esa-sceva/satcom-chunk-collection huggingface dataset:
# Region Pixel Voting Network (RPVNet) for 6D Pose Estimation from Monocular Image
## 2 Materials and Methods
### Model of the Problem
#### 2.1.2 Equivalence between Pose and Camera External Parameter  
Let us first review the pinhole model for monocular camera. Considering a point \(P\), its coordinates in WCS and CCS (camera coordinate system) are \(\mathbf{P}_{w}\) and \(\mathbf{P}_{c}\), respectively. Then,  
\[\mathbf{P}_{c}=\mathbf{R}_{c}^{w}\cdot\mathbf{P}_{w}+\mathbf{T}_{c}^{w} \tag{1}\]  
If \((x_{p},y_{p})\) is the coordinate of the projection point of P on the picture under the image coordinate system (ICS), given the intrinsic parameter \(K\), \((x_{p},y_{p})\) can be expressed as:  
\[\begin{pmatrix}x_{p}\\ y_{p}\\ 0\end{pmatrix}=\frac{1}{z_{c}}K\cdot\mathbf{P}_{c}\text{, where }z_{c}\text{ is z coordinate of }\mathbf{P}_{c}\text{ and }K=\begin{bmatrix}f_{x}&0&c_{x}\\ 0&f_{y}&c_{y}\\ 0&0&1\end{bmatrix} \tag{2}\]  
\([\mathbf{R}_{c}^{w}|\mathbf{T}_{c}^{w}]\) is known as the external parameter.  
According to Definition 2, if the monocular camera coordinate system CCS coincides with _Model_ and the WCS coincides with the OCS, then the object's pose \([\mathbf{R}_{m}^{o}|\mathbf{T}_{m}^{o}]\) in _Model_ is equivalent to the camera external parameter \([\mathbf{R}_{c}^{w}|\mathbf{T}_{c}^{w}]\). Thus, the following corollary can be drawn:  
**Corollary 1**.: _Given the object's model and the camera's external parameter \([\mathbf{R}_{c}^{w}|\mathbf{T}_{c}^{w}]\), then, for point \(P\) on the object, the coordinate of its projection in the image is:_  
\[\begin{pmatrix}x_{p}\\ y_{p}\\ 1\end{pmatrix}=\frac{1}{z_{c}}K\cdot(\mathbf{R}_{c}^{w}\cdot\mathbf{P}_{m}+\mathbf{T}_{c}^{w}) \tag{3}\]
# Fine-Grained Boundary Conditions in Field-Based Routing
###### Abstract  
In the realm of industrial wireless mesh networks, an efficient routing protocol is highly demanded to play a crucial role in ensuring that packets are efficiently directed along shorter and congestion-free routes toward gateways. Field-based routing has emerged as a promising solution to tackle these network challenges. This routing approach draws inspiration from physics and employs a differential equation to model its behavior in finding efficient routes. Given the fundamental significance of boundary conditions in physics, where they play an essential role in shaping the solutions to the equation, exploring the impact of boundary conditions on field-based routing behavior within network domains becomes highly significant. However, despite their influence, the impact of boundary conditions has remained unexplored in existing studies on field-based routing. In this context, our work explores the boundary condition problem and introduces new advanced fine-grained boundary conditions for field-based routing. We demonstrate the superior performance of our proposed scheme, highlighting the substantial role of boundary conditions in network behavior. Our work holds significant value in that it explores the boundary condition problem, an aspect largely overlooked in previous research, and provides a viable solution, underscoring its crucial importance in routing enhancement.  
Keywords:industrial wireless mesh network; field-based routing; boundary condition; virtual node +
Footnote †: journal:
# Modelling the Microphone-Related Timbral Brightness of Record Signals
## 2 Ratings of Brightness
### Recording Stimuli  
The selected microphones were set up in a circular array of 150 mm diameter, as shown in Figure 1, in an ITU-R BS 1116 compliant listening room [10] (which has an acoustic that is dry but not anechoic). All sources (instruments and musicians) were positioned 1.5-2 m from the array, no closer than ten times the array size. This setup has been shown previously by the authors to ensure that the perceived differences between the recordings made using any two of the microphones are predominantly due to the differing characteristics of those microphones, rather than due to their differing positions in the array [9].  
