esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Description of the Research Methodology
### Characteristics of the Experimental Sections of the Streets and the Equipment Used
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Name of the Street** & **Description** & \multicolumn{2}{c}{**Number of Lanes,**} \\  & & & **Total Width of the Roadway** \\ \hline Station 1 & Ave. Heroiv & Main street; one-way traffic; there is no & \\  & Kharkova & pedestrian traffic; there is no development & 3 lanes, \\  & & in the vicinity of the roadway; green spaces & 9.0 m. \\  & & on the right side of the road & \\  & & Main street; two-way traffic; there is no & \\ Station 2 & Ave. Traktorobudivnykiv & pedestrian traffic; there is no development & 4 lanes (2 in each direction), \\  & & in the vicinity of the roadway; green spaces & 12.3 m. \\  & & on the right side of the road & \\  & & Local street; one-way traffic; sidevalks on & \\ Station 3 & St. 12-ho Kvitnia & both sides of the street; there is no & 2 lanes, \\  & & construction in the vicinity of the roadway; & 8.6 m. \\  & & green spaces on both sides of the street & \\  & & Local street; two-way traffic; the sidewalk & \\ Station 4 & St. Myru & of one street; there is no construction in the & 2 lanes (1 in each direction), \\  & & vicinity of the roadway; green spaces on & 6.9 m. \\  & & both sides of the street & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of road conditions at speed measurement areas. Figure 1: Location of measuring stations on the city map. During the measurement, the radar was directed at a single-passenger car moving along the road section at a distance of 80 to 100 m. At the same time, the operator tried to stand as close as possible to the roadway in order to reduce the measurement error due to the sine effect. In the process of working with the radar, it was recorded that some drivers began to reduce their speed when an operator with the device pointed at them came into their field of vision, so it was decided to record the speed of cars when they moved away from the operator. The speeds of passenger cars were measured, the number of which is more than 90% of the traffic flow. The sample size was 50 cars for each section. In order to match the driving mode of the car whose speed is measured to the conditions of free movement, single vehicles or those that are the leaders of a pack of cars are selected.
# Prediction Models for Diabetes in Children and Adolescents: A Review
## 3 Prediction Models for Type 2 Diabetes (T2D)
### Socieconomic Status  
Research indicates significant socioeconomic disparities in children and adolescent T2D prevalence depending on the country. In the United States, 23.6% of adolescents with T2D belong to a high socioeconomic status (SES) group, while in India, the figure reaches 88.5% [26]. A study conducted in England and Wales between 2009 and 2016 on children and youth under 19 with T2D found that less than half lived in the most disadvantaged areas [36]. Low SES is a well-documented risk factor for obesity and T2D, primarily due to limited access to nutritious foods and lower levels of physical activity. In contrast, in developing countries, higher SES groups tend to engage in less physical activity and consume greater amounts of fat, salt, and processed foods compared to those with lower SES [51]. It is especially seen in populations in urban areas in comparison to rural areas: the annual incidence of T2D in urban areas is twice as high as in rural areas [12]. In economically thriving regions undergoing industrialization and urbanization, dietary shifts and declining physical activity have contributed to increased pediatric obesity rates, which is a recognized risk factor for T2D. In 2019, research concluded that low-middle and middle Socio-Demographic Index (SDI) countries had the highest incidence and disability-adjusted life year (DALY) rates, while low-SDI countries exhibited a lower incidence but higher mortality rates [14; 44]. More recently, the incidence and burden of T2D have risen among children and adolescents, and young adults, particularly in lower socioeconomic countries [74]. A significant proportion of youth with T2D live below the poverty line or come from low-resourced homes [65]. Socioeconomically disadvantaged youth are disproportionately affected by T2D, further exacerbating health inequalities [47].  
Beyond obesity as the leading global risk factor for T2D [45], regional socio-demographic factors also play a role, which is influenced by SES and SDI. Household air pollution and low fruit intake are prominent in low-SDI regions, while ambient air pollution and smoking are significant in high-SDI regions. The rising burden of early-onset T2D underscores the need for targeted interventions based on socio-demographic contexts. Without action, disparities will worsen, leading to poorer outcomes. Urgent implementation of predictive models and holistic prevention strategies is crucial, alongside better youth engagement and addressing intergenerational risk factors to reduce the growing burden.
