esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 3 Softway4 IoT  
This section briefly describes SOFTWAY4 IoT [32, 33, 34, 35], a project funded by the National Network for Higher Education, Research and Innovation (RNP, Brazil). It is an IoT communication solution developed, implemented and deployed as a pilot in a smart campus at Federal University of Goias (UFG, Brazil). The solution addresses four main problems: (i) provide support for multiple wireless technologies for IoT; (ii) minimise the impact of choosing a wireless technology for IoT not yet consolidated; (iii) mitigate potential risks related to the network security of the connected devices; and (iv) support connectivity of the devices to the infrastructure for data collection and processing, typically a cloud computing infrastructure.  
The solution chooses to interact with IoT devices by means of the cloud and fog/edge computing, doing so by creating virtual network slices that accommodate a certain service or device in compliance with its resources. Fog computing can be described as a decentralised computation process where its memory and processing power reside between the data source and the cloud. It is considered to be a powerful method because it distributes its computational effort through the network edge. Due to the solution architecture, the application is given the power to choose from where to consume the produced data. It can be available from either a (public or restricted) cloud or a fog infrastructure. Figure 1 summarizes the SOFTWAY4 IoT concept.  
Because conventional networks do not take advantage of SDR, each considered wireless technology would need a dedicated gateway or physical interface in order to be able to perform communication. As SDR allows for multiple technologies to operate over the same gateway and even adds/removes these possibilities by means of software only, the process of handling different wireless technologies is then considered to be simplified, as shown in Figure 2.  
Figure 1: The SOFTware-defined gateWAY and fog computing for Internet of Things (SOFTWAY4 IoT) interaction with different devices. FCMan, Fog Computing Manager; SMan, Slice Manager; NSMan, Network Security Manager; VIX, Virtual Air Interface of Technology X; VIMan, Virtual Interface Manager; WebGM, Web-based Gateway Manager.  
Figure 2: SOFTWAY4 IoT dealing with multiple wireless technologies.
## 3 Modeling of Base-Isolated Structure with Piezoelectric Friction Dampers
### Equation of Motion for a Base-Isolated Structure  
Consider an \(n\)-degree of freedom base-isolated structure with piezoelectric friction dampers at the isolation floor subject to seismic acceleration \(\ddot{x}_{g}\). The equation governing the dynamic response of the structural system is given by  
\[\mathbf{M}\ddot{\mathbf{x}}(t)+\mathbf{C}\dot{\mathbf{x}}(t)+\mathbf{K}\mathbf{x }(t)=-\mathbf{M}\ddot{\mathbf{x}}_{g}(t)+\mathbf{D}F(t) \tag{1}\]  
where \(\mathbf{M}\), \(\mathbf{C}\), \(\mathbf{K}\) represent the \(n\times n\) mass, damping, and stiffness matrices, respectively; \(\mathbf{x}(t)\), \(\dot{\mathbf{x}}(t)\), and \(\ddot{\mathbf{x}}(t)\) are \(n\times 1\) displacement, velocity, and acceleration vectors, respectively; \(\mathbf{I}\) is an \(n\)-dimensional identity matrix; \(\mathbf{D}\) is an \(n\times 1\) damper location vector; and \(F(t)\) is the damping force of damper and LRB.  
