esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 \(V_{oc}\) Loss Due to Rear Surface Metallization
### Measured \(V_{oc}\) on Test Samples  
To confirm the calculated results in Table 6 experimentally, test samples based on epitaxial on FZ wafers were made to examine the recombination introduced by the rear pattern and contacts. The test  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Steps** & **Component** & \(J_{0}\) **(A/cm\({}^{2}\))** & \(V_{oc}\) **(mV)** & Steps \\ \hline
3 & \(J_{0 bs}+J_{0 es}\) & \(1.04\times 10^{-13}\) & 687.0 & Rear SiO\({}_{2}\) \\ \(-\) & \(J_{0e-pc}\) & \(9.76\times 10^{-14}\) & \(-\) & \(-\) \\
4 & \(J_{0 bs}+J_{0 es}+J_{0 es}\) & \(2.02\times 10^{-13}\) & 670.0 & Rear contact \\ \hline \end{tabular}
\end{table}
Table 6: \(J_{0}\) and \(V_{oc}\) values of the thin silicon solar cell after rear surface metallization. \(J_{0e-pc}\) is the reverse saturation current density introduced by the point contact on the rear surface.  
Figure 4: Structure of the test samples for the UTSi solar cells based on epi on FZ wafer.  
\begin{table}
\begin{tabular}{c c c} \hline
**ID** & **Lifetime (\(\mu\)s)** & \(iV_{oc}\) **(mV)** \\ \hline Test samples & 383–674 & 682–703 \\ \hline \end{tabular}
\end{table}
Table 5: Lifetime and \(iV_{oc}\) of epi on FZ wafer test samples.  
structures are shown in Figure 5, where the left image is the structure after passivation, the right image is the structure after point contact patterning and metallization. The minority carrier lifetime, and \(iV_{oc}\) and \(V_{oc}\) of these samples were measured at each step as shown in Table 7. The rear point contact patterning and metallization led to a slight decrease in lifetime, \(iV_{oc}\) and \(V_{oc}\). The best \(V_{oc}\) measured after aluminium metallization was as high as 673 mV. The final \(V_{oc}\) values were in the range of 665-673 mV, which are consistent with the calculated value of 670 mV in Table 5. This result demonstrates that the calculation is valid.
# Genetic Programming to Remove Impulse Noise in Color Images
## 3 Preliminaries: Color Image Denoising  
Let \(I\) be an image with red-green-blue (RGB) color space with values from 0 to 255. The image \(I\) is represented by a two-dimensional matrix consisting of \(N\) pixels. Each color pixel \(\vec{x}_{i}\) is a three-dimensional vector, with one dimension for each color primary or channel, where \(i=1\ldots N\) indicates the pixel location on the image domain.
# An Efficient Algorithm to Determine Probabilistic Bisimulation
## 1 Introduction
### Related Work  
Probabilistic bisimulation preserves logic equivalence for PCTL [11]. In [12], [PERSON] c.s. reported up to logarithmic state space reduction obtained by probabilistic bisimulation minimisation for DTMCs. Quotienting modulo probabilistic bisimulation is based on the algorithm in [7]. In the same vein, [PERSON] et al. proposed symbolic probabilistic bisimulation minimisation to reduce computation time for model checking PCTL in a setting for DTMCs [13], where an SMT solver is exploited to do the splitting of blocks. Partition reduction modulo probabilistic bisimulation is also used as an ingredient in a counter-example guided abstraction refinement approach (CEGAR) for model checking for PCTL by [PERSON] et al. in [14].  
For CTMCs, [PERSON] et al. proposed the notion of contextual lumpability based on lumpable bisimulation in [15]. Their reduction technique uses the Valmari-Franceschinis algorithm for Markov chain lumping mentioned earlier. [PERSON] and [PERSON] [16] characterised probabilistic bisimulation of PLTSs as a partition shell in the setting of abstract interpretation. The algorithm for probabilistic bisimulation that comes with such a characterisation turns out to coincide with that in [4]. A similar result applies to the coalgebraic approach to partition refinement in [17] that yields a general bisimulation decision procedure, which can be instantiated with probabilistic system types.  
