esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatio-Temporal Variations of Ecosystem Water Use Efficiency and Its Drivers in Southwest China
## 3 Results  
### Spatial Distribution Characteristics of Ecosystem WUE, GPP, and ET  
The spatial distribution patterns of GPP, ET, and ecosystem WUE were analyzed in terms of three dimensions describing geospatial location: longitude, latitude, and elevation. As shown in Figure 2, the multi-year averages of GPP, ET, and ecosystem WUE were 2009.41 gC m\({}^{-2}\) yr\({}^{-1}\), 719.01 mm yr\({}^{-1}\), and 2.80 gC mm\({}^{-1}\) m\({}^{-2}\) yr\({}^{-1}\), respectively. The spatial distributions of GPP, ET, and ecosystem WUE were relatively similar, with multi-year averages showing distributions of high values in the south and low values in the north, and all increased with decreasing latitude, as illustrated in the statistical plots on the left of each panel in Figure 2.  
As demonstrated by the combination of Figures 1b and 2, the comparatively high values of GPP, ET, and ecosystem WUE are distributed in non-karst landform areas in Guangxi, southeastern Guizhou, and southern Yunnan. GPP, ET, and ecosystem WUE fluctuate in a \"W\" pattern with increasing longitude (Figure 2, bottom panel). The spatial distribution is influenced by geomorphology, with two low valley areas and numerous karst landforms distributed along the longitude.  
The combination of Figure 1b and the right panel of Figure 2 demonstrates that topographically, GPP, ET, and ecosystem WUE decreased with increasing elevation. As an explanation for this obvious phenomenon, the higher the altitude, the lower the temperature and precipitation, which are unfavorable for the growth, photosynthesis, and transpiration of vegetation; thus, the lower GPP, ET, and ecosystem WUE.  
Figure 2: Spatial patterns of multi-year mean values of (**a**) GPP, (**b**) ET, and (**c**) ecosystem WUE. The statistics of longitude, latitude, and elevation are presented in the bottom panel, left panel, and right panel, respectively.
# UAV Image Multi-Labeling with Data-Efficient Transformers
## 4 Experimental Results  
### Data Set Description  
In our experiments, two multi-label UAV datasets were used for evaluation: the Trento multi-label dataset and the Civezzano multi-label dataset. Some information about these datasets is summarized in Table 1, and samples from each dataset are shown in Figure 3.  
The Trento dataset consisted of UAV imagery acquired over the faculty of science of the University of Trento in Italy on 3 October 2011. The dataset was obtained using nadir acquisition performed with a Canon EOS 550D camera with a CMOS APS-C 18-megapixels sensor. The images of the dataset had the dimensions 224 \(\times\) 224 pixels, a ground sampling resolution of approximately 2 cm, and RGB spectral channels. The multi-label version was built upon the Trento single-label dataset, and it had 3415 images in total; 1000 were selected for training, and 2415 were kept for testing. The dataset was defined with 13 distinct class labels. These labels were: Asphalt, Grass, Tree, Pedestrian Crossing, Car, Person, Building Facade, Red roof, Dark roof, Vineyard, Solar Panel, Soil, and Shadow.  
The Civezzano multi-label dataset was a UAV dataset that has been acquired near the city of Civezzano in Italy, on 17 October 2012, at different off-nadir viewing angles. The acquisition was performed using a Canon EOS 550D picture camera equipped with a CMOS APS-C 18-megapixels sensor. Each UAV image had three channels (RGB) and a spatial resolution of 2 cm. The Civezzano dataset contained 3415 images, 1000 for training and 2415 for testing. The dataset's images were assigned to different 14 class labels: Asphalt, Grass, Tree, Vineyard, Low Vegetation, Car, Blue roof, White roof, Dark roof, Solar Panel, Building facade, Soil, Gravel, and Rocks.
