esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 2 Overview of the Study Area  
As it can be seen in Figure 1, Xiwuzhumuqin Banner (abbreviated as Xiwuzhumuqin or Xiwuzhumuqin Qi) is located in the central part of Inner Mongolia, in the eastern part of Xilingol League. The geographical coordinates range from 43\({}^{\circ}\)52\({}^{\prime}\) N to 45\({}^{\circ}\)23\({}^{\prime}\) N and 116\({}^{\circ}\)21\({}^{\prime}\) E to 119\({}^{\circ}\)23\({}^{\prime}\) E, covering a total area of 22,435 km\({}^{2}\). It borders Dongwuzhumuqin Banner to the north, Arukorqin Banner to the east, Balin Left Banner, Balin Right Banner, Linxi County, and Keshiketeng Banner to the south, and is adjacent to Xilinhot City to the west. The terrain of the region slopes from the southeast to the northwest, with an elevation range between 835 m and 1957 m, and an average elevation of over 1000 m. The main landforms include mountains, low mountain hills, and undulating plateaus. Mountains account for 24.9%, low mountain hills cover 27.7%, and plateaus make up 40.5% of the region's total area [17].  
Xiwuzhumuqin Banner was selected as the study area for its ecological and methodological significance. Ecologically, it is dominated by typical steppe ecosystems, which serve as a critical carbon sink and grazing resource in Inner Mongolia's grassland belt. The region's diverse microclimates, ranging from arid northwest (250 mm annual precipitation) to semi-humid southeast (350 mm), and topographic gradients (elevation 835-1957 m) create heterogeneous conditions ideal for testing multi-source data models. Methodologically, its large spatial extent (22,435 km\({}^{2}\)) and relatively undisturbed grassland landscapes provide a representative dataset for scaling remote sensing applications to similar semi-arid regions globally.  
In terms of climate, precipitation is uneven and varies significantly from year to year. The annual precipitation in the northwest is about 250 mm, while in the southeast it exceeds 350 mm, with the majority falling between July and August. The region has diverse soil types, with the predominant ones being chestnut calcareous soil (62.42%) and black calcareous soil (13.19%). The grassland types in this area mainly include meadow steppe, typical steppe, and wetland steppe. Major plant species include Stipa grandis, Leymus chinensis, Cleistogenes squarrosa, Artemisia frigida, and Agropyron cristatum [18].
## 1 Introduction
The mission of this paper is to compare space-based MODIS satellite-driven NPP data to ground-based inventory data driven NPP estimates using the Austrian National Forest Inventory Data. This Forest Inventory is based on a systematic permanent grid design with repeated observations [5,17]. The objectives of our study are:  
1. obtain MODIS NPP estimates (using different climate data) for comparing the results with terrestrial-driven NPP estimates
2. examine the potential effects of stand density, MODIS land cover types, stand age, species composition, ecoregion and elevation on NPP estimates by method.
# Flying Real-Time Network to Coordinate Disaster Relief Activities in Urban Areas +
## 7 Conclusions and Future Work  
In this paper we argue for the use of UAVs to provide digital communication support in SAR activities during first responses to extreme events such as floods, landslides, and earthquakes. In these scenarios, and particularly during the golden relief time, ensuring the real-time delivery of digital information makes a difference, easing the coordination of activities and reducing the impact of these events on the civil population.  
To the best of the authors' knowledge, there is no proposal reported in the literature that provides stable and real-time digital communication support in the field during the golden relief time. In order to advance the state-of-the-art in this study domain, this article proposes the use of a particular type of flying ad-hoc network, named the Flying Real-Time Network (FRTN), that uses a set of flying witness units to link the stationary and mobile devices deployed in the field. This network supports the transmission of messages from the field to the incident commander, and vice versa, considering time-constraints.  
The article also presents an analysis of these networks in order to prove real-time guarantees on the message transmissions. The results show that real-time requirements can be met by decomposing the message delivery in multiple independent stages. A synthetic disaster relief scenario, inspired by a real incident, was implemented using a discrete event network simulator. The simulation results corroborate the properties of the FRTN to support real-time message delivery. Therefore, this represents a step forward in the solution to a problem that has been difficult to address by the research community of the area.  
