esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Visual Validation Analysis  
Majority-based decision fusion methods clearly identify and justify three clusters as \"true\". Nevertheless, for transparency reasons, the authors have attempted to analyze the clustering results using other validation concepts by means of visual clustering assessment tools.  
Cluster analysis is an unsupervised and often exploratory issue without fixed, predefined expectations from the expert. Patterns in the data set that qualify to be interpreted as clusters could have very manifold shapes, configurations, and appearances. Therefore, in clustering validation assessment, there is room for the concept of visual analysis. Visible patterns in the data set may be identified as clusters. Effective plots for visual cluster validation include scatterplots, multidimensional plots, parallel coordinate plots, etc., in which the revealed clusters can be indicated by colors or shades, contour maps, dendrograms, and silhouette plots. Another broad category of visual validation methods is the visual assessment of tendency (VAT) family of algorithms, which reorders the pairwise dissimilarity matrix and then generates a cluster heat map that shows possible clusters in the data as dark blocks along the diagonal [106]. Moreover, the iVAT algorithm is an improved version of the VAT algorithm that more clearly shows the number of clusters, as well as their sizes [107]. The gap statistic can also be classified as a visual validation method. The basic idea of the gap statistics is to choose the number of clusters where the largest \"jump\" in within-cluster distance occurs.  
Visual clustering validation methods should be used as informal graphical illustrations, as a rule of thumb. Moreover, to begin with, such methods should be used with great caution since the visual representations of clusters may significantly vary with the nature of the parameter being tuned (dissimilarity measure, clustering algorithm, etc.) and the validation indices used. Nevertheless, the visual validation concept remains crucial and valuable for cluster validation assessment. The presented visual clustering validation methods are illustrated in Figure 6.  
Figure 6: Visual cluster validation plots of the clustered data set (379 data points): (**a**) gap statistic plot; (**b**) visualization of VAT validation method; (**c**) silhouette plot; (**d**) visualization of iVAT validation method.  
Figure 6 clearly shows that the visual methods reveal three \"true\" clusters. VAT and iVAT algorithms show the number of clusters, the visual sizes of clusters, and the distances among them. Therefore, Figure 6b,d display two clusters--near each other and far from the third. Nevertheless, the number of separate \"true\" clusters remains at three. This only justifies the veracity of clustering validation results presented by the majority-based decision fusion methods. The concluding statement is that there are significant differences between traditional, STEM, and STEAM educational modes in sensor-based engineering out-of-school courses in secondary school settings.
However, the published works [27; 28; 29; 30; 31; 32; 33; 34; 35] did not apply FC into the RIS-based TWR systems. This paper investigates TWR CR schemes that incorporate FC, RIS, and wirelessly energy harvesting (EH). In our model, two secondary sources aim to exchange data with the help of a RIS deployed in the network. Using FC, one source continuously sends FC packets until the other source has collected enough to fully recover the original data. Moreover, the transmit power of each source is adjusted based on an interference constraint set by a primary user and the energy harvested from a power station. The new points and main contributions of this work are summarized as follows:  
* Firstly, this paper considers two TWR schemes, i.e., conventional scheme (named Cov-Scm) and modified scheme (named Mod-Scm). The purpose of proposing the Mod-Scm scheme is to enhance the reliability of data transmission and reduce delay time, compared to the Cov-Scm. * Secondly, we derive closed-form expressions for OP at each source, system outage probability (SOP), and average number of FC packet transmissions needed for successful data exchange in the proposed schemes over Rayleigh fading channels. * Next, simulation results are presented to validate our analytical findings and compare the performance of the considered schemes. * Finally, we examine the effects of key parameters on overall performance. The results also present that the Mod-Scm scheme obtains better performance, as compared with the Cov-Scm scheme, in terms of reliability (OP, SOP) and delay time (average number of FC packet transmission). The remaining structure of this paper is as follows: Section 2 describes the system model for the considered TWR CR schemes along with operational principles. In Section 3, we compute the performance via mathematical expressions. Section 4 validates the analytical findings through simulations. Finally, Section 5 gives important conclusions and insights.
