esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The increase in new cyber threats is the result of the rapid growth of using the Internet, thus raising questions about the effectiveness of traditional Intrusion Detection Systems (IDSs). Machine learning (ML) technology is used to enhance cybersecurity in general and especially for reactive approaches, such as traditional IDSs. In several instances, it is seen that a single assailant may direct their efforts towards different servers belonging to an organization. This behavior is often perceived by IDSs as infrequent attacks, thus diminishing the effectiveness of detection. In this context, this paper aims to create a machine learning-based IDS model able to detect malicious traffic received by different organizational network interfaces. A centralized proxy server is designed to receive all the incoming traffic at the organization's servers, scan the traffic by using the proposed IDS, and then redirect the traffic to the requested server. The proposed IDS was evaluated by using three datasets: CIC-MalMem-2022, CIC-IDS-2018, and CIC-IDS-2017. The XGBoost model showed exceptional performance in rapid detection, achieving 99.96%, 99.73%, and 99.84% accuracy rates within short time intervals. The Stacking model achieved the highest level of accuracy among the evaluated models. The developed IDS demonstrated superior accuracy and detection time outcomes compared with previous research in the field.  
Keywords: intrusion detection systems (IDSs); machine learning (ML); datasets +
Footnote †: journal:
# A System Architecture of a Fusion System for Multiple LiDARs Image Processing
## 4 Implementation and Evaluation
### Experimental Result  
We measured the response time of the fusion system according to the number of connected LiDAR sensors. In the experiment, when a single LiDAR sensor is connected to the data fusion system, the client response time is approximately 20 ms. In contrast, the server response time is 4 ms.  
Figure 8 represents the response time comparison between sequential processing (_Server_s, Client_s) and multiprocessing (_Server_m, Client_m_). The red vertical lines present the differential of response time between sequential processing and multiprocessing. As the number of LiDARs is increasing, the differential of response time also has increased. Although the server response time is the same, the client response time is different. As we mentioned earlier, the sequential processing leads to linear increase in response time when the number of LiDAR sensors is increased. However, since the multiprocessing is capable of concurrent processing, the response time gradually converges as the number of LiDAR sensor increases. Therefore, as the number of connected LiDAR sensors increases, the gap of the response time between sequential processing and multiprocessing is growing.  
Figure 7: Experimental environment using multiple LiDARs.  
Figure 9 represents the response time of the clients when varying from one to four the number of connected LiDAR sensors. As the number of connected LiDAR sensors increases, the response time of the fusion system stabilizes. It is observed that the client response times converge to approximately 13 ms as shown in Figures 8 and 9. Furthermore, in the experiment results the response time of the client decreases, but the response time of the server increases based on the amount of merged data. The reason is that inside the fusion system, multiple clients process concurrently and only one server processes the clients' data. In the fusion system, although a single server is used to process multiple LiDAR sensors' data, the response time does not exceed the processing threshold.  
Figure 8: Response time comparison between sequential processing and multiprocessing.  
Figure 9: Response time comparison according to the number of connected LiDARs.
# Development of a Digital System for Monitoring Emergency Conditions in 20 kV Distribution Networks
## 5 Conclusions  
Effective digitalization of the power industry is only achievable through the integration of knowledge and technologies from fields such as digital measurements, data transmission, and radio signal processing. This paper addresses the initial stage of solving this challenge, which requires further refinement. The author intends to continue research and development of this topic at a higher technological level, which will become possible as deeper knowledge in the digitalization and automation of 20 kV power grid monitoring is acquired.  
This paper presents a basic version of an information-technological network that enables the monitoring of emergency modes in power grids using digital technologies for measuring zero-sequence voltages. Key components of the network monitoring system and the operational requirements for these components are outlined. Two types of measurement modules are considered, each performing the function of a programmable voltage monitor and equipped with interfaces for transmitting data to a processing server. Fault location diagnosis is achieved by recording abnormally high zero-sequence voltage values, significantly exceeding normal levels during nonemergency conditions [2,23,24].  
Simulation results of data transmission channels with different modulation types demonstrated the advantages of LoRaWAN technology in terms of resistance to interference. This allowed for the selection of appropriate equipment for the construction of the information-technological network. However, this monitoring method requires further development, which will be addressed in future publications with innovative proposals.  