The next step in this research initiative is to consider the message routing within the FRTN when a whole switching network is deployed to collect the shared information. This requires an important amount of TWUs to provide the necessary connectivity and reduce the amount of rules. In this scenario, the real-time analysis should include message congestion and energy consumption considerations.  
[PERSON] and [PERSON] designed the main aspects of the network model and its analytical evaluation. [PERSON] and [PERSON] designed and implemented the algorithms for the placement of the Team-centered Witness Units. [PERSON] designed and implemented the simulations used to evaluate the network behavior. [PERSON] and [PERSON] analyzed the simulation and analytical results, and determined their validity and the coherence between them for each study scenario. All the authors participated in the preparation and improvement of this article.  
This work has been partially supported by the Spanish Government under contract TIN2016-77836-C2-2-R, and by the Generalitat de Catalunya as Consolidated Research Group 2017-SGR-990.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Net Ecosystem \(Co_{2}\) Exchange, Environmental Variable, and Phenology Measurements  
Above-canopy eddy covariance flux towers were established in the LWS and HWS in July 2015 and July 2018, respectively, to measure CO\({}_{2}\) exchange in poplar forest ecosystems. During the same period, the CO\({}_{2}\)/H\({}_{2}\)O flux was measured using an open-path CO\({}_{2}\)/H\({}_{2}\)O infrared gas analyzer (LI-7500A, Li-COR, Inc. Lincoln, NE, USA), and anemometric data were recorded using a three-dimensional sonic anemometer (WindMaster, Gill, UK). Raw data were collected and recorded at 10 Hz. The entire system was powered by solar energy. The upper and lower windows were cleaned manually twice a month.  
The ancillary measurements included net radiation (CNR4 net radiometer; Kipp & Zonen, Delft, The Netherlands) in the HWS and global radiation (LI-200R pyranometer; Li-COR Inc.) in the LWS. In addition, we measured photosynthetically active radiation (PAR; LI-190R quantum sensor, Li-COR, Inc.), TA, and relative humidity (HMP155 temperature probe, Vaisala Group, Helsinki, Finland), and soil temperature/water content at depths of 5, 15, and 25 cm (TEROS 12 sensor, METER Group, Pullman, WA, USA) in both the LWS and HWS. Missing meteorological data were acquired using open-source data from local meteorological administrations and the ERA-5 dataset.  
We installed a PhenoCam on top of the towers in both the LWS and HWS in March 2019 (StarDot NetCam SC; StarDot Technologies, Buena Park, CA, USA). The cameras collected continuous full-color and infrared images every 30 min from 8 AM to 6 PM. The camera at HWS was out of service from May to September in 2020 and 2021, as well as in November and December of 2022. It operated normally for a total of 960 days, capturing over 9600 valid photos. The camera at LSW was out of service from May to August in  
Figure 1: Map of the research sites.  
2021 and in July of 2022. It operated normally for a total of 1074 days, capturing over 20,000 valid photos.
# PCONet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
## 3 The Proposed PCQNet
### The Training Strategy of PCQNet  
In the offline stage, we compute the precoding matrices in advance by the aforementioned Algorithm 1 and generate the training, testing, and evaluation datasets. In the online stage, we can directly obtain the low dimensional feedback bitstreams with the well-trained neural network. The gradient of the quantization module is treated as a constant to make the network differentiable, and for this reason the encoder and the recovery network can be trained end-to-end. We jointly optimized the encoder and the decoder modules with back-propagation and gradients can pass through the quantization layer during back-propagation.  
We formulate the feedback of the precoding matrices into a reconstruction problem by \(\hat{\mathbf{F}}_{k}=g_{\phi}(\mathcal{Q}_{\varphi}(\beta,f_{\theta}(\mathbf{ F}_{k},\theta_{k})),\phi_{k})\). The auto-encoder is optimized by updating the network parameters \(\theta_{k}\) and \(\phi_{k}\), which can be applied for all UEs. The loss function is the NMSE, which quantifies the difference between the recovered precoding matrices and the original ones with  
\[\text{NMSE}=\frac{\mathbb{E}\Big{[}\big{\|}\hat{\mathbf{F}}-\mathbf{F}\big{\|} _{F}^{2}\Big{]}}{\mathbb{E}\Big{[}\big{\|}\mathbf{F}\big{\|}_{F}^{2}\Big{]}} \tag{11}\]  
The PCQNet is trained and evaluated on an Nvidia GeForce 3090 platform. We use the Adam [27] optimizer with a batch size of 32 and the training process stops early with a patience of eight epochs, where the maximum number of training epochs is 1000. We apply the adaptive learning rate schedule with a factor of 0.8. If the loss does not improve for four epochs in a row, the learning rate is reduced.
