esa-sceva/satcom-chunk-collection huggingface dataset:
# Carrier Dynamics in InGaN/GaN on the Basis of Different In Concentrations
## 4 Discussion  
From the measurement results of FWM, numerous defects existed in sample PIN27. FWM can only monitor Da and \(\tau_{\text{R}}\) values with excitation energy density I\({}_{0}\) of 8.8 mJ/cm\({}^{2}\). The values of Da and \(\tau_{\text{R}}\) reached 2.73 cm\({}^{2}\)/s and 0.15 ns, respectively. Only one FWM measurement was observed given the remarkably high In doping concentration, whereas a number of high defects were generated in the P-type GaN layer. For sample PIN15, CSI27, and SI27, the values of Da and \(\tau_{\text{R}}\) totaled 2.56 cm\({}^{2}\)/s and 0.45 ns, 1.83 cm\({}^{2}\)/s and 1.59 ns, and 1.73 cm\({}^{2}\)/s and 1.48 ns, respectively. Comparison of PIN15 with PIN27 revealed that as In content increased from 15% to 27%, the D value increased from 2.56 cm\({}^{2}\)/s to 2.73 cm\({}^{2}\)/s. The grating times of CSI27 and SI27 total \(\tau_{\text{G}}=1.59\) ns and \(\tau_{\text{G}}=1.48\) ns, respectively, with no significant difference. Finally, we integrated the XRD, PL, and FWM measurement results, mainly based on the results of FWM measurement of the quality of samples SI27, CSI27, PIN27, and PIN15. According to results, CSI27 showed the best performance, followed by SI27, PIN 27, and finally PIN15.
# Topology Optimization Based Parametric Design of Balloon Borne Telescope's Primary Mirror
## 3 Parametric Design of Primary Mirror
### Theory of Compromise Programming Method
Considering the differences in the deformation of the mirror under gravity parallel to the optical axis and perpendicular to the optical axis, the corresponding weight factors were set to 0.2, 0.3, 0.3, and 0.2, respectively. The optimization algorithm is used to find the maximum and minimum values of each parameter to establish the compromise programming method mathematical model. After 501 optimization iterations, the maximum and minimum values of the primary mirror parameters were obtained. The objective function of parametric design is shown in Formula (17). \[\min\Gamma(t)=\left\{0.2^{2}\Big{[}\frac{R_{x}(t)-1.647e^{-6}}{2.977e^{-6}-1.64 7e^{-6}}\Big{]}^{2}+0.3^{2}\Big{[}\frac{R_{z}(t)-4.684e^{-6}}{7.234e^{-6}-4.684e ^{-6}}\Big{]}^{2}+0.3^{2}\Big{[}\frac{m(t)-0.029}{0.04069-0.029}\Big{]}^{2}+0.2 ^{2}\Big{[}\frac{f(t)-2002.4}{2437-2002.4}\Big{]}^{2}\right\}^{\frac{1}{2}} \tag{17}\]
# Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals
## 4 Method Validation and Application
### Tool Wear Real-Time Estimation and Early Warning  
It is very important to identify tool wear and forecast tool failure in the milling process by monitored signals. To achieve this goal, the method is realized in MATLAB and the calculated results can be shown in time, from which tool condition monitoring and tool wear estimation can display concurrently As shown in Figure 9, real-time monitoring signals are displayed once per 0.05 s and tool wear estimation is updated a point after a milling, which is designed for about 4 s. Tool wear estimation can be calculated and shown timely during the milling process, which is a useful application in modern manufacturing industry. The warning line that is regarded as the maximum tool wear can be set according to actual processing requirements. In Figure 9, 0.13 mm is chosen as the safety tool wear line.  
The figure is a screenshot of the dynamic display and tool wear estimation can be updated in real time according to machining signals.
