esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Recommender Systems to Predict Personalized Film Age Ratings for Parents
## 5 Experimental Results  
### Dataset  
We conducted our experiments using the age ratings provided by the users from the Common Sense Media site [9] (data were obtained from [9] and are available from [9], with permission from Common Sense Media). The site allows its users to specify their own age ratings on any movie they want. This enables our system to train its models on the personal age ratings provided by each user and predict personalized ratings on unrated movies, similar to how preference ratings are used in classic recommender system scenarios. The original dataset contains \(50,781\) age classifications on \(12,545\) movies, by \(45,030\) users. The dataset was split randomly into training and test sets, with the test set containing approximately \(6\%\) of the triplets of the dataset. Figure 3 shows the distribution of the various age values over all entries in the test set.
# Extreme Wave Loading on a Vertical Circular Cylinder
## 4 Extreme Wave Loads on the Vertical Cylinder  
### Impact Force on a Vertical Truncated Cylinder  
Example time histories of the free surface elevation measured in front of the cylinder (\(x\) = 8.9 m) and the subsequent impact forces on the vertical truncated cylinder are shown in Figure 10. Here \(T_{p}\) = 1.5 s, \(A_{p}\) = 0.1 m, and \(h_{ag}\) = 0.06 m. Recall that the front stagnation point of the cylinder was arranged at \(x\) = 9.3 m.  
It is clear from the figure that (both horizontal and vertical) forces are induced when the wave crests exceed the cylinder bottom at \(t\) = 30.44 s and 32 s. Steep rises and sharp peaks are observed in both horizontal and vertical forces due to the impingement of the wave crests and the bottom slamming during water entry, respectively. The following negative horizontal force is introduced by the reverse flow impacting onto the back face of the cylinder, while the water exiting during the impact process leads to the large negative (suction) force vertically. A similar phenomenon was reported by [22] for wave interactions on an elevated rectangular deck. It also can be seen from the figure that the peak vertical force is larger than that of the horizontal force, and the duration of the (vertical) suction phase is relatively longer than the wave impacting/slamming.  
The effect of the air gap/inundation level on the impact force is investigated in Figure 11. It can be seen that both the horizontal and vertical forces increase with the increasing inundation level (=undisturbed wave crest-air gap), as expected. Generally, the horizontal force is found to increase with the wave steepness, although the increase is mild for smaller inundation levels. An opposite trend is found for the vertical force, i.e., the vertical force decreases with the increasing wave steepness. The results from the Morison equation for a truncated cylinder [39] are also included. It can be seen that our measurements agree well with the Morison equation when the cylinder size (i.e., for cases with \(k_{p}A_{p}\) = 0.144 and \(k_{p}R\) = 0.149) is within the expected range of validity of the Morison equation (i.e., \(k_{p}R\) < 0.157). While for larger cylinders, the Morison equation overestimates the horizontal force in general. This further supports/confirms the capability of the present scaled physical experiments in modeling wave-truncated cylinder interactions.  
Figure 10: Time histories of the disturbed free surface elevation at \(x\) = 8.9 m (top) and the horizontal (middle) and vertical (bottom) forces. The embedded subplots in the middle and bottom plots show the close-up views of the corresponding impact forces. Here, \(T_{p}\) = 1.5 s, \(A_{p}\) = 0.1 m, and the air gap \(h_{\text{ag}}\) = 0.06 m (red dashed line).
