esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background: Identification of Dynamic Systems by Unscented Kalman Filter
### A Brief Review of Unscented Kalman Filter
In detail, this \"time-update\" is performed as  
\[\begin{array}{c}\mathbf{\chi}_{k+1\text{jk}}^{[i]}=f\left(\mathbf{\chi}_{k \text{jk}}^{[i]}\right)\\ \mathbf{\hat{x}}_{k+1\text{jk}}=\sum\limits_{i=0}^{2n_{\text{X}}}W_{m}^{i} \mathbf{\chi}_{k+1\text{jk}}^{[i]}\\ \mathbf{P}_{k+1\text{jk}}^{\text{xx}}=\sum\limits_{i=0}^{2n_{\text{X}}}W_{c}^{ i}\bigg{[}\mathbf{\chi}_{k+1\text{jk}}^{[i]}-\mathbf{\hat  
For the \"measurement-update\" of the prior estimates in Figure 1, SPs are first generated as  
\[ \begin{split}\mathbf{\chi}_{k+1|k}^{[0]}=\hat{\mathbf{\chi}}_{k+1|k}\\ \mathbf{\chi}_{k+1|k}^{[i]}=\hat{\mathbf{\chi}}_{k+1|k}+\left(\sqrt{\mathbf{ \mathrm{p}}_{k+1|k}^{\mathbf{xx}}}\right)^{i},\ i=1,\ldots,n_{\mathbf{X}}\\ \mathbf{\chi}_{k+1|k}^{[i]}=\hat{\mathbf{\chi}}_{k+1|k}-\left(\sqrt{\mathbf{ \mathrm{p}}_{k+1|k}^{\mathbf{xx}}}\right)^{i-n_{\mathbf{X}}},i=n_{\mathbf{X}}+ 1,\ldots,2n_{\mathbf{X}}\end{split}  \tag{4}\]  
Then, the measurement-updates are performed to obtain the following sequentially:  
\[ \begin{split}\zeta_{k+1|k}^{[i]}=h\Big{(}\mathbf{\chi}_{k+1|k}^{[i]} \Big{)},\ i=0,\ldots,2n_{\mathbf{X}}\\ \hat{\mathbf{\mathrm{z}}}_{k+1|k}=\sum\limits_{i=0}^{2n_{\mathbf{X}}} W_{m}^{i}\zeta_{k+1|k}^{[i]}\\ v_{k+1}=\mathbf{\mathrm{z}}_{k+1}-\hat{\mathbf{\mathrm{z}}}_{k+1|k}\\ P_{k+1|k}^{\mathbf{zz}}=\sum\limits_{i=0}^{2n_{\mathbf{X}}}W_{\text {C}}^{\text{I}}\Big{[}\zeta_{k+1|k}^{[i]}-\hat{\mathbf{\mathrm{z}}}_{k+1|k}\Big{]} \Big{[}\zeta_{k+1|k}^{[i]}-\hat{\mathbf{\mathrm{z}}}_{k+1|k}\Big{]}^{\text{T}}+ \mathbf{\mathrm{R}}_{k+1}\\ P_{k+1|k}^{\mathbf{zz}}=\sum\limits_{i=0}^{2n_{\mathbf{X}}}W_{ \text{C}}^{\text{I}}\Big{[}\mathbf{\chi}_{k+1|k}^{[i]}-\hat{\mathbf{\mathrm{x}}}_{k+1| k}\Big{]}\Big{[}\zeta_{k+1|k}^{[i]}-\hat{\mathbf{\mathrm{z}}}_{k+1|k}\Big{]}^{\text{T}}\\ K=\mathbf{\mathrm{P}}_{k+1|k}^{\mathbf{zz}}\Big{(}\mathbf{\mathrm{P}}_{k+1| k}^{\mathbf{zz}}\Big{)}^{-1}\end{split}  \tag{5}\]  
where \(\zeta_{k+1|k}^{[i]}\) is an SP calculated by substituting \(\mathbf{\chi}_{k+1|k}^{[i]}\) to the function \(h(\cdot)\); \(v_{k}\) is an innovation vector, i.e., theoretical residuals; \(\mathbf{\mathrm{P}}_{k+1|k}^{\mathbf{zz}}\) and \(\mathbf{\mathrm{P}}_{k+1|k}^{\mathbf{zz}}\) are covariance of the measurements and cross covariance of state and measurements; and \(\mathbf{\mathrm{K}}\) is the calculated Kalman gain. Finally, the results of the two updates are used to obtain the posterior estimates of the states and covariance matrix by the \"state-update\" step, i.e.,  
\[ \begin{split}\hat{\mathbf{\mathrm{x}}}_{k+1|k+1}=\hat{\mathbf{\mathrm{x} }}_{k+1|k}+\mathbf{\mathrm{K}}\mathbf{\mathrm{r}}_{k+1}\\ \mathbf{\mathrm{P}}_{k+1|k+1}^{\mathbf{xx}}=\mathbf{\mathrm{P}}_{k+1|k}^{ \mathbf{xx}}-\mathbf{\mathrm{K}}\mathbf{\mathrm{P}}_{k+1|k}^{\mathbf{zz}}\mathbf{\mathrm{ K}}^{\text{T}}\end{split}  \tag{6}\]  
These time-, measurement- and state-updates are performed at each time step \(k\) using the results of the previous step as illustrated in Figure 1.  
