esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study
### The Main Prediction Results
#### 5.3.4 The Remarkable Error Analysis in Test Set  
The prediction error of the M2 GSNet is analyzed as Figure 10. In this figure, the M2 GSNet is compared with the MLP and LSTM since they are the most commonly used machine learning and deep learning methods. The prediction results are visualized.  
In the left array of the figure, it is the 4 th-hour prediction results in the test set by three methods. The predicted values are compared with the true value. In the right array, the location of scenarios with larger prediction errors are visualized. The prediction error analysis is also very important because it can tell us which kind of scenario is difficult to be predicted. Then we can design methods to deal with it in the future. For each time step, we used different colors and different sizes to represent the prediction errors. The darker the color and the smaller the size, the smaller the prediction error. Since the \(Ind_{i}\) of most of scenarios are smaller than 0.15, we classify the prediction errors into four categories. If the prediction error according to \(Ind_{i}\) in Equation (19) is smaller than 0.05 p.u, it is the first category. This type includes the scenarios that are predicted rather accurately. If the the prediction error is between 0.05 and 0.1, it is the second category, and the color of this category in the figure is at 5. If the prediction error is between 0.1 and 0.15, it is the third category, and the color of those scenarios is at 10. If the prediction error is higher than 0.15, it is the fourth category and the color of them is at 20. So, different colors and sizes can reflect the prediction results of the same scenario. We counted the ratio of the different categories in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Sample Type Divided by \(Ind_{i}\)**} \\ \cline{2-5}  & **[0,0.05]** & **(0.05,0.10]** & **(0.10,0.15]** & **>0.15** \\ \hline M2 GSNet & 52.1 & 34.4 & 11.85 & 1.65 \\ LSTM & 50 & 33 & 11.55 & 5.45 \\ MLP & 53.05 & 31.15 & 10.55 & 5.25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sample Ratio of Different Category (%).  
Figure 10: The prediction error analysis.  
From Figure 10 and Table 3, it is obvious that the M2 GSNet method has better performance since it has less points belonging to the high prediction error category. However, it also can be found from Figure 10 that most light color and large size points are located in the turning point of the wind fluctuation process which means it is hard to predict and often leads to higher prediction errors.
# Symbol-Level Selective Channel Estimation in Packet-Based OFDM Systems
###### Abstract  
Wireless access in vehicular environments to support wireless communication between vehicles has been developed to provide road safety and infotainment services. In vehicular environments where the channel changes rapidly, channel estimation is very important in improving the reliability of wireless communication. Therefore, numerous channel estimation schemes have been proposed; however, none of the schemes proposed so far can perform well over the entire signal-to-noise ratio (SNR) region. In this paper, we propose a novel channel estimation scheme that selectively uses the better scheme between two channel estimation schemes on a symbol-by-symbol basis. The results show that the proposed scheme performs symbol-by-symbol selection of the better channel estimation scheme within a packet, and thus shows excellent performance over the entire SNR region in vehicular environments in terms of the bit error rate and packet error rate.  
correlation coefficient; IEEE 802.11p; orthogonal frequency-division multiplexing (OFDM); selective channel estimation; vehicular environments +
Footnote †: journal:  
0000-0001-6500-7900-3899][PERSON]
# Control and Position Tracking for UAVs
## 2 An Overview of the Published Articles
It was developed with the use of a curve fitting tool and a triangle polytope to reduce the number of vertices. The problem of time-varying payload is more important for quadrotors because of the fact that it directly affects the vehicle dynamics through a changed mass, the position of the mass center, and moments of inertia; therefore, an adequate control scheme is required. The LPV control scheme designed for the LPV model of a quadrotorbased on the LMI results in robust stability and quadratic \(H_{\infty}\) performance. The 3D CAD model of the quadrotor was developed with the use of SOLIDWORKS, and the time-varying payload was simulated as a liquid tank mounted under the quadrotor with different water levels. The effectiveness of the proposed control scheme was verified through numerical simulations simultaneously comparing it with the LTI control scheme. The results confirmed the possibility of the control scheme's usage in such applications where the quadrotor payload is changing in time like fire-fighting UAVs or agricultural drones used in crop spraying. Therefore, it would be interesting to also see experiments with a real quadrotor.