# A New BDS-2 Satellite Clock Bias Prediction Algorithm with an Improved Exponential Smoothing Method
## 3 Experimental Data and Evaluation Indicators
### Evaluation Criteria  
To fully assess the predicted performances of these models, the root mean square (RMS) and Range (the absolute value of the error between the maximum errors and the minimum errors) are used as the statistics to evaluate the models' forecast results. Among them, the RMS represents the forecast accuracy of the model, and the Range denotes the stability of the model. The expression of RMS and Range can be written as [41]  
\[RMS=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(error^{(i)}\right)^{2}}{n}} \tag{20}\]  
\[Range=\left|error^{(i)}_{\max}-error^{(i)}_{\min}\right| \tag{21}\]  
where, \(error^{(i)}=\hat{x}_{i}-x_{i}\), \((i=1,2,\cdots,n)\) refer to the forecast error of the model; \(\hat{x}_{i}\) and \(x_{i}\) indicate the predicted and real clock bias in epoch \(i\), respectively; \(n\) is the number of observed clock bias. \(error^{(i)}_{\max}\) and \(error^{(i)}_{\min}\) represent the maximum and minimum value of the model prediction errors, respectively.  
As shown in Equations (20) and (21), values of RMS and Range will be obtained by using different models. Also, the smaller the values of RMS and Range represent the better-predicted performances of the model.
## 2 Observing-System and Power-System Considerations
### Mathematical Modeling of the Power System  
#### 2.4.1 PV Array  
In this study, polycrystalline PV panels were designed and assembled by TYUT. In the Arctic Ocean and Antarctica, all PV panels were designed to be positioned in a fixed direction parallel to the ground, which can be considered an optimum angle for PV panels installed in the polar regions. The key specifications of the TYUT PV panel are presented in Table 2.  
The relationship between output of solar power and radiation intensity based on an individual PV cell can be obtained from the Equation (1):  
\[E_{solar}=[P_{max}-P_{max}(T-T_{stc})\cdot 0.4\%]\cdot R\beta_{1}\beta_{2}\beta_{3} \tag{1}\]  
where \(E_{solar}\) is the output of solar power (Wh/day); \(P_{max}\) is the maximum power at standard test conditions (15 W); \(T\) is the ambient temperature (\({}^{\circ}\)C); \(T_{stc}\) is the ambient temperature at standard test conditions (25 \({}^{\circ}\)C); \(R\) is the average radiation intensity (KWh/m\({}^{2}\)/day); \(\beta_{1}\) is the soiling losses factor 0.97; \(\beta_{2}\) is the non-MPPT point coefficient 0.96; \(\beta_{3}\) is antireverse diode coefficient 0.98.
###### Abstract
In every cluster, the head vehicle is selected to satisfy the maximum interests of the network. However, in the case of disruptive communication links due to cluster head vehicle mobility, the vehicles create an interest-broadcasting storm, and, the nomination process to select the new cluster head vehicle starts again. This increases the network load and communication delay and decreases the packet delivery ratio. We propose a method for mitigating broadcasting storms using multihead nomination clustering in vehicular content centric networks. The novel contributions of this method are given below:  
* It extends the hello packet header to extract the information from neighboring vehicles. This information is used to select the most appropriate head vehicles \((HV)\) in the clustered network. * If there is more than one head vehicle \(HV\) in the network, then its information is shared with the RSU. The RSU receives that information and maintains it in the cluster information table \((CIT)\). * Depending on information, the RSU selects the most appropriate \(HV\) to satisfy the maximum number of interests in the network. * If the interesting content is not available in the network, then interest is forwarded from \(HV\) to RSU. If there is a disruptive communication link, the RSU utilizes the \(CIT\) entries to select the new \(HV\) to eliminate the broadcasting storm in the network. The motivation of the proposed scheme is to mitigate the broadcasting storm on disruptive communication links in the network. Moreover, the proposed scheme is intended to avoid the re-selection process of head vehicles by maintaining a unique cluster information table (CIT). It mitigates the excessive packet flow in the network. However, if the requested content is not available in the network, then the proposed scheme also enables the only head vehicle to send a request to RSU. This process minimizes the bottleneck effect on RSU. The objective for proposed scheme to minimize the communication delay, increase the communication efficiency and mitigates the broadcasting storm on disruptive communication links.For The remaining paper is structured as follows. Section 2 briefly explains the previous contributions of the related literature. Section 3 gives the overview of the system model in detail. Section 4 discusses the simulation setup and its results in detail. The conclusion and future work are explained in Section 5.
