esa-sceva/satcom-chunk-collection huggingface dataset:
# Teleportation-Based Error Correction Protocol of Time-Frequency Qubit States
## 1 Introduction
However, the creation of the frequency entanglement structure for single photon encoding is difficult to realize experimentally with the current technology, as it requires performing a frequency entanglement operation between two single photons [27]. Therefore, inspired by the teleportation-based error correction protocol for quadrature encoding [28; 29], we develop a teleportation-based error correction protocol for frequency qubit states that uses only linear optical elements and allows the simultaneous correction of both time and frequency variables. In the teleportation-based error correction protocol, the noisy frequency qubit state is entangled with a less noisy frequency entangled state. One of the members of the entangled pair is combined into a balanced beam splitter with the noisy single-photon state, and then Bell measurements are performed. The error correction is performed naturally since the remaining frequency single-photon state belongs to the entangled state that was less noisy in the time and frequency domains compared to the initial state. Since the protocol requires the use of a Bell measurement, we also describe how to experimentally implement such a measurement for the two types of frequency qubits by using Mach-Zehnder interferometry. The proposed protocol is intrinsically probabilistic and consists of the teleportation of non-orthogonal states [30], instead of orthogonal ones [31; 32]. The non-orthogonality of the state reduces the efficiency of the teleportation-based error correction protocol. For that reason, we mention that photon number-resolving detectors can help increase the probability of success of the protocol by reducing the number of rejected measurement events. Finally, the advantage of discretizing a grid state into a qubit state by combining the even and odd peaks, rather than considering the state as a time-frequency qudit, is convenient because it simplifies the optical implementation of grid state manipulations. The paper is organized as follows. In Section 2, we provide a reminder of the definition of the time-frequency cat and GKP states and the reason for the experimental difficulty behind the natural error correction scheme of the time-frequency GKP states which requires frequency entanglement gates. In Section 3, we explain the optical equivalent of the polarizing beam splitter, a Mach-Zehnder interferometer, which allows separating spatially the two logical states of the time-frequency cat and GKP qubits. Such an interferometer is crucial for implementing [PERSON]'s measurement in the time-frequency degree of freedom. In Section 4, we present a teleportation-based error correction protocol for the time-frequency GKP state, which makes use of [PERSON]'s measurement. The protocol is intrinsically probabilistic and can be achieved with the current experimental devices. Finally, in Section 5, we summarize our results and present new perspectives.
# Discrete Element Analysis of Grouting Reinforcement and Slurry Diffusion in Overburden Strata
## 3 Model Establishment and Parameter Calibration
### Calibration of Cement Slurry Flow Parameters (Marsh Funnel Test)  
The Marsh funnel test is a simple, rapid, and practical field method primarily used to evaluate the relative viscosity of non-Newtonian fluids, such as cement slurries or other grouting materials. It offers a standardized and reproducible approach for assessing the flowability and consistency of slurry mixtures, serving as a useful indicator of their workability under field conditions. As a classical rheological test method, the Marsh funnel test quantitatively evaluates the workability of slurry by measuring its outflow time through a standardized geometric container. In this study, a stable slurry with a  
Figure 3: Geometric model and boundary conditions (two-dimensional space). (**a**) Computational geometry model. (**b**) Model boundary conditions.  
water-cement ratio of 0.7 is used, and its component mass ratio is cement-water-water reducer-bentonite = 1:0.7:0.015:0.005. The water exudation rate within 2 h is measured to be \(\leq\)1% in laboratory tests. To improve computational efficiency, the following optimization strategies are employed in the numerical modeling: (1) Geometric similarity criterion: key dimensions of the standard funnel are reproduced at a 1:2 scale. (2) Particle discretization: the characteristic particle size of cement particles is set to 1 mm, and 1341 discrete elements are generated using a random packing algorithm. (3) Contact constitutive modeling: the adhesive rolling resistance linear model is adopted to characterize particle interactions. (4) Parameter inversion and calibration: through iterative optimization of mesoscopic parameters, the simulated outflow time converges to the reference value of 45 s obtained from physical testing. The key geometrical parameters of the Marsh funnel are illustrated in Figure 5a,b.
