esa-sceva/satcom-chunk-collection huggingface dataset:
# A Two-Dimensional Non-Destructive Beam Monitoring Detector for Ion Beams
## 3 Experimental Results
### Comparison with Reference Beam Profile Measurements  
The correct functioning of the \(\pi^{2}\) detector was validated by means of two independent experiments: in the first one, the measurement of a parallel beam was compared with the measurement of the same beam provided by a FWT-60 radiochromic film; in the second one, the measurement of a focused beam was compared with the measurement provided by the UniBeAM detector.  
In the first experiment (Figure 4b), the film was attached with adhesive tape to the back of a \((3\times 3)\) cm\({}^{2}\) collimator, 10 cm upstream of the \(\pi^{2}\). The second quadrupole magnet of the BTL was switched off to obtain a parallel beam, i.e., with identical profile at the position of the film and detector. The film was irradiated up to an integrated proton flux of \(3\cdot 10^{12}\) cm\({}^{-2}\), thus, well within its linear response range. During that time, 10 images were acquired by the \(\pi^{2}\) and averaged in the post-processing for the comparison with the film. Figure 7 shows the two measurements, where the two-dimensional beam images have been projected onto the horizontal and vertical axes, for comparison. Both measurements agree within 5%.  
For the comparison with the UniBeAM (Figure 4c), both devices were simultaneously installed on the beam line, with the UniBeAM about 10 cm upstream of the \(\pi^{2}\); the current on the quadrupole magnets was set to obtain a focused beam, fully contained in the sensitive area of both devices. The beam profile measured by the \(\pi^{2}\) is shown in Figure 8. The image projections onto the horizontal and vertical axes are compared to the UniBeAM profiles in Figure 9, with good agreement. For a better visualisation, the profiles have been normalised and re-centred. Since the beam is focused, and the \(\pi^{2}\) monitor is placed after the UniBeAM, the narrowing of the profile from one detector to the other is to be expected. It should be noted that the comparison in this case is only qualitative, since neither of the two instruments was calibrated for the intensity levels obtained with focused beams.  
Figure 6: Average blue pixel value in the collimated region, as a function of \(\phi\cdot T_{exp}\), for three different exposure time settings. The dashed black line represents a 3 rd order polynomial fit to the data.  
Figure 8: \(\pi^{2}\) image of the focused beam used for the comparison with the UniBeaM reference detector.  
Figure 7: Comparison of the horizontal (**left**) and vertical (**right**) beam profiles measured by the \(\pi^{2}\) detector, with an FWT-60 radiochromic film placed 10 cm upstream of the detector.  
Figure 9: Comparison of the horizontal (**left**) and vertical (**right**) beam profiles measured by the \(\pi^{2}\) and the UniBeaM detectors. The profiles have been normalised and re-centred for better visualisation.
## 3 Results and Discussion
### Volatile Compounds
#### 3.4.9 Benzenoids  
Certain benzenoids are derived from the phenylpropanol synthesis pathway in grapes, while others are produced during fermentation from aromatic amino acids like phenylalanine and tyrosine. They can be further transformed by yeast, forming benzenoid aldehydes, alcohols, acids, esters, and other derivatives. Some benzenoid compounds can enter grapes and wines as a result of environmental pollution. Despite a high number of identified benzenoid compounds, only a few were affected by yeast species (Table 13). Control SCE treatment wines had higher concentrations of toluene, 1,2,3-trimethylbenzene, 4-ethyl-\(m\)-xylene, ethyl phenethyl ether, acetophenone, ethyl _o_-methylbenzoate, 3-methylacetophenone, 2-methylnaphthalene, 1-methylnaphthalene, and phenylacetic acid in 2021, and ethyl hydrocinnamate and 3-hydroxy-4-phenyl-2-butanone in 2022. The concentrations of benzyl acetate and ethyl 2-phenylacetate were higher in SCE wines of both harvest years. The higher levels of several derivates from the phenylalanine yeast metabolism, such as ethyl phenethyl ether, phenylacetic acid, and 2-phenylacetate, coincided with the higher levels of major phenylalanine products in SCE wines, such as 2-phenylethanol (Table 6) and 2-phenethyl acetate (Table 11). SPA wines contained higher concentrations of several benzenoids, such as 3,4-dimethylacetophenone in 2021, 4-ethylbenzoic acid and \(p\)-tert-butylbenzoic acid in 2022, as well as 2,5-dimethylcotonophenone in both years. The effect of harvest year was significant for particular compounds from this group.
