esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Experiment of 2D-3D Registration
#### 4.1.2 PDNT Experiment  
According to the proposed method, the Cesium digital earth platform was used as the 3D GIS system in the application of the digital twin city, and the virtual perspective and inverse depth maps of the digital twin city model were generated according to the estimated pose, as shown in Figure 5. The perspective image was matched with the real image, and 3D coordinates for the feature points were obtained from the inverse depth image.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**CamPOS**} & \multicolumn{2}{c}{**Actual**} & \multicolumn{2}{c}{**Error**} \\ \cline{2-6}
**Image** & **Position (m)** & **Orientation (\({}^{\circ}\))** & **Position (m)** & **Orientation (\({}^{\circ}\))** & **Position(m)** & **Orientation (\({}^{\circ}\))** \\  & **(X/Y/Z)** & **(Y/P/R)** & **(X/Y/Z)** & **(Y/P/R)** & **(X/Y/Z)** & **(Y/P/R)** \\ \hline \multirow{3}{*}{P1} & \(-2093390.42\) & \(284.9\) & \(-2093394.17\) & \(269.67\) & \(3.75\) & \(15.23\) \\  & \(4806197.15\) & \(-13.5\) & \(4806194.13\) & \(-5.78\) & \(3.02\) & \(-7.72\) \\  & \(3621100.70\) & \(360.0\) & \(3621107.46\) & \(353.68\) & \(-6.76\) & \(6.32\) \\ \hline \multirow{3}{*}{P2} & \(-2093850.41\) & \(217.2\) & \(-2093851.67\) & \(222.46\) & \(1.26\) & \(-5.26\) \\  & \(4806374.77\) & \(3.1\) & \(4806372.58\) & \(10.63\) & \(2.18\) & \(-7.53\) \\ \cline{1-1}  & \(3620496.15\) & \(359.0\) & \(3620501.33\) & \(1.39\) & \(-5.18\) & \(-2.39\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Poses of the Real Images.  
Figure 4: Real images and virtual scenes. (**a**,**c**) Real images taken with a vision sensor; (**b**,**d**) digital twin virtual scene.
## 4 Results
### Limitations of the First-Order Approximate Solution and Region of Validity  
To assess the accuracy of the KBM method, the particular case with linear stiffness (\(\delta_{h}=0\)) and pure Coulomb friction (\(\zeta=0,\ \mu\
eq 0\)) is initially considered. [PERSON] [22] derived the closed-form solution of the oscillation amplitude under the hypothesis of continuous motion as follows:  
\[A=F_{0}\sqrt{V^{2}-\left(\frac{\mu}{F_{0}}\right)^{2}U^{2}} \tag{35}\]and, under the assumption of one-stop per half-cycle, the relationship describing the boundary below which stick-slip phenomena occur is  
\[\begin{split} A>F_{0}\sqrt{\frac{V^{2}}{1+\left(\frac{\mu\Omega^{2}} {\Omega}\right)^{2}}}\\ V(\Omega)=\frac{1}{1-\Omega^{2}},\ \ U(\Omega)=\frac{\sin\left(\frac{\pi}{ \Omega}\right)}{\Omega\left(1+\cos\left(\frac{\pi}{\Omega}\right)\right)},\\ S(\Omega)=\max_{0\leq\tau\leq\pi/\Omega}\left[\frac{\Omega\sin \left(\tau\right)+U\Omega^{2}\left(\cos\left(\Omega\right)+\cos\left(\tau \right)\right)}{\sin\left(\Omega\tau\right)}\right]\end{split} \tag{36}\]  
where \(V(\Omega)\) and \(U(\Omega)\) are known as _response_ and _damping_ functions, respectively. As pointed out in [22], to easily obtain the boundary, it is convenient to notice that \(S\geq\ 1\) for \(\Omega<1/2\), while \(S=1\) for \(\Omega>1/2\).  
Figure 2a presents the comparison among the analytical solution (35), the KBM and the direct integration method (see Appendix A). For several values of the ratio \(\mu/F_{0}\), the agreement between the numerical and the analytical solutions is excellent. However, the KBM shows discrepancies with the other methods in the case of higher values of \(\mu/F_{0}\), and particularly at low oscillation amplitude. Nevertheless, the qualitative behavior is captured.  
