esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Internet-of-things networks consist of multiple sensor devices spread over a wide area. In order to protect the data from unauthorized access and tampering, it is essential to ensure secure communication between the sensor devices and the central server. This security measure aims to guarantee authenticity, confidentiality, and data integrity. Unlike traditional computing systems, sensor node devices are often limited regarding memory and computing power. Lightweight communication protocols, such as LoRaWAN, were introduced to overcome these limitations. However, despite the lightweight feature, the protocol is vulnerable to different types of attacks. This proposal presents a highly secure key establishment protocol that combines two cryptography schemes: Elliptic Curve Qu-Vanstone and signcryption key encapsulation. The protocol provides a method to establish a secure channel that inherits the security properties of the two schemes. Also, it allows for fast rekeying with only one exchange message, significantly reducing the handshake complexity in low-bandwidth communication. In addition, the selected schemes complement each other and share the same mathematical operations in elliptic curve cryptography. Moreover, with the rise of a community-friendly platform like RISC-V, we implemented the protocol on a RISC-V system to evaluate its overheads regarding the cycle count and execution time.  
IoT; secure communication; key establishment; signcryption; ECQV; RISC-V
## 4 Discussion
### Data Privacy Attacks and Protections  
Data security and privacy are critical issues in medical applications. In FL, it is usual for all nodes to calculate and upload their local model weights and parameters to an aggregate server. The steps of uploading and processing the weights and parameters may leak sensitive patient information contained in the medical data. The possible attacks include model inversion and membership inference attacks, which may leak patient data to an attacker. The common solutions for data privacy protection include differential privacy and homomorphic encryption [21] based techniques, which can guarantee the security of transferring the local weights and parameters in federated learning. In the following subsection, we describe the possible data privacy attacks and protections in FL.
## 3 Results  
To estimate how frequently RO measurements from a constellation of low Earth-orbiting satellites will encounter heavy precipitation in nature, orbital simulation experiments were carried out where the RO ray paths were superimposed on the nearest 30 minute GPM IMERG precipitation product [27]. A number of different orbit scenarios were examined, varying the number of satellites, orbital altitudes and inclinations, and the relative time separation between satellites in the constellation configuration. For brevity, one such configuration (three satellites) is described in Section 3.1, with summaries of findings for other orbit scenarios described in Section 3.2.
## 4 Conclusions  
In this paper, we reported on the effects of ALD-Al\({}_{2}\)O\({}_{3}\) passivation on multilayer MoS\({}_{2}\) TFTs and the temperature-dependent variation of the TFT performance. High-\(k\) Al\({}_{2}\)O\({}_{3}\) layers were uniformly passivated over the entire surface of MoS\({}_{2}\) TFTs with the aid of UV-ozone treatment leading to (i) huge reductions of the hysteretic transfer curves; (ii) saturation current enhancement of the output characteristics; and (iii) increases in \(\mu_{\text{eff}}\) of approximately 40.4%. Based on investigations of the temperature-dependent TFT characteristics including intrinsic carrier mobility (\(\mu_{0}\)) extracted by the \(Y\)-function method, we could conclude that the dominant transport mechanism is thermionic emission in our Al\({}_{2}\)O\({}_{3}\)-passivated MoS\({}_{2}\) TFTs with a considerable Schottky barrier between the S/D electrodes and active channel. In addition, the proposed approach at relatively high (i.e., realistic device working) temperatures can be employed to realize stable and reproducible electronic devices for robust and practical applications.  