The topic of digitalizing power transmission line monitoring requires ongoing attention, and the author plans to continue research in this field, offering new solutions to address pressing issues.  
Conceptualization, S.I., A.B. and G.N.; methodology, S.I.; software, A.Z. and A.Y.; validation, A.P., A.Z. and A.Y.; formal analysis, A.M.; investigation, S.I.; resources, A.B., S.I. and A.M.; data curation, G.N.; writing--original draft preparation, A.B., S.I., A.Z. and G.N.; writing--reviewing and editing, A.Y., A.P. and A.M.; visualization, A.B. and S.I.; supervision, S.I.; project administration, A.B.; funding acquisition, S.I. All authors have read and agreed to the published version of the manuscript.  
Participation in the research of the authors [PERSON], [PERSON], [PERSON], [PERSON], A.Y., A.P. and A.M. was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP26199519, 2025).  
No new data were created or analyzed in this study.  
The authors declare no conflicts of interest.
## 2 Concepts, Forms, and Classification of Non-Conventional Lanes
### Main Forms of Non-Conventional Lanes  
#### 2.2.1. Contraflow Left-Turn Lane (CLL)  
Contraflow left-turn lanes provide additional storage space and capacity for left-turning vehicles by borrowing part of the space from the opposite exit lane. This design is particularly suitable for intersections with high left-turn demand but with excess space on the opposite exit.  
[PERSON] et al. [9] conducted a systematic analysis of CLL design, establishing a left-turn capacity estimation model, and their research showed that CLL can significantly improve the capacity of left-turn movements. [PERSON] et al. [10] assessed the safety of contraflow left-turn intersections through field data, observing 22,830 left-turning vehicles at 7 EFL intersections in China, of which 9793 vehicles used the mixed-use area.  
[PERSON] et al. [11] further studied queue length estimation methods for CLL, proposing a queue model based on shockwave theory and determining the minimum distance between upstream and downstream intersections. [PERSON] et al. [12] used cellular automata methods to simulate and analyze vehicle operations in CLL, studying the influence of various factorson CLL operation effects, including vehicle types, driver types, and the proportion of U-turn vehicles.  
[PERSON] et al. [13] developed a cellular automata model to simulate lane-changing behaviors in CLL. [PERSON] et al. [14] systematically studied the signal timing and geometric design requirements of CLL, proposing a systematic method for determining CLL length and signal timing plans. [PERSON] et al. [15] proposed a CLL signal timing optimization method based on the cell transmission model, establishing a mixed-integer linear programming model that considers traffic dynamics and road space capacity, optimizing contraflow lane length, signal phase sequence, and green time duration, which reduced delay by 9.63% and increased capacity by 11.48% after optimization.
# Microstructure and Mechanical Properties of Cement Mortar Containing Phase Change Materials
## 4 Conclusions  
This study was conducted to evaluate the microstructure and mechanical properties of cement mortar according to the type and percentage of the PCM added to the mix. To this end, we evaluated the feasible amount of PCM-Ba and PCM-Sr when mixed into cement mortar with mix ratios of 0% to 15%. The conclusions drawn from this study for limited laboratories are as follows:  
1. The microstructural analysis results show that the crystalloid content of cement mortar without PCM differs by about 3% from the crystalloid content of cement mortar with PCM regardless of the type of PCM used, and not much difference is evident in the formation of crystals between cement mortar with and without PCM. PCM-Ba and PCM-Sr were found to act on the hydration reactions within the cement matrix, generating oxides of Ba-oxide and Sr-oxide in addition to hydration compounds. Also, we found that the cement paste containing Br produced macdonaldite (BaCa\({}_{4}\)Si\({}_{16}\)O\({}_{36}\)(OH)\({}_{2}\)\(\bullet\)10(H\({}_{2}\)O)).
2. PCM mix ratios of 3% to 15% do not have much effect on flexibility. We also found that the use of PCM-Sr decreases the workability compared to the PCM-Ba at the same mix percentage.