Rewriting Equation (1) in state-space form:  
\[\dot{\mathbf{z}}(t)=\mathbf{A}\mathbf{z}(t)+\mathbf{B}F(t)+\mathbf{H}\ddot{x} _{g}(t) \tag{2}\]  
where  
\[\mathbf{z}(t)=\left[\begin{array}{c}\mathbf{x}(t)\\ \dot{\mathbf{x}}(t)\end{array}\right]\text{, }\mathbf{A}=\left[\begin{array}{ cc}\mathbf{0}&\mathbf{I}\\ -\mathbf{M}^{-1}\mathbf{K}&\mathbf{M}^{-1}\mathbf{C}\end{array}\right]\text{, }\mathbf{B}=\left[ \begin{array}{cc}\mathbf{0}\\ -\mathbf{M}^{-1}\mathbf{D}\end{array}\right]\text{, }\mathbf{H}=\left[ \begin{array}{cc}\mathbf{0}\\ -\mathbf{I}\end{array}\right]\text{.} \tag{3}\]
## 4 Design Generation and User Evaluation
### Design Efficiency Verification  
In order to verify the efficiency of the Dual-Path Optimization Model in UI design tasks, six graduate students who are proficient in UI design were invited to act as the control group [86]. All graduate students had professional UI design abilities and proficient software skills. They participated in the execution of the traditional design process and the \"two-path optimization model\" process, carrying out three types of standardized design for the ordering interface. The actual time consumed in each design phase was recorded through time logs, and the time required by the traditional manual design method and the \"two-path optimization model\" process was determined. The results show that, in the first-draft-generation stage, the traditional design method takes 42.5 h, while the \"two-path optimization model\" process takes only 1.2 h, which is the most significant time compression. The Dual-Path Optimization Model saved the most time in the full schematic design phase, with a reduction of 123.9 h. In the design conception phase, the time difference between the traditional method and the optimization model is relatively small due to the need for creative diffusion, as shown in Table 15. These data indicate that the Dual-Path Optimization Model has significant advantages in improving design efficiency, especially in the first draft and the generation of the complete solution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Design Phases**} & \multicolumn{3}{c}{**Average Time Taken (h)**} & \multirow{2}{*}{
\begin{tabular}{c} **Efficiency** \\ **Improvement \({}^{*}\)** \\ \end{tabular} } \\  & \multicolumn{1}{c}{**Traditional Manual**} & \multicolumn{1}{c}{**Dual-Path Optimization**} & \\  & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Model Flow**} & \\ \hline Design & 3.1 & 0.8 & 74.2 \\ conceptualization & 42.5 & 1.2 & 97.2\% \\ First draft generation & 127.3 & 3.4 & 97.3\% \\ Full program & 127.3 & & \\ \hline \hline \end{tabular}
\end{table}
Table 15: Design time comparison table.  
Figure 9: Design scheme display. Source: own processing.
# Special Issue on Advanced Fiber-Reinforced Cementitious Composites
## 3 Investigation of the Influence on Tensile and Electrical Properties by Steel Fibers
The bending strengths obtained from the cross-sectional analysis using the material test results were compared with the test results of the beams. Overall, the beams with 1.0% inclusions produced lower estimates of tensile strength, while the beams with 1.5% inclusions produced higher estimates of tensile strength. When the tensile strength of UHPFRC beams was estimated by comparing the analysis results of the moment-curvature curve with the test results of the beams, the estimated tensile strength of UHPFRC increased as the compressive strength increased [8]. [PERSON] et al. experimentally investigated the bending and shearing behavior of UHPFRC type 5 by performing a four-point load test on it. The purpose of this study is to evaluate the flexural and shearing behavior of ultra-high performance fiber beams and compare it with the behavior of ordinary-strength beams and the equations available in the literature. Experiments were conducted with cross-sectional size and fiber reinforcement ratios as variables. It was found that the UHPFRC beams failed primarily in bending, while the normal strength beams failed primarily in shearing. The bending and shearing capacities of the UHPFRC specimens were up to 3.5 times greater than those of the conventional-strength specimens; on average, the bending and shearing loads of the UHPFRC specimens were 1.50 and 6.40 times higher than those of the normal strength specimens for the smaller sections, and 2.46 and 2.42 times higher for the larger sections, respectively [9]. The research presented here offers valuable insights into the ultimate strength, tensile behavior, and flexural/shearing behavior of UHPFRC beams. The studies have employed rigorous experimental programs, investigating variables like fiber content, design recommendations, and cross-sectional size. The results demonstrate significant improvements in strength and performance due to the incorporation of steel fibers in UHPFRC. However, a comprehensive assessment should consider additional factors, including sample size, statistical analyses, and potential experimental limitations.
