esa-sceva/satcom-chunk-collection huggingface dataset:
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 4 Test and Results
### Avispa  
Automated Validation of Internet Security Protocols and Applications (AVISPA) [16], is a cryptographic protocol verification tool. It was developed in the artificial intelligence laboratory at the University of Genoa, Italy, in conjunction with other institutions. The architecture used by the tool can be seen in Figure 12, which indicates that the protocol must enter the High-Level Protocol Specification Language (HLPSL), then the analysis model must be chosen for the tool to check the protocol; finally, the output returns the result of testing the protocol on the model. Each of these models is specified below.  
Figure 11: Cryptographic protocol verification block diagram.  
Figure 10: Protocol verification methodology.  
All the protocols verified in the AVISPA tool must be specified in the HLPSL language. This language is based on roles: the basic roles of representation of each entity and the role in which they are specified to represent the scenario. Each of the roles is independent of the others and has initial information or parameters of the entities, and the communication one has with others. A complete specification of the language is presented in [16].
## Evaluation and Results
### Test Scenarios and Results  
Each scenario utilised one of four different interference ranges, consistent across all nodes and the sink. This demonstrated the effectiveness of the MADCAL algorithm as interference ranges began generously and were then contracted, to the point where they barely covered one-hop between nodes. Within each scenario the MSN speed was altered between 2 mps, 10 mps, 20 mps, and 40 mps. Therefore, results can be compared between the sink moving very slowly, only encountering individual nodes a small amount of times, to a high sink speed meaning that many passes of each node were possible in the same simulation time.  
Results were first obtained for the network implementation with an MSN, but with the existing standard duty cycling with CCA and check interval. This made no allowances for sink mobility. Result metrics were of average energy consumption amongst significant nodes and MAC layer frames received by the sink node. Tests were conducted using both topologies as illustrated in Figures 1 and 2. The most significant difference across the two topologies was in the assignment of significant nodes.  
Node location was more stable and, although interference range was altered across tests, the significant nodes did not change and remain as nodes 1-6, 10, 11, 15, 16, and 20-25. However, in the case of the random topology, as interference ranges were altered, so were the significant nodes. This is covered in greater detail later.
## 3 The Important Role of Optic Flow in Postural Control
### The Functional Role of the Foveal and Peripheral Visual Field  
In 1999, [PERSON] et al. summarized the various roles that have been ascribed to central and peripheral vision in perception and the control of self-motion [40]. The authors reported that three hypotheses have been postulated about the functional role of the foveal and peripheral visual field in the control of posture: (1) peripheral dominance, (2) retinal invariance, and (3) differential sensitivity to radial flow.  
The peripheral dominance hypothesis states that peripheral vision is predominant for the perception of self-motion, while central vision is predominant for the perception of object-motion, meaning that the retinal position of the stimulus plays an important role in self-motion perception.  
The retinal invariance hypothesis states that optic flow provides the necessary information to perceive both self-motion and object motion independent of the retinal stimulation locus. Thus, according to this view, there is no functional specialization for self-motion.  
The functional sensitivity hypothesis states that the optic flow provides information for both self-motion and object-motion, but the central and peripheral parts of the retina are differentially sensitive to the optic flow patterns. According to this view, the central vision is more sensitive to radial, rotational, and lamellar optic flow patterns, while the peripheral vision is sensitive to lamellar optic flow, but insensitive to radial and rotational optic flow.  
In the past two decades, many studies sought to uncover the functional differences between central and peripheral vision to determine which of these hypotheses was the most correct. Here we review those studies, trying to elucidate the reasons that led to such controversial results.
# Pido: Predictive Delay Optimization for Intertidal Wireless Sensor Networks
## 4 Design of Pido
### The Prediction of \(S^{{}^{\prime}}(t+\tau)\)
#### 4.3.1 Indicators  
To improve the prediction accuracy, we propose an indirect method by using implicit indicators which are the above-water duration, the number of available neighbors and the number of available links.  
**Above-water duration (\(D_{av}\)):** Though tidal periodicity performs poorly when it is utilized alone, it does provide a rough means of forecasting nodes status. As we assume above, each node maintains a timer \(\Delta_{i}\) to record the above-water duration, and we observe the recorded data has correlated periodic cycles. Based on this, we select the above-water duration (\(D_{av}\)) as one of the implicit indicators of node status.  
