esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Material and Methods
### XAI Techniques  
In general, post hoc XAI techniques can provide global explanations (i.e., how the trained model makes predictions) and/or local explanations (i.e., how the model arrivesat a prediction for a single instance). It helps to understand the distribution of prediction outputs as a function of the input features [23]. The adoption of XAI methods in healthcare is becoming increasingly crucial. A recent systematic review highlights the need for transparency and trust in clinical AI models, particularly through interpretable predictions [64,65].  
In the present study, global and local explanations were explored with two different model-agnostic, post hoc XAI techniques: SHAP and the PDP. Firstly, SHAP is a feature importance technique that offers local explanations by calculating an additive measure (known as the Shapley value) with local accuracy and consistency for each feature of the single instance [66]. SHAP also provides global explanations by aggregating all the Shapley values concerning every single instance of the dataset using the \"Kernel SHAP\" implementation. Here, we used (i) the SHAP summary dot plot implementation [66] to quantify the contribution of each feature's value to the global model's prediction, and (ii) waterfall plots to describe the specific influence of the feature values on the local prediction of an instance. Secondly, PDPs provide visual explanations about the marginal effect on the predicted outcome of a given feature over the range of its observed values [67]. In this way, PDPs can visualize the trend in a specific feature's influence on the global prediction's probability. The SHAP and PDP analyses were implemented as part of a \"local framework\" with the SHAP Python library 0.35.0 [68] and PDPbox 0.21 [69], respectively. In this paper, the XAI procedure was applied to the best model assessed in the previous step.
## 3 Materials and Methods
### Data Pre-Processing Techniques  
Imbalanced datasets have a severe skew in the class distribution. The available datasets for training and testing the optimized CNN model are imbalanced as depicted in Figure 3. For instance, the HAM10000 dataset includes seven classes of skin cancer where NEV lesion class has the 6705 images and the DF lesion class has 115 images only. This problem can lead to a bias during the training stage that can reduce the predictive performance of the model by undermining the minority classes in favor of the majority classes. This is a problem as it is typically the minority class predictions that are most important. Many approaches can address the class imbalance challenges, such as randomly resampling the training dataset. The two main approaches to randomly resampling an imbalanced dataset are to delete examples from the majority class, called undersampling, or duplicate examples from the minority class called oversampling [33]. In this work, we have used an oversampling approach to increase the number of minority classes without duplicating examples.  
The augmentation technique was employed to balance the datasets instead of using duplication methods, as it offers an improved training data balance for the proposed model. The augmentation process involved applying elastic deformations through random displacements. These elastic deformations utilized local distortions, random affine transformations with random displacements, and smoothing operations with a Gaussian filter. Simple random rotations were also used in the augmentation process to improve performance further. The new augmented images were created by performing operations such as rotating, panning, twisting, and filling any gaps. Also. to ensure there were no mixed classes, they were produced using a separate approach for each class from the dataset. Figure 4 illustrates the augmented images that effectively address the imbalance issues in the datasets. As a result, the number of samples in the datasets were increased to 46,935, 102,976, 4116, and 46,935 images for HAM10000, ISIC2019, ISIC2017 and ISIC2018, respectively, (Figure 3).  
After carefully using the image augmentation technique to balance the datasets the CNN is more likely to provide reliable results. Pre-processing of high-quality datasets can cause reliable models to provide outstanding results. After the augmentation has been applied, the images can possess variations in size as well as possess multi-scale and multi-resolution features. The next and most critical step in the pre-processing phase is to make sure that all the images are of equal scale and resolution through cropping, resizing, and re-sampling. We use a relatively small image size of \(50\times 50\) as an input in this work because it allows for a more manageable feature map. The images are then normalized by first computing the mean pixel value, subtracting this value over the entire dataset to center the data, calculating the standard deviation, and dividing the pixel values by this value.
## 4 Bayesian Game among Community Operators
### Community Operator's Profit Model  
Community operator's profit mainly comes from DR purchaser, gas boiler, and energy storage. Accordingly, we can obtain the following profit model.  
1. Profit from DR purchaser From DR purchaser, community operator's income is the earning for selling DR resource, and cost is the punishment for DR resource's breach. According to the bidding price, when DR resource of community operator \(n\) is the \(i\)th grade, then the profit from DR purchaser can be expressed as \[W_{n}^{\text{DR}}=\sum_{t=1}^{T}\left(p_{i}^{t}x_{n}^{t}-q_{i}E\delta_{n}^{t} -c_{\text{user}}\left(x_{n}^{t}-E\delta_{n}^{t}\right)\right)\] (6) where \(q_{i}\) is the punishment for \(i\)th grade of DR resource's breach, \(c_{\text{user}}\) is the price paid to consumers.
