esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Parametric Study
### Conceptualization of the Portable Directional Antenna Scheme
Figure 4: Simulated impedance bandwidth (\(\,\)\(\,\)\(S_{11}\)\(\,\)\(\leq-10\) dB) of the antenna models I, II, and III. Figure 3: Conceptualization of the portable directional antenna scheme: **(a)** the Yagi-Uda antenna (model I), **(b)** the modified antenna with the U-shaped reflector and director (model II), and **(c)** the proposed portable antenna scheme (model III). Figure 6 compares the simulated xz- and yz-plane radiation patterns of the antenna models I, II, and III. The three antenna models exhibited similar radiation patterns. However, models I and II failed to cover the target frequency band (Figure 4). The xz- and yz-plane cross-polarizations of model III were below \(-40\) dB and \(-20\) dB, with the corresponding half-power beam width (HPBW) of \(149^{\circ}\), \(194^{\circ}\), and \(158^{\circ}\) and \(99^{\circ}\), \(92.8^{\circ}\), and \(84.2^{\circ}\) at 470, 680, and 890 MHz, respectively. The xz- and yz-plane back lobe levels were \(-10\) dB.
## 6 Experimental Evaluation of SRF-IoT Framework
### Comparison with Related Works  
This section focuses on the discussion and the comparison of the results obtained from the proposed SRF-IoT framework with other similar studies.  
Figure 9: Median SRF-IDS packet overhead in SRF-IoT scenario after deploying 5 to 15 SRF-IDS detectors.  
In Table 6, a comparison of our work with four similar studies is presented. Specifically, the evaluation results from the studies of MRTS [24], SRPL-RP [26], and SecTrust-RPL [26, 28] are compared with our proposed SRF-IoT framework. The metrics used in the comparison are the PDR, parent switches, and packets dropped. In addition, the total number of nodes deployed in each experiment is compared.  
The related works that are used for comparison purposes explore rank or blackhole attackers in medium scale networks. As we wanted to have a similar basis for comparing all the related studies, the results from Section 6 are referenced and used. That specific section evaluates the SRF-IoT framework in a scenario where 30 benign nodes are deployed, and 6 nodes launch a combination of blackhole and rank attacks. Thus, simulations have similar configurations with the other work.  
As it can be seen from Table 6, most of the studies provide the PDR metric, whereas only one of them provides the parent switch and another one the packets dropped metrics. In our work, we provide all of the aforementioned metrics. Looking at the PDR metric, the highest PDR value is achieved by SRPL-RP with 98.48% in a small network of 16 nodes plus the 4 rank attackers. The second highest PDR value is from our SRF-IoT framework, which achieves 92.8% in a network of 30 benign nodes plus 6 attackers that launch combined blackhole and rank attacks. The MRTS study follows with a PDR up to 90% in a network with 27 nodes plus the 3 blackhole attackers. SecTrust-RPL exhibits the lowest PDR with a value of 80%, with the same number of nodes are deployed in the network as in MRTS. Comparing the rest of the metrics, parent switches of the SRF-IoT framework are slightly more than the 80 switches observed in the MRTS work. Regarding packets dropped, our work keeps the percentage near 8%, which is very low in comparison with the 22-23% recorded in the SecTrust-RPL study.  
All in all, it has been shown that the proposed SRF-IoT is an effective solution that achieves the best results among the current related studies that deploy fewer nodes and study only single attacks. SRF-IoT was evaluated in a larger network than other studies using a combination of blackhole and rank attacks and demonstrates superior performance in most cases. Nevertheless, given the variation in the results with the variation in the number of detector nodes, the study of the application of machine learning techniques to dynamically optimise the number of detector nodes could be considered as a future enhancement.
