esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Analytical Technologies and Data Interpretation  
Untargeted salivary metabolomics and proteomics rely on advanced analytical technologies to explore the comprehensive molecular composition of saliva without prior knowledge of specific targets. In metabolomics, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the primary tools. NMR is non-destructive, highly reproducible, and quantitative, but has lower sensitivity and signal overlap limitations [54]. MS, particularly when coupled with chromatography (UPLC-MS or GC-MS), offers high sensitivity and wide metabolite coverage, though it is destructive and susceptible to ion suppression [125]. Proteomics predominantly employs liquid chromatography-tandem mass spectrometry (LC-MS/MS), which allows sensitive and high-throughput protein identification. Techniques like label-free quantification (LFQ) and isobaric tagging (e.g., TMT or iTRAQ) support relative protein quantification, though they vary in cost and complexity [126,127]. Some variables in the results among the cited studies may be due to the variability of interlaboratory calibration, even though using NMR Spectroscopy guarantees more accuracy in the reproducibility of the data than using MS (Table 7) [125].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **NMR Spectroscopy** & \
\begin{tabular}{c} **Mass Spectrometry** \\ **(MS)** \\ \end{tabular} & **References** \\ \hline \multirow{4}{*}{Sensitivity} & Low (\(\upmu\)M range), & \multirow{4}{*}{
\begin{tabular}{c} Very high \\ (rM–pM range), \\ especially with HR-MS. \\ \end{tabular} } & \multirow{4}{*}{[125,128,129,130,131]} \\  & & & \\ \cline{1-1} \cline{3-4}  & Highly accurate, can & & Quantification is \\ \cline{1-1} \cline{3-4}  & quantify multiple & relative unless internal & [125,128,129,130,131] \\ \cline{1-1} \cline{3-4}  & analytes. & standards are used & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Characteristics of NMR Spectroscopy and MS.  
Data interpretation in both omics fields involves preprocessing (normalization, transformation, scaling), statistical analysis (univariate and multivariate approaches, such as PCA or PLS-DA), and machine learning for classification. Identification of molecules relies on databases like HMDB, METLIN, UniProt, and KEGG, with annotation tools such as XCMS or Mzmine [134]. Finally, pathway analysis using platforms like MetaboAnalyst or Reactome helps contextualize findings within biological systems, such as inflammation or immune response pathways relevant to periodontal disease [44; 135].
## 1 Introduction
### Performance of Field-Scale Models and Role of Sentinel-2  
In 2023, [PERSON] et al. [29] conducted a systematic review of field-scale yield predictions, analyzing 23 models from various studies. Model performance, indicated by R\({}^{2}\) values, ranged from 0.23 to 0.92 and was influenced by factors such as crop type, location, lead time to harvest, the type of tool (e.g., satellite or UAV), the VI used, and the method applied. The highest-performing models were those utilizing UAVs or high spatio-temporal resolution satellites on demand [30; 31; 32; 33; 34]. Among models using free satellite data, the best was [PERSON]'s Gaussian process regression model (integrating Sentinel-2 and Landsat data) [35], which was based on 5 years of field-scale yield data from Iran, which achieved R\({}^{2}\) values between 0.69 and 0.84 depending on temporal training settings and agricultural field differences. The review's findings indicated that high resolution and the development of site-specific models can significantly improve field-scale yield forecasts.  
To this aim, the Sentinel-2 (S2) satellite constellation offers significant advantages [36; 37], providing free-of-charge imagery with improved features [38]. This accessibility benefits users working with low-income crops (like wheat) [39; 40]. Previously, Landsat was the only free option for field scale modeling in Europe, offering two scenes per month at a 30 m resolution. Since June 2017, S2 has provided six images per month with a 5-day revisit time and 10 m resolution [41; 42], allowing for more detailed VI assessments and reducing the impact of persistent cloud cover [41]. Although S2 has enhanced yield estimation compared to Landsat [43], its long-term data availability remains limited, and crop rotation further restricts consistent data collection [44]. Combining S2 with Landsat can improve data availability but may introduce inhomogeneity and uncertainty, especially in the near-infrared band [45]. Nevertheless, S2 has been successfully used in yield estimation across various crops in several studies [36; 46; 47; 48; 49].
