esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Datasets
### Segmentation Datasets  
* **COVID-19 CT Lung and Infection Segmentation Dataset**[208]. The CT images in this dataset are collected from five public datasets, which include StructSeg 2019, NSCLC, MSD Lung Tumor, COVID-19-CT-Seg and MosMed. This dataset contains 20 labeled COVID-19 CT scans. Left lung, right lung and infections are labeled by two radiologists and verified by an experienced radiologist;
* **COVID-19 CT Segmentation Dataset**[209]. 110 axial CT images from 60 patients with COVID-19 are in this dataset, which is segmented by a radiologist. Three types of objects, including ground-glass, consolidation and pleural effusion, are annotated. Ground-glass opacities are in blue, consolidation is in yellow and pleural effusion is in green. The images are in JPG format, while other information, such as age and sex, are also extracted. This dataset is suitable for the training of both the classification model and detection model;
* **MOSMEDDATA**[210]. A total of 1110 CT images were provided by municipal hospitals in Moscow, Russia. A small subset of studies (n = 50) has been annotated by the experts of the Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department. During the annotation, for every given image, ground-glass opacifications and regions of consolidation were selected as positive (white) pixels on the corresponding binary pixel mask. This dataset also includes other information, such as age and gender;
* **COVIDGR Dataset**[211]. COVIDGR Dataset is a balanced X-ray dataset that covered all levels of severity of illness, from normal with positive RT-PCR, mild and moderate to severe. Data were collected from an expert radiologist team of the Hospital Universitario San Cecilio, and there are 852 X-ray images in total;
* **BIMCV COVID-19+**[212]. BIMCV COVID-19+ is a 389.27 GB annotated dataset that consists of both X-ray and CT images. Data were collected from public sources, including COVID-CT-Dataset, COVID-19 dataset and COVID-19 RADIOGRAPHY DATABASE. Data were also collected from some private datasets. There are 23,527 images in total, 23 of which were annotated by a team of expert radiologists. Two types of objects, including ground-glass and consolidation, are annotated. Ground-glass opacities are in green, and consolidation is in purple. Images are stored at a high resolution and entities are localized with anatomical labels in a Medical Imaging Data Structure (MIDS) format. The dataset also contains other information, such as sex, age, diagnostics, survival, etc.
# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 6 Conclusions  
The essence of image matching lies in the prediction model for the corresponding points and the elimination of matching gross errors. In contrast to related studies, we performed special processing to address both problems. The relatively independent calculation model parameters of rotation, scale, and translation can provide stable prediction results. Applying RPC to obtain the rotation and scale parameters led to satisfactory image positioning precision. The translation parameters generated by OPC exploit the stability of the overall correlation. While performing matching gross error elimination, the local facet vectors of points to be determined can be obtained statistically. Consequently, local properties and overall statistics were considered, thereby avoiding the problem of large-scale adjustment calculation failure due to the strong correlation of the observed values.  
The proposed method can achieve precise matching even when the resolution of one image is three times higher than that of the other image, as shown in the first experiment. The accuracy and efficiency of this method was compared to those of SIFT, SURF, and A-SIFT; the proposed method fully met application requirements. Research data indicated that SIFT and SURF matching have more mismatched points and lower efficiency than our method. In contrast, the A-SIFT results are satisfactory; however, this method is time-consuming, which makes it unsuitable for automated processing. The matching results obtained by the proposed method enhanced the direct positioning precision from 100 m to under 8 m. Using the reference image for comparison, the position error of checkpoints in the x direction was between 2.7 and 6 m, whereas that in the y direction was between 1 and 3.5 m. The research results verified the effectiveness of our matching strategy. The proposed method has several limitations that require further investigation. First, the error elimination procedure through TIN could be simplified to enhance processing efficiency. Second, during image resampling, the model for calculating tie points has many drawbacks that might introduce excessive noise. Future studies will focus on solving these problems.  
