esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing Groundwater Level with a Unified Seasonal Outlook and Hydrological Modeling Projection
###### Abstract  
Although the annual rainfall in Taiwan is high, water shortages still occasionally occur owing to its nonuniform temporal and spatial distribution. At these times, the groundwater is considered an acceptable alternative water source. Groundwater is of particular value because it is considered a clean and reliable source of fresh water. To prevent water scarcity, this study utilized seasonal forecasting by incorporating hydrological models to evaluate the seasonal groundwater level. The seasonal prospective issued by the Central Weather Bureau of Taiwan (CWB) was combined with weather generator data to construct seasonal weather forecasts as the input for hydrological models. A rainfall-runoff model, HEC-HMS, and a coupled groundwater and surface water model, WASH123D, were applied to simulate the seasonal groundwater levels. The Fengshan Creek basin in northern Taiwan was selected as a study site to test the proposed approach. The simulations demonstrated stability and feasibility, and the results agreed with the observed groundwater table. The calibrations indicated that the average errors of river stage were 0.850 for \(R^{2}\), 0.279 for root-mean-square error (RMSE), and 0.824 for efficiency coefficient (CE). The simulation also revealed that the simulated groundwater table corresponded with observed hydrographs very well (\(R^{2}\) of 0.607, RMSE of 0.282 m, and CE of 0.621). The parameters were verified in this study, and they were deemed practical and adequate for subsequent seasonal assessment. The seasonal forecast of 2018 at Guanxi station indicated that the 25 th and 75 th percentiles of simulated annual rainfall were within 1921-3285 mm and the actual annual rainfall was 2031 mm. Its seasonal rainfall outlook was around 30% accurate for forecasts of three consecutive months in 2018. Similarly, at Xinpu station, its seasonal rainfall outlook was about 40% accurate, and the amount of annual rainfall (1295 mm) was within the range of the 25 th and 75 th percentiles (1193-1852 mm). This revealed that the actual annual precipitations at both Guanxi and Xinpu station corresponded with the range of 25 th and 75 th percentiles of simulated rainfall, even if the accurate rate for the 3 month seasonal forecast had some error. The subsequent groundwater simulations were overestimated because the amount of actual rainfall was far lower than the average of the historical record in some dry season months. However, the amount of rainfall returned to normal values during the wet seasons, where the seasonal forecast and observation results were similar.  
unified seasonal outlook; WASH123D; groundwater level; rainfall forecasting; hydrological modeling +
Footnote †: journal: Applied Soft. 2020, 10, 8882; doi:10.3390/app10248882
## 2 Results and Discussion
### Analysis of \"in Vivo\" VOCs
tridentata_ \\ \hline
**Compounds** & **RT \({}^{\star}\)** & **Mean (\(n=3\))\({}^{\star}\)** & **SD \({}^{\star}\)** & **Mean (\(n=3\))\({}^{\star}\)** & **SD \({}^{\star}\)** & **Mean (\(n=3\))\({}^{\star}\)** & **SD \({}^{\star}\)** & **Mean (\(n=3\))\({}^{\star}\)** & **SD \({}^{\star}\)** \\ \hline hexane & 1.50 & 0.96 & 0.01 & ND & - & ND & - & 7.14\({}^{\star}\) & 0.03 \\ octane & 2.21 & 0.39 & 0.14 & 0.16 & 0.13 & 0.08 & 0.08 & 0.14 & 0.00 \\ butane & 2.26 & ND & - & ND & - & 0.17 & 0.17 & 1.09 & 0.15 \\
1-octene & 2.53 & ND & - & ND & - & 0.17 & 0.17 & 0.22 & 0.00 \\ nonane & 3.25 & ND & - & ND & - & 0.22 & 0.22 & 1.36 & 0.32 \\ undecane & 10.25 & 0.73 & 0.65 & 3.56\({}^{\ddagger}\) & 0.00 & ND & - & 0.76 & 0.01 \\