# Centralized vs. Decentralized Cloud Computing in Healthcare
## 7 Results
#### 7.3.4 Security and Privacy of Patient Data  
Such models are classified as centralized cloud computing models in healthcare and store a vast amount of patient data in one location; they are vulnerable to cyber threats and hackers because patient data are highly valuable to hackers if compromised. In the case of an efficient breach, the unauthorized disclosure or theft of a patient's personal information can have significant adverse consequences on their lives. It is crucial to implement sufficient steps to safegu patients' information against unauthorized access [54; 58]. Decentralized cloud computing, in contrast, offers a method of storing data on several nodes or servers that are spread across different networks [61]. This approach has several advantages in relation to confidentiality and security since data fragmentation and any other methods of encryption can further the goal of keeping data away from prying eyes. In employing this technique, data are scaled down and distributed into fragments residing in different nodes so that no unlawful invader can penetrate them too easily [71]. In addition, there is encryption that implements measures to safeguard the information in case it is intercepted. It also makes the nodes more secure and private, which is ideal for processing patient-sensitive information and crucial healthcare entities.
## 3 Comparison between Flow-3D, MIKE 3 FM, and 1D Exact Riemann Solver Predictions
### Free Surface
#### 3.1.2 Free Surface during the Late Stage (\(T\geq 9.899\))  
To further study the free-surface profile features during the late stage (\(T\geq 9.899\)) after the dam break, the experimental setup by [PERSON] et al. [39] was used to compare the results computed by the two numerical models (Flow-3D and MIKE 3 FM) and the analytical solution of Stoker's solution [9] over a longer time in the wet-bed. The experimental and computed water surface levels for the dry-bed condition are presented here. The differences in the free-surface profile when using Flow-3D and MIKE 3 FM models decreased very well with respect to one another, especially in the simulated results, and the discrepancies in the wavefront location also became slight (Figure 9). The RMSE results in Table 2 also show that the Flow-3D model offered a similar performance to that of MIKE 3 FM, with 0.02 as the minimum and 0.04 as the maximum value. However, as seen, the experimental curves and simulated curves deviated from the corresponding exact curves for \(T=49.497\), with the RMSE values for the 1D analytical solution reaching 0.26, the worst performance of all the models. This is because the 1D analytical Ritter (dry bed) or Stoker (wet bed) solutions are only applicable to the dam-break flow in an infinite length for both an upstream reservoir and downstream, with the consequence being that the water surface along the channel is constant over time, but it does not produce the reflections from the walls in a way that affects the depth profiles. This outcome indicates that no reflected negative wave is expected, and the reservoir will never empty [43]. Either the Flow-3D or the MIKE 3 FM model can be selected as an appropriate model for predicting the free surface during the late stage after the dam break. These behaviors illustrate that, as time passes, the MIKE 3 FM successfully predicted the flow features after dam failure like the RANS method (Flow-3D). The only exception to this success was during the initial and late stages, because the vertical velocity became progressively minor compared to the horizontal velocity while the shallow-water assumptions became more realistic.
###### Abstract  
In the field of direction of arrival (DOA) estimation, a common assumption is that array noise follows a uniform Gaussian white noise model. However, practical systems often encounter non-ideal noise conditions, such as non-uniform or colored noise, due to sensor characteristics and external environmental factors. Traditional DOA estimation techniques experience significant performance degradation in the presence of colored noise, necessitating the exploration of specialized DOA estimation methods for such environments. This study introduces a DOA estimation method for acoustic vector arrays based on a spectrum attention mechanism (SAM). By employing SAM as an adaptive filter and constructing a double-branch model combining a convolutional neural network (CNN) and long short-term memory (LSTM), the method extracts the spatial and temporal features of signals, and effectively reduces the frequency components of colored noise, enhancing DOA estimation accuracy in colored noise scenarios. At an SNR of \(-5\) dB, it achieves an accuracy rate of 85% while maintaining a low RMSE of only \(2.03^{\circ}\).  
DOA estimation; acoustic vector array; colored noise; attention; deep learning Article
## 3 Proposed Identification Methods
### Proposed Hybrid Methods  
To overcome the limitations of the PSO method and enhance its solution refinement capabilities, we introduce a new hybrid approach, the HPSO-QN method. This method merges the global search capability of PSO with the fast convergence and local search proficiency of the quasi-Newton method. While PSO is known for its global search capability, it can be slow to converge and prone to getting trapped in locally optimal solutions. Conversely, the quasi-Newton method offers fast convergence but is limited to the local minima on multi-modal problems and is sensitive to initial conditions.  
