esa-sceva/satcom-chunk-collection huggingface dataset:
# Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
## 5 Proposed CCAM Solution for Autonomous Driving
### Sensors and Devices  
The right set of information is imperative for an autonomous vehicle to drive through a complex dynamic environment. To capture the information, we can make use of various types of sensors and devices. For our proposed CCAM infrastructure, we choose to have cameras, LiDARs, RADAR, etc. The reasons behind using these sensors and devices are as follows. The camera-based sensors perform capturing in the form of images and/or videos, These visuals capture the road segments, vehicles, pedestrians, traffic signals and sign-boards, and real-time events happening in the vicinity of the RSU. The LiDAR sensors perform 3D mapping the world around the RSU. A LiDAR's measurements become more precise as its layer count increase. The RADAR sensors perform reliable object detection and can locate stable or moving objects. Consider Figure 3, which illustrates the components of the proposed architecture at this layer. Through the implementation of sensory data fusion and filtering approaches, we can create patterns for different use cases of the collected environmental data (via on-road sensors) as well as data from vehicles. This is accomplished by deploying an IoT middleware, an optimization and machine learning toolbox, and a decision toolbox in the eRSU (cf. Figure 2). By analyzing data from connected on-road deployed sensors and vehicles, the eRSU creates information (map) at the edge. The eRSU creates new information (map) based on the information collected from connected road sensors and vehicles. The information gained from external environments is termed _Perception at Edge_ (PAE). Hence, more informed decision making is performed when PAE is shared with vehicles. An illustration of the components of the proposed architecture at this layer can be found in Figure 3.  
It is imperative to select the right set of sensors, computing infrastructure, and communication infrastructure carefully to meet the aforementioned capabilities. A technical description is given in Table 2, along with the features and techn  
Figure 3: An overview of the Edge architecture for CCAM infrastructure. sensors. The last column describes the measurements made by each sensor. Please note that the sensors' selection is based on the practical implementation of similar projects. As shown in the following figures, the suggested set of sensors at the edge provides the perception (in addition to detecting and tracking objects). The activity analysis outcomes are depicted in Figure 4a, the environmental parameters are shown in Figure 4b, and the generated perception can be visualized in Figure 4c.
# Bisector-Based Tracking of In Plane Subpixel Translations and Rotations
## 4 Results
### Experimental Results
#### 4.2.2 Video Processing and Object Tracking Calculation
They are related following (9), which can be simplified into:  
\[\Delta y(x,y) =\Delta y_{BU}+\Delta y_{BP}(x)-y+r\,\sin(\gamma+\theta)=\Delta y _{B}(x)-y+r\,\sin(\gamma+\theta), \tag{10}\] \[\Delta y\left(x_{D},y_{D}\right) =\Delta y_{B}(x_{D})-y_{D}+r_{D}\,\sin(\gamma_{D}+\theta).\]  
We have compared the data from the contact measurer \(\left[\mathrm{Y}\left(x_{D},y_{D}\right)\right]_{t}\equiv\Delta y(x_{D},y_{D})\) with the angles computed with the proposed technique, \(\left[\theta\right]_{t}\equiv\theta\), through Equation (10). We imposed the bending values \(\Delta y_{BU}=\ -0.0904\) mm and \(\Delta y_{B}=\ -1.9611\cdot 10^{-2}\) mm, the positions \(y_{D}=17.5\) mm and \(r_{D}=194.23\) mm, and the angle \(\gamma_{D}=5.17\)\(deg\), according to the theory, and computed the linear least-square fitting. The results from the fitting are shown in Figure 12, with a \(p\)-value <0.05, root mean squared error equal to 0.16 mm, and \(R=0.99\). Figure 11: (**a**) Region of interest (ROI) of a frame of the recorded sequence by the camera focusing on the contact measurer. (**b**) Binarized and Cartesian-to-Polar transformed image of the ROI. (**c**) Sum of intensity of the pixels in Figure 11b by columns. Columns have previously been discretized to cover the range from 0 to 1 mm with steps of 0.01 mm, according to the accuracy of the device. Figure 12: Vertical displacement computed from Equation (10) with theoretical values for \(\Delta y_{B}\), \(r_{D}\), \(y_{D}\), and \(\gamma_{D}\), and the torsional angle obtained with the proposed method versus the vertical displacement measured with the contact measurer at the point D (black dots). The blue line represents the linear least-square fitting. Theoretically, for the point D, the total deflection is \(\Delta y(x_{D},y_{D})=\Delta y_{BU}+\Delta y_{BP}(200)+\Delta y_{T}(200,\,17.5)=-4.54\) mm. Again, the theoretical value differs from the measurements, if we compare that value with those shown in Figure 12 (around \(-3.50\) mm). This fact made us doubt the accuracy of the parameters used for the theoretical calculations. Probably, some inhomogeneities (density, shape, weldings) in the steel pieces of the structure affect the inertial moments, and therefore the position of the shearing center and/or the torsional angle are not accurately predicted.
