esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In [17], a control strategy for reactive power compensation using storage system was studied. This study aimed to improve system stability by a proper prediction of reactive behavior and demand for different operation conditions. In [18], the authors presented the stability analysis using the load and generation levels as a direction vector for the base system through continuation power flow (CPF) under normal condition and contingency. The authors of [19] proposed a wavelet transform (WT) based on data analysis to extract the features from real-time active power and RMS (root mean square) voltage of the power grid. This study applied a hybrid classification technique based on particle swarm optimization (PSO) and support vector machines (SVM) to classify the features and diagnose different types of faults in a smart grid system. Previous studies investigated the use of control devices in a variety of ways based on different optimization methods. Most of these studies were focused on required number of control device without considering the optimum placement and number of these devices. The proposed methods can technically be feasible, but economically they are not acceptable. Therefore, reducing number of control devices in a system is another important objective. To solve the trade-off problem, the multiobjective optimization is an excellent tool. Additionally, the increase in tap position changing can reduce contacts lifespan and accelerate deterioration of transformer oil in the switching process [20]. This paper provides a method of multiobjective optimization of auto-tap-changer pole transformer (ATCTr), in term of optimum number and placement of tap position changes. Meanwhile, a multiobjective optimization using genetic algorithm [21; 22; 23; 24; 25] is applied to minimize voltage deviation. In Section 2, characteristics of system model and problem description are discussed. Section 3 presents the methodology, follows by the simulation result and comparison in Section 4. At last, Section 5 concludes simulations findings and briefs novelty and effectiveness of the study.
## 4 Conclusions  
In summary, we have developed a rapid, full-scale and label-free SERS analysis of mouse brain based on BP-metal 2D nanoprobe. The 2D SERS substrate was composed of a flake-like BP nanosheet with a large number of Au nanoparticles attached to it. BP-AuNSs exhibited excellent SERS activity, whose Raman enhancement effects on common organic dyes and brain tissues were several times higher than that of traditional citrate-stabilized AuNPs. In view of this, a fast and full-scale SERS mapping of the mouse brain was carried out under the 0.1 s integration time (56 ms per pixel). Subsequent SERS texture analysis showed a distinct interior texture of brain tissue that was well in accordance with the data by classical H&E analysis. The SERS spectral lines also displayed the biochemical molecular information of the various encephalic regions in brain tissue. The variation analysis of the SERS signals unveiled the biochemical composition difference between the left and the right hemispheres, which might be conducive to reply the different physiological and psychological functions of the two hemispheres.  
Conceptualization, Z.L.; Data curation, D.L.; Funding acquisition, Z.L.; Investigation, T.G. and D.L.; Methodology, W.Z.; Resources, W.Z.; Supervision, L.C.; Validation, F.D.; Writing--original draft, T.G. and Z.L.; Writing--review & editing, L.C.  
This work was supported by the National Natural Science Foundation of China (61675072, 11874021, 61335011, 21505047 and 61275187), and the Science and Technology Project of Guangdong Province of China (2017A020215059). The authors declare no conflict of interest.
###### Abstract  
This paper presents a new deep neural network (DNN)-based speech enhancement algorithm by integrating the distilled knowledge from the traditional statistical-based method. Unlike the other DNN-based methods, which usually train many different models on the same data and then average their predictions, or use a large number of noise types to enlarge the simulated noisy speech, the proposed method does not train a whole ensemble of models and does not require a mass of simulated noisy speech. It first trains a discriminator network and a generator network simultaneously using the adversarial learning method. Then, the discriminator network and generator network are re-trained by distilling knowledge from the statistical method, which is inspired by the knowledge distillation in a neural network. Finally, the generator network is fine-tuned using real noisy speech. Experiments on CHiME4 data sets demonstrate that the proposed method achieves a more robust performance than the compared DNN-based method in terms of perceptual speech quality.  
