esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Control and Monitoring Measures for Abrupt Geological Deformation of Shield Crossing  
### Deformation Control Measures  
#### 5.1.1 Surface Settlement and Engineering Measures  
(1) Synchronous grouting  
In the construction process of this project, considering the economic cost, grouting is usually used to reinforce the soft soil layer in the Baochang shield section, which lays the foundation for the following tunnel excavation. If the grouting pressure is too small, it will be difficult to inject the slurry into the pores of the soil layer during the grouting process. On the contrary, excessive grouting pressure will cause certain disturbances to the soft layer of soil in front of the tunnel excavation. So, the grouting strength should be well controlled in the process of excavation to ensure that the granularity, viscosity, and moisture content of the slurry are within a reasonable range and conform to the grouting construction specifications.  
(2) Correcting shield posture  
When shield tunneling, especially in complex strata, the posture of the shield will inevitably change. When the jack is used to rectify the shield posture, the included angle between the tunnel center line and the shield axis should be guaranteed to change within the scope permitted by the project, so as to avoid the stratum loss caused by the excessive included angle and the surface deformation, but in the process of correction. To control the scale well, if the deviation correction is excessive, it will be difficult for the shield to keep straight ahead, which will greatly increase the disturbance to the surrounding rock and increase the ground settlement. Therefore, the excavation route should be planned in advance before construction. During the construction process, the monitoring of the surface deformation should be strengthened and the shield machine should not be adjusted as much as possible so that the shield machine can proceed in a straight line.
## 3 Results and Discussion
### Flow Field Structures of the Investigated Droplets
As a result, the gravitational effect can also induce two opposite radial flows in the cross section of the droplet, which were also observed in sessile droplet evaporation by [PERSON] et al. [14]. It should be mentioned that the radial flows induced by the gravitational effect have opposite directions to the main radial flows induced by the upward vapor flow, which can disturb the main flows. Under the Marangoni effect, the liquid tends to move toward positions with higher surface tension and temperature [22]. Since the surface tension of \(\mathrm{PODE_{4}}\) (30.7 mN/m [20]) is higher than that of the \(\mathrm{PODE_{2}}\) (\(\sim\)25 mN/m [20]), the solutal Marangoni effect can drive the liquid flow toward positions with higher content of \(\mathrm{PODE_{4}}\). This effect consequently induces the solutal Marangoni flow from the droplet interior to the droplet surface, which has a different disturbance effect to the main flows from the gravitational effect. On the other hand, a temperature gradient also exists on the droplet surface since the supporting fiber heated by the flame can raise the nearby surface temperature. Thus, the thermal Marangoni effect can induce the thermal Marangoni flow from the top of the droplet to the bottom along the droplet surface, which has a similar disturbance effect to the gravitational effect. According to previous studies, the flow velocities caused by the gravitational effect and Marangoni effect are relatively small, at the scale of \(\upmu\)m/s [14; 15] while the flow velocities caused by the upward vapor flow can be at the scale of cm/s [10; 16]. Therefore, the main flow field structures of the investigated binary droplets are still controlled by the upward vapor flow, while local flow field structures at certain regions are disturbed slightly by the gravitational effect and Marangoni effect, which result in the discrepancies of vortices in the binary droplets.
# Chemometric Classification of Feta Cheese Authenticity via ATR-FTIR Spectroscopy
## 3 Results  
### ELISA Screening for Cow's Milk Adulteration in Feta Samples  
The ELISA screening for cow's milk adulteration in Feta samples confirmed the authenticity of the majority of specimens. Out of the fifty Feta samples analyzed using the Bio-Shield Cow Cheese ELISA kit, only four were found to contain detectable levels of bovine milk proteins above the method's quantification limit (LOQ = 0.10%), and were therefore excluded from the spectral dataset used for chemometric modeling. The exclusion of suspect samples ensured that the FTIR models were trained exclusively on verified authentic Feta specimens, enhancing the reliability and interpretability of classification outcomes. As a result, a total of 86 samples (46 authentic Feta and 40 non-Feta white printed cheeses) were retained and used in all subsequent chemometric modeling.
