esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Acoustic Wave Sensor for C-Reactive Protein Detection
## 1 Introduction
Surface acoustic wave design has several inherent advantages, such as high sensitivity and portability, high analogy, mass producibility, and the ability to produce different frequency responses (10 MHz to 1 GHz) by changing the geometry of interdigital transducers (IDT) [29; 30]. All of these advantages have attracted many researchers to study this field. The first SAW interdigital transducers on a piezoelectric substrate/film were proposed with the use of cascaded equivalent circuits [31]. Afterwards, several other techniques were developed that characterized SAW properties for the thin metal layer and the effect of metallic grating finger thickness [32; 33]. When an alternating current (AC) signal is applied to the input IDT, which is transmitted through the surface of the piezoelectric material, the surface waves travel across the sensing area and are very sensitive to variations on the sensor surface. Afterwards, the wave signal is converted back into an electrical signal at another output IDT. The frequency or amplitude changes at the resulting output signal can be correlated to the corresponding mass and mechanical variations on the sensing surface. In this paper, we fabricated a SAW sensor for CRP detection by using LiNbO\({}_{3}\) material with piezoelectric properties. In the future, such sensors can be used for CRP detection in medical science to explore the surface acoustic wave components with high sensitivity and to establish an assessment of the risk of developing cancer and other chronic diseases. After fabricating the SAW structure, the bio-sensing area of the gold surface was immobilized by surface modification. We can measure the sensor response with different concentrations of CRP that show the different drift values (insertion loss). In this CRP detection technique, the electrical detection of various CRP concentrations from 0.1 ug/mL to 1 mg/mL was measured in a linear relationship.
## 3 Results
### Mangrove Extent and Age Estimation
#### 3.3.3 Age Map  
The age map (Figure 13) was estimated and referenced to November 2019 and derived backward to 2009. The age map indicates that the mangrove expansion of forests being attached to the mainland began in 2014. In comparison, the isolated forests (on the delta lobes) were found to have started expanding in 2016, most likely initiated by the mangrove planting in the northern delta, in contrast to the natural mangrove succession that took place in the southern delta.  
Figure 10: Time series of porong mangrove extent derived from Landsat 7, Landsat 8, and Sentinel 1–Sentinel 2 imagery (the time-series animation is provided as an online material: Animation S1).  
By taking advantage of the age map and the high-resolution CHM, a relationship of mangrove height dependent on stand age was setup for the two Porong delta lobes. A mean height distribution across the mangrove age map was calculated by clipping the mangroves' age polygon to a CHM raster. Figure 14 compares the age-height relationships from the delta lobes' mangroves. The northern delta mangroves were consistently taller than the southern ones at the same stand age. By only taking the mangrove trees into account that were taller than 1.3 m, the average annual mangrove growth of the northern delta amounted to 2.26 m yr\({}^{-1}\). In contrast, the mangroves in the southern delta had an annual mangrove growth rate of 1.71 m yr\({}^{-1}\).  
Figure 11: Details of the mangrove extent estimated by satellite imagery from the 2009 dry season to November 2019.  
Figure 12: Time series of mangrove extent area development on (**A**) the region of interest (ROI) extends from 112.8585\({}^{\circ}\) S, \(-7.5418^{\circ}\) E to 112.8927\({}^{\circ}\) S, \(-7.5879^{\circ}\) E and focusing on (**B**) the Porong Estuary and the newly developed delta lobes which obviously exhibits an increasing trend of area development after 2011. The right figures show the time-series of mangrove area development for each region (**A**,**B**).  
Figure 13: Map of mangroves age distribution in Porong Estuary as estimated with respect to the reference period of November 2019.
# Unsupervised Transformer-Based Anomaly Detection in ECG Signals
## 3 Materials and Methods
### Anomaly Score and Threshold  
Because we implemented an unsupervised schema, the model was trained on only an N heartbeat sequence. The model predicts the original values of the input sequence. The loss function for our model is the mean-absolute error (MAE) [37] between the input signal and the predicted signal as shown below in Equation (7). As it is common to use root mean square error (RMSE) such as in [18], we have first attempted to use it as a loss function. However, we found that the MAE revealed the best choice because it performs better.  
\[\text{loss}= \frac{1}{\text{n}}\sum_{i=1}^{\text{n}}\left|\text{y}_{\text{i}}- \text{x}_{\text{i}}\right| \tag{7}\]  
Figure 4: Illustration of the scaled dot-product attention (**left**) and multi-head attention consisting of several attention layers running in parallel (**right**).  