# Structural Damage Detection through Dual-Channel Pseudo-Supervised Learning
## 3 Validation by a Numerical Model
### IASC-ASCE SHM Benchmark Dataset
In this paper, two convolutional kernels of size 5 were used for convolution operations. * LSTM: The LSTM model can capture long-range dependencies in sequence data through the gate-control mechanism, enabling the learning of crucial long-range dependency features. This model was constructed as a two-layer bidirectional LSTM with the dimension of 128 in this paper. * Multi-head CNN: By introducing multiple parallel convolutional branches (heads), this model can learn features at different scales. Each branch focuses on different time- or frequency-domain information, and the multiple branches are eventually fused to provide a comprehensive description of the structural damage. To further validate the effectiveness of integrating local and global features for structural damage detection and to assess the efficacy of combining CNNs and the self-attention mechanism in the Conformer network, we also included CNN-LSTM and Transformers as additional baseline models. The details of the Transformer and CNN-LSTM models are presented as follows:  
* CNN-LSTM: This model, in this paper, first used a 1D CNN with a convolutional kernel of 15 to extract spatial features. Then, a two-layer LSTM model with 256 dimensions was used for long-sequence modeling. * Transformers: Through the self-attention mechanism, the transformer model can effectively capture global dependencies in sequences. In structural damage detection, it models the acceleration response data collected by sensors to learn features related to identifying damage. In this paper, we constructed a four-layer Transformer block with 8 heads and with a dimension of 512. Furthermore, to evaluate the performance of DCPS learning, in addition to Conformer+Conformer, we compared the combinations of CNN+CNN and CNN+Conformer, as shown in Figure 6. Finally, we conducted tests at multiple noise levels to demonstrate the improvement of model noise immunity through DCPS learning and its robustness. Hyperparameters: For the Conformer model and models in DCPS learning, the Adam optimizer was used for a gradient descent, and the learning rate was 0.0001. The batch size was set to 32, and a total of 300 epochs were used for training and validation. In addition, we used 4 Conformer blocks and set 2 heads in the multi-head self-attention layer, with a dropout of 0.4 for the self-attention module and a dropout of 0.1.
## 1 Introduction
### Ultra-violet Photofunctionalization on Titanium Surface
#### 1.3.2 Direct Electrostatic Interactions  
[PERSON] et al. [66] mentioned that changes of interfacial energies between solid surfaces and liquid play an important role in the photoinduction phenomenon. The irradiation of TiO\({}_{2}\) to UV light results in the excitation of an electron from the valence band to the conduction band of TiO\({}_{2}\), thereby producing negative-electron (e\({}^{-}\)) and positive-hole (h\({}^{+}\)) pairs. The positive hole on the superficial layer of TiO\({}_{2}\) increases the surface free energy to become more electropositive. The divalent cations following UV-treated titanium surfaces act as direct attractants for cells, and the positively charged TiO\({}_{2}\) surface can attach directly to negatively charged proteins and cells without requiring ionic bridges such as calcium ions (Ca\({}^{2+}\)) to attract proteins and cells. The electropositive titanium surfaces exhibit a regulatory role by determining their bioactivity and attracting negatively charged proteins, blood, and cells on the titanium surfaces.  
Notably, naturally occurring sunlight consists of both UVA and UVC. Although UVC does not penetrate the ozone layer, it can be produced by a germicidal UV lamp. A UV light energy greater than 3.2 eV is required to induce TiO\({}_{2}\) photocatalytic activity [67]. [PERSON] et al. [21] demonstrated that UVA and UVC treatments both produced titanium surfaces with high wettability (contact angle \(<\)5\({}^{\circ}\)) under different underlying mechanisms. UVA serves as an energy source for the photocatalytic reaction of TiO\({}_{2}\), where the electron-hole pair is generated. Thus, hydrocarbons on the TiO\({}_{2}\) surface are removed via reaction with reactive oxygen species (as presented in Figure 2). UVC induces the photolysis of hydrocarbon compounds from the TiO\({}_{2}\) surface [43; 69], thereby causing the latter to decompose directly, which can lead to superhydrophilicity. In the study by [PERSON] et al. [41], initial cell osteoblast attachment and proliferation were enhanced on the UVC-treated surface, but not UVA-treated surface, despite comparable increased wettability. Similar cell reactions were also reported by [PERSON] et al. [21] and [PERSON] et al. [70]. In a dog animal model, [PERSON] et al. [71] showed a significant increase in BIC after two weeks of healing with the implant treated with UVA.  
Figure 2: Schematic photoexcitation of an electron on the TiO\({}_{2}\) surface and the creation of holes, which attract water molecules to generate hydroxyl radicals and superoxide ions. (Adapted from [67]).
