esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy and Security in Digital Health Contact-Tracing: A Narrative Review
## 3 Results
### Data Protection and Control
#### 3.2.4 Security of Data  
Security includes the protection of the data collected from contact-tracing, and it involves implementing the right policies and practices. Security concerns were reported as a reason for not intending to use the contact-tracing app [54; 61; 100]. Among various concerns, users showed a high level of concern related to data security when using contact-tracing apps [21; 113], and people perceived insufficient data transparency and expressed concerns about potential data leaks [103].  
Safety concerns, including potential data leaks, are among the frequently cited barriers to adopting contact-tracing apps [113]. Perceived security risks were identified as significant barriers towards adoption in Indian study [114]. Cybersecurity, constituting 18% of the total concerns, was mentioned as another reason [27]. In the USA, participants frequently cited concerns about data security as a primary reason for their reluctance to share data with contact-tracing technology [115]. On the Isle of Wight, some participants conveyed a sense of feeling spied on, emphasising data security concerns [116].  
In Scotland, participants raised concerns about the confidentiality of the data they were required to provide, uncertainties about its usage, and potential impact on their contacts [86]. In Scotland, people sought reassurance that their data would remain confidential and not be shared with third parties outside the NHS [86]. In an Australian study, many individuals expressed concern about the security of the contact-tracing app. Some participants believed the app might not be secure enough and could be hacked, leading to unauthorised use of their information [22].  
In New Zealand, a minority of participants cited privacy and security concerns that discouraged them from using the app [99]. In a study conducted in the USA, more than 70% of users agreed that they felt their health information was kept private while using contact-tracing apps [26].  
In the Netherlands, the likelihood of a participant downloading an app was 3.33 times higher when informed that their data would be protected by law than when no information was provided about how their data would be handled [117]. In Turkey, participants who were presented with information about data protection laws were significantly more willing to download the app than participants who were presented with a COVID-19 call to action that did not mention data protection [117].
# Strain Transfer Analysis of a Clamped Fiber Bragg Grating Sensor
## 3 Theoretical Strain Transfer Analysis  
A clamped FBG sensor model comprises an \(m\)-segment and an \(n\)-segment of the fiber, as shown in Figure 1, where the \(m\)-segment is glued, and the \(n\)-segment is unrestrained. The \(m\)-segment length is defined as the interlayer length, and the \(n\)-segment length as the gauge length. A, B, C, D points are boundary points of interlayer. A micro unit of an \(m\)-segment is shown in Figure 2, and a mechanical model of a micro unit of the interlayer is shown in Figure 3. Three appropriate assumptions are applied in this study to simplify the model,  
1. The optical fiber is linearly elastic with its \(m\)-segment and \(n\)-segment having the same mechanical properties. The mechanical properties of optical fiber are impacted by taking advantage of phase masks and ultraviolet irradiation methods when processing FBG writing. The FBG sensor does not carry the external force directly, and the axial strain is supposed to transfer from the host material to the optical fiber by the interlayer shear deformation.
2. The interlayer is a hollow cylinder, which is homogeneously coated on the fiber surface to undergo the shear deformation, regardless of its axial deformation.
3. There is no relative slip between the FBG sensor and the interlayer.
## 2 Materials and Methods
### ISEA4H Subdivision Model of the Temporal and Spatial Grid of the Earth System's Layers
#### 2.2.1 Basic Concept
2.4 Subdivision Model of the Layer Surface of the ISEA4H-ESSG Temporal and Spatial Grid of the Earth System's Layers  
ISEA4H uses a regular icosahedron to fit the Earth's surface and establishes a subdivision model on the surface of the regular icosahedron. The following figure is a schematic diagram of the regular icosahedron used to fit the Earth's surface when it is unfolded on a spherical surface. A regular icosahedron has twenty regular triangles, which can be divided into twenty complete ground hexagonal tiles on the triangular surfaces and corner-missing hexagonal tiles composed of twelve vertex triangles, as shown in Figure 4.  
In the global grid, the subdivision apertures are based on the hexagonal ones, including the three-aperture, four-aperture, and seven-aperture, and there are four commonly used hexagonal subdivision structures, as shown in Figure 5. The red hexagon representing the parent unit is subdivided into several black hexagons representing the sub-units. (a) is the three-aperture subdivision structure, (b) and (c) are the four-aperture subdivision structures, and (d) is the seven-aperture subdivision structure. Among them, there is a problem of direction transformation in the seven-aperture subdivision, which affects the efficiency of the grid-coding operation. However, the four-aperture hexagonal subdivision is relatively simple and convenient for coding operations. According to this subdivision structure, the subdivision structure shown in the figure is formed on each triangular surface, and based on this rule, recursive hierarchical subdivision is carried out to generate higher-level grid units, as shown in Figure 6.  
