esa-sceva/satcom-chunk-collection huggingface dataset:
# Passive Thermal Management of Tablet PCs Using Phase Change Materials: Intermittent Operation
###### Abstract  
This paper presents an experimental study of thermal management in a tablet PC under intermittent operation, with phase change materials (PCMs) encapsulated in a very thin aluminized laminate film container. It complements a study of the same system under continuous operation that was published in 2018. Two different types of PCMs were used for the experimental work; PT-37 and \(n\)-eicosane. A commercially available tablet PC was used as a test subject to ensure representative dimensions and material properties of every intricate part of the real tablet PC. For intermittent operation, the cycle used corresponds to fifteen minutes of operation (heat generation) followed by fifteen minutes of rest, to imitate a regular usage pattern of a tablet PC. It was observed that thin PCM thermal energy storage (TES) units are capable of providing a reduction in the rate of temperature increase during transient operations for both the electronics and the tablet cover. Reduction in peak temperature of the heat source and external surfaces of the tablet PC was also observed. At the maximum of 8 W operating power, PCMs were able to reduce the back-cover temperature by 20 \({}^{\circ}\)C. At all power inputs, heat storage in PCMs resulted in back-cover temperatures lower than 40 \({}^{\circ}\)C. Moreover, it was found that the PCM thermal management system tested was not affected by the inclination of the tablet PC. Results obtained from this study confirm that thin PCM encapsulation is indeed a suitable solution to control the temperature in tablet PCs during intermittent operation.  
Keywords:phase change materials; thin thermal storage system; temperature management; modern portable electronics; intermittent operation +
Footnote †: journal: Applied Physics Letters
# GNSS and LiDAR Integrated Navigation Method in Orchards with Intermitt GNSS Dropout
## 3 Results  
### Experiment Setting  
To verify the performance of the orchard navigation system proposed in this paper, tests on its path-tracking and positioning functions were conducted in an apple orchard in Beijing City, as shown in Figure 12. Five rows of apple trees were selected in the experimental area, spaced approximately 3.5 m apart, with each row being about 95 m long, and the orchard floor was covered in weeds. The robot vehicle walked through the rows of apple trees, covering a total distance of about 360 m. During the test, a dynamic switching GNSS/INS-LiDAR positioning mode was used within the rows of trees, while the GNSS/INS positioning method was employed at the row ends. Given that the positioning information error obtained by the GNSS/INS navigation system under good signal conditions is equal to or less than 1.5 cm, this experiment used it as the actual path trajectory during the orchard traversal process.
## 4 Methods
### Measuring Performance Inaccuracy
#### 4.4.1 Training Tasks  
In the training tasks, the participant played along with the audiovisual highlighted sequence. Here, a \"correct\" performance would constitute playing the correct pitches at the correct times in the sequence; that is, matching the sequence being producedurally and visually onscreen. For each _target note_ in the audiovisual demo, a window of 666 ms (the interonset interval at 90 bpm), centred around the target note, was created. The first performed note within this window to match the target's MIDI note number was used. In this way, inserted notes which did not match the expected MIDI note were not penalized if the \"correct\" note was played at some point in the time window. If no matching MIDI note number was identified within this window, a zero score was allocated for this target note in the sequence. The first matching MIDI note number was then assigned a score reflecting its timing accuracy--a value of 1, if played at the same time as the target; linearly reducing to 0 as the timing error increases to the boundary of the window (\(\pm\)333 ms). The resulting scores are summed. As a final step, the total is normalized by dividing it by the number of notes in the target sequence and multiplied by 100. This puts accuracy onto the same scale for target sequences of different lengths, such that 100 always means the target sequence has been performed perfectly. The formula to calculate a participant's inaccuracy in the training sequences of the scales and arpeggios is  
\[Inaccuracy =100-Accuracy,\text{ where}\] \[Accuracy =\frac{100}{N}\sum_{n=1}^{N}1-\frac{|t_{p}-t_{n}|}{333}, \tag{1}\]  
and  
* \(N\) is the total number of notes in the target sequence
* \(t_{n}\) is the time in milliseconds of \(n\)th target note
* \(t_{p}\) is the time in milliseconds of first performed note within the \(t_{n}\pm 333\) time window that matches the pitch of the target note.
# Automatic Point-Cloud Registration for Quality Control in Building Works
## 3 Results and Discussion  
### Case Study  
The validation of the methodology proposed is performed through its application to two different scenarios of a residential building located in Badalona (Spain). The building has asymmetric U-shaped floor plan (Figure 8a). In the moment of the acquisition, the building was being constructed with a life expectation of 50 years. The plan is 25,000 m\({}^{2}\) and is vertically distributed in two basements dedicated to parking, one base floor dedicated to commercial offices and the upper floors destined to housing. The building is divided in two blocks, north and south, with 14 and 13 floors, respectively.  
