esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Input/Reactions from Participants
### Human Motion Tracking via Depth Sensors  
Microsoft Kinect and similar depth sensing devices such as Asus Xtion PRO enabled a new way of capturing the gestures as well as facial expressions of the participant [132; 133].  
In [11], a participant can move the robot to the left or right via the left or right wave gestures. The OpenNI and NITE software packages were used to capture the human motion and to recognize the gestures. In [101], Microsoft Kinect sensor was used to track the participant's movement to determine how closely the participant imitated the pre-scripted and tele-operated movement of the robot. Metrics used include the range of motion of join angles, response delay, and the distance between two time series representing the movement of the robot and that of the participant.  
For studies with social robots, depth cameras are often used to track the presence and the activities of the participant for offline analysis regarding the similarity of the imitation and the original actions performed by the robot, as done in [92; 101]. In [92], the pose of the participant and the libs were first identified using the depth frames, and subsequently the head region in the corresponding color image frames were cropped. From the cropped face, the pose of the head was determined using a computer vision algorithm. From the head pose data extracted from the logged data during the experiment, the joint attention behaviors of children with ASD and typical developing children are compared. The imitation score was determined by the fraction of postured successfully imitated by the participant.  
Enabled by inexpensive depth sensing devices such as Microsoft Kinect, it becomes possible to engage children with ASD with motion-based touchless serious games [22; 23; 25; 134]. Unlike other serious games, a participant engages in the game play by moving his enter body. The participant is being continuously tracked by the depth sensor and an avatar representing the participant becomes part of the game displayed. These types of games not only could be more engaging, but also may enhance the motor skills of the participants too.  
[PERSON] et al. reported their work on using Kinect to track a participant's gesture and assess the quality of the imitation based a set of predefined rules [135]. Feedback is provided in realtime to the participant. [PERSON] et al. [136] used Kinect to track the engagement level of the participant. [PERSON] et al. also used Kinect to track the activity of the participant when he/she is interacting with a social robot [137]. Kinect has also been used to track the movement patterns of the participations [29] and for self-modeling intervention for children with ASD [138].
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 UAV Detection and Identification
### Anti-UAV in IoT Environments
#### 2.5.1 Detect and Identify UAVs Trying to Hack IoT  
The anti-UAVs mentioned above are all researched on UAVs invading areas of interest. In the IoT environment, people sometimes only want to detect and identify anonymous UAVs that attempt to invade the IoT. A blockchain-based UAV authentication network is an effective solution [81; 82].  
BANDA [81] (Blockchain-Assisted Network for Drone Authentication) is a system that leverages blockchain technology to enhance the authentication and security of UAVs. It operates by accepting real-time inputs about UAV information and add-on packages through cloud-based infrastructure. These inputs are then verified using proof-of-authority algorithms and smart contracts. The decentralized nature of BANDA is achieved through the deployment of systems on UAVs, ground control stations, and UAV defense systems. This distributed network allows for secure authentication and prevents malicious attacks and intrusions from anonymous UAVs. By utilizing a blockchain-based approach, BANDA ensures the integrity and authenticity of UAV information, enabling reliable identification and verification processes. This helps in mitigating security risks associated with unauthorized or malicious UAV activities. The optimized application of BANDA enables robust protection against potential threats and enhances the overall security of UAV operations.  
RBFNNs [82], which stands for Radial Basis Function Neural Networks, is a blockchain-based model that enhances data integrity and storage capabilities, enabling intelligent decision-making across different Internet of Drone (IoD) environments. By leveraging blockchain technology, this model facilitates decentralized predictive analytics and the application of deep learning methods in a distributed manner. It proves to be a feasible and effective approach for the IoD environment. The utilization of blockchain in developing decentralized predictive analytics and models ensures data integrity and enables secure sharing of deep learning methods. This approach aligns well with the requirements and constraints imposed by network intrusion detection, making RBFNNs a suitable choice for developing classifiers in such scenarios. By combining the strengths of blockchain and deep learning, RBFNNs provide a robust and secure framework for intelligent decision making in the IoD environment. It offers the potential for reliable and efficient network intrusion detection while maintaining data integrity and compliance with the constraints of the system.
