esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 5 Results
### Evaluation on Stage 3: OCSVM + dAE + DBSCAN  
The DBSCAN model operates as the third stage in the intrusion detection system, handling data flagged as positive by the stage 1 one-class SVM (OCSVM) and stage 2 dAE. The evaluation of the stage 3 DBSCAN uses an epsilon value of 0.2 and a minimum of 800 points for clustering. The clustering model is assessed against commonly used clustering algorithms including K-means [39], fuzzy C-means (FC-Means) [38], hierarchical clustering [40] (specifically divisive hierarchical clustering--DHC), and OPTICS [37], utilizing network traffic datasets for evaluation. The scatter plots in Figures 15 and 16 display the experimental results for CSECIC-IDS2018 and CIC-IDS2017, respectively. The experimental results show that the proposed clustering method effectively separates instances into eight groups, including benign ones, for both CSECIC-IDS2018 and CIC-IDS2017  
Figure 14: Performance measures of stage 2 detection OCSVM + dAE.  
Figure 13: Confusion matrix of detection stage 2 OCSVM + dAE on CIC-IDS2017.  
datasets. It demonstrates high accuracy in identifying positive and negative instances while maintaining a balanced performance in classification.  
Table 5 illustrates how the proposed method consistently outperforms alternative clustering algorithms across a range of metrics, highlighting its effectiveness in clustering tasks in the CIC-IDS2017 dataset. The proposed method attains the highest recall score (0.9907), indicating its robust capability to identify positive instances effectively, also suggesting a minimal false negative rate.  
Figure 17 illustrates that the proposed method outperforms others in the CSECIC-IDS2018 dataset, showcasing higher F1-score, recall, precision, FNR, and accuracy. The proposed approach achieves the highest overall correctness of clustering at 0.9881. The proposed approach achieves the highest F1-measure at 0.993, signifying a well-balanced performance between precision and recall.  
Figure 16: DBSCAN clustering on CIC-IDS2017.  
Figure 15: DBSCAN clustering on CSECIC-IDS2018.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **Specificity** & **Accuracy** & **F-Measure** & **FNR** \\ \hline OPTICS [37] & 0.9331 & 0.9345 & 0.9298 & 0.9324 & 0.9338 & 0.0655 \\ FC-Means [38] & 0.9451 & 0.9464 & 0.9418 & 0.9443 & 0.9458 & 0.0536 \\ K-Means [39] & 0.9540 & 0.9583 & 0.9507 & 0.9532 & 0.9547 & 0.0447 \\ Hierarchical [40] & 0.9509 & 0.9522 & 0.9476 & 0.9501 & 0.9516 & 0.0478 \\ Proposed & 0.9948 & 0.9907 & 0.9806 & 0.9927 & 0.9886 & 0.0093 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of stage-3 clustering OCSVM + dAE + DBSCAN on CIC-IDS2017.
# CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things
## 3 Cluster Tree-Based Routing Protocol
### Route Establishment
#### Objective Function  
It helps to find the optimal route among the multiple routes in the DODAG. In CT-RPL, the objective function of node i \(OF_{(QU,ETX)}(l)\) is calculated from the routing metrics queue utilization and expected transmission count. CT-RPL provides better results when the \(w_{1}\), \(w_{2}\), \(w_{3}\), and \(w_{4}\) weight values are 0.25, 0.25, 0.25, and 0.25, respectively. The \(OF\) calculation is given in Equation (19).  
