esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Quality Assessment of Augmented Reality Glasses as Medical Display Devices (HoloLens 2)
###### Abstract  
This study allows clinical stakeholders to systematically assess the image quality of see-through augmented reality headsets in accordance with established medical display standards. The adapted methods support the integration of AR glasses, such as HoloLens 2, as medical display devices within clinical workflows. By quantifying essential image quality metrics, the proposed approach ensures that AR displays convey critical medical information with adequate fidelity for safe clinical use.  
See-through augmented reality glasses, such as HoloLens 2, are increasingly adopted in medical settings; however, their efficacy as medical display devices remains unclear, as current evaluation protocols are designed for traditional monitors. This study examined whether the established display-evaluation techniques apply to HoloLens 2 and whether it meets standards for primary and secondary medical displays. HoloLens 2 was assessed for overall image quality, luminance, grayscale consistency, and color uniformity. Five participants rated the TG18-OIO pattern under ambient lighting conditions of 2.4 and 138.7 lx. Minimum and maximum luminance were measured using the TG18-LN12-03 and -18 patterns, targeting \(\geq\) 300 cd/m\({}^{2}\) and a luminance ratio \(\geq\) 250. Grayscale conformity to the standard grayscale display function allowed deviations of 10% for primary and 20% for secondary displays. Color uniformity was measured at five screen positions for red, green, and blue, with a chromaticity limit of 0.01 for primary displays. HoloLens 2 satisfied four of the ten primary and four of the seven secondary overall-quality criteria, achieving a maximum luminance of 2366 cd/m\({}^{2}\) and a luminance ratio of 1478.75. Grayscale uniformity was within tolerance for 10 of the 15 primary and 13 of the 15 secondary measurements, while 25 of the 30 color uniformity values exceeded the threshold. The adapted evaluation methods facilitate a systematic assessment of HoloLens 2 as a medical display. Owing to inadequate grayscale and color representation, the headset is unsuitable as a primary diagnostic display; for secondary use, requirements must be assessed based on specific application requirements.  
augmented reality (AR); see-through display; HoloLens 2; image quality; performance testing; medical display devices +
Footnote †: journal: Applied Sciences
# Detection and Optimization of Photovoltaic Arrays' Tilt Angles Using Remote Sensing Data
## 4 Discussion
### Real-World Constraints  
Even when all required input data are available and the proposed method achieves high accuracy in the detection of PV arrays and their tilt angle optimization, certain practical considerations may limit its direct applicability. The method assumes that the tilt angle of each PV array can be feasibly adjusted; in reality, mechanical or structural constraints can prevent physical alterations. However, a physical change in the tilt angle can be made if a renovation or longer maintenance is performed on the given PV system, where the expected physical workload cost should be lower than the expected long-term energy production gains from the tilt angle adjustment. Finally, while the method can estimate optimal tilt angles from a purely technical point of view, real-world installations must also account for the broader structural and possible regional regulatory conditions governing the design and maintenance of the PV systems.
## 3 Results and Discussion  
### Shear Viscosity Calculations  
Shear viscosity is extracted from the diffusion coefficients according to Equation (2). By separating the calculations in a parallel and a perpendicular direction, \(\mu_{P}\) and \(\mu_{T}\), respectively, we obtain a close-up view of the internal mechanism that affects ion movement inside each cell. For the narrowest cell under investigation (\(h\) = 3 nm), in Figure 2a, we observe that there exists increased shear viscosity perpendicular to the wall, \(\mu_{T}\), significantly higher that the parallel component, \(\mu_{P}\). As the strength of \(E\) increases from \(E\) = 0.0 to 1.0 V/A, \(\mu_{T}\) decreases monotonically. In contrast, \(\mu_{P}\) remains unaffected by the electric field value. The channel shear viscosity is, though, unaffected, remaining around the water bulk value for the range of \(E\) investigated here. In a weaker \(E\) range, the viscosity component parallel to the electric field has been found to increase monotonically with the electric field strength, while the perpendicular component first decreases and then increases with \(E\)[41]. We also note that experimental results do not always adhere to simulation, and this is attributed to the lower \(E\) strength applied, compared to simulated nanoscale systems [42].  