# A Review of Sharp-Switching Band-Modulation Devices
## 1 Introduction
### Principle of Band-Modulation  
Although the lateral PNPN structure looks like a floating-body thyristor, its operating principle is different, as it relies on band-modulation effects, rather than impact ionization (Section 3). The device is biased in forward mode with V\({}_{\text{A}}\) > 0. The electrostatic doping builds up three P-N junctions along the body; their energy barriers prevent the injection of holes from the anode and of electrons from the cathode into the body. The device stays in blocked mode, until the gate or anode voltage becomes large enough to collapse the barriers and let a large cathode-anode current flow. Corresponding band diagrams and a detailed description are given in Section 3.1. The switching characteristics are extremely sharp and inspired the name Z\({}^{2}\)-FET, for Zerosubthreshold swing and _Zero_ impact ionization [13]. The earlier variant with two top gates, as in Figure 2b, was named a field-effect diode (FED) [15; 16].  
Figure 2: Configuration of band-modulation devices: (**a**) \(Z^{2}\)–FET fabricated on FD-SOI and (**b**) field-effect diode implementable in nanowires and FinFETs.
## 2 Materials and Methods
### Tomographic Evaluation  
Before implant placement, dental cone beam computed tomography (CB) was done for surgical planning. All patients performed CBs at baseline and 12 and 24 months after prosthesis installation for bone level evaluation. All scans were acquired utilizing identical apparatus (KODAK 9000 Extraoral Imaging system, Carestream Health, Rochester, NY, USA).  
The exposure settings selected were FOV of 240 \(\times\) 190 mm, 74 kV, and 10 mA, with a 10.68-s acquisition time, allowing secure protocol with minimal radiation [23]. The software used was CS3D Imaging (Carestream Dental, Atlanta, GA, USA).  
Peri-implant bone level (mm) corresponded to the linear measurement made from the implant platform to the crestal bone (mesial, distal, buccal, and lingually) in the sagittal and coronal slices, recorded every 2 mm (Figure 1). A single research investigator viewed the scans (PHFD) and made all tomographic measurements. Intra-examiner calibration was done using 15 images from patients who received implants for overdenture rehabilitation. The measure was performed twice in 24 h. The reproducibility was calculated as 90%.  
Figure 1: Schematic illustration of the method of measurement of the crestal bone height.
# Temporal Logic Planning and Receding Horizon Control for Signal Source Localization
## 2 Preliminaries  
### Kinematics of Mobile Robots  
The turtlebot robot, shown in Figure 1, is used to locate the signal source, and the corresponding kinematics model is described by  
\[\left\{\begin{array}{l}\theta(k+1)=\theta(k)+\omega(k)\Delta t\\ x(k+1)=x(k)+\frac{\mu(k)}{\omega(k)}\big{(}\sin(\theta(k+1))-\sin(\theta(k)) \big{)}\\ y(k+1)=y(k)+\frac{\mu(k)}{\omega(k)}\big{(}\cos(\theta(k))-\cos(\theta(k+1)) \big{)}\end{array}\right. \tag{1}\]  
where \([x(k),y(k)]\) are the positions of mobile robots; \(\theta(k)\) is the orientation angle; \(\omega(k)\) is the angular velocity at time \(k\); \(\mu(k)\) is linear velocity at time \(k\); \(\Delta t\) is the sampling period; and \(u_{k}=[\mu(k),\omega(k)]\).  
Figure 1: The Turtlebot robot.
# Two Low-Level Feature Distributions Based No Reference Image Quality Assessment
## 4 Experiments
### Experimental Results and Analysis  
#### 4.2.1 Performance on Individual Databases  
In this section, the overall performance of the various IQA models will be tested on each individual database. Each database is divided into training sets and test sets; random selection of 80% of the database constitutes the training set and the remaining 20% makes the test set. Then through 1000 times of cross-validation and the median SROCC and PLCC values are recorded as shown in Table 4. The competing algorithms including four classic ones FR IQA: PSNR, SSIM [16], FSIM [17], VSI [38], eight classic ones NR IQA: NIQE [22], ILNIQE [39], BIQI [30], DIIVINE [31], BLIINDS2 [40], BRISQUE [21], GMLOG [41], NFERM [42], and five state-of-the-art ones deep learning: Dip IQA [43], OG IQA [20], Deep IQA [7], MEON [8], CNN [44].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Database** & **Source** & **Distortion** & **Distortion** & **Subjects** & **Subjective** \\  & **Images** & **Types** & **Images** & **Number** & **Scores** \\ \hline LIVE & 29 & 5 & 779 & 161 & 0–100 \\ TID2008 & 25 & 17 & 1700 & 838 & 0–9 \\ CSIQ & 30 & 6 & 866 & 35 & 0–1 \\ MLCT & 14 & 2 & 168 & 16 & 1–5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Benchmark database for IQA performance validation.  