# Satellite and Ground Based Thermal Observation of the 2014 Effusive Eruption at Stromboli Volcano
## 6 Conclusions  
This is the first time a long term observation of thermal radiance at an active volcano was performed using a small satellite, in this case TET-1, which provides unsaturated data at high spatial resolution in MIR and TIR bands. TET-1 data were not available for the whole eruption period, but we were still able to retrieve _TADR_ with a combined use of TET-1 and MODIS data. The results correspond well to field observation data using a combination of cameras operating in MIR and TIR bands. The mean output rate for the entire eruption equals 0.87 m\({}^{3}\)/s, which is higher than in the 2002-2003 eruption, but less than in the 2007 eruption. These differences can be partially explained by the model based on Poiseuille flow, where the pivotal role belongs to the height difference between the crater terrace and the effusive vent, _i.e._, the driving magmatic pressure. During the three-month-long eruption Stromboli erupted an estimated volume of 7.4 \(\times\) 10\({}^{6}\) m\({}^{3}\) of material.  
This research has been supported by a grant from the German Science Foundation (DFG), number ZA659/1-1. The TET-1 data are courtesy of German Aerospace Centre (DLR). We would also like to thank the members of the University of Hamburg who took part in the Stromboli 2014 campaign, especially [PERSON], who performed excellent work during the preparation for the campaign, and [PERSON], who supported us with GPS data. Finally, we would also like to thank [PERSON], [PERSON], and [PERSON] for their contributions in the debate on the TET-1 point spread function.  
[PERSON] designed the study and analyzed the field and satellite data. [PERSON] organized the field campaign and analyzed the results of the theoretical model. [PERSON] prepared the satellite data. All authors have read and edited the manuscript.  
The authors declare no conflict of interest.
# A Novel DV-HOP and APIT Localization Algorithm with BAT-SA Algorithm
## 2 DV-HOP-Based Methodology  
The main notion underlying the DV-HOP method is that unknown nodes must first ascertain the beacon node's maximum hop count. After multiplying the ratio of the average hop to the necessary minimum hops, the hop average distance has been determined. As the last step, when computing the distance between the unknown node and beacon node, the existing DV-HOP algorithms have a number of problems, some of which are listed below. Beacon localization is less accurate. Lower coverage rate in sparse environments. Figure 2 describes the design of nodes in a WSN with the conventional DV-Hop approach [8; 9; 10]. At the start, every anchor node broadcasts a signal to the entire WSN. The anchor node is identified by initializing one as count value for the HOP. The minimum count value isconsidered by the beacon. Astale node is detected as a beacon with a higher hop count value than a specific anchor node, and it is disregarded as a result [11; 12; 13]. The network floods the new nodes that it does not identify as stale nodes. At each intermediate hop, these nodes increase their value for the hop count. Every node in the network finds a path to its anchor node using this technique that requires the fewest hops. Figure 3 depicts DV-HOP implementation. The widely used APIT range-free localization technique has certain shortcomings. In the main structure of the APIT method, the target nodes detect the place of the anchor nodes first. This is performedusing the beacon signals that the target nodes receive from anchor nodes. These beacon signals are generated by anchor nodes. Beacon signals carry a variety of data, including the IDs of anchor nodes andthe power used for transmission and for the position in the transmission. Generally, anchor nodes consume more power and are equipped for the localization of the devices. The target nodes select from the list three of the 'n' anchor nodes. During APIT process, target nodes attempt to identifytheir own locations by using the anchor. The target node searches for a position within the designated triangulation area. The target nodes would generate C3 triangles by testing each combination. The target node finds its COG inorder to determine  
Figure 1: WSN network.  
its estimated position by locating the intersection of all probable triangles. Figure 4 shows the generic triangle area generation using the APIT method. The theoretical idea of point-in-triangulation (PIT) demonstrates whether the localization triangle zone is present on the exterior or interior of the zone. A TABC with threeanchor nodes andonetarget node is constructed while keepingin mind the target node T. T can travel in a way that either keeps it a long way from the triangle's vertices or brings it fairlyclose to them. The 'T'target node is present at the exterior of the triangle, and if this is the case, theninside the triangle if the opposite is true [14].  
Figure 4: Overview of APIT algorithm.  
Figure 3: DV-HOP methodology.  
