esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
1. Water content is the main factor affecting the electrical conductivity of GO/CCs. The conductivity of GO/CCs decreases linearly with the increase in water loss, and the rate of conductivity decreases slowly when water loss exceeds 6%. The addition of GO has little effect on conductivity when the water content is close to saturation. When the water content is not saturated, a 0.1% GO concentration can significantly increase the electrical conductivity of GO/CCs.
2. Temperature has a significant effect on the conductivity of GO/CCs, and the current intensity of GO/CCs increases tenfold when the temperature is increased from 10 \({}^{\circ}\)C to 70 \({}^{\circ}\)C. The fitted curves yield a quadratic correlation between the conductivity of GO/CCs and the temperature change.
3. The addition of GO attenuates the polarization phenomenon of GO/CCs. When water loss is <8% and the GO content is \(\geq\)0.05%, the current intensity value decrease during the polarization of GO/CCs is linearly correlated with water loss. This could be used to gauge the water content of GO/CCs.
4. The combined effect of ion migration in the water and electron migration on the GO surface constitutes the conductive mechanism of GO/CCs. Ion migration interferes with the electron migration paths, which are connected when the ionic conductivity is weakened.  
This study reveals the conductivity mechanism of GO/CCs and the effects of water content and GO doping on their conductivity. The conductivity of GO/CCs originates from the combined effect of ionic conductivity in microscopic pore water and the electronic conductivity on the surface of GO. In particular, the polarization of GO/CC was investigated and the phenomenon of spike fluctuation in the current curve was found for the first  
Figure 10: SEM of pure cement specimens and GO/CC specimens with 0.05% and 0.1% GO dosages.  
time. GO is believed to be the cause of this phenomenon. Cement-based sensors should be considered for waterproofing treatment or the elimination of moisture perturbation. This provides a reference for the design of cement-based sensors for GO/CCs and advances the application of cement-based sensors in natural environments.  
Conceptualization, Z.L., H.X. and K.Z.; methodology, Z.L., H.X. and F.Y.; supervision, H.X.; formal analysis, Z.L. and K.Z.; investigation, Z.L.; resources, R.G.; data curation, R.G.; writing--original draft preparation, Z.L.; writing--review and editing, K.Z. and H.X.; visualization, F.Y.; software, F.Y. and R.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 11962009).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors would like to thank the Yunnan Key Laboratory of Disaster Reduction in Civil Engineering. We also thank [PERSON] for the guidance and assistance provided to the first author of this study.  
The authors declare no conflicts of interest.
###### Abstract  
In both scientific and industrial fields, there has been a notable increase in attention toward High-Altitude Pseudo-Satellites (HAPSs) in recent years. This surge is driven by their distinct advantages over traditional satellites and Remotely Piloted Aircraft Systems (RPASs). These benefits are particularly evident in critical areas such as intelligent transportation systems, surveillance, remote sensing, traffic and environmental monitoring, emergency communications, disaster relief efforts, and the facilitation of large-scale temporary events. This review provides an overview of key aspects related to the propellers and propulsion systems of HAPSs. To date, propellers remain the most efficient means of propulsion for high-altitude applications. However, due to the unique operational conditions at stratospheric altitudes, propeller design necessitates specific approaches that differ from those applied in conventional applications. After a brief overview of the propulsion systems proposed in the literature or employed by HAPSs, focusing on both the technical challenges and advancements in this emerging field, this review integrates theoretical foundations, historical design approaches, and the latest multi-fidelity optimization techniques to provide a comprehensive comparison of propeller design methods for HAPSs. It identifies key trends, including the growing use of CFD-based simulations methodologies, which contribute to notable performance improvements. Additionally, the review includes a critical assessment of experimental methods for performance evaluation. These developments have enabled the design of propellers with efficiencies exceeding 85%, offering valuable insights for the next generation of high-endurance, high-altitude platforms.  