# A Review of Ghost Imaging via Sparsity Constraints
## 3 Applications of GISC
### GISC Spectral Camera
According to the theory of the GISC spectral camera [20], the correlation function of intensity fluctuations between the spatial intensity fluctuations in the pre-determined reference arm and the test arm is  
\[\Delta G^{(2)}(r_{i}^{\prime},\lambda_{l}^{\prime}) \tag{8}\] \[\propto\left\{\left\{T_{i}(r_{i}^{\prime},k_{l}^{\prime})\right\} _{r_{i}^{\prime}}\otimes\left\{\exp\left\{2\left[2\pi\omega(n-1)k_{l}^{\prime }\right]^{2}\right\}\right\}\left\{\exp\left[-\left(\frac{z_{2}r_{i}^{\prime} }{(z_{1}+z_{2})\zeta}\right)^{2}\right]-1\right\}\right\}_{r_{i}^{\prime}} \right\}_{k_{l}^{\prime}}\] \[\otimes\left\{\exp\left\{-\left[2\pi\omega(n-1)\right]^{2}k_{l}^{ \prime 2}\right\}\right\}_{k_{l}^{\prime}},\]  
where \(T_{i}(r_{i}^{\prime},\lambda_{l}^{\prime})\) is the spectral images of the object, \(\omega\) and \(\zeta\) are the height standard deviation and transverse correlation length of the spatial random phase modulator respectively, \(n\) is the refractive index, \(k_{l}^{\prime}=\frac{1}{\lambda_{l}^{\prime}}\otimes\) denotes the operation of convolution. Equation (8) specifies that the spectral images of an object can be separated from the correlation function of intensity fluctuations \(\Delta G^{(2)}(r_{i}^{\prime},\lambda_{l}^{\prime})\), while  
Figure 3: Schematic of the GISC spectral camera: (\(a\)) the object plane, (\(b\)) the first image plane, (\(c\)) the speckles plane, (1) an imaging system, (2) a spatial random phase modulator, (3) a microscope objective, (4) a CCD detector [20]. the spatial and spectral resolution are determined by the normalized second-order correlation of light fluctuations corresponding to different pixels and wavelengths in the first image plane '\(b\)'. Based on this model, we set up a prototype of the GISC spectral camera. To improve the reconstruction quality with low sampling rate, the structure sparsity and low-effective-rank constraints are applied in practice [77]. Figure 4 shows the experimental results of spectral imaging for vehicles, concrete ground and trees, etc. It indicates that the prototype has the capability of snapshot spectral imaging for natural scenes. As a new optical imaging technology, the GISC spectral camera provides a unique solution for spectral imaging of dynamic processes. This GISC imaging solution may also be expanded to other multi-dimensional information (such as polarization) acquisitions, ultra-fast measurements [60; 61], and super-resolution imaging [78]. With the improvement of GISC spectral camera, we believe it can be applied not only to remote sensing, but also to the consumer market.
