esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Total Ionizing Dose (TID) Radiation Test  
The total ionizing dose (TID) radiation test was conducted at the unit system level before any other space environment test, such as a thermal vacuum test (TVT). The primary objective of the test was to show that the RPi CM3+ stays operational and that the interfaces were stable in radiated environments. The central processing unit (CPU) of BCM2837B0 and embedded multimedia card (eMMC) are the main components that need to be observed by running the 'flash test' script. The secondary objective was to prove that the DL algorithm (training and classification tests) can be executed, and the last objective was to measure the power consumption during the TID test. The RPi CM3+ was exposed to a radiation source of Co-60 in the chamber facility. Six hours of the test were equivalent to 200 Gy of radiation simulated for two years in orbit. Similar tests were also carried out by [PERSON] [41] on RPi CM3 and by [PERSON] et al. [42] on Jetson Nano GPU. The differences between the test in this study and that in the study by [PERSON] [41] were the type of radiation source and the amount of radiated energy.  
The first objective was verified using a pseudo-random number generator. A known seed was used to generate files with uniformly dispersed byte values. Each file was seeded differently, resulting in unique files. This way, the same test can be run multiple times and create identical test files each time. During the TID test, a total of 75 files were generated and compared with the equal number of files previously created in the computer. Later, each generated file was hashed using the sha256 algorithm. This hash function had the virtue of returning a 256-bit-long result that was significantly different, even with only a single bit of difference. Additionally, hash collisions between separate files were improbable, and none have been documented so far for the sha256 algorithm. Each file was generated in about two minutes using RPi CM3+. After generating a collection of 15 files, a 30-min delay was added. The reason for this was to manually execute the DL algorithm. On the following day, the hashes were compared between the reference test (before the TID test), the actual test, and the post-functionality test using RPi CM3+. The results showed that no discrepancies were discovered for every 75 files. It verified that the eMMC on RPi CM3+ could stay operationally stable for two years in orbit. However, several anomalies were observed on the CPU of RPi CM3+. Overall, the flash test code was run five times and twice for the DL algorithms (training and classification tests) with a second of time sampling. Based on Figure 8, the RPi CM3+ consumed extra power without executing any code that started right before the execution of flash test5 (at 4 h). The issues were recovered after running the DL training test2 (at 4 h 55 min) and rebooting the RPi CM3+ at the end of the TID test. It showed that a single event latch-up occurred at the time of 133 Gy being exposed to the test article.  
Figure 8: Power consumption of Raspberry Pi Compute Module 3+ during radiation test.
# Topology Optimization of Functionally Graded Structure for Thermal Management of Cooling Plate
## 3 Evaluation and Analysis  
In this section, the effectiveness of the proposed method for topology optimization of cooling plate is evaluated, and uniform and nonuniform thermal load cases are utilized to  
Figure 1: Flowchart of the topology optimization pipeline.  
demonstrate the advancement of the functionally graded structure for cooling performance. Figure 2 shows the layout of the battery and cooling plate. Here, COMSOL Multiphysics 6.0 has been utilized for numerical simulation. The interpolation parameters \(q_{k},q_{p},q_{a},q_{c}\) were set to 0.005, respectively. The maximum penalty force was set to \(10^{7}\), the volume constraint for the fluid domain was set to 0.5, the mesh size and global filter radius were set to 1 mm, respectively, and the projection parameters \(\eta=0.5\), \(\beta=4\). The Method of Moving Asymptotes was introduced as the solver for the numerical simulation. The material property for topology optimization of the cooling plate is organized in Table 1, where \(\rho,k,C_{p}\), and \(\
u\) are the density, conduction coefficient, heat capacity at constant pressure, and dynamic viscosity, respectively.