**Supplementary Materials:** The following are available online at www.mdpi.com/2076-3417/8/3/424/s1, Figure S1: (**a**) Cross-sectional TEM image of a MoS\({}_{2}\) encapsulated by Al\({}_{2}\)O\({}_{3}\). Inset: FFT patterns of the MoS\({}_{2}\) obtained in the area marked with the white dashed line. (**b**) HAADF image of a MoS\({}_{2}\) TFT with Al\({}_{2}\)O\({}_{3}\) passivation; (**c**) atomic percentage of the atoms contained in the area marked with red and green rectangles in (**b**). Figure S2: Raman and (**b**) XPS spectra of (i) pristine and (ii) UV-ozone treated MoS\({}_{2}\). Figure S3: Transfer characteristics of 14 MoS\({}_{2}\) TFTs before (black line) and after (red line) Al\({}_{2}\)O\({}_{3}\) passivation., Figure S4: Transfer characteristics of six MoS\({}_{2}\) TFTs without Al\({}_{2}\)O\({}_{3}\) passivation before (i.e., pristine) and after UV-ozone treatment.  
**Acknowledgments:** This research was supported in part by a National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT, MSIT) (No. 2016R1C1B1016344, 2015R1A1A1A05027489) and the Ministry of Trade, Industry and Energy and Korea Evaluation Industrial Technology through the Industrial Strategic Technology Development Program (No. 10079571, 10080348). Support from the Korea Research Fellowship program funded by the MSIT and Future Planning through the NRF (No. 2017H1D3A1A02014116) and the Basic Science Research Program through the NRF funded by the Ministry of Education (No. 2017R1D1A1B030353150) is also gratefully acknowledged.  
**Author Contributions:** [PERSON] and S.K. conceived and designed the experiments. [PERSON] and [PERSON] performed the experiments. [PERSON], S.K. and [PERSON] analyzed the results. All the authors wrote and reviewed the manuscript. [PERSON] and [PERSON] contributed to this work equally.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications
## 4 Simulation and Numerical Results  
This section uses numerical simulations to evaluate the performance achieved by the proposed RBF-GAT for the sum-rate maximization problem. We first set the simulation parameters of the training neural network and IRS-assisted LEO satellite communication system. Then, we compare the RBF-GAT with several benchmarks proposed in prior works. Finally, we show the simulation results and analyze the computational complexities of the proposed RBF-GAT method. The simulation experiments conducted in this study were performed on a computer equipped with an Intel(R) Core(TM) i7-8700 processor @3.19 GHz, 64 GB RAM. The simulation platform utilized Python 3.6, and the neural network in the RBF-GAT was constructed using TensorFlow 1.6.
## 3 Experimental Setup  
### Dataset  
The input images used in this research are collected using the Scolioscan system (Figure 9) (Model SCN801, Telefield Medical Imaging Ltd., developed in Hong Kong), which generates 3D volume projection images (VPI) using a 3D ultrasound imaging technique. The experimental procedures involving human subjects were approved by the Institutional Review Board. The subjects gave informed consent for their inclusion in this study as required, and the work adheres to the Declaration of Helsinki.  
A total of 109 images, collected from 109 patients (82 females and 27 males) with an age range of 15.6 \(\pm\) 2.7 years and having different degrees of spine deformity, were used retrospectively.  
Nine 2D coronal images of different depths were extracted from one 3D ultrasound VPI image [16]. Since the quality of individual images varies greatly, human experts were employed to manually assess the clarity of the lateral bony features represented in an image and select the best image for a given patient. Subsequently, 109 2D vertebral coronal images formed the input dataset for this work, and each 2D image was then resized uniformly to 2574 \(\times\) 640-pixel and converted to the '.png' format.  
The truth mask for each 2D coronal image was carried out by experts from Hong Kong Polytechnic University. Some sample input 2D coronal images, along with their expert  
Figure 9: Scolioscan system [2].  
generated truth masks, are shown in Figure 10a-d. The labelling of the truth mask was conducted based on some key features: (1) There should be six lumbar bony features (LBF); (2) the thoracic bony features (TBFs) in the lumbar region are not labelled as they are not generally visible in ultrasound images; (3) if the last pair of TBFs (i.e., the T12 level) is not visible, it is labelled according to the judgment of the experts. The input raw datasets of 109 spine ultrasound images were randomly split into one training set of 79 images and one testing set of 30 images.