## 2 Theory
### Fog Computing  
Fog computing was proposed by Cisco to solve latency problems in cloud-based computing systems. A cloud computing setup involves transferring large amounts of data from the client application to where the cloud server is located, which can lead to overheads in terms of performance, time, cost, and power consumption [10]. This can be critical for applications that need to run in real time and that require a minimal of latency and maximum throughput, such as surveillance, medical, and industrial systems, among others. In addition, most of the current Internet of Things (IoT) infrastructures do not have a homogeneous platform, which causes problems in the integration between the different heterogeneous hardware and software services for their communication with the cloud. The purpose of fog computing is to alleviate the problems that arise in applications where the processing of large amounts of constantly generated data that have to be processed with a minimum of latency is required. Fog computing addresses the above challenges through placing the fog nodes at the edge of the network, and this proximity to the device layer is in turn helpful in the following points [11]:  
* A reduction in latency, since the data generated by devices such as sensors and actuators can be served by the fog nodes in the local area network, which significantly reduces the movement of data over the Internet and provides fast and high-quality localized services.
* Increased interoperability, which helps integrate vertically fragmented IoT platforms. Fog nodes achieve this by supporting communication across a diverse range of protocols [12].
* Optimized bandwidth allocation. The data transfer rate or capacity of the network is augmented by the help of the fog layer, because part of the processing is carried out in the fog nodes and only more specific information is transferred to the cloud.
# Construction of Nighttime Cloud Layer Height and Classification of Cloud Types
## 4 Results and Discussion
### Performance of the NSRM Method
The profiles were reconstructed with the NSRM method (\(\alpha=0.3\) and \(\beta=1.5\)), the similar radiance matching (SRM) method, and the direct selection of the nearest donor. In reality, there is always a certain difference between the MODIS measurements and the C-C measurements of CTH along the track. Although as the MODIS algorithm improves, the difference is generally within several hundred meters, the difference in pixels with high cirrus clouds and multiple cloud layers remains large [33; 34]. Since the algorithm uses MODIS retrieved cloud top properties as constraints, the additional test provided an evaluation of the influence from the difference between separate sensors. Figure 8 shows the comparison between CTH inferred from the constructed profiles (from the donors) and the MODIS measurements (from the recipients) within 400 km of the track. The difference was generally within 2 km. The gray box indicates a portion of data that are suspected to have attributed a donor with high thin cloud layer on top of the MODIS detected low cloud layers. Therefore, a large difference between the CTHs is shown. Figure 6: MD (**a**) and RMSE (**b**) of MODIS CTH between the original pixels and the reconstructed pixels, changing with \(\alpha\) and dead zone. Figure 7: MD (**a**) and RMSE (**b**) of MODIS CTH between the original pixels and the reconstructed pixels, changing with \(\beta\) and dead zone.
