esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### SBAS-DInSAR Analysis
Displacement maps have been geocoded and superimposed on an amplitude SAR image of the investigated area, and are shown on the grid of coherent pixels that are common to both ASAR/ENVISAT and CSK SAR datasets. Displacements are measured along the satellite LOS: negative velocity values (yellow to red color) represent movements away from the satellite, whereas the blue-green colored pixels are representative of positive velocities. To ensure reliable results, we selected and displayed only coherent SAR pixels, namely, those characterized by a temporal coherence [52] greater than 0.65, and we discarded the low-coherent pixels from the following analyses. We note that temporal coherence [52] is a quality factor of the achieved SBAS-DInSAR LOS deformation time-series, which depends on the noise corrupting interferograms and the phase unwrapping errors that occur during the SBAS processing. Figure 4: LOS SBAS-DInSAR average displacement velocity maps, retrieved using the 2007–2010 ASAR/ENVISAT (**a**); and the 2014–2016 CSK (**b**) datasets, and shown on a common geocoded grid. The **white** rectangles represent the reclaimed area of Lingang New City, where the following comparative analysis has been performed. The black stars identify the locations of the spatial reference point.
## 5 Basic Concepts for the SMEP Method
### Selection by Compression Interval Pattern Rule
: _Let be \(l_{c,2k-1}\), \(l_{c,2k}\) and \(l_{c,2k+2}\) be consecutive compression intervals in \(C_{S^{c}}\) of \(S^{c}\) and let \(S_{c,i}\) and \(S_{c,j}\) be the beforehand selected sensor data in \(I_{2k-1}\) and \(I_{2k+2}\), respectively. Given \(B_{p}\), an 1-bit position sequence of \(S^{c}\), for \(i^{\prime}\), \(j^{\prime}\), \(p^{\prime}\) and \(q^{\prime}\) in the line interpolation error definition,_  
\(i^{\prime}\) _= (1 - 1)2^c + (BitSubstring(B_{p}\), \(c\), \(i\))_10_,_  
\(j^{\prime}\) _= (j - 1)2^c + (BitSubstring(B_{p}\), \(c\), \(j\))_10_,_  
\(p^{\prime}\) _= (p - 1)2^c + (BitSubstring(B_{p}\), \(c\), \(p\))_10_,_  
\(q^{\prime}\) _= (q - 1)2^c + (BitSubstring(B_{p}\), \(c\), \(q\))_10_._  
Moreover, for \(A\) and \(B\) in the definition,  
\[A = \frac{S_{c,t}-S_{c,tAk+1}}{j^{\prime}-p^{\prime}}\big{(}q^{\prime}-p^{ \prime}\big{)}+S_{c,tAk+1}\] \[B = \frac{S_{c,t}-S_{c,tAk+2}}{j^{\prime}-q^{\prime}}\big{(}p^{\prime }-q^{\prime}\big{)}+S_{c,tAk+2}\]  
Figure 7: Line interpolation error measure. For the proof of Lemma 5 refer to Appendix L.  
**Theorem 7**. : For selectable \(S_{p}\) and \(S_{q}\) in \(I_{2k}\) in the _line interpolation error_ definition (Definition 17),  
\[E_{p}=E_{4k+1}=\left|\frac{S_{c,i}-S_{c,4k+1}}{j^{\prime}-p^{\prime}}\big{(}q^{ \prime}-p^{\prime}\big{)}-\left(S_{c,4k+2}-S_{c,4k+1}\right)\right|\]  
and  
\[E_{q}=E_{4k+2}=\left|\frac{S_{c,i}-S_{c,4k+2}}{i^{\prime}-q^{\prime}}\big{(}q -p^{\prime}\big{)}-\ \left(S_{c,4k+2}-S_{c,4k+1}\right)\right|\]  
For the proof of Theorem 7 refer to Appendix E.  
