esa-sceva/satcom-chunk-collection huggingface dataset:
# Source/Load-Pull Noise Measurements at K\({}_{\text{a}}\) [PERSON]
## 4 Discussion  
As anticipated in the Introduction, the proposed black-box characterization approach blends together several ideas which have been around for some time, namely, the use of electro-mechanical switches rather than of tuners; the exploitation of both forward and reverse modes; the combination of measurements at multiple frequencies into single-frequency extractions. Out of these, the last is probably the most effective in producing robust results.  
In this respect, it is worth reappraising the observation, already made in the Introduction, that typical black-box approaches require several physical terminations in order to allow an extraction of the four noise parameters at one frequency. In the light of the presented linearization-based technique, that statement loses validity in the present case: mathematically, the adopted first-order approximation of the noise correlation matrix allows for as few as one single physical termination. (Indeed, the Authors have found that reasonable extractions can be obtained with one physical termination only, although at the cost of increasing \(B_{L}\) significantly.)  
Leaving now aside the mathematical foundation of the approach, the present implementation of the test bench also deserves some comments. In the Authors' opinion, the results presented in Section 3 are very encouraging. However, significant room for improvement can be found.  
First of all, the present version of the test bench, mainly set up as a proof of concept, has not been optimized yet with respect to the hardware side. In particular, the coaxial cables connecting the switches with the bias-T's are of mediocre quality, which results in significant loss in key paths of the measurement chain. As can be seen in Figure 5, the maximum magnitude of the source reflectances synthesized at section \(s\)-\(D1\) is rather poor, as due to the excessive loss of the input cable. On the other hand, the loss of the output  
Figure 7: Noise figure of the mixed-technology LNA from [22]. The measurement refers to the actual, realized amplifier. The simulation is obtained by using the black-box characterization to simulate the first stage. The other stages are in the 100 nm technology and maintain their previous models.  
cable impacts negatively on BLK2 and, ultimately, on the uncertainty associated with the single noise measurements. It is thus imperative to reduce in length and improve in quality those two cables in the future.  
Moreover, it should be further investigated whether or not the implemented algorithm for tuning the operating point can ensure sufficient stability of the DUT's behavior. If so, the online measurement of the DUT's small-signal parameters could be spared, with obvious beneficial effects on the setup architecture. For instance, it would be possible to reallocate section _s-P1_ to one further termination. Alternatively, although at the cost of manually switching the source terminations, the input switch could be eliminated altogether, and the input cable with it.
## 2 Dynamic Modeling of HDFRMS
### Mechanical Subsystem Model  
Link 1 and 2 of HDFRMS are rigid. However, the third link, like a slender beam, is flexible. Its vibration will occur because the end-effector with heavy payload and manipulator system have large overall motions. Only the transverse vibration is considered, except for the axial deformation and shear deformation. Therefore, the Euler-Bernoulli beam theory is sufficient to describe the flexible deformation and vibration of the third link. Concretely, the dynamic modeling of HDFRMS consists of two parts: mechanical subsystem modeling and hydraulic subsystem modeling. We derive the dynamics of the mechanical subsystem using vibration analysis result-based AMM via [PERSON]'s equation. The following assumptions are made based on the actual situation. Table 1 shows the nomenclature of the mechanical subsystem. **Assumption 1**. : _The payload is held tightly by the manipulator end-effector, and their contact is static._  
**Assumption 2**. : _The mass of actuators focuses on joints of the manipulator._  
Let \(XOY\) and \(X_{i}OY_{i}\) be the inertial Cartesian coordinate and the moving coordinates fixed on the robotic manipulator, respectively. Figure 2 illustrates the schematic diagram of four primary coordinate systems for HDRMES. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Definition** & **Symbol** & **Definition** \\ \hline \(L_{i}\) & length of \(i\)th link & \(r_{1}\) & upper surface radius of link 1 \\ \(\theta_{i}\) & angle of \(i\)th joint & \(r_{2}\) & lower surface radius of link 1 \\ \(m_{i}\) & mass of the \(i\)th actuator & \(M\) & mass of payload \\ \(\rho_{i}\) & mass density per unit length of \(i\)th link & \(EI\) & elastic stiffness \\ \(L_{11}L_{21}\) & installation location of the first & \(\omega(r,t)\) & elastic deformation of flexible link 3 \\ \(L_{22}L_{31}\) & hydraulic cylinder & \(\Phi_{j}(r)\) & \(j\)th mode shape function of flexible link 3 \\ \(g\) & acceleration of gravity & \(q_{j}(t)\) & \(j\)th modal displacement of flexible link 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notation, frequently used symbols of mechanical subsystem. Figure 1: Model of HDFRMS. Considering AMM, we find the elastic displacement \(\omega(r,t)\) of the third link as:  
\[\omega(r,t)\ =\ \sum_{j=1}^{n}\Phi_{j}(r)q_{j}(t) \tag{1}\]  
The higher order modes are ignored, and only previous order modes are considered in practical engineering application, because they can make greater impacts on system performances. When flexible link 3 is conceived as cantilever beam model, one end of this link is supported simply, and the other end is free [6]. Therefore, we can obtain the elastic displacement  
\[\omega(r,t)\ =\ \Phi_{1}(r)q_{1}(t)+\Phi_{2}(r)q_{2}(\mathrm{T}) \tag{2}\]  
\[\Phi_{j}(r)\ =\ sh\beta_{j}r\ +\ \zeta_{j}\sin\beta_{j}r \tag{3}\]  
where \(\zeta_{j}\ =\ sh\beta_{j}L_{3}/\sin\beta_{j}L_{3}\), \(\beta_{j}\ =\ (j+0.25)\pi/L_{3}\), \(j\ =\ 1,2\). \(r\) is position of any point on the third link, and \(0\ \leq\ r\ \leq\ L_{3}\).
