esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Lab-Built Scenario and Benchmarking
### Implemented Security Posture  
The lab setup meticulously implements ZT principles, with security controls mapped to the core pillars of the ZT model. In terms of identity and user security, access to GitLab repositories is strictly confined to authorized users, with MFA enforced for all logins, a robust SSH key management policy, and RBAC to ensure least privilege. For device security, the NVIDIA Orin devices undergo a foundational hardening process, disabling all unnecessary services and ports. Secure access to the local Git Server is enforced via reverse SSH tunnels, and firmware integrity is continuously monitored. The network is secured through isolation and micro-segmentation to limit lateral movement, with firewalls blocking all unauthorized outbound traffic.  
Application and workload security is maintained through several key practices. Runtime Application Self-Protection (RASP) is integrated to enable applications to detect and prevent attacks during execution [8]. All commits require cryptographic signing, and Software Bills of Materials (SBOMs) are generated and maintained to track dependencies, which are continuously scanned for vulnerabilities [28]. Data security is handled through access-controlled repositories, data classification schemes, and encrypted data transfers.  
Finally, robust analytics and automation are provided by comprehensive logging of all server activities and AI-driven anomaly detection systems that monitor for suspicious behaviors from both users and devices [8].
## 2 Experimental Programme
### Shear Capacity Calculation by Various Standards
#### 2.1.3. Ceb-Fip 1990  
In contrast to the Uniform Building Code (UBC) approach, the International Federation for Concrete and Precast Concrete (Federation international du beton) suggested a code for shear capacity calculation [41]. This model employs a single equation (Equation (6)) to determine the total shear capacity of a reinforced concrete member. This equation incorporates the combined effects of concrete contribution (\(V_{RD,C}\)) and shear reinforcement contribution (\(V_{RD,S}\)). The factors influencing \(V_{RD,C}\) include the shear span-to-depth ratio (\(d\)/\(a\)), main reinforcement ratio (\(\rho\)), and concrete compressive strength (\(f_{ck}\)), potentially with additional considerations. Similarly, \(V_{RD,S}\) is influenced by the shear reinforcement's spacing (\(s\)) in combination with the tensile strength (\(f_{yt}\)) for an unspecified factor likely related to its contribution to shear resistance (Equation (7)). A key difference from the previously discussed UBS method lies in the calculation of the effective depth. The use of a modified effective depth (\(z\)) in the CEB-FIP model could lead to slightly different shear resistance estimates compared to the UBC and ACI approaches. This difference would bemost pronounced for deeper members, where the reduction in effective depth due to the 0.9\(d\) factor would have a greater impact on the overall shear capacity calculation.  
\[V_{RD,C}=0.15\bigg{(}\frac{3d}{a}\bigg{)}^{1/3}\xi(100\rho_{w}f_{ck})^{1/3}b_{w}d \tag{6}\]  
\[V_{Rd,s}=\bigg{(}\frac{A_{v}f_{yt}}{s}\bigg{)}z(\cos\theta+\cot\alpha) \tag{7}\]  
\[\xi=1+\sqrt{200/d} \tag{8}\]  
\[z=0.9d \tag{9}\]  
It is important to note that both the UBC and CED-FIP model code are recognised engineering design standards with a proven track record of safety and performance. The choice of which approach to use in a particular design situation may depend upon factors such as local building codes, engineering judgement, and familiarity with the respective methodologies.
# Structure-Function Coupling Reveals Seizure Onset Connectivity Patterns
## 2 Materials and Methods
### Mapping Cortical Regions to the Nearest Electrode  
This section details the processes in Step 2 of Figure 1, where we used our previously described method [30] to create a subject-specific electrode warp and map each subject's cortical regions from the DK atlas to the nearest electrode. First, using the ANTs nonlinear registration tool, 21 electrodes in the standard MNI template space were warped to each participant's T1 image that had been registered to the diffusion image space (Figure 1 Step 2, a, i-iii). Next, we applied our inverse square method, which incorporates the inverse square equation shown in Figure 1 (Step 2, b, i) to produce a subject-specific, one-to-one mapping of each cortical region to its nearest electrode. The inputs were each subject's electrode warp and the cortical structure labels from Step 1. The inverse square equation holds that the light intensity of a source is inversely proportional to the square of the distance from the source. Thus, the inverse square method enabled the consideration of MRI voxel intensity in assessing the distance of each cortical region from each electrode's centre. Voxel intensity may represent the cortex's topological arrangement, endorsing postulation of the EEG signal strength from a given region relative to that region's distance from the scalp. The matrix in Figure 1, (Step 2, b, ii) depicts each region with one electrode name assigned--this electrode was the closest to that region. Subcortical regions (such as the hippocampus) were not assigned electrodes as their physical distance from the scalp and positioning below other cortical regions deemed them inaccessible for accurate measurement; thus they were removed from the analysis.
