esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The temporal mismatch between SIF\({}_{\text{inst}}\) and GPP\({}_{\text{daily}}\) causes uncertainties in the spatio-temporal patterns of SIF-GPP correlations; therefore, the upscaling of SIF from the instantaneous to daily scale is important for investigating the link between the SIF and GPP. In this paper, we upscaled the in-situSIF measurements from an instantaneous to daily scale using the ratio of the PAR\({}_{\text{inst}}\) to PAR\({}_{\text{daily}}\) as a temporal conversion factor. With the continuous tower-based spectral observations, combined with meteorological and flux measurements at two sites, the accuracy of the PAR-based method and the performance of upscaled SIF to track GPP were evaluated, which was also compared to the commonly used cos-based upscaled SIF. The conclusions are summarized as follows.  
First, the PAR-based method gives more accurate diurnal and SIF\({}_{\text{daily}}\) predictions than the cos-based method because the PAR-based method considers diurnal weather changes on cloudy days. In addition, the upscaled SIF\({}_{\text{daily}}\) using the PAR-based method shows a more robust and significant relationship with the APAR\({}_{\text{daily}}\) under different weather and latitudinal conditions. Finally, the upscaled SIF based on PAR has a stronger linear correlation with the GPP at both half-hour and daily scales than with either SIF\({}_{\text{inst}}\) or upscaled SIF\({}_{\text{daily}}\) based on \(\cos(\text{SZA})\) at the seasonal scale. These results confirm the importance of upscaling SIF from the instantaneous to daily scale and the advantage of our SIF upscaling approach.  
[PERSON] and [PERSON] conceived and designed the research; [PERSON] conducted the experiments and data analysis; [PERSON] contributed significantly to the research method and the manuscript revision. [PERSON], [PERSON], and [PERSON] provided important supports for the experiments of tower-based measurements.  
The authors gratefully acknowledge the financial support provided by the National Key Research and Development Program of China (2017 YFA0603001), and the National Natural Science Foundation of China (41701396).  
The authors declare no conflict of interest.
###### Abstract  
The spiral bevel gear in a tractor, unlike the other gears in the transmission, is one of the most vulnerable gears in terms of fatigue life, as it is consistently driven throughout the operations of the tractor. Conventional fatigue life tests of transmission gears require expensive equipment and repeated tests, and do not reflect dynamic field loads. The aim of this study is to develop a simulation model which can replace conventional fatigue life tests for actual gears, in order to evaluate the fatigue life of a tractor using dynamic field load data. A transmission simulation model including a spiral bevel gear was developed using commercial software. In order to measure the dynamic load of the tractor according to various field operations, an axle torque measurement system was developed, and field experiments were performed for major agricultural operations occurring in the field. Fatigue life was calculated using Rainflow cycle counting (RFC), the Smith-Watson-Topper (SWT) model, and S-N curves based on torque data measured in the field. The fatigue life under moldboard plow tillage, subsoiler tillage, rotary tillage, and baler operation were 13,599, 285, 278,884, and 525,977 h, respectively. The fatigue life of the tractor, according to subsoiler tillage and baler operation, was 0.104 and 192 times the service life, respectively, where the difference between these two operations was about 1846 times. The fatigue life of the tractor, according to the attached implement type, was significantly different. Therefore, it can be seen that the fatigue life of a tractor can be significantly different, depending on agricultural operation type which the farmer uses most often; this can be used as basic data for tractor design and evaluation. In addition, it is considered that the developed simulation model can be applied to fatigue life evaluation using dynamic field load data.  
agricultural tractor; spiral bevel gear; fatigue life; simulation model; dynamic field load
# Optimization of HS-SPME-GC-MS for the Determination of Volatile Flavor Compounds in Ningxiang Park
## 3 Results and Discussion
### Influencing Factors on GC-MS Analysis
#### 3.1.3 Effect of Heat Treatment on VFCs  
In this study, the materials underwent various levels of heat treatment. The findings demonstrated that as the heat treatment duration was extended, the ion peak appearance was delayed, the peak separation was decreased, and the separation effect of volatile chemicals was enhanced (Figure S3, Figure 1c). The total ion peak area dramatically decreased (Figure 1d) (\(p<0.05\)), and the generation of chemicals in the experiment was not sufficiently steady and repeatable when the heat treatment time exceeded 50 min. There was no significant difference in the types of volatile substances in Ningxiang pork treated with different heat treatment times (\(p>0.05\)), and the total amount of substances was the highest at 50 min.  