## 3 Methods
### Spiking-Softmax Method  
In this Section, we will specifically introduce the method of simulating the Softmax activation function in SNNs. Previous ANN-to-SNN conversion methods mainly focused on converting single-input activation functions, while the Softmax activation function requires processing a set of inputs. Considering this characteristic, we refer to the calculation equation of Softmax and propose the Spiking Exponential Neuron (SI-exp) and Spiking Collaboration Normalized Neuron (SI-norm) to simulate the exponentiation and normalization operations in the Softmax function, respectively. Then, we propose the Spiking-Softmax  
Figure 6: Structure of the Spike Integrated Transformer, where the Softmax activation function in the multi-head attention mechanism is replaced by Spiking–Softmax.  
Figure 7: ANN-to-SNN Conversion Process.  
method by integrating SI-exp and SI-norm neurons. As shown in Equation (8), \(\exp_{i}\) denotes the i-th SI-exp neuron, and norm denotes the SI-norm neuron.  
\[\text{Spiking\_Softmax}(x)=\frac{\exp_{i}(x)}{\text{norm}(\sum_{i}\exp_{i}(x))} \tag{8}\]  
The threshold voltage \(V_{th}\) of the SI-norm neuron is obtained by summing the outputs of all SI-exp neurons, simulating the summation operation of the Softmax function. The SI-norm neuron receives the outputs of SI-exp neurons and the threshold voltage \(V_{th}\), normalizing the outputs of the SI-exp neurons. Thus, the simulation of the Softmax function is achieved based on the integrated collaboration of SI-exp neurons and SI-norm neurons, thereby achieving the Spiking-Softmax method, as illustrated in Figure 8.
## 4 Conclusions  
This paper derived a closed formula to determine the transmitted power from an optical fiber bend slot. The slot is used as a transmitter to form an OLF array transmitter. This can be considered as a simple solution for many applications including wireless sensor networks, confined area communications, and underwater optical data collection. In this paper, we only simulate the BER performance for the OLF to show the visibility of using the OLF in a simple MIMO communication system that is designed for confined areas. In the future, we plan to extend our work to other applications.  
The derived formula provided a good fit for experimental data, as shown in Figure 5. The most noticeable point in this work is that the derived expression of the leaked power is a function of R2, while in [PERSON] [4] it was a function of R. [PERSON] calculated the leaked power in the cladding, while in our work, we derived an expression for the leaked power in the surrounding environment. Moreover, in this work we found that losses increase exponentially with decreasing bend radius, with significant losses observed for radii smaller than the mode field diameter. The study demonstrates that by accurately modeling and controlling bending loss, optical leaky feeders can be effectively implemented for special applications. These feeders can support high-speed communication in confined or challenging environments, such as in underground tunnels and in hospitals, where RF-based systems face limitations. Precise modeling also enables more efficient system designs, reducing the trial-and-error approach often required in optical system development.
## 2 Materials and Methods
### Methods Used  
#### 2.2.1 Adaptive Neural Fuzzy Inference System (ANFIS)  
ANFIS is a powerful Artificial Intelligent prediction system that uses combination of ML technique of neural networks and a fuzzy logic system [12; 13; 14]. The ANFIS structure was a five-layer as follows [15] (Figure 2):  
**Layer 1**: This was the fuzzification layer. It consisted of defined membership functions of the input variables. The output was a degree of membership value that was calculated based on a Gaussian membership function.  
\[\mu_{Ai}(x)=\exp\left[-\left(\frac{x-c_{i}}{2\delta_{i}^{2}}\right)^{2}\right] \tag{1}\]  
where \(c_{i}\), \(\delta_{i}\) are parameters of a membership function.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Criteria** & **X\({}_{1}\)** & **X\({}_{2}\)** & **X\({}_{3}\)** & **X\({}_{4}\)** & **X\({}_{5}\)** & **Y** \\ \hline Mean & 89.893 & 306.860 & 2.376 & 23.077 & 11.618 & 3.072 \\ Std & 26.776 & 92.131 & 7.937 & 13.685 & 3.289 & 1.904 \\ Min & 5 & 15.2 & 0 & 5 & 3 & 0.720 \\
25\% & 100 & 328 & 0 & 16 & 9 & 2.190 \\
50\% & 100 & 338 & 0 & 19 & 11 & 2.600 \\
75\% & 100 & 346 & 0 & 24 & 14 & 3.265 \\ Max & 100 & 380 & 100 & 119 & 33 & 37.520 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical Measures of Dataset Features.  
