esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Using Classifiers to Predict Relevant Change
### Definition of Relevant Change  
There are many applications that call for the use of automated change detection using remotely-sensed imagery. Each application has its own set of criteria that define types of changes that are relevant and not relevant. For example, following a natural disaster, emergency management authorities are likely only interested in identifying areas that have been damaged or destroyed. Additionally, insurance companies have an interest in knowing about changes to properties for which they underwrite policies (e.g., expansions of existing structures, new outbuildings being constructed, _etc._)  
[PERSON] and [PERSON] propose a taxonomy of the causes of changes in [20]. In this paper, we propose a scenario in which we are interested in the subset of anthropogenic changes that may require features on a map to be updated that are considered relevant change; all other features are not considered relevant change. Within this definition of relevant change, we include any new building or an extension to an existing building that is at least 200 square meters in area (_i.e._, approximately the size of a small residential house). Additionally, we consider any new road, parking lot or other impervious surface to be a relevant change. The demolition of any existing building or road is also considered to be a relevant change. Finally, disturbed earth that has been cleared for non-agricultural purposes (e.g., construction, deforestation, _etc._) is considered to be relevant change.  
Conversely, seasonal or transitory changes are not considered to be relevant changes for this particular experiment. For example, vehicles in parking lots or on roads, although a common sight, are not considered to be a relevant change. Changes to road surfaces, such as repaving, do not constitute a relevant change, because it is not a change that would require an update of features on a map. Agricultural changes (including planting crops, plowing fields, _etc._) and seasonal water body fluctuations are not relevant for this experiment. Finally, ephemeral changes, such as shadows or building glint (due to over-saturation of the sensor), leading to streaking, is not a relevant change.
## 2 Materials and Methods
### Synthesis and Characterization
#### 2.2.2 Synthesis of 2-((3-(4-Dodecylphenoxy)-2-hydroxypropyl)dimethylammonium)acetate  
Sodium bicarbonate (0.012 mol, 1.01 g) was slowly added to 30 mL ethanol solution with \(N\),\(N\)-dimethylglycine (0.012 mol, 1.24 g) and reacted at 80 \({}^{\circ}\)C for 1 h. Then, dodecyl phenol chlorol ether (0.01 mol, 3.54 g) was dissolved in hexane (15 mL) and slowly dropped into the above solution and reacted for 8 h. All solvents were vaporized under reduced pressure (\(-\)0.08 MPa/45 \({}^{\circ}\)C) to obtain a light yellow waxy viscous betaine crude product. Ethyl acetate (30 mL) was added to the crude product and stirred to reflux for 2 h, then cooled and filtered. The extracted solids were recrystallized several times in methanol and acetone mixed solvent system, and the resulting products were dried under a vacuum at 60 \({}^{\circ}\)C for 24 h.  
1-((3-(4-Dodecylphenoxy)-2-hydroxypropyl)dimethylamino)acetate: White solid, yield (79%), \({}^{\bf{1}}\)**H NMR** (500 MHz, CD\({}_{3}\)OD) \(\delta\) 0.87-0.90 (t, 3H, Ar-CH\({}_{2}\)-CH\({}_{2}\)-(CH\({}_{2}\))\({}_{9}\)**-\(\mathbf{CH_{3}}\))**, 1.25-1.37 (m, 18H, Ar-CH\({}_{2}\)-CH\({}_{2}\)-**-\(\mathbf{CH_{2}}\)-(CH\({}_{2}\))\({}_{9}\)**-\(\mathbf{CH_{3}}\)), 1.56-1.62 (m, 2H, Ar-CH\({}_{2}\)-**-\(\mathbf{CH_{2}}\)-(CH\({}_{2}\))**-\(\mathbf{CH_{3}}\)), 2.56-2.58 (d, 1H, Ar-O-CH\({}_{2}\)-CH(\({}_{2}\)-CH\({}_{2}\))\({}_{8}\)-\(\mathbf{CH_{3}}\)), 3.13 (s, 6H, Ar-O-CH\({}_{2}\)-CH(OH)-CH\({}_{2}\)-CN\({}^{+}\)(\(\mathbf{CH_{3}}\))\({}_{2}\)-CH\({}_{2}\)-COOH\({}^{-}\)), 3.48-3.72 (m, 2H, Ar-O-CH\({}_{2}\)-CH(OH)-CH\({}_{2}\)-N\({}^{+}\)(\(\mathbf{CH_{3}}\))\({}_{2}\)-CH\({}_{2}\)-COOH\({}^{-}\)), 3.93-4.19 (m, 2H, Ar-O-CH\({}_{2}\)-CH(OH)-CH\({}_{2}\)-CH(OH)-CH\({}_{2}\)-NH\({}^{+}\)(\(\mathbf{CH_{3}}\))\({}_{2}\)-CH\({}_{2}\)-COOH\({}^{-}\)), 4.69-4.74 (m, 1H, Ar-O-CH\({}_{2}\)-CH(OH)-CH\({}_{2}\)-NH\({}^{+}\)(\(\mathbf{CH_{3}}\))\({}_{2}\)-CH\({}_{2}\)-COOH\({}^{-}\)), 4.69-5.29 (m, 2H, Ar-O-CH\({}_{2}\)-CH(OH)-CH\({}_{2}\)-N\({}^{+}\)(\(\mathbf{CH_{3}}\))\({}_{2}\)-CH\({}_{2}\)-COOH\({}^{-}\)), 6.82-6.83 (d, 2H, **1,5-ArH)**, 7.08-7.09 (d, 2H, **2,4-ArH). \({}^{13}\)**C NMR** (125 MHz, CD\({}_{3}\)OD) \(\delta\) 14.12, 22.80, 29.11, 29.35, 29.55, 29.67, 29.79, 31.52, 31.64, 35.00, 52.27, 64.65, 66.10, 66.85, 67.21, 114.96, 128.16, 135.72, 157.47, 169.02.
