esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Radiators are used on satellites to dump waste heat into deep space. Technological advancements and demand for high power payloads on small nanosatellite platforms increase the need for better thermal management systems. This study analyzed a few passive thermal control methods, such as multi-layer insulation, thermal straps, heat pipes, and passive radiators. Thermal straps are often used as high conductive flexible thermal links to transfer heat from inaccessible locations in the satellite to various other parts. An experimental investigation is carried out on a custom-designed thermal strap to characterize its conductance for different heat loads. The passive radiator is designed and studied in detail for many parameters, such as radiating surface area, radiator mounting type, surface coating, and radiator mass for a 6U satellite platform. The effect of different orbital inclinations on the passive radiator is studied for the same satellite geometric parameters.  
Design, S.S.; methodology, S.S., A.C.; Analysis, S.S.; Testing and validation, S.S.; investigation, S.S., A.C., D.V., B.L.; resources, S.S.; writing--original draft preparation, S.S.; writing--review and editing, S.S., D.V., B.L.; supervision, A.C., D.V., B.L.; project administration, S.S., A.C. All authors have read and agreed to the published version of the manuscript.  
This work was completed from [PERSON]'s NTU startup grant \"CubeSat for Earth Science observations\".  
Not applicable.  
Data available on request due to restrictions on privacy.  
The corresponding author would like to thank the Satellite Research Centre (SaRC) at Nanyang technological University (NTU) for providing sufficient support in the form of software, facility, and past satellite mission data to carry out this study. Also, the author would like to thank CR technologies for supporting with an academic Thermal Desktop(r) licence.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript.  
Active Thermal Control System  
Passive Thermal Control System  
Multi-layer Insulation  
MWR Mid-Wave InfraRed  
Short-Wave InfraRed  
Thermal InfraRed  
DC Dark Current  
InfraRed  
EOM Earth Observation  
Atomic Oxygen  
Ultraviolet Radiation  
EOL End-of-Life  
Beginning-of-Life  
TCC Thermal Contact Conductance  
Thermal Vacuum Chamber  
LFO Low Earth Orbit
# Microchannel-Embedded D-Shaped Photonic Crystal Fiber-Based Highly Sensitive Plasmonic Biosensor
## 5 Conclusions  
In this paper, a modified D-shaped PCF-based plasmonic sensor with a larger semicircular open channel is proposed to detect RI from 1.33 to 1.44. Plasmonic material is applied inside the smooth inner surface of the semicircular-shaped channel rather than on the flat surface of an ordinary D-shaped sensor to avoid surface roughness. A large semicircular-shaped channel helps the easy coating of the plasmonic film with quick infiltration by the analyte to be detected and effortless cleaning of the channel. This unique design improves the coupling between fundamental and SPP modes by reducing the separation between the plasmonic layer and the solid core, which results in higher sensitivity. It is found numerically that the sensor has a high spectral sensitivity of 63,000 nm/RIU with a resolution of 1.59 \(\times\) 10\({}^{-06}\) RIU and an amplitude sensitivity of 1439 RIU\({}^{-1}\) with a resolution of 6.94 \(\times\) 10\({}^{-06}\) RIU. Additionally, the sensor is highly linear with an R-squared value of 0.99559, and the FOM of the sensor is 839.  
Conceptualization, J.M.N. and T.A.; writing--original draft preparation, J.M.N.; writing--review and editing, T.A., M.A.H., J.A. and N.H.H.; supervision, T.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data are generated using COMSOL Multiphysics software. The raw data are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
## 3 Field Measurements and Model Verification  
Field measurements were performed to determine the ground vibration caused by a metro train running on an ordinary track of Tianjin metro line 3 to calibrate the correctness and accuracy of the model calculations. The map of the test section is shown in Figure 5.