The foundations of the building are constructed with a rectangular base, 200 \(\times\) 45 cm and 260 \(\times\) 45 cm size, individually or in groups of two elements with capped piles for load transmission. Retaining walls are made of concrete with temporary anchoring.  
Figure 7: Method for filtering correspondences with ICP algorithm: (**a**) method based on distance; (**b**) method based on normal vectors, which application was discarded.  
In the moment of data acquisition, the building was under different periods of construction, allowing for the testing of the methodology proposed in different environments and complexities of construction works. Two floors were selected for the data acquisition: (i) Floor 1 in the north block, which presents 810 m\({}^{2}\) (Figure 8b) and (ii) the ground floor in the south block, with 845 m\({}^{2}\) (Figure 8c). These floors were selected due to their condition of construction and geometrical complexity, which can be considered as representative for construction works. During acquisition, there were walls, pillars, holes (for the lift and drainpipes), floors and ceilings present in the scenes, as well as construction materials such as scaffolds and boards.
## 3 Description of Datasets  
The datasets selected to test the proposed methodology were composed of three types of DEMs related to four cartographic sheets of about 30 km \(\times\) 19 km (Figure 3). These 12 DEMs are produced and published by the Instituto Geografico Nacional of Spain [24]. The main properties of these datasets are shown in Table 1, including spatial resolution, dimensions, maximum and minimum heights, and mean slope. Thus, we selected two DTMs with different spatial resolutions (25 and 5 m) and one DSM (spatial resolution of 5 m) from each zone. The selection of the zones was related to their relief. Our aim was to cover all possible cases from flat to mountainous areas (one flat: 1034; two intermedium: 946 and 1008; one mountainous: 1027). In this sense, we selected zones characterized by low to high elevations and slopes (Table 1). We have to note that the difference in spatial resolution (25 vs. 5 m) supposes a small reduction in the height variability and mean slope.  
The selection of datasets considering two different spatial resolutions (DETM25 and DTM05), representing the same terrain surface, focused on contrasting the capacity of our  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**DEM** & **Zone** & **Resolution (m)** & **Rows/Columns** & **Min./Max. (m)** & **Mean Slope (\%)** & **DEM-ID** \\ \hline DTM25 & & 25 & 1189/759 & 271/1307 & 14 & DTM25-0946 \\ DTM05 & 946 & 5 & 5937/3785 & 266/1414 & 15 & DTM05-0946 \\ DSM05 & & 5 & 5935/3783 & 266/1417 & 18 & DSM05-0946 \\ \hline DTM25 & & 25 & 1197/758 & 422/1591 & 19 & DTM25-1008 \\ DTM05 & 1008 & 5 & 5975/3785 & 422/1589 & 21 & DTM05-1008 \\ DSM05 & & 5 & 5973/3783 & 419/1589 & 25 & DSM05-1008 \\ \hline DTM25 & & 25 & 1194/750 & 741/3454 & 43 & DTM25-1027 \\ DTM05 & 1027 & 5 & 5963/3743 & 745/3455 & 46 & DTM05-1027 \\ DSM05 & & 5 & 5960/3741 & 746/3455 & 48 & DSM05-1027 \\ \hline DTM25 & & 25 & 1218/784 & \(-1/408\) & 5 & DTM25-1034 \\ DTM05 & 1034 & 5 & 6081/3911 & \(-2/406\) & 6 & DTM05-1034 \\ DSM05 & & 5 & 6079/3908 & \(-2/405\) & 8 & DSM05-1034 \\ \hline \end{tabular}
\end{table}
Table 1: Main characteristics of the DEMs used in this study.  
Figure 3: DEMs used in the application of the proposed methodology.  
method in these cases and determining the influence of this basic aspect of gridded data. In this sense, we selected a differential factor of five between their resolutions, which is a high value considering the purpose of quality assessments. In addition, we also wanted to analyze the feasibility of our approach to detect homologous points when the represented surfaces include important differences. For instance, the DTM05 uniquely represents the surface of the terrain while the DSM05 also includes other elements such as vegetation, constructions, etc. These elements could hide possible terrain points to be used, reducing the number of detected points.