1-tridee & 11.85 & 0.07 & 0.00 & ND & - & 0.11 & 0.11 & ND & - \\ dodecane & 14.88 & 0.13 & 0.02 & ND & - & 1.39 & 1.00 & ND & - \\ trideeane & 18.72 & ND & - & 0.11 & 0.00 & ND & - & 0.79 & 0.03 \\
4-methyl-tetradeane & 20.55 & ND & - & ND & - & 4.69\({}^{\ddagger}\) & 0.53 & 1.25 & 0.11 \\ pentadecane & 21.43 & 0.68 & 1.68 & ND & - & 1.20 & 0.57 & 0.86 & 0.15 \\ nonadecane & 22.47 & 0.73 & 0.00 & 0.71 & 0.00 & 0.21 & 0.19 & ND & - \\ heptadecane & 25.98 & 0.25 & 0.01 & ND & - & 0.73 & 0.57 & ND & - \\
2-trideeane & 29.86 & 0.59 & 0.00 & ND & - & 0.43 & 0.26 & 0.50 & 0.00 \\ \hline
**Total** & & & **4.53** & & **4.54** & & **9.4** & **14.11** & \\ \hline _Aldehydes_ & & & & & & & & & \\ pentanal & 4.78 & ND & - & ND & - & 0.15 & 0.16 & 0.11 & 0.01 \\ esanl & 9.02 & 1.48 & 1.97 & 0.42 & 0.34 & 0.64 & 0.44 & 0.33 & 0.03 \\
3-hexenal & 12.22 & 0.12 & 0.01 & ND & - & 0.50 & 0.36 & 0.16 & 0.10 \\ heptanal & 13.98 &
# Exploring Carbon-Fiber UAV Structures as Communication Antennas for Adaptive Relay Applications
## 2 Materials and Methods
### Experimental Setup for Antenna Characterization
#### 2.1.2 Reflection Coefficient  
The reflection coefficient is a key parameter in evaluating antenna performance. It quantifies the proportion of energy from an incident wave that is reflected back by the UAV antenna, typically due to impedance mismatches within the transmission medium.  
Figure 2: The anechoic chamber used in experiments where A—Antenna A; F—Oscilloscope and G—Signal generator.  
Each tube (antenna) was connected, one at a time, to a Vector Network Analyzer (VNA) via a precision-calibrated coaxial cable. To measure signal power and spectrum, a signal generator and a spectrum analyzer were used. Additionally, the influence of ambient electromagnetic fields was assessed using a dedicated field strength measurement device, as shown in Figure 3. The measuring devices used for the experimental tests were from the company Rohde & Schwarz, Munich, Germany.  
The tests conducted on the antennas included the analysis of the reflection coefficient and the Voltage Standing Wave Ratio (VSWR). The variation of the reflection coefficient was evaluated over the frequency range of 10 MHz to 3 GHz, and the VSWR values were experimentally determined using the VNA.  
The Vector Network Analyzer (VNA) is a critical measurement instrument that precisely determines the complex reflection coefficient (S\({}_{11}\)) for both antennas, excluding the phase component. Figure 3 illustrates the measurement setup, including the two antennas and the VNA.  
In Figure 3, elements 1 and 3 represent the two antennas, element 2 corresponds to the VNA, and element 4 is the display screen showing the measurement results.  
The Rohde & Schwarz ZVA24 Vector Network Analyzer is a high-precision instrument used for the characterization of radiofrequency (RF) and microwave components. This experiment was employed to measure the reflection coefficient (S11) of a directional antenna fabricated from carbon fiber. The device covers a wide frequency range, from 10 MHz to 24 GHz, enabling detailed analysis of antenna behavior across an extended bandwidth. The connection was established using a thick, calibrated coaxial cable, which ensures stability and minimizes signal loss, while real-time measurement results are displayed on the integrated color screen.  
The front panel is equipped with a numeric keypad, function buttons, and a multi-function rotary knob, allowing for precise control of measurement settings. The image shows an S11 measurement performed over the 10 MHz-3 GHz range, where resonance regions and impedance mismatches can be clearly identified. This type of analysis enables the evaluation of antenna efficiency and impedance matching with the transmission line, providing essential data for performance optimization in real-world applications.  
