esa-sceva/satcom-chunk-collection huggingface dataset:
# A Brief Review of Multipath TCP for Vehicular Networks
## 5 MPTCP in Vehicular Networks
### Existing Studies on MPTCP in Vehicular Environment
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Research Focus** & **Publication** & **Research Summary** \\ \hline V2I scenario & [PERSON] et al. [100] & A discussion on MPTCP for V2I scenarios. \\ \hline Transmission stability & [PERSON] et al. [101] & A study for MPTCP transmission stability in vehicular networks. \\ \hline \multirow{7}{*}{Throughput} & [PERSON] et al. [102] & A network coding based MPTCP for mobile device communications in heterogeneous networks. \\ \cline{2-3}  & [PERSON] et al. [103] & A network coding based MPTCP to solve communication bottleneck in VANETs. \\ \cline{2-3}  & [PERSON] et al. [104] & A proposal that apply Q-Learning on MPTCP to schedule the path and reduce the buffer blocking. \\ \cline{2-3}  & [PERSON] et al. [105] & A congestion control algorithm that considers the message priority and throughput. \\ \hline High speed train scenario & [PERSON] et al. [106] & A discussion of MPTCP for high speed train scenarios. \\ \hline \multirow{7}{*}{Architectural design} & [PERSON] et al. [107] & A collaborative MPTCP architecture that considers the device status and user preference. \\ \cline{2-3}  & [PERSON] et al. [108] & A mechanism that combines MPTCP with DPI to implement selective discarding on the sender side and partial reliability on the receiver side. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Studies on MPTCP in a vehicular ad hoc network. time prediction module, task offloading module, and remote task execution module. The response time prediction module and task offloading module are embedded in UAVs, while the remote task execution module is in the ground control system (GCS). The task offloading module communicates with the remote task execution module by MPTCP. Considered the high mobility feature of UAVs, the default scheduler of MPTCP is modified by considering the network handoff time.
## 1 Introduction
Due to the small amount of GLONASS data received by POD \(-\) X [1], all tests in this study are based on GPS data of the rear POD (POD \(-\) X) antenna, and the main purpose of this paper is to carry out a detailed quality assessment of the space-borne GPS data for six COSMIC-2 satellites, and realize the POD with the RD method, and evaluate the orbit accuracy by carrier-phase observation residual, overlapping orbits comparison and reference orbit comparison. Section 2 introduces the content of data quality analysis in detail, including the utilization of GPS observations, cycle slip ratio (o/slps), multipath error, SNR and _IOD_. Section 3 shows the relevant theories of the reduced dynamic method and the orbit determination strategy of six COSMIC-2 satellites, determines the orbit of COSMIC-2 with the reduced dynamic and evaluates the orbit accuracy using the carrier-phase residual, overlapping arcs comparison and the reference orbit comparison. Section 4 gives the relevant conclusions.
# Photo-Fermentative Bacteria Used for Hydrogen Production
## 2 Photo-Fertanative Bacteria for Hydrogen Production
### Rhodobacter  
_R._ _R.  
oxygen as a by-product [56]. These bacteria are facultative anaerobes that can switch metabolic pathways accordingly and can use a variety of organic compounds as carbon and energy sources. In these bacteria, bacteriochlorophyll absorb light energy to initiate photosynthesis, electrons are transferred from water to the photosynthetic reaction centers within a cell, and in return, protons are pumped into the periplasmic space, thus creating a proton gradient. The hydrogenase enzyme catalyzes the proton reduction reaction to produce hydrogen gas as the end product [57].  
[PERSON] et al. reduced the bacteriochlorophyll content of _R. capsulatus_ and used genetic engineering and transposon mutagenesis to create a mutant strain that produces 50.5% more hydrogen. The reduction in pigment allows more light to reach the bacteria, resulting in increased phototrophic growth and hydrogen production [58].  
_R. capsulatus_ strains have been widely used for hydrogen production. These microorganisms can utilize different carbon sources, such as glucose, acetate, or sucrose, under different process conditions, as shown in Table 3. However, challenges, such as low yield and sensitivity to environmental conditions, have become a challenge. Despite these limitations, the organism still has a great potential to be used as a natural hydrogen producer.
