esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The LIGOM Model  
LICOM is used to solve the Navier-Stokes equations. An ocean circulation model can simulate ocean temperature, salinity, velocity, and sea surface height under certain initial and boundary conditions. Meanwhile, the results of simulations can be used as the lower boundary conditions of atmospheric models and sea ice models. In addition, they can provide boundary conditions for regional ocean models. In addition, the results are capable of providing information on marine environment variables with an equably distributed space and continuous time in order to cover the shortages in the currently uneven observation data. This is beneficial for understanding the physical mechanisms of oceanographic processes. According to the original N-S equations, LIGOM uses a finite-difference discrete model equation to ensure the conservation of energy and volume that are transferred during a discrete process. Since there are considerable differences in the vertical direction during the processes of marine stratification and mixing, a method with different layers is used to solve the problem. LIGOM uses sea surface fluctuation with a free surface, including a surface gravity wave with a high speed and a Rossby wave with a low speed. In order to reduce the calculations, the model splits the surface wave mode and uses smaller time steps for integration, while it uses larger time steps for models that describe the vertical structure. During the process of integration, the interactions between the two time steps are kept. This method is called \"the decomposition and interaction of models\" [29]. The calculation of the barotropic model integral is considerably reduced through this decomposition. There are some procedures that cannot be captured by LICOM or by other ocean models, such as the procedure of turbulence. It is necessary to create processes of parameterization to describe these model-invisible procedures to realize their impacts. A low-resolution ocean model contains mesoscale vortex parameterization. However, a high-resolution ocean model (less than 10 km) can recognize mesoscale vortexes. Thus, there is no need for parameterization. Currently, LICOM has reached resolution levels of 10 km and even higher. It is capable of recognizing mesoscale vortexes and nicely simulating vortexes and their impacts. High-resolution ocean models use a corrected barotropic and baroclinic decomposition algorithm. Meanwhile, an improved double-adjustable and sticky disjunction diffusion scheme can be employed in the horizontal direction in the momentum equation and thermohaline equation. Therefore, mesoscale vortexes can be better simulated. The main control flow of LICOM is shown in Figure 1. The major processes in the integral loop include barotropic, baroclinic, and thermohaline processes, among others, and the Euler forward or leapfrog scheme is used.  
Figure 1: The control flow of LICOM.  
LICOM has a variety of choices of output format, including binary files and netcdf. A binary file can be converted into a netcdf file. Thus, the results can be easily handled and investigated by using various types of professional software.
## 5 Conclusions  
In this paper, a negative stiffness-based VCS is employed, as a supplement to a conventional BI, in the base of a multi-story residential RC building structure for seismic protection. The methodology for the implementation of the VCS is presented and the nonlinear dynamic model of the controlled structure is formed. A realistic displacement-dependent mechanism that generates controlled nonlinear negative stiffness is proposed, and the SSI effects are accounted with the use of nonlinear springs, with respect to the considered soil category, and are coupled in series with the stiffness of the conventional BI (\(k_{BI}\)) providing the total stiffness of the base level (\(k_{R}\)). The fixed VCS parameters are selected and the independent design variables are presented. The optimal design of the VCS follows a constrained optimization procedure to be as realistic as possible. Various VCS set of optimized parameters are obtained for different soil types in order to determine the effectiveness of the proposed vibration control strategy. Based on a comparison with a BI and the numerical results obtained, the following conclusive remarks can be made:  
Figure 11: Base relative displacements (cm) of the OS, BI, and VCS systems for the two selected real earthquakes for each soil type. Chi\(-\)Chi and Niigata for soft soil, JMA and Northridge for medium soil, and Kocaeli and Tabas for firm soil.  
The proposed dynamic model of the controlled structure accounts for the nonlinear dynamic behavior of the NS element and the effect of SSI;  
The VCS design is realistic, employing small added masses and realistic constraints;  
The VCS is effective in seismically protecting the superstructure for all the considered soil types (firm, medium, soft), and retaining at the same time the base displacements significantly lower as compared to a BI;  
The superstructure dynamic behavior is greatly improved with the proposed VCS. More specifically, the floor accelerations and interstory drift are reduced 60-80% as compared to the original multistory structure;  
The required base displacements are retained in reasonable ranges, in the order of a few centimeters (2-8 cm), and render the VCS a possible retrofitting option. As compared to a conventionally BI structure, reductions of 70-90% are observed.  
