esa-sceva/satcom-chunk-collection huggingface dataset:
# Managing Emergency Situations in VANET Through Heterogeneous Technologies Cooperation
## 7 Conclusions  
In this work, a new distributed architecture is proposed to improve ITS management for enhancing the performance of traffic solutions. To this aim, an E-OBU is proposed by exploiting VANET infrastructure to gather and disseminate information with the main goal to increase safety with a better management of road traffic. The E-OBU is able to recognize dangerous events and notify alerts to neighbor vehicles. The spreading of these messages permits to reduce collision number and probability of road blocks. The full integration of on-board devices such as GPS, proximity sensors and communication network infrastructure has permitted to enhance also the driving comfort. Under the point of view of ITS management, the main idea is to spread information collected by vehicles in the high architecture layers, distributing the elaboration tasks between local and global devices. In particular, the work shows how it is possible to attribute global policies and decision to HRTM to perform rerouting policies to balance vehicle density among lanes to mitigate congestion and reduce the experienced traveling time. On the other side the LRTM can perform local policies to take quick decision for tackling collisions or traffic jams events. Roads map has been modeled on the basis of a graph structure, where cross-roads represent the vertexes of the graph and lanes are its edges. Moreover, a protocol has been proposed to allow a more reliable and efficient data dissemination between layers. Finally simulative campaigns have been carried out to demonstrate the goodness of proposal: an E-OBU can enhance the safety level of drivers and may help to reduce the number of collisions. Moreover, with a safer environments it is possible to improve driver comfort increasing the average speed of vehicle and reducing the traveling time because of a lower number of collisions and forced re-routing. The achieved results have demonstrated that the proposed protocol that exploits the VANET infrastructure for vehicle communication can enhance several quality indexes along the roads, by allowing the spreading of important data in beaconing messages, based on the IEEE802.11p and DSRC communications.  
[PERSON] conceived and designed the system architecture and he performed simulation experiments; [PERSON] provided literature analysis and defined protocol interfaces and messages; [PERSON] conceived and designed the mathematical formulation of the proposed algorithm for the congestion control and management; [PERSON] analized the data results of the simuation experiments and supervised the overall paper writing.  
Figure 18: (**a**) _Extended On-Board Unit_ (E-OBU) Protocol data messages related to dangerous events; (**b**) RSU Protocol data messages involved in warning dissemination and rerouting activities.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# The Flexural Fatigue Behavior of Honeycomb Sandwich Composites Following Low Velocity Impacts
## 3 Results and Discussion
### Flexural Tests  
Figure 5 shows the images of specimens--with aluminum and CFRP face sheet materials--obtained after three-point bending tests for different face sheet thicknesses. Examining the images, in the model with an aluminum face sheet of 0.5 mm thickness, face wrinkling damage was observed at the first contact point of the cylinder used to apply the load. This damage was seen to be less in the model with a 1 mm face sheet thickness, while it was not seen in the specimen with a 1.5 mm face sheet thickness.  
For specimens with a 0.5 mm thick CFRP face sheet, local crushing was dominant in the honeycomb core to which the load was applied. The amount of deflection increased with increasing face sheet thickness and the highest flexural damage was observed in the model with a face sheet thickness of 1.5 mm (Figure 5).  
There was no surface damage observed in specimen 6 CFRP10a with a 0.5 mm face sheet thickness, while face sheet material damage was determined in the specimens with thicknesses of 1 and 1.5 mm.  
The first damage type seen in the core at the beginning of the test was cracks in the cell walls. With the continuation of the test, crushing of the cell walls and adhesive damage of the cell walls were observed. With the catastrophic failure seen in the samples, the damage has been concentrated especially in the support areas.  
Figure 6 shows applied force vs. deflection graphs for pristine specimens with aluminum and CFRP face sheet materials. For both face sheet materials, increasing face sheet thickness increased flexural strength. Moreover, for the same load values, deflection decreased due to increased face sheet thickness. For example, in the specimens with an aluminum face sheet, the amounts of deflection under a 2000 N load were measured to be 1.133 mm, 0.917 mm and 0.524 mm for 6 AL10a, 6 AL10b and 6 AL10c specimens, respectively.  
Figure 5: Specim images obtained after three-point bending tests.  
Figure 6: Effect of (**a**) face sheet thickness, (**b**) face sheet type and (**c**) core height on flexural strength.  