The MAE is the total absolute variation between each signal's actual value \(y_{i}\) and the model's prediction value \(x_{i}\). Through training, the model learned the saliency and activation characteristics of normal signals, resulting in a lower loss prediction error. The model detects anomalies in the testing data by calculating whether the predicted loss error is greater than a fixed threshold. In this work, the threshold equals two standard deviations above the mean loss of the trained normal data as the following equation:  
\[\text{Threshold}= \text{mean }(\text{train data loss})+2\times\text{standard deviation }(\text{train data loss}) \tag{8}\]  
After calculating the threshold using the training data, the procedure was converted into a binary classification problem. In the anomaly detection stage, the calculated threshold determines whether a data sequence is normal or abnormal by comparing its prediction error. Using testing data, a specific input sequence was categorically provided to the model, and the predicted error was then compared to the calculated threshold. The input sequence was categorized as anomalous if the predicted error exceeded the threshold; otherwise, it was categorized as normal. Figure 5 shows the graphical architectural procedure of our anomaly detection model for clarification.
# Research on Factors Affecting Solvers' Participation Time in Online Crowdsourcing Contests
###### Abstract  
A crowdsourcing contest is one of the most popular modes of crowdsourcing and is also an important tool for an enterprise to implement open innovation. The solvers' active participation is one of the major reasons for the success of crowdsourcing contests. Research on solvers' participation behavior is helpful in understanding the sustainability and incentives of solvers' participation in the online crowdsourcing platform. So, how to attract more solvers to participate and put in more effort is the focus of researchers. In this regard, previous studies mainly used the submission quantity to measure solvers' participation behavior and lacked an effective measure on the degree of participation effort expended by a solver. For the first time, we use solvers' participation time as a dependent variable to measure their effort in a crowdsourcing contest. Thus, we incorporate participation time into the solver's participation research. With the data from Taskcn.com, we analyze how participation time is affected four key factors including task design, task description, task process, and environment, respectively. We found that, first, for task design, higher task rewards will attract solvers to invest more time in the participation process and the relationship between participation time and task duration is inverted U-shaped. Second, for task description, the length of the task description has a negative impact on participation time and the task description attachment will positively influence the participation time. Third, for the task process, communication and supplementary explanations in a crowdsourcing process positively affect participation time. Fourth, for environmental factors, the task density of the crowdsourcing platform and the market price of all crowdsourcing contests have respectively negative and positive effects on participation time.  
Keywords:crowdsourcing contests; open innovation; participation time; task characteristics; participation behavior +
Footnote †: journal:
# Research on a Novel Nanosecond Marx Generator and Its Efficiency Analysis
## 1 Introduction  
Nanosecond Marx generators are widely used in many fields, such as material processing, biomedical engineering, high power microwave technology, and so on [1, 2, 3]. Fast semiconductor switches, such as insulated gate bipolar transistors (IGBTs), Metal-Oxide-Semiconductor field-effect transistors (MOSFETs), and bipolar junction transistors (BJTs) [4, 5], are the key components in these Marx generators. However, the switching speeds of IGBT and MOSFET decrease with the increase in the conduction current, then the front edges of output pulses are limited. A nanosecond conduction velocity could be obtained if the Collector and Emitter of the BJTs avalanche break down [6, 7]. Therefore, avalanche BJTs are often used to generate nanosecond high-voltage pulses in Marx generators. To obtain high-frequency nanosecond pulses with fast rising and falling edges, T. Pi et al. [8] improved the traditional Marx generator based on the avalanche BJTs and increased the charging speed. An output pulse with 1.08 kV and 50 kHz was obtained, and the energy efficiency of the generator was about 26.67%. W. Zhang et al. [9] adopted multiple Marx generators in parallel to increase the output current amplitudes. Nanosecond pulses with a rising time of 3.4 ns, an amplitude of 2.5 kV, and a repetitive frequency of 15 kHz were obtained over a 50 \(\Omega\) resistor load. The energy efficiency was about 38%. To improve the reliability of high-power Marx generators based on avalanche BJTs, W. Ding et al. [10] developed an auxiliary triggering topology. The output voltage and the rise time were 4 kV and 6 ns over a coaxial cable with 50 \(\Omega\) impedance. The voltage efficiency was about 37%.  
