esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Establish the DL Model
\[IoU=\frac{\text{Area}(\text{ground }\cap\text{ predict})}{\text{Area}(\text{ground }\cup\text{ predict})} \tag{1}\]  
\[P=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[R=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
\[F1\ score=\frac{\text{P }\times\text{ R }}{(\text{P }+\text{ R })/2} \tag{4}\]  
\[AP=\sum_{n}(R_{n}-R_{n-1})P_{n} \tag{5}\]  
where TP is the number of True positives; FP is the number of False positives; and FN is the number of False negatives; P is _Precision_; R is _Recall_, and AP is _Average Precision_. Table 2 shows the correlation between the number of convolutional layers, training image size, and training time. The performance of the Mask R-CNN model varied for different training image sizes and backbone models. Doubling the image size from 256 \(\times\) 256 to 512 \(\times\) 512 pixels increased the model performance in three ResNet models, but tripling the size to 768 \(\times\) 768 pixels degraded the performance in both ResNet101 and ResNet152. There was an increase in performance when the network was deeper, increasing from 50 to 101 layers deep. However, when the depth increased to 152 layers, ResNet152 delivered a lower accuracy than ResNet101. Although the number of stacked layers might enhance the model's features, a deeper network can reveal any degradation issues. This is because as the number of layers in a neural network grows, the accuracy levels may become saturated and thus gradually decline, thus degrading the model's performance considerably. With the same backbone model (ResNet101), the training time with input image sizes, at 768 and 512 pixels, was 19 h 35 min, and 9 h 56 min, respectively; the training image size clearly affects the total training time considerably. Although the latter has slightly lower accuracy than the former, it performed better in terms of AP, F1, and Recall, at 0.3976, 0.5980, and 0.4668, respectively. The pattern of training times was similar when increasing layers in the ResNet model, with deeper layers leading to longer learning processes. Although ResNet50 needs less training time than others, the performance is lower for all four accuracy metrics. Based on these test results, ResNet101 with a training image size of 512 pixels, seems a suitable combination as it delivers the optimum results, therefore, it was used for model fine-tuning in the next step.
# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
## 5 Environmental Impacts on Hardware-Based LQEs
### Environmental Impacts on LQI Based LQEs
#### 5.4.2 Environmental Impacts on LQI Based LQEs  
Using Equation (12), the Cubic model between \(\mu_{lqi}\) and \(PRR\) in three environments was plotted, as shown in Figure 22. It is clear that all the measured data from the three environments are basically coincident with the Cubic model. Using Equations (13) and (14), the piecewise linear model and LR model in the three environments were also plotted respectively, are shown in Figures 23 and 24. It is clear that all the measured data from the three environments are also basically coincident with two models.  
To describe the environmental impact on LQI based LQEs quantitatively, RMSE of the estimated \(PRR\) and real \(PRR\) in the three environments were calculated, as shown in Table 6. It can be seen that RMSEs of the three environments are almost the same, no matter whether the model is. Compared with that in the runway, RMSE of the Cubic model in the artificial lawn reduces by 19.63%, but increases by 20.94% in the corridor. RMSE of the piecewise linear model in the artificial lawn reduces by 28.30%, but increases by 30.12% in the corridor. RMSE of the LR model in the artificial lawn reduces by 22.14%, but increases by 23.00% in the corridor. That is to say, the mapping model between \(\mu_{lqi}\) and \(PRR\) constructed in a specific environment can be directly used in other environments.  
Figure 21: \(\mu_{lqi}\) vs. \(PRR\) in different environments.  
Figure 22: Effects of the cubic model in different environments.