\[OF_{(QU,ETX)}(l)=w_{1}\times QU(l)+w_{2}\times ETX(l)+w_{3}\times RER(l)+w_{4 }\times CTHC(l) \tag{19}\]
## 3 Results
In this zone, the flow spreads in the form of a radial jet, making it easier to form a drop of liquid metal of the optimal size. The oscillograms of pulsations of the atomizing flow are shown in Figure 7. It can be seen that the atomizing flow features a pulsating character with time intervals (pauses). The common feature for all pulsation frequencies is the presence of a pause in atomizing, which  
Figure 6: Gas spectrograms of the airflows: **(a)** without pulsation, **(b)** with pulsation frequency 75 Hz for conical nozzle, **(c)** with pulsation frequency 75 Hz for cylindrical nozzle. 1—Mach disc; 2—the zone of the initial gas-dynamic spike; 3—the transitional zone; 4—the main zone (the front of Mach disc). is needed for the formation of liquid metal on the electrodes' butts, with their subsequent detachment from the electrodes by the airflow. Application of the airflow pulsation makes it possible to retain molten particles in the process of a droplet formation before its detachment, thus decreasing the influence of oxygen upon the melt. The reduction in air oxygen influence under the flow pulsation was evaluated by the aluminum coating's microstructure analysis. As seen in Figure 8, the coatings' microstructure had a layered pattern characteristic for EAS overlays. Figure 8a represents the microstructure of the coating obtained at steady (without pulsations) airflow. It is obvious that such a coating has as many oxide inclusions as the thick (0.5-6.0 mm) films lying between the crystallized droplets (the oxides are shown by the arrows). In contrast, the coating deposited by pulsating (67 Hz) airflow is characterized by a much lower amount of oxide films with a small number of equiaxed non-metallic inclusions distributed inside the droplets (Figure 8b). Such changes in microstructure indicate the oxidation-reduction caused by the airflow pulsation. Figure 8: The microstructure of aluminum coating, deposited (**a**) without airflow pulsation and with air pulsation of frequency (**b**) 67 Hz and (**c**) 120 Hz. The oxides are depicted by the arrows. Figure 7: The oscillograms of the atomizing airflow with the pulsation’s frequency of (**a**) 20 Hz, (**b**) 50 Hz, (**c**) 100 Hz. The same observations were seen for the aluminum coatings obtained with different pulsation frequencies (up to 120 Hz--Figure 8c). The results are represented by the graph of the oxide volume fraction vs. the airflow pulsation frequency illustrated in Figure 9. It can be seen that the graph had a parabolic shape with the minimum value corresponding to the frequency of about 65 Hz. With an increase in pulsation frequency up to 65 Hz, the oxide volume fraction gradually decreased to 2.6 vol. %, which is 3.6 times lower compared to the steady airflow (9.3 vol. %). When the pulsation frequency increases from 65 Hz to 120 Hz, the oxide volume fraction grows to 5.5 vol. %, remaining below the level of the steady flow. Evaluating the obtained results, one can conclude that the frequency of the optimal pulsations for aluminum coating deposition lies within 65-75 Hz interval, which provides the reduction in the metal oxidation rate.
## 3 Parameter Optimization and Performance Test
### Optimization Results and Verification Test of Drive Cam Installation Angle
#### 3.2.2 Test Conditions  
The drive cam was installed according to the drive cam installation angle obtained above. To guarantee the accuracy of the installation angle and avoid cumulative errors in the installation process, all the drive cams were installed with the No. 1 position as the standard. The specific installation methods were as follows:  
1. Calibrate the electronic slope meter, set five drive cams into the drive shaft, and lock the drive cam at the No. 1 position to prevent it from rotating relative to the drive shaft, and install the drive shaft to the bearing seat;
2. Adjust the angle of the drive shaft by pressing a 3 mm thick steel plate against the mounting aid plane on the cams, and then observe the electronic gradiometer attached to the plate. When the gradiometer reading is \(0^{\circ}\), it indicates that the drive cam at the No. 1 position is in a horizontal state, and at this time, a locking ring should be installed at both ends of the drive shaft to fix it in order to adjust the driving shaft to the horizontal reference position;
3. Install the auxiliary plane by making the steel plate close to the drive cam at the No. 2 position, adjust the position of the drive cam, and observe the gradiometer reading. When the reading reaches \(72^{\circ}\), it means that the drive cam at the No. 2 position has been rotated to the predefined installation position, and at this time, it is fixed to the drive shaft through the top wire locking cam;
4. Repeat step (3) for the drive cams at the No. 3-5 positions to complete the installation of all the drive cams. It is particularly important to note that the above installation method is in an ideal state. In the actual installation operation, since it is performed manually and the installation auxiliary plane of the drive cam is small, the installation results will inevitably have errors, and after repeated debugging, the installation errors can be controlled to within \(\pm 1^{\circ}\).  
