esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Adsorption Study
#### 3.2.2 Impact of Solution pH  
The impact of pH on MB adsorption by nZVISLP was investigated by varying the solution pH within the range of 4-10 for a dosage of 1.2 g/L and C\({}_{0}\) of 10 mg/L, conducted at 25 \({}^{\circ}\)C. A 0.1 N HCl/NaOH mixture was added to maintain the pH at the required value. As seen in Figure 5b, the MB removal (%) was enhanced with a rise in solution pH. The pH of the solution was also checked after the adsorption process at different time intervals, whereby it was found that the solution pH changed slowly with time within the initial 60 min and then became constant, resulting in the adsorption changing in the initial 60 min and then becoming almost constant (Figure 5b). The pH of the solution changed to approximately its pH\({}_{\text{ZPC}}\) value. At acidic pH, a lower removal was observed due to the competitive interactions of H\({}^{+}\) and catatonic MB, as well as the repulsive force interaction between the dye cations and the positively charged sites. This was confirmed by the stable zeta potential magnitude between pH 4.0 and 8.0 (Figure 4b). Upon increasing pH (8.0 to 10.0), the MB adsorptive removal increased, due to the increase in the number of negatively charged sites, which led to the existence of an electrostatic force of attraction between the cationic MB and nZVISLP [38]. These results also confirmed the results reported in earlier studies [38; 39; 40], which showed an increase in cationic dye adsorption with a rise in pH. MB adsorption was almost constant at alkaline pH values (8-10).
# Femtosecond Laser Textured Surfaces for Radiative Cooling: Black Metals
###### Abstract  
There is a growing need for novel methods to modify the surfaces of a wide range of materials over large areas. Here, we demonstrate the creation of low-reflectance (\(R<2\%\)) surfaces in the near-to-mid infrared (IR) spectral window of 2-20 \(\upmu\)m by ablating W, Al, and Cu with high average intensity 20-120 TW/cm\({}^{2}\), 200 fs laser pulses at 1030 nm wavelength. The chemical modifications of the surfaces by laser ablation under ambient room conditions were analyzed using X-ray photoelectron spectroscopy (XPS). The results show a consistent decrease in the metallic component, accompanied by an increase in metal oxides. Energy dispersive spectroscopy (EDS) showed a similar increase in oxygen content over a micrometer depth scale. The reduced refractive index of the metal oxides compared to the corresponding metals contributes to the reduction in IR reflectance, combined with the formation of 3D hierarchically textured surface structures. These IR-black metals exhibit great potential for radiative cooling at elevated temperatures relevant to industrial and space applications.  
femtosecond laser ablation; high-intensity; direct energy deposition; radiative cooling; anti-reflective surfaces +
Footnote †: journal: Applied Sciences
## 3 The Proposed Scheme
### Secret Image Compression Phase  
In the proposed scheme, data compression is used to decrease the secret message size, which effectively increasing the embedding capacity. In order to minimize the cost of transmission in a limited bandwidth network, information needs to be compressed before delivery to improve transport efficiency. The proposed scheme is combined with MGEMD to increase embedding capacity. As a result, the multi-bit data hiding scheme for compressing secret messages has twice the embedding capacity. The maximum runs and total length information of RLE will be regarded as secret messages to hide in the cover image.  
```
Input: A cover image (\(S\)) with gray level image Output: A stego image (\(I^{{}^{\prime}}\))
```  
**Step 1.**: The gray level secret image is transformed into a binary stream.
**Step 2.**: Compressing the binary stream by RLE for the new secret message (\(S^{\prime}\)), which includes maximum runs, total length information and the embedding secret messages (\(s\)).
**Step 3.**: Check if the new secret is zero or one in the high-order bit. This information tells us the beginning bit is zero or one by using RLE. Then, the first pixel's LSB of the cover image will be changed. Simultaneously, we also count the zeros and ones to record the total length information.
**Step 4.**: Find parameters (\(n\) and \(k\)), such as \(2^{nk+1}\) > Max(runs) and \(n\geq k\). Quotient (\(Q\)) and remainder (\(R\)) are calculated from total length (\(L\)) information using Equation (5).  
\[\begin{array}{rcl}R&=&L\bmod 2^{nk+1}\\ Q&=&\frac{R-L}{2^{nk+1}}\end{array} \tag{5}\]  
**Step 5.**: For the second pixel to the last, decision variables \(n\) and \(k\) divide the pixels into \(n\) adjacent pixels \((x_{1},x_{2},\ldots,x_{n})\) as a non-overlapping group.
