esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Based on the measured soil and meteorological data from the National Meteorological Station in Henan Township, Wushen Banner, Ordos City, Inner Mongolia, China, we artificially generate the dynamic data of water, vapor, and heat transport as the \"true\" measurements. These data, after adding random disturbances, are used as objective variables to inversely estimate the hydrothermal parameters of the coupled water-vapor-heat model, and to evaluate the contribution of the different variables to the inversion results. Furthermore, considering the cost-effectiveness of field survey data, we believe that inverting hydrological parameters such as water content and temperature is the most practical approach. Since TDR can simultaneously measure both of these data types, we further analyzed the variations in water flux, vapor flux, and heat flux under these conditions. The rest of the paper is structured as follows: Section 2 introduces the study area, the coupled water-vapor-heat model, and the multi-objective optimization algorithm. Sections 3 and 4 show the experimental results and analyze the simulation performance, respectively. Finally, Section 5 summarizes the full text and proposes the basis for selecting data when inverting the parameters.
## 3 RR-FRC Under Chloride Ingress
### Corrosion Initiation and Corrosion Propagation in RR-FRC
#### 3.1.3 Other Parameters Influencing Corrosion Initiation and Propagation
[PERSON] et al. [66] performed numerical simulations to investigate the impact of fibres on the service life of concrete structures in a case study of a bridge edge beam exposed to de-icing salts in Sweden. Two different reinforcement layouts were considered per concrete type. On the one hand, this was done in order to consider the improved post-cracking behaviour of the fibres and, on the other hand, in order to investigate the impact of reinforcement detailing (spacing and bar diameter) on the cracking process and subsequent chloride-induced corrosion. The diffusion of chloride solution through uncracked concrete was modelled on [PERSON]'s second law of diffusion, whereas corrosion initiation in the cracked regions was modelled on the work by [PERSON] and [PERSON] [91]. The study used the DIANA 10.3 [92] finite element analysis software to model corrosion-induced cracking. The authors observed a correlation between \(w\) and the time required to initiate corrosion (same initiation time in uncracked regions and shorter initiation for wider cracks in all scenarios). Assuming the same corrosion rate in the propagation phase and the loss of load-bearing strength of 15% as a criterion when the structure cannot be used anymore, the authors concluded that the service life of the beam is increased by 58% to 94% in RR-SFRC (\(V_{\text{f}}=0.5\%\)) and by 104% to 154% in RR-SFRC (\(V_{\text{f}}=1.0\%\)) compared to the RC reference beams. [PERSON] et al. [82] applied electrical potential (3 V) to accelerate chloride-induced corrosion. Corrosion-induced cracks first appeared in RC elements after 27 weeks, followed by cracks in RR-PEFRC after 36 weeks, whereas no corrosion-induced cracks were observed in RR-HyFRC. After exposure, rebar was extracted, and the loss of mass and corroded area (expressed as corroded surface area divided by the total surface area of the bar) were measured. Rebar in the RC specimen was observed to be fully corroded, whereas for RR-PEFRC, the corroded area equalled 65.4%; for RR-HyFRC, it was only 11.8%. In descending order, the rebar mass loss was 69.9 g (RC), 20.0 g (RR-PEFRC) and only 1.0 g (RR-HyFRC). [PERSON] et al. [89] investigated the effects of recycled-polyethylene fibres (RPEF) on the chloride corrosion initiation and propagation. In this study, four different RR-RPEFRC were compared to the reference RC specimens. The authors observed corrosion initiation on the first day for all concrete types. Over the duration of the experiment, the values of \(i_{\text{corr}}\) showed a decreasing trend in the case of RC (ultimately indicating negligible corrosion rate), whereas specimens with fibres showed a high-to-very-high corrosion rate, with more severe corrosion in instances where long fibres were used. The authors attributed this to the random orientation of fibres in the concrete matrix and to the recycled-polyethylene fibres being susceptible to water absorption, thus potentially increasing the overall ingress of chlorides.
