esa-sceva/satcom-chunk-collection huggingface dataset:
# H-KPP: Hypervisor-Assisted Kernel Patch Protection
## 2 Background  
### Virtualization  
Many modern processors are equipped with extensions to their basic instruction set architecture that enables them to execute multiple operating systems simultaneously. H-KPP utilizes Intel's implementation of these extensions, which they call Virtual Machine Extensions (VMX). The software that governs the execution of the operating systems is called a _hypervisor_ and each operating system (with the processes it executes) is called a _guest_. Transitions from the hypervisor to the guest are called VM-entries and transitions from the guest to the hypervisor are called VM-exits. While VM entries occur voluntarily by the hypervisor, VM exits are caused by events during the guest's execution, e.g., page faults, execution of a privileged instruction, or a timer's timeout. The event that causes a VM exit is recorded for future use by the hypervisor. A special data structure called Virtual Machine Control Structure (VMCS) allows the hypervisor to specify the events that should trigger a VM exit and many other settings of the guest.  
Intel's Extended Page Table (EPT), a technology generally called Secondary Level Address Translation (SLAT), allows the hypervisor to configure a mapping between the physical address space, as it is perceived by a guest to the real physical address space. Similarly to the virtual page table, EPT allows the hypervisor to specify the access rights for each guest physical page. When a guest attempts to access a page that is either not mapped or has inappropriate access rights, an event called an EPT violation occurs, triggering a VM exit.  
While, in general, hypervisors enable the execution of multiple operating systems on a single machine, we propose to use a hypervisor environment for securing a single operating system. The idea to use such a limited hypervisor for providing additional security is not new [9; 10]. Unlike full hypervisors, which tend to degrade the overall system performance significantly, thin hypervisors may intercept only a small portion of the system's events, thus keeping the system performance at an optimum (see Section 5.2).
## 3 Active Fault-Tolerant Control System Design  
The active fault-tolerant control scheme is presented in this section. The control structure is shown in Figure 2. First, a fault diagnosis unit is proposed to estimate the magnitude of actuator faults. Then, a modified adaptive sliding mode backstepping (MASMBC) control is designed to tolerate actuator faults by combining the fault estimation and adaptive sliding mode back stepping control (ASMBC).
## 4 Results and Discussion
### The Linewidth  
The linewidth is measured by the delayed self-heterodyne technique. The measured spectrum is the Lorentz line-type. Therefore, the 3 dB bandwith is obtained by fitting the measurement spectrum to the Lorentz curve (the red curve). The 3 dB bandwith represents the spectral width when the maximum value of the spectrum is reduced by 3 dB. The 3 dB linewidth at a wavelength of 1555 nm is measured, as shown in Figure 3, and an ultra-narrow linewidth of 13.6 kHz is obtained.  
Combining Formulas (1)-(3), it can be seen that the linewidth is affected by many factors, such as the cavity surface reflectivity and refractivity index of external cavity. In our experiment, the external cavity structure includes two optical components, diffraction grating and interference filter. Both diffraction grating and the interference filter play a very important role in narrowing the linewidth. First, for the external cavity configuration, the effective reflectivity \(r_{1e}\) depends on the reflectivity of the rear facet (\(R_{1}\)) and the diffraction efficiency of the grating. Therefore, a rear facet with reflectivity less than 0.01% and  
Figure 3: The linewidth spectrum at a wavelength of 1550 nm is 13.6 kHz.  
Figure 2: Optical spectra of the output beam for the ECDLs with (**a**) an injection current of 150 mA and (**b**) an injection current of 200 mA. The tunable range is both 40 nm from 1525 nm to 1565 nm. The peaks in the tuning range correspond to different grating positions.  
diffraction grating with a diffraction efficiency of 90% are chosen to achieve large, effective reflectivity to narrow the linewidth. Second, the interference filter contributes to narrowing the linewidth. The filter is composed of alternating layers of dielectric materials, in which multiple reflections can be realized. We measured the optical spectra without the filter, as shown in Figure 4. We compared the SMSR before and after adding the filter; the specific results are shown in Table 1. It can be seen that the SMSR of the ECDL with a filter is improved because the interference filter can filter out the fluctuation of the noise floor. In this way, the filter can reduce the potential competition mode, improve the spectral purity of the main mode, and further narrow the linewidth.  
In the whole tuning range, the gain is relatively high at an injection current of 200 mA. The linewidth does not change obviously when the wavelength changes. In addition, according to the formula, the calculated linewidth can be optimized by improving the effective refractive index of the external cavity. In our experiment, the effective reflection of the external cavity is a function of the diffraction efficiency of the grating and the transmission of the filter. Therefore, the linewidth can be further narrowed by optimizing the dielectric film structure of the interference filter.
