esa-sceva/satcom-chunk-collection huggingface dataset:
# Compensation of Limited Bandwidth and Nonlinearity for Coherent Transponder
## 3 Experimental Results
### Measured Bandwidth and Nonlinearity
The nonlinear response \(\gamma\) is plotted in Figure 4 towards the right axis. One can notice that within the Nyquist frequency (\(\Omega_{NY}\)), the dependency of \(\gamma\) on \(\omega\) is small. In addition, all frequency components within \(\Omega_{NY}\) will have roughly the same amplitude when the limited bandwidth is compensated. Thus, we can define the average nonlinear response \(\gamma_{avg}\) within the Nyquist frequency. Since Nyquist pulse shaping is widely used to improve the spectral efficiency, we ignore the frequency components outside \(\Omega_{NY}\) which are eliminated by the sharp roll-off of the Nyquist filter. To mitigate the nonlinear response, a quadratic equation is first solved. There are two roots for the quadratic equation. We can choose the correct root because the normalized amplitude x is between 0 and 1. Furthermore, to obtain the coefficients of the Volterra filter for compensation of nonlinearity, a Taylor expansion is performed around the zero point, as shown below:  
\[ \begin{array}{l}y=\gamma_{avg}x^{2}+x\\ x=\Big{(}\sqrt{1+4\gamma_{avg}y}-1\Big{)}/2\gamma_{avg}\approx y-\gamma_{avg}y ^{2}+2\gamma_{avg}^{2}y^{3}\end{array} , \tag{8}\]  
As seen, the coefficients of a memoryless Volterra filter are exactly the coefficients of a Taylor expansion. Thus, a third-order memoryless Volterra filter can be implemented in DSP ASIC to reverse the nonlinear response. The coefficients of the Volterra filter are simple and directly correlated to nonlinear response \(\gamma_{avg}\). Furthermore, MZM's transfer function is a sinusoidal function. Its compensation function is \"_arcsin_\" function which can be realized through a LUT in the DSP ASIC. As discussed in [7], the coherent transmitter can be modeled as a Wiener system, where the static nonlinear response follows the dynamic linear response. To compensate for the Wiener system, a Hammerstein system is used, where the static nonlinear equalization is placed in front of the dynamic linear equalization [19]. Figure 5 shows the block diagram of the proposed DSP architecture. The digital signal is first pre-distorted by the third-order memoryless Volterra filter to mitigate the nonlinearity. Then, the sinusoidal transfer function of the MZM is compensated by the \"_arcsin_\" LUT. At last, the limited bandwidth is compensated through the pre-emphasis implemented by the FIR filter. The tap coefficients of the memoryless Volterra filter is decided by \(\gamma_{avg}\) and the tap coefficients of the FIR filter is set such that the frequency response of the FIR filter is inversely proportional to the limited bandwidth (\(\alpha\)). Figure 4: Bandwidth and nonlinearity of one tributary. Bandwidth is plotted on the left axis and nonlinearity is plotted on the right axis.
## 3 Methods
### Design of Experiments
#### 3.3.1 The Resilient Approaches  
Two different resilient approaches, as represented in Figure 4 as a complement to the previous figure, are compared to a baseline approach without any collaboration: a distributed collaborative approach (the one used today by the interviewed nursing care structures) and a centralized collaborative approach.  
In the baseline approach (named \"approach 0\" because it is not a resilient approach and is represented in red), there is no cooperation between the caregivers; when a caregiver finishes their route, they return to the health care center to carry out administrative tasks.  
In the distributed collaborative approach (named \"approach 1\" and represented in orange), when a caregiver finishes their route, they try to reach their colleagues to help  
Figure 4: Decision-making algorithm modeling the two resilient approaches and the baseline.  
Figure 3: Representation of the main simulation model.  
them. The caregiver knows each colleague's work areas and, therefore, starts by calling those closest to them. A colleague does not answer the call if they are out on a care visit unless the latter is disrupted, whereas a colleague who is driving will always respond using a hands-free kit. If the colleague does not answer the call, the caregiver moves on to the next closest individual. Otherwise, they ask them if they need help (i.e., if they are late because they experienced a disruption during their route). If this is the case, they will take a care visit from them; otherwise, they will remove this colleague from the list and move on to the next one. If they have called and spoken to all their colleagues, they go back to the health care center to carry out administrative tasks. Otherwise, they wait five minutes and call their colleagues who are still on the list again.  