All microphones were connected to a Presonus Digimax FS microphone preamplifier feeding an RME Fireface 800 audio interface. Audio was captured with a sample rate of 44.1 kHz and a bit-depth of 24 bits. The MEMS microphones were supplied with 2.7 V power and recorded through the instrument inputs of the preamplifier due to their high output impedance. To equalise differing microphone output levels, pink noise was reproduced at a measured level of 74 dB\({}_{SPL}\) at the microphone array through a Genelec 1032 loudspeaker positioned 1.5 m directly in front of the array. The input gain on each of the ten preamplifier channels was then adjusted to give a digital signal level of \(-\)36 dBFS at the audio interface.  
Recording 5 sources using 10 microphones provided 50 stimuli for rating. This number is in the upper half of the range of numbers of stimuli employed in the brightness-related perceptual studies cited in Section 3.1 (range 16-72, median 32, mean 37). All stimuli are publicly available in the data archive referenced in the Data Availability Statement at the end of this paper.
# Design of Tracking, Telemetry, Command (TT&C) and Data Transmission Integrated Signal in TDD Mode
## 4 Performance Analysis of the TT&C Signal and Data Transmission Signal
### Acquisition Performance of the TT&C Signal  
Because this paper adopts an integrated signal, the subpeak of the APSK signal will be interfered due to the existence of the BOC signal. In addition, the signal power of the data transmission signal is higher than that of the TT&C signal to ensure the bit error rate. This will further interfere with the acquisition of the BOCcos signal. In Figure 5, the acquisition probability of the CNR is given.  
Obviously, the acquisition probability of the integrated signal is lower than that of the single BOCcos signal. The degradation of performance is approximately 2\(\sim\)3 dB. However, for intersatellite links and aircraft links, the CNR is higher than that of the satellite ground link. Therefore, the performance degradation is acceptable.  
The clock cycle required for one acquisition process is shown in Table 2. Since the sampling frequency \(f_{s}\) is set to 245.52 MHz, 409,105 sample points will be cached within 0.2 ms. After that, downsampling is realized 12 times, and the sample point number is reduced to 4093. Then, the data are zero-padded to 4096 points and sent to the FFT.  
Considering other waiting times, the acquisition process in the FPGA set the margin to 200 clock cycles. The acquisition time \(T_{acq}=\frac{(111872+200)}{245.52\mathrm{MHz}}=0.456\) ms. For intersatellite links, the Doppler frequency search range is generally set to \(\pm 6\) kHz. Therefore, if 500 Hz is used as the step, the time for searching all frequency points is 11.4 ms. However, due to the existence of missed detections,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Process** & **Clock Cycle** \\ \hline Downsampling & 4096 \\ FFT of the downsampled signal & 4096\(\times\)12 \\ Complex multiplication between & \\ the local PN code and & 4096 \\ downsampled signal & \\ IFFT of the multiplication result & 4096\(\times\)12 \\ Maximum searches & 4096 \\ Threshold calculation & 1280 \\ Total & 111,872 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Required clock cycles for once acquisition process.  
Figure 5: The acquisition probabilities of an integrated signal and a BOC signal.  
the acquisition time will be longer. Due to the long detector is used to reduce the false probability, the resident time will increase at each frequency [33; 43]. Each frequency will be searched eight times at low SNR and three times at high SNR. Take six times as acquisition time, which is equal to 66.57 ms. For the TDD mode, the hardware can search the code phase and Doppler frequency when in the TDD mode receiving stage. Thus, the duration of the TDD mode receiving and transmitting stage should not less than acquisition time.