###### Abstract  
Recognizing aircraft automatically by using satellite images has different applications in both the civil and military sectors. However, due to the complexity and variety of the foreground and background of the analyzed images, it remains challenging to obtain a suitable representation of aircraft for identification. Many studies and solutions have been presented in the literature, but only a few studies have suggested handling the issue using semantic image segmentation techniques due to the lack of publicly labeled datasets. With the advancement of CNNs, researchers have presented some CNN architectures, such as U-Net, which has the ability to obtain very good performance using a small training dataset. The U-Net architecture has received much attention for segmenting 2D and 3D biomedical images and has been recognized to be highly successful for pixel-wise satellite image classification. In this paper, we propose a binary image segmentation model to recognize aircraft by exploiting and adopting the U-Net architecture for remote sensing satellite images. The proposed model does not require a significant amount of labeled data and alleviates the need for manual aircraft feature extraction. The public dense labeling remote sensing dataset is used to perform the experiments and measure the robustness and performance of the proposed model. The mean IoU and pixel accuracy are adopted as metrics to assess the obtained results. The results in the testing dataset indicate that the proposed model can achieve a 95.08% mean IoU and a pixel accuracy of 98.24%.  
remote; image; segmentation; aircraft; deep learning; ensemble; U-Net
# Ion Exchange MIEX(r) GOLD Resin as a Promising Sorbent for the Removal of PFAS Compounds
## 3 Results and Discussion  
### Characterization of MIEX(r) GOLD  
In this study, MIEX(r) GOLD resin was used as an ion exchange resin to remove several PFAS compounds from water. The morphological and chemical properties of MIEX(r) GOLD resin were studied via SEM imaging, zeta potential measurement, and FTIR analysis.  
Figure 1a,b represent SEM images of MIEX(r) GOLD resin at different magnifications. It can be seen that resin particles have irregular spherical shapes with rough surfaces. The particle size varies in a range of 100-250 um, and this finding is in agreement with the supplied product detail, i.e., mean particle size of 180-250 um.  
The zeta potential values of MIEX(r) GOLD resin over the pH range of 3-11 are presented in Figure 2a. The resins possess a positive surface charge over the whole pH range with decreasing surface charge above pH 6. This is attributed to the main functional groups of the resin being quaternary amines whose zeta potential value decreases with increasing pH.  
MIEX(r) GOLD resin is a chemically macroporous methacrylic strong base anion exchange resin. Typically, magnetic ion exchange resin shows characteristic peaks of tertiary ammonium, alkyl, and -CH\({}_{2}\) groups [22; 23; 24; 25]. The FTIR spectrum of MIEX(r) GOLD resin (Figure 2b) shows noticeable peaks at 1160 and 1720 cm\({}^{-1}\) from the stretching of C-O and C=O groups [26]. Other characteristic peaks of the resin were noticeable at 1476 cm\({}^{-1}\) from C-H symmetric bending (attached to the quaternary ammonium), 1387 cm\({}^{-1}\) due to CH\({}_{3}\) symmetric bending, 1240 cm\({}^{-1}\) from C-N stretching, and 967 cm\({}^{-1}\) due to CH\({}_{2}\) rocking [22]. In addition, a characteristic peak at 1636 cm\({}^{-1}\) was observed from the C=C skeleton vibration of the benzene ring [23; 26].  
Figure 1: SEM images of MIEX® GOLD resin at (**a**) low and (**b**) high magnifications.
## 3 State of the Art
### Non-RF-Based WuRs  
Previously, our attention was directed toward RF-based WuRs. However, RF signals are not viable in some settings. In industrial underwater environments, the use of acoustic waves is preferred as their absorption rate is three orders of magnitude lower than that of electromagnetic waves [56]. Acoustic wake-up receivers intended for underwater use have been proposed, providing energy-saving benefits for battery-powered nodes that are difficult to access [57]. This technology has potential for the long-term monitoring of wildlife, particularly whales, based on their vocalizations [58].  
Optical technology can also be used for this purpose. A performance comparable to those achieved using RF-based wake-up receivers has been attained [59] through the use of infrared wake-up, which functions on the same principle as how a TV remote awakens a TV.