## 2 System and Channel Model
### FSO Sub-Link  
Considering only Gamma-Gamma atmospheric turbulence and pointing errors, the PDF of the instantaneous SNR \(\gamma_{x,y}^{FSO}\) of the FSO sub-link in any one-hop hybrid FSO/RF link (when the subscript is \((x,y)=(i,j)\), it represents the \(j\)-th hop hybrid FSO/RF link of the \(i\)-th path; when the subscript is \((x,y)=(s,d)\), it represents the direct hybrid FSO/RF link) is [21]:  
\[f_{\gamma_{x}^{FSO}}(\gamma_{xy}^{FSO})=\frac{\rho_{xy}^{2}}{2\gamma_{xy}^{FSO }\Gamma(\alpha_{xy})\Gamma(\beta_{xy})}G_{1,3}^{3}\Bigg{(}\frac{\alpha_{xy} \beta_{xy}\rho_{xy}^{2}}{\rho_{xy}^{2}+1}\Bigg{(}\frac{\gamma_{xy}^{FSO}}{ \gamma_{xy}^{FSO}}\Bigg{)}^{\frac{1}{2}}\Bigg{|}\frac{\rho_{xy}^{2}+1}{\rho_{ xy}^{2},\alpha_{xy},\beta_{xy}}\Bigg{)} \tag{1}\]  
where \(\overline{\gamma}_{x,y}^{FSO}\) is the average SNR of one-hop FSO sub-link, \(\Gamma(\cdot)\) is a Gamma function, \(G_{\cdot,\cdot}^{\cdot,\cdot}(\cdot)\) is a Meijer-G function, \(\rho_{x,y}\) is the ratio of the equivalent beam radius of the receiver plane to the jitter standard deviation of the receiver plane. \(\alpha_{x,y}\) is a positive parameter, which is related to the effective number of large-scale vortices in the scattering process, and \(\beta_{x,y}\) is a quantity related to the attenuation parameter. Assuming that the beam is a spherical wave, \(\alpha_{x,y}\) and \(\beta_{x,y}\) can be given by the following Equations [23]:  
\[\alpha_{x,y}=\Bigg{[}\exp\Bigg{(}\frac{0.49\sigma_{x,y}^{2}}{\left(1+0.18d_{x,y }^{2}+0.56\sigma_{x,y}^{12/5}\right)^{7/6}}\Bigg{)}-1\Bigg{]}^{-1} \tag{2}\]  
\[\beta_{x,y}=\Bigg{[}\exp\Bigg{(}\frac{0.51\sigma_{x,y}^{2}(1+0.69\sigma_{x,y}^ {12/5})^{-5/6}}{\left(1+0.9d_{x,y}^{2}+0.62d_{x,y}^{2}\sigma_{12/5}^{12/5} \right)^{5/6}}\Bigg{)}-1\Bigg{]}^{-1} \tag{3}\]  
where \(d_{x,y}=\left(kD^{2}/4L_{x,y}\right)^{1/2}\), Rytov variance \(\sigma_{x,y}^{2}=0.5C_{n}^{2}k^{7/6}L_{x,y}^{11/6}\), \(k=2\pi/\lambda^{FSO}\) is the wave number, \(\lambda^{FSO}\) is the optical wavelength of FSO link, \(D\) is the diameter of receiver aperture, \(L_{x,y}\) is the one-hop link length, and \(C_{n}^{2}\) is the refractive index structure constant (\(m^{-2/3}\)). Integrate Equation (1) according to the formula \(F_{\gamma_{x,y}^{FSO}}=\int_{0}^{\gamma_{x,y}^{FSO}}f_{\gamma_{x,y}^{FSO}}( \gamma_{x,y}^{FSO})d\gamma_{x,y}^{FSO}\), and the CDF of \(\gamma_{x,y}^{FSO}\) can be expressed as:  
\[F_{\gamma_{x,y}^{FSO}}(\gamma_{x,y}^{FSO})=B_{x,y}G_{3,7}^{6,1}\Bigg{(}A_{x,y} \frac{\gamma_{x,y}^{FSO}}{\gamma_{x,y}^{FSO}}\bigg{|}\frac{C_{x,y}}{\gamma_{x,y}^{FSO}}\bigg{)} \tag{4}\]  
Figure 1: The schematic of the multi-hop parallel hybrid FSO/RF cooperation system.  
where \(B_{xy}=\frac{2^{n}x_{xy}+\theta_{xy}-3^{2}\rho_{xy}^{2}}{\pi\Gamma(\alpha_{xy}) \Gamma(\beta_{xy})}\), \(A_{xy}=\left(\frac{x_{xy}\theta_{xy}\rho_{xy}^{2}}{4\rho_{xy}^{2}+1}\right)^{2}\), \(D_{xy}=\frac{\rho_{xy}^{2}}{2}\), \(\frac{\rho_{xy}^{2}+1}{2}\), \(\frac{\alpha_{xy}}{2}\), \(\frac{\alpha_{xy}+1}{2}\), \(\frac{\beta_{xy}}{2}\), \(\frac{\beta_{xy}+1}{2}\), \(0\) and \(C_{xy}=1\), \(\frac{\rho_{xy}^{2}+1}{2}\), \(\frac{\rho_{xy}^{2}+2}{2}\).