## 3 Instruments Used and Data Capturing
### Accuracy Testing of BLK360 and ZEB-REVO Scanners  
The devices usability and their actual accuracy were first tested in the basement of the Faculty of Civil Engineering, Czech Technical University in Prague before departure to the Morocco expedition (Figure 5). For the purpose an objective analysis of the tested device accuracy, the basement space was mapped using manual measurements, then  
Figure 4: (**a**) Laser mobile scanner ZEB-REVO, (**b**,**c**) laser scanner BLK360.  
the Leica TCR3 total station was used, and three types of laser scanners followed. To compare the laser data, the most accurate laser phase scanner Surphaser with an accuracy of \(0.6\,\mathrm{mm}/10\,\mathrm{m}\) was used as a reference model. These data were compared to results derived from a small BLK360 scanner and a ZEB-REVO mobile personal scanner (Figure 6).  
The derived results were processed in the form of a table showing selected measured distances in the documented space (Table 1).  
Figure 5: (**a**) Floor plan and analyzed distances in the faculty basement, (**b**) a view of the faculty basement part.  
Figure 6: (**a**) Precise laser scanner Surphaser 25 HSX, (**b**) used targets for point clouds alignment, and (**c**) ZEB-REVO personal mobile laser scanner.  
Point clouds from laser scanners were compared to each other using a CloudCompare software.  
The comparison function Compute cloud/cloud distances was used to calculate distances between identical groups of cloud points. The function provides statistical indicators comparison in the form of arithmetic mean and standard deviation of distances between identical points; clouds can be colored according to the calculated deviations to visualize the comparison results.  
The arithmetic mean of the distances differences between the points derived from the Surphaser 25 HSX and Leica BLK 360 scanners was 6.35 mm with a standard deviation of 10.38 mm.  
The arithmetic mean of the differences in distances between points measured by the Surphaser 25 HSX and ZEB-REVO scanners was 10.12 mm with a standard deviation of 12.76 mm  
This test showed the applicability of both types of laser scanners for the documentation of historic buildings during the planned expedition.  
A comparison of the easy-to-transport instruments used is shown in the following table (Table 2).
# Algorithms for Finding Shortest Paths in Networks with Vertex Transfer Penalties
## 4 Methods Avoiding Expansion
### Extended Dijkstra Version 1
This leads to an overall worst case complexity of \(\mathcal{O}\left(nc_{\max}^{-}\right)+\mathcal{O}\left(\left(mc_{\max}^{-} \right)\lg\left(nc_{\max}^{-}\right)\right)\). Assuming graph connectivity, this simplifies to a growth rate for Extended-Dijkstra-V1 of  
\[f_{1} = \mathcal{O}\left(\left(mc_{\max}^{-}\right)\lg\left(nc_{\max}^{- }\right)\right). \tag{3}\]  
As previously noted, the complexity of Dijkstra using a binary heap is \(\mathcal{O}(m\lg n)\). Using a Kirby-Potts expansion, in the worst case this leads to a graph \(G^{\prime}\) with \(n^{\prime}=n(c_{\max}^{-}+c_{\max}^{+})\) vertices and \(m^{\prime}=m+nc_{\max}^{-}c_{\max}^{+}\) edges, giving an overall complexity of \(\mathcal{O}(m^{\prime}\lg n^{\prime})\), or  
\[f_{2} = \mathcal{O}\left(\left(m+nc_{\max}^{-}c_{\max}^{+}\right)\lg \left(n\left(c_{\max}^{-}+c_{\max}^{+}\right)\right)\right). \tag{4}\]  
Figure 6 compares \(f_{1}\) and \(f_{2}\) for a range of different parameter values. Note that \(f_{1}\) grows more quickly in all cases, demonstrating that Extended-Dijkstra-V1 is less efficient with regards to increases in the number of edges \(m\). The main reason for this is that, with Extended-Dijkstra-V1, each outgoing edge of a vertex \(v\) is considered for each incoming colour of \(v\). This results in the term \((mc_{\max}^{-})\) seen in Equation (3). In contrast, although a Kirby-Potts expansion results in a graph \(G^{\prime}\) with an increased  
Figure 5: Solution returned by Extended-Dijkstra-V1 using the graph from Figure 2 and source \((v_{1},1)\). All transfer penalties are assumed to be 1. The shortest path tree rooted at \((v_{1},1)\) is defined by the contents of \(P\) and is illustrated on the right. number of edges and vertices, each edge in \(G^{\prime}\) is considered only once using Dijkstra, which results in a slower growth rate. Note, however, that each chart in Figure 6 features an intersect, suggesting that Extended-Dijkstra-V1 is more efficient with very sparse graphs. (If Fibonacci heaps are used for \(Q\) instead of binary heaps, the growth rates for \(f_{1}\) and \(f_{2}\) are \(\mathcal{O}((mc_{\max}^{-})+(nc_{\max}^{-})\lg(nc_{\max}^{-}))\) and \(\mathcal{O}(m^{\prime}+n^{\prime}\lg n^{\prime})\) respectively. These feature the same noted properties as \(f_{1}\) and \(f_{2}\) except that growth is more gradual, and intersects occur at slightly higher values of \(m\).) For indicative purposes, the grey rectangles in the figure show the range of values for which planar digraphs exist (i.e., the right boundary of these rectangles occur at \(m=2(3n-6)\), which is the maximum possible number of directed edges in a planar digraph). Planar graphs are considered further in Section 5.
