esa-sceva/satcom-chunk-collection huggingface dataset:
### Contributions  
The main contributions of this research work are summarized in the development of the following two algorithms:  
1. An algorithm that uses input-output data corresponding to the control signal sent to the Internal Combustion Engine (ICE) and the voltage signal acquired from the electric generator. This algorithm aims to identify offline a T-S type fuzzy model of the power generation system, where the consequent consists of four state-space fuzzy rules. The model consists of four linear submodels, which represent the input-output operation within the learning space. In order to derive the fuzzy model, the consequents are established using the fuzzy data clustering technique, namely the fuzzy c-meansalgorithm. This method enables an optimal search for the premises of the fuzzy rules. In this technique, an open problem is to determine the fuzzy parameter \(m\), which allows classification of the data into groups with fuzzy affinity. In the present paper, we propose to employ the Particle Swarm Optimization (PSO) method to identify the optimal value of the fuzzy parameter \(m\) from within the search space.
* An algorithm that uses Parallel Distributed Compensation (PDC) to regulate the output voltage, whose gains are determined through the combination of pole placement and PSO methods. This control technique, rooted in the T-S fuzzy model, leverages the premises of the rules and adjusts the consequents using state feedback.  
The remainder of the article is organized as follows: In Section 2, the description of the power generation system is presented. In Section 3, the T-S fuzzy modelling algorithm with the assumptions in the form of clustering and linear consequents, as well as the construction of the fuzzy system, applied to the power generation system, are derived. The PDC controller for the voltage regulation of the system is shown in Section 4. Finally, the conclusions and future directions of this research work are presented in Section 5.
## 4 Results and Validation
### Isotropic Hardening  
A well-known fact is that the the elastic limit initially depends on the cumulated plastic strain; thus, an isotropic hardening model (VOC) was introduced to account for this phenomenon. The exponentialistropic hardening rule of VOC is formalized in Equation (32), where \(Q\) and \(b\) are the equation parameters and \(p\) is the same as in Equation (6). \(\sigma_{0}\) corresponds to the elastic limit when \(p=0\).  
\[\sigma_{\rm Y}=\sigma_{0}+Q(1-\exp(-bp)) \tag{32}\]  
For high values of the cumulated plastic strain \((p\!\to\!\infty)\), Equation (33) provides a relationship between the constant value of the elastic limit of the CKH model and VOC's equation.  
\[\sigma_{\rm L}=\sigma_{0}+Q \tag{33}\]  
The transient during the initial cycles, as shown in Figure 15, ending at the stabilized Cycle II, was used to find the value of \(Q\) and \(b\). In particular, the maximum stress value at the inversion points is described by Equation (34), whereas the minimum stress value is given by Equation (35).  
\[\sigma_{N}^{\rm max}=\sum_{i=1}^{4}\chi_{\rm i,N}^{\rm max}+\sigma_{\rm Y}(p) \tag{34}\]  
\[\sigma_{N}^{\rm min}=\sum_{i=1}^{4}\chi_{\rm i,N}^{\rm min}-\sigma_{\rm Y}(p) \tag{35}\]  
By substituting Equation (33) in Equation (32), Equation (36) can be obtained. \(\sigma_{\rm Y}\) is the experimental input found by exploiting Equations (34) and (35); therefore, the values of \(Q\) and \(b\) were obtained through a least-squares fit of Equation (36).  
\[\sigma_{\rm Y}-\sigma_{\rm L}=-Q\exp(-bp) \tag{36}\]  
The result of the least-squares fit operation is then resumed in Figure 16, where a comparison between the experimental data and the best-fit curve is provided.  
The numerical values of the constants of VOC istropic hardening model are reported in Table 5. There were no differences between those obtained for CIKH model with three backstress components and those obtained with four backstress components except for the value of \(\sigma_{0}\).
## 6 Types of DGs and Network Application
### Practical Implementation of ANN-Based IDMs  
Research in the field of islanding detection in the distribution system using neural networks goes in two directions. The first direction is the conceptual development and testing of the off-line model in order to obtain the best synergy between the selection of input data, data preparation, validation of these data with a neural network, and the obtained results, and this constitutes the majority of papers currently in this scientific field. The second direction is the online implementation of methods in the real power system, where there are fewer published papers [62; 96]. The aforementioned papers present online applications for the islanding detection of photovoltaic systems.  
