esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Dispersion of a Nonlinearly Chirped Pulse in SPM  
In order to generate few-cycle pulses after spectral broadening by SPM, it is important to carefully compensate the phase of the output pulse, not only GDD but also TOD. The origin of TOD generated during the SPM process is asymmetry of the temporal intensity profile that gives a time-dependent nonlinear refractive index. One of the factors that provide an asymmetric temporal profile is a nonlinear chirp, _i.e._, TOD applied to the initial FTL pulse. Thus, the effects of TOD on the pulse shape appear before and after SPM, being related to the GDD.  
Figure 2 is the TOD of the output pulse based on Equation (19) as a function of initial TOD for various values of \(B\). The initial GDD \(\mathbf{\phi^{\sigma}}\) is (a) 10, (b) \(-\)10, and (c) 0. In the case that a large amount of GDD is applied to the initial pulse, there is no great distinction between the TOD of the output pulse and the initial TOD, as shown in Figure 2(a,b). The slope tends to lessen slightly with increasing \(B\). This tendency appears prominently in Figure 2(c). This is because SPM reduces the asymmetry in the temporal profile shown in the nonlinearly chirped pulse. As described above, a positive GDD is added by SPM of the linearly chirped pulse, whether the initial chirp is up or down. On the other hand, SPM of a nonlinearly chirped pulse produces a TOD with the opposite sign to the initial TOD.  
Summarizing the results shown in Figure 2, we can conclude that the TOD of the output pulse nearly equals the initial TOD when the \(B\)-integral is much smaller than the absolute initial GDD \(\mathbf{\phi^{\sigma}}\). On the other hand, the TOD is near zero when the \(B\)-integral is much larger than the absolute initial GDD.
# Novel Microfluidic Septum to Optimize Energy Recovery in Single-Chamber Microbial Fuel Cells
## 1 Introduction
In the present work, we aimed at developing a new a-SCMFC configuration that conjugates the optimization of the electrolyte distribution under feeding conditions, by achieving an optimal drop-like shape, with the presence of a separator material suitable to minimize oxygen cross-over. As a solution, a modified a-SCMFC configuration by incorporating an intermediate microfluidic septum (IMS) was proposed. Such an IMS, without modifying the electrodes' spacing, occupies a significant portion of the internal cell's volume and features a pass-through opening. The primary intention behind the design of an IMS is to shape the reactor to exactly mimic the electrolyte flow patterns naturally occurring within the MFC during refills, so as to guarantee optimal electrolyte replenishment [31; 32]. Moreover, compared to the usual square a-SCMFC design, the addition of the IMS in the cell's chamber reduces the amount of electrolyte required for this filling, but leaves the surface area of the electrodes unchanged. In addition, thanks to its shape, the IMS can act as a separator material to reduce the oxygen crossover near to the anode's surfaces, maintaining a distance between anode and cathode close to 1.8 cm, resulting in an adequate distance to minimize the amount of oxygen dissolved in the proximity of anode electrodes [26]. Furthermore, implementing the IMS configuration, this work demonstrates the tremendous impact of controlled fluid dynamics on the overall performance of a-SCMFCs. To give evidence of the crucial role of IMS configuration to improve the overall devices' performances, the energy recovery (\(E_{rec}\)) factor, defined as the ratio between the output energy yield and the available electrolyte volume, has been analyzed [6; 33; 34; 35; 36; 37]. The IMS design simultaneously addresses both aspects, improving energy output by optimizing biofilm electrical activity while also reducing the volume of the single-chamber electrolyte. Furthermore, a-SCMFCs with the IMS achieved an energy recovery one order of magnitude higher than that achieved by control cells, thus demonstrating the key role of microfluidics inside MFCs. This study contributes to the advancement in MFC technology, providing an alternative solution that partly overcomes the disadvantages related to the absence of an ionic exchange membrane.
## 2 Results and Discussion
### Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)  
#### 2.5.1 Permuted Value  
An OPLS-DA model was used to further identify which specific aromatic components contribute to the differences between microcapsules prepared with various wall materials. The permutation test for the jasmine instant tea (Figure S3 in the Supplementary Materials) demonstrated that the model possesses predictive relevance. The R2 and Q2 values for the original model (R2 = 0.463, Q2 = \(-\)0.971) stand out when compared to the distribution of the permuted values, with intercepts of R2 = (0.0, 0.463) and Q2 = (0.0, \(-\)0.971). The non-interception of the zero line by the permuted R2 and Q2 values reinforces the model's robustness against overfitting. The permutation tests confirm that the OPLS-DA models captured not merely noise, but rather, genuine underlying patterns in the data related to the microencapsulation's effect on aroma preservation. The clear separation between the permuted and original model statistics indicates that the encapsulation process significantly impacts aroma profile retention, with certain treatments demonstrating a stronger preservation effect, as previously observed in the PCA and OPLS-DA results.