2. Profit from gas boiler From gas boiler, community operator's income is the earning for providing heat to users, and cost is the energy cost for natural gas, investment and maintenance cost. Accordingly, profit from gas boiler can be expressed as \[W_{n}^{\text{GB}}=\sum_{t=1}^{T}\left(s_{n}^{\text{h}}h_{n}^{t}-c_{\text{ gas}}r_{n}^{t}\right)-C_{n}^{\text{GB}}\] (7) where \(s_{n}^{\text{h}}\) is heat price charged from users; \(c_{\text{gas}}\) is natural gas price; \(C_{n}^{\text{GB}}\) is the investment and maintenance of gas boiler with the following formula \[C_{n}^{\text{GB}}=\frac{1}{365}k_{\text{GB}}^{\text{in}}\frac{r(1+r)^{y_{\text {GB}}}}{(1+r)^{y_{\text{GB}}}-1}S_{n}^{\text{GB}}+\sum_{t=1}^{T}k_{\text{GB}}^ {\text{on}}h_{n}^{t}\] (8) where \(k_{\text{GB}}^{\text{in}}\) and \(k_{\text{GB}}^{\text{on}}\) are investment price and maintenance price, respectively; \(r\) is the discount rate; \(y_{\text{GB}}\) is the lifetime of gas boiler; \(S_{n}^{\text{GB}}\) is the capacity of gas boiler.
3. Profit from energy storage From energy storage, community operator's income is the earning for providing electricity to users, and cost is the electricity cost for charging storage, investment and maintenance cost. Accordingly, profit from energy storage can be expressed as \[W_{n}^{\text{ES}}=\sum_{t=1}^{T}\left(s_{n}^{\text{e}}e_{n}^{\text{dis},t}-c_{ \text{ele}}e_{n}^{\text{dis},t}\right/\left(\eta_{\text{ch}}\eta_{\text{dis}} \right)\right)-C_{n}^{\text{ES}}\] (9) where \(s_{n}^{\text{e}}\) is electricity price charged from users; \(c_{\text{ele}}\) is electricity price charged by public grid; \(C_{n}^{\text{ES}}\) is the investment and maintenance of energy storage with the following formula\[C_{n}^{\text{ES}}=\frac{1}{365}k_{\text{ES}}^{\text{lin}}\frac{r(1+r)^{y_{\text{ES }}}}{(1+r)^{y_{\text{ES}}}-1}S_{n}^{\text{ES}}+\sum_{t=1}^{T}k_{\text{ES}}^{\text {on}}\left(e_{n}^{\text{dis},t}+e_{n}^{\text{dis},t}/\left(\eta_{\text{ch}} \eta_{\text{dis}}\right)\right) \tag{10}\]  
where \(k_{\text{ES}}^{\text{in}}\) and \(k_{\text{ES}}^{\text{on}}\) are investment price and maintenance price, respectively; \(y_{\text{ES}}\) is the lifetime of energy storage; \(S_{n}^{\text{ES}}\) is the capacity of energy storage.
## 3 Data Processing  
This section describes the main data processing modules. All of them account for efficient memory management routines. The modules are:  
* Pre-processing
* Land masking.
* Ship detection.  
Figure 1: High level scheme of SIMONS.  
Figure 2: Timing in data processing for generating products.  
* Ship classification.
* AIS correlation.
* Report generation.
# Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment
## 6 Conclusions and Future Works  
In this study, we present an AN-STUKF estimating algorithm to observe the state of the skid-steered wheeled vehicle. A 3-DOF dynamic model is built to describe the vehicle dynamic characteristics, in which the Brush nonlinear tire model is used to estimate the tire and road contact information. The strong tracking algorithm is used to enhance the stability of the UKF algorithm. The adaptive variance adjustment is used to increase the accuracy when the vehicle drives in strong nonlinear conditions. In this observer, longitudinal velocity, lateral velocity, and yaw rate of the vehicle are estimated. Then, based on these estimated states, the sideslip angle of vehicle body and instantaneous center of rotation (ICR) are calculated to reflect the vehicle motion characteristics.  
