esa-sceva/satcom-chunk-collection huggingface dataset:
# Negotiation-Free Encryption for Securing Vehicular Unicasting Communication
## 6 Performance Evaluation
### Performance Evaluation of NFEM
#### 6.2.2 Impact of Varying Beacon Transmission Frequency and Speed  
Figure 6 shows the performance of the Negotiation-free encryption algorithms in terms of delay with different beacon transmission frequencies and vehicle densities. In this figure, the average speed is 60 km/h. As we can see, when the vehicle density is less than 75 or the beacon transmission frequency is less than 8 Hz, the transmission delay is relatively small. With the increase of beacon transmission frequency and vehicle density, the delay increased.  
Figure 7 shows the performance of the Negotiation-free encryption algorithms in terms of delay with different beacon transmission frequencies and vehicle densities. In this figure, the beacon transmission frequency is 8 Hz. Generally, with the increase of the average speed and vehicle density, the delay rises. Sparser topologies satisfy the delay requirements. When the vehicle density is less than 75, the delay is basically below 200 ms.  
Figure 6: End-to-end delay (ms) vs. beacon transmission frequency (Hz).  
Figure 7: End-to-end delay (ms) vs. Average Speed (km/h).  
Using the Negotiation-free encryption algorithm, the percentage of delays in the entire transmission process by the WAVE and LTE technology is calculated according to the different beacon transmission frequencies. To see the entire transmission process and the delay proportion of different technologies, we set the simulated numbers of vehicles between 25 and 100, respectively. In Figure 8, the vehicle density is 25, and in Figure 9, the vehicle density is 100.  
From Figures 8 and 9, one can clearly see that the beacon transmission frequency has little effect on the delay of the LTE compared with WAVE in the transmission process of the Negotiation-free encryption algorithm. With the increase of the beacon transmission frequency, the delay between vehicles increases rapidly. Therefore, in the whole transmission process, the increase of the delay proportion of the Negotiation-free encryption algorithm is obvious.  
Experiments show that our Negotiation-free encryption algorithm transmission method can adapt well to the actual needs and performs well under the condition of a high density of vehicles and a high frequency of beacon conditions.
# Study on the Damage Process and Numerical Simulation of Tunnel Excavation in Water-Rich Soft Rock
## 2 Staged Softening Process of Water-Rich Soft Surrounding Rock Based on Tunneling
### Undisturbed Stage of Surrounding Rock  
After a long time of geological tectonic action, the underground rock is in the original stress state. Due to weathering and seismic action, there are tiny fissures in the rock mass, but the fissures are small in scale and not penetrating. Groundwater cannot flow freely in the fissures, and the softening effect on the rock mass is relatively low and almost negligible. Under this stage, the surrounding rock is in the original stress state, neither unloading softening nor water-softening occurs and all physical and mechanical indexes are relatively stable.
# Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems +
## 4 Experimental Setup and Results
### Numerical Experiments
#### 4.2.3 Overlapping Problems  
**Definition 3**.: _A function \(f(x_{1},x_{2},\ldots,x_{N})\) is overlapping if the subcomponents of these functions have some degree of overlap with its neighboring subcomponents._  
An example of an optimization problem with overlapping variables is  
\[f(\overline{x})=x_{1}x_{2}+x_{2}x_{3}, \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Algorithm** & **Win** & **Loss** & **Equal** & **Av. Rank** \\ \hline (6) & iCC & 84 & 42 & 74 & 32.6 \\ (3) & CC(4) & 75 & 55 & 70 & 27.6 \\ (4) & CC(8) & 56 & 55 & 89 & 25.2 \\ (2) & CC(2) & 66 & 65 & 69 & 24.2 \\ (1) & CC(1) & 68 & 94 & 38 & 22.4 \\ (5) & CC(10) & 39 & 77 & 84 & 21.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The sum of the Wilcoxon test results.  
Figure 4: Average ranks of algorithms on the \(F_{4}\)–\(F_{11}\) problems.  
