esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation Results via Simulation  
To validate the accurateness of the mathematical procedures and the correct operation of the proposed control, two simulation tests were built in PSIM software, one for standard of 120 V @ 60 Hz (Simulation test 1) and another for 220 V @ 50 Hz (Simulation test 2). Comparison of voltage stress in semiconductors with respect to the SS-SMC presented in [4] is also performed. The simulation step time was configured to be \(2.60417\times 10^{-7}\) s for the test 1 and \(3.125\times 10^{-7}\) s for the test 2. The parameters of the power converter and its control are listed in Tables 2 and 3. Parasitic resistances of elements were obtained from datasheets: Equivalent series resistances in inductors and capacitors (\(R_{L}\) and \(R_{C}\), respectively) and Drain-Source on-resistance in MOSFETs (\(R_{M}\)). Considering that the proposed control can be entirely implemented with analog electronics, digitalization effects were only considered for reference generation. The generation method presented in [18] was considered, where signals are produced by using a look-up table storing 128 samples of 10 bits for a cycle of the output signal. It is worth mentioning that these features are typical of low cost microcontroller.  
In both simulation tests, five points are enforced to assess the stationary and transient behavior. Selected conditions and time intervals are listed in Table 4. Sudden transitions are enforced during the load changes while ramp type transitions with intervals of 3 ms are applied for input voltage changes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Symbol** & **Value (60 Hz)** & **Value (50 Hz)** & **Unities** \\ \hline Proportional gain & \(K_{p}\) & 0.3 & 0.2 & \\ Integral gain & \(\alpha K_{p}\) & \(25\times 10^{-6}\) & \(20\times 10^{-6}\) & \\ Saturation limit & \(s_{0}\) & \(500\times 10^{3}\) & \(500\times 10^{3}\) & A/s \\ Hysteresis band width & \(2\delta\) & 2 & 2 & A \\ Cutoff frequency LPF & \(f_{c1}\) & 240 & 200 & Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Control parameters used in simulations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Element** & **Manuf/Reference** & **Parameter** & **Symbol** & **Value** & **Unities** \\ \hline Capacitors & KEMET [19] & Capacitance & \(C\) & 9 & \(\mu\)F \\  & & Series & \(R_{C}\) & 8.3 & m\(\Omega\) \\ \hline \multirow{2}{*}{Inductors} & \multirow{2}{*}{Bourns [20]} & Inductance & \(L\) & 120 & \(\mu\)H \\  & & Series & \(R_{L}\) & 28 & m\(\Omega\) \\ \hline MOSFETs & ROHM [21] & On-resistance & \(R_{M}\) & 196 & m\(\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the power converter in simulations.  
Figure 9: Block diagram of the outer voltage controllers.
## Materials and Methods  
### Chemicals  
Analytical standards, with at least 97% purity were purchased from Sigma-Aldrich China Co. (St. Louis, Missouri, USA). These analytical standards were Ethyl Acetate, Ethyl Isobutyrate, Isoamyl Acetate, Ethyl Hexanoate, Ethyl Lactate, Ethyl Leptaonate, Ethyl Caprylate, Ethyl Nonanoate, Hexyl Hexanoate, Ethyl Decanoate, Isobutanol, Isoamyl Alcohol, 2-Heptanol, (E)-3-Hexen-1-ol, Heptanol, Nonanal, Decanal, 2,3-Butanedione, 3-hydroxy-2-Butanone, 1-Octen-3-one, Benzaldehyde, Benzyl Alcohol, Ethyl Phenylacetate, Phenethyl Acetate, Phenylethyl Alcohol, Terpinolene, a-Terpineol, Linalool, 4-Terpinoleol, Citronellol, Geranyl Acetone, (E)-Nerolidol, \(\beta\)-Damascenone, Butyrolactone, 4-Mercapto-4-methylpentan-2-one (4 MMP), 3-(Methylthio)-Propionaldehyde, 4-Mercapto-4-Methyl-2-Pentanol (4 MMPPol), 3-(Methylthio)-1-Propanol, 3-Mercaptohexyl Acetate (3 MHA), Furfural, Acetic Acid, Butanoic Acid, Octanoic Acid and O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine hydrochloride (PFBHA). L-Menthol (internal standard, IS1), Methyl Hexanoate (IS2), p-Fluorobenzaldehyde (IS3), 2,2-Dimethyl Propanoic Acid (IS4) and Isopropyl Disulfide (IS6) were purchased from ANPEL Scientific Instrument Co., Ltd. (Shanghai, China). 2-Methoxy-3-([2H3] Isobutyl) Pyrazine (IS5) was purchased from Medical Isotopes, Inc. (NH, USA). HPLC-grade ethanol absolute (\(\geq\)99.8%), dichloromethane (\(\geq\)99.8%) and methanol (\(\geq\)99.9%) were also purchased from Sigma-Aldrich China Co. (St. Louis, MI, USA). Ultrapure water was made with Milli-Q purification system (Millipore, Bedford, MA), and it was boiled for 5 min before use.