This work was supported in part by the National Natural Science Foundation of China under Project No. 41701531. It was also supported in part by the Natural Science Foundation of Jiangsu Province under Project No. BK20170782, and by the Open Research Fund of State Key Laboratory of Tianjin Key Laboratory of Intelligent Information Processing in Remote Sensing under grant No. 2016-ZW-KFJJ-01. We would also like to convey our heartfelt gratitude to the reviewers and members of the editorial team for their comments and contributions. [PERSON], [PERSON] and [PERSON] conceived and designed the study. [PERSON] and [PERSON] performed the experiments and analyzed the corresponding results. [PERSON], [PERSON] and [PERSON] wrote the paper jointly.  
The authors declare no conflict of interest.
# Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing
## 5 Model Analysis
After introducing the vehicular fog computing solution that uses vehicles with idle resources as fog nodes, users can directly obtain content from fog nodes close to the user, and they no longer need to obtain content data from a relatively remote core network, through a long backhaul link. This can greatly save the waiting time between the user sending a request and being responded, thereby improving the user service quality experience.
# Protecting Private Communications in Cyber-Physical Systems through Physical Unclonable Functions
###### Abstract  
Cyber-physical systems (CPS) are envisioned to change the whole of society. New engineered systems joining physical and digital solutions are being employed in industry, education, etc. These new systems are networked by default, and private information is shared among the different components related to users, critical infrastructures, or business operations. In this context, it is essential to encrypt those communication links to protect such information. However, even most complicated schemes based on hybrid (asymmetric and symmetric) solutions, finally require physical devices to store a secret key. This approach is cryptographically weak, as any person with physical access to the device could obtain that key. Therefore, in this paper we propose the use of physical unclonable functions (PUF) to generate secret keys for lightweight encryption schemes. Using PUFs, any attempt to capture the key is changing the original secret stream, and even manufacturers are not able to build two identical PUFs. The proposed key generator is based on magnetic materials and lightweight pseudorandom number generators to meet the low-cost and small size requirements of CPS. In particular, materials with an activated exchange-bias effect are employed, together with simple copper coils. The encryption process can be based on a simple XOR gate because of the robustness of the proposed key generator. In order to evaluate the performance of the proposed technology, an experimental validation based on simulation scenarios is also provided.  
Keywords:cyber-physical systems; physical unclonable functions; streaming communications; security; encryption +
Footnote †: journal: Accepted: 27 March 2019; Published: 1 April 2019
# Vision-Based Pavement Marking Detection and Condition Assessment--A Case Study
## 4 Case Study Results  
To evaluate the applicability of the proposed framework for vision-based pavement marking detection and condition assessment, a case study on a real-life roadway pavement was designed and carried out. Main Roads Western Australia (MRWA)--the road agency of the Government of Western Australia--archives pavement video data of their roads. Such videos were originally recorded by a vehicle traveling at a uniform speed in both directions of traffic movement. This data collection method ensures that each frame in the video covers approximately a 20 m long road segment, and therefore, far field regions of the pavement can be eliminated for the current frame and analyzed in the next frame. Additionally, the two-direction filming method is especially necessary for roads in metro areas since they are normally too wide to fit into the camera's field of view. A videotape recording a total of 28 km highway pavement in Western Australia was retrieved. Preliminary tests were run beforehand to evaluate the performance of the candidate methods for each module. Next, a case study was conducted to further validate its effectiveness. The case study can be understood as a flowchart, as shown in Figure 6.  
Figure 6: Flowchart of case study.
## 5 Results
### Experimental Results
The efficiency of the BSTA gain adaptation can be seen clearly in Figure 6, where the chattering phenomena are effectively suppressed. It needs to be mentioned that the adaptation method with BF decreases the BSTA controller coefficients and, consequently, the output of the controller; therefore, the system dynamics were not changed noticeably. That can be confirmed straightforwardly with the RMS values of the system output and sliding variable in Table 3. The real-time experiment offers the same conclusion, which can be seen in Figures 6-15. The BSTA has good chattering suppression capability and the same closed-loop performance as the STA. Also, the real-time experiment confirms that the BF adaptation process leads the controller output to lower values, with remarkably lower power consumption and higher efficiency of the system. This can be confirmed by the \(RMS_{u}\) in Table 3; Table 4. For the positioning system, it is also very important to reduce the chattering phenomena in order to lower the system wear, due to the excessive small mechanical movement of the system, which is produced by the harsh and non-continuous controller output. The chattering phenomena also produce a power dissipation on the positioning system drive and increase the heat loss, which is closely related to the power efficiency of the system. The disturbance suppression capability of the BSTA is preserved, and remains the same as on the STA, which can be seen in Figures 11-15. In the presented experiment, the chattering phenomena are also caused by the digital implementation of the algorithm. The discrete implementation of SMC is called the quasi-sliding mode, and the chattering phenomena are proportional to the sampling period [34]. In the experimental results, the influence of the sampling time is effectively reduced.
# DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients
## 3 Experiments
### Experiments  
#### 3.2.1. Treadmill Tests  
The performance of the FES envelope predicted by DNN was compared with that of the conventional trapezoidal envelope in treadmill walking tests. Figure 8 shows the experimental setup with the FES control system. In the treadmill tests, each subject was instructed to walk naturally on the treadmill at five different walking speeds: 1, 2, 3, 4, and 5 km/h. The subject was equipped with the FES control system, as described in Section 3.1. For each walking speed, the two FES envelopes (DBE and TE) were applied in a random order, so that the subjects were not aware of which FES envelope was being used. To avoid the effect of muscle fatigue, more than 10 min of rest was given to each subject between the tests with the two FES envelopes. The duration of the test session for each walking speed was 30 s following 15 s of transition time for the subject to adapt to the speed.  
The range of motion of a joint during gait can be used as one way to assess the closeness of FES-induced walking to natural walking. Using a goniometer, the joint angle at the ankle was measured while the subject was walking on the treadmill. Performance of the DBE and TE were compared regarding the range of motion of the ankle joint during treadmill gait.  
Figure 7: FES device (**above**) and experimental shoes (**below**).
## 4 Results and Discussion
### Advantages of the Proposed Model
#### 4.5.1 Impact of 3-Tier Densely Connected Architecture and Validation Function  
3-TierDCFNet can preserve morphological features, pixel-level information, and the semantic pattern of images at each tier with an unbiased validation function due to the fusion of three state-of-the-art models. In a three-tier architecture, we implemented densely connected phenomena with \(n(n+1)/2\) direct connections for N networks. This strategy enhances the optimal pixel-level feature selection and the preservation of semantic information of morphological parameters. The output feature map of the preceding tier is given as input to the next tier, which helps to ignore the irrelevant feature selection that ultimately reduces the computational time of subsequent tiers. The validation function at each tier's output does not allow the model to proceed with the subsequent tier execution until the training proficiency exceeds the predefined threshold value. At tier-I, DenseNet [64] is implemented, which helps to examine the RBC elements at the pixel level. At tier-II, Efficient-Net [70] preserves the pixel-wise semantic information for analyzing morphological deformities in blood images to detect healthy and anemic-RBC elements. During tier-III, ShuffleNet [69] maintains comparable accuracy with the least computational power. The main objective of using three-tier architecture is to simultaneously examine the input images with densely connected networks to preserve pixel-level features and semantic information. These morphological features are then used to identify the severity level of anemia.
# DDS: A Delay-Based Differentiated Service Virtual Network Embedding Algorithm
## 5 Performance Evaluation
### Evaluation Results
#### 5.2.2. Revenue and Cost  
Figures 4-6 describe the average revenue, average cost, and revenue-cost ratio under different virtual network request arrival ratios. It can be seen that the algorithm of [14] has the minimum average cost and the maximum revenue-cost ratio, the revenue-cost ratio is 93.998% to 98.516%. This is because it adopts integer linear programming to solve based on minimizing cost, so it performs well in the average cost. Compared with other algorithms, our algorithm has the lowest average cost and the maximum revenue-cost ratio. The revenue-cost ratio is 79.515% to 83.781%, which is about 5% to 10% higher than other algorithms.