Conceptualization, K.A.K. and I.A.A.; methodology, K.A.K. and I.A.A.; software, K.A.K.; validation, K.A.K.; formal analysis, K.A.K.; investigation, K.A.K.; resources, K.A.K.; data curation, K.A.K.; writing--original draft preparation, K.A.K.; writing--review and editing, K.A.K., I.A.A. and E.J.S.; visualization, K.A.K.; supervision, I.A.A. and E.J.S.; project administration, K.A.K.; funding acquisition, K.A.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 6 Lessons Learned, Open Issues, and Research Challenges
### Lessons Learned
#### 6.1.6 Avoiding Routing Holes, Loops, and Energy Holes  
Routing holes (local minimum), routing loops, and energy holes are the common problems in position-based forwarding in high-mobility FANETs. The routing holes can be avoided by allocating minimum reward to the relay UAVs owing to taking the bad action by designing an intelligent reward function [22; 23; 24; 39]. The consideration of residual energy level in the reward function produces balance in energy consumption, which avoids the energy holes. SAIQL [56] tracks the relay UAVs in the end-to-end path so that none of the UAVs are selected more than once to avoid a routing loop in FANETs.
## 3 Results
The FA maps of the in vivo human volunteers were obtained after B\({}_{0}\) correction, similar to the original composite RF pulse B\({}_{1}\) mapping method [40]. The FA maps in the axial orientation showed similar results for both coils; however, in the coronal orientation, the FA maps indicated a slightly lower sensitivity of the proposed coil, which was due to the slight mismatch in the slice position. The proposed coil for the MRI experiments was only used in transmit and receive modes; however, if operating only in the TX mode, a dedicated multichannel RX RF coil can be utilized to further increase SNR and receiver sensitivity. \begin{table}
\begin{tabular}{c c c c c} \hline
**Loading** & **Coil** & **Axial** & **Sagittal** & **Coronal** \\ \hline \multirow{2}{*}{Cylindrical Phantom} & 2-port commercial circular birdcage & 0.14 & 0.13 & 0.12 \\ \cline{2-5}  & 4-port proposed split-type elliptical birdcage & 0.16 & 0.17 & 0.15 \\ \hline \multirow{2}{*}{In Vivo Human} & 2-port commercial circular birdcage & & 0.14 & \\ \cline{2-5}  & 4-port proposed split-type elliptical birdcage & & 0.16 & \\ \hline \end{tabular}
\end{table} 
Table 4: Pre-calibrated RF pulse power in cylindrical uniform phantom and in vivo human (min-0/max–1) for commercial circular birdcage and proposed split-type elliptical birdcage.
## 3 Proposed Method for Comparing between Original and Manipulated Videos
### Description of Image Processing, Optimization Search Method, and Comparison Algorithm
#### 3.2.3 Similarity Comparison Algorithm  
The histogram presents the distribution of the pixel values of an image. Similar images have a similar distribution of pixel values. In stage B of Figure 1, an image is presented as a normalized histogram. In stage D, three comparison algorithms, i.e., correlation [22], intersection [23], and Bhattacharyya distance [24], are applied to the histogram of a query video and the histogram of a comparison video to find equal videos. The reason for the application of the three algorithms is to supplement the disadvantages of each algorithm and enhance their advantages. Because the correlation-based algorithm compares the number of pixels with equal hues, the match rate is different depending on the shutter speed. The intersection-based algorithm has the highest match rate. The Bhattacharyya distance-based algorithm is slower but more accurate than intersection-based algorithms.  
\[d(H_{1},H_{2})=\frac{\sum_{I}\left(H_{1}(I)-\overline{H_{1}}\right)\left(H_{2 }(I)-\overline{H_{2}}\right)}{\sqrt{\sum_{I}\left(H_{1}(I)-\overline{H_{2}} \right)^{2}\sum_{I}\left(H_{2}(I)-\overline{H_{2}}\right)^{2}}} \tag{1}\]  
\[\overline{H_{k}}=\frac{1}{N}\sum_{I}H_{K}(I) \tag{2}\]  
\(H_{1}\), \(H_{2}\): histogram (query frame, comparison frame)  
\(I\): bin value of hue and saturation  
\(N\): the total bin count of hue and saturation  
\(K\): a random image frame  
Equations (1) and (2) present the formulas for the correlation algorithm. In terms of a correlation-based histogram comparison, if histograms are matched completely, the result value is 1; if not matched completely, \(-1\); and if not correlated, 0. In addition, \(H_{1}\) is the histogram of a query frame, and \(H_{2}\) is the histogram of a comparison frame. Moreover, \(I\) is the value of the \(X\)-axis of the histogram, which represents the bin value of the hue and saturation, and \(N\) is the total number of \(X\)-axis values of the histogram: The hue is 180, and the saturation is 256. Here, \(K\) indicates a frame.  
