esa-sceva/satcom-chunk-collection huggingface dataset:
# Preliminary Validation of Surface Reflections from Fengyun-3C Radio Occultation Data
## 5 Conclusions  
In this study, we discussed the reflection occurrence and extracted the reflected bending model of FY-3C RO events. First, we used a matching criterion to extract the FY-3C and COSMIC refractivity profiles for the year 2015. We picked the COSMIC RO cases with reflected signal components, as identified in the ROM SAF reflection database. The mean bias of matched profiles is less than 1%, and standard deviation is less than 2%, from the altitudes of 5 km to 25 km. From the surface to the altitude of 5 km, the mean biases and standard deviation of matched profiles are less than 1% and 2%. The results demonstrated that there is a small systematic difference in the matching profiles of FY-3C and COSMIC. We also found worse comparison results below 2 km when COSMIC profiles without reflected signals. This is probably related to the different cutting height of FY-3C and COSMIC profiles.  
We picked a mass of FY-3C cases from the matched events when COSMIC profiles with reflected signals and found that these FY-3C cases show different degrees of reflection, especially over the ocean at high latitudes. The reflected signal is quite different from the direct signal, and the reflected bending angle increases as the impact height decreases. The reflected signals of FY-3C could be separated from the direct signals and could be used operationally in FY-3C RO products.  
Like other GNSS RO missions, the land-reflected signals of FY-3C are not as numerous as those over the ocean, and the reflection effect is not obvious. Reflections over ocean present distinct seasonal and dimensional characteristics, but these are not obvious over land. FY-3C will continuously provide RO products until 2030, and further work will be expected to investigate the uncertainty of land reflections.  
Conceptualization, Y.X. and W.C.; methodology, Y.X. and W.C.; software, W.C.; validation, Y.X., W.C., X.L., B.Z., R.Z. and S.X.; writing--original draft preparation, W.C.; writing--review and editing, Y.X., W.C.; visualization, Y.X.; supervision, Y.X.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research Program of China (No. 2016 YFB0501900), National Natural Science Foundation of China (Grant No. 41674028 and 41274044).  
Not applicable.  
The authors would like to thank ROM SAF for the use of demonstration data. We are thankful to the CDAAC for providing COSMIC radio occultation data. Additionally, we would like to thank the National Satellite Meteorological Centre, Chinese Meteorological Administration for providing FY-3C radio occultation data.  
The authors declare no conflict of interest.
# DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks
## 4 A Delay-Constrained Duty-Cycle Scheduling Algorithm
### Group-Based Forwarding Algorithm  
The proposed scheme consists of two phases: the group determination phase and the data transmission phase. In the first phase, the group index of each node is assigned. Then, the sink collects the information necessary for duty cycle interval determination, such as the number of forwarding candidates and the number of nodes in each group. Nodes forward packets to the sink in the data transmission phase.
## 4 Analysis of Results
### Expected Variation in the Decoy State Protocol  
Due to non-ideal devices, physical disturbances, and probabilistic single photon sources, variations are expected in the protocol's operation. These variations directly impact the system's ability to detect PNS attacks and must be accounted for, thus, the security condition becomes  
\[\eta^{signal}=\eta^{decoy}\pm\Delta \tag{9}\]  
where \(\Delta\) represents the protocol's expected variation during quantum exchange. Variation in the decoy state efficiency is primarily considered because it exhibits significantly more variation than the signal state due to its reduced occurrence percentage and lower MPN.  
While there are many potential sources of variation (e.g., fluctuations in laser sources, polarization dependent losses, variations in decoy state MPNs, temperature changes, physical disturbances,unstable detector efficiencies, etc.), many of them can be ignored due to the rapid propagation of photons through optical fiber (i.e., 2/3 the speed of light \(\approx\)2 \(\times\) 10\({}^{8}\) m/s). More explicitly, quantum exchange rounds (i.e., 100,000 signal state detections [63]) are typically very short (e.g., \(<\)20 \(\times\) 10\({}^{-3}\) s) and many of these effects are orders of magnitude slower (e.g., temperature change due to direct sunlight). Thus, [PERSON]'s pulse-to-pulse variation is of primary interest, and specifically, variation in her laser source (e.g., a commercially available id300 pulsed laser [64]) and decoy state generator (e.g., an electronically controlled Variable Optical Attenuator (VOA) used to control the MPN of each signal, decoy, and vacuum pulse [65]).  