# Research on Improved Bridge Surface Disease Detection Algorithm Based on YOLOv7-Tiny-DBB
## 3 Algorithm Enhancement
### Enhanced YOLOv7-Tiny-DBB Algorithm  
The characteristics of apparent diseases in bridges include the following: (1) a diverse range of disease types and variable morphological features; (2) significant differences in the area occupied by diseases in images, particularly for the small target samples of bridge cracks, which may lead to instances where detection fails; (3) in some collected images, the distribution of bridge diseases is relatively concentrated with overlapping conditions, resulting in occurrences of missed detections and false positives during the inspection process. The YOLOv7-Tiny algorithm has been improved to address the characteristics of surface diseases in bridges. Firstly, a Diverse Branch Block (DBB) module is introduced to replace the ELAN-Tiny module in the backbone feature extraction network, as illustrated by the red dashed box in Figure 6. This modification effectively utilizes disease features of bridges at different scales, enabling multi-scale feature extraction and enriching the information related to bridge diseases, thereby enhancing the overall network's capability to capture disease-related features. Secondly, the original model's loss function CIoU is replaced with the MPDIoU loss function. By comparing the similarity between the predicted bounding boxes and the actual annotated bounding boxes during the bounding  
Figure 5: Schematic diagram of the loss function (\(\mathrm{L_{MPDIoU}}\)) calculation process.  
box regression process, this modification can achieve a faster convergence speed and more accurate regression results. The improved network structure of the YOLOv7-Tiny-DBB algorithm is illustrated in Figure 6.
## 3 Results
### Results of Triaxial Tests
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Size**} & **Soll** & \(\sigma_{p}^{*}\) & **\(\mathbf{z_{c}}\)** & **\(\mathbf{p_{c}}\)** & **\(\mathbf{p_{c}}\)** & **\(\mathbf{(p_{c}-m_{c})/\sigma^{*}}\)** & **\(\mathbf{p_{c}}\)** & **\(\mathbf{(p_{c}-m_{c})/\sigma^{*}}\)** & **\(\mathbf{(p_{c}-m_{c})/\sigma^{*}}\)** & **\(\mathbf{(p_{c}-m_{c})/\sigma^{*}}\)** & **\(\mathbf{(p_{c}-m_{c})/\sigma^{*}}\)** & **\(\mathbf{(p_{c}-m_{c})/\sigma^{*}}\)** \\  & **Type** & **Offa** & **Offa** & **Offa** & **\(\mathbf{(p_{c})}\)** & **\(\mathbf{(p_{c})}\)** & **\(\mathbf{(p_{c})}\)** & **\(\mathbf{(p_{c})}\)** & **\(\mathbf{(p_{c})}\)** & **\(\mathbf{(p_{c})}\)** & **\(\mathbf{(p_{c})}\)** & **\(\mathbf{(p_{c})}\)** & **\(\mathbf{(p_{c})}\)** & **\(\mathbf{(p_{c})}\)** & **\(\mathbf{(p_{c})}\)** & **\(\mathbf{(p_{c})}\)** & **\(\mathbf{(p_{c})}\)** & **\(\mathbf{(p_{c})}\)** \\ \hline Unsynow & GSabusCN & 0.094 & 0.0 & 1.286 & 1.286 & 13.739 & 2.544 & 2.544 & 2.544 & 27.179 & 13.440 & 388 & 3.880 & 0.223 & 2.382 \\ Audineine & GSab & 0.102 & 0.0 & 1.399 & 1.399 & 1.3734 & 2.802 & 2.802 & 2.725 & 13.782 & 407 & 4.070 & 0.237 & 2.323 \\  & GSab & 0.135 & 0.154 & 1.545 & 1.416 & 2.999 & 2.999 & 22.2248 & 10.706 & 424 & 4.240 & 1.780 & 2.040 & 1.780 \\ \hline Unsynow & GSab & 0.132 & 0.0 & 1.642 & 1.662 & 13.361 & 3.074 & 3.074 & 24.077 & 11.346 & 384 & 3.840 & 0.254 & 2.068 \\ Building & ssaCN & 0.137 & 0.0 & 1.583 & 1.583 & 1.583 & 1.580 & 2.829 & 2.829 & 20.695 & 10.15 & 390 & 3.900 & 0.298 & 2.176 \\ No. 37 & ssaCN & 0.170 & 0.0 & 1.631 & 1.631 & 1.994 & 2.604 & 2.604 & 15.318 & 5.724 & 398 & 3.980 & 0.340 & 1.997 \\ \hline Unsynow & GSab & 0.120 & 0.0 & 1.322 & 1.212 & 11.017 & 2.501 & 2.501 & 20.842 & 9.825 & 421 & 4.210 & 0.216 & 1.800 \\ Building & GSabusCN & 0.134 & 0.0 & 1.409 & 1.095 & 10.546 & 2.668 & 2.686 & 20.105 & 9.558 & 423 & 4.200 & 0.239 & 1.789 \\ No. 34 & ssaCN & 0.155 & 0.0 & 1.945 & 1.945 & 12.548 & 3.952 & 3.592 & 23.174 & 10.625 & 395 & 3.950 & 0.335 & 2.158 \\ \hline Bilenay & ssaCN & 0.158 & 0.097 & 1.510 & 1.513 & 8.1900 & 2.287 & 2.170 & 11.758 & 3.559 & 3.986 & 3.980 & 0.270 & 1.463 \\  & ssCN & 0.122 & 0.020 & 0.546 & 0.526 & 4.301 & 1.166 & 1.146 & 9.370 & 5.070 & 247 & 2.400 & 0.148 & 1.209 \\  & ssCN & 0.143 & 0.034 & 0.746 & 0.712 & 4.999 & 1.330 & 1.316 & 9.222 & 4.230 & 28.200 & 0.192 & 1.342 \\  & ssaCN & 0.189 & 0.068 & 1.267 & 1.199 & 6.341 & 2.075 & 2.007 & 10.613 & 4.273 & 277 & 2.773 & 0.209 & 1.103 \\ \hline Steggy & CI & 0.092 & 0.020 & 0.536 & 0.516 & 5.609 & 0.930 & 0.910 & 9.891 & 4.283 & 175 & 1.750 & 0.079 & 0.853 \\  & ClsCN & 0.117 & 0.050 & 0.724 & 0.64 & 5.571 & 1.417 & 1.097 & 9.831 & 3.615 & 188 & 1.800 & 0.094 & 0.083 \\  & ClsCN & 0.142 & 0.080 & 0.873 & 0.973 & 5.985 & 1.528 & 1.488 & 10.197 & 4.613 & 229 & 2.290 & 0.144 & 1.011 \\ \hline \hline \end{tabular}  
Notes: \({}^{\star}\)CUT TT—Triaxial Test on sample consolidated isotropically with shearing in undrained conditions.  
\end{table} 
Table 4: Input and output values for the statistical analysis.
# Flexible, Brain-Inspired Communication in Massive Wireless Networks
## 2 System Model  
Let us consider the single wireless link presented in Figure 1. The analog source (modulating) signal \(x_{\text{m}}\left(t\right)\) is processed in the transmitter (either digital or analog), and then, the signal at the radio frequency is amplified in the power amplifier (PA) and fed to the antenna. In the receiver, the opposite operations are performed on the signal received by the antenna. It is assumed that the wireless channel introduces additive, white Gaussian noise as a distortion. Therefore, channel equalization at the receiver is not considered. This simplifies the considered model and is a reasonable assumption in IoT systems that are typically narrowband. In order to determine the quality of wireless transmission (both analog and digital), receiver's analog output \(\hat{x}_{\text{m}}\left(t\right)\) is compared with source signal \(x_{\text{m}}\left(t\right)\) using the mean square error metric (MSE) defined as \(\mathbb{E}\left[\left(\hat{x}_{\text{m}}\left(t\right)-x_{\text{m}}\left(t\right) \right)^{2}\right]\) where expectation can be calculated over time or random input signal realizations.
# Simultaneous Lightwave and Power Transfer for Internet of Things Devices
## 3 System Design and Experimental Methodology  
The circuit of the designed system is shown in Figure 4. In addition to a 38.5-cm\({}^{2}\) mono-crystalline Si solar panel (SP), the system is composed of 3 main blocks: an IR motion sensor, switch circuit and power management unit (PMU), and a programmable system on chip (PSOC). The IR motion sensor is used only when the PSoC is in sleep mode and is responsible for triggering a waking-up interruption call when a movement happens. The switch circuit and the PMU are used to interface the SP, enabling the switching of the SP to the photovoltaic mode or photoconductive mode. In the photovoltaic mode, the SP is connected to a PMU that harvests the power and stores it in a battery or supercapacitor. In the present system, we use an EATON 470-mF supercapacitor. An MPPT is included in the PMU to achieve the best response from the SP when harvesting energy. In photoconductive mode, the SP, having a 3-dB bandwidth of 100 kHz, is reverse-polarized and is connected to a transimpedance amplifier (TIA) through the PSoC. If the signal to be received by the SP is analog modulation, a follower is connected internally in the PSoC, serving as a buffer for the Logger device (an oscilloscope or an ESP32 system). If the signal is modulated using an on-off keying (OOK) scheme, the follower is disconnected and a DAC generates a reference for the digitalization of the signal. The PSoC also comprises a matrix of resistors as feedback for the TIA.  
Figure 3: Time switching solar panel circuit model.
## 3 Performance Analysis
### Propagation Models
#### 3.3.1 Stanford University Interim Model (SUI)  
The SUI model was developed in 1999 as an extension of the work of AT&T and Erceg, aimed at creating a WiMAX model for frequencies above 2 GHz used in suburban, provincial, and metropolitan environments. The result was the SUI basic propagation loss model. The model is categorized into three classes for propagation loss calculations, which are Category A, Category B, and Category C [23,24].  
* Category A: Terrain with pronounced slopes and high vegetation density, resulting in significant signal losses.
* Category B: Flat terrain with intermediate losses and moderate tree density.
* Category C: Flat terrain with low losses and sparse vegetation density.  
Table 2 shows the parameters of frequency, transmitter and receiver height, as well as the distance over which the SUI model operates. It is important to mention that this model was designed for outdoor environments.  
Depending on the terrain category that best fits the study area, the corresponding parameters detailed in Table 3 are used.
## 5 The Unquantized GLRT Detector  
As a performance benchmark, we consider the performance of the GLRT detector, which receives the original local observations without quantization (also referred to as the unquantized GLRT detector). In this case, as shown in [18] (p. 203), the MLE of \(\sigma_{1}\) is \(\hat{\sigma_{1}^{2}}=\frac{1}{N}\sum_{n=1}^{N}x_{n}^{2}\). Therefore, we conclude that the unquantized GLRT detector decides \(H_{1}\) if:  
\[T^{\text{NQ}}(\mathbf{x})=\left(\frac{\sigma_{0}}{\hat{\sigma_{1}}}\right)^{N }\exp\left\{\frac{N}{2}\left(\frac{\hat{\sigma_{1}^{2}}}{\sigma_{0}^{2}}-1 \right)\right\}>\eta \tag{17}\]  
where the superscript 'NQ' is used to stand for no quantization. From an asymptotic analysis, we know that the modified test statistic \(2\ln T^{\text{NQ}}(\mathbf{x})\) asymptotically follows:  
\[2\ln T^{\text{NQ}}(\mathbf{x})\overset{a}{\sim}\left\{\begin{array}{ll}\chi_ {1}^{2},&\text{under }H_{0},\\ \chi_{1}^{\prime 2}(\lambda^{\text{NQ}}),&\text{under }H_{1},\end{array}\right. \tag{18}\]  
The noncentrality parameter is given by:  
\[\lambda^{\text{NQ}}=2N\left(\frac{\sigma_{1}}{\sigma_{0}}-1\right)^{2} \tag{19}\]  
From (19), \(\lambda^{\text{NQ}}\) is dependent on the number of sensors and standard deviations of the observations under hypotheses \(H_{0}\) and \(H_{1}\).
# Hybrid Resonant Converter with Three Half-Bridge Legs for Wide Voltage Operation
## 5 Conclusions  
The main contribution of the proposed converter is wide voltage operation capability compared to the conventional single-stage or two-stage converters. In conventional single-stage or two-stage PWM converters or resonant converters, the input voltage variation range is normally less than a 4:1 voltage range. The presented circuit has three equivalent operation circuits with half bridge to full bridge circuit topology to change the voltage gain between the output and input voltages. Each equivalent resonant circuit can achieve a 2:1 input voltage operation range, that is, \(V_{in,max}=2V_{in,min}\). Therefore,  
Figure 17: Measured circuit efficiencies: (**a**) low input voltage range; (**b**) medium input voltage; (**c**) high input voltage range.  
three equivalent resonant circuits can achieve an 8:1 wide input voltage range operation, that is, \(V_{in,max}\) = \(8V_{in,min}\). For the low input voltage range, the full-bridge resonant converter with low turns ratio of transformer is operated to obtain the higher voltage gain between the load voltage and input voltage. For the medium input voltage range, the other full bridge resonant circuit topology with high turns ratio is selected to obtain the lower voltage gain. The half bridge resonant converter is selected to achieve the lowest voltage gain under the high input voltage range. Therefore, the presented converter with three equivalent sub-circuits can accomplish an 8:1 wide input voltage operation. Due to resonant circuit characteristics, the power switches can have soft switching operation over wide range of load conditions. Three equivalent sub-circuits are selected according to the input voltage range with two Schmitt comparators. The applications of the presented resonant circuit can be the front stage of solar power conversion with wide input voltage variation. The theoretical converter characteristics were confirmed by the measured waveforms from a laboratory circuit. The measured results confirmed the converter performance with wide voltage range operation. The further works of this project are to increase the circuit efficiency by using the synchronous rectifiers on the output side and designing the isolated transformer with minimum core and copper losses.  
[PERSON] proposed and designed this project and wrote this paper. [PERSON] measured the circuit waveforms in the experiment. All authors have read and agreed to the published version of the manuscript. This research is funded by the Ministry of Science and Technology, Taiwan, under grant number MOST 108-2221-E-224-022-MY2. This research is funded by the Ministry of Science and Technology, Taiwan, under grant number MOST 108-2221-E-224-022-MY2. The authors declare no potential conflict of interest.
###### Abstract  
Harvesting main crop rice with a wide header at a required height can increase its ratoon crop yield by decreasing the stubble rolling rate. However, an increased harvest width brings in more input material in a specific time, causing the size enlargement or working speed slowdown of harvesters. To solve this issue, a double-cutterbar structure was proposed. To provide a design baseline for the structure, the cutterbar configuration methods based on rice plant deformation were developed and verified, stalk shearing tests were conducted, and a double-cutterbar prototype was fabricated and tested. The results indicated that the methods developed for the configuration of the upper and lower cutterbars could considerably implement their functions of lowering the straw-grain ratio and keeping stubble height as required. The shearing tests indicated that the level angle significantly influenced the total shearing energy on each cutting point in Internode 2 and 3 (\(p<0.05\)) due to the increased stalk cross sectional area. The stubble rolling rate, panicle straw length and stubble height of the developed harvester were 26.9 %, 125 \(\pm\) 80 mm and 332 \(\pm\) 22 mm, respectively, which could meet the requirements of main crop rice harvesting. The outcomes indicated the functionality and practicability of the double-cutterbar structure developed based on rice plant deformation and shearing properties.  
main crop rice; ratoon crop; double-cutterbar; plant deformation; shearing test +
Footnote †: journal: Applied Sciences
## 2 Bearing Failure Modes
### Radiographic Detection  
Radiographic detection, as a high-precision detection technology, relies on high-energy radiation penetrating materials and imaging to achieve bearing diagnosis. The principle of radiation detection is shown in Figure 8.  
Typical ray-based bearing detection methods include X-ray detection, computed tomography (CT), gamma ray detection, and infrared synchrotron radiation technology, each with unique advantages in different application scenarios.  
X-ray inspection is the use of X-ray penetration capability to scan the interior of bearings and discover cracks, pores, and other hidden defects on rolling elements or raceways through image analysis. Its advantages are high resolution and intuitive imaging, especially suitable for detecting internal problems that cannot be exposed on the surface. The limitation lies in its limited penetration ability, making it unsuitable for materials that are too thick or high-density.  
Computer tomography (CT) combined with X-ray multi-angle imaging can reconstruct three-dimensional images of the interior of bearings and provide comprehensive defect information [60; 61; 62; 63; 64]. The advantage lies in the ability to accurately detect bearing components with complex geometric shapes, but high-resolution CT equipment is expensive and not suitable for online detection.  
Gamma ray detection has high energy and strong penetrability and is suitable for detecting deep defects in thick-walled or large bearings [65]. Although it has a wider range of applications, it requires higher operational complexity and security.  
Figure 8: Principle of radiographic detection: When the radiation passes through the object being inspected, the absorption capacity of defective and non-defective parts is different.  
Infrared synchrotron radiation technology combines synchrotron radiation X-rays and infrared spectroscopy to analyze the microstructure and stress distribution of bearing materials, which is helpful for fault analysis and material research.  
Ray-based methods have high sensitivity and can detect defects at the micrometer level, making them a powerful supplement to other non-destructive testing methods. However, these methods require professional operational skills and high-cost equipment and are accompanied by radiation safety risks, so they are often used in practical applications for the detection of high-value and critical components. In addition, they are also used for material fatigue research and predicting the service life of bearings.  
With the improvement of radiographic imaging equipment and algorithms, radiation-based detection technology is developing towards higher efficiency and lower cost. At the same time, combining artificial intelligence for image processing will further improve the accuracy and automation level of detection.