# Integrity Analysis for GPS-Based Navigation of UAVs in Urban Environment
## 2 Integrity Monitoring
In general, 95 th percentile position error is used to characterize the accuracy of the navigation system and its protection levels. However, integrity risks are defined at the much stricter level with higher percentiles such as 99.9999 th in order to make sure the position  
Figure 4: (**a**) Good satellite geometry (GDOP = 2.5492), (**b**) Bad satellite geometry (GDOP = 9.8042). Figure 5: Obscuration effect in **a** typical urban environment. errors do not exceed specified HAL or VAL boundaries. For this kind of very sensitive estimation, the standard deviation of error distribution is not always sufficient for ensuring the required reliability. Gaussian overbound and sigma inflation methods have already been suggested in the recent researches for correction of Gaussian distribution. While cumulative distribution function (cdf) overbounding for unimodal and symmetric distributions [38] and paired overbounding [39] methods are the most common examples, a combined method which involved both methods has been investigated in [40]. This combined method is applied at the data processing stage of our study for sigma corrections.
# THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management
## 3 Overview  
Figure 1 gives a full picture of THBase. It is composed of three modules:  
* T-table: T-table is an HBase table for persisting trajectory data. Considering the pruning and footprints factors, it organizes the trajectory data based on segment model, and processes data partitioning by MO-based model to ensure centralized distribution of all data of the same MO. This module is detailed in Section 4.
* L-index: L-index is an in-memory local secondary index structure for each Region of T-table, and is built by Observer coprocessor when trajectory data is inserted in Region. It uses a hybrid grid structure to index the spatiotemporal attribute of all trajectory segments. During query processing, each L-index generates the candidate rowkey collection for querying the corresponding Region by pruning and sorting. (Detailed in Section 5).
* Query Processing Module: The Query Processing Module implements efficient support for three typical queries by calling Endpoint coprocessor in the HBase cluster. We give a detailed description in Section 6.
# Experimental and Numerical Investigations on the Slate Shearing Mechanical Behavior
## 4 Numerical Simulations
### Failure Behavior of Slate  
#### 4.2.1 Shear Strength Properties  
The shear stress-displacement curve obtained by the direct shear test for slate at \(\beta=0^{\circ}\) and \(\sigma_{\mathrm{n}}=10\) MPa is shown in Figure 12. Both \(\tau_{\mathrm{P}}\) and peak shear displacement increase with \(\sigma_{\mathrm{n}}\) increases, with \(\tau_{\mathrm{r}}\) being higher at \(\sigma_{\mathrm{n}}\) values of 15 MPa and 20 MPa. \(\tau_{\mathrm{P}}\) exhibits an increasing-decreasing-increasing pattern with increasing \(\beta\). Peak shear displacement is relatively small at \(\beta=0^{\circ}\) and \(90^{\circ}\), measuring 0.60 mm and 0.77 mm, respectively, whereas it is more substantial at \(\beta\) values of \(30^{\circ}\), \(45^{\circ}\), and \(60^{\circ}\). For \(\beta=0^{\circ}\) and \(90^{\circ}\), the curve segment after \(\tau_{\mathrm{P}}\) exhibits a multiple shear stress drop phenomenon. As the shear stress decreases to a certain extent, the slope of the curve becomes gentler, and the stick-slip phenomenon emerges. Unlike the curve for \(\beta=90^{\circ}\) and \(\beta=0^{\circ}\), when the stress after the peak decreases to a certain extent, there is a more pronounced fluctuation. At \(\beta=30^{\circ}\), the number of post-peak stress declines is lower, yet each decrease is more substantial. At \(\beta=45^{\circ}\) and \(60^{\circ}\), the shear stress changes slowly, shear displacement increases rapidly, and the shear-slip phenomenon becomes more pronounced. In general, the elastic modulus of slate remains unchanged with the increase in \(\beta\) and \(\sigma_{\mathrm{n}}\).  
At \(\sigma_{\mathrm{n}}=10\) MPa (Figures 4b and 12b), due to the simplified assumptions in the numerical model, it cannot fully reflect the heterogeneity and anisotropy of real rock materials. Therefore, there are some discrepancies between the experimental and simulation results. However, overall, there is a certain similarity in the strength response under different \(\beta\).
