esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Development and Validation
### Material Constitutive Models
#### 3.3.3 Damping Interlayer Constitutive Models  
Mooney-Rivlin model was adopted to simulate the rubber. In the Mooney-Rivlin model, the strain energy density function W is a linear combination of two invariants of the left Cauchy-Green deformation tensor. The strain energy density function for an incompressible Mooney-Rivlin material is as follows:  
\[\mathrm{W}\ =\ \mathrm{C}_{1}\big{(}\overline{\mathrm{I}}_{1}-3\big{)}+ \mathrm{C}_{2}\big{(}\overline{\mathrm{I}}_{2}-3\big{)} \tag{7}\]  
where \(\mathrm{C}_{1}\) and \(\mathrm{C}_{2}\) are empirically determined material constants, and \(\overline{\mathrm{I}}_{1}\) and \(\overline{\mathrm{I}}_{2}\) are the first and the second invariant of the deviatoric component of the left Cauchy-Green deformation tensor.  
It has been confirmed that material constants of the Mooney-Rivlin model are related to the linear elastic shear modulus G, which can be calculated as follows:  
\[\mathrm{G}\ =\ 2(\mathrm{C}_{1}+\mathrm{C}_{2}) \tag{8}\]  
It can be found from the strain contours of the FE model of the SDC wall (in Section 4) that during the axial deformation, the maximum strain of rubber parts (the rubber layer and rubber rolls) is less than 80%. According to the reference [44], compared with other constitutive models for rubber (such as the Neo-Hookean model and Ogden model), Mooney-Rovlin model is more appropriate for modeling the behavior of high damping rubber (such as chloroprene rubber) in small strain ranges.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 2 UAV Tracking System Overview  
A UAV tracking antenna system as depicted in Figure 2 which consists of the antenna subunit and the system subunit. The antenna subunit comprises of the antenna, the steering block, motors, gears, communication link, and power detectors. Meanwhile, the system subunit comprises of elevation, azimuth, the microprocessor, and signal detection system.
## 2 Case Studies and Operation Modes of the System
### Case Studies  
The system performance is assessed for three different case studies corresponding to three different climatic zones (cold, average and hot climate) with different buildings:  
* Case #1: A passive house located in Helsinki with a \"low-temperature heating\" distribution system and a PCM with a phase transition at 64 \({}^{\circ}\)C
* Case #2: A low energy building located in Strasbourg with an \"intermediate-temperature heating\" distribution system and a PCM with a phase transition at 64 \({}^{\circ}\)C
* Case #3: A refurbished building located in Athens with an \"intermediate-temperature heating\" system and a PCM with a phase transition at 64 \({}^{\circ}\)C  
To analyze and compare different scenarios, the size of the R32 air source HP was fixed while the sizes of the buildings--more precisely, the number of apartments--were varied. This approach has the advantage that the costs for the HP are fixed for each scenario. In order to calculate scalable heating, cooling, and DHW demands for each case study, a standard apartment with the following general constraints was defined:  
* The ground floor of the standard apartment is a square with 75 m\({}^{2}\) and the room height is 3 m.
* Two outer walls are considered which are oriented to the south and the west. The U-values for the walls are 0.09, 0.11, and 3 Wm\({}^{-2}\) K\({}^{-1}\) for the buildings located in Helsinki, Strasbourg, and Athens, respectively
* The wall (south and west) to window ratio is 20% and the U-values for the windows are 0.75, 0.9, and 5 Wm\({}^{-2}\) K\({}^{-1}\) for the buildings located in Helsinki, Strasbourg, and Athens, respectively.  
* If cooling is needed, the windows are shaded with a solar radiation transmittance of 15%.
* The ventilation rate is 0.8 m\({}^{3}\)m\({}^{-2}\)h\({}^{-1}\)
* The air heat recovery efficiency is 75% for the passive house located in Helsinki
* The heat gains from lights and equipment are 5 Wm\({}^{-2}\)
* The DHW consumption of each full-scale apartment was 5.845 kWh, which is comparable to a medium water consumption as defined in [18].