Probabilistic simulation for PLTSs has been treated in [4], too. In [18], maximum flow techniques are proposed to improve the complexity. [PERSON] and [PERSON] [19] presented a space-efficient algorithm based on partition refinement for simulation between probabilistic automata, which improves upon the algorithm for simulation by [PERSON] and [PERSON] [16] for concrete experiments taken from the PRISM benchmark suite. A polynomial algorithm, essentially cubic, for deciding weak and branching probabilistic bisimulation by [PERSON] and [PERSON], recasting the algorithm in [20], is presented in [21].
## 1 Introduction  
Static pressure spindles and slides are key parts in ultra-precision machine tools [1; 2; 3; 4]. Their stiffness is an important performance indicator for the machine tools. Static pressure spindles and slides are supported and lubricated by static pressure bearings that include aerostatic and hydrostatic bearings, which mainly use air and oil as lubricants [5; 6; 7; 8]. The performance of static pressure bearings can be accurately calculated based on the computational fluid dynamics (CFD) method [9; 10; 11; 12; 13]. However, the stiffness of static pressure spindles and slides is not that of their supporting bearings due to the influence of FSI, which is a common phenomenon in many fields [14; 15; 16; 17]. This is because the solid structures of static pressure spindles and slides are not rigid. They deform under the support forces from the supporting bearings, which will change the shapes and dimensions of the bearings and thus affect the stiffness.  
For the static pressure spindles and slides with small dimensions and stiffness, their solid deformation under the supporting forces is generally small, and the effect of FSI can be ignored. However, for static pressure spindles and slides with large dimensions and high load-carrying capacity, their solid deformation is large, and thus the effect of FSI has to be considered, or the calculation results may have a significant error. Researchers have proposed some methods to consider the effect of FSI on the stiffness of static pressure spindles and slides. Some researchers use a transient FSI method to study the stiffness of static pressure spindles [18; 19; 20; 21]. This method can well simulate structural deformation. However, many iterations are required to achieve convergence, which requires much computation time. Some researchers propose a steady FSI method to study the thrust stiffness of static pressure spindles, which can save lots of computation time compared with the transient method [22]. Because the structural stiffness of static pressure slides is relatively small, the influence of ISI is generally great. Some researchers adopt the transient FSI method to research the normal [23; 24] and lateral stiffness [25] of static pressure slides.  
These FSI methods can calculate the stiffness of static pressure spindles and slides accurately. However, the influence of FSI on stiffness is still unknown. When a static pressure spindle or slide is designed, designers have to calculate the stiffness under different design parameters to find the suitable parameters, which will prolong the design cycle. This paper aims to reveal the influence of FSI on the stiffness of static pressure spindles and slides. The lateral stiffness of an aerostatic slide is analyzed. Its relationship with the lateral stiffness of the solid structure and supporting bearings is deduced. A theory for the stiffness of static pressure spindles and slides with FSI considered is proposed. The theory is proven to be valid for the normal stiffness of the aerostatic slide and the thrust stiffness of an aerostatic spindle.
