esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### Eligibility Criteria  
There were several criteria identified for the inclusion of studies in the review. Studies included in the review were those that (a) were published between January 2006 and September 2021 and (b) included child participants or voices of children aged between 2-16 years old. Selected studies were not limited by design or language, and grey literature was included to ensure a comprehensive review. According to [PERSON] et al. [33], a scoping review outlines the research to date in a particular field of study, ultimately summarising the research findings and identifying the gaps in the literature. Although the studies may be identified systematically, a scoping review aims to identify all the relevant literature, no matter the study design and/or study quality. We chose to exclude studies older than fifteen years as speech-synthesis technology has substantially changed since then, and the voice output in SGDs often yielded poor results pre-2006, particularly considering speech naturalness and intelligibility. For example, [PERSON] et al. [34] reported that genuine speech was more intelligible than synthesised speech in 1987, whereas in 2020, [PERSON] et al. [14] stated that state-of-the-art text-to-speech systems have the capability to produce synthetic speech that is perceptually indistinguishable from genuine speech by human listeners. Thus, we included current speech-synthesis systems for ecological validity and because older systems were often less intelligible, which made it challenging to generalise and compare the study results to newer systems. Finally, we chose children aged between 2-16 years to reflect the typical school-age population that would likely make use of SGDs.
## 4 Performance Evaluation of Post-Installed Anchors
### Dynamic Loading Test
#### 4.2.4 Pull-Out Performance Evaluation  
In the case of the M10 set anchor, a pull-out failure of the steel anchor occurred. The pull-out loads in the cracked concrete specimens were smaller than those in the non-cracked concrete specimens. In the case of the M12 set anchor, a concrete cone failure occurred as in the static pull-out tests. As with M10, the pull-out loads in the cracked concrete specimens  
Figure 16: Comparison results of the dynamic shear tests for M10 anchors: (**a**) non-cracked concrete; (**b**) cracked concrete.  
Figure 17: Load–displacement curve of dynamic shear tests for M12 anchors.  
Figure 18: Comparison results of the dynamic shear tests for M12 anchors: (**a**) non-cracked concrete; (**b**) cracked concrete.  
were smaller than those in the non-cracked concrete specimens. It was found that the M10 set anchor exhibited a failure of the anchor itself before the concrete-anchor bond failure, owing to the relatively smaller diameter. When the maximum loads of the dynamic pull-out tests were compared with those of the static tests, the maximum pull-out loads under dynamic loads were found to be higher. This appears to be because the clearance between the concrete and the set anchor, which existed at the time of the set anchor installation, contributed to the concrete-anchor frictional force that was generated by vibration under the application of cyclic loading.  
Table 9 summarizes whether the pull-out maximum loads generated during the tests met 80% of the static pull-out performance (seismic performance verification criterion).  
* DPN10 and DPC10  
Figure 19 shows the load-displacement curves, and Figure 20 shows an average summary of the values for each case-specific dynamic pull-out test. As the figure shows, the dynamic pull-out performance of the M10 anchors does not met the seismic performance verification criteria (horizontal lines) for cracked concrete. Based on this, the set anchors of M10 are not considered suitable for anchors of fixed parts.  
* DPN12 and DPC12  
Figure 21 shows the load-displacement curves, and Figure 22 shows an average summary of the values for each case-specific dynamic pull-out test. As shown in the figure, both cracked and non-cracked concrete met the seismic performance verification criteria for the M12 anchor, as shown in the M10 anchor.  
Figure 21: Load–displacement curve of dynamic pull-out tests for M12 anchors.  
Figure 22: Comparison results of the dynamic pull-out tests for M12 anchors: (**a**) non-cracked concrete; (**b**) cracked concrete.
## 4 Discussion
### Limitations and Future Recommendations  
Although this study seems to have found positive effects of a trampoline-based training program for children with ASD, several limitations were observed. The variation in the individual abilities of the children with ASD may have influenced the results, as it made it difficult to establish a uniform prescription and implementation of the program. Additionally, the duration of the study may not have been sufficient to observe significant changes in all areas of motor development. The adherence of the children to the training program also varied, which impacted the program's effectiveness. Finally, the absence of a control group limited the ability to directly attribute the observed changes to trampoline intervention.  
The exploration of different sports modalities for children with ASD, as well as the development of specific interventions aimed at promoting the motor skills of these children,could be considered as future recommendations. For future studies, it is recommended to expand the sample to include other age groups, establish a control group for the comparison of effects, and extend the duration of the study. Furthermore, personalizing the training program, assessing multiple dimensions of development, and using standardized assessment tools are essential for obtaining a more comprehensive understanding of the program's benefits. It would also be interesting to include other modalities with different motor stimuli in future studies.  
