esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Improved NSGA-II
### Fast Non-Dominated Sorting and Crowdedness Calculation  
(1) Fast non-dominated sorting  
The initialization population is subjected to non-dominance sorting. The Pareto dominance relation is as follows: There are two target components, \(f_{1}(x)\) and \(f_{2}(x)\), and given two individuals \(X_{a}\) and \(X_{b}\) arbitrarily, individual a dominates individual b if the following two conditions hold. (1) For \(\forall m\in 1\), 2, both have \(f_{m}(X_{a})\leq f_{m}(X_{b})\). (2) \(\exists m\in 1\), 2 such that \(f_{m}(X_{a})<f_{m}(X_{b})\). If no one individual dominates it, it is referred to as a non-dominant solution. The Pareto rank of a non-dominated solution in a collection of solutions is 1. The Pareto rank of the remaining solutions is defined as 2, and so on, and all the Pareto ranks are obtained by eliminating the non-dominated solution from the collection of solutions.  
After non-dominance sorting, if there were initially 25 (1-25) individuals in the population, these 25 individuals are separated into multiple Pareto ranks, with rank 1 = (1, 3, 5), rank 2 = (2, 12, 13, 14), rank 3 = (4, 6, 8, 10, 19), rank 4 = (7, 11, 16, 18, 22, 25), and rank 5 = (9, 15, 17, 20, 21, 23, 24).  
(2) Crowding degree  
The crowding degree \(x\) is introduced to ensure that the set of Pareto solutions can be constrained to a dispersed and uniform Pareto front to make the obtained solutions more homogeneous in the target space. The formula is shown below:  
\[n_{d}=\sum_{m=1}^{o}\frac{(f_{m}(z+1)-f_{m}(z-1))}{f_{m}^{max}-f_{m}^{min}} \tag{19}\]  
where \(f_{m}(z+1)\) and \(f_{m}(z-1)\) are the values of the (z + 1) and (z - 1) individuals in the \(m\) target component, respectively, and \(f_{m}^{max}\) and \(f_{m}^{min}\) are the maximum and minimum values of the current target component. \(o\) is the total number of target components.  
Figure 6: Small-scale neighborhood structure process.
# Comparison between Laser and Stamping without Die (SWD) for Micro Tapered Hole Forming
## 4 Experimental Results and Discussion
### Microhole Morphology
#### 4.1.2 Laser Processing  
In terms of the experimental parameters of laser drilling, the laser pumping energy is 16, 17, 18 J, and the defocusing distance is +0.5--0.5 mm. Al6061 has a thickness of 300 \(\upmu\)m and is perforated by laser thermal ablation. The pulse required for perforation is recorded, and the microhole size is measured.  
The inlet and outlet morphologies of the laser processed holes are as shown in Figure 13. When the defocusing distance of the process is increased, the laser focus spot diameter increases, and the depth of the field increases accordingly. When the defocusing distance is +0.1-+0.5 mm, laser energy generates a keyhole due to high power density. Meanwhile, the melted material stack around the keyhole. The Heat affected zone (HAZ) width increases due to the defocusing. When the defocusing distance is \(-\)0.1--0.5 mm, the power density of laser is not diffracted along its path as positive defocusing, thus the hole diameter is smaller. Meanwhile, the required number of laser pulse for perforation is less with negative defocusing than with positive defocusing as shown in Figure 14 it shows the relationship between defocusing distance and required pulse number for perforation. The processing time for a focal length of \(-\)0.5 mm is the shortest, and may be because the energy density of negative defocus is higher than the positive defocus. The relationship between hole depth and defocusing distance has a similar trend, where the hole depth is inversely proportional to the defocusing distance. As shown in Figure 15, while there is an inlet diameter jump with 18 J laser processing and no obvious change in outlet diameter with 16 J laser processing, the inlet and outlet diameters increases with defocusing distance in other cases. Therefore, the taper angle of holes increases with the defocusing distance. The maximum taper angle is about 9.7\({}^{\circ}\). At the same defocusing distance, a negative defocus results in a larger HAZ width than a positive defocus and it is due to higher energy density.