# A Stigmergy-Based Differential Evolution
## 4 Experimental Results
### Convergence Speed  
For the convergence comparison between DE and SDE, we used the same four instances as in the previous experiment: dimensions of 100 and 500 and populations of 256 and 512 individuals. In this case, the iterations per run are increased to 20,000, with the same number of runs per experimental instance; nevertheless, both algorithms iterate either until the algorithm is near to the optimum (e.g., \(f(x^{*})=-0.99\frac{d(d+4)(d-1)}{6}\) for Trid), or until reaching 20,000 iterations. After either of the two conditions is true, then the best global fitness and the number of iterations are stored, in the case of DE. However, as SDE does not utilizes a global best, we use the fitness of the first individual in the population as the 'global best' to demonstrate the capability of the algorithm to distribute the improvements through all the candidate solutions but with decentralized control. Therefore, when either the fitness of the first individual is close to the real global optimum, or 20,000 iterations are achieved, a run is completed in SDE. The results are in Tables 7-10 with the best results in bold letters. It can be seen from the results of these experiments (Tables 7-10) that the proposed algorithm does not outperform DE on two-dimensional problems. Although SDE reaches the optimum, it performs a higher number of iterations than DE, however, the performance of SDE in high dimensions is remarkable compared with DE. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{3}{c}{**Execution Time \(\mu(\sigma)\) for 256 Individuals**} & \multicolumn{3}{c}{**Execution Time \(\mu(\sigma)\) for 512 Individuals**} \\ \hline
**Function** & **DE** & **SDE** & **Speedup** & **DE** & **SDE** & **Speedup** \\ \hline F23: Bohachevsky1 & 23.0564 (0.0598) & **21.3298 (0.2496)** & **1.08** & 24.6339 (0.0576) & **22.3473 (0.1723)** & **1.10** \\ F24: Egg Crate & 24.434 (0.0727) & **21.1914 (0.1658)** & **1.15** & 26.0469 (0.0843) & **22.5681 (0.2205)** & **1.15** \\ F25: Bohachevsky2 & 23.0607 (0.1270) & **21.1913 (0.2459)** & **1.09** & 24.7679 (0.0580) & **22.3699 (0.1505)** & **1.11** \\ F26: Bohachevsky3 & 23.1568 (0.0570) & **21.1915 (0.2585)** & **1.09** & 24.8482 (0.0853) & **22.3834 (0.2782)** & **1.11** \\ F27:Three-Hump & 24.6552 (0.1462) & **21.1373 (0.2239)** & **1.17** & 26.3881 (0.2053) & **22.2933 (0.2343)** & **1.18** \\ Camel & & & & & \\ \hline \end{tabular}
\end{table}
# Trajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network
## 2 Materials and Methods  
### Artificial Neural Networks  
ANNs are generalized mathematical models of biological nervous systems. The effects of the neuron synapses are represented mathematically by the connection weights that modulate the effect of the input signals, then a transfer function is used to depict the non-linear characteristic exhibited by neurons, and finally, to compute the neuron impulse it is necessary to consider the weighted sum of the input signals transformed by the transfer function. Adjusting the weights of a neural network in concordance with the chosen learning algorithm will determine the learning capability of the ANN [14].  
There are three classes of learning situations in neural networks: supervised, unsupervised, and reinforcement [14; 15]. An ANN is usually used for supervised learning, a machine learning technique that allows the collection or production of data output after learning a classifier using the available training samples. In other words, supervised learning means that an external teacher provides the desired signals on individual output nodes. Some of the most famous examples include the BP algorithm, the delta rule, and the perceptron rule. In unsupervised learning an output unit is trained to respond to pattern groups within the input, and the system must develop its own representation of the input stimuli. Finally, in reinforcement learning the system will discover the actions that yield more rewards by trying them. The characteristics of the trial-and-error search and delayed reward represent the most important features of the reinforcement learning technique.  
