esa-sceva/satcom-chunk-collection huggingface dataset:
# SAM-PAY: A Location-Based Authentication Method for Mobile Environments +
## 8 Conclusions  
In this paper, we have discussed several aspects that make designing context-enhanced authentication based on three or more authentication factors more challenging than their Internet counterparts. Our approach, named SAM-PAY, enables users to authenticate using a combination of contextual information, such as the geographical position (as determined through GPS/GNSS signals and certified by a ground telecommunication network component), and traditional authentication factors, such as based on what the user has and what the user knows. Certifying the location itself is a challenging task since various location-sensing technologies have emerged and can be used depending on the operation environment and the application scenario. Our contribution is threefold: (i) we exploited an LE component used for certifying a UT's location within an acceptable \"integrity risk\", and a UT that supports various location-sensing technologies, which makes it suitable for use in various location-based applications; (ii) we proposed the SAM-PAY authentication method that makes use of both traditional and contextual (i.e., location) authentication factors; (iii) we designed and implemented a proof of concept for the SAM-PAY method, in the form of a case scenario that allows users to perform payments at self-service gas stations, without the need to use credit or debit cards. Our experimental test-bed shows that the method proposed also proves to be resistant to some attacks like card-number theft or MITM attacks, but future works could investigate the service resilience in various attack scenarios including external attackers, compromised components (e.g., the LE, UT) and malicious users.  
Preliminary parts of this work (including the exploitation of a prototype LE for location certification and integration) have been performed in a concluded project \"ANTICIPANDO GALILEO: PRODOTTI E SERVIZ A SUPPORTO DELLA MOBILITA E DELLA SICUREZZA\" financed by Regione Piemonte (Italy). Part of the work related to the user authentication and identification in the SAM-PAY method has been performed by Dr. [PERSON] within the Ministerial Decree no. 1062/2021 and received funding from the FSE REACT-EU-PON Ricerca e Innovazione 2014-2020.
## 3 Results and Discussion
### Concentration Factor  
The concentration factor is an important characteristic of the membrane process because it shows the level of concentration in the reentate of each compound present in the feed buttermilk. The effect of the VRR on the concentration factor during ultrafiltration of sweet buttermilk is shown in Figure 4. The highest values of the concentration factor were obtained for fat, followed by dry matter, proteins, and ash. The correlation between the concentration factor and VRR is linear which is proved by the models shown in Table 3. The researchers in [41] found a correlation between the concentration factor and VRR for other types of dairy products. An increase in the concentration of protein and calcium with the increase in the volumetric concentration factor (VCF) during ultrafiltration of skimmed sheep milk was established by [42]. Our work shows a linear dependence of the concentration factor of all buttermilk compounds studied on the VRR. According to the authors in [43], the concentration of the protein fraction increased by a factor of 3 in the reentate during ultrafiltration until the volume concentration factor of 4. Concerning fats, under the same working conditions, they increased by 3.5 times. The concentration of buttermilk twice almost doubled its fat content using an ultrafiltration membrane of 50 kDa [1]. Our results showed similar results when sweet buttermilk was subjected to ultrafiltration--the concentration factor for proteins was 2.39, while it was 3.87 for fats. The concentration of fats and proteins in reententates obtained by ultrafiltration will be suitable for the production of many dairy products, for example, cheeses where these components are main, as well as ice cream.  
\begin{table}
\begin{tabular}{l l l} \hline
**Component** & **Power Model** & **Correlation Coefficient R** \\ \hline Proteins & P = 85.447 \(\times\) VRR\({}^{0.0563}\) & 0.987 \\ Fat & F = 94.901 \(\times\) VRR\({}^{0.0249}\) & 0.985 \\ Ash & A = 31.511 \(\times\) VRR\({}^{0.4482}\) & 0.997 \\ Dry matter & DM = 63.108 \(\times\) VRR\({}^{0.1594}\) & 0.999 \\ \hline \end{tabular} Note: P—proteins; F—fat; A—ash; DM—dry matter.  
