esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation of the Availability of Proposed Model  
### Numerical Simulation of Single-Column Explosives Blasting Test  
The numerical simulation of the single-column explosive blasting test was implemented to verify the availability of the proposed constitutive model.  
The model with the length, the width and the height all 4.0 m is established to simulate the single-column explosive blasting test (Figure 2). The upper surface of the model along the Z direction is a free surface, and other surfaces of the model are non-reflective. The parameters of custom constitutive material are listed in Table 1.  
In Figure 2, the origin (0, 0, 0) of the coordinate system is the top of the charge hole. The radii of the charge hole are 42 mm, and the charging length in the hole is 2.0 m. The explosives are detonated at the bottom of the borehole.  
The high-energy rock explosive model embedded in LS-DYNA, \"MAT_HIGH_EXPLO SIVE_BURN, determines the relationship between the detonation pressure and the volume variation of explosive after the explosions with the JWL state equation [23]. The JWL state equation (see Equation (29)) was proposed by [PERSON] based on [PERSON] and [PERSON]'s work in 1965.  
\[P_{\text{eos}}=\text{A}(1-\frac{\omega}{\text{R}_{1}V})^{-\text{R}_{1}V}+\text {B}(1-\frac{\omega}{\text{R}_{2}V})^{-\text{R}_{2}V}+\frac{\omega\text{E}_{ \text{en}}}{V} \tag{29}\]  
where \(P_{\text{eos}}\) is the pressure of detonation products; A, B, R\({}_{1}\), R\({}_{2}\) and \(\omega\) are parameters of JWL equation of state; \(V\) is relative volume; \(E_{\text{en}}\) is the specific internal energy of detonation products.  
The main parameters of explosive in the single-column explosives blasting test are listed in Table 2.
## 6 Conclusions and Discussion  
Quantum qubit commitment is a rather new concept, which was first proposed in 2018 [14]. It was pointed out in 2021 [15] that the quantum qubit commitment has some unique and important applications beyond quantum bit commitment in the upcoming era of quantum network. Therefore, it is both theoretically interesting and practically valuable to develop unconditional secure quantum qubit commitment protocols. In this paper, we proposed an unconditionally secure relativistic quantum qubit commitment protocol for the first time. Comparing to the existed quantum bit commitment, our protocol does not destroy the coherence of the committed qubit (the collapsed states of qubits B preserve the coherence of the committed qubit). This is the fundamental difference between quantum qubit commitment and quantum bit commitment.  
Since quantum qubit commitment is a stronger version of the quantum bit commitment, our quantum qubit commitment protocol of course can be used to fulfill quantum bit commitment. However, to preserve the coherence of the committed qubit, when our protocol is used to execute a quantum bit commitment, it needs more quantum resources (quantum entanglement in our protocol) than the dedicated quantum bit commitment protocols [9; 10; 12]. This is the limitation of our protocol.  
In the last two decades, relativistic quantum cryptography has become an important branch of quantum physics. Numerous researchers have carried out a great deal of research in this field, including revealing information causality as a physical principle in quantum physics [22] as well as developing relativistic quantum protocols with wide range of important applications, such as quantum tokens [23] and quantum tagging [24]. As our proposed protocol is the first unconditionally secure relativistic quantum qubit commitment scheme, we think that it comes timely in the rapid development of relativistic quantum cryptography and will help stimulate further interest in the theory and practical implementation of this field.  
Conceptualization, H.L. and L.Y.; funding acquisition, H.L. and L.Y.; investigation, H.L.; writing--original draft, H.L.; writing--review and editing, H.L. and L.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (Grant No. 2019 YFB2203903), the National Natural Science Foundations of China (Grant No. 62065006), the Natural Science Foundation of Guangxi (Grants No. 2019 GXNSFA245024 and No. 2020 GXNSFBA159059), the Science and Technology Project of Guangxi (Grant No. AD19245064), Guangxi Key Laboratory Project of Optoelectronic Information Processing (Grant No. GD20103), and Guangxi Key Laboratory Project of Automatic Detection Technology and Instrument (Grant No. YQ20103).  
Not applicable.  
The authors declare no conflict of interest.