## 2 Potential Application Areas of CR-WSNs
### Real-Time Surveillance Applications  
Real-time surveillance applications, such as traffic monitoring, biodiversity mapping, habitat monitoring, environmental monitoring, environmental conditions monitoring that affect crops and livestock, irrigation, underwater WSNs, vehicle tracking, inventory tracking, disaster relief operations, bridges or tunnel monitoring, require minimum channel access and communication delay. Some real-time surveillance applications are highly delay-sensitive and require high reliability. A delay due to a link failure can also occur in multihop WSNs if the channel condition is not good. On the other hand, WS nodes hop to another channel if they find another idle channel with a better condition in CR-WSNs. Channel aggregation and the use of multiple channels concurrently are possible in CR-WSNs to increase the channel bandwidth [45; 46].
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 2 Modelling the Network  
A network channel is a physical medium in which a signal can travel. The medium can be guided (e.g., wired medium) or unguided (i.e., wireless). A noise in networks is a disturbance in the transmission medium that affects the characteristics of the signal and its quality. Thus, a noisy channel is a medium of transmission with disturbances. Independent of the source of disturbance, a random disturbance in a transmission channel results in random bit errors. The amount of noise can be expressed as the number of bit errors it introduces. A higher BER means more noises in the channel. We use the packet dropping capabilities of the Riverbed Modeler simulation tool to simulate the consequence of different BERs on system performance.
## 4 Results and Discussion
### Discussion  
It is valuable to evaluate the enhancement of fracture permeability when adopting the large-size proppant instead of the small-sized proppant. For the fracture containing the large-sized cylindrical proppant, the definition of porosity is  
\[\varphi=1-\frac{\pi D^{2}}{4L_{p}^{2}} \tag{16}\]  
where \(D\) is the proppant diameter and \(L_{p}\) is the fracture element length. Then, the ratio of the wetted area is written as  
\[\frac{S_{p}}{S_{f}}=\frac{\pi Dh}{2L_{p}^{2}}=\frac{2h}{D}(1-\varphi) \tag{17}\]  
where \(S_{p}\) is the wetted area of the proppant, \(S_{f}\) is the fracture element area, and \(h\) is the proppant height. Considering a particular situation of \(D=h\), the ratio of the wetted area can be further written as  
\[\frac{S_{p}}{S_{f}}=2(1-\varphi) \tag{18}\]  
Then, the permeability proposed by Li et al. (2022) Li et al. (2022) can be rewritten as  
\[k =D^{2}\alpha e^{\beta\varphi}(1-e^{-2(1-\varphi)}) \tag{19}\] \[\alpha =9.18\times 10^{-5}(2-2\varphi)^{-1.35}\] \[\beta =7.39(2-2\varphi)^{0.0412}\]  
For the fracture containing the small-size proppant, the fracture permeability can be assessed using the power-law model proposed by [PERSON] (2017) [PERSON] (2017), as follows:  
\[k=0.204r_{e}^{2}\varphi^{4.58} \tag{20}\]  
where \(r_{e}\) is the equivalent radius of the small-sized proppant. Based on the analysis, we obtain the fracture permeability magnification when increasing the proppant size. The assessment model is given as  
\[k_{r}=20\bigg{(}\frac{D}{d}\bigg{)}^{2}\frac{\alpha e^{\beta\varphi}\Big{(}1- e^{-2(1-\varphi)}\Big{)}}{\varphi^{4.58}} \tag{21}\]  
Figure 23: Reservoir average pressure and production rate variation with the well number. (**a**) Comparison of the average pressure for case 1 and case 2. (**b**) Comparison of the production rate for case 1 and case 2.  
where \(d=2r_{\varepsilon}\) is the equivalent diameter of the small-sized proppant and \(D\) is the diameter of the large-sized proppant. It is noteworthy that Formula (21) is based on the premise of \(D=h\). Nonetheless, the Formula (21) can be used to assess the permeability magnification of the fracture.  
The detailed magnification curves are exhibited in Figure 24. It is seen that when \(D/h\) is constant, the permeability magnification increases with the increase in porosity. When the porosity is constant, the permeability magnification also increases with \(D/h\).  
In fact, Formula (21) can also be approximately used to assess the permeability enhancement of the reservoir. Objectively, the reservoir after fracturing contains many fractures. The magnification of fracture permeability can be mapped to the enhancement of reservoir permeability. Therefore, Formula (21) can be chosen as an approximate model to assess the potential of permeability enhancement of the reservoir when adopting the large-sized proppant.
