esa-sceva/satcom-chunk-collection huggingface dataset:
# Paint Removal with Pulsed Laser: Theory Simulation and Mechanism Analysis
## 3 Results and Discussion
### The Surface Morphology Analysis  
Morphology of the surface and fracture cross-section produced by the pulsed laser are shown in Figure 12a-d, respectively. It can be seen from Figure 12b,c that there were a large number of cracks and sheets on the cleaning surface, and the fracture cross-section of the residual paint layer had a \"cliff-like\" shape. There were some regular craters in the bottom of the cleaned areas of the fracture section, as shown in Figure 12c, which are usually observed in laser ablation of metals and organic materials [10]. The craters were a result of mechanical shock. A distinct layered structure was formed inside the paint layer, as shown in Figure 12d. The sheets were folded in the opposite direction of the pulsed laser action, which was a typical plane showing dynamic tensile damage trace of the material. Mechanical action along the direction of the folded sheets was also seen. Stress concentration and whitening of the fracture edge of the sheet layer was also visible.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Binding Energy/eV** & **Components** & **Area\%** \\ \hline
73.76 & Al\({}_{2}\)O\({}_{3}\) & 20.02 \\
74.27 & Al\({}_{2}\)O\({}_{3}\), [(C\({}_{2}\)H\({}_{5}\))\({}_{2}\)AlNNN]\({}_{3}\), SiO\({}_{2}\)(Al\({}_{2}\)O\({}_{3}\))\({}_{0.22}\), & 27.45 \\
74.78 & Al\({}_{2}\)O\({}_{3}\), Al\({}_{2}\)O\({}_{3}\)/Al, Al\({}_{6}\)Si\({}_{2}\)O\({}_{13}\), Al\({}_{2}\)OSiO\({}_{4}\) & 27.61 \\
75.48 & Al\({}_{2}\)O\({}_{3}\) & 24.92 \\
455.80 & TiN, TiO & 10.22 \\
458.00 & TiO\({}_{2}\) & 32.96 \\
458.88 & TiO\({}_{2}\), TiO\({}_{0.3}\)S\({}_{1.5}\), Ti\({}_{2}\)S\({}_{5}\) & 16.59 \\
463.60 & TiOS, TiO\({}_{0.3}\)S\({}_{1.5}\) & 40.23 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of peak separation of the XPS Al2p, Ti2p spectra, together with the binding energy and relative content of the Al2p, Ti2p spectra.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Binding Energy/eV** & **Components** & **Area\%** \\ \hline \multirow{4}{*}{Untreated surface} & 394.88 & C-N= & 7.63 \\  & 398.16 & C-N/C=N & 66.53 \\  & 398.96 & C-N(C)-C & 22.40 \\  & 401.12 & C-N & 3.44 \\ \hline \multirow{4}{*}{Cleaned surface} & \multirow{4}{*}{399.12} & R-C=NH, C-NH-C, -NH\({}_{2}\), & \\  & & [Mg(C\({}_{2}\)H\({}_{8}\)NO)\({}_{2}\)], [Mg(C\({}_{5}\)H\({}_{4}\)NCO)\({}_{2}\)], & 20.14 \\  & & C\({}_{48}\)H\({}_{2}\)N\({}_{8}\), (-CH\({}_{2}\)CH\({}_{2}\)NH)-n & \\ \cline{1-1}  & 399.71 & NH\({}_{3}\), -NH\({}_{2}\), C\({}_{36}\)H\({}_{46}\)N\({}_{4}\), & 43.48 \\ \cline{1-1}  & 400.29 & C\({}_{9}\)H\({}_{16}\)NO\({}_{2}\), NO\({}_{\infty}\), NH\({}_{3}\) & 34.07 \\ \cline{1-1}  & 402.99 & (CH\({}_{3}\))\({}_{3}\)NO, AlN, N\({}_{2}\) & 2.31 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the peak separation of the XPS N1s spectra, together with the binding energy and relative content of the N1s spectra.