For the \(h\) = 6 nm cell, where the effect of wall confinement is weaker on the transport properties of fluids [43], we observe that shear viscosity anisotropy is smaller compared to \(h\) = 3 nm, and its behavior is almost isotropic at \(E\) = 1.0 V/A.  
Shear viscosity is also examined on its dependence on wall/fluid interaction \(\varepsilon_{wf}/\varepsilon_{ff}\) in Figure 3, for cells of height \(h\) = 3-15 nm. For \(h\) = 3 nm (Figure 3a), all shear viscosity components acquire their maximum value for the strongly hydrophilic wall (\(\varepsilon_{wf}/\varepsilon_{ff}\ =\ 1.0\)). However, this effect is weak and does not have a strong impact on \(\mu\) values, and this is also the case for \(h\) = 6, 9 and 15 nm in Figure 3b-d, respectively.  
From a macroscopic point of view, studied under the Poisson-Boltzmann (PB) theory, it has been shown that the response of an electrolyte to an applied voltage leads to sharp ion concentration near a surface [21, 22]. This has major implications for the system dynamics, as changes in diffusion and viscosity have been observed. In fact, the viscosity, which increases in high-concentration solutions, dominates the concentration dependence of \(D\) for some systems. Nevertheless, PB theory has many shortcomings because it neglects ion-ion interactions and steric effects and it is proposed that the electrolyte dynamics are better described by a modified form of the PNP equations [12, 23].  
Figure 2: Shear viscosity, \(\mu\), in parallel and perpendicular to the wall direction, and as total channel value vs. the strength of the electric field, \(E\), for (**a**) \(h\) = 3 nm and (**b**) \(h\) = 6 nm.
# In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
## 4 Experiments
### Track Testing  
The track tests were performed in two different settings. First, the rail cart ran and collected data on a branch line with a 115 RE rail that is used infrequently and has somewhat degraded tie and ballast conditions. Second, it was set on a main line with a 136 RE rail with far better tie and ballast conditions. These tests were conducted to assess the system's performance in recording the necessary data at higher speeds, over more extensive distances, and in more realistic environments, including challenging conditions, such as those encountered on branch lines, which could represent worst-case scenarios.  
Figure 6 shows a sample section of the track testing for each setting. The raised and smooth surfaces represent the ties, while the lower and more irregular surfaces are the ballast. The highlighted sections mark the locations of the ties. As the plots indicate, the surface tracks are well-captured by the sensors. Note that the left and right measurements are slightly shifted to enable easier differentiation between the two. Plotting them without the shift would place them nearly on top of each other, making it more difficult to identify  
Figure 5: Laboratory system evaluation: (**a**) comparison between LiDAR sensor and inductive proximity sensor measurements. Orange-shaded areas correspond to ties and unshaded areas to ballasts; (**b**) response of the inductive proximity sensor to a sample tie, where gray dots indicate the “null” regions (no Hall effect measurement) or ballasts and orange dots show the “activated” regions (Hall effect measurement) or ties.  
any differences. Additionally, some data drop-ins observed during branch-line track testing (Figure 6a) were caused by sunlight interference with the lasers. This issue was addressed in future tests by adding shade covers to block the sunlight. As shown in Figure 6b, there were no drop-ins during the mainline testing because the covers were installed for this test. Additionally, the mainline testing results in Figure 6b display smoother tie surfaces compared to the branch-line testing. For example, some cracks are visible in Figure 6a but not in Figure 6b.  
It must be noted that the track tests in both settings were limited to walking speeds, in the range of approximately 1.5 to 4.0 mph (2.4 to 6.4 km/h). The track cart was remotely controlled by an operator who was following it. Although the cart can move at speeds of up to 10 mph (16 km/h), the test speeds were limited to speeds the operator could walk while safely negotiating the ties and ballasts.