## 4 Simulation Results  
In this section, the simulated results are presented and discussed. A single satellite pass was set as the baseline, while the key rates were compared for each different scenario. Initially, the requirements for operational satellite-to-ground QKD are discussed; subsequently, the effect of the key parameters, such as fiber coupling and receiver telescope aperture size, on the link's performance is studied, which is achieved by following the distribution of a single photon over terrestrial fiber links. For the analysis presented below, the system parameters were kept constant while others were placed between some other range of different values. The input parameters are included in Table 1 below.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parameter** & **Value** \\ \hline \(\lambda\) & Quantum channel wavelength & 1550 nm \\ \(f\) & Emission rate & 40–300 MHz \\ \(N_{t}\) & Transmitted block size & \(10^{10}\)–\(10^{13}\) bits \\ \(H_{\mathrm{sat}}\) & Satellite orbit altitude & 550/8000/36,000 km \\ \(D_{Tx}\) & Tx telescope aperture diameter & 0.3 m \\ \(D_{Rx}\) & Rx telescope aperture diameter & 0.28–2.3 m \\ \(a\) & Obscuration ratio & \(\sim\)0.1 \\ \(f/x\) & Focal ratio & \(\sim\)/\(f\)/8 \\ \(\eta_{sys}\) & AO/tip–tilt optical transmittance & 0.9 \\ \(H_{OGS}\) & OGS Altitude & 500–2340 m \\ \(\varepsilon\) & Min. elevation angle & 20\({}^{\circ}\) \\ \(\sigma_{p}\) & Pointing error variance & 0.7 μrad \\ \hline \hline \end{tabular}
\end{table}
Table 1: System assumptions and parameters.
## 3 Concept of the Graphical Knowledge Unit  
In 2015, a new way of representing density clusters was introduced for one dependent and one independent variable using a bitmap. The method was named the \"graphical knowledge unit\" (GKU) [25]. Overlapping data points are the main features that are used for creating a GKU. A higher number of overlapping ones makes a GKU more meaningful and understandable. From its origin, a GKU does not suffer from overlapping data failures. Mainly, a GKU visualizes data in the form of density clusters. Mostly, it is possible to identify high density visually and by means of an algorithm, depending on the color density. In addition, a GKU provides a mechanism for visualizing the amount and nature of the distribution of both missing and out of range data points along with the density clusters. Thus, the GKU is sought as a reliable approach for big data visualization, especially overlapping types.  
Nevertheless, the GKU is not a solution for visualizing multi-variable environments. An extended usage of the GKU for representing multi-variables using the same concept of the overlapping of data points is investigated in this work. As in the GKU, the success of multi-variable multi-layer (MVML)-GKU depends on having high data density for clustering purposes. Figure 1a shows a GKU used for representing 35,864 data points, and Figure 1b, c show the 3D representation and heat map representation of the GKU. Usually, for plotting 3D maps or heat maps, it is necessary to process the data for creating the required matrix. In contrast, while using a GKU, it is not necessary to create such a matrix and when adding data points. A GKU gradually forms the density areas by incrementing the color intensity in overlapped regions.  
In the proposed method, the concept of the GKU is used to represent a multi-variable big data environment in 2D space. Furthermore, the proposed method generates density clusters of all variables on the same bitmap, which can be identified by the naked eye. Most importantly, the proposed method does not suffer from overlapping data points and requires more overlapping for better visualization of density clusters. Thus, the proposed method is a robust density cluster representation and data visualization technique for multi-variable big data. Due to the very flexible nature of the proposed method, it will help to represent big data captured from dynamic bioprocesses.  
Figure 1: (**a**) Representation of 35,864 data points a graphical knowledge unit (GKU); (**b**) representation of the processed data of the same data in 3D; (**c**) representation of the processed data of the same data in a heat map.
###### Abstract  
The neodymium-doped yttrium aluminum garnet (Nd-YAG) laser is used for removal of pigmented skin patches and rejuvenation of skin. However, complications such as hyperpigmentation, hypopigmentation, and petechiae can occur after frequent treatments. Therefore, identifying the risk factors for such complications is important. The development of a multivariable logistic regression model with least absolute shrinkage and selection operator (LASSO) is needed to provide valid predictions about the incidence of post inflammatory hyperpigmentation complication probability (PIHCP) among patients treated with Nd-YAG laser toning. A total of 125 female patients undergoing laser toning therapy between January 2014 and January 2016 were examined for post-inflammatory hyperpigmentation (PIH) complications. Factor analysis was performed using 15 potential predictive risk factors of PIH determined by a physician. The LASSO algorithm with cross-validation was used to select the optimal number of predictive risk factors from the potential factors for a multivariate logistic regression PIH complication model. The optimal number of predictive risk factors for the model was five: immediate endpoints of laser (IEL), \(\alpha\)-hydroxy acid (AHA) peels, Fitzpatrick skin phototype (FSPT), acne, and melanoma. The area under the receiver operating characteristic curve (AUC) was 0.79 (95% CI, 0.70-0.88) in the optimal model. The overall performance of the LASSO-based PIHCP model was satisfactory based on the AUC, Omnibus, Nagelkerke R\({}^{2}\), and Hosmer-Lemeshow tests. This predictive risk factor model is useful to further optimize laser toning treatment related to PIH. The LASSO-based PIHCP model could be useful for decision-making.  