In addition to highlighting the theoretical importance of the study's results, it is crucial to discuss their practical implications for health professionals, educators, and the families of individuals with ASD. This may include how the results of the study can influence clinical practices, therapeutic interventions, or public policies regarding the promotion of physical activity.
# Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant
## 2 Background
### Graph Machine Learning and LLMs  
The convergence of graph machine learning (GML) and LLMs represents a transformative shift in how structured data is utilized in reasoning systems. As reviewed by another study, GML frameworks are increasingly enhanced by LLM capabilities, either by serializing graphs into tokens for graph-aware language modeling (e.g., GraphGPT), or by prompting LLMs to generate or reason over subgraphs in downstream tasks like link prediction, node classification, or evidence retrieval [31].  
This synergy is increasingly bidirectional. While GNNs enhance LLM-based models with inductive bias and topological priors, LLMs have proven effective at improving feature quality, data augmentation, and semantic alignment in graph-based tasks. In return, graphs help LLMs address limitations, such as hallucination, poor long-range reasoning, and lack of factual grounding, features especially important in regulated sectors like public administration and medicine [25].
## 2 Experimental Set Up
### Validation  
The extent of the water surface during a flood process can have daily changes. It is almost impossible to obtain an accurate map of inundation regions on a particular day. In general, most of the flood products are a rough outline of the main inundated areas. In order to achieve the qualitative and quantitative evaluation, we produce two approximate reference maps for the first and second case studies. Either of them is based on a remote sensing image over the same site and taken on the same date as the corresponding experiment data. The spatial resolution of the data used for generating the reference map is necessarily higher than that of the experiment data. For the first case study using HJ-1A/B CCD data, there is a scene of GF-1 WFV data that can meet the requirements. The technical parameters of the GF-1 WFV data are listed in Table 3. For the second case using GF-4 PMS data, no corresponding GF-1 WFV data could be found. Instead, we find a scene of HJ-1B CCD data that is qualified. The technical parameters of the HJ-1B CCD data can be found in Table 1.  
For the process of how the reference map is made, we use a traditional water extraction method. We take the first case study as an example. First, the selected GF-1 WFV image is geometrically registered to the experimental HJ-1A CCD data. Then, we calculate the NDWI of the GF-1 WFV image. Compared with the ground information from Google Earth software, we manually select a threshold in NDWI to separate water and non-water pixels. Finally, the binary water mask is resampled to the spatial resolution of HJ-1A CCD data (30 m). Similar processes are applied to the second case study. Given that there is no detailed ground truth available, and that it is not feasible to get one by field investigation, we use the reference map in this study as an approximation of the real inundated extent, helping to evaluate and compare the detection results qualitatively and quantitatively.
## 3 Results
### Analysis of the Point Cloud Horizontal and Vertical Positional Precision for Flight A

Table 8: Quantitative measures for the horizontal position differences of points within the five regions of interest for flight A after adjusting for the heading bias. Figure 15: Horizontal position differences between the single and dual-antenna generated point clouds for the five regions of points of flight A after adjusting for the heading bias. Equation (11) indicates that heading precision has no effect on the vertical coordinate of an observed lidar point. Therefore, the vertical position differences between the single and dual-antenna point clouds within any of the five regions would be zero. An inspection of Figure 16 indicates that the vertical position differences for points within the left, right, and middle regions are approximately zero, but the points within the top and bottom regions have opposite mean vertical position differences of 0.015 m and \(-\)0.013 m, respectively. As the top and bottom regions closely align with the direction of travel of the Mid-40 lidar sensor as shown in Figure 11, the vertical position differences are suspected to be a result of a small relative bias in the single-antenna GNSS/INS trajectory pitch estimates compared to the dual-antenna solution. Table 9 presents quantitative measures for the vertical position differences between the point clouds within the five FoV regions for flight A.