Drawing inspiration from the successes of previous hybrid techniques [35; 60; 61; 36], our proposed hybrid approach employs both the PSO and QN algorithms to identify the mechanical parameters of the 2 MM. Unlike standard PSO algorithms, this hybrid method leverages the PSO algorithm's strength for global search and the QN method's efficiency in local search. Initially, the PSO identifies a potential global optimal region in the search space, after which the algorithm switches to the QN method for more rapid convergence and solution refinement. This approach combines the benefits of both global and local search methods, incorporating a stopping criterion to avoid being trapped in local minima.  
Diversifying the approach further, we introduce three variations of the HPSO-QN method. Each variant begins with the PSO to find an initial _gBest_ solution, which then serves as the starting point for the QN method. The key difference between these methods lies in their strategies to select particles for a second run of the PSO algorithm after the QN method has identified a minimum. These strategies provide new approaches to hybridizing the PSO and QN methods, leveraging the strengths of each, while mitigating their limitations. The following bullet points provide a brief outline of these proposed methods:  
1. HPSO-QN Sequential Method: PSO finds the _gBest_ solution, which is then improved by QN.
2. HPSO-QN Single Local Search Method: PSO uses a stopping criterion to find the _gBest_ solution, which is then refined by the QN method.
3. HPSO-QN Multi Local Search Method: SPSO is used until a stopping criterion is met, then a percentage of the best particles are selected and improved using the QN method.
## 4 Experimental Results and Discussion
### Results and Discussion
#### 4.3.1 Gesture Classification Using k-NN Algorithm
That is, it was confirmed that even if the \(k\) values of k-NN were different, there was no significant effect on the prediction success rate of grab gesture measured for each subject. Finally, to confirm whether each subject's prediction success rate of the pinch gesture was affected by the \(k\) value, a one-way ANOVA was performed for the prediction success rate of the pinch gesture, with respect to the subject, when the \(k\) value was 3, 5, and 7. Table 7 shows the results of normality test to perform one-way ANOVA. As a result of the normality test, as shown in Table 7, the significance level (red boxes in Table 7) of both the Kolmogorov-Smirnov and the Shapiro-Wilk test was greater than 0.05, so the prediction success rate of pinch gesture data satisfies the normal distribution. Next, the result of the test for equality of variance, in consideration of the post hoc analysis of one-way ANOVA, is shown  
As a result of the test for equality of variance, as shown in Table 8, the significance level (red box in Table 8) of Levene was greater than 0.05, so the equality of variance was confirmed. Thus, Table 9 shows the results of one-way ANOVA. As a result of one-way ANOVA, as shown in Table 9, the significance level (red box in Table 9) was greater than 0.05, so the null hypothesis of one-way ANOVA was adopted. Based on this, even if the \(k\) value in k-NN was different, as 3, 5, and 7, respectively, it could be seen that there was no significant difference in the average prediction success rate of the pinch gesture for each subject. That is, it was confirmed that even if the \(k\) values of k-NN were different, there was no significant effect on the prediction success rate of pinch gestures measured for each subject. Up to now, it was examined whether the data for confirming the effectiveness of this study, such as the prediction time, prediction success rate of grab gestures, and the prediction success rate of pinch gestures, were affected by the \(k\) value of k-NN. As a result of the one-way ANOVA for the cases where \(k\) is 3, 5, and 7, it was confirmed that there was no significant difference according to the \(k\) value. Therefore, 3 was arbitrarily selected as the \(k\) value for examining the subsequent experimental results.