## 2 Project overview and In-Situ Monitoring
### Pre-Support Arrangements  
The 60 m test section divided into two parts was set in the exit section of the New Badaling tunnel in the direction of Zhangjiakou, to explore the applicability of various pre-supports within the loess ground, as shown in Figure 2. From DK70+889 to DK70+859 and from DK70+859 to DK70+829, the pipe roofs and leading ductules were selected as the pre-supports, respectively. Subsequently, the deformation law of tunnel displacements, the GSSs and the effect of ground reinforcement under the two types of pre-supports are discussed. The specific design parameters of the pre-supports are as follows:  
(1) Pipe roof  
From DK70+889 to DK70+859, 30-m-long pipe roofs were selected as the pre-supports, constructed within 140\({}^{\circ}\) of the tunnel arch ahead of the tunnel face. The pipe roofs were composed of \(\varphi\)108 hot-rolled seamless steel pipes, 4-6 m long each and connected by thread. Pipe roofs were installed at an angle of 1\({}^{\circ}\) to 3\({}^{\circ}\) to the tunnel longitudinal axis, with circumferential space of 40 cm (Figure 6). Pipe roofs were combined with grouting with a grouting pressure of 0.5-2.0 MPa.  
(2) Leading ductule  
From DK70+859 to DK70+829, leading ductules with a composition of 4-m-long \(\varphi\)42 hot-rolled seamless steel were adopted as the pre-supports. They were installed at an angle of 10\({}^{\circ}\) to 15\({}^{\circ}\) to the tunnel longitudinal axis, with the circumferential spacing of 30 cm. A minimum lap length of 1.3 m was considered for the two adjacent rings (Figure 7). Leading ductules were cooperated by grouting with a grouting pressure of 0.5-1.0 MPa.  
Figure 5: Longitudinal perspective of three-bench seven-step excavation method.  
Figure 6: Schematic image of the pipe roof (unit: mm): (**a**) Cross section; (**b**) Longitudinal section.
###### Abstract  
Structural health monitoring (SHM) techniques are used to assess the behavior of structures during or after construction. The high cost of sensors is the main reason for the limited use of the SHM techniques. The present study investigates the dynamic behavior (dynamic acceleration, semi-static displacement, frequency and damping ratio) of highway steel plate girder bridges using strain measurements. The double filtration and polynomial prediction methods are used to estimate the dynamic behavior of the bridge using real-time strain measurements. To verify the accuracy of the developed method, the field monitoring measurements of the WonHyo bridge is used. The bridge behavior under different truck speeds and weights is observed and evaluated. The displacement and acceleration measurements are used to examine the results of the proposed method. The results of this study demonstrate that the strain measurements can be used to obtain an accurate semi-static displacement and dominant frequency content of the bridge. The accuracy of the developed model for the semi-static and dynamic behaviors is 99% and 69%, respectively.  
strain; wavelet; dynamic characteristics; monitoring; highway steel bridge +
Footnote †: journal: Applied Sciences
# Design of a Compressor Test Rig for Immobilization of the Stall Cell
## 3 Characteristics of the Compressor and Immobilization of the Stall Cell  
### Characteristics of the Compressor  
All the quantitative data were derived from pressure signals acquired by Rosemount Corporation's Model 3051S pressure transducers. These transducers ensure a total error of \(\pm\)0.025% at full scale, introducing an absolute error of \(\pm\)3 Pa within their full pressure range of \(\pm\)6.22 kPa. The compressor's characteristics were measured at three nominal rotating speeds and various rotating speed ratios. The average speed of the two rotors was used as the nominal rotating speed and for nondimensionalizing the rotating speeds. Figure 13 illustrates the characteristic curves, with the flow coefficient on the abscissa and the pressure rise coefficient on the ordinate axis, encompassing both un-stalled and stalled states. Different types of stalls were observed for different rotating speed ratios: decreasing rotor speed ratio advanced stall onset, widened hysteresis ring, and reduced post-stall pressure rise coefficient. At a speed ratio of 1.33, an abrupt stall occurred with a high post-stall pressure rise but a small hysteresis ring. Below a speed ratio of 1.17, the secondary stall was characterized by a progressive first stall followed by an abrupt secondary stall with a small post-stall pressure rise and an increased hysteresis ring size. This paper primarily focuses on the deep stall status (as depicted in Figure 14). The stall cell in this study looked to be stationary at a speed ratio close to 5:3, but it was still rotating slowly, suggesting that other speed ratios had been tried.  
