esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Low Multipath Single Point and Differential Positioning
#### 3.2.1 Precise Point Positioning (PPP) Static Results  
The distance between the reference station and the test point was 5 km, hence the effect of the ionosphere would have been nearly identical at each point. Three hours of R10 measurements at the reference station made during the test were processed successively in L1/L2 and L1 only PPP mode. The coordinate differences provide as excellent estimate of the effect of the ionosphere during the test. The differences (L1/L2 \(-\) L1) were 1, \(-\)9 and 13 cm in the latitude, longitude and height components, hence insignificant on the GPSMap 66 L1 results. The R10 L1/L2 and GPSMap 66 L1 measurements at the test point were also processed in PPP static mode. The differences between the R10 and GPSMap 66 blue unit were 43, 39 and \(-\)35 cm in the latitude, longitude and height components. The corresponding differences for the GPSMap 66 green unit were 34, \(-\)32 and 2 cm. The results demonstrate the capability of the GPSMap 66 for PPP applications at the 1 m RMS or better level under low ionospheric conditions. The P10 data was also submitted to the NRCan PPP service in static mode. The outcome was returned in code only kinematic mode due to the noisy data. No carrier phase nor Galileo data was available on the P10 during the test.
# Plate Size Effects in Gravelly Soil Based on In Situ Plate Load Tests and Finite Element Analysis
## 3 In Situ Plate Load Test
### Field PLT Results
#### 3.2.2 Average Contact Pressure Settlement  
Figure 5 illustrates the stress-displacement curves (\(p\)-\(s\) curves) from the in situ PLT. The tests at P1-2 and P1-3 were completed successfully. However, the test at P1-1 was abruptly halted due to a partial collapse at the top of the test tunnel when the average contact stress reached 1.6 MPa, preventing the recording of the unloading phase.  
Analysis of Figure 4 showed that during the test, there was no significant extrusion, cracking, or uplift of the soil around the test plates at any of the three test points. The total settlement was well below 0.1 times the plate diameter. From the \(p\)-\(s\) curves in Figure 5, it is clear that no sudden increase in settlement occurred during loading. Therefore, it can be concluded that the soil did not fail during the test but remained in a densified state.  
Figure 4: Geological description of PLT sites.  
Based on the principles of elasticity [17], within the linear range of the average contact stress-settlement curve for a circular test plate, the plate settlement modulus (\(E_{PLT}\)) [8] can be calculated using Equation (1), where \(E_{PLT}\) is determined by the average contact stress (\(\Delta p\)) and the corresponding settlement displacement (\(\Delta s\)).  
\[E_{PLT}=K_{p}\frac{\pi d\big{(}1-\mu^{2}\big{)}}{4} \tag{1}\]  
Here, \(K_{p}\) represents the coefficient of subgrade reaction corresponding to PLT, \(K_{p}=\Delta p/\Delta s\), \(\Delta p\) is the average contact stress within the linear range of the \(p\)-\(s\) curves, and \(\Delta s\) is the corresponding settlement; \(B_{p}\) is the diameter of the rigid plate, and \(\mu\) is the Poisson's ratio of the soil. The results are presented in Table 2.
# Quadrotor Formation Strategies Based on Distributed Consensus and Model Predictive Controls
## 3 Dynamic Model of a Quadrotor
### Model Predictive Control
#### 4.2.1 Linear Quadratic Tracker  
This section shows the problem statement and the solution to the LQT. The derivation of the solution to the LQT is not covered in this section. If the readers are interested in the derivation, it can be found in Reference [16].  