## 4 Simulation Experiments
### Performance Comparison for High and Low UAV Speed Scenarios
However, according to 3 GPP Release 15, a major proportion of the UAVs incorporated into the cellular system are maintaining LoS connections with the BSs, and therefore the proposed algorithm can be found useful in many civil UAV application scenarios. The timely updating of the DoA estimations directly contributes to the performance of beamforming, as seen in Figure 6c,d for the two different speed scenarios. The red and blue lines represent the outputs of the DL beamformer combined with the proposed algorithm and the improved MUSIC algorithm, respectively, and the black and yellow lines represent the outputs of the LCMV beamformer combined with the proposed algorithm and the improved MUSIC algorithm, respectively. We summarize the statistics of these results in Table 3, where the average gains and standard variances of the DL and LCMV results are provided. In both figures, the proposed schemeusing the DL beamformer exhibits the highest performance, with average gains of 21.92 dB for the scenario in Figure 6c and 21.95 dB for the scenario in Figure 7b, whereas the performance of the conventional scheme using the LCMV beamformer is the worst, with corresponding average gains of \(-2.71\) dB and \(-0.88\) dB, respectively. It is also observed that the DL beamformer achieves much higher gains than the LCMV beamformer. However, it should be noted that its computational complexity is also much higher. For both beamformers, there are no significant margins between the low-speed and high-speed cases in terms of the average SINR or variance, thus demonstrating the stability of the proposed beamforming scheme. Figure 5: DoA estimation errors of the proposed and the improved MUSIC algorithms. (**a**) The UAV speed is 160 km/h. (**b**) The UAV speed is 60 km/h. Figure 6: SINR performance of the proposed and the conventional beamforming schemes. (**a**) The UAV speed is 160 km/h. (**b**) The UAV speed is 60 km/h. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Data**} & **Value** & **Value** \\  & **(Proposed Scheme)** & **(Conventional Scheme)** \\ \hline Standard variance (DL, UAV speed = 160 km/h) & 0.04 dB & 0.09 dB \\ Standard variance (LCMV, UAV speed = 160 km/h) & 0.50 dB & 0.92 dB \\ Average gain (DL, UAV speed = 160 km/h) & 21.92 dB & 21.31 dB \\ Average gain (LCMV, UAV speed = 160 km/h) & 2.70 dB & \(-\)2.71 dB \\ Standard variance (DL, UAV speed = 60 km/h) & 0.02 dB & 0.05 dB \\ Standard variance (LCMV, UAV speed = 60 km/h) & 0.16 dB & 0.53 dB \\ Average gain (DL, UAV speed = 60 km/h) & 21.95 dB & 21.48 dB \\ Average gain (LCMV, UAV speed = 60 km/h) & 3.46 dB & \(-\)0.88 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of the SINR performance.
# A Hybrid IVFF-AHP and Deep Reinforcement Learning Framework for an ATM Location and Routing Problem
## 3 The Proposed Methodology  
This section discusses a hybrid methodology based on IVFF-AHP and deep reinforcement learning to determine ideal ATM locations for Istanbul districts and to plan the optimum route between the determined ATM locations. This methodology aims to provide a reliable and easily accessible ATM location in a risky environment by integrating the clustering approach with the IVFF-AHP approach. The clustering approach determines the risks in the districts and the needs to be addressed, and the IVFF-AHP approach is used to obtain the criterion weights and ranking alternatives. In the last stage, the Double Deep Q-Network Reinforcement Learning (DDQN-RL) approach is applied, enabling vehicle routing between the locations determined by IVFF-AHP. Combining these two methodologies is unique to this paper and makes the methodology innovative. First, the clustering approaches used in the study are presented in the following subsection. In the second subsection, the preliminary concepts for Fermatean Fuzzy Sets are given, and then the steps of the IVFF-AHP methodology are given. In the last subsection, the proposed hybrid methodology is presented in Figure 1.  
Figure 1: The proposed hybrid methodology.  