# Using Statistical Shape Models to Optimize TKA Implant Design
## 2 Materials and Methods
### Generation of the SSM
#### 2.1.2 Generation of the SSM: Choice of the Reference System  
The first step in building an SSM is to align all the individual bone models. All bone models need to be represented in a reference system that ensures a good relative alignment of the models. The choice of the reference system influences the robustness of the SSM and its ease of use. The reference system should be easy to identify on all bone models (highly reproducible) and ideally correspond to the landmarks used in clinical practice. For the tibia, the origin was defined by the mid-point between the most proximal point on the medial tibial spine and the most proximal point on the lateral tibial spine. The proximal axis connects the origin to the mid of the malleoli. The AP axis connects the center of posterior cruciate ligament attachment to the medial third of the tibial tuberosity [20]. For the femur, the center of the femoral head and the surgical trans-epicondyl line were used to build the reference system [21; 22; 23]. For the patella, a coordinate system was defined with one axis aligned with the ridge of the patella [24]. An iterative method was used to define the ridge (proximodistal axis) and the most medial and lateral points of the patella (ML axis). The origin was defined on the usual resection plane of the patella, 6 mm below the patella peak along the AP axis. Use of this coordinate system facilitated resection surface analysis, component placement, and peak medialization analysis. The ability of this reference system to properly align the individual bone models (step 1 in creating the SSM) was evaluated as follows: two alternative SSM were built using the same CT dataset, but using a different method to align the individual bone models. For SSM1, the bone models were aligned by minimizing the RMS error for all models. For SSM2, the individual bone models were aligned using a reference system based on inertial moments. As a result, the SSM built with the patella coordinate system described above enabled us to capture the geometric variance in fewer SSM modes. Thus, the modes were more compact as they captured geometrical trends better.
## 1 Introduction
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Unit**} & \multicolumn{3}{c}{**Type**} \\ \cline{3-5}  & & **Mini** & **Small** & **Standard** \\ \hline Coefficient of performance & & & & \\ (\(T_{\text{cmd}}\) = 58 \({}^{\circ}\)C, \(T_{\text{empty}}\) = 3 \({}^{\circ}\)C) & - & 2.1 & 2.1 & 2.1 \\ \hline Mechanical power consumed by the compressor at full capacity during the initial 10 min & kW & 2.4 & 2.9 & 3.3 \\ \hline Mechanical power consumed by the compressor for 67\% duty cycle & kW & 1.6 & 1.9 & 2.2 \\ \hline