Increased face sheet thickness reduced the amount of damage formed on the face sheet after impact tests. Similarly, increasing face sheet thickness decreased flexural strength more after impact. The reason for this was that during impact loading on increasing face sheet thicknesses, more of the applied load was transferred to the cores.
## 3 Experimental Methodology
### Performance Evaluation Metrics  
To evaluate the performance of the proposed systems, the following aspects are investigated:  
1. Per class and overall comparison of different cepstral, temporal, and spectro-temporal features classified using various pre-trained neural network and machine learning models.
2. Effects of balancing the datasetAside from the standard accuracy, evaluations of the performances of different techniques were also compared and measured in terms of their F1-scores. This is defined to be a measure that takes into consideration both the recall and the precision, which are derived from the ratios of True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN) [68], which can be extracted from confusion matrices.  
The databases used for this research compose of unequal numbers of audio files per category. To account for the data imbalance, two different techniques are used:  
1. Balancing the Dataset  
Particularly used for the initial development and experiments conducted for this work, in this technique, the dataset was equalized across all levels in order to preserve a balanced dataset. This is done in order to avoid biasing in favour of specific categories with more samples. It is achieved by reducing the amount of data per level to match the minimum amount of data amongst the categories. Selection of the data was done randomly throughout the experiments.  
2. Using Weight-sensitive Performance Metrics  
Provided that the F1-score serves as the main performance metric used for the experiments conducted, it is crucial to ensure that these metrics are robust and unbiased, especially for multi-classification purposes. When taking the average F1-score for an unbalanced dataset, the amount of data per level may affect and skew the results for the mean F1-score in favour of the classes with the most amount of data. Therefore, we consider three different ways of calculating the mean F1-score, including the Weighted, Micro, and Macro F1-scores, in order to take into account for the dataset imbalance [69].
# Empirical Evaluation of TLS-Enhanced MQTT on IoT Devices for V2X Use Cases
## 5 Performance Evaluation
### TLS Handshake Evaluation
\begin{table}
\begin{tabular}{l|c c c c|c c c|c c c} \hline \hline  & \multicolumn{4}{c|}{**CPU (Mcycles)**} & \multicolumn{4}{c|}{**Execution Time (s)**} & \multicolumn{4}{c}{**Energy (J)**} \\ \hline \multirow{2}{*}{**Role**} & \multirow{2}{*}{no-TLS} & Key Exch. & Server Auth & Client Auth & \multirow{2}{*}{no-TLS} & Key Exch. & Server Auth & Client Auth & \multirow{2}{*}{no-TLS} & Key Exch. & Server Auth & Client Auth \\  & (ECDHE) & (Ed25519) & (Ed25519) & (Ed25519) & (Ed25519) & & & & & \\ \hline Publisher & 321 & 379.3 & 381.8 & 439.6 & 0.234 & 0.248 & 0.255 & 0.289 & 0.554 & 0.588 & 0.604 & 0.685 \\ Subscriber & 362 & 411.3 & 422.9 & 426 & 0.267 & 0.268 & 0.283 & 0.286 & 0.633 & 0.635 & 0.670 & 0.678 \\ Broker & 19 & 19.8 & 19.8 & 20.2 & 0.0118 & 0.0119 & 0.0121 & 0.0121 & 0.0222 & 0.0224 & 0.0228 & 0.0228 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: TLS handshake evaluation: CPU usage, time requirements, and energy consumption. introduce a measurable increase in execution time and energy usage for the publisher and subscriber, yet the impact on the broker remains minimal. The handshake process did not result in any significant changes in memory usage throughout all TLS phases. The maximum RSS remains constant at 21,400 KB for both the publisher and subscriber and at 11,600 KB for the broker, which shows that TLS handshake complexity affects latency more than memory usage. These findings demonstrate the trade-offs which occur when deploying MQTT securely. TLS 1.3 proves to be suitable for constrained IoT environments due to the fact that it introduces manageable handshake overhead while keeping memory usage minimal. Our results presented in this section follow the same general trend as those reported in [7]. Specifically, we observe a consistent increase in CPU usage across all roles as we move from no-TLS to the progressive integration of TLS Phases 1, 2, and 3. Similarly, as in their study, the maximum RSS remains stable on the client side, even when TLS is activated. However, a key distinction lies in the experimental environment: whereas [PERSON] et al. [7] conducted their evaluation using virtual machines hosted on server infrastructure, our measurements are derived from execution on real IoT devices, specifically Raspberry Pi 4\(B\) boards, over Ethernet.