\[d(H_{1},H_{2})=\sum_{I}\text{min}(H_{1}(I),H_{2}(I)) \tag{3}\]  
Equation (3) is the formula for the intersection-based algorithm of two histograms. If normalized histograms are matched completely, the result value is zero and if not matched completely, the value is 1.  
\[d(H_{1},H_{2})=\sqrt{1-\frac{1}{\sqrt{H_{1}H_{2}N^{2}}}\sum_{I}\sqrt{H_{1}(I)\times H _{2}(I)}} \tag{4}\]  
Equation (4) formulates the Bhattacharyya distance-based algorithm. If the two videos to be compared are matched completely, the Bhattacharyya distance d is 0. The lower the similarity, the closer the distance is to 1.  
If two videos are similar to the results of the three algorithms applied to 1 frame, and if more than ten similarity results per frame are drawn repeatedly, 2 videos are equal or similar.
## 4 Tests and Numerical Results
### Islanded Operating Mode Tests  
A second group of tests were performed to control the voltage magnitudes in islanded operating mode. The single line diagram of the islanded operating mode test case is shown in Figure 7.  
Figure 6: Voltage magnitudes in test system over time and active and reactive power outputs in overvoltage condition and with the control of all the resources.  
Figure 7: Single line diagram of the islanded mode test case.
## 4 Discussion  
OCT provides a unique non-invasive way to assess human tissues. It offers the benefits of ease of use within a routine clinical setting and there are no known safety concerns. However, because swept-source laser light is used for imaging, artifacts caused by saturation of the photodetectors by reflected light or electrical noise within the high-speed analog-to-digital sampling system can obscure the image. Furthermore, because the high resolution of OCT provides near-cellular resolution, only a section of the tissue being studied can be imaged at once. Finally, OCT identifies multiple structural layers, including surface and sub-surface structures. These three issues (the presence of artifacts, the small field of view, and the long depth of view) can make it difficult for physicians to clinically interpret an OCT image. Here, we presented a technique to automatically segment out the TM from OCT images of the human ear. It removes artifacts and subsurface structures that are not the TM and then labels the TM specifically. Thus, the physician is presented with a clear and obvious OCT view of just the TM, which will allow the identification of pathology that is not detectable by routine otoscopy.  
While neural-network-based automated segmentation of CT images of the human ear has been carried out before [37; 38; 39; 40], a similar approach for segmentation of OCT images of the human ear is novel. Automatic segmentation is particularly important for OCT since provides a small field of view compared to CT or MRI, which can image the entire body. To provide OCT coverage of an entire structure, multiple 3D images from multiple angles might be needed. Once the structure of interest has been segmented out from each image, the 3D images could then be stitched together to provide a comprehensive analysis of the area of interest. For example, each of our microscopic OCT images can image about 2/3 ds of the TM. Now that the TM can be segmented, we could envision a future where the user slowly moves the imaging probe around in a circle, collecting many overlapping 3D volumes, and then the algorithm automatically cleans them and stitches them together. In this way, we expect to collect large, high-resolution 3D images of the ear in clinic.
# CARA: A Congestion-Aware Routing Algorithm for Wireless Sensor Networks
## 3 Congestion-Aware Routing Algorithm
### Route Evaluation Parameters
#### 3.1.2 The Node Load Factor  
Because the wireless sensor network characteristics include many-to-one transmission, multi-hop forwarding, and centralized data acquisition, they can easily cause network congestion. Congestion can lead to many packets being dropped and increase data transmission delays, seriously limiting the transmission performance of the network. In this study, we accurately judge the node's congestion by establishing a network queuing model to solve network congestion.  
Wireless sensor network nodes perform the functions of data acquisition and data forwarding. Because sensor nodes are usually inexpensive, they mostly use microcontrollers as processing units, and their limited storage capacity makes cache queue process packets using the FIFO order. Packets entering the queue include packets generated by the node's acquisition data and packets forwarded by the neighbor node. The cache queue model for node \(j\) is shown in Figure 2.  
