esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Maximum Likelihood Estimator Formulated in Pole-Residue Modal Model
## 2 Structure of the Combined LSCF-MLE-PMM
### The MLE-PMM (2 rd Step)
#### 2.2.4 Estimation of the Uncertainty Bounds  
One of the main advantages of the ML-based algorithms is the possibility of estimating the confidence intervals for the identified modal parameters using the noise information measured together with the FRFs during the vibration tests. As shown in Reference [24], a good approximation of the covariance of the ML parameters \(\Theta_{MLE-PMM}\) is obtained by  
\[\text{Cov}(\left[Res\right],\left[LR\right],\left[UR\right],\lambda)\simeq \left[2\text{Re}\left(J_{i}^{H}J_{i}\right)\right]^{-1} \tag{25}\]  
with \(J_{i}\) standing for the Jacobian matrix evaluated in the last iteration of the Gaussian-Newton algorithm. By taking advantage of the structure of the Jacobian matrix and using the matrix inversion lemma [25], the covariance of the natural frequencies and damping ratios can be estimated independently from the covariance of the modal residues and from the covariance of the lower and upper residuals, as follows  
\[\text{Cov}(f_{n},\xi_{n})\simeq\frac{1}{2}M_{1}^{-1} \tag{26}\]  
The advantage of the parametrization used in Equation (8) is that, since all the linearization is implicitly performed during the computation of the Jacobian matrix, the variances of the natural frequencies and damping ratios are computed directly from the reduced normal matrix, \(M_{1}\). Therefore,the use of additional linearization formulas is avoided if one is only interested on these variances. However, if one is also interested on the variances of the real and imaginary parts of the poles, they need to be linearised around \(\text{Cov}(f_{n},\xi_{n})\) by means of the following linearization formulas  
\[\begin{split}\text{Var}(\text{Re}\left(\lambda_{m}\right))& \simeq 4\pi^{2}\begin{cases}f_{n_{m}}\\ \xi_{n_{m}}\end{cases}^{T}\text{Cov}(f_{n_{m}},\xi_{n_{m}})\begin{cases}f_{n_{ m}}\\ \xi_{n_{m}}\end{cases}\\ \text{Var}(\text{Im}\left(\lambda_{m}\right))&\simeq 4\pi^{2}(1-\xi_{n_{m}}^{2} )\begin{cases}1\\ \frac{-f_{n_{m}}\xi_{n_{m}}}{1-\xi_{n_{m}}^{2}}\end{cases}^{T}\text{Cov}(f_{n_ {m}},\xi_{n_{m}})\begin{cases}1\\ \frac{-f_{n_{m}}\xi_{n_{m}}}{1-\xi_{n_{m}}^{2}}\end{cases}\end{split} \tag{27}\]  
The covariance of the modal residues and the upper and lower residuals are estimated by  
\[\text{Cov}(\left[\text{\emph{Res}}\right]_{k},\left[\text{\emph{LR}}\right]_{k },\left[\text{\emph{UR}}\right]_{k})\simeq\frac{1}{2}\left(R_{k}^{-1}+R_{k}^{ -1}S_{k}M_{1}^{-1}S_{k}^{H}R_{k}^{-1}\right) \tag{28}\]  
Once the covariances of the real and imaginary parts of the modal residues are computed, the covariance of the mode shapes and of the modal participation factors are estimated by following the procedure described in Reference [26].
# The Design of an Energy Harvesting Wireless Sensor Node for Tracking Pink Iguanas
## 3 Device Design
### Energy Harvesting and Power Architecture  
As shown in Figure 3 the electric charges generated by the solar cell micro panels are accumulated in a super-capacitor. The charge of the super-capacitor is provided by the series of a harvester power management integrated circuit (BQ25570) and a super-capacitor charger (LTC3225). The BQ25570 can extract very small amount of power (down to microwatts) generated by the solar panels while the LTC3225 is designed to charge, at a selectable fixed output voltage, two super-capacitors connected in series. Automatic cell balancing prevents overvoltage damage.  
Figure 3: Node Architecture.  
Figure 2: Sensor board components placement.  
A software-controlled dual path DC-DC converter (LTC3106) manages the flow of charges from the super-capacitor to and from the battery. The super-capacitor is the primary power source. The LTC3106 can also charge the backup battery whenever an energy surplus is available. In absence of a load, the LTC3106 generates an output voltage up to 5 V from the input sources with a very low power stand-by consumption. When the solar power is unavailable, the LTC3106 switches to an alternative power sources such as the battery. Despite the device is ready for battery integration, for the specific Iguana tracking problem in the Galapagos Islands, the battery can not be used for environmental reasons.  