## 2 Theoretical Background
### Silicon-on-Insulator Slot-Waveguides  
Silicon-on-insulator (SOI) wafers consist of a stack of crystalline silicon-substrate, a buried oxide (BOX) layer, a second silicon layer and a cladding top-layer. The cladding material can be air (\(n\approx 1.0\)), silicon dioxide (\(\text{SiO}_{2},n=1.44\)) or an organic polymer (\(n\approx 1.5\)). In any case, the cladding material must have a lower refractive index than the silicon waveguide to ensure total internal reflection and thereby a guidance of the light wave. The purpose of the BOX-layer is to act as the lower cladding material. Furthermore, the BOX-layer has the advantage of less parasitic capacitance, which is crucial in high-speed modulator applications [17]. Figure 3 illustrates different kinds of integrated SOI waveguides, formed by etching of the silicon layer and enclosed by the deposition of a cladding material. While Figure 3a shows a conventional strip-waveguide, Figure 3b,c show two types of slot-waveguides, where the two silicon wires are separated by a slot (gap). The latter is responsible for a high optical field confinement and a high interaction with the EO slot material by the evanescent electric field of the propagating light. The slab in Figure 3c, also called strip-load, is used to contact the slot-waveguide to the electrodes of an EO-modulator device. With this application in mind, it is the configuration studied in this work. Furthermore, in case of SOH technology, PMMA+DR1 is deposited as the cladding material by spincoating into the slot region and covers the silicon wires and slabs [18].  
Figure 3: Cross sections of (**a**) conventional strip-waveguides, (**b**) slot-waveguides and (**c**) a slot-waveguide with slabs. The latter is the configuration studied in this work.  
Figure 2: Dispersion curves of PMMA+DR1 for different mass concentrations \(C\) of the contained DR1 chromophore, where \(n\) is the refractive index and \(\kappa\) the extinction coefficient.
## 2 Related Works
### Deep Learning-Based Remote Sensing Imagery Methods
#### 2.2.4 AF2  
[PERSON] et al. [11] proposed the adaptive focus framework (AF2) to perform aerial imagery segmentation, which is a specific semantic segmentation task for remote sensing images. The adaptive focus framework (AF2) offers a hierarchical strategy for the segmentation procedure and adaptively utilizes multiscale representations, which are generated by widely used convolutional neural network models.  
At each scale's representation, AF2 introduces a learnable module known as the adaptive confidence mechanism (ACM). This mechanism dynamically assesses the confidence of the scale's representation for different objects. It dynamically determines which scale of representation is most suitable for segmenting different objects based on their characteristics and complexities. AF2 employs the ACM to filter and select pixels that require accurate segmentation. The mechanism adapts to the specific object characteristics in the imagery and makes decisions on a per-pixel basis regarding the most appropriate representation to use.  
AF2 was evaluated on three benchmark datasets, and the results demonstrated that AF2 can significantly improve segmentation accuracy compared to existing methods.
## 4 Differences in Basin Architecture and Sedimentary Evolution
### Depressed Basins  
Compared with other types of basins, the formation of depressed basins is controlled by strike-slip transform faults with a unique architecture. Vertically, the development and distribution range of rift sequences are narrow, and the thickness of depression sequences is large. Horizontally, it displays the characteristics of a \"narrow continental shelf and steep continental slope.\"  
Taking the Angoche Basin as an example (Figure 5), the extension--strike-slip stress transformation caused by the Late Jurassic seafloor spreading made the East Gondwana and Madagascar separate from Africa along the SN-trending Davie Ridge and Mozambique Ridge and other transform faults, forming rifted basins with steep-dip boundary faults. After the evolution of rifts and passive continental margins, the Angoche Basin was formed under the control of strike-slip faults with steep dip angles. The Karoo-Jurassic rift sequences are narrow and deep, mainly filled with elastic deposits, and the overlying depression sequences of the drift period are thick, mainly filled with elastic deposits. Seismic data reveal that graben and half-graben units are distributed between rift and depression sequences, filled with wedge-shaped sediments. The seismic reflections in the rift sequences exhibit weak amplitude and near-blank architecture, and gravity flow deposits such as submarine fans are easily formed at the continental slope and continental uplift in the depression sequences. They are formed by the sand deposits on the basin coast collapsing along the steep slope under the disturbance of earthquakes, tsunamis, and other factors. It is speculated that they belong to the deep-water slip-collapse-debris flow-turbidity current sedimentary system [25].  
