esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this research, we explored the feasibility of characterizing adipocyte size in human adipose tissue using PA measurement combined with a deep learning method. In the experiments on _ex vivo_ human adipose tissues, we first studied different networks with various numbers of layers and the results indicated that the network depth has a great influence on performance of network. As shown in Figure 4, the 7-layer network yielded the best performance and resulted in a nearly 10% decrease of MRE compared with the 2-layer network when training on the 12.5 to 16.5 MHz spectral band. When trained on different spectral bands and the entire spectral band, our network found out the optimally fitted relationship between the PA signal spectrum and the adipocyte size measured by histology. The most sensitive spectral band turned out to be relative to adipocyte size which can be evaluated by Equation (5). PA signals are characterized with a broad-band property; combining all spectral components resulted in an MRE decrease of 3.48% compared with training on the most sensitive spectral band in our study. Compared to conventional PASA method, using a deep learning method to fit the nonlinear relationship can better utilize the rich information in the power spectrum and an improvement of 12.84 % on MRE was achieved. The results of our research show the validity of analyzing PA signals in the frequency domain using deep learning which can be a novel method for quantitative and noninvasive evaluation of biological tissues, e.g., characterizing human adipose tissue cellular phenotype in the context of the clinical management of obese patients. In our further study, a larger data set will be built and a method of combining multiple wavelengths, instead of just using 1210 nm laser illumination, will be tested to improve accuracy of evaluation for clinical application.  
Conceptualization and methodology, J.Y. and X.W.; Data Curation, M.C., T.F., A.R.W., N.A.B., C.N.L. and R.W.O.; Software, X.M. and C.M.; Formal Analysis, X.M.; Writing, X.M. and C.M.; Supervision, Q.S., J.Y., Q.C. and X.W.  
This research was funded by the National Key Research and Development Program of China (No. 2017 YFC0111402), the Natural Science Funds of Jiangsu Province of China (No. BK20181256), and by NIH grants R01 DK115190 (RWO, CNL) and R01 DK097449 (RWO).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Deep Learning** & **PASA** \\ \hline MAE/\(\mu\)m\({}^{2}\) & 394.19 & 891.20 \\ MRE & 9.30\% & 22.14\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between deep learning method and photoacoustic spectral analysis (PASA) method.
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 3 GAN-Enhanced CNN for Voiceprint Recognition
### Feature Extraction--MFCC
#### 3.2.1 Audio Data Loading and the MFCC Characteristics Calculations  
The 'librosa.load' function is used to load audio data, generating two primary outputs: an audio time series and the sample rate (the number of samples per second). The parameter'sr = 22,050 Hz' was chosen for this study to determine the sample rate. This function decodes the audio file, making it ready for processing.  
The 'librosa.feature.mfcc' function extracts the MFCC from the audio data. MFCC is a common feature used in speech recognition. For this study, the 'n_mfcc' parameter was set to 13, meaning that 13 MFCC feature variables were extracted from each audio sample.
# Quality and Defect Inspection of Green Coffee Beans Using a Computer Vision System
###### Abstract  
There is an increased industry demand for efficient and safe methods to select the best-quality coffee beans for a demanding market. Color, morphology, shape and size are important factors that help identify the best quality beans; however, conventional techniques based on visual and/or mechanical inspection are not sufficient to meet the requirements. Therefore, this paper presents an image processing and machine learning technique integrated with an Arduino Mega board, to evaluate those four important factors when selecting best-quality green coffee beans. For this purpose, the \(k\)-nearest neighbor algorithm is used to determine the quality of coffee beans and their corresponding defect types. The system consists of logical processes, image processing and the supervised learning algorithms that were programmed with MATLAB and then burned into the Arduino board. The results showed this method has a high effectiveness in classifying each single green coffee bean by identifying its main visual characteristics, and the system can handle several coffee beans present in a single image. Statistical analysis shows the process can identify defects and quality with high accuracy. The artificial vision method was helpful for the selection of quality coffee beans and may be useful to increase production, reduce production time and improve quality control.  