In the centralized collaborative approach (named \"approach 2\" and represented in green), the information is centralized and easily available to everyone. Using a device (e.g., a smartphone application), a caregiver knows directly if their colleagues need help and where they are. If this is the case, they will take a care visit; otherwise, they go back to the health care center to carry out administrative tasks.  
Finally, for the two approaches, 1 and 2, there are three possible sub-approaches. These sub-approaches are subsequently named X-1, X-2, and X-3, where X = approach 1 or 2. For X-1 approaches, when a colleague needs help, the available caregiver will take the next care visit closest to them. For X-2, they will take the next care visit indicated on the route, regardless of their distance from the patient. And for X-3, they will take the last care visit on the route.  
The three approaches (baseline and the two resilient ones) and the three sub-approaches are summarized in Table 1.
## 3 System Model
### Energy Depletion  
We consider the sensing energy model introduced in [PERSON] et al. (2018):  
\[e_{W}=\gamma(d-r_{min})^{\alpha}+\varepsilon_{W}, \tag{9}\]  
where \(\gamma,\alpha\) are sensor-specific constants; in exemplary cases, \(\alpha\in[2,4]\); \(\varepsilon_{W}\) describes the static power and the wake-up timing and wake-up frequency will affect the static power consumption, so \(\varepsilon_{W}\) is inextricably related to scheduling strategies. Equation (9) implies that the energy depletion of the perceptron during the working duration is determined by the distance at which an object appears. If a sensor is in sleep mode, then only the low-power timer and other necessary components remain active, viz.  
\[e_{S}=\varepsilon_{S}; \tag{10}\]  
\(\varepsilon_{S}\) represents the static power. Since frequent wake-up and sleep can affect the static power, that means \(\varepsilon_{S}\) is also a function of the scheduling strategy.  
The total energy depletion for the sensing circuit is going to be the weighted sum of these two,  
\[e=e_{S}S+e_{W}W, \tag{11}\]  
where \(W\) and \(S\) represent working duration and sleeping duration, respectively. Then the mathematical expectation is:  
\[\mathbb{E}[e]=e_{S}\dfrac{S}{S+W}+e_{W}\dfrac{W}{S+W}. \tag{12}\]  
Assign (9) and (10) to (11), we have:  
\[\mathbb{E}[e]=\varepsilon_{S}\dfrac{S}{S+W}+\left(\gamma(d-r_{min})^{\alpha}+ \varepsilon_{W}\right)\dfrac{W}{S+W}. \tag{13}\]
## 3 Methodologies
### Bayesian Optimization  
Selecting appropriate hyperparameters is crucial for ensuring the performance of the trained model. Common parameter optimization methods include grid search, random search, and Bayesian optimization [70]. Grid search performs poorly when the number of parameters is large, consuming excessive computational power and sometimes even failing to find the optimal solution. Random search selects parameters randomly and lacks intelligence in parameter selection. Moreover, neither grid search nor random search exploits the correlations between different hyperparameter combinations [71]. Currently, Bayesian optimization is becoming increasingly popular as an intelligent hyperparameter optimization algorithm and is being more widely applied [68]. Bayesian optimization is typically used in conjunction with classifiers such as logistic regression, SVM, Random Forests, XGBoost, and neural networks. Sequential Model-Based Optimization (SMBO) is the core procedure of Bayesian optimization algorithms [67]. The procedure includes two key functions: the surrogate function and the acquisition function. SMBO first establishes an initial surrogate model \(M_{0}\) and an experimental set \(H\). The surrogate model \(M_{0}\) serves to link the hyperparameter settings \(x\) with the loss function \(F\), while \(H\) records each set of hyperparameters along with their corresponding loss values and is used to update the surrogate model [72].  
Sequential Model-Based Optimization (SMBO) then obtains the optimal hyperparameters through the following iterative process:  
1. Search and obtain the locally optimal hyperparameters \(x^{*}\) on the current surrogate model \(M_{t-1}\) using the acquisition function.