# Optical Beamforming Networks for Millimeter-Wave Wireless Communications
## 2 Principles
### Principle of Phase Shifter Architectures  
Typically, the signal processing of phase shifter architectures is characterized by the heterodyne detection of coherent optical carriers. For example, two coherent optical carriers can be produced by two phase-locked lasers, respectively, as plotted in Figure 3. The first optical carrier from one laser is modulated by an electrical signal (RF1), and then the phases of the optical carrier and its modulation sidebands are shifted by the phase shifter or TTD; next, the optical carrier and one sideband are filtered out. The second optical carrier goes directly to the PD and combines with one sideband of the first optical carrier, generating a new electrical signal (RF2) via coherent beating. If the lower sideband of the first optical carrier is left behind, the optical signals before the PD can be expressed as follows:  
\[S(t)_{2}=A_{3}\text{exp}^{\text{i}(2\pi(f_{1}-f_{\text{RF1}})t+\phi)}+A_{4} \text{exp}^{\text{i}2\pi f_{2}t} \tag{8}\]  
where \(A_{3}\) and \(A_{4}\) are the amplitude of the sideband signal and the second optical carrier. Meanwhile, ignoring the components of direct current and high-order frequency, the electric current can be simplified as follows:  
\[I(t)_{2}=R\times[2A_{3}A_{4}\text{cos}(2\pi(f_{2}-f_{1}+f_{\text{RF1}})t-\phi)] \tag{9}\]  
This equation demonstrates that the phase shift in the optical domain can be transferred to one in the RF domain via heterodyne detection which can also change the frequency of the RF signal into a new frequency of \(f_{\text{RF2}}=f_{2}\,-f_{1}+f_{\text{RF1}}\). Derived from Equation (3), the relationship of the phase difference (\(\Delta\phi\)) between adjacent antennas with the beam angle (\(\theta_{0}\)) is expressed by the following equation.  
\[\Delta\phi=2\pi d\text{sin}(\theta_{0})/\lambda_{\text{RF}} \tag{10}\]  
Figure 3: Operation of the RF signal in one channel of a coherent OBFN.
## 4 Conclusions  
This study investigated the possibility of improving cathodic protection (CP) efficiency through the proposed conductive mortar. Laboratory experiments were conducted to first find the best combination of admixture and chemical agent by not only using the Brunauer-Emmett-Teller (BET) method to identify the specific surface areas, pore volumes, and average pore sizes but also the Wenner 4 probe test to measure resistivity. After selecting the best-performed combination with low resistivity and higher specific surface area/micropore volume, the sacrificial anode cathodic protection (SACP) system was tested to check the CP performance. From the laboratory tests, the following key results were obtained:  
* Among admixtures, higher specific surface area and micropore volume result in lower resistivity. A large specific surface area contributes to adsorbing a large amount of moisture, while micropores increase the possibility of maintaining moisture for a long time. Activated carbon has the highest specific surface area and micropore volume; thus, it can hold a large amount of moisture for a long time.
* Specimens with activated carbon show the lowest resistivity, and adding sodium hydroxide further decreases the resistivity. Sodium hydroxide is a high-alkali component with high electrical conductivity, and the resistivity is further reduced when activated carbon is mixed with sodium hydroxide.
* Based on CP performance evaluation, lower CP potential and higher CP current density were observed in the proposed activated-carbon-based conductive mortar specimen compared to the general mortar specimen. The higher the admixture ratio, the higher the CP performance, proving that the CP performance can be improved by adding the proposed admixture.
* The four-hour depolarization measurement showed that all specimens, including the general mortar, have more than 100 mV depolarization since the samples were exposed to freshwater and the rebar was close to the zinc mesh anode; however, the conductive mortar shows higher depolarization potential than the general mortar.  
Based on the measurements of resistivity, pore characteristics, CP potential, CP current density, and 4 h depolarization potential, the activated-carbon-based conductive mortar shows excellent performance in the laboratory environment. The proposed conductive mortar with the SACP system was applied to an actual bridge structure in South Korea, and in situ measurement results will be presented to demonstrate its feasibility in Part 2.  
Conceptualization, [PERSON]-M.H., C.J. and [PERSON]-[PERSON]; methodology, J.-M.H., C.J. and [PERSON]-A.J.; investigation, [PERSON]-M.H., C.J. and [PERSON]-[PERSON]; writing--original draft preparation, J.-M.H.; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable.  
The authors declare no conflict of interest.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 4 An INS Cyber-Physical Range  
According to [110], \"_Cyber ranges are interactive, simulated representations of an organization's local network, system, tools, and applications that are connected to a simulated Internet level environment. They provide a safe, legal environment to gain hands-on cyber skills and a secure environment for product development and security posture testing._\" Accordingly, a testbed with functionality allowing the testing of the security posture of cyber-physical systems, such as the INS, would constitute a \"_cyber-physical range_\".  
A cyber-physical range can be used for training, as it provides a controlled, interactive technology environment where trainees can learn how to detect and mitigate cyber attacks using the same kind of equipment that exists in the real world. The cyber-physical range allows attacks against the INS to be simulated, and to monitor a trainee's progress and performance in reacting to them; this also includes the assessment of how well a team executes incident response plans. Beyond training, a cyber-physical range can be used to experiment with new cyber defense technologies, as it provides a safe environment to solve complex cybersecurity problems where new ideas can be tested and the interaction of humans with emerging cybersecurity solutions can be assessed. Further, the cyber-physical range can be used to test the effectiveness and efficiency of existing, established cybersecurity solutions when applied in a specifically configured environment.  