## 5 The Technologies
### Digital Twin  
A DT is a virtual replica of the whole or partial physical system [129]. It is a key enabler of an Industry 4 ecosystem [130; 131]. In smart manufacturing contexts, DT systems can be used for simulating assembly strategies [62], simulating and managing intralogisticssystems [66], monitoring and optimising real-time production processes [131], managing energy consumption [17], facilitating training programmes in Industry 4 learning factories [131], and more.  
DT systems are capable of bi-directionally transmitting data between the physical assets and the virtual representations, which is the key distinction when compared to the digital model and digital shadow of the system, respectively [131]. There are three major considerations in designing a digital twin system in academic Industry 4 facilities: 3D virtual environment, communication protocols, and control methods.  
Firstly, a 3D virtual environment is a platform for creating digital representations of the physical assets in Industry 4 facilities [82; 129]. Most of the users choose plug-and-play 3D simulation software to establish the digital context, including Tecnomica Plant Simulation [33; 71; 107], Siemens NX MCD [62], Siemens CIROS [132], Unity3D [32; 114; 133], fastSUITE [98], AnyLogic(r) [65], Emulate3D [58], ExtendSim9 [134], VENIO ERP/PLM [135], FlexSim [38], Delmia [53], VEROSIM [60; 136], 3 DEXPERIENCE [67], Robot DK [11; 129], and ROS [137].  
On the other hand, some practitioners prefer to make long-term investments for developing their virtual platforms, which offer the teams greater freedoms and potential to broaden and deepen their research [102; 138].  
Secondly, there are two common instant communication protocols that are used in the current development of Industry 4 facilities for building digital twin systems, which are OPC UA [5; 60; 129; 132] and MQTT [132; 133]. Despite its long development period, ModBus still plays a key role in the Industry 4 digitised context [6; 129]. For example, recent research by [PERSON] et al. [132] demonstrated the use of CIROS, Node.Red, and OPC UA to create a digital twin system in their learning factory for business and engineering students.  
Lastly, on top of communication protocols, a suitable control method is also a key enabler for building an effective digital twin system [138]. Middleware software can be placed between the physical assets and the digital replica to stream the bi-directional data transmission [139]. For example, [PERSON] [129] discussed the application and the advancement of using CODESYS as control software. [PERSON] et al. [60] employed the Robot Operating System (ROS) as the middleware for controlling their robotic assembly cell. [PERSON] et al. [62] developed a customised control agent in Python (3.9.18) to coordinate the communication between the OPC UA server and client modules. Also, RFID and QR Code technology can be used for providing tracking capabilities in digital twin systems [67; 140].
# Clock Ensemble Algorithm Test in the Establishment of Space-Based Time Reference
## 3 Experimental Analysis  
In practice, both the ISL data and the satellite clock offsets data (calculated with PPP or ODTS technique) can be used for the establishment of a space-based timescale. Due to the serious discontinuity of the publicly available ISL data, here we adopt the MGEX satellite clock products to test the three clock ensemble algorithms mentioned in chapter 2. It is worth noting that the performance of the next-generation optical frequency standard will be better than the GNSS onboard clock. Therefore, the performance of all tested algorithms in future practical applications should be better than the analysis results in this paper.
# A 5 Gb/s Optoelectronic Receiver IC in 180 nm CMOS for Short-Distance Optical Interconnects
## 2 Circuit Description  
### On-Chip P+/NW APD  
On-chip APDs are fabricated by using a 180 nm CMOS process. Figure 2 illustrates the cross-sectional view of the fabricated on-chip APD, which shares the same configuration as the P+/N-well (NW) APD reported in [10].  
Figure 3a depicts the current-voltage characteristics of the fabricated on-chip CMOS P+/NW APD, measured under both dark and illuminated conditions. A notable increase in both dark and photocurrent is observed near the breakdown voltage of 11.1 V, which is attributed to the avalanche multiplication effects. These measurements were performed with an incident optical power of approximately -60 dBm. The responsivity \(R\) of the APD is calculated by using the following expression:  
\[R=\frac{I_{Illumination}-I_{dark}}{P_{opt}},\]  
Figure 1: Block diagram of the proposed CORIC.  
where \(I_{Illumination}\) and \(I_{dark}\) denote the measured currents with and without optical input, and \(P_{opt}\) is the optical power incident on the device.  