## 2 Materials and Methods
### Cell Culture Methods  
Caco-2 human adenocarcinoma cells (ECACC 86010202, purchased from Sigma-Aldrich, St. Louis, MA, USA) were cultured in Dulbecco's Modified Eagle's Medium (DMEM) with a 4.5 g/L D-glucose concentration and GlutaMAX addition (Gibco; ThermoFisher; Waltham, MA, USA) supplemented with 10% (\(v/v\)) fetal bovine serum (EurX, Gdansk, Poland) and an antibiotic-antimycotic mixture (streptomycin 100 mg/mL, penicillin 100 U/mL and amphotericin B 250 ng/mL; Biowest, Lakewood Ranch, FL, USA) in standard conditions (37 \({}^{\circ}\)C, 5% CO\({}_{2}\), 98% humidity). HT-29-MTX cells were cultured in the same conditions as Caco-2 cells. For the 2D differentiation experiments, Caco-2 and HT-29-MTX cell suspensions were mixed in a 9:1 ratio and placed in the upper compartments of 12-well 0.4 \(\upmu\)m PET Transwell inserts. The extracts were added to the upper chamber and the media was changed every second day. For spheroid culture, a mixture of Caco-2/HT-29-MTX (9:1 ratio, 40,000 cells/200 \(\upmu\)L of medium with 1% of Martigel(r) Corning, NY, USA) was placed on a Biofloat U-shaped plate (Sarstedt, Germany). The spheroids were formed after 24 h and photographed in the microscope every second or third day for 2 weeks. HepG2 human hepatocellular carcinoma cells (ATCC HB-8065, batch #70039681) were cultured in DMEM medium with a low glucose concentration (1.0 g/L, Gibco; Thermo Fisher; Waltham, MA, USA). Raw 264.7 and P388D1 murine monocytic leukemia cells were grown in RPMI 1640 medium with 10% FBS and the same supplements as for Caco-2 cells.
## 2 Analysis of Related Work
### Overview of ISO/IEC 27001  
The ISO/IEC 27001 standard by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) specifies the requirements for establishing, implementing, operating, monitoring, reviewing, maintaining, and improving information security management system (ISMS) [10]. The security standard assists in the task of developing secure complex systems in the following ways:  
* It gives a step-by-step approach to establish ISMS [10; 11; 12].
* It provides best practice recommendations on information security management, risks, and controls [10].  
In our previous paper, we presented the SysML/UML-based MBSE security profile that conforms to the ISO/IEC 27001 standard [12]. The ISO/IEC 27001 4.2.1 section provides foundational steps for managing risks at a very high level, and these steps are the subject of the system engineering workflow at an early stage. The process that should be captured in the MBSE model is as follows:  
* Define the risk assessment approach of the organization.
* Identify the risks.
* Analyze and evaluate the risks.
* Identify and evaluate options for the treatment of risks.
* Select control objectives and controls for the treatment of risks.  
One of the biggest MBSE returns on investment is that by validating and verifying system characteristics early, it enables fast feedback on requirements and design decisions [5; 6]. This leads to the conclusion that the security solution should be lean, too. The ISO/IEC 27001 standard adopts the \"Plan-Do-Check-Act\" (PDCA) model (Figure 1), which dictates that the ISMS should be continuously improved, and this principle suits the systems engineering process very well [10].  
Our main objective is to develop a feasible and efficient MBSE method for creating secure systems while respecting ISO/IEC 27001 requirements.