# An Actor-Based Formal Model and Runtime Environment for Resource-Bounded IoT Services +
## 4 Operational Semantics
### Transition Rules
#### 4.2.3 Receiving a Service Request  
On receiving a service request \(\rho\), the directory service uses the search function to determine the opportunity to serve \(\rho\). This transition is written as:  
\[\begin{array}{l}\big{\langle}\llbracket I:\ [R[ready(search)]\rrbracket_{s_{ d}},\alpha:\mathcal{O}]_{s_{d}},\ S\mid C\mid\langle s_{d}\Leftarrow(\rho,s)\rangle,\\ M\big{\rangle}\to\big{\langle}\llbracket I:\ [search(\rho,s)]_{s_{ d}},\alpha:\mathcal{O}]_{s_{d}},\ S\mid C\mid M\big{\rangle}\end{array} \tag{7}\]  
where \(s_{d}\) is the directory service, \(a_{s_{d}}\) is \(s_{d}\)'s coordinator actor, \(s\) is the name of the client service which sent \(\rho\), \(\langle s_{d}\Leftarrow(\rho,s)\rangle\) is a service request message sent to \(s_{d}\), and \(\rho\) is the service request. As a result of delivery of this message to actor \(a_{s_{d}}\), \(a_{s_{d}}\) uses the _search_function to first search for an existing service matching the requirements of \(\rho\), or a set of contributing services which could collectively contribute to serving \(\rho\).
## 5 Application of IWOA in the Array Antenna Pattern Synthesis  
### Signal Model of the Planar Phased Array Antenna  
Consider the planar phased array composed of \(N\) array elements shown in Figure 5, its far-field radiation pattern is  
\[E(\theta,\phi)=\sum_{n=1}^{N}g(\theta,\phi)w_{n}^{*}e^{ik(x_{n}\sin\theta\cos \phi+y_{n}\sin\theta\sin\phi)} \tag{16}\]  
\[=g(\theta,\phi)\sum_{n=1}^{N}|w|_{n}e^{ik(x_{n}\sin\theta\cos\phi+y_{n}\sin \theta\sin\phi)+j\varphi_{n}} \tag{17}\]  
where \(g(\theta,\phi)\) is the element pattern function, \(k=2\pi/\lambda\), \(\lambda\) is the wavelength, \((x_{n},y_{n})\) is the coordinate position of the \(n\)th array element, and \(|w_{n}|\cdot e^{j\varphi_{n}}\) is the amplitude excitation factor and phase excitation factor of the corresponding array element.  
In this paper, the IOWA is applied to the actual engineering project. Since the RF device of the antenna can only control the amplitude within a maximum range of 20 dB, we need to restrict the amplitude variation within 20 dB in the process of the pattern synthesis. In engineering applications, Wilkinson circuit is usually used to realize the signal combination. The combining circuit of small signal is narrow and can achieve a high machining accuracy, which makes sure the amplitude change can be controlled more accurately. In addition, since the power of each signal is not equal, the combination efficiency will be affected when the power difference is large, and the hardware will also be more difficult to design. Therefore, we must control the range of amplitude change within 20 dB. Of course, if the amplitude change is too small, the sidelobe suppression will be very poor. In practice, we achieve the attenuation control by using attenuators.  
The optimization goal is to achieve sidelobe suppression only by optimizing the amplitude of the excitation. The designed fitness function can be expressed as  
\[F_{MSLL}=\alpha_{1}|G_{0}\,-\,G_{des}|+\alpha_{2}|MSLL_{\max}\,-\,MSLL_{des}| \tag{18}\]  
where \(G_{0}\) is the calculated gain of the main lobe pointing angle, \(G_{des}\) is the designed gain of the main lobe pointing angle, \(MSLL_{\max}\) is the calculated maximum sidelobe level among  
Figure 5: The planar phased array model.  
the entire pattern range, \(MSLL_{des}\) is the designed maximum sidelobe level, \(\alpha_{1}\) and \(\alpha_{2}\) are two weight coefficients, and \(\alpha_{1}+\alpha_{2}=1\) in the design.
# Evaluation of Remote Sensing-Based Evapotranspiration
## 3 Methodology
### Experimental Design  
The flow chart of this study is shown in Figure 2. First, the accuracy of the three remote sensing-based ET datasets (FLUXCOM, GLEAM, and PML) is evaluated based on in situ observations of pan evaporation. Second, two calibration schemes, i.e., calibration with only runoff (Scheme I) and multi-variable calibration with both runoff and remote sensing-based ET datasets (Scheme II), are used to calibrate the parameters of the DHSVM model. Third, we compare the hydrological model performance based on Scheme I and Scheme II through the runoff and ET simulation at different temporal resolutions and hydrological signature analysis. Finally, we can draw a conclusion regarding whether incorporating remote sensing-based ET datasets into multi-variable calibration can improve the hydrological model performance in humid regions of East China.