# Structural Augmentation in Seismic Data for Fault Prediction
## 2 Methods
### Adding Virtual Faults
#### 2.2.1 Fault Displacement  
Different from conventional methods [68; 96], two additional parameters are applied to the fault displacement simulation in this study (Figure 4): the rotation angle of the fault displacement \(\Psi_{dis}\) and the rotation angle of the fault attenuation ellipse \(\Psi\). In nature, there are not only many dip-slip faults [97] but also many strike-slip faults [98]. Therefore, different strike-slip and dip-slip displacements can be combined on a fault surface to produce arbitrary oblique-slip directions. In addition, although the attenuation of fault displacement is widely considered to satisfy an elliptic equation [99; 100; 101], it has also been shown that the attenuation ellipse may have different rotation angles in different faults [102; 103]. We therefore model the near-field displacement of faults by the following equation:  
\[d(\tilde{X},\tilde{Y})=2 Amp_{d}(1-r(\tilde{X},\tilde{Y}))\sqrt{\left(\frac{1+ r(\tilde{X},\tilde{Y})}{2}\right)^{2}-r(\tilde{X},\tilde{Y})^{2}}\]  
\[r(\tilde{X},\tilde{Y})=\sqrt{\left(\frac{\cos\Psi(\tilde{X}-\tilde{X}_{0})- \sin\Psi(\tilde{Y}-\tilde{Y}_{0})}{L[\tilde{x}]}\right)^{2}+\left(\frac{\sin \Psi(\tilde{X}-\tilde{X}_{0})+\cos\Psi(\tilde{Y}-\tilde{Y}_{0})}{L[\tilde{y}] }\right)^{2}}\enspace, \tag{6}\]  
\[d(\tilde{X},\tilde{Y})[\tilde{x}],=d(\tilde{X},\tilde{Y})\sin\Psi_{dis}\qquad d (\tilde{X},\tilde{Y})[\tilde{y}]=d(\tilde{X},\tilde{Y})\cos\Psi_{dis}\]  
where the parameters \((\tilde{X}_{0},\tilde{Y}_{0})\), \((L[\tilde{x}],L[\tilde{y}])\) and \(Amp_{d}\) are the classical parameters of the displacement attenuation equation, controlling the center, radius, and amplitude of the  
Figure 3: Four parts of virtual fault structures. (**a**) Reference plane. (**b**) Near-field displacement. (**c**) Fault surface. (**d**) Fault drag.  
near-field displacement, respectively. The parameters \(d(\bar{X},\bar{Y})[x]\) and \(d(\bar{X},\bar{Y})[g]\) represent the components of displacement in the strike-slip direction \(\bar{X}\) and the dip-slip direction \(\bar{Y}\), respectively. On the basis of these classical parameters, numerous displacement patterns of oblique-slip faults can be simulated by different combinations of displacement rotation angles \(\Psi_{dis}\) and attenuation ellipse rotation angles \(\Psi\).
# Relative Positioning in Remote Areas Using a GNSS Dual Frequency Smartphone
## 4 Results  
Differential GNSS solutions for the Galileo + GPS combination and for the GPS-only and Galileo-only observations were calculated with the RTKLPOST software, using the different CORS reference stations listed in Table 1. The measurements were processed using the double-difference method as referred to in Section 2 and applying an Extended Kalman Filter (EKF) algorithm to calculate the final solutions. Tables in Sections 4.2-4.7  
Figure 14: RMS results for Galileo + GPS combination at Test 2.  
Figure 15: RMS results for Galileo + GPS combination using the smartphone (**a**) and Septentrio PolaR5 (**b**) at Test 3.
# Using Ground-Based Passive Reflectors for Improving UAV Landing
## 2 Navigation Systems for UAV Landing  
### Instrumental Landing System (ILS)  
An ILS [36; 37] consists of two main parts: the ground part, that sends the signal, and the airborne part, that receives and processes it. The ground equipment (see Figure 1) includes the following: a directional beacon (DB), glide slope beacon (GSB), inner marker (IM), middle marker (MM), and the outer marker (OM). The aircraft equipment is a set of two radio receivers with directional aerial antennae (the directional one and the glide path one). The directional and glide slope beacons are installed next to the runway: the directional beacon--near the opposite end of the runway along the centerline; the glide slope beacon--on the side of the runway abean the landing point from the runway threshold.  