**Step 6.**: Compute the value \(t=f_{c}(x_{1},x_{2},\ldots,x_{n})\) and the difference \(D\), i.e., \(D=(s-t)\bmod 2^{nk+1}\).
**Step 7.**: If \(D=0\), then cover pixels do not change; else if \(D=2^{nk}\), then \(x_{n}^{{}^{\prime}}=x_{n}+2^{
## 3 Simulation Setup  
### Conventional Model (C-Model)  
The simulation setup under the conventional discrete-time memoryless MIMO model (_C-model_) just considers that \(\mathbf{Y}\), the matrix with received signal samples in Equation (1), is available to the FC as if no signal processing is performed by each CR before the sample values are forwarded to the FC. Matrices \(\mathbf{X}\), \(\mathbf{H}\), \(\mathbf{V}\), and \(\mathbf{V}_{\text{IN}}\) under the _C-model_ are generated as follows: To simulate a Gaussian distributed noise-like transmitted signal, \(\mathbf{X}\) is formed by unitary variance (unitary power), independent and identically distributed (i.i.d.) zero mean complex Gaussian samples. The Gaussian distribution for the entries of \(\mathbf{X}\) is adopted because it accurately models several modulated signals, for instance the amplitude of a multicarrier signal, such as orthogonal frequency-division multiplexing (OFDM), with  
Figure 2: Gating waveform (top) and impulsive noise waveform (bottom).  
a large number of subcarriers, which is the preferred modulation technique in most modern wireless technologies, including several digital television standards. The elements in the channel matrix **H** are zero mean i.i.d. complex Gaussian variables that simulate a flat Rayleigh fading channel between each primary transmitter and sensor (CR), assumed to be constant during a sensing period and independent from one period to another. The entries in **V** and **V**IN are complex Gaussian variables that represent, respectively, the additive thermal and the impulse noise corrupting the received samples. The desired received signal-to-noise ratio (SNR), in dB, and the desired average IN power are guaranteed by making the variance of the noise samples equal to \(10^{-\mathrm{SNR}/10}\) and the variance of the IN samples equal to \(K10^{-\mathrm{SNR}/10}\), for an average IN power \(K\) times the thermal noise power. Moreover, matrix **H** is normalized so that \((1/mp)\|\textbf{H}\|_{F}^{2}=(1/mp)\text{tr}(\textbf{H}^{\dagger}\textbf{H})=1\).  
The received matrix \(\textbf{Y}=\textbf{H}\textbf{X}+\textbf{V}+\textbf{V}_{\text{IN}}\) in the conventional model is then assumed to be available at the FC, from which the covariance matrix **R** is computed, and then the eigenvalues \(\{\lambda_{i}\}\), \(i=1,2,\ldots,m\). The test statistics for GLRT, MMED, MED, and ED are respectively computed from Equations (3)-(6). In each detection technique, the corresponding test statistic is compared with a threshold computed from the desired false alarm probability, and a final decision upon the occupancy of the sensed channel is reached.