**Available neighbors (\(N_{nb}\)):** In an IT-WSN, nodes communicate with neighbors frequently to update the routing table. A sudden change of number of neighbors may be caused by the submergence or emergence of neighbors, which reveals the tide rising or falling. Therefore, the change of available neighbors (\(N_{nb}\)) is also used as an indicator of the future states of nodes.  
**Available links (\(N_{link}\)):** Link quality is sensitive to environmental changes and can be a sensitive indicator. During the low tide period, the number of available links are stable; during the rising tide period, the physical changes impact link connection. Therefore, the number of available links (\(N_{link}\)) is a direct indicator of the node status. Note that \(N_{nb}\) and \(N_{link}\) are different due to the asymmetric links, i.e., an available neighbor does not necessarily hold two available links.
# Flavor Chemistry of Virgin Olive Oil: An Overview
## 3 Factors Affecting the Flavor of VOO  
[PERSON] [81] suggested that two main factors regulate the rate of release of aroma compounds from foods, i.e., the volatility (thermodynamic factor) and the resistance to mass transfer from the product to the air (kinetic factor). From this point of view, the mechanism of the release, as well as the retention of aroma compounds from olive oil, depends primarily on the concentration and nature of volatile compounds present in olive oil. The nature and the level of aroma compounds in olive oil are more affected by different agronomical and technological factors [7; 20; 53].  
The second mechanism that affects the aroma release of VOO is the properties of aroma compounds such as their molecular size, shape, volatility, and polarity that could determine their higher or lower availability to the olfactory receptors [82; 83; 84; 85].  
The third mechanism relates to the aspects linked to the oral process [86; 87]. During olive oil tasting, factors such as salivation, mouth size, breathing and temperature are able to change the volatility of olive oil odorants and consequently VOO aroma. In addition, another mechanism that can happen is the potential interaction between the matrix components and the volatile compounds that could reduce the aroma release and change the odor perception of food [88; 89].
## 1 Introduction  
Therionic cathodes are electron sources used in a wide range of vacuum electron devices (VEDs) critical to both military and civilian applications. Examples include devices based on RF amplifying traveling wave tubes--satellite communication systems and military/civilian RADAR applications [1]), miniature thermionic diodes [2], and more [3, 4]. To meet increasing demand, high-power cathodes are being sought for THz or mm-wave devices [5] operating beyond the crowded kHz, MHz, and GHz frequency ranges. Delivering the necessary enhanced current densities while lowering operating temperatures requires improvements to existing cathode designs or the development of next-generation electron emitting materials.  
A significant refinement of uncoated thermionic dispenser cathodes [6, 7] has been reported with the addition of Sc or Sc\({}_{2}\)O\({}_{3}\) to the powder-processed porous W matrix [8]. While Sc-containing (\"scandate\") cathodes have exhibited higher emitted current densities than other dispenser cathodes (100-400 A/cm\({}^{2}\) at -850 \({}^{\circ}\)C [9, 10]), inconsistent performance and poor reproducibility have prevented scandate cathodes from being widely integrated into devices [11]. In addition, little is known about the amount of and manner by which the Ba-Ca-Al impregnate desorbs from scandate cathodes and the location within a vacuum device these materials may redepposit.  
In the following, measurements of the distribution of re-deposited desorption products after activation and operation of a set of scandate cathodes are reported. Particular attention is given to the fate of both Ca and Sc, as the addition of these elements to the cathode is known to improve electron emission [6, 7, 9, 10]. The desorption of Ca is of particular interest because recent compositional analysis of cathodes impregnated with a CaO-containing oxide mix did not locate Ca in the cathodes post-activation[12]. To assess the fate of chemical species desorbed from cathode surfaces, X-ray Photoelectron Spectroscopy (XPS) and energy dispersive X-ray spectroscopy (EDS) studies were conducted on components of a sealed glass envelope in which scandate cathodes had been activated and operated for ~26,000 h. Emission testing, while not the focus of this study, has been presented by the manufacturer for similar cathodes elsewhere [13; 14]. Combined with information about the activation and operating conditions after the vacuum envelope was sealed, the present results show that cathode activation and operation occur in an oxygen-poor environment, that little to no Sc is desorbing from the cathode surface, and that, while impregnate material is deposited on the anode itself, this material accounts for only on the order of 1% of all impregnate added during fabrication. These findings from this first-ever study of scandate cathodes after long-term operation provide new insight into the fitness of scandate cathodes for use as critical components in vacuum devices and suggest potential impacts on device performance due to deposition of desorption products on interior device surfaces.
