esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Electrode Characterization  
The physical properties of the two ACs, including the total specific surface area (SSA) and the pore volume, were measured by N\({}_{2}\) adsorption isotherms (ASAP 2020, Micromerities, Norcross, GA, USA) at -196 \({}^{\circ}\)C temperature after degassing them at 110 \({}^{\circ}\)C for 2 h and the isotherm data were calculated with the Brunauer-Emmett-Teller (BET) equation. Fourier-transform infrared spectroscopy (FT-IR, Nicolet 6700, Thermo Scientific, Waltham, MA, USA) was conducted to investigate the surface functional groups of both the ACs (P60 and YS-2), which were pelletized for measuring their IR spectra by mixing the samples with KBr salts. The spectra were scanned in a range of 4000\(\sim\)400 cm\({}^{-1}\). Hydrophilicities of the electrodes were measured using a contact angle analyzer (DSA 100, KRUSS, Hamburg, Germany) with the sessile drop method, in which a 3 \(\upmu\)L of DI water droplet was deposited onto the electrode and/or membrane surface at a minimum of five points and changes in contact angle were tracked for 1 min. The samples were dried for a minimum of 6 h in a vacuum oven at 70 \({}^{\circ}\)C. X-ray photoelectron spectroscopy (XPS, SIGMA PROBE, ThermoFisher Scientific, Waltham, MA, USA) was conducted to determine the PDA coating quality. XPS spectra were collected using a monochromatic Al K\(\upalpha\) from 0 to 1200 eV.
# Common-Ground Photovoltaic Inverters for Leakage Current Mitigation: Comparative Review
## 2 Classification of Common-Ground Converters
### Voltage-Source CG Converters (VSCG)
#### 2.2.3 Buck/Boost VSCG  
Figure 6 shows the converters that belong to this group. Table 7 summarizes the features and limitations of each converter.  
The following remarks can be reported about these converters:  
* The buck-boost feature offered by these converters makes it applicable for a wide range of power ratings. It can be used with module-integrated PV applications as well as with multi-series-connected PV modules;
* Among these converters, those introduced in [76; 77; 78; 79] can offer five voltage levels on their outputs. This can significantly reduce the required filter size. On the other hand, the converter introduced in [66] can offer only two voltage levels. Thus, a large filter size is needed. All other converters belonging to this category can offer three voltage levels;
* The converters presented in [73; 76; 77; 78; 79] cannot offer continuous input current. This can result in the improper operation of MPPT;
* Among these converters, those introduced in [75; 83] use a high number of diodes. This reduces efficiency. On the other hand, the converter introduced in [65] uses a high number of switches. This can significantly increase the cost and complexity of operation. These converters need this high number of semiconductors while offering only three voltage levels;
* Except for the converter presented in [64], all of these converters support bidirectional power flow; thus, they can be operated at different power factors;
* The converters introduced in [69; 70; 71; 72; 74; 75; 76; 77; 78] are suffering from asymmetric conducting paths during positive and negative half-cycles. Therefore, DC current component may be generated;
* The boosting gain offered by the converters presented in [65; 67; 76; 77; 78; 79; 80; 81] is relatively low compared to the other converters belonging to this category.
# IMM Filtering Algorithms for a Highly Maneuvering Fighter Aircraft: An Overview
## 10 Numerical Simulations and Results  
In this study, the motion patterns of a highly maneuvering aircraft are the same as the target's motion patterns in [64; 65], which are NCV, NCT+, NCV, NCT-, and NCV in sequence. In the experiments considered in this section, the acceleration of the target during the NCT+ and NCT- phases is \(3g\), \(4g\), \(5g\), and \(6g\), respectively, while only the case with an acceleration of \(4g\) was considered in [64]. The prior distribution of the aircraft's state and the power spectral densities (PSDs) of the process noise during the NCV and NCT phases are the same as in reference [64], as shown in Table 2. The impact of prior covariance on filtering accuracy was studied further in [65]. The AMTI radar moves with constant velocity in 2D along the X-axis and measures the range, azimuth, and radial velocity of the aircraft. The initial position and velocity of the sensor and measurement parameters are shown in Table 3. In the IMM filters, we use a constant transition probability matrix [8], which is given by  
\[\mathbf{\Pi}=\begin{bmatrix}0.95&0.025&0.025\\ 0.04&0.95&0.01\\ 0.04&0.01&0.95\end{bmatrix}. \tag{117}\]  
We used 1000 Monte Carlo runs to compute the various MoPs of the IMM-based filters. We analyze the MoP of the filters using three azimuth SDs of 1, 2, 4 mrad and two measurement time intervals of 0.5 and 1.0 s. For filter initialization, we draw a sample from the prior distribution in each Monte Carlo run and set it to the initial state estimate. Therefore, the initial state estimate is different from the true initial state in each Monte Carlo run.  