# A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome
## 5 Discussion
ELI5 ranked and assigned weights based on feature importance and created a tree map for debugging the decision tree classifier. Further, we deployed a Qlattice algorithm that automatically created a predictive model along with a Qgraph to explain the classifier. Feature Importance with Random Forest helped get insights into global predictions made by RF. Notably most tools explained the predictions based having the following featureswith highest significance: Follicle No. (L), Follicle No. (R), Skin darkening (Y/N), Weight gain (Y/N), hair growth(Y/N), Pimples (Y/N). Research has indicated the significance of these features in diagnosis [4]. XAI tools hence help visualize and interpret local predictions just as practitioners would consider during primary detection of PCOS. Multiple articles proposed machine learning models for screening PCOS using the same open-source Kaggle dataset used in this study. [PERSON] et al. [58] published a study for detecting PCOS patients with data mining tools. Classifiers such as SVM, MLP, RF, LR, and NB, along with Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, were adopted. RF, along with data sampling techniques, performed the best with accuracy, sensitivity, and precision of 95%, 94%, and 96%, respectively. [PERSON] et al. [59] This study compared Python (Spyder IDE) and Rapid Miner for diagnosis. Spyder IDE used a correlation matrix and recursive feature elimination with a logistic regression model, while RapidMiner used forward selection (with NB) and backward elimination (with DT) with a cross-validation operator. The engineered data trained seven ML models, KNN, SVM, RF, NB, Auto-MLP, AdaBoost, and Bagging with DT. Ten features were selected, with RapidMiner obtaining the highest average accuracies of the ML models at 85.97%. [PERSON] et al. [60] proposed an ML PCOS detection architecture named 'PCOCare'. Out of the 42 features, 30 were selected by the Chi-square method. They deployed RF, SVM (with Linear kernel and radial basis function kernel), NB, LR, and KNN. RF performed the best with accuracy, sensitivity, and precision metric scores at 91%, 94%, and 88%. [PERSON] et al. [61] trained multiple classifiers, KNN, NN, NB, SVM, LR, classification tree, and Linear discriminant (LD). After feature engineering, they selected 43 features, and LD performed the best with 92.60% accuracy. [PERSON] et al. [62] proposed a LIME PCOS diagnostic model, which used RF as its classifier. A chi-square test was deployed, and features with a \(p\)-value greater than 0.05 were considered statistically significant. RF obtained an accuracy of 86.03%. LIME described local predictions for the first five patients. We have tabulated the remaining model performance comparison in Table 8.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Sr. No.
## 2 Materials and Methods
### Methods
#### 2.3.3 NASADEM Correction for Vegetation Bias
Knowing (1) the precise time each laser pulse was emitted, (2) the pulse return time from the target, and (3) the shape of the return waveform allows the calculation of various parameters, such as the mean elevation of the lowest detected mode ZG, the mean elevation of the highest detected mode ZH, and the elevation of the highest detected signal ZT within each waveform. ZG is represented by a distinguishable curve that returns the latest. RH parameters stand for Relative Heights to ZG at which a certain percentage of the waveform energy occurs, e.g., RH100 is equal to ZT-ZG. of the tested approaches. For the Canopy Density 2000 Model, we assumed that it provides the percentage of vegetation included in the area of each cell as viewed from above. Costa Rica is a very biodiverse area; however, most of the high vegetation can be broadly categorised as evergreen broadleaf forests and palmate trees. Moreover, as can be observed on the global map of GFCH2019 in Figure 5 in [17], Costa Rica is a small country with some of the world's tallest forests. The Kapok tree (Ceiba pentandra), Pterygota excelsa, and white oak are the tallest species, which can reach about 60 m in height (according to [[https://www.monumentaltrees.com](https://www.monumentaltrees.com)]([https://www.monumentaltrees.com](https://www.monumentaltrees.com)), accessed on 11 August 2021). Most forests have complex vertical layer structures, including the overstorey (emergent canopy), canopy, understory, shrub layer, and ground level. Therefore, the mean canopy height is a very different measure from that of the maximum or dominant height [39]. Our goal is to find the best analytical approach for VB correction that could be applied to the total area of study independent of vegetation type or land use. Four approaches for computing VB using CHM2000 as canopy heights \(H_{\text{canopy}}\) and tree canopy cover for year 2000 as \(\rho_{\text{canopy}}\) were tested:  
1. VB1 = \(H_{\text{canopy}}\);
2. VB2 = \(H_{\text{canopy}}\rho_{\text{canopy}}\);
3. VB3 = \(aH_{\text{canopy}}\rho_{\text{canopy}}\);
4. VB4 = \(aH_{\text{canopy}}\). The scaling coefficient \(a\) in VB3 and VB4 was determined as the coefficient that would provide the best fit for a corrected NASADEM with respect to the ZG of LVIS2005.
# An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
## 4 ML for Outlier Detection  
Outlier detection in the context of the framework explained in Section 3.2 is the first functional block to realise the overall framework. If outliers are not removed, then the resulting Pols will be inconsistent. Such inconsistency may relate to poor data collection, for instance.  
Another use of outlier detection is to discover abnormal patterns. We intend to exclude all points and/or trajectory segments that impair trajectory detection.  
ML is, therefore, relevant to be considered in this context. The choice of specific ML algorithms requires an approach that best serves the requirements of the specific solution. ML algorithms relevant to outlier detection can be categorised as follows [30]:  
1. **Nearest neighbour-based**. Based on the comparison of the distances between various points and their nearest neighbours [31].
