esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Feature Extraction Approaches
### Line-Based Approaches
First, all palmprint images are preprocessed to segment only the necessary ROI. Then, the palmprint features are extracted using HOG-SGF. In the next phase, the dimensionality of the palmprint features is reduced using an efficient auto-encoder (AE). Finally, the regularized extreme learning machine (RELM) classifier is used for palm identification. [PERSON] et al. (2019) [49] developed a palmprint feature extraction network founded on the double biologically inspired transform (DBIT), aiming to elucidate the mechanisms through which the optical human system perceives palmprints. This network comprises two phases, each applying dual convolutional layers succeeded by sum pooling, rectified linear unit (ReLU) activation, and normalization and combination operations. The first stage activates orientation-selective filters to elicit line and edge responses. The subsequent layer drives rotation-, scale-, and translation-invariant feature maps. Additionally, Pearson correlation and weighted fusion techniques are combined to assess the features' discriminability and provide palmprint matching. In order to synthesize the reviewed studies in this subsection, Table 3 offers a summary overview, outlining the various palmprint feature extraction methodologies spanning the utilized techniques, leveraged datasets, implemented experimental protocols, and key findings. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Paper**} & \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Used Datasets**} & \multirow{2}{*}{**Evaluation Protocol**} & \multirow{2}{*}{**Acc. (\%)**} \\ \cline{3-4} \cline{6-7}  & & & **Name** & & **\#IDs.** & & **\#Imgs.** & \\ \hline
2002 & [PERSON] et al. [43] & Fourier transform & Private & 500 & 3000 & 1 Img/sub (Rand) for training, remain 5 Imgs for testing & 95.48 \\ \hline
2008 & [PERSON] et al. [44] & PL + LPP & PolyU & 100 & 600 & First session for training, second session for testing & 100.00 \\ \hline
2014 & [PERSON] et al. [45] & HOL & PolyU & 386 & 7752 & First 3 Imgs/sub from 1 st session for training, all images from 2 nd session for testing & 99.97 \\ \cline{3-4} \cline{6-7}  & & PolyU-MS & 500 & 6000 & & session for testing & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A comparative overview of line-based approaches (#ID: number of identities, #Imgs: number of images, Acc: valid accuracy).
# FAIDM for Medical Privacy Protection in 5G Telemedicine Systems
## 3 Proposed Scheme
### Constrained Node Registration Phase  
The constrained node \(CN_{ij}\) submits registration information to gateway node \(GW_{i}\) in this phase. The gateway node \(GW_{i}\) verifies the constrained node \(CN_{ij}\)'s legitimacy then issues private key \(S_{ij}\) and certificate \(\mathit{Cert}_{GW_{i}\to CN_{ij}}\) to complete this phase. Note that the gateway node \(GW_{i}\) computes private key \(S_{ij}\) by constrained node \(CN_{ij}\)'s registration information. Figure 4. illustrates process of constrained node registration phase. Detailed descriptions are stated as follows:  
Step 1: Constrained node \(CN_{ij}\) chooses an identifier \(ID_{ij}\) and a random number \(s_{ij}\) as his own secret, computes \(Q_{ij}\), and sends (\(ID_{ij}\), \(Q_{ij}\)) to gateway node \(GW_{i}\).  
Figure 4: Constrained node registration phase of proposed scheme.  
Figure 3: Gateway node registration phase of proposed scheme.  
\[Q_{\bar{y}}=T_{s_{\bar{y}}}(x)\bmod p \tag{4}\]  
Step 2: Upon receiving \(ID_{\bar{y}}\) from constrained node \(CN_{\bar{y}j}\), gateway node \(GW_{i}\) checks the format of \(ID_{\bar{y}\bar{i}}\). If \(ID_{\bar{y}\bar{i}}\) is valid, gateway node \(GW_{i}\) computes private key \(S_{\bar{y}\bar{i}}\) correspond to \(ID_{\bar{y}\bar{i}}\), generates \(Cert_{GW_{i}\to CN_{\bar{y}\bar{i}}}\) from \(Cert_{S\to GW_{i}}\), and sends (\(S_{\bar{y}\bar{i}}\), \(Cert_{GW_{i}\to CN_{\bar{y}\bar{i}}}\)) to constrained node \(CN_{\bar{y}\bar{i}}\) via secure channel.  
\[P_{\bar{y}\bar{i}}=H_{2}(Q_{\bar{y}\bar{i}},ID_{\bar{i}}) \tag{5}\]  
\[S_{\bar{y}\bar{i}}=S_{i}T_{s_{i}}(P_{\bar{y}\bar{i}})\bmod p \tag{6}\]  
Step 3: The constrained node \(CN_{\bar{y}\bar{i}}\) stores (\(S_{\bar{y}}\), \(Cert_{GW_{i}\to CN_{\bar{y}\bar{i}}}\)) to complete the constrained node registration phase.
# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## 1 Introduction

Table 1: _Cont._clustering to understanding the underlying factors through feature importance analysis. This dual approach provides both a macro-level view of how cities compare to one another and a micro-level understanding of which factors cities should focus on to improve their smartness. The research concludes by synthesizing the findings into actionable insights for city planners and policymakers. By knowing which dimensions of smartness to prioritize, urban leaders can focus their efforts on specific areas that will have the most significant impact, thereby improving the overall smartness and livability of their cities. The study's flow effectively bridges the gap between data analysis and practical policy recommendations, offering a roadmap for smart city development. The originality of this article lies in ranking smart cities through a dual-analysis framework. Firstly, it applies a range of unsupervised learning models to cluster cities based on the IMD Smart City Index (SCI) dataset. This clustering reveals intrinsic groupings and patterns among cities that are often overlooked in traditional rankings. Secondly, it employs supervised learning models, such as random forest and support vector machines (SVMs), to determine the importance of various features that contribute to a city's smartness. By integrating these two methodologies, the study not only provides a nuanced classification of smart cities but also identifies the critical factors driving these classifications. As per Figure 2, this work is organized as follows. Section 2 discusses the related work. Section 3 reviews the background of the models used, while Section 4 describes the experiments performed on the dataset. Section 5 comments on the results. Finally, Section 6 draws the conclusions and the directions of more extended research. Figure 2: Visual representation of the work’s structure.
## 2 System Model and Transmission Process  
Firstly, we develop a cooperative underlay CR-NOMA network model. Then, by describing the signal model, we propose the transmission process and analyze the achievable data rate at the receiver. Four possible decoding results at U\({}_{1}\) are given, and the transmit powers of the secondary and the candidate relay sets are expressed, respectively.
## 4 Discussion
### Validity  
In the current investigation, the Fitnessmeter app showed not only high validity values (r = 0.971) but also showed no significant differences (\(p>0.05\)) compared to the gold standard (Photocell). Other smartphone applications for measuring sprint and change of direction times have been recently developed and tested [39; 45; 46; 47] reported similar association values (r > 0.98) between MySprint app and timing gates in the determination of 40 m sprint times. Slightly lower validity levels were identified between COD timer app and timing gates from linear regression analyses (r = 0.95-0.96) and comparisons between instruments (\(p>0.05\)), when measuring change of direction times in different tests [39; 45]. In addition, no collinearity was identified in the present study (d = 1.99). It should be noted, however, that including multiple measurements from the same individual in regression analyses, as was included here, can occasionally lead to overestimations of fit and potential collinearity issues [48].
