esa-sceva/satcom-chunk-collection huggingface dataset:
gain while, at the same time, decreases the number of user equipment (UE) devices that can be served in a single transmission. Furthermore, it has been shown in [5] the presence of multicast traffic negatively affects the service performance of unicast one making provisioning of services requiring both types of transmissions a complex problem. Thus, the choice of the optimal density of mmWave BSs is a non-trivial task that becomes even more complicated when advanced services need to be provisioned.  
In this paper, we consider multi-layer virtual reality (VR). This application inherently requires both multicast and unicast network services for efficient provisioning in mmWave NR systems presenting extremely challenging conditions for mmWave NR systems. To the best of the authors' knowledge, most of the studies performed so far for multi-layer multicast/unicast services concentrated on optimizing an already provisioned deployment for given traffic conditions. However, for network operators, the question of estimating the required density of mmWave NR BSs for a given stochastic traffic load in a given area is equally important. This question has been loosely addressed in the literature. In this paper, we fill the above-mentioned void by proposing a simple model for the only provisioning of multi-layer VR service in prospective mmWave deployments. Based on this model, we develop the algorithm allowing us to assess the sought parameter.  
The main contributions of our study are as follows:  
* a mathematical model characterizing the fraction of VR multi-layer multicast sessions that can be served as a function of system parameters and the traffic load;
* numerical analysis of the minimum mmWave NR BS density that can deliver a given performance to multi-layer VR service as a function of the system and service characteristics.  
The rest of the paper is organized as follows. In Section 2 we briefly describe the considered service and related work. The system model is defined in Section 3. We solve the model and specify the algorithm for estimating the required density of NR BSs in Section 4. Numerical results are reported in Section 5. Conclusions are drawn in Section 6.
# A Content Hiding Method for Digital Hologram Using Multiple Fresnel Diffraction
## 2 Characteristics of Fresnel Diffraction  
### Numerical Calculation  
[PERSON]'s transform (or diffraction) is a mathematical equation that models the diffraction phenomenon of the electromagnetic wave from a point light source, which is as Equation (1) [14].  
\[g(x,y,z)=\frac{\exp^{jkz}}{j\lambda z}\int_{-\infty}^{\infty}E(u,v,0)\exp^{j \frac{k}{2\pi}\left\{((x-u)^{2}+(y-v)^{2}\right\}}\,dudv \tag{1}\]  
where \(E(u,v,0)\) is the complex amplitude of the electromagnetic wave from a point light source at \((u,v)\) on the source plane at \(z=0\), \(g(x,y,z)\) is the complex amplitude of the electromagnetic wave at \((x,y,z)\) resulting from diffraction, and \(k=2\pi/\lambda\) is wave number for wavelength \(\lambda\). If we make the discrete version of Equation (1), \(u\), \(v\), \(x\), and \(y\) are discretized to \(u^{\prime}\Delta u\), \(v^{\prime}\Delta v\), \(x^{\prime}\Delta x\), and \(y^{\prime}\Delta y\) where \(u^{\prime}\) or \(x^{\prime}\in 0,1,\ldots,M-1\) and \(v^{\prime}\) or \(y^{\prime}\in 0,1,...,N-1\), and \(M\times N\) is the resolution of the object points and the diffraction plane (here we assume that both planes have the same resolution, without losing any generality). \(\Delta u\times\Delta v\) and \(\Delta x\times\Delta y\) are the sampling distances on the source plane and the diffraction plane with the distance \(z\) from the source plane. The pixels in the source and the diffractions plane have the relationship of Equation (2).  
\[\Delta x=\frac{\lambda z}{M\Delta u},\Delta y=\frac{\lambda z}{N\Delta v} \tag{2}\]  
Thus, Equation (2) can be rewritten with a Fourier transform as Equation (3), where \(FT[x]\) means the Fourier transform of \(x\).  
\[g(x,y,z)=\exp^{j\frac{\pi}{\lambda z}\{(x^{\prime}\triangle x)^{2}+(y^{\prime} \triangle y)^{2}\}}FT\Bigg{[}E(u,v,0)\exp^{j\frac{\pi}{\lambda z}\{(u^{\prime} \triangle u)^{2}+(v^{\prime}\triangle v)^{2}\}}\Bigg{]} \tag{3}\]  
Equation (3) may be described as the light diffracting at a point \(P\) on the input plane (the hologram plane or the spatial light modulator (SLM)) and reaching the output plane (the diffraction plane), which is shown in Figure 1.