## 2 Deterministic Analysis Method
### Soil-Existing Tunnel Interaction
\begin{array}{l}w_{-2}=\left(\frac{k_{0}^{2}D_{\text{e}}^{2}l^{4}}{\left( \kappa GA\right)_{\text{eq}}^{2}}+\frac{4k_{0}D_{\text{e}}l^{2}}{\left( \kappa GA\right)_{\text{eq}}}+4-\frac{\left(k_{n-1}-k_{n+1}\right)D_{\text{e} }l^{2}}{\left(\kappa GA\right)_{\text{eq}}}\right)w_{0}\\ -\left(4+\frac{2k_{0}D_{\text{e}}^{2}l^{2}}{\left(\kappa GA\right)_{\text{eq }}}\right)w_{1}+w_{n-2}+\frac{k_{0}D_{\text{e}}l^{2}\left(l_{n-1}-l_{n+1} \right)}{\left(\kappa GA\right)_{\text{eq}}}+\frac{l_{0}D_{\text{e}}l^{2} \left(l_{k-1}-k_{n+1}\right)}{\left(\kappa GA\right)_{\text{eq}}}\\ -\left(\frac{2D_{\text{e}}l^{2}}{\left(\kappa GA\right)_{\text{eq}}}+\frac{k_ {\text{e}}D_{\text{e}}^{2}l^{4}}{\left(\kappa GA\right)_{\text{eq}}}\right)k _{0}U_{0}\end{array}  \tag{25}\]  
\[ \begin{array}{l}w_{n+2}=\left(\frac{k_{0}^{2}D_{\text{e}}^{2}l^{4}}{\left( \kappa GA\right)_{\text{eq}}^{2}}+\frac{4k_{0}D_{\text{e}}l^{2}}{\left( \kappa GA\right)_{\text{eq}}}+4-\frac{\left(k_{n-1}-k_{n+1}\right)D_{\text{e} }l^{2}}{\left(\kappa GA\right)_{\text{eq}}}\right)w_{n}\\ -\left(4+\frac{2k_{0}D_{\text{e}}l^{2}}{\left(\kappa GA\right)_{\text{eq}}} \right)w_{n-1}+w_{n-2}+\frac{k_{0}D_{\text{e}}l^{2}\left(l_{n-1}-l_{n+1} \right)}{\left(\kappa GA\right)_{\text{eq}}}+\frac{l_{0}D_{\text{e}}l^{2} \left(l_{k-1}-k_{n+1}\right)}{\left(\kappa GA\right)_{\text{eq}}}\\ -\left(\frac{2D_{\text{e}}l^{2}}{\left(\kappa GA\right)_{\text{eq}}}+\frac{k_ {\text{e}}D_{\text{e}}^{2}l^{4}}{\left(\kappa GA\right)_{\text{eq}}}\right) k_{n}U_{n}\end{array}  \tag{26}\]  
The matrix form for the tunnel settlement can be obtained through combining the discretized formulations of each node:  
\[\mathbf{w}_{(n+1)\times 1}=\left(\mathbf{K}_{1}-\mathbf{K}_{2}+\mathbf{K}_{3}- \mathbf{K}_{4}-\mathbf{K}_{5}\right)^{-1}(\mathbf{f}_{1}-\mathbf{f}_{2}- \mathbf{f}_{3}-\mathbf{f}_{4}-\mathbf{f}_{5}) \tag{27}\]  
where \(\mathbf{w}_{(n+1)\times 1}\) is the vector of the tunnel settlement (\(\mathbf{w}_{(n+1)\times 1}=\{w_{0},w_{1},w_{2},\ldots,w_{n-1},w_{n}\}^{\text{T}}\)); \(\mathbf{K}_{1}\), \(\mathbf{K}_{2}\), \(\mathbf{K}_{3}\), \(\mathbf{K}_{4}\) and \(\mathbf{K}_{5}\) are the stiffness matrices; \(\mathbf{f}_{1}\), \(\mathbf{f}_{2}\), \(\mathbf{f}_{3}\), \(\mathbf{f}_{4}\), \(\mathbf{f}_{5}\) are the loading vectors for the greenfield soil settlement. The explicit expressions for the above vectors and matrices are presented in Appendix A. Moreover, it is noted that the calculation process in the above analysis method only involves algebraic operations so high efficiency can be guaranteed in the following random analysis.