## 3 Results
### Correlation Analysis of NPP Influencing Factors
#### 3.2.2 Correlation Analysis of SM  
SM is directly related to the dynamics of plant photosynthesis and respiration. It is a water resource that can be directly used by vegetation for growth. SM is influenced by solar radiation and therefore displays longitudinal zonation in the study area, as shown in Figure 11a, which is equidistantly divided into six classes according to the distribution of SM value domains (176.54-463.89 mm). To explore the effect of SM on NPP, PCCs were calculated for each image element using the GIS platform. The results indicate that NPP and SM are mainly positively correlated (Figure 11b), and the positively correlated regions account for 59.32% of the total area of the Songnen Plain (0.5 \(<\) PCCs \(<\) 0.7). The significantly positively correlated (0.7 \(<\) PCCs \(<\) 1) areas are mainly concentrated in the central and southern parts of the study area, accounting for 14.73% of the total area. The regions with weak correlation (\(-0.5<\) PCCs \(<\) 0.5) are mainly concentrated in the northeastern and southwestern parts of the study area, accounting for 22.93% of the total area. Incorporating  
Figure 10: Boxplots of PCCs between NPP and meteorological factors in each land cover type.  
the land cover distribution in the Songnen Plain, a statistical analysis was performed on the PCCs between NPP and SM for each land cover type. The results indicate that there is a significant correlation between NPP and SM in the PAD dataset (Figure 11c), with PCCs in more than 75% of the study area exceeding 0.65. The mean of PCCs in the case of DRY was the maximum (0.73). However, the average of PCCs between NPP and SM was only about 0.55 in the case of SWA with more adequate SM, showing a relatively weak correlation. In the cases of FOR, GRA, and PAD, the mean value of PCCs was greater than 0.63, despite their wide range.
# Investigation of Debonding Effect in Internal Replacement Pipe System Under Lateral Loading
## 2 Finite Element Analysis
### Internal Replacement Pipe (IRP) System Modelling and Validation
#### 2.2.3 IRP System Validation
Accordingly, the following section provides a detailed analysis of the debonding mechanism in relation to the adhesive layer, with a particular focus on shear strength failure. The results from the generic epoxy testing were incorporated into the FEA model to achieve a more accurate numerical estimation of the experimental findings, thereby improving the precision of the overall analysis.
## 4 Analytical Implementation of VB-TPHD
### The Implementation of Gaussian Mixture VB-TPHD Filter
#### 4.1.3 The Step of Update  
After the prediction step, the update step is carried out under the framework of variational Bayesian. Gaussian mixture VB-TPHD calculates the new information and adjusts the mean according to the measurement value \(z_{k}\in Z_{k}\) and continuously updates the error covariance to obtain the optimal trajectory state and noise covariance.  