# IoT OS Platform: Software Infrastructure for Next-Gen Industrial IoT
## 2 Related Work and Key Challenges
### Key Challenges
#### 2.2.2 Real-Time  
Currently, the basic network topology of many IIoT systems is star-shaped, meaning devices connect in a single hop to a central IoT platform or to a gateway, which then connects to the central IoT platform [33]. In such topologies, any data transmission requires support from the central IoT platform, leading to network transmission latency and data processing latency. Industrial monitoring applications are not sensitive to latency, but control applications have high real-time requirements, only tolerating millisecond-level delays [24]. IIoT systems introduce edge computing nodes to perform some control and data analysis work closer to the devices, thereby reducing transmission and processing latency [4; 23; 34]. However, in complex scenarios, the deployment of edge nodes becomes difficult, and there are no systematic solutions to issues like network switching, data transmission, computing task distribution, and result acquisition [35]. Furthermore, some research points out that a key issue in edge computing is addressing the latency caused by physical distance in data access [36].
# Initial Results from SQUID Sensor: Analysis and Modeling for the ELF/VLF Atmospheric Noise
## 2 System Description  
As SQUIDs deploying low-transition temeprature superconductors (LTS) can provide a lower magnetic flux noise floor and the corresponding technology is much more refined than high-temperature ones, we adopt the LTS sensor as the antenna of the ELF/VLF receiver. The LTS receiver used in our experiment was fabricated by the Shanghai Institute of Microsystem and Information Technology (SIMIT), Chinese Academy of Sciences [22]. A block diagram of the receiver is shown in Figure 1.  
The system consists of three components, i.e., SQUID bootstrap circuit (SBC) and its readout circuit, development platform and computer. The SBC is a new dc SQUID readout circuit operating in the voltage bias mode [22], which can effectively reduce the preamplifier noise below the intrinsic noise of SQUID sensor. More details about the SBC in our experiment can be found in [22]. The development platform is based on CompactRIO developed by National Instruments, which has excellent adaptability in field environment. The high-precision data are sampled at a 100 kHz sampling rate by 24 bit analog-to-digital converters (ADC). The digital data are then stored in the computer and some postprocessing methods listed in later sections are performed on them. The noise floor of the SQUID sensor in the center of a medium magnetically shielded room measured by a spectrum analyzer (Agilent 35670A) is shown in Figure 2 and the noise is about 6 fT/\(\sqrt{\mathrm{Hz}}\)@1.51 kHz. The noise below 100 Hz is very large because the measurement was conducted at day time with the influence of some external disturbances like a subway nearby. The large low frequency noise presented in Figure 2 should be mostly contributed from the urban environment noise since it differs much at daytime and at midnight. At midnight, we can find that the corner frequency of the \(1/f\) noise is about 3 Hz and the low-frequency components are much smaller than that in Figure 2.  
Figure 1: The diagram of the low-temperature superconducting quantum interference device (SQUID) receiver.
# Data-Driven Network Latency Processing for Auxiliary Services in Virtual Power Plant
## 4 Data-Driven Network Latency Processing
### Network Latency Processing  
We treat the collected network latency as a time series to estimate and predict network latency. To facilitate prediction, we first need to guarantee the wide-sense stationary property of the time series by autocorrelation coefficient (ACC) and partial autocorrelation coefficient (PACC). The ACC expresses the linear correlation of data among adjacent observations of a random process, which can be estimated from the observed time series by the sample as  
\[\hat{\rho}_{D}(k)=\frac{\frac{1}{N-k}\sum_{i=1}^{N-k}[d_{i}d_{i+k}]-\hat{m}_{D }^{2}}{\phi_{D}^{2}},k=0,1,\ldots N-1  
with \(\hat{\gamma}_{D}(0)=1\), and  
\[\hat{\gamma}_{D}(k,j)=\hat{\gamma}_{D}(k-1,j)-\hat{\gamma}_{D}(k)\hat{\gamma}_{D} (k-1,k-j). \tag{9}\]  
The standard error of the sample PACC is approximated by  
\[S_{\text{PACC}}=\sqrt{1/N}. \tag{10}\]  
Hence, \(\pm 2S_{\text{PACC}}\) can be used as critical limits on the sample PACC to test the hypothesis of a white noise process.  
For a process with the time-varying mean, a stochastic trend model can be applied. The stochastic trend model is constructed by differencing the random process for \(diff\) times, i.e.,  
\[z_{t}=(1-B)^{diff}d_{t}, \tag{11}\]  
where \(B\) is the backshift operator such that \(B^{j}d_{t}=dt-j\).  