# Study on Impact-Echo Response of Concrete Column near the Edge
## 2 Background  
### Principle of Impact-Echo Method  
The impact-echo method is a non-destructive testing method that uses transient impact to excite low-frequency stress waves to propagate the inside of the object to be tested. The generated P and S waves propagate along the hemisphere wavefront inside the structure, and the R wave propagates on the surface of the structure to be measured. During the propagation of stress waves, reflections occur when significant defects are encountered in the medium [20]. The transient resonance caused by reflection changes the displacement of the surface of the structure to be measured, and then the signal generated by the resonance is received by the sensor near the impact point. Usually, the signal that the sensor is able to receive is generated by P waves. The displacement waveform signal with periodic property is obtained by signal amplification, and then the time-displacement signal can be converted into amplitude spectra via fast Fourier transform (FFT) [21]. The dominant frequency in the amplitude spectra reflects the thickness or internal defect of the structure. The specific location of the thickness or defect of the structure can be calculated using the following formula [22; 23].  
\[f=\frac{\beta k}{n}\frac{C_{P}}{T} \tag{1}\]  
where  
\[C_{P}=\sqrt{\frac{E(1-\
u)}{\rho(1+\
u)(1-2\
u)}} \tag{2}\]  
\(k\) is the geometric correction coefficient; \(T\) is the cross-sectional thickness of the structure; \(n\) is a constant that depends on the acoustic impedance and generally takes 2 or 4 [24];\(\beta\) is a correction coefficient with a value range of 0.75-0.96; C\({}_{P}\) is the longitudinal wave velocity; and \(E\), \(\rho\), and \(v\) are the elastic modulus, density, and Poisson's ratio of the test object, respectively [25; 26].
# DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks
## 5 Performance Evaluation
### Compared Algorithms  
In this subsection, we present algorithms with which the proposed DDS scheme is compared: the optimal anycast packet forwarding (OAPF) [16], the optimal geographical progress (OGP) [19], and two extended DASF [25] algorithms. The objective of OAPF and OGP is to minimize E2E delay with a given duty cycle interval while the DASF algorithm aims at satisfying the delay constraint in the duty-cycled WSNs.
# The Quest for Low Loss High Refractive Index Dielectric Materials for UV Photonic Applications
###### Abstract  
Nanostructured High Refractive Index (HRI) dielectric materials, when acting as nanoantennas or metasurfaces in the near-infrared (NIR) and visible (VIS) spectral ranges, can interact with light and show interesting scattering directionality properties. Also, HRI dielectric materials with low absorption in these spectral ranges show very low heat radiation when illuminated. Up to now, most of the studies of these kind of materials have been explored in the VIS-NIR. However, to the best of our knowledge, these properties have not been extended to the ultraviolet (UV), where their application in fields like photocatalysis, biosensing, surface-enhanced spectroscopies or light guiding and trapping can be of extraordinary relevance. Here, we present a detailed numerical study of the directional scattering properties, near-field enhancement and heat generation of several materials that can be good candidates for those applications in the UV. These materials include aluminum phosphide, aluminum arsenide, aluminum nitride, diamond, cerium dioxide and titanium dioxide. In this study, we compare their performance when forming either isolated nanoparticles or dimers to build either nanoantennas or unit cells for more complex metasurfaces.  
Keywords:high refractive index dielectrics; UV plasmonics; scattering +
Footnote †: journal: Applied Physics Letters
# Geophysical Monitoring of Leachate Injection in Pretreated Waste Landfill
## 2 Materials and Methods
### Geophysical Characterization  
The preliminary geophysical characterization of the landfill was carried out using ERT surveys for estimating the distribution of electrical resistivity on 2D vertical sections of the landfill. The survey focused on mapping the inner heterogeneity of the landfill to a depth of about 15 m within the waste.  
The data acquisition was carried out by 6 ERTs, with 48 electrodes for each profile, using a Wenner-Schlumberger array. The horizontal spacing between the electrodes was 2 m, and the distance among the parallel lines was about 10 m (Figure 1). Lines 1-5 are located over the flat area of the landfill and did not present any relevant morphological aspect of the topography. Line 6 crossed Lines 1-5 along the direction from south-east towards north-west; this latter line showed a severe elevation change of about 8-9 m toward the end of the profile. This is due to the local morphology of the landfill that is partially created as an embankment; the final part of Line 6 ended along the side slope of the embankment at a lower quote than Lines 1-5.  
The data acquisition allowed the measurement of more than 500 independent measurements of electrical resistivity along each transect. The data processing was performed using the code R2, which was developed at Lancaster University by [PERSON] [26; 27].
## 4 Results
### Improved Performance on the Argoverse Dataset
#### 4.2.2 Quantitative Evaluation  
Quantitatively, we show that our method shows significant improvements in APE over Semantic and BAAM on the successfully reconstructed trajectories. It is important to note that both Semantic and MM-VSM require a reconstruction step in order to be able to estimate poses. This means that we can only evaluate pose estimates on trajectories that are sufficiently long and detected by OpenPifPaf consistently enough to be reconstructed. However, as our goal is _not_ to evaluate an object detector but to improve the accuracy of pose estimation of already detected objects, our statistical analysis stands. Please note that for general, in-vehicle autonomous driving, 3D object detection applications metrics that describe _object presence_ detection accuracy (such as mAP) may be better. Please also note that BAAM or OpenPifPaf were _not retrained_ to the Argoverse dataset in order to show general applicability of as-is pretrained models.  