We present the numerical results in Sections 10.1-10.3 with varying parameters.
# UV-Polymerized Vinylimidazol Ionic Liquids for Permselective Membranes
## 3 Results and Discussion
### Flux Measurements  
Water flux was determined by the volume of water passing at a certain time at different pressures. Every membrane fabricated as described above needed a certain conditioning time in the experimental setup. Besides the synthesized PIL membranes, commercial ultrafiltration (UF) and nanofiltration (NF) membranes were also characterized in the same manner to allow for comparison with those synthesized in this study. All flux data as a function of the pressure are shown in the supporting information (SI).  
At first sight, using PIL membranes offers a broad range of flux rates by varying the side chain of the IL. Thus, the results demonstrate a linear behavior with small errors.  
The application with PILs B with butyl side chains yielded in higher flux than a commercial 10 kDa UF membrane. A porous structure of the layer was conceivable. The main drawback is the lack of mechanical stability during the experiments. Compared to other PIL membranes, PIL B ruptures easily and responds sensitively to condition changes.  
To improve the usability, experiments with varying amounts of D were performed as the pure polymeric layer of D (PILs D) showed excellent mechanical stability. Unfortunately, the flux was one of the lowest observed and not quantifiable below 5 bar. While mixed PIL membranes were possible, the influence of D on the properties was more intense than the influence of B. PILs BD3, an equal molar mix of both ILs, decreased the flux significantly compared to PILs B and struggled with blocking. No flow was observed below 2.5 bar, and above this pressure the membrane blocked, prohibiting any flux.  
Figure 2: Ionic liquids used in membrane formation experiments and the corresponding polymers (A: [VEItm]Br, B: [VBInIm]Br, C: [VHexIm]Br, D: [VDodecIm]Br, E: [VBInIm]Br, F: [VPrylIm]Br).  
Higher amounts of B improved the system. Both, PILs BD1 (B:D 20:1) and PILs BD2 (4:1) showed increased stability and usability. Even when the amount of D was lower, the flux decreased more than 180 times compared to PILs B and was only 13 times higher than PILs D. All mixed membranes showed lower flux than the commercial NF membrane under the same conditions. Table 1 gives an overview of the performance of PIL layers in the experiments, standardized to filtration area, time and pressure. The reproducibility of the PIL layers was also controlled by the flow.  
In most of the common organic solvents (such as dimethylformamide, alcohols, \(N\)-methyl-2-pyrrolidone, chloroform, dichloromethane, hydrocarbons, pyridine, diethyl ether, dimethyl sulfoxide, acetone, and ethyl acetate), the PIL membranes were mechanically stable at room temperature. Fixed in the filtration setup, they showed the same stability. However, it was not possible to determine flux and related data. In some cases, the flux was fluctuating strongly even after long-time preconditioning; in other cases, the shape of the polymeric layer changed by deformation and was not usable anymore. Thus, the application of these membranes with organic solvents seems to be not possible.
# Energy Efficient Load-Balancing Mechanism in Integrated IoT-Fog-Cloud Environment
###### Abstract  
The Internet of Things (IoT) and cloud computing have revolutionized the technological era unabatedly. These technologies have impacted our lives to a great extent. The traditional cloud model faces a variety of complications with the colossal growth of IoT and cloud applications, such as network instability, reduced bandwidth, and high latency. Fog computing is utilized to get around these problems, which brings IoT devices and cloud computing closer. Hence, to enhance system, process, and data performance, fog nodes are planted to disperse the load on cloud servers using fog computing, which helps reduce delay time and network traffic. Firstly, in this article, we highlight the various IoT-fog-cloud models for distributing the load uniformly. Secondly, an efficient solution is provided using fog computing for balancing load among fog devices. A performance evaluation of the proposed mechanism with existing techniques shows that the proposed strategy improves performance, energy consumption, throughput, and resource utilization while reducing response time.  