**Proposition 4**.: _Based on Assumptions 4-8, as PHD \(D_{\alpha_{k}}(\overline{X})\) can be predicted as the extended trajectory at time \(k\), the extended trajectory update of PHD \(D_{\alpha_{k}}(\overline{X})\) can take the following forms:_  
\[D_{\pi_{k}}(\overline{X})=\ (1-p_{D})\ D_{\alpha_{k}}(\overline{X}) \tag{42}\] \[+p_{D}\sum\limits_{z_{k}\in\mathcal{Z}_{k}}\sum\limits_{j=1}^{l_{ k}+\gamma_{k}}\left[w_{\pi_{k}}^{j}\text{N}\Big{(}X|t_{\pi_{k}}^{j},\hat{m}_{\pi_{k}} ^{j},\hat{p}_{\pi_{k}}^{j}\Big{)}\cdot\prod\limits_{l=1}^{d}\text{IG}\bigg{(} \left(\sigma_{\pi_{k}}^{l,j}\right)^{2};\alpha_{\pi_{k}}^{l,j},\beta_{\pi_{k}} ^{l,j}\bigg{)}\right],\]  
\[\alpha_{\pi_{k}}^{l,j}=\alpha_{\alpha_{k}}^{l,j}+0.5, \tag{43}\]  
\[\dot{H}\ =\ \Big{[}0_{1,\
u_{k}^{l}-1^{\prime}},1\Big{]}\otimes H, \tag{44}\]  
\[\dot{z}_{k}^{j}=\dot{H}\hat{m}_{\pi_{k}}^{j}, \tag{45}\]  
_To obtain the optimal parameters, it is supposed to adjust and update the components. The loop should be started with the record of the loop number \(n\):_  
\[\left(\sigma_{\pi_{k}}^{l,j,n}\right)^{2}=\ \text{diag}\left(\frac{\beta_{\pi_{k}} ^{1,j,n}}{\alpha_{\pi_{k}}^{1,j}},\ldots,\frac{\beta_{\pi_{k}}^{d,j,n}}{\alpha _{\pi_{k}}^{d,j}}\right) \tag{46}\]  
\[S_{k}^{j,n}=\left(\sigma_{\pi_{k}}^{l,j,n}\right)^{2}+\dot{H}\hat{p}_{\omega_{ k}}^{j}\Big{(}\dot{H}\Big{)}^{T}, \tag{47}\]  
\[K_{k}^{j,n}=\hat{p}_{\omega_{k}}^{j}\Big{(}\dot{H}\Big{)}^{T}\Big{(}S_{k}^{j,n }\Big{)}^{-1}, \tag{48}\]  
\[\hat{m}_{\pi_{k}}^{j,n}=\hat{m}_{\omega_{k}}^{j}+K_{k}^{j,n}\Big{(}z-\dot{z}_{ k}^{j}\Big{)}, \tag{49}\]  
\[\hat{p}_{\pi_{k}}^{j,n}=\hat{p}_{\omega_{k}}^{j}-K_{k}^{j,n}\dot{H}\hat{p}_{ \omega_{k}}^{j}, \tag{50}\]  
\[\beta_{\pi_{k}}^{j,n+1}=\beta_{\omega_{k}}^{j,l}+0.5\times\left(z_{k}-\dot{H} \hat{m}_{k}^{j,n}\right)^{2}+0.5\times\left(\dot{H}\hat{p}_{\pi_{k}}^{j,n}\Big{(} \dot{H}\Big{)}^{T}\right), \tag{51}\]  
_Assume that the predicted track set is updated according to the measurement set at time \(k\) and the cycle number \(n\) is recorded. Break the loop, and export \(\hat{m}_{\pi_{k}^{j,n}}^{j,n}\hat{p}_{\pi_{k}}^{j,n}\) and \(\left(\sigma_{\pi_{k}}^{l,j,n}\right)^{2}\) when \(\hat{m}_{\pi_{k}}^{j,n}\)tends to be a fixed value to calculate the weights of each Gaussian component updated:_  
\[w_{\pi_{k}}^{j}=\frac{p_{D}w_{\omega_{k}}^{j}\text{N}\Big{(}\dot{z}_{k^{\prime }}^{j},S_{k}^{j,n}\Big{)}}{\lambda_{c}V+p_{D}\sum_{j=1}^{l_{k}+\gamma_{k}}w_{ \omega_{k}}^{j}\text{N}\Big{(}\dot{z}_{k^{\prime}}^{j},S_{k}^{j,n}\Big{)}} \tag{52}\]  
We put the proof of Proposition 4 in Appendix D.
# Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions
## 2 Materials and Methods
### Methods
#### 2.4.3 Spatiotemporal Method  
[PERSON] et al. [32] used the spatial and temporal adaptive reflectance fusion model (STARFM) to fuse the NDVI and LST from clear days' Landsat and MODIS data. The basic idea of the model is to combine the advantages of Landsat's high spatial resolution and MODIS's high temporal resolution. Through the use of a mathematical model, the reflectivity data at the coarse spatial resolution of the predicted date are combined by using multiple pairs (two pairs, in practice) simultaneously during the observation period. The model makes the best of the advantages of high spatial resolution of Landsat and high temporal resolution of MODIS for data fusion. MODIS itself has a high temporal resolution, but there are few high-quality images. The temporal resolutions of AMSR2 and ERA5 data are also very high, and there are no invalid values in the data itself. Based on this idea, we introduce different data to fuse MODIS LST, and then compare the fusion results of the two data sources.