# On End-to-End Intelligent Automation of 6G Networks
## 2 6G: Preliminaries and Enabling Technologies
### Enabling Technologies
#### 2.2.1 Wireless Communication Technologies
This represents a rich source of bandwidth availability equivalent to 400 THz, including the infrared, visible light, and ultraviolet sub-bands [36]. Among the potential OWC systems to be adopted, visible light communication (VLC) is considered to be an important supplementary technology to the current radio frequency communication systems. VLC is communication using the frequency band between 400 THz and 800 THz [37]. Similar to the THz communication, the benefit of using VLC technology as a supplementary technology is two-fold. First, it offers high data transmission rates (up to 10 Gbps per wavelength [38]). Second, it can offer illumination solutions for indoor environments, particularly given the abundance of off-the-shelf optical devices available [38]. An added benefit of VLC that THz communication cannot offer is added security. More specifically, VLC can offer high physical layer security [39]. This is in contrast to THz communication, which can only offer partial physical layer data security [40]. Due to its characteristics, VLC is being proposed as a viable solution in multiple applications, such as indoor LiFi (light-based WiFi), vehicle-to-vehicle communication, and underwater communication, to name a few [37]. Accordingly, OWC in general and VLC specifically promise to play a major role in 6G networks to enable and facilitate the achievement of the desired goals. This should be done by taking into account some of the limitations caused by the deployment of OWC and VLC-based systems, such as frequent handovers, high inter-cell interference in dense deployments, high potential flickering, and the unsuitability for outdoor environments due to atmosphere-induced losses [41]. * Open Radio Access Network (O-RAN): Another crucial technology that will significantly contribute to 6G is O-RAN. O-RAN is a deployment approach for mobile fronthaul and midhaul networks that is completely disaggregated and reliant on the principles of cloud native applications [42; 43]. It is _\"a concept based on interoperability and standardization of RAN elements including a unified interconnection standard for white-box hardware and open source software elements from different vendors\"_[43]. As a result, the base station is transformed into a modular software stack that can run on commercial off-the-shelf hardware [43]. In turn, this allows different suppliers/companies to provide baseband and radio unit components that can work seamlessly together [43]. Accordingly, the main benefit of adopting O-RAN architecture is that it has the capability of unleashing novel levels of innovation by facilitating and reducing the RAN market entry requirements to new competitors [44]. Consequently, the O-RAN architecture is being proposed heavily as part of the development efforts of 6G networks [45].
# IAACaaS: IoT Application-Scoped Access Control as a Service +
## 4 IoT Application-Scoped Access Control as a Service  
After analyzing the requirements for access control in IoT, in this section, we propose an AC model that fulfills the requirements that were identified above. As explained in Section 2, for designing our solution, we take ideas from the existing solutions in the literature. We adopt the use of the OAuth 2.0 protocol to manage RBAC- and ABAC-based authorization policies with regard to sensors. We of course reuse the concept of the policy enforcement point to secure the protected resources in the system. However, we change in our model the way in which the authorization roles of subject and object are assigned to the entities. Making the device the subject that performs publish/subscribe requests to a service allows us to remove the token validation from them, decreasing the computational load. This decision also enables the possibility of having application-scoped authorization, with the same device acting with different roles depending on the service or application.  
Figure 1 shows the detailed architecture scheme of our model. We have considered the following entities:  
* an IoT sensor or actuator (called _Device_ for short);
* a _Service_, i.e., an application whose resources are willing to be accessed by the _Device_ and to be secured by the proposed architecture;
* a _Service Admin_ in charge of managing the security policies related to the _Service_;
* an Identity Provider (_IdP_), which includes relevant features like credentials' management, _Service_ registration and management of groups and roles;
* the Policy Administration, Decision and Enforcement Points (_PAP_, _PDP_ and _PEP_), which comprise the widely-known access control architecture [23];
* and a _Policies DB_ where policies are stored by the _PAP_ and checked by the _PDP_.  
Figure 1: As a service access control architecture.  
In the following subsections, the most important components of the model are deeply described. Finally, some sample flows are provided to illustrate the interaction between them.
# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 3 Modeling Process
### Markov Decision Process Modeling
#### 3.3.4 Training Process  
The model was trained using the PPO algorithm framework [33], which is easily tuned and easy to implement. The loss function of PPO is defined as [40]:  
\[L^{clip}(\theta) =E_{t}[min(r_{t}(\theta)adv_{t},clip(r_{t}(\theta),1-\epsilon_{clip },1+\epsilon_{clip})adv_{t})] \tag{19}\] \[L^{\forall f}(\theta) =MSE(\phi_{\theta}(S_{t}),R)\] \[L_{t}(\theta) =E_{t}[L_{i}^{clip}(\theta)-c_{1}*L_{t}^{\forall f}(\theta)+c_{2}* S[\tau_{\theta}](S_{t})]\]  
where \(adv_{t}\) is the advantage function, \(r_{t}(\theta)=\frac{\pi_{\theta}(A_{t}|S_{t})}{\pi_{\theta}{}_{old}(A_{t}|S_{ t})}\) denotes the probability ratio, and \(\phi_{\theta}(S_{t})\) is the state-value function. \(R\) is the discounted cumulative reward. \(c_{1}\) and \(c_{2}\) are hyperparameters and \(S[\pi_{\theta}]\) is an entropy bonus of \(\pi_{\theta}\). The critic network is built by the form of three fully connected layers, whose input is the hidden state \(h_{t}\) of the actor-network. The training process is illustrated in Algorithm 1.  