3. As the PCM addition rate increases, the bending strength decreases. Although the strength of the bending increases due to the increase in aging, PCM-Ba does not perform well in terms of flexural strength, even in the long term, so PCM-Sr is favorable for the performance in flexure.  
Figure 11: Effects of addition mix ratio of PCM on hydration heat properties.  
* The compressive strength evaluation shows a decrease in compressive strength due to the mixing of PCM into cement mortar, and the decrease in initial strength is significant. In addition, PCM-Ba showed a more rapid drop in compressive strength compared to PCM-Sr. Therefore, it is assumed that the application of PCM-Ba will require user review.
* The test results show that the temperature increase and maximum temperature release time of PCMs were delayed up to 5% of the mixing ratio. Both PCM-Ba and PCM-Sr are effective in reducing hydration heat development.  
[PERSON] and [PERSON]. contributed to the project idea development and experimental design, and [PERSON] and [PERSON] performed the experiment and data analysis. [PERSON] reviewed the final manuscript and contributed to the results, discussion, and conclusions.  
This research was funded by the Gachon University research fund of 2018 (GCU-2018-0317). Also this work has supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2017R1D1A1B03028748).  
The authors declare that there is no conflict of interests regarding the publication of this article.
## 3 Numerical Results and Discussions  
### Code Verifications  
To verify the accuracy of the present numerical results, three cases are compared with the previous data in the literature. The first case is the cantilevered composite model with the upper and lower surfaces by piezoelectric ceramics. The plates have four composite layers \([-45/45/-45/45]\). The total thickness of the composite model is 1 mm and each layer has the same thickness of 0.25 mm. The two outer piezoelectric layers are of 0.1 mm thickness. In addition, the adhesive layers are neglected. The panel consists of T300/976 graphite-epoxy composites, and the piezoelectric material is PZT F1195N, as presented in Table 1[21]. The centerline deflections of the composite model with various input voltages are presented in Figure 3. The results are in good agreement with the data obtained by [PERSON] et al. [21].  
Secondly, the deflection responses of the model under blast loads. The material properties of \(E\), \(\
u\), and \(\rho\) are 206.84 GPa, 0.3, and 7900 kg/m\({}^{3}\), respectively. The dynamic response is calculated using mode superposition method, and time step is 0.05 ms. Figure 4 shows the deflection history at the center of the model. The present results are almost same with the previous works by [PERSON] et al. [22].  
\begin{table}
\begin{tabular}{c c c} \hline
**Properties** & **PZT Piezoceramic** & **T300/976** \\ \hline [PERSON]’s modulus (GPa): \(E_{11}\) & 63.0 & 150 \\ \(E_{22}=E_{33}\) & 63.0 & 9.0 \\ Poisson’s ratio: \(v_{12}=v_{13}\) & 0.3 & 0.3 \\ \(v_{23}\) & 0.3 & 0.3 \\ Shear modulus (GPa): \(G_{12}=G_{13}\) & 24.2 & 7.10 \\ \(G_{23}\) & 24.2 & 2.50 \\ Density (kg/m\({}^{3}\)): \(\rho\) & 7600 & 1600 \\ Piezoelectric constants (m/V): \(d_{31}=d_{32}\) & \(254\times 10^{-12}\) & - \\ Electrical permittivity (F/m): \(\epsilon_{11}\)=\(\epsilon_{22}\) & \(15.3\times 10^{-9}\) & - \\ \(\epsilon_{33}\) & \(15.0\times 10^{-9}\) & - \\ \hline \end{tabular}
\end{table}
Table 1: Material properties [21].  
Figure 3: The centerline deflections with various input voltages.  
The last case is the polyester model with stiffeners placed at the center. Two opposite edges are free, and the others are clamped. The structure and stiffeners are made with the isotropic materials as follows: E = 7.0 GPa, G = 2.6 GPa, \(\rho\) = 1504.2 kg/m\({}^{3}\) and \(\
u\) = 0.345. The natural frequencies of the model are well-matched with the previous data [16; 23], as shown in Table 2.