## 4 Experimental Results
### Single Factor Covariate Analysis for Post-Test  
In the previous part of the statistical analysis, the results were used as covariates. The post-test scores of both groups were used as the variables for the effectiveness check. The independent sample single factor covariate analysis was performed. The statistical analysis used a significance level setting of 0.05 (within a 95% confidence interval). As can be seen from Table 4, the results of the single factor covariate analysis showed that the experimental and control groups had significant differences in the post-test results (\(F=11.588\), \(p<0.001\)). These results indicate that the learning performance of test group students who were taught using AR teaching videos was better than the control group students, who were taught using a traditional teaching approach. The difference of learning effectiveness between test and control groups (i.e., research hypothesis 1) has thus been proved.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test Item** & **Source** & **SS** & **df** & **MS** & **\(F\) Value** & **Significance** \\ \hline Post-test & \(\text{SS}_{\text{B}}\) & 5933.105 & 1 & 5933.105 & 11.588 & 0.002 ** \\ Difference between & \(\text{SS}_{\text{W}}\) & 19,455.870 & 38 & 511.997 & & \\ groups & \(\text{SS}_{\text{T}}\) & 25,388.975 & 39 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: One-way ANOVA summary table for two groups of learning outcomes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
\begin{tabular}{c} \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the testing whether the learning ability of both groups was the same and, thus, was comparable. This test was conducted in the pre-test stage of the experiment, in order to test whether the groups were comparable and showed similar learning ability. The results show no significant difference between groups.
# PTMD Control on a Benchmark TV Tower under Earthquake and Wind Load Excitations
## 6 Vibration Control with PTMD
### Parameter Studies  
In order to investigate the influence of the gap value on the control effectiveness, the peak and RMS displacement responses along the \(x\) direction are listed in Table 6 and Figure 15, with the gap value selected from 0.005 m to 3 m. The maximum gap is chosen with reference to the maximum relative displacement between the mass and the connected point during TMD cases' simulations. As listed in the table, when the gap becomes larger, the control effectiveness is approaching the TMD-controlled results, which means bounding can hardly happen or not enough impact energy is consumed. Along with the decreasing of the gap value, the control results improved until the gap reaches a certain value and starts to degenerate. This phenomenon arises from the fact that even though the gap value is small enough to constantly induce impact, the relative velocity before bounding is not large enough to dissipate more energy and yield deformation of the VE layer. In addition, the movement of the mass will also be confined by the small gaps and, thus, the mass will possess less kinetic and potential energy and reduce the control effectiveness. By fixing the gap value to 0.05 m, the influence of the mass ratio can be examined (Table 7). As seen in the table, both the TMD and PTMD systems tend to perform better with a larger mass ratio. However, the performance of the TMD varies significantly with the changing mass value and the excitations. Results show that the TMD can perform well under the El Centro excitations--both the peak and the RMS responses can have a reduction over 40%. In the Tianjin earthquake excitation cases, even though the mass of the TMD increases from 200 t to 500 t, the control effectiveness of the peak responses can only increase from \(-\)0.28% to 12.65%. On the contrary, promising results in which less variation are observed in the PTMD-controlled cases with the changing value of the mass ratio. The control results on the peak and the RMS varies little and remains above 50% in every cases. These results also indicate that the PTMD is able to achieve better control results with a lower mass ratio, which will result in easier installation and lower cost of the whole controlled system. In addition, the trend of the PTMD control results presented with respect to the variation of the intensity of the earthquake input clearly shows better improvement on the control efficiency under extreme excitations.
# Hybrid RF/VLC Network Architecture for the Internet of Things
## 4 System Design
### ZigBee Integration  
The ZigBee protocol uses the packet structure defined in the IEEE 802.15.4 standard (Figure 6). This implementation of IEEE 802.15.4 defines both PHY and MAC layers, and ensures the transmission between two endpoints with known MAC addresses. In this work, there is an asymmetrical network knowledge, as has been already explained. However, a specific HNF purpose to enable communication between nodes within the same ZigBee network has been defined.  