HAPSs; stratospheric propellers; propulsion systems +
Footnote †: journal: Applied sciences
## 4 Discussion
### General Implications and Study Limitations  
The most significant finding is from the identical initialization experiment. The persistence of high-performance variance and observation space divergence in games like Boxing and Qbert, even when starting from the same network weights, demonstrates that the problem is not merely one of poor initialization. The stochastic nature of the RL interaction process itself is sufficient to drive agents onto divergent paths. This suggests that achieving reproducible performance in complex environments requires more than just fixing random seeds; it may require fundamentally new approaches to guide exploration or to make learning more robust to variations in experience.  
Our comprehensive set of metrics--from visual t-SNE to quantitative measures of dispersion (entropy, TV distance, MMD) and network function (TD-error, RMSE, KLD)--presents a unified narrative. When agents see different things (high TV/MMD), their understanding of the world diverges (high-value RMSE), and their resulting behaviors diverge (high policy KLD). This work highlights that reporting only the mean and standard deviation of final scores can obscure the rich and varied behaviors that contribute to those scores. A deeper analysis of the underlying state visitation distributions is crucial for a comprehensive understanding of DRL algorithm behavior.  
We acknowledge that the empirical results of our study focus on the PPO algorithm within the Atari domain. This deliberate choice of scope enabled a deep and multifaceted analysis of the divergence phenomenon. However, it is reasonable to question how these findings generalize to other algorithms and environments. We hypothesize that the core mechanism--stochastic agent-environment interactions leading to divergent experiential trajectories and specialized policies--is a fundamental aspect of the reinforcement learning process and is not unique to PPO. For instance, off-policy algorithms like DQN or SAC, which utilize a replay buffer, might exhibit different dynamics. A replay buffer could mitigate divergence by averaging experiences across different trajectories. Conversely, it could also exacerbate the issue if certain types of \"lucky\" trajectories become overrepresented early in training. Similarly, extending this analysis to domains with continuous action spaces, such as robotics tasks in MuJoCo, or environments with sparser rewards, represents an important next step. Exploring how the structure of the state space and the nature of the reward function influence the degree of observation divergence is a compelling avenue for future research.
# Advances in the Noninvasive Diagnosis of Dry Eye Disease
## 10 Bulbar Redness Assessment  
Bulbar redness (BR) is a non-specific ocular condition caused by the vasodilation of the conjunctival and/or anterior sclal blood vessels, which is itself caused by enhanced blood flow to and capillary permeability in the anterior ocular tissues in response to various stimuli [89]. Hyperemia is a feature of several ocular conditions: inflammation of the anterior segment of the eye [90], the adverse effects of glaucoma medications [91; 92], contact lens wearing [93], allergic or infective conjunctivitis [94], and DED [95].  
Since the first introduction by [PERSON] and Chapman-Davies in 1987 of an image-based bulbar hyperemia grading scale [89], several subjective rating scales have been introduced to improve inter- or intra-observer variability [96; 97]. Scientific studies tried to overcome these limitations by developing novel models of computer-based photograph-analysis techniques, which have now progressed to automated image analysis through the application of artificial intelligence (AI) [98]. [PERSON] et al. developed the first computer-assisted conjunctival hyperemia quantification system by using the pixel edge detection analysis of a 3 \(\times\) 3 mm region of bulbar conjunctiva [99]. [PERSON] analyzed the mean relative redness (RR) and the blood vessel area ratio (VA) of digital images of the external eye by using a clinical photographic biomicroscope, an MVC video camera and computer imaging system, and custom software [100]. [PERSON] et al. developed an automated computer redness grading system (Ora, Inc., Andover, MA, USA, patent pending) for the evaluation of the location and prominence of fine horizontal conjunctival vessels in DED and demonstrated agreement with a group of investigators using the established clinical scale [101].  
[PERSON] et al. evaluated the clinical assessment of bulbar redness by using an Oculus Keratograph 5M Topographer (Oculus Optikgerate GmbH, Wetzlar, Germany) equipped with automated scanning and scoring software. They found a statistically significant correlation between the Oculus Index score and the scores determined using three subjective scales (the Institute for Eye Research scale, the Efron scale, and the Validated Bulbar Redness 10-picture grading scale) [96; 102]. Moreover, they found that the Keratograph yielded higher intra- and inter-observer reproducibility, suggesting that the Keratograph could be a time-saving device [103].  