# Ceramic Matrix Composites for Aero Engine Applications--A Review
## 5 Non-Oxide CMCs
### Titanium Carbide CMCs  
Titanium carbide (TiC) is a very common reinforcement in a variety of different matrix composites. TiC presents very good physical properties, such as a high melting point, low density, high hardness, and mechanical stiffness, as well as high thermal and electrical conductivity [147]. TiC is made from titanium dioxide reacting with carbon at temperatures greater than 1800 \({}^{\circ}\)C. It is a very hard refractory material with similar properties to tungsten carbide, but to develop a more efficient and hard-use matrix, TiC is bonded with nickel matrix cermet (ceramic metals) so that better precision, cutting speed, and smoothness of the final material is achieved [148]. This occurs because the chemical reaction between TiC-Nickel and Molybdenum (TiC-Ni-Mo) creates well-bonded phases within the cermet, and carbides dissolve [149]. This combination allows for higher hardness with lower density and a better modulus of elasticity. On the other hand, the thermal conductivity of titanium carbide and CMCs are found to be high, such as the thermal expansion, which makes the composite a good conductor of heat [149].  
Through time, many different compositions of nickel-bonded titanium carbides have been tested in a wide span of temperatures and compositions [150]. It was found that compositions with a lower percentage of nickel would allow for increased interphase bond strength and a decreasing number of microstructural flaws (which are stress concentrators) needed for nucleation and propagation of cracks, resulting in the removal of materials [151].  
Research studies mention that liquid phase sintering is a technique used to create titanium carbide CMCs [151]. Various compositions of TiC/NiMo cermet samples were manufactured and tested by changing the material weight compositions, resulting in higher nickel bond content, decreased bulk hardness of the TiC/NiMo composites, and higher wear rate [152]. Besides liquid phase sintering, melting solidification, and powder metallurgy (conventional pressing and sintering) techniques are implemented to fabricate this type of CMCs, due to the high Vickers Hardness of the TiC matrix [151; 153; 154; 155]. However, because of TiC's low density, a uniform distribution of hard phases is not easily achieved. During solidification, the TiC particles show a gradient distribution in the matrix with respect to size and volume fraction [156].
## 3 Possible Use Cases
### SCF Relaying in Multicell Topologies with Heterogeneous Access Networks  
As mentioned before, with fixed infrastructure node placement, the radio link performance depends greatly on users' location within the serving area. Current trends for future network deployments consider the deployment of wireless local area networks to support the increased capacity requirements and improve the link budget. In [27] the authors propose the use of connectivity maps where UTs are informed of the location of the different access technologies from the serving macrocell BSs. In this way, a UT can switch between the different radio access networks (RANs) depending on its location within the cell. With SCF relaying however, such location dependance has less of an impact in the ability of a UT to send its data through the target RAN. A node can postpone transmission such as a desired location is reached before offloading its data. In Figure 3 one such possible scenario is shown. In the figure the coverage map of two macrosite BSs and three WLAN access points (AP) are shown. In such a multiaccess scenario, traffic can be shifted to the preferred location for the target serving cell to be accessed.
# [PERSON] Glacier Status Analysis Based on Historical Data and Moving Detection Using RPAS
## 5 Data Capturing
### RPAS Data Capturing  
Before the flights, flight planning is necessary. The eMotion software is used with the eBee system running on a notebook. This software uses a common GNSS device for navigation. During the flight planning in this software, satellite images from ordinary free web sources (Google, Microsoft, Nokia, and others) are used as a map background. Unfortunately, in Greenland, actual satellite data from these sources was unavailable, and it was not possible to precisely define the exact parameters and destination of the drone flight. This proved to be a problem with flight planning [10; 13; 14]. The front of the glacier was hundreds of m away from the latest satellite images, so the flight plan had to be estimated on-site (Figure 7).  
One single day was set aside for the measurement, and there was not more time. Two changeable cameras were at the drone's disposal during the expedition. Unfortunately, due to a malfunction in the RGB camera, only the NIR (near-infrared) camera was used. As it turned out, the NIR images were more suitable for capturing ice than an RGB camera, because the glacier ice is off-white to light blue, which is not perfect for the image correlation used for image processing. Using the drone eBee, two overflights of the same area were performed on 16 July 2019 with a time span of about four hours. During the first flight, a total of 162 NIR images were taken (Figure 8). During the second flight, 170 NIR images were recorded. During the first flight, the signal was lost due to a very high rock cliff near the last flight line; the drone emergency system interrupted the flight and returned it to the take-off point. For this reason, the number of images is not the same for both flights; however, the loss of eight images did not affect the project. The last flight line was not fully completed; there was no time to repeat the flight and therefore the second flight was slightly modified.  