## 4 Experiment and Results
### Proposed Similarity Measure Experiment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Users** & **User 1** & **User 2** & **User 3** & **User 4** & **User 5** & **User 6** \\ \hline User 1 & 1.000000 & 0.999999 & 0.999133 & 0.999133 & 0.999133 & 0.999383 \\ User 2 & 0.999999 & 1.000000 & 0.999201 & 0.999201 & 0.999201 & 0.999323 \\ User 3 & 0.999133 & 0.999201 & 1.000000 & 1.000000 & 1.000000 & 0.997054 \\ User 4 & 0.999133 & 0.999201 & 1.000000 & 1.000000 & 1.000000 & 0.997054 \\ User 5 & 0.999133 & 0.999201 & 1.000000 & 1.000000 & 1.000000 & 0.997054 \\ User 6 & 0.999383 & 0.999323 & 0.997054 & 0.997054 & 0.997054 & 1.000000 \\ Max (for 100 users) & 1.000000 & & & & \\ Min (for 100 users) & 0.997906 & & & & \\ Mean(for 100 users) & 0.999568 & & & & \\ IQR & 0.000594 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Similarity Matrix based on Demographic. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Users** & **User 1** & **User 2** & **User 3** & **User 4** & **User 5** & **User 6** \\ \hline User 1 & 1.000000 & 0.857143 & 0.169031 & 0.104828 & 0.000000 & 0.000000 \\ User 2 & 0.857143 & 1.000000 & 0.169031 & 0.00000 & 0.000000 & 0.000000 \\ User 3 & 0.169031 & 0.169031  
accurate result. Moreover, the semantic between author and writing community was 82%. Consequently, the results from the Gensim FastText model used to calculate the semantic similarity gave accurate results.
## 5 Core Loss Calculation Results
Fourier analysis was performed on the phase A current in the constant power field weakening control area, and the main harmonic current of phase A with SVPWM at different speeds was obtained as shown in Figure 16. As can be seen from the figure, due to the different fundamental frequencies of different speeds, the main harmonics of the phase A current generated by the inverter were also different. Figure 17 shows the amplitude of each harmonic component of the tooth radial flux density and the yoke tangential flux density under SVPWM. Compared with Figure 10, it can be seen that the higher harmonics of the radial flux density of the teeth and the tangential flux density of the yoke increased significantly, and the main harmonics of the flux density at different speeds were consistent with the current harmonics. Compared with the sinusoidal current source, the harmonics generated by the inverter caused an increase in core loss. The core loss of the stator core loss under the sinusoidal current source and SVPWM and the increased core loss of SVPWM relative to the sinusoidal current source are shown in Figure 10:0  
It can be seen from Table 4 that in the field weakening control area (3200-6000 rpm), compared with the sinusoidal current source loading, the motor loss under inverter SVPWM increased significantly, and the stator core loss increased by more than 25%. Results of the Fourier analysis of the stator core loss under SVPWM and the stator iron core loss harmonics are shown in Figure 10:1  
Compared with Figure 16, the influence of time harmonic current on stator core loss can be clearly seen from Figure 10:2  
At different speeds, the stator core loss cloud chart is shown in Figure 10:3 It can be seen from the figure that the stator core loss of the motor was mainly distributed in the stator teeth, and the loss distribution at different speeds was slightly different. The higher the speed, the greater the local core loss. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Speed (rpm)** & **Sinusoidal Current Source (kW)** & **SVPWM (kW)** & **Increased Ratio (\%)** \\ \hline
3200 & 1.7780 & 2.3318 & 31.15 \\
4000 & 1.6641 & 2.1737 & 30.62 \\
5000 & 1.6515 & 2.0902 & 26.56 \\
6000 & 1.7109 & 2.1550 & 25.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Stator core loss under sinusoidal current source and SVPWM. Figure 15: Phase A harmonic current under different harmonic orders at 5000 rpm. Figure 16: Space vector pulse width modulation (SVPWM) phase A harmonic currents at different speeds. Figure 17: The amplitude of each harmonic component of the tooth radial flux density and yoke tangential flux density under SVPWM. Figure 18: _Cont._
# A Novel L-Shaped Metalens for Ultra-Wide Band (UWB) Antenna Gain Improvement
## 1 Introduction  
Ultra-wide band antennas in planar technology have attracted research interest during the last decade thanks to their high gain, wide bandwidth, compact size, high efficiency, stable radiation pattern, and low sidelobe level. In particular, planar Vivaldi antennas find application in different fields, such as ground-penetrating radar (GPR) [1], fifth-generation (5G) and ultra-wide band (UWB) communication systems [2; 3; 4; 5; 6], microwave imaging [7; 8], and medical applications [8]. Various technologies have been proposed in the literature to fabricate low-cost and highly compact Vivaldi antennas, employing laser etching/writing [2], standard printed circuit board (PCB) technology [3; 4; 5; 6; 7; 8], 3D additive manufacturing processes [9], and substrate-integrated waveguide (SIW) technology [10].  