## 5 Limitations and Future Directions  
This is a preliminary study; further research is needed to validate and improve the robustness of the segmentation method. The inclusion of a larger and more diverse dataset, along with additional dental structures and different pathological conditions, could enhance the generalization and applicability of the segmentation approach. In particular, a limitation in this sense could be the total absence in the test set of some classes of deciduous teeth for which no performance information is available. In a future expansion of the dataset, it would be appropriate to have a test set that covers all the available classes.  
Large-scale annotated datasets and standardized evaluation metrics are required to facilitate the development and comparison of segmentation methods.  
Additionally, in future studies, exploring the combination of two-dimensional and three-dimensional imaging modalities could further improve the accuracy and comprehensive analysis of dental structures.
## 2 Mission Concept
### Payloads  
The main scientific objective of this mission is to explore the performance of sensing in the shortwave infrared (SWIR) region and specifically in the range of 1000-1650 nm, to detect the levels of methane and carbon dioxide in the atmosphere [1]. In order to fulfill this science objective, MeznSat carries two payloads; the primary payload is a SWIR spectrometer, and the secondary payload is an RGB camera. The combination of these two payloads provides a unique feature for MeznSat, allowing monitoring of GHG with the ability to perform geolocation using the RGB camera.  
The chosen primary payload for this purpose is the Argus 2000 SWIR spectrometer (Figure 1). Argus 2000 is a miniature, low-cost, space-proven spectrometer that operates measuring only \(4.5\times 8\times 8\) cm in size. The instrument operates by dispersing light entering to be dispersed by a 300 groove/mm grating and directed onto a 256-element Indium-Gallium-Arsenide detector array that is cooled by a Peltier cooler in order to reduce dark currents. The spectral range of the spectrometer is 1.0 to 1.65 micrometers, and its spectral resolution is 6 nanometers across 100 spectral channels. Gases within the spectral range of the Argus 2000 spectrometer include oxygen (1.25 \(\upmu\)m), carbon dioxide (1.57 \(\upmu\)m, 1.61 \(\upmu\)m, and 2.05 \(\upmu\)m), water (900 \(\upmu\)m, 1.2 \(\upmu\)m, and 1.4 \(\upmu\)m), carbon monoxide (1.63 \(\upmu\)m), methane (1.67 \(\upmu\)m and 2.25 \(\upmu\)m), and hydrogen fluoride (1.265 \(\upmu\)m). Argus 2000 supports integration times between 0.5 and 4.096 s [13].  
The Argus 2000 optical design consists of a telescope lens system, field stop and a primary parabolic mirror (with focal length and diameter of 35 mm) to provide a collimated image of the surface tile into a surface grating with 300 groves per mm. The spectrally divide image is reflected by the reflective grating onto another mirror that focuses the first spectral order of the surface tile onto the InGaAs detector [14; 15].  
The secondary payload is an RGB Camera that is based on a low voltage CMOS image sensor (Figure 2). The camera is developed by Tokyo University of Science (TUS) and has a significant flight heritage [1; 16]. The camera utilizes the OV-9630 Image Sensor from Omnivision and uses a Microchip PIC microcontroller to interface between the image sensor and the on-board computer (OBC) [1]. A Universal-Asynchronous-Receiver-Transmitter is provided for communication with the OBC. The camera is capable of taking SXGA (1280 \(\times\) 1024) and VGA (640 \(\times\) 480) images. The camera has a small-footprint package of 45 mm \(\times\) 46 mm \(\times\) 15 mm, weighs 39 g and has total power consumption of 0.11 Watts.  
Figure 1: The Argus 2000 Spectrometer.  
Figure 2: The RGB Camera used for MeznSat [16].
## 3 Validation of the Model
### Simulation of the Bottom Slag  
The change of the water temperature of the reservoir directly affects the displacement, stress, and water quality of the dam project. The water storage process of the reservoir will affect the water temperature at the bottom. If the water storage time is in the cold season, because the earth and river water temperature are low, the low temperature water will directly accumulate at the bottom of the reservoir at the front of the dam, which will cause the temperature at the bottom to be too low in the early stage. At especially high altitudes or high latitudes, this phenomenon is more pronounced.  
