esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The opening and closing of plant stomata regulates the global water, carbon and energy cycles. Biophysical feedbacks on climate are highly dependent on transpiration, which is mediated by vegetation phenology and plant responses to stress conditions. Here, we explore the potential of satellite observations of solar-induced chlorophyll fluorescence (SIF)--normalized by photosynthetically-active radiation (PAR)--to diagnose the ratio of transpiration to potential evaporation ('transpiration efficiency', \(\uptau\)). This potential is validated at 25 eddy-covariance sites from seven biomes worldwide. The skill of the state-of-the-art land surface models (LSMs) from the earthH2 Observe project to estimate \(\uptau\) is also contrasted against eddy-covariance data. Despite its relatively coarse (0.5\({}^{\circ}\)) resolution, SIF/PAR estimates, based on data from the Global Ozone Monitoring Experiment 2 (GOME-2) and the Clouds and Earth's Radiant Energy System (CERES), correlate to the in situ \(\uptau\) significantly (average inter-site correlation of 0.59), with higher correlations during growing seasons (0.64) compared to decaying periods (0.53). In addition, the skill to diagnose the variability of in situ \(\uptau\) demonstrated by all LSMs is on average lower, indicating the potential of SIF data to constrain the formulations of transpiration in global models via, e.g., data assimilation. Overall, SIF/PAR estimates successfully capture the effect of phenological changes and environmental stress on natural ecosystem transpiration, adequately reflecting the timing of this variability without complex parameterizations.  
solar-induced chlorophyll fluorescence; transpiration; transpiration efficiency; GOME-2; eddy-covariance 2019 11 413 10.3390/rs11040413 18 February 2019
# An Improved High-Resolution Network-Based Method for Yoga-Pose Estimation
## 3 Structure of the SEPAM_HRNet Model  
The SEPAM_HRNet model proposed in this paper inherits the basic four-stage architecture of the original HRNet [4] network, and the model architecture diagram is shown in Figure 1.  
The SEPAM_HRNet model first passes through two convolutional layers that are downsampled on the basis of the original image, then passes through the Stage 1 phase, which consists of four SEPAMneck modules. After that, a staged progressive downsampling phase is performed, which consists of three stages--Stage 2, Stage 3, and Stage 4--with each branch of each stage consisting of four SEPAMblock modules. Gradually decreasing the resolution of the feature maps can reduce the loss of detailed features in the human posture feature maps caused by the large downsampling operation. The use of parallel branches with different resolutions and channel numbers in each stage avoids the loss of spatial information in the feature map during the downsampling process.  
The specific processing of the SEPAM_HRNet model is as follows:  
1. The input image is first pre-processed by two convolutional layers with a kernel size of 3 \(\times\) 3 and a step size of 2 (including BN and RELU operations). The input feature map's resolution is then downsampled by a factor of 4 to become 1/4 of the original, and the number of channels is increased from three channels to 64 channels.
2. The pre-processed feature map is sent into the Stage 1 stage, which is made up of four SEPAMneck modules, where the shallow features are removed from the feature map,while only the feature map's channel count is altered and the feature layer's size is left unchanged.
3. In Stages 2, 3, and 4, downsampling operations are performed progressively. The feature maps used in each stage have resolutions of 1/4, 1/8, 1/16, and 1/32 of the original resolution, corresponding to channel numbers C, 2C, 3C, and 4C, respectively. Each branch in each stage consists of four SEPAMblock modules. At the end of each stage, upsampling/downsampling is used to unify feature maps of different resolutions to the same size and perform element-wise addition for multi-scale feature fusion. Finally, only the output of the first high-resolution branch is subjected to a \(1\times 1\) convolution to obtain 17 keypoints heatmaps, which completes the keypoints prediction.  
In this paper, the SEPAM_HRNet model architecture is adopted, with the number of channels C = 32 (i.e., the number of feature map channels in the branch where the maximum resolution feature map is 32). As the stage progresses, the resolution and the number of channels of the feature map is adjusted for each stage. In this paper, referring to the idea of HRNet [4] literature, for every 2X downsampling branch, the number of feature map channels in the added branch is doubled, thus compensating for the loss of spatial localization caused by resolution degradation. The specific implementation methods of the SEPAMneck module and SEPAMblock module structures are described in the summary provided in Section 3.2.