The output of the LTC3106 is affected by a ripple [16] that results in a power noise that propagates through the microcontroller and the RF oscillator. Eventually the ripple results in a noisy communication. This effect is counteracted by the low drop-out (LDO) voltage regulator (NCP170) placed at the output of the LTC3106.  
To better control energy consumption, the board is divided in four independently powered sections corresponding to the GPS, the RF front-end, the sensor set and the flash memory respectively. The controller can selectively power each section according to the application requirements and the available energy. Additionally, the power control unit makes use of a high drain line to supply power to the RF booster. Specifically, the high drain line can provide up to 1 A of current to the Skyworks SE2435L power amplifier in order to achieve the largest RF gain. It is worth to note that the maximum output power can be dynamically controlled by the CC1310 microcontroller through a DAC that can change the supply current of the SE2435L module. The rationale of this line split is the presence of the booster that in turn calls for an high current drain that allows long range transmissions.
# The Analysis of Service Convergence in an Optical Access Network +
## 4 Results and Discussion  
### Comparison between UFMC and GFDM  
In the UFMC signal, 100 sub-bands were used, each with 100 sub-carriers, resulting in a total bit vector length of 10,000. For the GFDM signal, 128 sub-carriers were configured, with 34 used for data transmission, creating a bit vector length of 13,600. The bit vector length varies with M-QAM modulation orders. For example, with 4-QAM, the bit vector length in the UFMC is 20,000 bits, and in GFDM, it is 27,200 bits. Figure 3 shows that when using 4-QAM, the BER is acceptable up to a power of \(-12\) dBm, with the UFMC having 10 erroneous bits and GFDM 25. Beyond this power, the BER decreases, reaching zero in both cases, ensuring high-quality transmission. The BER threshold of \(10^{-3}\) is essential for comparing different modulation schemes (4-QAM, 16-QAM, 1024-QAM), as it reflects efficient transmission with minimal errors. While higher-order modulations typically increase the BER, maintaining a \(10^{-3}\) BER indicates the system's ability to efficiently manage complex signals. The constellation diagrams confirm signal recovery at higher power levels without issues.  
To summarize the results obtained, Figure 4 presents a comparison of the BER curves for each modulation order in the UFMC and GFDM waveforms. It is notable that the UFMC consistently performs better than GFDM in all cases. However, the recovered signal only achieves a BER lower than 0.001 in 4-QAM and 16-QAM modulations; in other modulations, the signal experiences significant degradation, making it impossible to recover within the BER requirements. The superior performance of the UFMC is attributed to the use of filters for each sub-band, allowing each group of sub-carriers to have its own filter and corresponding equalization.  
In contrast, GFDM treats all sub-carriers as a single band, which increases the likelihood of data degradation. This issue could be mitigated by using a cyclic prefix (CP), as in OFDM, although this would increase the components in the power spectral density (PSD), reducing bandwidth efficiency. For higher-order modulations, both the UFMC and GFDM exhibit high BERs, which are unacceptable in a PON environment. As the modulation order increases, the BER also increases, as more modulation states are managed, and it becomes more likely for a signal to deviate from its original position in the constellation  
Figure 3: Rx optical power vs. bit error rate for 4-QAM.  
diagram. Modulation states are closer together compared to lower orders, where it is easier to distinguish the original position of a transmitted signal.
## 5 Analysis and Discussion
### Attribution Analysis of the Influencing Factors of Land Subsidence  
Based on the influence factors of surface displacement introduced above, a null hypothesis experiment can be performed for each influence factor \(H_{\text{i}}\). \(H_{\text{i0}}\): the factor has no influence on surface settlement. \(H_{\text{i1}}\): the factor influences the surface settlement. Analysis of n coherent point targets for surface settlement in the study area is as follows:  
\[\begin{bmatrix}P_{11}&\cdots&P_{14}\\ \vdots&\ddots&\vdots\\ P_{n4}&\cdots&P_{n4}\end{bmatrix}\begin{bmatrix}H_{1}\\ \vdots\\ H_{4}\end{bmatrix}+\begin{bmatrix}\varepsilon_{1}\\ \vdots\\ \varepsilon_{4}\end{bmatrix}=X \tag{2}\]  
In Equation (2) above, when \(H_{\text{i0}}\) cannot be rejected, \(P_{ni}=0\). Conversely, when \(H_{\text{i1}}\) cannot be rejected, \(P_{ni}=1\). Moreover, \(\varepsilon_{\text{i}}\) denotes the other influencing factors not analyzed, and Y is the real settlement at the n target points.  