Figure 5: Convergence graphs for CC(m) and iCC on \(F_{7}\), and \(F_{11}\) problems with different population size (\(F_{\#}\)-_pop_size_: (a) \(F_{7}\)-150; (b) \(F_{11}\)-150).  
where \(x_{2}\) belongs to both subcomponents \(x_{1}x_{2}\) and \(x_{2}x_{3}\).  
\(F_{12}\)-\(F_{14}\) are problems from the overlapping class. The experimental results are shown in Figures 6 and 7, and Tables 7 and 8. Tables 7 and 8 have the same structure as Tables 3 and 4, respectively. We can see that iCC outperforms all CC(m) algorithms. iCC has the maximum and the minimum number of \"win\" and \"loss\" points, respectively, in comparison with CC(m) algorithms.
## 1 Introduction
The main advantage of the presented approach is the proposed spatially explicit methodology that can be practically implemented for regional planning purposes to improve the charging infrastructure for private and public passenger transport at the same time. This research can inform and support policymakers to foster the larger-scale deployment of charging infrastructure for EVs, allocating public resources efficiently and contributing to the present effort of reaching the decarbonization targets. The research also aims at offering a basis for further spatially explicit analysis targeting electric mobility and assisting regions similar to the case study for what concerns the characteristics of their socio-economic and transport system. The work presented in this paper was developed within the framework of the two projects. The MOBSTER project \"Electric mobility for sustainable tourism\" (2019-2022) is an EU-funded project within the Interreg Italy-Switzerland Cooperation Programme. The main project aim is to encourage the spread of electric mobility and promote sustainable tourism in cross-border regions of Italy and Switzerland. One of its specific goals is to investigate the need for electric mobility infrastructures for private transport (electric passenger vehicles and e-bikes for individual use). The second project, \"Evaluation of different scenarios to switch the regional bus fleet of an Italian alpine region to zero-emission buses\" (2020-2021), was commissioned to Eurac Research by STA--Strutture Trasporto Alto Adige S.p.A.--to analyze the feasibility of replacing the whole provincial bus fleet (around 750 buses) with zero-emission buses. Following this request, the need for locating the charging infrastructure was also analyzed. The paper is structured as follows: in Section 2, the literature review on the main relevant issues is listed, followed by a brief description of the case study in Section 3, and the presentation of the analytical methodology in Section 4. The results are described and discussed in Section 5, and the conclusions are presented in Section 6, followed by a brief outlook of the next steps in Section 7.
# A Conceptual Model for Joint Graphic Representation of
## 2 Review of Methods for Servo-Driven Machine Representation
### Technical Drawing Standards
#### 2.1.2 Kinematic Diagrams  
The ISO3952 standard [4] describes a collection of graphic symbols for the simplified representation of rigid bodies, their mechanical relations, and even their movements. This standard focuses mainly on kinematic and dynamic analysis. The use of schematic representations of linear axes based on the standard can be found in the literature in [5,6]. In addition, these diagrams are employed in servo-driven systems [7] and Computer Numerical Control (CNC) machines, such as in [8].  
Given its schematic orientation, the standard removes certain mechanical information such as constructive details of the solids (material, finish, assembly, etc.) to focus on relevant data for modeling such as center of mass, distances between joints, types of mechanical links, etc.  
Figure 2 shows the representation of the servo axis shown in Figure 1b according to this standard. Both the fixed and the moving elements can be identified more easily this way than in the mechanical drawings. However, the information about the type of kinematic pair is irrelevant from the motion control perspective and it requires familiarity with the symbology.  
Despite its orientation towards the kinematics of the mechanical system, the ISO3952 standard does not consider the joint representation with the motion commands described by the motion controller program, nor does it consider the kinematic relations generated by these commands, such as electronic gears, virtual servo axes, and other resources. Finally,  
Figure 1: Linear servo axis with ball screw. (**a**) Technical drawing of the lateral view. (**b**) 3D view.  
Figure 2: Representation of a servo axis according to the ISO3952 standard. (**a**) Basic symbol. (**b**) Admissible symbol.  
representing systems with several servo axes, such as a Cartesian system, according to ISO3952, would be complicated since it makes it difficult to have a system of views such as those on the mechanical drawings.  
Therefore, it does not seem appropriate as a unified representation system of mechanics and motion automation, but its simple and direct symbology may serve as a basis.