**In the histogram, the following can be observed:**  
* MM-VSM produces the most accurate pose estimates.
* Semantic produces the second-best peak accuracy with a large tail indicating many failed cases.
* BAAM produces larger but more consistent errors in APE than Semantic.  
These findings support our assumptions that using LiDAR data to produce a geometrically correct _Semantic Mesh Model_ reduces erroneous reconstructions, therefore significantly improving the pose estimation robustness of Semantic on the Argoverse dataset.  
In addition to the histogram (Figure 8), we also present a numerical analysis. Because our pose estimation relies on a direct least squares problem, there are cases where the result diverges. This means that simply calculating averages is misleading, as a handful of extremely large errors can severely degrade the results. We therefore classify extremely large errors as failures and perform a failure analysis. We use two separate gates: a standard and a strict gate. Table 1 reports how many frames each method loses when the standard gate (10 m, 45\({}^{\circ}\)) is applied and when the gate is tightened to 5 m, 30\({}^{\circ}\). MM-VSM (ours) ranks  
Figure 7: This is an example of a failed reconstruction produced by the image-only method [15] plotted in blue. The LiDAR-assisted correction is plotted in green.  
second-best for default robustness but becomes the most reliable once the stricter threshold is used. Semantic [15] is the most fragile under both settings.  
On the frames that are not classified as failures, we perform a deeper statistical evaluation. Tables 2 and 3 present the error statistics on the frames that survive the _default_ gate. In addition to the mean (\(\mu\)) and standard deviation (\(\sigma\)), we list the median and the 95-percentile (\(P_{95}\)) to characterize distribution tails. The results clearly show that on the non-failed detections, our method produces superior pose estimation compared to the relevant methods. This is true only if the reconstructed models for the vehicles are available and the object is detected in the image by OpenPifPaf.
## 9 Enabling Technologies for Energy Efficiency in UAV-Based Cellular Networks
### Network Slicing/Network Function Virtualization  
Network function virtualization (NFV) is a networking paradigm whereby network functions (such as address translation, firewall services, storage, routing, deep packet inspection, etc.) are decoupled from the underlying hardware and instead realized as software (hence the term _network softwarization_) that can be run on standard commercial off-the-shelf servers/equipment [291] and accessed via standardized interfaces. NFV helps to improve the flexibility, resilience, EE, and management of UAV-based networks. The softwarization of network functions leads to less processing at UAV-BSs, which significantly improves the energy usage of such BSs.  
In such software-defined network scenarios, the automation of network management functions will free the UAV from performing those functions, so that its on-board energy can be dedicated to performing other tasks [292]. For instance, the authors in [293] introduced an NFV scheme where spectrum processing, allocation, and management are dynamically implemented as virtualized functions to serve UAV-BSs. Cognitive radio services are also virtualized to enable the UAV to provide both cellular, Wi-Fi, and WIMAX servicesaccording to the application requirements for the coverage area. Offloading storage and processing functions to the ground control stations gives the UAVs higher endurance. NFV not only allows UAVs to save energy but also to be rapidly adapted for different purposes based on application demands. This tack was taken in [294] where small UAVs were programmed to be deployed by a network operator to offer any chosen service such as flight control, voice over IP telephony, routing, etc. The authors demonstrated a prototype of using such a system to provide IP telephony services.  
In implementation, a NFV infrastructure is used to host all the network shared hardware and virtual resources. In fact, by allowing a diverse set of network services to be run remotely on such shared infrastructure, NFV inherently enhances resource utilization [295], including on-board energy resources in UAVs.
## 4 Countermeasures against Thermal Covert Channel Attacks
### Extra Noise Generation by Random Numbers  
There are different methods introduced to generate random numbers. Some of them are very simple and some of them are very complicated. However, our purpose to use this operation is to create some extra temperature in a very simple method that does not require special types of equipment or lab to implement, and all researchers and designers can have very easy access to it [45; 46]. Therefore, for implementing our idea, we used C/C++ programming and int rolldie(int rand_ number) function to create some random numbers. Algorithm 1 illustrates the process of generating random numbers. In the first step, the system gets two different random numbers (known as X1 and X2), then sums them to achieve the final results.  
Figure 4: Proposed countermeasure process.  
```
1:Two random numbers X1 and X2
2:Output: Psuedorandom sequence period of X1 + X2
3:for n = 100, do
4:if\(n>0\)then
5: X1 = rollDie();
6: X2 = rollDie();
7: Count n = X1 + X2
8:return
9:endif
10:endfor
```  
**Algorithm 1** Psuedorandom Number Generator  
We started our experiment with 20 random numbers, however, it did not have any effect on our system, as the duration of this process was too short and did not generate significant temperature. Hence, we increased these numbers until we achieved the best results. The best result was reached when 100 random numbers were generated, and we got high BER and low power consumption. We considered this operation (generating random numbers) as one extra noise and then applied it to the transmission process. The results are shown in Table 1.  