\end{table}
Table 2: Power models for rejection factor of buttermilk components depending on the volume reduction ratio.  
Figure 4: Concentration factor at different volume reduction ratios during ultrafiltration of sweet buttermilk.
# Wide-TSNet: A Novel Hybrid Approach for Bitcoin Price Movement Classification
## 3 Wide-TSNet
### Image Generation Layer
#### 3.2.2 Markov Transition Field  
The Markov transition field (MTF) [11] is defined as a Matrix \(\mathbf{M}\).  
\[M=\begin{bmatrix}w_{ij|x_{1}\in q_{i},x_{1}\in q_{j}}&\cdots&w_{ij|x_{1}\in q_ {i},x_{n}\in q_{j}}\\ \vdots&\ddots&\vdots\\ w_{ij|x_{n}\in q_{i},x_{1}\in q_{j}}&\cdots&w_{ij|x_{n}\in q_{i},x_{n}\in q_{j}} \end{bmatrix}. \tag{1}\]  
A Q \(\times\) Q Markov transition matrix (referred to as W) is constructed by dividing the data (magnitude) into Q quantile bins. These quantile bins correspond to the dataat timestamps \(i\) and \(j\) along the temporal axis, denoted as \(\mathbf{q}_{i}\) and \(\mathbf{q}_{j}\) (where \(\boldsymbol{q}\in[1,\,\,\text{Q}]\)). The element \(M_{ij}\) in the Markov transition field (MTF) represents the transition probability from \(\boldsymbol{q}_{i}\) to \(\boldsymbol{q}_{j}\). Essentially, we extend the transition probability matrix \(\boldsymbol{W}\)--which captures transitions based on magnitude--into the MTF matrix by incorporating temporal positions. At each pixel, the probability of transitioning from the quantile at timestep \(i\) to the quantile at timestep \(j\) is assigned to \(M_{ij}\). Consequently, the MTF matrix \(\boldsymbol{M}\) effectively encodes multi-span transition probabilities.  
Figure 2 visually represents the Markov transition fields (MTFs) at two sample times, presented in grayscale and RGB formats. Grayscale is a type of image in which the value of each pixel is a single sample, representing an amount of light only; therefore, only concentration information is reflected. Grayscale images are composed solely of shades of gray, with the contrast ranging from black at the weakest intensity to white at the strongest.  
On the other hand, RGB (red, green, blue) is the color model used in digital screens worldwide. Color in the RGB model is defined using three integer values ranging from 0 to 255 for red, green, and blue, where a value of 0 denotes dark and a value of 255 denotes bright [36]. Using both the grayscale and RGB formats in the representation of MTFs allows for a comprehensive understanding of the time-series data, as each format provides a unique perspective on the data's inherent patterns and temporal dependencies.
