esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Sequential Sampling Method  
The concept of the sequential sampling (SS) method is illustrated in Figure 2, and this shows that the surrogate model converges to a true function by way of adding the infill samples to the current training samples. A core technique in the SS method is for the infill criteria to search the infill samples and refine the surrogate models for its own purpose (for example, the optimization or global quality of the surrogate model). There are numerous studies to evaluate and compare the infill criteria for sequential surrogate sampling [11; 15; 20; 21; 22]. These infill criteria include the mean squared error (MSE) [14], expected improvement (EI) [16], the probability of improvement (PoI) [23], statistical lower bound (SLB) [24], and so on. The PoI and SLB criteria are not suitable for the global surrogate modeling since they were developed to improve the prediction of the best value (pure exploitation). The MSE criterion can be used to add infill samples to the region that has high uncertainty in the predictions of the surrogate model (pure exploration), whereas the EI criterion can add infill samples for both exploitation and exploration of the response surface (balanced exploitation and exploration). Both criteria can be used as multiple-update-infill sampling methods to accelerate the surrogate modeling [10; 15]. In this study, a minimum energy design (MED) is investigated to evaluate its feasibility for the SS method. The MED was recently developed to find multiple alternative optima rather than to accelerate the search for global optimum [12]. This section describes three infill criteria for the multiple-update-infill sampling method to improve the global quality of the surrogate model. These infill criteria are provided with their concepts in the following sequential order: (1) the mean squared error (MSE), (2) expected improvement (EI), and (3) the minimum energy design (MED).
# Distributed Detection of Large-Scale Internet of Things Botnets Based on Graph Partitioning
## 3 Background
### Botnet Architecture  
A botnet refers to a network formed by infecting a large number of hosts with one or more propagation methods, enabling one-to-many control between the controller and the infected hosts. Based on the command and control (C&C) structure of botnets, the transmission of commands and critical information to the bots is defined, leading to three types: centralized, decentralized, and hybrid.  
Centralized botnets use a client/server (C/S) model where botnet hosts actively send requests to a limited number of C&C servers. The attacker distributes control commands and program resources through these C&C servers, as shown in Figure 2. However, the centralized topology has a fatal flaw, which is a single point of failure. If a C&C server is discovered, the botnet hosts cannot receive instructions and stop working. While this benefits white-hat security professionals maintaining network security, it is detrimental to malicious hackers seeking to cause disruptive security events.  
To overcome the single point of failure in centralized botnets and improve their robustness and flexibility, attackers have applied peer-to-peer technology, forming decentralized botnets known as P2P botnets, as shown in Figure 3. In P2P botnets, bot nodes act as both clients and servers, and each node is equal. Although command dissemination in P2P botnets is slower, the communication process does not rely on a specific node, making them difficult to measure, hijack, and shut down. P2P botnets have better scalability and robustness compared to traditional C/S structures but are more challenging to eliminate due to their large scale and defense difficulty.  
Figure 2: Centralized botnet.  
Figure 4 shows the structure of a hybrid botnet, which combines both centralized and decentralized elements, incorporating the advantages of both. An example is Pinkbot [46], which uses a P2P approach for less time-sensitive instructions and employs a C&C mode for distributing time-critical commands. The experimental dataset used in Section 5 consists of both centralized and decentralized botnets.  
Given the widespread use of IoT technology, detection methods for IoT botnets should possess distributed detection capabilities and utilize computational resources effectively. Various devices should be able to parallelize detection within the maximum allowable range rather than relying solely on a single server to handle massive data detection.
# Evaluation of Viscoelastic and Rotational Friction Dampers for Coupled Shear Wall System
## 3 Shake Table Test of Six-Story Coupled Wall System with Damper
### Experimental Test Results of the Six-Story Frame
#### 3.5.3 Results Under Kokuji Waves  
Figure 19 shows the maximum displacement response under Kokuji wave excitations at varying intensity levels (ranging from 20% to 100%, corresponding to 70 gal to 350 gal) for three configurations: the frame and shear wall, the frame and shear wall equipped with an RF damper, and the frame and shear wall with a VE damper. The results demonstrate that the RF damper consistently reduces story displacement by approximately 40% across all intensity levels, highlighting its robust energy dissipation capabilities, particularly under higher excitation intensities. In contrast, the VE damper exhibits a progressive improvement in displacement reduction, achieving a 22% reduction at Kokuji 20% and increasing to 35% at Kokuji 80% intensity, indicating its enhanced effectiveness in mitigating displacement as excitation levels increase.  