Figure 7: Flight no.1, senseFly eMotion 2 software; the map background from Google Earth was out of date (2016). The flight plan was prepared at the university before expedition, but the actual position of the glacier front was elsewhere. There was no internet on site, the flight plan had to be estimated and changed at the measurement site.  
The flight time took approximately 35 min at an altitude of 180 m. Due to the smaller capacity of the batteries in the frost, we did not choose a lower flight or a longer flight. Another problem was the high rocks on both sides of the glacier.  
The aim was to record both shores of the moraine (see Figure 7), which could not change in a few hours, unlike the movement of the glacier, which was supposed to be the largest somewhere in the middle.
## 3 Results and Discussion
### TDS-1 Data Analysis Using In-situ Soil Moisture Data  
When trying to correlate the TDS-1 GNSS-R reflectivity measurements with in situ soil moisture values, very few matchups exist, to the point that it is difficult to perform a robust fit over the Remedhus (Spain) and Yanco (NSW, Australia) sites. Only over Brazil, and taking a 30 km radius, enough matchups have been found to obtain representative results. Results are shown in Figure 16. Sensitivities are 7.39 dB/100%, 1.36 dB/100%, and \(-9.10\) dB/100% for incidence angles from 0\({}^{\circ}\) to 30\({}^{\circ}\), from 30\({}^{\circ}\) to 60\({}^{\circ}\), and from 60\({}^{\circ}\) to 90\({}^{\circ}\), respectively. For incidence angles from 0\({}^{\circ}\) to 30\({}^{\circ}\), the value obtained is nicely between 6.62 dB/100% and 9.37 dB/100% encountered in Section 3.2 with respect to L4 and L3 SMOS SM data, respectively. For incidence angles from 60\({}^{\circ}\) to 90\({}^{\circ}\), the derived sensitivity to SM is negative, which can be probably attributed to the small number of samples used and the reduced SM range.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & & & \multicolumn{3}{c}{**BRAZIL**} & \multicolumn{3}{c}{**IBERIAN PENINSULA**} & \multicolumn{3}{c}{**AUSTRALIA**} \\ \hline  & **Correct** & 0°-30° & 30°-60° & 60°-90° & 0°-30° & 30°-60° & 60°-90° & 0°-30° & 30°-60° & 60°-90° \\ \hline  & NO & 12,143 & 19,678 & 5132 & 1763 & 1726 & 505 & 6392 & 6752 & 1815 \\ SMOS L3 SM (25 km) & R & 12,108 & 19,649 & 5099 & 1733 & 1698 & 499 & 6390 & 6746 & 1815 \\  & V & 4455 & 7720 & 1741 & 489 & 723 & 204 & 2818 & 3295 & 706 \\  & V + R & 4455 & 7716 & 1741 & 486 & 719 & 204 & 2818 & 3295 & 706 \\ \hline  & NO & 6473 & 11,672 & 2977 & 2477 & 1413 & 335 & 4992 & 5616 & 1805 \\ SMOS L2 SM (1 km) & R & 6467 & 11,657 & 2945 & 1309 & 1308 & 368 & 4992 & 5610 & 1805 \\  & V & 2200 & 4419 & 1224 & 435 & 385 & 126 & 2871 & 2741 & 1139 \\  & V + R & 2200 & 4417 & 1224 & 849 & 552 & 134 & 2871 & 2741 & 1139 \\ \hline \end{tabular}
\end{table}
Table 4: Number of points of the regression lines shown in the Figures 13–15, and SMOS L2 SM (Low Resolutions) and SMOS L3 Soil Moisture (High Resolution). Note: NO” stands for no corrections applied, “R” for surface roughness correction applied only, “V” for vegetation corrections applied, and “V + R” for vegetation and surface roughness corrections applied. NO, R, and V cases not presented in Figures 13–15.  
Figure 16: Same as Figure 16, but over CEMADEM/Brazil, using a radius of \(\sim 30\) km from the soil moisture stations. Note the limited range of Soil Moisture values, as this region corresponds to the so-called Brazilian semi-arid.