Figure 3: Reflection coefficient setup, where A—Antenna A; B—Antenna B; C—Vector Network Analyzer and D—Display monitor.
## 3 Dynamic Model of UAV System and Tracking Differentiator
### Fractional-Order Tracking Differentiator
The system (26) is global asymptotically stable because one can obtain \(V(\chi_{1},\chi_{2})\rightarrow\infty\) if \(\chi_{1}\rightarrow\infty\) and \(\chi_{2}\rightarrow\infty\). This concludes the proof. \(\square\)  
Two other tracking differentiators were considered for comparison of the proposed tracking differentiator. The first one proposed by [PERSON] [29; 30] is expressed as  
\[ \begin{array}{l}\dot{\zeta}_{1}=v_{1}=-r_{1}|\zeta_{1}-\alpha|^{1/2}\,\text{ sign}(\zeta_{1}-\alpha)+\zeta_{2},\\ \dot{\zeta}_{2}=-r_{2}\,\text{sign}(\zeta_{2}-v_{1}).\end{array}  \tag{30}\]  
The second one proposed by [PERSON] et al. [27; 28], is the tangent sigmoid function tracking differentiator expressed by  
\[\begin{array}{l}\dot{\zeta}_{1}=\zeta_{2}\\ \dot{\zeta}_{2}=-\Lambda^{2}\Big{[}c_{1}\Big{\{}(\zeta_{1}-\alpha)+|\chi_{1}- \alpha|^{\beta}\,\text{tansig}(r_{1}(\zeta_{1}-\alpha))\Big{\}}+c_{2}\Big{\{} \zeta_{2}/\Lambda+|\zeta_{2}/\Lambda|^{\beta}\,\text{tansig}(r_{2}\zeta_{2}/ \Lambda)\Big{\}}\Big{]},\\ \text{where }\textit{tansig}(r_{1}x)=\frac{2}{e^{-2\pi\zeta_{1}x}+1}-1,\,0< \beta<1\text{ is a constant. The parameters of the tracking differentiators were selected as \(\Lambda=2\), \(c_{1}=1.5\), \(c_{2}=5.5\), \(r_{1}=1\), \(r_{2}=0.5\), \(\beta=0.5\), \(\gamma=0.8\), and \(\varepsilon=0.95\) under the initial condition of \(\alpha=0\), \(\zeta_{1}(0)=2\), and \(\zeta_{2}(0)=-2\). The simulated results are presented in Figure 2, where the proposed differentiator exhibits the most rapid converging performance comparing to those of the other systems. Figure 2: Comparative results for the four tracking differentiators. (**a**) \(\zeta_{1}\). (**b**) \(\zeta_{2}\).
# A W-Band Communication and Sensing Convergence System Enabled by Single OFDM Waveform
## 6 Conclusions  
We demonstrated a W-band simultaneous communication and sensing system operating at 97 GHz using a common 16-QAM OFDM waveform. The zero-delay-shift-based approach is proposed to overcome the sensing range limitation in the conventional OFDM-based sensing systems and enable range extension for the OFDM-based converged system. Both simulation and experimental measurements are performed in the W-band with a bandwidth of 3.9 GHz. Due to the large bandwidth available in the W-band, we achieve a sensing resolution of 0.042 m in range and 0.79 m/s in speed in the experiment. The target range well beyond the CPI is detected by using our proposed method. Furthermore, we also measure the 16-QAM OFDM communication performance, and the BER below the SD-FEC is achieved. The successful demonstration of the convergence of communication and sensing using the same waveform is a significant step towards future wireless applications.  
