esa-sceva/satcom-chunk-collection huggingface dataset:
# A Subjective Study on User Perception Aspects in Virtual Reality
## 4 Subjective Results and Discussions
### Remarks on Findings  
Based on the above discussions, we remark the findings as follows.  
Figure 14: Acceptability rates for VR and Non-VR modes (QP = 22 and Device set D1). (**a**) Content #1; (**b**) Content #2; (**c**) Content #3.  
Figure 15: Number of viewers in percent who prefers the VR mode to the non-VR mode (QP = 22 and Device set D1).  
Figure 13: Perceptual quality scores for VR and Non-VR modes (QP = 22 and Device set D1). (**a**) Content #1; (**b**) Content #2; (**c**) Content #3.  
* **QoE aspects**: 360 videos at the resolutions of 4 K and 2.5 K can offer good presence and perceptual quality scores to users. Both the presence and the perceptual quality are found to have strong effects on the acceptability. Also, the cybersickness is a serious issue for 360 video, especially for contents with fast camera motion.
* **Encoding parameters**: The QoE differences are trivial between versions encoded at the 4 K and 2.5 K resolutions and the QP values of 22 and 28. Yet, as the resolution decreases or the QP increases, the QoE aspects reduce quickly. In particular, 360 video should not be encoded at the resolutions of HD or lower and the QP values of 40 or higher when watching in the VR mode using HMD.
* **Content motion**: The bitrate for good QoE varies widely across different contents. The minimum bitrates of the three test contents are about 1.5 Mbps, 5.6 Mbps, and 11.9 Mbps. In addition, there exist strong correlations between camera motion and two QoE aspects of the cybersickness and the presence. Specifically, videos with fast camera motion cause more severe cybersickness than those with static or medium camera motion. In addition, users' presence is worst in videos with static or fast camera motion, and best in videos with medium camera motion.
* **Rendering device**: Device set D1 always provides higher presence and perceptual quality scores than device set D2, but the differences between them are not much as expected. Statistical results show that the effect of rendering device is \"small\" on the presence and the perceptual quality. Meanwhile, no statistically significant effect of rendering device are found on the acceptability and the cybersickness.
* **Rendering mode**: Although the acceptability rates and the perceptual quality scores in the non-VR mode are usually higher than those in the VR mode, users prefer the VR mode to the non-VR mode when watching video versions encoded at the resolutions of fHD or higher. For video versions with the HD resolution, watching in the non-VR mode is preferred to watching in the VR mode. Also, the results suggested that the presence aspect may be what makes users prefer the VR mode to the non-VR mode. In addition, the perceptual quality and acceptability rate cannot be used to compare different rendering modes since they are valid in a rendering mode only.
## 2 Emerging Algorithms Description
### Salp Swarm Algorithm
#### 2.3.3 SSA Algorithm  
The SSA mathematically models the process of salps to form chains in the oceanic environment. The salps population is classified into two categories: the leader salp and the followers salps. As the definitions imply, the leader salp stands at the top of the chain, whereas the other salps follow. Apparently, the leader salp is the population member of the salps chain with the best position vector. If we define as \(\vec{V}_{salp}\) the position vector for each salp of the swarm with respect to the position vector of the food (\(\vec{V}_{food}\)), which is the optimized position vector of the desired solution, SSA computes the position vectors of each member of the salps population. Random coefficients adjust the search mechanism between exploration and exploitation.  
Algorithm A3 (see Appendix A.3) presents the mathematical model in pseudo-code of the Salp Swarm Algorithm. The position vector of each salp in the chain \(\vec{V}_{salp}\) is ranked to determine the position vector of the food (\(\vec{V}_{food}\)). At each \(i\)-th iteration, the position vector of each salp is computed,  
\[\vec{V}_{salp}=\left\{\begin{array}{ll}\vec{V}_{food}+p_{1}\cdot(ub-lb) \cdot p_{2}+lb,&\text{if $j\leq NP/2$ and $p_{3}<0.5$}\\ \vec{V}_{food}-p_{1}\cdot(ub-lb)\cdot p_{2}+lb,&\text{if $j\leq NP/2$ and $p_{3}\geq 0.5$}\\ \frac{\vec{V}_{salp_{i}}+\vec{V}_{salp_{j}(i-1)}}{2},&\text{otherwise}\end{array}\right. \tag{7}\]  
where \(p_{1}=2\cdot e^{-\left(\frac{4i}{2\pi\hat{1}}\right)^{2}}\), \(p_{2}\), \(p_{3}=rnd\in[0,1]\), \(ub\) and \(lb\) being the upper and the lower boundaries of the optimization problem. Consequently, the objective function value of each salp is computed \(F(\vec{V}_{salp})\) and compared to the objective function value of the food. Based on the comparison, the position vector of the food \(\vec{V}_{food}\) as well as its corresponding objective function value, \(F(\vec{V}_{food})\), are updated.