On the one hand, the low temperature water increases the temperature difference between the inside and outside of the concrete on the upstream dam surface, and on the other hand, it cools the dam concrete too quickly. These two situations can easily lead to the appearance and expansion of temperature cracks in the dam. Some concrete gravity dams and arch dams located in areas which are extremely cold use the method of bottom slag at the front of the dam in the temperature control and crack prevention design avoid the cold shock of the low temperature water on the concrete of the upstream dam surface [39]. The theory is to prevent the occurrence of temperature cracks at the dam heel by increasing the stable temperature of the strong restraint part of the dam foundation [40, 41]. In this paper, the model is used to simulate the bottom slag based on the varying thicknesses in the Xulong project, and the results are shown in Figure 6.  
The temperature comparison of bottom slag with a thickness of 40 m (\(\Delta\)2095-\(\Delta\)2135) and no bottom slag is shown in Figure 6. It can be seen that the bottom slag can increase the temperature of the bottom of the reservoir within a certain range. The simulated results agree with the measured data, with an average difference of 0.51 \({}^{\circ}\)C. Figure 7 illustrates the water temperature distribution characteristics of bottom slag with various thicknesses. We also simulated bottom slag with a thickness of 50 m (\(\Delta\)2095-\(\Delta\)2145) and 60 m (\(\Delta\)2095-\(\Delta\)2155). It can be seen from the figure that the greater the thickness of the slag, the greater the increase in the temperature at the bottom of the reservoir. When the thickness is 40 m, 50 m, and 60 m, the temperature increases by 5.2 \({}^{\circ}\)C, 5.5 \({}^{\circ}\)C, and 6.0 \({}^{\circ}\)C, respectively. As the thickness of the slag increases, the affected range also increases, roughly 20 m upward from the top of the slag. As the bottom of the slag is affected by the temperature of the earth, so the temperature drops. To sum up, bottom slag  
Figure 6: Water temperature of Xulong Reservoir (bottom slag 40 m).  
is an effective way to increase the boundary temperature at the concrete dam heel, and the temperature can be increased by 5-6 \({}^{\circ}\)C.
## Rule Framework  
Regarding the software's mathematical steps required for the in-live performance evaluation, the following regulations have been analyzed:  
* Regulation ISO 5167-1:2003 [14]. In this document, the Technical Committee establishes the general principles for methods of measurement and computation of the flowrate of fluid flowing in a conduit by means of pressure differential devices. Thus, Regulation ISO 5167-3:2003 [15] provides the procedure for the evaluation of the incoming air mass flowrate to the compressor \(\dot{m}_{air}\) by means of an Air Nozzle. Via the calculation of an ideal air mass flow through an Air Nozzle with Bernoulli's equation and the implementation of discharge coefficient \(C_{D}\) and expansion coefficient \(\epsilon\), the real air mass flow can be rated;
* Resolution MEPC.177 (58) (NOx Technical Code 2008) [16]. This technical code, besides providing mandatory procedures for the testing, survey, and certification of marine diesel engines, gives a method to estimate exhaust mass flowrate \(\dot{m}_{exh}\) by means of Carbon Balance. The method is demonstrated in ISO 8178-1 [17] through the equivalence between carbon entering the engine versus carbon leaving the engine;
* Regulation ISO 8217:2010 [18]. Within this regulation, standard values for fuels (in terms of density, viscosity, flash point, and much more) are provided. These are used if correct values from tests on in-use fuels are not available;
* CIMAC Number 27[2007][19]. The overall efficiency of a turbocharger \(\eta_{TI}\) can be evaluated by using the general formula provided in this document. Anyway, some manufactures (ABB and Mitsubishi) allow using simplified formulas, derived from the general one, which are reported in manufacturer's technical notes;
* Resolution MEPC.176(58)2008 [20]. For air pollution emission in atmosphere (in terms principally of nitrogen oxides NO\({}_{X}\) and sulfur oxides SO\({}_{X}\) emissions), the worldwide thresholds are fixed in this International Convention. This resolution embeds the previous 1997 Protocol that sets limits on sulfur oxide and nitrogen oxide emissions from ship exhausts and prohibits deliberate emissions of ozone-depleting substances. As regards the European Union, the Directive EU 2016/802 [21], commonly known as \"EU sulphur Directive\", concerning the maximum amount of sulfur dioxide emissions in the atmosphere, and the Regulation C(2016)8381 [22], concerning the general and technical requirements for emission limits and type approval for non-road ICEs, are issued.