* The apartments were scaled to multiples of 1/4 of the full-scale. The DHW storage devices were considered in full-scale for each apartment.
* Fresh water for the decentralized DHW storage devices is provided at 12 \({}^{\circ}\)C.  
The heating and cooling demands for Cases #1-#3 were calculated with the aid of AITs Building Model Generator [19; 20]. Dependent on the assumed heating distribution system (low or medium temperature) and the part load ratio of the HP, the inlet temperatures to the heating/cooling system water were varied considering a linear dependence on the part load ratio. The heating, cooling and DHW demands, together with further assumptions considering the HPs, are summarized in Table 1.
## 4 An Engineering Application
### Analysis of the Pipeline Settlement Displacement
#### 4.2.2 Determination of Calculated Parameters
On the basis of the introduction above, GX05 is the coordinate origin and the distance between GX03 and GX05 is 40 m. The inclinometer hole data of CX4 in the III period of construction are as shown in Figure 16.  
Based on the research results of [PERSON] et al., it can be assumed that the ground settlement area is equal to the lateral area of the enclosure structure in this study [7]. Although this assumption may have some errors with actual conditions, under the conditions of standard construction during excavation, the error is relatively small. Using the inclinometer hole data of CX4, the lateral area of the enclosure structure is \(S_{\text{w}}=19.22\,\times\,10^{4}\) mm\({}^{2}\). The maximum settlement of CX4 can be calculated as  
\[ \begin{array}{l}\delta_{\text{max}}=\frac{2S_{\text{w}}}{z_{0}}=\frac{2 \times 19.22\times 10^{4}}{18,490}\\ =20.79\text{mm}\end{array} \]  
The ground settlement curve within the subsidence area is defined as triangular distribution, and the distance between the measuring point GX03 and edge of foundation pit is 11,209 mm. Thus, the settlement of GX03 can be expressed as  
\[ \begin{array}{l}\delta_{\text{GX03}}=\frac{20.79\times(18,490-11,209)}{18,490 }\\ =8.19\text{mm}\end{array} \]  
According to Equation (3), the ground settlement corresponding to the center of pipeline \(\delta\) can be calculated as  
\[\delta=\frac{8.19}{\cos\left(\frac{T}{2\times 150}\times 40\right)}=8.96\text{mm}\]  
Similarly, the settlements in the II and IV periods of construction are 6.88 mm and 5.02 mm, respectively.
## 4 Discussion
### Apparent Reflectance Spectral Distortion Analysis and Exploitation
Thus, while the remaining polarisation effects are expected to be within 1% of the threshold limit established by the FLEX mission [22], other effects such as stray-light contamination can deserve more attention when the observed image presents bright objects. In this respect, to make the apparent reflectance exploitation a solid strategy to improve the accuracy of the atmospheric correction process, future work should also consider the spectral distortions generated by each of these instrumental effects. In addition, this study assumed a Lambertian surface reflectance to create the simulated DB. Future work should therefore consider using a non-Lambertian surface reflectance. A more extensive analysis should also ascertain the most suitable regression algorithm to avoid local minima or divergent solutions. In addition, with respect to the use of a dimensionality reduction technique, it is necessary to determine which is the best method and the optimal number of components to be considered that keep the key spectral features intact. As a final remark, the regression algorithm was trained and validated here for the O\({}_{2}\)-A and O\({}_{2}\)-B bands separately. In the future, the refinement of the atmospheric correction by using both regions simultaneously to disentangle atmospheric distortions should also be investigated. For instance, while spectral changes in the scattering phase function are typically smooth, others such as the stray-light contamination will probably become stronger in the O\({}_{2}\)-A band than in the O\({}_{2}\)-B due to its dependency on the radiance spectral contrast. To the best of our knowledge, the use of the apparent reflectance is proposed here for the first time as a quality indicator. Although this opens up possibilities to implement the quality check into the processing of the FLEX/Sentinel-3 tandem mission, it requires a more in-depth inspection. Therefore, follow-up analyses are foreseen by using the FLEX End-to-End simulator [43], which has the versatility to assess the impact of all of the proposed improvements and subsequently quantify the impact on the estimated SIF.