Consider the following discrete state-space model with \(n\) states, \(m\) controls, and \(q\) outputs:  
\[\mathbf{x}_{k+1} = A\mathbf{x}_{k}+B\mathbf{u}_{k},\] \[\mathbf{y}_{k} = C\mathbf{x}_{k},\]  
where the system matrix \(A\in\mathbb{R}^{n\times n}\), the control matrix \(B\in\mathbb{R}^{n\times m}\), and the output matrix \(C\in\mathbb{R}^{q\times n}\). \(x_{k}\in\mathbb{R}^{n}\) is a column vector, which denotes the state at time step \(k\) in the considered time horizon. \(u_{k}\in\mathbb{R}^{m}\) is a column vector, which denotes the control at time step \(k\) in the considered time horizon. Next, define the linear quadratic cost function based on the control and output state in the discrete state-space model and the reference trajectory for the output state as follows:  
\[J=\frac{1}{2}(\mathbf{y}_{N_{p}}-\mathbf{r}_{N_{p}})^{T}P(\mathbf{y}_{N_{p}}-\mathbf{r}_{N_{p} })+\frac{1}{2}\sum_{k=0}^{N_{p}-1}\left[(\mathbf{y}_{k}-\mathbf{r}_{k})^{T}Q(\mathbf{y}_{k} -\mathbf{r}_{k})+\mathbf{u}_{K}^{T}R\mathbf{u}_{k}\right], \tag{17}\]  
where \(k\left(k=0,1,...,N_{p}\right)\) denotes the time step over the time horizon. \(\mathbf{r}_{k}\left(k=0,1,...,N_{p}\right)\) is a column vector, which denotes the reference trajectory for the output state at time step \(k\). \(P,Q,R\) are the weighting matrices. All the weighting matrices are symmetric. \(P,Q\in\mathbb{R}^{q\times q}\) are positive semidefinite. \(R\in\mathbb{R}^{m\times m}\) is positive definite:  
\[P\geq 0,\quad Q\geq 0,\quad R>0. \tag{18}\]The optimized control sequence \(\mathbf{u}_{k}\) (\(k=0,1,...,N_{p}-1\)) that minimizes the cost function \(J\) in Equation (17) can be obtained by solving the following equations:  
\[K_{k} = (B^{T}S_{k+1}B+R)^{-1}B^{T}S_{k+1}A,\quad S_{N_{p}}=C^{T}PC,\] \[S_{k} = A^{T}S_{k+1}(A-BK_{k})+C^{T}QC,\] \[\mathbf{v}_{k} = (A-BK_{k})^{T}\mathbf{v}_{k+1}+C^{T}Q\mathbf{r}_{k},\quad v_{N_{p}}=C^{T}P \mathbf{r}_{N_{p}}, \tag{19}\] \[K_{k}^{v} = (B^{T}S_{k+1}B+R)^{-1}B^{T},\] \[\mathbf{u}_{k} = -K_{k}\mathbf{x}_{k}+K_{k}^{v}\mathbf{v}_{k+1}.\]  
In Equation (19), \(K_{k}\), \(S_{k}\), and \(K_{k}^{v}\) can be solved offline since they only consider the matrices in the discrete state-space model (16) and the weighting matrices \(P\), \(Q\), and \(R\) given by the users. Furthermore, by solving \(\mathbf{v}_{k}\) backward in time, the optimal tracking control sequence can be obtained. Substituting the optimal control sequence into the discrete state-space model (16), the optimal tracking trajectory over the time horizon can be obtained.
# A Literature Review on the Charging Behaviour of Private Electric Vehicles
## 2 Literature Review and Method
### Analysis of Infrastructure Usage
The average recharge lasts about 4 h. Evening or nighttime charging behaviors account for 26% and 21% of total charging. For home charging, the research interest is mainly concentrated on the hourly distribution of recharges since the power levels involved are generally limited. Domestic charges are concentrated in the evening and at night [111; 118], with peaks in correspondence with the time slots before and after work [80; 119]. The SOC at the start of charge shows a wide range of values, with a tendency to fully charge the battery [120]. The influence of electricity pricing on the temporal distribution of charging is addressed in [121] using machine learning techniques. Data from smart electricity meters are often used to extract EV load curves [122; 123; 124; 125]. Users who own cars with higher-capacity batteries seem to favor home charging [116]. As far as workplace charging is concerned, this practice represents a valid charging opportunity, especially when it is perceived as cheaper than charging at home or when home charging is not available. Usage data show regular patterns on working days, with a low rate of exploitation on holidays [126; 127]. In [128], the application of a data mining model allowed the authors to study the shape of the typical daily profile, the predictability with respect to weather conditions, and the trend of the EV charging demand. In Table 2, we present an overview of the papers that explicitly cited the databases used therein. The sources are classified as follows: charging data, registered at the EVSE; mobility data, which represent traffic, GPS, or EV onboard monitor unit log data; and other data if sources different from the above are included in the study. Although cited in the papers, some of the resources are no longer available at present.
