esa-sceva/satcom-chunk-collection huggingface dataset:
# Natural Stones with a Self-Cleaning Surface via Self-Assembled Monolayers
## 4 Discussion
### Thermal Stability and Durability  
As shown in Figure 6a-c, the CAs of the three treated stones remained constant (\(\sim\)170\({}^{\circ}\)) after they were annealed at temperatures up to 400 \({}^{\circ}\)C. This experimental result is a reflection that the self-cleaning functionality of the three treated stones is thermally stable up to that temperature. The thermal stability of surfaces is superior to those surfaces coated with fluorinated low-surface-tension chemicals [21; 22; 32]. Upon annealing at 450 \({}^{\circ}\)C, the CAs dropped to much less than 10\({}^{\circ}\), suggesting that the surfaces of the three treated stones became highly hydrophilic. Photographs of a water drop wetting the surface at 450 \({}^{\circ}\)C is shown in the inserts in Figure 6a-c. The hydrophilicity of the surfaces annealed at 450 \({}^{\circ}\)C is readily explained by the oxidation [32] of the fluorocarbon chains of the FPA molecules in the SAMs attached to Al\({}_{2}\)O\({}_{3}\) particles. In the SEM images shown in Figure 6d-f, one can see that the morphology of the surfaces is almost unchanged. This is because the FPA molecular chains are \(\sim\)2 nm, so their oxidation will not impact the microstructures of the surface. The HAs shown as inserts in Figure 6a-c are 20-25\({}^{\circ}\) for samples annealed at temperatures up to 400 \({}^{\circ}\)C. When annealed at 450 \({}^{\circ}\)C, the HAs are close to zero, because both the advancing and receding angles are close to zero, due to the fact that the surface is hydrophilic now.  
The self-cleaning stone surfaces were exposed to the air for more than 180 days, and water/oil CAs measurement were carried out. The wettability of the surfaces remained unchanged, thus indicating that the durability of the protective layers is excellent and is comparable with the work conducted by our group [20]. We believe that the lifetime of the protective coatings is over one year.
## 6 Implementation
### Technical Details
#### 6.3.3 Platform Security Features  
The build-in security mechanisms of the OSGi platform support inner-platform protection, both for devices and agents, by restricting the bundle operation to predefined capabilities, such as which bundle could be started or stopped, by whom, and when. The JADE-S add-on safeguards the ACL messages by offering the Java Cryptography Extension (JCE), Java Authentication and Authorization Service (JAAS), and Java Secure Socket Extension (JSSE) extensions. JADE-S enables among others message encryption and signature, agent actions authorization against agent permissions, and user authentication [78].
## 3 Testing Results and Analysis
### Moisture Stability Test  
Due to the susceptibility of the lime-ash stabilized soil to cracking when saturated during the early stages of curing, this study primarily focuses on investigating the moisture stability of specimens after 7 days, 14 days, and 28 days of curing. Figure 8 illustrates the saturation conditions of the specimens after 7 days of curing. In Figure 8a, the original soil sample begins to develop cracks from the edges towards the center after soaking for 3 h. This is accompanied by the continuous emergence of a significant number of bubbles above the soil sample. Subsequently, the soil sample continues to disintegrate and accumulate at the bottom of the container. These outcomes indicate a relatively poor resistance to disintegration of the natural red clay soil. In Figure 8b, after the addition of lime and fly ash for improvement, the red clay soil's resistance to disintegration is significantly enhanced. The specimen maintains its structural integrity after 24 h of soaking, with no signs of disintegration. However, the lime-ash stabilized soil specimens still exhibit cracks, resulting in reduced strength. Upon the further addition of phosphogypsum for modification, the appearance of the lime-ash stabilized soil specimens remains entirely intact, with no cracks or particle detachment observed. This result indicates that the addition of phosphogypsum markedly addresses the shortcomings of the soil, enhancing its resistance to disintegration and overall stability.  
