esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Lipid Membrane-Based Biosensing Platforms
## 2 The Electroanalytical Aspect of Lipid Membrane Biosensors
Temperature changes affect the order of lipids and result in the structural phase transition of the bilayer: At low temperatures, the bilayer is in the gel phase (very packed with minimum diffusion of lipid molecules), whereas as temperature increases lipid packing becomes looser to reach a liquid crystalline phase [31]. The ability to modulate phase shifting upon binding of the analyte is another strategy in biosensing; biochemical interactions (i.e., nucleic acid interactions) on one side of the membrane may induce phase shifting, transient or permanent, which alters membrane permeability [32]. The use of mixtures of lipid solutions to fabricate membranes with domains of different structural phase transition temperatures [33] may, indeed, provide interesting sensing platforms and the capability of array engineering in small spaces. Despite, evidence suggests that the immobilization of bioelements on one side of the bilayer disturbs membrane symmetry and may induce budding in the domain of interaction [34], suppressing phase separation, unless interaction perturbs the hydrophobic region of the membrane. At any case, bioelement-target analyte interactions, disturb bioelement-lipid interactions that could allow micro-phase separation to occur. When designing a lipid membrane platform for biosensing, the modulation of the interplay between electrochemical parameters for enhancing signal generation should also take under consideration membrane stability. Any change of electrical potential, surface tension, surface charge density, etc., may increase the free energy of the system, leading to molecular re-arrangement propagating to rupture [35]; the former does not affect membrane functionality critically but the latter is catastrophic. This implies a very careful trade-off between sensitivity and analytical range: Extremely sensitive biosensors work reliably in narrow analytical ranges [1]. In applications that may require broad detection ranges (e.g., environmental monitoring), the sensitivity level should be compromised.
## 4 Detection Method
### Text OCR Layer  
The text OCR layer mainly performed text recognition on objects with YOLOv5 detection results as learning targets, so as to identify specific learning objects. The main steps are shown in Figure 11. The text OCR layer of this system used the CRNN model. The architecture of the CRNN model consisted of the following three parts: a Convolutional Layer (CNN), a Recurrent Layer (RNN), and a Fully Connected Layer. As shown in Figure 12, features were extracted using CNN first, followed by classification using recurrent neural networks and SoftMax to obtain output, before, finally, being combined with CTC to determine the characters.  
In OCR training, the CTC loss function was used for backpropagation. As shown in Formulas (3) and (4), the CTC loss function was the sum of the probabilities of all possible paths with a result of l, given input x, where pi represented the path equal to l after being changed by B. Because the CTC loss function could compare sequences of different lengths, the input sequence could be mapped to the output sequence, while allowing a certain sequence alignment error, thus improving the robustness and generalization performance of the model.  
\[\text{P}\Big{(}\frac{1}{\text{x}}\Big{)}=\sum_{\pi\in\text{B}^{-1}(1)}\text{P} \Big{(}\frac{\pi}{\text{x}}\Big{)} \tag{3}\]  
\[\text{P}\Big{(}\frac{1}{\text{x}}\Big{)}=\sum_{\pi\in\text{B}^{-1}(1)}\text{P} \Big{(}\frac{\pi}{\text{x}}\Big{)} \tag{4}\]  
As shown in Figure 13, the model clipped the image containing word objects, according to the boundary box. The clipping result was shown on the left of Figure 14. Then, the text information in the image was recognized according to the learned features. The recognition result was shown on the right of Figure 14, where the string represents the recognized text content and the corresponding probability. By following the above steps, the corresponding target object could be accurately identified.  
Figure 11: Text OCR layer flowchart.  
Figure 12: CRNN model structure diagram.
## 2 Network Model
### Basic Assumptions  
To facilitate the analysis of an UWSN, we model it as follows. Suppose that the sink (denoted by \(D\)) is located at the center of a circle of radius \(a\), where \(a\) is the largest possible distance between \(D\) and any sensor. Any sensor node (denoted by \(S\)), which intends to transmit a data packet to \(D\), is assumed to be uniformly distributed over the entire circle. Due to the limited transmission energy, a packet from its originating source node to the sink may need to be sequentially routed by a certain number of intermediate nodes. For the sake of easy and practical deployment, we assume that all nodes, including the source node and the intermediate nodes, employ a common transmission range \(r\). Consequently,  
Figure 1: The network model for 2D UWSNs [1,11].  
direct transmission to the sink occurs only when the sensor node is within a distance of \(r\) from the sink. Any node within the transmission range of a node is called its neighbor. We assume that some routing protocol is employed so that each node can establish the shortest path to the sink.  