## 2 Dataset and Experiments
### Experimental Design  
In this paper, a sequence of time-series images was selected from the Sentinel-1 dataset; then, the proposed method and the traditional method were used to detect changes in the number of vehicles in a nearby parking lot and BEJIING-HYUNDAI AUTO Enterprise. The latitude and longitude of the nearby parking lot are 39.9214\({}^{\circ}\) and 116.1958\({}^{\circ}\), respectively; the latitude and longitude of BEJIING-HYUNDAI AUTO Enterprise are 40.1047\({}^{\circ}\) and 116.6443\({}^{\circ}\), respectively. For the nearby parking lot experiment, six sets of ground truth data were collected through field observations. The performance of these two methods was quantitatively evaluated using the root mean square error (RMSE) to validate the effectiveness and robustness of the proposed approach.  
The PAZ-1 dataset is used to complete the change detection of the number of vehicles in the parking lot of the CCTV Tower, and then the truth data are used to verify the detection results. The latitude and longitude of the nearby parking are 39.9171\({}^{\circ}\) and 116.3000\({}^{\circ}\), respectively.
# Analysing and Computing the Impact of Geometric Asymmetric Coils on Transformer Stray Losses
## 3 Assessment and Model of the Stray Magnetic Field
### Model to Determine Stray Magnetic Field on Tank Walls  
In order to calculate the additional losses that occur in the tank walls, Equations (1)-(3) are used. However, it is necessary to first determine the effective value of \(\mid\)B\({}_{\text{d}}\mid\). Through the analysis performed to characterise \(\mid\)H\(\mid\) and the large amount of information obtained through finite element method (FEM) simulations and with the intention of helping the designer to estimate the stray magnetic field due to the magnetic imbalance as well as the geometric asymmetry of the windings, a model has been successfully derived. This model facilitates the estimation of the magnitude of \(\mid\)H\(\mid\) (expressed in A/m) and is expressed as follows:  
\[H_{e}=\frac{1}{K_{R}}\bigg{|}\bigg{(}\frac{N_{s}I_{s}}{h_{s}}-\frac{N_{p}I_{p }}{h_{p}}\bigg{)}\bigg{|}, \tag{6}\]  
where _Np_, _Ns_, _lp_ and _ls_ are turns and currents of the primary and secondary windings, respectively, _hp_, _wp_, _ls_ and _ws_ are heights and widths of the primary and secondary windings, respectively and \(K_{R}\) is calculated by the Rogowski factor given by [26] the following:  
\[K_{R}=1-\bigg{(}\frac{\text{wp}+\text{wsp}+\text{wp}}{\pi\text{hw}}\bigg{)} \bigg{(}1-e^{\frac{-\text{zlnw}}{(\text{wp}+\text{wp}+\text{wp})}}\bigg{)}, \tag{7}\]  
where _hw_ is the height of the core window. It is proposed that the estimation of the magnitude of \(\mid\)H\({}_{\text{d}}\mid\) at a distance d from the external winding (high voltage winding) of the coil is given by the following:  
\[H_{d}=H_{e}\bigg{(}e^{\frac{-d}{(\text{wp}+\text{wp}+\text{wp})}}\bigg{)}, \tag{8}\]  
Afterwards, the density of the magnetic field that reaches the walls of the tank would be given by the following:  
Figure 5: Trapezoidal characterisation of \(\mid\)H\(\mid\) for different cases analysed.  
\[B_{d}=\left(e^{\frac{-d}{(\text{wp}+\text{wp}+\text{wp})}}\right)H_{d}\mu_{r}\mu_{0}, \tag{9}\]  
where \(\mu r\) is the relative permeability and \(\mu_{0}=4\pi\times 10^{-7}\). From (6)-(9), it is possible to show that the proposed technique seeks to determine the stray magnetic field based on the difference in the magnetomotive force (ampere-turns) produced by each winding and thereby later determine the magnetic field density that will reach the tank walls at a distance \(d\) from the coil.
## 2 Background and Literature Review
### SDN Model for NDN
#### 2.2.3 C. Application Layer  
Through various APIs, the application layer offers access points for various services. Northbound interfaces are used on devices to implement numerous APIs that are built for this layer's diverse functions [21].
## 4 Experiment and Evaluation  
### Experimental Setup  
The robot used in this experiment is a pallet-type mobile robot, as shown in Figure 15, and it operates on ROS [31]. Table 2 lists the hardware specifications of the mobile robot. As shown in Figure 15, an Intel tracking camera T265 is mounted at the front of the robot. T265 has stereo fisheye cameras, and the FoV is 163 \(\pm\) 5 degrees. We use the left camera of T265 to obtain fisheye images for our experiment. Each fisheye image has a resolution of 848 \(\times\) 800. T265 also has an IMU because it performs localization based on visual-inertial odometry (VIO).  