2. Calculate the actual loss value y of \(x^{*}\).
3. Update \(x^{*}\) and \(y\) into the experimental set \(H\).
4. Retrain the surrogate model using the updated \(H\) to obtain a new surrogate model \(M_{t}\).  
In this study, we used the Tree-structured Parzen Estimator (TPE) as the acquisition function and Expected Improvement (EI) as the surrogate function. Multiple experiments were conducted in total, with each iteration repeated several times to minimize the loss function of the surrogate function, thereby improving the overall performance of the model. Upon completion of the iterations, the global optimal hyperparameters \(x^{*}\) corresponding to the minimal loss value \(y\) were obtained.
## 3 Axial Compression Testing of U-Shaped Steel Support Joints
### Test Results  
The joint displacement curves of the bearing capacity of U-steel joints under axial compression with different initial pre-tightening torque are shown in Figure 5, and the changes in bearing capacity under different initial torque after secondary pre-tightening are reported in Figure 6.  
Figure 4: Experimental system and process.  
Figure 5: Slip resistance–displacement curves.  
The analysis illustrated in Figure 5 revealed that the bearing capacity displacement curves were mainly divided into four stages. In the initial bearing stage (OA) at the initial stage of loading, the gaps at the joint are continuously compacted, the bearing capacity shows a rapid linear increase, and there is no obvious slip at the joint; At the first slip stage (AB), when the bearing capacity reaches the same level as the joint's slip resistance, the joint begins to slip, and the bearing capacity remains basically stable during this process. In the secondary pre-tightening stage (BC), after applying secondary pre-tightening torque to the joint, the joint stops slipping, and the bearing capacity of the specimen rapidly increases again in a linear fashion. During the second slip stage (CD), when the bearing capacity reaches the level of secondary slip resistance, the joint undergoes a second slippage event, and the bearing capacity remains basically stable.  
Figure 6 indicates that when the initial torque increased from 100 N-m to 200 N-m, the bearing capacity of the specimen increased by 58.08 kN, at a 76.8% rate of increase. From the perspective of each group, the increase in the bearing capacity of the specimens after secondary pre-tightening was more than 10%. Among the groups, the increase in bearing capacity in the middle-group specimens was the highest, increasing by 28.22 kN, and the increase in bearing capacity after secondary pre-tightening reached 28.10%.  
In summary, for the joints with different initial torques, the change trends for the displacement in bearing capacity before and after secondary pre-tightening were basically the same. However, when the initial pre-tightening torque was higher, the first slip resistance and second slip resistance of the specimens increased. There was a turning point in the improvement of the secondary pre-tightening effect; after exceeding a certain pre-tightening torque, the improvement effect of secondary pre-tightening diminished.
# Power System Reliability Assessment Considering Coal-Fired Unit Peaking Characteristics
## 6 Case Study
### Algorithm Verification  
When the peak shaving depth of the unit model is set to 0%, the unit model becomes a two-state model. To verify the rationality of the proposed method, three methods are compared for the original system and the original load curve: (1) the equivalent load duration curve method; (2) the simplified model of the proposed method (0% peak shaving depth); and (3) the proposed method (2% ramp rate, 60% peak shaving depth). The simulation results are shown in Table 3.  
According to the results in Table 3, when the peak shaving depth is set to 0% and internal constraints such as ramp rates are not considered, the algorithm proposed in this paper yields simulation results similar to the traditional equivalent load duration curve method, and the computational efficiency of the proposed method is improved by about two times, which verifies the effectiveness of the proposed algorithm. Further analysis shows that when the peak shaving depth is set to 60%, the ramp rate is 2%, and internal unit constraints are considered, the power generation of the base-load unit is similar to that of the traditional model. However, the power generation of the flexible peak shaving units shows greater fluctuations compared to the traditional simulation methods, while the power generation of the start-stop peak shaving units is generally higher than that of the traditional methods. This is because the flexible peak shaving units, when responding to load demands, take into account their dynamic adjustment characteristics, adjusting the output flexibly according to actual needs. However, due to constraints on the unit parameters, state transitions are limited, leading to larger fluctuations in power generation. Additionally, due to the limitations of ramping capacity, peak shaving units cannot respond quickly to load changes, forcing start-stop peak shaving units to respond more frequently, which results in higher power generation compared to the units in the traditional method.  