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multicolumn{3}{c}{**Failure Modes**} \\ \cline{2-4}  & **S** & **FS** & **F** \\ \hline \(\lambda\) & \(0.56\leq\lambda\leq 0.90\) & \(0.90\leq\lambda\leq 1.50\) & \(\lambda\geq 1.50\) \\ \(\rho_{hc,c}\),\(f_{y4}\)/\(f_{c}\) & \(\rho_{hc,c}\),\(f_{y4}\)/\(f_{c}\geq 1.43\) & \(0.71\leq\rho_{hc,c}\) \(f_{y4}\)/\(f_{c}\leq 1.43\) & \(0.14\leq\rho_{hc,c}\),\(f_{y4}\)/\(f_{c}\leq 0.71\) \\ \(\rho_{vc,s}\),\(f_{y1}\)/\(f_{c}\) & \(0.04\leq\rho_{vc,s}\),\(f_{y1}\)/\(f_{c}\leq 0.25\) & \(\rho_{vc,s}\),\(f_{y1}\)/\(f_{c}\geq 1.43\) & \(0.03\leq\rho_{vc,s}\),\(f_{y1}\)/\(f_{c}\leq 0.25\) \\ \(f_{a}\)(MPa) & \(328.1\leq f_{a}\leq 333.85\) & \(f_{a}\geq 384\) & \(333.85\leq f_{a}\leq 384\) \\ \(\rho_{vw,s}\),\(f_{y2}\)/\(f_{c}\) & \(\rho_{vw,s}\),\(f_{y2}\)/\(f_{c}\geq 0.045\) & \(0.041\leq\rho_{vw,s}\),\(f_{y2}\)/\(f_{c}\leq 0.045\) & \(0.021\leq\rho_{vw,s}\),\(f_{y2}\)/\(f_{c}\leq 0.041\) \\ \(A_{w}\)/\(A\) & \(0.63\leq A_{w}\)/\(A\leq 0.66\) & \(0.58\leq A_{w}\)/\(A\leq 0.59\) & \(0.59\leq A_{w}\)/\(A\leq 0.63\), \\  & & & \(A_{w}\)/\(A\geq 0.66\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The top 6 important features of STRC shear walls under different failure modes take a range of values. **Author Contributions:** Conceptualization, G.Y. and Z.M. ; methodology, G.Y. ; software (Python-3.6.5), G.Y. ; validation, G.Y. ; formal analysis, G.Y. and J.Z. ; investigation, J.Z. ; resources, J.Z. ; data curation, G.Y. ; writing--original draft preparation, G.Y. ; writing--review and editing, W.X. ; visualization, G.Y. ; supervision, W.X. and J.Z. ; project administration, Z.M. and J.Z. ; funding acquisition, J.Z. and W.X. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data are contained within the article. **Conflicts of Interest:** The authors declare no conflict of interest.
# Automatic Tortuosity Estimation of Nerve Fibers and Retinal Vessels in Ophthalmic Images
## 2 Methods
### Curvilinear Structure Tortuosity Representation
#### 2.2.1 Representation of the Curvature Orientation  
The curvature estimation is calculated from the parameters with position and orientation. Therefore we define a 3D space \(\mathbb{R}^{2}\times S^{1}\), in which every 2D curvilinear can be represented as a group \(g=(\mathbf{x},\theta)\). The term \(\mathbf{x}=(x,y)\in\mathbb{R}^{2}\) is identified by planar translations while \(\theta\in S^{1}\) is the rotation. In this space, the image processing operations rely on a rotating frame of reference, such that  
\[\{\mathbf{e}_{g}|_{g},\mathbf{e}_{\eta}|_{g},\mathbf{e}_{\theta}|_{g}\}=\{ \cos\theta\mathbf{e}_{x}+\sin\theta\mathbf{e}_{y},\cos\theta\mathbf{e}_{y}- \sin\theta\mathbf{e}_{y}\,\mathbf{e}_{\theta}\}. \tag{7}\]  
In order to describe the estimation process more clearly, we illustrate the orientation projection process in Figure 3. Take the synthetic image in Figure 3a as an example of the original curvilinear structures in 2D image. First the original curvilinear structures is deal with our enhancement method, and then projected to the 3D position-orientation space to get the orientation scores) [32; 33] (Figure 3b). For each curvilinear, the mapping process is achieved through the wavelet transform and convolution operations. The transformation formula with continuous form is as follows:  
\[S_{f}(\mathbf{x},\theta)=\int_{\mathbb{R}^{2}}\overline{\psi(\mathbf{R}_{ \theta}^{-1}(\mathbf{x}^{\prime}-\mathbf{x}))}f(\mathbf{x}^{\prime})d\mathbf{ x}^{\prime}, \tag{8}\]  
where \(S_{f}\) is the orientation score. In an image \(f\), each spatial location \((x,y)\) of a curvilinear can be represented as a group \((\mathbf{x},\theta)\) in 3D space. Here, the \(\psi\in\mathbb{L}_{2}(\mathbb{R}^{2})\) is the cake-wavelet transform, which is \(\theta\)-axis aligned with rotational symmetry. Finally, the curvilinear in 3D space is represented by the orientation scores for further curvature estimation.  
Figure 2: Overview of the proposed exponential curvature estimation on corneal nerve fibers and retinal vessels: (**a**) nerve fiber image; (**b**) enhanced nerve fiber image; (**c**) nerve fiber curvature map in 3D orientation score space with color-coded; (**d**) nerve fiber curvature map in the 2D space; (**e**) vessel image; (**f**) enhanced vessel image; (**g**) 3D vessel curvature map; (**h**) 2D vessel curvature map.