# Modelling and Experimental Testing of Passive Magnetic Bearings for Power Loss Reduction
## 4 Results and Discussion
### Dynamic Tests
and the friction losses are  
\[P_{f}=f_{C}r_{b}F_{N}\omega, \tag{17}\]  
where the contact reaction force \(F_{N}\) is  
\[F_{N}=F_{zlow}+F_{zup}-mg. \tag{18}\]  
The aerodynamic power loss \(P_{a}\) is expressed in Equation (16), according to [27], as a sum of terms that are functions of the angular speed \(\omega\) to the \(k+1\) power, with \(k\in[1,\ 1.83]\). The friction power loss \(P_{f}\) expressed in Equation (17) is linear with respect to the angular speed and proportional to the contact reaction force \(F_{N}\) (see Table 2). The aerodynamic effect is considered equal in the three configurations under the assumption that small PMB axial offsets do not significantly affect the airgaps between the stator and the rotor. Therefore, PMB configurations with different \(F_{z}\) values cause a variation of the rotor acceleration proportional to the variation of \(F_{N}\), visible in Figure 8b and independent of the angular speed. Consequently, the power loss reduction of Figure 9b is totally addressed to the change in the friction loss due to the PMB thrust effect:  
* At low speed, the power loss percentage reduction tends to the values of the weight percentage compensation listed in Table 2 because the aerodynamic effect tends to zero;
* At high speed, the aerodynamic losses are the most relevant; the percentage reduction in configuration C is still high and equal to 47.7% at 200 rad/s. The reduction in power losses results in a decrease in the energy required to operate the rotating system. From the perspective of energy storage applications, the power loss reduction improves the FESS discharge time performance.
# A Simplification Method for Point Cloud of T-Profile Steel Plate for Shipbuilding
## 4 Point Cloud Simplification Algorithm Based on Curvature Feature  
### Algorithm of Calculating the Surface Curvature Feature  
In the study, the least square method is used for calculating the coefficients of the fitting surface shown by Equation (7)  
\[Q=\sum_{k=1}^{30}\left[\sum_{j=2}^{0}\left(\sum_{i=j}^{0}c_{i,j-1}x_{k}^{i}y^{ j-i}\right)-z_{k}\right] \tag{7}\]  
where: \(x_{k}\), \(y_{k}\), \(z_{k}\) are the coordinates of the points in the sub-cube.  
Then, the coefficients are calculated separately in Equation (7) by making the partial differential zero  
\[\frac{\partial Q}{\partial c_{i,j-1}}=\sum_{k=1}^{32}\{[z_{k}-\sum_{j=2}^{0} \left(\sum_{i=j}^{0}c_{i,j-1}x_{k}^{i}y^{j-i}\right)]\}x_{k}^{1}y_{y}^{j-1} \tag{8}\]  
As a result, the quadratic equation of the fitting surface is determined.  
After obtaining the fitting surface equation, the average curvature of the points is derived based on the following first-order and second-order partial differentials  
\[Z_{x}=\frac{\partial Z}{\partial x},Z_{y}=\frac{\partial Z}{\partial y},Z_{xx} =\frac{\partial^{2}Z}{\partial x^{2}},Z_{yy}=\frac{\partial^{2}Z}{\partial y ^{2}},Z_{xy}=Z_{yx}=\frac{\partial^{2}Z}{\partial x\partial y} \tag{9}\]  
Then, the average curvature of the surface can be expressed as follows  
\[P_{i}=\frac{1}{2}\frac{(1+z_{y})^{2}z_{xx}+(1+z_{x})z_{yy}-2z_{x}z_{y}z_{xy}}{ (1+z_{x}^{2}+z_{y}^{2})^{\frac{3}{2}}} \tag{10}\]Based on the steps above, the average curvature \(P_{i}\) of the selected points in the field is obtained. Finally, the average curvature \(\overline{P}\) of all points in the region is obtained  
\[\overline{P}=\frac{\sum\limits_{i=1}^{n}P_{i}}{n} \tag{11}\]  
The average curvature can show the bending degree of the surface. If the average curvature of point cloud data within the sub-cube is \(P_{i}\leq\overline{P}\), it can be considered that the point cloud data in the sub-cube belong to a point with relatively flat distribution, and the points in the region are simplified by the random sampling method. When \(P_{i}\geq\overline{P}\), the points are considered as belonging to a dramatically changing region. The regional gravity algorithm is used for the simplification of the points in the dramatically changing regions.