# Geo-Electrical Detection of Impermeable Membranes in the Subsurface
## 7 Discussion  
Due to the finite dimensions of the tank, simulation of two remote electrodes showed reduced far looking ahead capability compared to a half-space and one remote electrode. As a result, the curvature of the kernel is far less pronounced than in the field situation. Still, the effect was sufficient in the laboratory to be able to pinpoint the position of the membrane. We expect that the membrane in the field will be detected from a larger distance than in the laboratory, or from the same distance but with a smaller tool. The smaller size for a field tool will make it easier to push into the ground.  
Two types of configurations are required for the detection of the position of the membrane. The \"far-looking\" detection is sensitive to the presence of the membrane from a relatively large distance. However, the measurement is also influenced by local resistivity variations. The \"near-looking\" detection provides information about this background resistivity, so small changes to the curvature in the curve can't be mistaken for the membrane. The background resistivity information is needed for the kernel in the inversion procedure. Curvatures in the kernel can either be due to heterogeneity in the soil or the presence of the  
Figure 11: Visualisation of the misfit for Mode 2 arrays of varying electrode spacing (y-axis), with one distance between the tool and the membrane per panel. The misfit values are colour-coded with black indicating the minimum values. The true location of the membrane is at 0.0 m. The peak shows the global minimum of the combined arrays with different sizes.  
membrane. In order to isolate the effect of the membrane, the background must be included in the kernel. There is also a minimum distance needed between the current electrode and the voltage dipole in the \"far\" detection configuration. The best results are obtained for a tool with a width of at least 45 cm. As mentioned though, we expect that the tool can have smaller dimensions, if we can utilize remote electrodes.  
Field situations often have some degree of heterogeneity in the subsurface. It is recommended to perform some surface investigations prior to the membrane detection using pushing the tool in the ground. This surface ERT survey will also give general information about the background resistivity.  
Other applications of this method are related to detection of very resistive or very conductive targets, where the exact burial depth is crucial. After adjusting the measuring strategy and processing based on the flowchart of Figure 5, targets smaller in size can also be detected.
## 3 Results and Discussion
### Mean Annual Precipitation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Time Scale** & & **3B42 RT** & **3B42V7** & **GPCP-1 DD** & **PERSIANN-CDR** & **CMORPH** & **APHRODITE** \\ \hline \multirow{6}{*}{Annual} & RMSE (mm) & 857.04 & 732.80 & 843.31 & 808.70 & 922.44 & 908.67 \\  & R\({}^{2}\) & 0.32 & 0.45 & 0.25 & 0.31 & 0.33 & **0.46** \\  & ME (mm) & 57.67 & 132.96 & \(-\)79.64 & 60.77 & \(-\)376.94 & \(-\)561.98 \\  & MAE (mm) & 651.39 & 540.48 & 640.40 & 618.65 & 689.60 & 684.54 \\  & RB (\%) & 2.02 & 4.65 & \(-\)2.79 & 2.13 & \(-\)13.18 & \(-\)19.65 \\ \hline \multirow{6}{*}{Northeast monsoon} & RMSE (mm) & 158.42 & 138.90 & 168.81 & 158.76 & 175.34 & 160.40 \\  & R\({}^{2}\) & 0.51 & **0.60** & 0.42 & 0.48 & 0.46 & 0.55 \\  & ME (mm) & 0.34 & 12.41 & \(-\)26.34 & \(-\)13.38 & \(-\)70.76 & \(-\)62.21 \\  & MAE (mm) & 107.17 & 94.00 & 112.05 & 108.34 & 115.14 & 101.47 \\  & RB (\%) & 0.12 & 4.29 & \(-\)9.10 & \(-\)4.62 & \(-\)24.45 & \(-\)21.50 \\ \hline \multirow{6}{*}{Southwest monsoon} & RMSE (mm) & 98.85 & 84.47 & 92.33 & 93.70 & 97.03 & 89.15 \\  & R\({}^{2}\) & 0.23 & **0.37** & 0.25 & 0.25 & 0.27 & 0.36 \\  & ME (mm) & 