Despite the introduction of novel incorporated instruments that make the clinical assessment of BR in DED quicker and easier, the majority of automatic grading systems are unable to differentiate bulbar conjunctival hyperemia from episcleral and scleral hyperemia; thus, there are many concerns over the use of BR as a biomarker [104].
# Wireless Positioning in IoT: A Look at Current and Future Trends
## 3 Designing an IoT Positioning System  
In its essence, a positioning system translates a set of measurements from well-known reference points into a coordinate pair. The reference points, known as anchors in localization terminology or Access Nodes (AN) in IoT terminology, act as a means for the device of interests, a mobile or an IoT tag, to be in a local or global reference frame. Depending on who makes the measurements, the positioning is considered to be network centric (i.e., when the anchors make the positioning-related measurements) or device centric (i.e., when the IoT end nodes or tags perform the positioning-related measurements).  
These two types of positioning have very different implications on security and privacy, which should always be carefully considered regarding the final application. For example, privacy-preserving positioning solutions are easier to be achieved in a device-centric approach than in a network-centric approach as the device would not need to disclose its position to the network.
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 5 Discussion and Interpretation of Results  
Anyone unfamiliar with TCP could assume that in the case of a packet drop, TCP would just need an additional \(RTT\) to retransmit the packet. The average packet rate for \(p_{drop}=0.00\%\) is:  
\[\frac{6854\text{ pkts}}{60.24\text{ s}}=113.778\text{ pkts}/\text{s} \tag{14}\]  
Thus, if 1% of the packets are dropped, that would mean that about 68.54 packets need to be retransmitted.  
Figure 10: Packet Exchange for Packet Loss and Retransmission.  
With the above-average packet rate, that would add:  
\[\frac{68.54\;\mathrm{pkts}}{113.778\;\mathrm{pkts/s}}=0.602\;\mathrm{s} \tag{15}\]  
Adding 0.602 s to the transfer time of \(p_{drop}=0\%\) would result in 60.82 s. This is still far less than the simulated transfer time of 66.83 s (\(p_{drop}=1\%\), 10 s subtracted from simulation result, see Table 4). This indicates that other factors need to be considered to explain the observed behavior.
# Control Strategies for Gait Tele-Rehabilitation System Based on Parallel Robotics
## 2 Materials and Methods
### Control Strategies
#### 2.2.1 Therapist Modulation Mode (TMM)  
An alternative to the FGTM, in which the robotic motion is performed without any online modulation, is to enable therapists to transfer movements in real-time to the gait simulator. In addition to the possibility of creating arbitrary lower extremity motion, the method is particularly useful for tele-rehabilitation.  
In order to track the therapist's feet trajectory, the adopted approach involves the use of sensors, such as wireless inertial sensors, attached to the feet. Other sensing systems may be used, particularly if reliable position tracking is desired. As illustrated in Figure 3b, based on the measured pose with respect to the initial position provided by such sensors, i.e., \({}^{p0}\mathbf{R}_{p}\) and \({}^{p0}\mathbf{t}_{p}\), the same computations described in Equation (1) may be used to provide high-level commands to the platforms. The main difference with the previous strategy is that those estimates are transferred in real time to the robot.  
In tele-rehabilitation, the corresponding time delay may produce instability. This is especially the case when therapy involves fast movements. In order to limit the effects of these potential issues, in addition to optimization of communication software to reduce delay and packet loss and use of low-pass filtering to minimize the effect of high-frequency fluctuations, control gains are reduced. Finally, two additional functionalities are proposed to improve the overall usability of this operation mode. First, the therapist may directly deactivate any DOF or reset his/her initial position at any moment. Moreover, the performed trajectories may be recorded to be used in open-loop, such as in FGTM.
# Scale-Model Experiment on the Delayed Failure Mechanism of Deep Tunnels in Brittle Rock
## 3 Research Methodology  
In this study, the delayed rockburst process was reproduced through scale-model tests and numerical simulations, and the mechanism of delayed rockburst occurrence was determined based on the relationship between the delayed failure time and the parameters of the scale-model tests and simulations. The detailed research methodology was completed according to the following steps:  
1. Conducted scale-model tests to determine the delayed failure model and time under different loading scheme. * Deep Rock Scale-model Testing Apparatus (DRSTA) preparation. * Drilling machine design and manufacture. * Scale factor determined based on the chamber size of DRSTA and tunnel diameter of the scaled model. * Similar material studied to simulate the brittle rock in Jinping site. * Monitoring system designed to obtain detailed data of the tunnel delayed rock burst.