# Seismic Risk Analysis of Existing Link Slab Bridges Using Novel Fragility Functions
## 4 Failure Rate and Result Discussion
### Failure Rates Associated with Different Damage Extension Scenarios  
In this section, the analysis of the vulnerability and risk is pushed a little bit further. Information about the extension of the failure mechanisms identified on each structural element is indeed provided, first in terms of fragility curves (derived according to Equations (6) and (7)) and then in terms of annual rates of exceedance.  
Results are presented separately for the three main families of structural elements, i.e., Bearings (Figure 12), Links (Figure 13), and Piers (Figure 14).  
Different percent intervals of damage extent are considered. Besides the fragility curves of each percentage range, the curve obtained by summing up all the single curves is superimposed with a solid black line, and this corresponds to the classic fragility curve of each element already presented in Figure 8 of Section 4.1. The only difference between the classic curves presented in Figure 8 and the current one in Figures 12-14 is in the lognormal assumption used only to compute the ones in Figure 8. However, apart from the different degrees of smoothness, these curves carry the same identical information on the global damage probability of single structural elements.  
The annual rates of exceedance are presented in Figure 15 in separate charts, one dedicated to each structural element. Damage and Collapse failure rates are compared using the usual legend of colors and symbols.  
The main conclusions drawn from these outcomes are listed below:  
* regarding the failure rates of the link bars, it can be observed how the fairly high level of collapse risk previously identified in Section 4.1 actually involves a very small number of elements (less than 25%), and this information notably helps to adjust this result interpretation, which, based on the sole numbers provided in Figure 9, may appear greater than it really is.
* It can be seen how the risk decreases rapidly as we look at higher damage extent scenarios, e.g., the MAF of having ruptures on 25-50% of links is one order of magnitude lower than the MAF of having less than 25% of elements failed.
* Similar comments can be made for other structural elements.
## 3 Assessment Procedure in Terms of Spectral Accelerations According to ADRS Spectrum  
The capacity-demand assessment procedure can be expressed through the ADRS spectrum [12; 26]. For each limit state, the spectrum \(S_{a}-S_{De}\) is defined by the means of the relationship \(S_{De}(T)=S_{a}(T)(T/2\pi)^{2}\). As regards the capacity, it is necessary to represent the characteristic points of the behavioral curve of the structure, in the ADRS plane. Of these points, it is necessary to know the abscissa, which is the displacement \(d_{LS}^{*}~{}=~{}d_{LS}/\Gamma\).  
It is necessary to distinguish between cases \(T^{*}>T_{C}\) and \(T^{*}<T_{C}\). If \(T^{*}>T_{C}\) 41, the capacity in terms of spectral acceleration relative to the limit state considered can be obtained through the following relationship:  
\[S_{aSL}~{}=~{}d_{LS}^{*}{\omega_{0}^{*}}^{2} \tag{18}\]  
The demand is represented by the spectral acceleration provided by the code, for the specific limit state, in the case of the equivalent SDOF system with the equivalent period of vibration \(T^{*}\).  
For the assessment procedure, the inequality \(S_{als}\geq S_{a}(T^{*})\) must be satisfied.  