## 5 Communication Dependability
### Cybersecurity for IEC61850 Multicast Communication in Wide-Area Applications  
WAN communication presents cybersecurity challenges and threats arising from the networked environment. Cyber-attacks to R-SV/R-GOOSE communication may not only modify or destroy critical monitoring and protection data but may also lead to undesired outages and damage to electrical components. Therefore, cybersecurity should be understood thoroughly in this context [35; 36], and security requirements must be satisfied. The security requirements depend on the wide-area applications. For example, IEC 61850-90-5 specifies security requirements for synchrophasors in which integrity and authentication are mandatory requirements, but confidentiality is an optional requirement.  
Whatever the wide-area application, cybersecurity solutions must be applied to R-SV/R-GOOSE communication to ensure data authenticity and thus the reliable operation of the application. This can be achieved by creating secured messages, a secured communication path, or both, as with the most common solutions shown in Table 3.
# A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications
## 6 Performance Evaluation
### Simulation Environment  
A brief description of used hardware and software components are provided in the following. The sending device uses the HackRF One card with the ANT500 antenna and the Nooelec Module Tiny TCXO 10 Mhz module, a very precise oscillator with very low phase noise. It has been chosen from the various options available for the HackRF One card in order to use GPS applications (see Figure 4). An Android smartphone (Samsung Galaxy S4) with the NMEA GPS software was used as the receiving device, which returns the GPS data in NMEA format (see Figure 4). A path consisting of eleven points on the map and then converted into NMEA format, to be simulated with the _GPS-SDR-SIM_ software has been created using Google Earth Pro software. From Google Earth Pro software, once a path has been choose, it is possible to generate a \"KML\" file which is then passed to another software: SatGen [31] in order to create the NMEA strings, see Figure 5. KML stands for Keyhole Markup Language is a file format used to display geographic data in an Earth browser such as Google Earth. The software interface allows to set in the form \"Dynamic settings\" some parameters regards the dynamic of the receiver by setting information about different accelerations and max speed that we have set to 100 km/h. Moreover, in the first graphic it is possible to view the path created in Google Earth Pro and in graphic below the speed profile of the receiver. The update rate for the route can be defined from 1 Hz to 100 Hz, this will defined the granularity of the route created in the software. We have set it to 10 Hz. This means to create 10 NMEA strings to each second.  
Subsequently, the simulator output file has been transferred in input to the HackRF One board using Windows 10 command as shown in Figure 6.  
Figure 4: HackRF One with its ANT500 antenna and Tiny TCXO 10 Mhz oscillator and smartphone used as GPS receiver.  
The GPS signal is transmitted by the HackRF One board towards the smartphone GPS receiver deactivating/activating the troposphere correction algorithm. The experimental results are given in the next section and they show tests made considering three different latitude coordinates.
## 4 Results
### Complexity and Cost Analysis of OrthoDETR
#### 4.4.2 Memory Usage and Training Cost  
By integrating the MLP-Mixer, OrthoDETR benefits from decreased memory usage, which results in lower training and inference costs. This streamlined architecture allows for efficient utilization of hardware resources and makes OrthoDETR more feasible for large-scale implementation. Moreover, the customized loss function contributes to better optimization during training, minimizing the required training epochs and overall associated costs.  
In summary, OrthoDETR exhibits a favorable balance between complexity, cost, and performance, making it a valuable tool for detecting orthopedic medical devices, especially in fine-grained management during workflow processes.  
Furthermore, our advanced DETR model demonstrates remarkable resilience when handling images that present a diverse array of challenges, such as variations in scale, occlusion, and background interference. This resilience stems from the implementation of multi-scale feature fusion modules, the employment of high-resolution inputs, and adjustments to the object query quantities within the model. These optimization tactics allow our refined DETR model to adapt effectively to a vast array of demanding visual scenarios, thereby elevating its performance in real-world applications. Consequently, these benefits equip our improved DETR model with the ability to address numerous challenges in practical contexts, ultimately enhancing the accuracy and practicality of orthopedic medical device recognition.  