The authors in [62] presented an active online method for islanding detection using a fuzzy neural network. The method is integrated into an inverter control unit that has the ability to function with fuzzy information to detect the islanding operation of the photovoltaic system. Data transformation was carried out using wavelet transform (WT) to reduce the data and gain the speed of the algorithm. A passive online method for islanding detection using a neural network is presented in [47]. The method is integrated into the protection device to avoid setting the threshold of the protection device and to perform intelligent islanding detection of the photovoltaic system. Data preparation was carried out using wavelet packet transform (WPT) in order to reduce data and increase the response of the protection device.  
There are a certain number of obstacles that should be taken into account when implementing the method in a real power system with distributed generation (online). The first obstacle is that the installed protective (control) equipment has certain inputs as physical variables that are followed by certain outputs that act on precisely specified circuit breakers, and with that, we are limited in the choice of input variables that feed the method.
# Concept Drift Detection Based on Deep Neural Networks and Autoencoders
## 4 Experiment
### Evaluation Metrics
Suppose there are \(k\) algorithms tested on \(N\) datasets, and the variable  
\[\mathcal{T}_{\mathcal{X}^{2}}=\frac{12N}{k(k+1)}[\sum_{i=1}^{k}r_{i}^{2}- \frac{k(k+1)^{2}}{4}] \tag{7}\]  
When both \(k\) and \(N\) are large, \(\mathcal{T}_{\mathcal{X}^{2}}\) follows a chi-squared distribution with \(k-1\) degrees of freedom. To improve the rigor of the test, we employ the following statistic:  
\[\mathcal{T}_{F}=\frac{(N-1)\mathcal{T}_{\mathcal{X}^{2}}}{N(k-1)-\mathcal{T}_ {\mathcal{X}^{2}}} \tag{8}\]  
where \(\mathcal{T}_{\mathcal{X}^{2}}\) is calculated using Equation (7). The statistic \(\mathcal{T}_{F}\) follows an F-distribution with degrees of freedom \((k-1)\) and \((k-1)(N-1)\). If the null hypothesis that \"all algorithms perform equally\" is rejected, we proceed with the Bonferroni-Dunn test to identify significant differences between pairs of algorithms. The Bonferroni-Dunn test determines the critical difference for the mean rank differences as follows:  
\[CD=q_{\alpha}\sqrt{\frac{k(k+1)}{6N}} \tag{9}\]  
where \(q_{\alpha}\) is determined by the significance level \(\alpha\) and and the number of algorithms \(k\). The false detection rate (\(FDR\)) is an important metric for evaluating the drift detection method's ability to detect and identify drifts. Following the metric proposed in [13], the calculation of \(FDR\) is given by  
\[FDR=\frac{\left|\left\{s^{i}\mid s^{i}\
otin C_{i}\text{ and }s^{i}\in S\right\} \right|}{|S|} \tag{10}\]  
where \(|S|\) represents the length of all drift detection output results and \(C_{i}\) represents the actual inserted drift points. The \(FDR\) measures the proportion of invalid detections in the drift detection output, with smaller values indicating better drift detection performance.
## 2 Results
### Effect of Diosgenin on relP and relQ Expression  
The effect of diosgenin on the expression of _relP_ and _relQ_, the two key genes involved in (p)ppGpp synthesis, was evaluated using reverse transcription-polymerase chain reaction (RT-PCR) on _S. aureus_ cells cultured with 80 \(\upmu\)M or 160 \(\upmu\)M diosgenin for 3 h (Figure 3). Gene expression levels were normalized to 16s rRNA levels and are expressed as fold changes relative to an untreated control.  
In cells treated with 80 \(\upmu\)M diosgenin, _relP_ expression decreased by 27% compared to that in control group cells. Treatment with 160 \(\upmu\)M diosgenin reduced _relP_ expression by 46%, indicating a dose-dependent suppression of _relP_ transcription in response to diosgenin. Conversely, _relQ_ expression exhibited a more pronounced reduction in response to diosgenin treatment. At 80 \(\upmu\)M diosgenin, _relQ_ expression decreased by 60% relative to the control, representing a substantial reduction. Interestingly, increasing the diosgenin concentration to 160 \(\upmu\)M resulted in a slightly lower 55% reduction. This suggests that _relQ_ expression was already substantially suppressed at 80 \(\upmu\)M, with no further dose-dependent decrease observed at 160 \(\upmu\)M.  
The differential responses of _relP_ and _relQ_ to diosgenin treatment indicate that distinct regulatory mechanisms underlie the expression of these (p)ppGpp synthase genes. While _relP_ expression demonstrates a clear dose-dependent reduction, diosgenin's suppressive effect on _relQ_ expression appears to reach a threshold at or below 80 \(\upmu\)M, beyond which increasing the diosgenin concentration has no effect. Overall, these findings suggest that diosgenin selectively modulates _S. aureus_' stringent response pathway by targeting _relP_ and _relQ_ transcription, potentially disrupting (p)ppGpp synthesis and thereby inhibiting persister cell formation.
