esa-sceva/satcom-chunk-collection huggingface dataset:
# Phase Center Corrections for BDS IGSO and MEO Satellites in IGb14 and IGSR3 Frame
## 3 Data Processing
Therefore, in this study, the constraints of PCOs with 10.0, 0.1, 10.0 m for X-PCO, Y-PCO and Z-PCO were set to improve the stability of estimates. The PCOs and PCVs for BDS satellites were estimated in two steps. First, with fixing the PCOs to the values in jgs14_2101.atx, we calculated the daily raw PCVs for BDS IGSO and MEO satellites. The daily raw PCVs were then converted to daily smooth PCVs. Afterwards, the series of daily PCVs were converted to satellite-specific as well as satellite-block-specific PCVs depending on the satellite manufacturers and types. With the estimated satellite-block-specific PCVs,  
Figure 3: Distribution of the ground stations used for estimating PCOs and phase center variations (PCVs) of BDS IGSO and MEO satellites. Red circles indicate International GNSS Monitoring and Assessment System (iGMAS) stations, whereas the black ones represent International GNSS Service (IGS) multi-GNSS experiment (MGEX) stations. the daily PCO parameters of each BDS satellite were estimated within IGb14 and IGSR3 frame. The daily PCOs were then converted to satellite-specific PCOs. Table 3 summarizes the strategy used in this study. It has already been revealed that the scatter of horizontal PCO estimates, particularly for X-PCO, depends on the \(\beta\)-angles [6]. Generally, the larger the \(\beta\)-angles, the greater the scatter. This could be attributed to the correlations between X-PCO and orbital parameters. In previous research performed by [PERSON] et al. [21] and [PERSON] et al. [29], the impacts of the solar radiation pressure (SRP) model on the horizontal PCO estimates have already been investigated and analyzed. They concluded that the 5-parameter CODE Extended Orbit Model (ECOM1) with a prior SRP model was suitable for X-PCO estimation. Hence, in this study, ECOM1 was used with a prior SRP model developed based on a similar approach used for SRP modeling of BDS GEO satellites [28].
# Fourier Transform (FT) Analysis of the Stress as a Tool to Follow the Fatigue Behavior of Metals
## 5 Strain Sweep Behavior  
To follow the dependence of the linear and nonlinear mechanical parameters on the applied strain, strain sweep tests were performed (the strain amplitude was changed, but \(R\) was kept constant). As shown in Figure 3, the three materials investigated show a \(\mid\)\(E^{*}\)\(\mid\) decrease at strain amplitudes larger than \(\varepsilon_{0}\) = 0.04%. For \(\varepsilon_{0}\) \(<\) 0.04%, the intensities of the higher harmonics decrease with an exponent of around \(-1\), as expected for experimental noise (motor and/or force transducer) and can be neglected, while the \(I_{3/1}\) intensity scales quadratically with \(\varepsilon_{0}\) above \(\varepsilon_{0}\) = 0.04% before leveling off. Additionally, the \(I_{2/1}\) intensity is always much lower than the \(I_{3/1}\) value.  
Figure 2: Fourier spectrum of the force response for (**a**) AL, (**b**) CRS and (**c**) brass at a frequency of 5 Hz and a strain amplitude of \(\varepsilon_{0}\) = 0.16%.