Until now, in this section we have used the terminology _selection by minimum error_ rule without its exact formal definition. Now, the definition of this rule is formally defined as the below:  
**Definition 18**. : _Given the two selectable sensor data, \(S_{c,4k+1}\) and \(S_{c,4k+2}\), in \(I_{c,2k}\) in \(C_{S_{c}}\) of \(S^{c}\), the selection by minimum error rule is defined as a rule to select \(S_{c,i}\) as \(S_{c+1,k}\) in the \(c+1\) th round compression such that:_  
\[p=\underset{i\in\{4k+1,\ 4k+2\}}{arg\ min}\left(E_{i}\right)\]
## 4 Control Scenarios
### Total Flow Chart of Service
#### 4.2.4 **[Service Package #4]** Connection of the Integrated Control Center and the Safety Center  
Figure 11 and Table 8 show a package that provides services to enable rapid reporting and real-time monitoring to fire stations and control centers after a fire occurs. The following shows each step:  
1. **Fire occurrence reporting step**: The server of each building that has collected the fire occurrence information recognizes the status and immediately reports to the safety center if the building manager cannot respond;
2. **Fire occurrence monitoring and dispatch step**: The safety center receives the report, monitors the fire situation in real time, and dispatches fire engines immediately;
3. **Real-time fire monitoring step**: The integrated control center monitors the fire situation in real time and continuously provides information on the fire situation to the rescuer;
4. **Arrival and rescue step of fire** area: The rescuer arrives at the fire area and rescues the occupants through AR-based smart building disaster management service of smart phones and smart pads.
## 3 Control Strategy Description
### Control Strategy for the Motor  
For the studied parallel HEV, the motor is a key component that can be controlled to compensate for the disturbance torque during clutch slipping. In this situation, the motor, as the sole power supplier for the parallel HEV, must also be able to achieve accurate tracking of the target velocity. Here, we consider a general nonlinear system, as follows:  
\[\left\{\begin{array}{l}\dot{x}(t)=f(x(t),u(t),t),x\in\mathbb{R}^{n}\\ x(t)_{|t=t_{0}}=x_{0},t_{0}\leq t\leq t_{f}\end{array}\right. \tag{5}\]  
where \(x(t)\) stands for the system state, \(u(t)\) denotes the control input, and \(t_{0}\) and \(t_{f}\) are the initial time and terminal time, respectively. For a given general performance function \(J(u)\), the control input \(u(t)\) should satisfy the following condition:  
\[\min\left\{J(u)=F\Big{(}x\Big{(}t_{f}\Big{)},t_{f}\Big{)}+\int_{t_{0}}^{t_{f}} \big{[}L(x,u,t)\big{]}dt\right\} \tag{6}\]  
\[\text{s.t.}\ |u(t)|\leq U,t\in\Big{[}t_{0},t_{f}\Big{]}\]  
where \(F(x(t_{f}),t_{f})\) is the terminal index function, and \(L(x,u,t)\) is the Lagrange function. \(U\) is the maximum of control input. Since the control input is independent of the Lagrange function, the above equation can be transformed into:  
\[\min\left\{J(u)=F\Big{(}x\Big{(}t_{f}\Big{)},t_{f}\Big{)}+\int_{t_{0}}^{t_{f}} \big{[}L(x,t)+u^{2}\big{]}dt\right\} \tag{7}\]  
The appropriate Lagrange function with positive constants \(M_{0}\) and \(\alpha\) can be defined by:  
\[L(x,\ t)\ =\ M_{0}\ \exp(-\alpha t) \tag{8}\]  
On the basis of this definition, we can further deduce and obtain the equation as follows.  
\[\begin{array}{l}\int_{t_{0}}^{t_{f}}L(x,t)dt=\int_{t_{0}}^{t_{f}}M_{0}\exp( -\alpha t)dt\\ =\frac{M_{0}}{\alpha}\Big{[}\exp(-\alpha t_{0})-\exp\Big{(}-\alpha t_{f}\Big{)} \Big{]}\\ \leq\frac{M_{0}}{\alpha}\Big{[}1-\exp\Big{(}-\alpha t_{f}\Big{)} \Big{]}\\ <\frac{M_{0}}{\alpha}\end{array} \tag{9}\]  
According to Equations (6), (7) and (9), the following inequality can be derived by  
\[F\Big{(}x\Big{(}t_{f}\Big{)},t_{f}\Big{)}+\Big{(}t_{f}-t_{0}\Big{)}U^{2}+\int _{t_{0}}^{t_{f}}M_{0}\exp(-\alpha t)dt\leq\min\left\{J(u)\right\} \tag{10}\]  
That is, if the control input \(u(t)\) is derived from Equation (8) and satisfies corresponding constraints, a smaller performance index than that of Equation (10) can be obtained. Considering the complexity of the motor control problem during the clutch slipping process, an SMC strategy was selected to solve this problem.  