# Preliminary Validation of Surface Reflections from Fengyun-3C Radio Occultation Data
## 4 Discussion  
Under the matching criteria, we found FY-3C RO profiles have good performance compared with COSMIC profiles. The consistency of FY-3C and COSMIC products has been successfully validated as well. Within the closer location area and temporal interval, FY-3C events present reflection signatures similar to COSMIC reflection events. However, some events from matched pairs do not present much clearer reflections. We speculate that the absence of reflections may result from poor surface conditions and turbulent weather variations.  
The refractivity profiles of FY-3C RO events do not start from the surface in most cases, but the reflections may occur at the surface. We reprocessed the new bending angle profiles of FY-3C from the surface to 40 km altitude. Then, we reconstructed the model of reflected bending angles from the surface of Earth. However, there are still some uncertainties in the reflected bending calculation, due to the influence of low SNR or atmospheric interference. Further, more appropriate algorithms could extract more information and physical mechanisms at a lower height. It is worth noting that the reflected signal retrieval is not straightforward in practice. It is a classic routine to use PM [46] or CT [26] methods to separate the reflected signal from the direct signal and to then calculate the direct reflected bending angles through a GO algorithm [26], which will be our next emphasis in a coming study. In the current study, we only focused on the reflection existence in FY-3C and calculated the model of reflected bend angles, which paves the way for digging FY-3C in the future.  
FY-3C RO products have been used in numerical weather prediction (NWP) for several years. The reflected signals with good prior information are able to provide substantial benefits of NWP. Taking advantage of reflected RO signals with independent information in lower height enables us to improve product quality. In our following work, specific statistics will be focused on the reflection distribution and physical characteristics of FY-3C.  
Figure 9: Bending angles of FY-3C event on 28 September 2015: in (**a**), the red curve shows the bending angles of refractivity profile from cutting height to 40 km altitude, the green dashed curve shows the bending angles by Equation (6) and the blue curve shows the recalculated bending angle by GO method from Earth’s surface to 40 km altitude. (**b**) shows the reflected bending angle model extracted by Equation (8).
# Evaluation of Adhesion and Hysteresis Friction of Rubber-Pavement System
## 8 Summary and Conclusions  
This study experimentally examined the relationship between the rubber-pavement adhesion and friction. We calculated the surface energy components for rubber materials by measuring the contact angle between rubber materials and different probe liquids. The contact angle was measured using a Sessile drop device. Similarly, the surface energy components of aggregate samples were measured using a USD device. The adhesive bond energy between rubber and aggregates was calculated based on the surface energy components of both materials. In addition, we measured the coefficient of friction between different rubber materials and HMA substrates prepared using different rock types. The results showed that there is a fair correlation between adhesive bond energy and friction between rubber and pavement surfaces. In addition, the results demonstrated that there is a strong relationship between friction and rubber elastic modulus. Softer rubber provided higher friction. The results of this study provide an experimental verification of the relationship between adhesion and friction of rubber and pavement surface. The adhesive bond energy and rubber rheological properties could be incorporated in computational models to study tire-pavement friction in different conditions (e.g., speed and temperature). We recommend testing more aggregate types and rubber materials to evaluate the adhesion friction.  
This publication was made possible by an NPRP award (NPRP No. 7-482-2-184: Thermo-Mechanical Tire-Pavement Interaction: Computational Modelling and Field Measurements) from the Qatar National Research Fund (QNRF - a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.  
[PERSON] and [PERSON] conceived, designed and performed the experiments, analyzed the data, and wrote the paper.  
The authors declare no conflict of interest.
## 5 Case Studies
### Parameter Selection  
The main parameter of the DSEC model is iteration round R. Under different values of R, the GM performances of the DSEC model on the validation set are analyzed and compared and the value range is set to [10, 20,..., 100]. The results of parameter analysis are shown in Figure 5. Taking into account the randomness of the AdaBoost classifier, the average results of 10 repeated experiments are utilized for comparison.  