**Highway driving** & & & & \\ \hline Average speed & km/h & 90 & 90 & 90 \\ \hline Fuel mileage without air conditioner & km/l & 24 & 18 & 12 \\ \hline Fuel consumption & l/h & 3.75 & 5.00 & 7.50 \\ \hline Heat liberated by combustion of fuel & kW & 34.38 & 45.83 & 68.75 \\ \hline Thermal efficiency & \% & 30 & 30 & 30 \\ \hline Mechanical power output & kW & 10.31 & 13.75 & 20.63 \\ \hline Percentage additional mechanical power consumed by the compressor at 67\% duty & \% & 15.47 & 13.92 & 10.83 \\ \hline Heat dissipated via exhaust, radiator, and surface of the engine block & kW & 24.06 & 32.08 & 48.13 \\ \hline Heat dissipated via exhaust (assuming 50\%) & kW & 12.03 & 16.04 & 24.06 \\ \hline
**City driving** & & & & \\ \hline Average speed & km/h & 40 & 40 & 40 \\ \hline Average speed including stops & km/h & 48 & 48 & 48 \\ \hline Fuel mileage without air conditioner & km/l & 17 & 12.5 & 8.5 \\ \hline Fuel consumption & l/h & 2.82 & 3.84 & 5.65 \\ \hline Heat liberated by combustion of fuel & kW & 25.88 & 35.20 & 51.76 \\ \hline Thermal efficiency & \% & 30 & 30 & 30 \\ \hline Mechanical power output & kW & 7.76 & 10.56 & 15.53 \\ \hline Percentage additional mechanical power consumed by the compressor at 67\% duty & \% & 20.54 & 18.13 & 14.38 \\ \hline Heat dissipated via exhaust, radiator, and surface of the engine block & kW & 18.12 & 24.64 & 36.24 \\ \hline Heat dissipated via exhaust (assuming 50\%) & kW & 9.06 & 12.32 & 18.12 \\ \hline
**Idling or traffic jam** & & & & \\ \hline Average speed including stops & km/h & 0 & 0 & 0 \\ \hline Fuel mileage without air conditioner & km/h & 0 & 0 & 0 \\ \hline Fuel consumption & & & & \\ (assuming 0.38 l/h per l displacement) (l/h) & 1/h & 0.25 & 0.57 & 0.95 \\ \hline Heat liberated by combustion of fuel & kW & 2.30 & 5.23 & 8.71 \\ \hline Net thermal efficiency & \% & 0 & 0 & 0 \\ \hline Mechanical power required to overcome internal friction and operate alternator, water pump, and power steering pump & kW & 1.06 & 2.40 & 4.00 \\ (1.6\(\times\) displacement) & & & & \\ \hline Percentage additional mechanical power consumed by the compressor at 67\% duty & \% & 151.06 & 79.76 & 55.83 \\ \hline Heat dissipated via exhaust, radiator, and surface of the engine block & kW & 0.74 & 1.68 & 2.80 \\ \hline Heat dissipated via exhaust (assuming 67\%) & kW & 0.50 & 1.13 & 1.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 2 Theoretical Formulation
### Governing Equation  
In order to accurately describe the motion of the sandwich plate, nine displacement components, namely \(w\), \(u_{\text{c}}\), \(u_{\text{d}}\), \(u_{\text{b}}\), \(v_{\text{c}}\), \(v_{\text{d}}\), \(v_{\text{b}}\), \(\gamma_{\text{xz,d}}\), and \(\gamma_{\text{yz,d}}\) are required. Subscripts c, d, and b represent the constraining layer, damping layer and base plate, respectively. \(w\), \(u\), and \(v\) represent the displacement along the \(o\)-\(z\) axis, \(o\)-\(x\) axis, and \(o\)-\(y\) axis, respectively. \(\gamma\) is the shear strain in the damping layer and its subscripts represent the shear plane, where the first subscript denotes the direction of the strain. Figure 2 shows the longitudinal displacement of the sandwich plate in the \(x\)-\(z\) plane. Based on assumptions c and d, the following relationship can be obtained  
\[ \begin{array}{l}u_{\text{c}}+\frac{h_{\text{c}}}{2}w_{,x}=u_{\text{d}}+ \frac{h_{\text{d}}}{2}(\gamma_{\text{xz,d}}-w_{,x})\\ u_{\text{b}}-\frac{h_{\text{b}}}{2}w_{,x}=u_{\text{d}}-\frac{h_{\text{d}}}{2 }(\gamma_{\text{xz,d}}-w_{,x})\end{array}  \tag{1}\]  