# Visible Light Communication: A System Perspective--Overview and Challenges
## 4 Hybrid VLC
### Pdc-Vlc
The system consists of an RF front-end which includes among other components, an antenna operating in 2.4 GHz, a power amplifier and a photodiode (as a receiver). In [84] [PERSON] et al. have designed a fibre-wireless-fibre (FWF) link for indoor optical wireless communications. Through simulation data rates of 416 and 208 Gb/s can be achieved with a wide field of view of 40 and 60 degrees, respectively. Although these data rates are for the fibre optic communication to avoid the signal conversation from optic to electrical and vice versa. However,the same concept can be extended to the backbone access network of the VLC network to remove the bottle neck of PLC network.
## 3 Results
### Image Processing Using Computational Algorithm  
The microsphere beads used in this research are inert, nontoxic and do not contaminate the device, so there is no requirement to move it to a dedicated biology laboratory. However, the manufacturer (Cospheric) does not specify the size distribution of the beads, hence the number of beads per milligram can vary (calculated with the given diameter range) between \(1.2\times 10^{7}\) and \(1.5\times 10^{9}\) (using ideal spheres and the material density 1.3 g/cc). This makes a measurement by volume and weight not suitable.  
Different computational methods and algorithms for biological microscopic image analysis have demonstrated a comprehensive contribution for efficient and quantitative study of microorganism cells [20; 21]. Experiments have been conducted in this paper to detect and quantify fluorescent microbeads (1-5 um) in a measured sample of a clean water. Detailed steps are outlined as: (i) taking 2 uL of sample via micropipette; (ii) placing one drop on top of a microscopic glass slide; (iii) captureing an image of the full drop under microscopic camera; (iv) capturing zoomed images of the same drop frame by frame and finally (v) analyzing captured images by using computational algorithms using Matlab. The Circular Hough Transform (CHT) algorithm [22; 23] based code in Matlab, is used to find circular/spherical shapes in the captured image as one frame of a drop, as discussed above. The CHT algorithm is used to mark/circle specific sized microbeads for quantification. CHT algorithm accuracy rate is affected by different parameters/factors, such as minimum and maximum radius of the target circular shape, object polarity in the sense of dark or bright, and computation method. The results using CHT algorithm are approximately 80%, as reported in Table 1.  
Figure 6 shows resulting images of different iterations, which refer to the number of runs of the CHT algorithm applied in each image capture. First, the image is converted to grayscale, as color images are not giving a good segmentation. Then, minimum and maximum radius are defined as 1 and 600 pixels respectively. Finally, the object polarity is set to dark.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Iteration** & **minRadius** & **maxRadius** & **Sensitivity** & **Total Circles** & **Total Beads** & **Accuracy** \\ \hline
1 & 1 & 10 & 0.992 & 127 & 238 & 53.36\% \\
2 & 1 & 30 & 0.992 & 153 & 238 & 64.29\% \\
3 & 1 & 50 & 0.992 & 169 & 238 & 71.01\% \\
4 & 1 & 100 & 0.992 & 170 & 238 & 71.43\% \\
5 & 1 & 600 & 0.992 & 190 & 238 & 79.83\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of different parameters used during different iterations.  
Figure 6: Original image and resulting images of different iterations, as shown in Table 1.
## 5 Results
### Visual Search Outcome
#### 5.1.2 Search Time  
In addition to the number of correct responses, the search time was analyzed for the evaluation of visual search outcome (see the summary in Figure 1). Note that the participant's search time was averaged over the five trials for each combination of the controlled variables. The statistical analysis showed that the mean search time was considerably affected not only by the stimulus physical properties (\(F_{1,44}=48.12\), \(p<0.001\) for target-distractor similarity, and \(F_{1,44}=36.40\), \(p<0.001\) for set size), but also by experience-adjacent group (\(F_{2,44}=4.00\), \(p=0.025\)). Furthermore, the post hoc Bonferroni adjusted pairwise t-test revealed that students spent a markedly longer time searching the three-dimensional visual information when compared to the residents (\(p=0.04\)) and radiologists (\(p=0.002\)), however, the task performance speed of residents and radiologists did not differ considerably (\(p=0.86\)). We remark that no interaction effects were found to be significant.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task Parameters** & & **Radiologists** & **Residents** & **Students** \\ \hline High target-distractor & Small set & 4.1 (0.3) & 4.0 (0.3) & 3.5 (0.2) \\ similarity (15\% difference) & Large set & 3.6 (0.3) & 3.9 (0.3) & 3.5 (0.2) \\ \hline Low target-distractor & Small set & 4.6 (0.2) & 4.4 (0.2) & 4.5 (0.1) \\ similarity (25\% difference) & Large set & 4.4 (0.3) & 4.3 (0.2) & 4.0 (0.2) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average number of visual search tasks completed correctly (SE). There were five tasks in total.  