Figure 20 shows the maximum acceleration response under Kokuji wave excitations at progressively increasing intensities. The RF damper demonstrates consistent acceleration control, reducing peak acceleration responses by approximately 12% to 18% across all intensity levels. In comparison, the VE damper exhibits an intensity-dependent performance, showing moderate acceleration reduction of up to 18%. These findings under-score the superior performance of the RF damper in high-intensity scenarios, while the VE damper demonstrates reliable and adaptive performance across varying load conditions. At higher seismic intensities, the performance differences between RF and VE dampers become more pronounced due to their distinct energy dissipation mechanisms. RF dampers rely on sliding friction, where the resisting shear force is capped by the friction force, preventing excessive load transfer to the structure. The RF dampers dissipated energy by sliding deformation with constant friction force. This makes them particularly effective in high-intensity scenarios, as their limiting force prevents structural overloading while maintaining consistent energy dissipation. In contrast, VE dampers generate shear forces that are velocity and displacement-dependent, as written in Equation (4), meaning that their resistance increases with higher deformation rates. While this adaptive behavior allows VE dampers to efficiently dissipate energy across varying load conditions, their force output can rise significantly under extreme shaking, potentially leading to higher structural demands.  
Figure 18: The maximum acceleration under sinusoidal waves: (**a**) 50 gal, (**b**) 100 gal.  
Figure 19: The maximum displacement under Kokuji waves: (**a**) 20%, (**b**) 40%, (**c**) 60%, (**d**) 80%, (**e**) 100%.
A very important aspect is increasing the accuracy of determining the position of designated vehicles using GNSS. To this end, it is worth applying techniques and methods for aligning observation results used in land surveying. The literature provides examples of the application of land surveying alignment methods in rail transport, e.g., [37] which described the application of the conditional observation alignment method or referred to the estimation of the accuracies obtained during traditional static surveys [38]. In [38] the authors describe the use of automated measurement systems based on trolleys for track surveying supported with automatic tacheometric surveys. Moreover, there are examples of the application of geodetic alignment methods in maritime transport, e.g., [39, 40]. These articles present the results of work related to the adaptation of a surveying method for aligning observation results, known as the method of least squares with conditional equations in mobile surveys. The alignment process was checked using the records of GNSS signals during the mobile surveying campaign conducted in July 2019 on a 246-km-long section between Tczew-Chojnice-Brusy-Chojnice-Tczew located in northern Poland.