## 2 System Structures  
### System Solution  
The overview of the proposed framework is described in Figure 1, which is a loosely coupled IMU/GNSS/LiDAR integrated navigation system based on factor graph optimization. The main structure of the system is constructed by IMU measurements, while GNSS and LiDAR provide auxiliary information to correct state variables.  
The navigation system can be divided into three parts: (1) Factor construction: the IMU pre-integration factors and IMU bias factors are constructed based on IMU measurements, and the pseudo-range difference algorithm and iterative closest point algorithm (ICP) are applied to deal with GNSS and LiDAR measurements. (2) Nonlinear optimization: the factor graph framework is constructed based on modelled factors, and the ISAM2 sliding window algorithm is applied for filtering. The proposed IMMFGO algorithm can change the weights of corresponding sensors to achieve an adaptive optimization function. The IMMFGO includes parts of the input interactions of models, smoothing and fusion of models, and probability update of models, while the system will finally output the navigation states. (3) Multi-stage fault detection, isolation, and recovery: the results of IMMFGO and the navigation state will be used for system fault detection, isolation, and recovery. The system will detect faults based on the results of IMMFGO and IMU pre-integration measurements, respectively. The detection results determine whether to isolate or restore the fault sensor, and the factor graph navigation framework will be reconstructed on time. The frames and the notations used in the system are defined as follows:  
* The coordinate of IMU is defined as \(\{\cdot\}^{\text{B}}\), which is set at the center of the IMU sensor and is the same as the body-fixed coordinate;
* The GNSS frame is defined as \(\{\cdot\}^{\text{G}}\), which is set as the WGS-84 coordinate, and the measurement of GNSS is transferred into a navigation coordinate by the transform matrix \(\mathbf{R}_{\text{G}}^{\text{W}}\);
* The LiDAR frame is defined as \(\{\cdot\}^{\text{L}}\), which is set at the center of the LiDAR sensor, and the transform matrix from the LiDAR coordinate into the navigation coordinate is \(\mathbf{R}_{\text{L}}^{\text{W}}\);  
Figure 1: Overview of the proposed method.  
* The world frame is the ENU frame and is defined as \(\{\cdot\}^{\text{W}}\), which is set at the initial position of the vehicle and is assumed to coincide with the initial LiDAR frame.
## 3 Q2: What Are the Challenges of the Current UIoT System
### Channel Characteristics of UIoT
#### 3.1.1 Underwater Channel  
Unlike terrestrial IoT, UIoT nodes typically communicate via acoustic, optical, RF and MI channels [27]. This results in long propagation delay, high battery consumption, high error rate, etc. Moreover, the behavior of each channel's characteristics is different in the UIoT environment [2; 3; 4]. For example, the bandwidth of the acoustic channel is only a small percentage when compared to the RF channel [28]. Furthermore, due to the open characteristics of this UIoT environment, the attackers can easily inject the malicious node and steal the data or hack the communication channel [29].
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 1 Introduction
Traditionally, geographically weighted regression (GWR) [49] is used to model spatial problems; however, due to its linear nature, the model cannot perform very well if the underlying relationships are non-linear [50]. This study aims to address the two above-mentioned issues. We propose to employ the geographically weighted random forest (GWRFR) model to predict crop yield based on different feature sets. GWRFR has two advantages over other models: (1) it has a non-linear nature due to the underlying random forest algorithm, and (2) the spatial nature of the GWRFR can explicitly model spatial problems [51]. To the best of our knowledge, little research has been conducted on the use of GWRFR in corn yield prediction. Moreover, a systematic comparison of the predicted yield derived from GWRFR with those derived from other non-spatial algorithms has not been reported in previous research. This study also employs a wide range of predictors to derive the best set of features to predict crop yield, with an emphasis on reducing the number of features. Specifically, we use the following five sets of features to study the impacts of feature selection on model performance: (1) full length features; (2) vegetation indices; (3) gross primary production (GPP); (4) climate data; and (5) soil data. Lastly, the usage of GPP as a predictor for crop yield with machine learning has rarely been studied. We use GWRFR to predict county-level corn yield with different sets of features and compare the results with the following five popular machine learning algorithms: multiple linear regression (MLR), partial least square regression (PLSR), support vector regression (SVR), decision tree regression (DTR), and random forests regression (RFR) to address the following research questions:  
1. Can GWRFR derive more accurate results in corn yield prediction in the US Corn Belt than other machine learning models? 2. How does feature selection affect the performance of machine learning models in county-level corn yield prediction? The remainder of this article is organized as follows. Section 2 includes the details about the study area, data, and methods used in this research. Section 3 presents the results, and Section 4 provides a further discussion on the results. The conclusions of this study are included in Section 5.
