esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Soil nitrogen is one of the crucial components for plant growth. An accurate diagnosis based on soil nitrogen information is the premise of scientific fertilization in precision agriculture. Soil nitrogen content acquisition based on near-infrared (NIR) spectroscopy shows the significant advantages of high accuracy, real-time analysis, and convenience. However, soil texture, soil moisture content, and drying temperature all affect soil nitrogen detection by NIR spectroscopy. In order to investigate the effects of drying temperature on calcium soil nitrogen detection and its characteristic bands, soil samples were detected at a 25 \({}^{\circ}\)C placement (ambient temperature) after 40 \({}^{\circ}\)C drying (medium temperature), 60 \({}^{\circ}\)C drying (medium-high temperature), 80 \({}^{\circ}\)C drying (high temperature), and 105 \({}^{\circ}\)C drying (extreme high temperature), respectively. Besides that, the original spectra were pretreated with five preprocessing methods, and the characteristic variables were selected by competitive adaptive reweighted squares (CARS) and backward interval partial least squares (BIPLS). The partial least squares (PLS) method was used for modeling and analysis. The predictive abilities were assessed using the coefficients of determination (\(R^{2}\)), the root mean squared error (RMSE), and the residual predictive deviation (RPD). As a result, the characteristic bands focus on 928-960 nm and 1638-1680 nm when soil was detected after 40 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C drying. Calcium soil obtained the best prediction accuracy (\(R^{2}_{p}=0.966,\ RMSE_{p}=0.128\frac{\text{g}}{\text{kg}},RPD=5.03\)) after 40 \({}^{\circ}\)C drying by the method of CARS-BIPLS-PLS. Meanwhile, the prediction model also performed well when soil was detected after 60 \({}^{\circ}\)C drying (\(R^{2}_{p}=0.946,\ RMSE_{p}=0.172\ \text{g/kg},RPD=4.53\)) and 80 \({}^{\circ}\)C drying (\(R^{2}_{p}=0.961,\ RMSE_{p}=0.143\frac{\text{g}}{\text{kg}},RPD=4.98\)). However, the calcium soil obtained the worst detection result when soil was placed at 25 \({}^{\circ}\)C. In conclusion, a low or extremely high drying temperature had an adverse influence on the soil nitrogen detection, and the 40 \({}^{\circ}\)C drying temperature as well as the CARS-BIPLS-PLS method were optimal to enhance the soil nitrogen detection accuracy.  
Keywords:soil nitrogen; NIR; drying temperature; characteristic bands; PLS; CARS; BIPLS +
Footnote †: journal: Applied Soft. 2018, 8, 269; doi:10.3390/app8020269
## 3 Results and Discussion
### Material Characterization
#### 3.1.5 Antioxidant Activity of N/S-Carbon QDs  
In Figure 11, the values of % DPPH radical inhibition calculated from Equation (2) are plotted as a function of N/S-Carbon QDs concentration. From the plotted values, the linear Equation (3) was fitted. From Equation (3), the effective concentration of aqueous N/S-Carbon QDs (EC50) solution that decreases the [DPPH\({}^{\bullet}\)] concentration by 50% was calculated as equal to 4.14 mg L\({}^{-1}\) or 4.14 mgmL\({}^{-1}\). This value is much lower than the EC50 values reported previously for other carbon QDs [41; 42; 43; 44] and implies a superior antioxidant activity of the obtained N/S-Carbon QDs. The [DPPH\({}^{\bullet}\)] antioxidant activity mechanism is reported to act through [DPPH\({}^{\bullet}\)] quenching by hydrogen atom transfer from the carboxyl, hydroxyl, and/or amino groups located in the surface of N/S-Carbon QDs [42; 45]. In our case, it is suggested that the mechanism of [DPPH\({}^{\bullet}\)] quenching by the hydrogen atoms of N/S-Carbon QDs is boosted further by the presence of calcium atoms. This significant antioxidant activity of obtained N/S-Carbon QDs indicate the potential use of such QDs in food preservation applications.  
Figure 11: Plot of calculated % DPPH radical inhibition values as a function of N/S-Carbon QDs concentration.  