Conceptualization, N.M.I. and X.Y.; software, N.M.I., S.W. and L.Z.; data curation, N.M.I.; validation, N.M.I., Z.L., M.S. and H.Z.; investigation, N.M.I.; resources, X.Y.; writing--original draft preparation, N.M.I.; writing--review and editing, N.M.I. and X.Y.; visualization, N.M.I.; supervision, X.Y.; project administration, X.Y.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key Research and Development Program of China under Grant 2018 YFB1801500, the Natural National Science Foundation of China under Grant 62101483, the Natural Science Foundation of Zhejiang Province under Grant LQ21F010015, and the Zhejiang Lab under Grant 2020 LC0 AD01.  
The authors declare no conflict of interest.
## 2 Material and Methods
### Statistical Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factor/Interaction** & **F-Value** & **df** & \(p\)**-Value** & \(\eta\)p\({}^{2}\) & \(\lambda\) \\ \hline Group & 3.491 & 3 & 0.018 & 0.105 & 10.473 \\ Measurement & 3628.679 & 4 & \textless{}0.001 & 0.976 & 14,514.716 \\ Time & 9.839 & 2 & \textless{}0.001 & 0.1 & 19.678 \\ Time \(\times\) Group & 4.531 & 6 & \textless{}0.001 & 0.132 & 27.186 \\ Measurement \(\times\) Group & 2.628 & 12 & 0.002 & 0.081 & 31.535 \\ Measurement \(\times\) Time & 3.455 & 8 & \textless{}0.001 & 0.037 & 27.642 \\ Measurement \(\times\) Time \(\times\) Group & 3.163 & 24 & \textless{}0.001 & 0.096 & 75.905 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of mixed model ANOVA for main effects and interactions. Figure 4: Results of 2 HAND for all groups across three measurement points. meaningful but not large-scale transformations, aligning with previous research on short-term VR interventions. MT showed a near-significant trend (\(p\) = 0.054, F = 3.010, \(\eta^{2}\)p = 0.064, \(\lambda\) = 6.020), while RT did not reach significance (\(p\) = 0.479, F = 0.741, \(\eta^{2}\)p = 0.017, \(\lambda\) = 1.483), reinforcing the notion that shorter VR interventions primarily enhance coordination rather than fundamental reaction time improvements. In the E28 group, significant changes were observed in EHC (\(p\) < 0.001, F = 23.193, \(\eta^{2}\)p = 0.345, \(\lambda\) = 46.386), RT (\(p\) < 0.001, F = 9.529, \(\eta^{2}\)p = 0.178, \(\lambda\) = 19.059), and MT (\(p\) = 0.004, F = 5.933, \(\eta^{2}\)p = 0.119, \(\lambda\) = 11.866), indicating robust cognitive-motor improvements over an extended training period. The large effect size for EHC suggests a substantial impact of VR training on eye-hand coordination, while the moderate effect sizes for RT and MT indicate that reaction time and movement speed improvements were present but less pronounced. AET and TPED did not show significant changes (\(p\) = 0.224 and \(p\) = 0.126, respectively), suggesting that these functions may either plateau with prolonged training or require different intervention approaches to elicit further improvements. Conversely, the C groups did not exhibit significant changes in cognitive function, except for a minor significance in AET within C28 (\(p\) = 0.046, F = 3.199, \(\eta^{2}\)p = 0.068, \(\lambda\) = 6.399). The small effect size in AET suggests that this improvement was marginal, reinforcing that VR training was the primary driver of cognitive-motor enhancements rather than external factors. The observed pattern of results supports the notion that short-term VR training is effective at enhancing coordination-based tasks, while prolonged training is more beneficial for reaction time improvements. The large effect size in EHC for E28 indicates that longer training leads to more pronounced gains in sensorimotor integration, while the lack of significance in TPED and AET suggests potential training thresholds where additional exposure does not necessarily yield further gains.
## 3 The Mathematical Model of Double-Coupled Duffing System
### Response Characteristics and Weak Signal Detection Principle of Double-Coupled Duffing System  
With the displacement of the vibrator as the abscissa and the velocity as the ordinate, the phase trajectory diagram of the Duffing system response can be drawn. The Duffing system will show the attractor, homoclinic orbit, periodic bifurcation, chaotic state, large-scale periodic state and other states in the phase trajectory as the amplitude of the built-in power increases. The double-coupled Duffing system is composed of two Duffing systems, and each Duffing system corresponds to a phase plane diagram. Because the phase trajectories of the two Duffing systems are similar, only one of them is calculated during analysis.  