The moisture stability coefficient, denoted as K, is determined by the ratio of the UCS of saturated specimens to the UCS of specimens under standard curing conditions without saturation. The calculated moisture stability coefficients are presented in Figure 9. It is evident from Figure 9 that phosphogypsum significantly enhances the moisture stability of the lime-ash-stabilized soil. When the phosphogypsum content is 4%, the specimens exhibit the highest moisture stability coefficient. However, when the phosphogypsum content exceeds 4%, the moisture stability coefficients of the modified lime-ash stabilized  
Figure 8: Moisture stability test: (**a**) Natural red clay soil; (**b**) 0% P (lime-ash stabilized soil); (**c**) 4% P; (**d**) 8% P; (**e**) 16% P; (**f**) 24% P.  
soil specimens are all lower than those of the unmodified specimens. Furthermore, with an increase in the curing duration, the moisture stability coefficients of the specimens gradually increase. In the early curing stages, the moisture stability of the specimens is relatively poor, with saturation significantly impacting strength. At 14 days of curing, the moisture stability coefficient of the specimens with 4% phosphogypsum content reaches 71%. By 28 days, it increases significantly to 91%, indicating that saturation has a minimal impact on strength at this point. This suggests that the addition of an appropriate amount of phosphogypsum promotes better consolidation of the lime-ash-stabilized soil over extended curing periods, enhancing its strength.
## 2 Materials and Methods
### Metric Set
#### 2.3.3 Metrics for the Joint Dimensions Accuracy  
The joint dimensions accuracy was operationalized with the following metrics:  
\(\text{PAD}_{\text{abs}}\) mean absolute distance between the actual and the estimated emotional state in PAD space (calculated using Pythagorean theorem):  
\[\text{PAD}_{\text{abs}}=\frac{\sum_{i}dd_{i}}{n} \tag{6}\]  
where  
\[dd_{i}=\sqrt{\left(p_{i}-p_{i}^{\prime}\right)^{2}+\left(a_{i}-a_{i}^{\prime} \right)^{2}+\left(d_{i}-d_{i}^{\prime}\right)^{2}} \tag{7}\]  
\(dd_{i}\)--direct distance between estimated and actual emotion calculated for all dimensions using Pythagorean theorem.  
\(p_{i}\), \(a_{i}\), \(d_{i}\)--the dimension value for \(i\)th word (retrieved from the lexicon).  
\(p_{i}^{\prime}\)\(a_{i}^{\prime}\)\(d_{i}^{\prime}\)--the dimension value for \(i\)th word (estimated using mapping).  
\(n\)--number of words within the set.  
(PAD\({}_{10}\)) relative number of estimates that differ from the actual value by less than 10% of the scale in each dimension.  
\[\text{PAD}_{10}=all_{10}/n \tag{8}\]  
where  
_all\({}_{10}\)_--number of words, for estimates, that differ from the actual value by less than 10% of the scale in each dimension.  
(PAD\({}_{20}\)) relative number of estimates that differ from the actual value less than 20% of the scale in each dimension.  
\[\text{PAD}_{20}=all_{20}/n \tag{9}\]  
where  
_all\({}_{20}\)_--number of estimates that differ from the actual value by less than 20% of the scale in each dimension,  
(PAD\({}_{\text{SD}}\)) relative number of estimates that differ from the actual value for less than standard deviation size  
\[\text{PAD}_{\text{SD}}=all_{SD}/n \tag{10}\]  
where  
_all\({}_{SD}\)_--number of words, for which absolute difference between the estimate and the actual value is smaller than the standard deviation for the word.  
(DD\({}_{10}\)) relative number of estimates that have direct distance from the estimate smaller than 10% of the scale.  
\[\text{DD}_{10}=dd_{10}/n \tag{11}\]  
where  
_dd\({}_{10}\)_--number of words, for which absolute distance between the estimate and the actual value (dd\({}_{i}\)) calculated for all dimensions is smaller than 10% of the scale.  
(DD\({}_{20}\)) relative number of estimates that have direct distance from the estimate smaller than 10% of the scale.  
\[\text{DD}_{20}=dd_{20}/n \tag{12}\]  
where  
_dd\({}_{20}\)_--number of words, for which absolute distance between the estimate and the actual value (dd\({}_{i}\)) calculated for all dimensions, is smaller than 20% of the scale.  