Keywords:coffee beans; Arduino Mega board; image processing; pattern recognition; machine learning; \(k\)-nearest neighbor algorithm +
Footnote †: journal: Applied
## 3 Methods for Image Captioning
### Visual Models
#### 3.1.2 Object Detection  
The two primary components of computer vision are classification and object detection. Identifying what is in an image is called classification while locating an object in an image is called object detection and localization. Finding the object's coordinates in an image makes detection more difficult. Region-based CNNs or regions with CNN features (R-CNNs) are two cutting-edge techniques for employing deep learning to recognize objects [42]. Below, we will introduce the R-CNN and its improvements: the Fast R-CNN [43] and the Faster R-CNN [44].  
**Regions with CNN Features (R-CNN).** R-CNN [42] extracts around 2000 region proposals from the input image. These proposed regions are often chosen at various scales, sizes, and shapes. Every region proposal will have a class and a ground-truth bounding box indicated on it. A pre-trained CNN should be chosen and truncated before the output layer. Resize each region proposal to the network's required input size, then output the region proposal's extracted features by forward propagation. Train multiple support vector machines to classify objects.  
However, this method has some problems. It is slow because the feature map is calculated for each region proposal. R-CNN has three parts, CNN, SVM, and Bounding Box Regressor, that must be trained separately. Large memory is required to save every feature map of each region proposal.  
**Fast Region-Based Convolutional Network (Fast R-CNN).** Fast R-CNN [43] appeared to solve the problems found in R-CNN. The region proposal is generated from a selective search algorithm. CNN inputs these region proposals with the image to generate the convolution feature map. The feature vector of a fixed length for each feature map is then extracted for each object proposal by a Region of Interest (RoI) pooling layer. Each feature vector is subsequently sent into the twin layers of the bounding-box regression and softmax classifier to categorize region suggestions and improve the location of the object's bounding box. Fast R-CNN obtains a higher mAP on PASCAL VOC 2012 [45] and trains the very deep VGG16 network nine times faster than R-CNN.  
**Faster R-CNN.** The Fast R-CNN model frequently creates region recommendations during a selective search to improve object detection accuracy. The Faster R-CNN [44] suggests changing from a selective search to a region proposal network to decrease region proposals without losing accuracy. In contrast to the Fast R-CNN, the Faster R-CNN transforms the region proposal strategy from a selective search to a region proposal network. The remaining parts of the model are unchanged.
## 5 Conclusions  
To document Holocene deltaic evolution of the Huanghe River, core YDZ-3 and marine surface sediments were studied in terms of sediment grain-size analysis, and paleo-superlobe migrations were tuned to sedimentary sequences. We found that the mean value of sediment grain size is 5.5 \(\pm\) 0.9 \(\Phi\) in core YDZ-3, and silt and sand particles changed relatively little with average values of 5.2 \(\pm\) 2.3% and 8.2 \(\pm\) 5.3%, respectively, while clay content is 86.4 \(\pm\) 8.5%. To isolate different sedimentary processes, sediment grain-size spectrum was mathematically partitioned into three subgroups by a Weibull-based equation, and their modal sizes are 61.1 \(\pm\) 28.9 \(\upmu\)m, 30.0 \(\pm\) 23.9 \(\upmu\)m, and 2.8 \(\pm\) 1.6 \(\upmu\)m, respectively. Based on these observations, eight intervals with abrupt changes in modal sizes were identified and then correlated to the seven paleo-superlobes of the Huanghe River in the Holocene. This correlation confirmed the presence of Paleo-superlobes Lijin (6400-5280 yr BP), Huanghua (4480-4190 yr BP), Jugezhuang (3880-3660 yr BP), Shajinzi (3070-2870 yr BP), Nigu (2780-2360 yr BP), Qikou (2140-2000 yr BP), and Kenli (1940-1780 and 1700-1650 yr BP) in the history. By tuning geomorphological events to the sedimentary proxy derived in this study, summer monsoon was proposed to be the major factor controlling deltaic evolution on centennial timescales. Therefore, we concluded that both summer and winter monsoons have played critical roles in deltaic evolution of the Huanghe River on different timescales and solar activities could be an important factor shaping geomorphological features in the Holocene.  