As pointed out in [12], the selection of transmission range influences energy consumption and network connectivity. The question is how to quantitatively analyze the influence, which is described in detail in the next section.
# Simultaneous Use of Digital Technologies and Industrial Robots in Manufacturing Firms
## 3 Research Methodology
### Measures and Statistical Methods  
The following statistical methods were used to test the relationship between industrial robot types and the selected digital technologies and the use of industrial robots based on specific firm characteristics: Chi-square test of independence, Phi coefficient, and logistic regression [41].  
[PERSON]'s chi-square test of independence is a method for evaluating if 2 variables are independent of each other. It is based on the assumption that the data are categorical or nominal (mutually exclusive categories such as yes or no), the analyzed data represent a random sample, and that the expected frequency of each cell is at least 5 or greater. [PERSON]'s exact test should be used if the frequency is less than 5 [41].  
The Phi coefficient is a special case of the Pearson product-moment correlation coefficient, which is employed when we wish to analyze the relationship between the levels of 2 dichotomous variables (in our case, there are many yes or no variables that are examples of dichotomous variables). If the values of the phi correlation coefficient are in the range of 0.1 and 0.3, then it is considered that there exists a small effect size. If they are in the range of 0.3 and 0.5, it is considered a medium effect size, and if the values are greater than 0.5, then we can say that the effect size is large [41].  
Lastly, the logistic regression will provide us with the likelihood that an observed type of robot will fall into 1 of 2 categories (Using or Not using) [41]. In our case, we will determine the likelihood that the firm is using a specific type of robot based on 7 digital technologies and the readiness index level.  
Several variables from the EMS questionnaire were used for the purpose of our research. The following questions were used as dependent variables:  
1. Type of industrial robot (industrial robot for manufacturing processes, industrial robots for handling processes, mobile industrial robots, collaborating robots and autonomous industrial robots);  
The following questions were used as independent variables:
2. Digital technologies (7 selected technologies);
3. Firm size (number of employees);
4. Readiness Index levels (level 0 to level 5).  
Simple descriptive statistics were used for the presentation of the general use of selected digital technologies and industrial robots. Descriptive statistics were also used to present the distribution of firms within all 6 Industry 4.0 Readiness Index levels.
# Fault Diagnosis of Vehicle Gerboxes Based on Adaptive Wavelet Threshold and LT-PCA-NGO-SVM
## 2 Principle of the Method
### Principle of Wavelet Threshold Denoising
#### 2.1.2 Noise Reduction Method  
To accurately extract fault features, a noise reduction method leveraging CEEMDAN and wavelet techniques has been proposed. This method effectively suppresses residual noise and false components, thereby accentuating fault characteristic frequencies. The specific steps are as follows:  
Step 1: Perform CEEMDAN decomposition on the acquired signal to obtain multiple intrinsic mode function (IMF) components ordered from high to low. Calculate the correlation coefficients for each component.  
Step 2: Determine the optimal value of the improved threshold function using SA.  
Step 3: Apply wavelet noise reduction to the IMF components with larger correlation coefficients, and reconstruct the signal after noise reduction.  
Step 4: Verify the signal after noise reduction using evaluation metrics. Alternatively, conduct energy spectrum analysis to extract the fault characteristic frequency.  
Figure 2 presents a visual representation of the noise reduction flowchart.  
Figure 1: Threshold function curve.
# Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar
## 4 Comparative Performance Analysis
### Investigated Pseudorandom Binary Sequences
#### 4.1.5 Golay Sequences  
Binary Golay sequences are defined in complementary pairs [34], whose sum of non-oversampled PACFs yields an ideal non-oversampled PACF in the absence of Doppler shifts. In this article, those complementary sequences are henceforth called Golay A and Golay B sequence. In order for Golay sequences A and B to be separately transmitted, a multiplexing strategy has to be chosen, being the most common one time-division multiplexing (TDM). Although CDM, e.g., with Hadamard codes, could be used and still keep the aforementioned complementary sequences binary, it would result in an even longer measurementtime [8; 16; 23]. To avoid mutual interference between those sequences, it is henceforth assumed that cyclic prefix (CPs) of length \(N_{\text{CP}}^{\text{Galaxy}}\) are pretended to each of them before their successive transmission. Assuming an ideal radar channel, each of the received complementary sequences is correlated with their corresponding transmit counterparts at the receiver side of the SISO-PMCW radar system after CP has been removed. The two resulting PACFs are then summed to yield an ultimate PACF. Although the resulting PACF usable length could be equal to \(N_{\text{PRBS}}\) due to the expected null sidelobes, the maximum unambiguous range of the SISO-PMCW radar system adopting complementary Golay sequences will be mostly constrained by the CP length \(N_{\text{CP}}^{\text{Galaxy}}\) that is appended to each of the complementary sequences. Consequently, the usable length of PACF resulting from the sum of the PACFs of the complementary Golay sequences A and B is \(N_{\text{PRBS,usable}}=N_{\text{CP}}^{\text{Galaxy}}\). In this article, CPs of length \(N_{\text{CP}}^{\text{Galaxy}}=N_{\text{PRBS}}\) will be henceforth assumed for each of the complementary sequences so that no reduction in \(N_{\text{PRBS,usable}}\) is experienced. Another possibility for binary Golay sequences is to transmit just one of the sequences from the complementary pair, i.e., Golay A or Golay B, and use it as a ZCZ sequence. In this context, PACFs with maximum usable length \(N_{\text{PRBS,usable}}<N_{\text{PRBS}}\) are observed.
###### Abstract  
Pulsed electric field (PEF) treatment is known as a method that can intensify heat- and mass-transfer-based processes such as osmotic dehydration, drying, or freeze-drying. However, the literature about its impact on quality of freeze-dried products is limited to a few raw materials. The aim of this study was to analyze the effect of PEF on the cell disintegration index, selected bioactive compounds, and physical quality parameters of freeze-dried pumpkin. The final quality of the freeze-dried product was evaluated by residual moisture content, color analysis, total phenolic content, total carotenoid content, sugars content, and hygroscopic properties. The application of PEF treatment induced the disintegration of pumpkin cells even at low energy input (0.11 kJ/kg), and the saturation level of electroporation was reached after 4 kJ/kg. PEF treatment at 2 kJ/kg allowed 40% more total carotenoids to be retained in comparison to the untreated sample. Furthermore, all PEF-treated freeze-dried pumpkin samples exhibited lower sucrose content but had higher glucose and fructose contents in comparison to the reference samples. However, this effect was more pronounced when the shelf temperature was equal to 40 \({}^{\circ}\)C.  
PEF; electroporation; lyophilization; freeze drying; pumpkin
###### Abstract  
This study aimed to investigate the effect of the type of sweetener used (xylitol, stevia, cane sugar) on the quality of liqueurs made from _Vaccinium myrtillus_ L. and _Vaccinium corymbosum_ L. fruits. The quality assessment was performed based on selected organoleptic and physicochemical features, with particular emphasis on the health-promoting potential of the produced beverages. The liqueurs were assessed in terms of their physicochemical parameters: pH, total acidity, density, total soluble solids, color, ethanol and polyphenol contents, and redox potential. Antioxidant capacities were determined by a 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging capacity assay and ferric reducing antioxidant power (FRAP). The Qualitative Descriptive Analysis method was employed for their sensory assessment. The sensory profiling method was used to determine the intensity of the flavor sensations. The study results showed that the type of sweetener did not affect the antioxidative properties of the liqueur. The ABTS test yielded values from 1081.88 to 1238.13 \(\upmu\)mol Tx/100 mL, the DPPH test from 348.8 to 367.88 \(\upmu\)mol Tx/100 mL, and the FRAP test from 594.20 to 653.20 \(\upmu\)mol FeSO\({}_{4}\)/100 mL. However, the sweetening substrate affected the content of polyphenic compounds in the resulting products, but by no more than 15%. The liqueur sweetened with xylitol had a comparable extract content to that sweetened with cane sugar. All three variants of liqueurs were accepted by the evaluation panel, and their overall qualities were comparable in the sensory assessment. It is, therefore, possible to produce a high-quality liqueur with a reduced caloric value, which will potentially increase its attractiveness for consumers.  