Figure 4: An unfolded diagram of the regular icosahedron. Figure 5: Hexagonal subdivision apertures: (**a**) three-aperture (3 sub-units); (**b**) four-aperture type 1; (**c**) four-aperture type 2; (**d**) seven-aperture (7 sub-units). Black is the n+1 th grid, and red is the nth grid. Figure 6: Iterative subdivision on the triangular surface: (**a**) describes the subdivision structures of the first and second layers, and (**b**) describes the hierarchical structures of the first, second, and third layers. The planar hexagonal grid subdivision results generated can be used to generate a spherical hexagonal grid spherical model through the inverse ISEA projection, as shown in the figure. The lines formed by the boundaries of these spherical hexagons divide the Earth's surface into various spatial subdivision curves with equal areas. The spherical grid formed by using these subdivision curves does not have the problem of polar oscillation, and the grid units are evenly distributed on the global surface. Moreover, the grid on the spherical surface has the characteristic of infinite subdivision, enabling the acquisition of grids at any granular scale through subdivision. The global subdivision result at the third level is shown in Figure 7.
## Appendix A Condensation Technology
### Efficiency of a Condensing Boiler  
The formula for determining the efficiency of a condensing boiler differs from those commonly used for standard and low-temperature boilers. It consists of two terms that encompass the effects of sensible and latent heat:  
* Sensible heat refers to the heat that causes a change in the temperature of a substance without changing its state. In condensing boilers, sensible heat is the energy used to raise the temperature of the water or air being heated;
* Latent heat is the heat absorbed or released during a phase change, such as when water vapor condenses into liquid water. Condensing boilers capture this latent heat from the water vapor in the flue gases, which significantly improves their efficiency.  
In the latent or condensation term, which describes the impact of latent heat from the condensation of water vapor in flue gases, an additional variable appears alongside the usual lower and higher heating values of the fuel. The condensation rate, \(\alpha\), represents the ratio of the actual to the theoretically possible amount of condensate. The boiler becomes more efficient as the value of \(\alpha\) increases. As the temperature of the flue gases decreases, the amount of condensate increases, leading to a higher condensation rate and thus lower flue gas losses. The equation for determining the efficiency of condensing boilers is given in Equation (A2):  
\[\eta_{cc}=1-\frac{q_{fg}-q_{irr}}{100}+\alpha\cdot\frac{HHV-LHV}{LHV}\] (A2)  
where  
\(\eta_{cc}-\)efficiency of condensing boilers (dimensionless).  
\(\frac{q_{fg}-q_{irr}}{100}\)-dimensionless term related to sensible heat.  
\(\alpha\cdot\frac{HHV-LHV}{LHV}\)-dimensionless term to latent heat.  
\(q_{fg}-\)losses of flue gases (%).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**in kWh/m3** & **Lower Heating Value (LHV)** & **Higher Heating Value (HHV)** \\ \hline Natural gas & \(\sim\)10 & \(\sim\)11 \\ Propane & 25.8 & 28.1 \\ City gas 1  & \(\sim\)4.2 & \(\sim\)5.0 \\ Heating oil 2  & \(\sim\)10 & \(\sim\)10.6 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Heating values of some typical fuels.  
\(q_{irr}=\left(t_{fg}-t_{air}\right)\left(\frac{A}{CO2}-B\right)\)--loss due to boiler radiation into the immediate surroundings (%).  
\(t_{fg}\)--temperature of flue gases (\({}^{\circ}\)C).  
\(t_{air}\)--temperature of air (\({}^{\circ}\)C).  
A,B--dimensionless terms for gases provided by public gas supply, where A = 0.66, and B = 0.009.  
CO2--volume content of CO\({}_{2}\) in flue gases and an indicator of combustion quality that depends on burner design (%).  
\(\alpha=\frac{V_{em}}{V_{ct}}\)--degree of condensation, which depends on boiler design and installation (dimensionless).  
\(V_{cnt}\)--measured amount of condensate (kg/m\({}^{3}\)).  
\(V_{ct}\)--theoretical amount of condensate (kg/m\({}^{3}\)).  
HHV--higher heating value (kWh/m\({}^{3}\)).  
LHV--lower heating value (kWh/m\({}^{3}\)).  