# Whole Organ Engineering: Approaches, Challenges, and Future Directions
## 3 History of Whole Organ Engineering
### Academic and Industry Pioneers  
Many leaders in the field of whole organ engineering have already been mentioned in this review. Dr. [PERSON], MD, is a pioneer in the field of whole organ decellularization and recellularization, with his work with the heart model being the first engineered whole organ [38]. Dr. [PERSON], DVM, PhD, MD is a leader in regenerative medicine research with a focus on the importance of the ECM microenvironment in engineering functional tissue, including through whole organ engineering. Dr. [PERSON], PhD is an expert in liver decellularization, having been the first to adapt [PERSON]'s decellularization for the liver and having been the corresponding author on many recent liver engineering techniques that utilize ECM as a bioscaffold [41; 42]. Dr. [PERSON], MD, was responsible for one of the earliest demonstrations of engineered organs being able to revive failing organ functionality by implanting engineered bladders in human patients, and has since performed research that has shown him and his group to be experts and pioneers in organ engineering and regenerative medicine, especially in urinary organ engineering [37]. Dr. [PERSON], MD, PhD, has done extensive work in whole liver engineering and particularly in using pluripotent stem cells to revive hepatic function of liver scaffolds sourced from cadavers [43]. In addition to the leaders in academia, there are many companies, often founded on the basis of technology developed through academia, that have made great strides in both research and endeavors to commercialize engineered organs and tissue. Miromatrix is developing engineered whole liver and kidney products derived from de-cell/re-cell technology, helping to lay the groundwork for the commercialization of engineered organs [44]. Tissue Regeneration Systems is using three-dimensional (3D)-printed scaffolds to generate bone grafts and implants for skeletal reconstruction, leveraging the ability of engineered organ constructs to be customized based on patients' needs [45]. Rokit Healthcare utilizes bioprinting technology to generate soft tissue constructs, aiming to generate patient-compatible grafts and organs [46]. Combined, these leaders in academia and industry are laying the foundation for greater development and future commercialization of whole organ technologies that could eliminate the disparity between the number of transplantable organs available and the individuals in need of transplants.
# Seismic Identification and Characterization of Deep Strike-Slip Faults in the Tarim Craton Basin
## 2 Geological Structure of Strike-Slip Faults
### Structural Styles  
From past outcrop studies and structural physics simulations, we have observed that strike-slip faults display a variety of structural patterns. Their intricate formation processes lead to multiple fault zones and the presence of smaller branch faults. This complexity contributes to the formation of intricate geological structures, petrophysical attributes, seepage characteristics, and pronounced heterogeneity [31]. Notably, the outcrops reveal the presence of fault zones within strike-slip faults, showing distinct variations in their distribution, with these fault zones primarily concentrated at fault intersections, transitions, overlaps, and terminations [32] (Figure 2). Moreover, physical simulation experiments demonstrate that the upward growth of strike-slip faults in deep cratonic carbonate basins initially leads to the development of echelon structures that manifest as a complex \"spiral-drag\" pattern, which subsequently connect through Y-shear faults [30]. Such sectional connections and interactions represent significant mechanisms that drive fault growth [33]. Additionally, the differences in the rock's composition, structure, and physical attributes play a crucial role in governing the formation and development of these faults within the geological setting.  
Figure 2: Geological structure of a strike-slip fault33. **(a)** Structural zones of strike-slip faults, including the principal displacement zone (PDZ), restraining band, and horsetail splay. The zone or plane of dip-slip or strike-slip accounts for the greatest proportion of accumulated strain. Subsidiary structures such as synthetic and antithetic faults and folds (e.g., fault splays, back thrusts, fracture zones, and en echelon folds) will be kinematically linked to the PDZ. **(b)** Outcrop of strike-slip fault. **(c)** Strike-slip fault interpretation (red lines) based on the outcrop. The strike-slip displacement in the fault zone causes various structural deformations in the surrounding area.