###### Abstract  
This paper utilizes prevalent deep learning techniques, such as Convolutional Neural Networks (CNNs) and Residual Neural Networks (ResNets), along with the well-established machine learning technique, Random Forest, to efficiently distinguish between common lithologies including coal, sandstone, limestone, and others. This approach is highly significant for resource extraction--such as coal, oil, natural gas, and groundwater--by streamlining the process and minimizing the need for the time-consuming manual interpretation of geophysical logging data. The natural gamma ray, density, and resistivity log data were collected from 22 wells in the mountainous region of Southern Sichuan, China, yielding approximately 70,000 samples for developing lithofacies prediction models. All the models achieved around 80% accuracy in classifying carbonaceous lithologies and up to 88% accuracy in predicting other lithologies. The trained models were applied to the logging data in the validation dataset, and the outputs were validated against geological core data, showing overall consistency, although variations in the classification results were observed across different wells. These findings suggest that deep learning techniques have the potential to develop a general model for effectively handling lithology classification with well logging data.  
Keywords: lithofacies prediction; well log; deep learning +
Footnote †: journal: Applied sciences
## 3 FEA Model and Validation
### Modelling Procedure  
The TBM excavation process is mimicked by the \"element death\" technique [46] with the detailed procedure as follows:  
1. Firstly, the initial geostatic stress of the soil was generated with the initial displacement of ground less than \(10^{-6}\) m.
2. Secondly, the soil elements of the first seven slices (the width of each slice was 1.2 m) at EB were removed to simulate the space occupied by the TBM after it was launched. The non-uniformly distributed displacement boundary (as demonstrated by the yellow arrows in Figure 6a) was applied at the soil nodes around the excavated soils to simulated the volume loss caused by the TBM. This assumed tunnel convergence is characterized by a parameter \(\delta\), which is the tunnel contraction around the excavation region. For detailed description of this non-uniformly distributed displacement boundary, please refer to the previous research of the URUP project [34]. The contraction parameter \(\delta\) can be expressed as: \[\delta=\frac{\delta_{max}}{2(\mathrm{R-R_{0}})}\Bigg{[}\sqrt{R^{2}-2R(R-R_{0} )cos(\theta+\pi/2)+(R-R_{0})^{2}}-R_{0}\Bigg{]}\] (15) where \(\delta_{max}\) is the contraction at the tunnel crown. \(R\) and \(R_{0}\) are the radius of the cutter head and shield body, respectively. \(\theta\) is the angle of the polar coordinate system with the origin located at the central of the excavation region. At the same time, the supporting pressure at the tunnel face and the grouting pressure at the TBM tail (as shown in purple arrows in Figure 6c) were activated.
3. Thereafter, one slice soil element in the model was deactivated with the corresponding displacement boundary activated and deactivated, as illustrated in Figure 6b; the grouting pressure was applied at adjacent soils with two rings behind the TBM tail, and corresponding lining and grout layer were activated simultaneously (as illustrated in Figure 6c).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \(\rho(\frac{\mathrm{kg}}{\mathrm{m}^{3}})\) & **E(MPa)** & \(\
u\) & \(\beta\) & \(d\)(kPa)** & \(K\) & \(e_{0}\) \\ \hline
2 & 1.39 & 15 & 0.30 & 31.9 & 42.8 & 0.828 & 1.031 \\
1b1 & 1.37 & 25 & 0.27 & 23.9 & 55.2 & 0.871 & 0.99 \\ J311 & 1.57 & 25 & 0.25 & 32.9 & 110.8 & 0.822 & 0.7 \\ J312 & 2.23 & 60 & 0.20 & & & & 0.5 \\ J313 & 2.54 & \(2.1\times 10^{-3}\) & 0.21 & & & & 0.3 \\ Lining & 2.5 & \(3.61\times 10^{-4}\) & 0.20 & & & & \\ Grout & 1.9 & 5 & 0.40 & & & & \\ \hline \hline \end{tabular} Note: \(\rho\): soil density; \(E\): elastic modulus; \(\
u\): Poisson’s ration; \(\beta\): inclination angle of yield surface in \(p-t\) stress space; \(d\): intercept of \(t\) axis of yield surface in \(p-t\) stress space; \(K\): flow stress ratio; \(e_{0}\): initial void ratio.  