## 4 Potential IIoT Applications
### Medical Applications  
As the Healthcare IIoT (HealthIIoT) will have a remarkable influence across the IIoT-driven healthcare industry [83], NTNs can strongly support welfare and mission-critical HealthIIoT applications [84]. Apart from extending coverage through S-IoT configurations, the deployment of UAVs as wireless relays can enhance data communication in body area networks (BANs), thus facilitating the provision of low-cost and timely HealthIIoT, e.g., real-time diagnosis and prescription, in remote and inaccessible areas [85]. In this direction, UAVs can initially collect data from wearable biomedical sensors that detect abnormal health conditions and then send the information to central devices. Additionally, UAVs can be used for monitoring endemics/epidemics at any area and remotely recording people's vital signals, e.g., body temperature and heart rate, as has been proven by the recent COVID-19 outbreak [86]. Patients and the aging population can be served by NTN-based telemonitoring and medical diagnosis from their homes through IoT-powered in-home healthcare (IHH) services, whereas interaction though videoconferencing between medical personnel and patients at home is also viable. Moreover, high bandwidth links can accommodate real-time medical imaging and remote robotic surgery [87]. To further improve the interaction of users with the NTN-aided HealthIIoT, recognizing the emotions of users via AI-based methods is also foreseen [88]. However, medical information should be protected from unauthorized access and publicly posting personal information should be avoided. Hence, data protection in the form of digital watermarking and authentication is crucial in a HealthIIoT system.
## 3 Improve L1 Optimization to Correct 100% of Corrupted Propagated Errors in Network Coding
### The Notes on Algorithm 1  
There are some notes on Algorithm 1. First, in the second step, coding equation is \(y=\left[\begin{array}{c}A\\ I_{n}\end{array}\right]\times x_{0}\) other than \(y=A\times x_{0}\). Second, the estimate of \(y\) is \(y^{d}=\left[\begin{array}{c}y_{A}\\ \left(y_{(m+n-C\lambda)-(n-k)}\right)^{d}\\ \left(y_{n-k}\right)^{d}\end{array}\right]\). \(y^{d}\) is divided into three parts: \(y_{A}\in R^{(\lambda\cdot\mathcal{C})\times 1}\), \(\left(y_{(m+n-C\lambda)-(n-k)}\right)^{d}\) and \(0^{(n-k)\times 1}\). Among the three parts, we know \(y_{A}\in R^{(\lambda\cdot\mathcal{C})\times 1}\) which is transmitted through a secret channel a priori, \(\left(y_{n-k}\right)^{d}=0^{(n-k)\times 1}\) is all zeroes. Both \(y_{A}\) and \(\left(y_{n-k}\right)^{d}\) allow us to know some prior information about \(y^{d}\). The ratio of \(y_{A}\) and \(\left(y_{n-k}\right)^{d}\) is \(\left(\lambda\cdot C+(n-k)\right)/\left(m+n\right)\). Even if \(y^{d}\) is polluted 100% by errors, we can indirectly decrease the error  
Figure 2: The diagrammatic sketch about Algorithm 1. The squares with white represent those added 323 zeros in the end of packets. Other graphic elements represent ordinary cases.  
rate by \((\lambda\cdot C+(n-k))/(m+n)\) magnitude. As long as the error rate is deceased less than 100% (is equal to 100%), we can apply the Lloptimization methods in [21] to perform error correction.  
Strictly speaking, the overall information rate is \(k/(m+n)\). In the model, if the error ratio is high (for example, 0.9), \(x_{0}\) has to be sparse enough. In the most extreme case, there is only one non-zero component in \(x_{0}\). At this point, the rate is \(1/(m+n)\). In experiments, a good combination of parameters is \(m=800\) and \(n=200\). At this moment, the rate is \(1/1000\) which is extremely low. However, we can control the ratio of \(y_{A}\) and \(\left(y_{n-k}\right)^{d}\), and then decrease the error rate indirectly. If the fraction of errors is smaller than 0.65, the number of non-zero components in \(x_{0}\) can be more. That means, the information \(k/(m+n)\) can increase fast and this model can be applied in a real environment.  
What is worthy to be mentioned most is, if the original message \(\kappa\) itself is sparse enough, there is no need to add zeroes to it. At this moment, the normalized information rate is \(n/(m+n)\). Under the condition where \(m=800\) and \(n=200\), the information rate is \(1/5\). This is a not bad information rate in the environment where the fraction of propagated errors is 100% in random network. In the sensor network, the messages which are usually very sparse can be corrected because a characteristic data may be collected many times. Our scheme is especially suitable for above environment.
## 4 Future Data
### Active Microwave Scatterometer  
2014 saw the failure of a relatively new scatterometer (OSCAT), but also sees the launch of a new Ku-band scatterometer: RapidScat, mounted on board the International Space Station. RapidScat is currently planned to operate through 2016. The continuity of the C-band scatterometer record appears to be safe with two currently operating ASCATs and the planned 2018 launch of MetOp-C with another ASCAT onboard. A joint Chinese-French platform is also planned for a 2018 launch, carrying two C-band scatterometers: SWIM and SCAT. Further missions are in planning stages and under discussion by groups, such as the Ocean Vector Winds Science Team ([[http://coaps.fsu.edu/scatterometry/meeting/past.php](http://coaps.fsu.edu/scatterometry/meeting/past.php)]([http://coaps.fsu.edu/scatterometry/meeting/past.php](http://coaps.fsu.edu/scatterometry/meeting/past.php))).