## 2 Materials and Methods  
### Description of the Research Sites  
#### 2.1.1 Limited Irrigation Research Farm (LIRF)  
The Limited Irrigation Research Farm (LIRF) is located in Greeley, Colorado (CO), USA, and is under the management of the United States Department of Agriculture--Agricultural Research Service (USDA--ARS). The farm is geographically located at a latitude of 40.4463\({}^{\circ}\) N, a longitude of 104.6371\({}^{\circ}\) W, and an elevation of 1432 m above mean sea level (ASL). The study involved two adjacent rectangular maize fields, each measuring 190 m by 110 m (Figure 1), where field data were collected during the periods between July and September of 2020 and 2021.  
For each respective field crop growth season, each field was subjected to different irrigation water management strategies. In 2020, the West Field, hereafter referred to as Field W, was fully irrigated. In this context, \"fully irrigated\" represents the conditions in which frequent irrigation events were scheduled to maintain soil water content in the crop (maize) soil root zone at non-water-stress levels. Conversely, the East Field, designated as Field E, was managed as a deficit-irrigated field, resulting in crop/soil water stress conditions throughout the growing season. In 2021, the irrigation water management practices were switched between these treatment plots. Field W was transformed into the deficit-irrigated field, while Field E became the fully irrigated plot. A summary of soil wetting events, which includes irrigation and rainfall, for the years 2020 and 2021, is provided in Table 1.  
Each maize field had the same irrigation system, a subsurface drip irrigation setup with laterals (pipes) buried at a depth of 0.23 m and emitters spaced every 0.30 m. The maize rows were north-south and were spaced 0.76 m apart. The distance between adjacent maize plants was 0.17 m. The planting density for maize remained consistent at 87,500 plants per hectare during both years. In 2020, the selected maize variety was drought  
Figure 1: False-color image of the LIRF research site near Greeley, CO, USA. The study maize fields were Fields W and E located in the southeast corner of the research farm.  
tolerant NK9227-5222A (Syngenta Inc., Basel, Switzerland). Planting took place on 6 May 2020, and the harvest occurred on 13 and 14 October 2020. In 2021, there was a change in maize varieties with the introduction of other drought-tolerant maize options, P9998Q and P0157 AMXT (Pioneer Hi-Bred International, Inc., Johnston, IA, USA), along with CH 194-49 DG (Channel Bio Corporation, Saint Louis, MO, USA). Planting for this year was carried out on 13 May 2021, and the harvest took place on 11 and 12 October 2021. Fields W and E, each comprising approximately 83% of their respective plots, were planted with the maize variety P0157 AMXT, as indicated in Figure 2. The experimental design and data collection stations at LIRF for the years 2020 and 2021 are shown in Figure 3. It is worth noting that the prevailing wind direction remained consistent: the wind was from the south (S) to southeast (SE) direction during both years of data collection.
## 5 Discussion  
The variation in the noise reduction performance provided by the developed ANC headrest system was relatively low (below 5%), and the dimension of the comfort bubble was seemingly suitable for practical system implementation. This achievement, which was typically dependent upon the frequency components of the acoustic signal being cancelled, was verified in the course of the experiments. However, one has to consider possible changes in the acoustic paths associated with the ANC system due to the passenger's head movement, which were not addressed in this work. The exact impact of this assumption cannot be clarified yet and requires more research.  
The disturbing noise field was simulated by artificially generating tonal and narrowband disturbances, replicating the propeller-induced noise within a reference test environment under stationary and transient conditions. The primary noise was played through a dedicated speaker placed in front of the mannequin. The system's effectiveness was validated using representative experiments on local active noise control under laboratory conditions, utilizing a conventional FxLMS algorithm for multi-channel application. The upstream reference signals were taken from the predicted primary narrowband noise acting on the cabin passenger areas where seats are located, which was assumed to be detectable by reference sensors that are not affected by the control field.  
Figure 10: Quiet zone around the passenger’s head by ANC. (**a**) Side view. (**b**) Front view.  
Figure 9: Sound intensity (W/m\({}^{2}\)) measured with and without ANC at left and right mics. (**a**) Sound intensity map without control. (**b**) Sound intensity map with control.  
However, the proposed approach was restricted to the active control of periodic or nearly periodic noise, which is very common in turboprop aircraft cabins. The accuracy of the amplitude and phase of the secondary sound field generated by the developed signal processing algorithm affected the noise reduction levels, which were different for the left and right ears when the two speakers were alternatively activated. The control performance observed at the two microphones was also slightly different when the two speakers were simultaneously activated.  
In summary, our results demonstrated that significant noise reductions can be achieved around a passenger's ears with reasonable spatial variability. However, it is worth noting that, although very promising, these sound attenuations were achieved within free-field conditions (semi-anechoic conditions), thus neglecting the typical standing waves and reflections occurring in actual aircraft cabins. Furthermore, it was tacitly assumed that broadband aerodynamic noise could be separately addressed by using conventional passive techniques. For this reason, an extensive study ahead of system implementation, which considers broadband components up to 500 Hz of interest for the actual operation, is recommended.