For any coherent point target in the study area, the amount of subsidence is \(\text{v}_{\text{i}}\). First, according to the distance L between the point target and the subway line, the relationship between the settlement of point target \(\text{v}_{\text{i}}\) and distance L is compared point by point. When L \(<\) 100 m and \(\text{v}_{\text{i}}<-5\) mm/a, \(P_{n1}=1\); otherwise, the value is 0. If the point target is in the building development area, when the point target is in the building complex development area, \(P_{n2}=1\); otherwise, the value is 0. If the point target is in the urban village area, when the point target is in the urban village area, \(P_{n3}=1\); otherwise, the value is 0. Finally, If the trend of point target deformation has seasonal periodicity, \(P_{n4}=1\); otherwise, the value is 0. Therefore, whether the surface subsidence is influenced by single or multiple influencing factors can be analyzed based on the above information [32].  
In Figure 18, the green points represent the impact on surface deformation due to the construction of the subway, accounting for 5.5% of the PS points. The blue dots represent the surface deformation problem due to the development of building clusters, accounting for 29% of the PS points. The red points represent surface deformation due to the unsound foundations of self-built houses in urban villages, accounting for 53.5% of the PS points. The orange point represents the surface settlement caused by the parallel action of two driving factors. The yellow point represents the Dianchi International Exhibition Center due to its periodic deformation caused by the structure of the building itself, accounting for 12% of the PS points. The surface settlement caused by the unstable foundation of the self-built house in the urban village is the dominant factor.
# Clinical Validation of a New Enhanced Stent Imaging Method
## 3 Results
### Patient and Lesion Characteristics
#### 3.1.2 Quantitative Analysis  
The quantitative analysis included 93 lesions treated in a total of 76 patients with a mean age of 69.2 \(\pm\) 9.1 years, of which, 71.1% (\(n\) = 54) were men. The left anterior descending artery was treated in 63.4% (\(n\) = 59) and calcifications burden was moderate in 46.1% (\(n\) = 35) of cases. The baseline clinical, lesion, and stent characteristics of the study population are outlined in Table 2. A total of 98 stents were implanted with a mean diameter of 3.16 \(\pm\) 0.46 mm at a mean inflation pressure of 11 \(\pm\) 2 atm for which a mean  
\begin{table}
\begin{tabular}{l r} \hline \hline  & **N** \\ \hline Number of patients & 140 \\ Mean age (years) & 64.7 \(\pm\) 10.7 \\ Male gender & 101 (72.1\%) \\ Mean body mass index (kg/m\({}^{2}\)) & 29.1 \(\pm\) 5.9 \\ \hline
**Risk factors** & \\ Hypertension & 91 (65\%) \\ Dyslipidemia & 119 (85\%) \\ Diabetes mellitus & 54 (38.6\%) \\ Current smoker & 37 (26.4\%) \\ \hline
**Indication for PCI procedure** & \\ Stable angina & 45 (26.5\%) \\ Unstable angina & 46 (27.1\%) \\ Non-ST elevation myocardial infarction & 26 (15.3\%) \\ Other & \\ \hline
**Medical history** & \\ Peripheral artery disease & 38 (27.1\%) \\ \hline
**Treated coronary artery** & 157 \\ Number of lesions treated & 113 (72\%) \\ Calcifications & 62 (39.5\%) \\ Left anterior descending artery & 24 (15.3\%) \\ Left circumflex artery & 53 (33.8\%) \\ Right coronary artery & 8 (5.1\%) \\ Left main coronary artery & 10 (6.4\%) \\ Other & \\ \hline
**Stents** & \\ Number of stents implanted & 170 \\
**Coating** & **Scaffold** & **Struts Thickness (\(\mu\)m)** \\ Everolimus & \\  & PtCr & 81 \\  & PtCr & 74 \\ Sirolimus & \\  & CoCr & 60 \\  & CoCr & 80 \\  & 316L & 100 \\  & Mg & 150 \\ Paclitaxel & PtCr & 81 \\ TiNO & CoCr & 75 \\ BMS & & CoCr & 80 \\ \hline
**BMS** = bare metal stent; **CoCr** = cobalt chromium; **PtCr** = platinum chromium; **TiNO** = titanium nitric oxide; **316L** = 316L stainless steel. \\ \end{tabular}
\end{table}
Table 1: Qualitative cohort baseline clinical, lesion, and stent characteristics.  