A similar observation can be pointed out in Figure 2b where the KBM is compared to direct integration. For low \(\mu/F_{0}\), the averaging method provides a reliable means of studying the qualitative behavior of piecewise nonlinear stiffness isolators in the presence of combined viscous and Coulomb damping. However, caution is required when \(\mu/F_{0}\) is close to the threshold \(\pi/4\) that leads to the sticking phenomenon, as identified in Equation (27): in the case of non-smooth nonlinearities, asymptotic methods, such as the KBM, provides approximate expression that are affected by the Gibbs phenomenon. Therefore, in case of high friction and stick-slip behavior, alternative methods, such as direct integration or enhanced HBMs [48; 49; 50], should be used.
## Materials and Methods
### Pretreatment of the EC Solution  
The dominant content (>80%) of EC solution is PG and VG. Flavor or nicotine may be added to this mixture according to the preference of the EC user [40]. Note that PG and VG have higher viscosity values than water (the usual matrix for LC analysis). Hence, if the EC sample is analyzed without a pretreatment step (such as dilution), it is difficult to do the LC-based analysis of TSNA [41; 42; 43].  
To compare the required dilution to successfully elute TSNA in EC solution, two kinds of solvent, ammonium acetate (AA) and ACN, were selected. Previously, AA was used as the extraction solution for the analysis of TSNAs by ISO and WHO [16]. ACN was selected as an additional dilution solution to test in this study, because it can elute many chemicals in HPLC and it has similar performance to methanol. ACN is usually used as salting-out extraction solvent for analyzing \(N\)-nitrosamines [44].  
In the first liquid experiment stage (L-S1), to determine the dilution factor, TSNA spiked samples were prepared at a concentration of 100 ng mL\({}^{-1}\) by adding three PSs and NAT-LS to a PG/VG mixture (1:1, \(w/w\)). The spiked sample was systematically diluted 20, 50, and 100 fold using each of the two dilution solvents (AA or ACN) and 1 \(\upmu\)L of each diluted sample was analyzed by LC-MS/MS. Triplicate analysis for each sample was performed. In the second liquid experiment stage (L-S2), the spiked samples were prepared at three concentrations of 50, 250, and 500 ng mL\({}^{-1}\) by adding three PSs and NAT-LS into the PG/VG mixture. These spiked samples were then diluted with AA or ACN at a fixed dilution factor of 50, which was determined by the result of L-S1 analysis. One microliter of each diluted sample was analyzed by a LC-MS/MS. Triplicate analysis for each sample was conducted.
## 2 Materials and Methods
### Measurement and Characterization  
The output voltage and current were measured by using an oscilloscope (Wavesurfer 3022, Lecroy, Chestnut Ridge, NY, USA) and a current preamplifier (SR570, Stanford research systems, Sunnyvale, CA, USA), respectively. The charge density was measured by using an electrometer (6514, Keithley, Solon, OH, USA). The custom-made vertical pressure machine was used to measure the triboelectric outputs. The Kelvin probe force microscopy (KPFM) was obtained using an XE7 (Park systems, Suwon, Korea). X-ray diffraction (XRD) analysis was performed using a D8 discover ([PERSON], Billerica, MA, USA). The electrostatic voltmeter (ESVM) was measured using an ARS-S005 (SECOS, New Taipei City, Taiwan). The capacitor charging test was performed by using a high-precision digital multimeter (34401A, Agilent, Santa Clara, CA, USA). The polarization versus the electric field curves was measured by using a ferroelectric tester (RT66C, Radiant Tech, Albuquerque, NW, USA). Fourier-transform infrared spectroscopy (FTIR) analysis was conducted using Alpha (Bruker, Billerica, MA, USA). We must note that the active area, where vertical force was applied on the devices, was kept at 1 \(\times\) 1 cm\({}^{2}\).
## 4 Experiment
### Performance Comparisons
#### 4.3.2 Comparison of Hidden Feature Extraction Methods  
The effectiveness of the improved classification model in this task was verified by comparing different hidden feature extraction networks. The deep learning models used were Resnet50, MFN, and MFN-SRM, and the loss function was ArcFace Loss for all. Resnet50 is a classic deep neural network that performs well in the task of image feature extraction. MFN was the network before improvement, and its model parameter quantity was much lower than that of Resnet50. MFN-SRM is a feature extraction network improved  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Sampling rate & 16 kHz \\ Window size & 1024 \\ Hop size & 512 \\ Number of Mel-filter banks & 128 \\ Angular margin penalty t & 0.7 \\ Feature scale s & 30 \\ Batch size M & 64 \\ Step size & 0.0001 \\ Number of normal sound classes & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter settings.  
by adding the SRM module to MFN. It can be seen from Table 5 that at different measuring point positions, the AUC of the detection results of MFN compared with Resnet50 increased by 8.42%, 4.4%, 7.12%, 3.38%, and 8.31%. After the SRM module was added to MFN, the AUC at five different measuring points increased by 4.49%, 4.18%, 0.2%, 8.99%, and 5.81%. The drawback is that the number of parameters of the model increased slightly.