# QoS Aware and Fault Tolerance Based Software-Defined Vehicular Networks Using Cloud-Fog Computing
## 2 Literature Review
### Scheduling SDVN Models
Based on current literature, we investigated and found the following limitations of SDVN communications:  
* Response time of messages/data need to reduce
* Fault tolerance mechanism should be used to reduce tasks/messages failure ratio
* Mobility aware protocols need to develop for SDVN communications
* Cache-based scheduling algorithms needed to reduce energy consumption
# Device Management and Data Transport in IoT Networks Based on Visible Light Communication
## 4 Experimentation and Discussion
### Testbed Configuration  
In the _virtual museum_ service scenario, there are lots of exhibition rooms. Each room has multiple exhibition items, each of which is associated with a VT. Figure 12 shows the network configuration for the _virtual museum_, in which AA is located at the exhibition room and connected to PS. Multiple VTs are connected to AA through Ethernet in the room. The PS is connected to Internet for museum administrator. A museum visitor (user), who is equipped with VR, will move across different VTs in the room. The VR Service Channel between VR and AA is based on Wi-Fi connection.  
For experimentation, we implemented the proposed scheme by using oneM2M-based platform software, called the _Mobius_ platform [30], which is based on the standard of oneM2M release 1. In the Mobius platform, the _Mobius server_ for PS is implemented as _Infrastructure Node Common Services Entity (IN-CSE)_ in oneM2M [31], the _nCube-Rosemary_ for AA is implemented as _Middle Node of Common Services Entity (MN-CSE)_ in oneM2M [32], and _nCube-Thyme_ is used for implementation of VT and VR as _Application Dedicated Node of Application Entity (ADN-AE)_ in oneM2M [33]. The _Mobius server_ for PS is installed on a desktop PC with _Ubuntu 16.04.1 LTS_, and the _nCube-Rosemary_ for AA is installed  
Figure 11: VR handover across VTs.  
Figure 12: Testbed network configuration for _virtual museum_ service.  
on a _Raspberry Pi_ with _Raspbian OS_. VT is also installed on _Raspberry Pi_ with _nCube-Thyme_ and a VLC-capable commercial LED light. Another _Raspberry Pi_ is used for VR with Linux OS, VLC receiver, and _nCube-Thyme_. PS is equipped with its own DHCPv6 server, which is implemented using the _dnsmasq_ package from the _Ubuntu_ repository. AA also has its own DHCPv6 server, and the functionality of Access Point (AP) is implemented by the _hostapd_ package from the _Raspbian_ repository.  
Figure 13 illustrates the protocol stacks used in implementation and experimentation. PS and AA are connected through Ethernet. The current version of VT is a _Field Programmable Gate Array (FPGA)_-controlled commercial LED light and a pre-installed firmware for VLC function. An enhanced version of VT will be considered for further study. The VR is attached to the FPGA-controlled Photo Diode (PD) for functionality as a VLC receiver. The VR receives the VLC data through _Universal Asynchronous Receiver/Transmitter (ULART)_ communication with the VLC receiver device. The VR service channel between AA and VR is established by using Wi-Fi.  
For VLC communication, VT and VR use _Pulse Width Modulation (PWM)_-based modulation. The black horizontal lines of VT, as shown in Figures 12 and 13, imply that the VLC frames are transmitted through visible light. The PD-based VLC receiver detects the VLC frames from the received light at PD. The structure of a VLC frame used in the experiment is depicted in Figure 14. In experimentation, we employed 6 VIs, and each VT has a unique VT ID.
## 2 Problem Description and Mathematical Modelling
### Mathematical Modelling  
The reciprocating motion means that the heat source starts to make straight movement from some point in the medium and return to the outset after a full motion cycle. Then, the heat source repeats this kind of reciprocating process. As the heat transfer medium is infinite, we assume the outset of heat source as origin point and the move path direction as \(x\) axis. Then, we construct the corresponding rectangular coordinate system to study the heat transfer problem. The schematic diagram of the physical model is exhibited as Figure 2.  
Heat conduction happens in a homogeneous and isotropic medium of the infinite extension in all directions, of which the initial temperature is \(t_{\infty}\). The point heat source starts periodically reciprocating motion along the \(x\) axis in a rectangular coordinate region (\(x,y\), \(z\)). Introducing the surplus temperature \(\theta=t(x,y,z,t)-t_{\infty}\) and heating rate operator \(g_{\rm p}\) of moving heat source, the general mathematical model of three-dimensional heat transfer problem subjected to a point heat source can be expressed as  
\[\frac{\partial^{2}\theta}{\partial x^{2}}+\frac{\partial^{2}\theta}{\partial y ^{2}}+\frac{\partial^{2}\theta}{\partial z^{2}}+\frac{1}{k}g_{\rm p}(t)\delta \big{(}x-x^{\prime}\big{)}=\frac{1}{\alpha}\frac{\partial\theta}{\partial t}, \hskip 14.226378 pt-\infty<x,y,z<+\infty \tag{3}\]  
with initial condition  
\[\theta=I(x,y,z,t),\hskip 14.226378 ptt=0,\hskip 14.226378 pt-\infty<x,y,z<+\infty \tag{4}\]  
where \(\delta\) function is the density representation of point source as a centrally distributed physical quantity, \(\delta(x-x^{\prime})\) represents the space distribution of point heat resource in the  
Figure 1: Displacement curves of practical and simplified motion.  