Figure 3: Block diagram of proposed coordinated control strategy during clutch slipping.
# Biomethane Potential Test: Influence of Inoculum and the Digestion System
## 2 Materials and Methods
### Digestion Systems
#### 2.1.1 Hohenheim Biogas Yield Test (D-HBT)  
D-HBT consisted of 100 mL syringes, which are placed in a continuously rotating drum with 129 places for syringes placed in an incubator [22]. The substrates in the syringes are agitated by the rotation of the drum at a speed of 1.2 rpm. The volume was measured manually by reading from a scale on the syringes. The methane percentage was measured by an infrared-spectrometric methane- sensor (\"Advanced Gasmitter\", Pronova Analysetechnik, Berlin, Germany) [23]. The sensor was calibrated directly before and after each measurement with a calibration gas mixture of 40% CO\({}_{2}\) and 60% CH\({}_{4}\) (G325792, Westfalen AG, Munster, Germany). The tests were performed in two variants: in dry gas (D-HBT1) and in wet gas (D-HBT2). For dry gas measurements in D-HBT1 the gas was dried with an absorbent (SICAPENT(r), Merck, Darmstadt, Germany). For the wet gas measurement, the water content of the gas was considered--by a correction of the gas volume \(V_{0}\) (mL) according VDI 4360 [5] Equation (1).  
\[V_{0}=\frac{V\cdot(p-p_{W})\ T_{0}}{p_{0}\cdot T} \tag{1}\]  
where \(V\) (mL) and \(p\) (hPa) are the measured volume and pressure, \(p_{W}\) (hPa) is the water vapor pressure at operation temperature \(T\) (K), and \(p_{0}\) (hPa) and \(T_{0}\) (K) the standard pressure and temperature (1013.3 hPa, 273.15 K).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Code** & **System** & **Volume L** & **Agitation** & **Gas Quality** \\ \hline D-HBT & Hohenheim biogas yield test & 0.1 & Continuous & Infrared \\ D-BFT & Bergedor fermentation test & 1.5 & Frequently & Infrared \\ D-EUD & Eudiometer & 1.5 & Continuous & Infrared \\ D-AMP & Automatic methane potential test & 0.5 & Continuous & None \\ \hline \hline \end{tabular}
\end{table}
Table 2: Digestion systems used for biochemical methane potential test: digestion volume, frequency of agitation and method for determining the gas quality.  
Figure 1: Scheme of Hohenheim biogas yield test (D-HBT), Bergedorf fermentation test (D-BFT), Eudiometer (D-EUD), and Automatic methane potential test (D-AMP). Reproduced with permission from VDI 4630 [5].
## 2 Structures and Calculation Methods  
In Wyckoff coordinates, LiMnPbSn-type quaternary Heusler alloy VFeScZ possibly has three different structures: Type-I, V atom is located at (0, 0, 0), Fe atom is at (0.5, 0.5, 0.5), Sc atom is at (0.75, 0.75, 0.75), and \(Z\) (Z = Sb, As, P) is at (0.25, 0.25, 0.25); type-II, the coordinates of the V and Sc atoms are swapped; and type-III, the coordinates of the Fe and Sc atoms are swapped based on the  
Figure 1: Schematic density of states of (**a**) semiconductor, (**b**) half-metal, and (**c,d**) spin gapless semiconductor. “\(\uparrow\)” and “\(\downarrow\)” indicate spin-up and spin-down channel, respectively.  
type-I configuration. In our calculations, the initial crystal constant of 6.0 A in VFeScZ is adopted by comparing with the structure of Heusler alloy CoFeCrZ (Z = Sb, As P) [14]. After geometry optimization, the lowest-energy type-I, -II, and -III structures of VFeScZ are obtained. In the lowest-energy state, the structure, magnetism, and DOS are detected. To reveal the pressure effect in VFeScZ alloy, the geometry optimization is implemented once for every 5 or 10 GPa additional pressures until the alloy becomes an ordinary nonmagnetic metal. Usually, more pressure detection is done near the transformation point in VFeScZ for accurately describing the electronic transition behavior.  
