Signal degradation due to the presence of an obstacle between two vehicles.  
Figure 5: Signal attenuation due to the presence of a bridge.