Boiling, frying, and roasting are examples of heat treatment that will directly alter the chemical reaction between two taste precursors [45]. As a method of food heat treatment, high-temperature heat treatment has the potential to partially dissolve and degrade some flavoring compounds and to accelerate the creation of new taste compounds via chemical reactions. The initial Maillard reaction will be competitively inhibited by the fatty aldehydes created by lipid oxidation if the duration is too short, and the production of flavor compounds will be reduced. These results will have an impact on the extraction effect. The Maillard reaction will produce compounds that prevent lipid oxidation if the reaction period is too long [46], and the principal flavor compounds, such as aldehydes, alcohols, and ketones, will produce esters and alkanes, which will lower the quality of the substances detected by GC-MS. In this study, the overall volatile matter and total content of Ningxiang pork decreased when heat-treated in a 100 \({}^{\circ}\)C water bath for more than 50 min, and the overall flavor of the meat, such as overcooked flavor [47; 48; 49], was reduced when heat-treated for too long. Therefore, the heat treatment time of Ningxiang pork should not exceed 50 min.
## 5 Demonstration Using TerraSAR-X  
After analyzing the theoretical performance of the proposed method, results from acquisitions using the TerraSAR-X satellites are shown. The data were recorded using the so-called 'Aperture Switching' mode which is an experimental mode with dedicated commanding. The satellite was not designed for such experimental data takes which results in additional constraints and draw-backs, e.g., a too broad illumination of the scene for the given timing, which causes additional range ambiguities.  
As analyzed before, a notch and a boresight beam pattern are used for the two coherent SAR datasets. The theoretical antenna pattern of both beams in power and phase as generated by the TerraSAR-X antenna model are shown in Figures 3 and 4 along with the calculated difference pattern to be found in the measured data later. The presented data has been acquired by interleaved sampling of the two SAR datasets and doubling the PRF as described above.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 3 An Overview on IoT Communication Protocols and Frameworks
### Amqp  
The _Advanced Message Queueing Protocol (AMQP)_[9; 10; 11] (ISO 19464) is an open standard, message-based protocol that assists cross-platform application communication to be easily built via mediator entities, _brokers_, and queues. This protocol is considered to be the \"Internet Protocol for Business Messaging\" ([[https://www.amqp.org/](https://www.amqp.org/)]([https://www.amqp.org/](https://www.amqp.org/)) (accessed on 15 May 2021)) and is widely used by entities such as JP Morgan, which relies on AMQP to process over 1 billion messages a day, or the Deutsche Borse. Moreover, _NASA_ used it for the Nebula Cloud Computing Platform [12].  
AMQP provides a good basis for the integration of software derived from multiple vendors given that it supports common interaction patterns: one way, request/response, publish/subscribe, transactions, and store-and-forward. It does this with flow-control, multiplexing, security, recovery and a portable data representation that enables message filtering. Its messaging strategy supports both point-to-point and hub-and-spoke (broker-based) communications, and it is based on binary messaging. With AMQP, containers communicate via multiplexed sessions that depend on a reliable byte stream with in-order delivery, e.g., TCP.  
It is, therefore, a unidirectional binary IP-based messaging protocol, which allows asynchronous communication between publishers and subscribers to occur with some reliability. To do that, AMQP defines the following elements:  
* Exchange, entity which receives messages and applies message-based routing.
* Binding, entity in charge of defining rules to bind exchange to queues.  
* Queue, which supports the asynchronous communication between publishers and subscribers, by storing messages sent by Exchanges, and routed via the binding rules to subscribers.  
An AMQP network consists of a broker and applications connected in a star. Brokers can be added for redundancy, and sometimes connected in federations. AMQP adopts the HTTP view of the Internet as a set of large hubs serving many users. In an AMQP network, applications address the broker, which is the only \"stable\" piece of the network. This means the only IP addresses or domain name known to all involved applications concerns the brokers. This implies that all publishers and consumers need to know beforehand the URL of brokers. A first advantage of AQMP is the support of asynchronous communication. A second concerns the integrated QoS features, which provide guaranteed message delivery. interoperability and the capability to provide secure connections. However, AMQP transmissions require more bandwidth than its counterpart MQTT, and it does not support resource discovery.
## 4 Simulation Results
### Monthly PV Energy Production and Efficiency Gains  
Figure 4 compares the monthly PV energy generation with three MPPT methods. The energy generation is expressed in kilowatt-hours (kWh) and plotted against the months of the year. It is seen that the RL-based MPPT method achieves the highest energy production across all months, with a noticeable advantage during the summer months (e.g., June, July, and August) when solar irradiance is at its peak. This demonstrates the RL algorithm's ability to dynamically optimize the operating point of the PV array under varying conditions. The ANN-based MPPT method also performs well, with energy production values  
Figure 3: Battery state-of-charge (SOC) variation.  
close to those of the RL method. However, slight underperformance is observed during the transition months (e.g., March and October), likely due to the ANN model's reliance on training data, which may not fully capture transitional weather patterns. The conventional MPPT method generates the least energy, particularly during the winter months (e.g., December and January). This is attributed to its inability to accurately track the maximum power point (MPP) under rapidly changing conditions.  