## 4 Parameter Estimation
### Proposed \(\kappa\) Estimator
#### 4.1.2 Estimated \(\kappa\) in Each Frame  
Under direct sound absence hypothesis \(H_{0}(l)\), Equation (4) becomes \(\lambda_{z}(k,l)=\lambda_{r}(k,l)\), and substituting it into Equation (6) yields Equation (15)  
\[\lambda_{z}(k,l)=\exp\Bigl{\{}\tfrac{-13.8R}{T_{0}(k)f_{s}}\Bigr{\}}[(1-\kappa )\lambda_{r}(k,l-1)+\kappa\lambda_{z}(k,l-1)]. \tag{15}\]After some algebra, Equation (15) can be rewritten as Equation (16)  
\[\kappa=\frac{\exp\Bigl{\{}\frac{13.8R}{T_{60}(k)f_{s}}\Bigr{\}}\lambda_{z}(k,l)- \lambda_{r}(k,l-1)}{\lambda_{z}(k,l-1)-\lambda_{r}(k,l-1)}. \tag{16}\]  
Then, the estimated \(\kappa\) in the \(l\)th frame is determined in Equation (17) by averaging Equation (16) in the frequency domain  
\[\widehat{\kappa}(l)=\frac{\sum\limits_{k=0}^{K-1}\left[\exp\Bigl{\{}\frac{13. 8R}{T_{60}(k)f_{s}}\Bigr{\}}\lambda_{z}(k,l)-\lambda_{r}(k,l-1)\Bigr{]}\right.}{ \sum\limits_{k=0}^{K-1}\left[\lambda_{z}(k,l-1)-\lambda_{r}(k,l-1)\right]}. \tag{17}\]  
Note that the numerator and the denominator of Equation (16) are separately averaged in order to avoid division by zero.  
Equation (17) is similar to the conventional estimator Equation (18) [9]. However, Equation (17) is derived under direct sound absence hypothesis using Equation (6). Hence, the proposed estimator using a probability-based framework to update \(\kappa\), rather than a simple heuristic used in conventional estimator. Further comparison can be found in Section 5.  
\[\widehat{\kappa}(l)=\frac{\exp\Bigl{\{}\frac{13.8R\kappa_{N}}{T_{60}(k)f_{s}} \Bigr{\}}\sum\limits_{k=0}^{K-1}\lambda_{z}(k,l)-\sum\limits_{k=0}^{K-1} \lambda_{l}(k,l-N_{\epsilon})}{\sum\limits_{k=0}^{K-1}\lambda_{z}(k,l-N_{ \epsilon})-\sum\limits_{k=0}^{K-1}\lambda_{l}(k,l-N_{\epsilon})}. \tag{18}\]
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 1 Introduction
### Conventional OFDM Channel-Estimation Methods
In contrast, minimum mean square error (MMSE) estimation [20; 21] typically shows better performance compared to the LS in a noisy environment, since it takes into consideration the channel statistics along with noise variance. However, in real-time underwater environments, these statistics are not always available or easily estimable.
