esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background of Proposed Methodology  
A Wireless Sensor Network (WSN) is an emerging communication technology that impressed its footprint in almost every field of application. Recently, this sensor network has been utilized in urban areas to monitor living environments. The inclusion of wireless communication in these urban areas has transferred it to smart cities. The present research focuses on the deployment of WSNs with better security and energy efficiency in smart building applications. The crucial constraints faced in any type of sensor network are security and efficient energy. Many scholars have designed an enormous routing protocol to achieve efficient energy and security. The clustering method is the most commonly used approach for framing routing protocols in WSNs. However, this traditional clustering has limitations, such as complexity, time consumption, and difficulty in achieving an improved packet delivery ratio. The proposed architecture is designed based on grid clustering to attain energy-organized and trust-based routing for achieving enhanced data transmission in WSNs for smart building applicationsto overcome these limitations. The basic architecture of the proposed WSN framework for smart city applications is shown in Figure 1. TheWSN framework designed specifically for smart building applications is illustrated in the previous diagram. The proposed routing model was processed in two phases. An energy-efficient routing protocol is designed in the first phase, and in the second phase, a trust-based algorithm is included to enhance data security. The process carried out in the first phase is explained as follows: The initial step considered in any type of WSN is node deployment. Approximately eight sensor nodes that perform various functions, including temperature and air monitoring, were deployed in each building. The nodes deployed in each building were considered to be static. The primary purpose of each deployed node is to transmit the sensed information to the base station and from the base station to the server via the gateway for monitoring. Data transmission in the proposed study was performed using a grid-based clustering approach.  
In this approach, the formation of grids with the help of the deployed nodes was initially achieved. The grid organizer is elected using the sailfish optimizer in the second step, and the best relay node is selected using a fuzzy expert system to design the optimal  
Figure 1: Magnetization as a function of an applied field.  
routing protocol in the third step. In the second phase, a two-fish encryption algorithm is introduced to enhance the security of the transmitted data. The stepwise process involved in the proposed framework is briefly described in the following section.
## 2 Materials and Methods
### Drought Efforts on Rubber GPP
Error bars represent 95% confidence intervals, and black lines indicate minimum and maximum values. Figure 10: Deviations of GPP under short-term drought conditions during different seasons. Error bars represent 95% confidence intervals, and black lines indicate minimum and maximum values. Using the three-month SPEI to define drought events (Figure 10), it is observed that during the rainy season, the GPP deviations at the DR, XR, and NR sites are not significantly different. At the SR site, the GPP deviation under drought conditions is higher compared to non-drought conditions. During the cool dry season, the GPP deviations under drought conditions at the DR, XR, and NR sites are negative. The values are \(-4.80\), \(-4.03\), and \(-2.92\) g C m\({}^{-2}\) 8 day\({}^{-1}\), respectively, which are about 22%, 10%, and 10% below the average value during the same period, respectively. At the SR site, the GPP deviation under drought conditions is 0.89 g C m\({}^{-2}\) 8 day\({}^{-1}\) higher than under non-drought conditions, about 2% over the average. During the warm dry season, the four sites exhibit more complex patterns. At the DR site, the GPP deviation during drought is slightly lower than non-drought by 0.94 g C m\({}^{-2}\) 8 day\({}^{-1}\), about 4% below the average. At the XR site, the GPP deviation during drought in the warm dry season is higher than under non-drought conditions. At the NR and SR sites, the GPP deviations during drought in the warm dry season are significantly lower than those during non-drought periods, being approximately 14% and 5% below the average value for the same period. Using the twelve-month SPEI to define drought events (Figure 11), it is observed that during the rainy season, the GPP deviations under drought and non-drought conditions at all four sites are not significantly different. During the cool dry season, the GPP deviations during drought at the DR, XR, and NR sites are significantly lower than during non-drought periods. Specifically, these values are \(-6.46\), \(-4.18\), and \(-4.21\) g C m\({}^{-2}\) 8 day\({}^{-1}\), respectively, representing decreases of approximately 29%, 11%, and 14%, respectively. At the SR site, the GPP deviation under drought conditions is 2.32 g C m\({}^{-2}\) 8 day\({}^{-1}\) (5%) higher than under non-drought conditions. During the warm dry season, the four sites show relatively consistent patterns, with GPP deviations during drought being lower than during non-drought periods. At the DR site, the GPP deviation is the largest, reaching 9.38 g C m\({}^{-2}\) 8 day\({}^{-1}\), representing a decrease of 29%. At the XR site, it is also significantly lower by 8.41 g C m\({}^{-2}\) 8 day\({}^{-1}\), with an approximate decrease of 6%. At the NR and SR sites, the deviations are smaller, at 4.68 and 4.49 g C m\({}^{-2}\) 8 day\({}^{-1}\), respectively, corresponding to decreases of 14% and 12%, respectively.