It can be seen from Figure 5 that, with the increase of R, the GM value increases rapidly at first and then gradually remains stable. Considering the classification performance and model complexity, the value of R is set to 40.
## 2 Literature Review
### Latest Developments in SCADA  
SCADA systems continue to evolve with new capabilities enabled by emerging technologies. The growing utilization of cloud computing and software-defined networking provides opportunities to enhance SCADA flexibility and scalability. Research has explored migrating traditional SCADA architectures to cloud-based models for benefits like elastic resource allocation, pay-as-you-go costs, and off-site redundancy [15]. Cloud solutions also enable mobile access and remote management of dispersed SCADA assets via Internet connectivity [16]. Integration of Industrial Internet of Things (IIoT) technologies is another area of ongoing development. [PERSON] et al. [17] have examined leveraging wireless sensor networks and edge computing platforms to modernize supervisory control functions through real-time analytics at the network edge. This helps address issues like bandwidth limitations arising from centralized cloud models. Incorporating machine learning and AI into SCADA is also an active research area, with potential applications including predictive maintenance, advanced situation awareness, and autonomous optimization.
## 3 Computational Results
### Optical Depth Discretizations
For example, changing the OD in these layers to 0.022 reduced the error from 6% to 0.8%, and adopting an exponential scheme further reduced the error from 0.8% to 0.3%. Further decreasing the OD in the two layers would increase the maximum error. Scheme 2 did not work for OD = 2 cases because in Scheme 2, the OD in the first and the last layer would always be less than 0.02, and therefore, the optimal value of 0.022 could not be reached. Other cases with higher OD, such as OD = 10 and OD = 15, were also examined and similar observations were made. The authors admit that the rationale behind this optimal value of OD = 0.022 for the first layer and the last layer is not fully understood as yet, although the importance of the first and last layer has been shown. Further analytical investigations regarding this optimal value/scheme will be incorporated in future works. In existing experiments, transmitted photons in \(\theta_{t}\)-90\({}^{\circ}\) could not be readily accessed. As such, the relative error in the detectable regimes was more significant for potential spectroscopy methods, as least at present. For the sake of discussion, this angular range was set to 0-30\({}^{\circ}\) (transmitted photons) and 150-180\({}^{\circ}\) (reflected photons). The averaged absolute relative error in this range was used to evaluate the performance of different OD discretization schemes. Figure 7 depicts the averaged error with different OD discretization schemes. As seen in Figure 7, averaged absolute relative errors for all cases were quite low, from 0.05% to 0.3% with various phase functions and different ODs. It is also worth noting that the Markov chain approximation error for phase function 3 was significantly higher than phase function 1 and phase function 2. Such a discrepancy was believed to be caused by the discretization of the phase function, since phase function 3 was more spatially complex than the other two. An appropriate zenith angle discretization scheme may be needed for challenging phase functions, as will be discussed in Section 3.3 of this work. Finally, it is evident that the required averaged error was not remarkably affected by OD discretization. This observation was also verified with numerical repeats with other boundary conditions. Although OD discretization seemed to have a limited impact in the angular range of 0-30\({}^{\circ}\) and 150-180\({}^{\circ}\), it was still recommended to adopt the scheme in setting the OD of the first and last layer to a value of 0.022 to incorporate likely measurements near 90\({}^{\circ}\). Figure 6: Absolute relative error of Markov chain angular distribution predictions compared to Monte Carlo, with OD discretization scheme 2. Panel (**a**). Phase function 1, OD = 5; Panel (**b**). Phase function 2, OD = 5; Panel (**c**). Phase function 3, OD = 5. Red dash lines indicate an error threshold of 1.0%, and blue dash lines indicate a threshold of 0.1%.