## 3 Network Model
The link set \(E\) at a particular time instant \(t\) may be more appropriately denoted by \(E\left(t\right)\) to emphasize the temporal dependence. In this article, we drop \(\left(t\right)\) for convenience. Figure 1: The architecture of an integrated remote wireless sensor and satellite network for emergency scenarios. Without loss of generality, we further assume that each node \(n\), where \(n\in N\), can transmit at a maximum data rate \(R_{n}\left(t\right)\) bit/s at time \(t\) over a common information channel. It is immaterial to the capacity result if the channel is broken into several sub-channels of capacity \(R_{n}^{\left(1\right)}\left(t\right),R_{n}^{\left(2\right)}\left(t\right), \cdots,R_{n}^{\left(M\right)}\left(t\right)\) bit/s, as long as \(\sum_{m=1}^{M}R_{n}^{\left(m\right)}\left(t\right)=R_{n}\left(t\right)\). With this assumption, we can ignore some unconcerned physical layer details and focus on the topological aspects of the network that determine the capacity. Thus, our results can be readily extended to incorporate the situation where each link has a different and known capacity.
# Acoustic Voice Analysis as a Tool for Assessing Nasal Obstruction: A Systematic Review
## 3 Results
### Using Voice to Assess Nasal Obstruction
#### 3.2.2 Subjective Scales: Perceptual Voice Evaluation and PROMs  
This section presents a summary of the findings of studies that evaluated voice using subjective tools, including the GIRBAS scale, the Voice Handicap Index (VHI), and the Visual Analogue Scale (VAS), to assess nasal obstruction and its modification after therapeutic measures (mainly surgical).  
GIRBAS The GIRBAS scale is a clinician-rated tool designed for the perceptual assessment of voice quality [47]. In one study [19], the assessment of patients undergoing surgery for bilateral nasal polyposis revealed no significant differences between preoperative and postoperative scores.  
Voice handicap index (VHI) The VHI is a widely used tool that relies on patient reports to assess the effects of voice disorders in functional, physical, and emotional domains [48]. In one study [21], Group A exhibited a marked improvement in VHI scores postoperatively, although statistical significance was not indicated; additionally, no statistically significant changes were noted in Group B. Another study [22] indicated statistically significant reductions in VHI-30 scores at one and three months after surgery for nasal obstruction. Other studies [23; 25] have documented a statistically significant improvement in VHI only in a selected group of patients after surgery for nasal obstruction.  
Visual analogue scale (VAS) In a study [19], patient satisfaction regarding voice quality after surgery for nasal obstruction showed a progressive increase over time (\(p<0.05\)). In another study [24], the right- and left-sided nasal obstruction degree VAS values of the study group were significantly higher than those of the controls (\(p<0.01\)).  
Reported voice change after surgery In one study [27], following septoplasty, 61% of patients with septal deviation reported no change in their voice, 36% reported improvement, and 3% experienced deterioration.
## 1 Introduction
Finally, the metasurface is fabricated and measured, and the experimental measurement results show that our designed metasurface can effectively generate multiple pseudo Bessel beams. The proposed method and device have shown their effectiveness, which provide a new way for the application of one-to-many WPT or wireless communication.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 4 Discussion
### Applications of Flexibility Quantification
#### 4.1.3 DR Program Development and Implementation  
One of the key challenges associated with integrating DR with distribution grid operation is estimating the level of flexibility accurately [72]. Although one of the key goals of quantifying demand flexibility is to reduce system and customer costs [8, 9, 10, 11, 34, 38, 43, 44, 45, 51, 54, 58], inaccurate estimations can lead to increased costs at various levels. Overestimating potential demand reduction could result in a mismatch between load and generation, leading to higher operational costs as the utility would be forced to revert to more expensive generation resources to meet demand. On the other hand, underestimating potential demand reduction may result in curtailing more demand than necessary, which results in financial losses for the electric utility and inconvenience for end users.  
Most current DR programs implemented for residential customers are of the DLC type, where, during times of need, the electric utility remotely and directly turns off certain loads--often A/C units--typically with little to no coordination with the customer. The benefit of DLC is that the amount of demand reduction can be predicted with high accuracy, making it a reliable and dispatchable resource. However, many building appliances are not compatible with DLC due to technical limitations, privacy concerns, or user convenience issues [5]. This introduces a missed opportunity for both the utility and the customers. As modern buildings continue to be equipped with advanced home energy management systems (HEMS), opportunities are emerging for utilities to implement more advanced DR programs in which customers (or their HEMS units) decide to comply with a DR event or conversely, to opt out. While this creates possibilities to include a more diverse set of appliances and, hence, more potential for flexibility, it introduces unique challenges related to predicting what households will comply with the DR event and by how much.  