Figure 2: WSN configuration using DV-HOP algorithm.
# Traffic Prediction of Space-Integrated Ground Information Network Using the GTCN Algorithm
###### Abstract  
The traffic data of the intelligent network with the Space-Integrated Ground Information Network have the characteristics of robust traffic burst, large traffic data, and high volatility compared with the ground traffic. We propose a Gated Temporal Convolution Network (GTCN) algorithm to solve the above problems, the algorithm segments the traffic time series into equal length according to the autocorrelation of the network traffic, and creates the feature sequence by extracting the segmented features using Temporal Convolution Network (TCN), to learn the burst and fluctuation of the intelligent network traffic of the Space-Integrated Ground Information Intelligent Network and to improve the processing efficiency of the traffic time series. Then, the feature sequences of each subsection data are stored, screened, and retained through the gated unit. Finally, the network traffic data are accurately predicted using the characteristics of the sequences stored by the TCN and the gating unit. The simulation experiments showed that, compared with the comparison algorithms, the accuracy of the proposed algorithm is improved by at least 58.72%, the training speed of the algorithm is improved by 84.05%, and the algorithm execution speed is improved by 71.92%, which meet the requirements of the accuracy and timeliness of the traffic prediction model of the Space-Integrated Ground Information Intelligent Network.  
Space-Integrated Ground Information Network; network traffic prediction; GTCN 202211906[[https://doi.org/10.3390/electronics11060906](https://doi.org/10.3390/electronics11060906)]([https://doi.org/10.3390/electronics11060906](https://doi.org/10.3390/electronics11060906))
# A Watermark-Based In-Situ Access Control Model for Image Big Data
## 4 Proposed Scheme
### Case Study  
It is a trend to incorporate multiple maps from one location into one map as multiple layers. For a better explanation, we separate a combined map with multiple layers into three individual maps. In this case study, three maps of Shanghai are displayed in Figure 5[23], which includes a remote sensing image, a geologic map, and a city planning map. These three maps describe three aspects of the same location. A combinative map can provide various aspects of one location in one map by multiple layers, which facilitates fast linkages to relevant information within one area.  
Figure 5: A combinative map of Shanghai with multiple layers. The first one is sensing image. The second one is a geologic map. The third one is a city planning map.  
The security levels of roles and corresponding layers are embedded into maps as watermarks, and thus access control strategies can be obtained from distributed maps without consulting servers. Accessors present their roles to a dedicated client tool such as an image browser, and specific areas that can be accessed by presented roles will be determined by the client tool.  
In one map, accessible areas are encrypted by corresponding keys (e.g., \(a_{i}\) is encrypted by \(k_{i}\)\((i=1,2,...,n)\)). Only someone who presents the correct key (e.g., \(k_{i}\)) can view the corresponding encrypted areas (\(a_{i}\)). We also provide a kind of hierarchical access by hierarchical encryptions for areas. That is, keys at lower security levels can be derived by keys at higher security levels (e.g., \(k_{i+1}\) can be derived by \(k_{i}\)). Thus, areas for roles in lower security levels can also be decrypted and viewed by roles with higher security levels. Upon request for images by an accessor, the image browser will prompt the accessor to present their key (e.g., \(k_{i}\)). The image browser will compute \(f(k_{i})\) and use it to decrypt corresponding areas.  
In combinative maps, one area consists of multiple aspects presented in layers. For example, geology, remote sensing, and city planning are three layers of a single city, Shanghai. Some accessors may only be able to access one layer among them. Accessors present their roles and keys to reveal corresponding layers.
## 4 Experimental Results
### Thematic Accuracy Evaluation with Spectral Index  
The performance of the conventional and proposed Pan sharpening methods were evaluated with several quality indices and visual interpretation in this research. However, the effects of Pan sharpening on the information extraction, process such as image classification, segmentation and index-based analysis is another important concern that requires conservation of spectral properties of the MS image after Pan sharpening. One of the indirect methods frequently used to evaluate the abovementioned situation is to apply the spectral index on the MS and Pan sharpened images, and investigate their consistency. As only visible bands of the images were used in this research, the visible atmospherically resistant index (VARI) proposed by [PERSON] et al., 2002 Gitelson et al. (2002) was used for the evaluation as it uses the all visible bands for calculation.  
