esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The emergence of the Internet of Vehicles (IoV) has revolutionized intelligent transportation and communication systems. However, IoV presents many complex and ever-changing security challenges and thus requires robust cybersecurity protocols. This paper comprehensively describes and evaluates ensemble learning approaches for multi-class intrusion detection systems in the IoV environment. The study evaluates several approaches, such as stacking, voting, boosting, and bagging. A comprehensive review of the literature spanning 2020 to 2025 reveals important trends and topics that require further investigation and the relative merits of different ensemble approaches. The NSL-KDD, CICIDS2017, and UNSW-NB15 datasets are widely used to evaluate the performance of Ensemble Learning-Based Intrusion Detection Systems (ELIDS). ELIDS evaluation is usually carried out using some popular performance metrics, including Precision, Accuracy, Recall, F1-score, and Area Under Receiver Operating Characteristic Curve (AUC-ROC), which were used to evaluate and measure the effectiveness of different ensemble learning methods. Given the increasing complexity and frequency of cyber threats in IoV environments, ensemble learning methods such as bagging, boosting, and stacking enhance adaptability and robustness. These methods aggregate multiple learners to improve detection rates, reduce false positives, and ensure more resilient intrusion detection models that can evolve alongside emerging attack patterns.  
ensemble learning; machine learning; internet of vehicles (IoV); intrusion detection system (IDS); multi-class IDS; vehicular ad hoc networks (VANETs) 1
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 3 Sensors and Actuators in the IoMT
### Role of Body Sensors in the loMT
#### 3.1.3 Electrocardiogram (ECG) Sensors  
ECG sensors [20] monitor and diagnose heart conditions by measuring the electrical activity of the heart. These sensors are widely used in clinical settings, ambulatory monitoring, and home healthcare. One prominent example of an ECG sensor is the AliveCor KardiaMobile EKG Monitor. This compact and portable device can be connected to a smartphone to record single-lead ECGs any time and anywhere. It detects arrhythmias, monitors heart health, and facilitates remote cardiac monitoring. The device has a user-friendly interface and seamless mobile integration, making it a convenient solution for proactive heart health monitoring.
## 8 Discussion  
Three sets of features extracted from vibration signals were compared for classification of multi-fault in a reciprocating compressor. All sets of features are highly accurate for classification of valve and roller bearing multi-fault. The highest accuracy is reached with RF classification models trained with features extracted from vibration signals collected from a single sensor. Concerning the RF classification models, the Ensemble Subspace k-NN is more accurate with average classification accuracies higher than 99%. The Ensemble Bagged Tree model is also highly accurate. However, their average classification accuracy is slightly lower considering the CCM features. Statistical feature dataset is small and simple to extract, and it allows a highly accurate classification of the set of 13 faulty conditions under study. This set of features has been selected and applied to fault classification in gearboxes and roller bearings [39] providing high classification accuracy. The set of features based on SD is also simple to calculate and provides a high classification accuracy. This set of features has also been used for classifying faults in gearboxes using SVM classifiers [26] providing high accuracy. Although the CCM has been used successfully in biomedical signal processing applications [30; 40] their application in fault detection is an original contribution of this paper. This feature is conceptually simple as well as their calculation.  
The previous research in [20] used the same test-bed of a two-stage reciprocating compressor for recording a fault dataset of 17 different conditions for intake/discharge valves. This multi-fault dataset included vibration signals measured from four accelerometers. In that research, the roller bearing faults were not considered. A deep learning approach based on long short-term memory (LSTM) models was developed for fault classification. The LSTM based method attained a classification accuracy of 93%. We also performed an experiment for classifying the 17 valve conditions using the proposed SD and CCM features. The results of classification are shown on Table 4. The accuracy by using the SD features is 100% considering each classification model, and the results are similar for each sensor. The accuracy with the CCM features is higher than 97.9% using the ESK classification model. The highest accuracy is 99.8% using the ESK model trained with vibration signals from the accelerometer A2. Classification results with the EBT model provided lower accuracy; however, the classification accuracy is 98.7% using vibration signals from the accelerometer A2. In this application, the classification accuracy provided by classical statistical features is lower regarding the proposed SD and CCM features. In this case, the higher classification accuracy is obtained with the EBT classification model trained with features extracted from vibration signals collected by the accelerometer A2. The accuracy is 83.5%.