Flight director systems (that determine the aircraft location relative to the glide path and display it) are sensitive to the signals from the ILS, that are distorted by the various objects in the vicinity, such as houses, hangars, and nearby planes and cars, which can produce significant interference [27]. Ground slopes, hills, and other terrain uneveness, can also reflect the signal and, thus, interfere with the readings. All of these factors show the limits of the ILS's reliability. For the ILS to function normally [38], additional regulations related to plane movement on the ground have to be implemented.  
There exist some factors pertaining to the glide path selection and aircraft descent that limit the utility of the ILS [39; 40]. The main limitation is the absence of the optimal glide path for each specific aircraft, which necessitates sticking to a single glide path, with the vertical 3-degree approach slope. Besides that, the location of the glide path beacon and its relatively high frequency make it impossible to factor in the angles of deviation during the final approach phase corresponding with the inner glide path beacon area (in this area,  
Figure 1: Glide-path-forming principle in ILS with the schematic positions.  
the antenna array radiation pattern is yet to be formed). That is why a radio altimeter is commonly used for landing instead.
# A Review: Factors Affecting Internal Two-Phase Flow-Induced Vibrations
## 3 Factors Affecting Dominant Frequency
[66] observed a similar behavior in their experimental study, considering that this numerical study used their experiments for validation purposes. [PERSON] et al. [71] and [PERSON] et al. [97] reported the presence of fluid effect on natural frequencies of pipes. [PERSON] et al. found that this effect is more dominant in small diameter pipes when compared to large diameter pipes. The natural frequencies of the pipe increased with the increasing gas volume fraction. In large diameter pipes, the risk of resonance was higher since force fluctuations have low frequencies, which is equivalent with the observed low natural frequencies of large pipes. Furthermore, [PERSON] et al. [97] reported that increasing the fluid density decreases the natural frequencies of the pipe. [PERSON] and [PERSON] [63] performed spectral analysis of excitation forces and reported that the excitation component of forces always lies below 60 Hz, which agrees with the findings of [PERSON] and [PERSON] [8]. Analysis of force spectra showed that predominant frequency increases with the increase in mixture velocity for a given void fraction. The predominant frequency is very clear for flows with intermediate void fraction values (50% and 75%). [PERSON] and [PERSON] [63] also proposed a formula to determine dimensionless frequency, \(\overline{f}\), using Equation (15), to correlate the dimensionless force and frequency data, as follows:  
\[\overline{f}=\frac{fD}{V_{m}} \tag{15}\]  
In conclusion, the literature shows that dominant frequency increases with an increase in the mixture velocity and decreases with an increase in pipe diameter. The frequency range lies between 0 and 60 Hz. To investigate FIV successfully, the vibration of experimental flow loop should be diminished, and the frequency of experimental loop must be determined to avoid resonance. Moreover, while evaluating the natural frequency of pipe structure, the effect of added mass due to fluid should not be ignored. The literature on this phenomenon is very limited and requires further research. Furthermore, it should be noted that removing the risk of resonance does not abolish the risk of fatigue failure or excessive stress. Stress calculations are recommended to be performed in detail and the force magnitude and frequency equation can be correlated with stress to illustrate resonance and fatigue.
# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
## 2 Materials and Methods
### Roti  
ROTI was defined by [PERSON] et al. [29] to evaluate the occurrence of scintillation in the ionosphere. ROTI is estimated at the sampling rate of 1/30 Hz and a time interval of 5 min, as originally defined by [PERSON] et al. [29]. Additionally, a moving average is applied in the ROTI calculation so that its time interval is consistent with other parameters.
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 5 Methodology
### Structural Analysis
#### 5.2.2 Seismic Analysis  
Seismic performance evaluation is conducted using static or dynamic analysis methodologies, depending on the complexity of the structural system:  
* Nonlinear static pushover analysis is frequently utilized to estimate structural capacity under increasing lateral loads until a predefined target displacement is reached [50];
* Advanced pushover techniques, such as adaptive pushover analysis and modal pushover analysis, are employed to capture higher-mode effects and progressive damage patterns more accurately [49];
* Time-history analysis may be implemented for structures requiring a more precise representation of inelastic seismic behavior [3].