## 2 Conceptual Motivation
Moreover, IRS-assisted wireless communication systems based on deep machine learning [16; 20] could be a possible applicable emerging field in metaverse scenarios. Motivated by the aforementioned useful applications, conceptualization, and possible field of the metaverse toward 6G, this paper focuses on the wireless communication system in the physical world environments for enhancing the high data rate and high throughput between the physical world and virtual environments. In this paper, we propose a novel physical layer of technology in an IRS-assisted communication system to implement the above proposal in the metaverse environment. More specifically, an IRS-assisted multi-user (MU) multiple-input single-output (MISO) system is proposed to analyze the bit error rate (BER) and spectral efficiency (SE); the achievable outcome is reflected in the metaverse environment for the fulfillment of the goal of 6G wireless communications. The major contributions of this paper are as follows:  
* A novel conceptualization of metaverse scenarios with an IRS-assisted MISO system is proposed for the 6G-enabled wireless communication system. We propose the concept of physical layer communication (PHC), which provides novel connectivity in the physical world and metaverse by enhancing the desired achievable rate, bandwidth, and low latency for smooth communication paradigms. Thus, IRS-assisted wireless communication enhances the overall network performance for 6G communication expectations in metaverse scenarios. * In PHC, the IRS-cascaded communication channel between the based station (BS) and IRS and the IRS and user equipment (UE) is considered to overcome the signal loss attributed to various obstacles, such as buildings, trees, and so on. Thus, PHC can run the digital twin of the real world and virtual world and reduce the bandwidth requirement and latency using semantic communication. * To remove the inter-user interference (IUI), the BS employs zero-forcing (ZF) precoding with power allocation for the effective channel. Additionally, the water-filling technique [21] is employed for effective power distribution. * Finally, the BER, SE, and throughput are observed with respect to the different modulation schemes and IRS elements. The simulation results demonstrate that these results can play a vital role in achieving the goal of 6G in the metaverse. The rest of the paper is organized as follows. Section 3 describes the metaverse-enabled system modeling. Section 4 describes communication channel modeling with spectral efficiency and precoder formulation. Section 5 presents simulation results; finally, future research directions and conclusions are represented in Sections 6 and 7, respectively.
# Low Discrepancy Sparse Phased Array Antennas
## 1 Introduction
A low discrepancy element distribution appears random, but the elements also appear to be evenly distributed across the aperture. Discrepancy theory has its origin in a paper by [PERSON] on the uniform distribution of sequences [27]. Different LDSs have been introduced from the early 1960s, including the Hammersley point set [28], the Sobol sequence [29], the Faure sequence [30], and the Niederreiter sequence [31]. LDSs found their first applications in the 1990s for numerical analysis and integration for numerical simulation, in the fields of computer graphics [25], computational physics [32], and finance engineering [33]. The application of LDS to the generation of sample points for Monte Carlo sampling (i.e., the quasi-Monte Carlo approach) is theoretically superior to a standard Monte Carlo technique. To our knowledge, the first paper to apply an LDS (Hammersley sequence) to element spacing explored sparse aperiodic spacing on a spherical array [34]. The advantages of LDS for sparse phased array design demonstrated that the Hammersley sequence maintains the large separation between the elements, while reducing the grating lobes compared to element spacings derived from random, pseudo-random, and uniform plus jitter sequences [35]. In this paper we use low discrepancy sequences to distribute elements in a sparse planar array aperture while maintaining the following properties:* Sufficient elements to achieve a desired gain. * Aperture size is large enough to achieve the desired beamwidth. * Elements have a minimum separation distance, so they can physically fit into the aperture and mutual coupling is not a problem. * No GLs present at maximum scan angles. * The average element spacing is greater than \(\lambda\). We study sparse LDS arrays with 86% less elements than a fully populated array, and present numerical results that demonstrate these sampling techniques are capable of completely removing the grating lobes of sparse arrays. We recall that that a fully populated array has a uniform spacing of half-wavelength between its elements. The mathematical formulation for implementing LDS generated element lattice for sparse planar arrays is presented. Multiple array configurations are also studied, and we show that these LDS techniques are not impacted by the type/shape of the array aperture. Finally, we show that in comparison between the LDS techniques, Poisson disk sampling technique outperforms all other approaches and is the recommended LDS technique for sparse arrays. This paper is organized as follows. In Section 2, we describe the mathematical formulation of the LDS sampling techniques. In Section 3 we implement these techniques on a large square planar array with an aperture size of \(32\lambda\times 32\lambda\). We then extend this study to other array types in Section 4. Beam scanning performance of the arrays is studied in Section 5, followed by conclusions in Section 6.