# Congestion-Free Ant Traffic: Jam Absorption Mechanism in Multiple Platoons
## 2 Model and Simulation Scenario
### Model
#### 2.1.1 Stage I: Ant Motion
In the third case, the next cell contains no ants \((s_{i+1}(t)=0)\), but it contains a detectable level of pheromones below saturation \((1<\sigma_{i+1}(t)<\sigma_{(sat)})\). In that case, the instantaneous velocity of the ant depends on the pheromone concentration in the next cell. For the analysis presented in this paper, we use a deterministic equation to represent velocity changes related to the third case. Deterministic dynamics represents high sensitivity to pheromones, as well as low inertia of ants on the ant trail. In this scenario (the third case), the value of the prefactor \(\mathbf{a}\) is decided based on \(\sigma_{sat}\) and \(v_{max}\) (upper velocity limit \((=1)\)). For the functioning of the model within given velocity limits, \((\mathbf{a}\times\sigma_{sat}\leq v_{max}+\Omega_{i+1}-v_{min})\) should be satisfied. On the other hand, a lower value of \(\mathbf{a}\times\sigma_{sat}\) will lead to a lower velocity range. Therefore, considering the above restriction and mathematical simplicity, we selected \(\mathbf{a}\times\sigma_{sat}=0.8\), and assuming \(\sigma_{sat}=80\), we chose \(\mathbf{a}=0.01\) (for further explanation about \(\sigma_{sat}\), check Appendix A.2). In the fourth and final case, the next cell contains no ant, but the level of pheromone is above saturation. In that case, the velocity of the given ant becomes equal to the velocity at \(\sigma_{sat}\), which can be calculated by using the third case equation \((v_{min}+\mathbf{a}\times\sigma_{sat}-\Omega_{i+1})\). As explained above, for the analysis in this paper, we assumed \(\sigma_{sat}=80\). As specified by Equation (2), the new position \(p_{j}(t+1)\) of the given ant is calculated by adding the position of the ant at time \(t\) to the distance traveled in unit time \((v_{j}(t))\). It is interesting to note that the dynamics presented in Equation (1) resemble the dynamics in the Nagel-Schreckenberg model where the velocity of an agent depends on the headway distance, whereas in the case of our model, the velocity of an agent depends on the pheromone concentration. This resemblance indicates that both models are analogous to each other, which indicates that there might be some similarities between the two traffic systems [29].