If \(T^{*}<T_{C}\) and \(q>1\), according to the equality energy criterion, there is a different procedure to evaluate the capacity that leads to the anelastic spectrum whose parameters are listed below:  
\[F_{IS}^{*}~{}=~{}\frac{m^{*}S_{a}(T^{*})}{q_{IS}} \tag{19}\]  
\[q_{IS}~{}=~{}1~{}+~{}(\mu_{IS}~{}-~{}1)\frac{T^{*}}{T_{C}} \tag{20}\]  
\[S_{aSL}~{}=~{}q_{IS}\frac{F_{IS}^{*}}{m^{*}} \tag{21}\]  
If \(T^{*}<T_{C}\) and \(q\leq 1\), it results:  
\[F_{SL}^{*}~{}=~{}m^{*}S_{a}(T^{*}) \tag{22}\]  
\[S_{aSL}~{}=~{}\frac{F_{IS}^{*}}{m^{*}} \tag{23}\]  
\[m^{*}~{}=~{}\sum_{k=1}^{n}m_{k}F_{k} \tag{24}\]  
The checking is verified when the inequality \(S_{als}\geq S_{a}(T^{*})\) is satisfied.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **GMRFs** & **SMRFs** & **OMRFs** \\ \hline a5 & 1.0606602 & 0.976295 & 97.159963 \\ b5 & 0.6787599 & 1.027818 & 25.416893 \\ a6 & 1.0528759 & 0.975839 & 1.8666626 \\ b6 & 0.7200734 & 1.030732 & \(-\)0.429104 \\ a71 & 1.16745
# A Novel Alternating \(\mu\)-Law Companding Algorithm for PAPR Reduction in OFDM Systems
## 2 System Model
### Orthogonal Frequency Division Multiplexing (OFDM) Systems  
Figure 1 shows the basic architecture of the OFDM systems. First, the input data are modulated by quadrature amplitude modulation (QAM); then, serial signals are generated and converted into parallel signals \(\mathbf{X}=[X_{0},X_{1},\dots,X_{N-1}]^{T}\). Turning \(\mathbf{X}\) into an OFDM signal \(\mathbf{x}=[x_{0},x_{1},\dots,x_{N-1}]^{T}\) by IFFT, the \(n\)th tone of \(\mathbf{x}\) can be denoted by [6]:  
\[x[n]=\frac{1}{\sqrt{N}}\sum_{k=0}^{N-1}X[k]\varepsilon^{\frac{2\pi kn}{N}},0 \leq n\leq N-1, \tag{1}\]  
where \(X[k]\) is the \(k\)th tone of the QAM signal, and \(N\) denotes the number of subcarriers.  
Finally, the parallel OFDM signals are transformed into serial and transmitted through a digital-to-analog converter (D/A converter) and a high-power amplifier (HPA) into the channel with additive white Gaussian noise (AWGN) that conforms independent and identically distributed (i.i.d.). Next, we generate the output signal \(\mathbf{r}\) as  
\[\mathbf{r}=\mathrm{H}\mathbf{x}_{h}+\mathbf{n}, \tag{2}\]  
where H is the channel matrix, \(\mathbf{x}_{h}\) is the signal after passing through HPA, and \(\mathbf{n}\) is the noise. Then, the signal \(\mathbf{r}\) is sequentially restored to the original signal at the receiving end by the analog-to-digital converter (A/D converter), fast Fourier transform (FFT), and QAM de-modulator.  
Figure 1: The basic architecture block diagram of OFDM systems.
## 5 Experimental Results and Discussions
### Battlefield 2:  
For this experimental test, the battlefield subject of the examination has an increased number of threats, totaling thirty elements distributed over a domain with a length of 1000 units. As seen in Table 5, SMOBHC clearly outperforms all its competitors' algorithms in terms of the best, worst, mean, and std. values, except for the std. value obtained by SMO for the third-dimension problem. Statistically, SMOBHC outperforms all examined methods except SMO, where there is no significant difference between them. Figures 13-15 demonstrates that SMOBHC converges faster to the optimal flight path than all other algorithms for a 30-dimensional problem, but as the dimension increases to 60 and 90, the convergence speed of all algorithms decreases, with SMOBHC having the lowest. Additionally, the final result is better than all competitors. The boxplot illustrations in Figure 16 show the dominance of SMOBHC over its competitor algorithms. Figure 17 illustrates the flight paths of SMOBHC, SMO, and six other algorithms (selected as examples) for the third-dimension problem on the battlefield. The first observation is that the SMOBHC-optimized flight path is superior to all tested methods in terms of flight path stability and avoidance of local optima. On the other hand, SMO is trapped in a local optimum, similar to PSO and GWO, while BA, CS, and HS algorithms fail to produce an acceptable quality flight path.  
