esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Wind Turbine and Control Algorithm
### Control Algorithm
#### 2.2.3 Linear Quadratic Regulator Based on Fuzzy Control Algorithm  
LQRF is a proposed algorithm for the optimal control of wind turbines through enhancing the traditional LQR control algorithm, which is vulnerable to uncertainties in the turbine's state, by integrating fuzzy logic [14]. The target trajectories of LQR control, fuzzy logic, and gain scheduling are determined by the estimated wind speed, thus requiring high wind speed estimation accuracy. The time derivative of the state-space vector \(\dot{x}\) can be expressed in terms of the matrices \(A\) and \(B\), the state-space vector \(x\), and the input vector \(u\). The output vector \(\mathrm{y}\) can be represented as a relationship between the matrix \(C\) and the state-space vector. Equation (3) represents the wind turbine in state-space form, with matrices \(A\), \(B\), and \(C\) considered in the LQR control design. Equation (4) represents the state, input, and output vectors of the linear system, respectively. The inputs include the nacelle fore-aft displacement \(d\), nacelle fore-aft velocity \(\dot{d}\), rotor speed \(\Omega_{r}\), pitch angle \(\beta\), pitch rate \(\dot{\beta}\), generator torque \(T_{g}\), and the time integral of rotor speed. The outputs are the pitch angle command \(\beta_{CMD}\) and the generator torque command \(T_{CMD}\).  
\[\dot{x}=A_{matrix}x+B_{matrix}u \tag{3}\]  
\[\mathrm{y}=C_{matrix}x;\]  
\[x=\begin{bmatrix}d&\dot{d}&\Omega_{r}&\beta&\dot{\beta}&T_{g}&\int\Omega_{r}dt \end{bmatrix}^{T} \tag{4}\]  
\[u=\begin{bmatrix}\beta_{CMD}&T_{CMD}\end{bmatrix}^{T};\;\;y=\Omega_{r}\]  
The optimal state feedback gain for minimizing the objective function, provided in quadratic form of the output and input in Equation (4), can be obtained by solving the algebraic Riccati equation [13]. Additionally, fuzzy logic was applied for the purpose of interpolating nonlinear systems and control inputs. As shown in Figure 1, the LQRF control algorithm was applied to the target wind turbine control algorithm, replacing the existing power control algorithm to perform power control. Detailed information on the design of the LQRF control algorithm can be found in the literature, published through previous research [14].
## 4 Numerical Simulation
### Settlement Analysis and Comparisons  
To verify the correctness of the numerical analysis method, the settlement data from the model tests were multiplied by a similar factor, and the variation law of the load-settlement curve of the GRPS embankment was compared and analyzed as shown in Figure 11. It should be noted that the numerical simulation results of load-settlement under embankment loading are approximately the same as the trend obtained from the test; both show an increase with the increase in embankment load. The settlement of the model test is slightly higher than the numerical simulation results, and the maximum settlement of the model test and numerical simulation is 8.10 mm and 7.43 mm; the error of both is 8.14%, which shows that the numerical analysis model can reflect the actual situation more realistically. Figure 11a demonstrates that the soil deformation from the middle of the roadbed to the bottom in the dispersion gradually decreases, and the settlement at the foot of the embankment is much smaller than the surface settlement, which shows that the pulling film effect of the geogrid suppresses the deformation of the GRPS structure.
## 5 The Technologies
### Advanced Robotics  
Industrial robots were one of the iconic hallmarks in the era of automation and mass production [98]. Robots can operate in tougher working conditions than humans, such as areas with limited space, high temperatures and high humidity, and exposure tohazards [101]. Nevertheless, their rigid manipulators could only be employed for some highly repeatable tasks at the early development stage (e.g., welding, painting, pick and place) [11]. However, it is believed that the significant development of advanced and smart robotics in recent years is a key solution to relieve the difficulty of hiring human labourers for low-skilled, repetitive tasks in the era of Industry 4 [1; 11; 53; 121].  
It is required for industrial robots in the Industry 4 era to become smarter and more adaptive, which can be achieved by integrating more effective control and programming methods for industrial robots [47; 122]. [PERSON] et al. [53] have discussed a strategy to offer a novel offline programming method. [PERSON], [PERSON], and [PERSON] [3] have proposed a novel control strategy to enable batch-size-of-one production in robotic flexible assembly cells. Furthermore, another work by [PERSON], [PERSON], and [PERSON] [13] has verified a new redundancy strategy that can be implemented in robotic flexible assembly cells to mitigate the impact of individual robot failures.  