# Exploring Multi-Stage GAN with Self-Attention for Speech Enhancement
## 5 Results and Discussion  
This section presents the experimental results and ablation study on the effectiveness of self-attention in the ISEGAN-Self-Attention and the DSEGAN-Self-Attention networks. The results are reported quantitatively (see Tables 1 and 2) and through spectrograms (see Figure 6) to show the quality of the enhanced speech signals produced by the networks. The quantitative results presented in Tables 1 and 2 are based on the averaged objective evaluations of the ISEGAN-Self-Attention and the DSEGAN-Self-Attention, respectively, on the 824 wave files in the test set of the noisy dataset introduced by [PERSON] et al. [50]. In Table 1, we compare the results of the ISEGAN-Self-Attention with the attention blocks at the fourth and fifth convolutional layers of the encoder and the decoder to the baseline results reported in [PERSON] et al. [22; 23] and, likewise, the same for the results of the DSEGAN-Self-Attention network presented in Table 2. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Noisy**} & \multirow{2}{*}{**Weiner**} & \multirow{2}{*}{**SEGAN**} & \multicolumn{6}{c}{**ISEGAN**} & \multicolumn{6}{c}{**ISEGAN-Self-Attention**} \\ \cline{6-11}  & & & & & \(\mathbf{N=2}\) & \(\mathbf{N=3}\) & \(\mathbf{N=4}\) & \(\mathbf{N=2}\) & \(\mathbf{N=3}\) & \(\mathbf{N=4}\) \\ \hline PESQ & 1.97 & 2.22 & 2.19 & 2.34 & 2.24 & 2.19 & 2.21 & **2.66** & 2.58 & 2.59 \\ CSIG & 3.35 & 3.23 & 3.39 & **3.52** & 3.23 & 2.96 & 3.00 & 3.40 & 3.35 & 3.38 \\ CBAK & 2.44 & 2.68 & 2.90 & 3.04 & 2.95 & 2.88 & 2.92 & 3.15 & 3.09 & **3.18** \\ COVID & 2.63 & 2.67 & 2.76 & 2.91 & 2.69 & 2.52 & 2.55 & **3.04** & 2.97 & 3.01 \\ SSNR & 1.68 & 5.07 & 7.36 & 8.05 & 8.17 & 8.11 & 8.86 & **9.04** & 8.90 & **9.04** \\ STOI & 92.10 & - & 93.12 & 93.32 & 93.29 & **93.35** & 93.29 & 93.30 & **93.35** & 93.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison between ISEGAN-Self-Attention and the baseline methods based on the objective evaluation metrics. The highest score per metric is highlighted in **bold**, and the second best is underlined. As expected, the objective evaluations of both the ISEGAN-Self-Attention and the DSEGAN-Self-Attention networks showed an improvement over the ISEGAN and the DSEGAN without self-attention in all the speech quality metrics. For example, when \(N=2\), the ISEGAN-Self-Attention network achieved average improvements of 18.75%, 6.78%, and 10.64% in PESQ, COVL, and SSNR, respectively, over ISEGAN without self-attention; with \(N=4\), average improvements of 8.90% and 0.03% in CBAK and SSNR, respectively, were also achieved (see Table 1).
## 2 Basic Principles
### Physics-Informed Neural Network  
The PINN comprises two main components, i.e., an artificial neural network (ANN) and a physics-informed loss function. To date, many different types of ANNs have been proposed for use in different research tasks and each of them has differed in terms of structural design [35]. An ANN is a type of artificial intelligence which is inspired by the structure of biological neural networks. It is composed of interconnected neurons which process and transmit data to produce one or more output values. A typical ANN structure consists of an input layer, hidden layers, and an output layer. The input layer is responsible for receiving data, the hidden layers process and transform the data, and the output layer produces the output. The weights of the connections between the neurons are automatically adjusted by the optimizers based on the input data, allowing the ANN to \"learn\" as it processes more and more data. Figure 1 shows a typical ANN structure with \(\mathbf{x}\) and \(\mathbf{u}\) as the input and output, respectively. The mark \(A[n]\) in the figure represents the \(nth\) layer of the ANN. More details about ANNs can be found in [36].  