# RM-Line: A Ray-Model-Based Straight-Line Extraction Method for the Grid Map of Mobile Robot
## 3 Results
### Evaluation Standard  
We quantitatively evaluate the performance of the algorithm using three parameters: the number of lines, the average distance from grids to the lines, and the running time.  
(1) Number of lines  
One of the reasons why the number of lines is used as an evaluation parameter is because the robot needs to compress the map and reload it.  
As shown in Figure 7a, the first time a robot works in an environment, a grid map is built. As shown in Figure 7b, after the first job, the robot extracts line segments from the grid map and stores them. As shown in Figure 7c, when the robot works in this environment again, the stored line segments are reloaded, and a grid map is generated accordingly.  
It can be known from the storage and reloading process of the grid map that the number of line segments affects the storage space and efficiency.  
(2) Average distance from grids to lines  
From the storage and reloading process of the grid map, it can be known that the closer the extracted line segment is to the outline of the obstacle, the more accurate the reloaded map is. Therefore, the distance from grids to lines is used as an evaluation parameter.  
The distance from grids to lines is shown in Figure 8, where L0-L5 are the straight lines obtained using the algorithm, the grass green grids are the obstacle grids in the edge map, and the black dots are their center. The black lines are the distance from the center of the grid to the nearest straight line. For example, the closest hint to grid Pi is L5, and the distance is di. The average point-to-line distance refers to the average of the distances from all grid points to the nearest line, as shown in Equation (5).  
\[DisPL_{ave}=\frac{\sum_{n=0}^{N}d_{n}}{N}, \tag{5}\]  
where \(N\) is the number of obstacle grids, and \(d_{n}\) is the distance from grid \(P_{n}\) to the nearest line.  
The smaller the \(DisPL_{ave}\), the closer the obtained straight line is to the real contour, i.e., the higher the accuracy.  
Figure 7: Compressed storage and reloading of the grid map: (**a**) origin map; (**b**) line segments; (**c**) map reloaded with lines.
# Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm
## 4 Trajectory Optimization  
It is well known that the fold path generated by the JPS algorithm is not conducive to the robot's motion in real work scenarios. Therefore, this paper uses polynomials for trajectory optimization based on the AP-JPS algorithm. The derivatives of each order of the polynomial are used as the position, velocity, acceleration, jerk, and snap of the trajectory. Since it is difficult to adapt single segment polynomials to more complex scenarios, and considering the continuity of position, velocity, acceleration and rate of change of acceleration at the junction between two moving trajectories of the robot, the snap function is used as the minimization objective in this paper, and the path is smoothed using the minimum snap objective function to solve the seventh-order polynomial minimally.  
Figure 8: Example of the pathing process. (**a**) (e,3) is selected as the next search point, (**b**) (b,7) succeeds (e,3) as the next search point, (**c**) comparison of paths before and after optimization.