Collaborative industrial robots, also known as cobots, have been broadly deployed in a smart manufacturing context in recent years [123]. In the reviewed articles, cobots are one of the major manipulator classes deployed in academic Industry 4 facilities. There are two types of application scenarios for using collaborative robots in manufacturing environments. One scenario is to configure cobots to work alone with limited or no interaction with human operators. Another one is to achieve seamless integration and collaboration between humans and cobots in manufacturing.  
The employment of collaborative robots has the potential to significantly reduce the total assembly processing time [7; 9] and enable cobots to handle more complicated tasks [3; 11; 49]. End-effector changes can be automated using predefined algorithms integrated into robotic control systems [3]. In addition, with the integration of AI, machine vision, and mixed reality (AR and VR) in robotics, there is even greater potential for robots to contribute to manufacturing processes [11]. For example, [PERSON], [PERSON], and [PERSON] [86] have investigated an application of using cobots in a mixed-reality learning environment.  
In addition to cobots, mobile robots are the other type of robotics. Automated guided vehicles (AGVs) are typical examples in this category, which can offer flexible and autonomous material handling services on the factory floor [26; 39; 41; 67; 97; 107]. In some cases, collaborative robots can be mounted onto an AGV to perform specific tasks [49; 107].
## 2 Main Factors Affecting Spectral Reflection Characteristics of Leaves
### Structure--Near-Infrared  
In the near-infrared spectrum (700-1300 nm), the epidermis of leaves generally does not contain any pigments. The epidermis and cell wall cellulose are transparent, so the leaf absorption is less than 10%. The radiation incident on the leaves is either reflected or transmitted [29]. When natural light shines on leaves, it is reflected, absorbed, and transmitted successively in the upper epidermal tissues, mesophyll tissues, and lower epidermal tissues. The cell structural differences among different tissues lead to their different responses to incident light. The reflectance reaches about 50% of the infrared region, which is mainly determined by the profile of the leaf structure. For bifacial leaves, 5% to 20% of the volume of tightly arranged palisade tissues and 50% to 90% of the volume of loosely arranged spongy tissues are occupied by air, and the numbers of wall-air  
Figure 2: Solar spectral reflectance and transmittance of plant leaves [24].  
interfaces also differ between these two types of tissues. The light incident on mesophyll tissues will be strongly scattered under the action of multiple air-wall interfaces, and this effect of sponge tissues is stronger than that of palisade tissues. In this way, the light will be scattered more times before reaching the lower epidermis of the leaf, which helps plants absorb light. Thus, under the action of multiple scattering mechanisms, the near-infrared plateau characteristics on the reflection spectra of bifacial leaves and isofacial leaves of angiosperms with different mesophyll structural characteristics are quite different. Due to the aforementioned reasons, the mesophyll tissues of leaves strongly reflect near-infrared light at 800-1300 nm, and they thus form a relatively gentle plateau on the spectrum curve, with reflectance of 40-60% and an absorption rate less than 15%. The interface formed by the air between cells and the wet cell wall is the main reason why the reflection spectrum of leaves shows the above characteristics [30].