```
0: Tasks list
0: The new mission planned index list
1: Initialize policy params \(\theta\)
2:for scene in training Scenarios do
3: Import a new scene
4:for Iteration = 1, 2,..., \(I\)do
5: Parameters initialization
6:for\(t\) = 1, 2,..., \(t_{m}\)do
7: Get action \(A_{t}\) from policy \(\theta_{old}\)
8: Update new state \(S_{t+1}\)
9: Calculate instant reward \(R_{t}\)
10: Put \([S_{t},A_{t},R_{t},S_{t+1}]\) into memory buffer \(B\)
11: Calculate advantage estimates \(adv_{1}\), \(adv_{2}\),..., \(adv_{t_{m}}\)
12:endfor
13:for epoch = 1, 2,..., \(K\)do
14: Sample a random minibatch of \(N\) transitions \([S_{t},A_{t},R_{t},S_{t+1}]\) from \(B\)
15: Computing training loss as in (19)
16: Update \(\theta\) with stochastic gradient ascent
17:\(\theta_{old}\leftarrow\theta\)
18:endfor
19:endfor
20:endfor
```  
**Algorithm 1** Training process based on PPO
# Design and Study of a New Wave Actuator for a Boat
## 2 Mathematical Modeling of Wave Actuator
### Modeling of the Wave Actuator
#### 2.4.1 Average Cruise Speed of the Boat  
Ideal conditions are applied for convenience in formulating mathematical equations, ignoring energy dissipation due to friction and heat generation. We divided it into two phases in a wave cycle: the highest part of the wave, called the crest, and the lowest part called the trough. The wave force \(F_{w}\) is averaged over half a period at the wave crest, which is equivalent to the mean force \(F_{w}\). During the half-cycle of the wave crest from \(tA\) to \(tB\), using the acceleration as the second derivative of the difference between the wave position and the boat position, the product of the boat's mass and the acceleration is the wave force \(F_{w}\) (the concentrated force). We considered the division between the wave force derivative and the time subtraction \(tA\) and \(tB\) as the mean force \(F_{w}\). The wave force \(F_{w}\) is a concentrated force for two reasons: the wave parameters indicate that this is a short wave, not considering the wave shape that affects the wet area value of the boat, and which do not consider the variation in the wave force based on the position of the boat.  
\[F_{w}=\frac{\int_{t_{A}}^{tB}ma\ dt}{\Delta t} \tag{27}\]  
where acceleration is  
\[a=\frac{d^{2}(\zeta-z)}{dt^{2}} \tag{28}\]  
\(\zeta\) is the wave position equation, \(z\) is the boat's position, \(tA\) is the start time, and tB is the end time. During the downward phase of the wave, the current velocity \(V_{r}\) becomes zero. The average force value during the wave-up phase on the left side of Figure 5 is expressed using Equation (27). The average velocity over one period of wave cycle as presented in Figure 6 is expressed in Equation (29).  
\[Mean\ V_{drag}=\frac{1}{T}\int_{0}^{T}V_{drag}(t)dt \tag{29}\]where  
\(V_{drug}\) is the velocity at time \(t\).  
\(T\) is the period of regular waves, determined by the frequency \(f\).
# Constrained Connectivity in Bounded X-Width Multi-Interface Networks
## 1 Introduction
### Our Results
In fact, since in general the problem has been shown to be NP-hard even for graphs of maximum degree \(\Delta\geq 4\) with interfaces of unitary costs, it is worth investigating when the problem becomes affordable. As shown in Table 1, many graph classes have been already investigated, including paths, trees, rings, complete graphs, complete bipartite graphs, and series-parallel graphs. Hence our work is a continuation of this investigation, involving graphs of bounded treewidth, pathwidth or carvingwidth. In particular, while for graphs of bounded treewidth (and hence also for graphs of bounded pathwidth or carvingwidth) we prove that _CMI(2)_ is solvable in polynomial time, in the specific cases of graphs with bounded pathwidth and graphs with bounded carvingwidth we also provide specific polynomial time algorithms that solve _CMI(2)_.