## 2 Theory and Method  
### Theory  
The field of application deep learning is constantly expanding, and it is widely used in seismic data processing. The current method of using neural networks to solve the problem of frequency extension is as follows. The data produced by the main frequency are designated as full frequency band data, while the data corresponding to each individual frequency band are referred to as frequency band-slice data. It is then posited that a specific functional relationship exists between the full frequency band data and the frequency band-slice data:  
\[\mathrm{Y(x)=G(M(x))} \tag{1}\]  
x is the full seismogram represented by a matrix; Y represents the full frequency band data; M represents the frequency band-slice data; G represents the mapping relationship between the two. The mapping relationship is approximated by introducing the neural network BE-Transformer, and the expression is as follows:  
\[\overline{Y}(x,\alpha)=\mathrm{Net}(M(x,\alpha)) \tag{2}\]  
\(\overline{Y}\) represents the predicted complete full band data; Net represents the BiGRU-Extended Transformer network structure; \(\alpha\) is a hyperparameter. This mapping is non-linear and can handle complex patterns and laws. The neural network continuously adjusts the learning parameter \(\alpha\) to achieve a good fit between the input and output so that:  
\[\mathrm{Y(x)\approx\overline{Y}(x,\alpha)} \tag{3}\]  
During the neural network training process, the implicit relationship between data is established by solving the least squares optimization problem. The objective function expression is as follows:  
\[\mathrm{J(M,\alpha,Y)=argmin}\underset{i}{\sum}\mathrm{L}[\mathrm{Net}(M(x_{i},\alpha),Y_{i}] \tag{4}\]  
In the formula, J is the network optimization objective function; L is the loss function. The iterative idea approaches the optimal solution by using the gradient descent method and expresses the network parameters \(\alpha_{i+1}\) as:  
\[\alpha_{i+1}=\alpha_{t}+\beta[\
abla\alpha_{i=1,2,\ldots,J}(M,\alpha_{i},Y)] \tag{5}\]  
In the formula, \(\alpha_{t}\) is the network parameters after the t-th iteration; \(\beta\) is the update directions; \(\
abla\alpha_{i=1,2,\ldots,t}\) is the gradients relative to J. Through optimization algorithms and back-propagation mechanisms, neural networks can gradually approach the objective function and improve the accuracy of predictions.
# Optoelectronic Instrumentation and Measurement Strategies for Optical Chemical (Bio)Sensing
## 4 Design of Applications
### Fiber Optic Oxygen Sensor Based on Phosphorescence Lifetime Measurement  
Oxygen is undoubtedly one of the most important analytes on earth. Unfortunately, few sensors are able to measure concentrations of 100 nM or less [50; 51]. In these cases, fiber optic sensors have proven to be indispensable tools for oxygen quantification, replacing the conventional Clark electrodes. Optical oxygen sensors are based on the attenuation of the luminosity of a phosphorescent indicator by the analyte. The latest generation of indicators is dominated by phosphorescent complexes with platinum group metals that have lifetimes of between a few microseconds and a few milliseconds.