All tasks are done by using the CASTEP code within the density functional theory (DFT). PBE GGA functional is adopted to describe the exchange-correlation interaction [21]. In all calculations, the Vanderbilt-type ultrasoft pseudopotentials [22] and the valence electron configurations of V (3d\({}^{3}\)4s\({}^{2}\)), Fe (3d\({}^{6}\)4s\({}^{2}\)), Sc (3d\({}^{1}\)4s\({}^{2}\)), P (3s\({}^{2}\)3p\({}^{3}\)), As (4s\({}^{2}\)4p\({}^{3}\)), and Sb (5s\({}^{2}\)5p\({}^{3}\)) are used. In geometry optimization of VFeScZ bulks, the ferromagnetic states in all alloys are initially assumed and the options of the spin-polarization are checked. In the self-consistent field (SCF) task, the 6 \(\times\) 6 \(\times\) 6 mesh of special \(k\)-points in the Brillouin zone, the SCF convergence criterion of 1 \(\times\) 10\({}^{-6}\) eV/atom, and energy cutoff of 380 eV is applied, respectively. To satisfy the criterion of total energy convergence, the maximal force convergence criterion of 0.02 eV/A is chosen when the positions of atoms are relaxed. The total energy convergence criterion of 1 \(\times\) 10\({}^{-5}\) is used for all calculations. In our work, all technical parameters were tested carefully to ensure the accuracy of the results. By GGA + PBE scheme, quaternary Heusler alloy CoFeMnSi was tested. The crystal constant and the magnetic and electronic properties are in accordance with the results from Ref. [7]. So we think the GGA + PBE scheme adopted in the present work is reasonable for dealing with the VFeScZ system.
# KQI Performance Evaluation of 3 GPP LBT Priorities for Indoor Unlicensed Coexistence Scenarios
## 4 Performance Indicators
### Background Traffic User Performance Indicators  
In contrast to these, the User Performance Indicators (UPI) are outlined. Such metrics are measured to quantify its performance of the background traffic generated by the users. These are measured in this context from the background FTP traffic: mean throughput, mean delay and mean jitter measured at IP level. Since the MAC to PHY delay is fixed to a value of 2 ms, the resulting delay is computed as the time between the first IP packet of the file was sent until its arrival in the receiver. Finally, the throughput is calculated as the IP packet size divided by its delay [30].
## 2 Detection Algorithms
### Signal Model  
The received beat signal in the baseband domain can be expressed as  
\[y[n]=\sum_{k=1}^{K}A_{k}\cos(\frac{2B}{T_{\text{sweep}}}\tau_{k}nT_{s}-\frac{B} {T_{\text{sweep}}}\tau_{k}^{2}+\Delta\phi_{\text{PN}}[n])+w[n],\quad n=0,1, \ldots,N-1, \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Detection Technique** & **Phase Noise Handling** &
\begin{tabular}{c} **Hardware** \\ **Consideration** \\ \end{tabular} & **Validation** \\ \hline
[13] & \
\begin{tabular}{c} Deep CNN-based peak \\ detection \\ \end{tabular} &
\begin{tabular}{c} Requires training \\ dataset \\ \end{tabular} & Not addressed & Simulated only \\ \hline
[14] & Adaptive CFAR & Moderate resilience & Not addressed & Simulated \\ \hline
[22] &
\begin{tabular}{c} Subspace detection \\ (e.g., MUSIC, MPM) \\ \end{tabular} & Limited to ideal noise & No & Simulation \\ \hline
[16] &
\begin{tabular}{c} Fast FFT + local peak \\ estimation \\ \end{tabular} & Poor at low SNR & FPGA-targeted & Hardware tested \\ \hline
[29] &
\begin{tabular}{c} Phase noise modeling \\ in FMCW \\ \end{tabular} & Yes (analytical) & No & Model-based \\ \hline
**This Work** & \begin{tabular}{c} **LTSP + CA-CFAR +** \\ **SVD + MPM** \\ \end{tabular} & \begin{tabular}{c} **High resilience under** \\ **phase noise** \\ \end{tabular} & \
\begin{tabular}{c} **FPGA (BRAM, LUT,** \\ **DSP estimated)** \\ \end{tabular} &
\begin{tabular}{c} **Simulation +** \\ **measurement** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with related works in FMCW radar peak detection.  