Figure 6: The filtered upper envelope of network latency.  
For a process with the time-varying variance, the Box-Cox power transformation is applied to stabilize the variance by  
\[T(d_{t})=\frac{d_{t}^{\
u}-1}{\
u G^{\
u-1}(D)}, \tag{12}\]  
where \(G(D)\) is the geometric mean of \(d_{1},d_{2},\ldots,d_{N}\), i.e.,  
\[G(D)=\left(\prod_{i=1}^{N}d_{i}\right)^{1/N} \tag{13}\]  
and \(\
u\) is a parameter determining the transformation. The criterion for selecting \(\
u\) for variance-stabilizing transformation is to minimize the residual sum of squares  
\[SS(\
u)=\sum_{t=1}^{N}(T(d_{t})-\hat{m}_{D})^{2}. \tag{14}\]  
An \(ARIMA(p,diff,q)\) model of the nonstationary random process \(D\) is expressed as  
\[\left(1-\sum_{i=1}^{p}\phi_{i}B^{i
## 2 Materials and Methods
### Data Analysis  
#### 2.3.1 Hotspot Analysis  
GPS point data (Section 2.2.2) representing sheep locations at 1-h intervals were used as incidents, while 20 m \(\times\) 20 m regions, configured in a regular array (i.e., fishnet), were utilized as the aggregation polygons (zones). The camp boundaries were used to define the bounding polygon (i.e., where incidents are possible). Sheep tend to congregate around  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Formulation** & **Reference** \\ \hline \multirow{2}{*}{Atmospherically resistant vegetation index} & \multirow{2}{*}{ARVI = \(\frac{\text{NIR}-\text{(2\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text{\text{ \text{ \text{ \text{   }}}}}}}}} \text{Red})}}+\text{(Blue)}}}{\text{(NIR)}+\text{($2\text{\text{\  
the watering points during prolonged periods of the day to rest. They also tend to move along the boundary of the camps to rest in the corners. Buffer zones within 10 m from camp boundaries and 30 m from watering points were consequently created and used to exclude non-grazing events (GPS records).  
The Getis-Ord Gi* statistic, as implemented in the Optimized Hot Spot Analysis (OHA) tool of ArcGIS 10.7 [84], was used to identify statistically significant spatial clustering in the GPS data. Getis-Ord Gi* statistics have been shown to be superior to other clustering techniques such as kernel density estimation (KDE), which uses a kernel function to fit a smoothly tapered surface to each GPS point [85]. The GI* statistic considers each feature within the context of its neighbouring features. For a feature to be a statistically significant hot spot, it must have a high value and be surrounded by other features with similarly high values. The local sum for a feature and neighbours features is compared proportionally to the sum of all features. When the local sum is different from the expected local sum, and when that difference is too large to be the result of random chance, then a statistically significant z-score results [84]. The GI* statistic determines clustering intensity at high or low levels and provides statistical measures in the form of z-scores and \(p\)-values to indicate statistical significance. Zones were determined to be part of significant hotspots when the z-score was higher than 2 (at 95% confidence level), and for cold spots, when the z-score was lower than \(-2\) (at 95% confidence level).  
The OHA classified each zone in the grazing camp as either a statistically significant hot spot, cold spot or as not significant [86; 87]. The OHA was conducted to characterize the monthly spatial distribution of sheep across the grazing camps by identifying areas within the camps that were most frequently visited by sheep (hot spots) and areas least visited (cold spots). This allowed for an examination of the underlying spatial processes that may be driving the hot and cold spot zones across the grazing camps. Maps depicting the hot and cold spots were created to visually (qualitatively) analyse GPS data. Data processing, spatial analysis and the making of maps were carried out using ArcGIS 10.7 software.
# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 1 Introduction
Ensuring the accuracy and reliability of the information provided by these systems is critical, as misinformation can have serious consequences in healthcare [9]. There are also privacy and data security concerns, particularly when dealing with sensitive healthinformation [14]. In addition, the issue of public trust in AI systems remains a significant factor that can affect their effectiveness [5]. Despite these challenges, the potential benefits of AI chatbots in health emergency management are considerable. Their ability to provide immediate, scalable, and personalized responses makes them valuable tools in modern crisis communication strategies. As AI technology continues to advance, these systems are becoming more sophisticated, able to understand context, adapt to new information, and even detect emotional states to provide more empathetic responses [15]. In recent years, it has become clear that artificial intelligence chatbots are not just technological innovations, but essential tools in the fight against public health threats, bridging the gap between authorities and the public and ensuring that vital information reaches those who need it most, when they need it most. The ongoing development and integration of artificial intelligence chatbots into the public health infrastructure is a critical area of research and implementation. This review aims to explore the current state of the use of AI chatbots in public health emergencies, their benefits, challenges, and potential future developments, providing insights into how these technologies can be optimized to improve public health response efforts. By harnessing the power of AI in public health communication, we can potentially create more resilient and responsive health systems that can meet the complex demands of future public health emergencies. In this paper, we systematically explore the role of artificial intelligence-based chatbots in public health emergencies, with a focus on infectious disease outbreaks. After a review of the psychological relevance of chatbots in health communication, we analyze their historical development, from early experiments such as ELIZA and PARRY to current advancements with generative artificial intelligence models (Section 2). Next, we examine the specific applications of these tools in genetics and genomics (Section 3), as well as their use for the dynamic management of healthcare spaces during epidemics (Section 4). The paper continues with an analysis of the potential of chatbots as statistical consultants in epidemiology (Section 5) and their reliability in clinical decision support (Section 6). Finally, we address the main challenges and limitations related to their use (Section 7), concluding with a discussion of future prospects and research needs to further improve the integration of these tools into public health strategies (Section 8).
## Appendix A
### Short Review on Research Work with Solution-Diffusion-Based Models \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Objective** & **Osmotic** & **Type of** \\  & **Process** & **Membrane** \\ \hline
[22] & \
\begin{tabular}{c} Considers the asymmetry between the semipermeable \\ membrane and the porous support structure. \\ \end{tabular} & PRO &
\begin{tabular}{c} Hollow \\ Fiber \\ \end{tabular} \\ \hline
[23] & \
\begin{tabular}{c} Considers a more general case where the net flux in the \\ transition zone due to the concentration in the support layer \\ is nonzero. \\ \end{tabular} & PRO &
\begin{tabular}{c} Hollow \\ Fiber \\ \end{tabular} \\ \hline
[24] &
\begin{tabular}{c} Proposes a two-parameter water transport equation to \\ represent the permeation due to osmotic pressure and the \\ permeation due to applied pressure on the \\ high-concentration side. \\ \end{tabular} & PRO & Hollow Fiber \\ \hline
[7] &
\begin{tabular}{c} Defines the effective osmotic pressure by considering \\ internal concentration polarization. They propose a \\ simplified model when the feed is distilled water. \\ \end{tabular} & PRO & Flat Sheet \\ \hline
[25,26] &
\begin{tabular}{c} Conducts studies exploring the effects of internal \\ concentration polarization (ICP) and external concentration \\ polarization (ECP). They explore the effects of membrane \\ orientation AL-FS and AL-DS. \\ \end{tabular} & PRO, FO & Flat Sheet \\ \hline
[27] &
\begin{tabular}{c} Presents an equation for the water flux that includes terms \\ for external concentration polarization of dilution (DECP) \\ and internal concentration polarization of concentration \\ (CICP). In this equation, all terms can be measured or \\ calculated, and it was validated with experimental results. \\ \end{tabular} & PRO & Flat Sheet \\ \hline
[28] &
\begin{tabular}{c} Suggests that the volumetric and solute permeability \\ coefficients, A and B, along with the parameter S, can \\ describe the osmotic efficiency of a membrane. Equation \\ system to simulate PRO module \\ \end{tabular} & PRO & Flat Sheet \\ \hline
[29] &
\begin{tabular}{c} Presents expressions for the water flux are presented as a \\ logarithmic function, directly proportional to the mass \\ transfer factor in the porous substrate. They include an \\ expression to calculate the concentration at any point in a \\ modified spiral-wound module for PRO. \\ \end{tabular} & PRO & Flat Sheet \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Some of the most representative mathematical models for pressure-retarded osmosis over the years.