# Quantifying Moss Response to Metal Contaminant Exposure Using Laser-Induced Fluorescence
## 2 Materials and Methods
### Laboratory Treatment of Moss with Copper
The fronds were then removed from the DI water, dried, and immersed in 10 mL of 10 mM ethylenediaminetetraacetic acid (EDTA) solution [35]. Fronds were submerged and shaken in EDTA solution for 45 min. The process was then repeated in a fresh fraction of 10 mL of EDTA for 30 min. The two EDTA fractions were combined for extracellular Cu analysis. Samples were then blotted dry, weighed, and then dried in a furnace at 50 \({}^{\circ}\)C for 24 h before cooling for 24 h in a desiccator. The dry weight of cooled fronds was recorded and, finally, samples were submerged in 10 mL of 1 M HNO\({}_{3}\) for 30 min of shaking to induce partial digestion. Only selected samples were analyzed for copper content, specifically 3 pairs (left, center, and right of moss mass) from each sample from days 1, 3, and 10 and 1 pair (center of moss mass) for all the other days. The individual DI water, EDTA, and nitric acid fractions were analyzed for Cu concentration using a sector-field inductively coupled plasma-mass spectrometer (ICP-MS, Thermo-Fisher Element 2; Thermo Fisher Scientific, Waltham, MA, USA) at the University of Southern Mississippi Center for Trace Analysis (CETA). A self-aspirating nebulizer (Elemental Scientific, Omaha, NE, USA) with low flow (100 \(\upmu\)L/min) and a Teflon spray chamber was utilized. Cu-63 was determined in medium resolution, and calibration was conducted using external standards made in 0.16 M ultrapure nitric acid. These were then checked against standard reference waters from the U.S. Geological Survey. There was also an in-house consistency standard measured to ensure a sensitivity check, long-term stability, and instrumental drift correction. Cu analysis was also conducted for solution blanks of DI, EDTA, and HNO\({}_{3}\) and non-treated fronds from trial 1 to determine baseline Cu concentrations. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**Individual and Cumulative Cu Dose in mmol/cm\({}^{2}\)**} \\ \hline
**Day** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline
**Trial 1** & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) \\
**Trial 2** & 0.538 (0.538) & 0.0538 (1.076) & 0.1076 (0.1076) & 0.538 (1.614) & 0.1614 (0.538 (2.152)) & 0.2152 (0.538 (2.690)) & 0.2690 (2.690) \\
**Trial 3** & 1.077 (1.077) & 0 (1.077) & 1.077 (2.154) & 0.2154 (1.0215) & 1.077 (2.313) & 0.3231 (0.323) & 1.077 (4.308) & 0 (4.308) & 1.077 (5.385) & 0 (5.385) \\
**Trial 4** & 1.615 (1.615) & 1.615 (3.230) & 0 (3.230) & 1.615 (4.845) & 0 (4.845) & 1.615 (6.46) & 0 (6.460) & 1.615 (8.075) & 0 (8.075) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental doses of metal added each day (\(\upmu\)mol Cu per cm\({}^{2}\)), with cumulative concentrations of Cu reached by that day of the experiment provided in parentheses.
## 4 Numerical Simulation for the Highway Subgrade Stability
### Numerical Results and Discussion
#### 4.3.3 Influences of ELB Height on the Subgrade Stability  
Figure 10 shows the relationship between the stability factors and the ELB height (\(B=5\) m, \(B=7.5\) m, \(B=10\) m). The stability factors increase gradually with the ELB height increase. This is also agreeable with the analytical results in Section 3.2. The relationship between the stability factors and the ELB height was fitted using a linear equation. The fitting formula and correlation coefficients are shown in Table 3. The correlation coefficients are equal to or more than 0.97. This implies that the linear equation is good for fitting the relationship. From Table 3, it is seen that the slope of the fitting formula is 0.01 for the ELB width \(B=5\) m and \(B=7.5\) m. The slope is 0.013 for the ELB width \(B=10\) m. The slopes of the fitting formula increase with the ELB width increase.  
\begin{table}
\begin{tabular}{c c c} \hline
**Height/\(H\)** & **Fitting Formula** & **Correlation Coefficient** \\ \hline
3 m & \(F_{s}=0.014B+1.08\) & 0.99 \\
4 m & \(F_{s}=0.016B+1.08\) & 0.98 \\
5 m & \(F_{s}=0.016B+1.08\) & 0.98 \\
6 m & \(F_{s}=0.016B+1.10\) & 0.98 \\ \hline \end{tabular}
\end{table}
Table 2: Fitting formula between the stability factor and ELB width.  
Figure 9: The stability factors of the subgrade with different ELB widths.  
In Table 2, the minimum slope of the fitting formula is 0.014, while the maximum slope of the fitting formula in Table 3 is 0.013. The slope of the fitting formula in Table 2 is greater than that in Table 3. This means that the increased ratio of stability factors with the width increase is greater than that with the height increase. It can be concluded that the influences of ELB width on the subgrade stability factor are greater than that of ELB height. This is consistent with [PERSON]'s research conclusion [8].  
In summary, with the ELB width and height increase, the subgrade stability factors increase. The ELB width has a greater influence on the subgrade stability factors than the ELB height. For the ELB design, the scheme with bigger ELB width is recommended to ensure subgrade stability.