where \(K\) denotes the total number of propagation paths contributing to the received beat signal. These paths may include both direct line-of-sight (LOS) and multipath components. Each term in the summation represents a reflected signal corresponding to a distinct propagation path, where \(A_{k}\) is the amplitude of the \(k\)-th path, \(\tau_{k}\) is its associated time delay, and \(\Delta\phi_{\text{PN}}[n]\) is the phase noise sampled at time instance \(n\), \(N\) is the number of samples per chirp, \(B\) is the chirp bandwidth, \(T_{\text{sweep}}\) is the chirp sweep time, \(T_{s}\) is the sampling time, \(w[n]\sim\mathcal{CN}(0,\sigma^{2})\) is complex white Gaussian noise. The summation over \(K\) thus captures the aggregate effect of multiple reflections in the received signal.
## 2 Materials and Methods
### Curing and Aging Methods  
Composites were subjected to different processes:  
1. Curing composites. For the physical and chemical attack tests, specimens were subjected to two different curing processes: a) standard curing (SC) for 28 days in a humid chamber at 20 \(\pm\) 1 \({}^{\circ}\)C and 90% relative humidity; b) to accelerate pozzolanic reactions, accelerated curing (Acc) was carried out (7 days at 20 \({}^{\circ}\)C and 21 days of immersion in water at 55 \({}^{\circ}\)C);  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **CEM 1 52.5R** & **Limestone Filler** & **Pozzolan** & **Siliceous Sand** & **AR Fiber** & **Superplastizicer** & **Mixing Water** \\ \hline C & 1153.7 & 384.6 & 0 & 1025.5 & 93 & 15.4 & 529.8 \\ \hline GFA60 & 461.5 & 153.8 & 923 & 1025.5 & 93 & 12.3 & 531.6 \\ \hline GFA50+SSF10 & 461.5 & 153.8 & 769.1 + 153.8 & 1025.5 & 93 & 20 & 527.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dose of the manufactured GRC mortars (values in g).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Oxide** & **SiO2** & **Al2O3** & **Fe2O3** & **CaO** & **MgO** & **SO3** & **K2O** & **Na2O** & **LOI \({}^{*}\)** \\ \hline CEM & 22.05 & 5.44 & 3.64 & 62.53 & 2.43 & 3.75 & 1.42 & - & 1.6 \\ I/52.5R & 40.94 & 24.65 & 13.59 & 9.83 & 1.59 & 1.60 & 1.40 & 0.34 & 2.44 \\ DSF & 90.77 & 0.46 & 4.53 & 0.78 & 0.23 & 0.02 & 0.37 & 0.21 & 3.43 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of Portland cement and pozzolanic materials (%). FA= fly ash; DSF: densified silica fume. \({}^{*}\) indicates loss on ignition.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Mean Particle Diameter (\(\mu\)m)** & **d (0.1) \(\mu\)m** & **d (0.5) \(\mu\)m** & **d (0.9) \(\mu\)m** \\ \hline FA & 29.92 & 2.30 & 18.55 & 74.32 \\ GFA & 10.38 & 1.46 & 8.19 & 22.25 \\ DSF & 76.67 & 7.95 & 34.82 & 199.55 \\ SSF & 23.47 & 2.45 & 18.36 & 52.57 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Particle size parameters of fly ash before (FA) and after milling (GFA), and silica fume before (DSF) and after the sonication process (SSF).  
