esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
For GPS medium-long baseline real-time kinematic (RTK) positioning, the troposphere parameter is introduced along with coordinates, and the model is ill-conditioned due to its strong correlation with the height parameter. For BeiDou Navigation Satellite System (BDS), additional difficulties occur due to its special satellite constellation. In fact, relative zenith troposphere delay (RZTD) derived from high-precision empirical zenith troposphere models can be introduced. Thus, the model strength can be improved, which is also called the RZTD-constrained RTK model. In this contribution, we first analyze the factors affecting the precision of BDS medium-long baseline RTK; thereafter, 15 baselines ranging from 38 km to 167 km in different troposphere conditions are processed to assess the performance of RZTD-constrained RTK. Results show that the troposphere parameter is difficult to distinguish from the height component, even with long time filtering for BDS-only RTK. Due to the lack of variation in geometry for the BDS geostationary Earth orbit satellite, the long convergence time of ambiguity parameters may reduce the height precision of GPS/BDS-combined RTK in the initial period. When the RZTD-constrained model was used in BDS and GPS/BDS-combined situations compared with the traditional RTK, the standard deviation of the height component for the fixed solution was reduced by 52.4% and 34.0%, respectively.  
BDS; zenith troposphere delay; medium-long baseline RTK; satellite geometric structure; geostationary Earth orbit satellite +
Footnote †: journal: Sensors
# Common-Mode Voltage Analysis and Reduction for the Quasi-Z-Source Inverter with a Split Inductor
## 1 Introduction
However, the maximum modulation index decreased dramatically. To tackle the limited modulation index, the three-phase four-leg qZSI was proposed in [28; 29] with an extra phase leg. In [30], a recovery diode was adopted in the negative side of the PV array to cut off the power flow of the CMV when the ZSI is operating in the ST state. However, the ZSI will be disconnected from the PV array, which results in discontinuous input conditions. To further take advantage of the qZSI system, it is essential to deal with the high-frequency components in the CMV that result from the ST state. Meanwhile, further explorations are needed to make the CMV reduction strategies for the conventional VSI also available for the qZSI system. Figure 1: Schematic of a three-phase grid-connected quasi-Z-source inverter system, where the quasi-Z-source network (qZSn) consists of two inductors \(L_{1}\), \(L_{2}\), two capacitors \(C_{1}\), \(C_{2}\) and a diode \(D\), \(L_{f}\) is the output filter, \(v_{g}\) is the grid voltage, \(C_{pv}\) is the parasitic capacitance between the PV array and the ground and \(i_{\text{leak}}\) is the leakage current. In light of the above, this paper explores the CMV reduction strategies for the qZSI system based on the methods for the conventional VSI without introducing additional circuits. By splitting the input inductor into two parts, which maintains the total inductance, the high-frequency components due to the ST state can be reduced to some extent. With the inductor-splitting method, all the conventional CMV reduction strategies for the VSI can be used for the qZSI with appropriate ST state insertion. More importantly, the proposed split-inductor qZSI shows the same operating features as the original qZSI. The rest of this paper is organized as follows. The CMV of the qZSI is derived in Section 2, in which the effect of the ST state on the CMV of the qZSI is discussed. The CMV reduction strategies for the conventional VSI are presented in Section 3 in detail. The CMV of the proposed input split-inductor qZSI is explored when the conventional CMV reduction strategies are used in Section 4. Simulations and experimental tests demonstrate the effectiveness of the proposed strategy for the qZSI in terms of CMV reduction in Section 5. Finally, concluding remarks are given in Section 6.
# Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs
## 1 Introduction
The selection of distance deviation coefficient on node measured distance The distance deviation coefficient is used to evaluate the deviation degree of the distances calculated by the log-distance distribution model and the triangle centroid algorithm, respectively, and a distance deviation coefficients set is established. The median of the distance deviation coefficient set is selected to characterize the measured distance deviation of all nodes, which can better reflect the overall distance deviation characteristic. 3. Construction of the iterative positioning algorithm based on distance correction The distance deviation coefficient median is used as an iteration factor for the iterative positioning algorithm. In the process of each iteration, the median of the distance deviation coefficients is used to correct the distance from the last positioning, obtaining a distance value closer to the real value. The triangle centroid localization algorithm is iteratively applied to reduce the positioning fluctuation error and improve positioning accuracy. The rest of this paper is organized as follows. A brief review of related works is presented in Section 2, while the proposed iterative positioning algorithm is introduced in Section 3. The iterative positioning model is constructed in Section 4, the experimental results and corresponding analyses are given in Section 5, and, finally, conclusions and general discussions are summarized in Section 6.
