esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Collection and Preparation  
### Data Acquisition  
The ship employed in the study is a bulk carrier and its characteristics are listed in Table 2 The initial dataset reflected an operational period of 32 months while the sampling period was set to be 1 min, accumulating approximately 1.5 million measurements.  
For the data collection task, the LAROS(tm) Continuous Monitoring System (L-CMS) was used [31]. The L-CMS allows the synchronized and reliable signals and data collection from any type of sensor, measuring device, instrument, or control system, following the process presented in [32]. Figure 5 presents the overall system architecture.  
\begin{table}
\begin{tabular}{c c} \hline
**Metric Definition** & **Meaning** \\ \hline \(Accuracy=\frac{TP+TN}{TP+TP+TN+FN}\) & The ratio of correctly evaluated samples \\ \hline \(Precision=\frac{TP}{TP+TP}\) & The rate of correctly labeling a sample as positive \\ \hline \(Recall=\frac{TP}{TP+TN}\) & The rate of identifying correctly a positively labeled observation \\ \hline \(Specificity=\frac{TN}{TN+TP}\) & The ratio of correctly identifying a negatively labeled observation \\ \hline \(F1=\frac{2 TP}{2 TP+FP+FN}\) & The harmonic mean of precision and recall \\ \hline \end{tabular}
\end{table}
Table 1: Metrics definition.  
Figure 5: Operational flow of LAROS(tm) CMS providing synchronized and reliable data.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Length & 300.00 (m) \\ Breadth (Moulded) & 55.00 (m) \\ Depth (Moulded) & 25.00 (m) \\ \hline \end{tabular}
\end{table}
Table 2: Ship characteristics.  
More specifically, a secure wireless mesh network (IEEE 802.15.4 MESH) is installed inside each floor of the vessel, based on LAROST(tm) Smart Collectors. After acquisition, the data are further processed to the gateway periodically and are easily selectable by the end user. Additional layers and payload encryption are used to ensure both quality of service and cyber security at the edge level. This architecture enables the scalability and extendibility of the system together with efficient operations within the vessel's harsh environment. The preprocessed data are further delivered to the onboard server where they are stored locally for a predefined period (normally 6 months) and are also transferred via normal satellite communication to the next processing level (cloud). At this level, fleetwide data are collected and processed based on dedicated data analytical software tools. Figure 6 presents the signal sources and the specific parameters of interest to our work. With exception of the signals pertinent to the exhaust gas temperature of the main engine's six cylinders, the purpose of the other parameters was to facilitate the data preprocessing phase of our study, for instance, through the filtering process.
## 2 Numerical Study: Physical Understanding of Wave Propagation Mechanism
### Results: Shock Wave Propagation Visualization
This shows that the shock wave behavior (propagation speed > speed of sound) tended to go back to the acoustic pressure wave behavior (propagation speed = speed of sound) with propagation distance. The change in wave velocity observed in the \(15D_{d}\)-length region around the leak would complicate the localization system if one of the sensors were located too close to the leak location. This is a plausible  
Figure 6: Shock wave visualization of the 2D simulation: static pressure contour plot at, respectively, \(t_{1}=9\times 10^{-5}\) s, \(t_{2}=5\times 10^{-5}\) s, and \(t_{3}=4\times 10^{-5}\) s.  