0.69 & 10.78 & 6.06 & 16.10 & \(-\)6.32 & \(-\)30.52 \\  & MAE (mm) & 75.10 & 65.05 & 71.36 & 73.07 & 73.06 & 65.16 \\  & RB (\%) & 0.37 & 5.74 & 3.22 & 8.56 & \(-\)3.36 & \(-\)16.23 \\ \hline \multirow{6}{*}{Monthly} & RMSE (mm) & 130.31 & 111.53 & 130.35 & 125.60 & 134.48 & 126.26 \\  & R\({}^{2}\) & 0.45 & **0.56** & 0.39 & 0.43 & 0.40 & 0.52 \\ \cline{1-1}  & ME (mm) & 4.81 & 11.08 & \(-\)6.64 & 5.06 & \(-\)31.41 & \(-\)46.83 \\ \cline{1-1}  & MAE & 91.86 & 78.30 & 89.90 & 89.04 & 91.31 & 83.33 \\ \cline{1-1}  & RB (\%) & 2.02 & 4.65 & \(-\)2.79 & 2.13 & \(-\)13.18 & \(-\)19.65 \\ \hline \multirow{6}{*}{Daily} & RMSE (mm) & 18.11 & 18.35 & 17.25 & 16.68 & 16.77 & 16.55 \\  & R\({}^{2}\) & 0.15 & 0.15 & 0.09 & 0.11 & **0.16** & 0.12 \\ \cline{1-1}  & ME (mm) & 0.16 & 0.36 & \(-\)0.22 & 0.17 & \(-\)1.03 & \(-\)1.54 \\ \cline{1-1}  & MAE (mm) & 8.91 & 8.99 & 9.18 & 9.15 & 8.14 & 8.29 \\ \cline{1-1}  & RB (\%) & 2.02 & 4.65 & \(-\)2.79 & 2.13 & \(-\)13.18 & \(-\)19.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical analysis of the annual, seasonal, monthly, and daily precipitation (2003–2007) between precipitation products and rain gauges.
# A Decentralized, Flat-Structured Control System for Chiller Plants +
## 4 Case Study Based on New System
### Results and Analysis
The comparison of the energy use of the main device in each month is shown in Figure 11. Table 7 lists the monthly energy use of the main device from May to September. From 19 September 2018, the system started to operate the energy-saving optimization strategy. In September, the total energy use of the chiller plant system decreased significantly compared with the previous four months, 267,953 kWh less than that in August and 33,996 kWh less than that in May. At present, the decentralized control system has the following functions:  
1. System remote monitoring. Users with permission can browse the operation status, failure status, key operation parameters, operation energy use, etc. of each controlled device by logging in the webpage; and can control the start-stop state of each equipment according to the authority, or set the parameters such as rotation speed, frequency, chiller outlet water temperature setting value, etc. 2. The operation and maintenance of the equipment are simplified without complicated manual operation procedures. The daily time switch machine can be set by setting the schedule. The management personnel can set and switch modes according to the scene, thus adapting to flexible and changeable management requirements. 3. Realize the local safety protection and sequential start-stop of the equipment independently. 4. Through coordinated control among equipment to reduce energy use in system operation. During system operation period, through energy-saving optimization APP to realize efficient and optimized operation of the system. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Energy Use Category**} & **Traditional Control** & **Decentralized Control** & \multirow{2}{*}{**Energy Saving Rate**} \\  & **(9.16–9.18)** & **(9.19–9.21)** & \\ \hline Chiller (kWh) & 14,472 & 13,030 & 10\% \\ Condenser Water Pump & 3660 & 2895 & 20.9\% \\ (kWh) & 4705 & 3783 & 19.6\% \\ Cooling Tower (kWh) & 817 & 299 & 63.4\% \\ Total (kWh) & 23,654 & 20,007 & 15.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of energy use under different control systems. Figure 10: Comparison of the energy use of the main device. In the commissioning phase, the decentralized control system eliminates manual network configuration commissioning work. In the maintenance phase, the operation and maintenance of the equipment is simplified, and no complicated manual operation process is required. Thus, it is time saving and cost saving. In summary, the proposed decentralized, flat-structured HVAC automation system is energy efficiency, operability and cost saving.