\end{table}
Table 1: Material parameters of FEA model [34].  
Figure 6: FEA modelling procedure: (**a**) step 1; (**b**) non-uniform contraction applied at the TBM boundary; and (**c**) activation of lining and grouting layer (redrawn after [34]).
# Radio Network Planning towards 5G mmWave Standalone Small-Cell Architectures
The optimization algorithm used in this paper is the evolution of the algorithm previously used for the analysis of Long-Term Evolution (LTE) systems [7]. In this study, the analysis is focused on network dimensioning for the mmWave band at 28 GHz. Hence, the propagation module is modified accordingly and a path loss model based on results of field trials at the 28 GHz band is applied. For this analysis only microcells are considered with tri-sector directional antennas, as well as omnidirectional antenna patterns. Moreover, the system module is upgraded to encompass 5G rate calculations, while the core analysis is focused on the impact of high-density microcell deployments on the spectral efficiency and throughput efficiency of these systems. The remainder of the paper is organized as follows: Section 2 starts with the formulation of the radio network planning optimization problem and then explains the developed simulation methodology in terms of the employed propagation model, the necessary 5G system characteristics and the planning optimization method. Section 3 presents the results for different 5G standalone small-cell network deployments, namely dense, very dense and ultra-dense. Finally, concluding remarks are provided in Section 4.
## 3 Gut pH Cannot Predict Sensitivity to Bt Proteins or Susceptibility to Bt Colonization  
[PERSON] suggests that an alkaline gut renders animals susceptible to toxins produced by Bt. While many Bt targets do have alkaline guts [those in the Diptera suborder Nematocera (mosquitoes and midges) and Lepidoptera (butterflies and moths)], many targets also have acidic guts or near-neutral guts. For example, dipterans of the suborders Brachycera (horse flies and hover flies) and Cyclorlorphah (house flies [9], blowflies, fruit flies [10]) have guts that are neutral-to-slightly acidic, particularly in the foregut regions. Diptera with acidic foreguts such as _Musca domestica_ have a documented susceptibility to Cry1 Ba1 [11] and have a gut pH that ranges from 6.1 (anterior) through 3.1 (middle) to 6.8 (posterior) [9]. Other examples include Coleoptera from Tenebrionidae, such as _Tribolium castaneum_, which is susceptible to the Bt proteins Mpp23/Xpp37 [12] and has a foregut-midgut-hindgut pH profile of 5.2, 7.2-7.6 and 3.6-4.6 [13]; _Tenebrio molitor_, which is susceptible to the Bt protein Cry3 Aa [14] and has a gut pH ranging from 5.2 to 8.2 anterior to posterior [15]; the cryosomeld, _Diabrotica vegfiera vegfiera_, which is susceptible to Cry1 Bh1 [16], Cry3b [17] App6 Aa1 and Gpp34/Tpp35 [18] and has a gut pH of 5.75 [19]; _Leptinotars decmelinenta_, which is susceptible to Cry3 Aa and Cry3 Ab [20], Cry7 Aa2 [21], Mpp51 Aa1 [22] and Mpp51 Aa2 [23] and has a foregut-midgut-hindgut pH profile of 5.9, 5.9-6.6 and 6.5 [13]. Beyond insects, the nematode _Caenorhabditis elegans_ is susceptible to App6 Aa1, Cry5 Ba1, Cry21 Aa1 [24], Cry21 Fa1, Cry21 Ha1 [25], Cry5 Ca1 and Cry5 Da1 [26] and this nematode has a gut pH ranging from 5.96 +/\(-\) 0.31 in the anterior pharynx to 3.59 +/\(-\) 0.09 in the posterior intestine [27]. We must conclude that gut pH is not a predictor of pesticidal protein sensitivity.