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 4 Taxonomy of IoD
### Architecture
#### 4.1.2 Communication Protocols  
IoD enables multiple communication protocols to support and transfer data between the nodes. Authors propose a drone planner which assists in two communication protocols, namely the MAVLink protocol and ROSLink protocol. The MAVLink is a lightweight message marshaling protocol and ROSLink is integrated with the robot operating system which provides robots into the IoT [28]. The efficiency of the communication protocol is decided by the node movements and behavior in the network [29]. Furthermore, the communication protocol for IoD has to accelerate in routing competencies where data communication between the source node to target nodes is crucial [30]. Due to the above reasons, selection of the communication protocol for IoD should be analyzed thoroughly.
## 3 Novel Cooling Strategy
### Input and Boundary Conditions  
In this study, water was used as the cooling fluid. For consistency, all cross-section areas (0.0008 m\({}^{2}\)), the shape (serpentine), the total length of the ducts (12.637 m) and the cooling fluid inlet conditions, i.e., temperature (\(T_{\text{in}}=298\) K) and mass flow rate \(\dot{m}_{in}\cong 0.1\) kg/s of the fluid through the duct, were kept constant in all simulation cases. The back sheet was subjected to a heat flux \(q^{\prime\prime}=6400\) W/m\({}^{2}\) from the top. The surrounding temperature and emissivity of the back sheet were assumed as \(T_{\text{sur}}=298\) K and \(\varepsilon=0.9\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No** & **Geometry** & **Dimensions** & **Width** & **Perimeter * (mm)** & **Depth** & **Hydraulic Diameter** \\ \hline
1. & & & \(w\) (mm) & \(p\) (mm) & \(d\) (mm) & \(D_{h}\) (mm) \\ \hline
2. & & & \(w\) (mm) & \(p\) (mm) & \(p\) (mm) \\ \hline
3. & & & \(w\) (mm) & \(p\) (mm) & \(p\) (mm) \\ \hline
4. & & & \(w\) (mm) & \(p\) (mm) & \(p\) (mm) \\ \hline \hline \end{tabular}
* Perimeter \(p\) is the sum of length of the sides of the channel minus the top side or width \(w\).  
\end{table}
Table 1: Geometric characteristics of the different cooling duct cross-sections considered in this study.
## 2 Related Work
### Bloom Filter  
A Bloom filter [45] represents the membership information of a set using a simple bit-vector-based data structure. Bloom filters have recently been applied in many applications [46; 47; 48; 49] and proposed in others [50; 51; 52]. A Bloom filter uses a hash function \(k\) on an \(m\) bit array that is initialized to 0 to store the elements \(n\) of a set into an array. The Bloom filter has two different operations: programming and querying. In programming, the Bloom filter sets the corresponding cell from 0 to 1 using the hash indices of the element \(n\) of the set obtained by the hash function \(k\). Querying is performed to determine whether a given input is included in the set. The query results for a standard Bloom filter (SBF) are positive or negative. In querying, if all the cells corresponding to the hash indices are 1, then the Bloom filter produces a positive result, which means that the given input is in the elements of the set. If at least one of the cells corresponding to the hash index is 0, the Bloom filter produces a negative result, indicating that the input is not an element of the set. However, the Bloom filter can produce a false positive result even if the given input is not an element of the set but never produces a false negative result. The probability of false positives is as follows [53; 54]:  
\[f=(1-p)^{k}=(1-(1-\frac{1}{m})^{kn})^{k} \tag{1}\]  
where \(p\) is the probability that a cell in the Bloom filter remains in state 0 after programming the Bloom filter as element \(n\), assuming that the hash function \(k\) is perfectly random.  
The optimal number of hash functions \(k\) that minimizes the false positive probability is as follows [53; 54]:  
\[k=\frac{m}{2^{\lceil\log_{2}n\rceil}}ln2 \tag{2}\]  
A counting Bloom filter (CBF) [55] stores multiple bits in one cell; the number of cells indicates the number of mapped elements. The CBF structure improves on the SBF disadvantage that its elements are impossible to delete. However, CBFs consume a massive amount of memory and can produce false negative results. The quaternary Bloom filter (QBF) [56] is a proposed structure that can eliminate false negative CBF results. A QBF has two bits per cell and allows the elements of a set to be inserted and deleted. When more than two elements are mapped to a cell, the corresponding cell is represented with an X. In querying, if all mapped cells of a given input are X, the QBF result is indeterminable, so the given input cannot be identified as positive or negative. In deleting, if all cells mapped to an element are X, the element cannot be deleted.