Figure 12: The radial distribution scheme for experimental measurements.  
Figure 13: Characteristic curves of the compressor at different speed ratios.  
A fast-response pressure transducer was installed at the front of the first rotor to detect the rotating speed of the stall cell. The transducer, manufactured by All Sensors Corporation, has a full scale of \(\pm\)10 inch H2O (or 2.49 kPa) and a maximum error of \(\pm\)0.5% at full scale. Figure 14 illustrates the variation in the cell rotating speed versus the rotating speed ratios, indicating that the stall cell was immobilized at a rotating speed ratio close to 1.1, which confirms our hypothesis that an appropriate speed ratio between two rows can keep it stationary. However, this value is inconsistent with its designed one (5:3 or 1.67), suggesting that its actual rotating speed ratio with respect to the first rotor is 47.6% rather than 37.5%. This observation indicates that the rotor and stator exert distinct influences on the rotation speed of the stall cell. Remarkably, the rotor exhibits a more pronounced impact on the stall cell's rotation speed than the stator. Consequently, it becomes imperative to relatively augment the rear rotor's speed in order to effectively \"drag\" the stall cell, which tends to rotate in closer synchronization with the front rotor.
## 3 Results and Discussion
### Quantifying detection distance - a new methodology  
Both search and rescue and detection dog handlers who work dogs off-leash over large areas have observed and describe sudden changes in direction of the dog's nose followed by a subsequent change in the dog's direction of travel. This is believed to occur when the dog enters what could be envisioned as a line of detectable scent that the dog identifies as its trained target odor (Figure 3). Because this behavior was qualitatively described by search dog handlers but scientifically undocumented and because nothing was known about tortoise scent, it was unclear whether or not the methodology used in this study would allow us to obtain data for detection distance analyses. The use of GPS location data collected at the highest repeat increment possible (1-second) and the ability to differentially-correct the data to centimeter level accuracy of both the dog movement and the tortoise location was imperative to achieve accurate results. Even with WAAS enabled (Wide Area Augmentation System), uncorrected GPS data are not accurate enough to determine precise detection distances, primarily because both detection distances and the moment of change in direction towards the target source could be within the horizontal error of the GPS unit.  
A GPS that collects real-time differentially corrected data, which downloads both the GPS signals and the correction factors and then applies the geometric corrections internally is ideal although post processing data will also provide accurate track data. At present such GPS units are costly and are relatively large and heavy. To prevent physical injury to the dog from wearing a GPS the unit should be counter-balanced. This also keeps the GPS antennae pointed upward to maximize continuity in satellite reception. Dogs need to be comfortable with and accustomed to working while wearing equipment for detection distance data to yield valid results. Desensitizing working dogs to such equipment is simple and does not require much time. These fine details are important to ensure that the dogs are working uninhibited by equipment or experiencing distress. Either of these factors may affect their responses and thus affect the detection distance data.