# Current Harmonic Suppression Algorithm for Asymmetric Dual Three-Phase PMSM
## 3 Analysis of Dead-Time Effects
Applying the inverse Park transformation, the error voltages in the anti-synchronous rotating coordinate axis with the negative angular frequency \(+w_{e}\), which is defined as the \(x1\)-\(y1\) coordinate axis, become  
\[\left[ \begin{array}{c}U_{wd}^{x1}\\ U_{yd}^{y1}\end{array} \right]=\left[ \begin{array}{cc}\cos\theta&-\sin\theta \\ \sin\theta&\cos\theta\end{array} \right]\!\!\left[ \begin{array}{c}U_{xd}\\ U_{yd}\end{array} \right] \tag{9}\] \[=\frac{4U_{d}}{\pi}\left[ \begin{array}{c}\sum\limits_{n=6k}^{ \infty}\frac{\sin[n(w_{e}t+\delta)-\delta]}{n}+\sum\limits_{n=6k}^{\infty} \frac{\sin[n(w_{e}t+\delta)+\delta]}{n}\\ -\sum\limits_{n=6k}^{\infty}\frac{\cos[n(w_{e}t+\delta)-\delta]}{n}+\sum\limits _{n=6k}^{\infty}\frac{\cos[n(w_{e}t+\delta)+\delta]}{n}\end{array} \right] \qquad k=1,3,5...\]  
where \(U_{xd}^{x1}\) and \(U_{yd}^{y1}\) are the error voltages in the \(x1\)- and \(y1\)- axes respectively. After the transformation, it is obvious that the sixth-order voltage components are the main contents of the error voltages. It will influence the motor currents, which will in turn affect the control performance. Assuming that the motor back EMF contains only the fundamental frequency, the sixth-order current harmonics in the virtual anti-synchronous rotating \(x1\)-\(y1\) coordinate axis in \(x\)-\(y\) subspace are  
\[\left[ \begin{array}{c}I_{x1}^{6}\\ I_{y1}^{6}\end{array} \right]=\frac{4U_{d}}{\pi\sqrt{R^{2}+(6w_{e}L_{2})^{2}}} \left[ \begin{array}{c}\frac{\sin(6w_{e}t+5\delta-\phi)}{5}+\frac{\sin(6w_{ e}t+7\delta-\phi)}{7}\\ \frac{\cos(6w_{e}t+5\delta-\phi)}{5}+\frac{\cos(6w_{e}t+7\delta-\phi)}{7} \end{array} \right] \tag{10}\]  
where \(\phi=\tan^{-1}\frac{6w_{e}L_{2}}{R}\). Figure 5: Space diagram of current vector angle. It can be seen that the sixth-order current harmonics are dependent upon the speed and the parameters of the motor.
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
## 2 Background
### Location Privacy
#### 2.3.4 Cloaking Granularity  
It was suggested to resolve the limitations of the k-anonymity \"Cloaking granularity\". In the case of cloaking granularity, the cloaked region should be greater than the threshold defined by the user [27]. Place k-anonymity does not prohibit the release of user details, it preserves the privacy of the consumer. Cloaking granularity does not require the identification of the individual to be revealed, but cannot shield users from attacks linked to identity in the case of public awareness of the user's identity.
# Accurate Instance-Based Segmentation for Boundary Detection in Robot Grasping Application
## 4 Experimental Results and Analysis  
### Experiment Preparation  
The experiments were built with a 3D camera, Kinect V1, object including a red box, a cup, a cylinder, a white box, a computer equipped Intel core i5 9 th Gen and Nvidia GeForce GTX 1650 graphics card. Objects were disorderly located from 0.4 m to 3.5 m far from the camera since the camera could not capture the distance of the object out of this space. An application was developed by support of C#, Kinect for Window SDK 1.8 to connect to the 3D camera and acquire both color image and depth image. The application would associate with Kinect V1, and both of the two types of image would be directly delivered to the viewer. It made us easily capture the image for the experiments. The captured depth image would be served for 3D algorithms. Meanwhile, the captured color images, which have the same 640 \(\times\) 480 resolution, were used as the dataset of Mask R-CNN. However, before using the dataset of Mask R-CNN, the color image had to surpass an essential step--the calibration step. This essential step not only calibrates the color images as 2D image processing, but also maps the different coordinates of the color image and depth image. Two-dimensional cameras, IR Emitter and IR Depth Sensor, are arranged differently in Kinect V1. Moreover, the 2D and 3D cameras do not have the same resolution. In particular, the 2D camera has a 640 \(\times\) 480 resolution, and the 3D camera has a 320 \(\times\) 240 resolution. Thus, this calibration and mapping coordinate step is the very first step in combining 2D algorithms and 3D algorithms.  
We equipped all the experimental instruments with 4 classes of object. Using the Keras ImageDataGenerator library, we generate new images by horizontal and vertical flipping, rotating (\(\pm\)2 degree), shifting height and width, changing brightness. Both original and generated images are used together to form a dataset. The dataset has 1000 color images, in total, of four classes: blue box, cup, cylinder, and white box. We trained this dataset with COCO [27] pre-trained, which is trained with 80 classes. For training, we choose some arguments for configuration such as the number of epochs is 30 with 100 steps each, learning rate equals is 0.001. We used a mini-batch size of 1 image per GPU and trained the model for 11k iterations. We used a weight decay of 0.0001, a learning momentum of 0.9 and a Gradient norm clipping rate of 5.0. The training process took approximately four hours under a GTX 1650 GPU with 12 GB of VRAM and this setting. The training process is illustrated as shown in Figure 6. We implemented experiments on both backbone Resnet 50 and Resnet 101 to consider what network will be effective in combining with 3D algorithms.
