esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Evaluations of NLOS and Linearization Errors on UWB Positioning
## 2 Methods
### Extended Kalman Filter  
The standard Kalman filter (KF) applies to linear systems [29]. When the state transition function and/or observation function are nonlinear transformations, the standard KF cannot be used. The EKF linearizes the nonlinear system locally and can be applied to the weak nonlinear system [30]. The core idea is to apply the Taylor expansion to the nonlinear function at the filtering value and ignore the quadratic and above terms [31]. A typical nonlinear model can be expressed as [32]  
\[\left\{\begin{array}{l}\mathbf{X}_{k}=\mathbf{f}(\mathbf{X}_{k-1})+\mathbf{w}_{k}\\ \mathbf{Z}_{k}=\mathbf{h}(\mathbf{X}_{K})+\mathbf{v}_{k}\end{array}\right. \tag{3}\]  
where \(\mathbf{X}_{k}\) is the state vector; \(\mathbf{f}(\mathbf{X}_{k-1})\) is the system nonlinear function; \(\mathbf{Z}_{k}\) is the measured value at time \(k\); \(\mathbf{w}_{k}\) and \(\mathbf{v}_{k}\) represent process noise and measurement noise, respectively; \(\mathbf{h}(\mathbf{X}_{k})\) represents the nonlinear observation function [33].  
The normal speed model is used as the equation of state. The state vector consists of position (\(\mathbf{p}\)) and velocity (\(\dot{\mathbf{p}}\))  
\[\mathbf{X}_{k}=\underbrace{\begin{pmatrix}\mathbf{I}&\mathbf{I}\Delta t\\ \mathbf{0}&\mathbf{I}\end{pmatrix}}_{\text{F}}\underbrace{\begin{pmatrix}\mathbf{p}\\ \mathbf{p}\end{pmatrix}}_{\mathbf{x}_{k-1}}+\underbrace{\begin{pmatrix}\mathbf{0}\\ \mathbf{w}_{\dot{\mathbf{p}}}\end{pmatrix}}_{\mathbf{w}_{k}} \tag{4}\]  
where \(\mathbf{F}\) represents the state transfer matrix; \(\mathbf{I}\) is the unit vector; \(\mathbf{w}_{\dot{\mathbf{p}}}\) is the velocity noise.  
The EKF algorithm consists of two parts, with (1) Time-update [34]  
\[\mathbf{\hat{X}}_{k+1|k}=F\mathbf{\hat{X}}_{k}+\mathbf{w}_{k} \tag{5}\]  
\[\mathbf{P}_{k+1|k}=F\mathbf{P}_{k}\text{F}^{\text{T}}+\mathbf{Q} \tag{6}\]  
and (2) Measurement-update [35]  
\[\mathbf{K}_{k+1}=\mathbf{P}_{k+1|k}\mathbf{H}_{k+1}^{\text{T}}(\mathbf{H}_{k+1}\mathbf{P}_{k+1|k} \mathbf{H}_{k+1}^{\text{T}}+\mathbf{R})^{-1} \tag{7}\]  
\[\mathbf{\hat{X}}_{k+1}=\mathbf{\hat{X}}_{k+1|k}+\mathbf{K}_{k+1}(\mathbf{Z}_{k+1}-\mathbf{h}(\mathbf{ \hat{X}}_{k+1|k})) \tag{8}\]  
\[\mathbf{P}_{k+1}=\mathbf{P}_{k+1|k}-\mathbf{K}_{k+1}\mathbf{H}_{k+1}\mathbf{P}_{k+1|k} \tag{9}\]  
where \(\mathbf{Q}\) is the system noise covariance matrix; \(\mathbf{R}\) is the measurement noise covariance matrix; \(\mathbf{H}_{k+1}\) is the Jacobian matrix.