Demand flexibility models can inform the development and implementation of DR programs in various ways. As noted in the papers reviewed, by providing more accurate forecasts, DR events can be rolled out in a more targeted and localized fashion, with higher reliability. These forecasts can be used to determine optimal incentive structures that maximize customer participation while minimizing the financial burden on the utility [25; 48]. Lastly, these models can be used by electric utilities in customer targeting [1; 24; 25; 27; 31; 34; 45; 50; 51; 63]. Flexibility analysis can indicate which users are generally more flexible than others, what appliances are more reliable for achieving the targeted demand reduction, and what times are best to dispatch DR in which parts of the grid. This information can assist DR program managers in targeting customers more efficiently and in a more localized fashion.
## 4 Simulation Results and Analysis
### Dynamic Localization Performance  
Figure 13 illustrates the system's performance in tracking a robot moving in a circular path. The actual path (blue line) forms a perfect circle, centered at (1, 1) with a radius of 0.5 m. Estimated positions (colored dots) generally follow the circular path, demonstrating the system's ability to track dynamic movement. The color gradient of the estimated positions provides insight into the error distribution during motion. Lighter colors (lower errors) seem to dominate, indicating generally good tracking performance. Some areas of the circle show darker colored estimates, suggesting higher errors. These could be points where the robot's motion aligns unfavorably with the beacon arrangement. The beacons (blue dots at the corners) provide context for understanding how their positioning affects the tracking accuracy at different points along the path.  
Figure 12: Distribution of localization errors. Blue histogram bars show error frequency.  
Figure 13: Robot localization during circular motion. The blue line represents the actual path, while colored dots show estimated positions. The color gradient indicates the magnitude of error, with the yellow color representing larger errors and the blue color smaller errors. Red circles indicate beacon positions at (0, 0), (2, 0), and (1, 0).  
Figure 14 complements the circular motion analysis by showing how the localization error varies with the position of the robot. The error plot shows a periodic pattern, corresponding to the robot's revolutions around the circular path. The shape of the measured circle is due to the robot changing position slightly between measurements.
# CoAR: Congestion-Aware Routing Protocol for Low Power and Lossy Networks for IoT Applications
## 5 Performance Evaluation
### Impacts of Varying Number of Nodes  
In this section, we measure the different dimensions of LLNs to test the scalability of the protocol. The performance of the protocols varies according to the network size. In this regard, we analyze the influence of variation in network size. For this, we vary the number of deployed nodes in the network from 16 to 41. The traffic load to each network is kept at 480 packets per minute. The rest of the parameters remain the same.  
Figure 9(a) shows that CoAR maintains relatively low packet loss as compared to other protocols. With the growth of network size, more traffic flows can inject into the network and lead to congestion. Nevertheless, CoAR can change their congested parents to keep the queue utilization lower and bypass congested path, so that the lower packet loss rates are achieved. In contrast, with the increasing number of nodes, the packet loss ratio in RPL is higher than ECRM and CoAR. Because the default objective function in RPL cannot change the parents suffering from severe congestion. On the other hand, the packet loss ratio in ECRM marginally improved as compared to RPL; however, it is still not satisfactory for the proposed application scenario. As a result, at the network size of 16 and 41, CoAR outperforms RPL by 43.7% and 51.8% respectively.  
As the number of nodes increases in the network, CoAR gets the opportunity to explore more alternate parents to forward a large number of packets to the destination and thus improves the throughput. Figure 9(b) shows the inclination in PRR with an increased number of nodes in the network. When there are 26 and 41 nodes in the network, the maximum PRR of the protocols is approximately 74.6% and 48.2% in CoAR, 48.3% and 27.7% in ECRM, and 16.4% and 10.7% in RPL, respectively. For a varying number of nodes, CoAR maintains the average of 23.2% and 48.7% better performance than ECRM and RPL, respectively.  
As depicted in Figure 9(c), for the increased number of nodes, CoAR improves overall throughput as compared to the protocols. When the network size is higher, the network throughput achieved by CoAR is 8.12% greater than ECRM. Unlike the protocols, CoAR dynamically set the \(A_{\tau}\) and allows the affected nodes to avoid persistent congestion situation. Besides this, \(NI\) metrics check the nodes vicinity during the parent selection, especially when network size is increased. Therefore, we notice that as the number of nodes increases, the CoAR performs better because of its resource control mechanism can operate properly. The RPL also marginally improved, but its performance is very low in terms of satisfying the application scenarios.  