## 3 Proposed Method
### Pseudocode
#### 3.6.1. Initialization (Executed by All Nodes)  
1. \(colour=white\)
2. \(C_{id}=-1\)
3. _Broadcast Beacon\((id,\)\(s,\)\(dir,\) colour, \(C_{id})\) periodically_  
3.6.2. Node '\(u\)', upon Receiving Beacon Message from Node \(v\) (Executed by All Nodes)  
1. \(if\)\(dir=dir_{v}\)then
2. \(if\)\(v\
otin N_{u}\) then \(N_{u}=N_{u}\cup\{v\}\)
3. \(if\)\(colour=black\) and \(colour_{v}=grey\) then execute Section 3.6.5
4. \(if\)\(C_{id}=-1\) and \(colour_{v}=black\) then \(C_{id}=v;\ colour=blue\)
5. \(else\)\(if\ colour=blue\) then \(C_{id}=C_{id}+v;\ colour=grey\)  
3.6.3. Calculate Degree of Cohesion (Executed by All Candidate Cluster Heads)  
1. \(DC=\frac{1}{|N_{u}|}\ \sum_{j\in N_{u}}\left(\frac{min(S_{u},\ S_{v})}{max(S_{u},\ S_{v})} \ast\frac{1}{d_{ave}}\right)\)
2. _Broadcast DC up to 2-hop neighbours_  
3.6.4. Upon Receiving Messages from Candidate Cluster Heads (CCH) within 2-Hop Neighbors (Executed by Candidate Cluster Heads Only)  
1. \(\forall v\in CCH\ if\ max(DC_{v})=DC\ then\ color=black;\ C_{id}=id\)
2. _Broadcast CH message to neighbours_  
3.6.5. Upon Receiving Neighborhood Information from Candidate Gateways (CG) (Executed by Cluster Heads Only)  
1. \(Add\)\(id\)\(to\)\(CG\)
2. \(Split\)\(C_{id}\) and add them to \(CH_{g}\)  
3.6.6. After Receiving Neighborhood Information from all Candidate Gateways (CG) in Its Neighborhood (Executed by Cluster Heads Only)  
1. \(CH_{covered}=\varnothing\)
2. \(CH=\bigcup_{g\in CG}CH_{g}\)
3. \(While\)\(CH_{covered}\
eq CH\)
4. \(For\ each\ g\in CG\)
5. \(CH_{diff}=CH_{g}-CH_{covered}\)
6. \(id=\max\left(|CH_{diff}|\right)\)
7. \(GW_{selected}=GW_{selected}\cup id\)
8. \(CH_{covered}=CH_{covered}\cup CH_{g}\)
9. _Broadcast \(GW_{selected}\)_  
3.6.7. Upon Receiving Gateway Selection Message: (Executed by Candidate Gateways only)  
1. \(if\ id\in GW_{selected}\) _then color \(=\) red else color \(=\) blue_  
3.6.8. When a Clustered Node u Did Not Receive a Beacon Message from Its CH for Continuous Three Beacon Intervals: (Executed by All Nodes)  
1. \(color=white\)
2. \(C_{id}=-1\)
3. \(for\ each\ v\in N_{u}\)4. \(if\)\(dir=dir_{v}\) then
5. \(if\)\(color_{v}=black\) then \(C_{id}=v\); \(color=blue\); and exit
6. \(for\) each \(v\in N_{u}\)
7. \(if\)\(dir=dir_{v}\) then
8. \(if\)\(color_{v}=grey\) then \(color=cyan\); and exit
9. \(color=yellow\) and execute Sections 3.6.3 and 3.6.4
# Sensory Profile, Consumers' Perception and Liking of Wheat-Rye Bread Fortified with Dietary Fibre
## 2 Materials and Methods
### Consumer Study  
Consumers sensory evaluation of bread was performed in two evaluation condition: blind and informed. In blind condition participants had to assess liking of appearance, odour, taste/flavour, texture, and overall liking of each bread sample with 4%, 8%, 12%, 16%, 20% of flour replaced with oat fibre using a 9-point hedonic scale ranging from \"I dislike extremely\" to \"I like extremely\" (1--dislike extremely, 2--dislike very much, 3--dislike, 4--dislike slightly, 5--neither like nor dislike, 6--like slightly, 7--like, 8--like very much and 9--like extremely). Control sample without DF was also included in the profiling.  
Both crust and crumb were analysed together for the taste/flavour and texture attributes, whereas appearance attributes were assessed on the crumb only.  
Additionally, participants were asked to evaluate healthiness, naturalness and attractiveness of each sample using a 7-point scale ranging from 1--\"low healthiness\", \"low naturalness\", low attractiveness\" to 7--\"high healthiness\", \"high naturalness\", high attractiveness\", and declare if they are willing to buy such bread indicating maximum price they would pay for a standard package of 700 g.  
To determine the impact of information on consumers' perception and liking of DF fortified bread, they were presented with samples 8%, 12% of flour replaced with oat fibre and a control sample. The samples were labelled with two types of information either with the nutritional information \"source of dietary fibre\" or the health-related information \"dietary fibre helps to maintain appropriate body mass\" (Table 1).