This purpose is the Data Frame (0x03), which concatenates the destination node ID and a variable length data field (1-47 bytes). Figure 7 graphs an example frame exchange diagram. In this figure, initial MAC request and MAC response packets have been included to illustrate the coordinator's address discovery procedure.  
In its current version, the system does not support ZigBee data exchange between nodes belonging to different subnetworks. However, this capacity could be easily introduced to modify the HNF frame by indicating not only the type of destination network but also a sub-network identifier (or a hybrid network address). Since the objective of this work is designing and implementing a multi-technology IoT application with a clearly defined backbone (LoRaWAN), this additional use case has not been considered.
# Effects of Resonant Electromagnetic Fields on Biofilm Formation in _Pseudomonas aeruginosa_
## 2 Materials and Methods
### Microbiological Growth and Biofilm Assay
#### 2.3.3 Assay for Biofilm Adhesion to Polystyrene Pegs  
The ability of the PAO1 strains to adhere to and form biofilms on the polystyrene pegs was analyzed by crystal violet staining, as reported previously [27]. Briefly, bacteria were allowed to growth for 20 h in the peg-lid static system. Hereafter, the peg-lid was transferred to a \"washing microtiter plate\" with 180 \(\upmu\)L saline solution (PBS) to wash the pegs and remove non-adhering cells. The peg-lid was then transferred to a microtiter plate with 160 \(\upmu\)L 0.1% crystal violet (CV) and left to stain for 20 min. To remove unbound crystal violet, the lids were then washed three times in three individual \"washing microtiter plates\" with 180 \(\upmu\)L PBS. Bacterial adhesion to the peg-lid was measured by detaching adhering CV-stained cells through placing the peg-lids in a microtiter plate containing 180 \(\upmu\)L 99% ethanol. A plate reader (Synergy H1, BioTek, Agilent, Santa Clara, CA, USA) was then used to measure the CV optical density at OD590.
## 2 Mechanical Properties of SFRC Exposed to High Temperature
### Stress-Strain Relationship  
The mechanical response of concrete is typically expressed by the stress-strain relationship, which is important for determining concrete performance in the design process of the concrete structure. To reasonably assess the fire resistance of SFRC structures, the stress-strain relationship of SFRC after being subjected to high temperatures has been studied. It has been reported [36; 37; 42; 44; 70; 78; 79] that with the increase in temperature, the slopes of the stress-strain curves of SFRC tended to be gentle in both the pre-peak and post-peak stages, the peak stress moved downward and rightward, and the curves became flatter. The stress-strain curves of concrete reinforced by different steel fiber contents (0%, 1%, and 2%) after exposure to different temperatures are shown in Figure 9. It is clear that the peak stress decreases as the temperature increases gradually, whereas the strain at the peak stress increases with temperature. [PERSON] et al. [36] and [PERSON] et al. [42] reported that the shape of the stress-strain curve of SFRC beyond elevated temperatures changed significantly owing to the presence of steel fibers. Under the same heat treatment conditions, the stress-strain curves of SFRC exhibit a more flattened descending branch and a larger area that is surrounded by the stress-strain curve and coordinate axis, compared with plain concrete. [PERSON] et al. [78] discovered that the area under the stress-strain curve increased gradually with the dosage of steel fiber. The energy absorption capacity (toughness) of concrete is defined as the area under the stress-strain curve calculated to a specified strain value, which is the maximum strain value in the stress-strain curve [80; 81]. The addition of steel fiber improved the toughness of heated concrete significantly.  
Figure 8: Effects of exposure temperature and steel fiber content on unstable fracture toughness [13].  
Figure 9: Stress–strain curves of SFRC exposed to different temperatures with steel fiber dosage of 0% (**a**), 1% (**b**) and 2% (**c**) [44].  
In this part, the residual mechanical properties of SFRC exposed toa high temperature were described, and a detailed description of the effect of steel fiber on the compressive strength, flexural strength, tensile strength, elastic properties, fracture properties, and stress-strain relationships of heated concrete was provided. When the maximum exposure temperature was less than 800 \({}^{\circ}\)C, the residual mechanical properties of SFRC can still be better maintained owing to the inherently high melting temperature and bridging crack effect. Beyond 800 \({}^{\circ}\)C, the effect of steel fibers is weakened gradually owing to oxidation and corrosion. In addition, the residual mechanical properties of SFRC are affected by the volume content, physical parameters (aspect ratio and geometric type) of steel fibers, and the temperature history of the sample (heating rate and cooling system).