## 5. Drought Rate Evaluation Discussion
### RANS-Based Drought Rate (DR) Evaluation  
The calculation of \(DR\) based on the results of RANS CFD simulations is a straightforward procedure since all needed quantities (velocity, turbulence intensity, and air temperature) are provided by the solution. Steady-state distributions of \(DR\) are given in Figure 8 at the horizontal planes \(z=0.1\) m, \(z=0.6\) m, \(z=1.1\) m, and \(z=1.7\) m for two cases with \(\theta=25^{\circ}\) (Figure 8a) and \(\theta=45^{\circ}\) (Figure 8b).  
According to the plots, the RANS-obtained \(DR\) values correspond, in general, to a small percentage of people, dissatisfied due to draught. However, the room classification differs from one level to  
Figure 8. RANS-computed fields of draught rate (\(DR\)) at four cross-sections of the room for two cases: (a) \(\theta=25^{\circ}\) and (b) \(\theta=45^{\circ}\); red circles indicate the A-A vertical line position.  
another, and there is strong spatial non-uniformity in the distributions due to localization of the steady-state jets. At \(\theta=25^{\circ}\) when the jets are attached to the ceiling (Figure 8a) at \(z=0.1\) m and \(z=0.6\) m \(DR\leq 10\%\) is detected almost everywhere (EN 16798-1:2019 category I). At \(z=1.1\) m there are at least two localized zones with \(10\%\leq DR\leq 20\%\) (EN 16798-1:2019 category II). At \(z=1.7\) m there are several zones where \(20\%\leq DR\leq 30\%\) (EN 16798-1:2019 category III). At \(\theta=45^{\circ}\) with the detached jets (Figure 8b) the \(DR\) values are locally higher: at \(z=0.1\) m and \(z=0.6\) m \(DR\leq 10\%\) is again visible almost everywhere (EN 16798-1:2019 [28] category I), but there are several spots where \(10\%\leq DR\leq 15\%\). There are 16 high-\(DR\) spots at \(z=1.1\) m that correspond to the jet penetration locations, where \(25\%\leq DR\leq 30\%\) there. These spots are more pronounced at \(z=1.7\) m, with \(30\%\leq DR\leq 35\%\) inside the jets.  
The conclusion from the engineering RANS data analysis is that the ventilation scheme used is designed well and provide satisfying \(DR\) values. However, the RANS computations provide information on mean components of velocity that define the mean velocity magnitude, or simply \"velocity\". To evaluate the thermal comfort indices (and DR in particular), the time-averaged velocity magnitude, \(<\)\(V\)\(>\), called the mean \"speed\" distributions, must be used [42]. It is known that in case of large velocity fluctuations that is detected for the test classroom, both in the experiments and in the unsteady computations, the \"speed\" values could be much higher than the \"velocity\" values [40].
## 3 Results and Discussion
### Properties of Hardened Mortar
#### 3.2.2 Compressive Strength  
Figure 11 demonstrates the results of measuring the compressive strength of conductive CFRCM and SFRCM at the age of 28 days according to the variation in fiber volume fractions in comparison to PM. As evidenced in Figure 11, the average compressive strength of PM was measured to be 45.9 MPa, and as the fiber volume fractions increased, all of the compressive strengths tended to decrease gradually. In all FRCM except for CFRCM, the target compressive strength turned out to be 35 MPa or more, and in the case of SFRCM, it  
Figure 10: The effect of fiber volume fractions on the flexural strength of developed conductive CFRCM, SFRCM versus PM specimens.  