Figure 3 illustrates [PERSON]'s modeled variation when calibrated to produce weak coherent optical pulses with an MPN of 0.55. Because of the large number of pulses, the 99.9% Prediction Interval (PI) characterizes her expected MPN variation well. This means [PERSON] will generate pulses with an MPN between 0.49 and 0.61 nearly 100% of the time. Thus, variations in generating signal, decoy, and vacuum pulses should be expected and addressed when considering the effectiveness of the decoy state protocol in detecting PNS attacks.
## 4 The Proposed Routing Strategy  
In this section, we will introduce our inter-satellite routing algorithm and the first and last hop selection methods for ground stations to access and exit the constellation. Our inter-satellite routing algorithm is a heuristic method based on GA, which can effectively solve the UMCF problem and assign a unique path for each commodity. Our first and last hop selection methods for ground stations are simple methods based on the greedy strategy, which can select the optimal satellite node as the first and last hop according to the source and destination of the commodity. We will describe these two methods in detail in Sections 4.1 and 4.2, respectively. Before introducing the GA to our problem, we will introduce linear relaxation for MILP, the constraint in our problem. \(t_{ei}\in\{0,1\}\) represents that each commodity can only select a single fixed path to send traffic. This is an NP-hard problem. By replacing this constraint with \(t_{ei}\in[0,1]\), we obtain the linear relaxation form of the problem. In the linear relaxation problem, each commodity is allowed to split its traffic among multiple paths. This linear relaxation problem can reduce the complexity in solving the original problem.
## 4 Joint Task Offloading, Resource Allocation, and Security Assurance Algorithm
### Problem Transformation  
C1.3 is an integer constraint, thus the optimization problem P1 is mixed integer non-linear programming, non-convex, and non-deterministic polynomials (NP) problem [59]. In order to solve P1, we set \(t_{\Xi_{i}}\!=\!\max\!\left\{T_{i,j}^{loc},T_{i,j}^{UAV}\right\}\) and transform P1 into an equivalent form as shown in Lemma 1.  
**Lemma 1**.: _The optimization problem P1 can be equivalently recast as the following problem P2._  
\[\begin{split}\text{P2}:&\min_{\mathbf{x}, \lambda,\mathbf{f}}T(\mathbf{x},\lambda,\mathbf{f})\!=\!\min_{\mathbf{x}, \lambda,\mathbf{f}}\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{M}x_{i,j}t_{\Xi_{i}} \\ s.t.&\text{C2.1}:\sum\limits_{j=1}^{M}x_{i,j}t_{\Xi_ {i}}\leq T^{th},\ \forall i\in\mathbf{N}\\ &\text{C2.2}:T_{i,j}^{loc}\leq t_{\Xi_{i}},\ \forall i\in\mathbf{N},\ \forall j\in \mathbf{M}\\ &\text{C2.3}:T_{i,j}^{UAV}\leq t_{\Xi_{i}},\ \forall i\in\mathbf{N},\ \forall j\in \mathbf{M}\\ &\text{C2.4}:\sum\limits_{j=1}^{M}x_{i,j}=1,\ \forall i\in\mathbf{N}\\ &\text{C2.5}:x_{i,j}\in\{0,1\},\ \forall i\in\mathbf{N},\ \forall j\in\mathbf{M}\\ &\text{C2.6}:0\leq\sum\limits_{i=1}^{N}x_{i,j}f_{i,j}\leq F_{j}, \ \forall j\in\mathbf{M}\\ &\text{C2.7}:0\leq f_{i,j}\leq F_{i},\ \forall i\in\mathbf{N},\ \forall j\in \mathbf{M}\\ &\text{C2.8}:0\leq\lambda_{i,j}\leq 1,\ \forall i\in\mathbf{N},\ \forall j\in \mathbf{M}\\ &\text{C2.9}:R_{e}\leq R_{th},\ \forall i\in\mathbf{N},\ \forall j\in \mathbf{M}.\end{split} \tag{16}\]  
Proof of Lemma 1.: See the Appendix A.  