Figure 1: Algorithm of Unscented Kalman Filter (UKF).
## 4 Experiments and Results  
To further illustrate the effectiveness of the algorithm for detecting clusters of different shapes and densities in close proximity to each other, experiments were conducted on several synthetic datasets and the UCI (University of California) real dataset, which is a public repository used for machine learning and data mining research, and we compared them with current state-of-the-art clustering algorithms using three external evaluation metrics. The selected datasets are characterized by ambiguous boundaries and highly overlapping samples, especially R15, Pathbased, D31, S1, DS577, etc., with the aim of demonstrating the applicability of the proposed method. Finally, the effect of the nearest neighbor parameter K on the clustering results is analyzed. All experiments were conducted using a PC configured with a i9-12900H 2.50 GHz processor Windows 11 operating system, 16 GB RAM, and Python 3.11.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Dataset** & **K-Means** & **DBSCAN** & **DPC** & **RNN-DBSCAN** & **LDP-MST** & **HCDC** & **CPDD-ID** \\ \hline Aggregation & 0.35 & 0.82 & 1.71 & 0.98 & 0.41 & 0.92 & **0.315** \\ Spiral & 0.09 & 0.31 & 1.42 & 0.08 & 0.12 & 0.14 & 0.15 \\ R15 & 0.85 & 0.44 & 0.99 & 0.56 & 0.25 & 0.55 & 0.46 \\ Pathbased & 0.02 & 0.50 & 1.31 & 0.07 & 0.20 & 0.10 & 0.23 \\ Jain & 0.05 & 0.30 & 1.49 & 0.11 & 0.33 & 0.14 & 0.18 \\ D31 & 8.03 & 13.85 & 37.51 & 14.52 & 0.96 & 13.24 & 5.09 \\ Zelink1 & 0.02 & 0.40 & 1.36 & 0.06 & 0.18 & 0.11 & 0.15 \\ S1 & 9.73 & 37.80 & 105.07 & 36.84 & 5.92 & 41.33 & 5.07 \\ DS577 & 0.03 & 0.56 & 3.18 & 0.28 & 0.39 & 0.33 & 0.33 \\ T4.8K & 6.80 & 79.97 & 202.21 & 150.80 & 10.24 & 94.50 & 25.70 \\ \hline \end{tabular}
\end{table}
Table 1: Runtime comparison on synthetic datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Dataset** & **K-Means** & **DBSCAN** & **DPC** & **RNN-DBSCAN** & **LDP-MST** & **HCDC** & **CPDD-ID** \\ \hline Thyroid & 0.03 & 0.01 & 1.07 & 0.04 & 0.08 & 0.03 & 0.62 \\ Wine & 0.30 & 0.05 & 0.29 & 0.05 & 0.01 & 0.08 & 0.16 \\ Ionosphere & 0.05 & 0.04 & 0.52 & 0.11 & 0.15 & 0.12 & 0.06 \\ Wireless & 0.38 & 0.30 & 4.52 & 3.51 & 0.67 & 3.56 & 31.30 \\ Ecoli & 1.21 & 0.48 & 1.12 & 0.23 & 0.29 & 0.34 & 0.66 \\ Pima & 0.14 & 0.24 & 0.34 & 0.49 & 0.18 & 0.56 & 1.80 \\ Yeast & 6.82 & 3.38 & 19.92 & 5.69 & 5.85 & 3.94 & 4.34 \\ Balance & 0.22 & 0.32 & 0.25 & 0.34 & 0.20 & 0.48 & 0.09 \\ Satimage & 25.33 & 24.32 & 26.68 & 35.45 & 23.52 & 37.55 & 29.14 \\ Breast & 1.87 & 0.98 & - & 0.90 & 1.45 & 1.08 & 1.04 \\ \hline \end{tabular}
\end{table}
Table 2: Runtime comparison on synthetic datasets.
# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## Experiments
### Features Importance Analysis  
Non-numeric columns such as Id, City, Country, and the target column SmartCity_Index have been dropped from the features used for modeling. The target variable SmartCity_Index is isolated for prediction. A total of 80% of the data are used for training and 20% are used for testing, ensuring that the model is trained on one portion of the data and evaluated on another to assess its generalization ability. After the training process, the model makes predictions on the test set. Several performance metrics are computed to evaluate its effectiveness: (i) the mean squared error (MSE), which measures the average squared difference between actual and predicted values; (ii) the root mean squared error (RMSE), the square root of the MSE and a more interpretable measure; (iii) the R-squared (\(R^{2}\)) value, which explains the proportion of variance in the target variable that is predictable from the features, and finally, (iv) Average \(R^{2}\) across 5 folds. Cross-validation involves splitting the data into several folds or subsets. In 5-fold cross-validation, the dataset is divided into 5 equal parts. The model is trained on 4 parts and validated on the remaining 1 part. This process is repeated 5 times, each time using a different fold for validation while training on the rest.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Cluster** & **Smart Cities** \\ \hline Cluster 0 & Taipei, Adelaide, Melbourne, Tokyo, Perth, Auckland, Osaka, Kuala Lumpur \\ Cluster 1 & Geneva, Frankfurt am Main, San Francisco, Bochum, Stuttgart, London, \\  & Bordeaux, Milan, Nantes, Brussels, Marseille, Leeds, Lahti, Dublin, \\  & Verona, Nice, Tallinn, Catania, Bayreuth, Turin, Florence, Birmingham, \\  & Bari, Bratislava, Ljubljana, Perugia, Tel Aviv, Barcelona, Ancona, Lisbon, \\  & Rome, Genova, Naples, Riga \\ Cluster 2 & Madrid, Daejeon, Abu Dhabi, Seoul, Dubai, Budapest, Hong Kong, Prague, \\  & Shanghai, Moscow, Beijing, St Petersburg \\ Cluster 3 & Oslo, Bergen, Amsterdam, Copenhagen, Stockholm, Montreal, Vienna, \\  & Singapore, Boston, Zurich, Trondheim, Vasteras, Aalborg, Ottawa, \\  & Washington DC, Stavanger, Los Angeles, Helsinki, Vancouver, Berlin, \\  & Toronto, New York, Chicago, Dusseldorf, Reykjavik, Lyon, Helsingborg, \\  & Munich, Philadelphia, Hamburg, Luxembourg, Aarhus, Turku, Espoo, Paris, \\  & Hannover, Strasbourg, Joensuu, Sydney, Oulu, Cologne, Hameenlinna, Tampere, \\  & Jyvaskyla, Vantaa, Calgary, Edmonton \\ \hline \hline \end{tabular}
\end{table}
Table 9: SOM clustering of smart city indices in tabular form.