Figure 9: Network energy consumption.
# Capturing a Space Target Using a Flexible Space Robot
## 2 Dynamic Model of Space Robot
Based on the single direction recursive construction method, we can write the configuration velocity of the system as [34]  
\[\mathbf{v}=\mathbf{G}\dot{\mathbf{y}},\ \dot{\mathbf{v}}=\mathbf{G}\ddot{\mathbf{y}}+\mathbf{g}\overset{ \overset{\mathbf{\Lambda}}}{\mathbf{I}}_{N} \tag{4}\]  
where \(\mathbf{y}\in\Re^{N}\) is the independent generalized coordinate vector of the robot system, composed of the generalized coordinates of the robot base (\(B_{1}\)), the joint coordinates of the manipulator, the modal coordinates of the flexible links \(B_{2}\) and \(B_{3}\), and the generalized coordinates of \(B_{6}\) and \(B_{7}\). The generalized coordinates of the robot base contain three translational ones and three rotational ones, eight joint coordinates of the manipulator, six modal coordinates of \(B_{2}\), and six modal coordinates of \(B_{3}\). Therefore, the number of independent generalized coordinates of the robot system is twenty-six, namely \(N=26\). The parameters \(\mathbf{G},\mathbf{g}\), and \(\overset{\mathbf{\Lambda}}{\mathbf{I}}_{N}\) in Equation (4) are all constant matrices or vectors, and their expressions can be found in Ref. [30]. Using Equations (3) and (4), Equation (3) can be written as  
\[\Delta\dot{\mathbf{y}}^{\text{T}}[-\mathbf{Z}\ddot{\mathbf{y}}-\mathbf{z}+\mathbf{h}+\mathbf{f}^{\mathbf{y }}]=0 \tag{5}\]  
where \(\mathbf{Z}=\mathbf{G}^{\text{T}}\mathbf{M}\mathbf{G}\), \(\mathbf{z}=\mathbf{G}^{\text{T}}(\mathbf{f}^{\omega}+\mathbf{f}^{\mu}+\mathbf{M}\overset{\mathbf{ \Lambda}}{\mathbf{g}\overset{\mathbf{\Lambda}}{\mathbf{I}}_{N}})\) and \(\mathbf{h}=\mathbf{G}^{\text{T}}\mathbf{f}^{\rho}\), and \(\mathbf{f}^{\mathbf{y}}\) is the vector of the generalized force of the system. Since all the elements of \(\mathbf{y}\) are independent, Equation (5) becomes  
\[-\mathbf{Z}\ddot{\mathbf{y}}-\mathbf{z}+\mathbf{h}+\mathbf{f}^{\mathbf{y}}=0 \tag{6}\]  
This formula is the dynamic equation of the flexible space robot system. We have established the dynamic model for this system, for details please see Ref. [34].
# The Improved A* Algorithm for Quadrotor UAVs under Forest Obstacle Avoidance Path Planning
## 2 Materials and Methods
### Improved A* Algorithm
#### 2.2.4 Smoothing Based on Quasi-Uniform Cubic B-Spline Curves  
After removing the turning points, the smoothness of the path will be improved, and the number of turning points, total turn angle, and path length will be reduced, but the path is not yet better smooth due to the presence of some necessary corners.  
B-spline is a special manifestation of the spline curve, which can flexibly smooth the path by taking advantage of its merits, such as convex hull, geometric invariance, local support, and non-negativity [47] to better adapt to various actual trajectory requirements.  
To ensure that the optimized path encompasses both the starting and target points while maintaining continuous smoothness, this paper utilizes quasi-uniform cubic B-spline  
Figure 2: (**a**) Schematic diagram of local optimum; and (**b**) strategies for removing redundant turning points.  
curves to smooth the path. However, in practical applications, excessive smoothness may lead to collisions with obstacles. To mitigate this issue, the search range of control points is constrained, ensuring that the path after quasi-uniform cubic B-spline curve processing remains close to the path after removing redundant turning points. To achieve this, the paper sets the search radius of the control points accordingly.  
\[R\leq 0.6r \tag{7}\]  
where \(R\) is the search radius of the control points and \(r\) is the grid grain size.