Due to the highly complex coupling among optimization variables and mixed combinatorial feature, the solution of P2 is still challenging. Then, we further decouple the primal problem P2 into two subproblems, i.e., load balancing problem and task offloading problem. Specifically, we first determine \(\mathbf{f}\) and \(\lambda\) to get \(\mathbf{x}\). Then, we obtain \(\mathbf{x}\) under given \(\mathbf{f}\) and \(\lambda\). Finally, we repeat this process until convergence.  
Figure 4: General architecture of the proposed solution.
## 2 Materials and Methods
### CT Radiation Dose  
Dose data, such as volume weighted CT Dose Index (CTDI\({}_{\text{vol}}\)) and dose length product (DLP), were collected from the CT console. CTDI\({}_{\text{vol}}\) is the most established radiation metric for CT and considered an important value for quality assurance. CTDI\({}_{\text{vol}}\) is defined as:  
\[\text{CTDI}_{\text{vol}}=\frac{\text{CTDI}_{\text{w}}}{\text{pitch factor}} \tag{1}\]  
where CTDI\({}_{\text{w}}\) is the weighted average dose across a single slice and pitch is the distance of the table moving in one 360\({}^{\circ}\) gantry rotation. DLP is calculated by multiplying the scan range of the patients with the CTDI\({}_{\text{vol}}\), and it represents the overall dose output along the z-axis. Furthermore, the effective dose (E) is estimated by multiplying the DLP with a conversion coefficient factor (CC) [13].  
The organ dose of the phantom was measured by using TLD-100 (LiF: Mg, Ti) with dimensions of 3.2 \(\times\) 3.2 \(\times\) 0.6 mm. TLD-100 is the common passive dosimeter used in the measurement of radiation dose due to high sensitivity and response to the photon of X-ray. A total of 55 TLD-100 were used in this study and calibrated and compared with standard ionization chamber. The calibration process should be done to obtain a conversion factor (CF) in each TLD-100. Nine TLD-100 were used to measure background radiation. The CF was computed based on the equation:  
\[\text{CF}=\frac{\text{Q}_{\text{rad}}}{\left(\text{M}-\text{M}_{\text{background }}-\text{C}_{\text{background}}\right)} \tag{2}\]  
where \(\text{Q}_{\text{rad}}\) is energy photons from X-ray beam, M is the result of the Harshaw TLD reader, \(\text{M}_{\text{background}}\) is the background reading from TLD reader, and \(\text{C}_{\text{background}}\) is the background reading from TLD-100.  
All the TLD-100 were read out using a Harshaw 3500 TLD reader (Thermo Scientific, Waltham, MA, USA), combined with an operating software for all Harshaw TLD reader known as WinREMS after 24-h exposure. Before reuse, the TLD-100 chips were annealed using an oven (TLD annealing oven, PTW, Freiburg, Germany) for 1 h at 400 \({}^{\circ}\)C and 2 h at 100 \({}^{\circ}\)C followed by rapid cooling to make the TLD chips reusable. All the TLD-100 kept inside labeled plastic and sealed before inserting into the hole of the phantom. In this study, four TLD chips were inserted into the phantom slab no 9, 12, and 14 to represent lung, liver, and stomach, three TLD were inserted into slab no 7 and 19 to represent thyroid and gonads, and five TLD was placed on top of the phantom at the area of the thorax (slab number 8 and 11), abdomen (slab number 12 and 13), and pelvis (slab number 18) for the skin dose measurement.