Figure 2: Schematic diagram of moving heat source in an infinite medium.  
axis [22], m, \(k\) is the thermal conductivity, and W/m\(\cdot\)K, \(k=\alpha\rho c\). \(\alpha\) is the thermal diffusivity, m\({}^{2}\)/s.  
Due to the periodical characteristics of point heat source, departure and back travels consume the same time, so that the integral limit 0\(-t\) can be split by the interval \(T/2\). Solutions to temperature field can be calculated as  
\[\theta=\left(\int_{0}^{\frac{T}{2}}+\int_{\frac{T}{2}}^{T}+\int_{T}^{\frac{3T}{ 2}}+\cdots\int_{(K-1)T}^{(K-\frac{1}{2})T}+\int_{(K-\frac{1}{2})T}^{KT}+\int_{ KT}^{t}\right)Gg_{\rm{P}}(\tau){\rm{d}}\tau \tag{5}\]  
Tallying up the reciprocating regular pattern, the integral form of surplus temperature can be generally expressed as  
\[\theta=\left(\sum_{N=0}^{K-1}\int_{NT}^{(N+\frac{1}{2})T}+\sum_{N=0}^{K-1}\int _{(N+\frac{1}{2})T}^{(N+1)T}+\int_{KT}^{t}\right)Gg_{\rm{P}}(\tau){\rm{d}}\tau \tag{6}\]  
where \(K=1\), 2, \(\cdots\) and \(NT\)-\((N+1/2)T\) represents the time period of departure while \((N+1/2)T\)-\((N+1)T\) represents that of back tracking. \(KT\)-\(t\) represents the rest time after deducting all the complete cycles. \(G\) is Green's function of the corresponding heat transfer problem.
# A Review of Photothermal Detection Techniques for Gas Sensing Applications
## 3 Fiber-Based Photothermal Gas Sensors
### Pulsed PTS in Hollow-Core Fibers
Multimode transmission of the light taking part in the sensing results in modal interferences, which are susceptible to bending of the fiber and mechanical and acoustic disturbances. In most cases, modal interferences significantly decrease the SNR. Moreover, small diameters of the hollow core drastically increase the time required to exchange the measured gas sample, and hence the response time of the sensor [48]. Fortunately, these issues have been perceived by researchers working on novel structures of HCFs. The result is the currently observed struggle to design and draw HCFs with low attenuation in the mid-IR wavelength region [49; 50; 51]. Preliminary experimental results have been published, validating the possibility of using e.g., antiresonant hollow-core fibers in the 3-4 \(\upmu\)m wavelength region for various optical applications [52; 53; 54], which gives perspective for novel configurations of absorption and PT gas sensors. Figure 9: Transmission of 1-cm-thick fused silica glass in function of wavelength.