The seedlings used in the test were the same as the batch of seedlings used in the net seedling drip measurement test.
## 3 Experimental Results and Discussion
### Electrical Characteristics of PEF During the Treatment Process  
During the PEF treatment process, the electrical characteristics were monitored. For all the experimental conditions, except the no-load treatment condition, a 7.5 kV applied voltage was set at the HVDC source, and the current from the power source was 0.81 mA. Under this operating condition, the specific energy would be 43.93 kJ/kg, approximately [3; 13; 32]. Figure 7a,b show the pulsed voltage and current waveforms across the treatment chamber during the PEF treatment. As presented in Figure 7a, the high voltage pulses were around 5.91 kV for the no-load treatment condition (measured across the output terminal of the HV switch without the treatment chamber). This implied that there was a large voltage drop in the system, which was in the part before the HV switch, of almost 1.59 kV. This is because a large ballast resistor used for the current limitations causes a large voltage drop across it. Moreover, the voltage drop would possibly be due to the small leakage current through the parasitic elements in the treatment system. However, this leakage current was very small and undetectable with a shunt resistor. It could be noticed that the average voltage pulses decreased as the number of the treatment chambers increased. The average voltage pulses, across the treatment chambers, were 2.30, 1.65, and 1.42 kV for \(N_{HV1}\)\(N_{HV2}\), and \(N_{HV3}\), respectively. As presented in Figure 7b, the current pulses were generated through the conductive liquid media during the existence of voltage pulses. The magnitude of the average current pulses had an opposite trend to that of the voltage pulses, which were 0.95, 2.23, and 5.18 A for \(N_{HV1}\)\(N_{HV2}\), and \(N_{HV3}\), respectively.  
Figure 6: Simulation results for (**a**) the liquid temperature along the \(z\)-axis and (**b**) the liquid temperatures at positions A and B.  
Figure 7: Electrical characteristics of (**a**) pulsed voltages, (**b**) pulsed currents, and (**c**) voltage waveforms for various conditions.  
Figure 7c presents the waveform of a single voltage pulse across the treatment chamber. It could be noticed that the pulse voltage in the no-load treatment condition was almost constant, around 5.91 kV, with a pulse width of around 75 \(\upmu\)S. Meanwhile, in other conditions, the pulsed voltage rapidly decreased with different discharge times corresponding to the number of the treatment chambers. As the liquid medium bridges the high voltage and ground electrodes, it hence acts as a liquid resistor. A series of treatment zones causes a parallel connection of the liquid resistors since the treatment chambers have common high voltage and ground electrodes. Therefore, the increase in the treatment zones increases the number of current flowing paths. Consequently, the more treatment units there are, the faster the discharge time. This factor explains the characteristics of the pulsed voltage magnitude, which dropped across multiple electrode units.
## 5 Results
### Sensitivity Analysis  
Sensitivity analysis was used to determine the impact of criterion weights on the final ranking of alternatives [65]. These analyses can be performed in different ways [66; 67; 68; 69]. In the performed sensitivity analysis, the weights obtained by the SWARA method were applied. When performing this sensitivity analysis, each individual criterion is reduced by 90%, while the other criteria increase in proportion to that reduction. At the initial weight, the sum of the weight values was equal to one. As the value of one criterion decreases, that sum decreases. For this reason, it is necessary to proportionally increase the weights of the other criteria so that the sum of the weights is equal to one. First, the value of one of the criteria is reduced and then, the values of the other criteria are gradually increased until their sum is approximately equal to one. In the example of scenario S1, the weight value of criterion C1 is reduced from 0.113 to 0.011, while the values of other criteria are increased by 11% in order to compensate for the decrease in the weight value of one criterion. In this way, the sum of the weights is approximately equal to 1, that is, in this way, it is 0.9956, which is almost 1. In other scenarios, the values of other individual criteria decrease, while the values of other criteria that do not decrease increase by 11%. In this way, the impact of that criterion on the ranking of the alternatives was examined, which means that the alternative had better indicators compared to the alternative that is ranked better. The results of this sensitivity analysis show that there is a change in the ranking of suppliers SUP6 and SUP8 (Figure 4). According to three criteria, SUP 6 was ranked worse. In order for the SUP8 supplier to be better than the SUP6 supplier, they need to meet these three criteria.  