## 4 Methodology  
### General Model Framework  
The schematic diagram of the model we designed is illustrated in Figure 1. Broadly, the model can be divided into two main components: the first part is the graph diffusion augmentation section and the second part comprises the neighborhood information aggregation layers of the Graph Neural Network (GNN), along with the attention layers for each neighborhood aggregation information, and the readout layer that consolidates the overall representation of the computation graph. Initially, the operational parameters of the automated production workshop's processing center are structured into a graph dataset, and the original graph dataset undergoes graph diffusion augmentation to generate additional expanded data. These data then enter the GNN for the neighborhood information aggregation section, where, through multiple iterations and multistep neighborhood information aggregation, preliminary node representations are obtained. Subsequently, after attention operations and the readout layer, the final graph feature representation is acquired. The classifier then produces probability outputs.  
Figure 1: The overall framework of GraphDHNA model. The original data are constructed into an undirected graph, which is enhanced by diffusion and inputted into the neighborhood information aggregation layer, in which hierarchical aggregation and hop-attention allocation are carried out, and then the diagnosis results are obtained through the readout layer and classifier.
# Identification of Water Hammering for Centrifugal Pump Drive Systems
## 2 Methodology
### Proposed Method  
Water hammering is a critical problem in pumping systems and recognized as a hydraulic shock when a pressure surge and wave are created in the fluid, and there is sudden momentum change.  
In this work, the pressure head for each valve is measured at different pressures. As the flow rate of the pumping system changes and the velocity of water also changes, the pressure head of the pumping system also changes. When the pumping system pressure head becomes less than the vapor pressure head of the pump, water hammering occurs. Every pump has its own net positive suction head (NPSH). Vapor pressure head is a temperature-dependent gas pressure head. The vapor pressure is that pressure when vapor and liquid are in the equilibrium phase. If it exceeds the value of suction pressure, the flow rate will change, velocity will change, and the overall momentum of the system will change. For this sudden change, the pressure head will decrease and becomes less than the vapor pressure head; a cavitation problem occurs, and with this cavitation problem, due to velocity changes, water hammering also occurs. The pressure value of the pumping system aims to convert the pressure-head value, which has a strong relationship with the flow rate of the pumping system. In this work, a fault is created in the pumping system by the sudden external closing and opening of the valve. After feature extraction, the velocity of water and pressure head of the pumping system values are used as training and testing datasets, and a training model verifies whether the suction pressure head is less than the vapor pressure head or not. If the suction pressure head is less than the vapor pressure head, then the cavitation problem occurs such that, in the proposed method, a water hammering problem is also seen. Otherwise, no-fault will be there (Figure 2). Along with the cavitation problem, if the velocity of the pumping system suddenly changes and flow rate also changes, then a water hammering problem occurs. In the proposed method, both problems are seen, but as the paper concentrates on water hammering problems only; the other problem is not discussed. Here, P is the pressure and V\({}_{\text{P}}\) denote the vapor pressure of the pump. Pressure, speed, acceleration, velocity, and flow rate of the pumping system have been collected to detect water hammering faults, which are taken as input variables for this experimental research investigation.  
Figure 2: Generalized algorithm of water hammering.
# Large Scale Implementations for Twitter Sentiment Classification
## 4 Sentiment Analysis Classification Framework
### Algorithmic Description
#### 4.4.4 Sentiment Classification  
This is the final step of our proposed approach. In this job, we aggregate for all feature vectors \(u\) in the test set, the \(k\) vectors with the lowest Euclidean distance to \(u\), thus forming \(V_{k}\). Then, we assign to \(u\) the label (class) \(l\in L\) of the majority of \(V_{k}\), or the \(neu\) label if \(V_{k}=\emptyset\). The Algorithm 4 is given below.  