speech enhancement; deep neural network; generative adversarial network; distill knowledge +
Footnote †: journal: Applied Physics Letters  
_Article_
###### Abstract
Furthermore, the key generation process is critical to the security of an encryption algorithm, as the strength of the key directly impacts the security of the encrypted data. Therefore, it is important to use strong and secure key generation methods and to properly manage and protect the keys. This article develops an IoMT with a blockchain-based smart healthcare system using encryption with an optimal deep learning (BSHS-EODL) model. The presented BSHS-EODL technique comprises a few processes: data collection, image encryption, and data classification. Initially, the IoMT devices enable data collection processes, and the gathered images are stored in BC for security. Then, image encryption is employed to encrypt the data, and its key generation process is performed via the dingo optimization algorithm (DOA). Finally, the BSHS-EODL technique performs disease diagnosis comprising SqueezeNet, Bayesian optimization (BO) based parameter tuning, and voting extreme learning machine (VELM). A comprehensive set of simulation analyses is performed on medical datasets. The rest of the paper is organized as follows: Section 2 discusses the related works, Section 3 demonstrates the proposed approach, performance validation is depicted in Section 4, and the conclusion is shown in Section 5.
## 2 Distribution Network Flexibility Resources and Flexibility Evaluation Indicators
### Two-Layer Planning Model of SOP and ESS
#### 3.2.1 Upper-Layer Planning Model  
The upper layer model is employed to identify the optimal location and capacity of SOP and ESS, with the objective of economic optimization, as reflected by the minimization of the annual integrated cost. The objective function \(F_{up}\) comprises the investment and maintenance cost of SOP and ESS to be planned, with equal annual values, as well as the annual purchase cost and loss cost of ADN.  
\[\text{min}F_{up}=C_{I}^{SOP}+C_{OM}^{SOP}+C_{I}^{ESS}+C_{OM}^{ESS}+C^{PC}+C^{L} \tag{20}\]  
\[C_{1}^{SOP}=\frac{\lambda(1+\lambda)^{N_{sup}}}{(1+\lambda)^{N_{sup}}-1}\sum \limits_{i,j\in\Omega_{b}}C^{SOP}S_{ij}^{SOP} \tag{21}\]  
\[C_{OM}^{SOP}=\eta_{SOP}\sum\limits_{i,j\in\Omega_{b}}S_{ij}^{SOP} \tag{22}\]  
\[C_{I}^{ESS}=\frac{\lambda(1+\lambda)^{N_{ESS}}}{(1+\lambda)^{N_{ ESS}}-1}\sum\limits_{i\in\Omega_{ESS}}(C_{e}^{ESS}E_{i}^{ESS}+C_{P}^{ ESS}P_{i}^{ESS}) \tag{23}\]  
\[C_{OM}^{ESS}=\sum\limits_{l=1}^{T}\sum\limits_{s=1}^{S}\sum\limits_{i\in \Omega_{ESS}}\eta_{ESS}\Big{|}P_{t,s}^{ESS}\Big{|} \tag{24}\]  
\[C^{PC}=365\sum\limits_{t=1}^{T}\sum\limits_{s=1}^{S}c_{g}P_{t,s}^{MG} \tag{25}\]  
\[C^{L}=\int\limits_{0}^{8760}\rho P_{t}^{L}\Lambda dtt \tag{26}\]  
\[P_{t}^{L}=\sum\limits_{s=1}^{S}\left(\sum\limits_{j\in\Omega_{b}}r_{ij}l_{s, t,ij}^{2}+\sum\limits_{i=1}^{N_{b}}P_{s,t,i}^{SOP,L}\right)\times p(s) \tag{27}\]  
where \(C_{I}^{SOP}\), \(C_{OM}^{SOP}\), \(C_{I}^{ESS}\), and \(C_{OM}^{ESS}\) are the investment and maintenance costs of SOP and ESS; \(C^{PG}\) is the cost of purchasing power from a higher main network; \(C^{L}\) is the annual loss cost of ADN; \(\lambda\) is the discount rate; \(y_{SOP}\) is the service life of SOP; \(C^{SOP}\) is the SOP unit capacity investment cost; \(\eta_{SOP}\) is the maintenance cost per unit capacity of SOP; \(y_{ESS}\) is the economic service life of ESS; \(C_{e}^{ESS}\) and \(C_{P}^{ESS}\) are the investment cost per unit capacity and power of ESS; \(\eta_{ESS}\) is the maintenance cost per unit capacity of ESS; \(S\) is the number of scenarios; \(c_{g}\) is the unit price of power purchased from the higher network; \(P_{t}^{MG}\) is the active power injected into ADN from the superior main network. \(\rho\) is the cost of network loss per unit power; \(P_{t}^{L}\) is the active power loss of ADN; \(N_{N}\) is the number of system nodes; \(P_{s,t,i}^{SOP,L}\) is the SOP device loss; \(\Omega_{b}\) is the set of system branch circuits; \(p(s)\) denotes the probability of scenario \(s\).