The theoretical amount of condensate \(V_{ct}\) for natural gas (methane, CH\({}_{4}\)) is 1.6 kg/m\({}^{3}\).
# Study of NVIS Channel for USN Protocol Definition in Antarctica
## 5 Frame Protocol Defined and HF Frame Protocol Standards
For similar OFDM configurations (T\({}_{\text{S}}\) = 10 ms, N\({}_{\text{SC}}\) = 32, BW = 3.2 kHz, Modulation = QPSK, Bitrate = 4.102 Kbps) is achieved 50% probabilities to obtain a BER lower than 0.46 in comparison of both skywave and groundwave frame protocols designed where we have 50% probabilities to obtain a BER lower than \(10^{-4}\) and \(10^{-3}\) as we can see at Figure 19.  
In addition to the comparison of Figure 19, in Table 4, we compare the main characteristics of the frame protocol designed with the HF Frames Standards STANAG and MIL-STD\(-\)188-110D. As we can see, the preamble total time and data block time designed is very similar to the HF standards  
Figure 19: OFDM configurations CDF simulation. frames preamble and data block time. Nonetheless, the bitrate is lower for the designed frame protocol because of the modulations used for each frame protocol. Despite the low bitrate, it remains enough for most USNs and the use of this frame structure improves the robustness to ensure the data link.
## 2 Materials and Methods
### Gait Parameters Estimation from Smart Walker with Onboard Sensors  
The participant's weight distribution during walking was measured using the prototype Smart Walker. Figure 4 shows both left- and right-side forces recording from the four sensors with respect to time. Since the walker was easy to use for both groups of participants, and osteoporosis participants have experience in walker usage; thus, the data received have no significant errors and missing values. Therefore, no filters were used for data preprocessing before data analysis. The participants initiated walks by striking their right heel to the ground. At each heel strike we obtain the peak of force. The fluctuations in forces gave valuable information for the calculation of spatiotemporal gait parameters including time, stride time, stride length, cadence, step length, step time, and velocity. The force on the handlebar increased on the same side and lowered on the opposing side when users initiated a heel strike [14]. Hence, the number of steps taken by the users were easily determined by counting the number of inflection points as shown in Figure 4.  
Figure 3: Validation of sensor measurement after calibration. (**a**) The sensor on the right handlebar, and (**b**) the sensor on the left handlebar.  
For simplified data visualization, forces of two sensors attached on the left side (FsRLs +FsFLs) were added and resulted in a single force peak signal for the left heel strike, presented as the FLeft heel strike peak in Figure 5. Similarly, force measurements from the two sensors on the right side (FsRRs +FsFRs) were added to obtain one force peak signal, presented as the Fright heel strike peak in Figure 5.  
The differences in the forces recorded from left and right sides, occurrence of peaks at specific time and distance covered by the user, the following gait parameters were measured:  
\[F_{diff}=F_{right\;\;\text{{{heled strike peak}}}}-F_{left\;\;\text{{{heled strike peak}}}} \tag{3}\]  
Moreover, the following parameters were also measured:  
Step Time: Average time in seconds between minimum-maximum (right) and minimum-maximum (left).  
Stride Time: Average time in seconds between maximum-maximum (right) and minimum-minimum (left).  
Number of steps: Number of inflection points.  
Time required to complete walk: Number of seconds that a user takes to complete the walk.  
Figure 4: Force sensor reading. Force sensor on rear right side (FsRRs (N)), force sensor on rear left side (FsRLs (N)), force sensor on front right side (FsFRs (N)), and force sensor on front left side (FsFLs (N)).  
Figure 5: Sum of forces measured by sensor on rear and front (left) and on rear and front (right) during walk test using smart walker.
# Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs
## 4 The Construction of the Iterative Positioning Model  
In the procedure of iterative positioning, the unknown target node in the positioning triangle area has a significant influence on its positioning accuracy. The positioning error of the target node located in the positioning triangle area is much smaller than that of the target node outside the positioning triangle area. To improve the positioning accuracy, an iterative positioning model is established in the paper, as shown in Figure 4.  
In Figure 4, the quadrilateral \(A_{1}A_{2}A_{3}A_{4}\) is a square, the point \(O\) is its center, and the points \(B_{1}\), \(B_{2}\), \(B_{3}\), and \(B_{4}\) are the midpoints of the respective sides. According to the connection shown in Figure 4, the square \(A_{1}A_{2}A_{3}A_{4}\) is subdivided into eight triangular regions: region 1 to region 8. The anchor nodes (9 in total) are placed at points \(A_{1}\), \(A_{2}\), \(A_{3}\), \(A_{4}\), \(B_{1}\), \(B_{2}\), \(B_{3}\), \(B_{4}\), and \(O\).  