# Developments, Trends, and Challenges in Optimization of Ship Energy Systems
## 4 Historical Development of Optimization of Ship Energy Systems
### Design Point Versus Optimization based on Operating Profile-Time in Optimization  
Design optimization at a single operating state (design or nominal point) is relatively easy, because there is no need to consider partial load performance of equipment, and the dimensionality of the optimization problem (number of independent variables) remains relatively low. In addition, if the optimization objective is a thermodynamic one, e.g., maximization of efficiency or minimization of fuel consumption, there is no need to consider time. However, time of operation either in steady state or under variable conditions is needed, if the economic performance of the system is assessed, even with operation at a single point.  
The problem becomes more complex, if the optimization is performed considering various operating states. Table 3 gives information on these issues, as they are treated in the articles of Table 1.  
The simplest problem, optimization at a single operating point (design) point is the most frequent in Table 3 (35.09%). The operating profile is approximated with a number of operating states in 18 articles, while an hourly profile is considered in 6 articles. In each state or hour, the various loads (propulsion, electric, and thermal) are given together with the duration, usually in number of hours in a period, e.g., in a round trip or in a typical year of operation of the ship. An alternative way is the consideration of the frequency of occurrence of the various loads in discrete values in a certain period, e.g., in a typical year of operation. Four articles follow this approach.  
Speed optimization of the ship in real time is performed in two articles under environmental conditions changing with time. The whole route is divided in \(n\) time steps.  
Figure 1: Distribution of number of articles to optimization types.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Operating Conditions**} & \multicolumn{2}{c}{**Articles**} \\ \cline{2-3}  & **Number** & **Percentage** \\ \hline Design point & 20 & 35.09 \\ Two–six operating states & 15 & 26.32 \\ More than six operating states & 3 & 5.26 \\ Hourly load profile for a certain period of time & 6 & 10.52 \\ Frequency of occurrence of various discrete loads & 4 & 7.02 \\ Speed optimization in real time & 2 & 3.51 \\ Continuous load profile for a certain period of time & 7 & 12.28 \\ \hline Total: & 57 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Consideration of operating conditions in optimization and number of articles.  
Instead of discrete loads, load profiles continuous in time for a certain period are considered in seven articles.  
Only six articles apply dynamic optimization (No. 14, 24, 32, 46, 48, and 56). One article (No. 20) applies dynamic simulation of the system, while the transient behavior of the system is simulated after design, in order to guarantee stable operation after transients.
# Pipeline Leakage localization Based on FBG Hoop Strain Sensors Combined with BP Neural Network
## 2 FBG Hoop Strain Sensor
### FBG Hop Strain Sensor Development  
Figure 1 shows the schematic diagram and the encapsulated FBG hop strain sensor. Generally, the sensor is composed of a fiber Bragg grating, two gripper tubes, two gripper blocks, a protective tube, a movable end and a fixed end. The mechanical structure of the FBG hop strain sensor has the function of strain sensitivity amplification, which is demonstrated in the previous research [15]. The encapsulated sensor shown in Figure 1b has a specially-designed pre-tension function to ensure tight connection to the curved surface of the pipe. The anchorage member is fixed onto the surface of the measured pipeline before installing the optic fiber component. Then, the fiber can be tensioned and tightly installed. The method of fixing the anchorage member on the pipeline may be adhesion or welding, depending on the pipeline material. For example, in the following model pipeline experiment, the anchorage member is adhered using epoxy resin.
# A 3D-Printed Enclosed Twist Dielectric Resonator Antenna with Circular Polarization
## 2 Dielectric Resonator Antenna Design and Parametric Studies
### Twist with Box  
The twist structure itself can have mechanical issues in implementation due to its stability and fragility. To overcome this, a dielectric box was added which has a relative permittivity \(\varepsilon_{r2}=2\) so as to be as transparent as possible, but also to ensure a good rigidity when using 3D printing, in terms of infill percentage, in order to obtain that permittivity. The schematic of the box and its dimensions are shown in Figure 6.  
To obtain the final dimensions of the box, a parametric study is conducted for its different dimensions. These results are shown in Figure 7. To reduce the probability of printing errors, we first fixed the height of the box at 1 mm higher than the four stacked rectangles, reaching a total \(h_{box}=25\) mm. Second, regarding the rotation of the inner boxes, an angle of \(\theta=20\) degrees was chosen based on the conducted parametric studies, giving a good compromise between the matching and the axial ratio when including an outer dielectric box. Finally, the box dimensions are set to \(x_{box}=y_{box}=19\) mm because of the resulting axial ratio achieved with these values.