cloud computing; fog computing; Internet of Things; load balancing; optimization; energy consumption; wearable sensors and devices Article
## 5 Simulation Results
### Nonlinear Sytsem  
Consider a continuous-time nonlinear system with the external disturbance given as  
\[\dot{x}=f(x(t))+g(x(t))u(t)+h(x(t))v(t) \tag{69}\]  
where  
\[f(x) =\begin{bmatrix}-x_{1}+x_{2}\\ -0.5x_{1}-0.5x_{2}(1-\cos((2x_{1})+2)^{2})\end{bmatrix}\] \[g(x) =\begin{bmatrix}0\\ \cos(2x_{1})+2\end{bmatrix},h(x)=\begin{bmatrix}0\\ \sin(4x_{1}+2)\end{bmatrix}\]  
The state vectors are denoted as \(x=[x_{1},x_{2}]^{\mathsf{T}}\), and the initial state is \(x_{0}=[1,-1]^{\mathsf{T}}\). The constrained input control is satisfied with \(u\in\{u\in\mathbb{R}:\|u\|\leq 1\}\). Choose \(Q=l_{2}\) and \(R=I_{1}\). The activation function is selected as \(\phi(x)=[x_{1}^{2},x_{1}x_{2},x_{2}^{2}]^{\mathsf{T}}\). Then, the approximated weight vectors are denoted as \(\hat{\mathcal{W}}_{c}=[\hat{W}_{c_{1}},\hat{W}_{c_{2}},\hat{W}_{c_{3}}]\). The NN learning and triggering parameters are needed in this paper: \(\gamma=0.4\), \(L\in(0,1)\), \(T=0.1\), \(\alpha_{c}=0.25\). The external disturbance is selected as \(v(t)=5\cos(t)e^{-t}\) similarly.  
The two state trajectories of the closed-loop system are described in Figure 7. It is obvious that \(x_{1}\) and \(x_{2}\) gradually converge to asymptotically stable. The ideal critic weight is \([W_{c_{1}},W_{c_{2}},W_{c_{3}}]^{\mathsf{T}}\). The NNs can be ensured to converge under the action of PE condition. Figure 8 demonstrates the approximable critic weight vectors are converged to \([0.5468,0.8876,-0.4125]^{\mathsf{T}}\) after learning. From Figure 9, we can obtain the evolution of ETC policies \(u(\hat{x}_{k})\) and \(v(\hat{x}_{k})\). Under the condition of constrained input, the control law remains no more than \(1\), that is \(\|u_{max}\|<1\). The trajectory of the event error and the triggering threshold are shown in Figure 10. It can be seen intuitively that the event-triggered errors are always within the triggering thresholds. Figure 11 indicates the event-triggered sampling intervals under the event-triggered mechanism. From Table 2, it has only \(157\) sampling states are performed based on an event-triggered strategy. Likewise, states will be sampled at each time by using a time-triggered strategy.  
Figure 7: The evolution of \(x_{1}(t)\) and \(x_{2}(t)\).  
Figure 8: Three vectors for a single-critic network.  
Figure 9: The control law \(u(\hat{x}_{k})\) and the perturbation law \(v(\hat{x}_{k})\).  
Figure 11: The event-triggered sampling intervals  
Figure 10: The event error \(\left\|e_{k}\right\|^{2}\) and the triggering threshold \(e_{T}\).
## 3 Results
### Coordination Number Analysis  
The coordination number, representing the count of adjacent particles in direct contact with a central particle, serves as a key parameter for evaluating aggregate packing characteristics. Using the established contact point data, coordination numbers for individual coarse aggregates were computed, with results detailed in Table 5 and Figure 13.  
The analysis shows distinct variations in coordination number distribution across CPAM formulations. CPAM4 displays weak aggregate interlock, with coordination numbers mainly in the 0-1 range, reflecting inadequate contact between coarse aggregates to form a stable skeleton structure. By contrast, CPAM2 and CPAM3 exhibit optimal packing characteristics, with coordination numbers primarily in the 2-3 range and peak values up to 5. This efficient contact configuration enables the formation of a durable aggregate skeleton, improving overall mixture stability.  
Figure 13: The coarse aggregate coordination number of CPAMs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & \multicolumn{3}{c}{**Coarse Aggregate Quantity Corresponding to Coordination Number**} \\ \cline{2-7}
**Number** & **0** & **1** & **2** & **3** & **4** & **5** \\ \hline CPAM1 & 1512 & 2258 & 1277 & 542 & 66 & 41 \\ CPAM2 & 982 & 1786 & 1662 & 1017 & 325 & 170 \\ CPAM3 & 1015 & 2021 & 1608 & 863 & 201 & 124 \\ CPAM4 & 2081 & 2937 & 810 & 145 & 35 & 0 \\ CPAM5 & 1939 & 2454 & 899 & 211 & 51 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistics on the coordination number of coarse aggregates.  
The identified relationships between contact characteristics and aggregate compactness form the basis for strength-performance correlation analysis. In Figure 14a, Marshall stability tests demonstrate a direct correlation between contact number and mixture strength, where greater inter-particle contacts enhance load-bearing capacity and prolong pothole repair service life.  