From the research mentioned above, it can be found that the energy efficiency of the generators using avalanche BJTs is low, and the analytical method needs to be improved. So far, all these Marx generators based on avalanche BJTs are charged by DC voltage supplies,which causes the energy efficiency to be lower than 50% [8; 9]. The charging loops are indicated by the blue arrow lines, and the discharging loop is indicated by the red line in Figure 1. This paper proposes a novel nanosecond Marx generator using a series-resonant charging power supply based on the avalanche BJTs. The energy efficiency is analyzed in detail, and an optimization scheme is proposed to improve the energy efficiency. From the experimental results, the charging efficiency of the capacitors and the energy efficiency of this Marx generator have been significantly improved.
## 4 Discussion
### Potential Limitations of the Study  
Several limitations are to be acknowledged in the present study. The content of whey protein in milk samples may exhibit variability due to factors such as seasonality and the nutritional composition of the cows' feed. The BTX electroparoit utilized in this study is constrained to a maximum voltage of 3 kV. By using cuvettes with a 0.1 cm-gap treatment chamber, we managed to increase the voltage to 24 kV/cm; however, this was the threshold beyond which electrical discharges started to occur. Consequently, the impact of higher voltages on bacterial inactivation was not investigated.  
The bacterial load of the raw milk could also differ depending on the refrigeration and storage conditions, introducing another potential source of variability. Furthermore, the UHT pasteurization technique could not be reproduced in our laboratory setting. As a result, we were unable to replicate all the pasteurization techniques commonly employed in the dairy industry, limiting our capacity to assess their respective impacts on the nutritional properties of raw milk.  
Additionally, only the thermal pasteurization techniques used in this study were based on parameters used in the dairy industry. We did not take into account the potential effects of other processing stages such as creaming, homogenization, and refrigeration that are typically part of an industrial-scale processing line. This could potentially limit the generalizability of our findings to real-world industrial processes.  
Figure 14: Standard production method of whey protein powder (**a**) and a novel method for producing liquid whey protein using PEFs (**b**).
# Experimental Validation of Peer-to-Peer Distributed Voltage Control System
## 5 Device-to-Device Communication
### D2D Communication Modules
#### 5.2.1 Implemented Embedded Firmware  
The application software is written in C (Dev-C++, Cambridge, MA, USA) and operates on top of the FreeRTOS embedded operation system (10.0.1, Real Time Engineers Ltd., Bristol, UK). The software has been developed using Eclipse (Kepler Service Release 2, Eclipse Foundation, Inc., Ottawa, ON, Canada) and compiled with GNU Compiler Collection (GCC) (7.1, Free Software Foundation, Boston, MA, USA) for Advanced RISC Machine (ARM) processors (ARMv8.3-A, Acorn, Cambridge, England, UK). The high level structural diagram of the embedded firmware is depicted in Figure 7 and it is composed of the three threads: main thread, radio thread, and UART thread. The main thread is  
Figure 6: Structure of the D2D communication module (USB: universal serial bus, UART: universal asynchronous receiver-transmitter, FTDI: future technology devices international (semiconductor device company), VCP: virtual communication port).  
initialized after the basic initialization procedures (setting clock, checking the module and configuring the peripherals, blinking LEDs). The main thread initializes the UART thread for communicating with the physically connected agent, the radio thread for controlling the radio transceiver, and the server data structure for storing the data from the agents. The UART drivers are implemented based on direct memory access (DMA) and use a timer to detect end of a packet. Due to this reason, agents should enable for at least a 5 ms idle time between the sequential UART packets.  
The server data structure is implemented as a table listing the identifiers of the agents and the most recent data from them. The server structure is accessed and can be modified by either the radio thread or the UART thread. The D2D module can be configured to periodically report the complete table (i.e., the data from all other agents) to its agent.  