## 4 Common Latitude Power Series Expansions with Geocentric Latitude as Variable  
In related theories of geodesy and map projection, the geocentric latitude \(\phi\) is used as an auxiliary variable in addition to using the geodetic latitude \(B\) as the independent variable. We use geocentric latitude as the independent variable because it simplifies the theoretical problems of space geodesy, especially geometric problems. For example, in the ellipsoidal small projection, the projection from the ellipsoid onto the sphere is analyzed based on  
\begin{table}
\begin{tabular}{l c} \hline \multicolumn{1}{c}{**Expand to \(e^{10}\) Based on the First**} & **Expand to \(n^{5}\) Based on the Third** \\ \multicolumn{1}{c}{**Eccentricity \(e\)**} & **Flattening \(n\)** \\ \hline \(\left\{\begin{array}{l}m_{2}=-\frac{1}{3}e^{2}-\frac{1}{3}e^{4}-\frac{5}{6}e^ {6}-\frac{1}{72}e^{8}-\frac{21}{512}e^{10}\\ m_{4}=\frac{1}{3}e^{4}+\frac{1}{3}e^{6}+\frac{3}{3}e^{6}+\frac{1}{102}e^{10} \\ m_{6}=-\frac{1}{3}e^{6}-\frac{1}{16}e^{8}-\frac{9}{1024}e^{10}\\ m_{8}=\frac{1}{1024}e^{8}+\frac{512}{512}e^{10}\\ m_{10}=-\frac{1}{5120}e^{10}\end{array}\right.\) & \(\left\{\begin{array}{l}m_{2}=-n\\ m_{4}=\frac{1}{2}n^{2}\\ m_{6}=-\frac{1}{3}n^{3}\\ m_{8}=\frac{1}{4}n^{4}\\ m_{10}=-\frac{1}{5}n^{5}\end{array}\right.\) \\ \hline \end{tabular}
\end{table}
Table 4: Coefficients in Equation (14).  
\begin{table}
\begin{tabular}{l c} \hline \multicolumn{1}{c}{**Expand to \(e^{10}\) Based on the First**} & **Expand to \(n^{5}\) Based on the Third** \\ \multicolumn{1}{c}{**Eccentricity \(e\)**} & **Flattening \(n\)** \\ \hline \(\left\{\begin{array}{l}n_{2}=-\frac{1}{3}e^{2}-\frac{1}{3}e^{4}-\frac{1}{3} e^{6}-\frac{1}{72}e^{8}-\frac{21}{512}e^{10}\\ m_{4}=\frac{1}{3}e^{4}+\frac{1}{3}e^{6}+\frac{3}{3}e^{6}+\frac{1}{102}e^{10} \\ n_{6}=-\frac{1}{3}e^{6}-\frac{1}{16}e^{8}-\frac{1}{16}e^{10}\\ n_{8}=\frac{1}{4}e^{8}+\frac{1}{3}e^{6}e^{10}\\ n_{10}=-\frac{32}{5}n^{5}\end{array}\right.\) \\ \hline \end{tabular}
\end{table}
Table 5: Coefficients in Equation (16).  
the geocentric latitude. Geocentric latitude also plays an important role in determining satellite orbit and measuring altitude. These problems often involve converting geocentric latitudes to other common latitudes [32]. Using Mathematica, the power series of the common latitude with geocentric latitude as a variable based on eccentricity \(e\) and the third flattening \(n\) are expressed as detailed below.
