esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Corrosion Behavior of Waterborne Polyurethane Coating with High Thermal Conductivity
## 3 Results and Discussion
### Topography Observation  
The corrosion protection performance of the waterborne polyurethane coating with the best thermal conductivity is evaluated through salt spray experiments. Figure 3 shows the surface topography of the samples after the salt spray test. It can be seen that the sample surface before the salt spray test is flat and depicts a sea gray color. After 6 d of salt spray tests, there are some rust spots on the surface of the sample without a composite filler (Figure 3g). As time elapses, the rust spots evidently increase (Figure 3h), which suggests that the corrosive ions have penetrated into the substrate, causing corrosion of the substrate metal. In this case, the formed iron cations and iron oxides will diffuse outward [19], which will further damage the coating's resistance to penetration and reduce its protective performance. However, for the coating with 2 wt % composites filler, the coating surface displays no apparent rust spots and blisters at the full stage of salt spray tests, suggesting that the modified coating can provide excellent protection performance.  
Considering the importance of the coating adhesion for the service reliability, the adhesion strength of the modified coating with 2 wt % fillers is tested by the pull-off test,  
Figure 3: The surface topography of the samples at different period of salt spray test; (**a–d**) the topography of the coating with composites fillers after 0 d, 3 d, 6 d, and 10 d, respectively; (**e–h**) the topography of the coating without fillers after 0 d, 3 d, 6 d, and 10 d, respectively.  
Figure 2: The thermal conductivity results of waterborne polyurethane coating.  
as shown in Figure 4. The initial adhesion strength of the modified coating is around 12 Mpa, which approaches the value of the similar waterborne polyurethane coating in the previously published study [20]. As the salt spray time extends, the coating adhesion strength gradually decreases due to the infiltration of electrolyte. After 10 d of salt spray tests, it is still up to 8.5 Mpa, indicating that the modified coating greatly inhibits the penetration of the corrosive ions, which is a basis for the superior anticorrosion performance of coating.
## 2 Materials and Methods  
The proposed workflow consists of the following four steps (Figure 1).  
1. On-site McSL video recording: We record a surgery case with a McSL (Figure 1a). At this point, no additional effort is required (e.g., no calibration pattern recording is necessary).
2. Playback with our McSL video player: The user (e.g., medical trainees) plays the video and selects a frame to be reconstructed in 3D (Figure 1b, Section 2.3).
3. McSL calibration: Upon frame selection, we run our calibration algorithm to calculate the cameras' intrinsic and extrinsic parameters (Figure 1c, Section 2.1).
4. View synthesis: The 3D frame data are generated and saved (Section 2.2), and the selected frame is highlighted in the player. Therefore, the user can switch between the  
Figure 1: Pipeline of our system. Our system consists of four steps, including **(a)** on-site McSL video recording, **(b)** playback with our McSL video player, **(c)** McSL calibration, and **(d)** view synthesis on an autostereoscopic display. The view synthesis is available only after a successful calibration, which is challenging with non-feature-rich multi-view images in surgical videos. We, therefore, implement a calibration strategy that utilizes multiple frames over time.  
2D and 3D viewers. We provide ways to avoid occlusions in surgical fields. Given that the frame is in 3D representation, we can display the frame on an autostereoscopic display (Figure 1d).  
Our McSL consists of the following components.  
* The one used to capture the polysymadactyl surgery and the cleft lip surgery: stand-alone shadowless lamp (DAI-ICHI SHOMEI CO., LTD., LEDXII 5S) \(+\) camera (LUCID Vision Labs, PHX032S-CC) \(\times\) 5 units;
* The one used to capture the cleft lip surgery and the accessory auric surgery: stand-alone shadowless lamp (DAI-ICHI SHOMEI CO., LTD., LEDXIV 5S) \(+\) HDR camera (LUCID Vision Labs, TritonHDR) \(\times\) 5 units.
# Space Efficiency in European High-Rise Timber Buildings
## 3 Results
### Key Architectural Considerations: Function, Core Planning, and Building Form  
Figure 4a shows a predominant preference for residential use, comprising over 70%. This trend is driven by several factors [62; 63; 64], including the environmental benefits of timber. Additionally, timber buildings offer psychological and health benefits, improving mental and physical health. High-rise timber towers meet the market demand, are economically viable, and address housing needs in crowded cities in Europe.  
