esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Research Contributions  
Following are the major contributions of this article.  
* We discuss emerging technologies (i.e., AI, IoT, cloud computing, and blockchain) and emphasise the important role they play in the smart healthcare systems as a means of offering predictive, automated, computationally inexpensive, and reliable security services.
* We discuss the security challenges associated with the healthcare ecosystem (i.e., patient information systems, wearable systems, implantable and electronic health records) to encourage researchers from industry and academia to provide endogenous security solutions.
* We design a comprehensive taxonomy of existing security countermeasures to mitigate security threats in the smart healthcare environment.
* We proposed a real-world case study by designing an AI and blockchain-based secure architecture that confront malware samples and network attacks for smart healthcare systems. The proposed architecture is evaluated using various performance metrics, such as accuracy, scalability, and dynamic malware analysis.
# The Interaction between the LEO Satellite Constellation and the Space Debris Environment
## 4 Analysis of Constellation Collision Maneuver
### Phase 2: Collision Avoidance Maneuver during Working in Orbit  
During the operation of the satellite, it operates in a near-circular orbit at the height of 550 km, with a semi-major axis of 6928 km, an orbital inclination of 53\({}^{\circ}\) and an eccentricity of 0.0001. The calculation time is from January 2021. The on-orbit collision avoidance simulation is carried out for 5 years, and the annual collision probability and flux with detectable objects and all objects are obtained, as shown in Table 7.  
Figure 8 shows the relationship between the total number of circumvention and ACPL value during satellite operation in orbit. When the ACPL value is \(10^{-4}\), the total number of avoidance of all satellites is about 1300, with an average annual avoidance of 260, as can be seen from Figure 9, if the corresponding full maneuver avoidance is carried out, the  
\begin{table}
\begin{tabular}{c c c c} \hline \(ACP_{d}\) & \(ACP_{w}\) & \(Flux_{d}\) **(/m2/a)** & \(Flux_{w}\) **(/m2/a)** \\ \hline \(1.67\times 10^{-4}\) & \(1.72\times 10^{-4}\) & \(4.58\times 10^{-6}\) & \(4.74\times 10^{-6}\) \\ \hline \end{tabular}
\end{table}
Table 7: Single satellite annual collision probability (ACP) and space object flux during Phase 2.  
Figure 6: The relationship between the mean number of avoidance manoeuvres and ACPL during Phase 1.  
Figure 7: The relationship between risk and the mean number of avoidance manoeuvres during Phase 1.  
collision risk can be reduced by 60%, and the remaining collision risk for all space objects is 20%. When the ACPL value is \(10^{-5}\), all mobile avoidance can reduce the remaining risk to less than 5%, but too many avoidance times will lead to an increase in fuel demand, so it is necessary to make a reasonable choice of ACPL value according to the mission situation.
## 2 Background
### Coverage and Connectivity in WSN
 \tag{3}\]  
**Probabilistic sensing model**: This model is more practical and comprehensive than the binary model; it assumes that the sensed event, sensor design, and environmental conditions are all stochastic in nature. The coverage equation can be expressed as follows:  
\[\mathrm{C_{xy}}\left(\mathrm{Si}\right)=\left\{ \begin{array}{cc}0,&\mathrm{ if}\ \mathrm{Rs}\ +\ \mathrm{Re}\ \leq\ \mathrm{d}\left(\mathrm{Si},\mathrm{P}\right)\\ \mathrm{e}^{-\delta\alpha^{\beta}}&\mathrm{if}\ \mathrm{Rs}\ -\ \mathrm{Re}\ <\ \mathrm{d}\left(\mathrm{Si},\mathrm{P}\right)< \mathrm{Rs}+\mathrm{Re}\\ 1,&\mathrm{if}\ \mathrm{Rs}-\mathrm{Re}\ \geq\ \mathrm{d}\left(\mathrm{Si},\mathrm{P}\right) \end{array} \right. \tag{4}\]  
where  
**Re** is the uncertainty measure in sensor detecon, 0 \(<\) Re \(<\) Rs,  
a = d(Si,P) \(-\) (Rs \(-\) Re);  
\(\delta\) and \(\beta\) are parameters that measure the detection probability when the target in a distance equal to  
Rs \(-\) Re \(<\) d(Si,P) \(<\) Rs \(+\) Re. The probabilistic sensing model is represented in Figure 9b. Figure 9: Sensing models. (**a**) Binary model. (**b**) Probabilistic model.