Figure 13: Comparative convergence curves of algorithms for battlefield 2 with \(D=30\).  
Figure 14: Comparative convergence curves of algorithms for battlefield 2 with \(D=60\).  
Figure 15: Comparative convergence curves of algorithms for battlefield 2 with \(D=90\).
## 1 Introduction  
Splinting is a part of the management of the teeth after traumatic injuries. The splinting protocol has changed over the years. The trend to ligate or splint the injured teeth using rigid, prolonged fixation, the same as in jaw fractures, was maintained for many years. However, these splinting principles, which are still necessary for the treatment of broken jaws, have proven to be inadequate for the management of dental trauma [1]. The effect of splinting on healing has for many years been well documented in animal studies [2, 3]. Striving to improve the quality of splinting, to optimize the healing of damaged dental tissues, to simplify techniques, and the availability of materials, caused the devices like arch bars, wire ligatures, or cap splints to no longer be recommended. They have been replaced by flexible splints fixed with adhesive techniques [4].  
A dental trauma splint is defined as a device used to protect and stabilize a traumatized tooth in its natural position. The wide variety of dental trauma splints available allows a dentist to select such material that meets specific requirements of an individual patient. The materials that are currently used to stabilize the teeth are mostly wires (with the diameter being no greater than 0.4 mm), titanium trauma splint (TTS), and orthodontic brackets. A nylon fishing line fixed with a composite resin is also a good alternative to use as a splinting device. Fiber, composite resin, multiphase resin, and removable splints are also employed by dentists. For review, see [PERSON] and [PERSON] 2019 [4].  
An optimal splint should be commonly accessible in the dental office and easy to apply and remove. It should ensure proper fixation of the tooth during the healing period. Splinting materials should be passive and elastic, allowing for physiological tooth mobility. The comfort of wearing trauma splints is also important. This is why the device used must not impair oral hygiene and speech, interfere with occlusal movements, or traumatic oral soft tissues [4].  
Among all of the requirements for trauma splints, the rigidity of splinting materials has been the focus of most studies. In only two of these studies, the rigidity was evaluated in vivo in healthy [5] and injured [6] patients. Most authors conducted research using a human cadaveric model [7], an animal [8], and artificial models [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19].  
Recently, in an experimental study, the power chain has been shown to be a suitable device for splitting [18]. Before using this material in trauma patients, it is advisable to evaluate its properties in a clinical study on healthy volunteers and to compare the results with those obtained for commonly used splints.  
The main objective of this study was to evaluate and compare three different materials used for splinting in human volunteers. Two of the splints tested known as a wire-composite splint and a titanium trauma splint (TTS) are commonly used in case of dental injuries. The third material, known as the power chain, is made of connected elastic ligatures, which are used in orthodontics mainly for space closure.
# Analysis of Surface EMG Parameters in the Overhead Deep Squat Performance
## 2 Materials and Methods
### Investigated Parameters Description
#### 2.2.4 Co-Activation Index  
The co-activation index (\(CAI\)) is a quantitative measure used to assess the simultaneous activation of agonist and antagonist muscles during movement. \(CAI\) is calculated as follows [35; 36]:  
\[CAI=\frac{2\cdot\min(M_{1},M_{2})}{M_{1}+M_{2}}, \tag{10}\]  
where \(M_{1}\) and \(M_{2}\) are the normalized sEMG values (e.g., %MVC) of two muscles (typically agonist and antagonist), while \(\min(M_{1},M_{2})\) is the smaller of the two values. The value of \(CAI\) ranges from 0 to 1. A value of 0 indicates no co-activation, while 1 indicates equal and maximal simultaneous activation of both muscles.  
The selection of muscle pairs for the \(CAI\) calculation was guided by their functional roles during the deep squat. The goal was to capture co-contraction patterns between muscles that either act as agonist-antagonist pairs or play a stabilizing role. Surface EMG signals normalized to %MVC were used to \(CAI\) calculation. Muscle pairs selected for \(CAI\) analysis are presented in Table 2.
## 4 Grating Coupler  
Using the grating coupler is also an effective way to improve the coupling efficiency between the single-mode fiber (SMF) and waveguide reference wavefront source (RWS). Different from Y-branch coupler, grating coupler can be placed at any position of the waveguide RWS and edge of the waveguide RWS does not need to be polished. In this section, firstly, structure of the grating coupler is introduced and key parameters that determine the coupling efficiency are optimized. Secondly, 3 dB fabrication tolerance, 3 dB alignment tolerance, and polarization-dependent loss are analyzed, respectively.