For the architecture of an ANN there are three types of neuron layers: input, hidden, and output. The behavior of the network will depend on the interaction between different neurons. For example, in feed-forward networks the signal flow goes from input to output units in a feed-forward direction, and the data processing can cover multiple layers of units, but no feedback connections exist [14]. They are also known as multi-layer perceptron (MLP) neural networks and are the most popular neural networks where input-output connections can be achieved by adjusting the associated weights in the network [16]. Feed-forward neural networks form the basis of many important neural networks currently used, such as convolutional neural networks (CNN) and recurrent neural networks (RNN). [PERSON] et al. [17] show an example of a feed-forward neural network in Figure 1.
## 3 Mobility-Aware Duty Cycling
### MAC Implementation  
The MADCAL algorithm is implemented at the MAC layer, where the greatest amount of energy is consumed amongst network layers [35]. The MAC implementation utilised in this study is a lightweight carrier-sense multiple access (CSMA) implementation which reflects the core functionality of the IEEE 802.15.4 standard [36; 37]. This uses CCA and the transmission of preambles. Figure 3 demonstrates this MAC implementation, with the location of the MADCAL functionality highlighted to show how the normal wake-up schedule is intercepted.
###### Abstract  
Photovoltaic output is affected by solar irradiance, ambient temperature, instantaneous cloud cluster, etc., and the output sequence shows obvious intermittent and random features, which creates great difficulty for photovoltaic output prediction. Aiming at the problem of low predictability of photovoltaic power generation, a combined photovoltaic output prediction method based on variational mode decomposition (VMD), maximum relevance minimum redundancy (mRMR) and deep belief network (DBN) is proposed. The method uses VMD to decompose the photovoltaic output sequence into modal components of different characteristics, and determines the main characteristic factors of each modal component by mRMR, and the DBN model is used to fit the modal components and the corresponding characteristic factors, then the predicted results of each modal component is superimposed to obtain the predicted value of the photovoltaic output. By using the data of a certain photovoltaic power station in Yunnan for comparative experiments, it is found that the model proposed in this paper improves the prediction accuracy of photovoltaic output.  
Keywords: photovoltaic output prediction; VMD; mRMR; DBN; feature selection +
Footnote †: journal: Applied Sciences
## 1 Introduction
Consequently, the proposed methodology seeks to improve the predictive capabilities of such models. To summarize, the Mean-CAM method enables the CNN to focus solely on valuable information for recognition, eliminating irrelevant details, complemented by the colorization of grayscale images and enhancement of dark shades through the trained DCGAN model. The main contributions of this paper are summarized as follows:  
* To mitigate the challenges associated with an absence of color information and to enhance the visual quality of dark images during their input to a CNN model, we introduced and trained a proficient framework. This framework employs a DCGAN model for the purpose of colorizing grayscale and dark images. * To improve the predictive ability of CNNs, we introduced a novel framework, termed Mean-CAM-CNN. This framework is designed to guide the CNN's focus towards the most salient common region, encapsulating the most pertinent features. The Mean-CAM process incorporates CAMs to delineate an RoI from images belonging to the same class. This selective mechanism isolates discriminative features that furnish pertinent representations essential for the task of ear recognition. * We conducted an extensive evaluation of the proposed methodologies by employing two widely recognized and challenging ear recognition datasets. This evaluation encompassed both graphical and statistical analyses to comprehensively assess the performance outcomes. The paper's structure is as follows: Section 2 reviews related studies in recently published articles about ear recognition; Section 3 outlines the methods proposed in this study to address the issue of deep ear identification; Section 4 discusses the experimental analysis conducted in this study; and finally, Section 5 resumes the fundamental conclusions.