# Fully Leveraging Deep Learning Methods for Constructing Retinal Fundus Photomatages
## 3 Experimental Results  
### Dataset and Experimental Environment  
We used fovea-centered retinal fundus images from the Seoul National University Bundang Hospital Retina Image Archive (SBRIA), which contains images that were obtained at the health screening center and ophthalmology outpatient clinic at Seoul National University Bundang Hospital obtained from 1 June 2003, through 30 June 2016, using various fundus cameras (CF60 Uvi and CR6-45 NM [Canon, Utsunomiya, Japan]; VX-10, VX-10a, nonnymd 7, and GENESIS-D [Kowa Optimed, Tokyo, Japan]) [5; 16; 23]. Our experimental test dataset comprises 62 image sequences that were collected from both eyes of 31 patients, where each image subset has a minimum of six to maximum nine frames, totalling 454 images, or 7.32 frames per subset. Each image is of resolution 3872 \(\times\) 2592.  
Experiments were performed on a system with 3.50 GHz i9-9900x CPU [Intel, Santa Clara, USA] and two GeForce RTX 2080 ti GPUs [Nvidia, Santa Clara, USA]. We used the TorchVision package of PyTorch [24] as the implementation of the Faster R-CNN in disc and fovea detection, and the OpenCV implementation of SURF [25], and the SimpleITK implementation of B-spline registration [26]. PlantCV python-based opencv and PlantCV libraries were used to perform the technique. PlantCV is an opencv source library and it is used to find branch points in blood vessel images. All code was implemented in Python 3.
## 2 Materials and Methods
### Characterization Techniques  
FTIR spectra were recorded employing the KBr disk method in an FTIR spectrometer (Nicolet iS10, Thermo scientific, Madison, WI, USA), in the 4000-500 cm\({}^{-1}\) wavenumber range with a resolution of 4 cm\({}^{-1}\) and a total number of scans per spectrum of 16. The surface morphology of SBA-15-PSSA was analyzed by TEM (using a JEM-1011 instrument, JEOL, Tokyo, Japan). TGA experiments were performed using a Mettler Toledo TGA/DSC 1 Star system (Columbus, OH, USA). Specimens were heated from 25 to 1000 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min under an N\({}_{2}\) gas flowrate of 20 mL/min. The surface area and porosity of SBA-15 was assessed via N\({}_{2}\) adsorption-desorption isotherms using a Gemini VII 2390 V1.03 (V1.03 t) instrument from Micromertics (Norcross, GA, USA). The samples were outgassed at 350 \({}^{\circ}\)C for 3 h before the measurements were performed. The samples' surface area was calculated using the Brunauer-Emmett-Teller (BET) equation over the P/P\({}_{0}\) value range between 0.051 and 0.252, whereby a linear relationship is maintained. The pore size distribution in mesoporous silica were calculated using the Barrett-Joyner-Halenda (BJH) model based on the adsorption branch of the isotherm. The progression of the esterification reaction was assessed through quantification of the residual reactant (AA) in reference to the internal standard (nDD) by gas chromatography (GC) using a GC-7890 instrument from Agilent technologies Inc. (Santa Clara, CA, USA) equipped with a flame ionization detector and a 19019S-001 HP-PONA GC column.  
Figure 1: Preparation of SBA-15-supported polystyrene sulfonic acids (SBA-15-PSSA) solid catalyst. APTES: (3-aminopropyl) triethoxysilane; BMPB: 2-bromo-2-methylpropionyl bromide; SBA-15: pore-expanded mesoporous silica; SSNa: sodium 4-styrensoflunate.
# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 2 Networked Microgrids' Configuration
### Formation
#### 2.1.5 Game Theory Approaches  
In the domain of NMGs, cooperative game theory finds widespread application [47; 48; 49; 50], as each microgrid operates autonomously, resembling an independent player. This characteristic renders game theory a valuable tool for modeling interactions and strategic interdependence among microgrids. Within this conceptual framework, each microgrid strives to optimize various objectives, such as minimizing restoration costs [47], reducing power loss [48; 49], and minimizing operational costs [50]. However, the actions of one microgrid can have repercussions on others, leading to the emergence of a complex network of interdependencies.  
