esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Monitoring Device Architecture: Hardware  
The monitoring device is composed of ESP32, a battery, light emitting diodes (LEDs), and the following commonly used low-cost sensors:  
* GUVA-S12 SD ultraviolet radiation sensor [29];
* KY-038 noise sensor [30];
* Light-dependent resistor (LDR) [31];
* DHT11 temperature and humidity sensor [32];
* MQ-2 flammable gases sensor [33];
* Shinyei PPD42 NS dust sensor [32; 34].  
The choice of sensors considered low-cost options are commonly used in similar solutions, such as in [13; 14; 15; 16; 17; 18; 19; 20]. ESP32 is a series of low-cost and low-power microcontrollers and is also a system-on-a-chip (SoC) with an integrated microcontroller, Wi-Fi, and Bluetooth. ESP32 is a dual-core system commonly used for academic and industrial purposes, such as automation, wearables, and cloud-based IoT applications. The modules have a wide operating temperature range from \(-40\)\({}^{\circ}\)C to \(105\)\({}^{\circ}\)C, being capable of functioning reliably in industrial environments [35]. In the proposed system, ESP32 runs an embedded application that reads the sensors mentioned above, connects to the Wi-Fi network, and sends the measures to the server, as detailed in the next sections. A simplified diagram of the monitoring device is shown in Figure 1.  
Figure 1: Monitoring device diagram.  
Firstly, the monitoring device was mounted on a breadboard to verify correct operation in terms of sensor readings, Wi-Fi network connectivity, and communication with the cloud server. The ESP32 sensors and the device assembled on a PCB are shown in Figure 2.  
The embedded application that runs in the monitoring devices is explained in the next section.  
Figure 2: ESP32 and sensors (**a**) and device assembled (**b**).
# A Multi-Degree of Freedom Tuned Mass Damper Design for Vibration Mitigation of a Suspension Bridge
## 4 Structural Design of 2 DOFs TMD
### Conceptual Design of the TMDs  
By studying the characteristics of human-induced vibration, it is found that the first four modes of suspension bridge are easy to be stimulated to produce resonance phenomenon, which is the primary target of vibration reduction. The targeted mode shapes are shown in Figure 8.  
The optimal location of a TMD is at the maximum modal displacement. Hence, the center of the deck (point \(A\)) is the optimal location for the second bending and the first torsional modes of the deck (2B,1T), while for the first bending and the second torsional modes (1B,2T), it is at the quarter length of the deck (point \(B\)). The motion of the TMDs only along the vibration direction of the two pairs can reach the best vibration control. Two TMDs have the same geometric relationship as shown in Figure 9. Therefore, the dimension of the freedom space \(N\) is two, and the dimension of the constraint space is four, according to Equation (26).  
The translational freedom line and rotational freedom lines are orthogonal in Figure 9a. For the constraint space, the four constraint force lines intersect with the rotational freedom line and are orthogonal with the translational freedom line. Here the constraint couple lines can be ignored due to the fact that four constraint force lines have already formed the expected constraints. Thus, the corresponding constraint space can be divided into two pairs, each pair contains two parallel constraint force lines, and these two pairs are orthogonal (Figure 9b). Then constraint force lines are implemented by flexure elements, each of them can provide single DOF constraint along its axial direction (Figure 9c). Therefore, the exact constraints are formed on the TMDs and the expected DOFs are defined.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Freedom Space (\(\mathbf{\xi}_{2}\))**} \\ \cline{3-4}
**Geometric Condition** & & **Rotational Freedom Line** & **Translational Freedom Line** \\  & & **(\(\mathbf{h}_{2}\) = 0)** & **(\(\mathbf{h}_{2}\) = \(\infty\))** \\ \hline \multirow{4}{*}{**Constraint space (\(\mathbf{\xi}_{1}\))**} & Constraint force line & Coplanar (intersecting or parallel) \(a_{12}\) sin\(a_{12}\) = 0 & Perpendicular \(\alpha_{12}\) = 90\({}^{\circ}\) \\ \cline{1-1} \cline{2-4}  & Constraint couple line & Perpendicular \(\alpha_{12}\) = 90\({}^{\circ}\) & Arbitrary \\ \hline \hline \end{tabular}
\end{table}
Table 3: Geometric relationship between the freedom and constraint lines.