## 2 Mathematical Model  
If \(\overline{V}=\left[\overline{u}(\overline{\xi},\overline{\eta})\ \overline{v}( \overline{\xi},\overline{\eta})\ \overline{w}(\overline{\xi},\overline{\eta})\right]\)--the velocity of a viscous, incompressible, steady, and electrically-conducting Newtonian fluid containing 40% hafnium (Hf) particles--the fluid flow is assumed to pass through three different types of symmetric channels, each of finite length \"\(L\)\", respectively. A relation mathematically defining each channel along  
For Geometry. 3:  
\[H(\overline{\xi})=\left\{\begin{array}{cc}a-b\;Sin^{2}\Big{(}\frac{\pi\overline{ \xi}}{\lambda}\Big{)};&\mbox{When }\frac{11}{2}<\overline{\xi}<\frac{33}{7},\\ 0.5\;a;&\mbox{Otherwise.}\end{array}\right. \tag{3}\]  
As the flow is comprised of hafnium particles and water, the velocities in the dimensional form, of both fluid phase and particle phase, are given as  
\[\overline{V}_{f}=\Big{[}\overline{u}_{f}(\overline{\xi},\overline{\eta})\;\; \overline{v}_{f}(\overline{\xi},\overline{\eta})\;\;\overline{w}_{f}( \overline{\xi},\overline{\eta})\Big{]}, \tag{4}\]  
\[\overline{V}_{p}=\big{[}\overline{u}_{p}(\overline{\xi},\overline{\eta})\;\; \overline{v}_{p}(\overline{\xi},\overline{\eta})\;\;\overline{w}_{p}(\overline {\xi},\overline{\eta})\big{]}. \tag{5}\]  
Considering the flow to be entirely in the axial-direction. Then Equations (4) and (5) take the form  
\[\overline{V}_{f}=\Big{[}\overline{u}_{f}(\overline{\xi},\overline{\eta})\; 0\;\;0\Big{]}, \tag{6}\]  
\[\overline{V}_{p}=\big{[}\overline{u}_{p}(\overline{\xi},\overline{\eta})\;0 \;\;0\big{]}. \tag{7}\]  
Moreover, the fluid also experiences the influences of static transverse magnetic field along with the electrokinetic body force in the axial direction. In view of Equations (6) and (7), the flow governing equations for each phase have been given as:  
(i) For the fluid phase:  
\[\frac{\partial\overline{u}_{f}}{\partial\overline{\xi}}+\frac{\partial \overline{v}_{f}}{\partial\overline{\eta}}=0, \tag{8}\]  
\[\rho_{f}(1-C)\Big{[}\overline{u}_{f}\frac{\partial\overline{u}_{f}}{\partial \overline{\xi}}+\overline{v}_{f}\frac{\partial\overline{u}_{f}}{\partial \overline{\eta}}\Big{]} =-(1-C)\frac{\partial\overline{p}}{\partial\overline{\xi}}+\mu_{s }(1-C)\Big{[}\frac{\partial^{2}\overline{u}_{f}}{\partial\overline{\xi}^{2}}+ \frac{\partial^{2}\overline{u}_{f}}{\partial\overline{\eta}^{2}}\Big{]}+CS \Big{(}\overline{u}_{p}-\overline{u}_{f}\Big{)} \tag{9}\] \[-\sigma{B_{0}}^{2}\overline{u}_{f}+\Big{[}\frac{\partial^{2} \overline{\xi}}{\partial\overline{\xi}}+\frac{\partial^{2}\overline{\xi}}{ \partial\overline{\eta}^{2}}\Big{]}\overline{\xi}_{\xi}.\]  
(ii) For the particle phase:  
\[\frac{\partial\overline{u}_{p}}{\partial\overline{\xi}}+\frac{\partial \overline{v}_{p}}{\partial\overline{\eta}}=0, \tag{10}\]  
\[\rho_{f}C\Big{[}\overline{u}_{p}\frac{\partial\overline{u}_{p}}{\partial \overline{\xi}}+\overline{v}_{p}\frac{\partial\overline{u}_{p}}{\partial \overline{\eta}}\Big{]}=-C\frac{\partial\overline{p}}{\partial\overline{\xi} }+CS\Big{(}\overline{u}_{f}-\overline{u}_{p}\Big{)}. \tag{11}\]  
Figure 3: Physical sketch of the geometry.