Despite the advantage of game theory in considering all potential combinations among variables, this complexity is heightened. In response to the intricacies associated with game theory methods, the concept of the Darts game is introduced in [51; 52]. This strategy is designed to discover the optimal configuration of NMGs with a central focus on minimizing the costs linked to restoration. The approach involves nonlinear AC power flow analysis to assess power distribution among MGs, and it accounts for radial topology constraints, emergency-demand-response-program constraints, and electrical constraints.
# Hybrid Chirp Signal Design for Improved Long-Range (LoRa) Communications
## 2 Related Work  
Enhancing LoRa performance is challenging in the presence of noise/interference. In [10], spreading factor allocation strategies were used to enhance LoRa capacity, whereas in [14], different MAC layer techniques were used. Furthermore, in [15], the density of  
Figure 1: Time-frequency representation of traditional LoRa modulation for \(\mathcal{S}=2\). (**a**) \(k=0\), (**b**) \(k=1\), (**c**) \(k=2\), (**d**) \(k=3\).  
LoRa gateways was increased to improve the LoRa capacity. Research on the LoRa physical layer has also been conducted to improve the LoRa performance. For instance, in [16], capture effect and successive interference cancellation were considered to represent LoRa capacity. In [17], a combination of CSS and on-off keying was used to transmit LoRa signals simultaneously. In [18], chirp spread spectrum (CSS) modulation was compared with a binary phase-shift keying (BPSK); the results showed that CSS is more robust against interference compared to BPSK. To enhance LoRa capacity, error correction schemes were used in [19], whereas [20] used interleaved chirp spreading. In [21], the co-existence of LoRa and interleaved chirp spreading was investigated.
# Counter-Terrorism Video Analysis Using Hash-Based Algorithms
## 6 Implementation and Performance Study
### Frame Extraction Rate Choice  
Our experiments try to find the perfect balance between enough frames to find matches and not enough to cause false positives. A 5% probability of getting a false positive from any given video is set to be the maximum, so we apply the following restriction:  
\[n_{originalframes}\cdot n_{newframes}\cdot\mathbb{P}[\textit{false positive}]\leq 0.05\]  
where \(\mathbb{P}[\textit{false positive}]=4.011564\times 10^{-10}\) as calculated in our tests, \(n_{originalframes}\) refers to the number of frames of the original video and \(n_{newframes}\) refers to the number of frames of the new video. Our tests showed that the best performance is achieved with a 3-5 f/s rate in the new video and the original video respectively, which resulted in 1,166,914 and 5370 frames. It has a 1.214% error rate at detecting copied videos.
## 4 Discussion
Consistent with these findings, this study also identified linalool and trans-\(\beta\)-ionone as key floral components based on odor descriptions from **previous literature**[5]. Additionally, \(\alpha\)-cedrene and nonanal, which contribute to floral aromas [23], were found to play significant roles in enhancing the aroma quality of black tea. The contents of linalool, \(\alpha\)-cedrene, and nonanal were significantly upregulated under different shaking combinations, particularly in the YLS treatment, in both withered tea leaves and dry black teas. Trans-\(\beta\)-ionone, with its distinctive \"violet-like\" aroma [5], is a carotenoid-derived compound that can be produced either through enzymatic reactions during fermentation or thermal degradation during drying [36]. As a result, it was not detected in witnessed leaves, but its content in black tea increased remarkably in YLS-treated samples. Consistent with sensory evaluations, YLS, which exhibited the highest content of key floral components, also achieved the highest aroma score, characterized by a long-lasting floral aroma. In conclusion, our findings highlight the significant impact of shaking and yellow-light withering on the aroma quality of black tea. The YLS treatment, in particular, demonstrated the most pronounced enhancement in floral aroma components, suggesting its potential as a valuable technique in black tea processing. Future research should integrate olfactory measurements and molecular sensory science to further elucidate the specific contributions of individual compounds to black tea aroma. This approach will provide a more comprehensive understanding of aroma formation and enable the development of targeted strategies for producing high-quality black tea.