###### Abstract  
Synthetic aperture radar (SAR) altimeters can achieve higher spatial resolution and signal-to-noise ratio (SNR) than conventional altimeters by Doppler beam sharpening or focused SAR imaging methods. To improve the estimation accuracy of waveform re-tracking, several average echo power models for SAR altimetry have been proposed in previous works. However, these models were mainly proposed for satellite altimeters and are not applicable to high-mobility platforms such as aircraft, unmanned aerial vehicles (UAVs), and missiles, which may have a large antenna mis-pointing angle and significant vertical movement. In this paper, we propose a novel semi-analytical waveform model and signal processing method for SAR altimeters with vertical movement and large antenna mis-pointing angles. A new semi-analytical expression that can be numerically computed for the flat pulse response (PSIR) is proposed. The 2D delay-Doppler map is then obtained by numerical computation of the convolution between the proposed analytical function, the probability density function, and the time/frequency point target response of the radar. A novel delay compensation method based on size interpolation for SAR altimeters with vertical movement is proposed to obtain the multilook echo, which can optimally handle non-integer delays and maintain signal frequency characteristics. In addition, a height estimation method based on least squares (LS) estimation is proposed. The LS estimator does not have an analytical solution, and requires iterative solving through gradient descent. We evaluate the performance of the proposed estimation strategy using simulated data for typical airborne scenarios. When the mis-pointing angles are within 10 degrees, the normalized quadratic error (NQE) of the proposed model is less than \(10^{-10}\) and the RMSE of \(\tau\) obtained by the re-tracking method fitted by the proposed model is less than 0.2 m, which indicates the high applicability of the model and accuracy of the re-tracking method.  
synthetic aperture radar altimetry; waveform re-tracking: least squares estimation 20232329266
# Evaluation of Three-Dimensional Environmental Hydraulic Modeling in Scour Hole
## 4 Conclusions  
This study focused on comparing the performance of two numerical models--the open-source package OpenFOAM and the commercial software Ansys Fluent--when simulating the near-wall flow, or the turbulent flow in a scour hole to be specific. Both codes were set up similarly in conditions and schemes. The results of flow pattern streamline, velocity profiles, and turbulent kinetic energy were collected and compared to previous laboratory experimental data. Overall, both models showed a good ability of capturing the flow behavior, such as circulation flow that happens in a scour hole, as well as the development of velocity and turbulence. Although they cannot perfectly archive the results as the laboratory experimental data, the performances by the two models are reasonable, and especially quite similar for the first two categories, while only showing a small difference in the last one.  
In conclusion, the calculation of OpenFOAM is better than Ansys Fluent for the flow in a scour hole near the wall as shown in this study. According to the energy cascade principle, the higher turbulent kinetic energy produced by OpenFOAM can result in creating more vortices and therefore can lead to the higher mixing in the scour hole region than by Ansys Fluent. This result can be a useful note when make a choice between the models.  
Conceptualization, T.H.T.N. and J.A.; methodology, J.A.; software, T.H.T.N. and D.J.; validation, T.H.T.N., S.P. and D.J.; formal analysis, T.H.T.N.; investigation, T.H.T.N. and J.A.; resources, S.P.; data curation, S.P.; writing--original draft preparation, T.H.T.N.; writing--review and editing, J.A.; visualization, T.H.T.N.; supervision, J.A.; project administration, J.A.; funding acquisition, J.A. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Post-Doctoral Research Program (2018) through Incheon National University.  
Not applicable.  
The authors declare no conflict of interest.