Set the frequency of the built-in power to 1 rad/s. ODE45 is used to solve the response of the double-coupled Duffing system. The discrete time interval \(t_{s}\) is taken as 0.1 s. In Figure 2, the evolution law of the phase trajectory and the time domain waveform of the first Duffing system response with the amplitude change of the built-in power is shown.  
Figure 2: Cont.  
According to the phase trajectory and time domain waveform evolution law in Figure 2, the double-coupled Duffing system shows different types of dynamic response characteristics with the increase in the amplitude of the built-in power. There are critical points between the states. When the amplitude of the built-in force is at the critical point, a small increment will cause a sudden change in the system response state. Therefore, when there is a weak component in the signal to be measured with the same frequency as the built-in force, superposing it with the built-in force into the double-coupled Duffing system will cause a sudden change in the system response so as to achieve weak signal detection. The research shows that the Duffing system is insensitive to signals with different frequencies from the built-in driving force and has excellent weak signal detection ability and anti-noise performance.
## 6 Problem Solving
### Solving Steps
#### 6.3.2 Analysis of Results  
Under the premise of ensuring the same population size, dimension, number of iterations, and related parameters, the above VRPTW was solved by using the ACO algorithm, BOA, AEFA, and SC-AEFA. The results are described below.  
It can be seen from Table 6 that the SC-AEFA obtained better results under the premise of less time, which shows that the SC-AEFA is superior to the other three algorithms in terms of both the computing speed and the optimization accuracy. In order to more clearly observe the convergence trend of the SC-AEFA and the other three algorithms, the algorithm iteration convergence graph is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Required Vehicle** & **Algorithm Time** & **Cost** \\ \hline AEFA & 4 & 87.60 s & 559.35 \\ SC-AEFA & 4 & 78.76 s & 556.86 \\ BOA & 4 & 80.68 s & 561.36 \\ ACO & 4 & 80.04 s & 678.75 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The comparison of the results.  
Figure 6: The distribution map of the customer points and distribution centers.  
Figure 7: The comparison of the algorithm convergence effect.  
From the convergence comparison chart, it can be seen that the effect of the ant colony algorithm was the worst, and the convergence trend was very weak. The BOA was prone to premature convergence, resulting in it taking a long time to jump out of the local optimum, and the convergence accuracy was not high. Both the AEFA and SC-AEFA had better convergence trends, but the SC-AEFA had a significantly higher accuracy.  
It can be seen from Table 7 that the optimal value, the worst value, and the average value obtained by the algorithm proposed in this paper were better than those of the other three algorithms. At the same time, in the 20 independent repeated experiments, the SC-AEFA algorithm's earliest convergence iteration number was 23, which was the fastest among the four algorithms. Additionally, the optimal value obtained by the algorithm proposed was 556.12, while the theoretical value of the example was 553.81. In comparison, it can be found that the degradation degree of the algorithm's performance was not obvious, which indicates that the optimization results of the algorithm proposed in this paper are effective in solving the VRPTW.
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 4 AD Stability or Inhibition
### Effect of Organic Loading Rate (OLR)  
Providing an adequate OLR helps to ensure a stable digestion process and an optimum biogas yield during the process [126]. Feedstock composition will determine the level of biochemical activities that will take place in the digester. Consequently, overload or undersupply will affect the process performance [6].  
Anaerobic stability depends on the harmonic relations between acid formers and methane formers [127], and these two types of microorganisms differ widely in terms of physiology and nutritional needs [128]. In the case of easily degradable substrates with low buffering capacity, high loading rate will lead to formation of VFAs, pH will be dropped, and methanogenic bacteria will be inhibited [19; 22; 23; 49; 129]. For commercial biogas production, protein-rich feedstocks are preferred over other materials as they have high bio-methane potential (BMP) [130; 131]. Unfortunately, high loads of such materials are often associated with process instability because of the release of ammonia nitrogen (NH\({}_{3}\)-N) [6; 97; 132], causing inhibition of the process.