# Polymer/Silica Hybrid Waveguide Thermo-Optic VOA Covering O-Band
## 5 Conclusions  
In a summary, we demonstrate broadband polymer/silica hybrid waveguide thermo-optic VOA based on MZIs. We optimize MMIs, used in the VOA, to achieve wide bandwidth, low loss, and high fabrication tolerance performance. The VOA is fabricated through low-cost contact lithography and wet etching. At 1310 nm, an ER of 18.64 dB is achieved under 8.72 mW. The rise and fall time of the VOA are 184 \(\upmu\)s and 180 \(\upmu\)s, respectively. Over the O-band, the attenuation is larger than 6.99 dB. The VOA will be integrated with CWDM, for a power equalization function, monolithically.  
**Author Contributions:** Conceptualization, Y.Y. and D.Z.; methodology, Y.Y. and Y.D.; software, Y.Y. and Y.D.; validation, Y.Y., M.Y. and Y.L.; formal analysis, Y.Y., M.Y. and Y.L.; investigation,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Ref.** & **Platform** & **IL (dB)** & **ER (dB)** & **PC (mW)** & **Work Range** & **RT (\(\upmu\)s)** & **FT (\(\upmu\)s)** \\ \hline [12] & Silica on silicon with air trench & 0.7 & 30 & 155 & 1550 nm & N.A. & N.A. \\ [13] & Silica on silicon with & 0.8 & 39 & 95 & 1550 nm & N.A. & N.A. \\ [8] & Silica on silicon with suspended & & & & & & \\  & narrow ridge structure & & &  
X.X.; resources, Y.W. and D.Z.; data curation, Y.Y. and M.Y.; writing--original draft preparation, Y.Y.; writing--review and editing, Y.Y., M.Y. and Y.D.; supervision, D.Z.; project administration, Y.W.; funding acquisition, D.Z.; 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 (No. 2019 YFB2203001), and the Science and Technology Development Plan of Jilin Province (20190302010 GX).  
Not applicable.  
The authors declare no conflict of interest.
# Implementation of an IoT Based Radar Sensor Network for Wastewater Management
## 6 Background
### LoRaWAN MAC and Security
#### 6.2.2 Cayenne Packet Encoding  
To translate the measurements obtained utilising technology from the portable radar device, a method to encode the data into a packet size of less than 11 bytes (LoRa Data Rate 2, Spreading Factor 10) was required. The Cayenne Low Power Payload (LPP) [32] provides a convenient and simple encoding technique to send data over LPWAN networks such as LoRaWAN. The Cayenne LPP is compliant with the spreading factor payload size restrictions, and allows the device to send  
Figure 28: TTN Event packet capture.  
Figure 27: Data Packet life-cycle.  
multiple sensor data at one time. For this sensor two values are required to be monitored; both well level (metres) and flow rate (litres per second), and is encoded as follows:  
8 bytes per packet = 2 'channels' worth of data:  
_Channel 1: Level (metres)_  
1 Byte = Channel 1 Identifier  
= 0x01  
1 Byte = Channel 1 Type Identifier  
= 0x02 **(Analog Input)**  
2 Bytes = Channel 1 Data = 0x00 to 0 xFF **(Range)**  
\(=-327.68\) to 327.67 **m**  
_Data Resolution = 0.01 **(Per bit, Signed)**_  
_Channel 2: Flow (L/s)_  
1 Byte = Channel 2 Identifier  
= 0x02  
1 Byte = Channel 2 Type Identifier  
= 0x02 **(Analog Input)**  
2 Bytes = Channel 2 Data = 0x00 to 0 xFF **(Range)**  
_Data Resolution = 0.01 **(Per bit, Signed)**_
# MILP-Based Approach for High-Altitude Region Pavement Maintenance Decision Optimization
## 2 Problem Formulation
Specifically, we consider multiple road segments rather than a single segment, focusing on monthly maintenance plans rather than only annual ones, and use optimization theory to study the pavement maintenance decision problem in high-altitude areas, that is, the network-level optimization of pavement maintenance decisions in high-altitude regions. Furthermore, to better define the problem background and facilitate model construction, the following basic assumptions are proposed:  