In Figure 14b, penetration strength analysis indicates an inverse linear relationship with the contact number, showing better compaction performance at higher contact densities. This pattern suggests that increased inter-particle contacts improve aggregate interlock and packing density, leading to superior initial strength development. These properties are especially advantageous for supporting traffic loads immediately after pothole repair.  
These results collectively establish the coordination number as a reliable microstructural parameter for predicting and optimizing CPAM performance, especially regarding strength development and compaction characteristics.
# Infrared and Visible Image Fusion via Feature-Oriented Dual-Module Complementary
## 4 Experimental Setting and Results Analysis
### Subjective Comparisons
The CSF and SeAFusion methods are extreme cases: one is too dim while the other is too bright, and both are fusion results that look unnatural; and the image information of the three lights in the distance is also lost, as shown in the blue enlarged boxes in Figure 4g,i. The DDCGAN in Figure 4h destroys the original texture structure of the source image, resulting in serious artifacts and noise. The AUIF and YDTR methods have improved considerably from the perspective of infrared brightness extraction; the detail information, however, is lost, _e.g._, the three lights in the blue magnified area in Figure 4f as well as the lightness and texture of the tree trunk in the red magnified area in Figure 4k. By contrast, our method has higher image quality owing to the well-preserved details of the infrared targets and visible features. Similar conclusions are observable in Figure 5. The overall contrast of the VggML, Resnet50, and BayF methods are low, resulting in unrecognizable letters on the ground. Although the contrast of AUIF, SeAFusion, and YDTR have improved considerably, the over enhanced contrast makes the fused image look too bright, causing the texture on the wheel to be lost, as can be verified from the blue enlarged areas in Figure 5f,i,k. In addition, the methods based on CSF, DDCGAN, and VEHMD produce artifacts and noise, e.g., the outline of the tree within the blue magnified area. On the contrary, the fused image generated by the proposed algorithm in Figure 5l looks natural and preserves the finer details in the source images.
## Appendix A  
### Proof of Proposition 1  
Consider the PN model shown in Figure 6. By assumption, initially at \(M_{0}\) (time \(\tau_{0}\)), we have \(M_{0}(p_{1s})=M_{0}(p_{2c})=M_{0}(p_{3s})=1\), \(V(M_{0}(p_{1s},\,\phi_{1}))=\zeta_{1}\), \(V(M_{0}(p_{2c},\,\phi_{2}))=\alpha_{2}\), and \(V(M_{0}(p_{3s},\,\phi_{2}))=\mu\times\alpha_{2}\). At this marking, \(t_{23}\) fires with color \(\phi_{2}\) to feed \(p_{2}\) (\(DS_{2}\)), but \(t_{13}\) cannot fire to feed \(p_{1}\) (\(DS_{1}\)). As \(\zeta_{1}\geq\sigma\) holds, we fire \(t_{14}\) (or SCF is adopted) with color \(\phi_{1}\) to feed \(p_{1}\) and fire \(y\) with color \(\phi_{1}\) to charge \(p_{1s}\) simultaneously. At time \(\tau_{1}=\tau_{0}+\Psi\), \(M_{1}\) is reached such that \(M_{1}(p_{1s})=M_{1}(p_{3c})=1\), \(V(M_{1}(p_{1s},\,\phi_{1}))=\zeta_{1}\)\(+\)\(\alpha_{2}\), and \(V(M_{1}(p_{3c},\,\phi_{2}))=\mu\times\alpha_{2}\). At this marking, we fire \(t_{33}\) with color \(\phi_{2}\) to feed \(p_{2}\) and \(t_{14}\) continuesits firing, and \(y\) fires with color \(\phi_{1}\) and volume \(\mu\times\alpha_{1}-\alpha\) to charge \(p_{1s}\) as \(\mu\geq\)\(f_{\rm pmax}/f_{ds1}\). At time \(\tau_{2}=\tau_{1}+(\mu\times\alpha_{1}-\alpha)/f_{p\rm max}=\tau_{0}+(\mu\times \alpha_{1})/f_{p\rm max}\), \(M_{2}\) is reached such that \(M_{2}(p_{1s})=M_{2}(p_{3c})=1\), \(V(M_{2}(p_{1s},\)\(\phi_{1}))=\zeta_{1}+(\mu\times\alpha_{1}\times f_{ds2})/f_{p\rm max }\leq\zeta_{1}\), and \(V(M_{2}(p_{3c},\)\(\phi_{2}))=(\mu+1)\times\alpha_{2}-(\mu\times\alpha_{1}\times f_{ds2})/f_{p\rm max}\). Then, \(t_{14}\) and \(t_{33}\) continue their firing with color \(\phi_{1}\) and \(\phi_{2}\) to feed \(p_{1}\) and \(p_{2}\), respectively, and \(y\) fires with color \(\phi_{2}\) and volume \(\mu\times\alpha_{2}\) to charge \(p_{2s}\). At \(\tau_{3}=\tau_{2}+(\mu\times\alpha_{2})/f_{p\rm max}=\tau_{0}+\mu\times\Psi\), \(M_{3}\) is reached such that \(M_{3}(p_{1s})=M_{3}(p_{2s})=M_{3}(p_{3c})=1\), \(V(M_{3}(p_{1s},\)\(\phi_{1}))=\zeta_{1}\), \(V(M_{3}(p_{2s},\)\(\phi_{2}))=\mu\times\alpha_{2}\), and \(V(M_{3}(p_{3c},\)\(\phi_{2}))=\alpha_{2}\). This marking is equivalent to \(M_{0}\). Hence, \(CTK_{1}\) is feeding \(DS_{1}\) under the SCF mode during \([\tau_{0},\)\(\tau_{3}]\) and \(\Pi=\mu\times\Psi\), resulting in \(\delta=0.5\).