# A High-Efficiency Super-Resolution Reconstruction Method for Ultrasound Microvascular Imaging
## 2 Materials and Methods
### In Vivo Experiment Setups
#### Parameters Measurement  
We use the Full-Width at Half-Maximum (FWHM), defined as the \(-6\) dB bandwidth for the mainlobe, and Peak Sidelobe Level (PSL), defined as the peak value of the first sidelobe, to quantify the performance of different methods for imaging microvasculars. The former corresponds to the lateral resolution and the latter corresponds to the sidelobe level. To compare the efficiency of different methods, we also record their runtime in seconds.
## 3 Results and Discussion
### Results of the Core Flooding Test
Figure 10: Oil recovery and differential pressure (**left**) during the core flooding test, and contact angles (**right**) in pre- and post-treatment at a reaction time of 24 h.  
Figure 9: Oil recovery and differential pressure (**left**) during the core flooding test, and contact angles (**right**) in pre- and post-treatment at a reaction time of 12 h.  
When the reaction time was short, the nanofluid did not sufficiently react on the rock surface, leading to reduced adsorption and permeability by the nanoparticles. However, owing to its low reaction rate, it may not significantly alter the wettability. Therefore, the role of fluid-to-fluid interactions and fluid-to-rock interactions (interpreted through changes in pressure and contact angle) is diminished. Fluid-to-fluid and fluid-to-rock reactions were observed when an appropriate reaction time was provided. Some permeability reduction and wettability changes occurred. These results also confirmed improved recovery rates, with a reaction time of 24 h being the best-case scenario in this experiment. As the reaction time increased, the permeability reduction effect, which is one aspect of the fluid-to-rock interaction, became more pronounced, leading to a decrease in oil recovery efficiency. Therefore, designing an appropriate reaction time is crucial for maximizing the effects of rock-fluid interactions in EOR. To achieve this, analyzing the reaction rates and their impact via a fluid-to-rock interaction analysis is necessary. Table 6 summarizes the experimental results of the core flooding test. The incremental oil recovery ranged from 7.2% to 26.1%, with varying reaction times. Considering the high residual oil in carbonate reservoirs after waterflooding, nanofluid injection with GPTMS-SiO\({}_{2}\) appears to have been successful for EOR. The contact-angle differences ranged from 9.7\({}^{\circ}\) to 123.1\({}^{\circ}\) with nanofluid treatment,  
Figure 11: Oil recovery and differential pressure (**left**) during the core flooding test, and contact angles (**right**) in pre- and post-treatment at a reaction time of 48 h.  
Figure 12: Oil recovery and differential pressure (**left**) during the core flooding test, and contact angles (**right**) in pre- and post-treatment after a reaction time of 72 h.  
and wettability improvement was observed in all samples with reaction times exceeding 12 h. This implies that sufficient reaction time is necessary to enhance the wettability of a carbonate rock. Permeability exhibited a decrease with an increase in reaction time, with a permeability ratio of 0.78 at a 12 h reaction time and 0.11 at a 72 h reaction time, respectively.
