esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Lightweight URL-Based Phishing Detection
## 3 Methodology
### Model Selection and Tuning  
Based on the F1 scores of the algorithms (Table 4), Random Forest was the best candidate algorithm to be selected. The F1-score is used here as it is fit for generalising performance. Furthermore, Figure 1 presents both the ROC and confusion matrix for the Random Forest model. The maximum area under the curve (AOC) points out that the model can easily distinguish between the two classes of URLs. The confusion matrix reveals that it is uncommon for the model to mislabel URLs; only 104 URLs out of 10 thousand URLs were misclassified.  
Progressing to the next phase, we conducted the process of hyperparameter tuning or optimisation which consists of training a model with different values for its parameters. Trying different permutations of values for these parameters influences prediction accuracy, thus improving overall performance. It is worth noting that hyperparameter tuning is computationally expensive. For this reason, only the three algorithms with the best performance were selected for optimisation, namely: (a) Random Forest, (b) Support Vector Machine, and (c) Multi-Layer Perceptron.  
During the optimisation of the Random Forest classifier, the number of n_estimators is increased to raise the number of decision trees. By doing so, the model can deliver better predictions at the expense of both training and prediction time. As such, the number of n_estimators is set to either 150, 295 or 350. Next, the max_depth of each decision tree is set to be either 15, 18 or 21. The last optimisation involves the max_features parameter. This sets the number of maximum features provided to each tree to _auto_ and _sqrt_.  
Tuning the SVM classifier involves searching for the best performing C parameter, which influences the misclassification threshold of the model. A classifier with a greater C value will use a smaller-margin hyperplane, rendering the model less prone to delivering wrong predictions. However, a bigger C value will increase the chance of overfitting. Finally, the values of the C fed into the GridSearchCV are 10, 100 and 1000.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **F1-Score** \\ \hline
**Naive** Bayes & 98.37 \\
**Decision Tree** & 98.74 \\
**Random Forest** & 98.86 \\
**Support Vector Machine** & 98.76 \\
**Multi-Layer Perceptron** & 97.21 \\ \hline \hline \end{tabular}
\end{table}
Table 4: F-measure results of models’ training.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Example Result** \\ \hline
**Original** & example.com \\
**Addition** & example.com \\
**Bitsquatting** & azample.com \\
**Homoglyph** & example.com \\
**Hyphenation** & example-e.com \\
**Insertion** & example.com \\
**Omission** & example.com \\
**Repetition** & example.com \\
**Replacement** & example.com \\
**Subdomain** & ex.ample.com \\
**Transposition** & example.com \\
**Vowel-swap** & exomple.com \\
**TLD inclusion** & examplecom.com \\ \hline \hline \end{tabular}
\end{table}
Table 3: Domain variation techniques.
# Practical Implementation of a Blockchain-Enabled SDN for Large-Scale Infrastructure Networks
## 3 System Proposal
Thus, by replicating the mentioned model within the whole federation of service providers, the implemented integrated blockchain would have an increase in benefits from the perspective of the features and properties that come with the blockchain. The importance of extending the blockchain over the SDN networks is given by the constraint that the blockchain has--the necessity for a sufficient extension to render data modification difficult or virtually impossible. This goal can be achieved by including as many service providers as possible in the federation of SDN networks. Benefiting from such an organization, the abstraction of the federation to what is referred to as a virtual ISP is easier to comprehend and implement. This is also the focus presented in [25], where a collaborative game theory approach is proposed for validating the feasibility of integrating a blockchain solution in a federation of service providers. A set of incentives organized as a mechanism is proposed in order to stimulate cooperation between multiple service providers and to demonstrate that it is more beneficial for service providers to adhere to such a federation. Drawing from the outlined proposed model, several use cases warrant exploration. A potential application scenario involves leveraging the permissioned blockchain within the ISP federation to promote collaboration among ISPs. Service providers would have the opportunity to disseminate public information using the blockchain, offering benefits and assistance to fellow participants within the federation. This approach ensures the accessibility of information to all involved parties. Communicating and crowd-sharing information regarding problems on different segments of the networks can be of benefit to other ISPs. This approach facilitates the implementation of effective measures for resolving and mitigating the identified challenges. Solutions to different issues of common interest can also be stored in the blockchain, including information pertaining to security issues, threats, vulnerabilities and recommended solutions. Thus, the distributed ledger will also act as a history logger. Regarding ISPs' interaction, several advantageous use cases could be derived. As presented in [6,7], it may be beneficial to use shared caching mechanisms for collaborative information retention. The proposed solution would allow for incorporating negotiatedprices and contracts into the integrated blockchain, for enhanced operational efficiency. This approach ensures the immutability of information, allowing for historical verification and analysis. In the context outlined by [8], the ISPs could leverage the integrated blockchain within their SDN infrastructure to record agreements with CPs. This approach ensures the availability and transparency of information while preventing data alteration.