## 3 Results  
### Fixed Point Selection and Baseline Network Diagram  
Accurate geodetic network adjustment begins with the careful selection of stable control stations. In this study, ten permanent GNSS stations from the IGS CORE network were chosen to serve as global reference anchors. These stations offer long-term positional stability and are recognized under the ITRF2014 framework, ensuring consistency with international geodetic standards [48; 49].  
To reinforce the domestic network, seventeen continuously operating reference stations (CORS) within South Korea were selected based on criteria from ICSM (2017) [50] and NGS (2011) [51]:  
1. Continuous and stable GNSS observations suitable for long-term positioning applications.
2. No undocumented hardware changes during the adjustment period.
3. Complete and validated station metadata including antenna, receiver, and site information.
4. Even and appropriate spatial distribution across the national territory.  
Figure 3: Data processing workflow of GAMIT/GLOBK for integrated geodetic network adjustment based on GNSS baseline analysis.  
Stations exhibiting nonlinear motion, incomplete metadata, or known equipment alterations were excluded to avoid introducing regional bias into the adjustment process [52; 53].  
The resulting set of IGS and domestic CORS ensured both geodetic stability and spatial uniformity across the network.  
Figure 4 illustrates the locations of the selected domestic CORS stations.
# Awareness to Action: Student Knowledge of and Responses to an Early Alert System
### Early Alert Systems in Higher Education  
Early alert systems can be used to identify students who would benefit from an influx of support and notify student success staff (e.g., administrators, advisors) of these students [12]. These systems are sometimes referred to as early warning systems when they are used only to identify students experiencing academic or related challenges [15; 19]. However, these systems can also be used to provide positive feedback (e.g., kudos to a student for a strong start to the semester). For that reason, we use the neutral term early alert system.  
Initial early alert systems relied on reports submitted manually by an instructor detailing student behaviors (e.g., missing attendance, not submitting assignments) [10]. We refer to these as human-driven early alert systems because they are initiated by a human action (as compared to automated early alert systems that use student data captured by university systems) [20; 21; 22]. For example, an early alert system implemented at Vincennes University in the early 1990s allowed instructors to send postcards to students when they started to accrue absences [23]. Academic advisors also received a list of students who had been flagged for non-attendance. Data collected following system implementation indicated that the early alert system reduced the number of students who dropped, failed, or withdrew from courses, and significantly increased passing grades in early morning and evening classes.  
Many colleges and universities continue to use human-driven early alert systems [24]. Alerts from these systems may be particularly beneficial for students because they signal that an instructor notices and cares about them [25]. Past research on student persistence has found that when students and faculty engage in one-on-one interactions, students are more likely to perceive themselves as members of the college community, and this perception decreases the likelihood of attrition [13; 26; 27]. Additionally, a survey of undergraduates who considered dropping out revealed that social support was a key factor in their decision to stay enrolled [28]. These findings align with key theories of persistence and motivation. [PERSON]'s [13] model of institutional departure, for example, suggests that social integration (e.g., connections with peers and faculty) is a key influence on students' likelihood of degree completion. Likewise, self-determination theory [8] posits that relationship with others in one's context (e.g., classroom, university) bolsters self-directed motivation. Therefore, implementation of an early alert system, particularly one that is human-driven, has the potential to improve persistence by connecting students to members of the university.
# An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction
## 5 Discussion  
### Theoretical Implications  
This study contributes to EWS research [51; 64; 65], with a specific focus on the implementation of an Intelligent Early Warning System (IEWS) in the manufacturing sector, distinguishing it from previous works such as [31; 32]. Although similar to [54], which employs a Bayesian Hypothesis Testing-based model for anomaly detection, this study shifts the focus to predicting the most frequent error classes using deep learning techniques and the power of the attention mechanism.  