Figure 1: Model of system; (**a**) front view; (**b**) left view. where \(w_{,x}\) represents the partial derivative of \(w\) with respect to \(x\) (\(w_{,x}=\frac{\partial w}{\partial x}\)); \(h_{\text{c}}\), \(h_{\text{d}}\), and \(h_{\text{b}}\) are thicknesses of the constrained layer, damping layer and base plate, respectively. From Equation (1), \(u_{\text{d}}\), \(v_{\text{d}}\), \(\gamma_{\text{xz,d}}\), and \(\gamma_{\text{yz,d}}\) can also be expressed as  
\[ \begin{array}{l}u_{\text{d}}=\frac{u_{\text{c}}+u_{\text{b}}}{2}+\frac{h_{ \text{c}}+h_{\text{b}}}{4}w_{,x},v_{\text{d}}=\frac{v_{\text{c}}+v_{\text{b}}}{ 2}+\frac{h_{\text{c}}+h_{\text{b}}}{4}w_{,y}\\ \gamma_{\text{xz,d}}=\frac{h_{\text{c}}-u_{\text{b}}}{h_{\text{d}}}+\frac{h_{ \text{d}}}{h_{\text{d}}}w_{,x},\gamma_{\text{yz,d}}=\frac{h_{\text{c}}-v_{ \text{b}}}{h_{\text{d}}}+\frac{h_{\text{d}}}{h_{\text{d}}}w_{,y}\end{array}  \tag{2}\]  
where \(h=h_{\text{d}}+(h_{\text{c}}+\ h_{\text{b}})/2\), \(w_{,y}\) represents the partial derivative of \(w\) with respect to \(y\) (\(w_{,y}=\frac{\partial w}{\partial y}\)). Based on assumptions (a) and (b), constrained layer and base plate are under plane stress. The stain-displacement relationship is:  
\[ \begin{array}{l}\varepsilon_{\text{x,c}}=u_{\text{c},x}-zw_{,xx},\varepsilon _{\text{y,c}}=v_{\text{c},y}-zw_{,yy}\\ \varepsilon_{\text{x,b}}=u_{\text{b},x}-zw_{,xx},\varepsilon_{\text{y,b}}=v_{ \text{b},y}-zw_{,yy}\\ \gamma_{\text{xy,c}}=u_{\text{c},y}+v_{\text{c},x}-2 zw_{,xy},\gamma_{\text{xy, b}}=u_{\text{b},y}+v_{\text{b},x}-2 zw_{,xy}\end{array}  \tag{3}\]  
where \(w_{,xx}\), \(w_{,y\prime}\), \(w_{,xy}\) represent the partial derivatives of \(w_{,x}\) (\(w_{,y}\)) with respect to \(x\) (\(y\)) (\(w_{,xx}=\frac{\partial^{2}w}{\partial x^{2}}\), \(w_{,yy}=\frac{\partial^{2}w}{\partial y^{2}}\), \(w_{,xy}=\frac{\partial^{2}w}{\partial x\partial y}\)). The corresponding constitutive relation is:  
\[
# Augmented Reality in Surgery: A Scoping Review
## 4 Results
### Oncology  
AR application is frequent in oncology, being used for osteosarcoma [53], mandibular [54], kidney and prostate cancer [55], meningioma [56], urological cancer, intracranial [57], neuro-oncological [58], and cancer of the liver [14]. Indeed, AR application ensures an accurate visualization of the tumor, identifying its edges and position during surgeries. The capability to visualize the real anatomical structures, such as convolutions, grooves, blood vessels and nervous tracts, allows control during their resection, and permits surgeons to try to eradicate the tumor while removing as little of the surrounding healthy tissue as possible [59; 60; 61; 62]. Adequate planning also provides bone information that, together with information about the tumor, can lead to its successful removal [54]. Furthermore, the application of the AR to the innovative twin digital simulation technique can also be a medical support tool. In particular, this solution may allow oncologists to monitor and control the patient in addition to predicting the outcome of cancer through the development of appropriate simulation models and the creation of appropriate data sets [63].