The proposed methodology offers a comprehensive, risk-based, data-driven approach to supporting decisions on ATM location selection and cash-handling processes within the banking sector. Dynamic and adaptive solutions have been developed at strategic and operational levels through the combined use of clustering, IVFF-AHP, and DDQN-RL methods. Real-life conditions have been considered in the model by incorporating public safety data into the location selection process, and safe routes have been obtained using the risk matrix created with real traffic data during the routing phase. Therefore, the study provides both theoretical contributions compared to classical models in the literature and proposes an original method with high applicability for the sector.
# Towards a Highly Available Model for Processing Service Requests Based on Distributed Hash Tables
###### Abstract  
This work aims to identify techniques leading to a highly available request processing service by using the natural decentralization and the dispersion power of the hash function involved in a Distributed Hash Table (DHT). High availability is present mainly in systems that: scale well, are balanced and are fault tolerant. These are essential features of the Distributed Hash Tables (DHTs), which have been used mainly for storage purposes. The novelty of this paper's approach is essentially based on hash functions and decentralized Distributed Hash Tables (DHTs), which lead to highly available data solutions, which a main building block to obtain an improved platform that offers high availability for processing clients' requests. It is achieved by using a database constructed also on a DHT, which gives high availability to its data. Further, the model requires _no changes_ in the interface, that the request processing service already has towards its clients. Subsequently, the DHT layer is added, for the service to run on top of it, and also a load balancing front end, in order to make it highly available, towards its clients. The paper shows, via experimental validation, the good qualities of the new request processing service, by arguing its improved scalability, load balancing and fault tolerance model.  
Distributed Hash Table (DHT); high availability; decentralization; fault tolerance; scalability; load balancing; highly available data +
Footnote †: journal: 2022, 10, 831. [[https://doi.org/10.3390/math10050831](https://doi.org/10.3390/math10050831)]([https://doi.org/10.3390/math10050831](https://doi.org/10.3390/math10050831))
# On Sharing Spatial Data with Uncertainty Integration Amongst Multiple Robots Having Different Maps
## 1 Introduction
### Related Works
However, there were many limitations which are addressed in this extended work. The new major contributions are:  
1. **Uncertainty Integration in the Improved Confidence Decay Mechanism:** The previous work [12] did not consider the amount of estimated positional uncertainty of obstacles in the confidence decay. Both were decoupled entities. However, this was a serious drawback in the previous work because irrespective of the amount of positional uncertainty, confidence of all the obstacles decayed at the same rate. This caused several false map updates corresponding to dynamic obstacles which generally have large uncertainty associated. In the extended work, we have mathematically modeled the integration of positional uncertainty in the confidence decay mechanism. This is discussed in 'Section 4.1 Integrating Uncertainty in Confidence Decay Mechanism'. 2. **New Experiments with Heterogeneous Maps with Different Sensors:** Another shortcoming of the previous work was that it only worked with the same type of 2D grid-maps made with the same type of sensors. However, in the extended work, we include new experiments with heterogeneous maps (3 dimensional RGBD map and 2D grid-map) made from different sensors. In this regard, the merits of using the 'node-map' as a means of smoothly sharing information coherently between heterogeneous maps are also discussed. This is discussed in 'Section 6.1 Experiments with Heterogeneous Maps'. 3. **New Experiments in Dynamic Environment with Moving People and Testing Under Pressure:** The previous work only worked with static obstacles. In the extended work, new experiments have been performed to test the method when people are randomly moving in the vicinity of the robot and obstructing its navigation. In this regard, the tight coupling of new obstacle's uncertainty in the confidence decay mechanism plays a vital role to avoid false map-updates corresponding to the dynamic obstacles. This is discussed in 'Section 6.2. Results with Dynamic Entities (Moving Obstacle)'. The comparison of the previous work with the extended work is summarized in Table 1. In addition, the proposed work discusses the algorithm to generate the T-node map. The paper starts by first explaining the correspondence problem in different maps in Section 2. The node-map representation is explained in Section 3. Section 4 briefly explains obstacle removal and update in the nodemap and Section 4.1 explains the integration of positional uncertainty in the confidence decay mechanism. Further, using this coupling with Extended Kalman Filter is explained in Section 5 with detailed algorithm. The experimental results are discussed in Section 6. Section 6.1 explains about the experiments with heterogeneous maps and Section 6.2 discusses the results with dynamic entities (moving people). Finally, Section 7 concludes the paper.