This shift introduces a new perspective in the theoretical discourse around EWSs, moving from anomaly detection to event anticipation, thereby aligning more closely with the proactive needs of predictive maintenance strategies.  
The study also contributes methodologically by proposing a multi-model classification strategy, where each model is specialised in identifying a specific class of critical errors. This approach departs from the common use of monolithic classifiers and opens new avenues for research on modular and scalable architectures for industrial fault prediction. The integration of attention layers into LSTM networks further supports the theoretical discourse on temporal feature selection, allowing the model to identify which segments of the time series carry the most predictive value, a mechanism particularly relevant in scenarios where the importance of historical data fluctuates over time.
## 2 Fast Deploying Monitoring System
### Composition of FDMS  
An FDMS is an adaptive landslide monitoring system that is based on an ad-hoc network. An ad-hoc network is more secure, robust, stable, and reliable when compared with traditional bus and star networks. Figure 2 is the typical structure of a FDMS. In Figure 2, each station is composed of several sensors, a data acquisition instrument, and an ad-hoc router. The ad-hoc routers have GPRS/3G/4G communication modules and form a wireless local ad-hoc network using LoRa technology. They communicate with each other by multi-hops and can also act as access point (AP) nodes that have the ability to gain access to the external network. In the wireless local ad-hoc network, when one node breaks down, the network will find new paths to skip this node through a routing algorithm, which improves the network's robustness. The FDMS has three working modes. One is the Normal mode as shown in Figure 3a; another is the Communication fault mode as shown in Figure 3b. In this mode, if the GPRS/3G/4G communication module in some of the ad-hoc routers breaks down, the system finds a new routing path to send the data. The third is the Beidou satellite communication mode as shown in Figure 3c. This mode means that if the GPRS/3G/4G communication modules in all of the ad-hoc routers break down, then the Beidou satellite communication system will be started. With the advantages mentioned above, the FDMS is highly robust and works well in field environments, especially in places where there are no mobile signals, or the signals are weak. Furthermore, the measuring equipment is specially designed to make it deployed easily and quickly, as described in more detail below.  
Figure 1: Traditional landslide monitoring system.
# Prediction and Comparative Analysis of Software Reliability Model Based on NHPP and Deep Learning
## 3 Numerical Example
### Confidence Interval  
In this study, GRU, the software reliability model that showed the best results among the proposed models, was used to obtain the confidence interval. In addition, the difference in the distribution between the predicted and actual data values of the other models was compared. The confidence interval follows Equation (22). Since software failures follow a Poisson distribution, we take the mean and variance as \(\hat{m}(t)\). \(z_{\alpha}\) is defined as the \(100(1-\alpha)/2\) percentile of the standard normal distribution [35]. A 95% confidence interval was used.  
\[\hat{m}(t)+z_{\alpha}\sqrt{\hat{m}(t)} \tag{22}\]  
Figure 4: (**a**) Estimated and predicted value for 80% of Dataset 2; (**b**) Estimated and predicted value for 90% of Dataset 2.  
Figure 3: (**a**) Estimated and predicted value for 80% of Dataset 1; (**b**) Estimated and predicted value for 90% of Dataset 1.  
Tables A3 and A4 show the predicted values and corresponding confidence intervals for the remaining 20% and 10% of the data based on the estimated and trained models for 80% and 90% of Dataset 1. The bolded values indicate that the actual data values do not fall within the prediction intervals. For 80% of Dataset 1, the other NHPP software reliability models, except for PNZ, PZ, and Vtub, and the software reliability models using deep learning, predicted well within the 95% confidence interval. For 90% of Dataset 1, the other NHPP software reliability models (except PNZ, PZ, TP, and UDPF) and the software reliability models using deep learning performed well within the 95% confidence interval.  