## 5 Interoperability, Standardization and Security  
The vision of fully autonomous, scalable irrigation management hinges on the integration of diverse components within the system. This necessitates addressing interoperability, ensuring that sensors, actuators, controllers, and software platforms can communicate and exchange data effectively, regardless of their origins or underlying technologies [127]. Standardization complements this by providing a common framework for data formats, protocols, and interfaces, fostering consistency and compatibility across the entire irrigation management pipeline. The lack of widespread interoperability and standardization has presented a significant roadblock to the adoption of automated irrigation systems [15]. The prevalence of proprietary protocols and data formats creates difficulties in integrating components from various vendors into a unified system [128]. This fragmentation leads to elevated costs, increased complexity, and limited functionality. Table 5 summarizes the key data transfer and interoperability protocols used in irrigation systems, along with their key features and typical applications. Interoperability in precision agriculture encompasses a range of protocols and standards, addressing everything from low-level sensor data transmission to high-level data exchange between farm management systems. The adoption of these technologies in real-world irrigation projects has demonstrated their potential to enable integration and unlock the benefits of precision agriculture. Several key solutions have emerged to promote interoperability and standardization in this field. At the data exchange level, agroXML offers a standardized format that facilitates communication between diverse software applications and platforms within the agricultural ecosystem [140]. This standardization allows automated irrigation systems to seamlessly share data with farm management software and other precision agriculture technologies [128]. Complementing this, ISOBUS (ISO 11783) provides a standardized communication protocol for agricultural machinery and implements. This protocol enables the integration of automated irrigation systems with other equipment, supporting advanced practices such as variable rate irrigation [24]. For efficient data transmission in IoT-based irrigation systems, the message queuing telemetry transport (MQTT) has become a popular choice [129]. This lightweight messaging protocol ensures reliable communication between devices and servers, crucial for real-time irrigation management [129]. Building on this, the OGC SensorThings API offers a RESTful interface for real-time sensor data access and management, facilitating seamless integration with web-based platforms and applications [22]. Finally, SensorML provides a standardized method for describing sensor characteristics and observations. This standardization potentially enables the easy integration of data from various sensor types into automated irrigation systems, further enhancing the interoperability of precision agriculture technologies [141].
## 3 Results
### Response of Vegetation Coverage to LULC Change
#### 3.3.2 Contribution Rate Index  
The contribution rate index indicates that the contribution of vegetation coverage changes in regions where farmland, forest land, water bodies, and built-up land remain unchanged is greater than the transfer of corresponding LULC types to other types (Figure 8a). The contribution rate of cultivated land change to vegetation coverage change is \(-24.837\%\) to \(22.1\%\) (mean \(-0.597\%\) to \(2.372\%\)), while the contribution rate of cultivated land transfer to water bodies (\(-0.116\%\)) and built-up land (\(-0.597\%\)) is lower than \(0\%\), indicating the deterioration of vegetation coverage. Among all LULC transfer types, the region with unchanged forest land has the largest variation in contributions to vegetation coverage change (\(-34.614\%\) to \(50.16\%\)), with the highest average of \(8.414\%\). The contribution rate of forest land transfer to other LULC types and vegetation coverage change is relatively small. The contribution rate of grassland change to vegetation coverage change is \(-16.239\%\) to  
Figure 7: The distribution of vegetation coverage responses to LULC changes. (**a**) Distribution of NDVI change from 2000 to 2020. (**b**) Distribution of LULC transfer from 2000 to 2020. (**c**) Range and mean NDVI changes under different land-use transfers, where the upper and lower ends of the column represent the range of NDVI changes and the blue points represent the mean NDVI changes.  
16.902% (mean 0.035% to 4.82%). The contribution rate of water body changes to vegetation coverage changes is \(-\)13.446% to 22.747% (mean \(-\)1.063% to 1.306%). The contribution rate of changes in built-up land to vegetation coverage is \(-\)23.305% to 21.641% (mean \(-\)0.066% to 0.756%). The contribution rate of unused land change to vegetation coverage change is \(-\)4.368% to 7.177% (mean \(-\)0.19% to 2.173%).  
The net contribution rate index shows that there are differences in the net contribution rate of different LULC change types to vegetation coverage change (Figure 8b). The net contribution rate of cultivated land change to vegetation coverage change is \(-\)3.086% to 4.628% (mean \(-\)0.39%), while the net contribution rate of forest land change to vegetation coverage change has the largest variation range (\(-\)38.903% to 23.144%), but the average is the lowest (\(-\)6.179%). The net contribution rate of grassland change to vegetation coverage change is smaller than that of forest land (\(-\)16.73% to 17.85%), but the average is the highest (3.805%). The net contribution rate of water body changes to vegetation coverage change is \(-\)10.996% to 2.858% (mean \(-\)0.292%). The net contribution rate of built-up land change to vegetation cover change has the smallest range (1.162% to 3.669%, mean 2.428%), all of which are positive values. The net contribution rate of unused land change to vegetation cover change is \(-\)9.922% to 14.82% (mean 3.309%).