## 5 Swarm-Based Solution
### Optimal Sink Selection Problem
#### 5.1.1 Pheromone Calculation  
The pheromone value is updated using the distance hop metric since the selected sink node should offer the most suitable trade-off between energy utilization and in-time data delivery. The increasing density of the pheromone value is as follows:  
\[\Delta\tau_{ss_{i}}^{1}=DhtM_{s_{i}}^{s} \tag{62}\]  
The increase in the pheromone density is defined as:  
\[\tau_{ss_{i}}^{1}(t)=(1-\rho_{1})\tau_{ss_{i}}^{1}(t-1)+\rho_{1}\Delta\tau_{ss _{i}}^{1} \tag{63}\]  
where \(\rho_{1}\in(0,1)\)\(\rho\in\) UOTE is the evaporation factor [7].
# Generative Aspect Sentiment Quad Prediction with Self-Inference Template
## 5 Results and Discussion  
### Main Results  
The experimental results for various methods are reported in Table 2. For the ASQP dataset that does not contain implicit opinions, our model significantly improves various metrics compared to the Paraphrase method. The F1 scores for Rest15 and Rest16 are increased by 2.05% and 2.33%, respectively. In comparison to DLO and ILO methods, our Self-Inference Template slightly lags behind ILO on Rest15 but outperforms DLO and ILO on Rest16 without increasing the time cost. After adding prefix prompts, the model achieves the best results on Rest16. Combining prefix prompts and Mask operations on the smaller dataset Rest15 leads to a substantial improvement in model performance. With the assistance of these two methods, the model achieves optimal results on both datasets, with improvements of 1.02% and 1.73% compared to the Self-Inference Template.  
For the ACOS dataset containing implicit opinion terms, the Self-Inference Template, compared to the Paraphrase method, showed a 2.94% improvement in F1 score on ACOS_Restaurant. This indicates that the Self-Inference Template, by guiding the model to think step by step, indeed enhances the model's reasoning ability. However, for the ACOS_Laptop dataset, the improvement in the Self-Inference Template was marginal. This could be attributed to the excessive number of aspect categories in ACOS_Laptop, coupled with imbalanced data distribution among different aspect categories. The training set of ACOS_Laptop comprises a total of 114 aspect categories, with only 10 categories appearing in the tuples more than 100 times, and over half of the aspect categories appearing in tuples fewer than 10 times. The model struggles to adequately learn from each aspect category's data. Despite the guidance provided by the Self-Inference Template for thoughtful reasoning, the model faces challenges in correctly classifying aspect categories with numerous classes and limited training examples. However, with the addition of prefix prompts and Mask operations, the F1 score for ACOS_Laptop increased by 1.44%. This indicates that our two methods effectively assist the model in learning.  
Table 3 records the runtime of Paraphrase, SIT, and the combination of SIT with two methods. It can be observed that, compared to the Paraphrase model, the runtime of the Self-Inference Template has almost remained unchanged. The addition of the two small enhancements to the model has also had no impact on runtime.
## 3 Methodology
### Diffusion Module
The inverse diffusion process is the key to generating high-quality samples through the model. The model trains a denoising network to learn how to progressively remove noise from the noised latent representations to recover the original latent representations. Over several iterations, the model progressively improves the quality of generated samples. The model can effectively capture the complex structures and long-range dependencies of the data, thus generating highly realistic samples. The success of this process depends on carefully designed denoising networks, effective training strategies, and the accurate modeling of the data distribution in the latent space. Lastly, there is the conditional mechanism, which enables the model to introduce external information into the generation process, allowing it to produce samples that meet specific requirements. Through the conditional mechanism, the model can utilize additional input information, such as textual descriptions, categorical labels, or data from other modalities, to control the attributes and content of the generated results. Methods to implement the conditional mechanism typically involve encoding the condition information into vector representations and then integrating them with the latent representations at each step of the diffusion process. This introduces conditional information into the denoising network so that the model can refer to conditions while denoising, thereby generating samples that match the conditions. By having to learn the associations between conditional information and data distributions during training, the model can accurately capture and reflect the conditional information during generation, improving the quality and variety of the generated results. These three components work together to allow the latent diffusion model to efficiently conduct data generation in the latent space while achieving precise control over the generated content. Perceptual compression reduces the dimensions and computational complexity of the data, latent diffusion carries out efficient diffusion processes in the compressed space, and the conditional mechanism provides flexibility so that the model to generate specific samples as needed.