# Decentralized and Secure Blockchain Solution for Tamper-Proof Logging Events
## 5 Results and Discussion
### Feasibility in an Enterprise System  
The proposed solution is designed to effectively manage the large volume of log data generated within an enterprise system. However, the cost of sending transactions to the blockchain for certification remains a concern. In the case of Cardano, transaction costs are influenced by two components as shown in Figure 9: the transaction fee and the UTxO storage cost [31]. In our study, only one SHA2 hash was stored for each transaction, resulting in a fixed size of 32 KB with a corresponding cost of 200,000 localaces. In contrast, transaction costs on the Solana blockchain vary based on network congestion; therefore, they are non-deterministic [32]. These costs are calculated as the sum of a base fee and a priority fee as shown in Figure 10. Costs increase when more compute units (CU) are consumed, reflecting the computational resources used by the transaction on the network. In our study, the average cost was 5001 lamports per transaction.  
The rate of logs generated per second varies for each company, but for the purpose of this discussion, we will consider a rate of one log per second, which implies around 86,400 logs daily [11]. Approximately 10% of these logs are critical, which equates to 8640 logs per day. Through experimentation, we have found that sending all critical logs is not affordable due to cost constraints, specifically 164.16 \(\upepsilon\) per day in Solana and 552.96 \(\upepsilon\) per day in Cardano. To minimize costs, it is advisable to operate during periods of low congestion or reduce the transaction priority, which allows taking advantage of the lowest fees available. To further mitigate this issue, we can employ the advanced filtering functions of Syslog-ng to selectively filter the most crucial logs, thereby reducing costs while ensuring that the most important events are certified.  
Although the costs may be substantial, they are justifiable given the critical importance of data integrity, legal compliance, and security, particularly in industries such as finance  
Figure 9: Transaction cost breakdown in Cardano.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Events/Second** & **Log Module (ms)** & **From Event to Blockchain (ms)** &
\begin{tabular}{c} **From Event to Log** \\ **Management Server (ms)** \\ \end{tabular} \\ \hline
1 & 16.57 \(\pm\) 6.48 & 22,171.70 \(\pm\) 30,389.99 & 4357.8 \(\pm\) 6855.37 \\
10 & 7.59 \(\pm\) 4.43 & 240,995.88 \(\pm\) 164,862.90 & 219,455.22 \(\pm\) 167,  
and healthcare, where non-repudiation and long-term verifiability are paramount. In the finance sector, immutable log storage plays a crucial role in preventing fraud and adhering to regulatory standards like PCI DSS [33]. The investment in this technology can be rationalized by selectively applying blockchain to high-risk transactions. Likewise, in healthcare, it is essential to uphold the integrity of electronic health records (EHR) and manage access to sensitive medical information in order to comply with regulations like HIPAA [34].
# Two-Stage Metaheuristic Algorithms for Order-Batching and Routing Problems
## 4 Proposed Algorithm
### Neighborhood Searching  
To minimize the number of batches and shorten the total picking-route distance, we used neighborhood searching to determine the optimal neighboring solution for batching and picking problems. These two problems require different neighboring solution designs. First, to effectively reduce the number of batches, a mutation procedure is applied. For example, when a mutated order is the only order in a batch, the mutation of the order from the original batch to another batch can reduce the batch by one. In addition, a swap procedure changes the existing combination of batches in pairs according to a fixed total number of batches. Second, according to existing picking solutions, we designed a swap procedure to improve picking routes to a small extent. Moreover, to increase the extent of improvement, the insertion procedure is adopted for generating neighboring solutions by inserting one order into another and, hence, moving the remaining items in the order by one place.