Figure 10: Microtox bioassay gamma versus concentration plot.
###### Abstract
The response spectrum of CsPbBr\({}_{3}\) perovskite (bandgap about 2.3 eV) photodetectors (PDs) exactly covers the propagation window of sea water, intrinsically preventing the impact of long-wavelength light. Perovskite PDs have been reported for their application in UWOC previously [27; 28; 29]. [PERSON] et al., using a Br-terminated MXene-treated perovskite PD as a receiver, demonstrated UWOC with data rate of 128,000 baud per second [28], and a long response time of 29.9 \(\upmu\)s was the limitation of the data rate. [PERSON] et al. employed an ultralow dark current and self-powered perovskite PD as a receiver in an UWOC system and demonstrated a transmission rate of only 300 bps. Improving the response speed and reducing the noise current of perovskite PDs is of great importance to achieve a high speed of data transmission. Recently, our group have demonstrated a high-performance CsPbBr\({}_{3}\) perovskite PD with low noise, high responsivity, and a fast response speed, showing its significant potential in applications of UWOC [30]. In this work, a cost-efficient UWOC system was demonstrated using solution-processed CsPbBr\({}_{3}\) perovskite PDs as receivers, and based on Verilog hardware description language and FPGA platforms. With a fast response time (rise time of 82 ns, fall time of 710 ns), low noise of 4.3 \(\times\) 10\({}^{-14}\) A Hz\({}^{-1/2}\), and high responsivity of 0.317 A/W, CsPbBr\({}_{3}\) perovskite PD-based UWOC has achieved a transmission rate of 6.25 Mbps with a low bit error rate (BER) of 0.36%. The performance of perovskite PDs in UWOC was compared with a commercial silicon PD with similar device properties to study the factors of PD affecting communication quality. Due to a larger bandgap with an absorption cutoff wavelength of around 530 nm, perovskite PDs exhibit lower background noise under different ambient light intensities. They show better communication stability before reaching the data rate threshold of 6.25 Mbps, while the silicon PD exhibits a gradually increasing BER, which is likely thanks to the slight influence of noise from ambient light for perovskite PDs. The BER of perovskite PDs increases rapidly after the threshold, due to the long fall time causing difficulty in distinguishing switching signals. Therefore, reducing the fall time of perovskite PDs and the adoption of advanced coding methods will help to further improve the performance of UWOC systems based on perovskite PDs. This work not only demonstrates the huge potential of solution-processed perovskite PDs as receivers for a cost-efficient UWOC system, but also provides tips for achieving a higher data rate UWOC system for practical application.
## 3 Proposed HEMOGWO for SCOS
### Population Initialization  
In the search mechanism of MOGWO, the distribution characteristics of the initial population directly influence the convergence speed and the quality of the optimization results. Traditional random initialization strategies employ a completely random distribution within the solution space. Although this approach is simple to implement, it may lead to uneven population distribution and low individual dispersion, making the search process prone to premature convergence and unstable efficiency. To address this issue, this study proposes a population initialization strategy based on Tent chaotic mapping. By leveraging the ergodicity and inherent regularity of chaotic systems, this method ensures population diversity while enabling uniform exploration of the solution space, thereby significantly enhancing the algorithm's global search capability and convergence speed [24].  
Tent chaotic mapping, as a typical one-dimensional chaotic system, exhibits dynamic behavior governed by a piecewise linear iterative equation. The mathematical expression for this mapping is provided in Equation (12).  
\[x_{n+1}=\begin{cases}\mu x_{n},&x_{n}<0.5\\ \mu(1-x_{n}),&x_{n}\geq 0.5\end{cases} \tag{12}\]  
When the control parameter \(\mu=2\), the system enters a fully chaotic state, capable of generating non-periodic trajectories with uniform distribution properties. Compared to traditional random number generation methods, the chaotic sequences produced by the Tent map offer superior spatial coverage and more consistent ergodic behavior. This is because the intrinsic properties of chaotic systems guarantee unbiased traversal across the domain, avoiding the distribution gaps or local clustering that may result from random sampling. By mapping the chaotic sequence to the dimensions of the optimization problem's solution space, the initial population is ensured to be uniformly distributed within the decision space, with significant individual differences. This effectively prevents the clustering effect often caused by conventional random initialization and establishes a solid foundation of diversity for the subsequent optimization process.