Figure 2: Basic structure of the adaptive network based fuzzy inference system (ANFIS).  
**Layer 2**: This layer executed fuzzy AND of the previous part of the fuzzy rules.  
\[w_{i}=\mu_{Ai}(x)*\mu_{Bi}(x) \tag{2}\]  
**Layer 3**: This was the normalized layer. This layer normalized the membership functions.  
\[\overline{w}_{i}=\frac{w_{i}}{\sum_{i}w_{i}} \tag{3}\]  
**Layer 4**: This was the defuzzification layer. This layer executed the consequent part of the fuzzy rules.  
\[\overline{w}_{i}f_{i}=\overline{w}_{i}(p_{i}x+q_{i}y+r_{i}) \tag{4}\]  
where \(p_{i}\), \(q_{i}\), \(r_{i}\) are linear parameters.  
**Layer 5**: This output and combination layer was calculated by summing up the outputs of previous layers.  
\[\sum_{i}\overline{w}_{i}f_{i}=\frac{\sum_{i}w_{i}f_{i}}{\sum_{i}w_{i}} \tag{5}\]
## 4 Results  
### Multicollinearity Test  
At the initial stage in model testing, we assessed the descriptive statistics for all study variables. To assess the association between any two parameters in the model's formulation, we conducted correlation analysis (Table 3). At the \(p<0.01\) level, all model variables showed statistically significant relationships. Anxiety and dejection, as well as anxiety and anger, were found to have positive relationships. Furthermore, happiness and anxiety, as well as happiness and dejection, had negative relationships.  
Multicollinearity refers to the occurrence of high correlations among two or more variables [34] and can be detected by using variance inflation factors (VIFs). SmartPLS (v.3.3.3) analysis was used to compute the VIF values. All VIFs were found to be \(<\)5.0 and thus were acceptable according to [PERSON] et al. [33]. An issue in multicollinearity may emerge if the VIF coefficient is larger than 5.0. No VIF coefficient values exceeded 5.0 in the model findings, thus indicating an absence of collinearity issues in the model results (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **VIF** & & **VIF** & & **VIF** \\ \hline ANG1 & 1.548 & HAP1 & 2.591 & ANX1 & 2.030 \\ ANG2 & 1.965 & HAP2 & 2.521 & ANX2 & 1.829 \\ ANG3 & 1.390 & HAP3 & 2.201 & ANX3 & 2.487 \\ ANG4 & 1.687 & HAP4 & 2.285 & ANX4 & 1.571 \\ DEJ1 & 1.578 & HAP5 & 1.593 & ANX5 & 1.614 \\ DEJ2 & 1.524 & HAP6 & 1.646 & ANX6 & 1.694 \\ DEJ3 & 1.489 & HAP7 & 1.392 & ANX7 & 1.229 \\ DEJ4 & 1.275 & & & & ANX8 & 2.263 \\ DEJ5 & 1.482 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: VIF values.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Demographic Variable**} & **All (N = 332)** \\ \hline  & **Frequency** & **(\%)** \\ \hline Gender & & \\
1. Male & 260 & 78.3 \\
2. Female & 72 & 21.7 \\ \hline Age & & \\
1. Less than 25 & 23 & 6.9 \\
2. 25–30 & 51 & 15.4 \\
3. 31–40 & 119 & 35.8 \\
4. Older than 40 & 139 & 41.9 \\ \hline Work experience & & \\
1. Less than 5 years & 60 & 18.1 \\
2. 5–10 years & 41 & 12.3 \\
3. 11–20 years & 152 & 45.8 \\
4. More than 20 years & 79 & 23.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Demographic information.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Mean** & **S.D.** & **ANX** & **HAP** & **DEJ** & **ANG** \\ \hline ANX & 1.81 & 0.64 & - & - & - \\ HAP & 2.90 & 0.77 & \(-\)0.63 & - & - \\ DEJ & 1.66 & 0.65 & 0.79 & \(-\)0.59 & - & - \\ ANG & 1.85 & 0.67 & 0.77 & \(-\)0.64 & 0.79 & - \\ \hline \hline \end{tabular} Notes: Correlations are significant at the \(p\leq 0.01\) level. Abbreviations: anxiety (ANX), happiness (HAP), dejection (DEJ), and anger (ANG).  