## 2 Experimental Conditions and Methods
### Evaluation Methods of Tool Wear and Breakage
#### 2.2.3. Diameter Reduction  
The diameter reduction of the disc cutter is a parameter to measure the overall degree of breakage of the disc cutter. In the cutting process, damage conditions such as micro-chipping and chipping at multiple positions of the cutting edge will have a direct impact on the change in tool diameter. In this paper, the diameter reduction is selected as the parameter to characterize the diameter change of the disc cutter after overall damage. The flowchart for calculating the diameter reduction of the disc cutter is shown in Figure 7. Firstly, the point cloud scanning technology was used to scan the contour of the flank face before and after the wear of the disc cutter, and the specific point cloud scanning results are shown in Figure 8. Then, MATLAB was used to convert the line laser three-dimensional point cloud data into a two-dimensional grayscale image. The conversion process would inevitably produce noise points, as shown in the red box in Figure 7, so image filtering and denoising were performed under the premise of considering the calculation accuracy. After that, the edge detection of the disc cutter's cutting edge was performed. The classical edge extraction operator Canny operator edge detection was chosen for image processing because the detection technique using image edge features was very mature and had high accuracy, and the processing results had good edge detection continuity and high clarity [29]. Finally, the least squares method was used to calculate the diameter of the disc cutter for the cutting edge. In the calculation result, the green line represented the disc cutter edge acquisition trajectory, and the red line represented the fitted circle after the cutting process as determined by the least squares method. The diameter reduction was the difference between the initial diameter of the disc cutter and the calculated diameter after this cutting process was completed.  
Figure 6: Fixture for disc cutter inspection and the measurement of flank wear \(VB\). (**a**) Fixture for disc cutter inspection; (**b**) Flank wear \(VB\).
## 5 Vulnerabilities of SDN to LDDoS Attacks
### LDDoS Attack against Application Layer
#### 5.1.3 Northbound Interface Saturation  
SDN fosters creativity and programmability with northbound APIs that connect the SDN controller to applications operating across the network. It enables software designers in creating apps that allow the network to be programmable. This produces northbound APIs without widely accepted standards [90]. This gap is thought to offer vulnerabilities to a variety of security risks originating from trust concerns, app authorizations, and illegal application gain access. Because of these defense concerns, LDDoS attacks directed against the controller may produce congestion on the northbound interface. For instance, the LDDoS attack will utilize a bandwidth on the northbound channel; if a traffic inspection application listens in on Packet-In arriving from the switch in the direction of the controller, the network over time is overwhelmed [91].
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Holographic Metasurfaces  
#### 3.3.1 General Description  
The emerging role of holographic metasurfaces in the landscape of wireless communication and sensing technologies has been nothing less than transformational. Holographic metasurfaces enable important functions in the emerging 6G network, thanks to their unique possibility of engineering electromagnetic waves of high quality. Advanced functionalities may include beamforming, holographic imaging, and dynamic reconfigurability. Unlike conventional phased-array systems, holographic metasurfaces achieve their performance due to dynamic control over amplitude, phase, and polarization of electromagnetic fields. This flexibility solves the main problems of modern networks: achieving high spectral and energy efficiency. Moreover, thanks to their reconfigurability, they will be applied in many different domains, including integrated sensing, imaging, smart healthcare, and energy-efficient communication, making them pivotal technologies for next-generation networks (refs. [71; 72; 73; 74]).
## 1 Introduction
Figure 1: CL in the building environment. Figure 2: CL in the forest environment. The adaptive Kalman filter is an effective method to solve the unknown noise covariance matrix [34; 35]. It can estimate system noise and measure noise online. In this study, we proposed an adaptive extended Kalman filter decentralized cooperative localization (DCLAEKF) algorithm for firefighters. In the DCL system of firefighters, sensors of IMU, magnetometer, barometer, GNSS and UWB were used to obtain an accurate localization result. Every firefighter acted as a local fusion center, and the exchange of information took place only when relative measurements occurred. In this way, the burden of system communication was reduced significantly. The NLOS error of UWB was identified, and its adverse influence on the system accuracy was reduced by the adaptive filter. This avoided discarding the NLOS measurements directly, which reduced the effectiveness of measurement feedback. The main contributions of the research are outlined as follows:  
1. The DCL for firefighter localization was proposed. This method differed from the CCL structure, and it is also distinct from the other distributed structures proposed by other researchers. In our proposed DCL structure, we derived explicit expressions for the inter-member collaboration instead of using approximations, which led to higher localization accuracy. 2. To address the issue of the reduced localization accuracy caused by NLOS errors in UWB systems, we proposed using an adaptive extended Kalman filter algorithm to estimate measurement noise. In contrast to fixed noise parameter settings, which reduce the adverse effects of NLOS errors, the adaptive filter showed greater adaptability to the time-varying noise caused by environmental changes. The structure of this paper is as follows. Section 2 introduces the system model, and the DCLAEKF algorithm is derived. Section 3 introduces the experiments and validates the proposed algorithm. Finally, Section 4 concludes this work.