## 2 Review of the USOVA3D Public Database and the Evaluation Protocol  
The USOVA3D public database of annotated 3D ultrasound images of ovaries is summarised briefly in this sequel, while details can be found in our previous work [7]. The database consists of 35 entries, which are provided into training (16 entries) and a testing (19 entries) sets. Each entry contains \(5\) volumes (3D images), namely, the original ovarian ultrasound volume, two times manually annotated ovary and two times manually annotated follicles. Manual annotations (segmentations) were provided independently by two experienced medical experts (i.e., by rater 1 and rater 2). Its volume size is between \([101\dots 229]\,\times\,[89\dots 193]\,\times\,[115\dots 247]\) voxels, with the voxel dimensions 0.2 mm \(\times\) 0.2 mm \(\times\) 0.2 mm. The raters' manual annotations are considered as 'ground truth'. The USOVA3D database is accessible at the web address: [[https://usova3d.um.si](https://usova3d.um.si)]([https://usova3d.um.si](https://usova3d.um.si)) (accessed on 1 January 2022).  
As is widely accepted, the use of training data is foreseen for the development of detection algorithms, and a testing set is intended for an efficiency evaluation. The USOVA3D database introduces a small change in respect of this established concept. Only the original ovarian ultrasound volumes are available to researchers in the testing set, but not the manual annotations of the ovaries and follicles. Validation of the new detection method is, therefore, performed exclusively using the USOVA3D services on the USOVA3D portal, as the 'ground truth' for the testing set is stored on the USOVA3D portal only, and is not part of the publicly accessible USOVA3D database. Such design of the USOVA3D database leads to a more fair validation and comparison of algorithms.
# Influence of Joint Stiffness and Motion Time on the Trajectories of Underactuated Robots
## 1 Introduction
### Contributions of This Work  
Previous works chiefly focused either on the definition of suitable control systems [31; 32; 33; 34; 35] or on the mechanical design (e.g., [36; 37]). The considered movements were generally slow (e.g., more than a couple of seconds), so sometimes, they were not suitable for industrial uses, in which fast movements are required. Typical problems that arise in the industrial environment were scarcely addressed:  
* The effect of motion time on the trajectories and on the swept area of the robot;
* The need of via-points to generate specific trajectories and avoid collisions with obstacles;
* The effect of friction in the nonactuated joint;
* The effect of spring stiffness on the generated trajectories and on the swept area;
* The energy consumed by the robot to perform the task.  
This research deals with the mechanical modeling and simulation of a differentially flat 3-DOF robot. This robot moves in the horizontal plane and is equipped with two actuators and one nonactuated joint with a torsional spring. The focus is on industrial applications in work cells with obstacles. The main contributions of this work are:  
* The introduction of friction in the mechanical model of the nonactuated joint;
* The analysis of the influence of torsional stiffness on trajectories;
* The analysis of the influence of motion time on trajectories;
* The development of an algorithm for the calculation of the area swept by the robot;
* The calculation of motor torques as functions of torsional stiffness and motion time.  
These analyses are useful, because different trajectories of the end-effector and of the robot lead to different areas being swept in the workspace, which make possible the avoidance of obstacles in the work cell. The study was carried out in a Matlab-Simulink environment. Calculated results were validated by means of experimental tests on a subsystem of the simulated robot, including only one actuated joint and one nonactuated joint.  
The paper is structured as follows: Section 2 presents the mathematical model of a 3-DOF robot; Sections 3 and 4 analyze the effect of joint stiffness and motion time on the motion of the robot; Section 5 shows how joint stiffness and motion time can be combined to optimize the dynamic behavior of the system; Section 6 presents the experimental validation; finally, conclusions are drawn in Section 7.
# Optimal Arrangement Strategy of IoT Sensors in Urban Drainage Networks: A Review
## 4 Summary and Recommendations  
This review focuses on a strategy for the optimal layout of IoT sensors for UDNs, covering the general process, principal methods, and application cases. The main conclusions are listed as follows:  
1. The monitoring of drainage networks primarily reflects their operational status and detects and identifies abnormal states through hydraulic and water quality parameters. Different monitoring areas correspond to distinct monitoring indexes, which, in turn, gives rise to varying layout schemes. Nevertheless, the arrangement of IoT sensors for different networks should strive to provide the maximum amount of valid information.
2. In practical scenarios, multiple types of sensors are required for collaborative monitoring and sensing of UDNs. The sensor arrangement methods, based on information theory, statistical theory, and complex network theory, have their own characteristics and adaptation of application. Modularly combining these methods or employing hybrid optimal strategies can be useful for the design and evaluation of optimal layout solutions.
3. The quantitative evaluation of monitoring schemes is mainly conducted based on the degree of objective achievement, performance evaluation indexes of optimization algorithms, comparisons with the original sensor layout scheme, or contrasts in the effects achieved by different optimization methods. Nevertheless, a systematic and standardized quantitative evaluation process and methodology for monitoring schemes remain to be established. The current optimized layout methods may not be optimal and deserve further study.