# An Energy-Efficient Integration of a Digital Modulator and a Class-D Amplifier
## 5 Experimental Setup and Results
### Prototype  
To fully demonstrate the concept, a circuit has been designed in interactive software Multisim [18] that not only provides powerful simulation and analysis, but is also used for the printed circuit board (PCB) design. A schematic of the proposed circuit prototype is shown in Figure 13. The circuit can be divided into five major blocks for better comprehension. On the left side, the 555 timer IC generates a sawtooth waveform of \(f_{c}=19.8\) kHz to be used as a carrier, and the OPAMP LM358 shown at the bottom adjusts the gain and offset of the waveform. The second block is the comparator where a binary input signal is applied to the \(+ve\) input of the IC LM393, while the carrier is connected to the \(-ve\) input. The output of the comparator is a pulse width-modulated (PWM) signal. The third block is the driver circuitry and works as a buffer and designed using IC LM358 to prevent the loading effect and to drive the fourth block, i.e., IRLZ44N Power MOSFET transistors. The amplified signal is fed into the fifth block, i.e., the second-order band-pass passive LC filter circuit removes unwanted signal components and provides an amplified PSK-modulated signal at the output.
# A New Method of Angle Measurement Error Analysis of Rotary Encoders
## 4 Detection and Verification Method
### Installation Eccentricity Detection  
#### 4.2.1 The Principle of Detecting Installation Eccentricity  
The experimental device and the method to detect the eccentricity amplitude and direction are introduced in this section. As shown in Figure 9, a CCD camera is mounted on the microscope. A circular hole is symmetrical to the reading head with the rotating shaft. The microscope is adjusted to make sure that the hole is just in the middle of the field of view.  
Figure 8: Relationships between the radial error motions of the grating disk and test spheres.  
At every 6 degrees, the CCD camera gets the images of the radial grating, and, meanwhile, the rotation angles are measured by the rotary encoder. The coordinate of the geometric center of the grating disk and the radius of the fitting circle of the rotary scales edge at each position can be calculated by using the image processing technology. Figure 10 shows the geometric relationships of the parameters calculated by the image processing technology. The middle point in pictures is set as reference point \(R(x_{r},y_{r})\), and then the distance between the grating geometric center and the reference point is calculated, which is named \(l\). The radius of fitting circle of rotary scales edge, which we named \(r_{fitting}\), is already known. The distance between rotary scales edge and reference point called \(d_{ref}\) can be calculated by the equation \(d_{ref}=l-r_{fitting}\), and for every position, we get data \(d_{refk},k=1,2,\cdots,60\).  
The function \(d_{ref}(\theta)\) can be developed as a 10 order Fourier series, represented by Equation (10):  
\[d_{ref}(\theta)=c_{0}+\sum_{i=1}^{10}\left(c_{i}\sin(i\theta)+d_{i}\cos(i \theta)\right). \tag{10}\]  
The parameters \(c_{0},c_{1},d_{1},\cdots,c_{10},d_{10}\) can be calculated by using the least-square fitting method from data \(d_{refk},k=1,2,\cdots,60\). The first harmonic is caused by the installation eccentricity of the grating disk, so the magnitude of eccentricity \(e\) can be calculated by Equation (11):  
\[e=\sqrt{{c_{1}}^{2}+{d_{1}}^{2}}. \tag{11}\]  
Figure 10: Geometrical relationships of the parameters calculated by image processing method.  
Figure 9: Experimental setup for installation eccentricity detection.  
The phase of eccentricity \(\theta_{0}\) can be expressed as Equation (12):  
\[\theta_{0}=\left\{\begin{array}{ll}\arctan(\frac{c_{1}}{d_{1}}),&d_{1}>0\\ \arctan(\frac{c_{1}}{d_{1}})+\pi,&d_{1}<0\\ 0,&c_{1}=0\;\;d_{1}=1\\ \pi,&c_{1}=0\;\;d_{1}=-1\end{array}\right.. \tag{12}\]
###### Abstract  
This study explores optimizing Synthetic Aperture Radar (SAR) satellite constellation scheduling for multi-imaging missions in densely targeted areas using an in-house-developed Modified Dynamic Programming (MDP) algorithm. By employing Mixed-Integer Linear Programming (MILP) to define the mission planning problem, this research aims to maximize observation of high-value targets within restricted planning horizons. Numerical simulations, covering a wide range of target numbers and satellite configurations, reveal the MDP algorithm's superior mission allocation efficiency, enhanced success rates, and reduced revisit times compared to the greedy algorithm. The findings underscore the MDP algorithm's improved operational efficiency and planning robustness for complex imaging tasks, demonstrating significant advancements over traditional approaches.  