During our work, we considered two important metrics: bit error rate and power consumption. In our proposed countermeasure, it can be difficult for the attackers to identify or guess the temperature signal pattern, so the sensitive information cannot easily leak. Consider that a system has a thermal covert channel with a transmission frequency of 100 Hz. Figure 5 shows the temperature signal without and with the proposed countermeasure. As Figure 5 illustrates, when the proposed countermeasure is applied, it shows a significantly different thermal profile.  
In our proposed countermeasure, the BER of the TCC reaches 94%, and the power consumption did not increase significantly. A high BER indicates that our proposed countermeasure can be utilized to fight TCC attacks. On the other hand, system overheating leads to increased power consumption throughout the transmission process. Consequently, this issue is addressed in this paper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Countermeasures** & **Generate Random Numbers** & **Bit Error Rate** & **Avg. Power Consumption (w)** \\ \hline One Extra Noise & 20 & 22\% & 22.50966 \\  & 50 & 22\% & 22.80794 \\  & 100 & 94\% & 23.74631 \\  & 150 & 94\% & 23.96022 \\ \hline \hline \end{tabular}
\end{table}
Table 1: One extra noise with different random numbers.  
Figure 5: Temperature timing diagrams of the receiver core without **(a)** and with proposed countermeasure on the transmitter core (**b**).
# Opto-Electronic Oscillators for Micro- and Millimeter Wave Signal Generation
## 3 Recent Development of the OEO's Application
### Optical Signal Distribution for the 5G Radio Access Network
In other words, to avoid the power penalty of the OEO signal distribution over the ODN, the TDCM can be used at each base station. The only challenge associated with the proposal is that the TDCM requires knowledge of the exact optical length. This challenge can be solved by using a proportional-integral-derivative (PID) controller so that the TDCM can be automatically tuned by comparing the maximum RF power seen in the RF spectrum. On the other hand, there are other possible options, such as using the 1310-nm wavelength or special fibers such as a dispersion-shifted fiber (DSF). However, the use of the 1310-nm wavelength has some disadvantages such as larger optical losses compared to the C band, and the optical nonlinearities are larger in this band. In addition, DSF requires modification of the infrastructure, which might not be a cost-effective and feasible approach for a RAN.
# Double-Consensus Based Distributed Optimal Energy Management for Multiple Energy Hubs
## 1 Introduction
Therein, a distributed algorithm was introduced to find the Nash equilibrium. Furthermore, the interaction among smart energy hubs was further modeled as an ordinal potential game in [27], where a distributed algorithm was proposed to solve this problem. Considering the autonomy and self-interest among energy hubs, their cooperative interaction was formulated as a bargaining cooperative game in [28], where an alternating direction method of multipliers was applied to find the optimal solutions. It is worth noting that the existing game-theoretic-based distributed approaches can achieve a partial shift from a centralized manner to a distributed manner. However, they also require a cloud computing center, a central price coordinator, or a virtual coordinator to obtain the optimal operations, which may not be regarded as fully distributed methods. Since there are some global supply-demand balance constraints and strong coupling relationship among variables, it is also a challenge to design an algorithm to solve the EMP of multiple energy hubs in a fully distributed manner. To address these issues, a novel distributed algorithm, referred as the DDC algorithm, is presented. By introducing the consensus concept and designing a new projection operation method, our proposed approach can make the global computation process divided into each energy hub. As a result, each energy hub can locally calculate its optimal operation. Compared to the existing literature, the major contributions of the presented distributed algorithm are summarized as follows:  
1. By making use of some change of variables, the strongly coupled form between variables in global equality constraints are transformed to the local function and inequality constraints. With this effort, the EMP is formulated as a kind of distributed coupled optimization problem. 2. The proposed DDC algorithm, which does not require a price coordinator or a leader to collect global parameters, can solve the EMP in a fully distributed fashion. In contrast to centralized algorithms, the proposed algorithm is more flexible, reliable and robust, etc. To the best knowledge of the authors, there still exist very few papers concerned about solving the EMP for multiple energy hubs in a fully distributed fashion. 3. The strong coupling problem, existing in the objective function and constraint limits, can be effectively solved by implementing the proposed DDC algorithm. Moreover, the rigorous optimality and convergence analysis are presented only under strong connectivity conditions, which is more reasonable and general, and possesses less conservatism. The rest of this paper is organized as follows. In Section 2, the system model and optimal conditions are briefly introduced. In Section 3, some basic knowledge is firstly introduced. Then, the proposed DDC algorithm and the projection operation method are presented. In Section 4, several case studies are presented to show the effectiveness of the proposed DDC algorithm. Section 5 concludes the paper.