# A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine
## 2 Background
### Post-Copy  
In contrary to the pre-copy algorithm, the post-copy algorithm transfers the memory data to the destination after the new VM in the destination begins. The first step of the post-copy algorithm is to suspend the VM at the source. Then, the VCPUs, device states, and some kernel data are transferred to the destination. Then, the new VM resumed at the destination. Last, memory data at the source are transferred to the destination mainly by two ways: on-demand paging and active pushing.  
On-demand paging is the simplest way to synchronize the memory data. Once the VM starts at the destination and visits the memory page, which is not synchronized, it will result in page faults. The VM will then get the corresponding pages from the source. However, the network delay will cause a seriously degradation of the VM performance. From the user's perspective, it feels like the server crashed when the page faults occur. So, we should try to avoid page faults resulting in the migration process. Besides, if there is no other mechanism, the migration process can last a long time because some part memory pages of the VM may not have been visited in a long term.  
Active pushing is one way to reduce the duration of residual dependencies on the source node. The source will remain until the migration process is complete. The source VM will proactively push the remaining memory pages to the destination.  
Figure 1: Overview of pre-copy algorithm.  
In fact, on-demand paging and active pushing always work together in the post-copy algorithm. The post-copy algorithm ensures that all memory data are transferred only once, so the post-copy strategy has minimal total migration time closer to its equivalent time achieved by non-live VM migration. However, the disadvantages of post-copy algorithm are obvious: too many page faults may create network traffic, and the performance of the VM is seriously affected.
## 4 Results and Discussions  
### Spatial and Temporal Distribution Characteristics of Water Conservation Function in Yunnan Province  
The water conservation function fully reflects the process and capacity of the ecosystem to maintain water [39]. As shown in Figure 2, from 2000 to 2018, the water conservation depth in the study area decreased first and then increased, and generally maintained an upward trend. The average conservation depth over the years was 65.58 mm. The average depth of conservation in 2005 was 59.95 mm, a decrease of 12.7% compared with 2000. The average conservation depth in 2010 was 59.74 mm, slightly lower than that in 2005, and then recovered to 73.79 mm in 2018. During the study period, the average water conservation depth in the study area increased by 3.5%. Its spatial distribution pattern is basically stable, showing a distribution pattern of high in the southwest and low in the northeast. High value areas are concentrated in the north of Dehong, Pu'er, Xishuangbanna and the south of Wenshan mountain, with an average value of 166-349 mm. Low value areas are distributed in densely populated areas in the north of the study area, with an average value of 0-40 mm. Their distribution pattern is directly related to the average annual precipitation and the actual evapotranspiration of the region.
## 3 Results and Discussion
### Effect of Process Parameters
#### 3.1.6 Effect of Ultrasonic Frequency  
Besides the aforementioned process parameters, ultrasonic frequency is another important parameter for the UAE of quercetin and rutin in this work. The effect of ultrasonic frequency on the yield of quercetin and rutin was studied. The result is illustrated in Figure 8. As can been seen, similar to other process parameters, the extraction yield is dependent on different ultrasonic frequency. That is, the extraction yield increased with the increase in frequency of 20-80 kHz. Then, it displayed a gradual decrease from 80 to 120 kHz. The possible reason is that ultrasonic frequency of 80 kHz is closer the resonant frequency of the target component cell. Similarly, our previous studies [41; 42; 43] also found a highest extraction yield at one optimal frequency during the extraction of rutin from _Sophora japonica_ and hesperidin from tangerine peels. Thus, 80 kHz as the optimum ultrasonic frequency is used for the UAE of quercetin and rutin.  
Figure 8: Effect of ultrasound frequency using 60% concentration of ethanol solution with 40 mL/g of ratio at 30 \({}^{\circ}\)C and 200 W for 30 min on the extraction of quercetin and rutin.  
Figure 7: Effect of extraction temperature using 60% concentration of ethanol solution with 40 mL/g of ratio at 80 kHz and 200 W for 30 min on the extraction of quercetin and rutin.