## 1 Introduction
However, no similar development was observed in amateur athletes, and PAPE may occur at a minimal level [14]. However, some studies examined the effect of PAPE on one-time sprints. A study demonstrated that combined conditioning activity and strength protocols could be incorporated into warm-up routines instead of traditional warm-ups to enhance immediate sprint performance in amateur soccer players [18]. Contrary to that study, another study did not report the effect of PAPE on short-distance linear sprint performance after preload [13]. Although there is a widespread belief that high preloads reveal PAPE, there is currently no consensus on specific methods for preloads, including the intensity and volume of these methods, the recommended rest period following these methods, and the duration of the effectiveness of PAPE. Researchers have agreed on an experimental design that elicits PAPE after squat jumps and ballistic bench shots [19; 20]. However, more studies are needed to understand whether high preloads elicit PAPE in more functional movements such as RSA [14]. These movements require speed, power, and endurance, making them essential for soccer success. This study highlights the potential of strength-focused warm-up protocols, particularly PAPE, to significantly improve soccer players' RSA. By emphasizing the link between strength development and enhanced RSA, these findings could present alternative training approaches for soccer athletes, leading to improved on-field performance and more effective conditioning strategies. Although the precise mechanisms of PAPE in the context of intense warm-ups remain unclear, the potential for enhancing RSA through preloads emphasizes the need for further investigation into this promising training strategy for soccer players. Thus, the occurrence of PAPE in athletes following intense warm-ups remains unclear. Although the individual effect of PAPE on different performance metrics has been investigated [21], the individual responses of amateur male soccer players on the RAST have not yet been explored. Investigating the potential effects of PAPE on RSA is essential, as enhancing neuromuscular efficiency through preloads could improve repeated sprint performance, which is crucial for success in high-intensity intermittent team sports, such as soccer. This study aimed to investigate the effects of post-activation performance enhancement protocols created using different preloading volumes on repeated sprint ability performance in amateur male soccer players. In this study, it was assumed that PAPE protocols would positively affect the repeated sprint performance of amateur male soccer players.
# Study on Effective Temporal Data Retrieval Leveraging Complex Indexed Architecture
## 2 Temporal Database Architecture
### Proposed Improvement  
The limitation of the attribute level temporal system defined in the previous paragraph is correlated with the effectiveness of the Update statement. If multiple attributes are updated at the same time, each of them requests inserting a new row into the temporal layer, which creates performance limitation and the bottleneck of the whole system. Group level temporal architecture is based on synchronization groups detection. The managed granularity of the temporal layer can be either the attribute itself or the whole synchronized group, which is detected and managed automatically. Figure 6 shows the architecture. Black arrows show the data flow during the synchronization group detection. Input data are analyzed in the first phase. If any relevant synchronization is detected, the Group manager is contacted (2). In the third phase, the new group is created and its temporal definition is stored in the temporal management (4). The red arrow represents the input data stream to the system. In comparison with the attribute oriented solution, external systems cannot access actual valid states directly, whereas the group detection would be avoided.  
In this paper, we propose the extension of the group level temporal architecture by shifting the processing to the second level. Data themselves are not synchronized only in a time manner, by the positional data are taken care of. Individual data are segmented into categories by the added layer specifying data positions. The defined area is divided into individual polygons, which form the non-overlapping regions. From the interface point of view, temporal and spatial data are managed either separately, or the whole spatio-temporal perspective can be used. Each attribute can be managed either separately or can be formed into the group by covering multiple attributes, which are updated synchronously. In that case, it brings the benefits, whereas the whole group (which is dynamic and its validity is temporal, as well) is referenced in the temporal layer, not just individual attributes. In principles, if the group is composed of 5 synchronized attributes, only one Insert statement is performed in the temporal layer, instead of adding 5 new rows. The temporal aspect, however, does not need to be the only layer of processing and data grouping. In the proposed solution, spatial architecture can be used, as well. Many times, namely, data are produced and transferred using the ad-hoc network with specific reliability, speed of the transfer, and failure resistance. Therefore, individual data can be grouped based on the positions forming the complex data image with the defined granularity and frequency.  
Architecture is in Figure 7 extending the temporal concept by the spatial reference. In comparison with the core group level system, the interface is extended covering the spatial positions and categorization. Note, that the region definition can evolve and the temporal layer must reflect the structure changes.