Figure 4 also compares the annual PV energy production for the three MPPT methods and presents the percentage efficiency gains of the RL and ANN methods over the traditional method. It is determined that the RL-based MPPT method achieves the highest annual energy production, followed closely by the ANN-based method. The traditional method produces the least energy, exhibiting a significant performance gap compared to the advanced methods. The efficiency gains of the RL method over the traditional method range from 10% to 15%, depending on the month, with the highest gains observed during the summer months. The ANN method achieves efficiency gains of 8% to 12%, demonstrating its effectiveness in improving energy production. The results confirm that advanced MPPT methods significantly enhance the efficiency of PV systems, with RL-based MPPT offering the greatest performance improvements.
## 3 Methods
### Monitoring the Stability of the Base Station
**(a)** Displacement time series of the North-South (NS) direction; **(b)** displacement time series of the East-West (EW) direction; **(c)** displacement time series of the Upper-Down (UD) direction. The shaded period indicates the GPS-derived relative positions before the enforcement of the antenna monument of the base GPS on 17 April 2018. The vertical displacement time series of the base station (Figure 5c) indicates a linear trend toward the downward direction. The same downward trend was presented by the PPP solutions of GPS1, as shown in Figure 4e. In order to assess the long-term stability of the base station, we also investigated daily PPP solutions of a permanent GPS station (BJUT) outside of the study area. BJUT is located on the campus of the Beijing University of Technology, which is approximately 250 km away from the base GPS station. The displacement time series of BJUT depicts seasonal movements of the ground surface in both horizontal and vertical directions. The seasonal up and down of the ground surface is substantial with peak-to-trough amplitude of over two centimeters. GPS-derived positional time series are well known to have periodic variations attributed to a combination of annual and half-annual cyclical sinusoids [39]. A seasonal ground deformation model (vertical) was developed to simulate GPS-derived ground displacement time series in North China [37], which can be described as:  
\[Seasonal_{UD}(t)=-0.35\times\cos(2\pi t)+0.45\times\sin(2\pi t)+0.1\times\cos (4\pi t)-0.13\times\sin(4\pi t), \tag{1}\]  
where \(t\) is decimal year. The unit of the seasonal motion is in centimeters. The vertical oscillations could be a combination of true seasonal up and down movement of ground surface and certain periodic errors associated with the data modeling used in PPP processing. The red line in Figure 5c represents the seasonal model. The vertical displacement time series of BJUT agrees considerably well with the regional seasonal model. It turns out that the downward movement of the base GPS from April to December is coincident with the down-term of the seasonal ground movements. It is difficult to recognize seasonal motions from a dataset shorter than one year. Figures 4 and 5 reveal that a regional stable reference frame and a seasonal ground surface deformation model are critically important for assessing the long-term stability of reference stations.
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 3 Sound Source Localization Methods for Two-Dimensional (2D) Structures
### Triangulation Method
#### 3.1.3 Triangular Positioning Method under Uncertain Arrival Time Difference of Sound Waves  
[PERSON] et al. [13] proposed a new fully probabilistic method for estimating time-of-flight based on a Bayesian inverse problem that provided a reliable estimate of the time-of-flight for each sensor independently. The prediction was then used as an input to the Bayesian inverse problem for damage localization.  
[PERSON] et al. [14] proposed an improved AE-based damage source localization method for wind turbine blades and used five methods to determine the most reasonable TDOA to improve the accuracy of the damage source localization.  
[PERSON] et al. [15] proposed a Bayesian source localization strategy which was able to cope with complex situations. In this new framework, a Gaussian process was first used to model the relationship between the source location and the corresponding TDOA values for multiple sensor pairs. When an AE event of unknown origin was observed, a mapping was generated that quantified the likelihood of the emission location across the surface of the structure.  
[PERSON] et al. [16] proposed an autonomous analysis of the wavefield for impact diagnostics based on computer vision, i.e., identifying the location of the impact and reconstructing the impact force time-course.  
[PERSON] et al. [17] proposed a machine-learning method for localizing composite panels within a five-peak narrowband modulated sinusoidal signal excitation source and a back propagation (BP) neural network. The idea was to use the arrival time of the first wave packet of the five-peak wave signals to locate the AE source.  
[PERSON] et al. [18] used the magnitude of fiber optic sensor signals to estimate the distance between each sensor and the impact location. To achieve a higher correlation between signal magnitude and distance, the former was calculated using a rough estimate of the signal near the arrival time. Then, the accuracy of estimating the signal magnitude distance was improved by neural network training. Finally, triangulation was used to localize the impact source.  