Tables A5 and A6 show the predicted values and corresponding confidence intervals for the remaining 20% and 10% of the data based on the estimated and trained models for 80% and 90% of Dataset 2. For 80% of Dataset 2, the GO, PNZ, and TP models showed results where the actual data was not within the prediction confidence interval at all, while the DS model showed results where the actual data was within the 95% prediction confidence interval up to the 108 th time, the YID, TC, DPF, and UDPF models up to the 109 th time, and the PZ, Vtub model up to the 110 th time. For 90% of Dataset 2, the NHPP software reliability models showed that the PZ, Vtub, DPF, and UDPF models resulted in the actual data falling within the 95% prediction confidence interval up to the 118 th time. Except for the PZ, Vtub, DPF, and UDPF models, none of the NHPP software reliability models had the actual data fall within the predicted confidence interval. The software reliability models using deep learning performed well, with predictions within the 95% confidence interval. Except for the models that do not fall within the 95% confidence intervals shown in Tables A3-A6, the remaining models showed good results in following the data trend, and the software reliability models using deep learning showed good estimation and prediction results in following the data trend.
## 4 Design of Dynamic Cache Allocation Mechanism
### Weight Definition
#### 4.1.1 Definition 1: NACK Weight  
In reliable multicast, the role of cache is to retransmit lost data as close as possible. The NACKs received by the multicast tree node are more likely to belong to the multicast group with more lossy downstream links in the multicast tree, thus the number of NACKs of the multicast group reflects the packet loss probability of the multicast group. To avoid unreasonable cache reallocation caused by transient changes in the number of NACKs, we adopt a weighted moving average of the number of NACK. As shown in Equation (1), let \(NACK_{i,n}\) be the number of NACK of multicast group \(i\) in the \(n\)-th statistical period, and the weighted moving average of NACK \(EWMA\_NACK_{i,n}\) is calculated according to [27].  
\[EWMA\_NACK_{i,n}=(1-\theta)\times EWMA\_NACK_{i,n-1}+\theta\times NACK_{i,n} \tag{1}\]  
where, \(\theta(0<\theta<1)\) is a weighting factor.  
Then, we calculate the ratio of the number of NACK of each multicast group to the total number of NACK received by the MTN. The higher the NACK ratio of a multicast group is, the higher the packet loss probability of the multicast group is, and the greater the possibility that the multicast group needs to perform retransmission is. At the same MTN, \(N_{i,n}\) represents the NACK weight of multicast group \(i\) in the \(n\)-th statistical period.  
\[N_{i,n}=(EWMA\_NACK_{i,n})/\sum_{j=1}^{M}EWMA\_NACK_{j,n} \tag{2}\]
# Developments in Contact Lens Imaging: New Applications of Optical Coherence Tomography
## 2 Clinical Uses in CL Practice
### CL Complications  
ASOCT is a device that acquires data about different corneal structural changes, including possible complications after CL wearing. ASOCT is capable of visualizing the cornea in detail and objectively assessing the location, size, depth, shape, and light-scattering properties of the changed tissue, so it can be used to monitor the disease process. Corneal opacities or haze due to scattering, which causes an incorrect identification of the corneal surface reflections, can be precisely mapped using ASOCT, providing the corneal and epithelial thickness and the depth of the lesion [2; 4; 5; 9; 31]. All lenses need to interact with the tear film, and it is inevitable that the lens surface will have deposits. The integrity of the CL and the condition of the lens surface can also be examined using ASOCT [22].  
[PERSON] et al. noticed the potential of the OCT to assess the effect of CL on the morphology of the corneascleral transition zone [29]. The edge of the soft CL is likely to exert greater pressure in the limbal/scleral region and may cause subtle changes in the anterior layers of the cornea (especially in the perilimbal zone) that can be accurately visualized in ASOCT [9; 29; 31]. The zone of the lens with the greatest thickness difference (e.g., stabilization zones of the toric lens or the junction between the pigmented and clear regions in heavily-pigmented cosmetic soft lenses) is likely to influence the changes in morphometry [29].  