There is a target node \(X\) in the quadrilateral \(A_{1}B_{1}OB_{4}\) in Figure 4 and the points closest to the point \(X\) are points \(A_{1}\), \(B_{1}\), \(O\), and \(B_{4}\) in turn. The node X is included in both \(\Delta A_{1}B_{1}B_{4}\) and \(\Delta A_{1}B_{1}O\). In \(\Delta A_{1}B_{1}B_{4}\), the coordinates \((x_{\rm G1},y_{\rm G1})\) are calculated by the iterative positioning algorithm. In order to  
Figure 3: A flow chart of the iterative positioning algorithm.The \(C_{dev}\) is the distance deviation coefficient in Figure 1. The \(C_{m-dev}\) is the median of the distance deviation coefficients \(C_{dev}\).
## 1 Introduction  
Detecting Region Of Interest (ROI) for video sequences is widely applied both in surveillance/monitor systems and in video encoders. In recent decades, people attempt to define the concept of ROI based on Human Visual System [1]. Also, an ROI-based video encoding mode is adopted both in previous research [2; 3] and in latest video codecs such as VPX [4] or HEVC [5]. The notion of ROI is within an evolutionary process, In earlier works [6; 7], researchers treat faces as ROI details and adjust quantization parameters (QP) in video compression for ROI encoded blocks. Other works such as [8; 9] regard moving or high contrast image segments as ROI details for video codecs. As the concept of the \"objected-based\" video coding raised from MPEG-4 [10], researchers begin to focus on moving objects extraction and encoding. Hence, ROI is defined more likely as the foreground moving objects in video clips. During the recent five years, many fore-background separation methods are developed based on moving objects detection. The low-rank decomposition method is able to solve the change detection problem by outlier pursuit algorithm, in which the changing components are stored in a sparse matrix. Principle Component Analysis (PCA) has been proposed as a state-of-the-art method for low-rank matrix recovery [11]. However, the classical PCA can be easily corrupted by noise. With the rapid development of artificial intelligence, objects discovery is also used in navigation system designs for autonomous cars, in which computers would recognize objects and make the planning without specialized sensors [12]. Therefore, it is essential to design a detection algorithm for both accuracy and efficiency, so that typical objects in videos or images can be automatically discovered real time to help the computer understand their contents. In addition, object detection can be embedded into the scope of motion estimation (ME) for video coding architectures in our previous works [13; 14; 15]. Specifically, the detection method based on ME can be embedded into some ME processors. For example, the custom-instruction with the combination of synchronous dynamic random access memory (SDRAM) and on-chip memory architecture-based Nios II processor [16; 17], and the FPGA architecture-based processor [18].
## 3 Results and Discussion
### Use of Primary Energy Results by Window Shading System Characteristics
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Configuration** & **North** & **Northeast** & **East** & **Southeast** & **South** & **Southwest** & **West** & **Northwest** \\ \hline WSHA-1 1 & 14.21 & 23.67 & 30.31 & 23.16 & 14.38 & 23.76 & 30.30 & 23.03 \\ WSHA-2 & \(-\)10.2\% & \(-\)11.1\% & \(-\)9.8\% & \(-\)9.7\% & \(-\)7.4\% & \(-\)9.4\% & \(-\)9.2\% & \(-\)10.5\% \\ WSHA-3 & \(-\)17.4\% & \(-\)23.4\% & \(-\)23.8\% & \(-\)20.1\% & \(-\)13.2\% & \(-\)19.7\% & \(-\)22.6\% & \(-\)21.9\% \\ WSHA-4 & \(-\)11.0\% & \(-\)14.7\% & \(-\)6.5\% & \(-\)14.2\% & \(-\)11.3\% & \(-\)14.2\% & \(-\)6.6\% & \(-\)14.8\% \\ WSHA-5 & \(-\)26.0\% & \(-\)35.6\% & \(-\)29.5\% & \(-\)32.2\% & \(-\)22.7\% & \(-\)31.8\% & \(-\)28.4\% & \(-\)34.6\% \\ WSHA-6 & 14.4\% & 9.8\% & 6.4\% & 10.6\% & 14.3\% & 9.8\% & 6.5\% & 10.7\% \\ \hline \end{tabular}  
* Primary energy consumption for cooling the base case (in kWh/(m\({}^{2}\cdot\)year)).  