## 4 Review of Case Studies: Electric Vehicle Routing Optimization
\begin{table}
\begin{tabular}{c c c c c} \hline
**Study** & **Application Domain** & **Optimization Technique** & **Performance Metrics** & **Results** \\ \hline [PERSON] et al. [37] & Distribution of perishable goods & GA-based heuristic & Logistics cost, quality constraints & \begin{tabular}{c} Heuristic provides computationally \\ efficient solutions with minimal loss in \\ solution quality, considering time \\ windows and deterioration rates. \\ \end{tabular} \\ [PERSON] and [PERSON] [58] & General VRP (GVRP) & Iterative improvement approach & \begin{tabular}{c} Time window restrictions, \\ heterogeneous fleet, route restrictions \\ \end{tabular} & \begin{tabular}{c} Proposed approach successfully handles \\ multiple vehicles and transportation \\ requests in EVRP. \\ \end{tabular} \\ [PERSON] [39] & Hybrid VRP (HVRP) & Tabu search-based heuristic & \begin{tabular}{c} Vehicle cost, total distance traveled, \\ penalty cost for delays \\ \end{tabular} & \begin{tabular}{c} Heuristic algorithm effectively addresses \\ HVRP with time window constraints and \\ penalized delays. \\ \end{tabular} \\ [PERSON] et al. [40] & Hybrid VEP (HVRP) & MILP & Optimal vehicle routes & \begin{tabular}{c} Branch and cut algorithm-based approach \\ obtains optimal vehicle routes in HVRP. \\ \end{tabular} \\ [PERSON] et al. [41] & Hybrid VRP (HVRP) & \begin{tabular}{c} Iterated local search (ILS) \\ metaheuristic \\ \end{tabular} & Optimal solutions for HVRP variants & \begin{tabular}{c} IIS maneuver provides optimal \\ solutions for HVRP variants and \\ different numbers of customers and \\ fleet sizes. \\ \end{tabular} \\ [PERSON] and [PERSON] [42] & Hybrid VRP (HVRP) & Sequential insertion-based heuristic & \begin{tabular}{c} Permissable overloads, penalty cost \\ for overloads \\ \end{tabular} & \begin{tabular}{c} Heuristic algorithm effectively addresses \\ HVRP considering permissible overloads \\ and penalty cost for overloads. \\ \end{tabular} \\ [PERSON] et al. [44] & Hybrid VEP (HVRP) & GA, branch and bound & \begin{tabular}{c} Time windows, multi-product \\ deliveries, limited resources \\ \end{tabular} & \begin{tabular}{c} GA and branch and bound approaches \\ find optimal solutions for HVRP with time windows, multi-product deliveries, and limited resources. \\ and limited resources. \\ \end{tabular} \\ [PERSON] et al. [45] & GreenIRP & Mixed-integer linear programming model & Total cost, fuel consumption, emission & \begin{tabular}{c} Mixed-integer linear programming model \\ minimizers total cost, considering \\ load-dependent fuel consumption and \\ emission costs. \\ \end{tabular} \\ [PERSON] et al. [46] & \
\begin{tabular}{c} Multi-objective VRP with \\ simultaneous pick-up and delivery \\ and time window constraints \\ (Mo-EVRS/DDTW) \\ \end{tabular} & Multi-objective GA (NSGA-II) & Total logistics cost, average customer satisfaction &
\begin{tabular}{c} Improved NSGA-II algorithm reduces \\ logistics costs while maintaining high \\ customer satisfaction levels, optimizing \\ the distribution process. \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._
## 3 Results  
### Demographic Data  
The study took place from January to December 2022. A total of 110 patients were screened until the required 60 patients were reached (see Figure 1). After the baseline visit, two patients of the control group had to be excluded because they took medications that could affect the results. Additionally, three patients (two from the control group and one from the test group) were unable to attend the week 3 or week 6 visits due to work-related reasons. The final distribution of patients is presented in Table 1. No statistically significant differences were found between