# Numerical Analysis on Spanwise Correlation of Vortex-Induced Force of Split Double-Box Beam
## 4 Results and Analysis
### Analysis of the Results of Three-Dimensional Vortex-Induced Vibration
#### 4.3.1 Spanwise Correlation of Aerodynamic Coefficients When the Bridge Is in the Vibrational State
Moreover, different from the continuous decrease of the pressure's spanwise correlation coefficient in a static state, the pressure's spanwise correlation coefficient tends to a relatively stable correlation value gradually in the vibrational state, and the spanwise correlation coefficient of pressure is relatively higher as well. A possible reason is that  
Figure 11: Spanwise correlation of aerodynamic coefficients in the vibrational state. (**a**) Spanwise correlation of lift coefficient; (**b**) Spanwise correlation of torque coefficient; (**c**) Spanwise correlation of resistance coefficient. Figure 12: Correlation of pressure coefficient at each measuring point. (**a**) Spanwise correlation of pressure coefficient at 1# measuring point; (**b**) Spanwise correlation of pressure coefficient at 2# measuring point; (**c**) Spanwise correlation of pressure coefficient at two measuring points when \(\text{Vr}=6.21\). the natural vibration of the main beam structure plays a definite role in controlling the shedding of the vortex in the vortex-induced vibration of the bridge. 3.3 Spanwise Correlation of Wake Wind Velocity of the Model when the Bridge Is in the Vibrational State  
In this section, dynamic grid technology and the method of embedding UDF into solver FLUENT are used to carry out the spanwise correlation numerical simulation of fluctuating wind velocity in the wake region under vibrational state when the reduced wind velocity is 5.65, 6.21, and 6.68. The variation of the spanwise correlation coefficient of the main beam in wake region with spanwise spacing is shown in Figure 13.  
It can be found in Figure 13 that:  
* The trend of the spanwise correlation coefficient of fluctuating wind velocity in the wake area in the vibration state is basically the same as that in a static state. * Moreover, the spanwise correlation of the fluctuating wind in the vibrational state is stronger than that in the static state both in the along-wind and vertical-wind direction, especially the vertical-wind direction, in which the spanwise correlation coefficient is much higher. A possible reason is that the vortex vibration of the bridge is in the vertical direction, while the form of vibration has a great impact on the shedding of the wake during vibration. Therefore, the mutual disturbance of the spanwise airflow should be fully considered during the controlling of vortex vibration.
## 5 The Convergence Analysis  
The convergence analysis of the presented key variable separation-based recursive least squares algorithm for a dual-rate Hammerstein system is simply explained as follows.  
Assume that the \(\sigma\) algebra sequence \(\mathcal{F}_{kq}=\sigma(v(kq),v(kq-1),v(kq-2),\cdots)\) generated by \(v(kq)\), and \(\{\mathbf{v}(kq),\mathcal{F}_{kq}\}\) is a martingale difference sequence on a probability space \(\{\Omega,\mathcal{F},P\}\)[22]. The sequence \(\{v(kq)\}\) satisfies:  
\[(A1) \mathrm{E}[v(kq)|\mathcal{F}_{kq-q}]=0,\;\mathrm{a.s.}\] \[(A2) \mathrm{E}[v^{2}(kq)|\mathcal{F}_{kq-q}]=\sigma^{2}(kq)\leqslant \bar{\sigma}^{2}<\infty,\;\mathrm{a.s.}\] \[(A3) \limsup_{t\to\infty}\frac{1}{t}\sum_{i=q}^{kq}v^{2}(i)\leqslant \bar{\sigma}^{2}<\infty,\;\mathrm{a.s.}\]  
Theorem 1: For the key variable separation-based recursive least squares algorithm in this paper, assume that (A1)-(A3) hold and that the estimated information vector \(\hat{\mathbf{\psi}}(kq)\) and matrix \(\mathbf{T}(kq)\) arepersistently exciting, _i.e._, there exist constants \(0<C_{1}\leq C_{2}<\infty\) and an integer \(N>n_{0}\)\((n_{0}=nq+n+2)\), such that for \(k>n_{0}\), the following strong persistent excitation conditions hold:  
\[(A4) C_{1}\mathbf{I} \leqslant\frac{1}{N}\sum_{k=0}^{N-1}\hat{\mathbf{\psi}}(kq)\hat{\mathbf{ \psi}}^{\rm{T}}(kq)\leqslant C_{2}\mathbf{I},\ {\rm{a.s.}}\] \[(A5) C_{1}\mathbf{I} \leqslant\frac{1}{N}\sum_{k=0}^{N-1}\mathbf{T}^{\rm{T}}(kq)\mathbf{T}(kq) \leqslant C_{2}\mathbf{I},\ {\rm{a.s.}}\]  
Then, the parameter estimation vectors \(\hat{\mathbf{\Theta}}(kq)\) and \(\hat{\mathbf{\theta}}(kq)\) consistently converge to the true parameter vectors \(\mathbf{\Theta}\) and \(\mathbf{\theta}\).  