```
1:Input: Feature Vectors in the test set
2:functionMap(\(k1,v1\))
3:\(t\_id=\textbf{get}\textbf{TweedIdx}(v1);val=\textbf{getValue}(v1)\);
4:output(\(t\_id,val\));
5:endfunction
6:functionReduce(\(k2,v2\))
7:\(l\_k=\textbf{getKNN}(v2)\);
8:\(H=\textit{HashMap}<\textit{Class}\textit{,Occurences}>\{\}\);
9:\(H=\textbf{findClassOccur}(l\_k)\);
10:\(max=0\);max(\textit{class}=\textbf{null}\);
11:for all\(entry\in H\)do
12:if\(entry.occur>max\)then
13:\(max\textit{-entry.occur}\);
14:\(max\textit{Class}=\textit{entry.class}\);
15:endif
16:endfor
17:output(\(k2,maxClass\));
18:endfunction
```  
**Algorithm 4**MapReduce Job 4  
The _Map_ function is very simple and it just dispatches the key-values pairs it receives to the _Reduce_ function. For each feature vector \(u\) in the test set, the Reduce function keeps the \(k\) feature vectors with the lowest distance to \(v\) and then estimates the prevailing sentiment label \(l\) (if exists) among these vectors. Finally, it assigns the label \(l\) to \(u\).
## 3 Test Results and Discussion
### Load-GFRP Bar Tensile Strain Relationships  
Strain gauges were fixed at the upper surface of the deck, specifically at the junction between the barrier and the deck, for all specimens to assess the tensile strain developed in the GFRP bars. Figure 12 illustrates the tensile strains observed in the diagonal GFRP bars located at the front face of the barrier under loading conditions. It is important to note that only three strain gauges were utilized for the interior specimens (B-1, B-2, and B-3), while six strain gauges were employed for the specimens at the end locations (B-2, B-4, and B-6). The average tensile strains recorded in the GFRP bars were 6504, 4765, 5802, 5495, 15,848, and 7226 \(\mu\varepsilon\) for specimens B-1, B-2, B-3, B-4, B-5, and B-6, respectively. According to the manufacturer's certification data sheet, the ultimate strain for the GFRP bars is 16,700 \(\mu\varepsilon\). Consequently, none of the specimens reached the maximum tensile strain at the measurement location, except specimen B-5. Although the experimental ultimate strain observed in specimen B-5 was slightly lower than the specified ultimate strain for the GFRP bars in the manufacturer's datasheet by 5%, tensile rupture did not occur in the GFRP. This is attributed to the fact that the design values provided by the manufacturer are based on statistical data, which indicates that the characteristic tensile strength of the bar is lower than the mean value.  
Figure 12: Load–GFRP bar strain relationships for all specimens.
[PERSON] the control system can intelligently achieve the established goals and ensure the real-time control of the system. The combination of machine learning (ML) and MPC has better performance in path tracking control. In [20; 21], the learning-based model predictive control (LB-MPC) is applied to the path tracking control in mobile platforms. During the flight, a dual extended Kalman filter was used as a method for learning by the quadrotor to learn about its uncertainties, while an MPC was used to solve the optimization control problem. The LB-MPC technique rigorously combines statistics and learning with control engineering while providing levels of guarantees about safety, robustness, and convergence. It handles system constraints, optimizes performance with respect to a cost function, uses statistical identification tools to learn model uncertainties, and provably converges. Afterwards, the LB-MPC method was researched [22; 23; 24], and different schemes were designed [25; 26]. There are various ML techniques that have been explored and applied to MPC, such as regression learning [27], reinforcement learning [28], and deep learning [29; 30]. In [31], LB-MPC is reviewed from the perspective of security control, and the current applications of LB-MPC in the control field have been discussed. The LB-MPC method has been demonstrated to result in competitive high-performance control systems and has the potential to reduce modeling and engineering effort in controller design. The development process of LB-MPC is shown in Figure 1.  
The paper is organized as follows. Section 2 summarizes the LB-MPC technique that combines the advantages of ML and MPC. Section 3 introduces the application of path tracking control in mobile platforms by considering three aspects. Section 4 discusses some research challenges faced by LB-MPC. Finally, Section 5 concludes this paper regarding the learning-based model predictive control technique and its application in the field of mobile platforms for path tracking control.