The packet generating rate of node \(j\) is \(\lambda_{j}^{e}\). The packet receiving rate from node \(j\)'s neighbor node \(i\) is \(\lambda_{i,j}^{f}\). The packet arrival rate of node \(j\) can then be expressed as:  
\[\lambda_{j}=\lambda_{j}^{e}+\sum_{\begin{array}{c}i=1\\ i\
eq j\end{array}}\lambda_{i,j}^{f} \tag{5}\]  
When monitoring class sensor network applications, nodes process packets only to select the next hop for them, according to predetermined routing rules. When a node's packet arrival rate is close to its forwarding capacity, the node cache queue will be nearly  
Figure 1: The spatial relationships between the nodes.  
Figure 2: Node \(j\) queue model.  
full. If other nodes simultaneously forward data to it, packets can be lost because they are unacceptable. In this case, the node also becomes a congestion node. Therefore, we introduce the node load factor to evaluate the node's ability to route and forward packets in the queue. At time \(t\), node \(j\)'s load factor \(L_{j}^{t}\) is:  
\[L_{j}^{t}=\frac{c_{j}^{t}}{\lambda_{j}^{t}+q_{j}^{t}} \tag{6}\]  
Here, \(c_{j}^{t}\) is the packet service rate of node \(j\) at time \(t\), i.e., the number of packets that node \(j\) can handle in a unit of time. The packet arrival rate for node \(j\) is \(\lambda_{j}^{t}\). \(q_{j}^{t}\) is the node \(j\)'s current cache queue size. The node load factor dynamically reflects the processing of the node and its ability to receive packets. For the node with a significant load factor, its data forwarding ability is strong. The packet stay time in the node cache queue is short, and the node can assume more forwarding tasks and accept more packets. The routing protocol should avoid sending more packets to nodes with a small load factor to prevent network congestion and packet loss.
## 3 Results
### On Site Effectiveness of UV-SAN System
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Position** & **Pathogen and** **Concentration** & **Exposure Time** **[min]** & **Irradiation** **[m/cm\({}^{2}\)]** & **Growth after Incubation** **[24 h]** & **48 h** \\ \hline \multirow{4}{*}{HeadRest} & _Kleb. Pne. ESBL+_ & 10 & 76 & None & None \\ \cline{2-5}  & _0.5, 1, 2 McF_* & 20 & 152 & None & None \\ \cline{2-5}  & & 30 & 228 & None & None \\ \cline{2-5}  & _Staph. aur. MRSA_ & 10 & 76 & None & None \\ \cline{2-5}  & _0.5, 1, 2 McF_* & 20 & 152 & None & None \\ \cline{2-5}  & & 30 & 228 & None & None \\ \hline \multirow{4}{*}{Armrest} & _Kleb. Pne. ESBL+_ & 10 & 44 & None & None \\ \cline{2-5}  & _0.5, 1, 2 McF_* & 20 & 88 & None & None \\ \cline{2-5}  & & 30 & 132 & None & None \\ \cline{2-5}  & _Staph. aur. MRSA_ & 10 & 44 & None & None \\ \cline{2-5}  & _Staph. aur. MRSA_ & 20 & 88 & None & None \\ \cline{2-5}  & _0.5, 1, 2 McF_* & 30 & 132 & None & None \\ \cline{2-5}  & & 10 & 45 & None & None \\ \hline \multirow{4}{*}{Chair} & _Kleb. Pne. ESBL+_ & 10 & 45 & None & None \\ \cline{2-5}  & _0.5, 1, 2 McF_* & 20 & 90 & None & None \\ \cline{2-5}  & & 30 & 135 & None & None \\ \cline{2-5}  & _Staph. aur. MRSA_ & 10 & 45 & None & None \\ \cline{2-5}  & _Staph. aur. MRSA_ & 20 & 90 & None & None \\ \cline{2-5}  & _0.5 McF_ & 30 & 135 & None & None \\ \cline{2-5}  & & 10 & 18 & None & None \\ \cline{2-5}  & _Kleb. Pne. ESBL+_ & 20 & 36 & None & None \\ \cline{2-5}  & _0.5, 1, 2 McF_* & 30 & 54 & None & None \\ \cline{2-5}  & & 10 & 18 & 2 & 2 \\ \cline{2-5}  & _Staph. aur. MRSA_ & 20 & 36 & 2 & 2 \\ \cline{2-5}  & zone & 30 & 54 & 2 & 2 \\ \cline{2-5}  & _Staph. aur. MRSA_ & 10 & 18 & \textgreater{}100 & \textgreater{}100 \\ \cline{2-5}  & _Staph. aur. MRSA_ & 30 & 54 & 2 & 2 \\ \cline{2-5}  & _Staph. aur. MRSA_ & 10 & 18 & \textgreater{}100 & \textgreater{}100 \\ \cline{2-5}  & _Staph. aur. MRSA_ & 20 & 36 & 3 & 3 \\ \cline{2-5}  & _Staph. aur. MRSA_ & 10 & 18 & \textgreater{}100 & \textgreater{}100 \\ \cline{2-5}  & _Staph. aur. MRSA_ & 20 & 36 & 3 & 3 \\ \cline{2-5}  & _Staph. aur. MRSA_ & 10 & 18 & \textgreater{}100 & \textgreater{}100 \\ \cline{2-5}  & _Staph. aur. MRSA_ & 20 & 36 & 3 & 3 \\ \cline{2-5}  & _Staph. aur. MRSA_ & 10 & 18 & \textgreater{}100 & \textgreater{}100 \\ \cline{2-5}  & _Staph. aur. MRSA_ & 20 & 36 & 3 & 3 \\ \cline{2-5}  & _Staph. aur. MRSA_ & 10 & 18 & \textgreater{}100 & \textgreater{}100 \\ \cline{2-5}  & _Staph. aur. MRSA_ & 20 & 36 & 3 & 3 \\ \cline{2-5}  & _Staph. aur. MRSA_ & 2 McF_ & 30 & 54 & 2 & 2 \\ \hline \hline \end{tabular}  
* Test repeated for three different concentration with same result.  