## 6 Discussion
### The Dip Angle of Peripheral Faults  
At the Calderas of Sierra Negra and Alcedo volcanoes, the geometry of the fault is derived from forward modeling of the surface deformation. The results demonstrated that peripheral faults have subvertical angles. However, the detailed results remain unclear and, in some cases, controversial. For example, at the caldera of Sierra Negra, field observations and InSAR data from the 1997-1998 trapdoor faulting event indicate that the trapdoor faults are mainly south-dipping normal faults [13]. Conversely, GPS and InSAR data from the 2005 faulting event suggest that the faults are north-dipping reverse faults [37].  
In this study, seismic interpretation, kinematic modeling, and limit analysis were used to constrain the geometry of the peripheral faults. The seismic interpretation shows that the emplacement of saucer-shaped mafic sills can induce peripheral faulting and that the fault is subvertical. The dip angle of the fault is not accurately constrained by the seismic profile, perhaps due to the seismic resolution. The results of limit analysis and kinematic models are fairly consistent, both indicating that the fault has a dip angle of 90\({}^{\circ}\). The results are also similar to the seismic observations. Therefore, it is suggested that the real dip angle of the peripheral fault is 90\({}^{\circ}\). It can also be concluded that kinematic models and limit analysis are effective methods for studying the geometry of peripheral faults.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Column Mobility Model (CMM)
#### 6.1.3 Throughput  
Figure 4 provides a comprehensive depiction of how varying numbers and percentages of mobile and malicious nodes influence throughput in mobile networks using the CMM mobility model, both with and without the Hello Flood attack conditions. Each line on the graph represents a specific percentage of mobile nodes, ranging from 10% to 40%, and delineates the impact on throughput. A consistent observation is the trend of decreasing throughput as the number of nodes increases, indicating diminished data transmission efficiency with higher network density. Additionally, the presence of attacker nodes exacerbates this decline, resulting in even lower throughput values. Notably, the highest throughput is observed in scenarios without the attack, characterized by 10% mobile nodes and 10 total nodes. Conversely, the lowest throughput is observed in scenarios with the Hello Flood attack, particularly with 40% mobile attacker nodes and 60 total nodes. This disparity underscores the significant influence of both network size and malicious activity on throughput performance in mobile networks.  
The CMM has a notable impact on decreasing throughput in network environments due to various factors. The CMM's structured mobility patterns, where nodes move in columns or groups, can lead to congestion in specific areas as nodes converge, intensifying competition for communication resources and resulting in increased packet collisions and retransmissions, ultimately diminishing throughput. Moreover, the dynamic movement inherent to the CMM induces frequent changes in network topology, necessitating continual route recalculations that destabilize routing paths, leading to suboptimal routing and prolonged packet delivery times, further reducing throughput. Additionally, as nodes converge within columns, network resources like bandwidth and processing power may become strained, leading to bottlenecks and queuing delays that hamper overall throughput. Furthermore, the boundary handling mechanisms used by the CMM add extra delays to packet transmission, especially when nodes move between boundary transitions, which makes the drop in throughput even worse. Along with the CMM's effects, the Hello Flood attack makes the drop in throughput even worse by flooding the network with too much traffic, oversaturating communication channels, and using up all of the network's resources. This makes congestion, routing instability, and delays worse, ultimately lowering throughput even more.  
Figure 4: Throughput without and with attack using CMM.
## 1 Introduction  
GPR is shallow geophysical detection equipment based on the transmission and reception of high-frequency electromagnetic pulse waves. Because of the difference in the electrical parameters of the subsurface medium, we can analyze and infer the structural and physical property characteristics of the medium [1; 2; 3; 4]. GPR is also a non-destructive testing method for pile foundation, which has the advantages of high detection efficiency and accuracy [5; 6]. It is difficult to detect some potential damage hidden in the pile foundation using a low strain testing method. GPR can be used to retest and provide an accurate analysis to ensure the engineering quality of pile foundation.  