# Effects of the Weight and Balance of Head-Mounted Displays on Physical Load
## 5 Discussion
### Questionnaire Results
#### 5.3.3 Center of Mass  
Figure 12 shows a line profile resembling an inverse proportional hyperbolic curve. The Q4 curve showing a score of 2.8 can be approximated by the following equation, which shows an inversely proportional trend:  
\[f(x)=6384.3x^{-1}+435.8(R^{2}=0.91). \tag{1}\]  
Our results suggest that fatigue will be constant if the product of the weight and the balance (i.e., the distance from the COM) is constant. The product of the weight and the balance represents the torque required to support the HMD. Thus, the torque experienced by the neck joint can be regarded as an indicator of the physical load exerted by the HMD. Guidelines for the production of helmet-mounted displays [15] show a similar relationship between weight, balance, and fatigue. Therefore, helmet- and head-mounted displays appear to exhibit certain shared characteristics.  
Shifting the COM of the HMD from 2 cm to 4 cm in front of the tragion notch results in the physical load increasing rapidly. Regardless of the weight of the HMD, if the COM lies within 2 cm in front of the tragion notch, the physical load remains almost constant. [PERSON] and [PERSON] [10] reported similar results for participants in a look-down posture condition. As we did not instruct participants to adopt a particular posture, they exhibited a variety in their postures. Despite these variations in user posture during our experiment, it is encouraging that professional- and consumer-use HMDs with different shapes and weights yielded similar results. By investigating content that requires upward looking and other postures, the results reported by [PERSON] and [PERSON] [10] may be applicable to consumer-use HMDs.  
Our results are based on subjective responses. More objective evaluations are needed to verify the anatomical and physiological effects. For example, future work is to estimate the load torque applied to the neck joint from the posture and weights of human and HMD, and compare it with the present results.
# Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics
## 7 Results and Analysis
### EDCNN Performance Evaluation  
EDCNN was compared with two models, namely typical CNN and SELU CNN [37], for wind power forecasting (Figure 4). For performance evaluation of wind power forecasting, three evaluation indicators were used: Mean Absolute Error (MAE), Normalized Root Mean Square Error (NRMSE) and Mean Absolute Percentage Error (MAPE) (Table 3). MAPE, NRMSE, and MAE are widely used to evaluate the performance of wind power forecasting models [22; 26; 47; 48]. All the results shown in Figure 4 and Table 3 were taken on one day (24 h) of every season, i.e., 1 January (winter), 1 April (spring), 1 July (summer) and 1 October (autumn).
# A Novel Multipath Transmission Scheme for Information-Centric Networking
## 4 Path Management  
In our scheme, an ICN router bound with an MPSID can constitute a one-hop indirect path, which can provide a data relay service for end-to-end transmission. Therefore, making full use of path resources is vital. To this end, we propose in this section a path management mechanism in detail. Path management consists of two phases: path selection and path switching. In the path selection phase, we select several relay nodes as initial temporary solutions based on historical transmission information. In the path switching phase, we measure the congestion degree of these selected paths. If any of these paths experience heavy congestion, we discard this congested path and find an alternative.
## 7 Testing and Evaluation
### Sumo
#### 7.1.2 SUMO Simulation Procedure  
A road network consists of nodes (junctions) and edges (i.e., roads that connect various junctions). Each node features coordinators (x,y). First, the node file is created. The extension of the node file is (.rod.XML). Then, after this, the edge file is created and the file extension is (.edg.XML). Then, after this, the edge-type file is created with file extension (.type.XML). Then, after this, the route file is created with file extension (.rou.XML). The node files, edge files and type file, when combined, will generate the whole network file (.net.XML). After creating the net file, we need to create the route file defining the acceleration, deceleration, vehicle type, length and max speed. Finally, the configuration file has to be added, in which the beginning value is 0 s and the end value is 5000 s. These values can be changed. The input parameters below were used in the SUMO simulation.  
Length of Car: 4 m  
Junction Type: Three-Junction  
Vehicle Type: Car  
Vehicle Colour: Yellow  
Traffic Signal Time: 0.05 s  
Each lane has a unique ID to show the cars travelling in that lane. SUMO creates the trips on its own by editing the parameters. Once the trips are calculated, it will run the simulation. Once the simulation is run, as seen in Figure 13, the vehicles are moved along the journey from Point A to Point B. The simulation ran for 503 s and was then stopped to show that all vehicles in the system have completed their journey. The resulting time from the SUMO simulation, to go from Junction 1 to Junction 3, was 500 s, while the resulting time using analytical modelling was 501.954 s.