# Automatic Roadway Features Detection with Oriented Object Detection
## 4 Case Study
### Dataset Design  
The dataset was collected in the GCR and is composed of 473 km travelled between local roads, collector roads, regional and primary arterial roads, and regional and primary highways, as shown in Figure 5. The dataset was fully annotated with oriented object bounding boxes. Table 2 details the distances covered for each roadway class, such variety of road classes in the dataset allowed validating the proposed methods on roadway classes that are unseen during training and development time. A front-facing camera was designed to capture an image every 100 m, with each image including the longitude, latitude, altitude, time and average vehicle speed. The data collection plan was designed to cover a period of over two years from January 2017 to August 2019 and covered times from 6:00 a.m. to 12:00 p.m. and from 2:00 p.m. to 7:00 p.m. The complete dataset is composed of 4732 samples (divided to 3714 for training and validation and 1018 samples for testing) with a resolution of 2560 \(\times\) 1440 pixels captured every 100 m. In order to label the collected images, a multi-platform object oriented bounding boxes (OBB) annotation tool was developed.  
Figure 4: Roadway objects, from left to right and top to bottom: (**a**) traffic sign, (**b**) marking, (**c**) left and right barriers, (**d**) left and right curbs, (**e**) light pole, (**f**) bus, (**g**) motorcycle, (**h**) car, (**i**) heavy vehicle, (**j**) public transportation, (**k**) buildings, (**l**) billboards, (**m**) trees, (**n**) fence, and (**o**) pedestrian waiting areas.
## 5 Performance Evaluation and Results Analysis
### Average Energy Spent per Delivered Data Bit  
The average energy spent per delivered data bit metric represents the amount of energy spent by the network to successfully delivery each data bit to its destination. Thus, the less energy spent to deliver the data successfully, the higher the power efficiency of the network. The results obtained by this metric are affected by the energy consumption of the nodes and the packet delivery ratio. The metric is computed using Equation (3):  
\[AES=\frac{\sum_{i=1}^{N}Ec_{i}}{\sum_{i=1}^{N}Pr_{i}*M_{length}} \tag{3}\]  
Figure 6: Packet delivery ratio in function of number of nodes.  
where \(N\) is the number of nodes in the network, \(Ec_{i}\) is the total energy consumed (in millijoules) by each node \(i\), \(M_{length}\) is the length of the data message in bits, and \(Pr_{i}\) is the number data packets received by each node \(i\).  
Figure 7 shows the results obtained for the AES metric. In all studied network scenarios with more than 25 nodes, the proposed LOADng-IoT was able to obtain better results when compared with the other studied approaches. Considering the grid sparse scenario with 64 nodes, LOADng-IoT outperformed LOADng by \(\sim\)168%, and LOADng-SmartRREQ by \(\sim\)125%. In the mobility scenario, also with 64 nodes, the proposed approach is able to give a performance \(\sim\)73% better when compared with LOADng. In general, the reduction in the number of control messages used in the route discovery process allowed the proposed protocol to use fewer radio transmissions, provoking the energy consumption decrement. The improved process of route discovery for Internet nodes also enabled the protocol to find closer destinations to forward the Internet messages. Thus, the data messages delivered to the Internet-connected nodes required fewer transmissions, contributing to the reduction of energy consumption. Less transmission also implies a lower probability of packet loss caused by radio interference or packet collisions. Hence, by reducing the energy consumption and reaching a high packet delivery ratio, LOADng-IoT was able to spend less energy to deliver each data bit, exposing a high power efficiency in comparison with LOADng and LOADng-SmartRREQ.
# Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway
## 2 Materials and Methods
### Methods
#### 2.2.1 Sentinel-1 Preprocessing and Slope Correction  
All S1 GRD data from the fjord ice-free months June to November of the years 2018 and 2019 over Tromso Municipality were preprocessed with NORCE's geocoding software [38] using the 10 m Norwegian digital elevation model (DEM). Header information in the S1 *SAFE folder includes the necessary parameters for radiometric calibration and the exact satellite orbit information for georeferencing and terrain correction with the DEM. GRD files are, therefore, directly converted into georeferenced, radiometrically corrected gamma-naught \(\gamma^{\circ}\) radar backscatter images in dB for both polarizations: vertical polarized co-polarization \(\gamma^{\circ}\) (VV) and vertically emitted, horizontally received cross-polarization \(\gamma^{\circ}\) (VH). Adjacent single scenes of the same orbit are directly processed together into one seamless continuous SAR image. The data are projected onto a 10 m pixel grid in UTM zone 33 N.  