# Experimental Demonstration of a Multichannel Elastic Wave Filter in a Phononic Crystal Slab
## 2 Theory and Experimental Methods
The _Bloch-Floquet_ approach is considered as follows:  
\[u_{i}(x+a\cdot m,y+a\cdot n,z)=u_{i}(x,y,z)e^{-u\cdot(k_{x}m+k_{y}n)} \tag{1}\]  
where \(k_{x}\) and \(k_{y}\) are the components of the _Bloch_ wave vectors along the \(x\) and \(y\) directions. While \(u_{i}\) is the displacement with \(i=x\), \(y\) and \(z\). The _Bloch_ boundary conditions describe the phase relation of propagating wave on the boundaries with Bloch wave vector \(k\). The displacement from one boundary to the other boundary in the directions \(x\) and \(y\) can therefore be written as: \(\mathbf{u}(x)=e^{-\imath(ak_{x}m)}\mathbf{u}(x)\) and \(\mathbf{u}(y)=e^{-\imath(ak_{y}n)}\mathbf{u}(y)\) respectively. The equation for the case of harmonic homogeneous dynamical equilibrium, however, takes the partial derivative as the following forms. \[\rho\omega^{2}\mathbf{u}+\
abla[\mathbf{C}\cdot\varepsilon]=0 \tag{2}\]  
where \(\rho\) is the mass density, \(\omega\) is the angular frequency, \(\mathbf{u}\in\mathrm{I\!R}^{3}\) is the displacement vector, \(\mathbf{C}\) is the elastic tensor and \(\varepsilon=\
abla_{sym}\mathbf{u}=\frac{1}{2}\left[(\
abla\mathbf{u})^{T}+ \
abla\mathbf{u}\right]\) is the strain tensor. Equation (2) combined with _Bloch-Floquet_ theorem of Equation (1) have been solved using FEM. The eigenvalues are plotted usually against two wave vector parameters, \(k_{x}\) and \(k_{y}\), the taken value in the interval \(\left[0,\ \ \frac{\pi}{a}\right]\) of the first Brillouin zone.
# A VHF Band Small CRLH Antenna Using Double-Sided Meander Lines
## 3 Fabrication and Measurement
Figure 9: Receiving power measurement setup of the proposed antenna. Table 3 compares the commercialized military antenna and the previous CRLH transmission line antenna with the proposed antenna in this paper. The commercialized military antennas have high gain but are limited in the operation of communication equipment due to the antenna length of 1-2 m. Moreover, the CRLH transmission line antenna studied previously has an extremely low gain. However, the proposed antenna in this paper has a miniaturized antenna size in the VHF band and shows that it can be used as a receiving antenna.
## 2 Predictive Model Based on ESN
### Network Architecture  
The network structure of an ESN model is shown in Figure 1. It consists of an input layer, a state reservoir and an output layer. Its core is a state reservoir composed of randomly and sparsely connected neurons. On the one hand, the reservoir can be regarded as a high-dimensional nonlinear expansion of the input signal. On the other hand, it has the ability of short-term memory. Under the action of external input, the dynamic driving system of \"input state output\" is formed. In the training process, we only need to learn the weights from the reservoir to the output layer by solving some linear regression equations.  
Suppose the input to be \(u(n)\in\Re^{N_{u}}\), the output to be \(y(n)\in\Re^{N_{y}}\), the number of units in the reservoir as \(N_{x}\) and the objective of an echo state network as RMSE (i.e., root of mean squared error), that is,  
\[E(y,y^{target})=\frac{1}{N_{y}}\sum_{i=1}^{N_{y}}\sqrt{\frac{1}{T}\sum_{n=1}^{ T}\left(y_{i}(n)-y_{i}^{target}(n)\right)^{2}}, \tag{1}\]  
where \(n=1,2,\cdots,T\) are discrete time points and \(T\) indicates the number of training samples.  