###### Abstract  
The global navigation satellite system (GNSS) has been applied to many areas, e.g., the autonomous ground vehicle, unmanned aerial vehicle (UAV), precision agriculture, smart city, and the GNSS-reflectometry (GNSS-R), being of considerable significance over the past few decades. Unfortunately, the GNSS signal performance has the high risk of being reduced by the environmental interference. The vector tracking (VT) technique is promising to enhance the robustness in high dynamics as well as improve the sensitivity against the weak environment of the GNSS receiver. However, the time-correlated error coupled in the receiver clock estimations in terms of the VT loop can decrease the accuracy of the navigation solution. There are few works present dealing with this issue. In this work, the Allan variance is accordingly exploited to specify a model which is expected to account for this type of error based on the 1 st-order Gauss-Markov (GM) process. Then, it is used for proposing an enhanced Kalman filter (KF) by which this error can be suppressed. Furthermore, the proposed system model makes use of the innovation sequence so that the process covariance matrix can be adaptively adjusted and updated. The field tests demonstrate the performance of the proposed adaptive vector-tracking time-correlated error suppressed Kalman filter (A-VTTCES-KF). When compared with the results produced by the ordinary adaptive KF algorithm in terms of the VT loop, the real-time kinematic (RTK) positioning and code-based differential global positioning system (DGPS) positioning accuracies have been improved by 14.17% and 9.73%, respectively. On the other hand, the RTK positioning performance has been increased by maximum 21.40% when compared with the results obtained from the commercial low-cost U-Blox receiver.  
global navigation satellite system (GNSS); software-defined receiver (SDR); vector tracking (VT); Kalman filter (KF); Allan variance; time-correlated error; Gauss-Markov (GM) process; innovation sequence; RTKLIB +
Footnote †: journal: Remote Sensing
## 6 Conclusions, Recommendations, Future Directions, and Limitations
### Policy Recommendations  
As the banking sector transitions toward a fully digital model, policymakers face the urgent challenge of ensuring that this transformation is both inclusive and sustainable. The gradual phasing out of ATMs, combined with the increasing reliance on digital tools, calls for strategic investments in digital infrastructure to bridge the accessibility gap. Policymakers must prioritize the development of high-speed internet connectivity, particularly in rural and underserved areas, to guarantee equal access to digital banking services. Additionally, efforts to enhance digital financial literacy are important for empowering individuals, especially those from vulnerable populations, to adapt to a banking ecosystem dominated by technology.  
Regulatory frameworks must also evolve to address the growing reliance on AI-based tools, such as chatbots and robo-advisors. Policymakers should enforce transparency standards for algorithmic decision-making to prevent biases and ensure equitable access to services. Cybersecurity is another significant area, requiring robust regulations to protect customer data and mitigate risks associated with digital financial systems. Beyond safeguarding user trust, these measures can foster innovation by creating a secure environment for the adoption of advanced technologies.  
The elimination of physical banking infrastructure, such as ATMs, raises concerns about financial exclusion for certain demographics. Policymakers should explore alternative mechanisms to maintain access to essential banking services, such as mobile banking units or community financial hubs, particularly in regions where digital adoption remains low. Supporting the transition through fiscal incentives for banks investing in automation, as well as subsidies for fintech collaborations, can accelerate the adoption of innovative solutions while minimizing disruptions.  
This study highlights the importance of interoperability in digital financial systems, urging policymakers to promote collaboration among banks, fintech companies, and public institutions. Such cooperation can ensure the seamless integration of automation technologies across platforms, enhancing efficiency while maintaining fair competition. Additionally, regular monitoring of the social and economic impacts of digital transformation is essential for adaptive policy making, enabling governments to respond effectively to emerging challenges and opportunities in the financial ecosystem.
## 4 Data Understanding and Preparation
### Data Pre-Processing  
The initial step after the data understanding is the preparation of the data to the form suitable for the classifier training. When working with the textual documents, data pre-processing usually involves a series of tasks of text cleaning, formatting and creation of its representation used in the model training. Notably, in this case, the texts contain slang expressions, emoticons, incomplete words, typos, etc.; which are usually addressed in the pre-processing. However, in case of abusive language/toxic comments detection from the social media (as well as other similar tasks), it should be noticed, that abusive/toxic content is often associated with non-standard textual content (e.g., slang, upper case letters, emoticons, etc.). Authors in [6] point out, that in a domain such as antisocial behaviour detection on the Internet, it is essential and useful to keep the data in their original form as much as possible. Application of standard pre-processing methods such as lowercasing, stopwords removal or stemming could lead to the loss of the individuality and specific features of both, the content of the message that the author wants to submit and also for the author himself. By using the standard pre-processing methods, in this case, we may lose important traits, crucial in the process of extracting features. In our experiments, one of our primary motivation was to explore this hypothesis.  