## 3 Experimental Setup Testing and Analysis Results
### Concrete Object Crack Detection
#### 3.2.2 Testing a Concrete Object with Cracks (Object 2)  
Object 2 was 30 cm in thickness with a crack 15-cm deep in the concrete. The impact-echo device struck one side of the studied object. The microphone was utilized to collect sounds, and the sound card processed the recording and produced the audio file to be imported into MATLAB for simulation, resulting in the waveform graph shown in Figure 12. Figure 12a presents the incident impact point wave graph. Figure 12b shows the waveform graph of the first reflected wave. The t\({}_{1}\) is the impact time (243.3 \(\upmu\)s), whereas t\({}_{2}\) is the reflected wave receiving time (256.7 \(\upmu\)s). The time lapse is expressed as t\({}_{2}-\)t\({}_{1}\) =13.4 \(\upmu\)s. Because the distance between the impact device and receiver was 5 cm, of the P-wave was 3748 m/s. Figure 12c presents the overall reflective wave from MATLAB simulation. Figure 12d reveals two pronounced frequencies, signal A at 6.1 KHz and signal B at 12.4 KHz obtained using FFT. A thickness of 29.5 cm and a crack depth of 14.5 cm were obtained in this study calculated by the wave velocity of 3748 m/s. A total of six crack depth measurements were performed in this object and the measured values were 14.3 cm, 14.5 cm, 13.9 cm, 14.6 cm, 15.3 cm, and 14.8 cm. The average value of the crack depth matched the actual crack depth of the studied object that was originally manufactured by the concrete industry.
# Image Magnification Based on Bicubic Approximation with Edge as Constraint
## 5 Experimental Results
### Numerical Accuracy (PSNR and SSIM)
The results in Table 2 show that, for BSD100, UrBan100, and AIS image sets, the average PSNR and SSIM values of the new method are the largest except for the SSIM value for AIS. Therefore, for the four sets of comparison data, the image quality and similarity of the magnified images by the new method are the best among the 8 methods used for comparison.
# The Integration of Azure Sphere and Azure Cloud Services for Internet of Things
## System Design
### System Security  
In order to ensure security through all layers of the IoT system, the security policies and rules were divided into four categories as shown in Table 2, i.e., device security, connection security, cloud security [41] and application security.  
For device security, the MT3620 MCU used in this system is identified as a critical component that is designed according to the seven highest levels of security properties [33]. Meanwhile, certificate-based authentication, instead of passwords, is utilized to prove identities when communicating with the  
Figure 2: Azure Sphere device provision with Security Service and Device Provisioning Service.  
cloud servers. More details about authentication process can be found in Section 3.4. Furthermore, to ensure the security of the application and its data, four strategies including limited access to external resources, application capabilities, signing images and device capabilities are implemented.  
For connection security, the communications between MT3620 and the cloud gateway are secured by the wolfSSL [42], which is an industry-standard Transport Layer Security (TLS) library targeted at IoT, embedded and RTOS environments. Specifically, HTTP Secure (HTTPS) and Message Queuing Telemetry Transport (MQTT) protocols are used not only for efficient resource usage but also for reliable message delivery.  
For cloud security, Azure Active Directory (AAD) is used for user authentication and authorization. Azure Stream Analytics is ISO 27001 and ISO 27018 certified [43], which means that information security management and personal data protection in the cloud is ensured. All data written to the Azure Table Storage is encrypted with 256-bit Advanced Encryption Standard (AES) encryption, which is considered as one of the strongest block ciphers available [44].  
For application security, the designed solution achieves 128-bit AES algorithm for data encryption between the CC2530 ZigBee nodes. Specifically, the CC2530 chipset takes advantage of AES-CCM (Counter with CBC-MAC) mode to encrypt and decrypt data. The global variables zgPreConfigKeys and DSECURE in the configuration file \"f8 wConfig.cfg\" are set to TRUE and 1, respectively, to enable AES security. Secondly, application capabilities, device capabilities and signing deployment are introduced to ensure the application security running on MT3620. Specifically, application capabilities are defined in application manifest file to declare the authorized use of resources that a given application requires. Device capabilities of the Azure Sphere device are granted by the Azure Sphere Security Service and are stored in flash memory. All image packages deployed to an Azure Sphere device must be signed with an SDK signing key. Before the application is uploaded to the chip by sideloading or over-the-air (OTA) method, the packages should be signed to ensure its security.
# SACT: A New Model of Covert Communication Based on SDN
## 5 Experimental Results
### Detection Rate  
The interval arrival delay of packets is a random variable with normal distribution in the process of network transmission. Different network congestion leads to different normal distribution (different mean and standard deviation) of interval arrival delay. In the experiment, in order to simulate the influence of network jitter under different normal distribution on the detection rate of the system, the network jitter time satisfying different normal distribution is added to the packet delay sequence.  