# Testing and Analysis of MOSFET-Based Absorber Integrated Antenna for 5G/WiMAX/WLAN Applications
## 5 Conclusion and Future Recommendations  
The 3D EM/circuit co-simulation of the MOSFET-based absorber integrated antenna has been observed to have a suitable reflection absorbing capability by satisfying the condition for MOSFET absorption. This device, when directly connected to the power amplifier's output, reduces the effect of impedance mismatch between the PA and the antenna because the reflected power that would have resulted in arching within the PA structure is absorbed by the MOSFET absorber in the antenna construct. Another advantage of this design is that the model can function as a rectifying antenna for energy harvesting. Furthermore, the dual material antenna used in the proposed integrated design was designed to resonate at 2.5 GHz (2 GHz\(\sim\)3 GHz) and 5.3 GHz (4.6 GHz\(\sim\)6.1 GHz), hence making it suitable for the lower and upper bands of WLAN and WiMAX and the middle band of 5G technology (2.3 GHz to 2.4 GHz and 2.5 GHz to 2.69 GHz). This antenna design can be used in the front end of communication systems, signal intelligence systems, and other forms of transportation (intelligence, surveillance, and reconnaissance) systems.  
As a continuation of this work, we will present a block-by-block performance analysis of the proposed model to validate the usability of the 3D electromagnetic circuit simulation design in the 5G regime. Thereafter, a fabricated device will be realized.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Terminal** & **Voltage (mV)** & **Current (mA)** \\ \hline Source (P11) & 0 & \(-\)3.6 \\ \hline Drain(P10) & 466 & 3.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: MOSFET drain and source terminal parameters.  
Figure 14: Drain/source (**a**) voltage and (**b**) current.  
**Author Contributions:** E.O.O. and V.M.S. have conducted this research; E.O.O. has designed, fabricated, and analysed the model with data and wrote this article; V.M.S. has verified the result and V.M.S. has finalized the article. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 4 Results and Evaluations
### Results of the Solar Module  
The solar modules were evaluated using five parameters. The results are shown in Table 7. These parameters were evaluated according to the day of the month with the largest average energy output and the one with the smallest average energy output.  
Suppose that the solar panel was located at a latitude of 22\({}^{\circ}\) and a longitude of 114\({}^{\circ}\), and was tilted 22 degrees, as determined by data from the surface meteorology and solar energy database from NASA [12]:  
* the largest radiation of month = 5.6 (kWh/m\({}^{2}\)/day);
* the smallest radiation of month = 4.63 (kWh/m\({}^{2}\)/day).  
Therefore, the results were calculated based on the above maximum and minimum radiations and are shown in Table 7:  
1. **Solar panel output**: as obtained from the manufacturer's data sheet: panel conversion efficiency = 18%; solar array area = 0.5625 m\({}^{2}\). Thus, the solar panel output can be obtained by Equation (1).
2. **Solar module output**: As shown in Equation (2), to calculate the solar module output, the conversion efficiency has to be known, which is the efficiency of PWM solar energy to convert solar panel energy into electrical energy. This is estimated based on the efficiency loss of the charging process of the PWM charging controller. As calculated in Section 5.2, the conversation efficiency is 62%. Thus, the solar module output can be obtained by Equation (2).  
\begin{table}
\begin{tabular}{c c} \hline
**Devices** & **Power Consumption in an Hour** \\ \hline Satellite terminal & 5.2 W \\ Raspberry Pi & 1.08 W \\ \hline \end{tabular}
\end{table}
Table 6: The power consumption of the devices.  
\begin{table}
\begin{tabular}{l c c} \hline
**Parameter** & **Largest Radiation** & **Smallest Radiation** \\ \hline Solar Panel Output & 486.94 Wh/day & 332.34 Wh/day \\ Solar Module Output & 288.16 Wh/day & 196.67 Wh/day \\ System Power Consumption & 150.72 Wh/day & 150.72 Wh/day \\ Energy Surplus & 132.85 Wh/day & 41.36 Wh/day \\ Time to Fully Charge Battery & 5.41 days & 17.40 days \\ \hline \end{tabular}
\end{table}
Table 7: The parameters for the solar module.  