liqueur; sweetener; _Vaccinium myrtillus_ L.; _Vaccinium corymbosum_ L.; oxidation-reduction potential; polyphenols
# Smartphone-Based Task Scheduling in UAV Networks for Disaster Relief
## 6 Simulation Results
This reflects the effectiveness of our algorithm and its ability to converge quickly. To validate the performance of MOMIB in various data environments, we conducted tests under different conditions. Specifically, the performance of MOMIB was evaluated in a random environment (RE) and a true environment (TE). The RE entailed the random allocation of task types and was the environment used in all the above experiments. Since sufficient experimentation was conducted and validated, we do not repeat it here. The TE simulated the data distribution of real IoT scenarios, in which the various types of applications were roughly distributed in proportions of 35%, 15%, 20%, 20% and 10%, according to the order in Table 1[20; 21]. Figure 6 shows the performance of MOMIB with iterations in different environments. It can be observed that MOMIB achieved optimization and convergence in different environments, which further validates the stability of the proposed algorithm. Figure 4: Variation in system performance with number of SPs in mild disaster scenario. (**a**) Variation in total UAV overhead with number of SPs. (**b**) Variation in total SPs utility with number of SPs. Figure 5: Variation in each objective with MOMIB iterations. (**a**) System overhead of UAVs. (**b**) Execution utility of SPs.
## 3 Results and Discussion
### Effect of Ionic Strength on the Plasmon Field of the Gold Nanoparticles  
With an increase of the ionic strength induced by including sodium chloride and increasing its concentration, the interparticle distance between the gold nanoparticles was effectively reduced [17]. Figure 2 shows the UV-vis spectra for the different values of concentration of sodium chloride up to 100 mM. For higher values of concentration of sodium chloride, irreversible aggregation and loss of gold nanoparticles was obtained. The concentration of gold nanoparticles was estimated using the absorbance at 400 nm [18]. The concentration of gold nanoparticles was ca. \(2.7\times 10^{18}\) nanoparticles/L in all cases.  
There is almost no modification on the plasmon peak of the gold nanoparticles for a sodium chloride concentration up to 50 mM, with a slight shift to higher values of wavelength at 60 and 70 mM of sodium chloride. The plasmon band at 80 mM of sodium chloride displays a different shape with respect to the others, being the limit value for a a change in the tendency of shift of the plasmon band. This modification of the plasmon band, including a broad shoulder at ca. 640 nm, is ascribed to the appearance of large aggregates of nanoparticles. Such aggregation is mediated by the shielding of the repulsive electrostatic interactions of the citrate ions coating the nanoparticles by the sodium  
Figure 1: Citrate-capped gold nanoparticles. (**a**) Transmission electron microscopy image of the gold nanoparticles. (**b**) Histogram including the values of size of the gold nanoparticles.  
Figure 2: Effect of ionic strength on the plasmon characteristics of the gold nanoparticles. (**a**) UV-vis spectra of the dispersion of gold nanoparticles at different concentrations of sodium chloride from 0 mM (black line) to 100 mM (brown line). (**b**) Values of wavelength corresponding to the plasmon peaks of the gold nanoparticles against ionic strength. The line is included as a guide for the eye.  
ions in solution. Yet, the more noticeable shift in the plasmon peak occurs at high values of ionic strength, as expected by the enhanced aggregation of the nanoparticles. In this case, a dispersion of gold nanoparticles in 90 and 100 mM of sodium chloride leads to a plasmon peak at ca. 630 nm, see Table 1. Note that, in addition to the significant shift, the plasmon peaks becomes broader at high values of sodium chloride concentration.
# Automatic Roadway Features Detection with Oriented Object Detection
## 3 System Overview
### Model Architecture  
As one of the best one-stage object detectors in terms of detection accuracy and speed, YOLOv3 model architecture [5] was adopted as the base detector in this research. YOLOv3 divides the image into a grid of S \(\times\) S cells, and for each grid cell it produces nine different predictions of different scales and sizes for the potential object centered at the grid cell. Each prediction includes a score that represents the confidence of the object existence in the corresponding cell. A list of anchor boxes defining the most common object bounding boxes' scales is pre-defined, and the network predicts the scale offsets from such anchor boxes. The base architecture of YOLOv3 was modified to predict undirected oriented object bounding boxes instead of horizontal bounding boxes.  