# Maximization of Average Achievable Rate for NOMA-UAV Dual-User Communication System Assisted by RIS
## 3 Problem Formulation  
After the expression of \(\overline{R}\) is obtained, we aim to jointly optimize UAV power allocation, RIS phase, and UAV flight trajectory to maximize the average achievable rate, subject to the constraints on UAV flight trajectory and NOMA decoding sequence. We use \(Q\triangleq\{q[k],k\in{1,2,...,K}\}\) to represent the UAV trajectory, \(\Phi_{1,2}\triangleq\{\Theta_{1,2}[k],k\in{1,2,...,K}\}\) to represent the RIS phase, and \(P_{1,2}\triangleq\{p_{1,2}[k],k\in{1,2,...,K}\}\) to represent the UAV transmission power. Therefore, we can express the optimization problem as  
\[\mathrm{P}(1) :=\max_{P,\Phi,Q}\overline{R}\] \[s.t. 0\leq\theta_{(m,n)}[k]\leq 2\pi,\forall m,n,k \tag{18}\] \[\left\|q[k+1]-q[k]\right\|^{2}\leq D^{2},k\in\{1,2,...,K-1\}\] \[\left\|q[K]-q_{e}\right\|^{2}\leq D^{2},\] \[q[1]=q_{s},\] \[H_{2}[k]\geq H_{1}[k],\] \[p_{2}[k]\leq p_{1}[k],\] \[p_{2}[k]+p_{1}[k]\leq P_{max},\] \[p_{2}[k]\geq 0,k\in\{1,2,...,K\},\]
## 2 Materials and Methods
### The Degradation of Spatial Resolution  
To better simulate the XCH4 of pixels detected by the satellite, it is essential to model the radiance received by instruments across varying spatial resolutions. [PERSON] et al. (2022) modeled methane plumes with a spatial resolution of 10 m and averaged these simulations to generate 50 m satellite pseudo-observations [38]. When the spatial resolution of the satellite pixels exceeded the resolution of the plume diffusion, the radiance received was jointly influenced by the \(\Delta\)XCH4 within the mixed grids, as shown in Figure 3. The radiance observed by the satellite was the average of surrounding grids with different XCH4. When the spatial resolution was coarsened to encompass areas with varying XCH4, the resulting radiance could be represented as \(R_{c_{pixel}}\), as illustrated in Equation (4):  
\[R_{c_{pixel}}=\sum_{i=1}^{n}{(\Delta S_{i}R_{c_{i}})}/S \tag{4}\]  
where \(R_{c}\) is the radiance of the sub-region, \(\Delta S_{i}\) represents the area of the i-th small sub-region in the pixel, and \(S\) represents the total area of the detection pixel. Starting with the initial 10 m of simulated plumes, we extended the spatial resolution to 30 m, 60 m, 120 m, 250 m, 500 m, and 1000 m. This range of spatial resolutions encompasses most of the current hyperspectral imagers, ensuring that the simulations are relevant to the existing satellite instruments.
## 2 Random Finite Element Method
### Random Finite Element Method  
The random finite element method (RFEM) was employed to perform the stochastic analysis of the skirted foundation's bearing capacity, considering spatially variable soil undrained shear strength. The primary concept of RFEM involves incorporating the random field data generated in Section 2.1 into the finite element model simulated in Section 2.2. The general steps are summarized as follows.  
Figure 3: Finite element model of skirted foundation.  
1. A deterministic finite element model, coupling the skirted foundation and soil, is established in ABAQUS software, with the soil elements shown in Figure 1 numbered from 1 to 5300;
2. The non-stationary random field of \(S_{u}\) is generated using the SRM proposed in Section 2.1. Each value in the random field data is mapped to an element in the finite element model based on its unique position number. Subsequently, each realization of the soil random field is assigned to the deterministic model, replacing the soil undrained shear strength to generate a new calculation document for the subsequent finite element analysis. This process was executed using Python coding;
3. The number of MCS (\(n\)) is determined, and the finite element calculation in Section 2.2 is repeated after generating the random field data. The stochastic analysis comprises \(n\) finite element calculations.  
Selecting an appropriate MCS number is crucial for obtaining stable and accurate results. The evolution of the mean value and standard deviation of \(N_{CH}\) against the required MCS numbers in case Ani-3 are shown in Figure 4. The converging trend suggests that \(n\)= 600 is sufficient to provide stable results for the stochastic analysis.