\end{table}
Table 3: Descriptive statistics and correlation values.
## 4 Aerodynamic Performance Enhancements
### Optimal Blade Tip Designs  
The tip vortex behavior corresponding to the rotor blade tip shape remarkably dominates the aerodynamic performance (shaft power), the aerodynamic thrust force, and the aero-acoustic sound in the wind turbine. Figure 11 shows the spanwise aerodynamic contribution in the out-of-plane and in-plane directions on each rotor blade section, where the out-of-plane and in-plane directions indicate the axial direction of the rotor blade and the tangential direction perpendicular to the axial direction, respectively. It is found that the aerodynamic force contribution of the rotor blade is significantly dominated by that of the rotor blade tip area. In this study, two different types of blade tip designs, the winglet and ogege type, were studied for the aerodynamic performance improvement of current WTG model as shown in Figure 12.  
Figure 11: Spanwise aerodynamic force contribution.  
Figure 12: Blade tip designs: winglet and ogege type. (**a**) Winglet tip, (**b**) ogege tip.  
Figure 13 shows the tip vortex vorticity distribution of original blade tip design, optimized winglet type, and ogee type. Here, the vorticity distribution is on the 3 m downstream plane perpendicular to the tangential direction from the rotor blade tip trailing edge. The performance, that is, the shaft power was increased for the optimized winglet tip (Figure 13b) and ogee type (Figure 13c) compared to the base tip design (Figure 13a), while the aerodynamic load and the vorticity strength were decreased as desired. The simulation results are summarized in Table 3.  
As shown in Figure 12a, several design parameters such as the spanwise installation position, cant angle, taper ratio, and sweep angle should be considered to design the winglet tip shape. However, the winglet tip shape design of the present study was carried out using only two design parameters of the installation position and cant angle. The determinants of the ogee tip shape are the spanwise chord length and pitch axis position distribution as shown in Figure 12b.  
The aerodynamic characteristics are closely related to the reduction of the induced drag and the rotor tip loss which are significantly dominated by the vorticity strength of the tip vortex. Furthermore, the reduction of the tip vortex vorticity strength leads to the reduction of the aero-acoustic sound generated by the tip vortex behavior.  
Figure 13: Three-dimensional vorticity distribution according to various rotor blade tip. (**a**) Original tip design (base), (**b**) optimized winglet tip design, (**c**) optimized ogee tip design.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Items** & **Original** & **Winglet** & **Ogee** \\ \hline Shaft power (\%) & (base) & +0.54 & +0.08 \\ Blade root thrust moment (\%) & (base) & \(-0.08\) & \(-0.21\) \\ Vorticity (1/second) & (base) & Decreased & Decreased \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of aerodynamic characteristics.
# To Cache or Not to Cache
## 4 Evaluation
### Setup
#### 4.1.2 Experimental Setup  
We built cache simulators for every algorithm to process block I/O workloads and report on the number of reads, writes, cache hits and misses, and total writes incurred. When possible, we used the original authors' version of the algorithm implementation. To simulate sufficient cache space as well as I/O activity, the size of the cache was set to be a fraction of the _workload footprint_, defined as the total size of all unique data accessed. This fractional cache size was varied from 1% to 20% of each workload's footprint in our simulations (more specifically 1%, 2%, 5%, 10%, 15%, and 20%).