# Tilt-Induced Error Compensation with Vision-Based Method for Polarization Navigation
## 2 Principle of Visual Horizontal Attitude Angle Measurement  
The position and angle of the horizon in the image are intricately connected to the attitude of the UAV. When the attitude of the UAV changes, the position and angle of the horizon in the image will correspondingly change. Therefore, by detecting the position and angle of the horizon in the image, the attitude variations of the UAV can be inferred. This procedure has two key issues: first, the precise extraction of the horizon from the image; second, the application of the extracted horizon for attitude estimation. The following sections of this paper will systematically elaborate on these two core tasks, with the specific technical approach illustrated in Figure 1.
# Research and Development of a Large-Scale Axial-Flux Generator for Hydrokinetic Power System
## 2 Materials and Methods
### Study of Design Parameters  
Numerical models and simulations, or virtual prototyping, are relatively cheaper and faster when compared to physical ones, especially for large-scale structures. Despite design simplicity, there are different parameters that could be examined using virtual prototyping techniques that could form a large domain of values for varied parameters. Based on experience, a group of several design parameters are marked to be studied:  
* Air gap--this is probably the most important geometry parameter, and its minimum is searched for to improve the design performance. It is limited by technology requirements specific to such a large-scale product.
* Coil thickness--this parameter is related to the coil current density, and it influences the electromagnetic performance of the examined generator.
* Number of rotor disks--this parameter of the concept is related to the cost-effectiveness of the design, and it is limited by the required capacity of the generator.
* Planned design variants and simulations are shown as a scheme in Figure 3 to present the variety of models that are to be prepared. This plan presumes three stages:  
Figure 2: Preliminary AFPM concept: (**a**) characteristic dimensions and schematics; (**b**) virtual prototype of geometry.  
* Stage I--a general review of parameter sensitivity. It includes three different virtual prototypes with gradually added changes. The first model is used to assess air-gap decreases' influence over system performance, while the other two models introduce the rest of the design parameters.
* Stage II--it is a combination of air-gap and coil-thickness variants to achieve an improved design solution.
* Stage III is dedicated to the evaluation of the final design and a comparison with the preliminary concept.
## Materials and Methods
### Batch Experiments  
The adsorption tests were conducted at different initial P concentrations in NaH\({}_{2}\)PO\({}_{4}\) solution with an Al-Ti oxide dosage of 0.2 g/L in 100 mL of solution. The initial pH of the solutions was adjusted to 6.8 by adding H\({}_{2}\)SO\({}_{4}\) or NaOH. After a few hours of continuous stirring, the suspensions were separated by centrifugation (2500 rpm, 20 min). P concentrations in the supernatant solutions were measured by the molybdenum blue method [32] using an ultraviolet-visible spectrophotometer (721N, Shanghai Spectrum, Shanghai, China) at a wavelength of 700 nm. To evaluate the temperature involved in P removal, similar experiments, as described above, were performed by transferring the sealed reaction vessel to a thermostatic shaker bath (293 K, 303 K, 313 K, and 323 K). The P solutions with P concentration ranging from 2.5 to 50 mg/L were prepared from a stock solution. The P removal efficiency (\(R\)) was calculated by Equation (1):  
\[R=\frac{(C_{0}-C_{\rm e})}{C_{0}}\times 100 \tag{1}\]  
and the adsorption capacity of P at equilibrium (\(q_{\rm e}\)) and at time \(t\) (\(q_{\rm t}\)) were calculated by Equations (2) and (3):  
\[q_{e}=\frac{V(C_{0}-C_{e})}{W} \tag{2}\]  
\[q_{t}=\frac{V(C_{0}-C_{\rm t})}{W} \tag{3}\]where \(R\) denotes the removal efficiency (%), \(q_{\rm e}\) and \(q_{t}\) denote the amount of phosphorus adsorbed at equilibrium and time \(t\), respectively ((mg-P)/g), and \(C_{0}\) and \(C_{e}\) are the initial and equilibrium concentrations of P in aqueous solution (mg/L), respectively. \(C_{t}\) is the P concentration at time \(t\) (mg/L). \(V\) denotes the volume of P solution (L), and \(W\) is the adsorbent weight (g). Each experiment was conducted three times and the average values were used for calculation.  