In the experimental test, two driving conditions are considered to verify the accuracy of the proposed estimating algorithm. The accelerating straight driving condition verifies that the AN-STUKF traces faster than the traditional UKF. The experimental test in turning condition at small radius verifies that the AN-STUKF performs better than the traditional UKF when the vehicle drives in high nonlinear conditions. Then, the sideslip angle of vehicle body and the instantaneous center of rotation are computed by the estimation results. These two states' estimation results reflect that the instability increases in small radius turning conditions.  
However, even though the AN-STUKF performs some improvement in estimation accuracy and tracing rate, the application of this estimation algorithm still requires investigation. The parameters of the adaptive variance matrix adjustment require heavy workload to tune according to the experimental data and the linear method cannot satisfy all driving conditions. The computational burden also needs to be considered and this is an important reason why the linear method is used in the noise matrix adjustment instead of a more accurate exponent method. The accuracy of the used nonlinear tire model also needs to be verified. The kinetic model would be considered into the estimation model to improve the robustness of the observer.  
Conceptualization, X.Z. and S.Y.; methodology, X.Z. and X.Y.; software, X.Y.; validation, S.Y. and X.L.; formal analysis, X.Z.; experiment, Q.L., X.Y., X.L., and X.Q.; data processing, X.Z. and X.Y.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z., X.Y., and S.Y.; supervision, S.Y. and X.L.; project administration, X.L.; funding acquisition, X.L. and X.Q. All authors have read and agreed to the published version of the manuscript.  
Figure 19: The instantaneous center of rotation (ICRs) of the vehicle when the vehicle turns.  
**Funding:** This work was funded by the Key Laboratory of Equipment Pre-Research Fund General Project under grant 6142213180205.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Tensile Test Results
Figure 3: Plot of Young’s modulus and stress. Figure 2: Plot of stress–strain. that, at these concentrations, the studied fillers improved the resistance to breakage of the material. The Young's modulus of the materials obtained was determined based on the results of the stress and strain tests. The sample of PVA alone had a Young's modulus of 8.083 MPa; when adding Chi to the PVA material, the Young's modulus increased to 10.806 MPa. The molecular structure of Chi gives it physicochemical characteristics that improve the mechanical properties of PVA. This agrees with the results of tensile strength, since Chi was associated with an increase in the mechanical resistance of the material and a decrease in the percentage of deformation. Table 2 shows the results of [PERSON]' analysis on the Young's modulus of the samples, with a significance level of 0.05 for the statistical calculation of the standard deviation. Increasing the percentage of any of the fillers caused an increase in the Young's modulus of the material (see Figure 3), which also agrees with the behavior of tensile strength. The B4 PVA-Chi/PPy0.3 film showed better tensile test properties, rigidity, and toughness, with a Young's modulus of 19.67 MPa, almost double compared to the 10.80 MPa of the PVA-Chi film. These results indicated that there was a positive interaction between PPy and the PVA-Chi material, turning it into a material with potential structural applications. The B15 PVA-Chi/PPy0.3/PEDOT: PSS0.1/MWCNT0.1/CB0.1 material, which contained the four fillers (0.3 g of PPy and 0.1 g each of MWCNT, CB, and PEDOT: PSS) had a Young's modulus of 17.579 MPa, slightly less than the value for the B4 PVA-Chi/PPy0.3 material, which was 19.67 MPa. This may be since although PPy provided mechanical resistance to the materials, CB and MWCNT particles were dispersed using an ultrasonic bath, which could have affected the molecular structure of the materials.
## 3 Results and Discussion
### Parameter Sensitivity Analysis
#### 3.4.2 Sensitivity Analysis of Fatigue Model Parameters  
The objectives of Case Studies 2 and 3 were to predict the fatigue cracking using unaged and short-aged (RTFOT) parameters respectively. This study only focused on the prevailing temperature range affecting tropical regions. As obtained in Case Study 1, the phase angle, softening point and test temperature and frequency showed high sensitivity to the variation of G*.sin\(\delta\) parameter. The Sobol indices showed low sensitivity of G* and viscosity of the binder to the variation of G*.sin\(\delta\) parameter as shown in Figure 6.