At present, research on GPR mainly focuses on the following directions: [PERSON] applied the method of reverse-time prestack depth migration to amplitude reconstruction in complex environments [7]. [PERSON] and [PERSON] used GPR to detect a near-surface geothermal river valley using contemporary data decomposition techniques [8]. [PERSON] et al. proposed a novel method to remove GPR background noise based on the similarity of non-neighboring regions [9]. [PERSON] et al. proposed a prediction algorithm for data analysis in GPR-based surveys [10]. [PERSON] and [PERSON] used time-reversal-based processing techniques to enhance GPR's detection ability of targets [11]. [PERSON] et al. applied the curvelet transform for clutter and noise removal in GPR data [12]. [PERSON] et al. investigated the dielectric properties of a railway ballast using different GPR antennas and frequency systems [13]. [PERSON] and [PERSON] studied the shearlet transform to remove noise suppression and direct wave arrivals in GPR data [14]. In some special places, three-dimensional GPR images can provide a more intuitive interpretation [15]. [PERSON] and [PERSON] developed an analytic approach to estimate asphalt pavement thickness using 3-D ground-penetrating radar [16]. [PERSON] and [PERSON] researched the application of GPR in detecting deeply embedded reinforcing bars in pile foundation [17].  
The literature review above shows that the applications of GPR mainly include geological engineering investigations, disaster geology surveys, foundation detection, military applications, archaeological and environmental pollution investigations, highway and tunnel engineering detection, as well as other fields. However, there are limited studies on its application in non-destructive testing of U-shaped pile foundation. The integrity of the pile is a very important part of the whole project quality. According to the theory of electromagnetic wave propagation of GPR, this georadar can be used to investigate the pile quality within a certain range of pile length. When the pile length is not more than 30 m, the receiving antenna can receive the effective radar echo signal.
# Throughput Enhancement in Downlink MU-MIMO Using Multiple Dimensions
## 3 Proposed Scheme
### Received SINR  
In this subsection, signal to interference plus noise ratio (SINR) for the symbols in a superposed signal is represented to consider the performance of proposed scheme. If it is assumed that the two symbols are superposed in one superposed signal, i.e., \(s_{i}=\sqrt{P_{1}}\hat{s}_{i,1}+\sqrt{P_{2}}\hat{s}_{i,1}\), the received signal at the \(i\)-th receiving antenna for each user after eliminating MUI can be defined as follows,  
\[y_{i}=\lambda_{i}(\sqrt{P_{1}}\hat{s}_{i,1}+\sqrt{P_{2}}\hat{s}_{i,2}), \tag{14}\]  
where \(i\) is received antenna index, \(\lambda_{i}\) is a channel gain in one MIMO parallel channel. In this case, \(P_{1}<P_{2}\) subject to total power constraint. Therefore, \(\hat{s}_{i,1}\) is a weak symbol and \(\hat{s}_{i,2}\) is a strong symbol. Then the received SINR for the strong symbol is defined as follows,  
\[\text{SINR}_{\hat{s}_{2}}=\frac{\lambda_{i}P_{2}}{\sigma^{2}+\lambda_{i}P_{1}}. \tag{15}\]  
The weak symbol can be decoded by employing SIC. If it assumed that perfect SIC decoding is conducted, the received SINR for weak symbol is defined as follows,  
\[\text{SINR}_{\hat{s}_{1}}=\frac{\lambda_{i}P_{1}}{\sigma^{2}}. \tag{16}\]  
In the proposed scheme, SINR shows two negative effects in terms of system throughput. First, the weak symbol acts as an interference for received SINR of the strong symbol. As shown in Equation (15), \(\lambda_{i}P_{2}\) term is considered as noise. Second, strong symbols is likely be decoded incorrectly.If a strong symbol is not properly decoded, it can adversely affect the decoding of weak symbol. The negative effects for the system throughput are restated in the simulation result. Although there are some degradations in terms of throughput on exploiting the power domain, the system throughput is increased since the number of transmit symbols is linearly increased at high SNR.