Thus, a reliable method to generate \(N_{e}\) profiles would help build a better understanding of the global ionosphere at any given time. Many of the past methods use a top-down \"onion peeling\" method by way of performing an Abel inversion on the L1 and L2 excess phase [11; 12; 13; 14; 15; 16]. These methods, however, struggle to accurately measure the low-level ionosphere [17; 18; 19]. An improved Abel retrieval method that accounts for spherical non-uniformity has been developed, showing improved performance at E- and F-region altitudes [20]. More recently, an Abel inversion assisted with an Artificial Neural Network (ANN) background NmF2 model also showed improvement over the classical Abel inversion when compared to ionosonde NmF2 measurements [21]. To specifically improve GNSS-RO D- and E-region Electron Density Profile (EDP) estimates, a new bottom-up approach that uses the optimal estimation method to invert excess phase measurements while removing the F-region contribution has recently been developed [22; 23]. A preliminary validation of this method was provided in [23], comparing GNSS-RO derived profiles with measured ionosondes at Hermanus, South Africa, and they were found to be generally in good agreement for the E-region. The purpose of this research is to perform an in-depth comparison of the bottom-up method with ionosonde measurements and Faraday-International Reference Ionosphere (FIRI)-modeled profiles at four sites over a wide latitude range: Fortaleza, Brazil, in the equatorial region; Hermanus, South Africa and I-Cheon, South Korea, in the mid-latitude region; and Gakona, Alaska, in the polar region. GNSS-RO profiles are compared against ionograms within 2\({}^{\circ}\) and 30 min of the occultation that showed a clear E-region with no sporadic-E (\(E_{s}\)) present, and also with FIRI model-generated profiles. EDPs and virtual heights are analyzed to compare and contrast the different remote sensing methods and to provide insight into future improvements for both GNSS-RO and ionogram inversion techniques.
## 4 Security Notions
### Security Proof
\begin{array}{ll}E(x_{d}^{j})&=\sum\limits_{l\in ind_{i}[s]}\left(x_{(l,d)}^ {j}+r_{(l,d)}^{j}\right)\text{ mod }M(1\leq s\leq k)\\ &=(x_{(i,d)}^{j}+r_{(i,d)}^{j}+\sum\limits_{c\in ind_{i}[s]-j}\left(x_{(c,d)}^ {j}+r_{(c,d)}^{j}\right)\text{ mod }M\\ \end{array}  \tag{15}\]In the Equations (14) and (15), the adversary [PERSON] cannot solve the two equations at the d\({}^{\text{th}}\) timestamp and gain the exact \(x^{i}_{(j,d)}\) and even if he knows \(r^{j}_{(i,d)}=-r^{i}_{(j,d)}\), as the two equations have three unknown variables, so it is more impossible for [PERSON] to acquire \(x^{i}_{d}\) and \(x^{j}_{d}\) which ensures the scheme's security. Temporal aggregation  
\[ \begin{array}{ll}E(\sum\limits_{d=1}^{T}x^{i}_{d})&=\sum\limits_{d=1}^{T} \sum\limits_{l\in ind_{i}[s]}(x^{i}_{(l,d)}+r^{i}_{(l,d)})\bmod M(1\leq s\leq k )\\ &=(\sum\limits_{d=1}^{T}(x^{i}_{(j,d)}+r^{i}_{(j,d)})+\sum\limits_{d=1}^{T} \sum\limits_{c\in ind_{i}[s]-j}(x^{i}_{(c,d)}+r^{i}_{(c,d)}))\bmod M\\ &=((x^{i}_{(j,T)}+r^{i}_{(j,T)})+\sum\limits_{d=1}^{T-1}(x^{i}_{(j,d)}+r^{i}_{ (j,d)})+\sum\limits_{d=1}^{T}\sum\limits_{c\in ind_{i}[s]-j}(x^{i}_{(c,d)}+r^{ i}_{(c,d)}))\bmod M\\ &=(x^{i}_{(j,T)}+\sum\limits_{d=1}^{T-1}x^{i}_{(j,d)}+\sum\limits_{d=1}^{T} \sum\limits_{c\in ind_{i}[s]-j}(x^{i}_{(c,d)}+r^{i}_{(c,d)}))\bmod M\\ &\quad E(\sum\limits_{d=1}^{T}x^{i}_{d})\text{= }(x^{i}_{(i,T)}+\sum\limits_{d=1}^{T-1} x^{i}_{(i,d)}+\sum\limits_{d=1}^{T}\sum\limits_{c\in ind_{j}[s]-i}(x^{i}_{(c,d)}+r^{ j}_{(c,d)}))\bmod M\\ \end{array}  \tag{17}\]  