explanation for the remaining localization error in the experiments of [PERSON] et al. [10] since they assumed constant propagation speeds in small length ducts. Therefore, both sensors should be placed far enough from the critical fatigue zone where cracks can occur or the localization method should be adapted taking into account the non-constant velocity evolution of the wave in order to be applicable in any situation. When employed as a leak detection principle for long pipelines, this shock wave behavior around the leak with non-constant propagation speed is clearly less important than for the eSHM-system. Due to the longer propagation distance, the non-constant behavior of the velocity close to the leak will be averaged out. In Figure 8, the shock front pressure is extracted at different locations along the duct walls as a function of time. The locations at which the pressure is extracted over time are indicated with a black cube on the corresponding pressure contour plots. The red curve gives the first pressure jump measured by the imaginary sensor located on the lower wall of the duct, just under the leak position. Later, the second pressure step was measured on the upper wall of the duct at a distance \(2D_{d}\) from the leak position (see blue curve). Finally, due to the curved shape of the shock front, the third pressure jump (yellow curve) was measured around \(1\times 10^{-5}\) s later w.r.t the blue curve. The pressure jump in blue did not undergo any reflection before being measured. This explains why its amplitude was lower than for the red and yellow pressure jumps. The red pressure jump had a higher amplitude since it was measured just at the hard wall reflection location. With traveling distance, the amplitude of the shock front lowered due to friction with the wall. The increasing surface of the spherical shock after its reflection under the leak location was also responsible for the amplitude decrease. The shock started at the leak location as a point of energy. This energy was then distributed over the expanding surface of the shock front. For the blue and yellow curves, a second pressure jump was observed at a later time. These jumps are identified in the corresponding contour plots as being the reflections of the shock arriving later at the sensor locations. Figure 7: (**a**) Wave amplitude evolution with propagation distance. (**b**) Wave propagation velocity evolution with propagation distance.
# Solution-Based Synthesis and Processing of Metal Chalcogenides for Thermoelectric Applications
## 2 Solution-Based Approaches for Synthesis of Metal Chalcogenides
### Liquid-Based Exfoliation Method
The fabricated porous SnSe\({}_{1-x}\)S\({}_{x}\) nanosheets exhibit a low thermal conductivity of 0.4 W m\({}^{-1}\)K\({}^{-1}\) and a maximal ZT value of 0.12 at 310 K, which is significantly higher than that of pristine SnSe. Room-temperature ionic liquids are superior exfoliants as they possess attractive physical and chemical properties including tunable viscosity, low vapor pressure, high ionic conductivity, and high thermal stability, and they can potentially be recycled. In 2015, [PERSON] and coworkers studied the liquid exfoliation of Bi\({}_{2}\)Te\({}_{3}\) nanoplatelets in an ionic liquid (1-butyl-3-methylimidazol chloride, [C\({}_{4}\)mim]Cl) solvent (Figure 2b) [45]. Both experiments and molecular dynamics simulations demonstrated that the [C\({}_{4}\)mim]+ cation is the dominant factor for the interlayer sliding and separation of Bi\({}_{2}\)Te\({}_{3}\) layers. Bi\({}_{2}\)Te\({}_{3}\) nanoplatelets in [C\({}_{4}\)mim]Cl were synthesized at 0.05 wt % through 1-h tip sonication at a temperature of 80\(-\)150 \({}^{\circ}\)C and a power of 225-600 W. After sonication, as-synthesized nanoplatelets with a thickness of 1 to 2 \(\upmu\)m were exfoliated to 15 \(\pm\) 8 nm nanosheets. Later, the same group examined different exfoliation solvents and dispersion of Bi\({}_{2}\)Te\({}_{3}\) from eight imidazolium-based ionic liquids at temperatures between 350 to 550 K [46]. Figure 2: (**a**) Schematic illustration of how layered Bi\({}_{2}\)Te\({}_{3}\) can be intercalated with lithium and exfoliated with water to form a colloidal suspension of Bi\({}_{2}\)Te\({}_{3}\) nanosheets. (**b**) Schematic of the ionic liquid exfoliation process to generate 2D nanosheets. Readapted with permission from The Royal Society of Chemistry [43] and American Chemical Society [45]. Electrochemical exfoliation methods are gaining great research interest as the procedure is simple and the synthetic conditions are mild and easily controllable with high efficiencies. They can be applied to layered bulk materials in a liquid electrolyte to induce structural expansion. Promising methods for large-scale exfoliation of 2D materials include cathodic exfoliation or anodic oxidation-cationic intercalation. These layered bulk materials contain robust in-plane covalent bonds, but out-of-plane bonds that are held by weak van der Waals forces allow easy exfoliation into thin 2D nanosheets by charged ions [47]. In 2016, [PERSON] et al. reported the exfoliation of layered Bi\({}_{2}\)Te\({}_{3}\) via electrochemistry (Figure 3) [48]. In this method, small flakes from the synthesized crystals were electrically connected to one electrode through a copper tape with a counter electrode made of Pt foil. Exfoliation was simply carried out by alternating the voltages from the anode (+10 V) and cathode (-10 V) to a solution of 0.5 M Na\({}_{2}\)SO\({}_{4}\) electrolyte containing the crystals.