Horizontal east-west zoning: from west to east, according to the current stratigraphic dip, it can be divided into an upper gentle slope belt (less than 1\({}^{\circ}\)), a middle steep slope belt (1-4\({}^{\circ}\)), and a lower gentle slope belt (less than 2\({}^{\circ}\)), roughly corresponding to the continental shelf, continental slope, continental rise, and abyssal plain. The continental shelf area has the smallest sedimentary thickness and more faults, while the continental slope area has the largest sediment thickness, and the sedimentary characteristics of wedge-shaped deep-water gravity flow are obvious. Hence, it is speculated that slip-collapse-debris flow sediments are mainly distributed in this tectonic environment, and faults are not developed. The continental rise area has a larger sedimentary thickness, flat strata, and undeveloped faults. It is mainly characterized by parallel reflection architectures and is speculated to be dominated by turbidite, fine sandstone, and shale deposits.  
Figure 4: Two-dimensional seismic section AA\({}^{\prime}\) of the Obbia Basin (its location is shown in Figure 1). There is a significant difference between the two sets of sequences (lower-rift and upper-depression) in the vertical direction.
# Cluster-Centered Visualization Techniques for Fuzzy Clustering Results to Judge Single Clusters
## 4 Cluster-Centered Visualization Techniques
### Method: Angle-Mapping  
The method AM considers the data points from the viewpoint of one central cluster whose prototype is placed at the origin of the two-dimensional visualization space. The distances of the data points to this cluster center are preserved in the visualization. Polar coordinates are used for the visualization on the plane. The radius for the polar coordinates of a data point is defined by the distance of the data point to the prototype of the central cluster. The visualizations try to preserve angles as much as possible. Since not all angles can be preserved, angles between data vectors that have similar distances are prioritized by the visualization. For this purpose, the data points are placed in the visualization step by step. The zero angle is assigned to the first point. For the following points, the point with the most similar radius among those points that were already placed in the plane is selected, and the angle between these two points is preserved. Since there are two possible angles for the new points: the positive or the negative original angle. To decide between the positive and negative angles, the point with the second most similar radius to the new point among the already placed points is selected to minimize the error of the angle between the new point and this second selected point among the already placed points.
## 5 Implementation of the Proposed Model
### Design Phase
#### 5.2.2 User Profile Acquisition Stage  
At this stage, a set of states were defined based on configurable parameters, such as speed, music, and the number of obstacles (Table 2). To define the states, test sessions were conducted with a duration of 5 min, where the player used the virtual environment, then proceeded to generate the states of satisfaction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Mode** & **Description** \\ \hline \multirow{3}{*}{Speed} & S1 & Speed is set at 10 km/h. \\  & S2 & The speed is set at 15 km/h. \\  & S3 & The speed is set at 20 km/h. \\ \hline \multirow{3}{*}{Music} & M1 & Song: Tusa by [PERSON] and [PERSON] \\  & M2 & Canción: Sex on Fire de Kings of Leon \\  & M3 & Song: You’ll Be Under My Wheels by The Prodigy \\ \hline \multirow{3}{*}{Obstacles} & O1 & Less than 10 obstacles on the track \\  & O2 & Between 10 and 20 obstacles on the track \\ \cline{1-1}  & O3 & Between 20 and 30 obstacles on the track \\ \hline \hline \end{tabular}
\end{table}
Table 2: Customized parameters of the virtual environment. Each variable has 3 different options to select from a total of 24 combinations.  
Figure 13 represents the user preferences process. First, the user enters a testing stage and chooses an option to acquire his preferences, said preferences can be obtained from the following options.  
* Option 1: the user enters the test and, once in the test, selects the parameters he/she finds satisfactory.
* Option 2: the user enters the test and once it is completed, the user selects the preferred parameters.  
Once the user's preferences are obtained, the data is preprocessed. Here, a combination of the parameters is assigned to a set of satisfaction states \(S_{t}=\{s_{0},s_{1},s_{2},\ldots,s_{6}\}\)--states corresponding to the Q-learning algorithm. Subsequently, a reward matrix (Equation (2))  
Figure 12: The virtual environment adapts to the user’s preferences by accessing the desired parameters at the user preference acquisition stage.  