## 4 Establishment of a Prediction Model for Dual-Mode Shield Tunneling Parameters
### Evaluation Index  
In the predictive model, various evaluation metrics are employed, each emphasizing different aspects, and relying on a single metric lacks comprehensiveness in model assessment. To evaluate the model's performance, three distinct evaluation metrics are employed: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), defined as follows:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\big{|}g_{i}-y_{i}\big{|}, \tag{15}\]  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & **Cutterhead Speed** & **Thrust Pressure** & **Tunneling Speed** & **Cutterhead Torque** & **Total Thrust** \\  & **SNR** & **PSNR** & **SNR** & **PSNR** & **PSNR** & **SNR** & **PSNR** & **SNR** & **PSNR** \\ \hline Mean filter algorithm & 42.10 & 43.14 & 22.57 & 25.49 & 17.72 & 23.14 & 18.96 & 24.78 & 33.19 & 34.65 \\ \hline Improved mean filter algorithm & 42.68 & 43.72 & 22.58 & 25.49 & 17.84 & 23.27 & 18.96 & 24.79 & 33.19 & 34.65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prediction effect ranking analysis.  
\[RMSE =\sqrt{\frac{1}{n}\sum_{i=1}^{n}(g_{i}-y_{i})^{2}}, \tag{16}\] \[MAPE =\frac{100\%}{n}\sum_{i=1}^{n}\left|\frac{\hat{y}_{i}-y_{i}}{y_{i} }\right|, \tag{17}\]  
In the formula, \(y_{i}\) is the real value and \(\hat{y}_{i}\) is the predicted value. MAE is a commonly used metric for assessing the accuracy of predictive models, reflecting the degree of closeness between actual and predicted values. Similarly, RMSE is employed to gauge predictive model accuracy. In contrast to MAE, RMSE imposes a higher penalty by squaring the errors, making it more sensitive to significant errors and outliers. MAPE is highly sensitive to extreme values, limiting its ability to handle outliers effectively. However, it expresses errors as a percentage, providing interpretability and comparability.
## 2 Related Work
### Physical Layer Authentication (PLA)
[25] presented a data-adaptive matrix to capture time-varying channel characteristics and combined it with Convolutional Neural Network (CNN) to achieve physical layer security authentication. [PERSON] and [PERSON] [26] presented an adaptive lightweight physical layer authentication scheme using 5G Up-Link CSI features as classification evidence. This scheme can improve accuracy through the utilization of antenna diversity. Physical layer authentication schemes based on channel characteristics are relatively easier to implement as they do not require additional devices. However, these schemes rely on stable and distinguishable channel characteristics, which are not applicable to the classical 5G MU-MIMO scenario due to the variable and dynamic characteristics of wireless channels, and thus become a challenge for the aforementioned physical layer authentication.
## 2 Materials and Methods
### Volatile Compounds  
The volatile compounds isolation from the wine matrix was done using HS-SPME (headspace solid-phase micro-extraction) followed by GC-MS and GC-FID (gas chromatography with two types of detectors, mass spectrometry and flame ionization, respectively). Approximately 15 mL of wine, 1.5 g of sodium chloride and 10 \(\mu\)L of benzyl acetate (internal standard at 1000 mg L\({}^{-1}\)) were introduced into vials (50 mL) with polypropylene caps and PTFE/silicone septa, and the vial was heated up in a water bath with controlled temperature (50 \({}^{\circ}\)C) and automatic stirring. A 50/30 \(\upmu\)m DVB/CAR/PDMS (divinylbenzene/carboxen/polydimethylsiloxane) fiber of 1 cm and high capacity for adsorbing wine-volatile compounds was placed in the vial headspace for 40 min. Desorption of wine compounds from the SPME fiber was done by heating it up for 3 min in the GC injection port.  
A Shimadzu GC-17A gas chromatograph (Shimadzu Corporation, Kyoto, Japan) coupled with mass spectrometer (MS) detector, Shimadzu GC-MS QP-5050A was used for the identification of the wine volatile compounds. The separation was done in an SLB-5 ms fused silica capillary column of 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m film thickness, 5% diphenyl, and 95% dimethyl-siloxane (Supelco Analytical, Barcelona, Spain). Helium was used as the carrier gas (1.2 mL min\({}^{-1}\)) in splitless mode, and chromatographic conditions were: (a) 80 \({}^{\circ}\)C; (b) increase of 3.0 \({}^{\circ}\)C min\({}^{-1}\) from 80 to 170 \({}^{\circ}\)C; (c) increase of 25 \({}^{\circ}\)C min\({}^{-1}\) from 170 to 300 \({}^{\circ}\)C and hold for 5.8 min. The working temperatures for the injector and detector were 250 and 300 \({}^{\circ}\)C, respectively. Three different methods were used for the identification of the wine volatile compounds: _(i)_ retention indices, _(ii)_ GC-MS retention times of authentic chemicals, and _(iii)_ mass spectra of standards and those of the Wiley spectral library.  