Furthermore, from the perspectives of LOLP and EENS, the values obtained using the proposed method are higher than those calculated by traditional simulation methods. This can be attributed to the fact that our method incorporates the impact of losses occurring during flexible deep load-following operations, thereby better reflecting the operational constraints faced by generating units in real-world scenarios. Unlike traditional models, which assume that units consistently operate at their rated output, the proposed approach fully captures the dynamic adjustments of units during load-following operations. As a result, the obtained reliability indices more accurately align with actual system behavior.
## 4 Conclusions and Discussion  
We studied applicability of CV QKD over satellite links considering coherent and squeezed-state protocols, taking into account realistic satellite passage, atmospheric effects, finite data ensemble size, system clock rate and data processing efficiency. We show that the protocols are very sensitive to channel noise at the respective loss levels so that either set-up stabilization resulting in drastic decrease of noise or relaxation of security assumptions to individual or passive collective attacks is required for implementation over the low Earth orbit satellites. Satellite pass segmentation provides another viable option to reduce channel fading thus improving the secure key rate and allowing to establish the secure  
Figure 5: Maximal tolerable excess noise in the case of active collective attacks on squeezed- or coherent-state protocols (solid and dashed lines respectively) with receiver aperture size \(a=0.5\) (**left**) and \(0.75\) (**right**). From top to bottom: asymptotic regime, finite-size regime with repetition rate (from bottom to top) \(100\) MHz, \(1\) GHz, and \(10\) GHz. Squeezing \(V_{S}\geq 0.1\) and modulation variance \(V_{M}\) are optimized.  
Figure 6: Secure key rate without channel subdivision (solid lines) and with channel subdivision in \(3\) segments (dashed lines) versus orbit altitude \(H\) (km), secure against collective attacks in the finite-size regime with number of data points determined by (from darker to lighter shade) repetition rate \(100\) MHz, \(1\) GHz, \(10\) GHz for the squeezed-state protocol with optimized squeezing \(V_{S}\geq 0.1\) in the presence of untrusted excess noise \(\epsilon=10^{-4}\) related to the channel output. Aperture size \(a=0.5\) m (**left**) and \(a=0.75\) m (**right**).  
link with satellites at higher altitudes. We show that the use of squeezing can make the protocols more applicable in the satellite links, allowing for higher attenuation and levels of noise at the given security assumptions, and it has to be optimized in the collective attacks scenario. On the other hand, in the case of trusted noise assumption and at high repetition rates, squeezed-state CV QKD can tolerate attenuation levels up to 42 dB, which may open the possibility of use over geostationary satellites. The obtained results are promising for satellite-based QKD, potentially applicable in daylight conditions.  
Formal analysis, Visualization, Writing--original draft, Writing--review and editing, I.D. and V.C.U. All authors have read and agreed to the published version of the manuscript.  
Authors acknowledge support from the project LTC17086 of INTER-EXCELLENCE program of the Czech Ministry of Education, project 19-23739S of the Czech Science Foundation and EU H2020 Quantum Flagship initiative project No. 820466, CiViQ.  
The authors declare no conflict of interest.