# A Decision Support System for Melanoma Diagnosis from Dermoscopic Images
## 4 Implemented CAD System
### Feature Extraction
#### 4.2.3 Color Block  
The aim of the block in question is the definition of a palette of colors composing the segmented lesion area to be used as a color feature for lesion classification. For this purpose, a lesion segmentation is carried out in coherent color areas by a \(k\)-means algorithm with an \(N_{c}\) number of areas, where \(N_{c}\) is a tunable integer positive. The implemented CAD system provides physicians with the option of viewing these color areas, which may help in assessing the lesion scoring.  
In each color-based segmented area using k-means, the color mean value is evaluated considering the color components of the RGB image being tested. For each image of the database, a palette of \(N_{c}\) different colors is obtained.  
To compare palettes of different skin lesion images, a color transformation from RGB to Hue, Saturation, and Value (HSV) is implemented, and only the \(H\) and \(S\) components are retained, as the V component is illumination-dependent, while \(H\) and \(S\) components define the tint. With this approach, a color space is selected that has a relation with human perception and characterizes the color model like the human mind [36].  
To compare different palettes corresponding to different lesions within a classification system, a sorting mechanism is required. To this aim, a complex number is defined, which is expressed as:  
\[X_{n}=H_{n}+j\,S_{n}\text{ for }1\leq n\leq N_{c} \tag{5}\]  
where \(H_{n}\) and \(S_{n}\) are the hue and saturation values of the \(n^{\text{th}}\) detected color.  
For each ROI, a color-based feature vector is created (named palette) that contains the \(X_{n}\) numbers, sorted in ascending order of \(arg(X_{n})\), denoting with \(arg(X_{n})\) the argument of \(X_{n}\). In Figure 5, the color palette of the image previously segmented is shown.  
Changes in pigment inside a skin lesion are important details for an accurate classification; hence, each pixel of the color-based segmented areas is analyzed, and the difference between the pixel color and the mean color value of the segmented area to which that pixel belongs is measured. The color difference between the lesion area and the mean color of each segmented area obtained by the k-means procedure is evaluated to compose the difference image, known as dyschromia. The image corresponding to the difference previously considered is shown in Figure 6a. Such an image, magnified by a tunable parameter, is submitted to clinicians as accompanying information about the local color variation. The implemented procedure provides the option of analyzing the dyschromia image if deemed appropriate. The image obtained by substituting the mean color in each area, named the \"flattened image\", is shown in Figure 6b.  
Figure 5: An example of a color palette extracted by the image.
## 4 Discussion
### Uncertainty in Large-Scale Datasets--The Role of Fuzzy and Interval Data in Enhancing DSSs  
In the realm of Decision Support Systems (DSSs), the accuracy and reliability of the underlying data are paramount. However, large-scale datasets often contain errors due to various factors such as noise, incompleteness, and inconsistency [56]. Various methods have been used to tackle this problem, one of them being the consideration of uncertainty in the data. For example, in [57] a decision-support-modeling workflow is presented where early quantification of model uncertainty guides decisions about ongoing model design and deployment. Uncertainty quantification in the framework of DSSs relies largely on a robust definition of the probability distributions of the parameters, which express system expert knowledge. Apart from this, fuzzy logic theory offers a method for approximate reasoning and qualitative data modeling, which can address the challenges of uncertainty in decision support systems. For example, a fuzzy-logic-based decision support system has been developed for dietary clinical decisions for patients with multiple chronic conditions [58]. This method could be extended to incorporate the CMSs presented in this study, leading to a more robust methodology. Moreover, the use of interval numbers, which specify a range within which the true value lies, rather than a single precise value, allows DSSs to work with a finite range of possible values, providing a buffer against the imprecision of input data. This can lead to more robust and resilient decision-making processes, as the system does not rely on the accuracy of a single data point but considers a spectrum of probabilities.
## 1 Introduction
Because of the electrical nature of the resistivity measurements, the presence of ions within the section of concrete to be tested should affect the results. Because the electrical resistivity is closely related to the porosity of the concrete, it is possible to judge the resistance to chloride penetration, as shown in Table 1. Electrical resistivity is well correlated with certain performance characteristics of concrete, such as the chloride diffusivity, water absorption, and corrosion rate of the embedded steel. The technique also shows promise as a quality assurance tool for fresh and hardened concrete,  
Figure 1: Modified schematic sketch of the steel corrosion sequence in concrete (updated from [3]). as shown in Table 2. It is also a function of the concentration of the ions and their mobility in the pore solution [16; 17]. However, only the correlation between the durability criteria and electrical resistance was presented in terms of chloride penetration, and the change in electrical resistivity due to the presence of the chloride ions in carbonated or non-carbonated concrete was not illustrated. This paper is intended to evaluate the variation characteristics of the electrical resistivity, depending on the chloride concentration, for carbonated and non-carbonated concrete. Many structures are deteriorated by chloride or the combined deterioration by carbonation and chlorides, and this mechanism is very complicated, as shown in Figure 2. The variation in electrical resistivity was examined over time after mixing chloride ions in the concrete with various _w/c_ ratios and after carbonation in the concrete with chloride ions. Therefore, electrical resistivity still can be useful even after the concrete is deteriorated by carbonation and chloride penetration. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Chloride Ion** & **AASHTO TP 95 [1]** & **AASHTO T358-15 [18]** & **Thomas [19]** \\ \cline{2-3}
**Penetrability** & **100 mm \(\times\) 200 mm Cylinder (K\(\Omega\cdot\)cm)** & **150 mm \(\times\) 300 mm Cylinder (K\(\Omega\cdot\)cm)** \\ \hline High & \textless{}12 & \textless{}9.5 & \textless{}5 \\ \hline Moderate & 12-21 & 9.5–16.5 & 5–10 \\ \hline Low & 21–37 & 16.5–29 & 10–20 \\ \hline Very low & 37–254 & 29–199 & 20–200 \\ \hline Negligible & \textgreater{}254 & \textgreater{}199 & \textgreater{}200 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Chloride ions penetrability. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Chloride Ion Penetrability** & **ASHTO TP 95 [1] (K\(\Omega\cdot\)cm)** & **ASTM C 1202 [20] (Coulombs)** \\ \hline High & \textless{}12 & \textgreater{}4000 \\ \hline Moderate & 12–21 & 2000–4000 \\ \hline Low & 21–37 & 1000–2000 \\ \hline Very low & 37–254 & 100–1000 \\ \hline Negligible & \textgreater{}254 & \textless{}100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of chloride penetrability levels established for standards based on electrical resistivity (AASHTO TP 95) and charged passed (ASTM C 1202). Figure 2: Mechanism of interaction between carbonation and chloride penetration [21].
# A Throughput Analysis Using a Non-Saturated Markov Chain Model for LTE-LAA and WLAN Coexistence
## 2 Analysis of LTE-LAA and WLAN System Throughputs
### Interference Measured at an LTE-LAA Device
where \(B\) represents the bandwidth of a subchannel, and \(\kappa_{bw}\) is the system efficiency factor accounting for various system-level overheads. The parameters \(\kappa_{c}\) and \(\kappa_{sintr}\) together adjust for the SINR implementation efficiency of the modulation and coding schemes [19].
# An Enhanced Fusion Strategy for Reliable Attitude Measurement Utilizing Vision and Inertial Sensors
## 4 Experimental Test and Comparison
### Comparison Among Different Methods by Repeated Experiments with Round-Trip-Step-up Motion  
Repeated experiments are conducted to compare the performance among different methods. The motion trajectory of the moving object is designed as displayed in Figure 8, which we called the round-trip-step-up motion. The pitch axis is from \(-20\) degrees to \(+20\) degrees in the round-trip form and the yaw axis is from \(-20\) degrees to \(+20\) degrees in the step-up form.  