For each criteria, the best two IQA metrics are highlighted in bold. The main observations are as follows. First, TLLFD is closer to the human subjective evaluation of difference mean opinion score (DMOS) on all four databases. Second, TLLFD significantly outperforms PSNR and SSIM. Unfortunately, only CNN evaluation results on LIVE database as well as Dip IQA and Deep IQA evaluation results on CSIQ database are available. Third, compared with other deep learning methods, TLLFD has better quality prediction performance. On LIVE, the SROCC and PLCC values of TLLFD method reach 0.96. On TID2008, the value of SROCC and PLCC are close to 0.94. On MICT, the value of SROCC and PLCC are close to 0.92.
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 7 Malware Detection Tools
### VirusTotal  
VirusTotal is an economically accessible resource for assessing the security of files, passwords, and addresses. It can be accessed both via a computer application and an internet-based platform. The deployment of multiple antivirus algorithms facilitates the detection of a range of malicious entities such as viruses, worms, trojans, and other forms of adware. VirusTotal utilizes more than 70 anti-malware analyzers and URL/domain blacklisting services in its inspections, in addition to other methods for discriminating against dubious matters. VirusTotal enables the capability for any individual with a computer to transmit a file from their computing device. Apart from its primary public interface, PC uploaders, browser extensions, and an Application Programming Interface (API) are all viable avenues for the transmission of data to VirusTotal. The web UI receives the greatest consideration when examining the various publicly available registration forms. The specifications for the public, HTTP-based protocol can be formulated in any language. In addition to its primary task, VirusTotal also comprises several auxiliary tools, including the VirusTotal forum, which allows users to submit reports on files and addresses and to exchange interpretations of the outcomes amongst each other. This can be beneficial in distinguishing between potentially malicious data and false positives, which are those benign items that have erroneously been labeled as hazardous by the security system [23].
# Gaussian Quantum Trajectories for the Variational Simulation of Open Quantum-Optical Systems
## Quantum Trajectories for Expectation Values and Gaussianity
### Quantum Trajectories for Expectation Values
#### Photon-Counting Unraveling  
According to the quantum trajectory method, this master Equation (1) is unraveled into the evolution of stochastic wavefunctions by performing continuous measurement on the environment. The simplest unraveling is photon-counting (PC) detection: in between detector-clicks, the wavefunction is propagated as:  
\[i\partial_{t}|\widetilde{\psi}\rangle=\left(\hat{\mathrm{H}}-i\frac{\gamma}{2 }\,\hat{\mathrm{a}}^{\dagger}\,\hat{\mathrm{a}}\right)|\widetilde{\psi}\rangle, \tag{2}\]  
where the tilde notation (\(\widetilde{\cdot}\)) denotes that the norm of the wavefunction is not conserved. The detection of a photon corresponds to a discrete jump  
\[|\widetilde{\psi}\rangle\rightarrow\hat{\mathrm{a}}\,|\widetilde{\psi}\rangle /\big{\|}\hat{\mathrm{a}}\,|\widetilde{\psi}\rangle\big{\|} \tag{3}\]  
when the norm has decreased to \(\big{\|}\widetilde{\psi}\rangle\big{\|}^{2}=R\), where the random number \(R\) has a uniform distribution on the interval \([0,1]\).  
In terms of unnormalized expectation values, Equation (2) corresponds to the evolution:  
\[\widetilde{d\big{\langle}\big{\rangle}}= i\big{(}\widetilde{\big{[}\hat{\mathrm{H}},\hat{\mathrm{O}} \big{]}}\big{)}dt-\frac{\gamma}{2}\widetilde{\big{\langle}\hat{\mathrm{a}}^ {\dagger}\,\hat{\mathrm{a}}\,\hat{\mathrm{O}}\big{\rangle}}dt-\frac{\gamma}{2} \widetilde{\big{\langle}\hat{\mathrm{O}}\,\hat{\mathrm{a}}^{\dagger}\,\hat{ \mathrm{a}}\big{\rangle}}dt. \tag{4}\]  