Figure 3b presents the variation of responsivity with respect to the reverse bias voltages, showing that a peak responsivity of 2.72 A/W is achieved at 11.05 V [10]. The on-chip P\({}^{+}\)/NW APDs are designed with an octagonal shape to prevent premature edge breakdown with a diagonal length of 40 \(\upmu\)m in the optical window [11], which results in a parasitic capacitance of 0.49 pF.
# Monocular Vision-Based Depth Estimation of Forward-Looking Scenes for Mobile Platforms
## 2 Methodology
### Re-Projection  
Assuming that \(\mathbf{I}_{t-1}\) and \(\mathbf{I}_{t}\) are two adjacent frames, the positions of the space point \(\mathbf{P}\) in the two camera coordinate systems are (\(X_{1}\), \(Y_{1}\), \(Z_{1}\)) and (\(X_{2}\), \(Y_{2}\), \(Z_{2}\)). The mathematical relation between the two position coordinates can be established by the transformation relationship existing between the two camera coordinate systems.  
\[\begin{bmatrix}X_{2}\\ Y_{2}\\ Z_{2}\\ 1\end{bmatrix}=\begin{bmatrix}\mathbf{R}&\mathbf{T}\\ \mathbf{0}_{1\times 3}&1\end{bmatrix}\begin{bmatrix}X_{1}\\ Y_{1}\\ Z_{1}\\ 1\end{bmatrix} \tag{1}\]  
where \(\mathbf{R}\in\mathbb{R}^{3\times 3}\) is the rotation matrix and \(\mathbf{T}\in\mathbb{R}^{3\times 1}\) is the translation vector, which together is called the pose transformation matrix of the spatial point from the moment \(t-1\) to the moment \(t\). The position of the spatial point \(\mathbf{P}\) in the pixel coordinate systems in \(\mathbf{I}_{t-1}\) and \(\mathbf{I}_{t}\) are denoted as (\(u_{1}\), \(v_{1}\)) and (\(u_{2}\), \(v_{2}\)), respectively. Based on the principle of pinhole imaging and camera intrinsic matrix \(\mathbf{K}\), the relationships between (\(u_{1}\), \(v_{1}\)) and (\(X_{1}\), \(Y_{1}\), \(Z_{1}\)), as well as (\(u_{2}\), \(v_{2}\)) and (\(X_{2}\), \(Y_{2}\), \(Z_{2}\)), can be established as Equation (2).  
\[Z_{i}\begin{bmatrix}u_{i}\\ v_{i}\\ 1\end{bmatrix}=\begin{bmatrix}\mathbf{K}&\mathbf{0}_{3\times 1}\end{bmatrix} \begin{bmatrix}X_{i}\\ Y_{i}\\ Z_{i}\\ 1\end{bmatrix}. \tag{2}\]  
With the utilization of Equations (1) and (2), two equations can be derived by means of a sequence of mathematical transformations.  
\[Z_{2}\begin{bmatrix}u_{2}\\ v_{2}\\ 1\end{bmatrix}=\mathbf{K}\mathbf{R}\begin{bmatrix}X_{1}\\ Y_{1}\\ Z_{1}\end{bmatrix}+\mathbf{K}\text{ and }\begin{bmatrix}X_{1}\\ Y_{1}\\ Z_{1}\end{bmatrix}=Z_{1}\mathbf{K}^{-1}\begin{bmatrix}u_{1}\\ v_{1}\\ 1\end{bmatrix}. \tag{3}\]  
According to Equation (3), (\(u_{2}\), \(v_{2}\)) and \(Z_{2}\) can be represented as a function of \(\mathbf{K}\), \(\mathbf{R}\), \(\mathbf{T}\), and \(Z_{1}\).  
\[Z_{2}\begin{bmatrix}u_{2}\\ v_{2}\\ 1\end{bmatrix}=Z_{1}\mathbf{K}\mathbf{R}\mathbf{K}^{-1}\begin{bmatrix}u_{1}\\ v_{1}\\ 1\end{bmatrix}+\mathbf{K}\mathbf{T}. \tag{4}\]  
Equation (4) is the re-projection formula. From this re-projection formula, we can find that a new image \(\hat{\mathbf{I}}_{t}\) can be reconstructed based on Equation (4) and image \(\mathbf{I}_{t-1}\) if the pose transformation matrix (\(\mathbf{R}\), \(\mathbf{T}\)) and camera intrinsic matrix \(\mathbf{K}\) are known. The discrepancy between the reconstructed image \(\hat{\mathbf{I}}_{t}\) and the original image \(\mathbf{I}_{t}\) can be utilized to generate supervisory information. Therefore, the method in this paper necessitates the establishmentof two separate networks for estimating the depth map and pose transformation matrix (shown in Figure 1).