The above presented set of metrics (four per dimension and six joint-dimensions metrics) are used in the further analysis as accuracy measures.
## 3 The Time Source Techniques
### Low Earth Orbit
#### 3.2.1 Timing Mechanism  
LEO generally refers to satellites at altitudes ranging from approximately 80-100 km up to about 2000 km [47], with orbital periods of 128 min or less. Most artificial objects in space are located in LEO. The first Low Earth Orbit (LEO) satellites were launched more than 50 years ago, but it was not until the 1980s that the Iridium constellation was introduced, offering global low-latency narrowband communications. More recently, LEO-based broadband constellations, such as OneWeb, Starlink, and Kuiper, have been developed to provide high-capacity wireless connectivity worldwide, focusing on remote areas that are difficult to reach with traditional terrestrial infrastructure [48].  
Although broadband megaconstellations were not originally designed for Positioning, Navigation, and Timing (PNT) applications, they are engineered with rapid technological refresh capabilities, allowing adaptability for PNT through software or hardware updates [49]. Currently, one operational LEO timing service exists: the Satellite Time and Location (STL) service, provided by Satelles. The STL service delivers secure, GPS-independent time and location signals from LEO satellites [50]. These signals are transmitted via the Iridium constellation, which became operational in 1998 and consists of 66 satellites arranged in six orbital planes (11 satellites per plane) to ensure global coverage, as illustrated in Figure 1. Like GPS, STL signals are broadcast, though at slightly higher frequencies.  
The LEO satellites in the Iridium constellation orbit at an altitude of just 781 km, which is over 25 times closer to Earth than GPS satellites. At the poles, between six and eight satellites are typically visible at once, whereas only one satellite is usually visible from the equator. With an orbital period of approximately 100 min, these satellites complete about 14 orbits around Earth each day [50].  
LEO can provide a Stratum 0 UTC source of timing. A Stratum 0 source of timing refers to reference clock devices that provide UTC with minimal delay. High-precision timekeeping devices such as atomic clocks, GPS are Stratum 0 sources of timing, serving as the foundational elements in time synchronization protocols like the Network Time Protocol (NTP).
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 4 Performance Comparison
### Cost  
Cost is an essential parameter for application development in large-scale technologies. Implementation, planning, and operation and maintenance periods are considered for cost assumption. Each of the periods has key requirements like frequency spectrum, energy efficiency, battery life, and device density [69; 70]. In terms of cost, the ISM-band-based Sigfox, LoRa, and Z-Wave are all cheaper than the cellular-band-based NB-IoT, LTE-M, and RedCap. Among the cellular IoT protocols, RedCap is supposed to be more expensive than both NB-IoT and LTE-M [57]. Although Sigfox and LoRa are significantly cheaper than LTE-M and NB-IoT, respectively [25; 47; 71; 72; 73], between LoRa and Sigfox, LoRa is the more expensive one. LoRa has a long battery life by the framework, so the cost of replacing devices can be reduced. As architecture follows traditional wireless protocol regulation, deployment is straightforward. Similarly, Sigfox is designed to maximize energy efficiency by the ingrained framework. When it transmits data, Sigfox consumes very low power, and hardly any maintenance is required [73]. The above-stated features in the design help reduce the cost of LoRa and Sigfox. On the other hand, LTE-M uses power consumption features to extend device health for a long time, although licensed band standard and long coverage framework are more expensive than LPWAN protocol [25; 73]. Finally, in terms of device density, LoRaWAN is cost-efficient where the device density is low, and in metropolitan regions with a high device density, NB-IoT and LTE-M are cost-effective [74].