**Assumption 1:**_The total traffic demand within a road network is not affected by maintenance work. Multiple roads exist within a road network, and users can choose other roads when maintenance is conducted on a particular road. Although traffic flow on the segment is affected, the traffic demand within the road network does not change, and the potential traffic demand on the segment is relatively fixed._  
**Assumption 2:**_The maintenance work on a single segment can be completed within one month. To ensure the controllability of the maintenance period, this assumption is proposed. In practice, the segments to be maintained after segmentation are usually short (e.g., less than 1 km), resulting in shorter working periods._  
**Assumption 3:**_During the study period, a single segment can only choose one maintenance approach. For ease of decision-making, segments are usually divided based on distress characteristics. Therefore, a single segment can only adopt one maintenance approach, especially when the decision period is short._  
**Assumption 4:**_During the study period, each segment undergoes, at most, one type of maintenance work. This assumption is similar to the previous one, considering the characteristics of segment division, and is suitable for problems with shorter decision periods._
# Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images
## 4 Size-Scalable Candidate Region Proposal Algorithm
### Experiments  
#### 4.3.1 Dataset and Evaluation  
Two different scales of remote sensing image datasets are used in this paper:  
(1) SMALL-FIELD-RSIs: Each image was cropped from the Google Earth software, and all airplane objects were manual marked. As shown in Figure 3, for each airplane object, a minimum enclosing rectangle was used. Each bounding box was represented by its top-left and bottom-right coordinates: \(\left(x_{1},y_{1},x_{2},y_{2}\right)\). The dataset contained 980 high-resolution optical remote sensing images, and the image size was about 1300 \(\times\) 800 pixels, with the spatial resolution of the image being about 0.6 m. A total of 7452 airplane objects were marked.  
(2) LARGE-FIELD-RSIs: Each image was downloaded from the commercial professional remote sensing software, and the map level was 19, the scale 1:2257, the data source being from QuickBird satellite, and the spatial resolution 0.6 m. Compared to Google Earth software, the software can download any size of high-resolution optical remote sensing images with no watermark or other labels, and the images are relatively clearer. There were 110 images, and the average size of all images was about 5000 \(\times\) 5000 pixels. A total of 3380 airplane objects were manually marked. As shown in Figure 4, the left is a high-resolution remote sensing image of an airport (International Airport, Shenzhen, China), and the right is an enlarged view of the red area on the left. As can be seen, for the entire image, the airplane object was very small. When zooming out to a relatively small size, the airplane objects were almost invisible. Table 1 shows the statistical details of the two datasets.  
Figure 3: The annotation method of the airplane object.  
In the region proposal algorithm, we evaluated the improved algorithm by the DR-#WIN curve and MABO-#WIN curve, where DR refers to the detection rate and MABO refers to mean average best overlap, while #WIN refers to the number of candidate windows proposed.
# Drowsiness Detection in Drivers Using Facial Feature Analysis
## 3 Methods
### Static Threshold  
Figure 2 illustrates the block diagram of the static threshold algorithm, which consists of five primary components: (1) stream video capture, (2) frame extraction, (3) frame preprocessing, (4) calculation of ECR and MAR, and (5) assessment of driver state. Firstly, block 1 captures continuous video input. Secondly, block 2 isolates individual frames from the video stream. Thirdly, block 3 involves applying necessary enhancements or adjustments to optimize frame quality. Next, block 4 calculates the eye closure ratio and mouth aperture ratio. Finally, block 5 evaluates the driver's condition based on computed metrics. Each block plays a crucial role in the algorithm's functionality, collectively enabling real-time assessment of the driver's state. Below are comprehensive explanations of each block.
# A Novel System of Mixed RF/FSO UAV Communication Based on MRR and RIS by Adopting Hybrid Modulation
## 3 Performance Analysis
### Outage Probability
#### 3.1.1 End-to-End Uplink  
By substituting (11) into (13), the outage probability of the end-to-end uplink can be obtained as:  
\[P_{out\_up}=1-e^{-\frac{\gamma_{th}}{B\gamma_{1}}}\sum_{m=0}^{N-1}\frac{ \gamma_{th}^{m}}{(B\overline{\gamma_{1}})^{m}m!}\left(1+W\times G\begin{array} []{c}5,1\\ 2,6\end{array}\left(\frac{\alpha_{1}\alpha_{2}\beta_{1}\beta_{2}}{A_{0}} \sqrt{\frac{\gamma_{th}}{\tilde{\gamma}_{2}}}\bigg{|}\begin{array}{c}\phi_{1} \\ \phi_{2}\end{array}\right)\right). \tag{14}\]