Sensitivity analyses showed that regardless of increasing or decreasing the importance of the criteria, supplier SUP2 is the best in all scenarios and, thus, it is the best choice for MAMEX.
## 3 Algorithm Description
### Background  
* Control Channel  
Our DTS-AC protocol incorporates a notification system to facilitate communication between readers through a designated control channel (Figure 2). We utilize the two distinct control channels to efficiently manage resource allocation.  
Control Channel Structure:  
* Control Channel 1 (CC1): Dedicated to distributing frequencies for reuse.
* Control Channel 2 (CC2): Utilized for managing TS, enabling effective avoidance of RTI collisions.  
To mitigate RRI collisions, a reader identifies the presence of neighboring readers within its interference field on the data channel. It then employs the CC2 control channel to distribute TSs among the neighboring readers, thus preventing RTI collisions. Additionally, the CC2 control channel is utilized to distribute frequencies to neighboring readers, effectively preventing RRI collisions.  
Figure 2: Proposed background environment for RFID readers.  
* Frame Structures and Tables To control the assignment of frequencies and time slots, our protocol employs specific frame structures and tables within the notification system. B.1 Time Slot Control The control messages used (Table 2) are as follows:
* TYPE: Message type (e.g., REQUEST1, REQUEST2, RESPONSE1, RESPONSE2, TS_select).
* READER_SENDER: Identification of the source reader.
* READER_RECEIVER: Identification of the destination reader.
* ELIMINATE_TS: TS used by the source reader.  
The information stored in the readers' table is as follows:  
* TS_Enable: Authorized Time Slots for use.
* TS_Disable: Time Slots not allowed for use. B.2 Frequency Control The control messages used (Table 3) are as follows:
* USED_PROTOCOL: Protocol used, either FTDMA or CSMA.
* READER_IN_CHAIN: Readers using the same data channel.
* AFFECT_FREQ: Affected frequency.  
The information stored in the readers' table is as follows:  
* TYPE: Message type (e.g., REQUEST1, REQUEST2, RESPONSE, ADD_TO_CHAIN, NEW_CHAIN).
* READER_SENDER: Sender reader ID.
* READER_RECEIVER: Receiver reader ID.
* READER_IN_CHAIN: Readers using the same data channel.
* AFFECT_FREQ: Assigned frequency.
###### Abstract  
This paper presents a mathematical and statistical analysis of the security-performance trade-off in the context of the IoT Cloud architecture implemented at UBT Smart City. Through detailed modeling and real-world measurement data collected before and after the deployment of advanced security measures--such as VPN configuration, Network Security Groups (NSGs), Route Tables, and DDoS Protection--we quantify the impact of security on system performance. We propose a mathematical framework to evaluate the propagation delay of telemetry data through the system and employ queueing theory (M/M/1 model) to simulate the behavior of critical data processing services. Additionally, we perform hypothesis testing and statistical comparison to validate the significance of the observed performance changes. The results show an average delay increase of approximately 19% following the implementation of security mechanisms, highlighting the inevitable trade-off between enhanced security and operational speed. Finally, we introduce a multi-objective cost-delay function that can guide the selection of optimal security configurations by balancing latency and cost, providing a valuable tool for the future optimization of secure IoT infrastructures  
IoT; smart city; security; performance; Azure; queueing; VPN; NSG; delay; statistics 2025  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON].  
1 Faculty of Telecommunications, Technical University of Sofia, 1756 Sofia, Bulgaria; [EMAIL_ADDRESS] (B.Q.); [EMAIL_ADDRESS] (G.M.)