###### Abstract
We propose an approach based on neural networks with inspiration drawn from the operation of the feedback control system, in order to improve the estimation of the neural networks (NN) for the short-term tracking of the vehicles' displacement in these scenarios. The rest of the paper is structured as follows. In Section 2, we describe the challenging navigation problem, formulate the inertial navigation mathematical model and describe the structure of the proposed NN approach. In Section 3, we describe the Inertia and Odometry Vehicle Navigation Dataset (IO-VNBD) used as well as the setup of the experiment. In addition, we also define the metrics used to evaluate the performance of the NN technique and furthermore perform a comparative analysis of the IDNN, LSTM, Gated Recurrent Unit (GRU), Vanilla Recurrent Neural Network (vRNN) and MLP in terms of accuracy and computational efficiency. In Section 4, we discuss the results obtained and we conclude our work in Section 5.
## 4 Conclusions  
In this paper, the contamination phenomenon was quantified and represented as a contamination index based on transmittance. Through this analysis, the contamination behavior and its effect on  
Figure 11: The relative penetration depth according to location under a 1.0 L/min shielding gas flow rate.  
Figure 12: Correlation between the contamination index and \(S_{\rm r}\).  
welding quality were analyzed. The influence of the flow rate and the D\({}_{\text{nozzle}}\), which determine the performance of the protective system, were evaluated using the contamination index, and the correlation between the contamination index and welding quality were revealed. As a result, we can present the following conclusions:  
* The contamination of the coupling glass was successfully quantified using a contamination index based on measured transmittance. Penetration depth was found to be inversely proportional to the contamination index, which confirms that the contamination index defined in this study can be an effective index.
* Two mechanisms to prevent the contamination of coupling glass were postulated in this study. The first mechanism exploits an applied pressure difference across the aperture, which prohibits the intrusion of fume particles into the protective system. The second mechanism involves the deflection of fume trajectories inside the protective system. Both mechanisms are driven by shielding gas flow.
* When the shielding gas flow was 1.0 L/min or lower, the second mechanism worked in tandem with the first mechanism. An asymmetric pattern of contamination was observed due to the deflection of the welding fume trajectory, and the diameter of the shielding gas nozzle, which determines the flow speed, affected the contamination index.
* When proper shielding gas was not supplied, the coupling glass became contaminated during the welding trial and between welding trials. This caused the penetration depth to vary. The contamination can be controlled by selecting the proper gas flow rate and supply nozzle diameter.  
Investigation, Y.L. and J.C.; Methodology, Y.L. and J.C.; Supervision, B.-K.M. and C.K.; Writing--original draft, Y.L. and J.C.; Writing--review and editing, C.K.  
This research was funded by the Ministry of Trade, Industry, and Energy, Republic of Korea.  
The authors declare no conflict of interest.
# Hierarchical Reinforcement Learning for Crude Oil Supply Chain Scheduling
## 4 Hierarchical RL Scheduling Scheme
### Upper-Level Agent
#### 4.2.1 Markov Decision Process (MDP)  
The state of the RL agent includes all kinds of variable attributes in the facilities related to the problem, denoted as \(s_{t}=\{w^{t}_{e_{f}},w^{t}_{e_{r}},w^{t}_{e_{pf}},w^{t}_{e_{p}},m^{cl}_{f},m^ {cl}_{r},m^{d,t}_{r},m^{g,t}_{r},\bar{U}^{t}_{q_{q}}\}\), as detailed in Table 2.  
The actions of time step \(t\) of the upper-level agent are expected inventory volumes of crude, gasoline, and diesel in transfer stations and refineries, denoted as \(a_{t}=\{\hat{m}^{cl+1}_{f},\hat{m}^{cl+1}_{r},\)  
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Note** & **Dim** & **Notations** \\ \hline \(w^{t}_{e_{f}}\) & \(|E_{o}|\) & Crude supply volume from \(v_{o}\) to \(v_{f}\) \\ \(w^{t}_{e_{r}}\) & \(|E_{o}|\) & Crude supply volume from \(v_{o}\) to \(v_{r}\) \\ \(w^{t}_{e_{pf}}\) & \(|E_{pf}|\) & Crude purchase volume from \(v_{p}\) to \(v_{f}\) \\ \(w^{t}_{e_{r}}\) & \(|E_{p_{7}}|\) & Crude purchase volume from \(v_{p}\) to \(v_{r}\) \\ \(m^{t,t}_{f}\) & \(|V_{f}|\) & Crude inventory volume of \(v_{f}\) \\ \(m^{cl,t}_{r}\) & \(|V_{r}|\) & Crude inventory volume of \(v_{r}\) \\ \(m^{d,t}_{r}\) & \(|V_{r}|\) & Diesel inventory volume of \(v_{r}\) \\ \(m^{g,t}_{r}\) & \(|V_{r}|\) & Gasoline inventory volume of \(v_{r}\) \\ \(\bar{U}^{t}_{q_{r}}\) & \(|V_{r}|\) & Remained processing volume of \(v_{r}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: States of upper-level MDP.  