###### Abstract  
In this study, nano-graphene platelets (NG) and a phosphonium-phosphate-based ionic liquid (IL) were studied as additives to rotorcraft gearbox oil to improve the resistance to scuffing under starved lubricated conditions. Behavior under a loss of lubrication was evaluated using a high-speed ball-on-disk tribometer and a reciprocating cylindrical pin-on-disk tribometer. In addition, the scuffing load and friction over a wide range of sliding and entrainment speeds was determined. On the high-speed ball-on-disk tribometer, an oil blend containing both NG and IL additives was able to operate two times longer than an unadditivized oil before scuffing after the lubrication supply was removed. The increase in time to scuffing was larger for two additives together than the sum of their parts. The additives showed an increase in scuffing load individually but were detrimental when mixed together with a lower scuffing load. The combination of NG and IL show promise as additives to increase the operation time of gears during loss of lubrication, however at the potential cost of decreasing the load-carrying capacity of the oil.  
staved lubrication; ionic liquids; graphene; friction +
Footnote †: journal: Applied Sciences
## 4 Clinical Indications
### Pigmentation Disorders
Based on photographic documentation, the efficacy of the treatment was independently evaluated by two plastic surgery specialists, blinded to the clinical data. After 2-3 treatment cycles, significant or complete improvement was observed in both cases. There were no signs of recurrence during the three-year follow-up period, which suggests that the 755 nm PS alexandrite laser, in combination with a diffractive lens array or zoom handpiece, offers an effective, long-term, and safe treatment option for treating PIH [50]. [PERSON] et al. [43], evaluated the clinical efficacy of a 730 nm PS laser for treating freckles and solar lentigines. This laser operates with an ultrashort pulse duration and it allows for an efficient fragmentation of pigment within the target tissues. The study included 50 patients with Fitzpatrick skin types III-IV, who underwent two sessions with the 730 nm picosecond laser at one-month intervals. The rates of lesion clearance after one and two sessions were 53.1% and 78.4%, respectively, while the clearance rates at 1 and 6 months post-treatment were 78.7% and 78.4%, respectively. As many as 90% of the patients reported \(\geq\) 95% lesion reduction at six months of follow up, with the overall satisfaction rate of 86.0%. PIH was observed in only two patients with Fitzpatrick type IV, and no lesion recurrence was reported. These findings emphasize the superiority of the 730 nm PS laser in treating freckles and lentigines [43]. [PERSON] et al. [51], in their study involving 20 patients with Fitzpatrick skin types II-IV who underwent treatment for lentigines, reported comparable results. In addition to assessing the safety and efficacy of the 730 nm PS laser, these authors performed histopathological analyses to evaluate the impact of short laser pulses on the skin as compared to the effects of 532 nm PS pulses and 532 nm and 755 nm NS pulses. The 730 nm PS laser demonstrated high efficacy in reducing lentigines, with no adverse events and high patient satisfaction. Histological analysis revealed dispersed, focal vacuolization (5-10 \(\upmu\)m in diameter) of the epidermis and mild erythrocyte extravasation following 730 nm PS pulses, whereas the 755 nm nanosecond pulses led to diffuse epidermal vacuolization with vacuole coalescence (20-100 \(\upmu\)m), junctional separation, and mild erythrocyte extravasation. The 532 nm PS pulses resulted in dispersed, focal epidermal vacuolization with larger dermal vacuoles, extending to the depth of 500 \(\upmu\)m, while the 532 nm nanosecond pulses induced diffuse epidermal vacuolization, with vacuole coalescence and prominent dermal hemorrhage. These histological findings indicate that the 730 nm PS laser is a superior pigment-selective modality, with minimal disruption to the dermo-epidermal junction, which suggests that it may reduce healing time and minimize the risk of adverse events [51]. Studies showing the effects of using picosecond lasers to reduce hyperpigmentation are presented in Table 4.
## 3 Numerical Modeling of HDS
### Compressor  
The working process of the compressor is complex. The following assumptions are made to facilitate analysis. (1) The compression process is adiabatic. (2) Pressure loss during the inspiratory and exhaust processes can be neglected. (3) Adiabatic exponent of refrigerant is constant.  