Figure 1: Distribution of average search time, depending on the set size in search tasks with (**A**) high target-distractor similarity and (**B**) low target-distractor similarity.
## 6 Therapeutic Approaches of Dentin Hypersensitivity
### In-Office Therapeutic Treatments of Dentin Hypersensitivity
#### 6.2.5 Adhesive Agents  
In contrast to other agents that reduce DH, adhesive agents exert a prolonged or even permanent effect. When combined with resin composites, they create an occlusion of the dentin tubules, yielding a hybrid zone [68]. In the past, older adhesive agents formed this hybrid zone through the removal of the smear layer and etching of the dentin surface, allowing for deep resin penetration into the dentin tubules [120]. However, newer bonding agents alter the smear layer and integrate it into the hybrid zone. It is assumed that these modern adhesive agents can effectively address and act preventively against DH. An illustrative instance of an adhesive agent utilized to mitigate DH is Gluma (Heraeus, Hanau, Germany; Kulzer), which comprises hydroxyethyl methacrylate (HEMA, Amsterdam, The Netherlands), glutaraldehyde, and fluoride. Glutaldehyde prompts protein coagulation within the dentin tubules, while HEMA penetrates the tubules and blocks them. Clinical studies have substantiated Gluma's significant efficacy in DH reduction [121].
## 4 Results and Discussion
### Scenario 2: Smart Evacuation  
The configurations in Tables 3 and 4 were used in the simulations of case 1 and case 2, respectively. This scenario comprised two distinct cases: case 1 introduced a pre-evacuation delay, whereas case 2 involved no pre-evacuation delay. In both cases, the evacuation performance was evaluated based on the time required to evacuate all miners safely. Additionally, the effect of varying stamina was analyzed in both cases.  
Figure 8: Average evacuation time for a chaotic scenario with an increase in pre-evacuation time.  
Figure 7: Average evacuation time for a chaotic scenario with an increase in error.  
The objective of case 1 in the smart evacuation scenario was to evaluate the impact of pre-evacuation and intersection delays on the evacuation performance, assuming no mistakes or errors during evacuation. It should be noted that in this case, delays at intersections were constant. The study assessed the time required to safely evacuate all miners from the mine. The results showed a gradual increase in the average evacuation time as the pre-evacuation delay increased, under conditions of both uniform and varying stamina, as illustrated in Figure 9. These findings highlight the importance of effective pre-evacuation strategies in ensuring the prompt and successful evacuation of miners in emergencies. They also underscore the need for appropriate training and education of miners on evacuation protocols and the proper use of smart devices to minimize the pre-evacuation delay and enhance the overall evacuation performance.  
Case 2 of the smart evacuation scenario represented the ideal conditions for mine evacuation as it assumed no pre-evacuation delays, no delays at intersections, and no errors during the evacuation process. In this case, the evacuation performance was evaluated based on the time required to safely evacuate all miners. The results indicated a significant increase in the average evacuation time when variable stamina was employed compared  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation Data** & **Constant Stamina** & **Variable Stamina** \\ \hline Pre-evacuation delay (minutes) & 0 & 0 \\ \hline Delay at intersection (minutes) & 0 & 0 \\ \hline Probability of error (\%) & 0 & 0 \\ \hline Speed (m/s) & Constant & Variable \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for case 2 of the smart evacuation scenario.  
Figure 9: Average evacuation time for smart evacuation scenario case 1 with an increase in pre-evacuation time.  
with constant stamina, with a 31% increase as illustrated in Figure 10. This underscores that stamina plays a critical role in the evacuation performance, and the presence of varying stamina levels among miners can notably impact the overall evacuation time.