Conceptualization and methodology, L.Y.; formal analysis, Y.C., W.L., T.F. and Y.L.; original draft preparation, Y.C. and L.Y.; review and editing, Y.C., W.L., T.F. and Y.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant numbers 41602349 and 41706068, the Natural Science Foundation of Shanghai, grant number 19 ZR1459800, and the Beijing Natural Science Foundation, grant number 8204070.  
Not applicable.  
Data Availability Statement: Data are available on request the corresponding author ([PERSON]ADDRESS]).  
We thank [PERSON], [PERSON] in the East China Normal University for providing samples for this study.  
The authors declare no conflict of interest.
# Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction
## 4 Algorithm Design
### Resource Predictive Load Balancing SFC Migration Algorithm  
The pseudocode for the RP-LBM algorithm is shown in Algorithm 1. RP-LBM uses predicted future resource demands and the physical network graph as inputs to generate the SFC mapping strategy \(\pi\) (Lines 1-2). First, it calculates the resource utilization \(\eta_{R}\) for each physical node based on the prediction results (Line 3). If \(\eta_{R}\) exceeds the set threshold \(thr_{over}\) (Line 4), it selects an SFC to migrate on that node (Line 5) and it initializes the policy parameters \((\theta_{c},\ \theta_{a})\), the maximum number of iterations \(K_{max}\), and other relevant parameters (Line 6). In the main loop (Lines 7-19), it selects mapping actions \(a(t)\) from the policy \(\pi(s_{n}(t)|a_{n}(t),\theta_{a}^{n})\), executes the action to obtain an immediate reward \(r(t)\), and transitions to a new state \(s(t+1)\), while calculating the advantage function \(A(s_{n}(t),a_{n}(t))\). If constraints are not satisfied, it sets the immediate reward \(r(t)=-1/\varepsilon\) and reselects actions. Then, it computes the clipped surrogate objective for Proximal Policy Optimization and updates the policy parameters \(\theta_{a}\) using gradient ascent and the value function parameters \(\theta_{c}\) using gradient descent. If the resource utilization does not exceed the threshold, no action is taken (Line 20).  
```
1 Input: Prediction result \(c_{j,u}^{nf}(t+n)\), \(\ m_{j,u}^{nf}(t+n)(n=1,2,\ldots)\); Physical network diagram \(G=(V^{s},E^{s})\); SFC network diagram \(G_{i}^{nf}=\left(V_{i}^{nf},\ E_{i}^{nf}\right)\)
2 Output: SFC mapping strategy \(\pi\);
3 Calculate the resource utilization \(\eta_{R}\) of each physical node according to the prediction result;
4 if\(\eta_{R}\ \geq\ thr_{over}\) then
5 Select an SFC to migrate on that node;
6 Initialize \((\theta_{c},\ \theta_{a}),K_{max}\), M, \(((\varepsilon_{c},\ \varepsilon_{a}))\);
7 for episode = 1, \(\ldots\), M do
8 Select mapping actions \(a(t)\) from the strategies \(\pi(s_{n}(t)|a_{n}(t),\theta_{a}^{n})\);
9 if constraints are satisfied then
10 Execute the action \(a(t)\), get the instantaneous reward
11\(r(t)\) and transfer it to the state \(s(t+1)\);
12 Obtain the advantage function \(A(s_{n}(t),a_{n}(t))\);
13 else
14 Set instantaneous reward \(r(t)=-1/\varepsilon\), and re-select the action \(a(t)\) from the policy network;
15 endif
16 Compute the clipped surrogate objective for PPO
17 Update the policy parameters \(\theta_{a}\) using gradient ascent
18 Update the value function parameters \(\theta_{c}\) using gradient descent
19 endfor
20 endif
```  
**Algorithm 1** Resource Predictive Load Balancing SFC Migration Algorithm (RP-LBM)
## 1 Introduction
Such a combined experimental and computational approach is a good estimation method, but cannot be compared with a purely experimental measurement of the electron parameters due to approximations adopted during the calculations (for ex. the assumption of thermal equilibrium). Furthermore, for a pulsed low-current high-voltage discharge in the power range between 300 W and 1000 W, to the best of the authors knowledge, no data on a purely experimentally determined electron number density could be found in published works. The electron density can be estimated experimentally from the broadening of spectral lines due to the Stark effect. The so-called Stark effect arises in an ionized gas, i.e., plasma, due to the interaction of the atom's bound electrons with the electric field created by free electrons within the plasma surrounding that atom. Therefore, one of the aims of this work is to experimentally estimate the electron density of a pulsed low-current discharge operated at atmospheric pressure by means of emission spectroscopy. Furthermore, the heavy particle temperature is determined by the fitting of synthetic spectra to molecular bands of nitrogen transitions. The estimated heavy particle temperatures are also used for validation purposes. A commercially available pulsed plasma system Plasmabrush(r) PB3 produced by Relyon Plasma GmbH, Regensburg, Germany, is chosen as a test object. The system is characterized by a simple design comparable to that of other systems on the market.