## 6 Experimental Results and Analysis
### Time in the Air (ToA)  
It is noticeable that higher BW bandwidth gives a higher data rate [37], with shorter transmission time, as Equation (2) demonstrates. However, higher BW degrades the receiver sensitivity, as revealed by Equation (4), due to additional noise integration. For the transmission of a LoRa packet, its structure comprises three elements: preamble, header (optional) and payload.  
There are two types of LoRa packet format modes, explicit and implicit; where the header is removed from the packet, both the CR and payload size are fixed and must be manually configured on both sides of the radio link. Therefore, the implicit header mode reduces the transmission time of the packet, which is known in the LoRa literature as ToA calculated using Equation (3). For the study, we worked with the LoRa node parameters in Table 2.  
The packet structure in transmission consisted of the preamble plus the payload of 16 bytes, 8 bytes and 4 bytes with different SF. The result showed that the higher the SF, the airtime of a packet increases. These results can be seen in Figure 17.
###### Abstract  
This study proposes a time-dependent reliability analysis method for aqueduct structures based on concrete carbonation and finite element analysis. The primary goal of this study is to improve the reliability assessment of reinforced concrete aqueducs by incorporating environmental factors such as carbonation over time. First, a three-dimensional finite element model of a reinforced concrete aqueduct is established using the Midas 2022 Civil software, incorporating a time-varying function derived from a predictive model of concrete carbonation depth. Point estimation is then integrated with structural finite element analysis to calculate the first four moments of random variables as functions of concrete carbonation. Additionally, the original performance function is transformed into a normal distribution using dual power transformation and the Jarque-Bera test. The high-order unscented transformation (HUT) is subsequently employed to estimate the first four moments of the transformed performance function, facilitating the calculation of time-varying reliability indices for the carbonated concrete aqueduct. Based on the time-varying reliability index data, a reliability function corresponding to different time points is fitted and applied to service life prediction. The results demonstrate that the proposed method effectively reduces large errors associated with the fourth-moment method in calculating large reliability indices. Furthermore, the comparison with Monte Carlo simulation (MCS) results validates the high efficiency and accuracy of the proposed method, offering a valuable tool for addressing the reliability challenges of aqueducs exposed to carbonation and other environmental factors over time.  
finite element method; aqueduct; dual power transformation; HUT; time-dependent reliability +
Footnote †: journal: Applied sciences
## 10 Simulation Results and Discussion
### Electricity Cost vs. Electricity Price  
Figure 8 shows the electricity cost comparison of the WDO and PSO algorithms in the scheduled and unscheduled cases, respectively. It is clear form Figure 8 that EMC optimizes the energy consumption of home appliances by scheduling in low pricing time slots. During hours \((0\to 6)\), the scheduled electricity costs of both the PSO and WDO algorithms are comparatively the same, because EMC schedules the appliances according to the low pricing slots without taking into consideration the maximum capacity limit. During high price hours \((6\to 10)\), the electricity cost of the PSO algorithm is high, as compared to the WDO algorithm, because EMC schedules a greater number of appliances during these time slots. During mid-peak hours \((11\to 15)\), a greater number of appliances are turned on by the PSO algorithm as compared to WDO, so the electricity bill is higher. The remaining working cycles of all of the appliances are completed during low price hours \((15\to 25)\). It is clear that almost the electricity bill is the same during these hours. In Figure 9, the K-PSO algorithm schedules all appliances from hours \((0\to 24)\). During high peak hours \((7\to 10)\), a greater number of appliances are turned on as compared  
Figure 7: (**a**) Appliance I starting time; (**b**) Appliance II starting time; (**c**) Appliance III starting time; (**d**) Appliance IV starting time; (**e**) Appliance V starting time; (**f**) Appliance I starting time; appliance staring time of the unscheduled and scheduled cases.  
to the WDO algorithm, which does not turn on any appliances in these hours in order to save on the electricity bill. The remaining working cycles of appliances in the K-WDO case are completed during \((15\to 24)\) h. This is because during hours \((6\to 10)\), electricity prices are high, and the scheduler adjusts the working of all appliances to other time slots to save electricity cost. To sum up, we conclude that by using the K-WDO algorithm, electricity cost and high peaks are effectively reduced, which are useful in stabilizing the electric grid with user benefits.