2. Conducted the delayed failure time simulation of the scale-model testing. * Established a finite element model of the tunnel scaled model. * Initial geostress, material properties, boundary condition, etc., applied to the FEM model. * Numerical simulations under different loading scheme were conducted with Abaqus(V6.14) software.  
Figure 1: Jinping II headrace tunnel project schematics [18]: (**a**) project location; (**b**) geological conditions.  
Analyzed the results the scale-model tests and numerical simulations. Compared the time-to-failure of the scale-model tests and numerical simulations. Obtained the delayed failure mechanism based on the time-to-failure, rock properties, and geostress.
# Hybrid Deep Learning-Based Intrusion Detection System for RPL IoT Networks
## 6 Conclusions and Future Work  
This paper proposes a hybrid IDS deep learning-based model for detecting and classifying cyber-attacks for IoT networks based on the RPL routing protocol. Due to the lack of datasets that are developed for IoT traffic, a new dataset named IoTR-DS was created by simulating three types of common RPL attacks, which are DIS, Rank, and Wormhole attacks, in addition to the normal traffic. Unlike the existing techniques, which use sniffing and monitoring tools to collect the data traffic, in our IoTR-DS, the data attributes used for analysis are contained in the data packet and stored in the rod where the hybrid DL-IDS is implemented. The hybrid DL-IDS is composed of supervised DANN and semi-supervised DAE, which are designed to predict attacks that are trained and untrained. The goal was to design a scalable model for detecting new attacks where their traffic behavior was not seen before by comparing the traffic reconstruction loss with the normal traffic and the existing attack loss values.  
The evaluation results show a detection accuracy rate of 98% for multi-class attacks when using pre-trained attacks (known) and an average of 95% when predicting unseen two-attack behaviors (untrained). This high classification detection rate shows the good performance of the proposed model; it also indicates that the IoTR-DS dataset contains enough features and information to classify such attacks. Other attacks related to the routing and application layer will be included in the dataset and tested by the hybrid model in our future work.  
Writing--original draft preparation, Y.A.S.; review and editing, A.T. and R.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**DANN**} & \multicolumn{2}{c}{**J48**} & \multicolumn{2}{c}{**KNN**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**LSTM**} \\ \hline  & \begin{tabular}{c} **F1-** \\ **Score** \\ \end{tabular} & **Accuracy** & \begin{tabular}{c} **F1-** \\ **Score** \\ \end{tabular} & **Accuracy** & \begin{tabular}{c} **F1-** \\ **Score** \\ \end{tabular} & **Accuracy** & \
\begin{tabular}{c} **F1-** \\ **Score** \\ \end{tabular} & **Accuracy** &
\begin{tabular}{c} **F1-** \\ **Score** \\ \end{tabular} & **Accuracy** \\ \hline Normal & 0.99 & & 0.98 & 0.98 & 0.98 & 0.98 & 0.96 & \\ DIS Attack & 0.98 & 0.97 & 0.96 & 0.96 & 0.96 & 0.96 & \\ Rank Attack & 0.89 & 0.98 & 0.79 & 0.95 & 0.78 & 0.94 & 0.77 & 0.94 & 0.84 & 0.96 \\ Wormhole & 0.80 & & 0.75 & 0.67 & 0.61 & 0.77 & \\ \hline Average & 0.92 & & 0.87 & 0.84 & 0.83 & 0.88 & \\ \hline \hline \end{tabular}
\end{table}
Table 18: Performance of Different ML-DL Models.
## 5 Verification of Model Solution
### Comparison with Existing Models  
Based on the Mohr-Coulomb yield criterion, an elasto-plastic solution for the stress and radial displacement of the rock mass and structure was obtained [4]. Likewise, a model solution of frost-heaving force between the rock mass and structure was presented [10]. By assuming \(F\)0 = 0, \(\alpha_{\text{f}}=0\), \(b_{\text{r}}=0\), and \(k_{\text{f}}=0\) in the proposed mechanical model, the analytical solution of this study can be simultaneously compared with those of [PERSON] et al. [4] and [PERSON] et al. [10].  