# High-Fidelity Drone Simulation with Depth Camera Noise and Improved Air Drag Force Models
## 4 Conclusions and Discussion
### Discussion  
An essential aspect of using simulations is to provide them with sensor signals that mimic those generated by real sensors and to obtain the same control behavior as real drones in the same environment, which requires an accurate drag force model. It was shown that our depth sensor noise model successfully simulates a real-depth sensor, and our improved drag force model outperforms the existing Airsim drag force model. These results affirm the realism of our simulation environment for drone testing. Moreover, this simulator not only provides a realistic simulation environment but also opens up possibilities for studying drone object detection, SLAM (Simultaneous Localization and Mapping), and control algorithms using depth camera sensors before implementing them in real-world scenarios. One research scenario involves configuring various material properties for objects and testing drone behavior in confined spaces, such as underground tunnels, using realistic depth sensor signals generated by our model. Another application is simulating high-speed drone flights with surrounding obstacles like walls or gate frames, as seen in autonomous Drone Racing (ADR) [51; 52], the AlphaPilot [53]. Given that our drag force model demonstrates higher precision than the AirSim model at high speeds, it can be invaluable for competitors to fine-tune their algorithms before real-world tests. Additionally, our simulator is expected to yield superior results in reinforcement learning applications compared to existing simulators. The noise characteristics of widely used depth cameras, such as ASUS Xtion Pro Live, Occipital Structure IO, Orbbee Pro, Microsoft Kinect V2, Intel Realsense D435, Intel Realsense ZR300, Intel Realsense R200, Intel Realsense F200, Intel Realsense SR300, and Ensenso N35, often depend on the material or distance of the detected objects [25]. Given that our depth camera noise model is designed to be general, we believe it can be readily extended to accommodate these cameras. Furthermore, our air drag force model can be applied to any type of propeller, provided that the relationship between PWM and thrust values can be established. By following our approach, Airsim users can develop both the depth camera noise model and the improved air drag force model as ROS packages, making them accessible within the Airsim environment. We are also considering the integration of these models as plugins into the Airsim environment for our future work. In future work, we plan to make several enhancements. These include considering factors like the angle of the observed surface, variations in light conditions, and addressing other sources of noise, such as those arising from the edges of detected materials or caused
## 3 Pseudo-TCT and Verification Experiment
### Verification Experiment of Inter-Class Separability
#### 3.2.2 Three-Dimensional Visualization and Intra-Class Consistency Visualization  
To observe the spectral features of the pseudo-TCT images and check the separability of ground objects, seven typical ground objects were sampled at the same locations of the original image, TCT image and pseudo-TCT image: bare soil (including uncultivated fields),buildings, mountains, roads, shadows, vegetation (including cultivated fields) and water bodies. The first three bands of the original image, the TCT image and the pseudo-TCT image were composed into 3D spectral feature space, and the 3D scatter plot of the ground objects was drawn.  
The purpose of image enhancement is to increase intra-class consistency and improve separability between classes, making them easier for manual interpretation and machine processing. Compared with similar evaluation metrics, such as cosine similarity, Euclidean distance can take into account the differences in the distance and direction of variables. To verify whether the intra-class consistency of the ground objects of the pseudo-TCT images increased, the Euclidean distance between the pixel values of each point and the pixel values of eight neighboring points, each point with four pixel values, was calculated, and the average of the eight Euclidean distances was used to represent the similarity of the point to the surrounding eight points. This enables the visualization of intra-class consistency, where the grey value of the image is inversely proportional to the intra-class consistency, and a larger grey value indicates a poor intra-class consistency of the ground object. The formula for calculating Euclidean distance is the same as (4), so the formula for calculating the mean value of 8-neighborhood Euclidean distance is as follows:  
\[\mathrm{Grey}_{\mathrm{x}}=\frac{\sum_{1}^{8}\mathrm{D}(\mathrm{x},\ \mathrm{y}_{ \mathrm{i}})}{8} \tag{5}\]  
where: \(\mathrm{Grey}_{\mathrm{x}}\) is the mean Euclidean distance between the grey values of x point and the grey value of the eight neighboring points; \(\mathrm{D}(\mathrm{x},\mathrm{y}_{\mathrm{i}})\) is the distance between points x and y; x is the central point; and y is the i-th neighborhood point.
# Designing a Softwarized Network Deployed on a Fleet of Drones for Rural Zone Monitoring
## 2 Video Monitoring Platform Description  
The scenario we refer to in this paper is shown in Figure 1. It is a rural area where a fleet of drones realizes a video monitoring service for all the people that work there, or have any interest in monitoring it. It is constituted by a wireless mesh network that behaves as a backbone to allow easy and dynamic connections of video transmitter and video receiver devices. As illustrated in the sequel, the proposed platform is designed as a \"video monitoring as a service\" (VMaaS), which is managed by a video monitoring platform provider. It is not deployed for a specific user, but has the objective of capturing videos of a wide rural area, providing a service that can be used by any customer that has the interest of monitoring one or more portions of it.  