\end{table} 
Table 3: Results of UVC susceptibility test on MDR bacteria performed in the ambulance. significant decrease in bacterial growth at any of the concentrations used it was necessary to apply a UV-SAN for at least 20 min. Anyway, exposure up to 30 min did not ensure the absence of bacterial growth even at low concentrations (0.5 McF).
## 2 Methods
### Calculation of Feature Similarity  
For calculating the similarity of feature vectors, let \(\widetilde{F}^{s}\) denote the feature map obtained from the model image, and \(\widetilde{F}^{t}\) denote the feature map obtained from the test image. Moreover, \(H\) and \(N\) denote the difference of the features at each pixel point of the model image and the test image, and the number of similar features, respectively. Suppose:  
\[v_{i,j,k}=\left|\widetilde{F}^{t}_{i,j,k}-\widetilde{F}^{t}_{loc,k}\right|, \tag{3}\]  
where \(\widetilde{F}^{t}_{i,j,k}\) denotes the \(k\)-th feature at the pixel point \((i,j)\) of the test image and \(\widetilde{F}^{t}_{loc,k}\) denotes the \(k\)-th feature at the pixel point \(loc\) of the model image.  
Then, calculate the difference at the \(k\)-th feature of the pixel \((i,j)\) of the model image and the test image, which is \(h_{i,j,k}\), and calculate the similarity, which is \(m_{i,j,k}\):  
\[h_{i,j,k}=\left\|\widetilde{F}^{t}_{i,j,k}-\widetilde{F}^{t}_{loc,k}\right\|_{2} \tag{4}\]  
\[m_{i,j,k}=\left\{\begin{array}{l}1,v_{i,j,k}<0.5\\ 0,v_{i,j,k}\geq 0.5\end{array}\right., \tag{5}\]  
where \"0.5\" is the activation threshold, which can be adjusted according to the actual situation.  
Figure 5: The relationship between convolution kernel size and neighborhood texture.  
Figure 6: Image pyramid and convolution kernel pyramid. (**a**) Image pyramid; (**b**) Convolution kernel pyramid.  
Next, the heat map \(H\) and the similar quantity map \(N\) can be obtained:  
\[\left\{\begin{array}{l}H_{i,j}=\sqrt{\sum\limits_{k=0}^{n}{{{h_{i,j,k}}}}}\\ N_{i,j}=\sum\limits_{k=0}^{n}{{{m_{i,j,k}}}}\end{array}\right.. \tag{6}\]  
Generally, the smaller \(H_{i,j}\) is, the more similar the test image pixel point \((i,j)\) is to the model image pixel point _loc_. The larger \(N_{i,j}\) is, the more similar features the test image pixel point \((i,j)\) and the model image pixel point _loc_ have. Thus, the heat map \(H\) and the similar quantity map \(N\) can be fused into the final matching heat map:  
\[H_{i,j}^{s}=H_{i,j}\cdot\mathrm{e}^{-k\cdot N_{i,j}/n}, \tag{7}\]  
where \(k\) is the adjustment factor and \(n\) is the number of features. The maximum point of the heat map is the similarity point \(P_{s}\) in the test image that is most similar to the key point _loc_ in the model image.