Figure 13: Related elements in the user profile acquisition stage.  
is generated from the defined states. The mapping of the states with the combination of the parameters is presented in the Table 3.  
\[\mathbf{R}=\begin{bmatrix}s_{0}&s_{1}&s_{2}&s_{3}&s_{4}&s_{5}&s_{6}\\ -5&0&0&-1&-1&-1&-5\\ -1&-1&0&0&-1&-1&-1\\ -1&-1&0&0&-1&-1&-1\\ -11&-1&-1&0&0&-1&-1\\ -1&-1&-1&-1&-1&0&0\\ -1&-1&-1&-1&0&0&10\\ -5&-1&-1&-1&-1&0&10\\ \end{bmatrix} \tag{2}\]  
\[\bullet\quad S_{0}:\text{ extreme unlikable}\] \[\bullet\quad S_{1}:\text{unlikable}\] \[\bullet\quad S_{2}:\text{ more or less unlikable}\] \[\bullet\quad S_{3}:\text{neutral}\] \[\bullet\quad S_{4}:\text{ more or less likable}\] \[\bullet\quad S_{5}:\text{likable}\] \[\bullet\quad S_{6}:\text{ extreme likable}\]
_Algorithms_**2015**, \(8\), 632-644; doi:10.3390/a8030632  
_algorithms_  
ISSN 1999-4893  
www.mdpi.com/journal/algorithms  
Data Fusion Modeling for an RT3102 and Dewetron System Application in Hybrid Vehicle Stability Testing  
**[PERSON] 1,2,\({}^{\dagger,\ddagger,\star}\) and [PERSON] 1,2,\({}^{\dagger}\)**  
\({}^{1}\) College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China  
\({}^{2}\) Heilongjiang Institute of Technology, Harbin 150050, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel./Fax: +86-451-8802-8894.  
Academic Editor: [PERSON]  
_Received: 12 June 2015 / Accepted: 7 August 2015 / Published: 12 August 2015_  
**Abstract:** More and more hybrid electric vehicles are driven since they offer such advantages as energy savings and better active safety performance. Hybrid vehicles have two or more power driving systems and frequently switch working condition, so controlling stability is very important. In this work, a two-stage Kalman algorithm method is used to fuse data in hybrid vehicle stability testing. First, the RT3102 navigation system and Dewetron system are introduced. Second, a modeling of data fusion is proposed based on the Kalman filter. Then, this modeling is simulated and tested on a sample vehicle, using Carsim and Simulink software to test the results. The results showed the merits of this modeling.  
**Keywords:** data fusion; Kalman algorithm; vehicle stability; hybrid vehicle
## 1 Introduction  
The drilling and blasting method is a popular rock excavation technique in civil engineering. However, it has the disadvantages of blasting-induced ground vibration and noise, the former of which is the most critical factor with regard to the surrounding structures [1, 2]. Accordingly, the cut-blasting method is generally utilized to reduce vibration induced by blasting work [3, 4], during which the fragmented rocks should be discharged in the first round of blasting toward empty space (exavation face) [5, 6, 7]. Traditional cut methods (V-cut method and cylinder-cut method) involve concentrating the explosives at the cut area to facilitate good discharge of the crushed rock; however, this may increase blast-induced vibration. Moreover, when long hole blasting of more than 2 m is conducted, it is difficult to discharge the crushed rock; thus, the blasting efficiency may decrease.  
The long and large-diameter uncharged hole-boring (LLB) method is a cut-blasting method used to reduce vibration and involves creating an uncharged hole in the tunnel excavation direction [8]. This method utilizes an LLB hole that is horizontally drilled in the direction of the tunnel face. Notably, the diameter specification in South Korea is more than 250 mm. When a hole with a diameter of 362 mm is utilized, a 20-30% reduction in vibration efficiency can be achieved compared to that of traditional cut methods [9, 10]. Moreover, in some cases, it has been reported that vibration can be reduced by up to approximately 79% compared to the V-cut method using multiple large-diameter uncharged holes [11]. The LLB method is fundamentally advantageous for long and short hole blasting. [PERSON] et al. [12] compared the blasting efficiency of the LLB method involving short hole blasting with that of the traditional cylinder-cut method through 3D numerical analysis. However, blast-induced vibration in one case was greater in the former than in the latter when the advance was 2 m [8], highlighting the necessity of minimizing vibration within the allowable value while maximizing the blasting efficiency.  