In the Equations (16) and (17), the two equations with four unknown variables make the adversary \(A\) impossible to acquire \(x^{i}_{(j,d)}\) or \(x^{j}_{(i,d)}\). Hence, the encrypted aggregation method can ensure the individual, fine-grained meterings indistinguishable security as long as there is at least one uncompromised user in its pairwise set. Our security properties are based on the randomness of modular addition and stream cipher which is used to blind the individual meterings.
# Study on the Vibration Effects of Cyclic Blasting on Bridge Structures under Construction
## 5 Discussion
### Implications of Findings  
Our research confirms that cyclic blasting significantly affects the stability of adjacent bridge structures, with the vibration velocity at the bridge deck decreasing more rapidly than at the pier base as the distance from the tunnel increases. This differential response highlights the need for careful consideration of the location-specific impact when planning blasting operations near sensitive structures.  
The recorded peak vibration velocities, all within the range of 0.05 to 0.25 cm/s, fall well below the threshold for material damage. This indicates that the structural integrity of the bridge components can be maintained even with frequent blasting operations, provided that the blasting parameters are carefully controlled. The identification of Scheme 2 as the optimal blasting scheme underscores the importance of optimizing blasting parameters to balance construction efficiency and safety. The chosen parameters (a hole depth of 1.9 m, a linear charge density of 0.33 kg/t, and a blasting delay of 50 ms) ensure that vibration impacts remain within safe limits while maintaining effective progress in tunneling operations.
# IoT's Tiny Steps towards 5G: Telco's Perspective
## 4 IoT in 5G Performance Requirements
### Data Rate  
**Data rate** is the most important evaluation factor for generations of wireless communication networks [6]. It is contemplated in two ways: (1) peak data rate--defined as the maximum achievable data rate by the user; and (2) minimum guaranteed user data rate--defined as the minimum experience data rate by the user [16]. New mobile technologies are primarily driven by users' needs for higher data rates, as discussed in [8; 16; 99; 215; 216]. The expected values in 5G networks are 10 Gbps for minimum peak data rate and 100 Mbps as minimum guaranteed user data rate [5]. High data rate requirements are mainly posed by xMBB related use cases [9] like hotspots [214] or dense urban areas where in 95% of locations and time experience data rate by user should be 300 Mbps in downlink and 60 Mbps in uplink [100]. High data rate is also important in some activity-based classes of IoT applications, as shown in Table 4. Medium (e.g., monitoring of parking spaces) to high (e.g., monitoring of home video) data rate is needed in cases of mMTC monitoring activities. High data rate can be achieved by using a millimeter wave (mmWave) spectrum to share multi-gigabit data in the surrounding environment (i.e., mMTC monitoring with high data rate requirement) and to recognize an object via cloud in real time to find the optimal driving strategy instantaneously for IoT--autonomous vehicles application [101]. Data rates generated during transmissions of tracked medical assets, transport fleets, and ticked point of sale (POS) terminals is low (e.g., medium importance for ticketing and tracking activities in mMTC). In addition, low data rates are needed in uMTC monitoring and controlling (e.g., monitoring of health condition, control of driving).