## 4 Result and Discussion  
For convenience, the three processing schemes are marked as IFCB0, IFCB1 and IFCB2, respectively, in Table 5. The 70-day observations for all station are divided equally into seven sessions and 70 sessions to assess PPP-based time transfer performance. In total, there are approximately 266 and 2730 time transfer tests used in the experiment. The GLONASS PPP-based performance in terms of time transfer uncertainty is evaluated at the 68% and 95% confidence levels in the static mode. This statistic approach has been adopted by [PERSON] et al. [34] and [PERSON] et al. [13] for assessing PPP performance. The process of the application of the experiments is divided into four part. (1) Evaluating IFCBs with GLONASS-only PPP; (2) GLONASS PPP time transfer with identical receivers or mixed receivers are analyzed; (3) The performance of GLONASS PPP for different end users are presented; (4) The performances of the GLONASS PPPs with different IGS ACs products are investigated for different IFCB handling schemes.  
\begin{table}
\begin{tabular}{c c} \hline
**Items** & **Descriptions** \\ \hline Number of stations & 39 \\ \hline Estimator & Least squares (LSQ) estimator \\ \hline Sampling rate & 300 s \\ \hline Elevation cut-off & \(10^{\circ}\) \\ \hline Observation weighing & Sin 2(e) \\ \hline Phase wind-up & Corrected [29] \\ \hline \multirow{4}{*}{Topospheric delay} & ZHD: corrected with global pressure and temperature (GPT) [30] model \\  & using the formulas of [PERSON] [31] \\ \cline{2-3}  & ZWD: estimated as a continuous piecewise linear function (2 h parameter spacing), GMF mapping function [32] \\ \hline Tidal displacements & IERS conventions 2010 [33] \\ \hline Relativistic effect & Corrected \\ \hline Sagnac effect & Corrected \\ \hline Satellite antenna FCOs and PCVs & Fixed to the values from ig80\_adv \\ \hline Receiver clock & Estimated \\ \hline Station coordinates & Estimated as static or fixed \\ \hline Phase ambiguities & Estimated, constant for each continuous arc, float value \\ \hline GLONASS IFCBs & Constant for each handle scheme \\ \hline \end{tabular}
\end{table}
Table 4: Summary of GPS- and GLONASS-PPP processing strategies.
## 2 Proposed Thermal Model
### Simplified Lumped Parameter Thermal Model
As shown in Figure 3, both radial and axial heat transfer paths inside the motor are taken into consideration, and minimum thermal nodes are allowed to model the real thermal flow. The axial heat path mainly considers the thermal flow from the rotor node to the end cap node, which is conducted through the motor shaft. The radial heat path consists of the rotor node, stator node, casing node, and coolant node. The heat source, thermal resistance, and thermal capacity are marked in Figure 3 to indicate how the thermal model is developed according to the motor structure. According to the radial and axial heat flow of modelled motor, a five-node LPTN is proposed, which is shown in Figure 4. \(T_{W}\), \(T_{S}\), \(T_{R}\), \(T_{E}\), and \(T_{F}\) represent the temperature of the coolant, motor stator, motor rotor, end cap, and motor casing, respectively; \(R_{W\!F}\), \(R_{F\!S}\), \(R_{SR}\), \(R_{ER}\), and \(R_{EF}\) are the thermal resistance between the coolant and the motor casing, the thermal resistance between the motor casing and the motor stator, the thermal resistance between the motor stator and the motor rotor, the thermal resistance between the motor rotor and the endcap, and the thermal resistance between the endcap and the motor casing, respectively; \(C_{S}\), \(C_{R}\), and \(C_{E}\) represent the motor stator heat capacity, motor end cap heat capacity, respectively; \(P_{S}\) and \(P_{R}\) are the loss of the stator and the rotor of the motor, respectively. Figure 4: Proposed thermal network model. Figure 3: Radial and axial heat flow of the modelled motor. Figure 2: Cross section of modelled motor.
## 4 Design Architecture
### Distance Comparison Unit
#### Processing Elements  
The central components of the design are a generic number of processing elements, working in a systolic array. For the interconnection of the two signatures in the design and their associated indices, the data streams themself and shift ports are required. When the shift signal of the static signature is set, the signature and corresponding index is valid and stored locally in the PE. The previously stored value and index are transmitted to the next PE in the systolic array. The procedure is the same for the dynamic signature. In addition, a valid shift signal also triggers the calculation of the distance between the stored static and incoming dynamic signature.  
The results are output simultaneously with the indexes of the signatures that produced the result. Currently, the size of one result is 64-bit, where 10-bit are used for the result and 27-bit each are used to store the index of the input signatures.  
Figure 2: Architecture of the distance comparison units.  
This design allows for the calculation function to be adjusted to any comparison between two binary strings, while the rest of the design can remain unchanged.