# Use of CNN for Water Stress Identification in Rice Fields Using Thermal Imagery
## 2 Materials and Methods  
### Dataset  
The experiments were conducted in central Taiwan rice fields, mainly for three rice varieties, Tainan 11, HVA1 gene transgenic rice, and TN667. Referring to the irrigation amount setting in [7], the irrigation was classified as W1-W6, corresponding to 100-50% of normal irrigation. In addition, the grain yield decreased significantly after W4 (i.e., 70% irrigation). The goal of this study was to detect when water shortage is not serious; therefore, our experiment targets the relatively light water stress situations of W1-W3 (i.e., 100-80% irrigation). Well-watered, slight stress, and mild stress conditions were used to represent 100%, 90%, and 80% of the normal irrigation, respectively, and the data for each class are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Plants** & **Data Type of Input** & **Features and Classifiers** & **Remark** \\ \hline [PERSON] et al. [27] & Spathophyllum wallissii & RGB image & Texture features selected by PCA + MLP & Strict shooting conditions and prior knowledge requirements. \\ \hline [PERSON] et al. [28] & Corn, okra, and soybean & RGB image & CNN & RGB image lacks features and ignores degree information. \\ \hline [PERSON] et al. [29] & Corn & RGB image & CNN features + SVM & Too rough classification of water stress and lack of information on degree of consideration \\ \hline [PERSON] et al. [30] & Pomegranate trees & Thermal image & & consideration \\ \hline [PERSON] et al. [31] & Winter wheat & Thermal and RGB image & & Too rough classification of water stress and lack of information on degree of consideration \\ \hline [PERSON] et al. [32] & Sugarcane & Thermal image & & consideration \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of literature on water content of plant leaves.  
We conducted experiments on shooting from March 2020 to October 2020, where the planting distance of each plant was approximately 15 to 20 cm. An AVIO G100 EX/R300 infrared thermal imaging camera (AVIO, Turin, Italy) was used to capture the data in Table 2. For each picture, the thermal image (8-14 \(\upmu\)m) and the RGB image (400-700 nm) were captured at the same time, as shown in Figure 1 (taking HVA1 as an example). The upper and lower rows are the RGB image and the thermal image, respectively, with 100% irrigation to 80% irrigation from left to right. At the far right, the temperature is represented by color, with blue to red representing low to high temperatures, respectively.  
From Figure 1, it is difficult to distinguish the water stress from the rice RGB image. However, the thermal image in the lower row shows a significant temperature difference (rice fields reflect relatively low temperatures when watering is sufficient), confirming that the thermal image reflects the water irrigation situation.
## 3 Numerical Experiments and Analysis  
There are some variants of LSGO benchmarks. The first version was proposed in the CEC'08 special session on LSGO [23]. This benchmark set has seven high-dimensional optimization problems, \(D=\{100,~{}500,~{}1000\}\). Test problems are divided into two classes: unimodal and multimodal. Later, the LSGO CEC'10 benchmark set was proposed [24] as an improved version of LSGO CEC'08. The number of problems was increased to 20 by adding partially separable functions to increase the complexity of the benchmark. In this study, we use a recent version of the benchmark, namely the LSGO CEC'13 benchmark set [25]. This set consists of 15 high-dimensional continuous optimization problems, which are divided into five classes: fully separable (C1), partially additively separable (functions with a separable subcomponent (C2) and functions with no separable subcomponents (C3), overlapping (C4), and non-separable functions (C5). The number of variables of each problem is equal to 1000. The maximum number of fitness evaluations is 3.0\(\times\)10\({}^{6}\) in each independent run. The comparison of algorithms is based on the mean of the best-found values, which are obtained in 25 independent runs.  
The software implementation of CC-SHADE and CC-SHADE-ML was undertaken using the C++ programming language. We used the MPICH2 framework to parallel numerical experiments because the problems are computational complex. We built a computational cluster of 8 PCs based on AMD Ryzen CPUs. Each CPU has eight cores and sixteen threads (8C/16T). Thus, the total number of computational threads is 128. The operating system was Ubuntu 20.04.3 LTS.
## 3 The Proposed Tool
### Workflow
#### 3.1.1 Real-Time Analysis Tool  
1. Continuous Activities Monitoring: The proposed tool provides continuous surveillance of an organization's network, capturing real-time data that show the daily activities of the organization's employees.
2. Abnormality Identification: The system utilizes ML to identify abnormalities by examining anomalous daily activities of employees on the organization's network.
3. Immediate Alert Generation: Upon identifying abnormalities, the proposed tool promptly issues detailed alerts to the cybersecurity team for immediate action. Figure 2 shows an example of a generated alert.  
Figure 1: System workflow diagram.