# A Novel Magnetic Equivalent Circuit Model for the Corner of Inductor Core
## 2 Modeling and Analysis of Corner  
### Geometry of Square Core-Type Inductor  
In this paper, a symmetric square core-type inductor is designed, as shown in Figure 1, which uses Fe-Si power \(26\mu\)[18] material and contains four blocks of \(23\) mm \(\times 30\) mm \(\times 90\) mm in the core. Since the power core material is used, the core does not contain functional air gaps as a steel core inductor, in which the reluctance of the core is the main factor of the inductance. The geometry parameters are shown in Table 1. In the square inductor, there are four windings, a total of 40 turns, and each winding has 10 turns evenly distributed over four limbs, such that the four core limbs have even and identical flux distribution, and the corner has symmetrical flux distribution. This makes it easy to study the flux density distribution of the corner. In addition, the corners and the joints between the corner and limb account for 40% of the entire core volume, of which reluctance significantly contributes to the inductance calculation. Combined with the above geometric characteristics, the square inductor is a good model for studying the features of the corner.
[PERSON] [25] experimentally investigated the flow field around the caudal fin using an elastically deformable flat plate that simulates the self-propelled motion of aquatic organisms. As a representation of geometric shape, the three types of caudal fin with different trailing edges and different aspect ratios were selected and the velocity field measurements by PIV were performed at different forcing frequencies around the resonance. It was observed that the average velocity distribution of the caudal fin induced a jet-like flow in the wake of the caudal fin, which shows the generation of thrust. For the maximum value of thrust production, it was confirmed that the flapping frequency remains in the higher frequency region and did not match the structural resonant frequency. Motivated by the earlier observations, we extend the experimental work of [PERSON] et al. on a basic model of square-shaped flat plate in a flapping motion using three-dimensional numerical simulation for the fluid structure interaction of a flexible plate [25]. In the experimental work by [1], the authors reported that even using simple flexible foils and actuation only in heave, complex wake flows can be generated that strongly resemble fish locomotor wake patterns. A square flat plate is desirable as it allows the complex fluid structure interaction problems to be investigated. For solving the fixed and flexible boundaries, the coupled fluid structure interaction methods, which provide mathematically and computationally efficient algorithms, have been recently presented by some authors [26; 27]. In the current study, simulations are conducted with a solver provided in open-source CFD software OpenFOAM. The objective of this study is to ascertain the reliability and accuracy of the current methodology in three-dimensional analysis of fluid structure interaction of a square-shaped flexible plate. A quantitative analysis of thrust force generated by the plate, which is experimentally difficult to explore, is performed over a range of flapping frequencies. The mechanism of thrust generation is explained by analyzing the pressure distributions around the flapping plate and the relative deformation of trailing edge. Comparisons are made between the three different flapping frequencies present around the structural resonance to gain insight into the mechanism of the associated thrust generation.
## 3 Materials
### WiFi and Mesh Networks in Rural IoT  
One of the main challenges in rural IoT deployments is the lack of reliable connectivity. Unlike urban areas with high-speed networks and stable WiFi coverage, rural regions often lack even basic infrastructure. This directly impacts the implementation of monitoring systems, smart agriculture applications, and environmental sensing networks that depend on stable data transmission.  
In these scenarios, WiFi and mesh networks emerge as viable alternatives. WiFi, which uses the IEEE 802.11 standard [23], is characterized by its wide availability, low latency, and high data rates. However, in practice, it has some important limitations in rural environments. Its range is usually limited between 30 and 100 m [24] and the need for a continuous power supply can become a problem in areas that rely on solar or battery power [25]. Even with these limitations, WiFi can be a viable option in rural contexts where devices are concentrated in a well-managed area with access to power and network infrastructure.  
To extend the effective range of WiFi networks and improve resilience, mesh networks distribute communication across multiple nodes, allowing dynamic routing and broader coverage in hard-to-reach areas. This architecture is especially valuable in rural scenarios where infrastructure is sparse or geographically dispersed. Additionally, mesh networks support incremental expansion with minimal reconfiguration and are often compatible with low-power devices [26].  
Furthermore, hybrid approaches are becoming increasingly relevant. Integrating mesh networks with LoRaWAN [27; 28] or Zigbee [29; 30] enables connectivity across different distances and power constraints, combining the reliability of local mesh infrastructure with the range and efficiency of long-range and low-power technologies.  
Overall, while WiFi alone may not be optimal for all rural IoT scenarios, combining it with mesh topologies and other communication protocols provides a solid foundation for practical and scalable IoT deployments in these environments.