In conclusion, our proposed method, centered on an enhanced DETR, achieves exceptional performance in the precise recognition of orthopedic medical devices, as evidenced by our experimental results. These findings not only validate the effectiveness and superiority of Transformers in object detection tasks but also introduce a pioneering, industry-relevant solution for the identification of orthopedic medical devices. Moreover, this research has the potential to provide valuable insights for the development of future object detection tasks across various domains.
## 6 Testing Results
### Testing of the Detection Techniques Using Simulated Signals
The other disturbances misclassifications ranged between 2%-10%, which was an improvement. Concerning the second strategy exploiting the hybrid architecture, a significant improvement on the Transient response recognition was reached resulting in a 51.1% misclassification with the No Fault condition. In both, precision and recall, it showed more or less a 50% chance of miss-classification. For the other disturbances the misclassification ranges between 1.3% and 4.8% which is also an improvement. The hybrid CNN-LSTM with adjusted hyperparameters was able to detect the Transient disturbance in 48.9% of the signals where the transient was present. The other architectures failed completely in this task. Furthermore, this architecture obtained better results on the other disturbance classifications. Figure 9: (a) Confusion matrix of the LSTM training, (b) Confusion matrix of the LSTM validation, (c) Testing of the LSTM using a simulated voltage waveform. Figure 10: (a) Confusion matrix of the CNN training, (b) Confusion matrix of the CNN validation, (c) Testing of the CNN using a simulated voltage waveform. Figure 11: (**a**) Confusion matrix of the CNN-LSTM training, (**b**) Confusion matrix of the CNN-LSTM validation, (**c**) Testing of the CNN-LSTM using a simulated voltage waveform. Figure 12: (a) Confusion matrix of the CNN-LSTM with adjusted hyperparameters training, (b) Confusion matrix of the CNN-LSTM with adjusted hyperparameters validation, (c) Testing of the CNN-LSTM with adjusted hyperparameters using a simulated voltage waveform.
# A Novel 1-D CCANet for ECG Classification
## 1 Introduction and Related Work
#### Our Contribution  
The main novel contributions of this paper are summarized as follows:  
* We have designed a novel one-dimensional canonical correlation analysis network (1-D CCANet) to exploit two-lead ECGs for automatic classification of heartbeats that outperforms the state of the art;
* We have explored the use of handcrafted features in combination with a 1-D CCANet for ECG classification;
* Our proposal outperforms a solution based on a suitable one-dimensional ResNet that we have implemented for the sake of comparison.
## 3 Polymerization Reactions within AAO Templates
### Other Polymerization Reactions
#### 3.5.1 Coordination Polymerization  
It is worth mentioning the work of [PERSON] et al. on the synthesis of syndiotactic polystyrene (sPS) [144] using a metallocene catalyst within AAO membranes in conjunction with methylaluminoxane cocatalyst carried out in a silica modified AAO template with the aim of obtaining nanofibrils of ultrahigh molecular weight. First, the pore surfaces of these AAO films were coated with silica by the surface sol-gel method, afterwards the metallocene catalyst was supported onto the inner walls of a silica nanotube reactor to make a silica-supported metallocene catalyst (anchoring the catalyst onto the surfaces of porous silica particles). For the polymerization of styrene, the catalyst-deposited silica AAO membrane was placed over a liquid mixture of styrene and \(n\)-heptane of different concentrations and the reaction carried out at 70 \({}^{\circ}\)C. The authors found very thin sPS nanofibrils (<10 micron) grown at the catalytic sites on the pore walls that aggregated to form intertwined, rope-like nanofibrils with diameters in the range of 30-50 nm, which further intertwined into even larger 200 nm diameter polymer nanofibrils. The sPS synthesized in the silica nanotube reactor (SNTR) has an ultrahigh molecular weight (Mw: 928,000 g/mol) with a large fraction of 2,000,000-5,000,000 g/mol molecular weight polymers [144].  
Figure 12: Scheme of synthesis of PNIPAm-AAm nanopillars [74]. Copyright 2017. Reproduced with permission from the publisher: RSC.  