chart diameter of 3.25 \(\pm\) 0.47 mm was expected. Post-dilation was performed in 82.7% (\(n\) = 81) of cases using a non-compliant balloon in 85.5% (\(n\) = 71).
# Rapid Parametric Modeling and Robust Analysis for the Hypersonic Ascent Based on Gap Metrics
## 3 LPV Modeling Based on Gap Metric  
The linear model is obtained using the Taylor expansion method, and it is expressed by [29]  
\[\Delta\dot{\mathbf{X}}=\mathbf{A}\Delta\mathbf{X}+\mathbf{B}\Delta\mathbf{U} \tag{21}\]  
where \(\Delta\mathbf{X}=\mathbf{X}-\mathbf{X}_{\text{eq}}\), \(\Delta\mathbf{U}=\mathbf{U}-\mathbf{U}_{\text{eq}}\), \(\mathbf{X}=[h,V,\alpha,\theta,q]^{T}\), and \(\mathbf{U}=[\phi,\delta_{e}]\); the subscript \({}_{\text{eq}}\) indicates the trim value; \(\mathbf{A}\) and \(\mathbf{B}\) denote the linear model matrices.  
In addition, we select the normalized height \(\delta_{h}\) and velocity \(\delta_{V}\) as the scheduling variable, and they are expressed by  
\[\left\{\begin{array}{l}\delta_{V}=\dfrac{2V-(V_{\text{max}}+V_{\text{min}}) }{V_{\text{max}}-V_{\text{min}}}\\ \delta_{h}=\dfrac{2h-(h_{\text{max}}+h_{\text{min}})}{h_{\text{max}}-h_{\text {min}}}\end{array}\right. \tag{22}\]  
where \(V_{\text{max}}\), \(V_{\text{min}}\), \(h_{\text{max}}\), and \(h_{\text{min}}\) are maximum velocity, minimum velocity, maximum height, and minimum height over the whole flight range, respectively, and the flight envelope is divided into \(N_{V}\times N_{h}\) grid points where  
\[N_{V}=\dfrac{\delta_{V_{\text{max}}}-\delta_{V_{\text{min}}}}{\Delta\delta_{ V}}+1,N_{h}=\dfrac{\delta_{h_{\text{max}}}-\delta_{h_{\text{min}}}}{ \Delta\delta_{h}}+1\]  
For each flight point, the linear model \(\mathbf{G}_{ij}\) where \(i=1,\dots,N_{V}\) and \(j=1,\dots,N_{h}\) can be obtained. Based on that, the gap metric of the linear model for any two operating points is defined as \(\delta_{\text{gap}}\big{(}\mathbf{G}_{i,j},\mathbf{G}_{i+1,j}\big{)}\), and the gap metrics of \(V\) and \(h\) need to satisfy the following condition:  
\[\left\{\begin{array}{l}\int\dfrac{\partial\delta_{\text{gap}}}{\partial \delta_{V}}d\delta_{V}\leq\varepsilon_{\text{max}}\\ \int\dfrac{\partial\delta_{\text{gap}}}{\partial\delta_{h}}d\delta_{h}\leq \varepsilon_{\text{max}}\end{array}\right. \tag{23}\]  
where \(\varepsilon_{\text{max}}\) denotes the expected threshold of the gap metric.  
Based on the gap metric strategy, the linear variable parameter model of the aerospace vehicle is formulated. All operation points throughout the whole envelope are built as  
\[\left\{\begin{array}{l}\dot{\mathbf{X}}=\mathbf{A}(\delta_{V},\delta_{h})\mathbf{X}+\bm {B}(\delta_{V},\delta_{h})\mathbf{U}\\ \mathbf{Y}=\mathbf{C}(\delta_{V},\delta_{h})\mathbf{X}\end{array}\right. \tag{24}\]  
The matching relation between the linear variable parameter model and the original model is required, resulting in the following design. As soon as the testing results are satisfactory, this simplified model will substitute the original model for implementing the control law design and integrated optimization for the aerospace vehicle. The flowchart of the rapid modeling process and simulation is shown in Figure 4.