4. The existing research on the optimal layout of sensors predominantly focuses on spatial distribution and quantity. In the future, dynamic optimization strategies can be explored by implementing a phased deployment-feedback iteration approach. In the initial stage, sensors should be preferentially arranged at critical nodes. Subsequently, the arrangement scheme for the next stage can be adjusted and determined according to the collected feedback data and information gain. The dynamic optimization strategy has the potential to enhance the accuracy and objectivity of scheme design and evaluation.  
Conceptualization, T.G. and Y.W.; methodology, T.G. and Y.W.; software, Y.W.; validation, T.G. and Y.W.; formal analysis, Y.W.; investigation, Y.W.; resources, Y.M.; data curation, Y.W.; writing--original draft preparation, T.G. and Y.M.; writing--review and editing, Y.M. and Y.W.; visualization, T.G.; supervision, Y.M.; project administration, Y.M.; funding acquisition, Y.M. All authors have read and agreed to the published version of the manuscript.  
This research study is supported by the National Key R&D Program of China (Grant No. 2023 YFC3208905), the Natural Science Foundation of Zhejiang Province (Grant No. LZJWZ3E090009), the National Natural Science Foundation of China (Grant No. 52300122), and the Fundamental Research Funds for the Central Universities (226-2024-00033).  
The authors declare no conflicts of interest.
## 3 Integrated Navigation  
Before looking at the GNSS/INS integration algorithms, GNSS-only processing is briefly reviewed. To this end, in this work, the GNSS measurements are processed in single point mode, so no differential corrections are applied and the deployment of a reference station is unnecessary. Only pseudorange and Doppler (phase rate) observables are used.  
To account for the fact that satellite measurements at low elevation angles are generally noisier [28], the measurements are weighted the sine of the satellite elevation angle, as proposed in [19, 27]. To consider also the different accuracy related to the pseudorange and Doppler observables, the weight (reciprocal of variance) associated to the generic measurement is expressed by:  
\begin{table}
\begin{tabular}{l r r r} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**IMU Grade**} \\ \cline{2-4}  & **Navigation** & **Tactical** & **MEMS** \\ \hline
**Accelerometers** & & & \\ In-Run Bias (mg) & 0.025 & 1 & 2.5 \\ Turn-On Bias (mg) & - & - & 30 \\ Scale Factor (PPM) & 100 & 300 & 10,000 \\ VRW (g/\(\mathrm{\SIUnitSymbolMicro Hz}\)) & - & 2.16e–06 & 370e–06 \\ \hline
**Gyros** & & & \\ In-Run Bias ( \(\mathrm{\SIUnitSymbolMicro h}\)) & 0.0022 & 1 & \(<\)1,040 \\ Turn-On Bias ( \(\mathrm{\SIUnitSymbolMicro h}\)) & - & - & 5,400 \\ Scale Factor (PPM) & 5 & 150 & 10,000 \\ ARW (\(\mathrm{\SIUnitSymbolMicro h}\)/\(\mathrm{\SIUnitSymbolMicro Hz}\)) & 6.92 & 7.5 & 226.8 \\ Approx. Cost & \(>\)\$90,000 & \(>\)\$20,000 & \(<\)\$2,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of IMU characteristics for different grades of sensors (from [6, 27]).  
\[w_{ii}=\sin\left(el\right)/\sigma_{m}^{2} \tag{2}\]  
where \(\sigma_{m}^{2}\) is either the pseudorange variance \(\sigma_{PR}^{2}\) or the pseudorange rate variance \(\sigma_{PRdot}^{2}\).  
The GNSS solution is obtained using the WLS (Weighted Least Squares) method, whose equation is:  
\[\underline{\Delta x}=\left(H^{T}W\underline{H}\right)^{-1}H^{T}W\underline{ \Delta\rho} \tag{3}\]  
with \(\underline{\Delta\rho}\) being the measurement misclosure vector,  
\(H\) being the geometry (design) matrix,  
\(\underline{\Delta x}\) being the unknown vector of corrections to the current state estimates, and  
\(W\) being the diagonal weighting matrix whose elements \(w_{ii}\) are from Equation (2).  
The states are position, velocity and clock errors. If a single GNSS system is considered (e.g., GPS or GLONASS only) the clock error is modeled by two states: a bias and a drift. If two GNSS are combined (e.g., GPS/GLONASS case) a further state, representing the inter-systems time offset, must be included.