## 1 Introduction
Some prospective trials have studied the role of SBRT for spinal metastases. In a phase I/II trial that enrolled 61 patients with non-cervical spine tumors, the study showed a control rate of 88% with 18 months of follow-up with a single-fraction SBRT [22]. The RTOG 0631 was a phase II trial that included 44 patients with 1-3 spinal metastases. The administration of a 16 Gy single-fraction SBRT was shown to be feasible [23]. The phase III SC.24 clinical trial demonstrated that spinal SBRT was superior to cEBRT in terms of pain relief and has therefore been established as one of the standard treatments [24; 25]. Spinal SBRT is practiced today as an alternative to conventional palliative radiation in primary treatment, re-irradiation, and in the post-operative setting, albeit with no or very few randomized trials to support its practice. However, it is true that the limitations of spinal SBRT, as well as its possible complications, are known to a greater extent [20]. Possible complications include vertebral body fractures (VBF) (11-39%) and myelopathy (0.4%) [19; 24]. Another possibility is the appearance of the \"Pain Flare\" effect (14-68%), a transient increase in pain that can be controlled with corticosteroids as a preventive measure. The aim of this study is to describe our institution's experience with robotic SBRT for spinal metastases in terms of feasibility, oncological results, toxicities, and pain relieve. Also, we will analyze factors associated with responses following SBRT.
# Independent Double-Boost Interleaved Converter with Three-Level Output
## 2 Converter Topology Analysis
### DCM Operation of the Proposed Converter  
#### 2.3.1 Case 1  
Figure 6 shows the steady-state waveforms of inductor voltage and current for DCM operation in Case 1, where three different time intervals (D, D\({}_{1}\), and D\({}_{2}\)) can be observed in a switching period. These waveforms are specific to the regular DCM boost converter [34].  
From the average inductor voltage equation, the time interval defined by the time D\({}_{1}\) in Figure 6 can be expressed as:  
\[\mathrm{D_{1}}=\frac{2\mathrm{V_{in}D}}{\mathrm{V_{out}}-2\mathrm{V_{in}}} \tag{6}\]  
Considering the output C1 capacitor charge balance, the converter load resistance R, the current area A from Figure 6, and the peak current i\({}_{\mathrm{pk}}\), the diode D1 average current equation can be described as:  
Figure 4: CCM inductor voltage and current waveforms.  
Figure 5: Classic boost converter (purple line) and IDBIC (for D < 0.5 the orange line and for D > 0.5 the blue line) DC conversion ratio M(D) in CCM.  
\[\langle\mathrm{i_{D1}}\rangle=\frac{1}{\mathrm{T_{s}}}\mathrm{A}=\frac{1}{\mathrm{T_{ s}}}\left(\frac{1}{2}\mathrm{i_{pk}D_{1}T_{s}}\right)=\frac{\mathrm{V_{in}DD_{1}T_{s}}}{2 \mathrm{L_{1}}}=\frac{2\mathrm{V_{C1}}}{\mathrm{R}} \tag{7}\]  
By substituting the time interval \(\mathrm{D_{1}}\) into (7), the voltage gain in DCM Case 1 becomes:  
\[\frac{\mathrm{V_{out}}}{\mathrm{V_{in}}}=\frac{\left(2+\sqrt{4+\frac{8\mathrm{ D}^{2}}{\mathrm{K}}}\right)}{2} \tag{8}\]  
where:  
\[\mathrm{K}=\frac{2\mathrm{L_{1}}}{\mathrm{RT_{s}}} \tag{9}\]  
Using (8) and different values of the parameter \(\mathrm{K}\), Figure 7 shows the DC conversion ratio, M(D,K), of the IDBIC boost converter for DCM Case 1.