# Simulation Study of Thermal-Mechanical Coupling Fretting Wear of Ti-6 Al-4V Alloy
## 2 Numerical Calculation Method
### Model Solving Process  
Throughout the fretting wear process, there is always a sophisticated coupling relationship between heat, stress, and wear. In ABAQUS finite element software, a temperature-displacement coupled finite element model of fretting slip is established. The modified energy wear model is then introduced into the temperature-displacement coupled finite element analysis through the user subroutine UMESHMOTION to perform the coupling analysis of heat, stress, and wear. The numerical simulation flow of the coupled heat-force-wear fretting wear is shown in Figure 2, and the specific steps are as described in the following.  
1. In ABAQUS software, the finite element model of temperature-displacement coupling is established, and related parameters are set. The parameters include the friction coefficient and material parameters that vary with temperature, as well as the load, the number of fretting cycles, and the ambient temperature, etc. An INP file containing the model information is generated.
2. Solving the INP file to obtain information about the nodes in the contact area, including node temperature, node coordinates, contact stress, and relative slip distance.
3. Writing the UMESHMOTION wear subroutine based on the modified energy wear model; first calculate or update the energy wear coefficient \(\mathrm{K_{E}(T)}\) at the corresponding node using Equation (9) and the obtained node temperature T, and then calculate the wear amount according to Equation (4).  
Figure 1: Fitting effect of energy wear coefficient.  
Calling the wear subroutine at the end of the current fretting cycle to calculate the amount of wear for that fretting cycle.
5. Updating the node coordinates according to the wear amount of that cycle combined with the adaptive mesh technique (ALE) to generate the surface profile after wear and update the INP file.
6. Information such as temperature and stress of the node is recalculated based on the new surface profile, and then the energy wear coefficient K\({}_{\text{E}}\)(T) is updated.
7. Counting the number of fretting cycles n, accumulate the total wear for n cycles and record.
8. Determine whether the number of fretting cycles n reaches the target number of cycles N. If it does not, the judgment result is yes, and the cycle proceeds to steps (2)-(6). If it does, the judgment result is no, and the simulation ends.
## 2 Materials and Methods
### Fluorescence Microscopy Imaging  
The a-TiO\({}_{2}\)-NH\({}_{2}\) NPs coupled with lipids ready after preparation were characterized through wide-field fluorescence microscopy with colocalization method. The aim of this method is to evaluate the percentage of coupling between lipids and titania NPs. Samples were prepared by withdrawing 10 \(\upmu\)L of the lipid-coated a-TiO\({}_{2}\)-NH\({}_{2}\) NPs solution and depositing them on the microscope slide; then the drops were covered with a cover-glass slip and this was fixed with a common nail polish. The images were acquired using a wide-field optical fluorescence microscope Nikon Eclipse Ti, equipped with a super-bright wide spectrum Shutter Lambda XL source with a collection of four filter cubes. The images were acquired with 60\(\times\) and 100\(\times\) PLAN-APO immersion oil objectives and the data analyzed by the NIS-element software. Amorphous TiO\({}_{2}\)-NH\({}_{2}\) NPs labeled with Atto-550 NHS ester dye were used for this purpose and the lipids with DiOC\({}_{18}\), as described above. Images were thus acquired by exciting the dyes at two different wavelength channels: 550 nm (red channel) and 488 nm (green channel). The colocalization tool of NIS-Element software (NIS-Elements AR 4.5, Nikon) was used to evaluate the coupling percentages, as previously reported [58; 59]: after setting a threshold between 0.1 and 1 \(\upmu\)m to disregard larger aggregates, the spots in the red channel (identifying the a-TiO\({}_{2}\)-NH\({}_{2}\) NPs) and green channels (corresponding to the lipid bilayer vesicles) were counted and an overlay of the two images was performed, counting only the spots in which the two fluorescences were colocalized. About 600 fluorescent points were analyzed by this automatic routine by applying a dimensional threshold. The percentage of colocalization was then calculated with respect to the MSN channel with the following formula [Equation (1)]:  
\[\%\text{TiO}_{2}\text{ NPs colocalized = ($n^{\circ}$ a-TiO${}_{2}$-NH${}_{2}$ NPs colocalized)/($n^{\circ}$ a-TiO${}_{2}$-NH${}_{2}$ NPs)} \tag{1}\]
# A Novel UCP Model Based on Artificial Neural Networks and Orthogonal Arrays
## 3 New, Improved UCP--Our Approach
### The Methodology Used within the Improved UCP Model  
The appropriate methodology was selected for the experimental part of the UCP approach based on several trial experiments. The order of the experiment was constructed based on a robust design algorithm and is shown in Figure 3.  