###### Abstract  
Glasical lakes are an important component of the cryosphere in the Tibetan Plateau. In response to climate warming, they threaten the downstream lives, ecological environment, and public infrastructures through outburst floods within a short time. Although most of the efforts have been made toward extracting glacial lake outlines and detect their changes with remotely sensed images, the temporal frequency and spatial resolution of glacial lake datasets are generally not fine enough to reflect the detailed processes of glacial lake dynamics, especially for potentially dangerous glacial lakes with high-frequency variability. By using full time-series Sentinel-1A/1B imagery over a year, this study presents a new systematic method to extract the glacial lake outlines that have a fast variability in the southeastern Tibetan Plateau with a time interval of six days. Our approach was based on a level-set segmentation, combined with a median pixel composition of synthetic aperture radar (SAR) backscattering coefficients stacked as a regularization term, to robustly estimate the lake extent across the observed time range. The mapping results were validated against manually digitized lake outlines derived from Gaofen-2 panchromatic multi-spectral (GF-2 PMS) imagery, with an overall accuracy and kappa coefficient of 96.54% and 0.95, respectively. In comparison with results from classical supervised support vector machine (SVM) and unsupervised Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) methods, the proposed method proved to be much more robust and effective at detecting glacial lakes with irregular boundaries that have similar backscattering as the surroundings. This study also demonstrated the feasibility of time-series Sentinel-1A/1B SAR data in the continuous monitoring of glacial lake outline dynamics.  
g pacs: glacial lake mapping; Tibetan Plateau; median composite; Sentinel-1; time series +
Footnote †: journal: Journal of Environmental Research and Public Health
## 3 The Adaptive Droop Control of the ILC  
### The Topology of the ILC  
The ILC of a hybrid AC-DC microgrid adopts a voltage source converter (VSC) structure, and the topology is shown in Figure 6.  
The meanings of the variables in the figure are as follows: \(U_{\rm{abc}}\) is the AC bus voltage; \(I_{\rm{abc}}\) is the current flowing into the AC sub-microgrid; \(R_{\rm{f}}\), \(L_{\rm{f}}\), and \(C_{\rm{f}}\) are the filtering parameters; \(U_{\rm{Labc}}\) is the output voltage of ILC; \(I_{\rm{Labc}}\) is the output current of ILC; \(U_{\rm{dc}}\) is the DC bus voltage; C is the DC bus capacitance; and \(I_{\rm{dc}}\) is the DC sub-microgrid output current. The ILC transmission power \(P_{\rm{ILC}}\) moves in a positive direction from the DC sub-microgrid to the AC sub-microgrid.  
From Figure 6, the dynamic equation of the AC measured voltage of the ILC can be expressed as  
\[\left\{\begin{matrix}L_{\rm{f}}\frac{dI_{\rm{abc}}}{dt}=U_{\rm{Labc}}-R_{\rm{ f}}I_{\rm{Labc}}-U_{\rm{abc}}\\ {\rm{C}_{\rm{f}}\frac{dI_{\rm{dc}}}{dt}}=I_{\rm{Labc}}-I_{\rm{abc}}\end{matrix}\right. \tag{3}\]
## 3 Numerical Uncertainty Analysis
### Evaluation of the Results  
The order of an identified polynomial model was set from one to five, so that two methods of sparse regression (LSST and LSPL) are implemented with five different polynomial orders into data sets with five levels of noise intensity. For each method, 25 MCSs were executed in total for the evaluation. The identified model was analyzed regarding different aspects. First, the sparsity of the identified model was observed. The sparsity determines the complexity of a model. If a model is too complex or too simple, this model can be overfitted or underfitted, respectively [45]. Second, the convergence of the reconstructed time series indicates the usability of the identified model. The free vibration of a conservative system declines over time. An identified model can be discarded if the reconstructed signal diverges. Third, the eigenfrequency of a reconstruction characterizes the correctness of the numerical model of a vibration system. Lastly, the coefficient of determination \(R^{2}\) denotes how well the reconstructed time series fits the measurement. In the following paragraphs, the results of MCSs will be evaluated.