# Enhanced Bit Repair IP Fast Reroute Mechanism for Rapid Network Recovery
## 5 Evaluation of the Enhanced B-REP FRR Mechanism
Therefore, when R5 receives another EB-REP packet with an inaccurate Bit-String value specified by R1 (the route over R5-R10-R15-R16). R5 analyses the Bit-String and it knows that R10 is dead, and so it must respond. Therefore, R5 becomes the new S (exS). R5 examines its EB-REP BT table and finds a new alternative route for the selected destination (192.168.3.2). The result from this process is the new route via R9-R14-R15-R16 specified by a new Bit-String = 1 0000 0001 0100 0100, and with a new next-hop (R9). As the next step, R5 replaces the old Bit-String value with a new one and reroutes the packet towards the next-hop R9 with EB-REP ID = 3 (Figure 16). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Output Interface** & **Destination IP** & **Bit-String Value** \\ \hline Eth2 & 192.168.3.0/24 & 1 0000 0001 0000 1010 (LSB) \\ Eth4 & 192.168.10.0/24 & 0 0100 0010 000 0100 (LSB) \\  &... &... \\ \hline \hline Eth—Ethernet. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Router R1—EB-REP Backup Table. Figure 15: The EB-REP mechanism—inserting Bit-String. Router ID: 10.0.0.109 | B-REP ID: 2 Packet with BitString received Analyzing BitString... A route has been found! Setting next-hop: 10.0.0.137 Output interface is DOWN Found route in B-REP BT to destination 192.168.3.2 Sending datagram with BitString: 1 0000 0001 0100 0100  
It is important to note that this redirection process (failure detection on the already repaired path) is a new feature of the EB-REP mechanism. R9 receives the EB-REP packet and processes it as the router R; i.e., R9 analyses the BS, selects a new next-hop, and sets its bit in the packet's Bit-String to zero (Figure 17). This marking ensures that the packet has been processed on the router, thus preventing the formation of a loop between routers. Finally, according to the carried Bit-String (1 0000 0001 0100 0100), R9 looks for a directly connected EB-REP next-hop and forwards the packet to the R14 next-hop. The packet is now routed and delivered through R14 and R15 to R16. R14 and R15 use the same EB-REP processing as R9. However, the situation at R16 is different. R16 receives the EB-REP packet with the Bit-String value of 1 0000 0000 0000. The last bit in the Bit-String, which is also the bit set to position R16, indicates that R16 is the target (or destination) of the rerouting process. R16 therefore removes the EB-REP BIER header and decapsulates the packet (Figure 18). This operation reverts the packet to its original form, and R16 routes the packet as classic unicast data via its unicast routing table. The packet is now forwarded to the destination. In Table 11, we may observe this behavior as the output from the OMNeT++ simulation console. Figure 16: Output from R5—rerouting process. Figure 17: Output from R9—the processing of an EB-REP packet. Figure 18: Output from R16—decapsulation process.
# Application of Compact Folded-Arms Square Open-Loop Resonator to Bandpass Filter Design
## 3 Microstrip Filter Design
### Coupling Coefficient and External Quality Factor Extraction  
The arrangement for the fifth-order bandpass filter theoretical coupling coefficients (\(k_{1,2}\), \(k_{2,3}\), \(k_{3,4}\), \(k_{4,5}\)) between adjacent resonators (R1, R2, R3, R4, R5) is shown in Figure 7, alongside the theoretical external quality factor (\(Q_{ext}\)) between each port and the corresponding adjacent resonator. The theoretical coupling coefficient values are calculated using Equation (7) [1], while the theoretical external quality factor value is determined based on Equation (8) [1]. The practical filter coupling coefficients and the practical \(Q_{ext}\) are determined using Equations (9) and (10), respectively, as well as the detailed techniques reported in [1]. The resonator layouts and the electromagnetic (EM) simulation responses for obtaining the practical coupling coefficients, \(k\), and the practical \(Q_{ext}\) are shown in Figure 8.  
\[k_{d,d+1}=\
icefrac{{FBW}}{{\sqrt{g_{d}g_{d+1}}}}\ ;\ d=1,2,3,\ 2\ldots \tag{7}\]  
\[Q_{ext}=\
icefrac{{g_{0}g_{1}}}{{FBW}} \tag{8}\]  
\[k=\left(f_{2}^{2}-f_{1}^{2}\right)\bigg{/}\left(f_{2}^{2}+f_{1}^{2}\right) \tag{9}\]  
\[Q_{ext}=\
icefrac{{f_{0}}}{{f_{2}}}-f_{1} \tag{10}\]  
Figure 6: Practical dimensions of the proposed folded-arms square open–loop resonator at 2.2 GHz and the EM simulation response (all dimensions in mm).  
Figure 7: Fifth-order bandpass filter coupling coefficients and external quality factor arrangement (\(k_{1,2}=k_{4,5}=0.087\), \(k_{2,3}=k_{3,4}=0.064\), \(Q_{ext}=9.714\)).
# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 5 Conclusions and Future Research Directions
### Data Analytics  
The sensor network for IoT on the ground and patrolling UAVs will collect the data and forward it to the data management center using the cellular network. The data from multiple sensor nodes on the ground should be aggregated and properly structured using extract, transform, and load (ETL) approaches. Novel data predication models and visualization techniques should be studied to analyze and forecast the rate and direction of fire spread. A real-time data recommendation system should be proposed and the results can be made available for the wildfire management team and general public through our developed mobile application.
# An Overview of GEO Satellite Communication Simulation Systems
## 2 Coverage Analysis
### Data-Driven Methods  
Though geometric models and simulation tools like STK provide accurate assessments, they struggle with the complexity of intelligent and flexible beam control. As satellite systems become more dynamic and adaptive, data-driven methods are increasingly applied to enhance efficiency and accuracy in these challenging scenarios. For example, by leveraging the spatial clustering of user terminals, an adaptive multi-beam pattern and footprint plan were proposed in [18] where spot beams with flexible size and position were designed, thus enhancing the adaptability of high-throughput satellite systems. Unlike traditional fixed multi-beam pattern and footprint planning, it addresses the spatial and temporal heterogeneity of user demand, particularly in mobile broadband scenarios.
# Geometrical Calibration of a 2.5D Periapical Radiography System
###### Abstract  
The objective of this study was to develop a geometrical calibration method applicable to the 2.5D prototype Periapical Radiography System and estimate component position errors. A two-steel-ball phantom with a precisely known position was placed in front of a digital X-ray sensor for two-stage calibration. In the first stage, the following three parameters were estimated: (1) r, the distance between the focal spot and the rotation axis of the X-ray tube; (2) \(\psi\), the included angle between the straight line formed by the X-ray tube's focal spot and rotation axis and the straight line of the orthogonal sensor; and (3) L\({}_{4}\), the distance between the rotation axis and the plane where the two steel balls were positioned. In the second stage, the steel balls' positions were determined to calculate the positions of the X-ray tube on the x, y, and z axes. Computer simulation was used to verify the accuracy of the calibration method. The results indicate that for the calibration approach proposed in this study, the differences between the estimated errors and setting errors were smaller than 0.15% in the first and second stages, which is highly accurate, verifying its applicability to accurate calibration of the 2.5D Periapical Radiography System.  
geometric calibration; periapical radiography; computed tomosynthesis; quasi-3D imaging; 2.5D 202019006; doi:10.3390/app10030906
###### Abstract  
Pharmaceuticals are characterized by a wide range of physical, chemical, and biological properties and functionalities that contribute to their inherent complexity as compounds. Unfortunately, human carelessness during the production, use, and disposal of these compounds results in their presence in the environment. This study utilized a nanostructured TiO\({}_{2}\) film on a glass ring at the bottom of a reactor and simulated a solar radiation lamp as the radiation source for both photocatalytic and photolytic experiments, with the aim of unraveling the mechanism behind the degradation of trimethoprim (TMP), a pharmaceutical compound. This approach provides a novel perspective on the role of TiO\({}_{2}\) in the degradation of pharmaceuticals and could pave the way for more efficient and sustainable wastewater treatment methods. Scavenger studies were carried out using isopropanol, ammonium oxalate, and triethanolamine to examine the photocatalytic mechanism. Isopropanol and triethanolamine were found to impede the photocatalytic degradation of TMP, highlighting the significance of hydroxyl radicals and positive holes in the degradation process, while no inhibition was observed in the presence of ammonium oxalate. The complete degradation of TMP through photocatalysis under simulated solar radiation was observed in ultra-pure water in fewer than 3 h, as indicated by the results. Our findings suggest that utilizing natural solar radiation as a source of UV-A radiation in reactor configurations based on this approach holds promise for cost-effective pharmaceutical degradation treatment in wastewater treatment plants. The practical potential of this approach is supported by the results obtained under simulated solar radiation with an irradiation intensity in the UV-A region of 33 \(\pm\) 2 W/m\({}^{2}\).  
trimethoprim; photolysis; photocatalysis; simulated solar radiation; degradation mechanisms; degradation products; TiO\({}_{2}\)