## 2 Utilization of GNSS
### Basic GPS L1 Algorithm
When new measurements from the GPS receiver are available, they get processed in two steps:  
1. Preprocessing: Based on the a-posteriori values of receiver clock bias and drift from the last time step, the a-priori values of these quantities are propagated. Corrections for satellite and receiver clock errors as well as ionospheric and tropospheric refraction are applied to pseudo-ranges and pseudo-range-rates. The measurement noise covariance for pseudo-range and pseudo-range-rate measurements is calculated. All measurements are assumed to be uncorrelated with each other; the variances for pseudo-ranges and pseudo-range-rates are the same as in initialization mode. Lastly, positions and velocities of the received satellites are computed. 2. Measurement update: The innovation \(\delta z\) is formed as difference between the corrected pseudo-range and pseudo-range-rate measurements and their predicted counterparts. These predictions are based on the a-priori estimates of attitude, velocity and position. Plausibility checks for pseudo-ranges and pseudo-range-rates are employed to detect outliers. Finally, \(\delta z\) and the associated measurement noise covariance are used to determine corrections for the state vector's a-priori estimates. Simultaneously, the state vector's covariance matrix is updated to reflect the newly incorporated information.
## 3 Discussion
In addition, some residual cells showed an altered morphological aspect, which was presumably due to the activation of the multi-stress response within 24 h of peptide treatment. It is known that bacteria produce the toxic gas nitric oxide (NO) via the denitrification pathway for detoxifying nitrite produced in the cytoplasm. The _nir_BDC operon, which is positively regulated by nitrite, encodes for the cytoplasmic nitrite reductase NirB, which can reduce nitrite to NO [35]. This gas is identified as a key mediator of biofilm dispersal, and it can regulate biofilm dynamics in a variety of bacteria [36; 37]. Recently, [PERSON] and colleagues [38], according to [PERSON] [39; 40], found that in _P. aeruginosa_ and in _E. coli_ strains, NO stimulates biofilm dispersal or inhibits biofilm formation by affecting the motility. In our study, we observed that the _nir_B gene expression was induced in the presence of Esc(1-18) and Esc(1-21), suggesting a possible increase in NO with the consequent inhibition/dispersal of biofilm. During their growth, bacterial cells encounter not only nitrosative stress, but also oxidative and osmotic stresses. Bacteria are killed by a variety of lethal stressors, some of which promote a cascade of reactive oxygen species (ROS). It is known that in _E.coli_, the presence of ROS activates the antioxidant defense system, which is represented by enzymes such as the superoxide dismutase SodC and the catalase KatE [41]. In the last few years, different works have demonstrated that antimicrobial drugs and AMPs induce the formation of ROS [42; 43; 44; 45]. [PERSON] and colleagues [46] demonstrated that strains with mutations in the _osm_B gene, which encodes for a lipoprotein located in the outer membrane, were significantly more pressure sensitive than the wild-type strain. We investigated whether the selected peptides induced harmful levels of ROS and altered the osmotic balance by examining the transcription levels of _sodC_, _kat_E, and _osm_B in the _E.coli_ O157:H7 strain. The higher expression that we observed for these genes after peptide treatment suggests the activation of a multi-stress response by the bacterial cells. This could lead to an altered bacterial morphology (as observed in Figure 6), which is presumably due to the osmotic imbalance within 24 h.
# Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
## 1 Introduction
This model incorporates various preprocessing techniques that reflect the ABCD rule, along with ensemble models of ViT-B/16, DenseNet121, EfficientNet-B0, MobileNetV2, and ResNet50. Our model is trained and tested on the HAM10000 dataset [7], a widely used benchmark, and aims to classify a total of seven types of skin lesions. The novelty of our research is as follows:  
* We propose a novel ABC ensemble model for skin lesion classification by leveraging the ABCD rule, which reflects the clinical criteria used to assess lesions. The model consists of five distinct blocks, including two generalization blocks that learn overall image characteristics and three ABC-related blocks that focus on the asymmetry, border, and color features of skin lesions. Each block undergoes different preprocessing methods before passing through five classifiers: ViT-B/16, DenseNet121, EfficientNet-B0, MobileNetV2, and ResNet50. * To demonstrate the effectiveness of our model, we conduct experiments using 15 different model configurations. These configurations include models without preprocessing, models with various preprocessing methods, and ensemble models with and without weights. The results consistently show that the ABC ensemble model outperforms the other configurations in terms of accuracy, recall, precision, and F1-score, highlighting its superior performance in skin lesion classification. * Our model adopts a weighted soft voting approach, where higher weights are assigned to the generalization blocks that analyze overall image features. By doubling the weight assigned to the generalization blocks, we achieve improved classification performance. We experiment with various weight combinations to determine the optimal setup, showing that this weighted voting method enhances the overall accuracy and reliability of the model. * To further validate the effectiveness of our model, we perform Grad-CAM analysis, which allows us to visualize the regions of the skin lesion that each block focuses on during classification. The different preprocessing methods in our model highlight both general features and specific lesion characteristics such as asymmetry, border, and color. This analysis shows the interpretability and reliability of the model, ensuring that it is not only effective but also transparent in its decision-making process. The structure of this paper is as follows: Section 2 presents an overview of research on skin lesion classification using machine learning or deep learning approaches. Section 3 introduces the dataset, overall model architecture, data preprocessing, model detailed structure, ensemble classifier, and experiments. Section 4 reports on the classification performance and analyzes the ABC ensemble model across various experiments. Section 5 covers the limitations and retention future directions of our research. Finally, Section 6 summarizes our conclusions.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 4 Background on MAC Protocols
### Evaluation Metrics
#### 4.1.3 Packet Loss Ratio (PLR)  
PLR corresponds to the proportion of data lost or received with errors due to miscoodinations of frame transmissions, and that cannot be recovered over the total amount of data sent. The PLR is calculated as follows:  
\[PLR=\frac{P_{l}}{P_{s}}, \tag{3}\]  
where \(P_{l}\) is the number of lost packets and \(P_{s}\) is the number of packets sent. This ratio turns out to be important when energy efficiency is required, since a high PLR may trigger a high number of retransmissions when implementing a reliable link layer, which may mean more waste of energy. In general, the channel performance is analyzed by examining the supported channel load for a target PLR, which is commonly considered on the order of \(10^{-3}\) in the literature. In some cases, the normalized load achieved with a target \(PLR=10^{-3}\) is very low, making it necessary to consider worse PLR values in the analysis, e.g., \(PLR=10^{-2}\).  
The relation among the three metrics presented above is described by the following equation:  
\[S=C(1-PLR).\]
# Cardiac Nuclear Imaging Findings in Atypical Variants of Takotsubo Cardiomyopathy
## 4 Discussion and Limitations
Concerning the evaluation of sympathetic innervation, 123I-mIBG scintigraphy always revealed a marked reduction in tracer uptake in the involved area during the acute phase [72], reflecting the central role of abnormal myocardial adrenergic nervous system activation in the pathogenesis of the disease and non-invasively contributing to the final diagnosis of atypical TTC [81; 83]. Moreover, in one case, it was extremely useful in identifying a pheochromocytoma as a secondary cause of TLVDS [82]. As discussed above, sympathetic nervous system dysfunction may be the mechanism underlying the metabolic alterations revealed through both 123I-BMIPP scintigraphy [80] and 18F-FDG PET [70; 71; 72]. The transient nature of all these defects is always confirmed in follow-up studies [78], with a possible delay in the recovery of sympathetic function with respect to metabolic defects [76; 77]. However, these data are scarce and difficult to compare as the time occurring from the onset of symptoms to cardiac nuclear imaging in both acute phase and during follow-up is extremely variable. A mention of some additional limitations and drawbacks is needed. First of all, it is worth highlighting that the number of selected studies is quite limited. Both the rare occurrence of uncommon forms of TTC and the non-inclusion of cardiac nuclear imaging in the diagnostic pathway could be possible explanations for the low number of publications on this topic. Moreover, one of the major concerns in the analyzed literature is represented by the type of published papers, as the vast majority of them are single case reports or case series involving no more than two patients. We can speculate that the paucity of both retrospective analyses and prospective studies concerning cardiac nuclear imaging in TTC in general and atypical TTC variants in particular may represent the main obstacle preventing it from being included in the current diagnostic algorithms. An additional non-negligible limitation of most papers is represented by the lack of follow-up imaging, particularly useful for corroborating the transient nature of nuclear imaging findings.