optimal scheduling: synthetic aperture radar (SAR); satellite constellation; multi-imaging mission; high-density regional area; modified dynamic programming (MDP) Article
## 5 Conclusions  
To investigate the diversity of diterpenoids in _A. paniculuta_, the genomic data were analyzed and candidate terpene synthase genes were identified based on functional annotation. Functional validation using a yeast expression system led to the identification of a novel _ent_-copalyl diphosphate synthase and six sesquiterpene synthases. Gene structure and conserved motif analyses revealed the likely causes of functional loss in most sesquiterpene and monoterpene synthases in _A. paniculuta_, which may explain why diterpenoids are the dominant lineage-specific metabolites in this species. Molecular docking and site-directed mutagenesis further demonstrated that Val533 in ApTPS16 and Tyr534 in ApTPS17 are critical for their enzymatic activities. These findings provide new insights into the biosynthetic pathways of bioactive terpenoids in _A. paniculuta_, offering valuable targets for metabolic engineering aimed at enhancing the production of active ingredients or generating novel terpenoids with potential pharmaceutical applications.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/molecules30102208/s1](https://www.mdpi.com/article/10.3390/molecules30102208/s1)]([https://www.mdpi.com/article/10.3390/molecules30102208/s1](https://www.mdpi.com/article/10.3390/molecules30102208/s1)), Figure S1: Map of the distribution of conserved motifs and the intron-exon structures of ApTPS; Figure S2: GC-MS analysis of the products from the extractions of ApTPS6 or ApTPS7 couple with AtCPS or SmCPS in BY-T20 (in BY-T20) fermentation products; Figure S3: GC-MS analysis of the products from the extractions of TPS-a/b/g subfamily terpene synthases (in K197G) fermentation products; Table S1: The information of ApTPSs and AtTPSs; Table S2: Primers used in this study; Table S3: Homology matrix of TPS-b subfamily ApTPS.  
[PERSON] designed the study, data analysis, and prepared the manuscript. [PERSON] conducted experiments, [PERSON] and [PERSON] participated in the manuscript preparation and [PERSON] and [PERSON] are responsible for overall inspection and supervision of the work. All authors revised and improved the final manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Scientific and Technological Innovation Project of CACMS (CI2023E002), the Applied Basic Research Project of Yunnan Province (202401 AZ070001-006), the National Natural Science Foundation of China (82204573), the Fundamental Research Funds for the Central Public Welfare Research Institutes (ZZ14-YQ-044, ZZ16-YQ-039, ZZXT202204) and the Key Project at Central Government Level: The Ability Establishment of Sustainable Use for Valuable Chinese Medicine Resources (2060302).  
Not applicable.  
The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.
## Appendix B  
### Markov Chain  
Markov chains are powerful tools for modeling systems that evolve probabilistically. They provide a comprehensive understanding of changes in an ecosystem due to interactions with environmental and anthropogenic factors. In this study, states are used to symbolize different flood risk classes rather than directly representing meteorological or hydrological conditions. These classes are derived from predictions made by the Random Forest model, which incorporates various environmental variables to categorize each spatial unit into one of four distinct risk levels. This method provides a clear understanding of transition processes over time by utilizing a Markov chain to model the evolution of risk. For example, in a flood-prone area, each state corresponds to a level of risk, ranging from low to high. Transitions between these states occur based on specific probabilities that depend only on the current state and not on the history of the system. This feature is known as the Markov property or'short memory'.
## 4 Discussion of Prediction Results
### Case Study of an Urban Area  
#### Information of the Area  
To further demonstrate the proposed method, a case study of Shenzhen city is presented in this section. Studied buildings are RC frame buildings with no more than 10 stories. The distribution of studied buildings is shown in Figure 15. These building data were obtained from an online apartment rental company [36]. Note that the primary purpose of the case study is not to obtain the accurate seismic damage of Shenzhen city, but to demonstrate the effectiveness of the proposed method. If more reliable data become available in the future, a more accurate simulation can be performed using the methods presented in this work.
# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
## 4 Validation  
Following the research flowchart (Figure 1), multifaceted analysis of single and multiple UAV shooting pattern data was conducted to quantitatively verify the influence of shooting variables on Structure from Motion (SfM) result indicators (Table A1). Initially, the Kruskal-Wallis test was applied to establish the overall statistical significance of the basic shooting pattern 'type'. Subsequently, the influence of individual parameters within each pattern was quantified; Multiple Regression was used to model linear trends, while Random Forest Feature Importance assessed non-linear contributions. The interactive effects between key variable pairs were then visualized using Response Surface Methodology (RSM). To further characterize the performance space, Principal Component Analysis (PCA) and K-Means Clustering were employed to identify distinct outcome profiles. Finally, to identify the most efficient shooting strategies amidst conflicting objectives, TOPSIS was used to rank composite patterns, and Pareto Optimization was applied to delineate the optimal trade-off frontiers. The key findings and implications derived from the analysis are presented for each analytical methodology, and the study concluded by synthesizing these findings to discuss the contributions and limitations of the research.
# Electron Beam Characterization of REBCO-Coated Conductors at Cryogenic Conditions
## 2 Material and Methods
### Measurement Procedure  
The SEY and ESD analyses were performed first, as they were the most sensitive to the sample surface state. All SEY and ESD measurements were taken at cryogenic temperatures below 20 K and were done on intact as-received spots of the same RESCO sample. An XPS analysis subsequently followed the SEY and ESD measurements to investigate the surface chemical composition of the studied samples. An intact spot was chosen to capture the as-received surface state. Finally, the SEM and EDS analyses were done to study the sample surface morphology and localize in-depth bulk chemical composition. The XPS, SEM, and EDS were performed ex situ at ambient temperatures, as they were temperature-independent in this case.  
The two RESCO samples mounted on a flag-type sample holder were inserted into the UHV system via a load lock. Once the system was cold, the sample was rapidly heated to \(\sim\)100 K to desorb gases that could cryosorb from the background during the cool-down. Care was taken during the second and final cool-down from 100 K back to 12 K to ensure that the sample cooled down as the last, once again, mitigating the possibility of residual gas cryosorption. The sample was then ready for data-taking after the final cool-down when the system stabilized around T\({}_{S}\) = 12 K. Multiple SEY curves were acquired at different locations on the as-received sample to study variability within the sample itself. Measuring the SEY at the same locations at ambient temperatures prior to cool-down showed no tangible difference, as discussed below. Following the non-destructive SEY measurements, the ESD energy and dose dependencies were measured on two separate as-received spots of the same sample. The ESD energy dependence was measured first and was followed by a dose-dependent measurement at 300 eV to acquire a so-called conditioning curve. The SEY and ESD energy scans took about 5 min to finish, and the electron conditioning measurement took about 90 min. Once the ESD conditioning measurement was done, the SEY curve was acquired once again to capture the conditioned sample surface.
## 4 Results and Discussion
### Experiment on the Training and Validation
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Person**} & \multicolumn{10}{c}{**Beats \# (ECG-ID)**} & \multirow{2}{*}{**Predict**} & \multirow{2}{*}{**Patient**} & \multicolumn{10}{c}{**Beats \# (MIT-BIH)**} & \multirow{2}{*}{**Predict**} \\  & & **1** & & **2** & & **3** & **4** & **5** & **6** & **7** & **8** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline
1 & 28 & 72 & 72 & 1 & 1 & 1 & 16 & 44 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 9 & 1 \\
2 & 2 & 2 & 2 & 2 & 2 & 36 & 59 & 2 & 44 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 9 & 2 \\
3 & 3 & 3 & 3 & 24 & 3 & 3 & 3 & 44 & 3 & 3 & 3 & 3 & 3 & 3 & 3 & 9 & 3 \\
4 & 4 & 4 & 4 & 4 & 4 & 40 & 4 & 44 & 4 & 4 & 4 & 4 & 4 & 4 & 4 & 9 & 4 \\
5 & 5 & 5 & 5 & 5 & 5 & 5 & 5 & 5 & 44 & 5 & 5 & 5 & 5 & 5 & 5 & 5 & 9 & 5 \\
6 & 6 & 87 & 89 & 62 & 6 & 62 & 14 & 44 & 6/62 & 6 & 6 & 6 & 6 & 6 & 6 & 9 & 6 \\
7 & 7 & 7 & 7 & 7 & 7 & 7 & 7 & 7 & 44 & 7 & 7 & 7 & 24 & 7 & 30 & 30 & 9 & 7/30 \\
8 & 8 & 8 & 8 & 8 & 8 & 8 & 8 & 44 & 8 & 8 & 8 & 8 & 8 & 8 & 8 & 9 & 8 \\
9 & 9 & 9 & 9 & 79 & 79 & 9 & 79 & 44 & 9 & 9 & 45 & 9 & 9 & 9 & 9 & 9 & 9 \\
10 & 10 & 76 & 10 & 76 & 10 & 10 & 10 & 44 & 10 & 10 & 10 & 10 & 10 & 10 & 10 & 9 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Samples of authentication predictions of eight and six ECG beats for the ECG-ID and MIT-BIH databases.