laser toning; post inflammatory hyperpigmentation; LASSO; risk factors; PIHCP
# A Size-Dependent Cost Function to Solve the Inverse Elasticity Problem
## 3 Results
### Modulus Reconstruction by a One-Dimensional Coupled Model
For the proposed approach, the cost function is rewritten as  
\[F=\sum\limits_{i=1}^{2}\left(\!\!\frac{1}{2}\!\left(\int_{0}^{\frac{\left(L- \omega\right)}{2}}\left(u_{i}^{c}-u_{i}^{\text{\tiny{meas}}}\right)^{2}\!\! \mathrm{d}y+\int_{\frac{\left(L+\omega\right)}{2}}^{\frac{\left(L+\omega\right) }{2}}\left(u_{i}^{c}-u_{i}^{\text{\tiny{meas}}}\right)^{2}\!\!\mathrm{d}y+ \int_{\frac{\left(L+\omega\right)}{2}}^{L}\left(u_{i}^{c}-u_{i}^{\text{\tiny{ meas}}}\right)^{2}\!\!\mathrm{d}y\right)+a\!\left[\mu_{in}^{i}-1\right]\!\!\right) \tag{7}\]  
In this case, the relationship between the recovered shear moduli of these two inclusions is  
\[\frac{\left(\mu^{1}-\overline{\mu}\right)\!\!\left(L-a^{1}\right)^{2}\!\left( 1+\left(1-a^{1}\right)\!\!a^{1}\right)\!\!a^{1}}{\left(L\mu^{1}+a^{1}-\mu^{1} a^{1}\right)^{3}\!\!\left(L\overline{\mu}+a^{1}-a^{1}\overline{\mu}\right)} =\frac{\left(\mu^{2}-\overline{\mu}\right)\!\!\left(L-a^{2}\right)^{2}\!\left( 1+\left(1-a^{2}\right)\!\!a^{2}\right)\!\!a^{2}}{\left(L\mu^{2}+a^{2}-\mu^{2} a^{2}\right)^{3}\!\left(L\overline{\mu}+a^{2}-a^{2}\overline{\mu}\right)} \tag{8}\]  
If the recovered shear modulus \(\mu^{2}\) is determined, the recovered shear modulus of the inclusion of the left bar \(\mu^{1}\) can be calculated via Equation (6) or Equation (8). Thus, we can quantitatively compare these two methods by the significance of the underestimation of shear modulus \(\mu^{1}\). More specifically, we assume that the recovered shear modulus of the right bar \(\mu^{2}\) is underestimated by \(\varepsilon\). To this end, \(\mu^{2}=(1-\varepsilon)\overline{\mu}\), and \(\mu^{1}\) should be \((1-\varepsilon)\overline{\mu}\), as the target shear moduli of these two inclusions are the same. We define a relative error as \(\text{\it error}=\left(\mu^{1}-(1-\varepsilon)\overline{\mu}\right)\!/\left((1- \varepsilon)\overline{\mu}\right)\times 100\%\), and compute the relative error with varying sizes of the right inclusion \(a^{2}\), as shown in Figure 5. In Figure 5, the length of these two bars \(L\), the size of the left inclusion \(a^{1}\), and the target shear modulus \(\overline{\mu}\) are fixed to be 1, 0.1, and 5, respectively. Figure 4: A coupled one-dimensional (1-D) model: two linearly elastic and nonhomogeneous bars connected by two rigid plates. Figure 5 demonstrates that when the sizes of these two inclusions are the same, then \(\mu^{1}=\mu^{2}\), indicating no error in the reconstruction. However, increasing the size of the right inclusion causes underestimation in the shear moduli of the left inclusion, as the relative error is negative in both methods. The underestimation becomes more serious with the increase of the size of the right inclusion. Importantly, employing the proposed approach is capable of significantly reducing the relative error. This result is consistent with what we have observed in the 2-D simulated experiments presented in Section 3.1.