System power consumption: this is the sum of the power consumption of the single-broad computer and the satellite terminal for a day. By measurement, as shown in Table 6, the power consumption can be obtained by \((5.2+1.08)\) W \(\times\) 24 h = 150.72 Wh/day.
4. **Energy surplus:** the energy surplus per day can be determined by (solar system output - system power consumption).
5. **Time to fully charge battery:** the time it takes for the battery to fully charge can be determined by battery capacity/energy surplus per day. In this study, the factory-rated battery capacity was 720 Wh.
## 5 Discussion of Simulations and Results
### Comparative Study  
From the perspective of economics, an effective EM modeling approach has two functions: to provide a bidding decision-making tool for every EM participant so as to increase its own profit in EM competition; and to enhance the economic efficiency of the whole market, e.g., by improving SW and reducing AVLMP. If we compare our proposed GDCAC-based EM approach with other approaches, the superiority of our proposed approach can be verified in two ways. When approaches adopted by other participants for bidding decision-making are fixed, a specific participant can get more profit with our proposed approach in market competition than with other approaches. In addition, with the increase in the number of participants applying our proposed approach as their bidding decision-making tool, SW in the market increases and AVLMP in the market decreases. In [2], considering wind power penetration, it was verified that the fuzzy Q-learning-based hour-ahead EM modeling approach is superior to other approaches such as Q-learning, etc. in terms of improving SW and reducing AVLMP, etc. In this section, a comparison between our proposed approach and the fuzzy Q-learning approach is carried out by implementing simulations on Scenarios 1, 2, and 3, respectively. The obtained simulation results of three scenarios after 20,000 iterations are studied and compared, which mainly contain all agents' final profits, SWs and AVLMPs in those three scenarios (the ability to reach NE after enough iterations by applying the fuzzy Q-learning algorithm in EM modeling has been verified by [2]). In simulations on Scenarios 1, 2, and 3, we also make state variable 1 and 2 change randomly within interval [0 20] MW and [0 30] MW, respectively, in each iteration. Tables 5-7 demonstrate the obtained profit of every agent and the SW results of the three scenarios after 20,000 iterations in sample state (20, 30) MW, (10, 15) MW, and (4, 6) MW, respectively. The average AVLMP and SW of \(21\times 31\) sample states in three scenarios after 20,000 iterations are demonstrated in Table 8, in which \(21\times 31\) sample states constitute a discrete set as follows:  
\[\mathbf{Sap}=\{(x_{1},x_{2})|x_{1}\in\mathbf{AA},x_{1}\in\mathbf{BB}\}, \tag{43}\]  
where \(\mathbf{AA}=\{0,1,2,\ldots,20\}\) (MW), \(\mathbf{BB}=\{0,1,2,\ldots,30\}\) (MW). From Tables 5-7, it can be seen that:  
1. No matter which of the three sample states, in Table 5, NRGenCO1's final profit in Scenario 1 is 369.5193 ($/h), which is more than that in Scenario 2 (296.7915 ($/h)). In Table 6, NRGenCO1's
## 4 Materials and Methods
### Volatile Compound Analysis  
In a 20 mL headspace (HS) vial, a wine sample (1 mL) was spiked with 50 \(\upmu\)L of 2-octanol (2.13 mg/L in ethanol) as internal standard (IS) and added to previously introduced 1.5 g of sodium chloride. Extraction of volatiles was performed by headspace solid-phase microextraction (HS-SPME) using 2 cm long 50/30 \(\upmu\)m coated divinylbenzene/carbosen/polydimethylsiloxane (DVB/CAR/PDMS) fiber (Supelco, Sigma-Aldrich, Milan, Italy). GC-MS analysis was performed by Thermo Trace GC ultra gas chromatograph coupled to a Thermo Quantum XLS mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA), equipped with a PAL combi-xt (CTC, Zwingen, Switzerland) autosampler with an SPME option. The fiber conditioning, microextraction regime, and the configuration of the mass