## 4 Challenges and Future Work  
The challenge of the proposed method is the detection of weak and small targets in the case of complex background interference. Figure 10 shows the target detection results in a complex background. The left image is the original image, and the right image is an enlarged image corresponding to the cyan box in which the purple box is the detected target and the yellow dotted box is the missed target. As can be seen from the right image of Figure 10, due to the small size, poor contrast, and complex background, the target detection performance is decreased to a certain extent, which is a challenge faced by most infrared target detection algorithms and is also one of our works to focus on in the future.
# Performance Evaluation of GPS Auto-Surveying Techniques
## 3 Results
### Hatch Filter Averaging Constant Determination  
To obtain the Hatch Filter averaging constant that best fits the four different methods, four values were tested: 0.005, 0.01, 0.015, and 0.02. The results are presented in Table 1. It was observed that a higher value improves the LS and WLS mean error, at the cost of a slightly decreased precision, but only at the decimeter level. For the EKF and UKF, a lower value for the constant yielded better results in all metrics, but in the order of millimeters. A middle-ground value of \(\gamma=0.010\) was then chosen for the rest of the simulations in order to quantify the impact of the Hatch Filter in the positioning methods.
Computers_**2013**, \(2\), 152-175; doi:10.3390/computers2040152  
_Article_  
Static Three-Dimensional Fuzzy Routing Based on the Receiving Probability in Wireless Sensor Networks  
**[PERSON] 1,* and [PERSON] 2**  
1 Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran  
2 Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran; E-Mail: [EMAIL_ADDRESS] (S.K.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Cell Phone: +98-914-103-2156; Tel.: +98-411-236-4542.  
_Received: 4 July 2013; in revised form: 24 September 2013 / Accepted: 16 October 2013 / Published: 12 November 2013_  
**Abstract:** A Wireless Sensor Network (WSN) is a collection of low-cost, low-power and large-scale wireless sensor nodes. Routing protocols are an important topic in WSN. Every sensor node should use a proper mechanism to transmit the generated packets to its destination, usually a base station. In previous works, routing protocols use the global information of the network that causes the redundant packets to be increased. Moreover, it leads to an increase in the network traffic, to a decrease in the delivery ratio of data packets, and to a reduction in network life. In this paper, we propose a new inferential routing protocol called SFRRP (_Static Three-Dimensional Fuzzy Routing based on the Receiving Probability_). The proposed protocol solves the above mentioned problems considerably. The data packets are transmitted by hop-to-hop delivery to the base station. It uses a fuzzy procedure to transmit the sensed data or the buffered data packets to one of the neighbors called selected node. In the proposed fuzzy system, the _distance_ and _number of neighbors_ are input variables, while the _receiving probability_ is the output variable. SFRRP just uses the local neighborhood information to forward the packets and is not needed by any redundant packet for route discovery. The proposed protocol has some advantages such as a high delivery ratio, less delay time, high network life, and less network traffic. The performance of the proposed protocol surpasses the performance of the Flooding routing protocol in terms of delivery ratio, delay time and network lifetime.  
keywords: wireless sensor networks; routing protocols; fuzzy logic; receiving probability; network lifetime +
Footnote †: journal: Journal of Network Science
# Effect of Heat Treatment on Water Absorption of Chinese fir Using TD-NMR
## 2 Materials and Methods
### TD-NMR Experiments  
The TD-NMR experiments were carried out by a MesoMR23-060H-I NMR instrument (Niumag Co., Ltd., Suzhou, China) with a 0.52 Tesla permanent magnet (23 MHz proton resonance frequency), operating at 32.00 \({}^{\circ}\)C. The dimension of samples used for TD-NMR experiments was 50 mm \(\times\) 15 mm \(\times\) 15 mm. Similar to the process described in Section 2.1, the experiment samples were oven-dried and weighed before and after they were sealed with epoxy resin. After that, samples were immersed in distilled water in transparent vessels with the aid of a stainless steel net to prevent them from floating. At certain time intervals, the samples were taken out of the vessels and the extra water on the surface removed. The sample was placed into the probe one at a time after weighting. The Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence was used to measure the T\({}_{2}\) of the samples. 15,000 echoes with 64 scans were acquired, and the echo time and repetition time between two successive scans were 0.15 ms and 4 s, respectively.  