Metamaterials find applications in many fields. Lenses based on metamaterial technology (metalenses) are designed, optimized, and fabricated in order to improve antenna radiation performance [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. Non-resonant metamaterials (NRMs), based on conventional parallel-line unit cells, are used to enhance the gain [15; 16; 17] and to stabilize the radiation pattern [17]. In [18], a zero-index metamaterial (ZIM) by using meander line cells is proposed for obtaining a gain increase of about 4 dB of an antipodal Vivaldi antenna (AVA). A modified H shape cell is used for a ZIM lens to obtain a gain enhancement of up to 2.6 dB of an AVA slot antenna in frequency bands close to 60 GHz [19]. Epsilon-near-zero metamaterials (ENZMs) can be exploited to increase the gain of conventional [20] and double-slot AVAs [21]. Gradient refractive index (GRI) metasurface lenses, based on non-resonant unit cells, allow the improvement in the radiation properties if placed in front of the antenna at an optimized distance [22; 23].  
In this work, a novel metalens is designed to improve the radiation performance of an antipodal Vivaldi antenna, operating in the \(f_{AVA}=3\div 13.5\) GHz band. The preliminary design of the metalens has been performed with the S-parameter retrieval method (SPRM) [28; 29], and the optimization of the antenna with the metalens has been performed with full-wave numerical simulations. The L-shaped geometry of the unit cell is novel. Itsgeometry allows a design with a high degree of freedom with the aim of increasing the gain and maintaining, or slightly increasing, the bandwidth of an AVA. A prototype has been fabricated with the PCB process. The characterization of the fabricated prototype shows good agreement with the simulation. The measured maximum gain is increased from \(G_{max}=9.4\) dB for the case without the metalens to \(G_{max}=14.2\) dB with the metalens. Moreover, an increase of about 10% in the operation bandwidth and a maximum reduction of about 50% at \(f=14\) GHz in the half-power beamwidth is obtained.
## 2 Related Works
### Integration of IoT Platforms in Agriculture  
Recent studies have explored the integration of IoT technology with data collection and machine learning for analysis. These technologies were initially planned for use in smart cities but have since found significant parallels and applications in agriculture. For instance, [PERSON]. [17] employed deep learning convolutional neural networks (CNNs) to extract features from public infrastructure data, expediting evacuation procedures and contributing to smart city development. Similarly, using IoT platforms in agriculture can allow for the dynamic adjustment of irrigation systems and greenhouse environments based on real-time data, optimizing resource use and enhancing productivity. In [18], IoT technology facilitated urban development by improving public services and economic efficiency, similar to the way IoT in agriculture supports sustainable farming practices and increases yield through improved resource management.  
However, smart cities and agriculture share common challenges when relying predominantly on cloud computing, such as latency issues and network limitations that negatively impact real-time system responses [19]. To overcome these limitations, fog and edge computing have emerged as promising alternative technologies. For example, an intelligent tracking system based on ZigBee wireless networks was proposed for real-time data aggregation in smart cities [20]. In agriculture, similar wireless sensor networks (WSNs) are important tools for monitoring soil conditions, crop health, and livestock. These networks, similar to those used in urban environments, rely on dynamically configured nodes (such as sensors and actuators) that function efficiently within centralized monitoring systems.  
[PERSON]. [21] introduced a framework for IoT-enabled smart city applications using big data analytics. This has potential benefits for agriculture by enhancing the analysis of sensor data to improve decision making. However, a key limitation is the data loading speed, which can hinder real-time applications. This is particularly important in agriculture, where immediate responses to environmental changes are necessary for operations such as irrigation and pest management.  