The related parameters of the mechanical model were obtained from the Yuximolegait tunnel located in Yuximolegaidaban of Tianshan Mountain in Xinjiang Province, China. The altitude of the Yuximolegait tunnel site is approximately 3200 m. Moreover, the minimum temperature of the air in the tunnel is approximately \(-\)28 \({}^{\circ}\)C. The excavation radius of the Yuximolegait tunnel (_R_1) is 5.0 m, and the thickness of the support zone (_T\({}_{s}\)_) is 0.5 m. Additionally, the freezing depth of the surrounding rock (_R_2) is 7.0 m. The volumetric strain of the surrounding rock caused by frost heave (\(\epsilon_{\text{v}}\)) was 0.0055, and the initial ground stress (_P_0) was 2.5 MPa [4]. The other parameters are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Zone** & **Elastic Modulus/GPa** & **Poisson’s Ratio** & **Cohesion/MPa** & **Internal Friction Angle/\({}^{\circ}\)** \\ \hline Support zone & 28.0 & 0.16 & / & / \\ Frozen rock mass zone & 7.8 & 0.35 & 1.7 & 45 \\ Unfrozen rock mass zone & 4.6 & 0.33 & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameters based on the Yuximolegait tunnel.  
The comparison results between this study and existing models are shown in Figure 6. Noticeably, the stress distribution curves along the tunnel radial direction obtained from this study are similar to those from existing models. Regarding the plastic radius of the frozen rock mass zone, the values from this study, [PERSON] et al.'s [10] and [PERSON] et al.'s [4], were 5.938 m, 5.938 m, and 5.603 m, respectively. The deviation of the plastic radius between this study and [PERSON] et al.'s [4] was 5.6%. Regarding the frost-heaving force between the support zone and frozen rock mass, the values from this study, [PERSON] et al.'s [10] and [PERSON] et al.'s [4] were 2.309 MPa, 2.309 MPa, and 2.750 MPa, respectively. The deviation of the frost-heaving force between this study and [PERSON] et al.'s [4] was 16.0%. Notably, the discrepancies in the circumferential stress curves in support zone I and plastic zone III of the frozen rock mass between this study [10] and [PERSON] et al.'s [4] were due to the differences in plastic radius and frost-heaving force. This verifies the rationality and accuracy of the analytical solution obtained from the proposed mechanical model in this study.
## 2 Fault Type and Fault Feature Information of the Protection System
### Fault Feature Information of the Protection System  
Based on the complex fault types of the protection system summarized in Table 2, this paper selects four features of integrated alarm information, device self-checking information, link information of GOOSE and SV, and sampling value information as the feature information of fault diagnosis, which can comprehensively reflect the change in feature quantity caused by the fault of the protection system [15].  
The main function of the integrated alarm information is to reflect whether the protection system fails. If a fault occurs, the equipment will issue alarm information and upload it to the monitoring terminal, which can be used as one of the bases for equipment fault diagnosis while realizing fault warning.  
Device self-checking is an important function of an intelligent protection system. When any abnormality occurs in the operation process of the device, the device will record the abnormal information through the event-recording function for the operator to query.  
GOOSE (Generic Object-Oriented Substation Event) is equivalent to the DC control and signal cables in traditional substations, which transmit control instructions and signals. It mainly includes a switch/knife switch position, control switch position, abnormal/alarm signal, blocking signal, etc. SV (Sampled Value) is equivalent to the secondary AC cable in the traditional substation, which transmits the sampled instantaneous values of voltage and current, including the instantaneous value of voltage and current on the secondary side of the transformer. The link information of GOOSE and SV are important indicators to indicate whether the information links between the protection system equipment and between the equipment and the monitoring terminal work normally, reflecting the link connection state of the equipment.  
The sampling value information is the sampling value of three-phase voltage and current transmitted by two channels, which can reflect whether the voltage and current-sampling function of the protection system are normal.  
The protect system fault feature information, as shown in Table 3: