esa-sceva/satcom-chunk-collection huggingface dataset:
Remote Sens._**2013**, \(5\), 5926-5943; doi:10.3390/rs5115926  
_Remote Sensing_  
ISSN 2072-4292  
www.mdpi.com/journal/remotesensing  
A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US  
**[PERSON] 1,*, [PERSON] 1,2,*, [PERSON] 1, [PERSON] 1, [PERSON] 3, [PERSON] 4 and [PERSON] 5**  
1 Ministry of Education Key Laboratory for Earth System Modeling, and Center for Earth System Science, Tsinghua University, Beijing 100084, China; E-Mails: [EMAIL_ADDRESS] (C.Y.); [EMAIL_ADDRESS] (L.Y.)  
2 Department of Environmental Science, Policy and Management, University of California, Berkeley, Berkeley, CA 94720, USA  
3 Plant Functional Biology and Climate Change Cluster Department, University of Technology Sydney, Broadway, NSW 2007, Australia; E-Mail: [EMAIL_ADDRESS]  
4 School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583, USA; E-Mail: [EMAIL_ADDRESS]  
5 Department of Earth and Environment, Boston University, Boston, MA 02215, USA; E-Mail: [EMAIL_ADDRESS]  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (Q.X.); [EMAIL_ADDRESS] (P.G.); Tel.: +86-188-1025-3088; Fax: +86-10-6279-7284.  
_Received: 26 September 2013; in revised form: 6 November 2013 / Accepted: 7 November 2013 / Published: 14 November 2013_  
**Abstract:** Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop yields based on remotely sensed data due to the variation in radiation use efficiency (RUE) across crop types and the effects of spatial heterogeneity. In this paper, we propose a production efficiency model-based method to estimate corn and soybean yields with MODerate Resolution Imaging Spectroradiometer (MODIS) data by explicitly handling the following two issues: (1) field-measured RUE values for corn and soybean are applied to relatively pure pixels instead of the biome-wide RUE value prescribed in the MODIS vegetation productivity product (MOD17); and (2) contributions to productivity from vegetation other than crops in mixed pixels are deducted at the level of MODIS resolution. Our estimated yields statistically correlate with the national survey data for rainfed counties in the Midwestern US with low errors for both corn (R\({}^{2}\) = 0.77; RMSE = 0.89 MT/ha) and soybeans (R\({}^{2}\) = 0.66; RMSE = 0.38 MT/ha). Because the proposed algorithm does not require any retrospective analysis that constructs empirical relationships between the reported yields and remotely sensed data, it could monitor crop yields over large areas.  
keywords: remote sensing; crop yield; MODIS GPP; radiation use efficiency; spatial heterogeneity +
Footnote †: journal: Remote Sens.
## 2 Methods
### Sensitivity Analysis  
Sensitivity analysis is a tool that provides a comprehensive understanding of the workings of a computational model with respect to its parameters and a specified modelling outcome [23]. The model has 95 parameters, which include all resistances, compliances, vessel geometry attributes, time constants, and scaling factors. Parameters' descriptions and acronyms, as well as their control values relevant to this work, are provided in Table 1. Model behavior was defined as the total number of hypoperfusion events in the distal cerebral circulation over a 5000-heartbeat simulation.  
To permit sensitivity analysis, a control model population of \(10^{4}\) instances was constructed. To generate the population, 95 modelling parameters were each randomly sampled simultaneously from uniform distributions using a non-repetitive Mersenne Twister random number generator [24]. The sampling was constrained using Latin Hypercube Sampling [25]. The lower and upper limits adopted for each parameter's uniform distribution were obtained by multiplying the literature value by 0.5. for the lower limit, and by 2.0 for the upper limit. The adopted limits provided a large range sampling for each parameter. The model parameters and model outputs were stored for further analysis. Sensitivity analysis, which ranked parameters according to their impact on model behavior, was performed using partial rank correlation coefficients (PRCC) [26].  
Figure 4: Stochastic RR interval assignment. Top row: Probability distribution functions for sampled RR intervals in NSR (**A**) and AF (**B**) at shown heart rates. (**C**) Sampled RR intervals with mean of 70 bpm over the span of 5000 beats under NSR (blue line) and AF (red line) conditions.  