## 2 Materials and Methods
### The Methodology
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Name** & **Feature Formula** \\ \hline Peak & \(f_{\textit{peak}}=x_{\textit{max}}=max|x_{i}|\) \\ Peak-to-peak & \(f_{\textit{peak}2\textit{peak}}=|max(x_{i})-min(x_{i})|\) \\ Mean & \(f_{\textit{Mean}}=\frac{1}{N}\sum_{i=1}^{N}x_{i}\) \\ Root mean square (RMS) & \(f_{\textit{RMS}}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}x_{i}}^{2}\) \\ Crest factor (CF) & \(f_{\textit{Crest}}=f_{\textit{peak}}/f_{\textit{RMS}}\) \\ Kurtosis & \(f_{\textit{Kurt}}=\frac{1}{N}\sum_{i=1}^{N}\frac{(x_{i}-\overline{x})^{4}}{ \sigma^{4}}\) \\ Skewness & \(f_{\textit{Skew}}=\frac{1}{N}\sum_{i=1}^{N}\frac{(x_{i}-\overline{x})^{3}}{ \sigma^{2}}\) \\ Shape factor & \(f_{\textit{Shapef}}=f_{\textit{RMS}}/f_{\textit{Mean}}\) \\ Impulse factor & \(f_{\textit{Impulse}}=f_{\textit{peak}}/f_{\textit{Mean}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Extracted time features from raw signals. \(x_{i}\), is the signal (observation) at time \(i\), \(i=1,\ldots,S_{N}\), \(S_{N}\) is the length of the signal segments, \(\overline{x}\) is the mean value of \(x_{i}\), \(i=1\ldots S_{N}\), and \(\sigma^{3}\)\(\sigma^{4}\) are third and the fourth moment of \(x_{i}\), \(i=1\ldots S_{N}\), respectively. known settings C1 and C2, and novel setting C3. The generalization ability of the models is assessed using the classification models built during the training phase to make predictions on test 5 and compute the prediction accuracy (accuracy for setting recognition and RUL prediction error for prognostics). The generalization ability of constructed features is assessed by training classification and exponential degradation models on test 6, which includes three settings and evaluating the training accuracy and the Mean Square Error. Note that the k-NN in the fitness function of the classification-based GP is trained using the cross-validation method. Standard values of 80% of observations for the training set and 20% for the testing set have been used. Instead, the k-means algorithm performed in the clustering-based GP is iterated five times for each GP iteration to get the best data partitioning. In addition, the DT and k-NN used to evaluate the different feature sets are always trained using a 5-fold cross-validation. Finally, it is worth pointing out that the testing accuracy computed during the testing phase of the methodology refers to the prediction accuracy of models built in the training phase and not to the testing accuracy resulting from the cross-validation. The fitness functions used for training GPs are described in the following subsection.
# Aerial Coverage Analysis of Cellular Systems at LTE and mmWave Frequencies Using 3D City Models
## 3 Simulation Results
### LTE vs. mmWave
These results suggest that mmWave is a poor choice for aerial  
Figure 7: Cumulative distribution of SINR in full-load at different heights comparing LTE and mmWave. vehicles in a full-load scenario. However, mmWave enables usage of more advanced techniques such as beamforming and MaMIMO due to the use of large antenna arrays. These techniques can be used to decrease interference, especially for AUE. We can conclude that interference is still the main limiting factor for UAVs deployed in a cellular system, even when using mmWave, as described in the literature [4; 5; 6; 7; 26]. The result of an optimal altitude for UAVs right above rooftop height is in line with existing research [7; 8; 29]; however, our results show a higher coverage probability for ground users. This difference can be explained by the higher SINR threshold used in those papers. We found different coverage probabilities for UAVs and ground users, contrary to the authors of [3] who found a similar coverage probability for both ground and aerial users. This inconsistency can be a result of the fact that they performed experiments in a semi-urban environment with a lower density of base stations. Moreover, the fact that we assumed a worst-case network in full-load would also result in more interference.
###### Abstract  
Here, we report on the design and performance of a silicon photonic micro-transceiver required to operate in 5G and 6G environments at high ambient temperatures above 105 \({}^{\circ}\)C. The four-channel \"IOCore\" micro-transceiver incorporates a 1310 nm quantum dot laser system and operates at a data rate of 25 Gbps and higher. The 5 \(\times\) 5 mm micro-transceiver chip benefits from a multimode coupling interface for low-cost assembly and robust connectivity at high temperatures as well as an optical redundancy scheme, which increases reliability by over an order of magnitude.  
silicon photonics; 5G; 6G; co-packaged optics; data centers; integrated photonics; micro-transceiver; high-performance computer (HPC)
## 2 Materials and Methods
### Machine Learning Algorithms Used in the Study
#### 2.5.2 Random Forest Regression  
Random forest regression (RFR) stands out as a powerful ensemble learning algorithm widely recognized for its efficacy in both academic and practical settings, particularly in tasks involving classification and regression. The strength of RFR lies in its innovative use of bootstrap aggregation, which facilitates the creation of an ensemble comprising diverse, randomly constructed, and unpruned decision trees. The ensemble is meticulously crafted by systematically altering a group of decision trees, and a pivotal element in this process is the introduction of random feature selection. This strategic approach ensures a rich diversity of decision trees, making the judicious selection of different attributes imperative. Key hyperparameters govern the behavior of the RFR model. The max_depth parameter controls the maximum depth of individual decision trees, influencing the model's capacity to capture intricate relationships. The parameter min_samples_leaf determines the minimum number of samples required in a leaf node, guarding against nodes with too few samples. Simultaneously, min_samples_split sets the minimum number of samples needed to split an internal node, contributing to model generalization. The critical n_estimators parameter specifies the number of trees in the ensemble, influencing overall model performance. The careful tuning of these hyperparameters is essential to strike a balance, avoiding overfitting and ensuring the generalization capability of the random forest model. The RFR's ability to aggregate votes from diverse decision trees makes it a robust tool for diverse applications, with detailed explanations available in prior studies [55; 73; 74].
# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
## 5 Future Directions  
Research on physical layer cybersecurity for satellite communications is gaining importance as reliance on satellite systems continues to grow across various sectors, from defense and communication to navigation and Earth observation. The future directions is shown in Figure 9.
# Anomaly Detection Method for Unknown Protocols in a Power Plant ICS Network with Decision Tree
## 2 Materials and Methods
### Related Work
#### 2.1.1 AI Machine Learning  
Artificial intelligence technology refers to the ability of machines to perform human cognitive processes such as perception, information processing, and decision-making. AI can be divided into general AI and narrow AI. General AI refers to human-level intelligence, which means performing cognitive activities regardless of the level, type, or content. However, there is currently no AI technology that satisfies the definition of general AI, and it is expected to take several decades of further development. Narrow AI, on the other hand, refers to an algorithm that specifically solves a defined problem, and all currently used AI technologies are narrow AI [11; 12; 13; 14]. As shown in Table 1, the types of machine learning algorithms can be classified.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Types** & & **Description** \\ \hline \hline  & & Classification \\ \cline{2-3}  & - K-Nearest Neighbors [15] & - Support Vector Machine [16] \\ \cline{2-3} Supervised Learning & - Decision Tree [17] & - Random Forest [18; 19] \\ \cline{2-3}  & & Regression \\ \cline{2-3}  & - Linear Regression [20; 21] & - Logistic Regression [22] \\ \hline \hline  & - Clustering [23] & - Visualization [24] \\ \cline{2-3} Unsupervised Learning & - Dimensionality Reduction [25] & - Association Rule Learning [26] \\ \hline \hline  & - DQN (Deep Q-Network) [27] \\ \cline{2-3} Reinforcement Learning & - A3C (Asynchronous Advantage Actor–Critic) [28] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Machine learning types.  
The AI algorithm used in this study is determined by categorical prediction targets based on the number of data cases, and a decision tree is used during classification with the correct labels. The algorithm selection flow is illustrated in Figures 2 and 3, which show the steps for selecting the most appropriate algorithm for the dataset.  
In this study, we chose the decision tree as our machine learning algorithm because it is intuitive and well-suited for analyzing closed networks of private protocols. We also minimized the data preprocessing process to prevent overfitting and ensure that the importance of variables is fully understood. By doing so, we aimed to achieve accurate and reliable anomaly detection results that can help improve the security of industrial control systems.  
Figure 3: Flowchart for selecting machine learning techniques.  
Figure 2: Selecting the right machine learning technique [29].
# Mushroom Nutrition as Preventative Healthcare in Sub-Saharan Africa
## 7 Antitumour Activity of Mushrooms  
Usually, the causes of cancer are multifactorial, and they include genetic, environmental, and other risk factors. A recent meta-analysis of 213 studies, including 77 clinical studies, showed that _Ganoderma lucidum_ or _Coriolus versicolor_ mushrooms enhanced the efficacy and ameliorated their adverse effects, which lead to an improved quality of life in cancer patients [108,167].  
Mushroom lectins are a group of proteins/glycoprotein that can possess immunoblotting as well as direct cytotoxic activity toward tumour cell lines. In mushroom extracts and biomass, there are also some anticancer haemolysing proteins [168], enzyme lac-case [169], ribosome-inactivating proteins [170], and ubiquitin-conjugated proteins, which also display direct cytotoxic activity [171,172].  
Polysaccharides of mushrooms have antitumor activity, which is associated with the immunostimulatory effect that they can exert, since they activate foreign body reactions from the immune system [173]. This antitumor activity is not caused by a direct cytotoxic effect but via activation of the innate immune system of the host. The mechanism of action is related to the presence of pattern recognition receptors that can recognize the polysaccharides as pathogen-associated molecular patterns (PAMPs), due to its high molecular weight [174].  