After performing P adsorption, the used adsorbent was collected and washed thoroughly with water. The spent Al-Ti oxide adsorbent was immersed in 0.1 M NaOH with for 4 h for regeneration. The material was filtered, repeatedly washed with water, and subsequently dried at room temperature under vacuum. The reusability of the regenerated sample was evaluated through adsorption of 15.0 mg/L P solution.
# An Effective AUSM-Type Scheme for Both Cases of Low Mach Number and High Mach Number
## 6 The AUSMLS Scheme's Improvements for All Speeds
### Analysis on the AUSMAS Scheme's Behavior at Low Speeds  
As can be seen in Sections 5.1-5.3 and 6.1-6.3, the differences between AUSMAS and AUSMLS lie in the definitions of the coefficient \(D^{\rho}\) and the non-linear eigenvalues. In  
Figure 7: Dissipation coefficients of the pressure and the density.  
this section, we would like to give a brief proof to show that these differences have little influence on the schemes' resolutions at low speeds.  
The difference of the coefficient \(D^{\rho}\) has little influence on the performance of scheme at low speeds.  
As stated in Section 4.1, in low Mach limit, the physical pressure is proportional to the square of the Mach number. Thus, the function \(h_{1}\) in Equation (130) approaches 1 when the Mach number approaches 0. Correspondingly, the function \(t\) in Equation (126) approaches 1 and the coefficient \(D^{\rho}\) in Equation (139) is equal to the AUSMLS scheme's in Equation (106). Thus, the difference of the coefficient \(D^{\rho}\) has little influence on the schemes' performances at low speeds.  
The modifications of the non-linear eigenvalues have little influence on the performance of the scheme at low speeds.  
In the low Mach number limit, \(V_{n}\) is quite small and the modification of it can be disregarded at low speeds. Therefore, the performance of the scheme at low speeds is little influenced by the non-linear eigenvalues' modifications.  
All in all, AUSMAS is almost the same as AUSMLS at low speeds.  
Numerical Experiments  
Sod Shock Tube  
The initial conditions are given as \(\left(\rho,u,p\right)_{L}=\left(1,0,1\right),\ \left(\rho,u,p\right)_{R}= \left(0.125,0,0.1\right)\) with a computational grid of 400 cells. In terms of the time integration, the explicit fourth Runge-Kutta time-marching scheme is employed and the CFL number is equal to 0.5 in terms of the spatial accuracy; all these flux schemes are conducted with the first order reconstruction method.  
Figure 8 depicts density and pressure distributions obtained by different schemes at 1.8 s. As shown in the figure, the AUSMAS is able to produce similar steep gradients near the contact discontinuity and the shock as the others.  
Sod Shock Tube II  
In this test, the initial conditions are given as \(\left(\rho,u,p\right)_{L}=\left(3,0.9,3\right),\ \left(\rho,u,p\right)_{R}= \left(1,0.9,1\right)\). The length of the domain is 10 and is discretized with 400 cells. The explicit fourth Runge-Kutta time-marching scheme is adopted with the CFL number set to 0.5. In terms of the spatial accuracy, the first order reconstruction method is employed.  
Figure 8: Pressure and density distributions of the Sod shock tube test (\(t=1.8\) s).  
The schemes' pressure distributions and density distributions at 1.5 s are depicted in Figure 9. As can be seen in them, AUSM+ yields an unphysical expansion shock. AUSMAS is capable of avoiding this unphysical phenomenon like AUSMPW+ and SLAU. Therefore, AUSMAS is robust against the unphysical expansion shock.