To compute PRCC, the normally distributed parameters (\(x_{i}\)), as well as the observed outputs (\(y_{i}\)), were rank transformed. Then, the linear effects of other additional variables were accounted for by expressing each as a linear regression of the inputs:  
\[\hat{x}_{j}=a_{0}+\sum_{\begin{array}{c}k=1\\ k\
eq j\end{array}}^{N}a_{k}x_{k},\;and\;\hat{y}_{j}=b_{0}+\sum_{ \begin{array}{c}k=1\\ k\
eq j\end{array}}^{N}b_{k}x_{k}. \tag{6}\]  
Using residuals defined as \(r_{xi}=x_{j}-\hat{x}_{j}\) and \(r_{yi}=y_{j}-\hat{y}_{j}\), PRCC is defined as the correlation among these residuals normalized using their respective variances:  
\[PRCC\big{(}x_{i},y_{j}\big{)}=\frac{Cov\big{(}r_{xi},r_{yi}\big{)}}{Var(r_{xi} )Var\big{(}r_{yi}\big{)}}. \tag{7}\]  
As evident in Equation (6), PRCC assumes an underlying statistical model that is linear (regression), and the assumption of monotonicity provides the strength of the linear relationship between a given pair of a parameter and an output [26; 27]. The PRCC indices range from \(-1\) to +1.
## 3 Results and Discussions
### LaFe1-xNix Oxygen Carrier
#### 3.2.2 Effect of Ni Substitution on Toluene Conversion  
In this part, the performance of four LaFe\({}_{1\text{-}\text{x}}\)Ni\({}_{\text{x}}\) oxygen carriers was compared with LaFe and LaNi at 900 \({}^{\circ}\)C and the results are shown in Figure 9. When x = 0.2 (LaFe\({}_{0.8}\)Ni\({}_{0.2}\)), the gas products distribution was similar to that for LaFe. With x increasing to 0.4, 0.6, and 0.8, the partial oxidation was somewhat weakened due to less LaFeO\({}_{3}\), while the initial oxidation of H\({}_{2}\) into H\({}_{2}\)O was enhanced. However, the overall H\({}_{2}\) yield was promoted, and the maximum H\({}_{2}\) concentration (reached about 16%) was higher than that in the cases using LaFe and LaNi oxygen carriers. It should be ascribed to the synergistic effect of Fe and Ni in the oxygen carrier. Although not observed in the present investigation, Fe-Ni alloy could be formed in the oxygen carrier with the phase change of oxygen carrier during reactions. It is reported that the Fe-Ni alloy has a strong catalytic effect on CH\({}_{4}\) conversion [35] and toluene conversion [36]. Furthermore, our previous paper also reported that the Ni-Fe both tuned oxygen carrier LaFe\({}_{1\text{-}\text{x}}\)Ni\({}_{\text{x}}\) exhibited better performance on CH\({}_{4}\) conversion than LaFe or LaNi [37]. A decrease in H\({}_{2}\) concentration was observed in the late reaction for these oxygen carriers. It could be ascribed to the serious carbon deposition which covered the catalytic center. Overall, the Fe and Ni tuned oxygen carrier LaFe\({}_{1\text{-}\text{x}}\)Ni\({}_{\text{x}}\) exhibited a strong catalytic effect on promoting toluene conversion.  
Figure 8: Phase patterns of LaFe\({}_{1\text{-}\text{x}}\)Ni\({}_{\text{x}}\) oxygen carriers.
## 2 Methodology  
### Overall Architecture of YOLO-SUMAS  
Due to the small size of the inspection target of PCB circuit boards and the limited number of data sets, targeted improvement optimization is needed according to the feature distribution of the sample data when using the general YOLOv8 model for defect detection. According to the depth and width of the network, YOLOv8 can be categorized into five versions: YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. Among them, YOLOv8n, with a smaller number of parameters and good computational efficiency, is chosen as the baseline model for this study. Our design motivation is to address the three major challenges of PCB high-density, small-target defects: noise interference, positioning bias, and computational efficiency. On this basis, the images were input, the SCSA attention mechanism and MobileNetV4 neural network architecture were introduced into the backbone network part, the Unified-IoU loss function was used, and the ASF-SDI network structure was added to the neck part; adjusting the model structure, finally, it can effectively detect various defects in the PCB image, including short circuit, open circuit, spurious copper, and so on--the improved network structure diagram is shown in Figure 1. The modules work together: SCSA enhanced the image feature expression, MobileNetV4 performed lightweight reasoning for the model, ASF-SDI realized multi-scale fusion, and Unified-IoU optimized positioning.
# Non-Hertzian Elastohydrodynamic Contact Stress Calculation of High-Speed Ball Screws
###### Abstract  
In order to eliminate the calculation error of the Hertzian elastohydrodynamic contact stress due to the asymmetry of the contact region of the helix raceway, a non-Hertzian elastohydrodynamic contact stress calculation method based on the minimum excess principle was proposed. Firstly, the normal contact stresses of the screw raceway and the nut raceway were calculated by the Hertzian contact theory and the minimum excess principle, respectively. Subsequently, the Hertzian solution and the non-Hertzian solution of the elastohydrodynamic contact stress could be determined by the Reynolds equation under different helix angles and screw speeds. Finally, the friction torque test of the double-nut ball screws was designed and implemented on a self-designed bed for validation of the proposed method. The comparison showed that the experimental friction torque was the good agreement with the simulated friction torque, which verified the effectiveness and correctness of the non-Hertzian elastohydrodynamic contact stress calculation method. Under the large helix angle, the calculation accuracy of asperity contact stress for the non-Hertzian solution was more accurate than that of the Hertzian solution at the contact region of ball screws. Therefore, the non-Hertzian elastohydrodynamic contact stress considering the asymmetry of the raceway contact region could more accurately analyze the wear depth of the high-speed ball screws.  
ball screws; contact stress; minimum excess principle; elastohydrodynamic lubrication; non-Hertzian contact
## 2 The Method of Multi-DOF Image Automatic Acquisition
### The Process of System Acquisition  
The specific acquisition process is shown in Figure 4: The object is placed on the surface of the backlight source, and then the computer sends out the command to control the multi-axis motion. The \(x\)-axis and \(y\)-axis drive the object to move randomly in the plane after receiving the command. Meanwhile, \(z\)-axis, \(a\)-axis, and \(b\)-axis drive the industrial camera to move up and down, tilt and pitch in space after receiving the command. When the object moves randomly to a certain position, the industrial camera moving in space can track the position of the object (the center point of the camera's field of view coincides with the center of the object), the computer sends out the command to control the random illumination. At this time, the flat light sources on the inner wall of the system's cavity, the circular light source carried by the camera and the backlight light source mounted on the object are turned on or off randomly. Consequently, the camera can grab the images under different lighting conditions. Different objects move along random paths in a period, the camera collects images under different angles and illumination conditions, the whole acquisition process simulates all kinds of practical shooting conditions and obtains rich and diverse data.
## 5 Conclusions  
In this paper, the model of the MHD ARS combined with the Coriolis effect is studied, which is pivotal to extend the sensor's measurement scope throughout the whole bandwidth. Firstly, a comprehensive solving method of interaction between the flow field and the electromagnetic field suitable for the C-MHD ARS is proposed based on the conventional magnetic and electric approach. Then, the C-MHD ARS with an MHD pump in the middle fluid channel is designed. A numerical simulation of the MHD pump, Coriolis effect, and frequency response in the C-MHD ARS is made. Finally, the corresponding error analysis and experiment results are given to confirm the simulation results. The main conclusions are as follows:  
Figure 14: Frequency response experimental results of C-MHD ARS.  
The uniform radial velocity in the sensing channel could be induced when the flow is driven upward in the MHD pump. Then the Coriolis effect can be generated and is weakened when the rotating frequency increases.  
Notably, the performance of the MHD ARS at low frequency can be improved by introducing the Coriolis effect at low frequency. However, the corner frequencies of the two effects of the Coriolis effect and MHD effect are a little different, which may cause the fluctuation in the transition zone of the frequency response.  
The error source of the two effects combination may mainly be attributable to the item \(u_{r}/R\) in the modified simplified C-MHD, which should be minimized in the sensor design. It is difficult to acquire the ideal frequency characteristic curve directly by applying a fixed energized current in the MHD pump.  
Our study provides an error analysis method of MHD ARS combined with the Coriolis effect and offers a framework for future studies to minimize the error. In the future, the compensation signal processing method will be studied and a suitable closed-loop control method of the energized current will be researched. Besides, the reliability of the angular position and the acceleration measured by the MHD ARS should be studied deeply to expand its application in vibration testing and navigation.  