## 5 Confidence Intervals of \(R\)
### Bootstrap Confidence Intervals  
In this subsection, two confidence intervals based on the non-parametric bootstrap methods are proposed: (i) the percentile bootstrap method (Boot-\(p\)) (see [19]) and (ii) the bootstrap-t method (Boot-\(t\)) (see [20]).  
The algorithms for conducting the confidence intervals of \(R\) are presented as follows:  
1. [label=()]
2. Boot-\(p\) method: Step 1: From the sample \(x_{1},x_{2},...,x_{n}\) and \(y_{1},y_{2},...,y_{m}\) compute \(\hat{\alpha}\), \(\hat{\theta_{1}}\) and \(\hat{\theta_{2}}\). Step 2: Generate a bootstrap sample \(x_{1}^{*},...,x_{n}^{*}\) using \(\hat{\alpha}\) and \(\hat{\theta_{1}}\) and generate a bootstrap sample \(y_{1}^{*},...,y_{m}^{*}\) using \(\hat{\alpha}\) and \(\hat{\theta_{2}}\). Based on \(x_{1}^{*},...,x_{n}^{*}\) and \(y_{1}^{*},...,y_{m}^{*}\), compute the estimate of \(R\), say \(\hat{R}^{*}\). Step 3: Repeat Step 2 NBOOT times. Step 4: Let \(H_{1}(x)=P(\hat{R}^{*}\leq x)\) be the cumulative distribution function(cdf) of \(\hat{R}^{*}\). Define \(\hat{R}_{Boot-p}=H_{1}^{-1}(x)\) for a given \(x\). Thus, the approximate \(100(1-z)\%\) C.I. of \(R\) is given by: \[\left(\hat{R}_{Boot-p}(\frac{z}{2}),\hat{R}_{Boot-p}(1-\frac{z}{2})\right).\] Note: In this paper, \(\hat{R}^{*}\) can be computed using (14) in Step 2.
3. [label=()]
4. Boot-\(t\) method: Step 1: From the sample \(x_{1},...,x_{n}\) and \(y_{1},...,y_{m}\), compute \(\hat{\alpha}\), \(\hat{\theta_{1}}\) and \(\hat{\theta_{2}}\). Step 2: Generate a bootstrap sample \(x_{1}^{*},...,x_{n}^{*}\) using \(\hat{\alpha}\) and \(\hat{\theta_{1}}\) and generate a bootstrap sample \(y_{1}^{*},...,y_{m}^{*}\) using \(\hat{\alpha}\) and \(\hat{\theta_{2}}\). Based on \(x_{1}^{*},...,x_{n}^{*}\) and \(y_{1}^{*},...,y_{m}^{*}\), compute the estimate of \(R\) as \(\hat{R}^{*}\) and the statistic is \[T^{*}=\frac{\sqrt{n}(\hat{R}^{*}-\hat{R})}{\sqrt{Var(\hat{R}^{*})}}.\] Step 3: Repeat Step 2 NBOOT times. Step 4: Let \(H_{2}(x)=P(T^{*}\leq x)\) be the cumulative distribution function(cdf) of \(T^{*}\). Define \(\hat{R}_{Boot-t}=\hat{R}+n^{-\frac{1}{2}}\sqrt{Var(\hat{R})}H_{2}^{-1}(x)\) for a given \(x\). Thus, the approximate \(100(1-z)\%\) C.I. of \(R\) is given by: \[\left(\hat{R}_{Boot-t}(\frac{z}{2}),\hat{R}_{Boot-t}(1-\frac{z}{2})\right).\] Note: In this paper, \(Var(\hat{R}^{*})\) can be obtained using Theorem 2 in Step 2.