We performed a 3D numerical study using ANSYS LS-DYNA to investigate rock fragmentation in the LLB method for long hole blasting. A Johnson-Holmquist (JH-2) constitutive material model was utilized to simulate the behavior of rock in dynamic states. In addition, an LLB method combined with a new deck charge technique (modified LLB method) was proposed to improve the blasting efficiency. Although investigating the blasting process and its efficiency in an actual tunnel construction site yields the highest accuracy, many risks are involved because most tunnel construction sites where the LLB method is applied are concentrated in urban areas, which are highly sensitive to blast-induced vibration and construction schedules. Therefore, the aim of this numerical study is to evaluate the blast ability of the modified LLB method before testing is performed in a tunnel construction site.
# Strategies for Deploying a Sensor Network to Explore Planetary Lava Tubes
## 1 Introduction
### High-Resolution Lunar Imaging  
The high-resolution Narrow-Angle Camera (NAC) images the lunar surface at 0.5 to 1 m/pixel-resolution onboard the United States lunar satellite Lunar Reconnaissance Orbiter (LRO), launched in 2008, providing new details regarding the morphologies of the holes. LRO NAC took very clear, detailed images of MHH and its floor at high solar-elevation angles and confirmed the Marius feature to be nearly circular. From the NAC data, the floor axis lengths were estimated to be 59 m \(\times\) 50 m with a depth of 47 m [3,4]. The data confirmed that it is neither a normal impact crater nor a volcanic vent as no ejecta was seen surrounding the hole. Moreover, the lowest portion of the inner wall of MHH does not continuously follow the floor, which strongly suggests the existence of extended voids (possibly lava-tube caverns) at the bottom of the hole. Studying the geometry of the shadow on the floor suggests that the floor is flat as it is quite similar to the outline of the inner edge of the hole. Although the areas surrounding MHH are 3.56 Gyrs old [5], at least one layer under the surface formed 0.1 Gyrs earlier than the surface [1]. This sheds light on the possibility of a lava tube formed from the lower flow, around 3.6 Gyrs ago, corresponding to a time of high lunar volcanic activity [6]. Although the age of formation of lava tubes has been estimated, the age of formation of the hole structure is still unclear. However, it could be inferred that the hole structure formed possibly because the roof collapsed. The pile of rubble from the collapsed roof at the opening of the hole might have been crushed and flattened by meteorite bombardment and covered by regolith and soil from the inner walls. The meter-scale boulders seen from the NAC data on the floor may have fallen from the inner wall in recent times.  
The LRO NAC images of the Mare Tranquillitatis Hole and Mare Ingenii Hole also clarified the details of these features. The long axes of MTH and MIH are measured to be 98 m and 118 m, respectively, and the short axes to be 84 m and 68 m. The depths are  
Figure 1: (**a**) Section showing the area where the feature transitions from a chain of collapse pits to a continuous unclapsed segment (lava tube); (**b**) Marius Hill Hole (MHH); (**c**) Mare Tranquillitatis Hole (MTH); (**d**) Mare Ingenii Hole (MIH) [NASA/GSFC/Arizona State University].  
measured to be 107 m for MTH and 45 m for MIH from shadow measurements based on the LRO NAC images [3; 4]. No ejecta or boulders are seen around both the holes, implying that the holes are not volcanic vents. The surface surrounding MTH was formed 3.62 Gyrs ago, while two formation ages of the surrounding area of MIH are deduced, 3.17 Gyrs and 3.59 Gyrs, corresponding to a time of large amounts of lunar mare volcanism. As with MHH, the inner walls of both MTH and MIH do not follow the floor, which strongly suggests the existence of subsurface voids. Additionally, more than 200 pits were also discovered in the impact melt deposits.