## 1 Introduction
The optimization calculates a control sequence for the whole horizon such that the selected objectives are minimized, but only the control action for the next time step is implemented in the system [21]. In Ref. [20], the authors develop an approach for driver-aware vehicle control based on stochastic model predictive control with learning (SMPCL) combining the learning of a Markov chain that represents the driver behavior and a scenario-based approach for stochastic optimization. Ref. [22] presented an integrated model predictive control (IMPC) method that combined power management and adaptive velocity control during vehicle-following scenarios in reality, in order to synergistically examine driving safety and fuel economy during vehicle following scenarios of plug-in hybrid electric vehicles (PHEVs). In this paper, an MPC method based coordinated scheduling optimization for MES is proposed, in which the stochastic nature of EV availability respectively in office or residential sites is investigated considering people's driving patterns, and, moreover, the coordination of two kinds of complementary CHPs is achieved in satisfying the electricity and thermal demands. The main contribution of this paper can be summarized into the following aspects: (1) The uncertain EVs available charging/discharging electricity energy, respectively, in offices and residential buildings is described considering peoples' daily driving pattern, and represented by a number of scenarios generated by scenario generation and reduction techniques; (2) A stochastic MPC approach based electricity-heat coupled MES coordinated scheduling is developed, aiming to achieve the most economic operation in terms of operational costs and EV batteries' degradation costs, while considering operational constraints of various CHPs with complementary properties, which is formulated by a mixed-integer linear programming (MILP) problem and solved by the mature software Cplex. (3) For office or residential buildings, the working modes of EVs are analyzed and discussed; moreover, the energy generation efficiencies of two kinds of CHPs with fixed and variable heat-to-electricity ratio are analyzed. The rest of this paper is organized as follows: in Section 2, MES structures are introduced, including the system architecture and the components of EVs and CHPs. Section 3 illustrates the stochastic MPC approach for optimal scheduling coordination of MES, including the model and the solving procedure. In Section 4, the simulation results and discussions are described, and two cases are studied to demonstrate the proposed approach. Finally, the paper is concluded in Section 5.
## 5 Simulation Results and Analysis
### Distribution and Motion of UE  
As the UEs are any smart devices that are capable of aerial communication, in a disaster scenario they are distributed randomly in the AoI. We considered a static scenario where the UEs are distributed through random patterns in the AoI. A static scenario means one where the UE does not move from their position. Obstacles: in the simulation, we also carried out the experiment in an obstacle environment in order to check the stability of the proposed work in AoI.
## 3 Methodology
### Deep Learning Model  
The segmentation model is based on a Dynamic UNet with a pre-trained ResNet encoder. A transfer learning approach was utilized with a pre-trained ResNet encoder to address the challenge of insufficient training data. Transfer learning [26] is a widely used technique in computer vision tasks outside of medical image segmentation [27; 28]. In order to leverage transfer learning, the technique employed in the proposed solution is the Dynamic UNet (fast.ai, n.d.), based on the original Unet proposed by [PERSON] et al. [23]. A UNet architecture was chosen as it is the best methodology in terms of overall performance in medical imaging applications [21; 29]. The flexibility derived from using custom encoders provided by Dynamic UNet allows for a much greater degree of exploitation of transfer learning.  
Figure 3: Schematic of the proposed solution. The input image is inputted in a deep learning model, which outputs a generated mask. The segmentation mask is then applied to the input image. Notably, the upper part of the internal organs is removed, as highlighted by the red circle.  
The original UNet architecture can be divided into an encoding part, or \"downsampling\", and a decoding part, or \"upsampling\". The encoding side performs a similar task to a conventional CNN, with regular downsampling steps performed through the maxpool operation. At the same time, the decoding path follows a symmetrical structure, with the upsampling steps performed through a fractionally strided convolution layer. The symmetry allows for the activations of the downsampling layers to be concatenated to the activations of the upsampling layers. This offers better retention of spatial information throughout the upsampling path.  
The Dynamic UNet architecture was originally presented by [PERSON] and [PERSON] [25]. It follows the same basic architecture as the original UNet but adds a pre-trained model as the encoder. The approach not only achieves considerable improvements over the traditional UNet, but it also allows one to experiment with a variety of pre-trained encoders. In this study, the ResNet encoders were used, as it has been demonstrated that they reduce the reliance on regularization techniques [30].  
ResNets were first introduced in 2015 [31] to counteract the vanishing/exploding gradient problem in deeper networks, which had been a mainstay challenge in the deep learning research field since the inception of ConvNets [32]. The fundamental component of a ResNet is the residual block, which incorporates a \"skip connection\". The input to the block is passed through an identity mapping and is summed to the activation of a series of convolutional layers. ResNets can reach theoretically infinite depths, due to the self-regularization method provided by the identity mapping [31]. The flexibility of ResNets allows training for longer (more epochs), thus reducing the likelihood of overfitting [31].