## 4 Results and Discussion
### Time History Analysis
#### 4.2.5 On the Expansion Joint  
Upon contrasting the respective results from the tank wall and the expansion joints, it becomes apparent that the tensile stress within the expansion joints surpasses that within the wall. A comparison highlighting the peak tensile stress in the adjacent wall and the wall expansion joints for soil type 1 is presented in Figure 12. This value escalates when the tank is water-filled, with the peak point value reaching 23% and decreasing with a reduction in the water volume within the tank.  
As previously noted, the maximum tensile stress for the concrete considered in this study was 2.71 MPa. When comparing the maximum tensile stress in the wall and the adjoining wall expansion joints, it is observed that the expansion joints are fully engaged and reach up to 2.73 MPa. This result suggests a potential for cracking in the tank. Furthermore, leaks from the tank fluid may cause minor damage, potentially leading to irreversible harm.
## 6 Experiment
### Experimental Setup
#### 6.1.4 Evaluation Metric  
In the experimental evaluation metrics section, we primarily use the hit rate of the target item \(I\) and Normalized Discounted Cumulative Gain (NDCG) to assess the effectiveness of the poisoning attack in promoting the target item. Initially, for each target item, based on the original training dataset, we identify all users who have not interacted with the item and divide these users into two subsets: The target user set is randomly selected from the list of users who have never rated the item. These users are used to simulate interactions under the attack strategy, allowing for the calculation of reward values and optimization of the attack strategy. And the evaluation user set, consisting of the remaining users, is used as the basis for calculating evaluation metrics to measure the actual impact of the poisoning attack on the recommendation system.  
**HR@K:** Assume that each user's recommendation list contains \(k\) items. HR@K is calculated as the proportion of evaluation users whose \(top\)-k recommendation lists include the target item, derived using Equation (3).  
**NDCG@K:** Considering the ranking positions of items in the recommendation lists, by calculating NDCG, we can assess the effectiveness of the poisoning attack strategy in improving the ranking quality of the target item.  
HR@K and NDCG@K [39; 40; 41] are commonly used evaluation metrics in recommendation systems, which are used to measure the coverage and ranking quality of recommendation systems, respectively.
## 1 Introduction
### Automatic Verification
In particular, in this paper, we introduce a novel approach for the translation of the model checking problem for EMTLKD to the model checking problem of a variant of deontic-epistemic linear temporal logic (LTL), denoted as \(\text{ELTLKD}_{\text{q}}\). Moreover, we propose a modified SMT-based bounded model checking (BMC) approach for \(\text{ELTLKD}_{\text{q}}\), which allows for more efficient verification of safety-critical systems modeled as dense-timed deontic interpreted systems (DTDISs). Our approach enables rigorous verification of deontic real-time multi-agent systems (DRTMASs) by leveraging SMT-based BMC, making it particularly valuable for ensuring the reliability of safety-critical systems where failures could have severe consequences. Examples include railway interlocking systems, autonomous vehicle coordination, and critical infrastructure monitoring. It is important to note that unlike full model checking, which aims to verify properties over an unbounded execution space and is often subject to undecidability concerns in dense-time systems, BMC operates over a finite prefix of system executions. This means that we do not attempt to establish the universal truth of a specification over an infinite model but rather search for counterexamples within a given bound. Consequently, concerns about full decidability do not apply here, as BMC inherently operates within a decidable fragment by construction. This makes our approach practical for real-time multi-agent systems, where exploring the entire state space would be computationally infeasible. An alternative approach to ensuring the safety of real-time systems is Adaptive Dynamic Programming (ADP), which has been explored in control theory and reinforcement learning-based optimization techniques [35; 36; 37]. ADP-based methods focus on dynamically adjusting system behavior in response to environmental changes, often utilizing neural networks and adaptive control strategies to optimize performance under uncertainty. While these methods have been successfully applied to the control of nonlinear safety-critical systems, they do not provide exhaustive correctness guarantees, as they rely on approximations and empirical validation rather than formal verification. In contrast, our approach is based on model checking, which enables rigorous verification of all possible system executions, ensuring that specified safety and correctness properties hold under all conditions. This distinction is particularly important in applications where compliance with strict safety requirements is critical and cannot be left to adaptive optimization alone.