# A Systematic Survey of Multi-Factor Authentication for Cloud Infrastructure
## 1 Introduction
### Research Motivation  
The user authentication process has the sole aim of establishing user identity by means of verification of credentials of genuine users of the system. Single Factor Authentication (SFA) was popular due to its simplicity and user-friendliness [15; 19]. [PERSON] [20] observed that combining two or more strong authentication measures could increase the difficulty level for guessing and breaking the SFA type of authentication. Accordingly, to increase the degree of difficulty for the attacker, researchers developed the idea of using more than one factor in a cascading manner or using one factor within another factor of authentication. Such developments gave rise to the authentication method commonly known as Multi-Factor Authentication (MFA) [21; 22]. Though the overhead associated with processing more than one credential or factor check was initially relatively high, the advancement of technology and processing speed has been leveraged with newer-generation IT devices. However, based upon the type and categories of factors adopted for MFA, in almost all cases, the system requires special purpose or additional hardware equipment and software drivers associated with their fundamental functioning. Though additional checks for authentication are frustrating for the users, the same overhead is worth the value for secure user authentication and further to the entitled IT resources. [PERSON] et al. [23] have conducted a detailed survey on user convenience vis-a-vis difficulty with the introduction of MFA. [PERSON] et al. [24; 25] have carried out extensive analysis on _Authentication Management_ and _Authorization Management_ with respect to Security As A Service, covering human users of the cloud network [26].
# Airborne GNSS Reflectometry for Water Body Detection
###### Abstract  
This article is dedicated to the study of airborne GNSS-R signal processing techniques for water body detection and edge localization using a low-altitude airborne carrier with high rate reflectivity measurements. A GNSS-R setup on-board a carrier with reduced size and weight was developed for this application. We develop a radar technique for automatic GNSS signal segmentation in order to differentiate in-land water body surfaces based on the reflectivity measurements associated to different areas of reflection. Such measurements are derived from the GNSS signal amplitudes. We adapt a transitional model to characterize the changes in the measurements of the reflected GNSS signals from one area to another. We propose an on-line/off-line change detection algorithm for GNSS signal segmentation. A real flight experimentation took place in the context of this work obtaining reflections from different surfaces and landforms. We show, using the airborne GNSS measurements obtained, that the proposed radar technique detects in-land water body surfaces along the flight trajectory with high temporal (50 Hz ) and spatial resolution (order of 10 to 100  m\({}^{2}\)). We also show that we can localize the edges of the detected water body surfaces at meter accuracy.  
GNSS-R; airborne reflectometry; signal segmentation; reflectivity; water body detection; edge localization +
Footnote †: journal: remote sensing  
Article  
Airborne GNSS Reflectometry for Water Body Detection
Tackling these problems is essential to improving the durability and effectiveness of IoT systems. This work presents a task scheduling algorithm specifically tailored for IoT devices employed in disaster monitoring. This algorithm aims to decrease energy usage while ensuring prompt and effective reactions to natural disasters. The system effectively closes the current disparity between conventional energy management methods and the particular power requirements of catastrophe monitoring applications by optimizing power utilization. Consequently, the system not only greatly decreases power usage but also prolongs the lifespan of the instruments, offering a cost-efficient method for continuous monitoring in areas susceptible to disasters. This study examines the creation and execution of an inexpensive natural catastrophe monitoring system that utilizes Internet of Things (IoT) technologies. Although government money is available, the system's coverage is not comprehensive, so certain regions rely on local meteorological data for disaster management and planning. This research primarily focuses on two key contributions. Firstly, this research aims to improve disaster management in villages nationwide, particularly in flood-prone areas. It focuses on deploying low-cost IoT devices that are easily accessible and cost-effective because government funding does not cover all areas. This study also addresses the high energy consumption of sensor nodes, emphasizing the need for practical solutions to overcome the country's specific constraints. Secondly, it emphasizes the enhancement of power efficiency at the software level. This study examines the impact of power-consuming elements such as processing, sensing, and transmission modules on the overall energy usage of microcontroller units (MCUs) and sensors, which are already built for low power consumption. It emphasizes the importance of energy efficiency in these components. The rest of the paper is organized as follows: Section 2 presents a comprehensive review of related work. Section 3 describes the system architecture of our low-cost disaster monitoring system and the task scheduling algorithm. Section 4 outlines the experimental setup and results. In Section 5, we discuss the implications of our findings and potential improvements. Finally, Section 6 concludes the paper with a summary of the key contributions and future research directions.