The evaluation of the proposed method is carried out using an optical tracking system, as shown in Figure 16. The ground truth position of the robot's odometry is measured  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Hardware** \\ \hline Embedded Board & NVIDIA Jetson-Nano \\ Wheel Encoder & PHIDGET Optical Rotary Encoder \\ Motor & PHIDGET NEMA-17 Bipolar Stepper \\ Motor Controller & PHIDGET Stepper Bipolar HC \\ LiDAR & SLAMTEC RPLIDAR-S1 \\ Camera & Intel RealSense T265 Tracking Camera \\ Battery & NEXT-408 PB-UPS (40,800 mAh) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental pallet robot’s hardware specification.  
Figure 15: (a) A pallet-type robot; (b) An Intel T265 fisheye camera is attached in front of the pallet robot.  
using the OptiTrack V120 Trio pose-tracking device [32]. To track the robot, four optical markers are attached to the four corners of the pallet robot as shown in Figure 16b. In this study, we used the trajectory evaluation toolbox proposed in [33] to calculate the odometry translation error compared with GT.
# PDDQN-HHVBF Routing Protocol Based on Empirical Priority DDQN to Improve HHVBF
## 2 Related Works
A reward function was developed based on reinforcement learning to seek optimal routing rules. Before forwarding the data packet, a two-hop availability checking function was defined, which avoids routing holes and identifies the trap nodes. However, this protocol only focus on the static nodes. When nodes move influenced by ocean current movement, it will perform worse. Although the above methods can reduce the delay, packet loss ratio and energy consumption of UWSNs to a certain extent. However, there are no good research results for M-UWSNs with mobile nodes as source nodes. And too many underwater routing protocols with RL pay attention to find low hop paths rather than short distance paths, while the long distance of ocean communication is the significant reason for the packets collision and energy loss in underwater. Thus, problems of low network lifetime, high energy consumption, and low packet delivery ratio of M-UWSNs have not been better solved. Based on the above problems, this paper proposes the PDDDQN-HHVBF protocol, which aims at finding the optimal relay node to maximizing the packet delivery ratio, optimizing energy consumption and prolonging the network lifetime. In addition, when the AUV cannot find a suitable relay node during forwarding, this paper proposes the \"Store-Carry-Forward\" mechanism. In this way, AUV avoids wasting a lot of energy in searching for relay nodes or sending the same data packet repeatedly.
# A Hybrid Domain Color Image Watermarking Scheme Based on Hyperchaotic Mapping
## 3 The Proposed Watermarking Scheme
### Embedding Capacity Analysis  
The embedding capacity is a measure of the number of bits of information that can be inserted into the cover image [31]. In this scheme, a color cover image with a size of \(512\times 512\) is used, and a grayscale watermark image with a size of \(256\times 256\) is used. Therefore, the embedding capacity of the scheme is \((256\times 256\times 8)\) / \((512\times 512\times 3)=0.6667\) bpp. Most of the other watermarking schemes use \(64\times 64\) or \(128\times 128\) watermarks, and the embedding capacity is \(0.0417\) and \(0.1667\), respectively. The embedding capacity is low, and the scheme in this paper has high embedding capacity.
## 4 Design Analysis
### Finite Element Analysis
#### 4.7.3 Quasi-Static Analysis  
Quasi-static load is independent of time as it varies slowly. In addition, 46.6 N compression load will be applied on each rail at \(+Z\). Table 11 shows the three cases of loading.  
The maximum von Mises stress on the satellite was found to be 41.46, 92.02, and 97.3 MPa in analyses A, B, and C, respectively. The maximum deflection on the satellite was 0.027, 0.061, and 0.061 mm in analyses A, B, and C, respectively. Table 12 shows the locations of the maximum von Mises stresses and maximum deformations in each case. Stress levels on various parts of the satellite and deformations are displayed in Figures 14-16. The margin of safety (\(MS\)) for the various components was computed using a factor of safety (\(FOS\)) of 1.5 for yield strength (\(F_{ty}\)) and 2.0 for ultimate strength (\(F_{tu}\)). The  
Figure 13: Boundary conditions applied on the rail for (**a**) quasi-static analysis and (**b**) modal analysis.  
following equations were used to calculate the margin of safety. The results are shown in Table 13. All the results were within the safety criteria as shown in the tables below.  
\[MS=\frac{allowable\;stress}{maximum\;stress\times FOS}-1\geq 0, \tag{3}\]
## 5 Numerical Results  
In order to study the efficiency and the accuracy of the proposed augmented Tikhonov regularization method on dynamic load identification, three numerical simulation examples are studied. These include two multi-degrees-of-freedom system examples and a cantilever beam example.