## 3 Results and Discussion
### Catalytic Studies of the Quasi-Homogeneous and Heterogeneous Base Catalysts
In addition, the catalyst **SG-MNP-NH2** can be recycled in the condensation of _p_-anisaldehyde with nitroethane for four consecutive runs without significant decrease in its activity or selectivity. \begin{table}
\begin{tabular}{l l l l} \hline
**Entry** & **Catalyst** & **Solvent** & **Yield (\%)** \\ \hline
1 & **SG-MNP-NH2** & acetonitrile & 47 \\
2 & **SG-MNP-NH2** & dimethylformamide & 68 \\
3 & **SG-MNP-NH2** & dioxane & 53 \\
4 & **SG-MNP-NH2** & dimethyl sulfoxide & 34 \\
5 & **SG-MNP-NH2** & ethanol (95\%) & 48 \\
6 & **SG-MNP-NH2** & ethanol (dry) & 80 \\
7 & **SG-MNP-NH2** & benzene & 87 \\
8 & **SG-MNP-NH2** & heptane & 88 \\
9 & **SG-MNP-NH2** & cyclohexane & 90 \\
10 & **SG-MNP-NH2** & toluene & 85 \\
11 & **SG-MNP-NMe2** & toluene & 72\({}^{\text{b}}\) \\
12 & **SG-MNP-NMe2** & ethanol (dry) & 23\({}^{\text{b}}\) \\
13 & **SG-MNP-NMe2** & dimethyl sulfoxide & - \\ \hline \multicolumn{3}{l}{\({}^{a}\) Reaction conditions: 100 mg catalyst, 1.64 mmol _p_-anisaldehyde, 2.39 mmol nitroethane, 5 ml solvent, 80 \({}^{\circ}\)C, 16 h.} \\ \multicolumn{3}{l}{\({}^{b}\) Only 1-(4-methoxyphenyl)-2-nitropropan-1-ol was formed.} \\ \end{tabular}
\end{table} 
Table 1: Nitroaldol condensation of nitroethane with _p_-anisaldehyde in different organic solvents catalyzed by magnetically separable heterogeneous base catalysts \({}^{a}\).
## 3 Results and Discussion
### Sensitive Analysis of Parameters
#### 3.2.3 Effect of the Ratio of Liner Major Axis to Host Pipe Radius  
The pressure versus displacement curves when the value of \(a/R\) is changed are shown in Figure 11. As the value of \(a/R\) increases, the critical buckling pressure of the liner decreases and the displacement of the buckling lobe increases. Comparing the curves in Figure 11a,b, it can be found that the critical buckling pressure of the circular liner (\(q=0\)) is significantly higher than the elliptical liner (\(q=2.5\)). The critical buckling pressure in Group C-6 decreased by 46.21% compared to Group B-2 and the displacement of the buckling lobe increased by 100.52%, while the critical buckling pressure in Group C-1 decreased by 10.64% compared to Group B-1 and the displacement of the buckling lobe increased by 21.77%. Apparently, the increase rate and decrease rate caused by the decrease of \(a/R\) during the buckling equilibrium path is greater when the liner cross-section is circular (\(q=0\)) than when the liner cross-section is elliptical (\(q=5\)). The deformation of the buckling lobe when Group B-2 and Group C-6 reach the critical buckling pressure is shown in Figure 12 and the stress in the tensile part of the liner does not change significantly but the range of the detached portion increases when the value of \(a/R\) decreases, which leads to the decrease in critical buckling pressure and increase in the displacement of the buckling lobe.  
The comparison of the critical buckling pressure between numerical results and calculation results when the value of \(a/R\) is changed under different values of \(q\) is shown in Figure 13; the effect of the ratio of liner major axis to host pipe radius (\(a/R\)) in Equation (1) is ignored. However, the critical buckling pressure of the simulation results reduce by 33.95 kPa and 1.79 kPa under \(q=0\) and \(q=5\) when \(a/R\) changed from 0.999 to 0.99. The critical buckling pressure of the simulation results reduce by 3.77 kPa and 0.89 kPa under \(q=0\) and \(q=5\) when \(a/R\) changed from 0.99 to 0.98. The simulation results show that the critical buckling pressure of the liner decreases as the value of \(a/R\) is decreasing, while the reduction amount of the critical buckling pressure of the liner decreases due to the value of \(q\) increasing.  
Figure 10: Comparing the influence of increasing \(q\) for critical buckling pressure under different \(a/R\).