## 2 Description of General Idea  
As shown in Figure 1, the case study is a neighborhood grid consisting of several H-MGs, and each H-MG comprises non-responsive load and Distributed Energy Resources (DERs) including responsive load demand, Energy Storage (ES), dispatchable unit, and non-dispatchable unit. Each H-MG must first respond to its loads according to the non-dispatchable units output, and, ultimately, if it has a deficit in electrical/thermal power, it will be compensated by obtaining power from other H-MGs or vendors. On the other hand, if it has an excess in power generation, the H-MG cells the surplus to other H-MGs or purchasers. Therefore, each player must make a comparison between the prices offered by other H-MGs and retailers to select the optimal price offer for this competition. Producers and consumers have their own goals, buying and selling, accordingly. Therefore, each manufacturer is seeking to raise its profits by selling the highest amount of power produced to other H-MGs and retailers. On the other hand, each consumer tries to buy low-cost products to reduce their own outlay. Furthermore, since ER is the main objective of the strategies being presented in this paper, each H-MG should, if faced with a lack of power generation, firstly detect its deficiency through the ES, and then determine whether the existing dispatchable units violate the pollution indicators. Finally, it should solve the problem of power shortage according to the DR constraints and through the use of dispatchable units. Ultimately, power shortage will be mitigated by purchasing from other H-MGs and retailers. To meet the ER objective function, each of the H-MGs that are lacking in power should first provide their own deficiency from surplus generation of other H-MGs.  
Figure 1: A schematic of the general idea.
## 3 Results
### Effect of Treatment with Adaptogens
#### 3.2.4 Overall Findings  
Among the studies analyzed, five reported a significant reduction in both physiological and psychological stress-related measures following adaptogen treatment. In a RCT lasting 8 weeks, _W. somifera_ administration resulted in a significant reduction in serum cortisol levels and perceived stress in a sample of 52 males and females under chronic stress. Specifically, reductions in serum cortisol levels of 16.05% and 22.2% were observed at weeks 4 and 8, respectively. Similarly, perceived stress levels decreased by 22.1% at week 4 and 32.7% at week 8 [87]. Likewise, _W. somifera_ treatment significantly reduced salivary cortisol levels, self-perceived stress, and the severity of generalized anxiety disorder in a double-blind, 8-week RCT involving male and female participants with mild-to-moderate stress and anxiety levels [89]. Another RCT assessed the effects of _W. somifera_ in 58 highly stressed males and females over 8 weeks, where both 250 mg/day and 600 mg/day doses decreased serum cortisol levels compared to the placebo group [91]. In terms of perceived stress, reductions relative to BL were significantly higher in the treatment groups compared to the placebo in a dose-related trend.  
A combination of _W. somifera_ and _Abelmoschus esculentus_ contained in the proprietary composition CL18100F4 was evaluated in a 2-week RCT. The results demonstrated a significant reduction in serum cortisol levels in both 300 mg/day and 500 mg/day groups, with 27.21% and 33.01% reductions, respectively, compared to BL. Furthermore, perceived stress significantly decreased in both 300 mg/day and 500 mg/day groups, with reductions of 21.1% and 23.22%, respectively [90].  
In a 60-day RCT involving 215 healthy male and female athletes, ADAPT-232S and ADAPT-S supplementation prevented the exercise-induced increase in blood cortisol levels. While cortisol levels significantly increased in the placebo group at the end of the training season, levels in the ADAPT-232S and ADAPT-S groups remained significantly lower compared to baseline. Additionally, a significant reduction in perceived stress was observed in both groups [88]. Finally, blood cortisol and ACTH levels were analyzed with no significant differences found among groups [93].  
Conversely, several studies found no significant effects of adaptogen treatment on stress biomarkers or self-reported psychological measures. Specifically, [PERSON] et al. [93] reported no significant differences in blood cortisol or ACTH levels following psychobiotic intervention, suggesting a lack of measurable endocrine changes. Similarly, [PERSON] et al. [92] also observed no reduction in cortisol levels; however, their findings revealed a decrease in subjective stress perception, indicating that psychobiotics may exert beneficial effects on emotional appraisal even in the absence of detectable hormonal changes.