The state equation in a ESN can be expressed as  
\[\widetilde{x}(n)=\tanh\Bigl{(}\mathbf{W}^{in}[1:u(n)]+\mathbf{W}x(n-1)+\mathbf{W}^{fb}y(n- 1)\Bigr{)}, \tag{2}\]  
\[x(n)=(1-\alpha)x(n-1)+\alpha\widetilde{x}(n). \tag{3}\]  
Here, \(x(n)\in\Re^{N_{x}}\) is a unit in the reservoir and \(\widetilde{x}(n)\) indicates its update. The function \(\tanh(\cdot)\) is the hyperbolic tangent activation function. The symbol \([:]\) represents the concatenation of vectors or matrices in rows, \(\mathbf{W}^{in}\in\Re^{N_{x}\times(1+N_{u})}\) is the weight matrix which connects input units and the units in the reservoir, and \(\mathbf{W}\in\Re^{N_{x}\times N_{x}}\) is the weight matrix for the reservoir. These weights are all stochastically generated and kept unchanged in the learning process. The parameter \(\alpha\in(0,1]\) is a shrinkage parameter which can be explained as the updating speed of the reservoir. The smaller \(\alpha\) is, the longer historical memory is stored in the network. In order to decouple the relationship between reservoir and output, output learning is regarded as a feedback task by teacher forcing. Thus, \(\mathbf{W}^{fb}\) is the feedback connection weight from \(y(n-1)\) to \(\widetilde{x}(n)\). Obviously, the output feedback enhances the expression ability of the reservoir because it no longer simply relies on the  
Figure 1: The architecture of an echo state network.  
randomly generated input drive to construct the output. Additionally, it can dynamically adapt to the target task. Nevertheless, the output feedback often brings about the stability problem of the network. Hence, the output feedback connection is not introduced in the pitch motor early warning task in this paper.  
The output of the network is  
\[y(n)=\mathbf{W}^{out}[1;u(n);x(n)], \tag{4}\]  
where \(\mathbf{W}^{out}\in\Re^{N_{y}\times(1+N_{u}+N_{x})}\) is the output weight matrix. Similarly, a nonlinear transformation can be applied to Equation (4) by an activation function.
## 4 Threat Model for a Blockchain-Based Supply Chain System
### Security Requirements for Supply Chain Systems  
With recent advent of technologies like industrial IoT and 5G, modern supply chain management systems aim to digitally co-ordinate all sub-processes of supply chain from supplying products to delivering and/or resumption of the products. We considered the reliability of such systems as the pre-requisite of security. Accordingly, we scrutinize transparency, traceability, and privacy as the key requirements for the security of supply chain systems. _Transparency_ guarantees a transparent co-ordination among all sub-processes, which helps in immediate identification of inconsistencies or conflicts between any of the two sub-processes, in order to ensure timely supply of the product on one hand and to save the cost (e.g., logistics) on the other hand. _Traceability_ demands sufficient information recordings that can perfectly facilitate in tracing the product and interactions among entities, and performing auditing at later stages. Finally, preserving the _privacy_ of sensitive organizational information is important. Privacy can be archived by protecting the co-ordination between processes and communication between involving parties from possible security threats.  
In recent developments, blockchain has been used in supply chain in different ways to handle security and privacy issues [21; 22; 23; 24; 25; 26]. Importantly, blockchain integrated supply chain solutions are good in providing security and privacy of data that does not allow to modify the data records or to misuse the data. Also, such solutions facilitate audit for security and privacy violations as it has full trail of interactions among communicating parties.  
While such solutions are good at providing protection of data it does not help to detect any security threat (as discussed in Section 4) that arises from the computational and communication process. In detail, such solutions fail to detect any threat in real-time that occur when a computation or a communication goes wrong, such as the execution of a smart contract is compromised, or a communicating party gets compromised and shares undesired data, respectively.  
In this survey, we investigated state-of-the-art blockchain-based supply chain management-proposals, considering three security requirements of transparency, traceability, and privacy. In addition, we analysed the existing solutions based on the thread model presented in Section 4, including the types and sources of attacks. We classify efforts of building a secure blockchain-based supply chain system into two categories as academic and industrial. The Academic efforts and the industrial efforts are individually discussed in the following sections.