## 3 Results and Discussion
### Hardness
In this case, instead of using different samples with different grain sizes, the Hall-Petch curves were obtained by considering  
Figure 8: Micro-Vickers hardness with relative linear interpolation for all conditions plotted as a function of Z (**a**) and relative Hall-Petch plot (**b**). different areas of the same sample, corresponding to horizontal slices at different distances from the baseplate on the Z-axis cross-section. This analysis works under the assumption that the Hall-Petch correlation is applicable to local measurements of the same sample and that local variations of columnar grains impact on microstructure and hardness, as previously assumed. Because of the opposite trend that ArS samples showed with respect to the other conditions in hardness measurements, even in Hall-Petch curves the resulting line slopes are opposite. Furthermore, kSE-CB and kSE-HB are markedly different, confirming a certain influence of the platform temperature on the final microstructure, independent from grain width, confirming the \(\alpha^{\prime}\) size measurements provided in Figure 7. In fact, [PERSON] et al. [66] proved that both grain boundaries strengthening and microstructure refinement play an important role in raising Ti-6 Al-4V yield strength and both these phenomena must be taken into account when considering Hall-Petch plots. According to their results, grain size has a greater strengthening effect, although the authors worked on Ti-6 Al-4V produced by EBM, which is mainly characterized by a lamellar microstructure. Moreover, the distance from the platform might also indirectly influence the microstructure by directly affecting grain width and consequently the martensite size leading to a sort of graded microstructure along Z. In fact, grain boundaries are preferential nucleation spots for martensite needles [14]. Hence, a local inter-grain \(\alpha^{\prime}\) size variation along Z might be possible, leading to a different distribution in the bottom and the top part of the same grain, consequence of the different grain boundaries concentrations along the sample height. However further analysis is necessary to assess this possible local effect.
# Flow Dynamics of Sulfate-Modified Water/Polymer Flooding in Micromodels with Modified Wettability
## Materials and Methods
### Interfacial Viscoelastic Response (IFV)  
Interfacial viscoelastic properties between oil-brine phases were measured through the analysis of oil drop volume at the snap-off point. This approach did not provide direct measurements of interfacial viscoelasticity or elastic moduli. Rather, it provided an indirect measurement of oil-drop size analysis at the snap-off point, which correlated to the interfacial viscoelasticity. The IFV response was defined using the following steps:  
* An oil-drop of 2.5 \(\upmu\)L volume was produced through a syringe in the specific brine phase.
* A settlement time of 10 min was established for ionic equilibrium between both fluids. During this time, the ionic interaction between the oil polar compounds and brine divalent/monovalent ions was expected to happen at the interface.
* After a 10 min time interval, 2.5 \(\upmu\)L of oil was further injected to increase the oil-drop size.
* An additional 10 min were required until the expected ionic interaction between both phases was established.
* Subsequently, 2.5 \(\upmu\)L of oil was injected to increase the oil-drop volume further. This process continued until the oil-drop snap-off happened from the needle.
* The oil-drop experienced two opposite forces before snap-off happens. One force was the buoyancy force acting upwards due to oil density. The second force was the interfacial viscoelasticity acting downwards, establishing the oil drop attachment to needle and controlling the oil-drop snap-off. The oil-drop size went on increasing in case the viscoelastic force at the interface was higher than the buoyancy force.
* After a specific increase in drop size, the buoyancy force dominates the viscoelastic force and oil drop detachment happens from the needle.  
This investigation helped to study the formation of the viscoelastic layer at the fluids interface due to ionic reactions. The strong viscoelastic layer was expected to produce a more significant oil-drop volume before the snap-off point. [PERSON] et al. [24] and [PERSON] and [PERSON] [61] demonstrated that the elastic interfacial film was found to be more stable and resistant to snap off. This assisted the stable and continuous oil flow during flooding, while limiting the oil trapping in porous media, hence the correlation of the higher oil recovery during core flooding experiments.
## 4 Evaluation
### Performance of the Driving Intention Estimation  
The estimation accuracy of driving intention was measured. The cases were counted as a failure when the proposed system anticipated the lane-keeping, when in fact the vehicle conducted a lane-changing. On the other hand, the cases were counted as a false alarm when the proposed method judged that the adjacent vehicle would perform a lane change, when in fact the lane-changing did not occur. Although it is equally important to decrease the number of false alarms, the failure is the most dangerous case. Consequently, a recall with 100% accuracy must be achieved for the safety system. The evaluation was conducted using the \(F_{1}\) score, which is derived by  
\[F_{1}=2\times\frac{\text{precision}\times\text{recall}}{\text{precision}+ \text{recall}}. \tag{17}\]  
The precision represents the false-alarm rate, and the recall evaluates the failure rate. Through evaluation using the entire testing data, the \(F_{1}\) score of 99.2% was achieved with the proposed method. False alarms occurred in 95 cases among 5917 lane-keeping events. Otherwise, no failures occurred among 1010 lane-changing events. It was confirmed that the proposed method satisfies the requirement without failures and achieves the great accuracy with the \(F_{1}\) score of 99.2%.  