To provide convincing results, the performance of five different methods are conducted and the attitude angle errors of the pitch angle and yaw angle are shown in Figure 9, where the abscissa is time in seconds and the ordinate represents the pitch and yaw angle errors. It can be directly seen that considering the overall performance of the five methods in the viewpoint of stability and precision,  
Figure 8: Motion trajectory of pitch axis and yaw axis.  
Figure 7: Rotation transformation of (**a**) inertial data and (**b**) vision data.  
the proposed RBF-CKF method outperforms the EKF, RBF-EKF, CKF and back propagation neural network (BPNN) BP-CKF methods. Besides, during the period when the errors of other method is quite large, such as the 40-60 s for the pitch axis as well as 20-40 s and 40-60 s for the yaw axis, the RBF-CKF can stay steady, which indicates its stability. More importantly, it indicates the RBF-CKF algorithm can maintain good convergence during the intersampling of slow visual data.  
The reasons for the above phenomena are that when the visual measurements are available, attitude is calculated by normal filtering to fuse the inertial and visual data together. During the intersampling of the visual data, for the EKF or CKF method, attitude is calculated by inertial data only. For RBF-EKF and BP-CKF methods, the attitude calculated by inertial data is compensated by RBF and  
Figure 9: (**a**) The attitude errors of experiment one; (**b**) The attitude errors of experiment two; (**c**) The attitude errors of experiment three.  
BP prediction results before combining the EKF and CKF respectively. Finally, we need to output the final estimated attitudes. Furthermore, for the RBF-CKF method, the attitude angles calculated by inertial data are compensated by RBF prediction results before putting the final estimated attitudes as the output. The optimal estimation of RBF-CKF can be reflected in the pitch and yaw angle errors and it is determined that the performance of RBF-CKF is obviously superior to the other four methods.  
The maximum errors and Root Mean Squared Errors (RMSE) for different methods across five groups of experiments are shown in Table 2 and Figure 10. It can be clearly seen that RBF-CKF is more stable compared to the other methods. The running time of each method is shown in Table 3. Due to the complex structure of RBFNN as well as the process of establishing the training model and predicting the estimated error, the RBF-CKF running time is the longest among the five methods.
## 4 Discussion
Despite these limitations, this study provides an important initial step toward data-driven, spatially explicit sediment prediction in inland rivers. The consistency between the predicted patterns and the known seasonal trends supports the model's practical utility. Moreover, the framework is transferable and can be adapted to other rivers using locally sourced bathymetric and environmental data from field measurements, remote sensing, and hydrodynamic simulations. Future work will focus on incorporating more physical variables and high-resolution flow indicators to develop physics-informed machine learning models. In addition, expanding the dataset to cover a broader range of hydrological and sedimentary conditions will allow for more robust training, validation, and scenario testing under diverse flow regimes.
# Satellite-Assisted Disrupted Communications: IoT Case Study
## 4 Experiments
### Experimentation Parameters
Subsequently, each route was added to each node's routing table. Upon changes in the topology according to the _updateInterval_, relevant parameters that require updates were reconfigured, such as satellite positions and node routing tables. When the topology changes (in the next _updateInterval_), any relevant parameter that required update was reconfigured such as satellite positions and node routing tables. Finally, the _intersatelliteLink_ parameter enabled the communication among satellites with some preconditions such as the compatibility of links within each satellite [50]. In this context, satellites placed on the same plane or neighboring planes communicated even with the absence of a ground station between them. On the contrary, while disabled, the placement of a ground station between satellites was mandatory to achieve communication under these conditions.
## 2 Materials and Methods
### NDVI, GPP, and Radiation Datasets  
MODIS vegetation index product (MOD13C2) was used in this study because compared to other datasets (e.g., GIMMS NDVI3g), MODIS NDVI is based on narrow-band calculations specifically designed for vegetation monitoring. In addition, MOD13C2 is quality controlled global-gridded (0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) spatial resolution) monthly average product, which benefits from improved radiometric sensitivity and atmospheric/geometric (bi-directional radiation distribution function, BRDF) corrections [(23)].  