Analyzing Figure 4b reveals that the central core is the most used configuration, accounting for 57%. The peripheral arrangement comes next, making up 43%. Within high-rise timber developments, a focal core arrangement presents myriad advantages [65; 66; 67]:  
* Stability against sideways pressures, lessening requirements for reinforcement.
* Augmented available area by concentrating vital amenities.
* Increased effectiveness via reduced circulation lengths and simplified entryways.
* Elevated daylight and vistas owing to expanded window areas.
* Versatility for forthcoming adjustments requiring minimal structural alterations.
* Economies achieved via enhanced structural effectiveness.
* Liberty in facade design as the core structure bears the primary load.
* Reinforced fire safety with fire-retardant zones.  
Following the morphological classification of high-rise timber constructions, prismatic arrangement, representing 82%, occurred as the leading architectural choice, while free  
Figure 4: Examined cases by (**a**) function, (**b**) core type, and (**c**) form.  
Figure 5: Examined cases by (**a**) material and (**b**) system.  
configurations make up 16%, as depicted in Figure 4c. The widespread adoption of prismatic designs in the case study towers might be ascribed to various considerations [37, 68, 69]: 1. Structural efficacy, evenly dispersing loads. 2. Optimization of materials, in line with standard wooden construction supplies. 3. Simplified construction, as regular shapes reduce time and labor expenses. 4. Space use maximization, utilizing rectangular or square floor plans to their fullest extent. 5. Flexibility in facade design, accommodating various architectural elements and promoting energy efficiency. 6. Potential for modular construction, bolstering efficiency and quality management. 7. Considerations pertaining to fire safety involve effective compartmentalization and contemporary methodologies aimed at mitigating risks.
# Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition
## 2 Related Works
### Image Set Classification
#### 2.1.2 Dynamic Modeling Methods  
With dynamic modeling methods, each image set is modeled with some dynamic method, such as affine hull, convex hull, and so on. These modeling methods usually rely on learning of sparse or collaborative representation coefficients. For instance, in the ISCRC [3] algorithm, each probe image set or video was modeled as affine or convex hulls; then, these models were collaboratively reconstructed using all gallery image sets or videos. In the RNP [4] algorithm, the image sets were also modeled as affine hulls. Based on this modeling, the authors developed a new objective function to simultaneously learn the modeling coefficients and the distance metric. Similar to RNP, Dual Linear Regression Classification (DLRC) [16] also modeled image sets as affine hulls and reconstructed a virtual image using two different image sets; the distance between the two reconstruction images was used as the distance between the image sets. However, in DLRC, in each distance calculation, only two image sets were used: it cannot use the useful information from other gallery image sets. Based on DLRC, two new algorithms, Pairwise Linear Regression Classification  
Figure 2: The tree diagram of image set classification methods, and these methods can be grouped into two categories: static and dynamic modeling methods.  
(PLRC) [17] and Discriminative Residual Analysis (DRA) [18], were then developed by introducing different unrelated subspaces. Specifically, PLRC maximized the unrelated distance between two image sets and minimized the related distance to improve the classification results. DRA learned a projection matrix to project the reconstruction residual to a more discriminative space. In other words, the PLRC algorithm was first used to compute the related and unrelated distances between any two gallery image sets, and then, the projection matrix was used to learn a more discriminative space. Since the above related and unrelated distances were independently computed, Joint Metric Learning-based Class-specific representation (JMLC) [15] jointly learned the related and unrelated metrics and extended PLRC and DRA to the large-size image set classification task. Although dynamic modeling methods provide higher-accuracy recognition performance, they usually need to solve some sparse or collaborative optimization problems, and these problems are usually time-consuming.
# Motion, Static Force, and Efficiency Analysis of Planetary Gear Transmission Based on Graph Theory
## 3 Performance Analysis Example of Automatic Transmission
### Motion Analysis  
The motion analysis model of each gear of a 6 HP26 automatic transmission can be obtained by deleting the connection edges between basic components of each planetary row in Figure 10.  
Components of the first, second, and third planetary row are numbered 1, 2, \(\ldots\), 9 in the order of sun gear, ring, and planet carrier, and the rotational speeds of the components are \(n_{1}\), \(n_{2}\), \(\ldots\), \(n_{9}\), respectively. The characteristic parameters of the first, second, and third planetary row are \(\alpha_{1}=z_{\tau 1}/z_{\delta 1}=1.92\), \(\alpha_{2}=z_{\tau 2}/z_{\delta 2}=2.24\), \(\alpha_{3}=z_{\tau 3}/z_{\delta 3}=2.74\).  