# Wearable Plasma Pads for Biomedical Applications
## 2 Experimental Methods  
### Fabrication and Characterization of a Plasma Pad System  
#### 2.1.1 Fabrication of the Plasma Pad  
For the dielectric layer of the plasma pad, a commercial polyimide film (Kapton(r) film, DuPont, Wilmington, DE, USA) was selected, with a thickness ranging from several tens to hundreds of \(\upmu\)m. Kapton film has good electrical and physical characteristics, including a thermal resistance up to +400 \({}^{\circ}\)C, high chemical resistance, and a high breakdown voltage exceeding 6 kV for a film thickness of 120 \(\upmu\)m, with a field strength of 5 \(\times\) 10\({}^{5}\) V/cm [23]. For a hybrid-DBD plasma pad, the high-voltage electrode and the ground electrode were fabricated on the upper and lower surfaces of the 120-\(\upmu\)m-thick polyimide film of the plasma pad. The pad is electrically safe because the high-voltage film surface is covered with silicone rubber for insulation, while the cold plasma that is generated on the ground electrode surface is in contact with the skin and medical gauze covers the film surface of the ground electrode. To attach the pad to the skin, adhesive paste is applied around the periphery of the gauze, except in the area covering the ground electrode.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 3 Proposed Method  
Video-based person Re-ID focuses on establishing correlations between individuals appearing in video segments captured from different camera viewpoints. This task essentially revolves around metric learning, where the goal is to develop a function (\(\phi\)) to effectively map tracklets into a metric space, bringing similar tracklets closer together. This section introduces the Gait-Appearance Fusion Network (GAF-Net), a novel framework made up of the following three main components: (1) a gait feature extraction module, (2) an appearance feature extraction module, and (3) a gait and appearance feature fusion module. GAF-Net uses GaitGraph for gait feature extraction. This method combines skeleton pose estimation with a Graph Convolutional Network (GCN) to efficiently extract gait attributes. Appearance feature extraction is performed with various backbone architectures to extract image-level features. Finally, the fusion module combines these appearance and gait features to derive a unified and normalized representation of the individual. Figure 1 graphically illustrates the global architecture of GAF-Net. Hereafter, we consider a person labeled with identifier \(i\). This person is represented by a series of \(T\) RGB images of size \(W\times H\).  
This series can be seen as a four-dimensional (4D) tensor, denoted as \(\mathbf{A}^{i}\), as defined by \(\mathbbm{R}^{W\times H\times 3\times T}\).
## 4 Security Performance Analysis of Hybrid FSO/RF System
### Strict Positive Security Capacity  
Strict forward security capacity is defined as the probability that the instantaneous security capacity is greater than 0, which indicates the probability that the system can provide secure communication [33]. The SPSC of the hybrid system can be expressed as:  
\[SPSC=P_{r}\Big{\{}C_{s}\Big{(}\gamma^{MRC},\gamma^{E}\Big{)}>0\Big{\}}=1-SOP_{ L}|_{R_{s}=0}. \tag{22}\]  
Substituting Equations (20) and (21) into Equation (22), the SPSC of the hybrid system with the RF link eavesdropping and the FSO link eavesdropping can be obtained, respectively.
# Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning +
Second, the aforementioned approach only works in a supervised learning context. In other words, if segmentation annotations are not available for a particular dataset, then the approach at hand cannot be applied. In this paper, we propose a novel approach towards computer-aided identification of glaucoma, making it possible to both diagnose and localize this eye disease in fundus images with a high effectiveness. More precisely, we can summarize our contributions as follows:  
* **High diagnosis effectiveness:** Our predictive model comes with a high diagnosis accuracy of 96%, as well as a high sensitivity of 96% and a high specificity of 100% for Dataset-OD, a set of center-cropped fundus images highlighting the optic disc. Current state-of-the-art techniques for computer-aided diagnosis using deep learning [17] have an accuracy of 94%, a sensitivity of 92%, and a specificity of 96%. Furthermore, our predictive model is able to achieve an AUC score of 99%. * **Glaucomatous area localization:** Our predictive model does not only come with a high diagnosis accuracy; it is also able to localize the glaucomatous area, thus helping ophthalmologists obtain a diagnosis that is more trustworthy. * **Probability of glaucoma diagnosis:** Upon diagnosis of glaucoma, we provide a probability that represents the diagnostic confidence. * **Use of 1903 and 220 fundus images for training and testing, respectively:** The dataset used was curated by ophthalmologists working at Samsung Medical Center in Seoul, Korea. More details about this dataset, which can be made available upon request, can be found in Section 3. Furthermore, we also validated the effectiveness of our predictive model using an external dataset [28]. * **Medinoid:** We have developed Medinoid ([[http://www.medinoid.org](http://www.medinoid.org)]([http://www.medinoid.org](http://www.medinoid.org))), a publicly-available prototype web application for computer-aided diagnosis and localization of glaucoma in fundus images, integrating our predictive model. This web application is intuitive to use for medical doctors, as well as for people who have difficulties in accessing human experts and/or specialized medical imaging equipment. Note that this paper is an extension of work previously presented at a conference [29], leveraging deeper models so as to be able to increase the effectiveness of diagnosis and additional datasets so as to be able to better evaluate generalization power. Furthermore, we present more in-depth experimental results and introduce Medinoid, a web application for both glaucoma diagnosis and localization. The remainder of this paper is organized as follows. In Section 2, we provide an in-depth overview of our approach towards glaucoma diagnosis and localization. In Section 3, we outline our experimental setup, and in Section 4, we subsequently discuss the results obtained. Finally, we provide conclusions and directions for future research in Section 5.