###### Abstract  
This paper investigates the effect of complementary metaresonator for evaluation of vegetable oils in C and X bands. Tremendously increasing technology demands the exploration of complementary metaresonators for high performance in the related bands. This research probes the complementary mirror-symmetric S resonator (CMSSR) that can operate in two bands with compact size and high sensitivity features. The prime motivation behind the proposed technique is to utilize the dual notch resonance to estimate the dielectric constant of the oil under test (OUT). The proposed sensor is designed on a compact 30 \(\times\) 25 mm\({}^{2}\) and 1.6 mm thick FR-4 substrate. A 50 \(\Omega\) microstrip transmission line is printed on one side, while a unit cell of CMSSR is etched on the other side of the substrate to achieve dual notch resonance. A Teflon container is attached to CMSSR in the ground plane to act as a pool for the OUT. According to the simulated transmission spectrum, the proposed design manifested dual notch resonance precisely at 7.21 GHz (C band) and 8.97 GHz (X band). A prototype of complementary metaresonator sensor is fabricated and tested using CCEPAR AV3672D vector network analyzer. The comparison of measured and simulated data shows that the difference between the first resonance frequency is 0.01 GHz and the second is 0.04 GHz. Furthermore, a mathematical model is developed for the complementary metaresonator sensor to evaluate dielectric constant of the OUT in terms of the relevant, resonant frequency.  
complementary mirror-symmetric S resonator; dielectric constant; dual notch; metaresonator sensor; oil under test +
Footnote †: journal: Applied Sciences  
17 June 2021  
**Keywords:** complementary mirror-symmetric S resonator; dielectric constant; dual notch; metaresonator sensor; oil under test
###### Abstract  
Harsh underwater channels and energy constraints are the two critical issues of underwater acoustic (UWA) communications. To achieve a high channel estimation performance under a severe underwater channel, sparse Bayesian learning (SBL)-based channel estimation was adopted for UWA orthogonal frequency division multiplexing (OFDM) systems. Accurate channel estimation can guarantee the successful reception of transmitted data and reduce retransmission occurrences, thereby, leading to energy-efficient communications. However, SBL-based algorithms have improved performances in iterative ways, which require high power consumption. In this paper, a fast SBL algorithm based on a weighted learning rule for hyperparameters is proposed for channel estimation in a UWA-OFDM system. It was shown via numerical analysis that the proposed weighted learning rule enables fast convergence and more accurate channel estimation simultaneously. Simulation results confirm that the proposed algorithm achieves higher accuracy in channel estimation with much fewer iteration numbers in comparison to conventional SBL-based methods for a time-varying UWA channel.  
orthogonal frequency division multiplexing (OFDM); sparse Bayesian learning (SBL); underwater acoustic communications; channel estimation
## 6 Conclusions  
The metering-out chamber in SMEHS is often maintained near a lower pressure for energy-saving reasons. When the load drastically changes, the system is easy to oscillate. The damping characteristics of SMEHS are investigated and the novel active damping strategy is proposed in this work in order to suppress the oscillation. A robust backstepping controller combining with tracking differentiator and nonlinear disturbance observer is designed for the separating-metering electro-hydraulic system with the active damping strategy. The experimental results indicate that, when compared with the SMC controller, the maximum tracking error is reduced by 35%. By using the active damping strategy, the duration of oscillation time is reduced by 41%. The maximum overshoot is reduced by 10.5%. In summary, the proposed controller has excellent tracking performance, and the proposed active damping strategy effectively suppresses the oscillation. This work is suitable for the separating-metering system with low back pressure applications. However, the metering out chamber pressure will get increased, which means that the system has lower energy efficiency in order to increase loop damping. Figure 16: The pressure of cylinder chambers without ADS. Figure 17: The pressure of cylinder chambers with ADS. Figure 15: Position error (ADS restrained oscillation). Therefore, how to improve the energy efficiency of the active damping strategy in SMEHS is one of the issues to be addressed in the future. **Author Contributions:** Data curation, S.L. ; Funding acquisition, G.A. ; Investigation, S.L. and Y.G. ; Methodology, S.L. and J.H. ; Project administration, G.A. ; Software, S.L. and Y.G. ; Supervision, J.H. ; Validation, J.H. ; Writing-original draft, S.L. ; Writing-review & editing, J.H. All authors have read and agreed to the published version of the manuscript. **Funding:** This work reported here is funded by the Coordinative Creation Center of Taiyuan Heavy Machinery Equipment of China (1331 Project). **Conflicts of Interest:** The authors declare that there is no conflict of interests regarding the publication of this paper. **Nomenclature**
## 4 Conclusions  
In the present study, the control efficiency against cowpea thrips and operator exposure determination by aerial and ground spraying were investigated and compared. When spinerotam with the same dosage of active ingredient per hectare was applied, the field control efficacy against cowpea thrips sprayed by UAV was higher than that of the knapsack electric sprayer. Moreover, UAV spraying could greatly reduce water consumption and working time. When Allura Red as a pesticide surrogate was applied under similar spraying scenarios with the field control against thrips on cowpea, the potential dermal exposure of the operator to Allure Red sprayed by knapsack electric and UAV sprayers was carried out. The average total exposure of the knapsack operator was greatly higher than that of the UAV operator. The present research indicates that plant protection UAV spraying can minimize the pesticide exposure level without sacrificing the control efficiency of pests, which is the direction of development of modern intensive sustainable agriculture.  