\(\tilde{m}_{r}^{d,t+1}\), \(\tilde{m}_{r}^{g,t+1}\), and the total \(|V_{f}|+3\times|V_{r}|\) dimensions. Each dimension is continuous and bounded by the physical inventory limit \(ph_{i}^{k}\).  
The reward function Equation (8) consists of two parts. One is the inventory alert penalty \(\mathcal{P}^{t}\), which can be calculated by substituting \(a_{t}\) into Equation (5). The other is the feedback penalty \(J^{t}\) returned by the lower-level agent. The detailed definition of the feedback penalty \(J^{t}\) is described in Equation (18a); conceptually, it includes the total transportation cost \(\mathcal{H}^{t}\), as defined in Equation (6) and the deviation of \(\tilde{m}_{i}^{k}\) from constraints in Equation (7b). This encourages the actions of the upper-level agent to optimize the objective of the crude oil supply chain scheduling problem as well as satisfies the constraints.  
\[R(s_{t},a_{t})=-\alpha_{1}\mathcal{P}^{t}(a_{t})-\alpha_{2}J^{t} \tag{8}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) are the same parameters defined in Equation (7a).
# Equatorial Plasma Bubbles: A Review
## 3 Development of EPBs and Associated Irregularities: Theoretical Aspects
### Electromagnetic Rayleigh-Taylor Instability
[72] that studied the magnetic flux tube-integrated evolution of EPBs, an effective capacitance determined by the dielectric constant of the plasma, which depends on the Alfven speed, was introduced in their theoretical framework. However, in the flux tube-integrated approach, FACs did not appear. In order to determine how small the E region's Pederson conductivity must be to allow the growth of EPBs, [PERSON] [73] extended the transmission line model [70] to study the effect of E region conductivity on the non-linear developments of EPBs. A set of non-linear equations, which included mode coupling for a three-mode system, was used to derive a condition for unstable fixed states. This condition for the non-linear development of electromagnetic R-T instability resulting in chaotic behavior of the equatorial F region plasma was:  
\[\
u_{i}\ +\ \frac{\mu_{0}\ V_{A}^{2}\ \Sigma_{p}^{E}}{l}\ \ <\ \sqrt{\frac{g}{2\ L}} \tag{5}\]  
Here, \(\
u_{i}\ =\ \ v_{in}\ +\ \
u_{ie}\), which includes ion collision frequency with neutrals as well as electrons; \(V_{A}\big{(}=B/\sqrt{\mu_{0}\ N\ M}\big{)}\) is the Alfven speed and \(l\) is the length of the geomagnetic field line from the base of the equatorial F region to the E region in either hemisphere. This condition differs from that obtained by [PERSON] et al. [60] because the E region's Pedersen conductivity, together with equatorial F region polarizability, introduces a new time scale in the process of the development of an EPB: the time taken by the currents flowing through the E region to discharge the plasma bubble. Typical values of \(\Sigma_{p}^{E}\), \(V_{A}\), and \(l\) are approximately 1 mho, 300 km/s, and 1600 km, respectively. For these values, the time required to discharge an EPB is around 100 s, which is comparable to the other time scales associated with the phenomena: inverse of the growth rate and ion collision frequency. It can be seen from Equation (5) that the greater the height of the equatorial F region, the larger the value of \(l\) and the smaller the effect of the E region's conductivity on the chaotic evolution of the EPB. Height of the equatorial F region is known to be one of the most important factors in the development of EPBs, as shown by [PERSON] et al. [74] from a study of extensive radar observations of EPBs. The first three-dimensional modeling of the electromagnetic characteristics of EPBs has been carried out to study the dynamics of the Alfven waves associated with EPBs [75]. According to this study, it takes tens of seconds for the Alfven waves to map electric fields along a set of geomagnetic field lines. A recent study has investigated the role of neutral winds in determining the latitudinal shift of the source of Poynting flux associated with EPBs [76].
## 4 Experimental Results
### Results of Subset1
According to the list on the right side of the figure for Model2, the values of the 'ElecPrice' feature of the 10-day lagged (ElecPrice_t-10) and the current value provide the most support for the accurate prediction of Model2. The one-day lagged, two-day lagged and the current value of the 'DewPnt' feature is 0.83, and this value, which is greater than 0.74, improves Model2's prediction performance. The progress bar given on the left of the figure for Model3 shows that the prediction result of the model is 0.80. According to the list on the right side of the figure for Model3, the lagged values of the 'DryBulb' feature until the 14 th day negatively affect the model prediction score. In summary, for Subset1, where no feature selection was made, the variables 'Humidity', 'DewPnt', 'ElecPrice' strengthen the prediction performance of the models.