## 3 Results
### Algorithm Application and Clustering Results
#### 3.2.2 Selection of DBSCAN Hyper-Parameters  
For all the areas, the minimum samples value _minPoints_ has been set at 4, since the dataset used in this work has 2 dimensions. As regards _eps_, the approach mentioned in Section 3.1 has been implemented for the three areas. The results are shown in Figure 7, both in terms of plots of the mean of the distances between each point and its \(k\)th nearest neighbor, sorted in ascending order, and reporting the information needed to find the _eps_: the numbers of _ASC_ and _DES_ PSs, and their sum (tot. PSs), the area, the PSs density (PSs/m\({}^{2}\)), the boundaries \(k_{inf}\) and \(k_{sup}\), and, finally, the optimal value for neighborhood radius _eps_. The latter has been estimated as the mean of the ordinates of the maximum curvature points (represented with red circular markers) referred to each curve, given by a value of \(k\) included in the interval [\(k_{inf}\); \(k_{sup}\)]. According to Section 2.3.1, the ends of this interval are estimated based on a PSs density obtained by dividing the number of PSs for the area occupied by buildings, approximately the 50% of the total area for the case study areas. Then, \(k_{inf}\) has been obtained by multiplying the number of PSs in a m\({}^{2}\) for the minimum unity surface, set at 28 m\({}^{2}\) in this case, according to the Italian D.M. 05/07/1975 [50]. The value of \(k_{sup}\), indeed, has been evaluated taking into account that the greater distribution is generally on the roof, and other PSs can be found on the facades, so herein, a mean area of the buildings is estimated to be 250 m\({}^{2}\).  
The optimal values obtained for _eps_ are very similar for the three areas. These values are very close to the minimum distance between the buildings. Then, in absence of such analysis, for urban areas with similar features, an _eps_ value between 6 and 7 m could be suggested.
## 2 Materials and Methods  
### Dataset and Preprocessing  
In this study, experiments were performed using the FarmMap dataset compiled by the Korea Agency of Education, Promotion, and Information Service in Food, Agriculture, Forestry, and Fisheries (EPIS, Sejong-si, Republic of Korea) [46]. The dataset offers high-resolution orthophotos in GeoIIFF format as inputs and farmland polygon boundaries with farmland labels in SHP format. Each orthophoto comprises three RGB channels, and the ground sampling distance is fixed at 1 m per pixel.  
As summarized in Table 1, the data were partitioned into training, validation, and test subsets. All parcels were extracted from the designated 2021 imagery of the same target regions (94,241 parcels in total), then randomly split in a 9:1 ratio into training and validation subsets; as a result, both subsets belong to an identical spatio-temporal domain. In contrast, the test subset consists of 15,227 parcels collected in 2020 from different target regions, providing a spatio-temporal domain that differs from the training imagery. Representative parcel-level orthophoto crops from each domain are shown in Figure 3.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Year** & **Region** & **Parcels** & **Purpose** \\ \hline Train Dataset & 2021 & Gyeongsang, Daegu, Jeolla, Jeju, & 84,810 & Train \\ Val Dataset & 2020 & Seosan, Taean & 9431 & Same-domain eval \\ Test Dataset & 2020 & Geumsan, Namyangju & 15,227 & Diff-domain eval \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of dataset composition by year, region, and usage purpose.  
FarmMap includes 11 farmland classes whose parcel counts are highly imbalanced. In the combined Train + Val distribution (Figure 4a), orchard and growing field each exceed 10,000 parcels, whereas ginseng and tree nursery are limited to 563 and 694 parcels, respectively. Such disparity reveals a severe class imbalance that can bias a classifier toward majority classes during training.  
The marked shift in imbalance patterns in the test subset (Figure 4b) indicates that the observed distributional changes cannot be explained by class-ratio differences alone. Instead, these shifts point to an underlying domain shift driven by collection year and region. Therefore, farmland classification models must maintain robust performance under domain shifts.  
Because the FarmMap dataset is supplied with polygon coordinates and farmland labels in SHP format and orthophotos in GeoTIFF format, parcel-level images had to be created before classification. The Coordinate Reference System (CRS) values of the SHP file and of the GeoTIFF orthophotos were first harmonized. Using the transformed polygon coordinates, the orthophotos was clipped so that only the pixels inside the parcel were retained, while all exterior pixels were set to RGB (0, 0, 0) to mask the background. The resulting parcel image was then paired with its farmland label (e.g., plowed rice paddy), and all subsequent training and inference was performed exclusively on these parcel-level images.