spectrometer were set up as previously reported by [PERSON] et al. (2017). Briefly, solid-phase microextraction was followed by fiber desorption in the splitless mode (3 min at 250 \({}^{\circ}\)C), and the fiber was reconditioned between each sample (7 min at 270 \({}^{\circ}\)C). The column installed in the GC oven was 30 m \(\times\) 0.25 mm VF-WAXms, with a 0.25 \(\upmu\)m film thickness (Agilent Technologies Inc., Santa Clara, CA, USA). After the sample injection, the oven temperature was initially maintained at 40 \({}^{\circ}\)C for 2 min and graduallyramped at 6 \({}^{\circ}\)C/min up to 250 \({}^{\circ}\)C (sustained for 5 min). The detection was carried out by electron impact mass spectrometry (MS) in total ion current (TIC) mode, using ionization energy of 70 eV and ion source temperature of 230 \({}^{\circ}\)C. Using ThermoXcalibur software (1.1.1.03, Thermo Scientific, Milan, Italy), the semiquantitative analysis was carried out. The final concentration of detected compounds was expressed as \(\mu\)g/L of the standard internal 2-octanol, considering a response factor equal to 1. Where applicable, the linear temperature-programmed retention indices were calculated (Supplementary Materials Table S7) for a series of n-alkanes (C7-C30) and compared with those reported in literature [58; 59; 60; 61; 62; 63; 64; 65; 66].
## 5 Emerging Challenges and Opportunities
### Network Integration  
As a cyber-physical emergency response system may have to rely on cooperation among multiple networks, the integration and interference avoidance, among heterogeneous networks emerges as a significant issue.  
_Progress toward network integration standards:_ IP is not adequate as a standard for WSNs, and TinyOS [67] deviates from IP and has to be lightweight for power-saving purposes. The uIP [68] concept includes a low-power link built on IEEE 802.15.4-embedded devices to avoid a heavyweight IP stack. The IETF defines the RFC 4944 (6 LoWPAN) [69] to allow IPv6 datagrams to be carried over IEEE 802.15.4 frames by fragmentation and header compression. In [70], a IPv6-based network architecture is proposed to support duty-cycled link protocols, hop-by-hop forwarding and efficient routing protocols in WSNs. Multiple WSNs can be connected by _border routers_, which can forward IP datagrams, where each border router may also implement IPv4-to-IPv6 translation in compliance with conventional IPv4 networks. The emergence of WiFi Direct [71] allows mobile phones to exchange content directly based on peer-to-peer communications.  
_Interference avoidance among heterogeneous networks_: communication reliability becomes more difficult when multiple networks co-exist., e.g., WiFi, Bluetooth and IEEE 802.15.4 networks. References [72, 73] describe performance degradation in ZigBee networks that co-exist with WiFi networks. The major reason is that the ZigBee signal is too week to be detected by WiFi senders. Many efforts consider a pessimistic mechanism to _avoid_ using the channels occupied by other coexisting networks. In [74], superframes of multiple coexisting ZigBee networks are scheduled, while [75] allows a node to use different channels at different time points. In contrast, [76] proposes a header redundancy scheme to reduce _header corruption_, where multiple headers associated with a single ZigBee packet are sent. Since WiFi packets are typically shorter than ZigBee packets, longer ZigBee packets may cause WiFi transmitters to back off and shorter WiFi packets may corrupt ZigBee packet headers. Using a multiple header scheme, the first (corrupted) header can cause a WiFi transmitter to back off, and the second header can improve the correctness of detection by a ZigBee receiver. To reduce the number of forwarding messages between multiple mobile networks, the approach in [77] proposes a mechanism to detect the overlapping structure among them based on their density and the degree of connectivity. Interoperability among several heterogeneous networks is facilitated by a gateway selection scheme proposed in [78].