## 1 Introduction
However, there are still critical gaps in harmonizing data generation, treatment, and processing derived from the different links of which they are composed and that permit the use of data analytics techniques to facilitate decision-making through the identification of patterns, trends, and factors that, in turn, allow the improvement of the agri-food chain performance in the departments and the country. Accordingly, the questions sought to be answered through this literature review are: What are the barriers (limitations) and enablers (drivers) for implementing technologies associated with Food Informatics in agri-food chains? What are the analogous barriers (limitations) and enablers (drivers) for implementing technologies associated with Food Informatics from a global view of the chain? This study reviews relevant cases about the barriers and enablers to implementing Food Informatics technologies to address the research question: unlocking agri-food chain innovation. Moreover, by applying a comprehensive approach to the entire chain instead of an individual approach to each of its links, this work will contribute to strengthening the body of literature on barriers and enablers to implementing Food Informatics technologies in agri-food chains. The paper is structured as follows: Section 2 provides the theoretical background of the research, Section 3 presents the methodology applied in this paper, Section 4 provides the analysis of the results at various levels, including metrics, knowledge structures, and identified barriers and enablers for the production, processing, and marketing links, and Section 5 discusses the results obtained. Finally, Section 6 includes the conclusions from this study and recommendations for future research.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 4 Proposed Elliptic Curve Signcryption Based Security Protocol for Smart Card (ECSSP-SC)
### Registration Phase  
Both \(USR\) and the \(SVR\) register themselves with the registration center \(RGC\) in the registration phase of the ECSSP-SC. It is significant to note that all the steps in the process of registration are executed over a secure channel. Furthermore, it has been assumed that no communication can be intercepted by third party or intruder over the secure channel. There can be many forms of secure channels depending on the usage, including personal visits and courier service.
# Research and Analysis of MEMS Switches in Different Frequency Bands
## 2 Brief Introduction to MEMS Switches  
MEMS switches can be classified into capacitive shunt switches [13], capacitive series switches [14], direct current (DC) shunt switches [15], and DC series contact switches [16] based on the method of contact. The DC contact switches (shunt and series) show better isolation and insertion loss at a wide frequency from DC to gigahertz (GHz). However, the isolation is related to the parasitic capacitance, and the insertion loss depends on the contact resistance. In addition, the dielectric materials are much harder than the contact materials, which are typically gold (Au) or other metals, and friction and adhesion happen frequently in the DC contact switches. The capacitive series switches present better isolation and insertion loss in the frequency range of several GHz (typically 1-10 GHz), and the capacitive shunt switches show better isolation and insertion loss at higher frequency ranges (typically tens of GHz). Furthermore, the current MEMS switches can also be classified into piezoelectric [17], electrothermal [18], magnetic [19], and electrostatic [20] driving according to their actuation mechanisms. Magnetic and electrothermal actuations exhibit the good performance of high-contact force and low-actuation voltage, but the switch time is a little longer, and they consume more power [21; 22]. Piezoelectric actuation shows fast switch time and low-actuation voltage, but parasitic actuation occurs frequently due to the mismatch in the coefficients of thermal expansion [21]. Electrostatic actuation MEMS switches are most commonly used for their advantages of high isolation, low insertion loss, and low power loss [21]. Shunt capacitive MEMS switches are the most researched because of their excellent electromagnetic performance at high frequency ranges.  
The typical schematic structure of a standard shunt capacitive MEMS switch is shown in Figure 1a [23; 24]. The switch contains a silicon substrate, a silicon nitride insulator, a metal bridge, a coplanar waveguide (CPW) signal line, and two CPW ground lines. The movable bridge moves toward the dielectric layer when the pull-down voltage is applied. Then, the RF signal in the CPW signal line is isolated through the metal bridge because of the large capacitance between the CPW signal line and the metal bridge after the contact between the dielectric layer and metal bridge. When the pull-down voltage is removed, the metal bridge pulls up due to its elastic restoring force. Then, the RF signal passes through the signal line because of the much smaller capacitance between the CPW signal line and the metal bridge in this state. Figure 1b shows the schematic structure of the DC contact switch. The switch is in the \"ON\" state when the cantilever is in the \"Down\" state, which is the opposite of shunt capacitive MEMS switch.