The developed firmware implements a multi-stage error detection and correction system. In case of noncritical errors (e.g., wrong format of UART commands from the physically connected agent) the module recovers automatically. In case of severe mistakes (detected by the software or if the software hangs), the module reset procedure is initiated. After reset, the most recent state of the module is recovered. The restored data does not include the calculations made by the connected agent.
# Luminescence Tuning of Fluorinated Bistolanes via Electronic or Aggregated-Structure Control
## 3 Results and Discussion
### PL Behavior in the Solution Phase
Considering that the energy gap between the HOMO and LUMO (\(\Delta\)E\({}_{\rm H\text{-}L}\)) in the S\({}_{0}\) state decreases in the order **1 aB** (5.69 eV) \(>\)**1 aC** (5.55 eV) \(>\)**1 aC** (5.28 eV), increasing the \(\mu_{\parallel}\) value results in a narrower \(\Delta\)E\({}_{\rm H\text{-}L}\) and a shift of \(\lambda_{\rm abs}\) towards longer wavelengths: **1 aB** (338 nm) \(<\)**1 aC** (347 nm) \(<\)**1 aC** (360 nm). \(\Delta\)E\({}_{\rm H\text{-}L}\) in the S\({}_{1}\) state exhibited a similar tendency (**1 aB**\(>\)**1 aC**\(>\)**1 aC**), corresponding to the order of the \(\mu_{\parallel}\) values. Thus, these results clearly indicate that the electronic transitions for absorption and PL can be controlled by tuning \(\mu_{\parallel}\). Figure 5: (**a**) Absorption spectra of **1 aB–D** (solid lines) and **1 bB–D** (dashed lines) in CH\({}_{2}\)Cl\({}_{2}\) (\(1.0\times 10^{-5}\) mol L\({}^{-1}\)); (**b**) photoluminescence (PL) spectra of **1 aB–D** (solid lines) and **1 bB–D** (dashed lines) in CH\({}_{2}\)Cl\({}_{2}\) (\(1.0\times 10^{-6}\) mol L\({}^{-1}\)); (**c**) photographs under ultraviolet (UV) irradiation (\(\lambda_{\rm ex}\) = 365 nm) and CIE chromaticity diagram for the PL in CH\({}_{2}\)Cl\({}_{2}\) solution. The PL colors of luminophores with large \(\mu_{\parallel}\) values have often been reported to show obvious variations depending on the solvent polarity [40]. Therefore, we tested the PL sensing properties of CF\({}_{3}\)-substituted **1 bC** as a representative fluorinated bistolane in various solvents. Figure 6 shows the observed PL behavior, and the photophysical data are summarized in Table 2.  
Irradiating **1 bC** at 343 nm in less polar toluene (\(\varepsilon\) = 2.38, where \(\varepsilon\) is a dielectric constant at 25 \({}^{\circ}\)C and an indicator of solvent polarity [40]) produced deep-blue PL with a single PL band at \(\lambda_{\mathrm{PL}}\) = 409 nm, which was shifted towards shorter wavelengths by 0.282 eV compared with the \(\lambda_{\mathrm{PL}}\) in CH\({}_{2}\)Cl\({}_{2}\) (\(\varepsilon\) = 8.93). In contrast, dramatic shifts towards longer wavelengths were observed when more polar solvents, e.g., MeCN (\(\varepsilon\) = 35.9) and DMF (\(\varepsilon\) = 36.7), were employed; based on the CIE diagram, greenish-yellow PL was observed in both cases. Although THF (\(\varepsilon\) = 7.58) is less polar than CH\({}_{2}\)Cl\({}_{2}\), the PL band (\(\lambda_{\mathrm{PL}}\) = 482 nm) was shifted towards longer wavelengths by 0.177 eV. The unique PL behavior in THF is likely due to the oxygen atom, which allows THF to easily solvate **1 bC** with its large \(\mu_{\parallel}\) value through intermolecular interactions, such as hydrogen bonding, and may promote the nonradiative deactivation from the S\({}_{1}\) to the S\({}_{0}\) state [41; 42; 43]. Consequently, it was successfully demonstrated that the fluorinated bistolanes can be applied as PL sensing materials for solvent polarity.