Consequently, proinflammatory cytokines are produced in a cascade, including tumour necrosis factor alpha (TNF-\(\alpha\)), which are members of the IL-1 family that regulate immune homeostasis and the mechanisms against infections in recognition of foreign cells and tumour cells [175].  
Some structures of mushroom \(\beta\)-glucans are better adapted to specific receptors, which suggests a relationship between the structure and antitumor activity of polysaccharides, and it was found that mostly \(\beta\)-1,3-glucans have the highest antitumor activity [176; 177]. Triterpenes, the secondary compounds found in mushrooms, cause tumour cells to self-destruct (apoptosis) [178; 179].  
Polysaccharide extracts from _Hericium erinaceus_ are active against liver cancer cells in vitro and in vivo [180; 181]. The highest consumption of dietary mushrooms, including _Agaricus bisporus_ and _Lentinula edodes_, is associated with a decreased risk of breast cancer in premenopausal women and postmenopausal women [182].  
Maitake mushroom (_Grifola frondosa_) is one of the most popular edible medicinal mushrooms. The natural killer (NK) cells, which have the ability to eliminate target cells without prior immunization, show an important role in controlling viral infections and high cytotoxic activity in oncologic patients administered _G. frondosa_, and they significantly restrain tumour growth. This is achieved by an increased release of TNF-\(\alpha\) and IFN-\(\gamma\) from the spleen and a significant boost in IFN-\(\gamma\) and TNF-\(\alpha\) expressed in NK cells [183].
## 3 Proposed Method
### Message Embedding  
This subsection describes the two-phase message embedding. Figure 1 shows the two-phase message-embedding process, consisting of Phase-1 and Phase-2. The first phase, Phase-1, is denoted as the optimal color-transfer embedding phase, or OWCTE in short. In this phase, a color appearance similarity [33], which utilizes the Kullback-Leibler Divergence (KLD) technique [34], derives an optimal weight, \(W^{*}\). This optimal weight will be used in OWCT to construct a new image, where its color appearance differs from the source ones. Next, for each pixel, \(\mathbf{P}_{oucet}\), optimal weighted color-transfer embedding (OWCTE) injects the first secret message, an \(n\)-ary 3-tuple secret message vector, \(d_{sb}=(d_{n}^{1},\,d_{n}^{2},\,d_{n}^{3})\), into each pixel's Red, Green, and Blue channels. In this phase, we use a secret key, \(key_{1}\), aiming to achieve a multi-hider mechanism and increase the security of message embedding when producing the stego pixel, \(\mathbf{P}^{\prime}_{oucet}\). \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{d}\) & \(\mathbf{w_{1}}=7\) & \(\mathbf{w_{2}}=5\) & \(\mathbf{w_{3}}=2\) \\ \hline
1 & \(-1\) & 0 & 0 \\
2 & 0 & 0 & 1 \\
3 & 0 & \(-1\) & 0 \\
4 & 1 & 1 & 0 \\
5 & 0 & 1 & 0 \\
6 & 0 & 0 & \(-1\) \\
7 & 1 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The pixel alteration (PA) table for 3-tuple weight vector \(\mathbf{W}=(7,5,2)\). Then, in the second step, a second secret message, \(d_{um}\), which is in the \(m\)-ary notational system, is concealed in every stego pixel, \(\mathbf{P}^{\prime}_{ouct}\), without affecting the existing hidden message. Similarly, we employ a secret key, \(key_{2}\), for the purpose of multi-hider and security when producing the stego pixel, \(\mathbf{P}^{\prime}_{ouct}\). The output of Phase-1 is a stego OWCT image, \(l^{\prime\prime}_{ouct}\), where its color appearance is contributed from both the source and target images with an optimal weight. Every pixel, \(\mathbf{P}^{\prime\prime}_{ouct}\), has concealed the first secret vector, \(\mathbf{d}_{\mathbf{sb}}\), and the second secret message, \(d_{wm}\). The second phase, Phase-2, is referred to as the weighed modulus reversible data-hiding phase, or WMRDH in short. In this phase, we embed the third secret message, a \(k\)-ary message vector, \(\mathbf{d}_{\mathbf{du}}\) into every stego pixel, \(\mathbf{P}^{\prime\prime}_{ouct}\). Once again, we use a secret key, \(key_{3}\)