## 4 Results
### Macroscopic Appearances of Eroded Specimens  
Figure 5 illustrates the macroscopic appearances of specimens eroded by SC-CO\({}_{2}\) jet issuing from the three nozzles with different configurations around the optimum standoff distance under the same operating conditions. The inlet pressure was set constant as 40 MPa. The ambient pressure was set to 10 MPa, and the fluid temperature was set at 330 K. These photos were displayed and analyzed because around the optimum standoff distance, the aggressive rock erosion intensity affected by nozzle configuration shows the largest differences with respect to erosion volume. As can be observed from the photos in Figure 5, an erosion pit is formed in the central area of the specimen by the high-speed SC-CO\({}_{2}\) jet impingement. This phenomenon suggests that the erosion of artificial core specimen caused by the SC-CO\({}_{2}\) jet is a kind of \"drilling-type\" damage, which has been claimed a feature of jet impact erosion for rocks with comparatively high porosity, such as limestone, sandstone and so on [42; 43]. This kind of damage is characterized by rather deep depth with small diameter. Additionally, it can also be observed that the size of the erosion pit increases first and then decreases with the increasing standoff distance, and the maximum size can be achieved at the standoff distance of 3 and 4 for the convergent nozzle and the other two Laval nozzles, respectively. This is because, with the increase of standoff distance, the diameter of the jet beam increases while the energy of the jet declines [9]. Moreover, the macroscopic appearances of eroded specimens are in good agreement with the results of rock erosion by jets in previous related articles [15; 39], indicating the reliability of this experiment.  
Figure 5: _Cont._It can also be observed in Figure 5 that, for the Laval nozzles, the size of the pits on the surface of the specimens are larger than that of the pits eroded by the jet discharging from the convergent nozzle at the same standoff distance, indicating that the erosion capability of the SC-CO\({}_{2}\) jet can be enhanced by the Laval nozzles. This is mainly due to the fact that the Laval nozzle with a convergent-divergent longitudinal section can make the jet have a greater impact speed, and then produce a stronger impingement on the specimens than the convergent nozzle under the same experimental conditions [26; 29]. In addition, another interesting phenomenon that can be observed in the figure is that there exist small pieces of rock breakage around the erosion pits on the surface of eroded specimens when using the Laval nozzles. On the other hand, the macroscopic appearances of eroded specimens are relatively flat and smooth when using the convergent nozzle. These phenomena illustrate that, the specimens are eroded in a more intense way when using the Laval nozzles than that when employing the convergent nozzle. This difference in macroscopic appearances also suggests that the Laval nozzle with a convergent-divergent longitudinal section can enhance the impingement of the SC-CO\({}_{2}\) jet on the surface of the specimens.
# Analyzing and Controlling Inter-Head Diversity in Multi-Head Attention
## 3 Similarity Measures for Multi-Head Attention
### Centered Kernel Alignment (CKA)  
[PERSON] et al. [9] have introduced Centered Kernel Alignment (CKA) as a similarity measure between deep representations. The authors have pointed out a limitation of SVCCA that it is invariant to invertible linear transformation when dimension size exceeds the number of data, whereas CKA shows robustness regardless of a small number of data \(N\).  
CKA is calculated by normalizing an index of Hilbert-Schmidt Independence Criterion (HSIC) [27] in order to keep invariance to isotropic scaling. For a pair of heads  
Figure 1: Visualization of attention weights in single-head attention and multi-head attention. Each head in multi-head attention assigns different weights to each word.  
\(X_{i}=(x_{i1},x_{i2}...,x_{iN})^{T}\) and \(X_{j}=(x_{j1},x_{j2}...,x_{jN})^{T}\), we can define two matrices \(K_{kll}=\kappa(x_{ik},x_{il})\) and \(K_{jkl}=\kappa(x_{jk},x_{jl})\) where \(\kappa\) is kernel function. Then HSIC between two heads is computed as follows;  
\[\text{HSIC}(K_{i},K_{j})=\frac{1}{(N-1)^{2}}tr(K_{i}CK_{j}C), \tag{4}\]  
where \(C\) is a centering matrix \(C_{N}=I_{N}-\frac{1}{N}\mathbf{1}\mathbf{1}^{T}\), where \(\mathbf{1}\) is a vector of ones. CKA of a pair of heads is computed by normalizing HSIC [28; 29];  
\[\text{CKA}(K_{i},K_{j})=\frac{\text{HSIC}(K_{i},K_{j})}{\sqrt{\text{HSIC}(K_{i },K_{i})\text{HSIC}(K_{j},K_{j})}}. \tag{5}\]  
Finally, we define inter-head similarity using CKA as an average value over CKA of every possible pair of heads;  
\[\text{CKA}_{\text{multi}}=\frac{1}{\text{\# of pairs}}\sum_{i<j}\text{CKA}(K_{i },K_{j}). \tag{6}\]  
In this paper, CKA similarity is used as not only a tool for analyzing inter-head diversity as well as SVCCA statistics but also an augmented loss to control inter-head diversity.