## 2 Materials and Methods
### Implementation and Data Processing  
The model is implemented in Python, as this programming language offers powerful support for optimisation tasks and data processing. This is particularly due to the large number of available libraries that enable efficient mathematical modelling, numerical calculations, and data analysis. Pyomo, a flexible library for the mathematical formulation of complex optimisation problems [19], is used to model and solve MIP. Pyomo enables the use of various solvers, and the CBC (Coin-or Branch and Cut) solver is applied in this model [20]. CBC is an open-source solver based on branch-and-bound methods and is particularly efficient for medium-sized optimisation problems. However, the calculation time can increase significantly with increasing problem size, especially with a high number of binary variables and decision options. The planning horizon considered in the model is restricted to short-term decisions within a limited period of time. The computation time for the considered problem instance, which consists of 4 machines, 8 production orders (each with 3-4 subtasks) and 2 planned maintenance tasks, is measured to evaluate the computational efficiency of the model. The recorded computation times are 0.21 s for CPU processing time and 0.22 s for total elapsed time, indicating that the approach is efficient for small to medium-sized problems. However, as the number of machines, jobs or constraints increases significantly, the computational effort grows exponentially. The initial tests show that computational complexity increases exponentially with the number of binary variables, which is particularly relevant for larger production environments. Future improvements should explore decomposition techniques or heuristic approaches to maintain computational feasibility. This issue can be mitigated by using more advanced solvers such as CPLEX or Gurobi or by implementing heuristic approaches to improve computational efficiency. NumPy (Version: 2.0.2) is used for data processing in order to efficiently analyse and process large volumes of data. Matplotlib and Gantt (Version: 3.9.2) charts are applied to visualise the results, which show the machine allocation, production planning and maintenance intervals clearly. The input parameters of the model are derived from typical data for single- and small-batch production. These include machine capacities, wear limits, production order details, maintenance tasks and set-up times. These parameters enable a realistic representation of the production environment and practical optimisation of machine and maintenance planning. These aspects are described further in Section 2.5.
# Microwave-Supported Modulation of Functional Characteristics of Gluten-Free Breads
## 1 Introduction
Moreover, buckwheat and teff have been recognized for their unique phenolic compounds and antioxidant activities, which can contribute to the overall health benefits of the end products [24; 25; 26; 27; 28; 29]. While the specific impact of microwave hydrothermal treatment on the polyphenolic content and antioxidant activity of gluten-free flours was not directly addressed in the provided references, it is worth noting that the antioxidant activity of gluten-free flours can be influenced by various factors, including the presence of polyphenols. [PERSON] et al. (2019) investigated the partial replacement of wheat flour with gluten-free flours in bread and found that the antioxidant activity was more potent in all gluten-free flours tested [30]. [PERSON] et al. observed that the microwave processing of chia seeds increased the total phenolic content, FRAP, and reducing power, indicating enhanced antioxidant activity [31]. This suggests that gluten-free flours, including those that may have undergone microwave hydrothermal treatment, can potentially exhibit higher antioxidant activity compared to wheat flour. The aim of the study was to investigate the changes in free, bound, and total polyphenols content, and the percentage of bio-accessible polyphenols in flours after MW-HMT and in bread made with treated flours. The bread quality features like bake loss, specific volume, and color of crust and crumb, as well as texture, on the day of baking and after seven days of storage, were analyzed.