## 4 Validation
### Validation Results
#### 4.3.2 Real World vs. Simulated Number of Assisted Voyages  
Figure 7b presents the real-world number of instances of IB assistance per destination port, the corresponding simulated values for 35 individual simulation runs, and the mean of the simulated values. The presented numbers include all instances of IB assistance received by ships on their way  
Figure 7: Real-world vs. simulated port-specific number of ship arrivals (**a**), instances of IB assistance (**b**), and mean IB waiting times (**c**). The traffic data indicate the real-world performance of the system during the considered period. The corresponding simulated performance values are presented both in terms of the mean values of 35 independent simulation runs, and in terms of the outcome of each of those individual simulation runs. The outcomes of the individual simulation runs demonstrate the variability of the simulation output.  
to or from individual ports. As ships are often assisted at multiple times during a single voyage, the number of instances of assistance is higher than the number of ships that received assistance. As shown by the figure, for all ports the mean values of the simulations agree quite well with the data. However, the variation between individual simulation runs is significant. The standard deviation between the mean values and the data is 13%. As per the data, the total number of instances of assistance is 192 whereas the corresponding simulated mean value is 213 (+11%).
# Evaluation of BDS/GPS Multi-Frequency RTK Positioning Performance under Different Baseline Lengths
## 2 Multi-Frequency RTK Positioning Mathematical Model
### Kalman Filtering Theory for Long Baseline RTK Positioning
#### 2.3.2 Double-Differenced Troposphere Model  
Troposphere delays are divided into hydrostatic delay and wet delay. Hydrostatic delay accounts for 90% of the zenith total delay and can be calculated by modelling with an accuracy of millimeter level. The wet delay accounts for 10% of the zenith total delay, and it is difficult to calculate the wet delay by an accurate correction model because of the large variation in water vapor in the atmosphere [13]. Therefore, in long baseline RTK positioning, the hydrostatic delay is calculated by the Saastamoinen model, and the hydrostatic mapping function is calculated by the Neill Mapping Function. For the residual wet tropospheric delay, it can be estimated as an unknown parameter in Kalman Filtering. The DD residual wet tropospheric delay is defined as:  
\[T_{rb}^{jk}=[W(E_{r}^{j})T_{z,r}-W(E_{b}^{j})T_{z,b}]-[W(E_{r}^{k})T_{z,r}-W(E_{b }^{k})T_{z,b}] \tag{15}\]  
In Equation (15), \(T_{z}\) is the zenith troposphere wet delay, \(E\) is the elevation of the satellite, and \(W\) is the wet mapping function. Since the single-differenced tropospheric wet delay of satellites is strongly correlated and a set of single-difference tropospheric wet delays can usually be used to estimate the single-difference combination of all visible satellites, then Equation (15) can be rewritten as:  
\[T_{rb}^{jk}=[W(\alpha^{j})-W(\alpha^{k})](T_{z,r}^{j}-T_{z,b}^{j}) \tag{16}\]  
where:  
\[\alpha=\frac{E_{r}+E_{b}}{2}\]
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 3 System Model and Problem Statement
### Cognitive Metrics
#### 3.2.4 Short-Term Power Consumption Ratio  
In general, it is difficult to replace the battery of the sensors deployed in the wild field. If the energy of a specific node is quickly exhausted, it leads to a decrease in the network lifetime. To improve the overall network lifetime, we adopt the power consumption ratio as a decision variable to select a parent node. The power consumption ratio (\(E\)) can be calculated as,  
\[E=\frac{(\alpha-1)T_{cur}*Idle_{e}+(\alpha)L*Tx_{e}+(\alpha*D)Rx_{e}+Sleep_{ e}}{E_{total}}, \tag{4}\]  
where \(T_{cur}\) denotes the cumulative delay due to backoff or retransmissions. \(\alpha\) is the number of backoffs or frame retransmissions. \(L\) denotes the payload length. \(Idle_{e}\), \(Tx_{e}\), \(Rx_{e}\), and \(Sleep_{e}\) are the amount of energy consumption for each transceiver mode, and \(E_{total}\) is the total amount of energy.
# PC12 and THP-1 Cell Lines as Neuronal and Microglia Model in Neurobiological Research
## 3 Results and Discussion
### THP-1 Cells as Microglia Model  
The same tests (except for LDH assay) were also performed on a second cell line--THP-1. As mentioned earlier, in studies focusing on neurodegeneration and neuroinflammation, in addition to neuronal cultures, the microglia model is a useful research tool.  