\end{table} 
Table 10: Variation, relative to the base case (WSHA-1), in primary energy consumption of cooling for the different shading system design configurations, differentiated by orientation of the main facade. Results for dwelling type SFH-1. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Configuration** & **North** & **Northeast** & **East** & **Southeast** & **South** & **Southwest** & **West** & **Northwest** \\ \hline WSHA-1 & 14.21 & 23.67 & 30.31 & 23.16 & 14.38 & 23.76 & 30.30 & 23.03 \\ WSHA-2 & 12.76 & 21.04 & 27.34 & 20.91 & 13.32 & 21.53 & 27.50 & 20.62 \\ WSHA-3 & 11.74 & 18.14 & 23.11 & 18.51 & 12.48 & 19.07 & 23.46 & 17.99 \\ WSHA-4 & 12.64 & 20.20 & 28.34 & 19.86 & 12.76 & 20.39 & 28.31 & 19.62 \\ WSHA-5 & 10.51 & 15.24 & 21.36 & 15.70 & 11.12 & 16.21 & 21.69 & 15.07 \\ WSHA-6 & 16.26 & 25.98 & 32.26 & 25.61 & 16.44 & 26.08 & 32.28 & 25.49 \\ \hline \end{tabular}
\end{table} 
Table 9: Results of primary energy consumption of cooling (in kWh/(m\({}^{2}\cdot\)year)) for the different shading system design configurations, differentiated by orientation of the main facade. Results for dwelling type SFH-1. Figure 8 shows the results for CO\({}_{2}\) emissions due to primary energy consumption of cooling for each dwelling type and are differentiated according to the shading system installed in the building's window openings. For all dwelling types, emissions are minimal for the case of the WSH-5 system, with reductions of 2.37, 2.11, and 2.15 kgCO\({}_{2}\)/(m\({}^{2}\cdot\)year) for the dwelling types SFH-1, SFH-2, and SFH-3, respectively.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 3 Resource Management Techniques in C-RAN
### Radio Resource Management Techniques
#### 3.1.2 Joint Optimization Schemes  
In [44], [PERSON] et al. proposed DRAC, a two-stage design for dynamic resource allocation in a C-RAN in conjunction with real-time BBU-RRH assignment, considering the constraints in transmission power and Signal-to-interference-plus-noise-ratio (SINR) for user equipment (UE). In the first stage, they formulated the resource allocation problem as a mixed linear integer problem (MILP). They then solved it using the branch and cut algorithm (BCA), which associates the best spectrum set of frequency/time resources dynamically to incoming UEs. In the second stage, the optimal number of BBUs required and RRHs to be turned on to handle the traffic load is calculated. This problem was formulated as a multiple knapsack problem (MKP). Here, the RRHs are the objects, and the BBU capacity to handle the real-time traffic load is the knapsack. It was solved by IBM's linear solver CPLEX, which determined the optimal result using highly marginal computation time.  
[PERSON] et al. investigated joint downlink resource allocation and admission control for mobile users in an orthogonal frequency division multiple access (OFDMA)-based C-RAN as an optimization problem [45]. The objective was to determine the optimal PRB allocation to maximize the total user throughput in the system while considering the QoS and mobile user data rate requirements, maximum transmission power, and fronthaul link limitation constraints. They proposed a two-stage resource allocation and admission control (RAAC) algorithm. The resource allocation problem was first solved using the fixed time branch-and-cut algorithm without the data rate constraints. Then, the fast admission control algorithm was utilized based on the output of the former problem to select the largest set of mobile users to be accepted in the system, considering the dropped data rate constraint.  
[PERSON] et al. proposed a C-RAN structure with a mobile cloud (virtual machine) co-located in the BBU pool with BBUs, to investigate the joint energy minimization and resource allocation. The objective was to enhance both the performance and energy efficiency of the C-RAN in [46]. The architecture is such that the mobile cloud executes the computationally intensive task, whereas the BBUs are responsible for transferring the executed results to the RRH for sending it to the UE. The joint energy minimization is formulated as a non-convex optimization problem, which is further reformulated into an equivalent convex problem, based on power minimization and sum data rate maximization that is transformed into a minimization of weighted sum mean square error (MSE) problem. The objective is to minimize the energy cost of the mobile cloud and network considering the QoS, i.e., the time constraints. A WMMSE-based iterative algorithm is proposed. It can solve the joint resource allocation between the mobile cloud and C-RAN and thereby improve the system performance and energy saving.