## 3 Calculation and Simulation Results
### Lens Form Factor Design and Luminous Intensity Curve Comparison  
For practical application in interior lighting, a linear LED luminaire measuring 1200 mm in length was initially selected, utilizing an AL for the preliminary design. During the early stages of the design process, a very thin LED strip served as the light source. The dimensions of the lens were determined to be 1200 mm in length, approximately 10 mm in height, and 20 mm in width.  
To demonstrate the merits of the design methodology grounded on ideal luminous distribution curves, four distinct AL-TIR LED luminaire configurations, as depicted in Figure 4, were evaluated. The L1-AL0 luminaire, developed empirically prior to the formulation of ideal luminous intensity curves, serves as a foundational reference. In contrast, the L2-AL6 luminaire, conceptualized with the ideal luminous intensity curves in mind, showcased improved light beam utilization, albeit not fully optimized due to the absence of TIR optics. The lens profiles of L2-AL6, observed in the section perpendicular to the lens axis, exhibit an asymmetric shape. It consists of a semi-circular arc on the exterior, transitioning into linear segments to form the asymmetry, while the interior cross-section is segmented into distinct subsections, crafting a composite lens with convergent, divergent, and non-refractive zones. The precise placement of a thin LED strip at the lens' center, at variable distances, and the subsequent selection of configurations based on maximum intensity in the luminous intensity plot ensured optimal lighting distribution.  
Figure 3: The luminous intensity curves \(D(\theta)\) for various values of the parameter \(k\) (\(1\), \(2\), \(3\), \(6\)), with \(H=0.3\) m and \(W=4\) m, in polar (**left**) and rectangular plots (**right**).  
The prevalent design by [PERSON] et al. (2019), represented by L3 AL-STIR, features a central convergent lens flanked by TIR parabolic prisms on either side. However, a comprehensive analysis of these configurations necessitates a comparison against the ideal luminous intensity angular distribution requirements. The epitome of this evolution is seen in L4 AL-ATIR, which is benchmarked against the ideal luminous intensity curve K3. This optimal design, which will be detailed in the subsequent section, highlights how meticulous optics engineering can culminate in achieving uniform ceiling light distribution and enhanced efficacy.
## 3 Materials and Methods
### Reward Function  
This research endeavors to ensure consistently high perceptual quality and maximize user satisfaction while eliminating rebuffering. For that, we have formulated a reward function \(R=f(r,rb,q)\) that takes into account the rate of perceived quality change (r), the occurrences of rebuffering (rb), and the quality of each video segment (q).  
In this subsection, we describe the experimental study conducted to classify the various bitrates (qualities). For this experiment, we used the BigBuckBunny video sequence from the BigBuckBunny dataset [37]. Users participated in the tests by initialing their video sequences on different devices across various internet conditions, all within the context of an ongoing streaming session. Moreover, we programmed bitrate switches in the following manner: (\(\{144p,240p\}\), \(\{144p,360p\}\), \(\{144p,720p\}\)...). We meticulously observed the resulting changes in perceived quality.  
Based on the empirical findings, we categorized the qualities into three distinct classes: \(Q_{\text{high}}\), \(Q_{\text{medium}}\) and \(Q_{\text{poor}}\). These are visually depicted in Figure 1. To define these classes, we establish the following quality sets: \(Q_{\text{high}}=\{1080p,\,\ldots,\,2160p\}\) representing high quality, \(Q_{\text{medium}}=\{360p\ldots 720p\}\) representing medium quality, and \(Q_{\text{poor}}=\{144p\ldots 240p\}\) representing poor quality. The quality level \(qt\) of a given segment \(s_{i}\) can be classified into the quality set \(Q=\{Q_{\text{high}},Q_{\text{medium}},Q_{\text{poor}}\}\), while the bandwidth can be categorized into the set \(Bw=\{Bw_{\text{low}},Bw_{\text{medium}},Bw_{\text{high}}\}\).  
Figure 1: Classification of quality levels.