While cloud computing offers extensive storage and computational capabilities, its latency is a hindrance to time-sensitive agricultural applications [22]. This has led to the growing adoption of edge computing [23], which processes data closer to the source, reducing latency and improving the real-time performance required for tasks such as automated irrigation and pest detection. Overall, the integration of IoT platforms with edge and fog computing, combined with advanced data analysis, has the potential to revolutionize the management and efficiency of modern agriculture.
## 4 Experimental Framework
### Experimental Setting  
The proposed ProLSFEO-LDL algorithm is firstly applied, in a pre-preprocessing step, to the raw datasets obtaining a subset of training instances and a matrix of label-specific features. The subset of selected instances will make up the new pre-processed training set that the learner will train with. As we mentioned before, the selected learner is AA-kNN. Regarding the label-specific features,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Name** & **Formula** \\ \hline [PERSON](Cheby)\(\downarrow\) & \(Dis(D,\hat{D})=max_{j}|d_{j}-\hat{d}_{j}|\) \\ \hline Clark\(\downarrow\) & \([PERSON](D,\hat{D})=\sqrt{\sum_{j=1}^{\zeta}\frac{(\hat{d}_{j}-\hat{d}_{j})^{2}}{( \hat{d}_{j}+\hat{d}_{j})^{2}}}\) \\ \hline Canberra(Can)\(\downarrow\) & \([PERSON](D,\hat{D})=\sum_{j=1}^{c}\frac{|\hat{d}_{j}-\hat{d}_{j}|}{\hat{d}_{j}+\hat{d }_{j}}\) \\ \hline Kullback–Leibler(KL)\(\downarrow\) & \([PERSON](D,\hat{D})=\sum_{j=1}^{c}d_{j}\ln\frac{\hat{d}_{j}}{\hat{d}_{j}}\) \\ \hline Cosine(Cos)\(\uparrow\) & \([PERSON](D,\hat{D})=\frac{\sum_{j=1}d_{j}}{\sqrt{\sum_{j=1}^{c}d_{j}^{2}}\sqrt{\sum _{j=1}^{c}d_{j}^{2}}}\) \\ \hline Intersection(Inter)\(\uparrow\) & \([PERSON](D,\hat{D})=\sum_{j=1}^{c}min(d_{j},\hat{d}_{j})\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation measure for LDL learners. \(\downarrow\) means that the lowest value is the best and \(\uparrow\) means the opposite.  
we have adapted the AA-\(k\)NN algorithm to support the selection of features, for this, each label is predicted separately using only the characteristics marked as selected. The prediction obtained is normalized to make it compatible with the LDL constraints.  
The results will be compared with those obtained by the same learner without applying data reduction as a pre-processing step. All measures were computed over a merged set from the test predictions using a wrapped 10-fcv. Note that this procedure is over the entire process and differs from the cross-validation used to estimate the fitness function within the optimization process.  
The parameters used in experiments are summarized in Table 3. The AA-\(k\)NN algorithm used in the fitness function of ProLSFEO-LDL method and in the subsequent learner requires a value for \(k\), set to four neighbors. We have selected a small value for \(k\) in order to provide the most flexible fit with a low bias.
## 3 Results and Discussion
### Hydration Process and Microstructure of Ultrafine Calcium Silicate Cement Slurry
#### 3.5.3 SEM Analysis  
To further study the microstructure of oil well cement-based composite, the pure sample (without early-strength agent) and the cement sample added with 8% ultrafine calcium silicate were taken for micromorphology analysis. Figure 9a is the SEM photo of the cement sample without adding ultrafine calcium silicate, and Figure 9b is the SEM photo of the cement stone with 8% ultrafine calcium silicate. Generally, the hydration products of oil well cement mainly include fibrous calcium silicate hydrate and flaky calcium hydroxide. The more obvious the hydration products are, the higher the hydration degree of cement is. It can be seen from Figure 9a that there are granular, massive and isolated hydration products inside the blank cement stone. The hydration products of cement particles are less covered, the lap between hydration products is weak. For the 8% ultrafine calcium silicate sample, Figure 9b showed that there were more flocs of fibrous calcium silicate hydrate, and a small amount of flake Ca(OH)2 crystals were stacked on the amorphous C-S-H gel, and the hydration products formed dense and uniform, indicating a higher degree of hydration. The ultrafine calcium silicate promotes the hydration of oil well cement and improves the microstructure.  
Figure 8: XRD analysis of hydration products.  
Figure 9: SEM images of cement samples. (**a**) 0% ultrafine calcium silicate; (**b**) 8% ultrafine calcium silicate.