Based on the above assumptions, the compression process of the refrigerant can be described by the following equations [25].  
\[\frac{T_{o}}{T_{i}}=\left(\frac{P_{e}}{P_{c}}\right)^{\frac{k-1}{k}} \tag{1}\]  
\[m_{\tau j}=(\eta_{\tau}V_{th})/v_{i} \tag{2}\]  
\[W=\frac{kP_{e}}{k-1}\left[\left(\frac{P_{e}}{P_{c}}\right)^{\frac{k-1}{k}}-1 \right]v_{i} \tag{3}\]  
Figure 3: State of air in HDS.  
Figure 2: Schematic of the hybrid dehumidification system (HDS).  
where \(T_{o}\) and \(T_{i}\) are the exhaust temperature and inlet temperature, respectively. \(P_{c}\) and \(P_{e}\) are the condensation pressure and evaporation pressure, respectively. \(m_{rj}\) is the mass flow of the refrigerant. \(v_{i}\) is the specific volume of suction. \(\eta_{v}\) is the volumetric efficiency, and the \(\eta_{v}\) in this system is 0.9. \(V_{th}\) is the theoretical exhaust volume of the compressor. \(W\) is the energy consumption per unit mass. \(k\) is the adiabatic exponent of the refrigerant. The refrigerant in this system is R22, and the \(k\) of R22 is 1.16.
## 2 Small-Sample Adaptive Residual Life Prediction Model Based on Feature Matching Preprocessor-LSTM  
### Overall Framework of the Model  
Mechanical products residual life predictions obtained from the test data exist in the time scale, multi-failure state residual life prediction problems in the life test of mechanical products, usually only part of the system in the specified time failure, while another part of the system can still work properly. At this point, the data obtained are not only the failure time data of the system, but also the performance degradation data of the unfailing system. Although the overall life distribution of this type of system can be obtained by using only the failure time data, a more accurate residual life prediction can be obtained if the performance degradation data can also be used. Therefore, to address the problem of low accuracy of residual life prediction for small samples of mechanical products caused by multiple time scales and multiple failure states, the failure time data and performance degradation data are fused, and the failure rate and standard deviation of life characteristics are used as the residual life prediction criteria to visually reflect the probability of failure of a component or system at some point.  
The adaptive capability of the parameters is an important tool to maintain the real-time prediction of the residual life of small-sample mechanical products. However, most of them stay in the experimental stage with long cycle time and the required sample size is not applicable to the conditions of small-sample data. So, for the current demand of adaptive small-sample residual life prediction data, a life feature matching preprocessor model is established. On this basis, this paper uses a LSTM neural network to establish a small-sample adaptive residual life prediction model. The overall framework of the model is shown in Figure 1.
## 5 Electromagnetic Shielding by Nanofiber Mats in the X-Band and Ku-Band
[PERSON] et al., e.g., prepared a superhydrophobic EMI-shielding material by chemical deposition of silver nanoclusters on an electrospun polymer nanofiber mat and modified it by stearic acid to reach the superhydrophobic effect, enabling using it outdoors [94]. They found a very high shielding effectiveness of 90 dB which they attributed to the high electrical conductivity of 5.7 \(\times\) 10\({}^{6}\) S/m, the porous structure and interfacial polarization. Aiming at shielding in the X-band and the Ku-band, [PERSON] et al. prepared a composite from electrospun PAN and a multiwall carbon nanotube buckypaper by electrospinning  
Figure 7: Schematic of EM wave shielding for ZrC/SiC nanofiber mats. From [85], copyright (2021), with permission from Elsevier. and roller coating [95]. They found a shielding effectiveness of approximately 9-23 dB for a very thin system of 0.1 mm thickness. For the same frequency range, [PERSON] et al. prepared membranes of core-shell nanofibers with Co@C nanoparticles in the shell and graphitic N-doped carbon nanofibers, derived from PAN/PVP electrospun nanofibers, in the core [96]. They reached a high shielding effectiveness of 56 dB and high values along the whole X- and Ku-bands for sample thickness of 4.0 mm and 3.0 mm which they attributed to a superposition of dipolar polarization, interfacial polarization, magnetic coupling and multiple reflections inside the nanofiber mats. Concentrating on the Ku-band, [PERSON] et al. developed buckypaper/PAN nanofiber composite films by electrospinning PAN, preparing single-walled CNTs which were self-assembled to a buckypaper by infiltration through a nylon filter membrane on a Buchner funnel, and finally forming the composite films by infiltrating the single-walled CNTs into the PAN nanofiber mat [97]. They found conductivities in the range of 9-53 kS/m for the composites, as compared to the pure buckypaper with 57 kS/m, and a total shielding effectiveness of max. 65 dB, which was significantly larger than that of the pure buckypaper due to interfacial polarization along the CNTs and multi-reflections. As visible from these few examples, quite similar approaches are followed for EMI shielding in the Ku-band, as compared to the X-band.