In a similar way to the method in [23], Theorem 1 can be proven, and its proof is omitted here.
# Development of Automatic Accumulating Equipment for Roller-Type Onion Pot-Seeding Machine
## 1 Introduction  
A need for ergonomic design is emerging to prevent occupational musculoskeletal diseases that occur owing to the uncomfortable working postures and repeated motions of workers [1; 2]. In particular, agricultural works are often performed in harsh conditions such as high temperatures and dusty environment. Thus, studies to reduce the labor load and increase work efficiency of the workers are required [3].  
In the case of the roller-type pot-seeding machine used for onion seeding, the work efficiency is high, and the economic efficiency and durability are excellent [4]. However, pot trays that complete the seeding process must be carried one at a time by workers as there is no pot tray accumulating equipment. The labor load of the workers is very high because they have to repeat the uncomfortable posture of bending their backs to pick the pot trays until the end of the seeding work. Therefore, the development of an equipment to accumulate the seeded pot trays in multiple stages and carry them at once is required.  
[PERSON] et al. [5] developed accumulating equipment for wood to reduce the labor intensity and to shorten the time required in the wood packaging process. [PERSON] et al. [6] developed a high-efficiency accumulator suitable for cabbage-accumulating work through a mechanical and kinematic analysis. [PERSON] et al. [7] developed equipment capable of accumulating up to three pot trays for the automation of the seedling raising process in plant factories. The accumulating equipment used in the seedling raising area can be classified into the round bar type, angle beam type, and flat plate type depending on the element that lifts the pot trays. The round bar type and angle beam type have high power transmission efficiency and relatively low production cost because they use simple mechanical components such as chains and gears. These types, however, cannot be used when the flexibility of the pot trays is high because the contact area between the lift element and pot tray is narrow. For the flat platetype, the production cost is high and the structure is complicated because hydraulic devices such as actuators are used. However, it is not significantly affected by the flexibility of the pot trays because the contact area between the lift element and pot trays is wide [8]. As the pot trays used for onion seeding can be easily bent owing to their high flexibility, the flat plate-type accumulating equipment is required to reduce the bending of the pot trays, thereby minimizing the seed bounce in the accumulating process [9].  
In this study, flat plate-type accumulating equipment for a roller-type onion pot-seeding machine was developed, and appropriate operating conditions to minimize the seed bounce in the accumulating process were derived.