## 3 Evaluation
### Experimental Setup
#### 3.2.2 Classification Setting  
We devised an experiment where we employ our model before training a classifier to map both the training set and the test set. We then trained the classifier on the plain training set (\(D^{\text{Tr}}\)) augmented with the mapped training set (\(D^{\text{Tr}}\)) using our model, and we evaluated the classifier on the mapped test set (\(D^{\text{Tr}}\)). We compared our experiment with a baseline experiment where we trained the classifier on the plain training set (\(D^{\text{Tr}}\)) and evaluated it on the plain test set (\(D^{\text{Tr}}\)). We deployed a neural network (NN) and a linear SVM models for the classification task. We used a linear SVM as a classifier because improving accuracy with a linear classifier demonstrates the linear separability enhancement of transformed data points. We measured the accuracy for both classifier models (NN and LSVM) and the loss for the NN model. Experiments were repeated 10 times independently and average accuracy and average loss was calculated over the 10 runs for each experiment and compare the obtained results of the baseline experiment against ours. Both experiments with both  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **K-Means Init.** \\ **Method** \\ \end{tabular}  & \begin{tabular}{c} **Test Set** \\ \end{tabular}  & \begin{tabular}{c} **Time** \\ \end{tabular}  & \begin{tabular}{c} **Inertia** \\ \end{tabular}  & \begin{tabular}{c} **H** \\ \end{tabular}  & \begin{tabular}{c} **C** \\ \end{tabular}  & \begin{tabular}{c} **V\({}_{\beta}\)** \\ \end{tabular}  & \begin{tabular}{c} **ARI** \\ \end{tabular}  & \begin{tabular}{c} **AMI** \\ \end{tabular}  & \ \begin{tabular}{c} **S** \\ \end{tabular}  \\ \hline \multirow{7}{*}{MNIST} & K-Means++ & \(D^{\text{Tr}}\) & 119.802 s & 42,141,554 & 0.421 & 0.443 & 0.432 & 0.322 & 0.421 & 0.058 \\  & \(D^{\text{Tr}}\) & **32.010 s** & **14,454,470** & **0.658** & **0.680** & **0.669** & **0.586** & **0.658** & **0.396** \\  & Random & \(D^{\text{Tr}}\) & 62.014 s & 42,142,078 & 0.417 & 0.439 & 0.428 & 0.318 & 0.417 & 0.060 \\  & \(D^{\text{Tr}}\) & **31.006 s** & **14,454,470** & **0.658** & **0.680** & **0.669** & **0.586** & **0.658** & **0.381** \\  & PCA-Based & \(D^{\text{Tr}}\) & 14.302 s & 42,142,060 & 0.417 & 0.439 & 0.428 & 0.317 & 0.417 & 0.055 \\  & \(D^{\text{Tr}}\) & **8.425 s** & **14,454,470** & **0.658** & **0.680** & **0.669** & **0.586** & **0.658** & **0.394** \\ \hline \multirow{7}{*}{ \begin{tabular}{c} Fashion- \\ MNIST \\ \end{tabular}
## 2 Modeling of Distribution System Components
### PV Generation System and ESS System Modeling
#### 2.2.2 Model of a DC/DC Converter  
DC-DC converters such as buck or boost converters help realize highly flexible and efficient power control in DC circuits. This enabled us to track the optimal operating point in this case [29]. In this study, we employed a boost-type converter whose circuit diagram is illustrated in Figure 6.  
This converter is governed by the following equations:  
\[V_{0}=\frac{V}{1-\alpha} \tag{16}\]  
\[I_{0}=I(1-\alpha) \tag{17}\]  
where \(\alpha\), \(V_{0}\), and \(I_{0}\) denote the duty cycle, output voltage, and output current of the boost converter, respectively. Furthermore, \(\alpha\) is the boost converter duty cycle.  
The boost converter's inductance, input, and output capacitance are expressed as follows:  
\[\left\{\begin{array}{c}C_{1}=\frac{dV_{0}}{8L\Delta V_{0}f^{2}}\\ C_{2}=\frac{dI}{f\Delta V}\\ L=\frac{dV_{0}}{f\Delta I_{L}}\end{array}\right. \tag{18}\]  
where \(\Delta V_{0}\) and \(\Delta V\) denote the input and output desired amplitude, respectively. Furthermore, \(\Delta I_{L}\) and \(f\) represent the inductance current desired amplitude and the frequency, respectively.