## 3 System Model
### Communication Model  
We first present the model of communication for edge-cloud systems, where the environment has a number \(\mathcal{M}\) of small UAVs that are connected to the centralized cloud computing via a backbone router. In addition, each UAV is connected with a number \(\mathcal{N}\) of mobile devices that have highly intensive computation tasks that should be completed. We refer to the small UAVs' set and the mobile devices' set as \(\mathcal{M}=\{1,2,\ldots,M\}\), and \(\mathcal{N}=\{1,2,\ldots,N\}\), respectively.  
We denote \(\varphi_{i,j,a}\) as the decision offloading where the task of mobile device \(j\), which is connected with UAV \(i\), is assigned to be executed at the server \(a\), which can be a local execution (i.e., a = 0), cloud execution (i.e., \(a=M+1\)), or UAVs execution (i.e., \(a\in[1..M]\)). In particular, (\(\varphi_{i,j,a}=1\), where \(a\in[1..M]\)) denotes that the computation task of device \(j\), which is connected to UAV \(i\), will be transmitted and executed remotely at one of the available UAVs, while (\(\varphi_{i,j,0}=1\)) and (\(\varphi_{i,j,M+1}=1\)) denote that the task of mobile device \(j\) will be processed locally by itself or on the cloud server, respectively. Overall, each mobile device \(j\) must execute its computation task only one time, whether locally (\(a=0\)) or remotely ( \(a\in\{1,2,\ldots,M+1\}\) ), i.e., \(\sum_{a=0}^{M+1}\varphi_{i,j,a}=1\).  
Following [PERSON]'s law, the maximum uplink data rate between each mobile device \(j\) and the connected UAV to transmit its task's data through the communications channel can be expressed as [38]:  
\[r_{i,j}=B_{i}log_{2}(1+\frac{p_{i,j}g_{i}^{2}}{\omega_{i}B_{i}}), \tag{1}\]  
where \(B_{i}\) denotes the channel bandwidth between UAV \(i\) and its connected mobile device, \(p_{i,j}\), and \(\omega_{i}\) and \(g_{i}\) denote the transmission power of mobile devcie \(j\), the power density, and the gain of the channel between the mobile device and UAV. Guided by the intuition in [39], TDMA technology is utilized to mitigate the co-channel interference between device users.  
In this paper, the total overhead consumption in terms of energy and time for returning the output data size is neglected due to its size being smaller than the input data size. The link data rate for downloading result from server is larger than the uploading [40; 41; 42].
## 3 Compression Standards
### Image Properties Affecting Compression Performance  
A key issue to be addressed when designing new compression schemes which improve overall performance could be identifying relevant image properties that affect the compression efficiency. Two different classes of properties can be defined for DNA microarray images: general properties applicable to any image and properties specific to microarray images.  
General properties like resolution size or image entropy have the advantage of being more easily measurable, but also the disadvantage of not always providing much insight on the nature of DNA microarray images nor their compression. Image size does not correlate very well with the compression performance of any of the general or image-specific schemes shown in Tables 3, 5 and 6. On the other hand, average entropy is clearly related to the compression performance for microarray images, but this fact is not useful for designing new algorithms.  
Properties only applicable to DNA microarray images like spot quality and information uncertainty and their effect on compression could be very useful to understand the peculiarity of the underlying signal. Spot quality of microarray images and information uncertainty of microarray experiments (described in Subsection 2.6) may be useful in this regard. It would be very interesting to investigate the relationship of these two properties and the compression performance of different algorithms. Unfortunately, it is not feasible to do so in a straightforward way. Most quality assessment procedures rely on a previous segmentation of the image into foreground and background, but publicly available tools either require manual intervention, which is very inefficient and can cause imprecisions, or cannot be considered equally reliable for all datasets. Were an automatic method for assessing quality found, it would then be feasible to investigate the mentioned relationship. In addition, most information uncertainty measures require information of the genes associated to each spot, which is not available for the majority of the datasets used for benchmarking. If these data were available, it would be interesting to analyze the experimental deviations for the different datasets and their relationship with compression performance.