The stress and strain tensors are considered symmetrical in a two-dimensional solid mechanics problem; therefore, the physical variables that must be solved in a two-dimensional mechanics problem are \((u_{x},u_{y})\) and \((\sigma_{x},\sigma_{y},\sigma_{xy})\). In finite element analysis, the displacement and stress methods are two of the potential means of solving solid mechanics problems. Due to its convenience of usage and suitability for different kinds of boundary conditions, the displacement method is typically considered the first choice and as such has been widely used in many examples of commercial finite element analysis software, i.e., ABAQUS and ANSYS. To be consistent with the finite element method, the coordinates \((x,y)\) here are  
Figure 1: A physics-informed neural network containing two main components: an artificial neural network and a physics-informed loss function.  
treated as the input variables, and the variables of (\(u_{x}\),\(u_{y}\)) were selected as the output; thereafter, the remaining variables of interest, i.e., \(\sigma_{ij}\), \(\epsilon_{ij}\), can be directly calculated via differentiation.
# Fatigue Assessment of Prestressed Concrete Slab-Between-Girder Bridges
## 3 Results
### Assessment of the Case Study Bridge for Punching
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Number** & \begin{tabular}{c} \(V_{exp}\) \\ **(kN)** \\ \end{tabular} & \
\begin{tabular}{c} \(V_{BB,d}\) \\ **(kN)** \\ \end{tabular} &
\begin{tabular}{c} \(V_{Ed}\) \\ **(kN)** \\ \end{tabular} & \(V_{BB,d}/V_{Ed}\) \\ \hline BB1 & 348.7 & 1162.3 & 721.9 & 236.0 & 3.06 \\ BB2 & 321.4 & 1071.3 & 665.4 & 236.0 & 2.82 \\ BB7 & 345.9 & 1153.0 & 716.1 & 236.0 & 3.03 \\ BB19 & 317.8 & 1059.3 & 658.0 & 232.0 & 2.84 \\ FAT1S1 & 347.8 & 1159.3 & 720.1 & 232.0 & 3.10 \\ FAT7S1 & 393.7 & 1312.3 & 815.1 & 232.0 & 3.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Determination of the safety factor for the deck of Van Brienenoord Bridge. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Number** & \begin{tabular}{c} **Wheel Print** \\ **(mm \(\times\) mm)** \\ \end{tabular} & \
\begin{tabular}{c} \(V_{exp}\) \\ **(kN)** \\ \end{tabular} &
\begin{tabular}{c} \(V_{R,c}\) \\ **(kN)** \\ \end{tabular} & \(V_{exp}/V_{R,c}\) \\ \hline BB1 & \(200\times 200\) & 348.7 & 141.9 & 2.458 \\ BB2 & \(200\times 200\) & 321.4 & 141.9 & 2.266 \\ BB7 & \(200\times 200\) & 345.9 & 141.9 & 2.438 \\ BB19 & \(115\times 150\) & 317.8 & 121.6 & 2.613 \\ FAT1S1 & \(115\times 150\) & 347.8 & 124.4 & 2.795 \\ FAT7S1 & \(115\times 150\) & 393.7 & 127.4 & 3.091 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between the mean predicted punching capacity and punching capacity in experiment.
# Evolution and Testing of NB-IoT Solutions
## 4 NB-IoT Evolution
### UE Positioning Accuracy Enhancements for LTE (Release 14)  
There are many use cases where positioning brings significant value into the IoT space. In addition to tracking sensors and meters, locating wearables, vehicles, and goods is also of interest. In 3 GPP Release 14, there has been a significant effort to add positioning for IoT. Although NB-IoT is assumed to cover the lowest complexity use cases, this feature opens new possibilities.  
NB-IoT positioning is based on the same concept used for LTE positioning named Observed Time Difference Of Arrival (OTDOA). Specific radio signals named Narrowband Positioning Reference Signals (NPRS) are transmitted by multiple cells in configured periodic time opportunities. As shown in Figure 4, the device to be tracked measures and reports the relative timing of the signal received from each monitored cell.  