A reference architecture for a cyber-physical range and an outline of its instantiation for testing the cybersecurity posture of marine navigation systems were proposed in [111]. In this section, a more detailed specification of such a cyber-physical range for the INS is provided.
# Fiber Sensing in the 6G Era: Vision Transformers for \(\phi\)-OTDR-Based Road-Traffic Monitoring
## 3 Experimental Setup
### Dataset  
The images in the dataset are labeled with car density and average speed information. Due to the scarcity of more than 5 heavy vehicles in a 5 min time frame, a class for 5 or more vehicles was created, so six classes are considered for car counting. The quantity of samples per class used for car counting is displayed in Table 2a. For speed estimation, the images are divided into 14 classes, each representing a 5 km/h speed bin. As very high speeds are not allowed inside the university campus, only speeds up to 70 km/h are considered. To address class imbalance and avoid classification bias, the most populated class (i.e., \"0\") is limited to 251 images. The quantity of samples per class for speed estimation is shown in Table 2b. Vehicles whose speeds are in the range 5-20 km/h and above 55 km/h are uncommon in the monitored street. Data augmentation techniques are used to deal with this imbalance. For car counting, random contrast, zooming, translation, brightness, blurring, and additive noise are chosen as augmentation techniques. For average speed estimation, random contrast, brightness, blurring, and additive noise are set for data augmentation.  
The image filenames contain the vehicle quantity and average speed. A sample filename is \"3_28.12015_1300m_300s_500 ns_per100 ms_byte_Feb-23-2024_08_49_26\". Table 3 explains the parameters presented in the sample filename. The first parameter is the number of vehicles in the image. In the example, \"3\" indicates that YOLOv8 detected three heavy vehicles in the recorded time frame. The average speed, calculated from the box displacement in the YOLOv8 tracking function, is represented with five decimals in km/h. The fiber length is constant for the entire dataset, 1300 m. The collection period is also constant, 5 min or 300 s, for all images. The pulse width is constant at 500 ns. The collection period is constant at 100 ms. Data collection is carried out in BYTE mode.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **(a) Car Count** & **(b) Average Speed** \\ \hline
**Class** & **Samples** & **Class** & **Samples** \\ \hline
0 & 482 & 0–4.99 km/h & 251 \\
1 & 479 & 5–9.99 km/h & 1 \\
2 & 182 & 10–14.99 km/h & 3 \\
3 & 35 & 15–19.99 km/h & 9 \\
4 & 9 & 20–24.99 km/h & 36 \\
5+ & 11 & 25–29.99 km/h & 76 \\ – & – & 30–34.99 km/h & 171 \\ – & – & 35–39.99 km/h & 162 \\ – & – & 40–44.99 km/h & 137 \\ – & – & 45–49.99 km/h & 49 \\ – & – & 50–54.99 km/h & 51 \\ – & – & 55–59.99 km/h & 10 \\ – & – & 60–64.99 km/h & 1 \\ – & – & 65–70 km/h & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset classes for car count (a) and speed estimation (b).
# Effects of Velocity Profiles on Measuring Accuracy of Transit-Time Ultrasonic Flowmeter
## 2 Methodology
### Correction Factors for Measurement with Ultrasonic Flowmeter  
For a laminar flow, it is easy to see from Equations (15) and (18) that the laminar correction factor will be 0.75. That is to say, the real flowrate is the result of multiplying the reading flowrate by the correction factor.  
However, it is not so easy to get the final result for a turbulent flow because the correction factor is not a constant in this case. From Equation (26), the correction factor for a turbulent flow will be  
\[k_{t}=\frac{2n}{2n+1} \tag{27}\]  
The relationship between the exponent \(n\) and the Reynolds number in Table 1 can be expressed as  
\[n=f(\mathrm{Re}) \tag{28}\]  
The Reynolds number depends on the real flowrate \(Q\), as follows  
\[\mathrm{Re}=\frac{2\rho Q}{\pi\mu R} \tag{29}\]  
The relationship between real flowrate and the reading flowrate \(Q_{0}\) is  
\[Q=k_{t}Q_{0}=\frac{2n}{2n+1}Q_{0} \tag{30}\]  
The real flowrate can be obtained from the simultaneous solution of the above Equations (28) to (30). Figure 3 gives the correction factor for a turbulent flow.