the groups at the baseline visit for any of the clinical outcomes evaluated. In the test group, the sample consisted of 11 males and 18 females, ranging from 21 to 72 years of age, with a mean age of 39.24 (standard deviation, SD = 17.77 years), with three patients being current smokers. The control group consisted of 12 males and 14 females, ranging from 21 to 74 years, with a mean age of 43.23 (SD = 18.01 years), and two of the patients being current smokers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **All** & **Test Group** & **Control Group** & \(p\) \\ \hline
**n** & & 55 & 29 & 26 & \\ \hline
**Age** & Mean (SD) & 41.13 (0.17) & 39.24 (17.77) & 43.23 (18.01) & Mann–Whitney \\ \hline  & Min–Max & 21–74 & 21–72 & 21–74 & 0.438 \\ \hline
**Gender** & Male [n (\%)] & 23 (41.8) & 11 (37.9) & 12 (46.2) & Chi-square \\ \hline  & Female [n (\%)] & 32 (58.2) & 18 (62.1) & 14 (53.8) & 0.537 \\ \hline
**Smoking** & No [n (\%)] & 50 (90.9) & 26 (89.7) & 24 (92.3) & Fisher exact \\ \hline  & Yes [n (\%)] & 5 (9.1) & 3 (10.3) & 2 (7.7) & 1.000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic characteristics of the complete patient sample and by study group, analyzed by means of different statistical tests (see column “\(p\) value”).
## 7 Simulation Results and Analysis
### Simulation Results of FSO Link  
During the simulation, we evaluate the BER and outage probability of the FSO link. For the FSO link, the channel fading is considered to be a Gamma-Gamma stochastic process. In this section, the effects of pointing errors and different turbulence strengths on the performance of the FSO link are investigated for different values of \(\varsigma\). According to [41], we set the parameters as follows: the laser wavelength is \(\lambda_{FSO}\) = 1550 nm, the beam divergence angle is \(\theta\) = 2 mrad, the receiver diameter is 20 cm and the receiver aperture radius is 6 cm.  
Figure 6 depicts the BER of the FSO link for different strengths of atmospheric turbulence with \(\alpha\) = 2.289, \(\beta\) = 1.834 (moderate turbulence) and \(\alpha\) = 2.052, \(\beta\) = 1.335 (strong turbulence). Based on the previous derivation in the FSO system model, it is clear that \(\varsigma\) is inversely proportional to the pointing error displacement standard deviation \(\sigma_{s}\) and the pointing error is larger when \(\varsigma\)= 1.5. In Figure 6, an increase in the pointing error leads to a significant increase in the BER of FSO link for the same turbulent conditions. For the same pointing errors, strong turbulence has a greater impact on the BER performance of the FSO link. This is because strong turbulence causes large attenuation and phase disturbance during signals transmission, which affects the BER of the FSO link. It is worth noting that larger pointing errors lead to lower BER performance of the FSO link. However, in the case of slight misalignment, this can be considered negligible.  
Figure 7 depicts the effect of pointing error on the FSO link outage probability for several of the same atmospheric turbulence conditions as those described in Figure 6. We note that the outage probability increases with the strength of the atmospheric turbulence  
Figure 6: BER performance of FSO link with different turbulence strengths and pointing errors.  
and the severity of the pointing error. In fact, for the same turbulence strengths, the pointing error significantly affects the performance of the FSO link. In the moderate turbulence case, from \(\zeta=5\) to \(\zeta=1.5\), the outage probability of FSO link increases significantly; the same result is found for strong turbulence, where the outage probability is proportional to the severity of the pointing error. And the effect is somewhat larger in the case of strong turbulence.