2. Accelerated aging. Specimens were previously cured under standard conditions and placed in a thermostatic water bath at 55 \({}^{\circ}\)C for 28 days (SC+Ag). The equivalence in predictive durability terms is, according to [PERSON] et al. [24], 100 days at 20 \({}^{\circ}\)C and in a humid environment (UK conditions) for each aging day, which would amount to about 8 aging years under standard conditions.
## 3 An Improved Objective Function Based on SVDM
### The Improved Objective Function
Therefore, when the modeled damage factors converge to the actual damage factors, \(\mathbf{\mu}\rightarrow\overline{\mathbf{\mu}}\), then the modeled natural frequency converges to the measured natural frequency, \(\omega_{i}(\mathbf{\mu})\rightarrow\overline{\omega}_{i}\), and \(-\left|h_{aq}(\mathbf{\mu},\overline{\omega}_{i})\right|\rightarrow\min\). In the traditional objective function, the frequency error term \(\left(\frac{\omega_{i}(\mathbf{\mu})-\overline{\omega}_{i}}{\overline{\omega}_{i }}\right)^{2}\) also reaches its minimum value when \(\mathbf{\mu}\rightarrow\overline{\mathbf{\mu}}\). Therefore, the objective function can be modified by replacing \(\left(\frac{\omega_{i}(\mathbf{\mu})-\overline{\omega}_{i}}{\overline{\omega}_{i }}\right)^{2}\) with \(-\left|h_{aq}(\mathbf{\mu},\overline{\omega}_{i})\right|^{2}\). According to these two characteristics, an improved objective function of Equation (5) is proposed. It is obtained by replacing the mode shape and frequency parts of the theoretical FE model with the frequency response, as discussed above. \[f(\mathbf{\mu})=\alpha_{\mathbf{\varphi}}\sum_{i=1}^{n_{\mathbf{\varphi}}}(1-MAC\big{(} \mathbf{H}_{q}(\mathbf{\mu},\overline{\omega}_{i}),\overline{\mathbf{\varphi}}_{i}\big{)} )-\alpha_{\omega}\sum_{i=1}^{n_{\omega}}\sum_{a=1}^{n_{a}}\left|h_{aq}(\mathbf{\mu },\overline{\omega}_{i})\right|^{2} \tag{5}\]  
The frequency response \(h_{aq}(\mathbf{\mu},\overline{\omega}_{i})\) in Equation (5) can be quickly computed using the SVDM, which is described in the next section.
# Privacy and Security in Digital Health Contact-Tracing: A Narrative Review
## 4 Discussion
### Knowledge and Awareness  
The studies showed that understanding and awareness significantly influence individuals' decisions to accept or reject these apps. Concerns were linked to a perceived lack of information [27], transparency [50], and clarity [82] in the development and implementation of these apps. People felt that the information given by authorities was insufficient to address their worries, emphasising the need for transparency. Misinformation was a significant issue in communities where misconceptions about data privacy arose.  
Among other concerns, government communication strategies played an essential role. The lack of consistent information was a major concern. Clear and consistent information was essential to build trust in the government. A lack of a communication strategy is major issue in establishing trust towards the government and assuring individuals' safety for using contact-tracing applications.  
**Recommendations for government.** Governments should implement nationwide educational campaigns to dispel myths and misinformation about digital contact-tracing apps, using multimedia platforms to ensure messages reach diverse audiences, including vulnerable populations. This is because misinformation and a lack of awareness are major barriers to adoption; governments must take a proactive role in correcting misconceptions and providing clear, consistent information. Governments should also ensure transparency in app development and implementation, including open-source code and independent security evaluations, to address concerns about secrecy and foster informed decision-making. This is because transparency builds public confidence, empowering citizens with the knowledge necessary to make informed choices about using these apps.  
**Recommendations for app developers.** App developers should integrate clear, user-friendly explanations within the app about how data are collected, stored, and used. This is because transparent communication promotes trust and combats misinformation. Developers are responsible for addressing user concerns by providing accessible, accurate information directly through the app interface, ensuring users feel informed and secure. App developers should also embed features like frequently asked questions, interactive tutorials, and updates to address misconceptions and provide consistent, reliable information. This is because developers can counter misinformation by equipping users with credible, easily accessible knowledge. These features empower users to make informed decisions, thereby promoting app adoption and sustained use.