## 4 Experiments and Results
### Results and Analysis
#### 4.4.3 Experimental Results of Different Datasets  
For the accuracy metric, we compare our model with three baselines using different datasets in different areas, such as insurance, laws, and so on. The experimental results are shown in Figure 8. Compared with these baselines, which use a neural network or multi-networks for answer selection, our model could achieve better performance with an accuracy of 71.4%.  
For the MAP metric, we compared our method with three more baselines. As shown in Figure 9, the improved model is more accurate in extracting location information. The Bi-LSTM gating mechanism incorporates the features extracted by self-attention and the feature representation extracted by convolutional attention, which contains both global information and local features and improves the feature extraction capability. Therefore, the semantic feature extraction ability of the model in this paper is stronger in building a better answer selection model. The feasibility of the model is verified.
## 4 Results  
The NeuroPredict platform, currently undergoing experimental evaluation in a laboratory setting, has progressed through several development stages, focusing on high-fidelity multimodal data acquisition, modular integration, and AI functionality. Although it has not yet been deployed in clinical environments, the platform has reached a high level of technical maturity, allowing its architecture, signal processing workflows, and inference mechanisms to be evaluated under controlled conditions. Main implementation achievements and technical insights gained throughout the current development phases are included in the findings shown in this section.  
All results are derived from internal testing in a lab environment with simulated user interactions. The purpose was to assess the functional readiness of the platform's core modules--data acquisition, edge/cloud transmission, ML-based inference, and dashboard visualization.
# Designing and Managing a Smart Parking System Using Wireless Sensor Networks
## 3 Architecture of the Smart Parking System
Once a car has just parked, the sensor node detects the presence of the latter and sends the occupied state of this space with both identifiers to the gateway. Afterwards, this information will be sent and saved in the central server. To better illustrate the process of managing parking spaces in different parking areas, Algorithm 3 shows the operations performed by the sensor nodes in each parking area. ```
1: For i = 1 to N do // N: Number of parking areas in the city
2: For j = 1 to M do // M: Number of parking spaces in each parking. M change according to the parking area
3: If (Sensor node in (IDzi,IDpj) detects the presence of a vehicle) then
4: send occupied state of the parking space to the gateway to update it in the central server
5: Else
6: send available state of the parking space to the gateway to update it in the central server
7: End if
8: End for
9: End for
```  
**Algorithm 3** Algorithm surveillance parking spaces in different parking areas by sensor nodes. The wireless communication between the different sensor nodes and the gateway acts a crucial role in the deployment of the WSN network in the outdoor car parks, where much of the energy consumed by the nodes is highly dependent on the energy consumed during wireless communication:  
\[E_{Sensor}=E_{Sens}+E_{Proc}+E_{Comm}\]  
\[E_{Sensor}\approx E_{Comm}\]  
where \(E_{Sens}\) is the energy consumed during the detection process, \(E_{Proc}\) is the energy consumed during the treatment process, and \(E_{Comm}\) is the energy consumed during the communication process. To detect the availability of parking spaces in an outdoor parking lot in an efficient and reliable manner, the system must deploy one of the most used wireless communications technologies (Wi-Fi, Bluetooth, ZigBee, etc. ), so to minimize the energy consumption of the nodes and to increase the longevity of the WSN network. The networks based on ZigBee technology generally consume less energy compared to Wi-Fi and Bluetooth networks. The following table (Table 1) shows the advantages of ZigBee over other wireless communications technologies. On the other hand, the deployment of the WSN sensor network in outdoor car parks obliges sensor nodes to be installed in the ground, where the signal propagation is obstructed by cars and is affected by their noises. For this type of application, many previous works have shown that conventional wireless standards, such as Bluetooth or Wi-Fi, are not suitable for this type of network in terms of quality and performance compared to the ZigBee standard based on some parameters such BER (bit error rate) and SNR (signal-to-noise ratio) [19; 20; 21]. The following figure demonstrates the performance of ZigBee compared to other types of networks (Figure 10). For this reason, we will use ZigBee wireless communication technology, adopted by the proposed self-organization protocol, to extend the battery lifetime of the sensor nodes and to increase the longevity, efficiency, and performances of the WSN network in the outdoor car parks.