## 5 Key Challenges and Future Directions  
### Challenges  
The Internet of Vehicles' quick advancement is deteriorating numerous barriers in the development of smart cities and urban planning, but there are still numerous key challenges to overcome such as security, reliability, interoperability, real-time communication, multi-model sensing, and heterogeneity.  
* **Security and Privacy:** Applying different techniques need to handle secure communications. Moreover, the wireless nature of wireless sensor transmission poses a great risk to cyber-attacks than fixed-line infrastructure. Interference allows for easy eavesdropping or denial of wireless transmission. This makes it a prime target for hackers, with the significant potential catastrophic harm. Furthermore, sensors can be used without the need for safeguards or observation. This raises the danger of attackers exploiting the mobile nodes and gaining access to sensitive data or credentials to launch an attack via remote access. Sensors can potentially keep confidential material on the privacy of their clients.
* **Reliability:** Data dependability is critical for urban transportation operation and assessment, however, due to its minimal exposure and restricted capability of sensing wisps, as well as the lossy nature of transmissions, it became difficult while employing WSN. As a result, any proposed WSN architecture and communication protocols should incorporate providing reliable edge connections.
* **Interoperability:** Traffic control is an inter-optimizing issue that necessitates the improvement of a variety of weighted parameters such as gas emissions, number of stops, and traffic noise levels, among others. This multiplicity of feasible solutions necessitates a multiplicity of sensing devices, which is one of WSN's primary design goals.
* **Real-time Communication:** Various approaches involve real-time data collecting to make in-the-moment choices. A typical scenario is traffic signal control, in which variable crossroads planning is dependent on real-time traffic distribution.
* **Multi-model Sensing or Heterogeneity:** Due to the fast advancement of wireless technologies and many sensing devices, suggested systems must consider equipment diversity to assure compatibility and flexibility.
# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
## 2 Methodology
### Method Overview
#### 2.2.1 Image Preprocessing
By performing gray-level average processing on the images in the entire database, the overall gray-level average image could be obtained. The Gaussian blur method was used to find the gray-level distribution range of the static and dynamic objects (e.g., building structure, sky and water) on the image It has been found that the objects have similar gray level distribution. Gaussian blur method has been widely used in past research to reduce image noise and detail levels of images [27]. After Gaussian blur processing, the gray level distributions of objects in the image could be clearly distinguished. After analyzing the Gaussian blurred image, we find that the information of the building structure is concentrated in the range of 50 to 100. An image example with a gray level between 50 and 100 is shown in Figure 5.  
In the database, each image has a total number of 2,073,600 pixels. We calculated the mean square error of the gray value for the same point in all images so as to obtain the gray image as shown in Figure 6. In Figure 6, it could be seen that the sky area, which was  
Figure 4: Comprehensive image after gray weighted average. Figure 5: Image with grayscale 50–100. not useful for visibility evaluation, has been filtered out. However, the gray mean square error method cannot be used to remove the water background interference in the image. Therefore, we have chosen gray-level weighted averaging to remove the background (i.e., water area) interference.
## 3 Optimal Energy Consumption Model in HWSNs  
### System Model  
#### 3.1.1 Definition 1  
We consider a probabilistic WSN consisting of \(N\) sensors, denoted by \(s_{1},s_{2},\cdots,s_{N}\), respectively, and one sink deployed at the edge of a square area. All the sensor nodes know their location information. Furthermore, after the node is deployed, the position will not change. Each node has a unique identity and may determine the distance from the sender node based on the received signal intensity. Also, the transmission power can be adjusted freely by the nodes to save energy.
# Real-Time Stereo Visual Odometry Based on an Improved KLT Method
## 3 Detailed Description of the Method
### Bucketing and Triangulation  
Some studies have found that not all detected feature points are suitable for accurate tracking [16]. The work presented in [24] confirmed that feature selection can significantly reduce the number of iterations in the RANSAC scheme. This means that a subset of carefully selected features can not only prevent estimation bias, but also improve the real-time performance of SVO. Thus, it is generally required that the feature points should be uniformly distributed over the image plane, which can be implemented through bucketing technique [25]. In this case, each image is split into 50 \(\times\) 50 pixel-sized blocks, i.e., buckets. In every bucket, only the strongest feature is kept, and the others are discarded.  