Macrophages in their primary form (as an inherently heterogeneous group) are difficult to obtain in sufficient quantities and are usually replaced by the cancer-derived THP-1  
Figure 5: Effect of A\(\beta_{25\text{-}35}\) (**A,B**) or lipopolysaccharideaccharide (**C,D**) on differentiated PC12 cells: (**A**) average length of neurites after incubation with A\(\beta_{25\text{-}35}\); (**B**) average number of neurites after incubation with A\(\beta_{25\text{-}35}\); (**C**) average length of neurites after incubation with LPS; (**D**) average number of neurites after incubation with LPS; Control—cell culture incubated without LPS or A\(\beta_{25\text{-}35}\); \({}^{*}p<0.05\)—significant difference compared to control.  
cell line. Differentiation with macrophage colony-stimulating factor (M-CSF), retinoic acid, 1,25-dihydroxy vitamin D3 (vD3), or PMA causes the monocytic cells to acquire functional and morphological resemblance to macrophages (microglia) (Table 3). Contrary to the situation with PC12 cells, the need to differentiate THP-1 cells to constitute a macrophage model is widely recognized in the scientific community. However, it is worth noting that choosing the right PMA differentiation protocol is crucial to obtain reliable results [35,36].  
The THP-1 cell line is grown in suspension. Cells are spherical and regular in shape. They are small, grow singly and do not aggregate like PC12 cells (Figure 6A). THP-1 cells do not require modification of the culture vessels' surface to differentiate in the presence of the differentiation factor. After a 5-day incubation with 5 ng/mL PMA, it can be seen that the cells adhered to the surface and approximately 90% of cells have elongated morphology (Figure 6B).
## 2 Materials and Methods
### Validation of Numerical Model  
Model validations were further conducted in a curved channel based on the experimental data of supercritical nitrogen heat transfer obtained by [PERSON] at NASA Lewis Research Center [35]. The curved channel with a cross-sectional aspect ratio of 1 and a 45\({}^{\circ}\) bend with 137 mm mean radius in the heated portion was adopted for model validation. The inside and outside dimensions of the square channel were 4.14 mm \(\times\) 4.14 mm and 6.17 mm \(\times\) 6.17 mm, respectively. The inlet velocity was 21.94 m/s, the inlet temperature of fuel was 280 K, the back pressure was 8.3 MPa, and the heat flux was 0.28 MW/m\({}^{2}\).  
The experimental heat transfer data and numerical results are described in Figure 1. Here, the Nusselt number, \(Nu\), is the experimental Nusselt number, which is calculated by its definition, and the Nusselt number calculation, \(Nu_{calc}\), is the reference Nusselt number, which was obtained by a semi-empirically developed equation [36]:  
\[Nu_{calc}=0.023\text{Re}_{b}^{0.8}\text{Pr}_{b}^{0.4}\bigg{(}\frac{T_{w}}{T_{ b}}\bigg{)}^{-0.3}, \tag{11}\]  
where \(T_{b}\) is the bulk fluid temperature, and \(T_{w}\) is the wall temperature. Re and Pr are the Reynolds number and the Prandtl number, respectively.  
In the straight heat section (\(0<x/Dh<28\)), the maximum discrepancy of the ratio was less than 20%, which was within the uncertainty of the semi-empirical model. In the curved heat section (\(28<x/D_{h}<53.77\)), the discrepancy of the experimental data was greater than the numerical data, which is because the experimental data considered the effect of the electrical power input and the cooling enthalpy gain [36]. Very good agreements between experimental data and numerical results can be observed in Figure 1. Therefore, the numerical model is suitable to anticipate the heat transfer characteristics in a curved channel under supercritical conditions.  
Figure 1: Validation of heat transfer in curved channel.
## Materials and Methods
### Numerical Modelling
#### 2.3.3 Mesh Distribution  
Eight-node linear brick elements (C3D8R) with reduced integration were used in the solid part of the pipe, while four-node (S4R) elements with quad-dominated free mesh were used in shell parts of the pipe. Mesh sensitivity analysis was done to optimize accuracy of prediction and reduce the time required to complete analysis. Predictions were very sensitive to size of elements in the solid part of the pipe having the crack. Therefore, fine mesh distribution was used in it, and coarse mesh in the rest of the pipe. Final element dimensions in the solid were \(5\) mm \(\times\) 2.86 mm \(\times\) 0.85 mm in the longitudinal, circumferential, and thickness directions of the pipe respectively, and element size of 18 mm was used for the shell elements. Figure 3b shows mesh distribution in the model, with fine mesh near the crack and coarse mesh away from the crack to reduce computation cost. A total of 10 elements were used in the thickness direction of the pipe.