was almost similar to or slightly higher than that of PM, indicating equivalent or higher compressive strength. In the case of CFRCM containing 0.5% fiber as obtained from this test result, the compressive strength was decreased by 3.0 MPa compared to PM, while CFRCM containing 0.75% fiber was decreased by 4.8 MPa compared to PM. Besides, in the case of CFRCM containing 1.0% fiber, the compressive strength was decreased by about 8.7 MPa compared to PM, and CFRCM containing 1.25% fiber was decreased by about 20.1 MPa. In terms of the ratio, therefore, it could be said that the compressive strength of CFRCM containing 0.5, 0.75, and 1.0% fibers was reduced by about 6.5%, 10.4%, and 18.9%, respectively, compared to PM, and CFRCM containing 1.25% fiber was less by about 43.8% significantly. In the case of CFRCM containing 1.25% fiber, particularly, it is hard to uniformly disperse the fibers in the cement mortar, and agglomeration occurs when the fibers become entangled, and the compressive strength becomes significantly low instead due to insufficient dispersion and increased air voids. It is judged that the compressive strength of carbon fibers were lowered because the interfacial adhesion force between the fibers and matrix in the cement mortar after hardening was weak due to the non-hydrophilic material on the fiber surface. The research results were also reported in previous studies [9; 56]. However, in the case of SFRCM containing 0.5% fiber, the compressive strength increased by approximately 6.4 MPa compared to PM and, in SFRCM containing 0.75% fiber, the compressive strength increased by about 5.5 MPa compared to PM. Moreover, in the case of SFRCM containing 1.0% fiber, the compressive strength increased by about 2.2 MPa compared to PM and, in SFRCM containing 1.25% fiber, it was almost similar to 0.1 MPa. Consequently, in the case of SFRCM containing 0.5%, 0.75%, 1.0%, and 1.25% fibers, the compressive strength was improved by about 13.9%, 12.0%, 4.8%, and 0.2%, respectively, compared to PM. This increase in compressive strength is considered to be because the formation and propagation of micro cracks in the cement mortar can be suppressed due to the bridging role of steel fibers.
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 3 Effective Factors in the CO\({}_{2}\) Absorption by Nanofluid
### Effect of Temperature  
Temperature also plays a key role in CO\({}_{2}\) absorption enhancement by nanofluids. [PERSON] and [PERSON] introduced a novel CO\({}_{2}\) absorber consisting of a NaCl aqueous solution containing Al\({}_{2}\)O\({}_{3}\) nanoparticles. They assessed the solubility of CO\({}_{2}\) in this nanofluid at different temperatures and concentrations of Al\({}_{2}\)O\({}_{3}\). They reached 11%, 12.5%, and 8.7% enhancement in CO\({}_{2}\) capture at 30 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, and 10 \({}^{\circ}\)C, respectively, when Al\({}_{2}\)O\({}_{3}\) nanoparticle concentration was 0.01 vol% in the solution [92]. Another study carried out by [PERSON] et al., that was carried out in a bubble reactor indicated that the rate of CO\({}_{2}\) absorption increased by 4.5% at 20 \({}^{\circ}\)C and Al\({}_{2}\)O\({}_{3}\) concentration of 0.01 vol%; this enhancement was 5.6% when Al\({}_{2}\)O\({}_{3}\) was substituted by SiO\({}_{2}\) nanoparticles at the same temperature [104]. [PERSON] et al., achieved an eight percent enhancement in CO\({}_{2}\) absorption rate in a bubble reactor at Al\({}_{2}\)O\({}_{3}\) nanoparticle concentration of 0.01% and 10 \({}^{\circ}\)C. These results imply that better mass transfer efficiency was obtained at lower concentrations of nanoparticles [105].
# High-Throughput Cross-Technology Communication via Chip-Level Side Channel
## 4 Evaluation
### Evaluation Metrics  
We take the CTC symbol Accepted Ratio (CTC-SAR) and ZigBee Packet Accepted Ratio (ZigBee-PAR) as the key metrics to evaluate the performance of our design. CTC-SAR is the accepted ratio of the number of correct demodulated CTC symbols to the number of CTC symbols; it is calculated as follows:  
\[\frac{Number\ of\ correct\ demodulated\ CTC\ symbols}{Number\ of\ CTC\ symbols} \tag{3}\]  
ZigBee-PAR is the accepted ratio of the number of correct ZigBee packets to the number of all the receiving ZigBee packets. The packet will be lost if it fails the CRC check, even if only one symbol is corrupted. It is calculated as follows:  
\[\frac{Number\ of\ correct\ ZigBee\ packets}{Number\ of\ all\ the\ receiving\ ZigBee\ packets} \tag{4}\]