For GPP, we used monthly global products provided by the Max Planck Institute's Biogeochemical integration group (MPI-BGC). The MPI-BGC GPP products were generated through a machine learning algorithm upscaling flux-tower-based GPP into global 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) products [(24)]. Here we did not use MODIS GPP products (MOD17A2) because MOD17A2 is also based on LUE model and takes MODIS NDVI as a major input. Thus results might be biased when comparing MODIS GPP and VI-based LUE model.  
The incoming shortwave radiation (SWR) was used in this study as a proxy for PAR. Monthly global SWR data (gridded 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\)) provided by the National Aeronautics and Space Administration Clouds and the Earth's Radiant Energy System (NASA-CERES, a series of satellite instruments) are available at [[https://eosweb.larc.nasa.gov/order-data](https://eosweb.larc.nasa.gov/order-data)]([https://eosweb.larc.nasa.gov/order-data](https://eosweb.larc.nasa.gov/order-data)).
## 1 Introduction
Under the condition of ensuring ground coverage, the satellite's uniform spinning motion and the camera's uniform oscillating motion are used to derive the angular velocity of the satellite cone scanning and the angular velocity of the camera pendulum scanning. In order to improve the system performance, a dual-camera variable speed optical cone and pendulum scanning imaging mode is proposed. This method can more reasonably plan the coverage of each frame of images by adjusting the angular velocity of the camera pendulum scanning in real time, reduce the amount of data, lower the requirements for the attitude control capability of the satellite platform, and obtain higher-quality original images. These two novel operating modes are described in detail in this paper and simulated. Finally, the ground principle prototype is designed and ground verification experiments are carried out. The remaining of this paper is organized as the following: Section 2 proposes the optical cone and pendulum scanning imaging method and introduces the process of parameter design. Section 3 introduces the design methods of the two imaging modes according to the flowchart and deduces the relevant formulas in detail. Section 4 simulates and verifies two imaging modes and completes the ground verification experiments. Section 5 concludes this paper and proposes possible future research directions.
# Clone Node Detection Attacks and Mitigation Mechanisms in Static Wireless Sensor Networks
## 5 Ontology in Wireless Sensor Networks
### Ontology for Replication Attacks in Static Wireless Sensor Networks (ORASWSN)
If the attack is concerned about the Sybil attack, for example, it is just a matter of reconfiguring and redesigning the system security according to the needs and intentions of the cybersecurity analyst. The following diagram (Figure 6) encompasses the techniques, tools and strategies (a well-structured assembly of ideas) that can detect the clone node attack. Furthermore, it can also be used to minimize or mitigate this attack. (1) The first goal of this framework design is to detect the replication attack in a static wireless sensor network area. Thus, for this scheme to be effective, it has to be properly used. We cannot disregard the basic and common security measures that have already been used to prevent physical access to an important place. Even though they are popular and some of them have nothing to do with technology, they are still very significant. These fundamental measures can be cameras, fences, walls, and security guards, etc. Though it is obvious that at first glance those measures are the first thoughts of any individual when it comes to the security of an area, they should not be neglected. For the attacker to extract the information from a sensor, he has to physically capture that sensor; therefore, it is of great importance that we highlight those basic security measures. (2) The measures mentioned in (1) are considered to be the first security layer. It does not directly detect the clone node attacks, but it detects and helps catch the attacker. Using  
Figure 6: ORASWSN ontology-based approach in static WSNs. the intrusion detection system (IDS) tool can be helpful in terms of monitoring the network behavior and trigger an alert when the intruder starts interacting with any node in the system. Any modification of the nodes (for example, location change if the network uses the sink as the primary node, ID change, missed nodes, node replacement, and/or any node with a non-predicted behavior) must be sent to the system administrator. (3) The second goal of our scheme is to mitigate the attack. Having this mission planned, we recommend that the security layers be taken into consideration, from a fundamental standpoint to advanced security measures. Using encryption methods for the way keys are shared in the network, the communication between nodes, and the hashing phase is the second layer in the framework; however, the first measures are considered to prevent the attacker from extracting and modifying the captured nodes. Intrusion prevention systems (IPS) also fall into this category as it prevents any unintended activities of the nodes from occurring in the network by blocking them. (4) The main requirement of this ontology requires that a maximum of security layers be used properly to be efficient. Skipping a layer can severely affect the network.