2. **Density-based**[32]. Based on the measurement of the higher or lower density of points in a given region, which has resulted in a new definition of local outliers, with the same principle. Here we consider both LOF and DBSCAN as representative algorithms of this category.
3. **Distance-based**[33]. Defined by measuring the distances from a given point O to other points, resulting in points in its neighbourhood and outliers. Examples are _k-nearest neighbours (kNN)_, k-_means (k-MEANS) clustering_, and _learning vector quantisation (LVQ)_.  
Out of the mentioned approaches, this work considers DBSCAN and LOF as the basis for outlier detection in the proposed framework. DBSCAN and LOF are anomaly detection algorithms that use distance to track down the nearest neighbour clusters, based on the k-NN algorithm.  
The next subsections explain the two algorithms.
# Performance Analysis of Multihop Underground Magnetic Induction Communication
## 2 Underground MI Communication System
### Multihop MI Communication Model  
In this study, we developed an MI-WUSN by using a linear system model in a one-dimensional region \(\mathcal{R}\). To model an MI communication system, let us consider \(K\) nodes such that \(\mathcal{K}=\{1,2,3,\ldots,K\}\) is a set of nodes aligned in \(\mathcal{R}=[0,R]\), and let there be (\(K\)-2) relay nodes between the transmitter and receiver. The \(K\) nodes are arranged along \(\mathcal{R}\), establishing \(i\) equidistant hops such that \(h_{i}\in\{1,2,\ldots,H\}\), with \(H=K-1\). This path can be defined as an ordered set of nodes, that is, \(\mathcal{H}=0\leq h\leq H\), and each hop can be represented by \(h_{i}=|\mathcal{H}|\). The position of each node \(k\), where \(k=1,2,3,\ldots,K\), is denoted by \(x_{k}\). The source node is at \(x_{1}=0\) while the sink node is at \(x_{K}=R\). The distance between any two nodes \(k\) and \(k+1\) is \(r_{(k,k+1)}\). For equidistant nodes, we have \(r_{(1,2)}=r_{(2,3)}=\ldots=r_{(K-1,K)}\). The sink node is connected to a gateway node that transmits data to the above-ground channel for processing. A detailed visualisation of this notational convention is presented in Figure 3.  
In contrast to the traditional mesh networks [34] that depend on a routing technique to relay messages, the proposed linear network topology offers an inherently simplified routing mechanism. Each node in the linear network [26] propagates the message to the next node since communication is based on the strength of the mutual induction between two consecutive nodes. A mesh network topology would be a costly alternative to the suggested linear topology in terms of high deployment effort as well as low network lifetime, especially for challenging underground environment. To demonstrate how multi-hop communication can enhance the overall system robustness, we first develop an MI communication system based on the above strategy and then present a methodology for analytically evaluating the communication system's performance. We also analyse the impact of different channel states between hops for a multihop MI-WUSN.
###### Abstract  
This paper presents a summary of the optimization design process for a multi-objective, two-level engineering problem, utilizing the generalized inverse cascade method under uncertainty. The primary objective is to enhance the vibration isolation performance of a mounting system, considering the influence of uncertain factors on its stiffness. The focus is on determining the value range of the design variables at the bottom layer, ensuring that the design goal is met with a specified confidence level. To illustrate the application of this methodology, the optimization design of a powertrain mount is used as a case study. A data-driven approach is adopted, establishing a quantitative mapping relationship between mount stiffness, force transmission rate, modal decoupling rate, and other design indicators. This is achieved through the development of a CRBM-DBN approximate model, which combines Conditional Restricted Boltzmann Machines (CRBMs) and a Deep Belief Network (DBN). Additionally, an intelligent optimization algorithm and interval search technology are employed to determine the optimal design interval for the mount stiffness. Simulation and experimental verification are conducted using selected parameter combinations. The results demonstrate notable improvements in the vibration isolation performance, modal decoupling rate, and vehicle NVH performance when compared to the original state. These findings provide valuable insights for the interval optimization design of similar multi-objective, as well as two-level engineering problems, serving as useful references for future research and applications.  