# Study of Sea Fog Environment Polarization Transmission Characteristics
## 5 Experimental Results and Analysis
Circularly polarized light offers superior polarization retention. The experimental outcomes are essentially in line with the simulated outcomes. The effect of the incoming light wavelength on the polarization degree. In this section, the 30 g/L salt content data group is discussed. In order to understand the effect of wavelength on the degree of polarization during polarized light transmission, modeling and experimental findings are studied based on the starting state of polarization and three incoming light wavelengths. The modeling and experimental curves of the degree of polarization of polarized light with varying wavelengths, optical thicknesses, and polarizing angles are shown in Figures 11 and 12. Figure 11 compares simulation and experimental results for 0\({}^{\circ}\) linearly polarized light in three bands, while Figure 12 compares simulation and experimental results for left-handed circularly polarized light in three bands. Figure 8: \(\lambda=\) Simulation and experimental comparison of DOP change of 450 nm polarized light: (**a**) simulation results and (**b**) experimental results. Figure 7: Simulation and experimental comparison of the relationship between salt content at 671 nm and DOP: (**a**) simulation results and (**b**) experimental results. Figure 11 depicts the variation in the degree of polarization of polarized light of different wavelengths with optical thickness when the incident light has a linear polarization angle of 0 degrees, and Figure 12 depicts the variation in the degree of polarization of polarized light of different wavelengths with optical thickness when the incident light has a left-handed circular polarization angle of 0 degrees. Figure a depicts the simulation outcome, whereas Figure b depicts the experimental outcome. From a comparison of the results curves in Figures 11 and 12, it is clear that the degree of polarization of the polarized light of the three wavelengths decreases as the optical thickness increases, and the change trend is almost the same. When the optical thickness is modest, the degree of circular polarization or linear polarization at various wavelengths does not vary noticeably. However, when the optical thickness grows, the following relationship holds for the degree of polarization of the three bands:  
\[\text{Dop}_{450}<\text{Dop}_{532}<\text{Dop}_{671}\]  
Figure 10: \(\lambda=\) Simulation and experimental comparison of DOP change of 671 nm polarized light: (**a**) simulation results and (**b**) experimental results. Figure 9: \(\lambda=\) Simulation and experimental comparison of DOP change of 532 nm polarized light: (**a**) simulation results and (**b**) experimental results. In other words, when the optical thickness of the sea fog increases in the visible light spectrum, the longer the wavelength, the greater the polarization preservation, and the degree of polarization of circularly polarized light is much more than that of linearly polarized light.
# Conceptual Design of an Extended Reality Exercise Game for the Elderly
## 4 Discussion  
As a multi-mode, multi-task, multi-user, VR home exercise game for the elderly, _VRrowing_ has advantages compared with real sports/other themed VR exercise games/related VR rowing games. Of course, it is undeniable that there are also weakness that need refinement and improvement. Compared to real rowing sports, _VRrowing_ could simulate similar or richer scenes in the home, without the constraints of limiting environmental factors, such as time, space, weather, etc. It does not require the investment of sports equipment and reduces the risks encountered during sports.  
Compared to similar VR rowing games, _VRrowing_ is developed for the elderly, considering their characteristics and needs, and more in line with their cognition and habits. No additional sports equipment (for example a rowing machine) is required, and no specific requirement for a display device, is also one of the differences between _VRrowing_ and similar software. It could lower the players' cost input, enhancing the game's generalizability. However, at the same time, it leads to a lack of force feedback, which may affect the realism and immersion. Considering reducing the cost and simplifying the system, this original conceptual prototype version still adopts the limited external device configuration. Nevertheless, in the design and implementation of subsequent versions, the addition of external devices to provide interaction and feedback that is closer to the actual situation will not be ruled out.  
Not limited to the boating theme, _VRrowing_ integrates a variety of boat sports, multi-game modes, and multi-task functions to meet users with different physical fitness, exercise preferences, player types, and health needs. From the dimensions of exercise posture (sitting/standing), exercise body part (upper/lower/whole body), training aspect (physical/mental), and exercise intensity, _VRrowing_ could potentially provide a controllable and adjustable personal customised experience. To a certain extent, it aims to enrich the game content, increase the fun, and motivate players to continue using it. The use of in-game data is implemented in the analysis function. Exercise situations over time are counted, calculated and presented through the time/correct rate of tasks completed by the user,the training time/gliding distance, etc. In subsequent releases, the real-time dynamic adjustment of difficulty based on in-game data will be considered.  
Furthermore, reducing the risks of the elderly using VR exercise games should be highlighted in the prototype and implementation. This conceptual design mentioned the different levels in three modes (relaxation/training/competition), allowing users to begin with simpler and easier tasks and then level up. In this way, users could slowly adapt to exercising in a VR environment by gamified tasks and reducing the risks in exercises.
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 3 SDN, NFV, and CASB Technologies
### Software-Defined Network (SDN)
#### 3.1.1 Pillars of SDN  
Figure 2 illustrates the architecture of a mobile cloud system based on SDN. It emphasises SDN's centralised control and orchestration abilities, its support for programmability and automation, and the capability for network virtualisation, which allows for creating multiple virtual networks on the same infrastructure.  
1. Centralised Control and Orchestration: SDN centralises the control plane, allowing network administrators to oversee and direct network devices and traffic from a central controller.
2. Programmability and Automation: SDN offers a programmable interface, facilitating the configuration and management of network devices.  
Figure 2: SDN-based mobile cloud overview.  
Network Virtualisation: SDN separates the control plane from the data plane, enabling network virtualisation and allowing the creation of multiple virtual networks within the same physical infrastructure.