## 2 Materials and Methods
### Controller Design
Also, \(\hat{g}_{\phi}(x(t),t)\) is bounded such that \(\hat{g}_{\phi}(x(t),t)\in\mathcal{L}_{\infty}\) provided that \(\hat{\theta}(t)\) and \(x(t)\) are bounded \(\big{(}\)i.e., \(\hat{\theta}(t)\), \(x(t)\in\mathcal{L}_{\infty}\big{)}\)._  
Adding and subtracting \(Y(x(t),t,u(t))\dot{\theta}(t)\) in (11) and using (2), (12) and (13) yields a modified open-loop error system as  
\[ \begin{array}{lll}\dot{r}(t)&=&\chi_{\sigma}(t)+Y_{\phi}(x(t),t,u(t))\bar{ \theta}(t)+\hat{g}_{\phi}(x(t),t)u(t)\\ &&-\dot{x}_{d}(t)-\alpha k_{1}\mathrm{sgn}(r(t))-\alpha k_{2}r(t)-\alpha\beta e _{f}(t).\end{array}  \tag{14}\]  
Based on the open-loop error dynamics in (14), the control input is designed as  
\[u(t)\triangleq\hat{g}_{\phi}^{+}(x(t),t)\Big{[}-k_{3}r(t)+(k_{2}+\alpha\beta) e_{f}(t)+\dot{x}_{d}(t)\Big{]}, \tag{15}\]  
where \(k_{3}\in\mathbb{R}_{>0}\) is a user-defined control gain. Similarly, the parameter estimate law is designed as  
\[\hat{\theta}(t)\triangleq\mathrm{Proj}\Big{(}\Gamma Y_{\phi}^{T}(x(t),t,u(t))r (t)\Big{)}, \tag{16}\]  
where \(\Gamma\in\mathbb{R}^{q\times q}\) is a positive definite, diagonal gain matrix and \(\mathrm{Proj}(\cdot)\) denotes the smooth projection algorithm defined in [PERSON] et al. [41], which ensures that the estimate \(\hat{\theta}(t)\) stays within bounds defined in (3) for \(\theta(t)\). The controller from (15) is substituted into the open-loop error system in (14) to obtain a closed-loop error system as  
\[ \begin{array}{lll}\dot{r}(t)&=&Y_{\phi}(x(t),t,u(t))\bar{\theta}(t)-(k_{3}+ \alpha k_{2})r(t)\\ &&-\alpha k_{1}\mathrm{sgn}(r(t))+k_{2}e_{f}(t)+\chi_{\sigma}(t).\end{array}  \tag{17}\]  
The closed-loop error system in (17) has an SM term due to the implementation of the auxiliary error term \(e_{f}\). The SM term compensates for uncertain terms upper-bounded by constants in the dynamics. Due to the controller implementing the integral of the signum function, chattering is effectively reduced.
## 3 Results and Discussion
### Solar Fraction Analysis  
For a solar water heating system, the solar fraction, \(f\), is one of the major evaluation indicators, reflecting the contribution rate of solar energy to the hot water system. It is defined as the percentage of the heat collection of a solar collector to the total heat of the system, which can be calculated as Equation (2):  
\[f=Q_{s}/(Q_{s}+E_{c})\times 100\% \tag{2}\]  
where \(Q_{s}\) is the heat collected by the solar collector, MJ; \(E_{c}\) is the conventional energy consumption, MJ.  
In the present work, heat collected by solar collectors in the two systems, the electric boiler power consumption and the electricity consumption of the air source heat pump were measured. Based on the data, the average solar fractions for the three stages of the heating period, the transition season and the refrigeration period were calculated. The final calculation results are shown in Table 3.  
As shown in Table 3, the solar fraction of the two systems varied greatly in different seasons and had the same variation tendency, meaning it was the highest in the refrigeration period and the lowest in the heating period. The reason for this is that the most sufficient solar energy was obtained in the refrigeration period, so the solar collector could function adequately; however, in the heating period, sunshine was inadequate; thus, the electric boiler or the heat pump was mainly relied upon to heat the water.  