Figure 2: ANN architecture with one hidden layer (ANN-L36 prim).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **N** & **Min (PM)** & **Max (PM)** & **Mean (PM)** & **Std. Deviation (PM)** \\ \hline Dataset\_1 & 50 & 5775.0 & 7970.0 & 6506.940 & 653.0308 \\ Dataset\_2 & 21 & 6162.6 & 6525.3 & 6393.
## An Improved Physics-Based Model for Topographic Correction of Landsat TM Images  
**[PERSON] 1,\({}^{\dagger}\), [PERSON] 1,2,\({}^{\dagger}\), [PERSON] 1,2,* and [PERSON] 1,2**  
1 Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China; E-Mails: [EMAIL_ADDRESS] (A.L.); [EMAIL_ADDRESS] (Q.W.); [EMAIL_ADDRESS] (G.L.)  
2 University of Chinese Academy of Sciences, Beijing 100049, China  
\({}^{\dagger}\) These authors contributed equally to this work.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-28-8522-4131.  
Academic Editors: [PERSON], [PERSON] and [PERSON]  
_Received: 23 January 2015 / Accepted: 12 May 2015 / Published: 20 May 2015_  
**Abstract:** Optical remotely sensed images in mountainous areas are subject to radiometric distortions induced by topographic effects, which need to be corrected before quantitative applications. Based on Li model and Sandmeier model, this paper proposed an improved physics-based model for the topographic correction of Landsat Thematic Mapper (TM) images. The model employed Normalized Difference Vegetation Index (NDVI) thresholds to approximately divide land targets into eleven groups, due to NDVI's lower sensitivity to topography and its significant role in indicating land cover type. Within each group of terrestrial targets, corresponding MODIS BRDF (Bidirectional Reflectance Distribution Function) products were used to account for land surface's BRDF effect, and topographic effects are corrected without Lambertian assumption. The methodology was tested with two TM scenes of severely rugged mountain areas acquired under different sun elevation angles. Results demonstrated that reflectance of sun-averted slopes was evidently enhanced, and the overall quality of images was improved with topographic effect being effectively suppressed. Correlation coefficients between Near Infra-Red band reflectance and illumination condition reduced almost to zero, and coefficients of variance also showed some reduction. By comparison with the other two physics-based models (Sandmeier model and Li model), the proposed model showed favorable results on two tested Landsat scenes. With the almost half-century accumulation of Landsat data and the successive launch and operation of Landsat 8, the improved model in this paper can be potentially helpful for the topographic correction of Landsat and Landsat-like data.  