# Requirements for Crafting Virtual Network Packet Captures
## 3 Requirements
### Awareness  
Virtual networks differ from physical networks in various aspects which have to be handled during the transformation. Techniques such as migration or on-demand changes in the infrastructure, often initiated by a user, are common in virtual environments. A packet generator for virtual environments has to be aware of these situations and has to be able to create valid scenarios based on a given packet capture.  
Another event that can occur in virtual networks is the change of internal network packet information and addresses such as the IP range of a user network. A customer is able to change the assigned network and might change IP addresses, routes to some destinations, packet forwarding, firewalling or add additional NFV devices such as VPN gateways or load balancers. All these changes have effects on the network and have to be managed by a packet generator, i.e., by changing internal network information of the transferred network packets.  
A further difference to traditional hardware-based networks is the possibility to change protocols on the fly. This protocol swap is done to implement additional features scgh as TCP-offloading [53]. A packet generator should be able to manage such a protocol change by providing the ability of swapping from one protocol to another, which results in different NVO information such as protocol types or port numbers. A packet generator has to analyse and change the necessary information on the fly to create valid and usable capture files.
## 3 Results
### Participants' Perception Related to Wildland Fire-Induced Risks and Its Mitigation
#### 3.2.2 Views about the Characteristics of 70 m Buffer Zone  
In response to the question, \"In Panel (a) of the following figure (referred as Figure 4 in this document), [PERSON] et al. assumed that relevant authorities removed forest/vegetation between 30 m and 70 m buffers from the WUI to protect the structures in the nearby communities, i.e., Timberlea. Furthermore, panel (b) of the figure (i.e., Figure 4) shows the existence of dense vegetation (shown in reddish color) within 10 to 70 m from the WUI after the 2016 HRF event; and identifies as forest fire-induced vulnerable areas. In order to reduce the wildland fire-induced risk, we may emphasize that a vegetation zone of up to 70 m from the WUI would enhance the safety measures to reduce the wildland fire-induced risk. Do you agree with this statement? Please briefly explain your opinion.\", we realized that at least 68% of participants agreed with this statement (see Table 2). They also provided additional comments comprising of:  
* _\"I agree based on the logical perspective. 30m is safer than 10 m, 70 m is safer than 30 m. The distance between combustible material directly impacts the propagation or spread of fire.\";_
* _\"This 70 m buffer zone would be optimistic target for reducing the fire risk depending upon the weather condition.\";_
* _\"These buffer zones can be useful recreation/greenspace for the surrounding communities.\";_
* _\"The extended vegetation zone will enhance more safety against forest fire. The extended zone will ensure the forest fire blow of burning fuel burn completeness (the dry leaves particles and dry wood husk are the fuel of forest fire). This 30 to 70 m is enough space to control the fire by facilitating the fire itself to be as control burned, it will not catch the building/other object.\"_  
Note that participants also mentioned the importance of such 70 m buffer with some caution, e.g., the loss of wildlife habitat and cost of land acquisition and maintenance, in particular. In case of disagreements, one of the participants mentioned, _\"there must be a happy medium, because vegetation helps lowering the water table as most of the RMWB is built on marshlands, and 2016 Horse River Fire jumped over the Athabasca River which is at least 1000 m wide.\"_. Additionally, another participant noted, _\"I would believe that we should create and design a mosaic of different landscapes with different vegetation cover in order to reduce the expansion of wildfires.\"_, which might be important for reducing the fuel loading for fire severity reduction purposes.