The main elements of the video monitoring platform are described in Section 2.1, while Section 2.2 presents its architecture. Finally, Section 2.3 will describe the platform orchestrator, with its management role at both network and application layers.
## 1 Introduction
That study focused on the transition of the electric field value on the eyeball surface, and electrode placements that were capable of stimulating the nasal, central, and temporal sides of both eyeballs were described. The reported electrode placements were able to present a phosphene in three locations in the visual field for use as a walking support tool for the visually impaired. Obstacle recognition in these three directions is extremely important in walking support tools [12,13]. This is because visually impaired people need to be able to recognize obstacles in the forward direction, as well as obstacles in the left-right direction, in order to support spatial understanding [12]. However, dangerous are not only present at head height when walking outside. There is much information even in the lower part of the visual field that needs to be recognized, including train station platform edges, steps, and obstacles around their feet, in order to avoid tripping hazards. Nevertheless, methods for increasing the presentation accuracy of phosphenes in order to be able to present them in the lower part of the visual field have not been described. Therefore, in this paper, a method for presenting a phosphene in the lower visual field is described with the aim of developing a safer walking support tool for the visually impaired. For the purposes of validation, electric field simulation was selected to compare the results with the previous study, which demonstrated the presentation of phosphenes in three locations [11]. The contribution of this paper lies in describing the electrode placements that permit the presentation of phosphenes the lower visual field. Moreover, a method for controlling the local stimulated area around the eyeball is described by comparing the stimulated area with the electrode placements used in the previous study. Despite methods of obstacle detection and presentation being a necessary topic of discussion for the development of a walking support tool for visually impaired people, obstacle detection methods are not within the scope of this paper. Sonar-based obstacle detection and smartphone-based control of tACS devices are assumed as obstacle detection method in our walking support tool. The rest of the paper is organized as follows: the related work and background knowledge are described in Section 2, and Section 3 presents the hypothesis for designing electrode placements for presenting phosphenes in three locations of the lower visual field. The simulation conditions and evaluation results are presented in Section 4, and Section 5 discusses the results. Finally, the conclusions of this study are drawn in Section 6.
## 5 Seismic Responses of CSBs Under Non-Uniform Excitations
### Seismic Responses Subjected to the Non-Uniform Excitations  
During the tests, six non-uniform cases were carried out, shown in Table 4. The delay times are 0.1, 0.2 and 0.4 s, respectively. Based on the measured accelerations time history, we can get the displacement time histories through direct integration and baseline correction. The relative displacement is the displacement to tower base, which can be calculated by displacement subtracting in time history. The tower relative displacements envelopes are shown in Figure 26a,b. When subjected to the uniform excitations, the relative displacements on both tower tope nodes are consistent. While the tower relative displacements are quite different when subjected to the time delayed excitations. For non-uniform cases with different delay time (\(dt\)), the responses also seems quite different. However, the relative displacement responses excited by different \(dt\) seems to have no obvious rules to follow.  
Figure 27a,b show the maximum vertical deformation of girder subjected to different non-uniform excitations. As shown in Figure 27a, when non-uniform excited by ScEL, the vertical deformation on the mid-span (G3) are larger than uniform excitation. The reason is the symmetrical modes, such as the 4 th, 6 th modes, are more seriously excited by the non-uniform excitations. Secondly, comparing the non-uniform cases with different \(dt\), the deformation on the G4 is more sensitive than G2. It means wave passage effect has a greater impact on the nodes located farther away, such as G4, when the excitation is propagating from G1 to G5.  
Figure 26: Relative displacements response of towers subjected to (**a**) ScEL (**b**) ScTCU.  
Figure 27: The maximum vertical deformation of girder subjected to (**a**) ScEL; (**b**) ScTCU.  