Figure 1: System overview during deployment phase.  
Transfer learning [52] is applied. YOLOv3 was initially trained on MS COCO (Common Objects in Context) dataset [53], which is a rich object detection dataset featuring 123,287 training images representing 80 classes, and hence allows YOLOv3 CNN pipeline to detect a generic set of image objects features with a high precision. Then, the trained neural network was transferred from that broad domain to the specific one focusing on scenes from the roadway dataset. When training on the dataset, instead of starting the model iterative learning process from a random initial point, only the prediction layer is set to start from a random initial point, while the CNN pipeline starts from the knowledge gained from training on MS COCO dataset. The transfer learning is found necessary for the introduced model to converge to a reasonable detection performance; especially that the fine-tuning dataset is smaller than MS COCO dataset.
# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 3 System Model
### OFDM System  
Figure 1 shows the block diagram of the OFDM system in the multipath time-varying channel. At the transmitter, each group inputs a bit stream \(x=\{x_{0},x_{1},x_{2},\cdots,x_{2N_{c}-1}\}\) is digitally modulated to Quadrature Phase Shift Keying (QPSK) symbols \(X(k)\), where \(N_{c}\) is the number of subcarriers, \(x_{i}=\{0,1\}\), \(i=\{0,1,2,\cdots,2N_{c}-1\}\). Then the modulated signal is Invert Fast Fourier Transformation (IFFT) to obtain the time-domain signal \(x(n)\) of the modulated signal:  
\[x(n)=\text{IFFT}[x(k)]=\sum_{k=0}^{N_{IFFT}}X(k)e^{j\left(\frac{2\pi k}{N_{IFFT} }\right)},n=0,1,2,\cdots,N_{IFFT}-1 \tag{1}\]  
A Cyclic Prefix (CP)of length \(N_{g}\) is inserted to eliminate Inter Symbol Interference (ISI) caused by multipath channels, then we can obtain a new OFDM signal \(x_{f}(n)\) :  
\[x_{f}(n)=\left\{\begin{array}{cc}x(N_{IFFT}+n)&,n=-N_{g},-N_{g}+1,\cdots,-1\\ x(n)&,n=0,1,\cdots,N_{IFFT}-1\end{array}\right. \tag{2}\]  
Considering the multipath time-varying channel of the Single Input Single Output (SISO) model, i.e.,  
\[h(t)=\sum_{l=0}^{L-1}h_{l}\delta(t-\tau_{l})e^{j2\pi v_{l}t} \tag{3}\]  
where L represents the number of resolved multipath, \(l\) indicates the multipath channel index, \(\tau_{l}\) represents the time delay of the \(l\)-th path, \(h_{l}\) denotes the complex amplitude of the \(l\)-th path, \(v_{l}\) denotes the corresponding doppler shift, which can be calculated by the formula \(v_{l}=f_{c}v\cdot\cos\theta/c\), \(f_{c}\) denotes the carrier frequency, \(v\) denotes the moving speed of the mobile station, \(c\) denotes the speed of light, \(\theta\) denotes the signal arrival angle. Since this paper consider SISO systems, so \(\theta=0^{\circ}\), \(v_{c}=f_{c}v/c\).  
Then the downlink received signal \(y(n)\) can be expressed as:  
\[y(n)=x(n)\otimes h(n)+w(n) \tag{4}\]  
where \(\otimes\) denotes cyclic convolution, \(w(n)\) denotes Additive White Gaussian Noise (AWGN). CP is removed from the received signal, and then through Fast Fourier Transformation (FFT), the following received signal can be obtained:  
\[Y(k)=FFT[y(n)]=\frac{1}{N_{IFFT}}\sum_{n=0}^{N_{IFFT}-1}y(n)e^{-j(2\pi kn/N_{IFFT })},k=0,1,\cdots,N_{IFFT}-1 \tag{5}\]  
Figure 1: OFDM system architecture for multipath time-varying channels.  
At this time, the received signal \(Y(k)\) can also be expressed as:  
\[Y(k)=X(k)H(k)+W(k) \tag{6}\]