## 6 Numerical Results
### Achievable Rates  
In this subsection, we compare the theoretical rates achieved by IFP in Equation (22) with existent schemes. We consider the rates achieved by three other schemes: (i) the decode and forward scheme, which requires decoding the original signals at the relay; (ii) the compute and forward scheme without precoding; and (iii) compute and forward obtained by considering integral channels, which serves as an upper bound for achievable rates in CF. For fair comparison, we have adjusted the total power available at the transmitters in all the schemes to be equal to \(\gamma_{x}=\text{max}(\gamma_{w})\). The channel gains, **h** = [\(h_{1}\)\(h_{2}\)], are approximated by integers, **a** = [\(a_{1}\)\(a_{2}\)]. The \(log\) is taken to the base of two. The rates are measured in bits per channel use (bpcu). For decode and forward, the relay decodes one of the signals, treating the other signal as noise. Hence:  
\[R_{DF}=\text{log}^{+}\left(\frac{\gamma_{x}\mid h_{1}\mid^{2}}{\sigma^{2}+ \gamma_{x}\mid h_{2}\mid^{2}}\right)\]  
For compute and forward without precoding, the relay computes the linear combination of original signals, and the non-integer part of the channel contributes to additional self-noise. The achievable rate is given by:  
\[R_{CF}=\text{log}^{+}\left(\frac{\gamma_{x}\parallel\textbf{a}\parallel^{2}}{ \sigma^{2}+\gamma_{x}(\parallel\textbf{h}-\textbf{a}\parallel^{2})}\right)\]  
The upper bound of CF is computed considering the channels as integers (\(\textbf{h}=\textbf{a}\)) in which no additional self-noise is present. The rate achieved is given by:  
\[R_{CF}^{u}=\text{log}^{+}\left(\frac{\gamma_{x}\parallel\textbf{a}\parallel^{ 2}}{\sigma^{2}}\right)\]  
Figure 4: Outage probability curve. The horizontal axis shows the power available at the transmitter. The power available at the transmitter is assumed to be always greater than or equal to the signal power, hence \(\gamma_{a}>1\). With the increasing channel variance, \(\sigma_{h}\), the outage decreases.  
We compare these rates with the rates for our proposed scheme. Figure 5 shows the achievable rates of different schemes for varying SNR. Clearly, the proposed implementation approaches the upper bound at high SNRs. The CF scheme without precoding suffers from increased self-noise as the signal power increases.
# A GNSS/5G Integrated Three-Dimensional Positioning Scheme Based on D2D Communication
## 2 Methods
### GNSS/5G Integrated Three-Dimensional Positioning Scheme
#### 2.3.2 GNSS/5G Integrated Three-Dimensional Positioning Based on PF
```
Input: the initial distribution \(p\big{(}X_{0}^{i\mathcal{N}^{2}}\big{)}\)
1: Initialization: generate the initial state of the particles \(PX_{0}^{i\mathcal{N}^{2}}(f)\big{(}f=1,2,\cdots,N_{p}\big{)}\) according to \(P\big{(}X_{0}^{i\mathcal{N}^{2}}\big{)}\)
2:for all\(k=1,2,3,\cdots\)do
3: let \(q\Big{(}X_{k}^{i\mathcal{N}^{2}}\bigg{|}X_{k-1}^{i\mathcal{N}^{2}}\Big{)}=P \Big{(}X_{k}^{i\mathcal{N}^{2}}\bigg{|}X_{k-1}^{i\mathcal{N}^{2}}\Big{)}\) be the importance distribution
4: sample the particles at time slot \(k\) according to \(PX_{k}^{i\mathcal{N}^{2}}(f)\sim q\Big{(}X_{k}^{i\mathcal{N}^{2}}\Big{|}X_{k-1 }^{i\mathcal{N}^{2}}\Big{)}\)
5: calculate the weights of the particles at time slot \(k\): \(\omega_{k}^{i\mathcal{N}^{2}}(f)\propto P\Big{(}Z_{D,k}^{tr,i\mathcal{N}^{i}},\mathcal{N}_{k-1}^{i\mathcal{N}^{2}}\Big{|}X_{k}^{i\mathcal{N}^{2}}\Big{)}\)
6: normalize the weights: \(\hat{\omega}_{k}^{i\mathcal{N}^{2}}(f)=\omega_{k}^{i\mathcal{N}^{2}}(f)\Bigg{[} \sum\limits_{f=1}^{N_{p}}\omega_{k}^{i\mathcal{N}^{2}}(f)\Bigg{]}^{-1}\)
7: resampling the particles and the weight of each of them is \(\frac{1}{N_{p}}\)
8: calculate the estimated state at time slot \(k\) as \(\hat{X}_{k}^{i}=\frac{1}{N_{p}}\sum\limits_{f=1}^{N_{p}}PX_{k}^{i}(f)\) and output the result
9:end
```  
**Algorithm 2**GNSS/5G Integrated Three-Dimensional Positioning Algorithm