# CFD-Based Metamodeling of the Propagation Distribution of Styrene Spilled from a Ship
###### Abstract  
The present study aimed to numerically establish a new metamodel for predicting the propagation distribution of styrene, which is one of the hazardous and noxious substances (HNSs) spilled from ships. Three-dimensional computational fluid dynamics (CFD) simulations were conducted for 80 different scenarios to gather large amounts of data on the spatial distribution of the change in concentration over time. We used the commercial code of ANSYS Fluent (V.17.2) to solve the Reynolds-averaged Navier-Stokes equations, together with the scalar transport equation. Based on the CFD results, we adopted the well-known kriging model to create a metamodel that estimated the propagation velocity and spatial distributions by considering the effect of the current surface velocity, deep current velocity, surface layer depth, and crack position. The results show that the metamodel accurately predicted the changes in the local distribution of styrene over time. This model was also evaluated using the hidden-point test.  
hazardous and noxious substance (HNS); computational fluid dynamics (CFD); metamodel; kriging model; propagation velocity +
Footnote †: journal: Applied Sciences
# Quantum Authentication Evolution: Novel Approaches for Securing Quantum Key Distribution
## 2 Quantum Cryptography and Quantum Key Distribution  
The objective of cryptography is to securely transmit confidential information across insecure communication channels, safeguarding against potential eavesdropping and unauthorized access. Encryption plays a pivotal role in ensuring data security, often achieved through the utilization of pre-shared secret keys. The feasibility of achieving absolute security through such methods has been demonstrated, particularly with the use of a one-time pad (OTP) [32].  
In the OTP scenario, two users, [PERSON] and [PERSON], possess a key consisting of perfectly correlated bits, ensuring confidentiality during message transmission. The key is applied through a bitwise XOR operation with the message, resulting in a randomized bit string that is transmitted through an insecure channel. This process ensures that the transmitted message remains indecipherable, provided the key is used only once. However, securely distributing the key poses a significant challenge, as demonstrated by [PERSON]'s recognition of the key distribution problem [32]. While classical channels are inadequate for secure key exchange, leveraging quantum channels offers a solution. Quantum mechanics' unique features enable the transmission of bits with guaranteed confidentiality, as affirmed by the no-cloning theorem [14]. This theorem, coupled with quantum states' inherent collapse upon measurement, forms the foundation of quantum cryptography.  
QKD protocols facilitate secure key distribution, encompassing various approaches like prepare-measurement protocols such as BB84 [10] and entanglement-based protocols like E91 [11]. This study focuses on simulating the SARG04 and BB84 protocols [33].
## 4 Experimental Results and Discussions  
This paper uses the peak signal to noise ratio \((PSNR)\)[37], normalized correlation coefficient \((NC)\)[9], and bit error rate \((BER)\)[19] to evaluate the visibility and robustness of the watermarking scheme. \(PSNR\) is used to describe the fidelity performance, and \(NC\) is used to describe the watermarking robustness. \(MSE\)[19] is the mean square error of the data, which is expressed below:  
\[MSE=\frac{1}{X\times Y}\sum_{x=0}^{X-1}\sum_{y=0}^{Y-1}(I_{o}(x,y)-I_{w}(x,y)) ^{2}. \tag{27}\]  
The \(PSNR\) is defined as follows:  
\[PSNR=10\log_{10}\frac{255^{2}}{MSE}. \tag{28}\]  
Figure 8: Flowchart for extracting watermark.  
where \(I_{o}(x,y)\) is the host image, \(I_{w}(x,y)\) is the watermarked image. In addition, the bit error rate (BER) and normalized correlation (NC) are used to evaluate the performance in terms of the watermark's robustness, BER and NC are defined as follows:  
\[BER=\frac{\sum_{h=1}^{H}\sum_{g=1}^{G}w_{e}(h,g)-w_{o}(h,g)}{H\times G} \tag{29}\]  
\[NC=\frac{\sum_{h=1}^{H}\sum_{g=1}^{G}w_{e}(h,g)\times w_{o}(h,g)}{\sqrt{(\sum_{ h=1}^{H}\sum_{g=1}^{G}(w_{e})^{2})}\sqrt{(\sum_{h=1}^{H}\sum_{g=1}^{G}(w_{o})^{2})}} \tag{30}\]  
where \(w_{0}(h,g)\) is the original watermark, \(w_{e}(h,g)\) is the extraction watermark. \(w(h,g)\) is a watermark of size \(H\times G\).  
This section illustrates the performance of the scheme through a series of experiments, and only representative experimental results are given herein. The five parts include the QDFT transform and inverse QDFT transform, the geometric expression of pseudo-Zernike, optimal watermark strength, comparing the scheme with the existing schemes, and the forecasting performance of the MLS-SVR network.