Afterward, the 3-D coordinates of the selected features are calculated using intersecting rays projected through the stereo observation models, i.e., triangulation, as shown in Figure 2. In the absence of error, the rays of the same feature points in the stereo frame (\(\mathbf{q}_{l}\) and \(\mathbf{q}_{r}\)) intersect at point \(\mathbf{P}\) in the 3-D spatial space. However, due to image noise, camera model uncertainty, and matching error, they do not always intersect. The shorter the distance between the two rays is, the more accurate the results that stereo matching can obtain. In the implementation, the feature correspondences which intersection distance is greater than 0.1 m is eliminated.
## 6 Conclusions  
With respect to time-dependent damage and large deformation and instability in deep roadways, an engineering-scale model was established to simulate and analyze the evolution law of deformation, the number of cracks, and the damage degree of surrounding rock in deep roadways using UDEC. The following conclusions were obtained:  
(1) A strength attenuation of the contact surface during the rheological process was achieved based on the function between the cohesion and the time and the proportional relationship in the number of yielding deformation bodies in the model. The developed strength-attenuation-based rheological model can well describe the entire process of rock rheology.  
(2) The results of engineering-scale simulation analysis revealed that the expansion of shallow and deep cracks caused the primary plastic deformation and secondary rheological deformation of the surrounding rock. During the rheological process, massive internal cracks in the rock expanded to intensify the deformation of the roadway. After the surrounding rock entered the accelerated deformation stage, the rheological deformation rate increased by 21.4% every 55 days on average in the absence of further control measures, which ultimately induced severe roadway damage.  
(3) Based on the mechanism of time-dependent instability of deep soft-rock roadways, a control technology of high-preload bolt + deep- and shallow-borehole crack filling was proposed. Both the simulation results and the on-site observation data indicate that this technology can notably suppress crack propagation and the surrounding rock rheology, effectively control roadway deformation, and ensure the safety and stability of the roadway.  
In view of its effective application in the deep soft broken surrounding rock, it can be considered that this technology can provide a reference for designing support measures for deep and shallow fractured surrounding rock of roadways. Considering that water can reduce the effectiveness of the bolt support and crack filling, further research is needed to determine whether this technology is applicable to water-rich roadways.  
Conceptualization, B.W. and J.C.; methodology, C.L.; investigation, T.W.; writing--original draft preparation, B.W.; writing--review and editing, C.L., T.W. and W.S.; supervision, J.C. and X.W.; funding acquisition, B.W., J.C., T.W. and W.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (52204079, 52174105, 52104117, and 52204081) and the Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology (2023 jirc28).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors are grateful to Huaibei Mining Group Co. Ltd.  
The authors declare no conflict of interest.
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 3 Assessment of Processing Time and Memory Requirements
### Acquisition of a Pilot Channel with Assistance
#### 3.3.2 Coherent Integration Only with Semi-Parallel Implementation  
From the previous cases, it is easy to generalize to the case where \(N_{\rm A}\) accumulators test \(N_{\rm A}\) delays in parallel among the \(N_{\rm SCB}\) possible. The processing time for one frequency bin is  
\[\begin{array}{l}T_{\rm FB}=KN_{\rm FFT}N_{\rm S}\lceil\frac{N_{\rm SCB}}{N _{\rm A}}\rceil+2(N_{\rm FFT}+L)-(N_{\rm P}+N_{\rm Z})\\ =KN_{\rm S}\lceil\frac{N_{\rm SCB}}{N_{\rm A}}\rceil(2N_{\rm P}+N_{\rm Z})+3N_ {\rm P}+N_{\rm Z}+2L\\ =N_{\rm P}\Big{(}2 KN_{\rm S}\lceil\frac{N_{\rm SCB}}{N_{\rm A}}\rceil+3 \Big{)}+N_{\rm Z}\Big{(}KN_{\rm S}\lceil\frac{N_{\rm SCB}}{N_{\rm A}}\rceil+1 \Big{)}+2L.\end{array} \tag{21}\]