## 6 Preliminary System Validation
### Preliminary Results  
#### 6.2.1 Feasibility in Capturing Trajectories  
The raw trajectories captured by the tablet-based and robot-based systems are presented in Figure 10a and Figure 10b, respectively. The data were normalised and grouped according to each participant for the left and right hands, respectively. Since each participant performed two attempts for each trial, the figures present the first attempt in blue and the second attempt in orange.  
It is evident that both systems were able to capture the position trajectories from the labyrinths. On one hand, the tablet-based system recorded position data at an average frequency of 10 Hz. Additionally, the pressure of the stylus was also recorded for further analysis if needed. On the other hand, the robot-based system recorded the position of the robot's end-effector at an average frequency of 6.5 Hz. Due to the force sensors integrated into the robotic arm joints, several force-based parameters from the human-robot interaction were measured, including the interaction forces \(\left[F_{x},F_{y},F_{z}\right]\) according to the Cartesian coordinate frame of the robot's base (related to movement direction) and the torque \(\left[\tau_{x},\tau_{y},\tau_{z}\right]\) measured in the robot's tool (related to orientation changes). All parameters from both the tablet-based and robot-based systems were stored in.csv files for post-processing.  
It can be seen in Figure 10 that the trajectories performed with the tablet and the robotic arm are quite different. Trajectories performed with the tablet stylus are smoother than those performed with the robotic arm. Also, it can be seen that smoother traces were achieved with the right-hand case for both systems. This is reasonable since for most of the participants, the dominant hand is the right one. Note that when referring to smoothness, the trajectories can be compared to each other or against the reference or ideal path given by the central line. It should also be noted that users took different times to complete the tasks, which can influence the traces. In this context, it is necessary to use a method that allows comparing the recorded trajectories without considering the effects of drawing speeds.
# Cellular Entry, Cytotoxicityicity, and Antifungal Activity of Newly Synthesized Dendrimers
###### Abstract  
Dendrimers, 4-dimethylamino-1,8-naphthalimide (DAB) and its halogenated analog 3-bromo-4-dimethylamino-1,8-naphthalimide (DAB-Br), were evaluated on eukaryotic cells, human HFF-1 fibroblast cells, and five fungal species. Although both dendrimers have demonstrated antibacterial and antiviral potential, thus far, their effects on eukaryotic cells, particularly human and fungal cells, have not been investigated. For this purpose, their cytotoxicity, mechanisms of cellular entry, and antifungal activity were studied. Dynamic light scattering measurements revealed that both dendrimers exhibited positive surface charges (+28 to +35 mV), good colloidal stability, and nanoscale dimensions (117-234 nm), facilitating interactions with target cells. The MTT assay showed that DAB was more cytotoxic toward HFF-1 cells (IC\({}_{50}\) = 27 \(\mu\)g/mL) compared to DAB-Br (IC\({}_{50}\) = 68 \(\mu\)g/mL). In contrast, the resazurin-based antifungal assay demonstrated that DAB-Br had superior antifungal activity, achieving a lower minimum inhibitory concentration (0.148 \(\mu\)g/mL), compared to DAB (0.295 \(\mu\)g/mL). A trypan blue exclusion test revealed that both dendrimers entered cells through membrane permeabilization, either temporarily or permanently, depending on the concentration and exposure time. At concentrations above 30 \(\mu\)g/mL, irreversible permeabilization was observed within two hours of treatment, accompanied by a decrease in membrane lipid order, indicating altered membrane integrity and permeability. Conversely, at lower concentrations (7.5-15 \(\mu\)g/mL), dendrimers induced only temporary membrane permeabilization, with membranes remaining intact, suggesting a reversible interaction with the lipid bilayer. Conducting thorough and systematic research to fully explore their biological activities could provide valuable insight for future applications.  