# A Robust Conformal Framework for IoT-Based Predictive Maintenance
## 5 Robust Conformal Prognostic for Reliable Predictive Maintenance
### Robust Conformal Prognostic Design  
The proposed prognostic workflow, presented in Figure 8, begins by generating RUL estimations from prognostic algorithms. To compare the validation performances, these initial predictions passed through a regulation step where non-increasing monotonicity is enforced to ensure that RUL does not inappropriately increase over time. This represents a critical constraint in most degradation processes.  
Next, the framework proceeds to the calibration and residuals phase, where the regulated predictions are compared against true values from a calibration set (training set) to compute residuals, and the margin estimators are applied to these residuals to quantify the uncertainty. Specifically, this work considers the three main strategies introduced in Section 3.2 (naive, bootstrapping, weighted margin), which are commonly applied in time series conformal applications:  
* Naive margin (Quantile 1\(-\alpha\)) uses a straightforward quantile-based approach.
* Bootstrap margin (median bootstrap) leverages resampling to obtain robust estimates of variability.
* Weighted margin (exponential weighting) accounts for time-varying or instance-specific importance.  
The latter phase consists of comparing or combining the calculated margin with the final confidence. To further halve (without considering a superior CI) and conservatively adjust to avoid excessively wide intervals (selecting the appropriate confidence value \(\alpha\)), reflecting engineering judgment and domain requirements for safety and cost considerations. Given these conditions, the outlined approach constructs the final prediction interval, intentionally bounded above by the regulated prediction. It preserves the strictly decreasing RUL trajectory and mitigates undue sensitivity to sensor noise. The CI estimation provides coverage that aligns with the desired confidence levels. It gives the possibility to evaluate the proximity to the EoL score, which gives an idea of the model's conservativeness by calculating the time window distance between the conformal critical status (when CI predicts RUL = 0) and the actual EoL (when validation RUL = 0) to allow engineers and practitioners to evaluate whether immediate intervention is warranted or if further analysis is possible before triggering timely interventions.  
Overall, this end-to-end pipeline offers a coherent, physically plausible, and statistically reliable combination of conformal calibration and decision-oriented. The proposed forecasting framework avoids common pitfalls related to probabilistic methods (which may overfit idealized trends) and unregulated conformal approaches (which can be unduly influenced by sensor noise). This delivers robust intervals that expand the timely estimation of RUL, streamline maintenance scheduling, and ultimately improve the reliability and cost-effectiveness of PdM programs.
# Status of the SXFEL Facility
###### Abstract  
The Shanghai soft X-ray Free-Electron Laser facility (SXFEL) is being developed in two steps; the SXFEL test facility (SXFEL-TF), and the SXFEL user facility (SXFEL-UF). The SXFEL-TF is a critical development step towards the construction a soft X-ray FEL user facility in China, and is under commissioning at the Shanghai Synchrotron Radiation Facility (SSRF) campus. The test facility is going to generate 8.8 nm FEL radiation using an 840 MeV electron linac passing through the two-stage cascaded HGHG-HGHG or EEHG-HGHG (high-gain harmonic generation, echo-enabled harmonic generation) scheme. The construction of the SXFEL-TF started at the end of 2014. Its accelerator tunnel and klystron gallery were ready for equipment installation in April 2016, and the installation of the SXFEL-TF linac and radiator undulators were completed by the end of 2016. In the meantime, the SXFEL-UF, with a designated wavelength in the water window region, began construction in November 2016. This was based on upgrading the linac energy to 1.5 GeV, and the building of a second undulator line and five experimental end-stations. Construction status and the future plans of the SXFEL are reported in this paper.  
X-ray FEL; SXFEL; cascaded HGHG; cascaded EEHG-HGHG; water window +
Footnote †: journal: Applied Physics Letters B