generalized inverse cascade method; mounting system; uncertainty; interval
# Evaluating the Impact of Drone Signaling in Crosswalk Scenario
## 6 Conclusions  
In this paper, we tackled the problem of using drone signaling in a crosswalk scenario that is likely to happen in smart cities, as well as that involves a car and a vulnerable road user (a pedestrian). For this, we considered different types of car driver profiles and various trajectory configurations at the vicinity of the crosswalk. We emphasized that the UAV may help to improve both road safety and energy consumption. Indeed, after observing the driver behavior, the drone collects information about speed and acceleration and sends alerts to the driver in the case of potential harmful situation. This is repeated each perception and reaction distance to take into account possible driver reactions to this alert. We show that the earlier the driver reacts to the drone alert(s), the safer is the braking process. Moreover, we stated that the acceleration is the main parameter behind energy consumption. Hence, drone signaling is likely to help the driver to progressively reduce their speed by avoiding sudden braking. We recall that we assumed that Drone to Car communication delays are very low and that the drone has continuously access to both speed and acceleration values. Further work will be dedicated to studying the influence of these parameters on the drone signaling performance (namely depending on propagation conditions, communication channel modeling, and drone attitude), as well as taking into account a more complex scenario, considering models for both car arrivals (traffic models) and pedestrian presence at the crosswalk.  
Conceptualization, N.N., L.N., and J.N.; methodology, N.N., L.N., and J.N.; software, S.B. and N.N.; validation, L.N. and J.N.; formal analysis, S.B. and N.N.; investigation, S.B.and N.N.; writing--original draft preparation, S.B. and N.N.; writing--review and editing, L.N. and J.N.; supervision, L.N. and J.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work has been performed in collaboration with undergraduate students from Tunis Al Manar University ([PERSON] and [PERSON]). The authors thank them for their contributions in the software and simulation steps.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
U2 VUAV to Vehicle communication  
U2 IUAV to Infrastructure communication  
C-ITSCooperative Intelligent Transportation System  
UAVUnmanned Aerial VehicleV2X Vehicle to Everything communication X2V Everything to Vehicle communication VRU Vulnerable Road Users VLOS Visual Line Of Sight
# Skin Cancer Disease Detection Using Transfer Learning Technique
## 3 Materials and Methods
### Dataset
#### 3.1.1 SIIM-ISIC 2020 Dataset  
The ISIC-2020 Archive [69] comprises the world's most enormous number of quality-controlled skin lesions dermoscopic images publicly available for research. Several institutions contributed data from patients of various ages and sexual orientations. The dataset includes 33,126 dermoscopic images, 584 images related to malignant, and 32,542 benign skin lesions from more than 2000 patients. Every image is associated with one of these patients through a unique patient identifier. We used 11,670 images of benign class and 584 images of melanoma. Considering the data of these two classes is imbalanced. Therefore, to handle the class imbalance issue, 4522 melanoma images were included in the ISIC 2019 archive [70]. After that, various data augmentation strategies were performed, including rescaling, width shift, rotation, shear range, horizontal flip, and channel shift, which became 11,670 after augmentation. The reason behind using 11,670 images of benign is to tackle the class imbalance issue. The images of the benign class were selected arbitrarily from the whole set of images. See sample images in Figure 2 and details of the classes in Table 2.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Class Labels** & **Training** & **Validation** & **Testing** \\ \hline Melanoma & 8170 & 1750 & 1750 \\ Benign & 8170 & 1750 & 1750 \\ Total & 16,340 & 3500 & 3500 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the ISIC-2020 dataset.  
Figure 1: Proposed method flowchart.
## 5 Experiments and Results
### Environment  
At present, two kinds of simulators, SUMO and CityFlow, are widely used in the research of TSC problems. They can both customize the environment or simulate real-world road conditions. We chose SUMO as the simulator in our experiments. SUMO is an open-source, microscopic multi-modal traffic simulation software. It can be fine tuned for each vehicle, making it ideal for traffic-control research. SUMO realizes the customization of the environment by writing road-network files and vehicle files, and can also simulate the real environment by importing various types of map files. At the same time, SUMO comes with a variety of vehicle-following models and lane-changing models to simulate realistic vehicle-driving situations. SUMO provides a TraCI interface based on Python so that the algorithm and the environment can be easily connected. In conclusion, SUMO is a suitable environment to simulate and verify TSC problems.