Setting the standard deviation \((\sigma=4)\), respectively test the detection rate of different watermark interval delay step \(\Delta\) when the average jitter is 5 ms, 10 ms and 15 ms. A total of 8000 bits of information are transmitted. Setting the threshold parameter of confidence interval when determining cosine similarity as \([0.8,1]\), then the result is shown in Figure 8.  
Figure 9 is the effect of different standard deviations \((\sigma=2,4,8)\) on the watermark detection rate when the average network jitter is given (\(\mu=10\) ms). The experimental results show that the smaller the standard deviation of network jitter is, the less the influence on watermark detection is, and the higher the detection rate is. The experimental results can further prove that the larger the watermark interval delay step is, the smaller the impact of network jitter on watermark detection.  
Watermark redundancy refers to the situation that the same watermark information is embedded into the carrier data stream several times in succession. Usually, both sides of anonymous communication keep continuous communication for a certain period of time. The watermark information comes from the port identification information of switch, so the redundancy of watermark information will be produced. Therefore, it is necessary to test the influence of watermark redundancy on detection rate. When the network jitter is fixed \((\sigma=4,\mu=10)\), the influence of watermark redundancy times \(r\) on detection rate is tested. The results are shown in Figure 10. The results show that the more redundant the watermark is, the higher the detection rate is. When the number of redundant watermark is more than 5, the detection rate is higher than 0.9. In conclusion, the anonymous communication tracking model proposed in this paper can work reliably and stably in our simulated networks.  
Figure 8: Effect of network jitter on detection rate.  
Figure 9: Effect of standard deviation of network jitter on detection rate.
# Adversarial Data Augmentation on Breast MRI Segmentation
## 2 Methods
### Evaluation Metrics
#### 2.3.1 FID and SSIM  
The FID [18] evaluates the generator's performance by measuring the Frechet distance between two multivariate Gaussians, modeled by the 2048-dimensional activations of the Inception-v3 pool3 layer for real and synthetic images. Equation (6) details the FID calculation, where \(X_{r}\sim\mathcal{N}(\mu_{r},\Sigma_{r})\) and \(X_{g}\sim\mathcal{N}(\mu_{g},\Sigma_{g})\) refer, respectively, to the activations of the pooling layer mentioned above for real and fake images.  
\[\text{FID}=||\mu_{r}-\mu_{g}||^{2}+\text{Tr}(\Sigma_{r}+\Sigma_{g}-2(\Sigma_{r }\Sigma_{g})^{1/2}) \tag{6}\]  
The FID has a lower bound of 0 but has no upper bound. A lower FID value will indicate higher similarity between the real image and the corresponding synthetic one and, therefore, a better synthesis quality. In [18], it is shown that FID is more robust to noise and blurring than the Inception Score [27], besides also being more sensitive to mode collapse and capable of evaluating intra-class variability. However, this metric depends of the capabilities of the Inception network, it assumes that Gaussian distributions correctly model the feature maps extracted and only takes into consideration the first two order moments of the distributions, which may not be suitable approximations [28].  
The SSIM [19] is a three-aspect metric that looks at the luminance distortion (\(l\)), contrast distortion (\(c\)) and loss of structural correlation (\(s\)). Image distortions are less noticeable on bright or textured regions. In turn, this drove the usage of luminance and contrast distortion, respectively. The concept of close neighbouring pixels having high interdependence is leveraged for estimating the structural aspect of objects.  
For measuring the distance between two images (\(f\) and \(g\)), each of the three aspects is calculated as shown in Equation (7).  
\[\begin{cases}l(f,g)=\frac{2\mu_{f}\mu_{g}+C_{1}}{\mu_{f}^{2}+\sigma_{g}^{2}+C_ {1}}\\ c(f,g)=\frac{2\sigma_{f}\sigma_{g}+C_{2}}{\sigma_{f}+\sigma_{g}+C_{2}}\\ s(f,g)=\frac{\sigma_{f}\sigma_{g}+C_{3}}{\sigma_{f}\sigma_{g}+C_{3}}\\ \end{cases} \tag{7}\]  
Here, \(\mu_{f}\) and \(\mu_{g}\) refer to mean intensity values, \(\sigma_{f}\) and \(\sigma_{g}\) to the respective standard deviation, \(\sigma_{fg}\) to the co-variance matrix, and \(C_{1}\) to \(C_{3}\) to arbitrary constants. Equation (8) presents how to calculate the composite metric:  
\[\text{SSIM}(f,g)=l(f,g)c(f,g)s(f,g) \tag{8}\]  
SSIM's values range between \(-1\) and \(1\), where 0 reports no correlation between the images, 1 means equal images and \(-1\) indicates that the structure is inverted.