The LPP (LTE Positioning Protocol) core network protocol [13] is used for the exchange of information between the IoT devices and the E-SMLC (Enhanced Serving Mobile Location Centre), which is a dedicated core network entity for positioning functions. The network can also make use of power (RSRP) and quality (RSRQ) measurement reports from the devices, and take benefit from the timing advance measurements when the devices are active as additional sources of information.  
In more technical detail, the new Narrowband Positioning Reference Signals (NPRS) are defined in [14]. As also shown in Figure 5, different resource elements are used with incremental density depending on whether the signals are transmitted in-band 2 port, in-band 4 port, or in standalone and guard-band modes. Depending on the cell identity, up to six different pattern shifts are possible by applying offsets in frequency. It is important to note that NPRS patterns can be flexibly configured, and different neighbor cells could transmit simultaneously or interleaving/selectively muting patterns in time for reduced interference at the expense of larger acquisition periods. More information about positioning can be found in [15].  
Figure 4: OTDOA position tracking in NB-IoT.
# Air-Polishing Powders' Effect on the Color of CAD/CAM Restorative Materials
## 4 Discussion
In this in vitro study, it was observed that the APPs, which are used to remove stains, themselves cause discolorations on the restorative materials. Although they changed the color of the restorative materials within 50:50% acceptable limits, the majority of them exceeded the 50:50% perceptible limits, which means that 50% of the observers could notice the color difference [35]. Therefore, considering the findings of this study and the previous studies, regardless of the APP used, clinicians should consider avoiding or reducing the use of APPs on CAD/CAM restorative materials during supportive periodontal therapy, since they may have doubts about which restorative material they are facing intraorally. Another feasible method is to repolish the restorations after air-polishing, either with the finest polishing pastes and rubber cups or according to the manufacturer's instructions, if the restorative material is known, to smoothen the surface as much as possible and thereby sustain color stability [13; 28; 31]. There are some limitations to this study. Primarily, in vitro studies do not demonstrate the intra-oral environment completely in terms of coloring beverages, the washing effect of saliva, habits like smoking, brushing the teeth, thermal alterations, etc., which may affect the color change. In addition, the samples were not subjected to artificial aging because this study aimed to evaluate the effect of APP, not aging. Secondly, intra-oral feldspathic ceramic restorations are glazed, but most restorative materials used in this study did not require glaze as the manufacturers instructed, and in order not to add another variable to this study and to standardize the study protocol, the specimens were not glazed, but they were all mechanically polished with a diamond polishing solution. Further laboratory and clinical studies, without such limitations, should be executed to confirm the findings of this study. Although combining variables will not provide information on the effect of any single variable, aging and glaze can be added as variables to simulate the intraoral conditions better. In addition, this study analyzed only the color change and the SEM micrographs for surface analysis, but the surface roughness and gloss should also be investigated with a combination of repolishing procedures. Moreover, the influence of different APPs to CAD/CAM blocks stored in staining solutions such as coffee, coke, and black tea can also be researched in future studies in terms of optical and surface properties.
# An Overview of CubeSat Missions and Applications
## 9 Review Conclusions and Discussion
### CubeSats as Technology Demonstration Platforms
These technological convergences have effectively raised the ceiling on CubeSat capabilities while simultaneously lowering the barrier to entry for organizations developing sophisticated space missions.
# System Architecture Design of IoT-Based Smart Cities
## 6 Case Study Research
### Case Study-Smart Charging
The infrastructure cost includes setting up a centralized management system that can monitor and control the charging stations remotely. This system requires a reliable and robust network infrastructure to ensure the charging stations' real-time data are transmitted and processed efficiently. Additionally, maintaining the charging stations and the central management system requires ongoing upkeep, such as regular software updates, technical support, and hardware repairs. These costs can be offset by revenue generated from charging fees and other value-added services, such as advertising and data analytics.
## 4 Discussion  
### Main Findings  
Our results show that the implementation of a personalized patient pre-alignment strategy based on information retrieved from previous treatment fraction could lead to a reduction of up to around 46% in the magnitude of the CVs needed to adjust the final patient position before treatment. It was also demonstrated that pre-correcting translational components has, on average, a higher benefit on the magnitude of the correction vector than correcting rotational ones.