Figure 4: Potential dermal exposure of operators to Allure Red sprayed by knapsack electric and UAV sprayers (\(n=6\) for knapsack spraying including two operators and \(n=3\) for UAVs spraying).  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/app11209579/s1](https://www.mdpi.com/article/10.3390/app11209579/s1)]([https://www.mdpi.com/article/10.3390/app11209579/s1](https://www.mdpi.com/article/10.3390/app11209579/s1)), Video S1: The local usual spraying scenario for cowpea pest control with stretcher-mounted power sprayer.  
**Author Contributions:** Conceptualization, X.Y. and H.Y.; methodology, X.Y. and Y.Z.; software, D.Y.; validation, Y.Z. and X.L.; formal analysis, X.Y.; investigation, X.Y. and Y.Z.; resources, X.L.; data curation, D.Y.; writing--original draft preparation, X.Y.; writing--review and editing, D.Y. and H.Y.; visualization, X.Y.; supervision, H.Y.; project administration, X.Y.; funding acquisition, X.Y. and H.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (NSFC) (No. 32072468) and National Key Research and Development Plan of China (2016 YFD0200703).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Proposed Power Losses Model
### Proposed Conduction Losses Model
#### 3.1.2 The Solution of the Duty Cycle Based on SVPWM
\begin{array}{ll}\frac{1}{2}(1+\frac{\sqrt{3}}{2}m\cos(\alpha- \frac{\pi}{6}))&0\leq\alpha\leq\frac{\pi}{3}\\ \frac{1}{2}(1+\frac{3}{2}m\cos(\alpha))&\frac{\pi}{3}\leq\alpha\leq\frac{2\pi} {3}\\ \frac{1}{2}(1+\frac{\sqrt{3}}{2}m\cos(\alpha+\frac{\pi}{6}))&\frac{2\pi}{3} \leq\alpha\leq\pi\\ \frac{1}{2}(1+\frac{\sqrt{3}}{2}m\cos(\alpha-\frac{\pi}{6}))&-\pi\leq\alpha \leq-\frac{2\pi}{3}\\ \frac{1}{2}(1+\frac{3}{2}m\cos\alpha)&-\frac{2\pi}{3}\leq\alpha\leq-\frac{\pi} {3}\\ \frac{1}{2}(1+\frac{\sqrt{3}}{2}m\cos(\alpha+\frac{\pi}{6}))&-\frac{\pi}{3} \leq\alpha\leq 0\\ \end{array} \right. \tag{15}\]  
According to the FOC algorithm, the phase-current is in sinusoidal form. Assuming that the phase angle of the phase current is \(\theta\), the current can be expressed as:  
\[i(\theta)=I_{p}\cos\theta \tag{16}\]  
where \(I_{p}\) represents the amplitude of the phase current. Since PMSM is an inductive load, there is an angle difference between the voltage vector phase angle and the current vector phase angle. This angle is defined as power factor angle and can be expressed as:  
\[\alpha=\theta+\varphi \tag{17}\]  
Substitute Equations (16) and (17) into Equation (8):  
\[P_{light\_cur\_cond}(\theta)=\frac{1}{2\pi}\int_{-\frac{\pi}{2}}^{\frac{\pi}{ 2}}\left(V_{co}+R_{cr}\times I_{p}\cos\theta\right)\times I_{p}\cos\theta \times D(\theta+\varphi)d\theta \tag{18}\]  
From Equation (18), it can be seen that the calculation of power losses is in a piecewise integration form due to the piecewise duty cycle, which brings higher computation requirements. Therefore, it is necessary to find a more feasible engineering method to solve this problem.
## 5 Experimental Results and Analysis
### Comparative Experiment
Detection performance for this category is generally poor across various models, and the rotation information introduced by our method may have disrupted the effectiveness of this structure, further complicating the detection process. As for the GF category, the detection issues likely arise from the irregular and complex shape of golf courses. The rotation features do not align well with these complex layouts, making it challenging for the model to accurately detect the targets. Figure 10: Sample images from the APO and GF categories. Figure 9: Confusion matrix of multi-class classification performance on the DIOR-R dataset.