We decided to compare the models performance also from the aspect of standard pre-processing techniques used in the process and compared on the data pre-processed in a standard manner and on the data with no traditional pre-processing at all.  
During the experiments, where we used the data pre-processing, we studied the effect of the standard techniques commonly used in the textual data preparation:  
* Tokenization--splitting of strings into a tokens, representing the lexical units (e.g., words);
* Lowercasing--conversion of the entire text to lowercase (words with different cases map to the same lowercase form);
* Punctuation removal--removal of all punctuation marks within the sentence;
* Stop words removal (stop words--words with minimum information value, e.g., conjunctions, prepositions, confusions, etc.).  
Figure 6: Correlation of features and targets.
## 3 Establishment of a Single-Degree-of-Freedom Eddy Current Damping Vibration Suppression Model
### Establishment of Eddy Current Damping Vibration Suppression Model
\[\ddot{x_{0}}=i\omega A_{0}e^{\omega t}e^{-\phi_{0}i} \tag{8}\]0  
Let:  
\[\ddot{x_{0}}=i\omega A_{0}e^{\omega t}e^{-\phi_{0}i} \tag{8}\]1  
A comprehensive solution model is obtained based on Equations (15), (18), and (24). Because the vortex flow damping module is composed of multiple electromagnetic arrays, and the magnetic field formed by the electromagnetic array is not a linear superposition, but there is a complex coupling relationship between each other. To minimize the error calculated by the model, a correction coefficient \(\beta=1.3\) is introduced here [26], and the number of electromagnets is **q** = 8. The value of the correction coefficient \(\beta\) is referenced in [26]. Considering hysteresis loss, the relative length of the conductor plate to the magnetic pole being not infinite, and the non-uniformity of the magnetic field, the value of \(\beta\) is relatively conservative. Due to the fact that the value of \(\beta\) is based on certain experience, it has some limitations. Substituting Equation (24) into Equation (15) results in the following:  
\[\ddot{x_{0}}=i\omega A_{0}e^{\omega t}e^{-\phi_{0}i} \tag{8}\]2  
[PERSON] et al. [27] proposed a novel, non-contact, multi-layer electromagnetic spring with adjustable negative stiffness, which reduces the natural frequency of the system by using a variable stiffness model to achieve vibration isolation. The nonlinear factor of this model lies in the introduction of negative stiffness. Under this model, as the amplitude of vibration gradually increases, the efficiency of vibration suppression will gradually decrease, which is related to the interaction between magnetic fields. The model proposed by this article uses a variable damping vibration suppression model, which consumes vibration energy by adjusting damping in the vibration process so as to achieve the effect of vibration suppression. The nonlinear factor of this model lies in the introduction of eddy current effects. In this model, as the vibration amplitude gradually increases, the required current also gradually increases, and the losses of the coil and the iron core of the electromagnet also gradually increase. The efficiency of the vibration suppression will gradually decrease. However, regardless of which model is used, the linearity of the model is relatively high under low-frequency, low-speed vibration conditions or under small vibration conditions. Therefore, when exploring low-frequency, low-speed vibrations, the establishment of this model ignores the nonlinear part [26; 27; 28; 29]. The comprehensive solution model is as follows:  
\[\left\{ \begin{array}{l}\xi=\frac{10.4 TF^{2}+2S\sigma R_{n}{}^{2}c_{0}}{4S \sigma R_{n}{}^{2}\sqrt{km(1-\xi^{2})}}\\ A_{1}=A_{0}\sqrt{\frac{1+4\xi^{2}(1-\xi^{2})a^{2}}{1+(1-\xi^{2})^{2}a^{2}}}\\ \end{array} \right. \tag{27}\]