# Gas-Liquid Mixability Study in a Jet-Stirred Tank for Mineral Flotation
###### Abstract  
Micro- and nano-bubble jet stirring, as an emerging technology, shows great potential in complex mineral sorting. Flow field characteristics and structural parameters of the gas-liquid two-phase system can lead to uneven bubble distribution inside the reaction vessel. Gas-liquid mixing uniformity is crucial for evaluating stirring effects, as increasing the contact area enhances reaction efficiency. To improve flotation process efficiency and resource recovery, further investigation into flow field characteristics and structural optimization is necessary. The internal flow field of the jet-stirred tank was analyzed using computational fluid dynamics (CFDs) with the Eulerian multiphase flow model and the Renormalization Group (RNG) \(\mathrm{k}-\epsilon\) turbulence model. Various operating (feeding and aerating volumes) and structural parameters (nozzle direction, height, inner diameter, and radius ratio) were simulated. Dimensionless variance is a statistical metric used to assess gas-liquid mixing uniformity. The results indicated bubbles accumulated in the middle of the vessel, leading to uneven mixing. Lower velocities resulted in low gas volume fractions, while excessively high velocities increased differences between the center and near-wall regions. Optimal mixing uniformity was achieved with a circumferential nozzle direction, 80 mm height, 5.0 mm inner diameter, and 0.55 radius ratio.  
jet-stirred tank; numerical simulation; gas-liquid mixing; structural optimization +
Footnote †: journal: Applied Sciences
## 4 Results
### Inter-Set Performance Analysis  
Section 4.2 shows that most of the architectures perform well when tested with the holdout test sets from the original data set. In real environments the trained classifiers are likely to be presented with images that have been distorted by larger turbulence and attenuation than what was present in the training set. The tests in this section explore the architectures and how well they perform when presented with images outside of their training set. For example, how well does an architecture trained with the AL0 data set classify attenuated images from the AL4, AL8, and AL12 holdout sets?  
For this analysis, both underwater and free-space data sets are evaluated. For the underwater sets, the AL0 and the AL0-4 trained architectures are used. These architectures are evaluated against the AL0, AL4, AL8, and AL12 holdout sets. For the free-space data sets, the TB5 and TB5-10 trained architectures are used. These architectures are evaluated with the TB5, TB10, and TB15 holdout sets. In both cases, the results of interest are with data sets that fall outside the training sets. In the following tables, the results are ordered by ascending accuracies.  
Table 7 includes results from architectures that have been trained with the AL0 data set. The four columns include accuracies from AL0, AL4, AL8, and AL12 holdout sets. In looking at the performance of the AL4 test set, DenseNet and Wide ResNet give the best accuracies at 63.7% and 81.2% respectively.  
In evaluating architectures trained with the combined AL0 and AL4 data sets, Table 8 shows results ordered according to ascending results for the AL8 data set. DenseNet and ShuffleNet take the lead spots with 80.0% and 84.4% respectively.  
Tables 9 and 10 have similarly organized results for free-space data sets. ResNet and DenseNet (97.8% and 97.9%) have the best results for TB10 in Table 9, while DenseNet and ResNet (81.8% and 84.8%) take the lead spots in Table 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **AL0** & **AL4** & **ALS** & **AL12** \\ \hline MobileNet & 100.0 & 29.4 & 3.1 & 3.7 \\ AlexNet & 99.4 & 44.4 & 18.8 & 10.6 \\ SqueezeNet & 97.5 & 45.0 & 23.7 & 7.5 \\ ShuffleNet & 100.0 & 45.6 & 16.3 & 6.9 \\ ResNet & 100.0 & 46.9 & 13.8 & 15.6 \\ ResNeXt & 100.0 & 51.2 & 13.1 & 12.5 \\ DenseNet & 100.0 & 63.7 & 18.1 & 5.0 \\ Wide ResNet & 100.0 & 81.2 & 7.5 & 5.6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Underwater AL0 inter-set test. Architectures trained on ALO and tested against all AL data sets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **AL0** & **AL14** & **AL8** & **AL12** \\ \hline ResNet & 100.0 & 99.4 & 46.3 & 19.4 \\ AlexNet & 99.4 & 100.0 & 58.8 & 13.1 \\ MobileNet & 100.0 & 100.0 & 61.3 & 13.1 \\ Wide ResNet & 99.4 & 100.0 & 70.0 & 23.1 \\ SqueezeNet & 100.0 & 99.4 & 71.3 & 17.5 \\ ResNeXt & 100.0 & 99.4 & 79.4 & 36.9 \\ DenseNet & 100.0 & 100.0 & 80.0 & 40.0 \\ ShuffleNet & 100.0 & 100.0 & 84.4 & 45.0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Underwater AL0-4 inter-set test. Architectures trained on ALO-4 and tested against all AL data sets.