# Detecting DDoS Attacks in IoT-Based Networks Using Matrix Profile
###### Abstract  
The Internet of Things (IoT) is a swiftly developing technology in all sectors, with the number of devices that connect to the Internet has increased remarkably in recent years. However, most of these devices use cheap hardware and lack a concrete security defence system. This may encourage hackers to recruit these devices and use them to launch Distributed Denial of Service (DDoS) attacks, which is one of the main causes of concern among security engineers. This paper investigates the possibility of using a matrix profile to detect DDoS attacks in an IoT-based environment. According to our empirical experiments, the preliminary findings illustrate that the matrix profile algorithm can efficiently detect IoT-based DDoS attacks.  
Distributed Denial of Services (DDoS) attacks; Internet of Things (IoT); matrix profile; smart cities; anomaly detection +
Footnote †: journal: Applied  
DDoS attacks, including but not limited to revenge, damaging competitors, entertainment, personal challenges, etc. [10]. There are many kinds of DDoS attacks, including SYN flood, slow read attack, Mirai botnet, TTL expiry attack, etc. [11]. There are also many defence techniques that have been utilised to mitigate the effects of DDoS attacks, including but not limited to firewalls, routers, switches, upstream filtering, blocking ports, and intrusion-prevention systems [10; 12; 13]. However, most of these techniques have not been evaluated with respect to the simplicity and scalability of the methodology. Additionally, most of these solutions work on the victim's side.  
In this paper, we investigate the possibility of using the method of data structure and associated algorithms called \"matrix profile\" to detect DDoS attacks in smart cities. Using matrix profile can solve the problem of detecting IoT-based traffic anomalies (DDoS attacks) in smart cities. This study utilises matrix profile to identify the malicious behaviour of DDoS attacks via discovering respective symptoms rather than monitoring traffic details. The main reason behind this is that malicious behaviour can be characterised by a set of symptoms, such as high CPU utilisation, an increase in voltage usage, growth in the number of tasks, high memory usage, etc.; it could be challenging to detect DDoS attacks at the source (attacker side) as it is difficult to distinguish between legitimate and malicious traffic. Three IoT-based devices are used in this case study to launch a DDoS attack. The findings show that the accuracy of the matrix profile algorithm ranges from 93.33% to 100% when using time-series data, whereas the accuracy range is 80-100% when using Z-normalised time-series data. Additionally, the results show that using Z-normalised data with matrix profile results in a shorter calculation time.
# Development of a Stock Price Prediction Framework for Intelligent Media and Technical Analysis
## 2 Materials and Methods
### Methodology
#### 2.3.1 Sentiment Classifier  
Data from Twitter was first collected using an API called GetOldTweets [20] and then taken through the pre-processing stage, including labelling, to prepare for the sentiment classification.  
Step 1, Twitter scraper: In this step, the GetOldTweets python API gathered South African-based tweets to introduce South African grammar into the sentiment classifier. Tweets were collected from famous South African television shows, the Datamustfall movement, telecommunication companies on the JSE, and finally, the Stanford Twitter Sentiment Corpus. The paper uses hashtags \"#\" to collect relevant tweets. These hashtags are considered due to the amount of pertinent sentiment they provide to the classifier. The popular South African television dataset uses the following hashtags: Date-My-Family, South African Idols, and Uyajola99, which amounts to a total of 3000 tweets. The next 1000 come from the hashtag Datamustfall movement, and the last 3000 tweets are from telecommunication companies. The paper considered the following hashtags: Vodacom, Mtn, and Telkom, and excluded tweets from their companies' Twitter accounts for this data, as this only has relevant marketing tweets.  
Step 2, Data cleaning: To prepare the dataset for the training of the sentiment classifier, the dataset needed to go through a pre-processing step. As a result, a python function was written using regular expressions and a few other techniques to clean the dataset. The function first drops all empty rows in the dataset and turns all the letters into lower case. Next, the function removes all URLs, usernames, dates, whitespace, and the hashtag sign (#). Finally, columns are renamed to text, and all the collected tweets are targeted and labeled such that 0 = negative, 2 = neutral, and 4 = positive. This was conducted using the same method used to label the text in the Stanford Twitter Sentiment Corpus [21]. The final clean dataset consists of 14,000 tweets.  
Step 3, Sentiment analyser training: To train the sentiment classifier, a pre-trained language model, BERT (Bidirectional Encoder Representation from Transformers), was chosen [22]. The paper can train its sentiment analysis classifier using the Hugging Face Python library to fine-tune the BERT model [23].