# A Monopole UWB Antenna for WIFI 7/Bluetooth and Satellite Communication
## 2 Structure and Analysis
### Evolution of Multi-Frequency Antennas  
The design evolution process of the antenna is illustrated in Figures 2 and 3, showing the S-parameters situation for Ant 1-Ant 4. The initial structure is shown in Figure 2a, which is composed of a rectangular ground, microstrip line, and a short-sleeve-shaped radiator, marked as Ant 1. From Figure 3, it can be seen that the simulated results for this structure achieve the coverage of three frequency bands (2.18-3.01 GHz; 4.68-9.18 GHz; 12.57-13.93 GHz). To expand the bandwidth, we etch a semicircular slot on the upper part of the initially designed short-sleeve-shaped patch for changing the current path or extending the current, add a symmetrical rectangle to the cuffs on both sides to resonate at 5.1 GHz, and subtract the two symmetrical corners from the lower part. The introduced asymptotic structure changes the strong current distribution here, allowing for better excitation of the desired modes and helping to unfold the bandwidth, as shown in Figure 2b. As is evident from the corresponding S-parameter results, the modifications to the initial short-sleeve-shaped patch increase the bandwidth by 1.6 GHz and improve the partial matching effect compared to the case of Ant 1, but the return loss is still higher than \(-10\) dB in the 10-12 GHz range. By loading the symmetrical L-shaped patch method on both sides of the feeder, the radiation current path is extended, thereby reducing the return loss and expanding the working bandwidth. In addition, a rectangular slot is carved under the semicircular slot, again blocking the partial current and advancing the resonance frequency point from 6.8 GHz to 6.2 GHz, as shown in Figure 2c. The structure is marked as Ant 3. It can be deduced from the S-parameters of Ant2 and Ant3 that the impedance-matching performance can be improved by using symmetrical L-shaped patches on both sides of the feeder. Furthermore, cutting slots in the antenna feeder can change the current path, which can generate resonance. In this case, we inserted an inverted-U-shaped slot to block the current flow in that area, thus further improving matching around 10-13.2 GHz to make the antenna reach the standard of S11 \(<-10\) dB in the coverage frequency band, as shown in Figure 2d. For Ant 4, adding a U-shaped slot not only improves the matching effect, but also partially increases the high-frequency bandwidth. The final antenna structure covers wide frequency bands of 2.09-2.72 GHz, 4.79-5.35 GHz, and 5.6-14.59 GHz.  
Figure 3: Simulated results for Ant 1–Ant 4.  
Figure 2: Evolution of the proposed antenna: (**a**) Ant 1, (**b**) Ant 2, (**c**) Ant 3, (**d**) Ant 4.
# Spontaneous Brillouin Scattering Spectrum and Coherent Brillouin Gain in Optical Fibers
## 1 Introduction  
Brillouin light scattering (BLS) [1] and stimulated Brillouin scattering (SBS) [2] are extensively studied in optical fibers and guided optics for fundamentals [3; 4; 5; 6] and applications [7; 8; 9]. The usual model for SBS [10] describes the acoustic phonons or waves involved in the interaction as density fluctuations, similar to models of acoustic waves in fluids. In optical fibers, generally composed of solid materials such as fused silica, the same model is used as well and is known to faithfully explain experimental observations, providing it is understood to apply to longitudinal acoustic phonons or elastic waves only. The main achievements of the usual model include the explanation of the coherent Brillouin gain and the exponential growth of SBS [11; 12], with noise initiation of SBS described by random fluctuations generating thermal acoustic phonons [13].  
Recently, this model has been enriched because of new observations involving the polarization of elastic waves in solids: the whole family of normal modes of an optical fiber are involved in guided acoustic wave Brillouin scattering (GAWBS) [14; 15; 16]; hybrid phonons having coupled longitudinal and shear polarization have been observed in photonic crystal fibers [17]; anisotropic guided acousto-optical diffraction has been observed with polarized optical waves [18]; and all-optical generation of surface acoustic waves have been observed in microwires, tapered optical fibers and optical waveguides [19; 20; 21]. As a note, the vector polarization of elastic waves had long before been considered for the description of BLS and SBS in solids and high viscosity liquids interrogated with freely propagating light [22; 23; 24].  
In order to describe BLS in optical fibers with general elastic waves and acoustic phonons, we have previously introduced a model were vector displacements satisfy an elastodynamic equation subject to a driving electrostriction force [25; 26]. In this paper, we re-examine noise initiation of SBS,i.e., spontaneous BLS, in the light of this vector elastodynamic model. Of particular interest is the coincidence of the spontaneous BLS spectrum, resulting from random fluctuations inside the solid body, with the coherent Brillouin gain, applying to wave mixing (see Figure 1). Are they always proportional and under which conditions? As we show in the following, the experimental situations where either isolated elastic modes are present or a continuum of bulk elastic waves exists must be treated differently.