Motion characteristic matrix of a 6 HP26 automatic transmission:  
\[\mathbf{A}_{1}=\begin{bmatrix}1&\alpha_{1}&-(1+\alpha_{1})&0&0&0&0&0\\ 0&0&0&1&\alpha_{2}&-(1+\alpha_{2})&0&0&0\\ 0&0&0&0&0&1&-\alpha_{3}&\alpha_{3}-1\end{bmatrix}\]  
Connection matrix:  
\[\mathbf{A}_{2}=\begin{bmatrix}1&0&0&0&0&0&0&0&0\\ 0&0&0&0&1&0&0&-1&0\\ 0&0&0&0&0&1&0&0&-1\end{bmatrix}\]  
Gear matrix:  
\[\mathbf{C}_{1}=\begin{bmatrix}0&0&0&0&0&1&0&0&0\\ 0&0&1&0&0&0&-1&0&0\\ 0&1&0&0&0&0&0&0\end{bmatrix}\mathbf{C}_{2}=\begin{bmatrix}0&0&0&1&0&0&0&0&0\\ 0&0&1&0&0&0&-1&0&0\\ 0&1&0&0&0&0&0&0\end{bmatrix}\]  
\[\mathbf{C}_{3}=\begin{bmatrix}0&0&1&-1&0&0&0&0\\ 0&0&1&0&0&0&-1&0&0\\ 0&1&0&0&0&0&0&0\end{bmatrix}\mathbf{C}_{4}=\begin{bmatrix}0&1&0&0&0&0&0&-1\\ 0&0&1&0&0&0&-1&0&0\\ 0&1&0&0&0&0&0&0\end{bmatrix}\]  
\[\mathbf{C}_{5}=\begin{bmatrix}0&1&0&0&0&0&0&-1\\ 0&0&1&-1&0&0&0&0&0\\ 0&1&0&0&0&0&0&0\end{bmatrix}\mathbf{C}_{6}=\begin{bmatrix}0&1&0&0&0&0&0&-1\\ 0&0&0&1&0&0&0&0&0\\ 0&1&0&0&0&0&0&0\end{bmatrix}\]  
\[\mathbf{C}_{\text{R}}=\begin{bmatrix}0&0&1&-1&0&0&0&0\\ 0&0&0&0&0&1&0&0&0\\ 0&1&0&0&0&0&0&0\end{bmatrix}\]  
The transmission ratio and component speed of a 6 HP26 automatic transmission at each gear can be obtained from the following equation, as shown in Tables 4 and 5, respectively.
# The Effect of Laser Pulse Widths on Laser--Ag Nanoparticle Interaction: Femto- to Nanosecond Lasers
## 4 Results and Discussion  
### Simulation of Nanoparticle Temperature Distribution According to the Laser Pulse Width  
The distributions of the energy absorption rates were simulated according to the laser pulse width. Most of the irradiated energy was absorbed by the several-nanometer-thick surface layer of the nanoparticles. This may result in peeling due to thermal strain between the surface and the inner layers. The thermal effect of nanosecond laser pulses is thought to result in fragmentation because of the weaker force of the outermost atoms in the particles [31]. However, our simulation showed that the thermal effect of energy dispersion and interference on the outer surface is also important. The inhomogeneous distribution of energy absorption in nanoparticles leads to a partial deformation or evaporation of the surface, which can accelerate the fragmentation. In the nanosecond region, the thermal effect on nanoparticles increases with decreasing pulse widths. Assuming the same energy per pulse, when the nanoparticle surface is irradiated with a laser of pulse widths 4, 32, 64, and 100 ns, the surface temperature increases, which affects particle fragmentation. According to the simulation, the shorter the laser width pulse, the greater is the inhomogeneous increase in the energy absorption rate. The total energy absorption according to previous results was numerically calculated (Figure 4). The energy absorption by the nanoparticles increased markedly with decreasing pulse widths in the nanosecond range. Therefore, the rate of nanoparticle decomposition increased with decreasing laser pulse widths, leading to the production of smaller nanoparticles.  
Figure 4: Total energy absorption of nanoparticles as a function of the pulse width.  
Figure 3: Schematic diagram of the experimental setup.