## 7 IoT-Defined Application Protocols
### Data Distribution Service (DDS)  
IoT application is for certain QoS. These QoS include reliability/durability, security/encryption, and queueing/prioritizing. The data that are transmitted between the server and the receiver are known as topics. The methods of DataReader and DataWriter in subscriber and publisher, respectively, generate the data on the topic. Fundamentally, DDS is all about data. It does not control nodes, machines, or processes. The applications of DDS are designed for tagging and assignment in data and their secure or unrestricted access [52].  
DDS is an interoperable data exchange protocol that is used for real-time secure data transactions. It works on publish/subscribe message delivery time, durability, and security. If the subscriber wants the publisher to send the data in less time, the subscriber can embed the path/route information in the message. The publisher can also find the shortest path to the subscriber for achieving QoS. Similarly, updating security protocols makes the receiver a less visible intruder, and data distribution can also be done with some encryption for secure information exchange.  
This protocol is a good choice for IoT and machine-to-machine communication models if QoS is the main concern. DSS consists of a local database that is the collection of all topics handled by the domain. This database is termed the global data space (GDS). This local store gives the illusion to the connected applications that they can access the whole data (in actuality, the whole data concerning the application is also known as the ticket). Nodes connected to this local store possess data that are instantaneously/timely needed. The copies of data are respectively sent to the application after the application subscribes to it.  
Global space shares many different technologies such as sensors, mobile communication, embedded systems, and Windows- and Linux-based platforms and cloud applications. The communication is fast and with low latency because tickets can be accessed at any instant by any communicating node with attached QoS specification. DSS always provide dynamic discovery of publisher and subscriber and are somewhat called plug 'n' play-mechanism-based applications.  
The architecture makes it scalable and the administrative layer can control its subsequent fogs (data spaces) and edges (connected machines). Only this protocol can add any type of node to an existing pool of IoT and provides a simplified framework for distributed system development [52].  
The following are some of the qualities of DDS for which modern IoT supports its core development.
## 2 Developed Methodology
### Analysed PPF Models
#### 2.1.3 Support Vector Machines (SVM)  
Similar to ANNs, SVM forecasters are used to model any system no matter whether it is governed by linear or non-linear equations. In this study, the SVMs' regression skill is used to fit vector coefficients through the use of historical databases. Just as ANNs' use neurons to produce their predictions, SVMs use their vectors to forecast future values. Although SVMs and ANNs share similarities due to the fact that both optimize their parameters through regression algorithms, SVM models have the advantage of being able to change all of a model's parameters if a better configuration is found during the training step. However, ANNs are not able to calculate these variations and they maintain their parameters throughout the whole training step [51].  
To perform a regression activity, SVMs use different kinds of transformations, which are denoted as kernel functions. These kernel functions modify a model's vectors' coefficient values through an iterative process, until the point where the difference between the actual and forecasted value reduces to a residual error chosen by the user. In addition, the main advantage of kernel functions arises from the fact that no matter how complicated the real system is, kernel functions will be able to converge on the optimal solution without getting stuck in a local minima [53].
## 3 Results and Discussion
### Hydrogeochemical Characteristics
This process takes place when low salinity Ca\({}^{2+}\)-HCO\({}_{3}\)\({}^{-}\) waters flush high salinity waters from an aquifer, since Na-X and Ca-X\({}_{2}\) are exchangeable cations [31], with Ca\({}^{2+}\) favored in the sorption processes. However, this process is reversible and it is also possible that Na\({}^{+}\) ions, if high concentrations may saturate the alluvial sediments as a consequence of the interaction between the saline intrusion and the lithology with release of Ca\({}^{2+}\). The \"_natural softening_\" process can also involve Mg\({}^{2+}\) ions, resulting in a lowering of its concentrations in the groundwaters and the consequent increase of the amount of Na\({}^{+}\) released [14; 31]. _Natural softening_ as an explanation for the concentrations observed is also supported by the analysis of the Ca\({}^{2+}\) + Mg\({}^{2+}\) vs. HCO\({}_{3}\)\({}^{-}\) + SO\({}_{4}\)\({}^{2-}\), the HCO\({}_{3}\)\({}^{-}\) vs. the sum of total anions and the Ca\({}^{2+}\) vs. HCO\({}_{3}\)\({}^{-}\) plot, all shown in Figure 5. The fact than in the Ca\({}^{2+}\) + Mg\({}^{2+}\) vs. HCO\({}_{3}\)\({}^{-}\) + SO\({}_{4}\)\({}^{2-}\) plot the majority of the data does not deviate from the 1:1 line, can also be considered as evidence that the Ca\({}^{2+}\) and Mg\({}^{2+}\) chemistry is largely explained by the weathering process of both calcite and gypsum like minerals [10]. Furthermore, the deviation from the 1:1 ratio in the Ca\({}^{2+}\) vs. SO\({}_{4}\)\({}^{2-}\) plot indicates that this anion is not as influential to the Ca\({}^{2+}\) chemistry as other ions present in the groundwaters. This result also agrees with the findings reported by [14; 32] for this SSWB. Through a classical hydrogeochemical approach [14] is proposed that the main recharge sources for this aquifer are both the infiltration of local precipitation (especially in lowland areas) and runoff infiltration in the nearby hills. An additional evidence of the influence of rainfall and chemical weathering to the composition of the groundwaters studied, is the fact that all the samples fall between the 1:1 and the 1:2 ratio in the Ca\({}^{2+}\) vs. HCO\({}_{3}\)\({}^{-}\) plot, since groundwater derived from weathering of dolomite and calcite type minerals usually plot between these equivalent ratios [10].
## 4 NAEEA for Clustering Optimization of AUSCNs
### Fitness Evaluation  
The purpose of clustering optimization in AUSCNs is to lower the network energy expenditure. In the network energy consumption, communication energy consumption accounts for the majority. From Equation (3), it can be concluded that the total energy expenditure of the node is \(E_{All}\). Therefore, the fitness function of this evolutionary process is as follows:  
\[Fitness(I_{n})=\sum\limits_{l=1}^{L}{E_{All}(l\in\{1,2,\cdots,L\})} \tag{6}\]  
In this equation, \(I_{n}\) represents the \(n\)-th individual of the population, \(Fitness(I_{n})\) indicates the fitness of \(I_{n}\) and the energy expenditure of the cluster head selection scheme of \(I_{n}\). In the process of evolution, the lower the energy consumption, the lower the fitness, which also means the better the individual. On the contrary, the higher the energy consumption, the greater the fitness, and the individual is easier to be eliminated in the process of evolution.
## 5 Results and Discussion
### Contributions of Knowledge-Assisted Learning Methods
Figure 15 shows the learning process of the KA-DDPG algorithm, without sharpening the reward through use of the guiding reward method. Compared to the first case, KA-DDPG, without the guiding reward method, had a larger reward at the beginning of the learning process, but it grew more slowly and showed more volatility. The simulation results demonstrated that the guiding reward method in KA-DDPG led the learning process to converge faster, by sharpening the reward function. Figure 15 shows the total rewards of KA-DDPG, without the guiding reward method in each learning process, while Figure 15 shows the average of the total rewards. Without the assistance of the guiding reward method, KA-DDPG needed about 500 episodes to outperform the baseline, while only about 200 episodes were needed to achieve the same control performance, when using both assisted learning methods. Similarly, KA-DDPG, without the guiding reward method, needed about 800 episodes to converge, which was also slower. These simulation results demonstrate that the guiding reward method, also, has the ability to reduce the learning cost. However, unlike the criteria action method, which reduces the learning cost by reducing randomly selected actions, the guiding reward method achieves this purpose by sharpening the reward function. To summarize, the simulation results presented above demonstrate that both of the assisted learning methods proposed in this paper can reduce the learning cost of the KA-DDPG algorithm. The guiding reward method accelerates the learning process, by sharpening the updating reward with the assistance of the evaluation method, whereas the criteria action method achieves this through providing the learning direction by criteria actions, thus reducing the randomly searched actions. Figure 14: The learning process of KA-DDPG without the criteria action method: (**a**) total rewards for each learning process and (**b**) the average value of the total rewards.