The results showed that the heat collected by the two solar collectors was similar in the same period, but the air source heat pumps consumed much less electricity than the electric boiler. By examining the comparisons of the solar fractions of the two systems, it can be noted that the value of the SA-ASHP system was much larger than that of the solar-assisted electric boiler water heating systems.
## 3 Quantifying Transmission Reserve Requirements in Real-Time Dispatch
### Probabilistic Collocation Method with Correlation Control  
The PCM is used to quantify the statistical features of the uncertain line flows in the probabilistic real-time dispatch sub-problem. This method is efficient for handling uncertainties with a much smaller number of selected scenarios, i.e., collocation points (CPs), than what is required from the traditional random sampling techniques (e.g., MCS), and is considered an extension of the generalized point estimate method (PEM) [32]. Fully explained in [33], the CPs selected are based on Gaussian quadrature (GQ) rule that could reach an algebraic accuracy of \(2N-1\) by \(N\) CPs. Furthermore, a simple correlation control technique is also combined with the basic PCM to deal with the correlation between input variables, i.e., wind power outputs of multiple wind farms. The procedure of evaluating the mean and standard deviation of the random line flows is summarized as follows:  
(1) Suppose \(\mathbf{\xi}_{n},\ \forall n\in\mathcal{N}\) is a standard normal vector. Generate \(N_{C}\) CPs and their corresponding weights of \(\mathbf{\xi}_{n}\) using the Multiprecision Computing Toolbox [34]. The \(s\)th CP is denoted as \(\mathbf{\xi}_{n}^{s},\ s\in\mathcal{S}=\{1,2,\cdots N_{C}\},\forall n\in\mathcal{N}\), whose elements are independent of each other.  
(2) Transform the CP \(\mathbf{\xi}_{n}^{s}\) to the correlated ones \(\mathbf{z}_{n}^{s}\) by  
\[\mathbf{z}_{n}^{s}=\mathbf{L}_{n}^{s},\ s\in\mathcal{S},\ \forall n\in\mathcal{N} \tag{27}\]  
where \(\mathbf{L}\) is a lower triangular matrix, obtained by Cholesky decomposition of the correlation coefficient matrix \(\mathbf{R}_{W}\)  
\[\mathbf{R}_{W}=\mathbf{L}\mathbf{L}^{T} \tag{28}\]  
(3) Map each \(z_{n}\) from the correlated standard normal domain into the original space for the input wind power \(\hat{W}_{n}\)  
\[\hat{W}_{n}=F_{n}^{-1}(\Phi(z_{n})),\ n\in\mathcal{N} \tag{29}\]  
where \(\Phi\) and \(F_{n}\) are the cumulative probability function (CDF) of \(z_{n}\) and \(\hat{W}_{n}\), respectively. \(F_{n}^{-1}\) is the inverse CDF of \(\hat{W}_{n}\). Thus, the CPs of \(\hat{W}_{n}\) at each time step \(t\) is obtained and denoted as \(\hat{W}_{n}^{ts}\) with the corresponding weight \(\omega_{s}\).  
(4) Solve the deterministic optimization problem of real-time dispatch at each wind power scenario \(\hat{W}_{n}^{ts}\) to yield the associated output scenario, i.e., \(\hat{P}_{g}^{t,s}\) and \(\hat{P}_{l}^{t,s}\), and then substitute Equations (20) and (21) for a new expression of the line flow statistics  
\[\overline{P}_{l}^{t}=\sum_{s\in\mathcal{S}}\omega_{s}\hat{P}_{l}^{t,s},\ t\in \mathcal{T},\ l\in\mathcal{L} \tag{30}\]  
Figure 1: Flowchart of the proposed risk-averse stochastic unit commitment (SUC) with transmission reserves.  
\[\sigma_{P_{l}^{l}}^{2}=\sum_{s\in\mathcal{S}}\omega_{s}\left(\hat{P}_{l}^{t,s} \right)^{2}-\left(\overline{P}_{l}^{t}\right)^{2},\,t\in\mathcal{T},\,l\in \mathcal{L} \tag{31}\]