# A Review on Local Failure Probability Sensitivity Analysis
## 3 Local RSA in the Original Input Space
### Score Function Method for Sensitivity Analysis  
In order to estimate the derivatives of \(P_{f}\), the score function method was first presented in [16] and later in [11; 17]. It consists in introducing a density function in the domain integral, like in the important sampling framework [18]. The density is simply taken as the joint pdf of the inputs \(\mathbf{Z}\), and the sensitivity is written  
\[\frac{\partial P_{f}(\mathbf{\theta})}{\partial\theta_{\ell}}=\int_{\mathbb{R}^{d }}\mathbb{I}_{D_{f_{\mathbf{Z}}}}(\mathbf{z})\frac{\partial f_{\mathbf{Z}}( \mathbf{z};\mathbf{\theta})}{\partial\theta_{\ell}}\,\mathrm{d}\mathbf{z}=\int_{ \mathbb{R}^{d}}\mathbb{I}_{D_{f_{\mathbf{Z}}}}(\mathbf{z})\frac{1}{f_{\mathbf{ Z}}(\mathbf{z};\mathbf{\theta})}\frac{\partial f_{\mathbf{Z}}(\mathbf{z};\mathbf{\theta})}{ \partial\theta_{\ell}}f_{\mathbf{Z}}(\mathbf{z};\mathbf{\theta})\,\mathrm{d} \mathbf{z},\]  
for \(\ell=1,\ldots,m\). Noticing the following equality \(\partial\ln(f_{\mathbf{Z}}(\mathbf{z};\mathbf{\theta}))/\partial\theta_{\ell}=(1 /f_{\mathbf{Z}}(\mathbf{z};\mathbf{\theta}))\partial f_{\mathbf{Z}}(\mathbf{z}; \mathbf{\theta})/\partial\theta_{\ell}\) holds for all \(\mathbf{z}\in\mathbb{R}^{d}\), the derivative is thus equal to  
\[\frac{\partial P_{f}(\mathbf{\theta})}{\partial\theta_{\ell}}=\int_{\mathbb{R}^{d }}\mathbb{I}_{D_{f_{\mathbf{Z}}}}(\mathbf{z})\frac{\partial\ln(f_{\mathbf{Z}} (\mathbf{z};\mathbf{\theta}))}{\partial\theta_{\ell}}f_{\mathbf{Z}}(\mathbf{z}; \mathbf{\theta})\,\mathrm{d}\mathbf{z}=\mathbb{E}_{f_{\mathbf{Z}}}\Big{[}\mathbb{I }_{D_{f_{\mathbf{Z}}}}(\mathbf{Z})\frac{\partial\ln(f_{\mathbf{Z}}(\mathbf{Z}; \mathbf{\theta}))}{\partial\theta_{\ell}}\Big{]}.\]  
The derivative \(Q(\cdot)=\partial\ln(f_{\mathbf{Z}}(\cdot;\mathbf{\theta}))/\partial\theta_{\ell}\) is then called the _score function_. In order to compute the sensitivity with regard to the distribution parameters of \(\mathbf{Z}\), the following expected value must thus be evaluated: \(\mathbb{E}_{f_{\mathbf{Z}}}\Big{[}\mathbb{I}_{D_{f_{\mathbf{Z}}}}(\mathbf{Z} )Q(\mathbf{Z})\Big{]}\). This expected value can be estimated with a crude Monte Carlo (MC) method [19], and the MC estimate of the derivatives of \(P_{f}\) is then  
\[\frac{\widehat{\partial P_{f}(\mathbf{\theta})}^{\text{MC}}}{\partial\theta_{\ell }}=\frac{1}{N}\sum_{j=1}^{N}\mathbb{I}_{D_{f_{\mathbf{Z}}}}\Big{(}\mathbf{Z}^ {(j)}\Big{)}Q\Big{(}\mathbf{Z}^{(j)}\Big{)} \tag{6}\]  
where the observations \(\mathbf{Z}^{(j)}\) are independent and identically distributed (iid), generated from \(f_{\mathbf{Z}}\). Therefore, assuming the failure probability is estimated with a crude MC method and written \(\hat{P}_{f}^{\text{MC}}\), the same observations can be entirely reused here. The simulation budget is thus kept constant as the score function does not depend on the costly limit state function \(g\).
# Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction
## 4 Experiments  
### DataSet  
In order to verify the effectiveness of the DM-CPDP method, we validate the performance of the prediction model through experiments and compare it with other methods. In this paper, we selected 14 datasets in NASA and 3 datasets in SOFTLAB to experiment. The datasets of NASA and SOFTLAB are obtained from PROMISE repository. The PROMISE repository is constantly updating datasets. Compared to the datasets used in some of the previous papers [8; 12], the current datasets have some variation in the number of samples, but its internal structure has not changed to some extent. Since this paper constructing cross-project software defect prediction model based on multi-source datasets, the impact of some change in the number of samples on the experimental results is limited. To facilitate comparison with previous studies, we used previous versions of datasets maintained by the PROMISE repository. These datasets are shown in Table 1.  
These datasets are derived from different projects, and the data distribution as well as feature attributes are different. We choose the common attributes of the target set and the source domain dataset to build the prediction models. Table 2 shows the metrics of the software features used in the experiment. It mainly includes McCabe metric element, Line Countmetric element, Halstead basic metric element, and its extension DHalstead metric element.  
To facilitate comparison, we performed two parts of the experiment on the data sets: NASA to NASA, NASA to SOFTLAB.  
NASA to NASA: Only the NASA is used as the target set and the training set. Each dataset belonging to the NASA is chosen as the target set, and the remaining data sets are used as the training set.  
NASA to SOFTLAB: All datasets belonging to NASA are used as source domain datasets, and datasets in SOFTLAB are used as the target sets.
# Review of Liquid Argon Detector Technologies in the Neutrino Sector
###### Abstract  
Liquid Argon (LAr) is one of the most widely used scintillators in particle detection, due to its low cost, high availability and excellent scintillation properties. A large number of experiments in the neutrino sector are based around using LAr in one or more Time Projection Chambers (TPCs), leading to high resolution three-dimensional particle reconstruction. In this paper, we review and summarise a number of these Liquid Argon Time Projection Chamber (LArTPC) experiments, and briefly describe the specific technologies that they currently employ. This includes single phase LAr experiments (ICARUS T600, MicroBooNE, SBND, LArIAT, DUNE-SP, ProtoDUNE-SP, ArgonCube and Vertical Drift) and dual phase LAr experiments (DUNE-DP, WA105, ProtoDUNE-DP and ARIADNE). We also discuss some new avenues of research in the field of LArTPC readout, which show potential for wide-scale use in the near future.  
time projection chambers; noble liquid detectors; cryogenic detectors; neutrino detectors; micropattern gaseous detectors; photon detectors for UV; visible and IR photons (solid-state); large detector systems for particle and astroparticle physics +
Footnote †: journal: Nuclear Physics Letters B
## 4 Resulting Use Cases
### Comprehension of Concurrent Programming Concepts  
A more advanced use case was the one oriented toward facilitating the learning of concurrent programming concepts. In this sense, the ANGELA notation integrated in the ITS was ideal for visualizing the execution flow of the programs, specifically through the vehicle that went through the graphic representations as a thread of execution.  
Concurrent and real-time programming entailed some challenges in students who were used to working in programs with a single execution flow. This was due to the new concepts that had to be introduced to the student, such as processes, synchronization and communication mechanisms, etc. The proposed ITS could leverage the included notation to help visualize these concepts.  
The identification of the critical sections in a program, i.e., memory variables that are shared between different processes, would be noteworthy among the concepts that the notation could represent. Such a concept could be represented by painting yellow diagonal stripes on the roads and placing vehicles that would represent the different running threads with their PID on their sides. In the case of the proposal, trucks were used to represent such threads.  
In order to synchronize all the trucks (i.e., threads), we could use traffic lights to stop/start the movement of the trucks, that is to stop/start the running thread respectively to avoid them entering the critical section at the same time. To control the state of traffic lights, pressure plates were placed on the road to simulate the action of the wait/signal on the traffic light when the truck drove on it. Such behavior can be seen in Figure 8, where a thread with \(PID=3\) is running through the critical section. At the same time, another thread with \(PID=4\) is waiting at the traffic light before the critical section until the other thread drives on the pressure plate to signal the traffic light.  
Figure 8: The ITS in action using the ANGELA notation showing a use case of concurrent programming. In this context, a thread (a) tries to access the critical section of a program (c), currently accessed by another thread and protected by a traffic light with a barrier (b) controlled by a pressure plate for signaling the semaphore (d). A video playing the full sequence can be found at [[https://youtu.be/66](https://youtu.be/66) zaRSljbPA]([https://youtu.be/66](https://youtu.be/66) zaRSljbPA).