By choosing \(\hat{\mathrm{O}}=1\), we see that the evolution for the normalized expectation value \(\big{\langle}\hat{\mathrm{O}}\big{\rangle}=\widetilde{\big{\langle}\hat{ \mathrm{O}}\big{\rangle}}/\widetilde{\langle\hat{\mathrm{I}}\rangle}\) is given by:\[d\left\langle\hat{\mathcal{O}}\right\rangle= i\left\langle\left[\hat{\mathrm{H}},\hat{\mathcal{O}}\right] \right\rangle dt\] \[-\frac{\gamma}{2}\left\langle\hat{\mathrm{a}}^{\dagger}\,\hat{ \mathrm{a}}\,\hat{\mathcal{O}}\right\rangle dt-\frac{\gamma}{2}\left\langle \hat{\mathcal{O}}\,\hat{\mathrm{a}}^{\dagger}\,\hat{\mathrm{a}}\right\rangle dt +\gamma\left\langle\hat{\mathrm{a}}^{\dagger}\,\hat{\mathrm{a}}\right\rangle \left\langle\hat{\mathcal{O}}\right\rangle dt. \tag{5}\]  
Meanwhile, according to Equation (3), a jump affects the expectation value \(\left\langle\hat{\mathcal{O}}\right\rangle\) by:  
\[\left\langle\hat{\mathcal{O}}\right\rangle\xrightarrow{I}\frac{\left\langle \hat{\mathrm{a}}^{\dagger}\,\hat{\mathcal{O}}\,\hat{\mathrm{a}}\right\rangle} {\left\langle\hat{\mathrm{a}}^{\dagger}\,\hat{\mathrm{a}}\right\rangle}=\frac{ \left\langle\hat{\mathrm{a}}^{\dagger}\,\hat{\mathcal{O}}\,\hat{\mathrm{a}} \right\rangle}{\left\langle\hat{\mathrm{a}}^{\dagger}\,\hat{\mathrm{a}}\right\rangle}. \tag{6}\]  
Equations of the form (5) and (6) together then in principle provide a complete description of the evolution of expectation values for a trajectory under photon-counting unraveling.
## 5 Simulation Results  
In our application, we assume that the PD is able to receive the signals broadcast by different DVB-T emitters belonging to the same SFN. To analyze how the PD can handle these signals, we assume a scenario with \(N_{e}\) emitters. The distance between the PD and the \(i\)-th emitter is denoted as \(d_{e,i}\), the distance, \(d_{e,1}\), is associated to the closest emitter and the relative distance, \(\Delta_{i}=d_{e,1}-d_{e,j}\), with \(j=2,\cdots,N_{e}\), is introduced. If the PD is able to receive the closest signal (used as the reference  
Figure 9: Doppler effect.  
signal), the signals broadcast by emitters with \(\Delta_{i}<\Delta p_{max}\) can be detected, and the relative delays of these emitters can be estimated by using the same techniques used for the reception of the useful signal. The emitters with \(\Delta_{i}>\Delta p_{max}\) are seen as noise. In theory, in positioning applications, also, these distant emitters could be used, trying to detect them with a dedicated processing. However, they are generally blinded by the close emitters; so, in our study, they have not been considered. In our method, we assume that the PD starts working in DVB-mode after an initialization phase, which allows the PD to evaluate \(N_{e}\) and the initial distances, \(d_{e,i}\).  
One important aspect for positioning is the Doppler effect. Since all the emitters are fixed, the Doppler effect is introduced by the receiver's motion. In [20], this effect on the DVB-T system has been investigated. The results show that for 2 K QPSK, more than 400 km/h is tolerable, but for 8 k 64 QAM, the tolerance is less than 50 km/h. Therefore, in our application, especially in the 2 K mode experiment, the Doppler effect is negligible.
# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 3 Construction of Computing Platform in Micro-Nano Satellite
### The Commercial GPU in Integrated Information Flow Unit  
Compared with traditional large satellites, micro-nano satellites do not need a long lifetime. The disadvantage of a short lifetime can be compensated by networking and constellation. Therefore, the COTS components with the characteristics of low cost, superior performance, high integration and easy access are very suitable for on-board processing in micro-nano satellites.  
Embedded component Jetson TX2 by NVIDIA Corporation, which is a development board integrated four cores ARM Cortex-A57 CPU and 256 cores Basal GPU, can achieve a trillion times floating point number operations per second on the Linux system. Jetson TX2 is technically designed for computer vision, deep learning and other fields with the same size of a credit card. Low cost ($599), low power consumption (less than 7.5 W), small size (50 mm \(\times\) 87 mm), and light weight (85 g) are all suitable for on-board processing on micro-nano satellites. Jetson TX2 has the software environment for the development tools of deep learning, so the code and model of proposed method can be easily ported into Jetson TX2.  
Figure 5: Hardware architecture of the integrated information flow.