## 4 Design of MIM-QSMS Decoder for RC-QC-LDPC Codes
### LUT Optimization  
The SMIM-DE described above tracks the evolution of \(P_{R|X}^{(t,j)}\) and \(P_{S|X}^{(t,j)}\) at each layer and each iteration to construct the LUTs varying with different layers and iterations for decoding. For a predetermined maximum number of decoding iterations \(T_{\text{max}}\) and \(N_{b,\text{max}}\) layers of the target LDPC codes, the memory requirement for storing the LUTs constructed in this way grows linearly with \(N_{b,\text{max}}\times T_{\text{max}}\), which increases significantly in particular for the target LDPC codes with large \(N_{b,\text{max}}\). Thus, it is necessary to optimize the SMIM-DE to generate LUTs with less memory demand for hardware implementation. To this end, we propose an optimization method for the RC-MIM-QSMS decoder in a postprocessing manner to design the LUTs that only vary with decoding iterations, which we refer to as the iteration-specific LUTs in the rest of this paper. In particular, we first conduct the SMIM-DE at each iteration to obtain the conditional pmfs \(P_{S|X}^{(t,j)}\) for all layers. Similar to the case of the CN update, we assume that the C2V messages sent to a VN at the \(t\)th iteration are from \(N_{b,\text{max}}\) layers of all target LDPC codes. Thus, we combine the conditional pmfs from \(N_{b,\text{max}}\) layers into the iteration-specific pmf by considering the weighted expectation of the C2V message pmfs at the \(t\)th iteration such that  
\[\hat{P}_{S|X}^{(t)}(s|x)=\frac{\sum_{j=1}^{N_{b,\text{max}}}w_{j}\cdot P_{S|X} ^{(t,j)}(s|x)}{\sum_{j=1}^{N_{b,\text{max}}}w_{j}}. \tag{20}\]  
The iteration-specific reconstruction LUTs \(\phi_{ch}^{(t)}\) and \(\phi_{v}^{(t)}\) can be constructed by replacing \(P_{S|X}^{(t,j)}\) in (15) and (16) with \(\hat{P}_{S|X}^{(t)}\). The quantization LUT \(\Gamma_{v}^{(t)}\) for each iteration can also be obtained following (17)-(19). With these iteration-specific LUTs, the conditional pmf \(P_{R|X}^{(t,j)}\) requires to be further updated by the SMIM-DE for designing the LUTs at the next iteration. The LUT optimization repeats at each iteration until the preset maximum number of iterations \(T_{\text{max}}\) is reached. To summarize, we present the design flow of the RC-MIM-QSMS decoder with the LUT optimization in Algorithm 1. Note that by adopting the LUT optimization, the RC-MIM-QSMS decoder requires only four LUTs per iteration, which consists of two reconstruction LUTs (\(\phi_{ch}^{(t)}\) and \(\phi_{v}^{(t)}\)) and two quantization LUTs (\(\Gamma_{ch}\) and \(\Gamma_{v}^{(t)}\)). For the decoding process, all the LUTs are fixed for different received signal-to-noise ratios (SNRs). Furthermore, \(\Gamma_{ch}\) is fixed for all iterations while the other LUTs may vary with different iterations. ```
0:\(q_{m}\), \(q_{v}\), \(\rho(\xi)\), \(\theta(\xi)\), \(\sigma_{d}\), \(N_{b,\max}\), \(T_{\max}\). 0:\(\Gamma_{ch}\), \(\Gamma_{v}^{(t)}\), \(\phi_{ch}^{(t)}\), \(\phi_{v}^{(t)}\).