# Sphero-Conical Modeling for the Estimation of Very Long Baseline Interferometry Invariant Point
## 2 General Description
### Ground Surveying  
Ground surveying is generally conducted in a local frame, whose vertical axis is naturally determined from the local plumb line. Although the origin of the local frame can be set up anywhere within the site, CORS SEJN was chosen as the origin of the local reference frame. This is because it should eventually be aligned to the ITRF to be combined with other space-geodetic solutions, which is performed using the GNSS campaign at local pillars, including CORS SEJN. Therefore, the local tie vector is represented with respect to the GNSS pillar (SEJN). The orientation of the local reference frame will be defined using the local geodetic coordinate system of the CORS (SEJN); thus, there might be an offset called the \"deflection of the vertical\" between the geodetic vertical direction and the local gravity vector [25]. However, the magnitude is very small at the project site (approximately 6 arcseconds in the absolute sense), and it should be absorbed into the Helmert transformation parameters while transforming the local tie vector into the global reference frame.  
All possible slant distances and horizontal/vertical angles between the pillars were observed with a total station (an electronic theodolite integrated with an electronic distance-measuring instrument) [26]. Because it is not feasible to mount a glass-prism reflector at the antenna reference point (ARP), only the horizontal and vertical angles were measured from the pillars to the ARP of the CORS. Moreover, it is not possible to locate a center to the target on the ARP; thus, the horizontal and vertical angles are measured to the bottom-left and bottom-right of the antenna, and their average is used as a measurement of the ARP.  
To determine the local tie vector in the ITRF, the coordinates of the eight pillars and the CORS (SEJN) ARP should be estimated in both the local and global frames. A GNSS campaign was conducted by simultaneously collecting data from GNSS receivers mounted on all eight pillars and the CORS. Data were collected at a 30 s rate over seven 24 h sessions  
Figure 2: Flow chart to estimate the VLBI IVP [24]. The ground surveying and the GNSS campaign are conducted under two reference frames. The IVP estimated in local frame is transformed into the global frame via the conventional Helmert transformation.  
for one week. The GNSS data collected at the pillars were processed together with those from 51 international GNSS service core stations worldwide, resulting in a set of GNSS vectors aligned to ITRF 2014. The resulting vectors, along with their covariance matrices, were used as observations in the subsequent network adjustment.
## 2 Description of the Systems Under Investigation and Experimental Setups
### Free Vibration Testing
#### 4.1.1 Side Accelerometer  
Figure 7 shows the results of impact testing from the side accelerometers among all BARC systems. A comparison is made to the mode shapes found in Table 2 and the natural frequencies found in Table 3. Notice that all first modes in the central-cut systems are very similar to one another. Because of this observation, the response from impact testing in Figure 7 from mode 1 should be highest in the Y-direction. The impact testing results show this to be the case, all with peaks around 95 Hz. Comparing the fundamental frequency in impact testing to its counterpart in Table 3 shows a lower frequency. This is expected, though, since computational simulations consider ideal conditions. In reality, conditions can be as near ideal as possible, but nothing can match the perfection seen in FEA. Thus, dominant frequencies are expected to be lower during experimental testing.  
Figure 7: Impact testing results from the side accelerometer: (**a**) 0.5\({}^{\prime\prime}\) cut, (**b**) 0.25\({}^{\prime\prime}\) cut, (**c**) 0.1\({}^{\prime\prime}\) cut, and (**d**) no-cut BARC systems.  
Continuing the examination of the rest of the peaks in Figure 7, it is noted that almost all peaks have the highest response in the Y-direction. This is due to the dominant motion in the Y-direction for the side accelerometer, and also due to the excitation orientation in the Y-direction. In fact, inspecting Table 2 and the animation in the Supplementary File, the results show that nearly all side accelerometers in every mode move in the Y-direction in some capacity. Note that the plots of the no-cut system only reach 800 Hz, whereas mode 6 (according to the FEA) is somewhere near 1073 Hz. It should be mentioned that the impact and other experiments did not consider the sixth mode for the no-cut system.