## 2 GEO Grid ASTER EVI Products
### Vegetation Index Module  
The main issue in computing the EVI from ASTER is the fact that the ASTER sensor does not have a blue band, that is, Equation (1) is not directly applicable to ASTER spectral data. Therefore, three EVI algorithms have been programmed into the ASTER Grid EVI module, which are the MODIS Backup EVI [15], the two-band EVI without a blue band (EVI2) [16], and a coupled ASTER-MODIS EVI.
# Workspace Analysis of a Mobile Manipulator with Obstacle Avoidance in 3D Printing Tasks
## 4 Discussion  
In this article, we propose an analysis of the printing workspace of a mobile manipulator on the construction site with obstacles during the 3D printing task. The analysis of the results shows that the shape of the workspace varies according to the proposed construction element. Analysis of the mobile manipulator workspace is essential on construction sites to determine the accessibility and mobility of the robotic platform and to identify its limitations according to the size of the mobile manipulator. To study the workspace of the mobile manipulator, we propose an algorithm based on kinematic modelling that optimises the movement of the robotic platform and considers constraints such as obstacle avoidance, the collision between manipulator links and the collision between the mobile platform with the robotic arm.  
The printing workspace of the mobile manipulator is an envelope similar to the shape of the proposed construction element. We also noticed that the surface covers the workspace of the mobile manipulator changes in shape as it evades obstacles during 3D printing. Furthermore, we observed that when the height of the building elements varies, the error increases. However, the error of less than 10 mm is still tolerable for 3D printing on construction sites.  
Furthermore, it became apparent that the efficiency of the printing process of the building elements depends not only on the obstacle avoidance action of the mobile manipulator but also on the distance between the obstacles with the desired building element profiles.
# Design of a Noise Mitigation System Using Lightweight Graded Micro-Porous Material
## 3 Design, Fabrication, and Validation of Graded Micro-Porous Material System
### Validation by Normal Incidence Impedance Tests
#### 3.4.1 Graded uPHB Composite Material Combination of L50, M25, S25  
In this graded material combination, the layer with large particle sizes is twice as thick as the layers with medium and small particle sizes, with a total thickness of 50 mm. The predictions have been validated through experiments, and detailed discussions are provided.  
* Absorption Coefficient Response  
Figure 16 illustrates the experimental absorption coefficient alongside the predicted response for a graded material configuration composed of L50, M25, S25, with a total thickness of 50 mm. The predictions show reasonably good agreement with the experimental data. Specifically, the agreement is good between 250 and 500 Hz, though there is a slight underestimation in the 500 to 1000 Hz range, with a difference of about 0.05. Between 1000 to 1300 Hz, there is a good agreement, while beyond 1300 Hz up to 1600 Hz, there is again a slight underestimation of about 0.05 compared to the experimental data. Additionally, Figure 16 shows the absorption coefficient bounds for medium and large particle-size uPHB composite materials, both with a thickness of 50 mm. This comparison indicates that the graded material system surpasses the performance of medium and large particle-size uPHB composite materials beyond a frequency of 600 Hz. Consequently, it can be concluded that the L50, M25, S25, micro-structurally controlled graded material system stands out, achieving an engineered absorption coefficient \(\geq\)0.50 over 500 Hz, peaking at about 0.80 around 1200 Hz and remaining constant at higher frequencies.  
Figure 15: Predicted transmission loss versus frequency for graded material design.  
* Transmission Loss Response  
Figure 17 shows the experimental transmission loss alongside predictions for a graded material configuration composed of L50, M25, S25 with a total thickness of 50 mm. The predicted response agrees reasonably well with the experimental results. However, in the low-frequency range of 250 to 500 Hz, there is notable disparity, ranging from 1 to 4 dB, with the largest difference observed at 250 Hz, gradually diminishing as the frequency increases. Between 500 to 800 Hz, the difference is about 0 to 1 dB, while between 800 to 1600 Hz the prediction closely matches the experimental data. Additionally, the transmission loss response for medium and large particle-size uPHB composite materials is shown for comparison. It can be concluded that the transmission loss response for the selected graded material combination falls between the transmission loss bounds of medium and large particle-size uPHB composite materials. Notably, a design transmission loss \(\geq\) 20 dB over a broad frequency range (over 500 Hz) is achieved for the L50, M25, S25 graded material system.