\[VARI=\frac{Green-Red}{Green+Red-Blue}. \tag{9}\]  
The test site F3 was selected for this evaluation, as it is one of the most challenging sites in the dataset due to complex and heterogeneous land cover characteristics. The VARI index was applied on both the MS and CIELab Pan sharpened images, and a binary classification was performed with the use of the same threshold to map the manmade and natural lands in the region. According to results presented in Figure 4, same level of information extraction could be achieved with CIELab Pan sharpened image and it even provided better thematic representation by providing better geometric representations of the objects and less of the salt and pepper effect observed in the vegetated areas located in the north and south parts of the image.
# An Electric Load Simulator for Engine
## 2 System Design
### Performance Requirements  
The main technical requirements of the electric load simulator are listed below according to the technical parameters of the engine [21]:  
(1) In view of the fact that the pressure difference between the cylinder and the exhaust port could reach 8 bar during the exhaust stroke, the output force of the electromagnetic actuator should range from 0-320 N to simulate the gas force.  
(2) The maximum velocity of the exhaust valve changes from 2 m/s-3 m/s, so the power of the electric load simulator followed: \(P\leq 960\) W.  
(3) Bandwidth: \(\geq 80\) Hz.  
(4) The maximum stroke of the electric load simulator: 8-10 mm.  
(5) In order to improve the control accuracy of the system, static load accuracy requirements: \(\leq\)2% FSR (Full Scale Range), dynamic load accuracy requirements: \(\leq\)5% FSR.  
(6) Taking the performance and cost of electronic components and other factors into account to meet the system response performance, the system supply voltage followed: U \(\leq 30\) V.
# Development of a Caterpillar-Type Walker for the Elderly People
## 3 Results
### Upper and Lower Extremity Muscle Activity
#### 3.2.2 Obstacle Terrain
In contrast, when using CTW, there was a slight increase in muscle activity with obstacle heights of 1-2 cm, 2-3 cm, and 1-3 cm, respectively. The increases of 0.02%, 7%, 6% in TA, 1%, 1%, 2% in RF, 2%, 1%, 3% in GA, and 0.01%, and 4%, 4% in BF were observed. There was no significant difference in the lower extremity muscle activity according to the height difference. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**1 cm**} & \multicolumn{2}{c}{**2 cm**} & \multicolumn{2}{c}{**3 cm**} \\  & **GTW** & **CTW** & **GTW** & **CTW** & **GTW** & **CTW** \\ \hline \multirow{2}{*}{FDS} & \(14.41\pm 2.4\) & 12.80 \(\pm\) 2.7 & 20.28 \(\pm\) 2.5 & 12.12 \(\pm\) 2.8 & 22.17 \(\pm\) 2.6 & 11.95 \(\pm\) 2.8 \\  & (14.5, 11.9–16.7) & (11.5, 8.9–15.3) & (19.1, 13.4–25.9) & (11.5, 8.9–15.3) & (20.8, 16.1–26.0) & (11.09, 7.0–15.1) \\ \hline \multirow{2}{*}{BB} & \(10.23\pm 3.4\) & 6.19 \(\pm\) 2.4 & 16.18 \(\pm\) 3.1 & 6.29 \(\pm\) 2.7 & 17.68 \(\pm\) 2.5 & 6.78 \(\pm\) 2.7 \\  & (11.3, 8.4–13.7) & (4.8, 3.4–8.1) & (16.5, 13.2–18.6) & (4.8, 3.4–8.1) & (15.6, 