## 2 Background and the Related Work
### Controller Placement Problem (CPP)  
Since the existing form of SDN requires multiple controllers, the CPP is the issue of determining the optimum number of controllers as well as where to place them [19]. The CPP is an urgent problem that needs to be solved, asking how many controllers are needed and where they should be positioned [20]. The type, number, and location of these controllers directly affect the cost and average latency of control packets [21]. The CPP has three different components. The number of controllers to be placed in a network, their locations, and the function of assigning controllers to switches are all important to the design of an effective control plane [22].  
After performing an extensive review of the literature, [PERSON] et al. [23] conclude that no rigid placement rule exists in SDN that can be applied to every network. Dynamically adding and removing controllers is inevitable. Our concern in this article is not to present an issue in regard to the CPP but simply to evaluate the traffic classification-based load balancing made by a prepositioned SDN controller.
## 5 Conclusions  
In this study, the modulus of deformation \(E_{m}\) (pressuremeter modulus) and limit pressure \(P_{L}\) of weathered granite in South Korea were assessed based on the SPT-\(N_{60}\) value and chemical weathering index \(VR\). Nonlinear multiple regression analysis was conducted using SPT-\(N_{60}\) and \(VR\) as the independent variables, specifically for the values measured at three sites in South Korea. The analysis was used to derive empirical equations for \(E_{m}\) and \(P_{L}\), and the predictions of the equations were compared with the equations suggested in the previous relevant studies. The study and its findings can be summarized as follows:  
1. The relationships between the PMT and SPT-\(N_{60}\) results were derived by simple regression analyses, which revealed that \(E_{m}\) tended to increase with increasing SPT-\(N_{60}\). Relatively high correlations were observed when both an allometric model and linear model were utilized, as represented by \(R^{2}\) of 0.71. The correlations were lower for \(P_{L}\), as represented by the \(R^{2}\) values of 0.44 and 0.43 for the two models, respectively, revealing no significant difference.
2. The chemical weathering index that has been rarely considered in previous studies was used to evaluate the PMT results. The _normalized VR_ with vertical effective stress revealed a relatively good correlation with \(E_{m}\) (\(R^{2}=0.67\)). Therefore, it is useful  
Figure 11: Comparison of the measured and predicted values of (**a**) \(E_{m}\) and (**b**) \(P_{L}\).  
as a simple method for the estimation of \(E_{m}\) of weathered granite in preliminary site characterization.
3. Nonlinear multiple regression analyses were conducted by combining the allometric models that produced improved correlations between \(E_{m}\), \(P_{L}\), SPT-\(N_{60}\), and _normalized VR_. The obtained equations of \(E_{m}\) and \(P_{L}\) had better \(R^{2}\) values (0.76 and 0.46, respectively) than the equations obtained by simple regression in other studies. Even though the obtained slight increase in the coefficient of determination was lower than expected, this study is meaningful in that it is possible to obtain improved prediction of the \(E_{m}\) and \(P_{L}\) values of weathered granite by additionally considering \(VR\), which is a chemical weathering index that represents the physical properties of weathered granite.
4. Empirical equations suggested in other studies were based on a single variable (i.e., SPT-\(N_{60}\)) and can thus be only used to estimate either \(E_{m}\) or \(P_{L}\) for a particular SPT-\(N_{60}\). However, the empirical equations proposed in this paper utilize an additional variable \(VR\) can thus predict the \(E_{m}\) and \(P_{L}\) by considering both the degree of weathering and SPT-\(N_{60}\). To determine the engineering properties of geo-materials, this study proposes the \(E_{m}\) and \(P_{L}\) equations for use in geotechnical surveys of weathered granite in South Korea.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The authors declare no conflict of interest.
# Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach
## 4 Results and Experiment
### Performance of Machine Learning Models  
Table 2 provides a comprehensive summary of the performance of three machine learning models--Logistic Regression (LR), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks--in classifying stress levels based on environmental data. The evaluation metrics include accuracy, precision, recall, and F1 score, offering a detailed assessment of each model's effectiveness.  