dendrimers; HFF-1 cell line; fungi; cytotoxicity; antifungal agents +
Footnote †: journal: Applied sciences  
1
## 6 Conclusions  
In this work, we derived analytical expressions for 24 IG modes with the indices \(p=3\), 4, 5, and 6 via 2, 3, or 4 LG modes (more exactly, via their real or imaginary parts) and HG modes (see Equations (19), (25), (26), (30) and (34) and Table 1). The expansion coefficients of the IG modes in terms of LG and HG modes are expressed via the ellipticity parameter \(\varepsilon\). Using the derived formulas, one can immediately derive expressions for the IG modes when the parameter \(\varepsilon\) is either zero or approaches plus or minus infinity. This explicit dependence of the IG modes on the ellipticity parameter enables control of their intensity through adjustment of this parameter. We have derived the symmetry properties of both even and odd IG modes. In particular, we have demonstrated how these modes are interrelated when the ellipticity parameter undergoes a sign change. The obtained representations of IG modes via HG and LG modes are convenient not only from the theoretical point of view, as they reveal the properties of these beams without simulation, but also from the practical point of view. This practical application makes it relatively easy to program complex amplitudes of the IG modes for their numerical simulation or generation using a spatial light modulator. For large-distance atmospheric probing using IG beams, high-power laser beams are needed. For this purpose, it is convenient to use a method of coherent beam combining, as proposed in [21]. Ince-Gaussian beams can beeffectively used for encoding (or protecting) data in wireless information transmission, where the same beam \(\mathbf{IG}_{p,q}^{(e,\rho)}(\mathbf{r},\varepsilon)\) with constant \(p\), \(q\) values is transmitted, but the parameter \(\varepsilon\) is randomly changed.  
Conceptualization, E.G.A.; methodology, V.V.K. and E.G.A.; software, A.A.K.; validation, V.V.K. and E.G.A.; formal analysis, V.V.K.; investigation, V.V.K., E.G.A. and A.A.K.; resources, V.V.K.; data curation, E.G.A.; writing--original draft preparation, V.V.K.; writing--review and editing, V.V.K. and E.G.A.; visualization, A.A.K.; supervision, V.V.K.; project administration, V.V.K.; funding acquisition, V.V.K. and A.A.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the RUSSIAN SCIENCE FOUNDATION (Project No. 22-12-00137, theory and numerical simulation). The work was also performed within the STATE ASSIGNMENT of NRC \"Kurchatov Institute\" (Introduction and Conclusion).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Experimental Results and Discussion
### Analysis Results of Consolidation Test  
Figure 5 shows the curves between the primary compression strain \(\varepsilon_{p}\) and stress \(\sigma_{v}\) of L-MSW, M-MSW, and H-MSW specimens under different temperatures. Here, you can see that the primary compression strain and logarithm of stress exhibited a linear relationship under different temperatures in each group of samples. As the stress on the specimens increased from 25 kPa to 400 kPa with temperatures of 20 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C, the primary compression strain of L-MSW increased from 7.691%, 8.219%, and 9.539% to 26.584%, 28.517%, and 29.086%. The primary compression strain of M-MSW increased from 5.8%, 6.2%, and 8.218% to 30.557%, 32.638%, and 36.648%. The primary compression strain of H-MSW increased from 6.785%, 8.009%, and 7.254% to 37.558%, 40.553%, and 41.826%. The MPCI of each sample could be obtained by linear fitting of the settlement data. At temperatures of 20 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C, the MPCIs of L-MSW were 0.15443, 0.16629, and 0.16948; The MPCIs of M-MSW were 0.21109, 0.22323, and 0.23894, and the MPCIs of H-MSW were 0.26166, 0.27383, and 0.29623. The fitting degree of the curve was 0.9958-0.9997, and the results are good.