[PERSON] and [PERSON] conceived and designed the study. [PERSON] made the device's numerical simulation and error analysis. [PERSON] and [PERSON] carried out the sensor design in experiment. [PERSON] and [PERSON] performed the experiment. [PERSON] and [PERSON] wrote and revised the manuscript. All authors read and approved the manuscript.  
This work is supported by the Foundation of the National Natural Science Foundation of China (No. 61703303, U1533111, 61427810 and 61503279), the Natural Science Foundation of Tianjin City (No.17 JCQN)C04100 and 16 JCQNJC04500), the Project supported by State Key Laboratory of Precision Measuring Technology and Instruments (No. PILAB1705 and PILAB1707), the Research project of Tianjin Municipal Education Committee (No. 2017 KJ086) and the Program for Innovative Research Team in University of Tianjin (No. TD13-5036).  
The authors declare no conflict of interest.
## 5 STBC-Assisted Improvement between Various Modulation Formats  
To irradiate the benefits of using the STBC scheme to mitigate the MDL impairment in the SDM transmission systems, the performance results of the space-time coded FMF transmission with or without using the STBC-RLS scheme are presented in Figure 16, from which we can achieve a roughly 3-dB OSNR improvement for the PDM-QPSK signal at a \(10^{-3}\) BER, whereas the condition for the dotted line marked as Gaussian indicates the performance over a perfect MDL-free Gaussian channel.  
Additionally, for the higher-order modulation formats, the performance comparison plots for PDM-16 QAM and PDM-64 QAM signals with or without using the purposed STBC-RLS scheme are further shown in Figures 17 and 18, respectively.  
In summary, the performance comparison for various modulation formats with or without using the STBC-RLS scheme is summarized in Figure 19, whereas the scheme for not using STBC-RLS designates the conventional RLS scheme, from which we can observe that, as more bits per symbol are transmitted, a larger OSNR tolerance improvement could be accomplished by using STBC for higher-order modulation formats. The overall OSNR tolerance can be improved by means of the STBC approach by approximately 3.1, 4.9, and 7.8 dB for QPSK, 16 QAM, and 64 QAM with respect to BER.  
Figure 16: The bit error rate (BER) vs. optical signal-to-noise ratio (OSNR) comparison for PDM-QPSK signal in LP\({}_{11\text{a}}\) mode with or without using the STBC-RLS scheme [31].  
Figure 17: The BER vs. OSNR comparison for PDM-16 QAM signal in LP\({}_{11\text{a}}\) mode with or without using the STBC-RLS scheme [31].
## 4 Numerical Results
### Results and Discussion
#### 4.3.4 Single Secondary User Identification Performance on Different Social Media Platforms  
With the rapid advancement of the Internet, social media platforms have become the primary means of sharing images. Therefore, forensic efforts to identify the source camera model are increasingly focused on images that have been transmitted through these platforms. However, during transmission, each platform applies its own set of post-processing operations, such as JPEG compression and image rescaling, with the specific parameters for these processes typically unknown. These unknown and platform-specific operations can significantly alter the statistical characteristics of an image, potentially deviating the image data from its original distribution. Such alterations introduce substantial challenges for source camera identification, especially for methods that rely on fine-grained sensor noise patterns or statistical assumptions.Therefore, to ensure the practical applicability of the proposed method, it was essential to evaluate its robustness under real-world conditions involving post-processed images.  
To address the above challenges, we performed a series of experiments using the Forchheim dataset. This dataset includes both original images captured by smartphones and corresponding versions that have been post-processed by various social media platforms. As depicted in Figure 10, we first evaluated the identification task performed by a single secondary user, comparing source camera identification across the same set of images having undergone different post-processing steps. The results in Figure 10 show that, in all six experimental scenarios, our method consistently achieved a higher AUC compared to the method of [PERSON] [12]. Furthermore, the variations in identification accuracy for the same image processed by different platforms can be attributed to the unique effects of these platforms' processing operations on the original sensor noise of the image. Despite these variations, the findings from Figure 10 demonstrate that our proposed method remained robust in the face of real-world post-processing, maintaining reliable performance even when images have been altered by social media platforms.  
Figure 9: Identification results between different devices of the same camera model.