## 2 Establishment of Model  
The goal was to determine the blanking sequence of the semi products according to the order demand and to minimize the surplus material and maximize the utilization rate of the silicon steel coil. Assume that a total of \(n\) kinds of semi products are needed according to the order; \(bi\) is the width of the semi products; \(di\) is the quantity of the semi products with the same width; and \(B\) is the width of the silicon steel coil. Here, \(i\) represents the type of the semi products, and its value is \(i=1,2,\ldots,n\). Assume that the order demand is divided into \(m\) groups; \(a_{ij}\) represents the quantity of the type \(i\) blanking from a single silicon steel coil in group \(j\). Here, \(j\) represents the number of a group, and its value is \(j=1\), \(2,\ldots,m\). The coefficient matrix can be obtained as  
\[A=\begin{bmatrix}a_{11}&a_{12}&\cdots&a_{1m}\\ a_{21}&a_{22}&\cdots&a_{2m}\\ \vdots&\vdots&\ddots&\vdots\\ a_{n1}&a_{n2}&\cdots&a_{nm}\end{bmatrix} \tag{1}\]  
Suppose \(y_{j}\) represents the width of the residual material from a single silicon steel coil in the group \(j\), then  
\[y_{j}=B-\left(a_{1j}b_{1}+a_{2j}b_{2}+\cdots+a_{nj}b_{n}\right) \tag{2}\]  
where \(x_{j}\) represents the quantity of silicon steel coils needed in group \(j\), and the total quantity of silicon steel coils is  
\[X=\left(x_{1}\quad x_{2}\quad\cdots\quad x_{m}\right)^{T} \tag{3}\]  
Therefore, the sum width of the residual material is  
\[Y=y_{1}x_{1}+y_{2}x_{2}+\cdots+y_{m}x_{m}=\sum_{j=1}^{m}y_{j}x_{j} \tag{4}\]  
The goal was to minimize the residual material. Moreover, market experience shows that residual material with a width less than 60 mm can neither be used for the next blanking nor be directly cross-cut into silicon steel sheets and, thus, become waste. Therefore, it is necessary to guarantee that the width of the residual material is not less than 60 mm. Then, the optimization model of the silicon steel coil blanking is obtained as  
\[\text{objective function}:\ \min\sum_{j=1}^{m}y_{j}x_{j} \tag{5}\]  
\[\text{constraints}:\left\{\begin{array}{rl}\sum_{j=1}^{m}a_{ij}x_{j}&=d_{i} \ \ (i=1,2,\ldots,n)\\ x_{j}\geq 0\ \ (j=1,2,\ldots,m)\\ y_{j}\geq 60\end{array}\right. \tag{6}\]
# Advancing Brain Tumor Segmentation with Spectral-Spatial Graph Neural Networks
## 2 Methods and Material
### Data Preprocessing
#### 2.3.2 Creating Supervoxels
This metric is particularly informative when assessing the spatial distribution and orientation of voxels within each supervoxel. Shape uniformity is quantified using the coefficients of variation (CoV) of two aspect ratios: X/Y and Y/Z. For each supervoxel, the aspect ratios are calculated, and the CoV for each ratio is determined as \(\text{CoV}_{XY}=\frac{\sigma_{XY}}{\mu_{XY}}\) and \(\text{CoV}_{YZ}=\frac{\sigma_{YZ}}{\mu_{YZ}}\), where \(\sigma\) and \(\mu\) represent the standard deviation and mean of the respective aspect ratios across all supervoxels. These CoVs offer a standardized measure of dispersion, enabling a comparison of shape uniformity across the dataset. Unlike homogeneity, which focuses on the uniformity of intensity values within each supervoxel, shape uniformity assesses the physical dimensions and orientations of supervoxels. A lower CoV suggests more uniform shapes, indicative of regular, possibly spherical or cuboid supervoxels. In contrast, a higher CoV points to greater variability, signifying elongated or irregularly shaped supervoxels. By analyzing both CoV metrics (\(\text{CoV}_{XY}\) and \(\text{CoV}_{YZ}\)), a comprehensive view of the 3D shape characteristics is obtained. * Size Uniformity: In the evaluation of supervoxel segmentation algorithms for 3D brain MRI scans, size uniformity plays a vital role. It measures the consistency in the sizes of supervoxels across the segmented image. Size uniformity is assessed by first calculating the size of each supervoxel, typically the count of voxels it contains. The mean size, denoted as \(\mu_{\text{size}}\), and the standard deviation of sizes, \(\sigma_{\text{size}}\), are computed. The coefficient of variation, given by \(\text{CV}_{\text{size}}=\frac{\sigma_{\text{size}}}{\mu_{\text{size}}}\), serves as a normalized measure of size uniformity. A lower \(\text{CV}_{\text{size}}\) indicates a higher degree of uniformity, which is essential for the accuracy of medical image segmentation.