## 1 Introduction
The volume and velocity of market data require efficient processing mechanisms that can extract accurate patterns while filtering out noise. Figure 1: Example of a modern candlestick chart; screenshot from Ref. [5]. This research addresses these challenges through the development of ElliottAgents, a novel multi-agent framework where specialized agents collaborate to analyze market patterns. Each agent within the system performs a specific function, from data pre-processing and pattern recognition to strategy formulation and reporting, working collectively to enhance analytical accuracy. The system integrates LLMs to enable natural language processing capabilities while implementing RAG to ensure consistent access to domain knowledge [11; 12]. The platform incorporates a continuous learning architecture [13; 14], where DRL enables agents to improve their pattern recognition capabilities through iterative backtesting of historical data. This adaptive learning mechanism allows the system to systematically enhance its predictive accuracy by validating identified patterns against historical market behaviors, effectively combining the structured approach of EWP with the capabilities of modern AI. The practical applications of this research are demonstrated through testing on real market data, resulting in generation of actionable trading signals based on recognized patterns. The system's scalable architecture enables real-time market analysis, while its implementation of dynamic context management allows for improved adaptation to market conditions. The implementation of continuous learning mechanisms enhances pattern recognition accuracy, while maintaining interpretability crucial for market practitioners. Through extensive testing on historical market data, this research validates the effectiveness of combining traditional Elliott wave analysis with modern AI techniques through a multi-agent architecture. The results demonstrate that ElliottAgents provides analyses that can be utilized by both professional analysts and retail investors, contributing to both theoretical understanding and practical applications in financial forecasting. The rest of the paper describes technologies, implementation, and experiments and is structured as follows. Section 2 describes the foundational concepts and technologies underlying our work, including the EWP, LLMs, and DRL. Section 3 presents a survey of prior research in multi-agent systems for financial market analysis. Our multi-agent framework, ElliottAgents, is overviewed in Section 4, detailing its architecture and key components. The experimental setup and performance results of the ElliottAgents system for both stock and cryptocurrency markets are presented and discussed in Sections 5 and 6. Finally, Section 7 concludes the paper and highlights future research directions.
## 3 Components Modeling of Proposed Design
### DC-DC Boost Converter  
The main purpose of DC-DC converters is to regulate the voltage coming out of the battery pack and SC. The battery pack is connected to a unidirectional boost converter which steps up and supplies the voltage from the battery to the BLDC motor. The boost  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**EMF-a** & **EMF-b** & **EMF-c** & **Q-1** & **Q-2** & **Q-3** & **Q-4** & **Q-5** & **Q-6** & **H\({}_{\mathbf{a}}\)** & **H\({}_{\mathbf{b}}\)** & **H\({}_{\mathbf{c}}\)** \\ \hline
0 & 1 & \(-1\) & 0 & 0 & 1 & 0 & 0 & 1 & 1 & 1 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Commutation Logic Table for Gate and Decoder.  
Figure 3: Simulink model of brushless DC (BLDC) motor.  
converter adjusts the DC voltage from the battery pack to different levels to control the BLDC motor at varying speeds.  
The following Equations (9)-(12) were used in designing the boost converter for the proposed system. The boost converter parameters are given in Table 3.  
\[V_{o}=\frac{V_{i}}{1-K} \tag{9}\]  
\[C=\frac{K}{2 Rf} \tag{10}\]  
\[L=\frac{K(1-K)R}{2f} \tag{11}\]  
\[\text{Load Resistance }=\frac{V_{o}}{I_{o}} \tag{12}\]  
where, \(V_{o}\) represents the output voltage, \(V_{i}\) is the input voltage, while \(C\) and \(L\) repre-sent the capacitance and inductance of the boost converter, respectively, \(K\) is the duty cycle of the boost converter, \(R\) is the load resistance, and \(f\) is the switching frequency.
## Problem Formulation  
### Proposed System Model Architecture  
DSM is the entity responsible for the productive and smooth performance of a smart grid. The main function of DSM is the management of energy and controlling of all those activities which take place on consumer side. In this model, we are working on a university campus. Thus, to handle load scheduling in a proper manner, we have divided the university campus load into two sections, morning session and evening session. The morning session is further divided into offices, laboratories, classrooms, lavatories, main halls, a canteen and a library; meanwhile, the evening session consists of a staff hostel, student hostels, a canteen and searchlights, as given in the Table 1.  