## 4 Simulation and Fpga Implementation  
Our work aims at achieving a 20 Gbps rate wireless communication system, which has been mentioned before. A 64-parallel architecture could make the FPGA clock frequency running at around 156.25 MHz. A 32-parallel system will lead to 312.5 MHz FPGA clock frequency, which makes it very difficult for FPGA to ensure the stability while running. Even though a 128-parallel system needs the FPGA circuit runs only at 78.125 MHz, which is quite easy for nowadays FPGA circuit to guarantee its stability, but with the increase of the number parallel channels, the system error grows drastically. On the other hand, the number of parallel channels other than an integer multiple of two will lead to a waste of waste hardware resource on FPGA chip while routing, because FPGA is based on binary system. So, a 64-parallel architecture is the best choice for a 20 Gbps rate system.  
The proposed algorithm is verified in a baseband communication system. The modulation type is 16 QAM, bit rate is 20 Gbps, roll-off factor is 0.4, oversampling frequency \(f_{s}\) is 2 times the symbol rate \(R_{s}\), (\(f_{s}=2R_{s}\)), and the timing frequency and phase offset are 32 kHz and \(\pi\) respectively. The parallel source signal is quantized to 8 bits. The BER performance of the improved parallel architecture  
Figure 5: Implementation structure of loop Filter with error smoothing.  
simulated on MATLAB reveals its high efficiency. Furthermore, the implemented parallel architecture on Xilinx XC7 VX485T FPGA shows perfect consistency with simulation.
# Tailored Nanoparticles as Vaccine Components
## 4 Nanoparticles as Artificial Antigen-Presenting Cells--The Future of Immunotherapy?  
Traditionally, vaccines have aimed to bring about an immune response by stimulating DCs to present the provided antigen to T cells (see above). With a better understanding of the rules of T cell activation, it has recently become feasible to directly stimulate T cells using multivalent reagents that mimic activated dendritic cells, so-called artificial antigen-presenting cells (aAPCs). Because T cells only recognize processed antigen as peptide epitopes bound to an MHC molecule, the use of aAPCs is indicated where peptide-specific T cell stimulation is possible and desirable, for example, with T cells directed against a known tumor epitope. The aAPCs must carry at their surface the MHC molecule/peptide complexes that are needed to convey antigen specificity, and they must also display co-stimulatory molecules, especially ligands for the CD28 receptor on the T cells; they may also carry proteins or lipids that promote general cell adhesion, and they may contain activating cytokines for slow release [73,74]. Inorganic and polymeric nanoparticles of different sizes are being investigated for direct T cell stimulation. In addition, filaments such as dextran molecules or chemical analogs of peptides with a longer biological lifetime promise polyvalent backbones (though not strictly nanoparticles) [75]. The resulting possibilities for direct antigen-specific stimulation of individual T cell clones allow much more finely tuned manipulation of the desired immune response with boundless opportunities, including perhaps, in the future, the inactivation of regulatory (inhibitory) T cells (Tregs) or perhaps the generation of desirable Tregs to treat autoimmune diseases [76].
# Vertebral Center Points Locating and Cobb Angle Measurement Based on Deep Learning
## 6 Conclusions  
Accurate measurement is essential for scoliosis diagnosis. However, the current manual measurement method is inefficient, subjective, and unstable. Additionally, current computer-aided detection of the Cobb angle requires manual cropping or frame selection of X-ray images, which cannot achieve full automation. This paper proposes a deep learning-based vertebral center points localization algorithm, which fits the spine curve according to the predicted key points to calculate the Cobb angle. Experiments demonstrate that this method has good robustness and practicability, and its specific advantages are as follows:  
1. It can predict the entire X-ray input without the need for cropping while only keeping the thoracic and lumbar vertebrae that need to be predicted, which meets the practical application requirements.
2. It is more stable, rigorous, and rapid than traditional manual measurement.
3. The number of network parameters is small, saving memory resources and improving the detection speed while ensuring the accuracy.  
**Author Contributions:** Z.Z.: funding acquisition, project administration, writing--review and editing; J.Z.: conceptualization, methodology, writing--original draft; C.Y.: formal analysis, methodology. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partly supported by Program of Shanghai Academic/Technology Research Leader (22 XD1433500).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Author elects to not share data.  
**Acknowledgments:** The authors wish to acknowledge [PERSON] from Shanghai Sinan Satellite Navigation Technology Co., Ltd. for providing the experimental equipment and funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.