Figure 1: System Architecture: A comprehensive framework integrating IoT devices, data processing platforms, and machine learning models for real-time stress monitoring and prediction.  
The results highlight that the SVM model outperformed the others, achieving the highest accuracy (97.95%) and demonstrating balanced performance across all metrics. The Logistic Regression (LR) model also performed well, with a slightly lower accuracy of 96.95%. In contrast, the LSTM model showed moderate performance, with an accuracy of 70.03% but significantly lower precision (49%), likely due to the limited size of the training dataset and a lack of diverse temporal patterns in the environmental data. These findings suggest that while SVM is the most suitable for stress level classification in this context, the LSTM model's performance may be improved with further optimisation, such as hyperparameter tuning (e.g., adjusting learning rate, number of layers, and units per layer) and by using a more suitable dataset with stronger temporal dependencies. In addition, the lack of diverse temporal features in the dataset may have limited LSTM's ability to capture important patterns, potentially limiting its ability to improve prediction accuracy. This insight opens avenues for future work, where temporal models like LSTM might perform better with more data or refined feature engineering.
# NTT and Inverse NTT Quantum Circuits in CRYSTALS-Kyber for Post-Quantum Security Evaluation
###### Abstract  
The emergence of quantum computers threatens current cryptographic systems, and NIST is preparing for the post-quantum era through the post-quantum cryptography (PQC) contest. CRYSTALS-Kyber is a lattice-based cipher suite that is used as a PQC standard. Lattice-based cryptography is considered quantum-safe for quantum computing because a quantum algorithm that can more efficiently solve the lattice problem of lattice-based cryptography compared to a classic algorithm has not been reported as yet. In this paper, we present quantum circuits tailored to NTT and inverse NTT, employed for optimized polynomial multiplication within CRYSTALS-Kyber. The proposed quantum circuits operate at \(Z_{3359}[X]/(X^{256}+1)\), which are the parameters of CRYSTALS-Kyber. We provide an in-depth description of the NTT/InvNTT quantum circuit's operation and subsequently assess and analyze the quantum resources necessary for these functions. The NTT/InvNTT quantum circuits comprise four unique sub-functions, with the InvNTT additionally incorporating Barrett reduction. To the best of our knowledge, this represents the inaugural implementation of the CRYSTALS-Kyber NTT/InvNTT quantum circuits. We anticipate that our findings will aid in analyzing the security strengths of quantum computers for lattice-based cryptography.  
quantum circuit; CRYSTALS-Kyber; number theoretic transform (NTT); post-quantum cryptography (PQC) +
Footnote †: journal: Applied
## 3 The Problem of Selecting the Next Network
### Network Selection Process according to the Proactive Cross-Layer Protocol
#### 4.1.3 Model of Multinomial Logistic Regression  
A model of classification logistic regression is used for data in which the dependent variable is unordered or polytomous, and independent variables are continuous or categorical predictors. Under a multinomial logistic regression model, the probability that \(x\) belongs to class \(i\) is written as:  
\[\left(y^{(i)}=1\Big{|}x,w\right)=\frac{\exp\left(w^{(i)^{T}}x\right)}{\sum_{ y=1}^{m}\exp\left(w^{(i)^{T}}x\right)} \tag{9}\]  
For \(i\in\{1,\ldots,m\}\), where:\(w^{(i)}\) is the weight vector corresponding to class \(i\), in this case the class is a network and the superscript \({}^{T}\) denotes matrix transpose. For binary problems (\(m=2\)), this is known as a logistic regression model; for \(m>2\), the usual designation is multinomial logistic regression (or soft-max in the neural networks literature) because of the normalization condition.  
\[\sum_{y=1}^{m}P\left(y^{(i)}=1\middle|x,w\right)=1 \tag{10}\]  
The weight vector for one of the classes need not be estimated. Without loss of generality, we thus set \(w^{(m)}=0\), and the only parameters to be learned are the weight vectors \(w^{(i)}\) for \(i\,\in\{1,\ldots,m-1\}\).