## 3 Numerical Method  
The numerical method used in this work is based on finite differences. It can deal with tree-like mesh grids (see Figure 2b) and allows fast Cartesian discretizations, flexibility and accuracy, and local mesh refinement. In order to fit the discretization stencil near the interfaces between grid elements of different sizes, a robust method based on a moving least squares meshless interpolation technique is used to compute the weights of the finite difference approximation in a given hierarchical grid, allowing complex mesh configurations and preserving the overall accuracy of the resulting method.  
Figure 2a shows a schematic representation of the mesh refinement. Note that some of the points (variables) of the computational cells (red dots) must be approximated because they are not located in the center of the cell (e.g., the center of computational cell 1 is not the same as the location of the red dot used to compute the derivative of the property being evaluated). To solve this problem, we use a special adaptive least square interpolation (_MLS_). The method is known as HiG-Flow, and a detailed mathematical explanation can be found in [31,32].  
For the numerical solution of the Navier-Stokes equations together with the constitutive equation given by the gPTT model, the momentum Equation (6) is rewritten:  
\[\frac{\partial\mathbf{u}}{\partial t}+\mathbf{u}\cdot\
abla\mathbf{u}=-\
abla p +\frac{1}{Re}\
abla^{2}\mathbf{u}+\
abla\cdot\mathbf{S}+\mathbf{F}, \tag{11}\]  
\[\boldsymbol{\sigma}=\frac{2(1-\zeta)}{Re}\mathbf{D}+\mathbf{S}. \tag{12}\]  
From Equation (9), the rheological constitutive equation can be written as:  
\[\frac{\partial\boldsymbol{\sigma}}{\partial t}+(\mathbf{u}\cdot\
abla) \boldsymbol{\sigma}-\left[(\
abla\mathbf{u})^{t}\cdot\boldsymbol{\sigma}+ \boldsymbol{\sigma}\cdot\
abla\mathbf{u}\right]=\frac{1}{Wi}\mathbf{M}( \boldsymbol{\sigma}), \tag{13}\]  
where \(\mathbf{M}(\boldsymbol{\sigma})\) is given by Equation (14),  
\[\mathbf{M}(\boldsymbol{\sigma})=\frac{2(1-\zeta)}{Re}\mathbf{D}-\left[\Gamma( \beta)E_{\mathrm{n},\beta}\left(\frac{\varepsilon Re\;Wi\;\sigma_{kk}}{(1- \zeta)}\right)\right]\boldsymbol{\sigma}-\zeta\,Wi(\boldsymbol{\sigma}\cdot \mathbf{D}+\mathbf{D}\cdot\boldsymbol{\sigma}), \tag{14}\]  
and the parameter \(\xi\) (\(0\leq\xi\leq 1\)) accounts for the slip between the molecular network and the continuous medium. For \(\xi=0\) there is no slip and the motion becomes affine.  
Figure 2: (**a**) Mesh refinement and the need to use an adaptive least squares method; (**b**) dependency tree.  
The parameter \(\xi\) leads to a non-zero second normal-stress difference in shear, resulting in secondary flows in ducts having non-circular cross-sections. Since in this work we only consider 2D flows, and, due to the high number of parameters involved in the numerical simulations and the gPTT model itself, we have only considered the case when \(\xi=0\).
## 5 Results
### Baseline Scenario
Although, the area around Ibague in Tolima (west) is a large producer of foods, a large portion of them is delivered to other big cities. In general, Figure 6b indicates that the most traded products are grains, fruits and vegetables (F&V), and \"other products\". Potato and plantain chains are smaller chains because they only contain two or three products. Notice that production quantities of these two chains are virtually not delivered to the northern cities of Cartagena and Barranquilla (circles in the upper part of Figure 6b). The cost and profits analysis follows next. Figure 5: (**a**) Total tons of food supplied to the food hubs. (**b**) Participation of the agricultural chains in the baseline scenario. Figure 6: (**a**) Total tons delivered to each point of demand. (**b**) Distribution of delivered products by supply chains. We also compare the baseline in relation of the current situation of no hubs in which products are delivered directly to the cities. We can observe that all transportation and emission costs decrease with the hubs' network. This is achieved mainly due to reductions in the vehicles' operation fixed costs and emissions' costs (up to 95% reduction). This cost savings can be explained as fewer vehicles are required when cargo is consolidated at the hubs. In the scenario of no hubs, the number of vehicles used is approximately 8000 per week, considering that each farmer (or middlemen) travels to the cities in small trucks. With the hubs' network, the number of vehicles required to transport food is around 800, which represents a 90% reduction. The additional benefit is that fewer vehicles also reduce the total emissions of CO2, improving the carbon print. Figure 7 presents this comparison.