Figure 27b shows the girder deformation when subjected to the PNF ground motions (ScTCU). Similar to the previous Figure, the deformation on the node G4 is significant various for different non-uniform cases. In order to explain this rule more clearly, Figure 28 shows the deformation response time history diagrams subjected to different non-uniform cases. For the uniform case, the maximum deformation on the node G4 is larger than the node G2. However, for non-uniform with \(dt=0.4\) s, the maximum deformation on node G4 is significant reduced. Further, the peaking time is also different to the uniform cases. In contrast, the deformation response of node G2 is less different for different non-uniform cases.
# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.8 Change Detection  
This was a very specific task achieved for the environment monitoring in the case of building structure updates. Its main added value was an increasing analysis of how a building has evolved over time. This task was found in the work of [PERSON] et al. (2013). A sample visualization is presented in Figure 12.
## 3 Particle Swarm Optimization
### Recent Advancements in PSO for a Better Convergence Rate
#### 3.6.3 Fully Informed Particle Swarm Optimization  
Fully informed particle swarm optimization (FIPS) enhances the communication and information exchange among particles within a swarm [110]. In FIPS, each particle not only considers its personal best solution (pbest) and the global best solution (gbest), but also incorporates information from the best solutions of its neighboring particles. This fully informed approach allows for a more-comprehensive exploration of the search space and can lead to improved optimization performance. The communication topology can take different forms, such as a fully connected network or a spatially defined neighborhood structure [111]. Each particle maintains a set of information about its neighbors' best solutions, known as the \"flock\" information. This flock information consists of the position and fitness values of the neighboring particles' best solutions. The fully informed communication mechanism in FIPS facilitates cooperative interactions among particles, enabling them to share valuable information and guide each other toward promising regions in the search space [112]. This enhanced communication promotes a balance between exploration and exploitation, helping to avoid premature convergence to sub-optimal solutions.
## 3 Network Architecture and Hierarchical Model for Leak Detection and Localization
### Hierarchical Leak Detection and Localization Model  
According to the architecture of monitoring networks and the monitoring information features, we set up a hierarchical model for leak detection and localization as follows: in the first place, the original leak signals are easily disturbed by background noise and transmission route, which may lead to the ambiguity of detection information. In order to diminish the disturbance impact and improve the detection precision, wavelet transform technology is introduced to cope with original leak signals and the leak characteristic parameters are extracted as well. Secondly, SVM multiple classifiers are employed to integrate the leak characteristic parameters in monitoring networks. This kind of classifiers not only could enhance the ability of anti-interference, but also could accurately recognize the current state of a pipeline. Thirdly, due to the influence of monitoring position, sensor nodes in different positions may generate conflicting results, which makes it difficult for the monitoring networks to provide a correct decision. To solve this problem, a recognition result from a single node, which is regarded as the independent evidence, will be combined with results from other nodes at the sink to increase the focal degree of the correct proposition. Furthermore, if there has been a leak along the pipeline, the position of the leak point can be precisely determined by the localization results among nodes.  
Based on the analysis above, the process of leak detection and localization is divided into three parts, shown as Figure 2. At sensor nodes, we use wavelet transform technology to deal with original leak signals as well as obtain characteristic parameters of the signals in both the time and frequency domains. Then, we build the model of SVM multiple classifiers and acquire the recognition results of  
Figure 1: Architecture of pipeline monitoring sensor networks.  
each node. At the sink, we design an improved evidence combination rule based on the degree of reliability for the final decision making from the recognition results of several nodes, and judge the state of the pipeline. If the state corresponds to a leak, a weighted average localization algorithm based on time difference of arrival (TDOA) is introduced to precisely calculate the position of the leak point. The whole model adopts a hierarchical configuration, in which the result from the former level is regarded as the input of the next level. Thus the model is able to separately achieve multiple different levels of leak information processing, which ensures the accuracy and reliability of the diagnosis results.