Once the GRD data is processed into georeferenced and radiometric corrected images, an additional radiometric slope correction according to [39] is applied. This is less relevant in this project as the topography in the intertidal zone was not resolved in the DEM. An important issue, however, is to mask the areas without reliable data due to SAR shadow and overlay occurring at steep hills along the coast perpendicular to the SAR range direction.  
Instead of using NORCE's internal software that is set up for large-scale operational monitoring, the preprocessing steps can also be done with any other commercial SAR processing software or ESA's free openly available Sentinel-1 Toolbox from the Sentinel Application Platform (SNAP) [40]. Alternatively, preprocessed Sentinel-1 SAR data are also available on Google Earth Engine (GEE) [41; 42], but preprocessing on GEE is done with the lower-resolution ASTER DEM [41]. Using other preprocessing software should not affect the results, as long as the preprocessed output is gamma-naught \(\gamma^{\circ}\) radar backscatter in dB.
# Effect of Coupled Mechanical-Chemical Activation on Hydration Activity of Copper Slag Powder
## 2 Raw Materials and Experimental Methods
### Particle Size Distribution of Copper Slag  
The copper slag was ground for 30, 60, 90, and 120 min by a ball mill (SM\(\Phi\)500 \(\times\) 500) to prepare copper slag powder with different fineness. A laser particle size analyzer (Mastersizer 2000) was applied to test the particle size distribution of the copper slag powder and cement. The specific surface area of the copper slag and cement was determined according to the Chinese standard \"Testing Method for Specific Surface of Cement-Blaine Method\" (GB/T 8074-2008).
## 2 Methodology
### Mass/Length Change, Density, and Apparent Porosity  
The weight, height, and diameter of the geopolymer composites were measured using a mass balance accurate to 0.01 g and calipers accurate to 0.01 mm. Measurements were taken before and after heating, as well as after exposure to the molten Group 1 chlorides. Mass losses were determined by comparing sample weights before and after heating. Length changes were determined by comparing sample outer diameters before and after heating, similar to previous work [31]. Heat-treated sample densities and open porosities were measured before and after exposure to molten salt using the Archimedes method outlined in ASTM C20. Deionized water at 24 \({}^{\circ}\)C was used as the reference fluid. Samples were dried at 130 \({}^{\circ}\)C for 24 h and weighed, W\({}_{\text{Dry}}\). Next, samples were submerged in boiling water for at least 2 h to fill all open pores with fluid. Once cooled and still submerged, suspended samples were weighed to offset buoyant force, W\({}_{\text{Sub}}\). Finally, saturated samples were wiped of free water and weighed, W\({}_{\text{Sat}}\). Equations (3)-(7) below show how to calculate open porosity and bulk density using described sample weights.  
\[\begin{array}{cc}\text{V}_{\text{Bulk}}=\frac{(\text{W}_{\text{Sat}}-\text{ W}_{\text{Sub}})}{\text{Pr}_{\text{water}}}&\text{Equation (3). Bulk Volume}\\ \hline\text{V}_{\text{Open~{}Pores}}=\frac{(\text{W}_{\text{Sat}}-\text{W}_{ \text{Dry}})}{\text{Pr}_{\text{Water}}}&\text{Equation (4). Open~{}Pore~{}Volume}\\ \hline\text{V}_{\text{Matrix}}=\frac{(\text{W}_{\text{Dry}}-\text{W}_{\text{Sub }})}{\text{Pr}_{\text{Water}}}&\text{Equation (5). Apparent~{}Volume}\\ \hline\text{\rho}_{\text{Bulk}}=\frac{\text{W}_{\text{Dry}}}{\text{V}_{\text{ Bulk}}}=\frac{\text{W}_{\text{Dry}}+\text{Pr}_{\text{Water}}}{(\text{W}_{\text{Sat}}-\text{W}_{ \text{Sub}})}&\text{Equation (6). Bulk Density}\\ \hline\text{Open~{}Porosity}=\frac{\text{V}_{\text{Open~{}Pores}}}{\text{V}_{ \text{Bulk}}}=\frac{\text{W}_{\text{Sat}}-\text{W}_{\text{Dry}}}{\text{W}_{ \text{Sun}}-\text{W}_{\text{Sub}}}&\text{Equation (7). Open~{}Porosity}\\ \hline\end{array}\]