# A Novel Ring Spring Vibration Isolator for Metro Superstructure
## 4 Analysis of Vibration Isolation Structure
### Stiffness and Displacement  
The value of the vertical equivalent stiffness parameter of the isolator is changed to explore the effect of the vertical stiffness of the ring spring vibration isolator on the vibration isolation effect. The four vertical directions of 100 kN/mm, 250 kN/mm, 500 kN/mm and 1000 kN/mm are supplemented to obtain the peak acceleration of the vertical vibration of the structure (Figure 16). It can be seen that the vertical equivalent stiffness of the ring spring vibration isolator varies from 100 kN/mm to 1000 kN/mm. The isolator can still play the role of isolating metro vibration. Although the normal use of the superstructure will be affected when the vertical deformation is greater, smaller equivalent stiffness still leads to a better vibration isolation effect.  
We choose the absolute peak displacement of the column bottom node of the vibration isolation structure, and verify whether it is within the effective working range of the ring spring. As shown in Figure 17, it is the vertical displacement of the isolation system.  
When the stiffness changes from 100 kN/mm to 1000 kN/mm, the displacement is always within the effective working range. As the equivalent stiffness decreases, the vertical displacement also increases disproportionately. The displacement is mainly concentrated on the vibration isolator. The displacement of each floor is in overall translation. In terms of the vibration isolation effect and vertical deformation, the vertical stiffness of the ring spring vibration isolator is 500-1000 kN/mm.  
Figure 16: Peak acceleration as the stiffness changes.  
Figure 17: Vertical displacement as the stiffness changes.
## 3 Results and Discussion
### Thermal Stability  
The thermal stability and degradation profiles of the P(_bis_MEP)-based nanocomposites (and the corresponding individual components) were evaluated by thermogravimetric analysis under nitrogen atmosphere, and the data is summarized in Table 2. The P(_bis_MEP) homopolymer with a single weight-loss step degradation profile started to decompose close to 230 \({}^{\circ}\)C and reached a maximum decomposition temperature at about 304 \({}^{\circ}\)C, leaving a residue of ca. 40% at 800 \({}^{\circ}\)C almost certainly rich in phosphorus, in accordance with data reported elsewhere [38]. The TGA curve of pure BC, in addition to the dehydration below 100 \({}^{\circ}\)C (loss of ca. 5 wt %), showed a single-step degradation profile with initial and maximum decomposition temperatures at 266 \({}^{\circ}\)C and 347 \({}^{\circ}\)C, respectively, in good agreement with previously published data [21]. This single-step pathway is associated with the pyrolysis of the cellulose skeleton [39].  
The TGA thermograms of the P(_bis_MEP)/BC nanocomposite membranes exhibit an intermediate behavior between the individual components with a two-step weight-loss degradation profile (Table 2). The first stage with maximum decomposition temperature (T\({}_{\text{dmax1}}\)) of 234 \({}^{\circ}\)C for P(_bis_MEP)/BC_1 and 235 \({}^{\circ}\)C for P(_bis_MEP)/BC_2 is comparable to the degradation of the P(_bis_MEP) polymer backbone, whereas the second stage with maximum decomposition temperature (T\({}_{\text{dmax2}}\)) of 300 \({}^{\circ}\)C for P(_bis_MEP)/BC_1 and 303 \({}^{\circ}\)C for P(_bis_MEP)/BC_2 corresponds to the degradation of the cellulose skeleton. This is corroborated by the fact that the magnitude of the loss at T\({}_{\text{dmax2}}\) decreases with the reduction of the BC content. A weight-loss of about 60 wt % was obtained for P(_bis_MEP)/BC_1 and P(_bis_MEP)/BC_2 at temperatures up to 800 \({}^{\circ}\)C. Despite the reduction of the thermal stability of both nanocomposites in comparison with P(_bis_MEP) and BC, they are still thermally stable up to about 200 \({}^{\circ}\)C (loss of ca. 6 wt %), which is higher than the standard (<100 \({}^{\circ}\)C) and maximum (<140 \({}^{\circ}\)C) operating temperatures of the PEFCs technology [14; 40]. Moreover, these results are comparable to those reported for BC-based membranes containing the cross-linked poly(methacryloyloxyethyl phosphate) (PMOEP) as the polyelectrolyte [21], which is the diprotic acid version of the P(_bis_MEP) homopolymer.