The new technique, known as en face ASOCT, is capable of measuring the depth of corneal changes more precisely. Visualization in a frontal plane can reveal supplemental morphological details in patients with ocular surface conditions. This may improve the quality of diagnosis and be used to monitor the efficacy of treatments with no corneal contact [18].  
[PERSON] et al. applied ultra-high-resolution OCT to image the microvascular properties within the limbus that may occur after CL wear [56]. Early pilot cross-sectional study, performed by [PERSON] et al., suggests that the swept source OCTA used may be helpful for detecting areas of vascularization in the anterior segment [19].
## 2 Experimental Setup  
The configuration of the ECDL system is shown in Figure 1. To achieve the widely tunable ECDL, a commercial gain chip (Thorlabs, SAF1126H heatsink assembly, Newton, NJ, USA) was chosen, one facet of which was with ultra-low reflectivity (R\({}_{1}\) < 0.01%). And the reflectivity of another facet was about 10% (R\({}_{2}\)). The gain chip was accurately cooled by the thermoelectric cooler (TEC) and the measuring temperature was 20 \({}^{\circ}\)C. According to the simulation by ZEMAX, an aspherical lens (NA = 0.6) with focal length of 2.97 mm (Thorlabs, 355660-C, Newton, NJ, USA) was chosen to collimated the output beam from rear facet (R\({}_{1}\)). The collimated beam was diffracted on the surface of a diffractive grating mounted in Littrow configuration and the first order diffractive beam from the grating was fed back into the gain chip.  
To learn the influence of grating features, including diffraction efficiency and ruling density, on the SMSR and the tunable range, two gratings with different ruling densities were used in the external cavity system, and the parameters of different configurations are listed in Table 1. Configuration A had  
Figure 1: The configuration of the Littrow external-cavity diode laser (ECDL) system.  
the same grating as configuration B, and the grating parameter was 600 lines/mm ([PERSON], GR13-0616, Newton, NJ, USA). The grating diffraction efficiency of configuration A was about 90%. However, the grating diffraction efficiency of configuration B was changed by rotating the grating along the axis of incident beam to ensure that configuration A and B had the same experimental condition. For configuration B, the polarized direction of the incident beam was non-orthogonal to the direction of grating groove, as shown in Figure 2b, and the diffraction efficiency in the first order at the blazed wavelength of 1550 nm was about 17%. The diffractive grating II ([PERSON], GR13-1208, Newton, NJ, USA) in configuration C was 1200 lines/mm. And the diffraction efficiency was about 91% for the first order at the blazed wavelength of 1550 nm, which was almost the same as configuration A. For configuration A and C, the polarization direction of the incident beam was perpendicular to the direction of grating groove, as shown in Figure 2a.
## 3 Materials and Methods
### ORP-Controlled MEC System  
A redox potential electrode (METTLER P14805-SC-DPAS-KSS/225, Zurich, Switzerland) was used to measure ORP, which was regulated via power supply (PMC 1000/DC, AMETEK). For the continuous low-potential strategy (LPORP), the potential was maintained from \(-\)0.4 V to \(-\)0.6 V. When the ORP levels rose above the set point, additional reductive potential was applied, whereas when these levels fell below the set point, additional oxidative potential was applied. For the high-voltage electric pulse strategy (HPORP), a 1-s periodic potential was employed, with a \(-\)1 V potential applied for 0.5 s and no potential applied for the remaining 0.5 s. Whenever ORP levels rose above the set point, a periodic potential was applied. No potential was applied when ORP levels fell below the set point, with open-circuit voltage instead being monitored. The \(-\)1 V constant potential MEC system (CPMEC) had a \(-\)1 V and was constantly applied throughout the 48 h fermentation reaction period (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline
**MEC System** & **Potential (V)** & **Frequency (s/s)** & **ORP-Controlled** \\ \hline CPMEC & \(-\)1 & 1 & No \\ LPORP & \(-\)0.4\(\leftarrow\)\(-\)0.6 & 1 & Yes \\ HPORP & \(-\)1 & 0.5 & Yes \\ \hline \end{tabular}
\end{table}
Table 2: ORP-controlled MEC system.