## 2 An Efficient Algorithm for Moment-Independent Importance Analysis
### Moment-Independent Sensitivity Analysis Based on 4 PEM and SGLD  
Equation (3) can be interpreted as the first order raw moment of \(s(X_{i})\). Therefore, it can be re-written by 4 PEM according to Equation (6).  
\[E_{X_{i}}[s(X_{i})]=\int s(X_{i})f_{X_{i}}(x_{i})dx_{i}\approx\sum\limits_{j =1}^{4}w_{i,j}s\big{(}\chi_{i,j}\big{)} \tag{11}\]  
Since Equation (3) is the function of univariate \(X_{i}\), only the realization of \(s(X_{i})\) corresponding to the location \(\chi_{i,j}\) needs to be determined by  
\[s\big{(}\chi_{i,j}\big{)}=\int\Big{|}f_{Y}(y)-f_{Y|X_{i,j}}(y)\Big{|}dy \tag{12}\]  
in which \(f_{Y|X_{i,j}}(y)\) is the conditional PDF under the condition of \(X_{i}\) fixing at its 4 PEM locations \(\chi_{i,j}\). Thus, to get the MII index, only \(f_{Y}(y)\) and \(f_{Y|X_{i,j}}(y)\) need to be evaluated from the first four moments of \(Y\) and \(Y_{X=\chi_{i,j}}\), which can be obtained in Section 2.2.  
For the global sensitivity analysis of structural seismic demand, the requirements of PPD are (i) reconstruction unimodal distribution for the output, which is the inherent properties of seismic demand, (ii) more suitable for the skewed distribution of input since most uncertain parameters of a civil structure follow the non-negative distribution, and (iii) accurate estimation for the distribution tail because the distribution tail behavior is crucial for engineering applications [24]. By considering all of the three factors above and the trading among the existing PPD methods, the authors picked the SGLD method to reconstruct the \(f_{Y}(y)\) and \(f_{Y|X_{ij}}(y)\). The core idea of SGLD is to construct the unknown PDF by a parameterized function with undetermined parameters, which can be uniquely determined by the first four moments. The SGLD is briefly demonstrated as follows.  
The PDF model of SGLD is written as [23]  
\[f_{Y}(y)=\frac{\eta}{y-b}\exp\left(-\frac{1}{\beta\delta^{\beta}}\left|\ln \left(\frac{y-b}{\theta}\right)\right|^{\beta}\right),b<y<+\infty \tag{13}\]  
in which \(\eta=1/\left[2\beta^{1/\beta}\delta\,\Gamma(1+1/\beta)\right]\), \(\Gamma(\bullet)\) is the gamma function. In Equation (13), only four parameters \(b,\theta,\beta\) and \(\delta>0\) are to be identified and they can be determined by the first four raw moments of \(Z=(Y-b)/\theta\), which can be written by [24]  
\[E[Z^{q}]=\frac{1}{\Gamma(1/\beta)}\sum_{n=0}^{+\infty}\frac{(q\delta)^{2n}}{(2 n)!}\beta^{2n/\beta}\Gamma\left(\frac{2n+1}{\beta}\right) \tag{14}\]  
Eventually, the four parameters can be solved by these four equations: \(\alpha_{1,Z}=\alpha_{1,Y}/\theta-b\), \(\alpha_{2,Z}=\alpha_{2,Y}/\theta\), \(\alpha_{3,Z}=\alpha_{3,Y}\) and \(\alpha_{4,Z}=\alpha_{4,Y}\) in which \(\alpha_{i,\cdot}\) represents the \(i\)-th central moment. Thus, \(f_{Y}(y)\) and \(f_{Y|X_{ij}}(y)\) can be evaluated by the first four moments of \(Y\) and \(Y_{X=\chi_{i,j}}\) respectively. The MII index can be finally solved by Equations (11) and (4).