# Bans--Learning Steganography with a Binary Attention Mechanism
## 4 Experiments
### Steganalysis Experiments  
To ensure that our model is robust to steganalysis methods, we test our models using StegExpose [33] with linear interpolation of detection threshold from 0.00 to 1.00 with 0.01 as the step interval. The ROC curve is shown in Figure 17 where true positive stands for an embedded image correctly identified in which there are hidden data inside while a false positive means that a clean figure is falsely classified as an embedded image. The green solid line with a slope of 1 is the baseline of an intuitive random guessing classifier. The figure shows a comparison among our several models, StegNet [21] and Baluja-2017 [22] plotted in dash-line-connected scatter data. It demonstrates that StegExpose can only work a little better than random guessing and most BASN models perform better than StegNet and Baluja-2017.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **BSER (\%)** & **Payload (bpp)** \\ \hline Min-LSM-1 & 1.06\% & 1.29 \\ Min-LSM-2 & 0.67\% & 0.42 \\ Mean-LSM-1 & 2.22\% & 3.89 \\ Mean-LSM-2 & 3.14\% & 2.21 \\ Min-LSM-1-PS-0.6 & 0.74\% & 0.60 \\ Min-LSM-1-PS-0.8 & 0.66\% & 0.80 \\ Mean-LSM-1-PS-1.2 & 0.82\% & 1.20 \\ Mean-LSM-2-PS-1.2 & 0.93\% & 1.20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different embedding strategies comparison.  
Figure 16: Steganography using Mean Fusion with 1-bit LSM.  
Our model is also further examined with learning-based steganalysis methods including SPAM (Subtractive Pixel Adjacency Model) [34], SRM (Spatial Rich Model) [35], and YedroudjNet [36]. All of these models are trained with the same cover and embedded images as ours. Their corresponding ROC curves are shown in Figure 17. The SRM [35] method works quite well on our model with a larger payload capacity; however, in real-world applications, we can always keep our dataset private and thus ensure high security in resisting detection from learning-based steganalysis methods.
## 4 Simulation Results  
The proposed sliding surface and its control were performed in PSIM computer simulation software. The comparative analysis for PI, hybrid SMC-PI and the proposed SMC control strategies is presented in Section 4.1, while the surface dynamic behavior at start-up is discussed in Section 4.2. The values of the system parameters are given in Table 2. The PI control parameters used in simulations are \(K_{p}=0.2\) and \(K_{i}=0.2\) for the voltage loop and \(K_{p}=104\) and \(K_{i}=0\) for the current loop. The stability analysis and control tuning were performed in sistootol (MATLAB R2020a) by means of root locus analysis together with the \"Robust Response Time\" tuning method. The tuning steps and criteria such as voltage overshot lower than 10% and delay between internal and external loops can be consulted in [32]. In addition, the coefficient for SMC is \(\alpha=150\), which is in the stable admissible range.  
Figure 8: Adaptative hysteresis band modulator.  
Figure 7: Block diagram of the control system: (**a**) classical cascade PI controller; (**b**) hybrid PI-SMC; and (**c**) proposed SMC.