As is clear from the model, the campus receives a dedicated line from the distribution grid. In our proposed scheme, we assume that the university campus receives a price signal from the utility according to which it schedules its appliances in order to obtain aforementioned objectives. As mentioned earlier, we have divided the campus into blocks. Labs, classes and offices have some non-shiftable appliances, such as fans, tube lights, energy saver lights and exhaust fans. These appliances cannot be shifted from one-time slot to another time slot during scheduling. Similarly, there are some shiftable appliances, such as geysers, exhaust fans, laboratory equipment, tube well water pumps and water coolers. In order to reduce PAR and electricity cost, we have divided the time horizon of 8 h (the morning session time 8 a.m.-4 p.m.) into eight slots, and the evening session time of 16 h (4 p.m.-8 a.m.) in to 16 slots. Shifting of appliances is basically some kind of trade-off between users waiting time and cost. If a user uses an appliance during peak hours then he must pay a lot. Smart meters are allocated in each apartment for hourly computation of power consumption. The smart meters are responsible for exchanging mutual information between the end users and a utility. For example, the price information will be conveyed from the utility side to the user and corresponding energy consumption data will be sent from demand side to utility. A set of appliances is considered \(App_{n}=App_{1},App_{2},\ldots,App_{n}\). It is assured that every appliance is able to keep its connection with EMC uninterruptedly, and according to the time slots scheduled for the appliances. It is necessary for every appliance to complete its operation in the given time slot. The time duration of the earliest starting time and the latest finishing time must be followed. We make twenty four (24) time-slots for one day; one slot shows one hour. Our suggested system architecture is shown in Figure 1.  
Figure 1: Architecture of our model.
# A Data Secured Communication System Design Procedure with a Chaotic Carrier and Synergetic Observer
## 3 Methodology and the System Design Procedure
### Chaotic Generator Mathematical Model and Two-Channel Chaotic Transmitter Block Diagram  
Let us consider a dynamic chaos generator based on a \"strange\" attractor of [PERSON] [44], which describes the dynamic regimes by the following nonlinear differential equations  
\[\begin{array}{l}\dot{x}(t)=-y-z;\\ \dot{y}(t)=x+ay;\\ \dot{z}(t)=b+xz-cz,\end{array} \tag{1}\]  
where \(x\), \(y\) and \(z\) are state variables, and \(\mu^{0}=[a,b,c]\) is the vector of constant parameters.  
In accordance with the method of global reconstruction of dynamical systems [13], by using the following change of variables  
\[\begin{array}{l}x=X-aY;\\ y=Y;\\ z=aX-Y-Z,\end{array} \tag{2}\]  
we transform Equation (1) to the following form  
\[\begin{array}{l}\dot{X}(t)=Y;\\ \dot{Y}(t)=Z;\\ \dot{Z}(t)=f\Big{(}X,Y,Z,\mu^{0}\Big{)},\end{array} \tag{3}\]  
where  
\[f\Big{(}X,Y,Z,\mu^{0}\Big{)}=(a-1)X-Y+(a-1)Z-aX^{2}-aY^{2}+\Big{(}a^{2}+1\Big{)} XY+c(aX-Y-Z). \tag{4}\]  
The Rossler generator mathematical model in the form of Equations (3) and (4) provides transmission of variables \(x(t)\), \(y(t)\) and \(z(t)\) over a single communication channel with only one new variable \(Z(t)\). In verifying that Equations (1) and (3) are equivalent, in Figure 2 we show attractors of initial Rossler model (1) and reconstructed model (3).  
Thus, the transformed system (3) in the general case contains three unknown parameters \(a\), \(b\), and \(c\), which can be found by constructing a sy  
Figure 2: Rössler chaotic generator attractor for (**a**) initial and (**b**) reconstructed systems.  
As we decided to use a two-channel useful data transmission system, we perform modulation of parameters \(a\) and \(c\) from drive signals so that their value remains within the chaotic dynamic system model. This gives us the new control parameters of the Rossler system as follows.  
\[\begin{array}{l}a^{*}(t)=a+\mu_{1}(t);\\ c^{*}(t)=c+\mu_{2}(t).\end{array} \tag{5}\]  
The condition of chaotic mode existence in the system should be determined in the following intervals.  
\[\left\{\begin{array}{c}0.15\leq a\leq 0.25;\\ b=0.2;\\ 5\leq c\leq 6.\end{array}\right.\]  
So, the transmitter MATLAB/Simulink model structure according to model (3) had the form as in Figure 3. In Figure 3 the block \(dz\)/\(dt\) consists function (4), and inputs \(a\)(\(t\)) and \(c\)(\(t\)) were modulated with useful signals according to (5).  
Figure 4a,b display initial useful signals \(a\)(\(t\)) and \(c\)(\(t\)) with rectangular pulses at the inputs of the transmitter
## 3 Experiments and Results  
In this section, we firstly compare the theoretical derived EMSE in Section 2.3 with the simulated one to confirm the theoretical result. Then, we describe the asymmetry of queuing delay in a satellite network and verify the robustness and the adaptability of the GMACC in a satellite network delay prediction. Note that the experiments in this section are all computer simulations.