Wine volatile compounds were semi-quantified using a Shimadzu GC-17A with a flame ionization detector (FID). The chromatographic conditions used for the GC-FID were those previously described for the GC-MS analysis, injector working at 300 \({}^{\circ}\)C. N\({}_{2}\) was the carrier gas at a flow of 1 mL min\({}^{-1}\). Benzyl acetate was the internal standard and 10 \(\upmu\)L were added to each sample at a concentration of 1000 mg L\({}^{-1}\). Data should be consideredsemi-quantitative because no standard curves were done for each one of the wine volatile compounds. Analyses were run in triplicate, and results were expressed as mg L\({}^{-1}\).
## 3 Frying Process of French Fries and Potato Chips and Organoleptic Features of Ready Potato Snacks  
Both French fries and potato chips are obtained through the frying process, which is one of the most popular food processing methods alongside cooking, baking, and drying [2,26,30,33]. Traditionally obtained snacks are deep-fried in oil, which is heated to high temperatures (French fries--175-180 \({}^{\circ}\)C; chips--175-185 \({}^{\circ}\)C) [2,3,18,26,27,34,35]. During the course of this process (deep-fat frying), many transformations take place, such as simultaneous heat and mass transfer, which is connected with the flow of water vapor in opposite directions (in the form of bubbles) and oil on the surface of the fried potato pieces. As a result of this phenomenon, pores are formed on the surface of the fried product [36, 37, 38, 39, 40]. It also shapes the consistency of the ready product by creating a crispy and crunchy outer layer, while French fries are also floury inside [2,3,26,41]. The fat content of the fried potato pieces is also ultimately formed [33,39,40,42].  
There are also other specific organoleptic features typical of ready-made potato products, i.e., color, characteristic taste, and smell, which are related to the course of the processes accompanying the Maillard reaction [29; 43; 44; 45; 46].  
The color of French fries and potato chips is the first priority for consumers. Regardless of the method of preparing the ready potato snacks, they should be light and golden, without brown discoloration, black streaks, or stains [2; 26; 27; 43]. The color of French fries and potato chips is related to the content of reducing sugars in potatoes and their distribution over the tuber cross-section [3; 7; 26; 27]. The uneven distribution of sugars in the tuber--Figure 1 (higher sugar content in the top and stolon parts) may affect the browning of the ends of the French fries--the so-called \"sugar end\", as can be seen in Figure 1a, while in the potato chips, brown-tinged edges of the snack may appear, as demonstrated in Figure 1b [2; 47; 26].  
This effect in the fried product is the result of the Maillard reaction taking place with the participation of reducing sugars and free amino acids [8; 10; 48; 49; 11; 45], where a bitter taste may be produced if the brown color is too intense [8; 26; 45; 48; 49]. As a result of this reaction, melanoidin compounds are formed, which not only affect the color of the ready product, but also its taste [45; 46]. The taste and smell of potato snacks (French fries and potato chips) should be typical potato and oil, with no noticeable aftertaste or smell of bitterness, burning, or rancid fat [2; 26; 50]. During the frying process of snack products, many favorable visual and taste characteristics are formed, but high-temperature processing and the accompanying Maillard reaction also contribute to the formation of a harmful compound--acrylamide [8; 10; 45; 49; 51; 52].
# LEO-Based Satellite Constellation for Moving Target Detection
## 1 Introduction
Numerical results are given in Section 3 to verify the effectiveness of the proposed method, and the corresponding advantages are comprehensively analyzed in Section 4. Finally, we draw a brief conclusion in Section 5.