10.1–23.8) & (6.9, 4.5–9.0) \\ \hline \multirow{2}{*}{FDP} & \(19.34\pm 2.1\) & 18.8 \(\pm\) 3.1 & 24.23 \(\pm\) 3.1 & 19.03 \(\pm\) 4.1 & 24.92 \(\pm\) 2.7 & 20.84 \(\pm\) 4.1 \\  & (19.3, 14.9–22.7) & (14.7, 18.5–21.5) & (24.5, 18.0–29.3) & (18.6, 14.8–21.5) & (24.6, 19.7–30.2) & (21.0, 16.6–24.1) \\ \hline \multirow{2}{*}{TB} & \(13.92\pm 3.7\) & 11.09 \(\pm\) 3.5 & 21.81 \(\pm\) 3.8 & 11.28 \(\pm\) 4.5 & 23.19 \(\pm\) 3.8 & 11.94 \(\pm\) 4.5 \\  & (14.1, 11.7–16.6) & (9.0, 5.7–16.8) & (22.1, 18.4–26.3) & (9.5, 5.8–16.7) & (22.42, 20.2–26.3) & (11.9, 8.57–14.9) \\ \hline \multirow{2}{*}{TA} & \(12.29\pm 3.4\) & 10.37 \(\pm\) 2.8 & 17.89 \(\pm\) 4.1 & 10.34 \(\pm\) 4.1 & 19.22 \(\pm\) 4.1 & 14.73 \(\pm\) 4.1 \\  & (12.1, 10.4–14.8) & (9.8, 6.1–12.8) & (17.7, 15.9–19.4) & (10.0, 6.2–12.8) & (18.6, 14.1–24.4) & (13.5, 13.1–16.1) \\ \hline \multirow{2}{*}{RF} & \(16.40\pm 4.1\) & 14.38 \(\pm\) 2.7 & 17.32 \(\pm\) 4.5 & 14.56 \(\pm\) 4.3 & 17.33 \(\pm\) 4.5 & 15.68 \(\pm\) 4.3 \\  & (16.3, 14.9–18.7) & (14.3, 9.1–18.5) & (16.6, 12.5–21.3) & (14.38, 9.2–18.4) & (18.24, 12.97–21.0) & (14.6, 11.6–19.5) \\ \hline \multirow{2}{*}{GA} & \(16.6\pm 2.8\) & 15.1 \(\pm\) 3.1 & 19.62 \(\pm\) 3.4 & 15.45 \(\pm\) 2.4 & 21.82 \(\pm\) 3.4 & 16.17 \(\pm\) 2.4 \\  & (16.9, 13.7–20.6) & (13.2, 15.1–19.1) & (19.4, 15.9–22.9) & (15.0, 12.0–19.1) & (20.3, 17.8–24.4) & (15.5, 12.1–19.5) \\ \hline \multirow{2}{*}{BF} & \(17.5\pm 3.4\) & 15.45 \(\pm\) 4.1 & 20.46 \(\pm\) 3.5 & 15.42 \(\pm\) 2.5 & 21.48 \(\pm\) 3.5 & 17.2 \(\pm\) 22.5 \\  & (17.4, 15.4–20.8) & (14.2, 10.1–19.8) & (19.6, 17.1–23.8) & (14.29, 10.2–19.5) & (22.1, 15.1–26.1) & (16.9, 12.5–20.2) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Upper and lower extremity muscle activity in obstacle terrain (M \(\pm\) SD (median, IQR)).
## 4 Illustrative Experiment  
As an illustrative experiment, the dependency model-based multiple fault diagnosis method is applied to a control system. The dependency model of the control system is shown as Figure 2. Apart from the common fault \(f_{1}\), two branches are included in the model: (1) The first branch includes the faults \(f_{2}\) and \(f_{3}\), (2) the second branch includes the fault \(f_{4}\).  
In Figure 2, each fault state \(f_{i}\) (\(i=1,2,3\).) in the dependency model can be tested according to the corresponding test \(t_{i}\) along to the arrow. The results of some cases are obvious and definite, e.g.,  
* If the fault \(f_{1}\) happens, the observation results of all the tests including \(t_{1}\), \(t_{2}\), \(t_{3}\) and \(t_{4}\) must be 'Fail';
* If the fault \(f_{4}\) happens, the observation result of the test \(t_{4}\) is definitely 'Fail'.  
In the control system, the prior probability of each fault state is as follows: \(p\left(f_{1}\right)=0.1\), \(p\left(f_{2}\right)=0.2\), \(p\left(f_{3}\right)=0.3\), and \(p\left(f_{4}\right)=0.4\). Assume that the test result of each fault according to the observation result in the system is as follows: \(t_{1}=r_{1}=0\) ('Pass'), \(t_{2}=r_{2}=1\) ('Fail'), \(t_{3}=r_{3}=1\) ('Fail'), and \(t_{4}=r_{4}=1\) ('Fail').