# Extracting Sentence Embeddings from Pretrained Transformer Models
## 5 Conclusions
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Model** & **T0** & **T4** & **BERT** & **BERT + Avg.** & **Avg.** & **B2S** & **B2S-100** & **RE** \\ \cline{2-10}  & \multicolumn{3}{c}{**Last**} & \multicolumn{3}{c}{**First + Last**} & \multicolumn{3}{c}{**-**} & \\ \hline avg. & 47.0 & 48.2 & 51.8 & 54.1 & 50.7 & 51.7 & 51.7 & 46.5 \\ + normalize & 46.2 & 48.3 & 53.0 & 53.2 & 52.4 & **53.7** & **55.2** & 56.4 \\ + quantile-u & 46.6 & 48.5 & 51.9 & 53.9 & 51.7 & 50.8 & 52.9 & 48.2 \\ + quantile-u\({}^{W}\) & 46.9 & 48.0 & 53.6 & 53.4 & 53.0 & 51.4 & 51.1 & 47.8 \\ + whiten & 15.9 & 16.3 & 15.9 & 17.0 & 18.5 & 18.1 & 17.2 & 17.6 \\ + whiten\({}^{W}\) & 44.9 & 44.1 & 51.1 & 51.2 & 51.7 & 50.5 & 49.9 & 49.0 \\ + zscore & 47.5 & 49.0 & 53.0 & 53.3 & 51.4 & 50.0 & 50.5 & 46.4 \\ \hline id\({}_{l}^{W}\) & 48.6 & 47.2 & 50.5 & 52.1 & 48.5 & 49.4 & 50.8 & 45.4 \\ + normalize & 48.3 & 47.8 & 51.9 & 52.7 & 48.5 & 50.3 & 50.4 & 51.5 \\ + quantile-u & 49.0 & 47.9 & 52.7 & 51.9 & 48.8 & 49.8 & 49.9 & 46.4 \\ + quantile-u\({}^{W}\) & 48.3 & 47.9 & 52.7 & 52.8 & 48.3 & 49.4 & 49.6 & 46.0 \\ + whiten & 15.3 & 15.1 & 15.7 & 16.2 & 18.3 & 17.4 & 17.0 & 15.9 \\ + whiten\({}^{W}\) & 43.0 & 42.3 & 50.0 & 50.8 & 50.1 & 49.8 & 49.3 & 45.5 \\ + zscore & 49.3 & 46.7 & 52.2 & 51.4 & 48.0 & 48.9 & 49.1 & 46.3 \\ \hline id\({}_{l}^{T}\) & **50.8** & 50.5 & 55.0 & 55.2 & **54.5** & 51.7 & 51.7 & 49.0 \\ + normalize & 49.6 & 50.6 & **55.1** & 54.7 & 53.6 & 53.7 & 55.2 & **58.5** \\ + quantile-u & 49.7 & 51.0 & 53.5 & 54.3 & 53.7 & 50.8 & 52.9 & 50.5 \\ + quantile-u\({}^{W}\) & 50.0 & **52.3** & 53.4 & 54.8 & 53.3 & 51.4 & 51.1 & 51.7 \\ + whiten & 17.6 & 18.0 & 15.5 & 16.5 & 18.2 & 18.1 & 17.2 & 17.6 \\ + whiten\({}^{W}\) & 44.4 & 46.6 & 51.9 & 53.2 & 54.0 & 50.5 & 49.9 & 48.8 \\ + zscore & 49.7 & 50.0 & 53.5 & 55.4 & 53.8 & 50.0 & 50.5 & 47.8 \\ \hline -biases & 47.1 & 49.6 & 52.4 & 52.3 & 51.7 & 49.6 & 49.6 & 48.0 \\ + normalize & 47.3 & 49.3 & 54.0 & **55.7** & 53.4 & 51.3 & 51.3 & 55.1 \\ + quantile-u & 47.9 & 50.4 & 53.0 & 55.6 & 53.4 & 48.6 & 50.7 & 48.2 \\ + quantile-u\({}^{W}\) & 48.8 & 50.0 & 53.4 & 54.4 & 53.5 & 50.0 & 49.0 & 47.4 \\ + whiten\({}^{W}\) & 45.9 & 46.0 & 52.2 & 53.0 & 53.3 & 47.3 & 47.5 & 47.4 \\ + whiten & 16.3 & 16.9 & 16.4 & 17.4 & 18.7 & 18.7 & 17.7 & 18.5 \\ + zscore & 47.3 & 49.4 & 52.0 & 53.5 & 53.2 & 48.3 & 49.0 & 47.4 \\ \hline \([\)MASK\(]\) & 41.1 & 47.0 & & & & & & & \\ + normalize & 41.0 & 47.1 & & & & & & & \\ + quantile-u & 42.0 & 47.7 & & & & & & & \\ + quantile-u\({}^{W}\) & 41.9 & 47.4 & & & & & & & \\ + whiten & 14.9 & 16.6 & & & & & & \\ + whiten\({}^{W}\) & 38.4 & 41.7 & & & & & & & \\ + zscore & 41.6 & 46.7 & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Clustering accuracy dependence on token aggregation and post-processing techniques for the tweet dataset. The best result for each model is bolded, while underlined result is the best across all the models.
# Modeling the Power Consumption of Function-Level Code Relocation for Low-Power Embedded Systems
## 2 Code Migration Methodology Based on Function Relocation
This result indicates that the increased time complexity of the program selected for experimentation has an effect on the increase in the power reduction efficiency of function relocation. Furthermore, the efficiency varies depending on the segment of the volatile memory that is accessed [5; 6; 7; 8]. These results confirm the advantageous effect of the proposed function-level code relocation to reduce the amount of power consumed. The analyses conducted in previous studies were comparatively easy to achieve because they were based on a human-understandable programming language, yet they were limited to an abstract analysis based on experimental data. An in-depth analysis, however, requires substantial analysis based on actual program execution at the lower language level. Thus, in this work, we investigate effective program execution at the lower level of a language, e.g., at the instruction level, and attempt to observe the influence of code migration based on function-level code relocation on the rate of power reduction.