For the 1D and 2D nuclear magnetic resonance imaging (NMR imaging), a field gradient of 0.05 Tm\({}^{-1}\) was employed. The magnetic field exists perpendicular to the longitudinal direction of the wood sample, while the field gradient is nominally parallel to the longitudinal direction, therefore we could produce spatial resolution along the samples. In the 1D NMR imaging experiments, one-dimensional frequency coding was utilized to collect the MC profile along the longitudinal direction of the specimen, and the echo time and repetition time were 0.2 ms and 1 s, respectively. To perform 2D NMR imaging, multilayer spin echo imaging sequence was used to obtain images that could visualize the distribution of water in wood, the slice width was 2.5 mm, and the echo time and repetition time were 550 ms and 9.4 ms, respectively.  
To investigate the effect of heat treatment on water absorbing performance of wood, calibrations of the integral peak area of T\({}_{2}\) relaxation curve with respect to MC was performed for individual samples. The feasibility of using T\({}_{2}\) relaxation curve to monitor the dynamic change of moisture during the water uptake process could be evaluated according to the linear correlation coefficient (R\({}^{2}\)). The content of bound water and free water in the samples could be quantified during each water  
Figure 1: Sample preparation method.  
absorption stage, due to the fact that the integral area of the T\({}_{2}\) curve is in proportion to the water amount. The calculation was based on Equation (1):  
\[\text{MC}_{i}=\frac{(\text{Area}_{i}/\sum\text{Area})\times\text{WM}}{\text{OW}} \times 100(\%) \tag{1}\]  
where MC\({}_{i}\) is the content of bound water or free water; Area\({}_{i}\) is the integral area of bound water peak or free water peak; \(\Sigma\)Area is the total integral area of T\({}_{2}\) curve; WM is the water mass of the sample at each water absorption stage, which can be calculated by a dry basis; OW is the oven dry weight of the sample.
## 4 Uncertainty of the Geometrical Acoustic Prediction Models
### Absorption and Scattering Coefficients  
The predictive software considers \(\alpha_{\rm w}\) and s as input variables that have to be assigned to the surfaces of the model. Thus, it is important to evaluate the uncertainty (U) of the calculated values, due to the uncertainty of the absorption (U\(\alpha_{\rm w}\)) and scattering (Us) variables. These uncertainties were estimated to be higher than 0.05 and 0.15, respectively, as was found on the basis of the user's experience in [PERSON] [13] and [PERSON] et al. [49]. This case study considered only a few materials: stones and grass in particular. This allowed variations due to different \(\alpha_{\rm w}\) and s combinations regarding the cavea stone, which is the main surface considered in the model, to be investigated. To this aim, as shown in Figure 4b, twenty alternative materials were considered in both kinds of software, with \(\alpha_{\rm w}\) equal to 0.05, 0.10, 0.15, and 0.20, and with s equal to 0.25, 0.40, 0.55, 0.70, and 0.85. These values considered the possibility of having different degrees of damage on the steps of the cavea. In the case of the scattering coefficients of 0.85 [41], a perfectly preserved periodic triangular section with an angle of 45\({}^{\circ}\) has been considered, whereas in the case of scattering coefficient of 0.25, a heavily damaged cavea was represented.  
As suggested previously [50], the sensitivity coefficients were calculated in order to evaluate the uncertainty propagation. This evaluation was conducted considering the average simulation results of the 500 Hz and 1 kHz octave bands [2]. The variability of each simulated receiver was calculated, and no systematic effects were detected. Thus, the sensitivity coefficients were calculated considering the normalized values, with respect to the relevant average value. An appropriate mathematical model, based on linear regression, was defined so as to relate the simulated values of each acoustical parameter to the absorption and scattering coefficients [50; 51]. The expanded uncertainty was obtained as 2\(\sigma\), where \(\sigma\) is the standard deviation of the model [50].  
The expanded uncertainties for the O and C simulation software (U\({}_{\rm O}\) and U\({}_{\rm C}\)) are shown in Table 3. The uncertainty, due to the input variability of \(\alpha_{\rm w}\) and s, is lower than the JND for all the parameters, except for T\({}_{20}\) and C\({}_{80}\) when the C software is used. The lower uncertainty values are due to the software algorithm, which is less sensitive to variations in \(\alpha_{\rm w}\) and s.