###### Abstract  
In this study, we present a united sign coherence factor beamformer for coherent plane-wave compounding (CPWC). CPWC is capable of reaching an image quality comparable to the conventional B-mode with a much higher frame rate. Conventional coherence factor (CF) based beamformers for CPWC are based on one-dimensional (1D) frameworks, either in the spatial coherence dimension or angular coherence dimension. Both 1D frameworks do not take into account the coherence information of the dimensions of each other. In order to take full advantage of the radio-frequency (RF) data, this paper proposes a united framework containing both spatial and angular information for CPWC. A united sign coherence factor beamformer (uSCF), which combines the conventional sign coherence factor (SCF) and the united framework, is introduced in the paper as well. The proposed beamformer is compared with the conventional 1D SCF beamformers (spatial and angular dimension beamformers) using simulation, phantom and in vivo studies. In the in vivo images, the proposed method improves the contrast ratio (CR) and generalized contrast-to-noise ratio (gCNR) by 197% and 20% over CPWC. Compared with other 1D methods, uSCF also shows an improved contrast and lateral resolution on all datasets.  
Keywords:coherent plane-wave compounding (CPWC); spatial and angular coherence; united coherence +
Footnote †: journal: Applied Sciences
# Do We Get What We Need from Clinical Acoustic Voice Measurements?
## 2 Background: Current State of the Art and Challenges in Clinical Acoustic Measurements  
### Hypothesis Underlying Measurements and Current Clinical Assessment Recommendations  
Instrumental acoustic measurements rely on the hypothesis that the functionality of the human voice production system is reflected in measurable changes in the acoustic voice signal. Thanks to huge technical developments in the last 30 years, there are several measurement methods at hand for clinical applications, which are easy to apply and have a variously described relation to voice dysfunction [4; 5; 6]. According to current guidelines, both quantitative and qualitative characteristics of vocal output are measured to allow indirect conclusions to be drawn about the pathophysiology and extent of vocal function restriction [5; 7]. Quantitative measures comprise fundamental frequency (\(f_{0}\)) and voice intensity, or sound pressure level (SPL), measurements. These include speaking and singing voice range profiles (VRP), meaning distribution maps of \(f_{0}\) and SPL while performing different voice tasks, including sustained vowels, speaking, counting, reading, and singing [8]. Moreover, so-called voice quality measures, including jitter, Harmonics-to-Noise ratio (HNR), Cepstral Peak Prominence (CPP), and combined indices such as the Dysphonia Severity Index (DSI), have been recommended to objectively quantify dysphonia [5; 7; 8].  
Over the years, guidelines have been published proposing technical equipment, procedures, and speech tasks to promote the reproducibility and reliability of acoustic analysis. The most important recommendations are described in Table 1[5; 6; 7; 9].
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
## 6 Experimental Validation and Discussion
### Detectability of Planar Array Configurations
#### 6.2.2 Spherical Detectability  
Next, the characterisation of the spherical detection zone of the chipless RFID tags is carried out using the monostatic radar configuration as shown in Figure 23. The 3D printed antenna rotating system inspired by [37] is used to rotate the dual-polarisation patch antenna. The elevation interval, \(\theta\), ranges from \(-150^{\circ}\) to \(+150^{\circ}\) with a step size of \(10^{\circ}\) and the azimuth angle, \(\phi\), spans from \(0^{\circ}\) to \(+180^{\circ}\) with a step size of \(30^{\circ}\).  
The success of chipless RFID tag identification in 3D spherical coordinates is presented in Figure 24. In the unit cell tag, as shown in Figure 24a, the successful reading range of the tag varies from \(-60^{\circ}\leq\theta\leq+60^{\circ}\). Reading is unsuccessful for other elevation angles due to the ground plane of the chipless RFID tag. Similarly, all other array configurations show successful readings where \(\theta=0^{\circ}\)\(\phi=90^{\circ}\). However, the success of the 3D spherical identification narrows as the number of elements of the array increases as shown in Figure 24b-d. The far-field RCS pattern narrows and concentrates on the array's middle (i.e., less scattering) as array elements increase, causing a narrower spherical reading range.  
Figure 23: A 3D spherical identification of chipless RFID tag measurement setup.  
Figure 24: The 3D spherical detection success of chipless RFID tags. (**a**) Unit cell. (**b**) \(2\times 2\) array. (**c**) \(3\times 3\) array. (**d**) \(4\times 4\) array.