The synthesis of polyethylene was carried out by heterogeneous Ziegler-Natta polymerization within nanochannels of anodized aluminum oxide (AAO) membranes [75]. The catalyst was chemisorbed at the inner wall of the nanocavities and monomers diffused from the outside. Results indicated the formation of a highly stressed crystalline structure attributed to catalytic production of excess amounts of polyethylene inside the nanoconfined templates.
# Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
## 5 Experiment and Analysis
### Comparison of Demodulation Performance  
In order to further verify the performance of the proposed DRdA-CA, the demodulation experiments after denoising have also been done. We present two higher-order modulation modes, 16 QAM and 64 QAM, since their demodulations are more challenging in the low SNR range for practical communication systems. The RLS algorithm with better performance in the traditional methods and the DnCNN are selected as the comparison methods. Figure 11 shows the BER curves of demodulation for the 16 QAM and 64 QAM signals after denoising with different methods.  
From Figure 11, it can be seen that the three noise reduction methods can improve the BER performance of signal demodulation. The DnCNN improves BER more than the traditional RLS, and the proposed DRdA-CA considerably outperforms both of these two methods. When the SNR ranges from \(-\)12 dB to 8 dB, the proposed method decreases the BER by 22.3%\(\sim\)99.5% for 16 QAM and 12.6%\(\sim\)93.7% for 64 QAM compared to the non-denoising method. In addition, compared with RLS and DnCNN, DRdA-CA reduces the BER of 16 QAM by 63.5% and 40.5% on average, and makes the BER of 64 QAM decline by 46.7% and 18.6% on average, across the entire SNR range. For 16 QAM signals at an SNR of 8 dB, when using the RLS and DRdA-CA, the BER of demodulation is reduced by two orders of magnitude, from 0.05 to 0.0005. Besides, compared with DnCNN, the BER of DRdA-CA is only 1/6 of the former. When it comes to the 64 QAM signals at an SNR of 8 dB, compared with the non-denoising method, DRdA-CA makes BER decrease from 0.195 to 0.0123, and compared to the RLS and the DnCNN, it reduces the BER of 64 QAM by 91.49% and 43.34%, respectively.  
Figure 10: Classification accuracy of different networks for each modulation type. (a) DnCNN+CNN. (b) DRdA-CA+CNN.
## 6 Experimental Results
### Dealing with Large Datasets
#### 6.5.2 Probabilistic Approach  
As mentioned earlier, in this section, we conduct experiments only for datasets D8 and D9, which are those with highest average length. The performance of the statistical model has been explored thanks to the quality index of perplexity computed within ESCAPE. These results are shown in Table 14, where low perplexity values indicate better results.  
Regarding dataset D8 on Luggage reviews, the LogTF-IDF weighing strategy differs from the others since it provides a more detailed analysis and also discovers subtopics, in addition to the five main topics already discovered by the other schemas. Instead, this different level of result granularities is not present for the Video product category dataset (D9).  
The graphical visualization of the results obtained with D8 is then shown in Figure 6. In Figure 6a,c, we can see similar shapes and distribution of the documents between the clusters. In Figure 6b, it is possible to recognize an imbalance of the coloring of the points: the five main topics, containing a major number of documents, and three smaller subtopics.
## 3 Results and Discussion  
### Factors Influencing the Entrance and Exit Delamination Factors  
#### 3.1.1 Influence of Cutting Speed on the Entrance and Exit Delamination Factors  
Figure 4 shows the morphology of the entrance and exit at different cutting speeds, and Figure 5 shows the entrance and exit delamination factors at different cutting speeds. According to Figure 5b, the delamination factors of the entrance and exit gradually decreased with the increase of cutting speed. However, the delamination factor of the exit was significantly greater than that of the entrance. This may be due to the pushing action of the chisel edge of the drill bit at the exit, which made the stripping of the fiber more serious, and the delamination factor of the exit was larger. According to reference [3; 4; 5], the main cause of delamination on the pore surface was the thrust force in the drilling process. From Figures 4 and 5, the greater the cutting speed, the smaller the thrust force, and the lower the entrance and exit delamination factors. In addition, the large amount of cutting heat generated in the drilling process raised the drilling temperature and melted the resin matrix, which had a significant impact on the delamination tearing at the hole exit [1].