# Vibration Control of Multi-Modular VLFS in Random Sea Based on Stiffness-Adjustable Connectors
## 3 Vibration Control Method Based on the Stiffness-Adjustable Connector
### Mathematical Model of the Floating System  
Considering a three-dimensional model of a floating system with \(N\) modules in a column, the governing equation of each module based on [PERSON]'s law can be expressed as  
\[\mathbf{M}_{i}\ddot{\mathbf{x}}_{i}+(\mathbf{K}_{M,i}+\mathbf{R}_{i})\mathbf{ x}_{i}=\mathbf{f}_{i,w}+\mathbf{f}_{i,c},\hskip 14.226378 pti=1,2,\cdots,N \tag{1}\]  
where symbol \(\mathbf{x}_{i}=[x_{i},y_{i},z_{i},a_{i},\dot{p}_{i},\gamma_{i}]^{T}\) is the displacement vector of the \(i\)th module, representing the displacements of surge, sway, heave, roll, pitch, and yaw, respectively. It also indicates the displacement of the local frame in the global frame. On the left-hand side of Equation (1), symbol \(\mathbf{K}_{M,i}\) is the stiffness matrix of the mooring system, which is simplified as a constant matrix. Symbols \(\mathbf{M}_{i}\in\mathbb{R}^{6\times 6}\) and \(\mathbf{R}_{i}\in\mathbb{R}^{6\times 6}\) are the mass matrix and the buoyant restoring stiffness matrix of water, respectively, which is expressed by  
\[\mathbf{M}_{i}=\left[\begin{array}{cccccc}m&0&0&0&0&0\\ 0&m&0&0&0&0\\ 0&0&m&0&0&0\\ 0&0&0&I_{x}&0&0\\ 0&0&0&0&I_{y}&0\\ 0&0&0&0&0&I_{z}\end{array}\right] \tag{2}\]  
\[\mathbf{R}_{i}=-\rho g\left[\begin{array}{cccccc}0&0&0&0&0&0\\ 0&0&0&0&0&0\\ 0&0&-A_{w}&0&\iint_{Aw}x\mathrm{d}A&0\\ 0&0&0&-\big{(}V_{w}(z_{b}-z_{g})+\iint_{Aw}y^{2}\mathrm{d}A\big{)}&0\\ 0&0&\iint_{Aw}x\mathrm{d}A&0&-\big{(}V_{w}(z_{b}-z_{g})+\iint_{Aw}x^{2} \mathrm{d}A\big{)}&0\\ 0&0&0&0&0&0\end{array}\right]\]  
where symbol \(m\) is the mass of the \(i\)th module. The symbols \(I_{x}\), \(I_{y}\), and \(I_{z}\) are the rotary inertias for the \(x_{i}\), \(y_{i}\), and \(z_{i}\) axes, respectively. Symbol \(\rho\) is the density of water, and \(g\) is the acceleration of gravity. Symbol \(A_{w}\) is the water plane area, and \(V_{w}\) is the volume of the water. Symbol \(z_{b}\) is the design draft, and \(z_{g}\) is the height of the center of gravity. On the right-hand side of Equation (1), symbol \(\mathbf{f}_{i,w}\) expresses the wave excitation vector determined by the linear wave theory [36]. Symbol \(\mathbf{f}_{i,c}\) indicates the connector force vector acting on the \(i\)th module. Vectors \(\mathbf{f}_{i,w}\) and \(\mathbf{f}_{i,c}\) are derived as discussed in the paragraphs below.
# Parameter Observer Useable for the Condition Monitoring of a Capacitor
## 5 Conclusions  
This paper proposes a parameter observer designed to estimate the time constant of a first-order linear system in a free regime. The estimation, performed on the basis of the variation in the system's output, is possible also when this parameter is time-variable.  
The observer can be used for electrolytic capacitors for which the parameters are time-varying during transient processes. This is highlighted by discharging the capacitor through a resistor, and considered for the capacitor the equivalent R-C serial scheme. Since the voltage across the capacitor terminals is experimentally obtained in sampled form, the discrete time version of the observer is recommended. From the voltage curve generated for two-stage discharges, equivalent values for the parameters of the R-C serial scheme are determined using a processing procedure/algorithm with few operations. The procedure is applied in the paper to determine the equivalent parameters of the output capacitor of a DC-DC buck converter. Two capacitors with different nominal values are considered for the same converter scheme. The results obtained via offline processing support the general possibility of determining equivalent parameters for capacitors by using the parameter observers. The tracking process of the time constant variation is performed practically after approx. \(10^{-3}\) s. The obtained values for the equivalent capacitance are according to the spectral range of the discharge signal, while the values for the equivalent resistance belong to the range below 20 Hz.  
Due to the simplicity of the working algorithm, the parameter observer can be integrated for the condition monitoring of capacitors in maintenance and fault detection applications. As the observer is designed for first-order linear processes, its application should not be restricted to the area of electrolytic capacitors.  
**Author Contributions:** Conceptualization, C.B. and T.-L.D.; methodology, T.-L.D.; software, C.B. and D.-V.C.; validation, C.B.; formal analysis, C.B. and D.-V.C.; investigation, C.B.; resources, C.B., D.-V.C. and T.-L.D.; data curation, C.B. and D.-V.C.; writing--original draft preparation, T.-L.D.; writing--review and editing, C.B. and T.-L.D.; supervision, C.B. and T.-L.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** Did not report any data.  
The authors declare no conflict of interest.