# Multi-Line Prefetch Covert Channel with Huge Pages
## 2 Related Work  
Microarchitectural side channels have long been used for covert communication. Prior works such as Prime+Probe, Flush+Reload, and Spectre-family attacks demonstrate how cache occupancy and speculation artifacts can be exploited [11; 12].  
Our work is closely related to the \"Adversarial Prefetch\" attack by [PERSON] et al. [1], which showed that PREFETCHW can leak the coherence state of a cache line, enabling high-throughput covert channels over single-line access patterns. We build on this idea, generalizing to multi-line prefetching and integrating huge page memory mappings.  
Recent studies further extend covert channels' capacity and stealth:  
* PrefetchX ([PERSON] et al., 2023) [13] discovers a cross-core channel via the XPT prefetcher shared among cores, achieving 1.7 MB/s on Intel.
* BandwidthBreach ([PERSON] et al., 2023) [14] establishes covert channels through LFB/SQ contention in the memory pipeline, achieving over 10 Mbps.
* Write+Sync Covert Channels ([PERSON] et al., 2023) [15] demonstrate durable-write-based channels exploiting fsync() barriers on disk, effective even under strong cache defenses.
* SgxPectre Attacks [16] exploit speculative execution vulnerabilities in SGX to leak enclave secrets, demonstrating how transient execution can be used to bypass SGX's isolation guarantees. These highlight the importance of microarchitectural analysis in trusted computing.  
Compared to these, our channel uniquely combines huge page access, multi-line prefetching, and TEE compatibility via AMD SEV-SNP. Our throughput and accuracy trends (up to 88%) compare favorably while maintaining low detectability and leveraging coherence transitions observable even under cloud virtualization.
###### Abstract  
Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindella across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m\({}^{-2}\) and R\({}^{2}\) of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m\({}^{-2}\) and R\({}^{2}\) of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R\({}^{2}\) = 0.86) when compared to the RMSE of 0.03 mm and R\({}^{2}\) of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m\({}^{-2}\) and R\({}^{2}\) of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m\({}^{-2}\) and R\({}^{2}\) = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m\({}^{-2}\) and R\({}^{2}\) = 0.91 as compared to the wet season (RMSE = 10.75 g/m\({}^{-2}\) and R\({}^{2}\) = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients.  
g 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Throughflow Model
### Empirical Models
#### 2.4.1 Spanwise Loss Distribution  
In terms of the loss distribution in the throughflow simulation of the axial compressor, some studies have been implemented, in which the results seem to be basically matched with the 3D simulation or the real flows. However, the loss distribution in the throughflow simulation of the centrifugal compressor has not been noticed enough in the open literature. Additionally, difficulties are encountered in the application of the empirical correlations along the spanwise direction, since almost all the empirical correlations for the centrifugal compressors were originally established and verified in a 1D perspective, as concluded by [PERSON] et al. (2010) and [PERSON] et al. (2010). To deal with this problem, the spanwise-averaged/meanline parameters were employed to evaluate the \"total\" flow loss in the present solver. This \"total\" loss was then distributed with a prescribed form along the spanwise direction according to the specific type of loss. A uniform loss distribution along the spanwise is advised by [PERSON] (2018), and was adopted in the distribution of incidence loss, blade loading loss, skin friction loss, and mixing loss. Then, considering the source of loss production, a simple locally linear distribution was chosen for the clearance loss, disc friction loss, and recirculation loss. As for the vaneless diffuser loss, a parabolic distribution was employed to approximate the viscous effects from real endwall effects. The conditions prescribed for the linear and parabolic function are as follows:  
For the linear distribution, once the distribution range is specified:  
* The minimum value of the loss at one end is set as zero;
* The integral of the loss along the span is equal to the estimated \"total\" loss by a 1D approach.
* For the parabolic distribution:
* The spanwise location of the minimum loss value needs to be manually inputted and is usually chosen at mid-span;
* The ratio of the minimum loss value to the averaged one needs to be manually inputted. It takes the value of zero for the vaneless diffuser loss distribution in this study;
* The integral of the loss along the span is equal to the estimated \"total\" loss by a 1D approach.  
Both the linear and parabolic distribution forms along the span are depicted in Figure 1.  
The values that have to be manually entered in these distributions, such as the spanwise location and value of the minimum vaneless loss with a parabolic distribution, are specified with the view that they can represent some basic features of the meridional field of a compressor. In this way, the throughflow solver can be adopted as an independent analysis tool in the preliminary design stage with moderate accuracy in terms of meridional flow field prediction. The accuracy of these distributions can be further improved by the results from the 3D viscous computations and experimental measurements.  
Figure 1: Spanwise loss distributions.