[PERSON] et al. [19] proposed a simplified and robust arrival-time-based iterative planar source (IPS) localization algorithm for identifying arbitrarily selected multiple damage source locations for rectangular, circular and sawtooth otoacoustic emission sensor network arrangements. The outcomes of the proposed localization algorithm were then compared to the results of an evolutionary particle swarm optimization (PSO) algorithm for each sensor network arrangement to evaluate the performance of the AE source monitoring strategy.  
[PERSON] et al. (2020) extracted the arrival time features at different frequencies from time domain signals by continuous wavelet transform, mutual correlation and Hilbert transform. Two techniques, namely, image fusion and artificial neural network, were proposed to fuse multi-frequency features to detect impact defects in large aerospace structures.
## 5 Conclusions  
This paper proposes a framework that can achieve real-time rendering of the 3D scene aligned to the real world on an off-the-shelf AR HMD by reducing the amount of data transmitted whilst retaining all the necessary information for accurate and high-resolution 3D rendering. We have categorised deformation in a medical training simulation into local deformation by user interaction and global deformation as a result of the simulation scenario. Local and global deformation is transmitted in a different way to reduce the position error between a transmitted mesh and the original mesh. Control points embedding a target mesh are transmitted, and a surface mesh is constructed from the received control points for local interaction. Our framework can be applied to other medical simulations since the deformation of an organ model is often the result of both user interaction and the specific training scenario, as is the case for the VE simulation, or the deformation range can be restricted. The proposed framework can achieve efficient and accurate mesh transmission in the case of large deformations of the complete shape as a result of a given scenario, but it can also be used without global transmission in simulations with small deformation. This enables direct and seamless rendering of a virtual scene onto the real world on an HMD without the need for additional external real-time tracking, which is an important step in the further development and adoption of AR-based medical simulators.  
Methodology, M.K.; Software, M.K.; Supervision, F.B.; Validation, M.K.; Writing--original draft, M.K.; Writing--review & editing, F.B.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Imperial Health Charity grant number 161715a and The APC was funded by Imperial Open Access Fund.  
The authors declare no conflict of interest.
# Interpretation of Bridge Health Monitoring Data from Satellite InSAR Technology
## 2 Case Study
### Case Study Area and Environmental Loads  
The case-study area is 17.6 km\({}^{2}\), close to Mantua in northern Italy. It includes the villages of San Nicolo Po and Portiolo and is crossed by the Po River, Italy's longest and widest river. This area is subjected to a slow progressive subsidence of around 1.0-1.5 mm/year, as visible in the vertical displacement measurement accessible from the European Ground Motion Service [26].  
In the case study area, the subcontinental climate is characterised by high humidity, snowfall and fog in winter and a muggy, humid climate with little ventilation in summer. Temperatures vary between \(-\)5 \({}^{\circ}\)C and 40 \({}^{\circ}\)C throughout the year, with daily temperature variations of 10 \({}^{\circ}\)C to 15 \({}^{\circ}\)C around the average. Figure 5a shows the air temperature measured between UTC 5 p.m. and 5:30 p.m. from January 2014 to December 2021 by the meteorological station 5.6 km from the A22 Po River Bridge in Gorgo di San Benedetto Po (MN). As commonly known, the vertical deflection and longitudinal deformation of prestressed concrete bridges with a structural scheme similar to the A22 Po River Bridge strongly respond to temperature variation [27]. Thus, we expect to measure a periodical displacement of the A22 Po River Bridge from satellite SAR images, which is correlated to seasonal temperature variation.  
Significant water flow variations characterise the Po River; the water level has periodically increased up to 10 m in the period we are interested in, which caused the floodplain to fill up during heavy rains. Figure 5b shows the water level of the Po River from 2014 to 2021 measured between UTC 5 p.m. and 5:30 p.m. by the hydrometer of the Interregional Agency for the River Po (AIPO) installed on the Borgoforte Bridge, located just 7.4 km before the A22 Po River Bridge.  
Figure 5: Time series from 1 May 2014 to 12 May 2021 of **(a)** temperature variation in the case study area and **(b)** water flow level of the Po River.  
The lithology of the foundation soils was deduced from the results of surveys conducted during the construction close to Pier 7 and the South Abutment. Surveys show a predominance of uniform grain size sands with traces of silt in the higher levels and peaty traces at the deeper levels (>15 m from ground level). The survey report indicates an average value of the soil elastic modulus of 8 MPa, constant over a depth of 15 m from ground level, and a friction angle conservatively assumed to be \(\varphi=32^{\circ}\) at the base of the piles and zero cohesion over the entire length of the foundation piles.