## 4 Applications in Optimization
### Non-Evolutionary Chaotic Optimization Algorithms
#### 4.5.2 Improved Lozi Map-Based Chaotic Optimization Algorithm (ILCOA)
The authors suggested that the remarkable local and global search abilities of ILCOA grant it a distinct edge over other optimization methods, allowing it to sift through the entire search space for the global optimum. The efficacy of the proposed approach was highlighted by comparing its performance to those of three other algorithms: the Bird Mating Optimizer (BMO) [105], Improved Chaotic Whale Optimization Algorithm (ICWOA) [106], and LCOA in terms of Root Mean Square Error (RMSE), Relative Error, Mean Absolute Error (MAE), Normalized MAE, and Mean Bias Error. The results showed that ILCOA performs better than the other algorithms and has higher convergence speed and stability. Moreover, it was able to reach a smaller value of the objective function compared with LCOA. ILCOA was tested by Ebrahimi et al. [107] along with other algorithms to identify the parameter of the two chaotic Chen and Lorenz attractors as well as a permanent magnet synchronous motor system. Their simulation results ascertained the very good performance and high speed of this algorithm in identifying these systems in comparison with other algorithms.
## 4 Empirical Analysis
### Solving the Problem and Evaluating Solutions
#### 4.3.2 Evaluating Innovative Solutions with BWM  
On the basis of generating the above four innovation options, it is also necessary to evaluate and rank the above options using the BWM. Firstly, on the basis of the four-dimensional indicators such as development cost, novelty, feasibility and expected benefits, the BWM multi-criteria decision-making model can be applied to evaluate the four selected solutions on a trial basis as a whole;  
Secondly, five experts are invited to identify the best and worst solutions as well as the remaining solutions under each indicator and compare them with the best and worst solution. Due to space limitations, only an example evaluation of development cost indicators is presented in this paper, with the following steps:  
Step 1: Determine the best and worst option. According to the experts' discussion, solution 1 is the optimal solution and solution 3 is the worst solution. Step 2: The optimal solution is compared with the remaining solutions. Option 1 is compared with the remaining options separately and the result is BO = (1,3,8,4). Step 3: The remaining options are compared with the worst option. Compare each of the remaining solutions with option 3 and the result is OW = (9,7,1,3)\({}^{\text{T}}\). Step 4: Construct the mathematical planning problem and solve it. The following programming is solved and standard weights are determined via Lingo:  
\[ \begin{array}{l}\text{min}\xi\\ \text{s.t.}\\ |w_{1}-3\;*\;\text{w}_{2}|\leq\xi\\ |w_{1}-8\;*\;\text{w}_{3}|\leq\xi\\ |w_{1}-4\;*\;\text{w}_{4}|\leq\xi\\ |w_{2}-9\;*\;\text{w}_{3}|\leq\xi\\ |w_{4}-3\;*\;\text{w}_{3}|\leq\xi\\ w_{1}+w_{2}+w_{3}+w_{4}=1\\ w_{j}\geq 0,j=1,2,3,4\end{array} \]  
Solving the model leads to the following results: w\({}_{1}\) = 0.5388, w\({}_{2}\) = 0.2374, w\({}_{3}\) = 0.0457, w\({}_{4}\) = 0.1781. Repeating the above steps and calculating the weights of the four innovative solutions under the three indicators of novelty, feasibility and expected benefits respectively, the final ranking of each solution can be obtained, as shown in Table 8.  
It is easy to see that the four product innovation solutions proposed in this paper are, in descending order of priority, Solution 1, Solution 2, Solution 4, and Solution 3. Similarly, the scenarios arising from the filtration and mixing problems of slurry pumps are shown in Tables 9 and 10. \begin{table}
\begin{tabular}{l c c c c c c} \hline  & **Development Costs** & **Novelty** & **Feasibility** & **Expected Benefits** & **Weighted Score** & **Ranking** \\ \hline Solution1 & 0.5388 & 0.4389 & 0.3876 & 0.6351 & 0.5001 & 1 \\ Solution2 & 0.2374 & 0.2967 & 0.2581 & 0.3236 & 0.2790 & 2 \\ Solution3 & 0.0457 & 0.0981 & 0.1413 & 0.2099 & 0.1238 & 4 \\ Solution4 & 0.1781 & 0.1652 & 0.2987 & 0.3457 & 0.2469 & 3 \\ \hline \end{tabular}
\end{table} 
Table 8: Evaluation and ranking results by programme.