Figure 10 illustrates an example of the driving intention estimation. In the figure, \(\tau_{j}\) indicates the time of detecting the lane-changing of adjacent vehicle, and \(\tau_{c}\) represents the time of crossing the lane marking by the vehicle. It is confirmed that the lane-changing was successfully detected in advance before crossing the lane marking.  
Figure 9: Risk minimization: if there is no cut-in vehicle, the advanced adaptive cruise control (AACC) assesses the collision risks with respect to the preceding and following vehicles. Otherwise, the AACC assesses the collision risk generated from the cut-in vehicle based on the predicted lane-changing maneuver when the cut-in occurs.  
The system should detect a lane change as soon as possible, at the moment the lane changing maneuver starts. The detection speed \(\tau_{d}\) can be defined as follows,  
\[\tau_{d}=\tau_{c}-\tau_{j}. \tag{18}\]  
A large value of \(\tau_{d}\) implies that the ego vehicle acquires a sufficient time to react, improving driving safety. If the detection is delayed, the ego vehicle cannot be ready to react to the interruption of adjacent vehicles. Consequently, the accuracy and the detection speed of the driving intention estimation are critical factors determining the performance of the proposed system. However, there is a trade-off between accuracy and detection speed [31]. To overcome the limitation, our research group has tried to make a breakthrough, and one of considered approaches is described in our previous work [32]. Details on this point are explained in Section 5.
## 4 Results and Analysis
### Physical Analog Model's Results and Analysis
#### 4.1.2 Vertical Displacement of the Main Roof  
The results show that as the working face advances, the dynamic change in the main roof's vertical displacement can be divided into four stages:  
* The slow deformation stage (0-95 cm from working face's open-cutting)  
The overlying strata load was borne by the left boundary coal pillar and the filling body. Because the advancement of the working face was still short, the left boundary coal pillar bore most of the overlying strata load, which slowed down and reduced the vertical displacement. The maximum vertical displacement of the main roof was 1.2 mm (Figure 10a).  
* The rapid deformation stage (95-115 cm from the working face's open-cutting)  
Due to the sufficiently long advancement of the working face, the left boundary coal pillar cannot offer enough force to support most of the overlying strata load. Therefore, the main roof's vertical displacement increased rapidly, started to bend, and subsided, with a maximum vertical displacement value of 3.35 mm (Figure 10b).  
* The stable deformation stage (115-140 cm from the working face's open-cutting)  
As the working panel continued to advance, the filling body, with an 80% filling ratio, became the main body of support for the overlying strata load. Consequently, the vertical displacement of the main roof stabilized at a maximum value of 5.53 mm (Figure 10c).  
* The completely stable deformation stage (140-190 cm)  
When the working face reached its final advancement, the filling body was fully compacted and bore the overlying strata load above the goat area. At this stage, the maximum vertical displacement of the main roof was 5.68 mm, for which the dynamic change compared to the previous stage was quite small (Figure 10d).  
Figure 9: Displacement variation of overlying strata at the end of the mining process.  
Figure 10: Vertical displacement evolution characteristics of the main roof.
###### Abstract  
This article examines state-of-the-art research into the impact of virtual reality (VR) on brain-computer interface (BCI) performance: how the use of virtual reality can affect brain activity and neural plasticity in ways that can improve the performance of brain-computer interfaces in IoT control, e.g., for smart home purposes. Integrating BCI with VR improves the performance of brain-computer interfaces in IoT control by providing immersive, adaptive training environments that increase signal accuracy and user control. VR offers real-time feedback and simulations that help users refine their interactions with smart home systems, making the interface more intuitive and responsive. This combination ultimately leads to greater independence, efficiency, and ease of use, especially for users with mobility issues, in managing IoT-connected devices. The integration of BCI and VR shows great potential for transformative applications ranging from neurohabilitation and human-computer interaction to cognitive assessment and personalized therapeutic interventions for a variety of neurological and cognitive disorders. The literature review highlights the significant advances and multifaceted challenges in this rapidly evolving field. Particularly noteworthy is the emphasis on the importance of adaptive signal processing techniques, which are key to enhancing the overall control and immersion experienced by individuals in virtual environments. The value of multimodal integration, in which BCI technology is combined with complementary biosensors such as gaze tracking and motion capture, is also highlighted. The incorporation of advanced artificial intelligence (AI) techniques will revolutionize the way we approach the diagnosis and treatment of neurodegenerative conditions.  
computer science; control; virtual reality; brain-computer interface; artificial intelligence; cognitive function; information integration +
Footnote †: journal: applied sciences