**Keywords:** mountainous areas; topographic correction; NDVI; BRDF; Landsat TM
# Optimisation of Energy Transfer in Reluctance Coil Guns: Application to Soccer Ball Launchers
## 1 Introduction
### Comparison of Existing Soccer Ball Launching Systems
Figure 4: Human kicking sequence. Figure 5: Mechanical inertial rotating launcher. Table 1 shows a comparison between existing ball launchers, including humans. These solutions are very different. This comparison is done considering the weight and size for each system, as it is a strong constraint in our case study. It is important to note that the energy transferred to the ball is close to the maximum value for all systems, except for the robot leg. This one is based on an industrial actuator able to carry heavy loads, and largely oversized for launching a soccer balls in terms of torque and power. Because it is not used at full power, its ratio of energy transmitted to launcher volume is very low compared to other solutions. However, this solution takes too much space due to the rotation of the leg and is not relevant for a small size launcher. In conclusion of this section, the most relevant launching systems in terms of energy transferred to the ball for a given actuator volume are reluctance coil guns, with a ratio of energy transmitted to launcher volume better than rotating inertial launchers by a factor 2.5, and better than most humans by a factor 5. Since these electromagnetic coil guns seem to be the most promising solution for launching balls, this paper will only focus on improving that solution in order to maximize this energy transfer without changing the volume and the weight of the actuator. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Launcher**} & **Length** & **Width** & **Height** & **Volume** & **Weight** & **Ball Speed** & **Ball Energy** &
\begin{tabular}{c} _Energy_ \\ _Volume_ \\ _(_cm_) \\ \end{tabular} \\  & **(cm_)** & **(cm)** & **(cm3)** & **(kg)** & **(m-s\({}^{-1}\))** & **(j)** & **(j-dm\({}^{-3}\))** \\ \hline Soccer player leg & 160 & 20 & 80 & \(133\times 10^{3}\) & 20 & 36 & 290 & **2.18** \\ Rotating inertial launcher & 25 & 65 & 25 & \(40\times 10^{3}\) & 25 & 29 & 190 & **4.75** \\ Robot arm [9] & 240 & 240 & 30 & \(1360\times 10^{3}\) & 50 & 21 & 100 & **0.07** \\ Reluctance coil gun [11] & 30 & 9 & 9 & \(2.4\times 10^{3}\) & 4.5 & 11.4 & 29 & **12.08** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing ball launchers comparison. Figure 6: Mechanical robot leg. Figure 7: Tech United Reluctance Coil Gun.
# On the Analysis of Coverage Feedback in a Fuzzing Proprietary System
## 5 Discussion
### Example Case Where Coverage Feedback Is Meaningless  
Listing 3 represents an example implementation for checking the integrity of the PNG image header. In this case, checking the code coverage feedback might be totally useless considering that the probability of the fuzzer randomly matching the PNG signature or CRC checksum is practically zero. This is dependent on how the memcmp or integer value comparison is implemented. More specifically, more coverage can be measured if the comparison takes different execution cycles upon different input data although their byte length is the same. If the comparison is atomic (e.g., executed instruction sequence for entire comparison is identical even if two given data for comparison partially matches or not), the probability of the fuzzer discovering a new code coverage is 1 over 2 to the 64 th in the case of the PNG signature match and 1 over 2 to the 32 nd for the CRC check. To handle special cases such as file signature match, libFuzzer typically maintains dictionary or supplementary data structure such as the Table of Recently Compared data (TORC) to discover such special data, regardless of coverage feedback. Building a good dictionary is more important than coverage feedback in such cases. In the case of a CRC integrity check example, there is no feasible way for the fuzzer to automatically solve the condition solely based on coverage feedback. On the other hand, one may use more detailed and fine-grained program behavior measurement techniques. For example, cmplog of AFLL++ [19] and Sanitizer Coverage in libFuzzer provide fine-grained measurements that allow for the detailed assessment of processed data. With such advanced fuzzing support features, it could be possible to automatically solve such conditions in a small amount of time based on implementation details.  
```
1#include\"pmg.h\"
2#include\"crc.h\"
3...
4
5//Check the PNG signature
6 constuints_tpng_signature[8]=[137,80,78,71,13,10,26,10];
7 if(memamp(png_data,png_signature,8)!=0){
8//NotapNGsfile
9 return-1;
10}
11
12//next17 bytestoheader(IHDRchunkbytes).
13 uint32_tfile_crc=*(uint32_t*)(png_data+28);
14 uint32_tmemory_crc=calculate_crc32(png_data+8,17);
15
16 if(file_crc!=memory_crc){
17//IntegrityCheckFailed
18 return-1;
19}
20
21//parsercontinues...
22
23//parsercontinues...
```  
**Algorithm 3** Example implementation of PNG header integrity checking. Coverage feedback information does not help the fuzzer find a new code path (integrity check success) in such a case.